阶梯式碳交易机制以及优化调度模型求解算法是进行园区综合能源系统(community integrated energy sys-tem,CIES)优化调度的重要因素,现有文献对这两个因素的考虑不够全面.为此,文中在考虑阶梯式碳交易机制的基础上,提出采用近端策略优化(proximal policy optimization,PPO)算法求解CIES低碳优化调度问题.该方法基于低碳优化调度模型搭建强化学习交互环境,利用设备状态参数及运行参数定义智能体的状态、动作空间及奖励函数,再通过离线训练获取可生成最优策略的智能体.算例分析结果表明,采用PPO算法得到的CIES低碳优化调度方法能够充分发挥阶梯式碳交易机制减少碳排放量和提高能源利用率方面的优势.
A low-carbon optimization scheduling method of CIES based on PPO algorithm
The tiered carbon trading mechanism and optimization scheduling model solving algorithm are pivotal for the community integrated energy system(CIES).CIES plays a crucial role in optimizing scheduling,yet existing literature often does not fully consider these two factors.To address this gap,the adoption of the proximal policy optimization(PPO)algorithm is proposed,which incorporates a ladder-type carbon trading mechanism to solve the low-carbon optimization scheduling problem of CIES.This method constructs a reinforcement learning interactive environment based on a low-carbon optimization scheduling model.The intelligent agent's state,action space,and reward function are defined using device status and operating parameters.An intelligent agent capable of generating the optimal policy is obtained through offline training.Case study analysis results demonstrate that the low-carbon optimization scheduling scheme for CIES achieved through the PPO algorithm,effectively leverages the advantages of the tiered carbon trading mechanism,significantly reducing carbon emissions and improving energy utilization efficiency.
community integrated energy system(CIES)optimize schedulingproximal policy optimization(PPO)algorithmladder-type carbon trading mechanismpenalty coefficientcarbon emission
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