Distributed optimization of electricity-Gas-Heat integrated energy system with multi-agent deep reinforcement learning

The coordinated optimization problem of the electricity-gas-heat integrated energy system(IES)has the characteristics of strong coupling,non-convexity,and nonlinearity.The centralized optimization method has a high cost of communication and complex modeling.Meanwhile,the traditional numerical iterative solution cannot deal with uncertainty and solution efficiency,which is difficult to apply online.For the coordinated optimization problem of the electricity-gas-heat IES in this study,we constructed a model for the distributed IES with a dynamic distribution factor and transformed the centralized optimization problem into a distributed optimization problem in the multi-agent reinforcement learning environment using multi-agent deep deterministic policy gradient.Introducing the dynamic distribution factor allows the system to consider the impact of changes in real-time supply and demand on system optimization,dynamically coordinating different energy sources for complementary utilization and effectively improving the system economy.Compared with centralized optimization,the distributed model with multiple decision centers can achieve similar results while easing the pressure on system communication.The proposed method considers the dual uncertainty of renewable energy and load in the training.Compared with the traditional iterative solution method,it can better cope with uncertainty and realize real-time decision making of the system,which is conducive to the online application.Finally,we verify the effectiveness of the proposed method using an example of an IES coupled with three energy hub agents.

基于可调热电比和两阶段阶梯式碳交易的多主体综合能源系统低碳经济调度策略

国家“双碳目标”对综合能源系统群的经济性和低碳性提出了更高要求,如何从多层面统筹均衡多主体综合能源系统的经济性和低碳性已成为重要的研究内容。为此,基于运行层面和市场层面提出一种多主体综合能源系统低碳经济运行策略。首先,提出热电比可调的热电联产机组运行方式与控制模型;然后提出基于配额共享机制的内外两阶段阶梯式碳交易模式,引导多主体综合能源系统控制碳排放;最后,以购能成本、碳交易成本、新能源弃用成本和储能电站投资运行成本之和最小为目标函数,通过分段线性化,转化为混合整数线性规划问题进行求解。通过设置对比运行情景,验证所提策略的低碳性、经济性和有效性,并进行灵敏度分析,深度挖掘系统低碳化的内部动力,以期为监管部门决策提供理论参考。