The cost of the power distribution infrastructures is driven by the peak power encountered in the system. Therefore, the distribution network operators consider billing consumers behind a common transformer in the function of their peak demand and leave it to the consumers to manage their collective costs. This management problem is, however, not trivial. In this paper, we consider a multi-agent residential smart grid system, where each agent has local renewable energy production and energy storage, and all agents are connected to a local transformer. The objective is to develop an optimal policy that minimizes the economic cost consisting of both the spot-market cost for each consumer and their collective peak-power cost. We propose to use a parametric Model Predictive Control (MPC)-scheme to approximate the optimal policy. The optimality of this policy is limited by its finite horizon and inaccurate forecasts of the local power production-consumption. A Deterministic Policy Gradient (DPG) method is deployed to adjust the MPC parameters and improve the policy. Our simulations show that the proposed MPC-based Reinforcement Learning (RL) method can effectively decrease the long-term economic cost for this smart grid problem.
Model Predictive Control for Smart Grids with Multiple Electric-Vehicle Charging Stations
University of Technology Sydney（悉尼科技大学）；The University of New South Wales Sydney（新南威尔士大学）；Queen’s University Belfast（贝尔法斯特女王大学）；Princeton University（普林斯顿大学）
Bang-Bang Charging of Electrical Vehicles by Smart Grid Technology
University of Technology Sydney（悉尼科技大学）；Queen’s University Belfast（贝尔法斯特女王大学）；Princeton University（普林斯顿大学）；The University of New South Wales Sydney（新南威尔士大学）
Predictive Lagrangian Optimization for Constrained Reinforcement Learning
Weiyang College, Tsinghua University（清华大学未央学院）；Xingjian College, Tsinghua University（清华大学行健学院）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与出行学院）；College of Artificial Intelligence, Tsinghua University（清华大学人工智能学院）；Department of Mathematical Sciences, Tsinghua University（清华大学数学科学系）；School of Mechanical Engineering, University of Science and Technology Beijing（北京科技大学机械工程学院）
