The large-scale integration of distributed photovoltaics and emerging loads has made the insufficient hosting capacity of distribution networks a key physical bottleneck restricting the development of new-type power systems. Existing research on virtual power plants (VPPs) mostly focuses on commercial aggregation and optimal dispatch, lacking a systematic exploration of active support mechanisms for the underlying operational constraints of distribution networks. Stepping out of the single resource aggregation perspective, this paper proposes a fourstage evolutionary analysis framework for VPPs oriented towards enhancing distribution network hosting capacity, and systematically reviews international frontier practices from VPP 1.0 (aggregation arbitrage) to VPP 4.0 (hierarchical autonomy). Specifically, VPP 2.0 relies on the local autonomous control of smart inverters to mitigate voltage violations, effectively enhancing the static hosting capacity of distribution networks; VPP 3.0 introduces the Dynamic Operating Envelopes (DOEs) mechanism to decouple physical constraints from market optimization, deeply unlocking the spatiotemporal dynamic flexibility of the system; VPP 4.0 utilizes Cellular Energy Systems and distributed collaborative algorithms to address the scalability bottleneck under the integration of hundreds of millions of nodes. On this basis, considering China's practical national conditions—specifically the management system based on secondary substation areas (transformer service areas) and the massive scale of legacy non-smart assets—this paper proposes a three-stage localized evolution pathway encompassing physical capability upgrades, physical-market decoupling, and cellular autonomy at the secondary substation level. This research provides theoretical references and engineering guidelines for the transformation of VPPs in China from pure commercial aggregators to technical aggregators equipped with physical support capabilities for distribution networks.

The efficient operation of Virtual Power Plants (VPPs) relies on massive heterogeneous data interactions, facing the fundamental contradiction between data value mining and privacy protection. Addressing the challenges of data silos, single points of failure, and privacy leakage in centralized management, this paper reviews the application status and integration solutions of federated learning and trusted execution environment in VPPs. Through comparative analysis of blockchain and cryptographic collaboration schemes, the advantages of federated learning in privacy protection, collaboration efficiency, and technical adaptability are clarified. The applications of federated learning in VPP load forecasting, distributed energy resource coordination, electricity market trading, and demand response are systematically reviewed, along with an analysis of trusted execution environment mechanisms that ensure computational confidentiality and integrity through hardware isolation. The integration architecture of both technologies is emphasized: trusted execution environment addresses federated learning's vulnerabilities in parameter leakage and poisoning attack defense by protecting model aggregation processes and sensitive data processing, while federated learning's distributed characteristics overcome the performance and scalability bottlenecks of trusted hardware. This integrated technology realizes "data availability without visibility," providing a feasible path for building secure and efficient energy internet data collaboration, with prospects for challenges in performance optimization, side-channel defense, and standardization.

To address the cost recovery challenges of new-type energy storage projects and to identify the key economic determinants across different application scenarios, a cost analysis framework and economic quantification methodology tailored to revenue models for new-type energy storage applied at the generation, grid, and demand sides are proposed. Unit energy/capacity cost characterization models are derived using the operational-period pricing method, which are compatible with revenue models across different application scenarios. Multidimensional revenue quantification models are established by incorporating market-based income alongside traditional income. Case studies and sensitivity analyses are conducted using indicators such as annualized net revenue, dynamic payback period, and internal rate of return. The results indicate that while mature electrochemical energy storage demonstrates better economic performance in grid-side independent energy storage, its financial viability is significantly impacted by electricity market products, and the potential discontinuation of peak-shaving markets would lead to a substantial decline in revenue. For generation-side renewable energy stations, increasing the storage duration of energy storage can increase its grid electricity and enhance grid compatibility, thereby enhancing both electricity revenue and "Two Rules" (the grid compliance and ancillary service compensation mechanisms) revenue. The economics of demand-side energy storage depend on tariff structures （single-rate vs. two-part） and load characteristics. For large industrial customers with stable loads under two-part tariffs, optimal sizing of storage power and energy capacity is required to avoid losses caused by increased demand charges. From the perspective of investors and operators, this study systematically outlines a cost mechanism and economic analysis approach for new-type energy storage across generation, grid, and demand sides that are aligned with revenue models. By comprehensively incorporating key cash flow elements such as loan repayments and tax liabilities, it enables accurate dynamic economic assessment, providing a practical theoretical basis and operational guidance for cost control and decision-making in market-oriented environments.

A scientific and rational technical standard system for power carbon emission reduction (TSS-for-PCER) is a critical element in both of advancing the construction of a new electricity system (NES) and achieving carbon peaking and carbon neutrality goals. With the gradual construction of NES, it is clearly that the TSS-for-PCER shall cover the entire power industry chain. In this paper, based on the Hall three-dimensional structure, using the method of combining "top-down" and "bottom-up", firstly, the development status of technical standards for power carbon emission reduction was reviewed. Then, the standard requirements of various stakeholders in the industry were summarized. the standard requirements in the carbon reduction field from various stakeholders in the power industry is studied. Finally . a multi-level TSS-for-PCER architecture of "3+6+N+N" has been proposed. The TSS-for-PCER is characterized by 3 levels of basic support, core implementation, and management evaluation, 6 dimensions of basic universality, carbon emission accounting and verification, carbon emission monitoring, carbon reduction technology and equipment, carbon emission assessment and evaluation, and carbon emission management, 20 technical fields, and 33 categories. To maintain the advancement of the TSS-for-PCER, it is necessary to conduct regular evaluations of it and adjust the technical fields and corresponding subjects. Therefore, the number of both technical fields and categories are represented by N+N. Finally, specific implementation suggestions were provided from four aspects: implementation direction, path, guarantee, and development. The standard system has not only successfully filled the standard gap in the field of domestic power carbon emission reduction technology, but also achieved the coverage of the whole process of power carbon emission, which is systematic, progressiveness and scalable, providing a comprehensive and suitable standard guidance for the future development of the power industry carbon emission reduction.

With the acceleration of new power system construction, distributed photovoltaic aggregators are able to participate in the day-ahead energy and reserve joint market trading and obtain profits. However, due to differences in their interests, distributed photovoltaic aggregators, market trading centers, and distribution system operators exhibit complex market trading game behaviors. Accordingly, a single-leader multi-follower mixed-integer Stackelberg game framework is constructed for distributed photovoltaic aggregators participating in the day-ahead electricity market. The upper-level model aims to maximize the profit of the distributed photovoltaic aggregator (leader) by optimizing bidding strategies, while the lower-level model involves the market trading center (follower 1) conducting day-ahead joint market clearing, and the distribution system operator (follower 2) performing security verification on the market clearing results based on discrete control measures such as transformer tap changers and capacitor switching. To solve this multi-agent game model, the Karush-Kuhn-Tucker conditions and the Big-M method are first used to equivalently transform the follower 1 problem. Subsequently, a data-driven bilevel reconstruction algorithm is employed to solve the leader-follower game model with continuous and discrete variables. Finally, the accuracy and effectiveness of the game model and its solution algorithm are validated using a practical transmission and distribution system in a certain region.

In recent years, the rapid development of Fuel-Cell Hybrid Electric Vehicles (FCHEV) has effectively alleviated the peak-shaving challenges in new power systems caused by the "anti-peak" characteristics of renewable energy, promoting energy-transportation coupling and low-carbon emission reduction. This paper focuses on the impact of renewable energy output uncertainty and the charging response of FCHEV on microgrid optimization scheduling. By utilizing FCHEV as a link between multiple regions of the microgrid, a two-stage robust game model for the microgrid is established. To address the issue of low solving efficiency caused by continuously adding constraints to the master problem during the iteration process of the C&CG algorithm, an improved iNC&CG algorithm is proposed. Finally, simulation results demonstrate that the proposed two-stage robust game model for the microgrid can achieve a balance of interests between the microgrid and FCHEV users, effectively cope with the impact of renewable energy output uncertainty. And the advantages of the improved algorithm in solving such large-scale problems is verified.

To address the problems of renewable energy accommodation and supply–demand imbalance caused by the dynamic evolution of load demand over the life cycle of western mining areas, a life-cycle dynamic configuration method for the energy system of underground coal mines is investigated. Based on the production organization in the construction, early-stage mining, mid-stage mining and late-stage mining periods, a multi-energy coupling framework for electricity–heat–cooling is constructed to reflect the differences between above-ground and underground loads. A multi-stage mixed-integer linear programming model is developed, which integrates photovoltaic (PV) generation, electrical energy storage, chillers and external power/heat supply. The objective is to minimize the total life-cycle cost consisting of investment, operation and maintenance, purchased energy and carbon emission costs by optimally sizing PV, energy storage and primary network equipment, and by representing the temporal evolution of source–load relationships through typical-day load profiles and year-by-year capacity expansion decisions. A typical western underground mine is used as a case study, and two scenarios are compared: traditional static one-shot configuration and life-cycle dynamic configuration. The results show that the dynamic configuration increases the average installed PV capacity and renewable penetration through staged PV and storage expansion in key years, while restraining the required capacity of primary network equipment and substantially reducing curtailment over the whole life cycle. Compared with the static configuration, the dynamic configuration reduces the total life-cycle cost by about 17.9% and the carbon emission cost by about 50.2%. The proposed life-cycle dynamic configuration method can satisfy secure energy supply for mining areas while balancing economic performance and low-carbon goals, and it provides a technical reference for planning clean energy systems in western mining areas and similar energy-intensive industrial parks.

To address the challenges of diversified participants in the electricity market and resolve conflicts of interest among multiple virtual power plants (VPPs), this paper proposes a three-layer hybrid game strategy involving the distribution system operator (DSO), virtual power plant operator (VPPO), and user aggregator (UA) to support collaborative optimization of multi-VPP systems. First, a UA coalition is formed by aggregating various flexible resources, including photovoltaic prosumers, electric vehicle charging stations, and integrated energy loads. Next, a 3-layer hybrid game model is constructed, encompassing hierarchical energy transactions among DSO, VPPO, and UA, as well as peer-to-peer energy trading among UA entities, structured as a "Stackelberg game - Stackelberg game - cooperative game" framework. Finally, the proposed model is solved using the bisection method, Karush-Kuhn-Tucker conditions, and the alternating direction multiplier method. Case studies demonstrate that the proposed multi-layer hybrid game strategy reduces the operating costs of the photovoltaic prosumer UA and the electric vehicle charging station UA by 4.5% and 15.3%, respectively. Meanwhile, guided by the dynamic pricing mechanism of the DSO, the total profit of the system operators increases by 2.7%. This strategy can effectively balance the interests of multiple stakeholders, optimizing the energy trading and benefit allocation mechanisms among different operators.

To address the optimization problem of resource aggregation and bidding decision-making for virtual power plants (VPPs) participating in power peak shaving, a decision optimization model based on resource response capability and information gap decision theory (IGDT) is proposed. Considering four dimensions—response potential,fluctuation degree, duration, and response speed—an aggregation indicator system for distributed resources is constructed. A multi-objective aggregation optimization model is established, balancing the maximization of expected response revenue and the minimization of deviation penalty risk, to screen the optimal resource portfolio. The market transaction framework and bidding decision mechanism for VPPs participating in power peak shaving are designed. The IGDT theory is introduced to characterize the uncertainty of peak shaving compensation prices, and a risk-averse (RA) model is constructed to optimize bidding strategies.The simulation results show that the multi-objective optimization model of virtual power plant aggregation can take into account both economic and risk considerations. It can provide a theoretical method for virtual power plant aggregators to screen resources and reduce the risk of deviation punishment of virtual power plants. The IGDT-based bidding decision optimization model can help avoid the transaction risk caused by the uncertainty of peak compensation price, so that the virtual power plant can obtain reasonable response benefits.

system demonstrates that integrated optimization of these technologies reduces total system costs by 5.98% and curtailment rates for wind and solar power by 15.68%. The results validate that the proposed collaborative planning approach effectively integrates complementary advantages across different technologies, significantly lowering system costs and alleviating pressure on renewable energy integration. It also reveals that the flexibility regulation capacity of coal-fired power plants is influenced by their node position within the grid.

To address the issues of existing regional distributed photovoltaic (PV) power forecasting, such as heavy reliance on meteorological data, high operation and maintenance costs, poor data quality and insufficient result credibility,a joint credible forecasting method for PV power and energy is proposed. First, power measurements from smart meters and daily frozen energy data are jointly filtered, fused, and normalized to enhance data set quality. Second, a multi-time-scale, high-accuracy sequence-to-sequence (Seq2Seq) forecasting framework is developed, integrating historical and forecast data from centralized regional PV plants; a multi-time-scale loss function that jointly accounts for both power and energy is employed to optimize prediction accuracy. Finally, a model integrity verification scheme based on commit-and-prove succinct non-interactive argument of knowledge (cp-SNARKs) is designed to ensure result credibility while preserving model confidentiality. Experimental validation using real-world data from a city in North China demonstrates that the proposed method significantly reduces forecasting errors for both power and energy, thereby improving PV power prediction accuracy. Requiring no meteorological inputs or system modifications, the approach features high data quality, superior prediction accuracy, low operational cost, and strong verifiability, making it readily extensible to other time-series forecasting tasks such as load forecasting and wind power prediction.