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The increasing of distributed energy and flexible load incentivize market participants to participate in a more active market. A coordinated peer to peer (P2P) trading model with an aggregated alliance and reserve purchasing is proposed in this paper. Under such a coordinated trading model, the market participants form an alliance, where the agents perform aggregated alliance and purchase reserve from a mobile energy storage supplier. To reduce the risk of deviation penalty in P2P trading, the agents seek to maximize the welfare of the entire alliance in the P2P process, while considering product differentiation and deviation risks. The proposed trading model design comprises: (i) a coordinated P2P market design with the aggregated alliance and reserve purchasing, (ii) a two-stage P2P market-clearing model to maximize trading utility while reducing the risk of deviation. To solve this two-stage multivariable coupling problem in a distributed way, we propose a primal–dual based ADMM method. Through the case study, compared with the traditional P2P trading model, the proposed market model can reduce the deviation penalty and improve the comprehensive welfare, while the convergence can be effectively guaranteed.

Multi-energy microgrid (MEMG) is a typical realization of multi-energy system. Its day-ahead dispatch is crucial for the economic operation of MEMG. However, previous research usually considered the efficiency of multi-energy coupling equipment in MEMG as a constant value neglecting the non-linear relationship of heterogeneous energy conversion. With this, day-ahead dispatch would result in inappropriate plans and further impair safe operation of MEMG. To deal with this challenge, this paper studies a novel day-ahead dispatch model for MEMG by considering variable operating conditions of multi-energy coupling equipment. Specifically, first, a novel day-ahead dispatch model of MEMG is proposed. The day-ahead dispatch model incorporates refined energy conversion constraints, and these constraints can capture variable operating conditions of multi-energy coupling equipment. Then, due to the non-linear terms with respect to variable operating conditions, a piecewise linearization method is utilized to transform the proposed model into a mixed-integer linear programming formulation. Finally, simulation results show that our approach can obtain a more reasonable day-ahead dispatch scheme with multiple advantages, such as improving the overall operating economy, avoiding multi-energy power curtailment, and enhancing the potential of demand response.

Disorderly charging of large-scale electric vehicles (EVs) would result in the peak electricity consumption, damaging the safe and economic operation of power systems. As an important managing technique of distributed energy resources (DERs), virtual power plant (VPP) can effectively improve the charging strategies of EVs. With this, this paper proposes a bi-level optimal scheduling model of VPP with aggregated plug-in electric vehicles (PEVs) and a battery swapping station, and to optimize the charging/discharging of PEVs and batteries in battery swapping station. Based on the Stackelberg game, the leader is a VPP operator. It guides the charging/discharging behavior of PEVs using reasonable prices. As the followers, PEV owners adjust their charging/discharging strategies based on the leader’s price information. The Karush-Kuhn-Tucker (KKT) conditions are used to transform the bi-level model into a single-level formulation. Numerical results demonstrate that the proposed model can effectively improve overall economic benefits of the VPP, while respecting the interest of each PEV owners. Besides, charging/discharging of EVs are improved sustaining the safe operation of power systems.

With the rapid development of distributed renewable energy generation, the requirement of flexibility is also increasing. The traditional solution to increase flexibility is to conFigure energy storage. However, this method has some problems, such as high investment cost and large-area occupation. Therefore, it is a better method to fully exploit the flexibility of existing resources. This paper proposes an optimal dispatch method of the virtual power plant that aggregates distributed renewable energy resources and building water supply systems. It aims to explore the flexibility of building water supply systems. The proposed model is a mixed-integer non-linear programming model. Thus, a piecewise linearization method is applied to convert the proposed model to a mixedinteger linear programming model to make the model tractable. Simulation results show that the proposed method can not only increase operation economics of the virtual power plant but also enhance demand-side flexibility.

Due to the volatility of renewable energy and randomness of electricity load in virtual power plant (VPP), VPP will inevitably deviate from the pre-planned reference points in real-time operation process. Thus, real-time active power dispatch is essential to re-determine the power output of VPP’ components. Indeed, distributed resources of VPP often belong to different individuals. Coordinating various components of different individuals to participate in VPP realtime scheduling can realize incentive compatibility between VPP and individuals. To this end, this paper proposes real-time active power dispatch for VPP via a multi-agent leaderfollower game approach to achieve the purpose of real-time power balance and economic operation of VPP. In the multiagent leader-follower game, fuzzy control technology is used to generate bidding strategies for various individuals. Simulation results demonstrate the economy and effectiveness of the proposed real-time active power dispatching model for VPP.