With the abolition of mandatory energy storage allocation and the promotion of full market-based participation of renewable energy, the development of Shared Energy Storage (SES) in China is shifting from policy-driven to market-oriented. As leasing revenue constitutes the primary source of SES income, designing rational service fee packages has become a key strategy for adapting to this transition. However, existing SES pricing schemes generally suffer from insufficient systematic design, a lack of comprehensive consideration of renewable energy policy impacts, and overly simplified evaluation indicators. To address these issues, this study designs seven SES service fee pricing schemes based on three pricing dimensions—power, capacity, and period. Considering the incomplete information of renewable energy entities and the continuity of electricity prices, a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is employed to learn user behavior characteristics. Based on an in-depth analysis of renewable energy policies, distributed renewable energy projects are further classified into local-consumption projects and general distributed projects, and corresponding benefit models are developed to optimize user demand. These results are then incorporated as learning samples in the MADDPG framework to achieve dynamic optimization of SES service pricing. Furthermore, the proposed pricing schemes are comprehensively evaluated in terms of economic efficiency, participation, and fairness. Finally, real-world data from Inner Mongolia and Zhejiang Province are used for case studies to analyze the effects of mechanism electricity prices and green power direct transaction prices on the implementation and performance of the proposed SES pricing schemes.

Customer-side energy storage is crucial for reducing peak load pressure on the grid while lowering user electricity costs. However, in China, the economics of customer-side energy storage are constrained by high initial investment costs and insufficient peak-valley price spreads, increasing the dependence on government subsidies. In this study, an economic benefit model for commercial and industrial energy storage (CIES) is developed, considering seven incentive policies, including power-based subsidies, capacity-based subsidies, discharge-based subsidies, income tax reductions, and value added tax (VAT) exemptions, aiming to assess the impact of these incentive measures on the net present value (NPV) and internal rate of return (IRR) of CIES. Taking lithium iron phosphate (LFP) batteries as an example, an empirical analysis is conducted in 34 regions with regional time-ofuse (TOU) tariffs across 31 provincial-level administrative regions in mainland China. The results show that, first, for CIES, subsidies based on discharge volume have the most significant effect on economic benefits, especially in areas with higher average electricity prices and more peak periods. Second, tax incentives are suitable as the foundational incentives for regions with good economic benefits for CIES. Third, for regions with moderate peak-valley price spreads and only one peak period, higher levels of subsidies based on discharge, combined with certain tax incentives, are necessary to ensure good economic performance of the project. This study provides a more accurate perspective for evaluating the economic benefits of regional CIES and implementing incentive policies.

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The self-generation and self-use attributes of distributed photovoltaic increase the line costs of grid enterprises, and these costs are mainly borne by regional power users and grid enterprises. With the expansion of distributed photovoltaic scales and the rise of profit rates, distributed photovoltaic operators should also become one of the main contributors to the grid connection costs. Distributed photovoltaic grid connection cost related researches focus on its impact on the regional users of transmission and distribution prices. Alternative apportionment of grid connection cost on distributed photovoltaic related subjects of the impact of the research is less. Based on the theory of evolutionary game, this paper constructs a four-party game model among roof owners, grid enterprises, PV enterprises and the government, and takes 30 regions in China as examples to measure the impacts of different grid connection cost sharing schemes on the decision-making of the stakeholders. At the same time, it considers the impacts of the DPV cost, the grid connection cost and the proportion of self-consumption of DPV on the results of the evolutionary game. The results of this study are used to propose policy recommendations on the sharing of grid connection costs for distributed photovoltaic in China.

China’s current distributed photovoltaic grid connection cost channeling approach reduces the economic benefits of grid enterprises and brings unfairness among users. How to put forward appropriate distributed photovoltaic connection cost channeling methods according to the resource endowment and renewable energy consumption in different regions, achieving the promotion of distributed photovoltaic consumption while alleviating as much as possible the negative impacts of distributed photovoltaic connection on the grid and users are the aims of this paper. This paper firstly analyzes the cost composition of distributed photovoltaic grid connection in China as well as the channeling methods, and puts forward alternative channeling schemes; then it constructs a four-way evolutionary game model for roof providers, grid enterprises, photovoltaic enterprises, and the government under different channeling methods; at the same time, it also combines regional resource endowment, photovoltaic penetration rate, and other factors to construct a distributed photovoltaic grid-connection cost cross-subsidization indicator to categorize the regions, selects Hebei, Shanghai, and Guizhou for simulation in conjunction with the categorization results, at the same time, the impacts of grid costs, distributed photovoltaic costs, and other factors on the evolutionary results are took into account.

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To achieve the carbon peaking and carbon neutrality goals, China promoted county-wide photovoltaic projects (CWPVs) in 2021. A large number of distributed photovoltaic (DPV) installations have expanded the scale of prosumer. It also brings the inequity problem of prosumer, distribution network system operator (DSO) and consumer--DSO and consumer bear the costs such as access cost and depreciation of stock investment caused by DPV access. Combined with China's current electricity tariff system and the promotion of photovoltaic scale development, this paper proposes a standby fee model based on the principle of “beneficiary apportionment”. In this model, the standby fee mechanism for self-owned power plants is applied to DPV to reduce the implementation cost of the mechanism, and the principle of “beneficiary apportionment” is adopted to ensure the profitability of DPV. This paper takes the CWPV in Hunan Province, China as an example, calculates the standby fee under various electricity metering methods, and determines the most appropriate standby fee by considering factors such as output uncertainty of DPV. Finally, the difficulties in the implementation of standby fee are sorted out.

To reflect the future electricity demand variations in the industrial sector of western China under the "carbon peaking" and "carbon neutral" strategies, that the traditional methods of electricity consumption forecasting are no longer effective, this paper proposes a new hierarchy of electricity demand influencing factors based on the transition path of low-carbon and CO2 emission intensity constraints of the industrial sector. Then, the feedback equations of the influencing factors based on CO2 emission intensity is constructed, and a long-term electricity forecast model based on system dynamics is established. Finally, taking Ningxia Province as an example, the electricity demand and carbon emissions of the industrial sector are predicted under different policy constraint scenarios. The results show: (1) In the baseline scenario, electricity demand will tend to saturate with about 140.1TWh in 2030, and the CO2 emission level of electricity demand will peak at 72.52 million tons in 2029. (2) Compared with the system dynamics model without considering CO2 emission intensity, the mean absolute percentage error is reduced about 3.16 % by inputting the influence factors and regression coefficients designed in this paper. (3) Compared with other forecasting methods, the model proposed in this paper has the lowest mean absolute percentage error of 2.75 %. The model improves the accuracy and provides a reference for planning departments to develop energy transition plans, where the prediction results reflect the reality of the industrial sector in western China. Correspondingly, this model has reference value for relevant institutions in forecasting the electricity demand under the low-carbon development path in industrial sectors of different regions.

In the context of the construction of new power system, the installed scale of energy storage is steadily increasing in order to deal with the problem of safe and reliable operation of the system resulting from a large proportion of renewable energy installations connected to the grid. The pumped storage plants (PSP) have peak shaving, frequency modulation and standby functions which play a major role in ensuring the safety of the system and the consumption of renewable energy. By analysing the evolution of the pricing mechanism of transmission and distribution (T & D) tariffs and PSP, this paper analyses the influencing factors of PSP on T & D tariffs under different stages of electricity market development and establishes a conduction model. The empirical analysis shows that as the electricity market improves, the costs that PSP need to conduct through T & D tariffs continue to decrease. Therefore, it is necessary to accelerate the construction of the electricity market and to reflect the value of PSP through market regulation, in order to enable the PSP to recover part of their fixed capacity costs without relying on T & D tariffs, and to gradually achieve self-profitability.

Big data and artificial intelligence technology have promoted the all-round innovation of engineering cost.In order to improve the level of decision-making of distribution network engineering cost, aiming at the problems of many influencing cost factors and low prediction accuracy in distribution network, this paper proposed a distribution network overhead line engineering cost combination prediction model.Firstly, the missing of important data in the overhead line project of the distribution network was analyzed and processed.Seconelly, the important cost factors of overhead lines of the distribution network were selected based on the random forest algorithm.Finally, the least squares support vector machine model was used for cost prediction based on parameter optimization.By the comparison of the results of different prediction methods, it is confirmed that the cost prediction model constructed is closest to the measured value and can effectively improve the prediction speed and accuracy.The prediction model can provide an effective and practical method for realizing the cost prediction of overhead line project in distribution network.

To cope with such problems existed in pumped storage power stations in China as the pressure of investment cost recovery, the lack of social investment willingness and the lack of connection with market development, a two-part electricity price market connection mechanism of pumped storage power station was designed, in addition, a life cycle benefit evaluation model of pumped storage under the market-oriented mode was established to calculate the profit space of pumped storage participating in the market. By means of the life cycle period simulation of pumped storage power stations by capacity electric price and electrical capacity charge, it was found that the approved capacity electricity price existed a downward trend and this trend tended to be stable, and the capacity electricity charge emerged the U-shaped changing trend. Research results show that the designed two-part electricity price market connection mechanism can make the pumped storage power stations obtaining reasonable income in the electricity market and stepwisely reducing the proportion of the approved capacity electricity price covering the power station capacity to help the pumped storage power stations smoothly converted to the identity of an independent market subject.

随着双碳目标、新基建等政策的推行,电动汽车数量增长成为必然的趋势,电动汽车用户将成为需求响应的重要主体之一.用户在参加需求响应的过程中将会改变电动汽车充电时间以及电量,带来充电负荷需求的时空迁移,原有的充电设施规划结果需要调整.基于此,考虑价格型需求响应以及激励型需求响应对电动汽车规划结果的影响,首先从用户角度出发,以用户充电成本最低为目标函数建立不同需求响应模式下电动汽车充电行为模型,并提出峰谷电价下的电动汽车需求响应策略,预测充电需求时空分布;在给定初选站址的情况下,从投资者的角度出发,以建设运营总利润最大化为目标建立充电设施规划模型,对充电站是否建设以及建设规模进行规划.最后应用算例对不考虑需求响应、考虑价格型需求响应、考虑激励型需求响应下的电动汽车充电设施进行规划,并对需求响应参与度、需求响应价格对运营商投资收益率的敏感性进行了分析.

Pumped storage plant can help promote the low-carbon transformation of China's power system because of its fast response and energy time shift. Based on the pumped storage electricity price mechanism and conforming to the construction law of China's spot power market, this paper established a life cycle benefit evaluation model of pumped storage plant through different market stages, and the evaluation results can provide decision-making reference for investors and national policy makers. Through the life cycle simulation of the pumped storage plant, it is found that the capacity price approved by the government has a downward trend and tends to be stable, and the capacity electricity revenue shows a “U" change trend. The results show that the electricity price connection mechanism designed in this paper can make the pumped storage plant recover costs and obtain reasonable income in the electricity market. When the market mechanism is not perfect, gradually reducing the proportion of the approved capacity price covering the capacity of pumped storage plants will improve the economic benefits of pumped storage plant and help the pumped storage plant to smoothly convert to the status of an independent market subject.

With the implementation of policies related to the “dual carbon” target, comprehensive energy parks including wind power, photovoltaics and other renewable energy will increase. The uncertainty of load demand and renewable energy output is a major issue facing the optimization of multi-energy system dispatching represented by the combined cooling, heat and power (CCHP) system. Information gap decision theory method (IGDT) is currently a popular method for solving uncertainty, it improves the robustness of the optimization result by quantifying the maximum uncertainty offset that the target value can withstand. However, it needs to predict the relationship between the uncertain variable and the objective function value, which increases the workload and may cause errors. In addition, the traditional IGDT method does not consider the probability distribution of the uncertain variable, making the calculation result inconsistent with the actual situation. In this paper, probability distribution and IGDT are merged, and a new IGDT method is proposed to optimize the scheduling of situations with high probability of occurrence to solve the above problems. Finally, this paper uses Monte Carlo to randomly generate scenes and compares the scheme under the new IGDT with the scheme under the traditional IGDT, the new IGDT method is more robust.

Poverty alleviation is an important work of the Chinese government in recent years. According to the international poverty standards of the world bank, China's poverty reduction population accounts for more than 70% of the world. Photovoltaic poverty alleviation is a significant way for regions rich in solar energy resources to transform the advantages of renewable energy resources into the driving force of social and economic development. It is also an effective means for China to implement power poverty alleviation. While promoting emission reduction in these regions, it accelerates economic development and implements targeted poverty alleviation. This paper analyzes the comprehensive benefits of typical market entities of photovoltaic poverty alleviation projects, and establishes the environment, economic and social benefit evaluation models for poor households, the state and government, photovoltaic enterprises and grid corporation. In the model, we creatively put forward an index system to characterize the poverty alleviation effect, and consider the additional industrial benefits brought by photovoltaic poverty alleviation projects. Finally, a typical photovoltaic poverty alleviation project in Haiyuan County, Northwest China is selected to verify the feasibility of this model.

Distributed energy, mainly composed of new energy, plays an important role in promoting the development of new energy. At present, the development of distributed energy is greatly hindered by imperfect trading platform and unstable output of new energy. Blockchain is decentralized, autonomous and requires collaborative management. Its own technical characteristics have the inherent advantages of reconstructing the energy system. The alliance chain in the blockchain is more suitable for building a distributed energy trading platform. The paper constructs a distributed energy transaction model based on alliance blockchain, studies the integration mode of blockchain and distributed energy transaction, and explores the application of blockchain in distributed transaction. The paper provides a new idea for optimizing and reconstructing the traditional distributed energy trading platform, and providing decision support for promoting distributed energy trading.

Distributed energy based on clean energy has gradually become a progressively important part of the energy system due to increasingly prominent environmental problems and energy crises. Presently, the development of distributed energy has the phenomena of investment chaos, resource waste, and information asymmetry. We propose a grid-distributed energy system joint decision-making model based on the alliance blockchain to solve problems including the power generation strategy of distributed energy users and the grid investment plan connected to the distributed system. Firstly, we construct the return models of the distributed energy system and the power grid system separately. Secondly, combined with the analysis of the game mechanism of different entities, we propose a multi-agent decision-making model of the distributed energy system based on the alliance blockchain. Thirdly, based on this model, we optimize the revenue of the entire system through the dynamic game and use the alliance chain and smart contract to automatically execute. Finally, the model is solved by the iterative search method, and the entire simulation process is implemented in Ethereum using python. Based on the idea of joint decision making, our study considers the interests of all participants, ensures that the participants maximize their benefits in the game process, optimizes the investment decisions of each entity, and improves the effectiveness of the grid-distributed energy system decision making.

电网固定资产投资中绝大部分是电网基建投资,投资的经济效益关乎电网公司的可持续发展.在当前输配电价改革的背景下,电网基建投资的经济效益面临着较大的不确定性.结合国外的输配电价监管模式,分析我国未来的输配电价改革趋势.研究电网基建项目从建设到退役的全寿命周期成本和整个运行期的收益,建立了电网基建项目动态投入产出模型,该模型能够适应未来输配电价的动态变化.最后,结合案例进行电价预测、负荷预测和投入产出计算,得出项目的投入产出指标,验证了模型的有效性和准确性.

With the development of transmission and distribution price reform in China, pumped storage power station can not continue to be included in the effective assets of the power grid, and its cost can not be dredged through the transmission and distribution price, so it is urgent to find a way to protect its own income through the market. This paper innovatively proposes a "three-stage" competitive optimization model for pumped-storage power stations, using a quadratic programming algorithm with two consecutive iterations to convert the discrete programming problem into a linear convex programming problem, reducing the difficulty of calculation and improving the calculation accuracy. Finally, the reinforcement learning algorithm is used to obtain the real-time bidding strategy of the pumped storage power station, and continuous feedback is provided. The calculation example analysis shows that compared with the traditional model, the "three-stage" model can bring better benefits to the pumped storage power station, and when the actual value of demand fluctuates within-8%, the pumped storage power station has the ability to resist risks higher than the market average. And when the proportion of renewable energy increases from the current 8%e30%, the revenue of pumped storage power plants will drop by 20%. (c) 2021 Elsevier Ltd. All rights reserved.