Xingang Yang

To address the challenge of quantifying the coupling relationship between user-side reporting and bidding behavior and electricity market operational risks, this study proposes a method for electricity market risk assessment and propagation identification based on entropy-weighted TOPSIS and directed temporal-dependency analysis. First, a risk indicator system is constructed across four dimensions, including electricity supply and demand, price volatility, system operation, and user-side bidding credibility. Robust quantile standardization and the entropy-weighted TOPSIS method are employed to construct category-specific and composite risk-state indices and warning levels. Subsequently, the PCMCI+ method is used to identify candidate temporal relationships among risk indicators, and risk propagation pathways are screened through stability and temporal direction tests; on this basis, a dynamic model with pathway constraints is established to quantify the propagation strength between different risk categories based on impulse responses. The PJM case study shows that the framework can distinguish elevated risk-state periods and identify a sparse full-summer directed temporal-dependency pattern, while sensitivity and chronological split-sample analyses reveal that the detailed propagation structure is specification- and period-dependent. These results support the use of the framework for multidimensional risk-state monitoring while also defining the empirical boundary of its propagation interpretation.

As electricity demand increases with social development, the inadequate upgrade of distribution network infrastructure fails to meet peak demand. To address this issue, introducing source-load distributed resources to enhance power system flexibility has become a development trend that reduces distribution equipment retrofitting costs while satisfying peak grid demand. To this end, this paper proposes an optimal aggregation and scheduling strategy for distributed resources based on zonotopes. First, a Monte Carlo simulation-based scenario generation model is developed to supplement the scenario set for uncertain photovoltaic output. Second, a distributed resource aggregation method using zonotopes is proposed to determine the adjustable range of distributed resource clusters. Finally, an industrial park case study is conducted to validate the superiority of the proposed strategy in coordinating source-load distributed resources for peak-load shaving, achieving a peak shaving rate of 40.09% during peak demand periods.

Against the backdrop of rapidly growing distributed photovoltaics (DPVs) and mounting pressure on conventional frequency-regulation (FR) resources, this study proposes a day-ahead–intraday two-stage optimal scheduling strategy for aggregators of DPV + advanced energy storage participating in a joint energy–FR market. In the day-ahead stage (hourly resolution), a multi-aggregator-independent offering model is formulated that explicitly accounts for PV curtailment costs and storage operating/lifecycle costs. Subject to constraints on buy–sell transactions, PV output, storage charging/discharging power and state of charge (SOC), FR capacity, and power balance, the model co-optimizes energy and FR-capacity offers to maximize profit. In the intraday stage (15 min resolution), bidding deviation penalties are introduced, and a rolling optimization is employed to jointly adjust energy and FR dispatch/offers, reconfigure storage SOC in real time, reduce deviations from day-ahead schedules, and enhance economic performance. A three-aggregator case study indicates that, with deviation penalties considered, regulation-command tracking remains at a high level and PV utilization remains very high, while clearing costs decline and system frequency-response capability improves. The results demonstrate the proposed strategy’s implementability, economic efficiency, and scalability, enabling high-quality participation in ancillary services and promoting high-quality renewable integration under high-penetration distributed scenarios.

As an essential platform for aggregating and coordinating distributed energy resources (DERs), the virtual power plant (VPP) has attracted widespread attention in recent years. With the increasing scale of VPPs, energy interaction and sharing among VPP clusters (VPPCs) have become key approaches to improving energy utilization efficiency and reducing operational costs. Therefore, studying the coordinated operation mechanism of VPPCs is of great significance. This paper proposes a two-stage coordinated operation model for VPPCs based on energy interaction to enhance the overall economic performance and coordination of the cluster. In the day-ahead stage, a cooperative operation model based on Nash bargaining theory is constructed. The inherently non-convex and nonlinear problem is decomposed into a cluster-level benefit maximization subproblem and a benefit allocation subproblem. The Alternating Direction Method of Multipliers (ADMM) is employed to achieve distributed optimization, ensuring both the efficiency of coordination and the privacy and decision independence of each VPP. In the intra-day stage, to address the uncertainty in renewable generation and load demand, a real-time pricing mechanism based on the supply–demand ratio is designed. Each VPP performs short-term energy forecasting and submits real-time supply–demand information to the coordination center, which dynamically determines the price for the next trading interval according to the reported imbalance. This pricing mechanism facilitates real-time electricity sharing among VPPs. Finally, numerical case studies validate the effectiveness and practical value of the proposed model in improving both operational efficiency and fairness.