The large-scale integration of distributed photovoltaics and emerging loads has made limited distribution network hosting capacity a key physical bottleneck to 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 four-stage 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 envelope 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 enables “data availability without visibility”, and provides a feasible path for secure and efficient data collaboration in the energy internet. On the basis, this paper further discusses future challenges related to performance optimization, side-channel defense, and standardization.
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 three-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 plant (VPP) 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 VPP 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 VPP aggregation can take into account both economic and risk considerations. It can provide a theoretical method for VPP aggregators to screen resources and reduce the risk of deviation punishment of VPP. The IGDT-based bidding decision optimization model can help avoid the transaction risk caused by the uncertainty of peak compensation price, so that the VPP can obtain reasonable response benefits.
To address the coordinated challenge of economy, low-carbon performance, and robustness in the scheduling of virtual power plants (VPPs) under high-proportion renewable energy grid integration, this paper proposes a VPP optimization scheduling strategy based on electricity-carbon coupling and multi-time-scale distributionally robust optimization. Firstly, a joint electricity-carbon market framework integrating stepwise carbon trading and green certificate trading is constructed, which stimulates the low-carbon scheduling potential of VPPs through price signal linkage. Secondly, a three-level day-ahead-intraday-real-time optimization architecture is designed, with the core of formulating global plans, correcting prediction errors, and eliminating instantaneous deviations respectively, to achieve accurate scheduling across different time scales. Finally, in response to the uncertainties of wind and solar power output and market prices, a distributionally robust optimization method is adopted to build a Wasserstein ambiguity set model, which dynamically adjusts robust parameters to balance risks and benefits. The case study results show that the total cost of the proposed strategy is reduced to 12,300 yuan per day, a decrease of 32.4% compared with the traditional strategy; the renewable energy consumption rate reaches 99.5%, the carbon cost-benefit ratio is increased to 1.8, and the robustness compliance rate is as high as 98.2%. At the same time, the energy storage charge-discharge efficiency and the optimization effect of market revenue structure are significant. This strategy achieves an all-round improvement in economy, low-carbon performance, robustness, and energy utilization efficiency, providing an effective technical path for the optimal operation of VPPs in a multi-market environment.
Under the background of the “carbon peak and carbon neutrality” goals, the contradiction between the high proportion of renewable energy grid connection and the reliance on fossil energy has become increasingly prominent. To coordinate low-carbon constraints with energy security, this paper proposes an optimized scheduling model for virtual power plants (VPPs) based on the collaboration of carbon capture and storage (CCS), power-to-gas (P2G), and electric vehicles (EVs). This model builds an integrated framework of “emission reduction - conversion - benefit” by aggregating distributed resources such as gas turbine units, combined heat and power (CHP), wind power, photovoltaic power and EVs: Firstly, CCS is used to capture CO2; Secondly, through CCS-P2G, CO2 is converted into methane by utilizing the abandoned wind and photovoltaic energy, and the captured CO2 is consumed to form a carbon cycle. Finally, aggregated EVs participate in carbon market transactions and increase their profits by using the China certified emission reductions (CCERs) they generate. The case analysis based on Matlab/Cplex shows that compared with the traditional gas-CHP system model, the model proposed in this paper can reduce carbon emissions by 91.3% (from 2,466.9 tons to 214.34 tons), lower the cost of wind and solar power curtailage by 71,457 yuan, and increase the consumption rate of renewable energy. And by selling CCER, the net cost of VPP was reduced by 8,208 yuan. Ultimately, the overall net cost of VPP was reduced by 96,611 yuan. The research verified the effectiveness of multi-technology collaboration in enhancing the economic and environmental benefits of VPP, providing theoretical support and practical paths for the low-carbon transformation of the new power system.
The trading behavior of virtual power plant (VPP) in the carbon-electricity market will affect the harmonious development of the market. Therefore, this paper proposes a peer to peer (P2P) transaction matching method for VPPs to participate in the carbon-electricity convergence market considering trading harmony. Firstly, the overall framework for VPPs participating in P2P transaction mechanisms of the carbon-electricity convergence market is designed. Based on the operation plan of each unit of source, load and storage in VPP and the carbon emission coefficient of carbon-emitting units, a transaction capacity evaluation model for VPP to participate in the electricity market and carbon market is constructed. Meanwhile, by analyzing the impact of various historical transaction behaviors and performance behaviors of VPP on the harmonious development of the carbon-electricity trading market, the evaluation index of trading harmony for VPP participating in the carbon-electricity market is proposed. Secondly, a P2P transaction matching model for the primary carbon-electricity convergence market is constructed with the objective function of maximizing the total transaction acceptance of both parties, including the transaction harmony of both parties, the transaction price acceptance of both parties and the carbon neutrality achievement of both parties. Furthermore, a secondary carbon market P2P transaction mechanism is established as a supplementary part of the primary carbon-electricity convergence transaction market for the carbon rights transaction volume corresponding to the deviation between the actual executed electricity and the agreed electricity in the day-ahead electricity market. Finally, the simulation example verifies that VPP with high trading harmony and low carbon emissions are more competitive in the P2P transactions of the carbon-electricity convergence market.
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, progressive and scalable, providing a comprehensive and suitable standard guidance for the future development of the power industry carbon emission reduction.
To enhance the operational flexibility of coal-fired power plants in supporting high penetration of renewable energy, this paper focuses on the coordinated planning of regional coal-fired power flexibility upgrades. Existing research typically optimizes deep peak-shaving modifications on the boiler side of condensing units and thermal-electric decoupling technologies separately, with most studies neglecting the impact of grid structural constraints. this paper first systematically analyses the operational characteristics of four technical approaches: deep peak shaving modifications on the boiler side, zero-output modifications for low-pressure cylinders, electric boilers, and thermal storage devices. Subsequently, a mixed-integer linear programming model incorporating grid topology is constructed. This model aims to minimize total system costs, enabling the coordinated configuration and operational optimization of multiple technical pathways. A case study based on an enhanced IEEE 14-node 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.
The proposal of the national “dual carbon” strategic goals and the improvement of the carbon trading market have placed higher demands on the dispatch and operation strategies of microgrids. Aiming at emission reduction and cost lowering, this paper develops a dual-layer optimization model for low-carbon and economic operation of microgrids, which includes photovoltaic, wind power, diesel generator sets, and energy storage modules, while considering load constraints, charging/discharging constraints of storage devices, and carbon quota constraints. The outer-layer planning model introduces a carbon asset operation mechanism and uses an improved genetic algorithm to optimize microgrid operation strategies, improving economic benefits while ensuring low-carbon system operation. The inner-layer demand response model improves user electricity consumption behavior through incentive measures, optimizes the load curve, and feeds back the user response after implementing demand response to the outer-layer model. The inner and outer layers interact iteratively until both equipment configurations and load curves reach optimum, thereby achieving a bidirectional interaction between load and carbon emissions. Simulation results under multiple scenarios demonstrate that the proposed dual-layer optimization model can significantly reduce microgrid operation costs and carbon emissions. Sensitivity analysis results show that carbon trading prices have a significant impact on operation strategies and costs.
High-accuracy reconstruction of the operational state is essential for performance evaluation and fault diagnosis of wind turbines. However, in practice, inaccurate wind speed measurements often lead to significant deviations in data analysis and state reconstruction, which directly compromise the accuracy and timeliness of wind turbine operation and maintenance. To address this issue, this study starts from the overall mechanism of energy capture and transfer in wind turbines, establishes a power flow model that integrates physics-based principles with data-driven approaches, and proposes a bisection-based method for estimating the rotor equivalent wind speed. The results demonstrate that the proposed method achieves high-consistency reproduction of key state variables—including rotor speed, output power, and pitch angle—under a wide range of operating conditions, including below rated wind speed, near rated wind speed, and in high wind speed regions. The consistency coefficient of field reproduced (CCFR) obtained by this method significantly outperforms that of conventional methods relying on directly measured wind speeds, showing excellent engineering applicability and robustness. The proposed wind speed estimation method enhances the accuracy of operating condition reproduction and provides a reliable data foundation and technical means for engineering applications such as performance assessment, control parameter optimization, and fault early warning. It holds considerable value for practical field deployment.
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 knowledges (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.
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