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As an important means of mobilizing demand-side resources, peer-to-peer (P2P) energy trading has drawn more and more attention from scholars. This paper constructs a P2P energy trading framework considering prosumers' trading partner preferences (TPPs) and system risk. At first, we build the P2P trading models of prosumers equipped with different distributed energy resources (DERs), and TPP models. Secondly, to solve the established energy trading problem, a fully distributed double-consensus alternating direction method of multipliers (DC-ADMM) is proposed, which can achieve transaction consensus when considering market players' TPPs. Then, a risk-based security constrained economic dispatch (RB-SCED) model based on AC power flow is established for the first time, by which a distribution system operator (DSO) checks system security and obtains risk-based locational marginal prices (RLMPs). Moreover, double-regulated price signals related to RLMPs which contain grid utilization prices (GUPs) and DSO's retail prices realize management of players' transactions. In the end, the proposed method is applied to an IEEE33 bus distribution system. Results show the proposed method effectively reduces system risk and ensures secure operation of system without direct management.

Large-scale wind power integration brings great challenges to power system operation. The use of large-scale wind power in the electricity market has become a concern for many researchers. Demand response (DR) and energy storage systems (ESSs) play crucial roles in the consumption of large-scale wind power. In this paper, a detailed DR model is established, including price-based demand response (PBDR) and incentive-based demand response (IBDR). IBDR contains load shifting (LS) and load curtailment (LC). The IBDR model in this study not only includes its bidding and market clearing but also contains relevant constraints: maximum/minimum duration time, shifting/curtailment gap time, and shifting/curtailment frequency. A two-stage trading method, including a day-ahead (DA) market and a real-time (RT) market, is proposed. The method contains various market participants: conventional units (CUs), rapid adjustment units (RAUs), wind power, ESS, and multiple types of DR. The roles and economic benefits of various market participants in the consumption of large-scale wind power are analyzed in an IEEE 30 bus system, verifying the accuracy and validity of the model. The best DR scale and the suggestions of ESS are given. The results show that the proposed method can effectively utilize wind power and decrease system costs.

The AC / DC active distribution network (ADN) connected to a large number of renewable energy source (RES) and new loads such as EV will become the main form of the future ADN, which intensifies the volatility and uncertainty of the net load. To cope with this situation, considering various types of flexibility resources, this paper quantitatively evaluates the flexibility demand and supply in the system, and constructs a day-ahead flexible operation model of AC/DC ADN with a high proportion of RES. At first, the flexibility demand is quantified firstly by considering volatility and uncertainty comprehensively, and the conditional probability distribution of wind power volatility is obtained through the analysis of mutual information and other correlation coefficients. Then, a quantitative method to the time flexibility provided by orderly charging of electric vehicles (EVs) is proposed for the flexible cooperation with other resources. Besides, voltage source converter (VSC) with precise loss is considered as flexibility source to participate in dispatching. Furthermore, considering the preference of flexibility in different directions, a weighted node flexibility margin index is proposed. Considering the accuracy and computational efficiency, a second order cone relaxation method which is suitable for the elaborate VSC model is applied in this paper. In the end, Test results of an improved IEEE33-bus system with a three-terminal DC network show that the proposed model can improve the flexibility and reduce the operation cost of the ADN.

With the development of the electricity market, various stakeholders such as batteries, multi-microgrid (MMG), and electric vehicle (EV) clusters, can trade with either the distribution network or each other to meet their power balance needs and to maximize their profits. This paper proposes a two-level game model based on game theory to study the operation strategy of stakeholders in the distribution network. First, each stakeholder predicts its electricity demand profile. A Markov Decision Process (MDP) model of random variables is established to predict the charging and discharging power of the battery. Then, the two-level game is presented to let multi-stakeholder participate, in which different kinds of stakeholders have different game strategy limits. Additionally, suggestions for battery operation modes under different compensation coefficients are given to participate in the subsequent two-level game. An algorithm is proposed to allow stakeholders to merge or split self-adaptively based on Nondominated Sorting Genetic Algorithm II (NSGA-II) to optimize operation mode. Finally, the proposed model is applied to the PG and E69-bus distribution system and a practical 101-bus distribution system in China. The case studies show that different game strategy limits of the stakeholders will affect the distribution of the Nash equilibrium (NE) solutions. The multi-stakeholder system can better absorb regional unbalanced power through electricity transactions, and further increase the benefits of each stakeholder.