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Due to the depletion of traditional fossil energy, to improve energy efficiency and build a cost-effective integrated energy system has become an inevitable choice. Aiming at the problems that the traditional centralised scheduling method is difficult to reflect the multi-dimensional interests of different agents in the multi-energy microgrid system, and the application of artificial intelligence technology in integrated energy scheduling still needs further exploration, this manuscript proposed a hierarchical control optimisation learning method with consideration of multi-agent game. Firstly, the multi-energy microgrid was taken as the research object, the microgrid system architecture was analysed, and the multi-agent partition in the system was pursued based on different economic interests. Secondly, for the technical aspects involved in the integrated energy regulation and management, the management layers of the multi-energy microgrid were divided, and the functions of different management layers were analysed. Based on this, the regulation functions were realised by considering the Nash Q-learning and the artificial intelligence method of Petri-net. Finally, the learning and decision-making ability of the method through practical cases were analysed, and the effectiveness and applicability of the proposed method were explained. This study explores the application of artificial intelligence technology in energy Internet energy management.

The efficient integration of distributed energy resources (DERs) in buildings is a challenge that can be addressed through the deployment of multienergy microgrids (MGs). In this context, the Interreg SUDOE project IMPROVEMENT was launched at the end of the year 2019 with the aim of developing efficient solutions allowing public buildings with critical loads to be turned into net-zero-energy buildings (nZEBs). The work presented in this paper deals with the development of a predictive energy management system (PEMS) for the management of thermal resources and users’ thermal comfort in public buildings. Optimization-based/optimization-free model predictive control (MPC) algorithms are presented and validated in simulations using data collected in a public building equipped with a multienergy MG. Models of the thermal MG components were developed. The strategy currently used in the building relies on proportional–integral–derivative (PID) and rule-based (RB) controllers. The interconnection between the thermal part and the electrical part of the building-integrated MG is managed by taking advantage of the solar photovoltaic (PV) power generation surplus. The optimization-based MPC EMS has the best performance but is rather computationally expensive. The optimization-free MPC EMS is slightly less efficient but has a significantly reduced computational cost, making it the best solution for in situ implementation.

This study proposes a comprehensive framework for developing a multi-energy off-grid microgrid with the decoupled flow of thermal and electrical energies in a rural setting. A carbon-neutral microgrid with a hybrid generation system constituting a photovoltaic unit and a biofuel generator is proposed. In order to enhance the fuel utilisation efficiency, the biofuel generator is operated in combined cooling, heating, and power mode, and the recovered thermal energy forms the heat distribution network in the microgrid. The flexibility of system operation is improved by suitable multi-energy (electrical and thermal) storage. Firstly, an optimal sizing framework has been developed for the system as a mixed integer linear programming model. Secondly, a coordinated multi-energy management system (MEMS) has been developed for combined optimal dispatch of multiple generation and storage resources. The MEMS has been developed as a mixed integer non-linear programming model, which minimises system operational cost while considering minimum battery degradation to prolong its lifetime. Finally, a detailed economic analysis of the proposed system has been presented, highlighting the levellised cost of energy and net present value. Extensive case studies and simulation results depict the effectiveness and suitability of the proposed MEMS for the rural off-grid microgrid.