Data-Driven Assessment of Solar Surplus and Battery Storage for Cost and Emission Reduction in Sri Lanka’s Power System

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D. Gammanpila, A. C. Gammanpila, A. H.T.S Kularathna, N. K. Jayasooriya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8496983/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rapid growth in solar photovoltaic deployment across the Global South is increasingly constrained by temporal mismatches between generation and demand, resulting in renewable curtailment, inefficient system operation, and continued reliance on oil-based peaking generation. Grid-scale battery energy storage systems (BESS) are widely recognised as a key enabler of renewable integration; however, empirically grounded assessments of their operational and developmental value remain limited in emerging power systems. This study develops a high-resolution, data-driven analytical framework to quantify solar-excess availability and derive indicative battery-storage requirements for Sri Lanka’s national power system. Using 15-minute operational, irradiance, generation, and cost datasets obtained from national regulatory and system-planning institutions, the analysis integrates solar-generation potential modelling, solar-excess identification, and storage-dispatch simulation to evaluate feasible charge-discharge windows and system-level impacts. Results show that existing solar deployment already produces substantial and recurrent midday surplus energy concentrated within consistent 2-4-hour windows. Percentile-based sizing indicates that short-duration, grid-scale storage on the order of several hundred megawatts, with energy capacities of a few gigawatt-hours, is technically sufficient to capture the majority of daily solar surplus while displacing high-cost thermal generation during evening peak periods. The findings demonstrate that appropriately sized battery storage can enhance solar utilisation, reduce operating costs and emissions, and improve grid flexibility without requiring long-duration storage solutions. Beyond system-level efficiency gains, the results highlight the role of battery storage as a strategic enabler of energy security, affordability, and resilient low-carbon transitions in fuel-import-dependent power systems across the Global South. battery energy storage solar surplus renewable integration energy transition Global South Sri Lanka Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Electric power systems worldwide are undergoing a profound transformation as renewable energy sources increasingly displace conventional fossil-fuel-based generation in response to climate change mitigation, energy-security concerns, and sustainability imperatives (IPCC, 2022). While this transition delivers substantial environmental benefits, the rapid expansion of variable renewable energy sources, particularly solar and wind has introduced new operational and planning challenges. These include intermittency, temporal mismatches between generation and demand, and recurrent periods of surplus output that place increasing pressure on system flexibility and dispatch practices [ 1 ]. Such challenges are now widely recognised as central constraints on the efficient integration of renewables in modern power systems [ 2 ]. A range of flexibility mechanisms can be employed to manage the variability of renewable generation, including demand-side response, grid reinforcement, interconnection, and flexible generation resources such as hydropower [ 4 ]. The suitability of these options, however, is highly system-specific and constrained by institutional, geographic, and fiscal factors. In many emerging power systems, demand-side participation remains limited, transmission expansion faces long lead times, and hydropower flexibility is increasingly affected by climate variability. Within this context, battery energy storage systems (BESS) have emerged as a critical enabler of sustainable power-system transformation by providing temporal flexibility between energy supply and demand [ 3 ]. By storing energy during periods of high renewable availability or low system demand and discharging during peak-demand intervals, BESS can reduce renewable curtailment, improve load management, and limit reliance on high-cost and carbon-intensive peaking generation [ 5 ], [ 6 ]. Beyond short-term operational benefits, battery storage plays an increasingly important role in supporting broader sustainability objectives, including emissions reduction, system resilience, and long-term cost containment in electricity systems characterised by rising shares of variable renewable energy. These challenges and opportunities are particularly pronounced in Global South power systems, where rapid renewable deployment often occurs alongside constrained fiscal space, limited reserve margins, and growing electricity demand. Sri Lanka exemplifies this transition context. The country has committed to achieving 70% renewable electricity generation by 2030 and carbon neutrality by 2050 [ 7 ]. This transition is supported by national initiatives led by the Ministry of Power and Energy and the Sri Lanka Sustainable Energy Authority (SLSEA), including the Soorya Bala Sangramaya (“Battle for Solar Energy”) programme, which promotes both rooftop and utility-scale solar deployment [ 8 ]. As solar penetration has increased, however, the national grid has begun to experience recurrent midday energy surpluses, particularly during periods of high irradiance and relatively low demand [ 9 ]. In the absence of adequate storage or other flexibility mechanisms, these surpluses lead to renewable curtailment, inefficient system operation, and continued dependence on oil-based thermal generation during evening peak hours. Recognising these emerging system constraints, Sri Lanka has initiated its grid-scale battery storage projects aimed at absorbing excess solar generation and shifting it to periods of higher system demand [ 10 ]. These early investments reflect growing policy recognition that energy storage is not merely a supporting technology, but a central component of a sustainable and development-oriented energy transition. However, despite this recognition, there remains a limited empirical basis for quantifying the scale of storage required to effectively utilise solar surplus and translate renewable capacity targets into operational, economic, and environmental outcomes. Against this background, this study develops a data-driven analytical framework to quantify solar-excess availability and to estimate the corresponding grid-scale battery storage capacity required for effective utilisation within Sri Lanka’s power system. Using high-resolution datasets on generation cost, total generation, and solar irradiance, the analysis identifies feasible storage-sizing parameters. The findings aim to support evidence-based planning and investment decisions that enhance renewable-energy utilisation, reduce fossil-fuel dependence, and strengthen grid reliability in resource-constrained power systems. Methodology This study employs a multi-layer, data-driven analytical framework that integrates solar-resource modelling, system-cost profiling, and grid-scale battery storage simulation to evaluate the operational and environmental implications of utilizing solar-excess energy within Sri Lanka’s national power system. The framework is designed to capture high-frequency interactions between renewable generation, system costs, and storage operation under realistic grid conditions. The methodological approach comprises four principal components which are data acquisition and temporal harmonization of multi-source operational datasets, modelling of solar-generation potential and identification of solar-excess intervals, simulation of battery-storage dispatch under operational constraints and assessment of associated economic and environmental impacts arising from thermal generation displacement. 3.1 Data Acquisition This study constructs a high-resolution analytical framework by integrating empirical datasets obtained from Sri Lanka’s national energy institutions. System-wide electricity generation, fuel-specific marginal generation costs, and pollutant-specific emission intensities were sourced from the Public Utilities Commission of Sri Lanka (1SL), which publishes 15-minute operational dispatch and cost benchmarks aligned with the nationally approved tariff structure. Solar-resource data were obtained from the Sri Lanka Sustainable Energy Authority (SLSEA), which operates twelve precision-calibrated solar monitoring stations distributed across Sri Lanka’s major climatic zones. These stations provide 10-minute measurements of global horizontal irradiance (GHI) and photovoltaic (PV) potential. District-level installed solar capacity and associated technical specifications were obtained from the Ceylon Electricity Board (CEB). The analysis focuses on the first six months of the year, corresponding to the period for which continuous, quality-assured irradiance measurements were available across the SLSEA monitoring network. This interval captures a representative range of solar-generation conditions, including inter-monsoon and Southwest monsoon phases, enabling assessment of solar-excess behaviour and storage feasibility under varying irradiance and demand conditions. While the temporal scope does not extend to a full calendar year, the selected period provides sufficient variability to support robust estimation of operational storage requirements. 3.2 Data Conditioning and Temporal Harmonization To ensure consistency across datasets, all time series were harmonized to a common 15-minute temporal resolution. The 10-minute GHI data from SLSEA were resampled using an irradiance-preserving method to avoid bias introduced by temporal smoothing. Basic quality checks were applied to identify and remove outliers arising from sensor errors or data spikes. Missing observations were reconstructed using interpolation across neighbouring monitoring stations, taking advantage of spatial consistency within the twelve-station network. All data were converted to Sri Lanka Standard Time (GMT + 05:30) and temporally aligned to ensure coherence across sources. Data validity was verified by examining monthly irradiance patterns to confirm that key monsoonal characteristics were preserved. The final dataset comprises a continuous, multi-source, high-resolution time series covering the first six months of the year and is suitable for analysing solar-excess behaviour and battery-storage feasibility. 3.3 Solar Generation Potential Modelling Solar-generation potential was estimated using a capacity-weighted irradiance-power transformation consistent with the resolution and structure of the available datasets. For each SLSEA station, GHI measurements were converted into potential PV output using a standard efficiency-based scaling approach expressed as Eq. 1 below: $$\:Pi\left(t\right)=GHIi\left(t\right)\eta\:PV$$ 1 where ηPV​ represents the aggregate module-level conversion efficiency and Ci​ denotes the effective installed PV capacity allocated to station ii. This formulation provides a physically consistent, computationally tractable representation of solar availability without requiring detailed module temperature or angle-of-incidence modelling, which were not part of the empirical data collection. District-level installed capacities from CEB were spatially mapped to the twelve irradiance stations using a proportional allocation scheme. Aggregation across stations yielded a national 15-minute solar potential series for the full twelve months, capturing both spatial and seasonal variations in irradiance profiles. 3.4 Solar-Excess Identification Across the Annual Cycle Solar-excess periods were identified by comparing the modelled PV potential with actual renewable dispatch recorded by PUCSL. For each interval is given in Eq. 2 below. $$\:Eexcess\left(t\right)=max(Psolar,potential(t)-Prenewable,actual(t),0)$$ 2 where positive values represent underutilised or implicitly curtailed solar energy. Extending this calculation across all twelve months captures monsoon-season dips, inter-monsoon peaks, and seasonal shifts in demand-supply synchronisation. Operational constraints reflecting national dispatch priorities such as evening-peak charging restrictions were incorporated to avoid overstating storage feasibility. The resulting full-year solar-excess profile provides a high-fidelity classification of storage opportunities, renewable-utilisation gaps, and seasonal variability in surplus energy. 3.5. Analysis of Generation Cost Dynamics and Arbitrage Opportunity The generation cost dataset, obtained from the PUCSL, was analyzed to examine temporal variations in the unit cost of electricity generation throughout a typical 24-hour cycle. These costs, expressed in LKR per kilowatt-hour (LKR/kWh), represent the composite generation mix encompassing coal, oil (both CEB and IPP), hydro, wind, and solar sources under the January 2025 national tariff framework. By aligning the cost profile with the corresponding generation data, the study identified distinct low-cost and high-cost intervals within the daily cycle. This temporal mapping of generation cost against resource availability provided a foundational input for assessing energy-arbitrage potential.. 3.6. Battery Sizing and Benchmark Comparison This stage outlines the methodological approach used to determine the battery-storage capacity required to utilize the identified solar-excess energy and to benchmark the derived estimates against existing national initiatives. The total solar-excess energy, obtained through time-integration of positive generation differentials, was used as the primary input for sizing calculations. Power-capacity requirements were inferred from the maximum instantaneous surplus observed within the solar-excess window. To convert theoretical energy to practical battery capacity, standard parameters for grid-scale lithium-ion systems were applied, including round-trip efficiency and allowable depth of discharge. These factors provided an adjusted estimate of usable energy storage under realistic operating conditions. Following the sizing procedure, the results were systematically compared with publicly available specifications of Sri Lanka’s ongoing and proposed battery-energy-storage projects, such as pilot installations and grid-scale programs announced by the Ceylon Electricity Board. This benchmarking step ensured that the analytical framework remained aligned with current technological and policy developments, while establishing a consistent basis for interpreting subsequent results. 3.7 Battery Charge-Discharge Optimisation Framework To ensure that the battery-dispatch behaviour underlying the system-cost and environmental assessments reflects economically rational operation, a constrained charge-discharge optimisation framework was employed. The battery operation is formulated over discrete 15-minute intervals consistent with the system-operator dataset. The decision variables comprise battery charging energy \(\:{E}_{ch}\left(t\right)\) , battery discharging energy \(\:{E}_{dis}\left(t\right)\) , and state of charge \(\:SOC\left(t\right)\) at each interval \(\:t\) . The inter-temporal evolution of the battery state of charge is governed by the following balance Eq. (3). $$\:SOC\left(t+1\right)=SOC\left(t\right)+{\eta\:}_{ch}{E}_{ch}\left(t\right)-\frac{{E}_{dis}\left(t\right)}{{\eta\:}_{dis}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(3\right)$$ where \(\:{\eta\:}_{ch}\) and \(\:{\eta\:}_{dis}\) denote charging and discharging efficiencies, respectively. Battery operation is subject to standard operational constraints, including limits on energy capacity, maximum charging and discharging power, allowable depth of discharge, and non-negativity of decision variables. Charging actions are restricted to intervals identified as solar-excess periods, while discharging is permitted during periods characterised by elevated marginal thermal-generation costs. The resulting charge-discharge schedule is determined to minimise total system operating cost by maximising displacement of high-cost thermal generation, thereby providing the operational basis for the avoided-cost and emission-reductions. 3.8 System-Cost Reduction and Environmental Assessment Given Sri Lanka’s vertically integrated power structure without wholesale market pricing, economic impacts were evaluated using marginal thermal-displacement cost rather than price arbitrage. For each discharge interval, avoided thermal-generation cost was computed as Eq. 4 below. $$\:Cavoided\left(t\right)=Edis\left(t\right)\cdot\:Cthermal\left(t\right)\:\:-\:\:CSolar\left(t\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4\right)$$ In this formulation, \(\:{C}_{\text{avoided}}\left(t\right)\) denotes the avoided thermal-generation cost at time interval \(\:t\) , expressed in Sri Lankan Rupees (LKR). The term \(\:{E}_{\text{dis}}\left(t\right)\) represents the electrical energy discharged from the battery during interval \(\:t\) , measured in kilowatt-hours (kWh). The variable \(\:{C}_{\text{thermal}}\left(t\right)\) refers to the real-time marginal cost of thermal generation (LKR/kWh), computed from PUCSL’s publicly available fuel-cost and heat-rate datasets for oil-fired power plants. Together, these variables quantify the economic benefit accrued whenever battery discharge offsets otherwise required thermal generation. All variables are defined at a uniform 15-minute temporal resolution consistent with the system-operator dataset. Environmental effects were quantified by calculating avoided emissions whenever stored solar energy displaced thermal generation as Eq. 5 below. $$\:Avoided\:Emissionk\left(t\right)=Edis\left(t\right)\cdot\:EFk\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(5\right)$$ using PUCSL emission factors for CO₂, SO₂, NOₓ, and PM. These avoided emissions were subsequently monetised using pollutant-specific externality coefficients, reflecting public-health, ecological, and agricultural damages. Because the study spans all monsoonal phases, the resulting environmental-savings profile captures the seasonal interaction between solar availability, thermal dependence, and pollutant intensities. Annualised environmental savings were obtained by aggregating avoided-damage values across the full-year simulation. Results 4.1. Seasonal Dynamics of Solar Availability Sri Lanka’s solar resource exhibits pronounced monsoonal seasonality driven by the country’s unique climatic regime [ 19 ]. Analysis of the full-year irradiance dataset reveals strong asymmetry between monsoon and inter-monsoon periods, with solar potential peaking during the two inter-monsoon seasons (March-April and August-September) when clear-sky conditions and reduced cloud cover produce consistently high GHI profiles across all twelve SLSEA monitoring stations. In contrast, the Southwest monsoon (May-June) is marked by extensive cloudiness and increased atmospheric moisture, resulting in notably lower irradiance levels, while the Northeast monsoon (November-January) provides moderate but more variable solar availability due to shifting wind and cloud patterns. These patterns align with Sri Lanka’s established monsoonal climatology, which is characterised by distinct Yala and Maha monsoon regimes and strong intra-annual variability in solar [ 11 ]. When aggregated nationally, the observed seasonal fluctuations underscore the importance of using full-year irradiance records rather than single-month snapshots to accurately capture the operational dynamics and variability of solar generation potential in Sri Lanka. 4.2 Spatial Distribution of Solar Resource While seasonal dynamics describe how Sri Lanka’s solar resource varies over time, understanding the country’s solar-generation potential also requires examining its spatial distribution. Solar availability is not uniform across the island which it is shaped by complex interactions between regional climate regimes, topography, monsoonal cloud cover, and land-surface characteristics [ 13 ]. These spatial differences influence where large-scale solar development is most feasible, how much energy can be harvested from different regions, and the extent to which national output depends on geographically uneven resource strengths. Assessing the spatial variability of irradiance therefore provides essential context for interpreting modelled PV output and identifying opportunities for future solar expansion. Figure 1 presents the spatial distribution of long-term Direct Normal Irradiance (DNI) derived from the World Bank’s Global Solar Atlas. The map highlights a pronounced North-South gradient, with the Dry Zone, particularly the Northern, North Central, Eastern, and Southern provinces which are exhibiting DNI values exceeding 4.2–4.6 kWh/m² per day, reflecting clearer skies and semi-arid conditions. In contrast, Wet Zone regions such as Colombo, Gampaha, Kandy, and surrounding high-elevation areas show substantially lower irradiance due to persistent cloud cover and higher moisture content. Source: World Bank Group [ 20 ] 4.3. Spatial Distribution of Installed Solar Capacity To contextualise the spatial heterogeneity of solar deployment across Sri Lanka, the geographic to contextualise the spatial heterogeneity of solar deployment across Sri Lanka, the geographic distribution of installed PV capacity was mapped using district-level data obtained from the CEB. As of the study period, Sri Lanka’s total installed solar capacity is approximately 1.7 GW. Figure 2 illustrates the spatial allocation of this installed capacity across districts, highlighting pronounced regional variation. This spatial perspective provides essential context for interpreting subsequent analyses of solar generation potential, surplus formation, and the localisation of battery storage requirements within the national power system. 4.4. Multi-Day Solar Generation Patterns To examine the intra-day and inter-day temporal structure of solar generation, a multi-day diurnal heatmap of 15-minute modelled photovoltaic (PV) output was constructed for a selected 14-day period representative of typical system conditions. As depicted in Fig. 3 below. This representation highlights the progression of solar availability across consecutive days and illustrates how irradiance ramps, midday peaks, and evening declines manifest under typical system conditions. By visualising these day-to-day dynamics, the analysis provides insight into the temporal regularity of solar generation and the operational opportunities and constraints relevant for battery-charging strategies and grid-integration planning. 4.5. Identification of Solar Excess Periods The identification of solar excess periods was conducted to determine time intervals suitable for battery charging based on the relationship between solar supply potential and actual grid generation. The identification of solar excess periods was conducted to determine time intervals suitable for battery charging based on the relationship between solar supply potential and actual grid generation. The analysis focuses on the January-June period, which corresponds to Sri Lanka’s relatively high solar-availability months and captures both inter-monsoon and Southwest monsoon conditions, where solar-excess occurrence is most pronounced. The modelled solar potential time series was aligned with total system generation and generation cost data obtained from the PUCSL at a uniform 15-minute temporal resolution. A time interval was classified as a solar excess period when the modelled solar potential exceeded the observed renewable generation contribution to the grid. Such intervals represent instances in which the available solar resource surpassed the renewable component utilised within the grid mix, implying either renewable curtailment or unused solar generation capacity due to operational constraints. Daily solar-excess energy was computed by integrating positive solar-excess values across all 15-minute intervals within each day. This aggregation enables quantification of both the magnitude and variability of surplus solar energy at a daily resolution while preserving the high-frequency operational characteristics of solar generation. Table 1 summarises the descriptive statistics of daily solar-excess energy derived from the full study period. The results indicate that daily surplus solar energy frequently exceeds 1 GWh, with a mean value of approximately 1,030 MWh and a median of 1,019 MWh. Substantial day-to-day variability is observed, as reflected by the wide interquartile range and a standard deviation of 614 MWh. While some days exhibit minimal surplus, peak days reach excess levels as high as 2,350 MWh. Table 1 Daily solar-excess energy statistics based on high-resolution operational data. Indicator Daily Excess Energy (MWh) Mean 1030 Median 1019 25th percentile 535 75th percentile 1517 Maximum 2350 Standard deviation 614 This distribution highlights both the regular occurrence of surplus solar availability and the presence of high-surplus events, underscoring the need for flexible, short-duration storage capacity rather than sizing decisions based on isolated peak-day conditions. 4.6 Single-Day Operational Dynamics To visualise the intra-day operational implications of the statistically identified solar-excess patterns, the selected representative day was examined in detail. Figure 4 illustrates the hourly evolution of total system generation alongside modelled solar potential and actual solar output. During the early hours (00:00–05:30), total generation remained within the range of approximately 1,500-1,800 MW and was dominated by thermal sources. Following sunrise, solar output increased rapidly, peaking around midday. A consistent divergence between modelled solar potential and actual solar generation is observed between approximately 10:00 and 13:00, indicating periods of underutilised solar availability attributable to grid absorption limits or curtailment. These intervals correspond to potential charging windows during which surplus solar energy could be captured by battery storage systems rather than curtailed. Figure 5 directly compares modelled solar potential and realised solar generation, with shaded regions indicating solar-excess intervals. The frequency and magnitude of these excess periods on the representative day align with the broader temporal patterns identified in the multi-day analysis, reinforcing the suitability of short-duration storage for mitigating curtailment and enhancing renewable-energy utilisation. Importantly, this single-day analysis is intended solely to illustrate the operational manifestation of the statistically derived solar-excess characteristics and does not form the basis for storage sizing decisions, which are derived from the full dataset in subsequent sections. 4.7. Temporal structure of solar-excess events Following the quantification of daily solar-excess energy, this section examines the temporal structure of surplus generation by analysing contiguous solar-excess windows. Table 2 summarises the key characteristics of solar-excess charging windows identified from the operational dataset of 6 months. The results indicate that solar-excess events are typically of short to moderate duration, with a median window length of approximately 2 hours and an average duration of 2.68 hours. While longer events are occasionally observed, with a maximum duration of 8.75 hours, such cases are infrequent. The typical charging window occurs between 07:00 and 14:00, aligning closely with peak solar generation periods. In terms of magnitude, the average excess energy available within a window is approximately 629 MWh, with peak window energy reaching up to 2,350 MWh. The corresponding peak instantaneous surplus power is approximately 653 MW. Table 2 Characteristics of contiguous solar-excess charging windows. Metric Value Average duration (h) 2.68 Median duration (h) 2.00 Maximum duration (h) 8.75 Typical charging window 07:00–14:00 Average window energy (MWh) 629 Peak excess power (MW) 653 Figure 6 illustrates the diurnal distribution of solar-excess availability by combining the cumulative excess energy within each hourly interval with the frequency of surplus occurrences. The figure demonstrates a pronounced concentration of solar-excess events during the late morning and early afternoon hours. Both the magnitude of excess energy and the frequency of surplus intervals increase steadily from early morning, peak around midday, and decline towards the late afternoon. Taken together, the results in Table 2 and Fig. 5 indicate that solar-excess events are highly concentrated within a consistent midday window, typically lasting between 2 and 4 hours under normal operating conditions. The strong temporal alignment between peak excess energy and high occurrence frequency suggests that surplus generation is not only substantial but also predictably available during these hours. This temporal structure implies that short-duration battery storage systems, with discharge durations on the order of a few hours, are technically sufficient to absorb the majority of daily solar surplus without requiring long-duration energy storage. 4.8. Battery capacity determination Battery power capacity was determined from the distribution of instantaneous solar-excess power observed during the identified excess windows. The 90th percentile of peak surplus power was selected in order to capture the majority of solar-excess events while avoiding oversizing for rare extremes. This resulted in an indicative battery charging power requirement of approximately 540 MW. Battery energy capacity was derived from the distribution of cumulative solar-excess energy within contiguous excess windows. The 90th percentile of window-level excess energy, estimated at approximately 1,744 MWh, was adopted as a representative design value. To obtain the required installed battery capacity, this energy was adjusted to account for round-trip efficiency and allowable depth of discharge, and the round-trip efficiency assumed to be 0.90, and DoD is the operational depth of discharge was assumed to be 0.80. Applying these parameters yields a required battery energy capacity of approximately 2,422 MWh. The resulting power-to-energy ratio corresponds to an effective discharge duration of approximately 4–5 hours, which aligns closely with the observed temporal concentration of solar-excess availability within consistent midday windows. This indicates that short-duration, grid-scale battery storage is technically sufficient to capture the majority of daily solar surplus under typical operating conditions, without requiring long-duration energy storage solutions. The derived battery sizing parameters are consistent with established methodologies for storage design in renewable-integration and energy-arbitrage applications, where percentile-based approaches are commonly used to balance system adequacy against economic practicality [ 14 ], [ 15 ]. Table 3 summarises the resulting indicative battery power and energy capacity requirements. Table 3 Comparison Between Study-Based Battery Requirement and Existing / Proposed BESS Projects in Sri Lanka Project Rated Power (MW) Energy Capacity Status CEB Distributed BESS Program 160 640 MWh Called tenders for 16 substations [ 17 ] Hambantota Pilot Project 5 10.7 MWh Developed under a Korean grant [ 12 ] [ 16 ] Kolonnawa Grid-Scale 100 100 MWh Tender announced [ 18 ] Derived Battery Sizing 540 2400 MWh - When compared with existing and proposed battery energy-storage initiatives in Sri Lanka, the derived system-level battery requirement of approximately 540 MW power capacity and 2.4 GWh energy capacity is found to be of a comparable order of magnitude. The distributed 160 MW /640 MWh battery-energy-storage programme announced by the Ceylon Electricity Board (CEB) represents an important foundational step toward enabling solar-arbitrage operations at the system level, while the 5 MW / 10.7 MWh Hambantota pilot installation demonstrates the practical feasibility of short-duration storage at the regional scale. Although current projects are implemented in modular and distributed configurations, their aggregate scale is directionally consistent with the system-level storage requirements identified in this study, underscoring the feasibility of scaling battery storage to support renewable-energy integration across the national grid. It is important to note that the derived storage capacity does not imply a full charge-full discharge cycle within a single day. Charging occurs opportunistically during identified solar-excess intervals, while discharge is selectively deployed during high-cost evening peak periods dominated by oil-based thermal generation. Partial discharge and state-of-charge rollover across days are therefore permitted within the operational logic, reflecting realistic grid-scale battery-dispatch practices. The resulting sizing thus represents the capacity required to absorb typical midday solar surpluses and enable economically optimal peak shaving, rather than continuous full-energy shifting. 4.9 Optimised Battery Charge-Discharge Behaviour for a representative day To illustrate the operational behaviour of the optimised battery-dispatch framework, a representative day was selected from the analysis period and examined at 15-minute resolution. Figure 7 presents the temporal evolution of battery charging and discharging, state of charge (SOC), solar-excess availability, and total system demand. Total system demand is shown on a secondary axis to preserve visual clarity due to its larger magnitude, while storage-related variables are plotted on the primary axis. The results demonstrate clear temporal alignment between battery charging and identified solar-excess periods, with charging concentrated during midday hours when surplus solar availability is highest. As solar output declines toward the evening, the battery transitions to discharge mode, supplying energy during peak-demand periods characterised by elevated thermal-generation costs. The SOC trajectory remains within operational limits throughout the day and exhibits partial discharge rather than full daily cycling, reflecting realistic grid-scale battery operation. This behaviour confirms that the optimisation framework effectively captures surplus solar energy and redeploys it to mitigate evening peak demand, providing the operational basis for the system-cost and emission reductions reported in subsequent sections. 4.8. Economic Implications The economic impact of battery operation was assessed at the unit-cost level, reflecting the marginal cost differential between displaced thermal generation and the effective marginal cost of solar energy. During evening peak periods, marginal supply in Sri Lanka is typically provided by oil-fired thermal units, with a representative operating cost of approximately 80 LKR/kWh, based on PUCSL generation-cost data. In contrast, the marginal operating cost associated with stored solar energy is substantially lower, with effective unit costs for solar generation approximately 26 LKR/kWh, reflecting non-fuel contractual and operational components rather than variable fuel expenditure. Accordingly, each kilowatt-hour discharged from the battery during peak periods represents a net system-cost saving equivalent to the difference between oil-based thermal and solar-based marginal costs. This unit-cost differential underpins the economic rationale for battery-enabled solar shifting, demonstrating that storage deployment can reduce reliance on high-cost thermal generation without requiring market-based price arbitrage or investor-level revenue assumptions. 4.8. Environmental Implications By absorbing surplus solar generation during midday excess periods and displacing thermal generation during evening peak hours, the optimised battery storage system delivers measurable reductions in operational greenhouse gas emissions. Emission impacts were evaluated using a representative marginal emission factor of 0.80 tCO₂/MWh for displaced thermal generation, consistent with reported characteristics of Sri Lanka’s fossil-dominated marginal supply mix [ 21 ]. Under the demand-constrained dispatch framework, the battery discharges on the order of 1.5–1.7 GWh per day during high-cost evening peak periods, depending on system demand and solar availability. This level of discharge corresponds to avoided emissions of approximately 1,200-1,400 tCO₂ per day, reflecting direct displacement of oil-based thermal generation at the margin. When annualised over periods of sustained solar availability, the resulting emission reductions are estimated to be in the range of 0.30–0.35 MtCO₂ per year. While the present analysis focuses on operational emission impacts rather than a full lifecycle assessment of battery storage technologies, the results demonstrate that short-duration grid-scale battery storage can deliver substantial environmental benefits by improving solar-energy utilisation, reducing renewable curtailment, and lowering reliance on fossil-fuel-based generation during peak demand periods. Discussion The findings of this study highlight a critical and increasingly common challenge faced by power systems across the Global South: the coexistence of rapidly expanding variable renewable-energy capacity and persistent reliance on high-cost, fossil-based generation during peak demand periods. In the case of Sri Lanka, substantial midday solar surpluses are already evident, yet these resources remain underutilised due to structural limitations in system flexibility rather than a lack of renewable potential. This underscores the importance of storage not merely as an enabling technology, but as a system-level intervention that allows renewable energy to translate into tangible economic and environmental outcomes. The empirically derived battery-storage requirement demonstrates that meaningful system benefits can be achieved with moderate, short-duration storage capacities, rather than large-scale, long-duration solutions often associated with higher capital intensity. This finding is particularly relevant for developing economies, where fiscal constraints and investment risk necessitate carefully prioritised infrastructure deployment. The identification of a storage configuration that is technically sufficient, operationally realistic, and economically defensible suggests that renewable integration need not follow the cost-intensive pathways observed in advanced power systems. From a development perspective, the results emphasise that the value of battery storage in the Global South lies not only in emissions reduction, but also in reducing exposure to volatile fuel imports, improving system reliability, and lowering peak-period operating costs. By displacing marginal oil-fired generation during evening demand peaks, battery-enabled solar shifting contributes to greater energy security and shields the power system from external price shocks, which is an outcome of particular importance for import-dependent economies. The spatial implications of the analysis further reinforce the relevance of distributed, context-specific solutions. The concentration of solar surplus in high-resource districts such as Hambantota, Monaragala, and Ampara suggests that decentralised battery deployment can simultaneously address renewable curtailment and local grid constraints. Such an approach aligns with broader sustainable-development objectives, including regional economic participation, infrastructure resilience, and the potential for localised manufacturing and skills development in emerging clean-energy value chains. This study demonstrates the role of data-driven planning frameworks in supporting sustainable energy transitions. By leveraging high-frequency operational data rather than static planning assumptions, the analysis captures real system behaviour and temporal dynamics that are often overlooked in conventional planning exercises. This approach enables more adaptive and evidence-based decision-making, allowing policymakers to iteratively refine storage deployment strategies as renewable penetration increases and system conditions evolve. More broadly, the Sri Lankan case illustrates how countries in the Global South can pursue renewable-energy targets in a manner that balances ambition with practicality. Rather than treating storage as a future add-on, the findings suggest that integrating modest, strategically deployed battery systems early in the transition can unlock disproportionate system benefits. In this sense, battery storage emerges not only as a technological solution, but as a development-enabling asset that links renewable-energy expansion to affordability, resilience, and long-term sustainability. D. Limitations and Future Work This study constitutes an initial, data-driven assessment of battery storage requirements for facilitating solar integration in Sri Lanka’s power system. Nevertheless, several limitations should be acknowledged, which also point toward meaningful directions for future research. First, the analysis focuses on a restricted temporal window selected to ensure data completeness and high-resolution alignment across solar, generation, and cost datasets. While this period captures representative operational behaviour under high solar availability, it does not fully reflect seasonal variability associated with monsoonal cycles, hydropower inflows, or demand shifts. Extending the analysis to multi-seasonal and multi-year datasets would allow for a more robust characterisation of storage requirements under diverse climatic and hydrological conditions, particularly in systems where hydroelectric generation plays a significant balancing role. Second, the study concentrates on solar-driven surplus generation as the primary source of storage charging. Although this reflects the dominant driver of midday excess energy in Sri Lanka, future work could incorporate wind, biomass, and mini-hydro generation to evaluate cross-resource complementarities and their combined impact on storage utilisation. Such an extension would be particularly relevant as wind capacity expands and seasonal correlations between solar and wind resources become more pronounced. Third, battery sizing in the present framework is derived from statistical and energy-balance considerations, rather than from a fully optimised dynamic dispatch model. While this approach is appropriate for system-level planning and avoids overfitting to short-term operational conditions, it does not explicitly account for battery degradation, cycling constraints, or long-term cost recovery. Future studies could integrate optimisation-based or stochastic dispatch models to examine lifecycle performance, degradation-sensitive operation, and interactions with evolving tariff structures or market mechanisms. Finally, the analytical workflow employed here is largely deterministic and retrospective. Advancements in forecast-driven and AI-assisted energy analytics offer opportunities to enhance storage planning by incorporating probabilistic solar forecasting, adaptive charge discharge strategies, and real-time system feedback. Embedding such capabilities within cloud-based decision-support platforms could transform the present framework into a predictive and adaptive planning tool, supporting continuous refinement of storage deployment strategies as renewable penetration and system complexity increase. Conclusion This study underscores the strategic role of battery energy storage in enabling Sri Lanka’s renewable-energy transition by translating growing solar capacity into operational and system-level benefits. The analysis demonstrates how data-driven methodologies can bridge the gap between policy ambition and operational reality, providing decision-makers with quantitative insight into system flexibility, storage adequacy, and investment prioritisation. By grounding storage assessment in empirical high-resolution datasets, the proposed framework establishes a robust foundation for evidence-based planning that can adapt as renewable penetration and system complexity increase. As power systems become increasingly data-rich, the integration of advanced analytics, AI-assisted forecasting, and cloud-based modelling will be essential for managing variability, optimising dispatch, and maintaining grid stability. In this context, data-centric planning should be viewed not merely as a technical enhancement, but as a structural requirement for achieving resilient, cost-effective, and low-carbon electricity systems, particularly in emerging economies facing rapid renewable expansion and fiscal constraints. Beyond system-level efficiency gains, the findings have direct implications for sustainable development outcomes in emerging power systems. By enabling greater utilisation of domestically available solar resources and reducing reliance on oil-based thermal generation, grid-scale battery storage contributes to improved energy security, lower exposure to fuel price volatility, and reduced foreign-exchange outflows which are issues that are particularly acute in Global South economies. In the Sri Lankan context, short-duration battery storage can mitigate midday renewable curtailment while moderating evening peak costs, supporting more affordable and reliable electricity supply without large-scale infrastructure expansion. These co-benefits position battery storage not merely as a technical balancing asset, but as a strategic enabler of equitable and resilient energy transitions in developing countries. In conclusion, Sri Lanka’s pathway toward a sustainable energy future lies in coupling technological deployment with data intelligence. By embedding analytical insight into storage planning and system operation, renewable energy potential can be transformed into reliable, actionable, and equitable energy outcomes that support long-term development and climate objectives. Declarations Ethics, Consent to Participate, and Consent to Publish declarations Not applicable Clinical trial number not applicable. Competing interests The authors declare that they have no competing interests. Funding No external funding was received for conducting this study. Author Contribution W.D. Gammanpila (W.D.G.) conceptualised the study, performed data preprocessing, modelling, and analysis, and led the manuscript writing. A.C. Gammanpila (A.C.G.) contributed to methodological refinement, code validation, and manuscript editing. A.H.T.S. Kularathna (A.H.T.S.K.) supervised the research, provided guidance on study design, and reviewed the manuscript. N.K. Jayasooriya (N.K.J.) co-supervised the study, contributed to the theoretical and analytical framing, and reviewed and approved the final manuscript. Acknowledgements The authors gratefully acknowledge the support provided by the Department of Interdisciplinary Studies, Faculty of Engineering, and the Department of Computer Science, Faculty of Applied Sciences, University of Sri Jayewardenepura. The authors also thank all individuals who contributed valuable insights during the development of this work. Data Availability The datasets analysed during the current study are available from the corresponding author on reasonable request. References Intergovernmental Panel on Climate Change (IPCC). Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report. Cambridge University Press, Cambridge, UK. Chatzigeorgiou NG. A review on battery energy storage systems: Applications, developments, and research trends. Energy Rep, 12, 2024. Garttan G et al. Battery Energy Storage Systems: Energy Market Review, Energies, vol. 18, no. 15, 2025. Strbac G, Pudjianto D, Aunedi M, Djapic P, Teng F, Zhang X. Role and value of flexibility in facilitating cost-effective energy system decarbonisation. Progress Energy. 2020;2(4):042001. https://doi.org/10.1088/2516-1083/ab9b35 . Wankmüller F. Impact of battery degradation on energy arbitrage revenue. OSTI Rep, 2017. Md U, Hashmi A, Mukhopadhyay A, Bušić J, Elias, Kiedanski D. Optimal storage arbitrage under net metering using linear programming, arXiv preprint arXiv:1905.00418, 2019. Reniers JM, Mulder G, Ober-Blobaum S, Howey DA. Improving optimal control of grid-connected lithium-ion batteries through more accurate battery and degradation modelling. arXiv preprint arXiv:1710.04552, 2017. Sri Lanka Sustainable Energy Authority. Annual Report 2021, Colombo, Sri Lanka, 2021. Public Utilities Commission of Sri Lanka (PUCSL). Public consultation on rooftop solar PV development in Sri Lanka. Government Rep, 2022. Wijesena GHD, Amarasinghe AR. Solar energy and its role in Sri Lanka (Battle for Solar Energy / Soorya Bala Sangramaya). Int J Eng Trends Technol (IJETT). 2018;65(3):226–31. Malmgren BA, et al. Precipitation trends in Sri Lanka since the 1870s and relationships to El Niño–Southern Oscillation. Int J Climatol. 2003;23(10):1235–52. 10.1002/joc.921 . Ministry of Power and Energy. Sri Lanka), Construction of 5 MW/10.7 MWh battery energy storage system underway with Republic of Korea funding. Official Press Release; 2024. Khaniya B, Priyantha HG, Baduge N, Azamathulla HM, Rathnayake U. Impact of climate variability on hydropower generation: A case study from Sri Lanka. ISH J Hydraulic Eng. 2020;26(3):301–9. 10.1080/09715010.2018.1485516 . de la Torre S, Álvarez C, López A, Contreras J. Optimal battery sizing considering degradation for renewable energy systems. IET Renew Power Gener. 2019;13(11):1922–30. 10.1049/iet-rpg.2018.5489 . Rehman W, Wu J, Chatzivasileiadis P. Sizing energy storage system for energy arbitrage, Proc. IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 1–6, 2021, 10.1109/SmartGridComm.2021.9631948 . [. News D. Battery storage project planned for Hambantota, Daily News Online, Sept. 11, 2025. [Online]. Available: https://www.dailynews.lk/2025/09/11/business/854189/battery-storage-project-planned-for-hambantota [. Ceylon Electricity Board (CEB). Request for Proposals: 160 MW/640 MWh Distributed Battery Energy Storage Systems (BESS) – Volume II, Tender Ref: TR/REP&PM/ICB/2025/003/C, Colombo, Sri Lanka, Jul. 2025. [Online]. Available: https://www.ceb.lk/front_img/tender_pdf/250730120734160MW_BESS_RFP_Volume_II.pdf Ceylon Electricity Board (CEB). Procurement Notices: Grid-Scale BESS (Kolonnawa 100 MW/100 MWh), Tender Notice Portal, Colombo, Sri Lanka, 2025. [Online]. Available: https://ceb.lk/tender-notice/en [. Renne D et al. (2003) Solar Resource Assessment for Sri Lanka an Maldives [Preprint]. 10.2172/15004299 World Bank Group, ESMAP & Solargis. (2019). Global Solar Atlas: Solar Resource Map of Sri Lanka (DNI, 1999–2018). Washington, DC: World Bank. Available at: https://globalsolaratlas.info Public Utilities Commission of Sri Lanka (PUCSL). Study report on estimation of external cost of thermal power generation in Sri Lanka (Final draft report). Colombo: Public Utilities Commission of Sri Lanka; 2020. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8496983","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571990228,"identity":"c0206128-5672-499b-a749-0ae08304348b","order_by":0,"name":"W. D. Gammanpila","email":"data:image/png;base64,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","orcid":"","institution":"University of Sri Jayewardenepura","correspondingAuthor":true,"prefix":"","firstName":"W.","middleName":"D.","lastName":"Gammanpila","suffix":""},{"id":571990229,"identity":"9a459fd9-740a-4455-8b6b-801a7ce169e7","order_by":1,"name":"A. C. Gammanpila","email":"","orcid":"","institution":"University of Sri Jayewardenepura","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"C.","lastName":"Gammanpila","suffix":""},{"id":571990230,"identity":"abe6abec-c4d4-4459-acd5-a4aec18e8e68","order_by":2,"name":"A. H.T.S Kularathna","email":"","orcid":"","institution":"University of Sri Jayewardenepura","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"H.T.S","lastName":"Kularathna","suffix":""},{"id":571990231,"identity":"03631fa8-0612-47d8-9b84-17f4dc7e7c80","order_by":3,"name":"N. K. Jayasooriya","email":"","orcid":"","institution":"University of Sri Jayewardenepura","correspondingAuthor":false,"prefix":"","firstName":"N.","middleName":"K.","lastName":"Jayasooriya","suffix":""}],"badges":[],"createdAt":"2026-01-01 20:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8496983/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8496983/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101754468,"identity":"f81c01e8-ef33-4e16-96b7-6b5e75422dad","added_by":"auto","created_at":"2026-02-03 10:42:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1360029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpatial distribution of long-term DNI across Sri Lanka (1999-2018).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSource: World Bank Group [20]\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8496983/v1/44c26a96cbc7b04246fccaa1.png"},{"id":101744657,"identity":"373a76a6-2e16-40b5-9510-f8d74b1665f4","added_by":"auto","created_at":"2026-02-03 09:00:24","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":136588,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistrict-level distribution of installed solar PV capacity in Sri Lanka.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8496983/v1/3f5af319dfd308f431940504.jpeg"},{"id":101744658,"identity":"b9ee97fb-97f0-4458-a0a3-b54c847b3711","added_by":"auto","created_at":"2026-02-03 09:00:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":373622,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMulti-day diurnal heatmap of 15-minute modelled PV output\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8496983/v1/08246becbc798824c4bc10d2.jpg"},{"id":101754142,"identity":"372801ae-404b-46c2-8388-7940d97069ef","added_by":"auto","created_at":"2026-02-03 10:41:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":24212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTotal generation, modeled solar potential, and actual solar supply for a day\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8496983/v1/0d407fd9e1a1c2a4834dcd9e.png"},{"id":101754536,"identity":"e11c75b4-c4de-4f4f-94a6-d4d54f2030f4","added_by":"auto","created_at":"2026-02-03 10:42:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":17454,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSolar potential and actual solar generation for a representative date, with shaded regions indicating solar-excess intervals suitable for battery charging.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8496983/v1/e65c52ebedc52c130b2916e7.png"},{"id":101753413,"identity":"ddf321b8-1dfa-40dd-8ba3-16e3b709e34c","added_by":"auto","created_at":"2026-02-03 10:40:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":24625,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of Solar Excess Availability and frequency across 6 months\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8496983/v1/cce6e87a981bec7b1a44fd7b.png"},{"id":101744661,"identity":"f1bbaf0a-c103-435e-86ba-34f0ffb80610","added_by":"auto","created_at":"2026-02-03 09:00:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":63184,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBattery charging-discharging assessment for a representative day\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8496983/v1/300d771cde3b0302f0b1f583.png"},{"id":109540472,"identity":"a4325282-e0e7-40e8-87d3-57e751de5d21","added_by":"auto","created_at":"2026-05-19 09:56:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2056096,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8496983/v1/6d9417e1-bc3c-4ff2-b6dd-085d8fe44fd4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Data-Driven Assessment of Solar Surplus and Battery Storage for Cost and Emission Reduction in Sri Lanka’s Power System","fulltext":[{"header":"Introduction","content":"\u003cp\u003eElectric power systems worldwide are undergoing a profound transformation as renewable energy sources increasingly displace conventional fossil-fuel-based generation in response to climate change mitigation, energy-security concerns, and sustainability imperatives (IPCC, 2022). While this transition delivers substantial environmental benefits, the rapid expansion of variable renewable energy sources, particularly solar and wind has introduced new operational and planning challenges. These include intermittency, temporal mismatches between generation and demand, and recurrent periods of surplus output that place increasing pressure on system flexibility and dispatch practices [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Such challenges are now widely recognised as central constraints on the efficient integration of renewables in modern power systems [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA range of flexibility mechanisms can be employed to manage the variability of renewable generation, including demand-side response, grid reinforcement, interconnection, and flexible generation resources such as hydropower [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The suitability of these options, however, is highly system-specific and constrained by institutional, geographic, and fiscal factors. In many emerging power systems, demand-side participation remains limited, transmission expansion faces long lead times, and hydropower flexibility is increasingly affected by climate variability. Within this context, battery energy storage systems (BESS) have emerged as a critical enabler of sustainable power-system transformation by providing temporal flexibility between energy supply and demand [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. By storing energy during periods of high renewable availability or low system demand and discharging during peak-demand intervals, BESS can reduce renewable curtailment, improve load management, and limit reliance on high-cost and carbon-intensive peaking generation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Beyond short-term operational benefits, battery storage plays an increasingly important role in supporting broader sustainability objectives, including emissions reduction, system resilience, and long-term cost containment in electricity systems characterised by rising shares of variable renewable energy.\u003c/p\u003e \u003cp\u003eThese challenges and opportunities are particularly pronounced in Global South power systems, where rapid renewable deployment often occurs alongside constrained fiscal space, limited reserve margins, and growing electricity demand. Sri Lanka exemplifies this transition context. The country has committed to achieving 70% renewable electricity generation by 2030 and carbon neutrality by 2050 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This transition is supported by national initiatives led by the Ministry of Power and Energy and the Sri Lanka Sustainable Energy Authority (SLSEA), including the Soorya Bala Sangramaya (\u0026ldquo;Battle for Solar Energy\u0026rdquo;) programme, which promotes both rooftop and utility-scale solar deployment [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. As solar penetration has increased, however, the national grid has begun to experience recurrent midday energy surpluses, particularly during periods of high irradiance and relatively low demand [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In the absence of adequate storage or other flexibility mechanisms, these surpluses lead to renewable curtailment, inefficient system operation, and continued dependence on oil-based thermal generation during evening peak hours.\u003c/p\u003e \u003cp\u003eRecognising these emerging system constraints, Sri Lanka has initiated its grid-scale battery storage projects aimed at absorbing excess solar generation and shifting it to periods of higher system demand [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These early investments reflect growing policy recognition that energy storage is not merely a supporting technology, but a central component of a sustainable and development-oriented energy transition. However, despite this recognition, there remains a limited empirical basis for quantifying the scale of storage required to effectively utilise solar surplus and translate renewable capacity targets into operational, economic, and environmental outcomes.\u003c/p\u003e \u003cp\u003eAgainst this background, this study develops a data-driven analytical framework to quantify solar-excess availability and to estimate the corresponding grid-scale battery storage capacity required for effective utilisation within Sri Lanka\u0026rsquo;s power system. Using high-resolution datasets on generation cost, total generation, and solar irradiance, the analysis identifies feasible storage-sizing parameters. The findings aim to support evidence-based planning and investment decisions that enhance renewable-energy utilisation, reduce fossil-fuel dependence, and strengthen grid reliability in resource-constrained power systems.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study employs a multi-layer, data-driven analytical framework that integrates solar-resource modelling, system-cost profiling, and grid-scale battery storage simulation to evaluate the operational and environmental implications of utilizing solar-excess energy within Sri Lanka\u0026rsquo;s national power system. The framework is designed to capture high-frequency interactions between renewable generation, system costs, and storage operation under realistic grid conditions.\u003c/p\u003e \u003cp\u003eThe methodological approach comprises four principal components which are data acquisition and temporal harmonization of multi-source operational datasets, modelling of solar-generation potential and identification of solar-excess intervals, simulation of battery-storage dispatch under operational constraints and assessment of associated economic and environmental impacts arising from thermal generation displacement.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data Acquisition\u003c/h2\u003e \u003cp\u003eThis study constructs a high-resolution analytical framework by integrating empirical datasets obtained from Sri Lanka\u0026rsquo;s national energy institutions. System-wide electricity generation, fuel-specific marginal generation costs, and pollutant-specific emission intensities were sourced from the Public Utilities Commission of Sri Lanka (1SL), which publishes 15-minute operational dispatch and cost benchmarks aligned with the nationally approved tariff structure. Solar-resource data were obtained from the Sri Lanka Sustainable Energy Authority (SLSEA), which operates twelve precision-calibrated solar monitoring stations distributed across Sri Lanka\u0026rsquo;s major climatic zones. These stations provide 10-minute measurements of global horizontal irradiance (GHI) and photovoltaic (PV) potential.\u003c/p\u003e \u003cp\u003eDistrict-level installed solar capacity and associated technical specifications were obtained from the Ceylon Electricity Board (CEB). The analysis focuses on the first six months of the year, corresponding to the period for which continuous, quality-assured irradiance measurements were available across the SLSEA monitoring network. This interval captures a representative range of solar-generation conditions, including inter-monsoon and Southwest monsoon phases, enabling assessment of solar-excess behaviour and storage feasibility under varying irradiance and demand conditions. While the temporal scope does not extend to a full calendar year, the selected period provides sufficient variability to support robust estimation of operational storage requirements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data Conditioning and Temporal Harmonization\u003c/h2\u003e \u003cp\u003eTo ensure consistency across datasets, all time series were harmonized to a common 15-minute temporal resolution. The 10-minute GHI data from SLSEA were resampled using an irradiance-preserving method to avoid bias introduced by temporal smoothing. Basic quality checks were applied to identify and remove outliers arising from sensor errors or data spikes.\u003c/p\u003e \u003cp\u003eMissing observations were reconstructed using interpolation across neighbouring monitoring stations, taking advantage of spatial consistency within the twelve-station network. All data were converted to Sri Lanka Standard Time (GMT\u0026thinsp;+\u0026thinsp;05:30) and temporally aligned to ensure coherence across sources. Data validity was verified by examining monthly irradiance patterns to confirm that key monsoonal characteristics were preserved.\u003c/p\u003e \u003cp\u003eThe final dataset comprises a continuous, multi-source, high-resolution time series covering the first six months of the year and is suitable for analysing solar-excess behaviour and battery-storage feasibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Solar Generation Potential Modelling\u003c/h2\u003e \u003cp\u003eSolar-generation potential was estimated using a capacity-weighted irradiance-power transformation consistent with the resolution and structure of the available datasets. For each SLSEA station, GHI measurements were converted into potential PV output using a standard efficiency-based scaling approach expressed as Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Pi\\left(t\\right)=GHIi\\left(t\\right)\\eta\\:PV$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere ηPV​ represents the aggregate module-level conversion efficiency and Ci​ denotes the effective installed PV capacity allocated to station ii. This formulation provides a physically consistent, computationally tractable representation of solar availability without requiring detailed module temperature or angle-of-incidence modelling, which were not part of the empirical data collection. District-level installed capacities from CEB were spatially mapped to the twelve irradiance stations using a proportional allocation scheme. Aggregation across stations yielded a national 15-minute solar potential series for the full twelve months, capturing both spatial and seasonal variations in irradiance profiles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Solar-Excess Identification Across the Annual Cycle\u003c/h2\u003e \u003cp\u003eSolar-excess periods were identified by comparing the modelled PV potential with actual renewable dispatch recorded by PUCSL. For each interval is given in Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:Eexcess\\left(t\\right)=max(Psolar,potential(t)-Prenewable,actual(t),0)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere positive values represent underutilised or implicitly curtailed solar energy. Extending this calculation across all twelve months captures monsoon-season dips, inter-monsoon peaks, and seasonal shifts in demand-supply synchronisation. Operational constraints reflecting national dispatch priorities such as evening-peak charging restrictions were incorporated to avoid overstating storage feasibility. The resulting full-year solar-excess profile provides a high-fidelity classification of storage opportunities, renewable-utilisation gaps, and seasonal variability in surplus energy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Analysis of Generation Cost Dynamics and Arbitrage Opportunity\u003c/h2\u003e \u003cp\u003eThe generation cost dataset, obtained from the PUCSL, was analyzed to examine temporal variations in the unit cost of electricity generation throughout a typical 24-hour cycle. These costs, expressed in LKR per kilowatt-hour (LKR/kWh), represent the composite generation mix encompassing coal, oil (both CEB and IPP), hydro, wind, and solar sources under the January 2025 national tariff framework.\u003c/p\u003e \u003cp\u003eBy aligning the cost profile with the corresponding generation data, the study identified distinct low-cost and high-cost intervals within the daily cycle. This temporal mapping of generation cost against resource availability provided a foundational input for assessing energy-arbitrage potential..\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Battery Sizing and Benchmark Comparison\u003c/h2\u003e \u003cp\u003eThis stage outlines the methodological approach used to determine the battery-storage capacity required to utilize the identified solar-excess energy and to benchmark the derived estimates against existing national initiatives. The total solar-excess energy, obtained through time-integration of positive generation differentials, was used as the primary input for sizing calculations. Power-capacity requirements were inferred from the maximum instantaneous surplus observed within the solar-excess window.\u003c/p\u003e \u003cp\u003eTo convert theoretical energy to practical battery capacity, standard parameters for grid-scale lithium-ion systems were applied, including round-trip efficiency and allowable depth of discharge. These factors provided an adjusted estimate of usable energy storage under realistic operating conditions.\u003c/p\u003e \u003cp\u003eFollowing the sizing procedure, the results were systematically compared with publicly available specifications of Sri Lanka\u0026rsquo;s ongoing and proposed battery-energy-storage projects, such as pilot installations and grid-scale programs announced by the Ceylon Electricity Board. This benchmarking step ensured that the analytical framework remained aligned with current technological and policy developments, while establishing a consistent basis for interpreting subsequent results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Battery Charge-Discharge Optimisation Framework\u003c/h2\u003e \u003cp\u003eTo ensure that the battery-dispatch behaviour underlying the system-cost and environmental assessments reflects economically rational operation, a constrained charge-discharge optimisation framework was employed. The battery operation is formulated over discrete 15-minute intervals consistent with the system-operator dataset.\u003c/p\u003e \u003cp\u003eThe decision variables comprise battery charging energy \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{ch}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e, battery discharging energy \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{dis}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e, and state of charge \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:SOC\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e at each interval \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e. The inter-temporal evolution of the battery state of charge is governed by the following balance Eq.\u0026nbsp;(3).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:SOC\\left(t+1\\right)=SOC\\left(t\\right)+{\\eta\\:}_{ch}{E}_{ch}\\left(t\\right)-\\frac{{E}_{dis}\\left(t\\right)}{{\\eta\\:}_{dis}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{ch}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\eta\\:}_{dis}\\)\u003c/span\u003e\u003c/span\u003e denote charging and discharging efficiencies, respectively.\u003c/p\u003e \u003cp\u003eBattery operation is subject to standard operational constraints, including limits on energy capacity, maximum charging and discharging power, allowable depth of discharge, and non-negativity of decision variables. Charging actions are restricted to intervals identified as solar-excess periods, while discharging is permitted during periods characterised by elevated marginal thermal-generation costs.\u003c/p\u003e \u003cp\u003eThe resulting charge-discharge schedule is determined to minimise total system operating cost by maximising displacement of high-cost thermal generation, thereby providing the operational basis for the avoided-cost and emission-reductions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.8 System-Cost Reduction and Environmental Assessment\u003c/h2\u003e \u003cp\u003eGiven Sri Lanka\u0026rsquo;s vertically integrated power structure without wholesale market pricing, economic impacts were evaluated using marginal thermal-displacement cost rather than price arbitrage. For each discharge interval, avoided thermal-generation cost was computed as Eq.\u0026nbsp;4 below.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Cavoided\\left(t\\right)=Edis\\left(t\\right)\\cdot\\:Cthermal\\left(t\\right)\\:\\:-\\:\\:CSolar\\left(t\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this formulation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{\\text{avoided}}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e denotes the avoided thermal-generation cost at time interval \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e, expressed in Sri Lankan Rupees (LKR). The term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{\\text{dis}}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the electrical energy discharged from the battery during interval \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e, measured in kilowatt-hours (kWh). The variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{\\text{thermal}}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e refers to the real-time marginal cost of thermal generation (LKR/kWh), computed from PUCSL\u0026rsquo;s publicly available fuel-cost and heat-rate datasets for oil-fired power plants. Together, these variables quantify the economic benefit accrued whenever battery discharge offsets otherwise required thermal generation. All variables are defined at a uniform 15-minute temporal resolution consistent with the system-operator dataset.\u003c/p\u003e \u003cp\u003eEnvironmental effects were quantified by calculating avoided emissions whenever stored solar energy displaced thermal generation as Eq.\u0026nbsp;5 below.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:Avoided\\:Emissionk\\left(t\\right)=Edis\\left(t\\right)\\cdot\\:EFk\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(5\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eusing PUCSL emission factors for CO₂, SO₂, NOₓ, and PM. These avoided emissions were subsequently monetised using pollutant-specific externality coefficients, reflecting public-health, ecological, and agricultural damages. Because the study spans all monsoonal phases, the resulting environmental-savings profile captures the seasonal interaction between solar availability, thermal dependence, and pollutant intensities. Annualised environmental savings were obtained by aggregating avoided-damage values across the full-year simulation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Seasonal Dynamics of Solar Availability\u003c/h2\u003e \u003cp\u003eSri Lanka\u0026rsquo;s solar resource exhibits pronounced monsoonal seasonality driven by the country\u0026rsquo;s unique climatic regime [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Analysis of the full-year irradiance dataset reveals strong asymmetry between monsoon and inter-monsoon periods, with solar potential peaking during the two inter-monsoon seasons (March-April and August-September) when clear-sky conditions and reduced cloud cover produce consistently high GHI profiles across all twelve SLSEA monitoring stations.\u003c/p\u003e \u003cp\u003eIn contrast, the Southwest monsoon (May-June) is marked by extensive cloudiness and increased atmospheric moisture, resulting in notably lower irradiance levels, while the Northeast monsoon (November-January) provides moderate but more variable solar availability due to shifting wind and cloud patterns. These patterns align with Sri Lanka\u0026rsquo;s established monsoonal climatology, which is characterised by distinct Yala and Maha monsoon regimes and strong intra-annual variability in solar [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. When aggregated nationally, the observed seasonal fluctuations underscore the importance of using full-year irradiance records rather than single-month snapshots to accurately capture the operational dynamics and variability of solar generation potential in Sri Lanka.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Spatial Distribution of Solar Resource\u003c/h2\u003e \u003cp\u003eWhile seasonal dynamics describe how Sri Lanka\u0026rsquo;s solar resource varies over time, understanding the country\u0026rsquo;s solar-generation potential also requires examining its spatial distribution. Solar availability is not uniform across the island which it is shaped by complex interactions between regional climate regimes, topography, monsoonal cloud cover, and land-surface characteristics [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These spatial differences influence where large-scale solar development is most feasible, how much energy can be harvested from different regions, and the extent to which national output depends on geographically uneven resource strengths. Assessing the spatial variability of irradiance therefore provides essential context for interpreting modelled PV output and identifying opportunities for future solar expansion.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the spatial distribution of long-term Direct Normal Irradiance (DNI) derived from the World Bank\u0026rsquo;s Global Solar Atlas. The map highlights a pronounced North-South gradient, with the Dry Zone, particularly the Northern, North Central, Eastern, and Southern provinces which are exhibiting DNI values exceeding 4.2\u0026ndash;4.6 kWh/m\u0026sup2; per day, reflecting clearer skies and semi-arid conditions. In contrast, Wet Zone regions such as Colombo, Gampaha, Kandy, and surrounding high-elevation areas show substantially lower irradiance due to persistent cloud cover and higher moisture content.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: World Bank Group [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Spatial Distribution of Installed Solar Capacity\u003c/h2\u003e \u003cp\u003eTo contextualise the spatial heterogeneity of solar deployment across Sri Lanka, the geographic to contextualise the spatial heterogeneity of solar deployment across Sri Lanka, the geographic distribution of installed PV capacity was mapped using district-level data obtained from the CEB. As of the study period, Sri Lanka\u0026rsquo;s total installed solar capacity is approximately 1.7 GW. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the spatial allocation of this installed capacity across districts, highlighting pronounced regional variation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis spatial perspective provides essential context for interpreting subsequent analyses of solar generation potential, surplus formation, and the localisation of battery storage requirements within the national power system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Multi-Day Solar Generation Patterns\u003c/h2\u003e \u003cp\u003eTo examine the intra-day and inter-day temporal structure of solar generation, a multi-day diurnal heatmap of 15-minute modelled photovoltaic (PV) output was constructed for a selected 14-day period representative of typical system conditions. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis representation highlights the progression of solar availability across consecutive days and illustrates how irradiance ramps, midday peaks, and evening declines manifest under typical system conditions. By visualising these day-to-day dynamics, the analysis provides insight into the temporal regularity of solar generation and the operational opportunities and constraints relevant for battery-charging strategies and grid-integration planning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Identification of Solar Excess Periods\u003c/h2\u003e \u003cp\u003eThe identification of solar excess periods was conducted to determine time intervals suitable for battery charging based on the relationship between solar supply potential and actual grid generation. The identification of solar excess periods was conducted to determine time intervals suitable for battery charging based on the relationship between solar supply potential and actual grid generation. The analysis focuses on the January-June period, which corresponds to Sri Lanka\u0026rsquo;s relatively high solar-availability months and captures both inter-monsoon and Southwest monsoon conditions, where solar-excess occurrence is most pronounced. The modelled solar potential time series was aligned with total system generation and generation cost data obtained from the PUCSL at a uniform 15-minute temporal resolution.\u003c/p\u003e \u003cp\u003eA time interval was classified as a solar excess period when the modelled solar potential exceeded the observed renewable generation contribution to the grid. Such intervals represent instances in which the available solar resource surpassed the renewable component utilised within the grid mix, implying either renewable curtailment or unused solar generation capacity due to operational constraints.\u003c/p\u003e \u003cp\u003eDaily solar-excess energy was computed by integrating positive solar-excess values across all 15-minute intervals within each day. This aggregation enables quantification of both the magnitude and variability of surplus solar energy at a daily resolution while preserving the high-frequency operational characteristics of solar generation.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarises the descriptive statistics of daily solar-excess energy derived from the full study period. The results indicate that daily surplus solar energy frequently exceeds 1 GWh, with a mean value of approximately 1,030 MWh and a median of 1,019 MWh. Substantial day-to-day variability is observed, as reflected by the wide interquartile range and a standard deviation of 614 MWh. While some days exhibit minimal surplus, peak days reach excess levels as high as 2,350 MWh.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDaily solar-excess energy statistics based on high-resolution operational data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily Excess Energy (MWh)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25th percentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75th percentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis distribution highlights both the regular occurrence of surplus solar availability and the presence of high-surplus events, underscoring the need for flexible, short-duration storage capacity rather than sizing decisions based on isolated peak-day conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Single-Day Operational Dynamics\u003c/h2\u003e \u003cp\u003eTo visualise the intra-day operational implications of the statistically identified solar-excess patterns, the selected representative day was examined in detail. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the hourly evolution of total system generation alongside modelled solar potential and actual solar output. During the early hours (00:00\u0026ndash;05:30), total generation remained within the range of approximately 1,500-1,800 MW and was dominated by thermal sources. Following sunrise, solar output increased rapidly, peaking around midday.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA consistent divergence between modelled solar potential and actual solar generation is observed between approximately 10:00 and 13:00, indicating periods of underutilised solar availability attributable to grid absorption limits or curtailment. These intervals correspond to potential charging windows during which surplus solar energy could be captured by battery storage systems rather than curtailed.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e directly compares modelled solar potential and realised solar generation, with shaded regions indicating solar-excess intervals. The frequency and magnitude of these excess periods on the representative day align with the broader temporal patterns identified in the multi-day analysis, reinforcing the suitability of short-duration storage for mitigating curtailment and enhancing renewable-energy utilisation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eImportantly, this single-day analysis is intended solely to illustrate the operational manifestation of the statistically derived solar-excess characteristics and does not form the basis for storage sizing decisions, which are derived from the full dataset in subsequent sections.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.7. Temporal structure of solar-excess events\u003c/h2\u003e \u003cp\u003eFollowing the quantification of daily solar-excess energy, this section examines the temporal structure of surplus generation by analysing contiguous solar-excess windows. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarises the key characteristics of solar-excess charging windows identified from the operational dataset of 6 months. The results indicate that solar-excess events are typically of short to moderate duration, with a median window length of approximately 2 hours and an average duration of 2.68 hours.\u003c/p\u003e \u003cp\u003eWhile longer events are occasionally observed, with a maximum duration of 8.75 hours, such cases are infrequent. The typical charging window occurs between 07:00 and 14:00, aligning closely with peak solar generation periods. In terms of magnitude, the average excess energy available within a window is approximately 629 MWh, with peak window energy reaching up to 2,350 MWh. The corresponding peak instantaneous surplus power is approximately 653 MW.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of contiguous solar-excess charging windows.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage duration (h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian duration (h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum duration (h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypical charging window\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e07:00\u0026ndash;14:00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage window energy (MWh)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak excess power (MW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the diurnal distribution of solar-excess availability by combining the cumulative excess energy within each hourly interval with the frequency of surplus occurrences. The figure demonstrates a pronounced concentration of solar-excess events during the late morning and early afternoon hours. Both the magnitude of excess energy and the frequency of surplus intervals increase steadily from early morning, peak around midday, and decline towards the late afternoon.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTaken together, the results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e indicate that solar-excess events are highly concentrated within a consistent midday window, typically lasting between 2 and 4 hours under normal operating conditions. The strong temporal alignment between peak excess energy and high occurrence frequency suggests that surplus generation is not only substantial but also predictably available during these hours. This temporal structure implies that short-duration battery storage systems, with discharge durations on the order of a few hours, are technically sufficient to absorb the majority of daily solar surplus without requiring long-duration energy storage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.8. Battery capacity determination\u003c/h2\u003e \u003cp\u003eBattery power capacity was determined from the distribution of instantaneous solar-excess power observed during the identified excess windows. The 90th percentile of peak surplus power was selected in order to capture the majority of solar-excess events while avoiding oversizing for rare extremes. This resulted in an indicative battery charging power requirement of approximately 540 MW.\u003c/p\u003e \u003cp\u003eBattery energy capacity was derived from the distribution of cumulative solar-excess energy within contiguous excess windows. The 90th percentile of window-level excess energy, estimated at approximately 1,744 MWh, was adopted as a representative design value. To obtain the required installed battery capacity, this energy was adjusted to account for round-trip efficiency and allowable depth of discharge, and the round-trip efficiency assumed to be 0.90, and DoD is the operational depth of discharge was assumed to be 0.80. Applying these parameters yields a required battery energy capacity of approximately 2,422 MWh.\u003c/p\u003e \u003cp\u003eThe resulting power-to-energy ratio corresponds to an effective discharge duration of approximately 4\u0026ndash;5 hours, which aligns closely with the observed temporal concentration of solar-excess availability within consistent midday windows. This indicates that short-duration, grid-scale battery storage is technically sufficient to capture the majority of daily solar surplus under typical operating conditions, without requiring long-duration energy storage solutions.\u003c/p\u003e \u003cp\u003eThe derived battery sizing parameters are consistent with established methodologies for storage design in renewable-integration and energy-arbitrage applications, where percentile-based approaches are commonly used to balance system adequacy against economic practicality [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarises the resulting indicative battery power and energy capacity requirements.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison Between Study-Based Battery Requirement and Existing / Proposed BESS Projects in Sri Lanka\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRated Power (MW)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnergy Capacity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEB Distributed BESS Program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e640 MWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCalled tenders for 16 substations [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHambantota Pilot Project\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.7 MWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeveloped under a Korean grant [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKolonnawa Grid-Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 MWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTender announced [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDerived Battery Sizing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2400 MWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen compared with existing and proposed battery energy-storage initiatives in Sri Lanka, the derived system-level battery requirement of approximately 540 MW power capacity and 2.4 GWh energy capacity is found to be of a comparable order of magnitude. The distributed 160 MW /640 MWh battery-energy-storage programme announced by the Ceylon Electricity Board (CEB) represents an important foundational step toward enabling solar-arbitrage operations at the system level, while the 5 MW / 10.7 MWh Hambantota pilot installation demonstrates the practical feasibility of short-duration storage at the regional scale. Although current projects are implemented in modular and distributed configurations, their aggregate scale is directionally consistent with the system-level storage requirements identified in this study, underscoring the feasibility of scaling battery storage to support renewable-energy integration across the national grid.\u003c/p\u003e \u003cp\u003eIt is important to note that the derived storage capacity does not imply a full charge-full discharge cycle within a single day. Charging occurs opportunistically during identified solar-excess intervals, while discharge is selectively deployed during high-cost evening peak periods dominated by oil-based thermal generation. Partial discharge and state-of-charge rollover across days are therefore permitted within the operational logic, reflecting realistic grid-scale battery-dispatch practices. The resulting sizing thus represents the capacity required to absorb typical midday solar surpluses and enable economically optimal peak shaving, rather than continuous full-energy shifting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.9 Optimised Battery Charge-Discharge Behaviour for a representative day\u003c/h2\u003e \u003cp\u003eTo illustrate the operational behaviour of the optimised battery-dispatch framework, a representative day was selected from the analysis period and examined at 15-minute resolution. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the temporal evolution of battery charging and discharging, state of charge (SOC), solar-excess availability, and total system demand. Total system demand is shown on a secondary axis to preserve visual clarity due to its larger magnitude, while storage-related variables are plotted on the primary axis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results demonstrate clear temporal alignment between battery charging and identified solar-excess periods, with charging concentrated during midday hours when surplus solar availability is highest. As solar output declines toward the evening, the battery transitions to discharge mode, supplying energy during peak-demand periods characterised by elevated thermal-generation costs. The SOC trajectory remains within operational limits throughout the day and exhibits partial discharge rather than full daily cycling, reflecting realistic grid-scale battery operation. This behaviour confirms that the optimisation framework effectively captures surplus solar energy and redeploys it to mitigate evening peak demand, providing the operational basis for the system-cost and emission reductions reported in subsequent sections.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.8. Economic Implications\u003c/h2\u003e \u003cp\u003eThe economic impact of battery operation was assessed at the unit-cost level, reflecting the marginal cost differential between displaced thermal generation and the effective marginal cost of solar energy. During evening peak periods, marginal supply in Sri Lanka is typically provided by oil-fired thermal units, with a representative operating cost of approximately 80 LKR/kWh, based on PUCSL generation-cost data. In contrast, the marginal operating cost associated with stored solar energy is substantially lower, with effective unit costs for solar generation approximately 26 LKR/kWh, reflecting non-fuel contractual and operational components rather than variable fuel expenditure.\u003c/p\u003e \u003cp\u003eAccordingly, each kilowatt-hour discharged from the battery during peak periods represents a net system-cost saving equivalent to the difference between oil-based thermal and solar-based marginal costs. This unit-cost differential underpins the economic rationale for battery-enabled solar shifting, demonstrating that storage deployment can reduce reliance on high-cost thermal generation without requiring market-based price arbitrage or investor-level revenue assumptions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.8. Environmental Implications\u003c/h2\u003e \u003cp\u003eBy absorbing surplus solar generation during midday excess periods and displacing thermal generation during evening peak hours, the optimised battery storage system delivers measurable reductions in operational greenhouse gas emissions. Emission impacts were evaluated using a representative marginal emission factor of 0.80 tCO₂/MWh for displaced thermal generation, consistent with reported characteristics of Sri Lanka\u0026rsquo;s fossil-dominated marginal supply mix [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnder the demand-constrained dispatch framework, the battery discharges on the order of 1.5\u0026ndash;1.7 GWh per day during high-cost evening peak periods, depending on system demand and solar availability. This level of discharge corresponds to avoided emissions of approximately 1,200-1,400 tCO₂ per day, reflecting direct displacement of oil-based thermal generation at the margin.\u003c/p\u003e \u003cp\u003eWhen annualised over periods of sustained solar availability, the resulting emission reductions are estimated to be in the range of 0.30\u0026ndash;0.35 MtCO₂ per year. While the present analysis focuses on operational emission impacts rather than a full lifecycle assessment of battery storage technologies, the results demonstrate that short-duration grid-scale battery storage can deliver substantial environmental benefits by improving solar-energy utilisation, reducing renewable curtailment, and lowering reliance on fossil-fuel-based generation during peak demand periods.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study highlight a critical and increasingly common challenge faced by power systems across the Global South: the coexistence of rapidly expanding variable renewable-energy capacity and persistent reliance on high-cost, fossil-based generation during peak demand periods. In the case of Sri Lanka, substantial midday solar surpluses are already evident, yet these resources remain underutilised due to structural limitations in system flexibility rather than a lack of renewable potential. This underscores the importance of storage not merely as an enabling technology, but as a system-level intervention that allows renewable energy to translate into tangible economic and environmental outcomes.\u003c/p\u003e \u003cp\u003eThe empirically derived battery-storage requirement demonstrates that meaningful system benefits can be achieved with moderate, short-duration storage capacities, rather than large-scale, long-duration solutions often associated with higher capital intensity. This finding is particularly relevant for developing economies, where fiscal constraints and investment risk necessitate carefully prioritised infrastructure deployment. The identification of a storage configuration that is technically sufficient, operationally realistic, and economically defensible suggests that renewable integration need not follow the cost-intensive pathways observed in advanced power systems.\u003c/p\u003e \u003cp\u003eFrom a development perspective, the results emphasise that the value of battery storage in the Global South lies not only in emissions reduction, but also in reducing exposure to volatile fuel imports, improving system reliability, and lowering peak-period operating costs. By displacing marginal oil-fired generation during evening demand peaks, battery-enabled solar shifting contributes to greater energy security and shields the power system from external price shocks, which is an outcome of particular importance for import-dependent economies.\u003c/p\u003e \u003cp\u003eThe spatial implications of the analysis further reinforce the relevance of distributed, context-specific solutions. The concentration of solar surplus in high-resource districts such as Hambantota, Monaragala, and Ampara suggests that decentralised battery deployment can simultaneously address renewable curtailment and local grid constraints. Such an approach aligns with broader sustainable-development objectives, including regional economic participation, infrastructure resilience, and the potential for localised manufacturing and skills development in emerging clean-energy value chains. This study demonstrates the role of data-driven planning frameworks in supporting sustainable energy transitions. By leveraging high-frequency operational data rather than static planning assumptions, the analysis captures real system behaviour and temporal dynamics that are often overlooked in conventional planning exercises. This approach enables more adaptive and evidence-based decision-making, allowing policymakers to iteratively refine storage deployment strategies as renewable penetration increases and system conditions evolve.\u003c/p\u003e \u003cp\u003eMore broadly, the Sri Lankan case illustrates how countries in the Global South can pursue renewable-energy targets in a manner that balances ambition with practicality. Rather than treating storage as a future add-on, the findings suggest that integrating modest, strategically deployed battery systems early in the transition can unlock disproportionate system benefits. In this sense, battery storage emerges not only as a technological solution, but as a development-enabling asset that links renewable-energy expansion to affordability, resilience, and long-term sustainability.\u003c/p\u003e\n\u003ch3\u003eD. Limitations and Future Work\u003c/h3\u003e\n\u003cp\u003eThis study constitutes an initial, data-driven assessment of battery storage requirements for facilitating solar integration in Sri Lanka\u0026rsquo;s power system. Nevertheless, several limitations should be acknowledged, which also point toward meaningful directions for future research.\u003c/p\u003e \u003cp\u003eFirst, the analysis focuses on a restricted temporal window selected to ensure data completeness and high-resolution alignment across solar, generation, and cost datasets. While this period captures representative operational behaviour under high solar availability, it does not fully reflect seasonal variability associated with monsoonal cycles, hydropower inflows, or demand shifts. Extending the analysis to multi-seasonal and multi-year datasets would allow for a more robust characterisation of storage requirements under diverse climatic and hydrological conditions, particularly in systems where hydroelectric generation plays a significant balancing role.\u003c/p\u003e \u003cp\u003eSecond, the study concentrates on solar-driven surplus generation as the primary source of storage charging. Although this reflects the dominant driver of midday excess energy in Sri Lanka, future work could incorporate wind, biomass, and mini-hydro generation to evaluate cross-resource complementarities and their combined impact on storage utilisation. Such an extension would be particularly relevant as wind capacity expands and seasonal correlations between solar and wind resources become more pronounced.\u003c/p\u003e \u003cp\u003eThird, battery sizing in the present framework is derived from statistical and energy-balance considerations, rather than from a fully optimised dynamic dispatch model. While this approach is appropriate for system-level planning and avoids overfitting to short-term operational conditions, it does not explicitly account for battery degradation, cycling constraints, or long-term cost recovery. Future studies could integrate optimisation-based or stochastic dispatch models to examine lifecycle performance, degradation-sensitive operation, and interactions with evolving tariff structures or market mechanisms.\u003c/p\u003e \u003cp\u003eFinally, the analytical workflow employed here is largely deterministic and retrospective. Advancements in forecast-driven and AI-assisted energy analytics offer opportunities to enhance storage planning by incorporating probabilistic solar forecasting, adaptive charge discharge strategies, and real-time system feedback. Embedding such capabilities within cloud-based decision-support platforms could transform the present framework into a predictive and adaptive planning tool, supporting continuous refinement of storage deployment strategies as renewable penetration and system complexity increase.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study underscores the strategic role of battery energy storage in enabling Sri Lanka\u0026rsquo;s renewable-energy transition by translating growing solar capacity into operational and system-level benefits. The analysis demonstrates how data-driven methodologies can bridge the gap between policy ambition and operational reality, providing decision-makers with quantitative insight into system flexibility, storage adequacy, and investment prioritisation. By grounding storage assessment in empirical high-resolution datasets, the proposed framework establishes a robust foundation for evidence-based planning that can adapt as renewable penetration and system complexity increase.\u003c/p\u003e \u003cp\u003eAs power systems become increasingly data-rich, the integration of advanced analytics, AI-assisted forecasting, and cloud-based modelling will be essential for managing variability, optimising dispatch, and maintaining grid stability. In this context, data-centric planning should be viewed not merely as a technical enhancement, but as a structural requirement for achieving resilient, cost-effective, and low-carbon electricity systems, particularly in emerging economies facing rapid renewable expansion and fiscal constraints.\u003c/p\u003e \u003cp\u003eBeyond system-level efficiency gains, the findings have direct implications for sustainable development outcomes in emerging power systems. By enabling greater utilisation of domestically available solar resources and reducing reliance on oil-based thermal generation, grid-scale battery storage contributes to improved energy security, lower exposure to fuel price volatility, and reduced foreign-exchange outflows which are issues that are particularly acute in Global South economies. In the Sri Lankan context, short-duration battery storage can mitigate midday renewable curtailment while moderating evening peak costs, supporting more affordable and reliable electricity supply without large-scale infrastructure expansion. These co-benefits position battery storage not merely as a technical balancing asset, but as a strategic enabler of equitable and resilient energy transitions in developing countries.\u003c/p\u003e \u003cp\u003eIn conclusion, Sri Lanka\u0026rsquo;s pathway toward a sustainable energy future lies in coupling technological deployment with data intelligence. By embedding analytical insight into storage planning and system operation, renewable energy potential can be transformed into reliable, actionable, and equitable energy outcomes that support long-term development and climate objectives.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical trial number\u003c/strong\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo external funding was received for conducting this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW.D. Gammanpila (W.D.G.) conceptualised the study, performed data preprocessing, modelling, and analysis, and led the manuscript writing. A.C. Gammanpila (A.C.G.) contributed to methodological refinement, code validation, and manuscript editing. A.H.T.S. Kularathna (A.H.T.S.K.) supervised the research, provided guidance on study design, and reviewed the manuscript. N.K. Jayasooriya (N.K.J.) co-supervised the study, contributed to the theoretical and analytical framing, and reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors gratefully acknowledge the support provided by the Department of Interdisciplinary Studies, Faculty of Engineering, and the Department of Computer Science, Faculty of Applied Sciences, University of Sri Jayewardenepura. The authors also thank all individuals who contributed valuable insights during the development of this work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIntergovernmental Panel on Climate Change (IPCC). Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report. Cambridge University Press, Cambridge, UK.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatzigeorgiou NG. A review on battery energy storage systems: Applications, developments, and research trends. Energy Rep, 12, 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarttan G et al. 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Colombo: Public Utilities Commission of Sri Lanka; 2020.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"battery energy storage, solar surplus, renewable integration, energy transition, Global South, Sri Lanka","lastPublishedDoi":"10.21203/rs.3.rs-8496983/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8496983/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRapid growth in solar photovoltaic deployment across the Global South is increasingly constrained by temporal mismatches between generation and demand, resulting in renewable curtailment, inefficient system operation, and continued reliance on oil-based peaking generation. Grid-scale battery energy storage systems (BESS) are widely recognised as a key enabler of renewable integration; however, empirically grounded assessments of their operational and developmental value remain limited in emerging power systems. This study develops a high-resolution, data-driven analytical framework to quantify solar-excess availability and derive indicative battery-storage requirements for Sri Lanka\u0026rsquo;s national power system. Using 15-minute operational, irradiance, generation, and cost datasets obtained from national regulatory and system-planning institutions, the analysis integrates solar-generation potential modelling, solar-excess identification, and storage-dispatch simulation to evaluate feasible charge-discharge windows and system-level impacts. Results show that existing solar deployment already produces substantial and recurrent midday surplus energy concentrated within consistent 2-4-hour windows. Percentile-based sizing indicates that short-duration, grid-scale storage on the order of several hundred megawatts, with energy capacities of a few gigawatt-hours, is technically sufficient to capture the majority of daily solar surplus while displacing high-cost thermal generation during evening peak periods. The findings demonstrate that appropriately sized battery storage can enhance solar utilisation, reduce operating costs and emissions, and improve grid flexibility without requiring long-duration storage solutions. Beyond system-level efficiency gains, the results highlight the role of battery storage as a strategic enabler of energy security, affordability, and resilient low-carbon transitions in fuel-import-dependent power systems across the Global South.\u003c/p\u003e","manuscriptTitle":"Data-Driven Assessment of Solar Surplus and Battery Storage for Cost and Emission Reduction in Sri Lanka’s Power System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 09:00:17","doi":"10.21203/rs.3.rs-8496983/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"49b3f1fe-3cdb-494b-8507-f7b7680c4439","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-19T09:41:16+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T09:55:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 09:00:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8496983","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8496983","identity":"rs-8496983","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}