计及信息隐私保护的能源社区内热产消者楼宇分布式模型预测控制OA

Under the guidance of the"carbon peaking and carbon neutrality"strategic goals,China is accelerating the construction of a new-type energy system with new energy as the mainstay,and intelligent heating systems have become a key path to achieve the"dual carbon"goals.Thermal prosumers,relying on their bidirectional heat flow characteristics,are of great significance for improving the renewable energy absorption capacity and economic efficiency of regional heating systems.However,existing studies have problems such as neglecting heat transaction line losses,lacking information privacy protection,and insufficient adaptability of control strategies.To address these issues,this paper proposes a distributed model predictive control(DMPC)strategy for thermal prosumer buildings in energy communities considering information privacy protection. Firstly,a model of thermal prosumer buildings incorporating multiple distributed resources(solar collectors,electric heat pumps,and hot water storage tanks)is constructed,integrating the volatility of load demand and solar irradiance as well as heating pipeline loss factors,and a state-space equation reflecting the entire process of heat production,storage,and transaction is established.Secondly,to address the privacy leakage risks in information interaction during distributed regulation,an average consensus information interaction method based on the Hadamard transform is proposed,which hides real transaction information in false information via Hadamard matrices to achieve secure information sharing.Finally,aiming at minimizing the total operating cost of the energy community,rolling optimization is performed in combination with the DMPC algorithm,and the optimal control sequence is solved through information interaction between the independent controller of each building and adjacent nodes. Simulation results based on a 33-node heating distribution network show that the proposed strategy effectively promotes nearby heat transactions and reduces line losses from long-distance transmission while safeguarding the information privacy of thermal prosumers.Compared with the scenario without heat transactions,the total operating cost of the energy community is reduced by 17.46％,and the utilization rate of solar energy is significantly improved.Compared with the scenario ignoring privacy protection,the heat transaction power is increased by 46.57％,and the output of electric heat pumps is optimized by 27.75％.The algorithm exhibits good convergence and scalability under different building scales,and the average consensus error of heat prices can converge after 30 information interactions,making it suitable for the optimal regulation of large-scale energy communities. The following conclusions can be drawn from the simulation analysis: (1)An optimal operation framework of energy communities with thermal prosumer buildings is constructed,which can fully mobilize the flexibility of thermal prosumer buildings and significantly improve the utilization rate of solar energy in the energy community. (2)To address the privacy issues in information interaction during distributed optimal regulation,an average consensus information interaction method with privacy protection is proposed.It protects building information security while enhancing the enthusiasm for internal heat transactions in the energy community. (3)The DMPC method is adopted to optimize the regulation of energy communities with thermal prosumer buildings,which has good compatibility with the information interaction strategy and exhibits excellent reliability and scalability in solving large-scale optimal regulation problems.