Under the background of global energy conservation, the energy hub (EH)-based integrated energy system is becoming the transition direction of future energy structure. In this paper, we study the cooperative economic scheduling problem for multiple neighboring integrated energy systems on the basis of EH. Different with the traditional non-cooperative mode where each EH operates individually, these EHs constitute a cooperative community and can share energy among them. Considering the autonomy and self-interest of different EHs, the coordinated management problem is modeled as a bargaining cooperative game, where involved EHs will bargain with each other about the exchanged energy and the associated payments. The bargaining solution can achieve a fair and Pareto-optimal balance among the objective functions of different EHs. A distributed optimization is applied to find the bargaining solution of the cooperative system, to guarantee the autonomous scheduling and information privacy of EHs. Numerical studies demonstrate the effectiveness of the bargaining-based cooperative economic scheduling framework, and also show the improvement of benefits of the community system.

In this paper, a novel energy pricing and sharing strategy is proposed to solve the energy management and market bidding problems of a virtual energy station (VES) in multi-carrier energy systems. First, the energy cell-tissue-based group interaction framework and the multiagent-based communication and control architecture are established in the VES. Second, the general model of an energy cell is built, in which the integrated demand response process and the automatic generation control mode of a combined heat and power unit are designed. Third, according to the classification of energy cells, the aggregation rules of the VES without the participation of third-party agents are proposed, and the internal trading mechanism of the VES led by a higher energy cell is designed based on a Stackelberg game. Moreover, a three-stage hybrid stochastic robust optimization strategy is proposed to address uncertainties in market prices and renewable energy output. Finally, the optimization model is linearized and solved by duality theory, KKT conditions, and column-and-constraint generation (CC&G) algorithm. Simulation results prove the rationality and effectiveness of the framework and business model for the VES.

Battery energy storage (BES) and demand response (DR) are considered to be promising technologies to cope with the uncertainty of renewable energy sources (RES) and the load in the microgrid (MG). Considering the distinct prediction accuracies of the RES and load at different timescales, it is essential to incorporate the multi-timescale characteristics of BES and DR in MG energy management. Under this background, a hierarchical energy management framework is put forward for an MG including multi-timescale BES and DR to optimize operation with the uncertainty of RES as well as load. This framework comprises three stages of scheduling: day-ahead scheduling (DAS), hour-ahead scheduling (HAS), and real-time scheduling (RTS). In DAS, a scenario-based stochastic optimization model is established to minimize the expected operating cost of MG, while ensuring its safe operation. The HAS is utilized to bridge DAS and RTS. In RTS, a control strategy is proposed to eliminate the imbalanced power owing to the fluctuations of RES and load. Then, a decomposition-based algorithm is adopted to settle the models in DAS and HAS. Simulation results on a seven-bus MG validate the effectiveness of the proposed methodology.

Interconnected microgrids (MGs) system is regarded as one of the future power system configurations, where individual MGs can support each other through interchanging energy. This paper presents a community market structure to manage the energy trading among MGs and also with the main grid. In local layer, individual MGs would operate and bid as autonomous entities; in market layer, an independent third party would coordinate the energy transaction among different MGs by mimicking the role of auctioneer. To cost-effectively utilize the controllable units in MGs, the concept of adjustable power is discussed. Apart from the surplus or deficit energy, adjustable power is also incorporated in the individual MGs' bidding strategies. The local trading prices are determined via the market clearing. We analyze the economic benefits of such an energy trading mechanism and also the value of adjustable power. Numerical results demonstrate the effectiveness of the proposed framework for the energy trading among multiple MGs system.

In recent years, frequent typhoon-induced disasters have significantly increased the risk of power grid outages, posing severe challenges to the secure and stable operation of distribution grids with high penetration of distributed photovoltaic (PV) systems. Furthermore, during post-disaster recovery, the dual uncertainties of distributed PV output and the charging/discharging behavior of flexible resources such as electric vehicles (EVs) complicate the configuration and scheduling of mobile energy storage systems (MESS). To address these challenges, this paper proposes a two-stage robust optimization framework for dynamic recovery of distribution grids: Firstly, a multi-stage decision framework is developed, incorporating MESS site selection, network reconfiguration, and resource scheduling. Secondly, a spatiotemporal coupling model is designed to integrate the dynamic dispatch behavior of MESS with the temporal and spatial evolution of disaster scenarios, enabling dynamic path planning. Finally, a nested column-and-constraint generation (NC&CG) algorithm is employed to address the uncertainties in PV output intervals and EV demand fluctuations. Simulations on the IEEE 33-node system demonstrate that the proposed method improves grid resilience and economic efficiency while reducing operational risks.

Interconnected microgrids (MGs) system is regarded as one of the future power system configurations, where individual MGs can support each other through interchanging energy. Specifically, MGs with surplus power can trade with those with deficit power. This paper presents a two-level hierarchical structure to manage the energy trading among MGs and also with the main grid. In lower level, individual MGs would operate as self-controlled entities; in upper level, an independent and nonprofit third party would coordinate the energy transaction among different MGs. To guarantee the fairness and autonomy, a local energy exchange market is established, in the presence of the main grid. Apart from the surplus or deficit energy, adjustable power is also incorporated in the individual MGs’ bidding strategies. The local trading prices are determined via the market clearing. We analyze the economic benefits of such an energy trading mechanism and also the value of adjustable power originally hidden in MGs. Numerical results demonstrate the effectiveness of the proposed framework for the energy trading among multiple MGs system.