This paper studies the energy trading among flexible demand response aggregators (DRAs) and a distribution company (Disco) with self-owned generators. Instead of the conventional non-cooperative game based approach, the trading problem is formulated as a bargaining based cooperative model, where Disco and DRAs collaboratively decide the amounts of energy trade and the associated payments. This cooperative interaction can be beneficial to both Disco and DRAs, by reducing the aggregated peak demand and increasing the potential cost savings. The increased benefits from cooperation are fairly allocated among these participants, based on the Nash bargaining theory. Compared with the non-cooperative game based approach, the proposed bargaining cooperative model can further improve the benefits of Disco and DRAs. Moreover, the bargaining outcome can maximize the social welfare of the system. Considering the privacy and autonomy issues of participants, we utilize a decentralized solution to solve the bargaining problem, with minimum information exchange. Numerical studies demonstrate the effectiveness of the bargaining -based cooperative framework, and also show the improvement of benefits of the system.

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.

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 selfinterest 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.