Research on the feasible path of low-voltage distributed photovoltaics participating in grid peak shaving

Research on the feasible path of low-voltage distributed photovoltaics participating in grid peak shaving | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on the feasible path of low-voltage distributed photovoltaics participating in grid peak shaving Bing Wang, Lei Chen, Enshan Zhu, Xiao Cui, Zhongli Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7673186/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 Against the backdrop of global energy structure transformation and the "Dual Carbon" goals, the large-scale integration of distributed photovoltaics into low-voltage distribution networks poses dual challenges to both system operation and market mechanisms. This study begins with an analysis of standard policies and market mechanisms, and proposes a dynamic compensation model based on output contribution to address user responsibilities in peak shaving. By integrating vehicle-grid collaboration and photovoltaic-storage synergy modes, it explores effective methods to optimize peak shaving capabilities. Using market-oriented tools such as differentiated electricity pricing and interruptible load pricing, the study designs business models to incentivize flexible resources. Drawing on policy experiences from Europe and the United States, it formulates an integrated policy solution that includes dynamic adjustment of financial subsidies, streamlined grid connection procedures, and multi-stakeholder collaboration. The research demonstrates that the synergy between policy and market mechanisms can effectively enhance photovoltaic integration efficiency, providing a theoretical basis and practical guidance for the sustainable development of low-voltage distributed photovoltaics. Low-voltage distributed photovoltaics Grid peak shaving Market-oriented mechanisms Policy optimization Standards and specifications Figures Figure 1 Figure 2 1 Introduction With the deepening of global energy transition and the strategic goals of "carbon peak and carbon neutrality," renewable energy, represented by photovoltaic (PV) power, is developing at an unprecedented pace [ 1 , 2 ]. Among these, low-voltage distributed PV systems have been widely deployed in urban buildings, rural rooftops, and other areas due to their advantages such as flexible siting, proximity to demand, and low investment thresholds. However, their inherent intermittency, randomness, and volatility pose significant challenges to the safe, stable, and economical operation of power distribution networks, especially low-voltage distribution grids. The peak shaving task of traditional power grids is mainly undertaken by large power plants (such as thermal power and hydropower), which respond to load changes through a top-down centralized scheduling model. But with the continuous increase in the penetration rate of distributed photovoltaics, their power generation highly overlaps with the daytime load curve, resulting in a drastic change in the form of the net load curve of the power grid (actual load minus photovoltaic power generation), forming a significant "duck shaped curve". This phenomenon causes problems such as power reversal and local voltage exceeding limits during the midday photovoltaic peak, and forms a steep evening peak when the photovoltaic system exits and the load suddenly rises in the evening, exacerbating the peak shaving pressure on the power grid. Therefore, exploring how to guide large-scale and dispersed low-voltage distributed photovoltaic resources to actively and orderly participate in grid peak shaving, transforming "passive acceptance" into "active regulation", has significant theoretical value and practical significance for improving the grid's ability to absorb high proportion renewable energy, ensuring the safety and stability of the distribution system, delaying grid capacity expansion investment, and promoting the construction of new power systems. With the continuous increase in the penetration rate of distributed photovoltaics in new power systems, their automatic regulation and control problems have attracted widespread attention in academic and engineering fields. Existing research has mostly focused on autonomous control strategies at the level of distribution networks, substations, or power stations, and there is still a lack of collaborative regulation methods for power balance and peak shaving needs across the entire network. In terms of local regulation, reference [ 3 ] constructed a flexible photovoltaic regulation architecture for distribution stations based on "cloud edge end" collaboration, but did not involve a coordinated peak shaving strategy for distributed photovoltaics across voltage levels. Reference [ 4 ] explored adaptive control methods based on artificial intelligence; Reference [ 5 ] adopts model predictive control to achieve optimal scheduling of photovoltaic clusters; Reference [ 6 ] proposed a multi-level regulation framework for active distribution networks to improve response speed and effectiveness; Reference [ 7 ] utilizes artificial intelligence to assist in decision-making and optimize photovoltaic output instructions; References [ 8 , 9 ] have respectively improved control accuracy and efficiency from the perspectives of regional regulation and automatic generation control (AGC) within the power station. However, these methods are still limited to local scales and difficult to directly extend to the entire network application scenario. In recent years, with the continuous expansion of distributed photovoltaic scale, the idea of multi-level coordination and cluster regulation has gradually emerged. Reference [ 10 ] proposes a distributed scheduling mechanism for "cloud edge" collaboration under the virtual power plant architecture; Reference [ 11 ] suggests dividing large systems into hierarchical clusters containing distributed resources; Reference [ 12 ] established a regulatory system for cluster control and multi-level coordination to meet the operational needs of high penetration distributed power sources. These studies provide important references for the participation of distributed photovoltaics in system regulation, but there is still a lack of specialized group regulation and control scheme design for the entire grid peak shaving scenario, and a large-scale practice at the provincial power grid level has not yet been formed. 2 Technical specifications for low-voltage distributed photovoltaic access and analysis of peak shaving responsibilities 2.1 Technical specification framework for low-voltage distributed photovoltaic access The user side access technology specification has established a rigorous and comprehensive technical system through equipment selection, system design, and security protection, ensuring that low-voltage distributed photovoltaic systems can be efficiently, stably, and safely connected to the power grid. In the equipment selection process, photovoltaic modules, as the core components of energy conversion, must strictly comply with the GB/T 29195 − 2012 "Design, Identification, and finalization of ground crystalline silicon photovoltaic modules" standard. This standard provides clear regulations for multiple indicators such as performance, reliability, and durability of components. Among them, the photoelectric conversion efficiency η is the core parameter for evaluating the quality of components and must meet the following formula: $$\:\begin{array}{c}\eta\:=\frac{{P}_{out}}{{P}_{in}}\times\:100％\ge\:20％\#（1）\end{array}$$ In the formula, P out is the output power of the photovoltaic module, and P in is the incident light power. High conversion efficiency not only improves power generation revenue, but also reduces system costs to a certain extent. Taking a certain model of photovoltaic module as an example, under standard testing conditions, when the incident light power P in is 1000W/m ², if the output power P out reaches 200W or above, its conversion efficiency meets the specification requirements. The inverter, as a key equipment for converting direct current into alternating current and connecting it to the power grid, must comply with the GB/T 19964 − 2024 standard. The active power regulation rate v of the device is an important parameter that needs to meet the requirement of not less than 10% rated power/minute, as follows: $$\:\begin{array}{c}v=\frac{\varDelta\:P}{\varDelta\:t}\ge\:0.1\times\:{P}_{rated}/min\#（2）\end{array}$$ In the formula, ∆ P represents the power change, ∆ t represents the time change, and P rated refers to the rated power of the inverter. Taking an inverter with a rated power of 5kW as an example, its power regulation amount needs to reach 0.5kW or more within 1 minute, so that the inverter can quickly change its output power when the light intensity changes or the grid demand is adjusted, ensuring stable system operation. The system design is based on the basic principles of "safety, reliability, and economy", which is the key to ensuring the long-term stable operation of photovoltaic systems. When the installed photovoltaic capacity P PV exceeds 30% of the distribution transformer capacity P T , in order to balance power fluctuations, improve power quality, and enhance system stability, an energy storage system needs to be configured. The capacity of the energy storage system, C_ESS, is calculated according to the following formula: $$\:\begin{array}{c}{C}_{ESS}=k\times\:{P}_{PV}\#（3）\end{array}$$ The value of k is generally between 0.2–0.3. The access method adopts the mode of "single point access, multi-point metering", and distinguishes the flow of electricity through a 0.5s level dedicated metering meter. The safety protection regulations include two important areas: electrical safety and information security. For electrical safety, the grounding resistance R g must meet the requirement of R g ≤4Ω, and a TN-S grounding system must be used. The TN-S system strictly separates the working neutral line and the protective neutral line, which can effectively prevent electric shock accidents and equipment damage caused by insulation damage of electrical equipment. When a short circuit fault occurs in the circuit, a smaller grounding resistance can cause the short-circuit current to quickly flow into the ground, thereby prompting the protective device to quickly operate and cut off the faulty circuit in a timely manner. 2.2 Responsibilities and execution standards for low-voltage distributed photovoltaic peak shaving The legal basis for users' peak shaving obligations comes from regulations such as the Electricity Law and the Renewable Energy Law, which explicitly require distributed energy grid connected entities to bear the responsibility of grid regulation [ 13 ]. According to the Electricity Law of the People's Republic of China, power users have the responsibility to obey the unified dispatch of the power grid and ensure the safe and stable operation of the power system, which provides legal support for users' peak shaving responsibilities. In addition, the "Regulations on Power Grid Dispatching Management" have refined the implementation rules, requiring power generation facilities connected to the power grid to strictly abide by the dispatching rules and effectively fulfill their obligations of peak shaving and frequency regulation. According to the "Electricity Demand Side Management Measures", industrial and commercial users are required to configure peak shaving capabilities according to capacity standards. For example, distributed photovoltaic systems with a capacity exceeding 500kW must have a 10% real-time power regulation margin. In terms of implementing standards, the national standard GB/T 33593 − 2017 "Technical Requirements for Distributed Power Source Access to Power Grid" clearly stipulates that the peak shaving response time should not exceed 30 seconds, the power regulation accuracy should be ± 2%, and related equipment needs to pass third-party testing and certification [ 14 ]. In terms of industry standards, GB/T 19964 − 2024 "Technical Regulations for Connecting Photovoltaic Power Stations to Power Systems" specifies the requirements for distributed photovoltaic peak shaving capability: firstly, the active power regulation rate should comply with the formula: $$\:\begin{array}{c}v=\frac{\varDelta\:P}{\varDelta\:t}\ge\:10\%{P}_{rated}/min\#（4）\end{array}$$ In the formula, ∆ P represents the power change, ∆ t represents the time change, and P rated represents the rated power of the photovoltaic system, in order to ensure that the system can quickly respond to grid peak shaving instructions; Secondly, when the grid frequency deviates from 50 ± 0.2Hz, the photovoltaic system needs to adjust the output power according to the preset strategy. The specific adjustment amount can refer to the formula: $$\:\begin{array}{c}\varDelta\:{P}_{f}={K}_{f}\left({f}_{n}-f\right)\#（5）\end{array}$$ In the formula, K f is the frequency adjustment coefficient, f n is the rated frequency, and f is the actual frequency. Local governments have simultaneously introduced supporting policies to strengthen implementation effectiveness. The "Distributed Power Management Measures" of some provinces explicitly require that distributed photovoltaic projects with a capacity exceeding 100kW must be equipped with adjustable equipment and submit quarterly peak shaving capacity assessment reports; For users who fail to fulfill their peak shaving obligations, penalty measures such as suspending subsidy applications and restricting expansion will be implemented. At the same time, the market rules for peak shaving auxiliary services implemented by the National Energy Administration have clarified the compensation standards. When deep peak shaving (output below 30% of rated power) occurs, the compensation unit price is 0.3–0.8 yuan/kWh, providing economic incentives for users to fulfill their peak shaving responsibilities. 3 Implementation path of distributed photovoltaic peak shaving function The implementation of distributed photovoltaic peak shaving function requires collaborative promotion from three aspects: hardware configuration, control strategy, and collaborative management. In terms of hardware, intelligent inverters with fast adjustment function should be preferred, with a power adjustment rate of up to 20% P rated /min and support for IEC61850 communication protocol, which can achieve real-time data interaction with the power grid dispatch system. When the capacity of the photovoltaic system exceeds 30% of the distribution transformer capacity, an energy storage system needs to be installed. The capacity calculation can refer to the following formula: $$\:\begin{array}{c}{C}_{ESS}=\left(0.2-0.3\right){P}_{PV}\#（6）\end{array}$$ In the formula, P PV represents the installed capacity of photovoltaics. Taking a 100kW photovoltaic project as an example, the energy storage capacity needs to be controlled within the range of 20–30 kWh, and power fluctuations can be balanced by utilizing the fast charging and discharging characteristics of lithium batteries. In terms of control strategy, a hierarchical distributed architecture is adopted for precise adjustment. The underlying inverter dynamically adjusts the output power based on maximum power point tracking technology and real-time scheduling instructions; The mid-level controller integrates local load forecasting results with grid peak shaving requirements to optimize the coordinated operation strategy between photovoltaic systems and energy storage equipment; The top-level utilizes the regional energy management platform to achieve the aggregation and scheduling of multi-user resources. Taking the actual operating scenario as an example, during peak electricity consumption, the system prioritizes ensuring local load electricity and stores excess electricity in the energy storage system; After the power grid sends peak shaving instructions, the energy storage system quickly releases electrical energy and reduces photovoltaic output, thereby achieving the "peak shaving" function. In the field of collaborative management, establish a tripartite linkage mechanism of "power grid user service provider". Power grid enterprises use the electricity market platform to release peak shaving demands, and after receiving instructions, users are assisted by professional service providers to complete equipment renovation and strategy optimization. Introducing blockchain technology to achieve transparent measurement and trustworthy settlement of peak shaving electricity, ensuring the tamper proof nature of data. At the same time, a predictive control model is built to estimate photovoltaic output and electricity demand based on weather forecasts and historical load data. The power generation plan is adjusted 15–30 minutes in advance to improve the timeliness and accuracy of peak shaving response, and promote efficient matching between distributed photovoltaic resources and grid demand. In addition, with the help of virtual power plant technology to aggregate regional distributed photovoltaic resources, and through edge computing gateway to achieve command interaction with the power grid dispatching center, relying on the day ahead real-time two-level market mechanism, the contribution rate of peak shaving capacity can be increased to 1.8–2.5 times of single photovoltaic, significantly enhancing the capacity of large-scale peak shaving [ 15 ]. 4. Design of peak shaving compensation incentive mechanism 4.1 Construction of compensation model based on contribution degree The peak shaving compensation model based on contribution quantifies the actual value of user peak shaving behavior on power grid stability, achieving accurate allocation of compensation amounts. The model construction includes three core steps: establishing an indicator system, determining weights, and calculating compensation amounts. (1) Establishment of indicator system The core evaluation indicators include peak shaving capacity, response speed, and regulation accuracy. The specific definitions and calculation methods are as follows: Peak shaving capacity ( C p ): refers to the total amount of active power that users can adjust during peak shaving periods, measured in kW, and is a key indicator for evaluating users' peak shaving capabilities. Response speed ( V r ): measured by the time from receiving scheduling instructions to the start of power adjustment, in minutes, reflecting the user's response efficiency to scheduling instructions. Adjustment accuracy ( A c ): expressed as the deviation rate between the actual adjustment power and the commanded required power, calculated using the following formula: $$\:\begin{array}{c}{A}_{c}=1-\frac{\left|\varDelta\:{P}_{actual}-\varDelta\:{P}_{target}\right|}{\varDelta\:{P}_{target}}\times\:100％\#（7）\end{array}$$ In the formula, ∆P actual represents the actual adjustment power, and ∆P target represents the adjustment power required by the scheduling instruction. The closer the value of this indicator approaches 100%, the higher the adjustment accuracy. (2) Weight allocation and compensation calculation Referring to the "Operation Rules for Power Auxiliary Service Market" and many successful cases of provincial peak shaving markets in China, the weights of peak shaving capacity, response speed, and regulation accuracy in auxiliary service compensation can be set at 50%, 30%, and 20%. This allocation has different emphasis directions in practice in various places. The formula for calculating the compensation amount M is as follows: $$\:\begin{array}{c}M=\alpha\:\times\:\left(0.5\times\:{C}_{p}+0.3\times\:\frac{1}{{V}_{r}}+0.2\times\:{A}_{c}\right)\#（8）\end{array}$$ In the formula, α is used as the compensation unit price coefficient, and its value is dynamically determined based on the peak shaving period and the supply and demand situation of the power grid. Taking the Jiangsu electricity market as an example, the "Trading Rules for Peak shaving Auxiliary Services" clearly stipulate that the compensation unit price for deep peak shaving during peak hours is 0.8 yuan/kWh, and 0.3 yuan/kWh is set during off peak hours. 4.2 Incentive Scheme for Collaborative Peak shaving between Vehicles and Networks The incentive mechanism for vehicle network collaborative peak shaving can be established from three aspects: economic compensation, policy support, and technical support. In terms of economic compensation, explore the mechanism of "basic subsidies + tiered rewards": calculate the basic subsidies based on the charging capacity of electric vehicles participating in peak shaving, and provide subsidies at a standard of 0.1–0.2 yuan per kilowatt hour; The tiered rewards are set according to the peak shaving depth and response speed. On this basis, a peak valley electricity price linkage mechanism will be established synchronously. During peak hours, the electricity price for vehicles discharging into the grid will increase by 30% -50% on the basis of the benchmark electricity price [ 16 ], and during periods of underestimation, the electricity price for charging will decrease by 30%. The specific implementation path of the incentive mechanism is shown in Fig. 1 : Encourage car owners to actively participate in peak shaving, and stipulate that EV charging stations must have bidirectional power control functions. During peak hours of the power grid, electric energy can be transmitted to the grid at a rate of 0.5C, and a subsidy of 1.2 times the charging price can be obtained based on the amount of electricity returned, and charging service fees are waived. At the policy support level, the government can provide purchase subsidies for electric vehicles participating in vehicle network collaboration, such as adding an additional 5000–10000 yuan on top of the current subsidies for new energy vehicles; For enterprises that construct V2G charging piles, subsidies of 20% -30% of equipment investment will be provided, and land use tax and value-added tax will be reduced or exempted. In addition, the coordinated peak shaving of the vehicle network will be included in the green power certificate trading system. Each megawatt hour of peak shaving electricity can be exchanged for one green certificate, and enterprises can obtain additional income through trading green certificates; Allow third-party aggregators to integrate dispersed EV resources, provide additional capacity subsidies (5 yuan/kW·month) based on aggregation scale (≥ 10MW), and use blockchain technology to achieve transparent traceability of contribution. In the field of technical support, establish a unified vehicle network collaborative management platform to achieve real-time data exchange of vehicle status, power grid demand, and charging facilities. The platform uses intelligent algorithms to determine the optimal peak shaving strategy based on grid load forecasting and vehicle SOC (State of Charge), and sends peak shaving instructions and revenue estimation information to car owners. At the same time, we will establish a safety standard system for vehicle network collaboration, clarify the communication protocol, power control rules, and data encryption requirements between vehicles and the power grid, ensure the safety and stability of vehicle network collaborative peak shaving, dispel users' concerns about battery life and vehicle safety, and enhance their participation enthusiasm. 5 Economy and feasibility of the 5 light storage linkage modes 5.1 Energy storage configuration enhances the system's peak shaving capability Energy storage system configuration is an important way to enhance the peak shaving capability of photovoltaic systems, and its role can be quantitatively evaluated from two dimensions: power regulation and time transfer. Through the "peak shaving and valley filling" mechanism, the energy storage system can effectively enhance the combined peak shaving efficiency of solar energy storage. When the energy storage configuration capacity reaches 15% -30% of the photovoltaic installed capacity, the daily peak shaving capacity of the system can be increased by 40% -60%, and the response time can be shortened to milliseconds. Significantly improve the peak shaving performance of low-voltage distributed photovoltaic systems. At the level of time transfer, energy storage systems achieve cross temporal distribution of electrical energy. Assuming the installed capacity of the photovoltaic system is P PV , the capacity of the energy storage system is C ESS , and the discharge duration is T , the adjustable power P ESS of the energy storage system is: $$\:\begin{array}{c}{P}_{ESS}=\frac{{C}_{ESS}}{T}\#（9）\end{array}$$ Taking a typical daily load curve as an example, if equipped with an energy storage system with a photovoltaic installed capacity of 20% -30% (discharge duration of 2–4 hours), the system's peak shaving capacity can be increased by 40% -60%. For example, a solar energy storage project in an industrial park in Jiangsu Province is equipped with a 200kWh energy storage system that accounts for 20% of the installed photovoltaic capacity, with a discharge time of 2 hours. With the help of the energy storage system's "peak shaving and valley filling" function, the abandoned solar energy rate in the park has been reduced from 12% to 4%, and the pressure of power grid peak shaving has been significantly alleviated. The specific effect comparison is shown in Table 1 : Table 1 Comparison of Abandoned Solar Energy Rate and Peak shaving Pressure before and after Energy Storage Configuration Project abandonment rate power grid peak shaving pressure Unconfigured energy storage 12% high after configuring energy storage 4% low The collaborative regulation strategy between energy storage systems and photovoltaic inverters can further enhance the efficiency and capability of peak shaving. By combining MPPT and SOC control algorithms, the energy storage system can continuously adjust its charging or discharging based on the real-time demand of the power grid, the output of photovoltaics, and its own power status. When the frequency of the power grid deviates to 50 ± 0.2Hz, the energy storage system can prioritize responding to the received frequency modulation command and maintain the frequency deviation within the normal range through rapid charging and discharging operations; Under normal operating conditions, the task of "peak shaving and valley filling" is performed to adjust the power fluctuations of the power grid in real time. This precise collaborative control mechanism makes the photovoltaic energy storage linkage system a highly controllable and flexible power source, greatly enhancing the stability of the power grid operation. 5.2 Analysis of Economic and Social Benefits of Photovoltaic Energy Storage Collaboration The collaborative mode of light storage has demonstrated significant value in both the economic and social fields. At the level of economic benefits, relying on peak valley electricity price arbitrage and peak shaving compensation to achieve diversified income. In the peak valley electricity pricing mechanism, the energy storage system charges during the off peak period (when the electricity price drops by 50%) and discharges during the peak period (when the electricity price rises by 50%). Based on the industrial and commercial electricity price of 0.8 yuan/kWh, a single charge discharge cycle can generate a price difference profit of 0.8 yuan/kWh. When participating in the grid peak shaving auxiliary service market, deep peak shaving of energy storage systems (output below 30% of rated power) can receive a compensation of 0.3–0.8 yuan/kWh. Combined with the photovoltaic power generation income, the internal rate of return (IRR) of the project can be increased by 3–5 percentage points. From the perspective of the entire lifecycle (25 years), the LCOE of photovoltaic storage systems can be reduced to below 0.45 yuan/kWh, which is 23% -28% lower than that of independent photovoltaic power plants. As the cost of lithium batteries continues to decline at an average annual rate of 12% -15%, the investment payback period for energy storage systems has been shortened from the early 8–10 years to 5–7 years, and the economy has been significantly improved. In terms of social benefits, the photovoltaic storage linkage mode can effectively alleviate the pressure on the power grid caused by distributed photovoltaic access. By smoothing power fluctuations, the occurrence of power quality problems such as voltage exceeding limits and harmonic exceeding standards has been reduced, thereby lowering the cost of power grid renovation and upgrading. At the same time, this model improves the absorption capacity of renewable energy, reduces fossil energy consumption and carbon emissions. Taking the 1MW integrated solar energy storage project as an example, it can reduce approximately 1200 tons of carbon dioxide emissions annually, equivalent to planting 66000 trees; Every 100MW solar energy storage system saves an average of 12000 tons of standard coal per year, reduces CO₂ emissions by 31000 tons, and reduces investment in power distribution network expansion by 30% -40%. In addition, the light storage system provides users with backup power, enhances power supply reliability, ensures continuous power supply for critical loads in extreme weather or grid failures, and reduces losses caused by power outages. As shown in Fig. 2 , the photovoltaic energy storage collaborative mode enhances social benefits in multiple dimensions by reducing carbon emissions, stabilizing grid voltage, and ensuring continuous power supply for critical loads. The dual realization of economic and social value provides strong support for the sustainable development of low-voltage distributed photovoltaics. 6 Conclusion This study systematically analyzed the technical path and market mechanism of low-voltage distributed photovoltaics participating in grid peak shaving, and constructed a complete technical specification system including equipment selection, system design, and safety protection. A dynamic compensation model based on peak shaving capacity (50%), response speed (30%), and regulation accuracy (20%) was proposed, and it was verified that the photovoltaic storage collaborative mode can improve the system's peak shaving capacity by 40% -60% and shorten the investment payback period to 5–7 years. Research has confirmed that when configuring an energy storage system with a photovoltaic installed capacity of 20% -30%, the curtailment rate can be reduced from 12% to 4%. At the same time, through the "basic subsidy + tiered reward" vehicle network coordination mechanism, the peak discharge electricity price can be increased by 30% -50%. The research results indicate that the collaborative innovation of policies and market mechanisms not only reduces CO ₂ emissions by 1200 tons annually for a 1MW solar storage project, but also increases the contribution rate of peak shaving capacity to 1.8–2.5 times that of individual photovoltaics through virtual power plant technology, providing a practical and feasible solution for building a new type of power system with a high proportion of renewable energy. Declarations Funding Declaration This research was funded by State Grid Corporation of China Science and Technology Project grant number 5400-202313567A-3-2-ZN. Clinical trial Not applicable Consent to Publish declaration Not applicable Consent to Participate declaration Not applicable Data Availability All data generated or analysed during this study are included in this published article. Ethics Not Applicable References Xiang LI, Dan ZHANG, Qiuyan LI, et al. Hosting capacity evaluation of distributed generators accessing a medium voltage distribution network considering randomness[J]. Power Syst Prot Control. 2024;52(17):150–60. Linghao ZHANG, Xiaolong XIAO, Mingming SHI, et al. Coordinated control method of power balance between PV and load in distribution station based on energy storage regulation and control[J]. Distribution &Utilization. 2024;41(5):36–45. Yang LIU, Haidong YU, Wenbin LIU, et al. Architecture ofdistributed photovoltaic flexible regulation based on cloud-edgeend collaboration[J]. Shandong Electr Power. 2023;50(7):23–9. Feng LI, Jie DING, Caiqi ZHOU, et al. Key technologies of largescale grid-connected operation of distributed photovoltaic under new-type power system[J]. Power Syst Technol. 2024;48(1):184–96. Xiaoyu ZHENG, Yu JI, Ying ZHANG, et al. Coordinated optimal control of distributed photovoltaic cluster based on model predictive control[J]. Power Syst Clean Energy. 2019;35(7):66–74. Xiaobo DOU, Limin CHANG, Chunhua NI, et al. Multi-level dispatching and control of active distribution network for virtual cluster of distributed photovoltaic[J]. Autom Electr Power Syst. 2018;42(3):21–31. Xiaobo DOU, Chao CAI, Xiangmei DUAN, et al. Optimal control method of distributed PV considering model errors in distribution network[J]. Electr Power Autom Equip. 2019;39(12):41–8. Yixiao WANG, Liyan CUI, Zhenfeng LEI, et al. Research on the regional intelligent regulating system of distributed PV station[J]. Power Syst Prot Control. 2016;44(4):118–22. Shuchao WANG, Shengpeng DUAN, Wei HOU, et al. Hierarchical distributed control structure based AGC technology for photovoltaic power plants[J]. Autom Electr Power Syst. 2016;40(8):126–32. Chongqing KANG, Qixin CHEN, Jian SU, et al. Scientific problems and research framework of virtual power plant with enormous flexible distributed energy resources in new power system[J]. Autom Electr Power Syst. 2022;46(18):3–14. Haoyong CHEN, Bifei TAN, Liang WU et al. Operation and control of the new power systems based on hierarchical clusters[J].Proceedings of the CSEE, 2023, 43(2): 581–594. Wenchuan WU, Boming ZHANG, Hongbin SUN, et al. Energy management and distributed energy resources cluster control for active distribution networks[J]. Autom Electr Power Syst. 2020;44(9):111–8. YuanliangFan, HanWu. JianliLin. A distributed photovoltaic short-term power forecasting model based on lightweight AI for edge computing in low‐voltage distribution network[J].IET Renewable Power Generation,18, Issue 16. 2024. PP 3955–66. Qian CHEN, Jing BAI, Xiaoyue CHEN, et al. Review of the Impact of Distributed PV Grid Integration on the Power Grid[J]. Sci Technol Eng. 2024;24(27):11491–504. Zheng G, Zhu E, Zhang H. Low voltage distribution intelligent control system based on distributed photovoltaic grid connection[J]. Journal of Physics: Conference Series, Volume 2846, Issue 1. 2024. PP 012024–012024. Weiguo GU, Haixing ZHUGZHANG et al. Impact of Distributed Photovoltaic Grid Connection on Low-voltage Distribution Network[J]. Electr Eng, 2024(S1): 366–8. 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. 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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-7673186","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573959193,"identity":"83787a86-253b-410c-a3bc-6787dd6e2ea1","order_by":0,"name":"Bing Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBAC+4bjBx98/Pdfzv54A5FaDBjPJBvOYGM2ZjhzgFgtzAfMhHnYmBMZbiQQqcWc7UAaAw8PWwLjzMcbbzDU2EQT1GLZc/DYAwkJnjxm6bRiC4ZjabkNBPXcOJBuYGAgUcwmnWMmwdhwmAgt9x+YSSQkGCT2SJ4hUovBgQNmEgcOJCTOkOAhUotkAzCQGxsOGBvwAP2SQIxf+BmOH3z8t+GAnAH74Y03PtTYEOEXZEdKJJCiHKKFVB2jYBSMglEwMgAAA9RDK7huJP0AAAAASUVORK5CYII=","orcid":"","institution":"State Grid Hebei Electric Power Company Hengshui Power Supply Branch","correspondingAuthor":true,"prefix":"","firstName":"Bing","middleName":"","lastName":"Wang","suffix":""},{"id":573959195,"identity":"3f78cf48-41a3-47f6-a792-684f9a2d4d41","order_by":1,"name":"Lei Chen","email":"","orcid":"","institution":"State Grid Hebei Electric Power Company Hengshui Power Supply Branch","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Chen","suffix":""},{"id":573959203,"identity":"2b41e83a-9713-42bd-b4c4-78b57fe006f9","order_by":2,"name":"Enshan Zhu","email":"","orcid":"","institution":"State Grid Hebei Electric Power Company Hengshui Power Supply Branch","correspondingAuthor":false,"prefix":"","firstName":"Enshan","middleName":"","lastName":"Zhu","suffix":""},{"id":573959204,"identity":"2f1b29a8-c3c8-48ae-8380-2648dba8ed6f","order_by":3,"name":"Xiao Cui","email":"","orcid":"","institution":"State Grid Hebei Electric Power Company Hengshui Power Supply Branch","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Cui","suffix":""},{"id":573959211,"identity":"f5d6f7a1-3d39-4ca8-ba68-819ed335bea3","order_by":4,"name":"Zhongli Wang","email":"","orcid":"","institution":"State Grid Hebei Electric Power Company Hengshui Power Supply Branch","correspondingAuthor":false,"prefix":"","firstName":"Zhongli","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-09-22 11:24:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7673186/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7673186/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100370556,"identity":"89ca29f8-7fc5-41d9-bc38-29c0b9e75af0","added_by":"auto","created_at":"2026-01-16 08:06:25","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":123770,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7673186/v1/734a174aa057f9ababc50a1f.docx"},{"id":100225003,"identity":"f7792dc1-770c-4128-8c99-261a25f28ba1","added_by":"auto","created_at":"2026-01-14 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1","display":"","copyAsset":false,"role":"figure","size":122545,"visible":true,"origin":"","legend":"\u003cp\u003eReward mechanism diagram for vehicle network collaborative peak shaving\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7673186/v1/8df131ddba91a9e81d97551d.png"},{"id":100224999,"identity":"0d33f68a-ecfc-45ae-8c20-b82db8951475","added_by":"auto","created_at":"2026-01-14 10:13:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44989,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of Collaborative Enhancement of Social Benefits through Light Energy Storage\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7673186/v1/bc9172dcf70ec762f4beb116.png"},{"id":105896151,"identity":"04848f88-932b-427c-b01d-88b1abef6190","added_by":"auto","created_at":"2026-04-01 08:43:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":918159,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7673186/v1/437c5f86-f801-4eda-8688-b3bc648f9567.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the feasible path of low-voltage distributed photovoltaics participating in grid peak shaving","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWith the deepening of global energy transition and the strategic goals of \"carbon peak and carbon neutrality,\" renewable energy, represented by photovoltaic (PV) power, is developing at an unprecedented pace [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Among these, low-voltage distributed PV systems have been widely deployed in urban buildings, rural rooftops, and other areas due to their advantages such as flexible siting, proximity to demand, and low investment thresholds. However, their inherent intermittency, randomness, and volatility pose significant challenges to the safe, stable, and economical operation of power distribution networks, especially low-voltage distribution grids.\u003c/p\u003e \u003cp\u003eThe peak shaving task of traditional power grids is mainly undertaken by large power plants (such as thermal power and hydropower), which respond to load changes through a top-down centralized scheduling model. But with the continuous increase in the penetration rate of distributed photovoltaics, their power generation highly overlaps with the daytime load curve, resulting in a drastic change in the form of the net load curve of the power grid (actual load minus photovoltaic power generation), forming a significant \"duck shaped curve\". This phenomenon causes problems such as power reversal and local voltage exceeding limits during the midday photovoltaic peak, and forms a steep evening peak when the photovoltaic system exits and the load suddenly rises in the evening, exacerbating the peak shaving pressure on the power grid. Therefore, exploring how to guide large-scale and dispersed low-voltage distributed photovoltaic resources to actively and orderly participate in grid peak shaving, transforming \"passive acceptance\" into \"active regulation\", has significant theoretical value and practical significance for improving the grid's ability to absorb high proportion renewable energy, ensuring the safety and stability of the distribution system, delaying grid capacity expansion investment, and promoting the construction of new power systems.\u003c/p\u003e \u003cp\u003eWith the continuous increase in the penetration rate of distributed photovoltaics in new power systems, their automatic regulation and control problems have attracted widespread attention in academic and engineering fields. Existing research has mostly focused on autonomous control strategies at the level of distribution networks, substations, or power stations, and there is still a lack of collaborative regulation methods for power balance and peak shaving needs across the entire network.\u003c/p\u003e \u003cp\u003eIn terms of local regulation, reference [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] constructed a flexible photovoltaic regulation architecture for distribution stations based on \"cloud edge end\" collaboration, but did not involve a coordinated peak shaving strategy for distributed photovoltaics across voltage levels. Reference [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] explored adaptive control methods based on artificial intelligence; Reference [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] adopts model predictive control to achieve optimal scheduling of photovoltaic clusters; Reference [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] proposed a multi-level regulation framework for active distribution networks to improve response speed and effectiveness; Reference [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] utilizes artificial intelligence to assist in decision-making and optimize photovoltaic output instructions; References [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] have respectively improved control accuracy and efficiency from the perspectives of regional regulation and automatic generation control (AGC) within the power station. However, these methods are still limited to local scales and difficult to directly extend to the entire network application scenario.\u003c/p\u003e \u003cp\u003eIn recent years, with the continuous expansion of distributed photovoltaic scale, the idea of multi-level coordination and cluster regulation has gradually emerged. Reference [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] proposes a distributed scheduling mechanism for \"cloud edge\" collaboration under the virtual power plant architecture; Reference [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] suggests dividing large systems into hierarchical clusters containing distributed resources; Reference [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] established a regulatory system for cluster control and multi-level coordination to meet the operational needs of high penetration distributed power sources. These studies provide important references for the participation of distributed photovoltaics in system regulation, but there is still a lack of specialized group regulation and control scheme design for the entire grid peak shaving scenario, and a large-scale practice at the provincial power grid level has not yet been formed.\u003c/p\u003e"},{"header":"2 Technical specifications for low-voltage distributed photovoltaic access and analysis of peak shaving responsibilities","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Technical specification framework for low-voltage distributed photovoltaic access\u003c/h2\u003e \u003cp\u003eThe user side access technology specification has established a rigorous and comprehensive technical system through equipment selection, system design, and security protection, ensuring that low-voltage distributed photovoltaic systems can be efficiently, stably, and safely connected to the power grid.\u003c/p\u003e \u003cp\u003eIn the equipment selection process, photovoltaic modules, as the core components of energy conversion, must strictly comply with the GB/T 29195\u0026thinsp;\u0026minus;\u0026thinsp;2012 \"Design, Identification, and finalization of ground crystalline silicon photovoltaic modules\" standard. This standard provides clear regulations for multiple indicators such as performance, reliability, and durability of components. Among them, the photoelectric conversion efficiency η is the core parameter for evaluating the quality of components and must meet the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}\\eta\\:=\\frac{{P}_{out}}{{P}_{in}}\\times\\:100％\\ge\\:20％\\#（1）\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eout\u003c/sub\u003e is the output power of the photovoltaic module, and \u003cem\u003eP\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e is the incident light power. High conversion efficiency not only improves power generation revenue, but also reduces system costs to a certain extent. Taking a certain model of photovoltaic module as an example, under standard testing conditions, when the incident light power \u003cem\u003eP\u003c/em\u003e\u003csub\u003ein\u003c/sub\u003e is 1000W/m \u0026sup2;, if the output power \u003cem\u003eP\u003c/em\u003e\u003csub\u003eout\u003c/sub\u003e reaches 200W or above, its conversion efficiency meets the specification requirements.\u003c/p\u003e \u003cp\u003eThe inverter, as a key equipment for converting direct current into alternating current and connecting it to the power grid, must comply with the GB/T 19964\u0026thinsp;\u0026minus;\u0026thinsp;2024 standard. The active power regulation rate v of the device is an important parameter that needs to meet the requirement of not less than 10% rated power/minute, as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}v=\\frac{\\varDelta\\:P}{\\varDelta\\:t}\\ge\\:0.1\\times\\:{P}_{rated}/min\\#（2）\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, ∆\u003cem\u003eP\u003c/em\u003e represents the power change, ∆\u003cem\u003et\u003c/em\u003e represents the time change, and \u003cem\u003eP\u003c/em\u003e\u003csub\u003erated\u003c/sub\u003e refers to the rated power of the inverter. Taking an inverter with a rated power of 5kW as an example, its power regulation amount needs to reach 0.5kW or more within 1 minute, so that the inverter can quickly change its output power when the light intensity changes or the grid demand is adjusted, ensuring stable system operation.\u003c/p\u003e \u003cp\u003eThe system design is based on the basic principles of \"safety, reliability, and economy\", which is the key to ensuring the long-term stable operation of photovoltaic systems. When the installed photovoltaic capacity \u003cem\u003eP\u003c/em\u003e\u003csub\u003ePV\u003c/sub\u003e exceeds 30% of the distribution transformer capacity \u003cem\u003eP\u003c/em\u003e\u003csub\u003eT\u003c/sub\u003e, in order to balance power fluctuations, improve power quality, and enhance system stability, an energy storage system needs to be configured. The capacity of the energy storage system, C_ESS, is calculated according to the following formula:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{C}_{ESS}=k\\times\\:{P}_{PV}\\#（3）\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe value of \u003cem\u003ek\u003c/em\u003e is generally between 0.2\u0026ndash;0.3. The access method adopts the mode of \"single point access, multi-point metering\", and distinguishes the flow of electricity through a 0.5s level dedicated metering meter.\u003c/p\u003e \u003cp\u003eThe safety protection regulations include two important areas: electrical safety and information security. For electrical safety, the grounding resistance \u003cem\u003eR\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e must meet the requirement of \u003cem\u003eR\u003c/em\u003e\u003csub\u003eg\u003c/sub\u003e\u0026le;4Ω, and a TN-S grounding system must be used. The TN-S system strictly separates the working neutral line and the protective neutral line, which can effectively prevent electric shock accidents and equipment damage caused by insulation damage of electrical equipment. When a short circuit fault occurs in the circuit, a smaller grounding resistance can cause the short-circuit current to quickly flow into the ground, thereby prompting the protective device to quickly operate and cut off the faulty circuit in a timely manner.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Responsibilities and execution standards for low-voltage distributed photovoltaic peak shaving\u003c/h2\u003e \u003cp\u003eThe legal basis for users' peak shaving obligations comes from regulations such as the Electricity Law and the Renewable Energy Law, which explicitly require distributed energy grid connected entities to bear the responsibility of grid regulation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. According to the Electricity Law of the People's Republic of China, power users have the responsibility to obey the unified dispatch of the power grid and ensure the safe and stable operation of the power system, which provides legal support for users' peak shaving responsibilities. In addition, the \"Regulations on Power Grid Dispatching Management\" have refined the implementation rules, requiring power generation facilities connected to the power grid to strictly abide by the dispatching rules and effectively fulfill their obligations of peak shaving and frequency regulation. According to the \"Electricity Demand Side Management Measures\", industrial and commercial users are required to configure peak shaving capabilities according to capacity standards. For example, distributed photovoltaic systems with a capacity exceeding 500kW must have a 10% real-time power regulation margin. In terms of implementing standards, the national standard GB/T 33593\u0026thinsp;\u0026minus;\u0026thinsp;2017 \"Technical Requirements for Distributed Power Source Access to Power Grid\" clearly stipulates that the peak shaving response time should not exceed 30 seconds, the power regulation accuracy should be \u0026plusmn;\u0026thinsp;2%, and related equipment needs to pass third-party testing and certification [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn terms of industry standards, GB/T 19964\u0026thinsp;\u0026minus;\u0026thinsp;2024 \"Technical Regulations for Connecting Photovoltaic Power Stations to Power Systems\" specifies the requirements for distributed photovoltaic peak shaving capability: firstly, the active power regulation rate should comply with the formula:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}v=\\frac{\\varDelta\\:P}{\\varDelta\\:t}\\ge\\:10\\%{P}_{rated}/min\\#（4）\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, ∆\u003cem\u003eP\u003c/em\u003e represents the power change, ∆\u003cem\u003et\u003c/em\u003e represents the time change, and \u003cem\u003eP\u003c/em\u003e\u003csub\u003erated\u003c/sub\u003e represents the rated power of the photovoltaic system, in order to ensure that the system can quickly respond to grid peak shaving instructions; Secondly, when the grid frequency deviates from 50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2Hz, the photovoltaic system needs to adjust the output power according to the preset strategy. The specific adjustment amount can refer to the formula:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}\\varDelta\\:{P}_{f}={K}_{f}\\left({f}_{n}-f\\right)\\#（5）\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, \u003cem\u003eK\u003c/em\u003e\u003csub\u003ef\u003c/sub\u003e is the frequency adjustment coefficient, \u003cem\u003ef\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e is the rated frequency, and \u003cem\u003ef\u003c/em\u003e is the actual frequency.\u003c/p\u003e \u003cp\u003eLocal governments have simultaneously introduced supporting policies to strengthen implementation effectiveness. The \"Distributed Power Management Measures\" of some provinces explicitly require that distributed photovoltaic projects with a capacity exceeding 100kW must be equipped with adjustable equipment and submit quarterly peak shaving capacity assessment reports; For users who fail to fulfill their peak shaving obligations, penalty measures such as suspending subsidy applications and restricting expansion will be implemented. At the same time, the market rules for peak shaving auxiliary services implemented by the National Energy Administration have clarified the compensation standards. When deep peak shaving (output below 30% of rated power) occurs, the compensation unit price is 0.3\u0026ndash;0.8 yuan/kWh, providing economic incentives for users to fulfill their peak shaving responsibilities.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Implementation path of distributed photovoltaic peak shaving function","content":"\u003cp\u003eThe implementation of distributed photovoltaic peak shaving function requires collaborative promotion from three aspects: hardware configuration, control strategy, and collaborative management. In terms of hardware, intelligent inverters with fast adjustment function should be preferred, with a power adjustment rate of up to 20% \u003cem\u003eP\u003c/em\u003e\u003csub\u003erated\u003c/sub\u003e/min and support for IEC61850 communication protocol, which can achieve real-time data interaction with the power grid dispatch system. When the capacity of the photovoltaic system exceeds 30% of the distribution transformer capacity, an energy storage system needs to be installed. The capacity calculation can refer to the following formula:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{C}_{ESS}=\\left(0.2-0.3\\right){P}_{PV}\\#（6）\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, \u003cem\u003eP\u003c/em\u003e\u003csub\u003ePV\u003c/sub\u003e represents the installed capacity of photovoltaics. Taking a 100kW photovoltaic project as an example, the energy storage capacity needs to be controlled within the range of 20\u0026ndash;30 kWh, and power fluctuations can be balanced by utilizing the fast charging and discharging characteristics of lithium batteries.\u003c/p\u003e \u003cp\u003eIn terms of control strategy, a hierarchical distributed architecture is adopted for precise adjustment. The underlying inverter dynamically adjusts the output power based on maximum power point tracking technology and real-time scheduling instructions; The mid-level controller integrates local load forecasting results with grid peak shaving requirements to optimize the coordinated operation strategy between photovoltaic systems and energy storage equipment; The top-level utilizes the regional energy management platform to achieve the aggregation and scheduling of multi-user resources. Taking the actual operating scenario as an example, during peak electricity consumption, the system prioritizes ensuring local load electricity and stores excess electricity in the energy storage system; After the power grid sends peak shaving instructions, the energy storage system quickly releases electrical energy and reduces photovoltaic output, thereby achieving the \"peak shaving\" function.\u003c/p\u003e \u003cp\u003eIn the field of collaborative management, establish a tripartite linkage mechanism of \"power grid user service provider\". Power grid enterprises use the electricity market platform to release peak shaving demands, and after receiving instructions, users are assisted by professional service providers to complete equipment renovation and strategy optimization. Introducing blockchain technology to achieve transparent measurement and trustworthy settlement of peak shaving electricity, ensuring the tamper proof nature of data. At the same time, a predictive control model is built to estimate photovoltaic output and electricity demand based on weather forecasts and historical load data. The power generation plan is adjusted 15\u0026ndash;30 minutes in advance to improve the timeliness and accuracy of peak shaving response, and promote efficient matching between distributed photovoltaic resources and grid demand.\u003c/p\u003e \u003cp\u003eIn addition, with the help of virtual power plant technology to aggregate regional distributed photovoltaic resources, and through edge computing gateway to achieve command interaction with the power grid dispatching center, relying on the day ahead real-time two-level market mechanism, the contribution rate of peak shaving capacity can be increased to 1.8\u0026ndash;2.5 times of single photovoltaic, significantly enhancing the capacity of large-scale peak shaving [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e"},{"header":"4. Design of peak shaving compensation incentive mechanism","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Construction of compensation model based on contribution degree\u003c/h2\u003e \u003cp\u003eThe peak shaving compensation model based on contribution quantifies the actual value of user peak shaving behavior on power grid stability, achieving accurate allocation of compensation amounts. The model construction includes three core steps: establishing an indicator system, determining weights, and calculating compensation amounts.\u003c/p\u003e \u003cp\u003e(1) Establishment of indicator system\u003c/p\u003e \u003cp\u003eThe core evaluation indicators include peak shaving capacity, response speed, and regulation accuracy. The specific definitions and calculation methods are as follows:\u003c/p\u003e \u003cp\u003ePeak shaving capacity (\u003cem\u003eC\u003c/em\u003e\u003csub\u003ep\u003c/sub\u003e): refers to the total amount of active power that users can adjust during peak shaving periods, measured in kW, and is a key indicator for evaluating users' peak shaving capabilities.\u003c/p\u003e \u003cp\u003eResponse speed (\u003cem\u003eV\u003c/em\u003e\u003csub\u003er\u003c/sub\u003e): measured by the time from receiving scheduling instructions to the start of power adjustment, in minutes, reflecting the user's response efficiency to scheduling instructions.\u003c/p\u003e \u003cp\u003eAdjustment accuracy (\u003cem\u003eA\u003c/em\u003e\u003csub\u003ec\u003c/sub\u003e): expressed as the deviation rate between the actual adjustment power and the commanded required power, calculated using the following formula:\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{A}_{c}=1-\\frac{\\left|\\varDelta\\:{P}_{actual}-\\varDelta\\:{P}_{target}\\right|}{\\varDelta\\:{P}_{target}}\\times\\:100％\\#（7）\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, ∆P\u003csub\u003eactual\u003c/sub\u003e represents the actual adjustment power, and ∆P\u003csub\u003etarget\u003c/sub\u003e represents the adjustment power required by the scheduling instruction. The closer the value of this indicator approaches 100%, the higher the adjustment accuracy.\u003c/p\u003e \u003cp\u003e(2) Weight allocation and compensation calculation\u003c/p\u003e \u003cp\u003eReferring to the \"Operation Rules for Power Auxiliary Service Market\" and many successful cases of provincial peak shaving markets in China, the weights of peak shaving capacity, response speed, and regulation accuracy in auxiliary service compensation can be set at 50%, 30%, and 20%. This allocation has different emphasis directions in practice in various places.\u003c/p\u003e \u003cp\u003eThe formula for calculating the compensation amount \u003cem\u003eM\u003c/em\u003e is as follows:\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}M=\\alpha\\:\\times\\:\\left(0.5\\times\\:{C}_{p}+0.3\\times\\:\\frac{1}{{V}_{r}}+0.2\\times\\:{A}_{c}\\right)\\#（8）\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, \u003cem\u003eα\u003c/em\u003e is used as the compensation unit price coefficient, and its value is dynamically determined based on the peak shaving period and the supply and demand situation of the power grid. Taking the Jiangsu electricity market as an example, the \"Trading Rules for Peak shaving Auxiliary Services\" clearly stipulate that the compensation unit price for deep peak shaving during peak hours is 0.8 yuan/kWh, and 0.3 yuan/kWh is set during off peak hours.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Incentive Scheme for Collaborative Peak shaving between Vehicles and Networks\u003c/h2\u003e \u003cp\u003eThe incentive mechanism for vehicle network collaborative peak shaving can be established from three aspects: economic compensation, policy support, and technical support. In terms of economic compensation, explore the mechanism of \"basic subsidies\u0026thinsp;+\u0026thinsp;tiered rewards\": calculate the basic subsidies based on the charging capacity of electric vehicles participating in peak shaving, and provide subsidies at a standard of 0.1\u0026ndash;0.2 yuan per kilowatt hour; The tiered rewards are set according to the peak shaving depth and response speed. On this basis, a peak valley electricity price linkage mechanism will be established synchronously. During peak hours, the electricity price for vehicles discharging into the grid will increase by 30% -50% on the basis of the benchmark electricity price [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and during periods of underestimation, the electricity price for charging will decrease by 30%. The specific implementation path of the incentive mechanism is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEncourage car owners to actively participate in peak shaving, and stipulate that EV charging stations must have bidirectional power control functions. During peak hours of the power grid, electric energy can be transmitted to the grid at a rate of 0.5C, and a subsidy of 1.2 times the charging price can be obtained based on the amount of electricity returned, and charging service fees are waived.\u003c/p\u003e \u003cp\u003eAt the policy support level, the government can provide purchase subsidies for electric vehicles participating in vehicle network collaboration, such as adding an additional 5000\u0026ndash;10000 yuan on top of the current subsidies for new energy vehicles; For enterprises that construct V2G charging piles, subsidies of 20% -30% of equipment investment will be provided, and land use tax and value-added tax will be reduced or exempted. In addition, the coordinated peak shaving of the vehicle network will be included in the green power certificate trading system. Each megawatt hour of peak shaving electricity can be exchanged for one green certificate, and enterprises can obtain additional income through trading green certificates; Allow third-party aggregators to integrate dispersed EV resources, provide additional capacity subsidies (5 yuan/kW\u0026middot;month) based on aggregation scale (\u0026ge;\u0026thinsp;10MW), and use blockchain technology to achieve transparent traceability of contribution.\u003c/p\u003e \u003cp\u003eIn the field of technical support, establish a unified vehicle network collaborative management platform to achieve real-time data exchange of vehicle status, power grid demand, and charging facilities. The platform uses intelligent algorithms to determine the optimal peak shaving strategy based on grid load forecasting and vehicle SOC (State of Charge), and sends peak shaving instructions and revenue estimation information to car owners. At the same time, we will establish a safety standard system for vehicle network collaboration, clarify the communication protocol, power control rules, and data encryption requirements between vehicles and the power grid, ensure the safety and stability of vehicle network collaborative peak shaving, dispel users' concerns about battery life and vehicle safety, and enhance their participation enthusiasm.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Economy and feasibility of the 5 light storage linkage modes","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Energy storage configuration enhances the system's peak shaving capability\u003c/h2\u003e \u003cp\u003eEnergy storage system configuration is an important way to enhance the peak shaving capability of photovoltaic systems, and its role can be quantitatively evaluated from two dimensions: power regulation and time transfer. Through the \"peak shaving and valley filling\" mechanism, the energy storage system can effectively enhance the combined peak shaving efficiency of solar energy storage. When the energy storage configuration capacity reaches 15% -30% of the photovoltaic installed capacity, the daily peak shaving capacity of the system can be increased by 40% -60%, and the response time can be shortened to milliseconds. Significantly improve the peak shaving performance of low-voltage distributed photovoltaic systems.\u003c/p\u003e \u003cp\u003eAt the level of time transfer, energy storage systems achieve cross temporal distribution of electrical energy. Assuming the installed capacity of the photovoltaic system is \u003cem\u003eP\u003c/em\u003e\u003csub\u003ePV\u003c/sub\u003e, the capacity of the energy storage system is \u003cem\u003eC\u003c/em\u003e\u003csub\u003eESS\u003c/sub\u003e, and the discharge duration is \u003cem\u003eT\u003c/em\u003e, the adjustable power \u003cem\u003eP\u003c/em\u003e\u003csub\u003eESS\u003c/sub\u003e of the energy storage system is:\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{P}_{ESS}=\\frac{{C}_{ESS}}{T}\\#（9）\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTaking a typical daily load curve as an example, if equipped with an energy storage system with a photovoltaic installed capacity of 20% -30% (discharge duration of 2\u0026ndash;4 hours), the system's peak shaving capacity can be increased by 40% -60%. For example, a solar energy storage project in an industrial park in Jiangsu Province is equipped with a 200kWh energy storage system that accounts for 20% of the installed photovoltaic capacity, with a discharge time of 2 hours. With the help of the energy storage system's \"peak shaving and valley filling\" function, the abandoned solar energy rate in the park has been reduced from 12% to 4%, and the pressure of power grid peak shaving has been significantly alleviated. The specific effect comparison is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\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\u003eComparison of Abandoned Solar Energy Rate and Peak shaving Pressure before and after Energy Storage Configuration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\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\u003eabandonment rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epower grid peak shaving pressure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnconfigured energy storage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eafter configuring energy storage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elow\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\u003eThe collaborative regulation strategy between energy storage systems and photovoltaic inverters can further enhance the efficiency and capability of peak shaving. By combining MPPT and SOC control algorithms, the energy storage system can continuously adjust its charging or discharging based on the real-time demand of the power grid, the output of photovoltaics, and its own power status. When the frequency of the power grid deviates to 50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2Hz, the energy storage system can prioritize responding to the received frequency modulation command and maintain the frequency deviation within the normal range through rapid charging and discharging operations; Under normal operating conditions, the task of \"peak shaving and valley filling\" is performed to adjust the power fluctuations of the power grid in real time. This precise collaborative control mechanism makes the photovoltaic energy storage linkage system a highly controllable and flexible power source, greatly enhancing the stability of the power grid operation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Analysis of Economic and Social Benefits of Photovoltaic Energy Storage Collaboration\u003c/h2\u003e \u003cp\u003eThe collaborative mode of light storage has demonstrated significant value in both the economic and social fields. At the level of economic benefits, relying on peak valley electricity price arbitrage and peak shaving compensation to achieve diversified income. In the peak valley electricity pricing mechanism, the energy storage system charges during the off peak period (when the electricity price drops by 50%) and discharges during the peak period (when the electricity price rises by 50%). Based on the industrial and commercial electricity price of 0.8 yuan/kWh, a single charge discharge cycle can generate a price difference profit of 0.8 yuan/kWh. When participating in the grid peak shaving auxiliary service market, deep peak shaving of energy storage systems (output below 30% of rated power) can receive a compensation of 0.3\u0026ndash;0.8 yuan/kWh. Combined with the photovoltaic power generation income, the internal rate of return (IRR) of the project can be increased by 3\u0026ndash;5 percentage points.\u003c/p\u003e \u003cp\u003eFrom the perspective of the entire lifecycle (25 years), the LCOE of photovoltaic storage systems can be reduced to below 0.45 yuan/kWh, which is 23% -28% lower than that of independent photovoltaic power plants. As the cost of lithium batteries continues to decline at an average annual rate of 12% -15%, the investment payback period for energy storage systems has been shortened from the early 8\u0026ndash;10 years to 5\u0026ndash;7 years, and the economy has been significantly improved.\u003c/p\u003e \u003cp\u003eIn terms of social benefits, the photovoltaic storage linkage mode can effectively alleviate the pressure on the power grid caused by distributed photovoltaic access. By smoothing power fluctuations, the occurrence of power quality problems such as voltage exceeding limits and harmonic exceeding standards has been reduced, thereby lowering the cost of power grid renovation and upgrading. At the same time, this model improves the absorption capacity of renewable energy, reduces fossil energy consumption and carbon emissions. Taking the 1MW integrated solar energy storage project as an example, it can reduce approximately 1200 tons of carbon dioxide emissions annually, equivalent to planting 66000 trees; Every 100MW solar energy storage system saves an average of 12000 tons of standard coal per year, reduces CO₂ emissions by 31000 tons, and reduces investment in power distribution network expansion by 30% -40%. In addition, the light storage system provides users with backup power, enhances power supply reliability, ensures continuous power supply for critical loads in extreme weather or grid failures, and reduces losses caused by power outages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the photovoltaic energy storage collaborative mode enhances social benefits in multiple dimensions by reducing carbon emissions, stabilizing grid voltage, and ensuring continuous power supply for critical loads. The dual realization of economic and social value provides strong support for the sustainable development of low-voltage distributed photovoltaics.\u003c/p\u003e \u003c/div\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThis study systematically analyzed the technical path and market mechanism of low-voltage distributed photovoltaics participating in grid peak shaving, and constructed a complete technical specification system including equipment selection, system design, and safety protection. A dynamic compensation model based on peak shaving capacity (50%), response speed (30%), and regulation accuracy (20%) was proposed, and it was verified that the photovoltaic storage collaborative mode can improve the system's peak shaving capacity by 40% -60% and shorten the investment payback period to 5\u0026ndash;7 years. Research has confirmed that when configuring an energy storage system with a photovoltaic installed capacity of 20% -30%, the curtailment rate can be reduced from 12% to 4%. At the same time, through the \"basic subsidy\u0026thinsp;+\u0026thinsp;tiered reward\" vehicle network coordination mechanism, the peak discharge electricity price can be increased by 30% -50%. The research results indicate that the collaborative innovation of policies and market mechanisms not only reduces CO ₂ emissions by 1200 tons annually for a 1MW solar storage project, but also increases the contribution rate of peak shaving capacity to 1.8\u0026ndash;2.5 times that of individual photovoltaics through virtual power plant technology, providing a practical and feasible solution for building a new type of power system with a high proportion of renewable energy.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by State Grid Corporation of China Science and Technology Project grant number 5400-202313567A-3-2-ZN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXiang LI, Dan ZHANG, Qiuyan LI, et al. Hosting capacity evaluation of distributed generators accessing a medium voltage distribution network considering randomness[J]. Power Syst Prot Control. 2024;52(17):150\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLinghao ZHANG, Xiaolong XIAO, Mingming SHI, et al. Coordinated control method of power balance between PV and load in distribution station based on energy storage regulation and control[J]. Distribution \u0026amp;Utilization. 2024;41(5):36\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang LIU, Haidong YU, Wenbin LIU, et al. Architecture ofdistributed photovoltaic flexible regulation based on cloud-edgeend collaboration[J]. Shandong Electr Power. 2023;50(7):23\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng LI, Jie DING, Caiqi ZHOU, et al. Key technologies of largescale grid-connected operation of distributed photovoltaic under new-type power system[J]. Power Syst Technol. 2024;48(1):184\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiaoyu ZHENG, Yu JI, Ying ZHANG, et al. Coordinated optimal control of distributed photovoltaic cluster based on model predictive control[J]. Power Syst Clean Energy. 2019;35(7):66\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiaobo DOU, Limin CHANG, Chunhua NI, et al. Multi-level dispatching and control of active distribution network for virtual cluster of distributed photovoltaic[J]. Autom Electr Power Syst. 2018;42(3):21\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiaobo DOU, Chao CAI, Xiangmei DUAN, et al. Optimal control method of distributed PV considering model errors in distribution network[J]. Electr Power Autom Equip. 2019;39(12):41\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYixiao WANG, Liyan CUI, Zhenfeng LEI, et al. Research on the regional intelligent regulating system of distributed PV station[J]. Power Syst Prot Control. 2016;44(4):118\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShuchao WANG, Shengpeng DUAN, Wei HOU, et al. Hierarchical distributed control structure based AGC technology for photovoltaic power plants[J]. Autom Electr Power Syst. 2016;40(8):126\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChongqing KANG, Qixin CHEN, Jian SU, et al. Scientific problems and research framework of virtual power plant with enormous flexible distributed energy resources in new power system[J]. Autom Electr Power Syst. 2022;46(18):3\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaoyong CHEN, Bifei TAN, Liang WU et al. Operation and control of the new power systems based on hierarchical clusters[J].Proceedings of the CSEE, 2023, 43(2): 581\u0026ndash;594.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWenchuan WU, Boming ZHANG, Hongbin SUN, et al. Energy management and distributed energy resources cluster control for active distribution networks[J]. Autom Electr Power Syst. 2020;44(9):111\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuanliangFan, HanWu. JianliLin. A distributed photovoltaic short-term power forecasting model based on lightweight AI for edge computing in low‐voltage distribution network[J].IET Renewable Power Generation,18, Issue 16. 2024. PP 3955\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQian CHEN, Jing BAI, Xiaoyue CHEN, et al. Review of the Impact of Distributed PV Grid Integration on the Power Grid[J]. Sci Technol Eng. 2024;24(27):11491\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng G, Zhu E, Zhang H. Low voltage distribution intelligent control system based on distributed photovoltaic grid connection[J]. Journal of Physics: Conference Series, Volume 2846, Issue 1. 2024. PP 012024\u0026ndash;012024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiguo GU, Haixing ZHUGZHANG et al. Impact of Distributed Photovoltaic Grid Connection on Low-voltage Distribution Network[J]. Electr Eng, 2024(S1): 366\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Low-voltage distributed photovoltaics, Grid peak shaving, Market-oriented mechanisms, Policy optimization, Standards and specifications","lastPublishedDoi":"10.21203/rs.3.rs-7673186/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7673186/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgainst the backdrop of global energy structure transformation and the \"Dual Carbon\" goals, the large-scale integration of distributed photovoltaics into low-voltage distribution networks poses dual challenges to both system operation and market mechanisms. This study begins with an analysis of standard policies and market mechanisms, and proposes a dynamic compensation model based on output contribution to address user responsibilities in peak shaving. By integrating vehicle-grid collaboration and photovoltaic-storage synergy modes, it explores effective methods to optimize peak shaving capabilities. Using market-oriented tools such as differentiated electricity pricing and interruptible load pricing, the study designs business models to incentivize flexible resources. Drawing on policy experiences from Europe and the United States, it formulates an integrated policy solution that includes dynamic adjustment of financial subsidies, streamlined grid connection procedures, and multi-stakeholder collaboration. The research demonstrates that the synergy between policy and market mechanisms can effectively enhance photovoltaic integration efficiency, providing a theoretical basis and practical guidance for the sustainable development of low-voltage distributed photovoltaics.\u003c/p\u003e","manuscriptTitle":"Research on the feasible path of low-voltage distributed photovoltaics participating in grid peak shaving","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-14 10:13:21","doi":"10.21203/rs.3.rs-7673186/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":"a41dae5a-9085-46cd-b3c9-470817d9978e","owner":[],"postedDate":"January 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-01T08:41:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-14 10:13:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7673186","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7673186","identity":"rs-7673186","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}