Developing a reasonable, efficient distributed market transaction mechanism is an important issue in distribution systems. The gaming relation between distributed transaction market entities has yet to be fully elucidated in various trading links, and the impact of distributed transactions on distribution network operations has yet to be comprehensively analyzed. This paper proposes a novel distributed Peerto- Peer (P2P) day-ahead trading method under multi-microgrid congestion management in active distribution networks. First, a flexible load model for price-based demand response load and an autonomous microgrid economic scheduling model are constructed. Second, under normal operation of the distribution network, a non-cooperative game model and Stackelberg game model are employed to separately and comprehensively analyze gaming relationship among sellers, and between sellers and buyers. Thereafter, a congestion management method based on market capacity is established from the perspective of distribution network control centers. Finally, the impact of end energy consumption characteristics on microgrid economic scheduling and P2P trading is analyzed through a modified IEEE 33-node power distribution system. The economic and technical benefits such as congestion mitigation and network loss reduction that produced by P2P trading to the operation of microgrid systems are analysed with specific indicators.

Nowadays, electricity markets and carbon trading mechanisms can promote investment in renewable sources but also generate new uncertainties in decision-making. In this paper, a two-stage Wasserstein distributionally robust optimization (WDRO) model is presented to determine the optimal planning strategy for renewable energy generators (REGs) and energy storage systems (ESSs) in the distribution network. This model considers supply-side and demand-side uncertainties in the distribution network and the interaction uncertainty from the main grid which are depicted by the ambiguity sets based on the Wasserstein metric and historical data. Meanwhile, both 1-norm and ∞-norm Wasserstein metric constraints are considered to satisfy the decision-maker's different preference. Furthermore, to solve this WDRO model, a systematic solution method with a three-step process is developed. Numerical results from a modified IEEE 33-node system and a 130-node system in the real world demonstrate the advantages of the two-stage WDRO model and the effectiveness of the solution method.

A day-ahead economic scheduling method based on chance-constrained programming and probabilistic sequence operation is proposed in this paper for an electric vehicle (EV) battery swapping station (BSS), considering the dual uncertainties of swapping demand and photovoltaic (PV) generation. First of all, a BSS day-ahead scheduling model that can deal with the uncertainties is established by using the chance-constrained programming. The optimization objective is to minimize the cost of electricity purchased from the utility grid with the chance constraints of swapping demand satisfaction and the confidence level of the minimum cost. Then, the deterministic transformation of chance constraints is implemented based on probabilistic sequences of stochastic variables. Thereafter, the feasible solution space of the proposed model is determined based on the battery controllable load margin, and then the fast optimization method for the BSS day-ahead scheduling model is developed by combining the feasible solution space and genetic algorithm (GA). In order to evaluate the solution quality, a risk assessment method based on the probabilistic sequence for day-ahead scheduling solutions is proposed. Finally, the efficiency and applicability of the proposed method is verified through the comparative analysis on a PV-based BSS system. Results illustrate that the model can provides a more reasonable charging strategy for the BSS operators with different risk appetite.

A multi-energy microgrid has multiple terminal resources and multiple distributed components for energy production, conversion, and storage. By using this grid, an interconnected network with optimized multiple energy sources can be formed. This type of grid can minimize energy waste while laying the critical foundation for an energy Internet. The multi-energy microgrid must be formed properly to ensure multi-energy coupling and complement. However, critical technologies (e.g., reliability assessment) and configuration planning methods now need further research. In this study, a novel method for the reliability evaluation of a multi-energy supply is proposed, and an operation–planning double-layer optimization design method is investigated that considers reliability. On that basis, the effects of different configuration schemes on economy and reliability are quantitatively analyzed. First, the coupling relationship between multi-energy carriers in a typical multi-energy microgrid is analyzed; subsequently, the energy efficiency and economical models of the key equipment in the grid system are determined. Monte Carlo simulation and the Failure Mode and Effect Analysis (FMEA) method are applied to evaluate the reliability with sorted indicators. A double-layer optimization model is built for a multi-energy microgrid with the optimal configuration. The impact of configuration on the reliability and economical performance of the microgrid system is quantitatively analyzed based on actual calculations. The results obtained here are relative to the capacity, configuration, operation, and energy supply reliability of the multi-energy microgrid, and may serve as the feasible guidelines for future integrated energy systems.

False data injection (FDI), could cause severe uneconomic system operation and even large blackout, which is further compounded by the increasingly integrated fluctuating renewable generation. As a commonly conducted type of FDI, load redistribution (LR) attack is judiciously manipulated by attackers to alter the load measurement on power buses and affect the normal operation of power systems. In particular, LR attacks have been proved to easily bypass the detection of state estimation. This paper presents a novel distributionally robust optimization (DRO) for operating transmission systems against cyber-attacks while considering the uncertainty of renewable generation. The FDI imposed by an adversary aims to maximally alter system parameters and mislead system operations while the proposed optimization method is used to reduce the risks caused by FDI. Unlike the worst-case-oriented robust optimization, DRO neglects the extremely low-probability case and thus weakens the conservatism, resulting in more economical operation schemes. To obtain computational tractability, a semidefinite programming problem is reformulated and a constraint generation algorithm is utilized to efficiently solve the original problem in a hierarchical master and sub-problem framework. The proposed method can produce more secure and economic operation for the system of rich renewable under LR attacks, reducing load shedding and operation cost to benefit end customers, network operators, and renewable generation.