Distributed Optimal Power Flow for Electric Power Systems with High Penetration of Distributed Energy Resources

Abstract

Optimization technology is developing to the point of becoming a cost-effective enabler of increased power transfer asset utilization. This paper presents a smart decomposition technique for the traditional optimal power flow (OPF) algorithm to allow distributed optimal power flow (DOPF) calculations without relying on a centralized controller. Hence, it develops a feasible distributed architectures for the electric power industry. The proposed method is implemented using Monte Carlo Tree Search based reinforcement learning (MCTS-RL) algorithm. This reduces computational complexity and allows to avoid difficulties associated with stochastic modeling often used to capture the random nature of distributed energy resources (DER) units and loads. The efficiency of the optimization process is improved when the DOPF reflects the fast response capability of the optimal solution. This contribution provides results for a real-time dispatchable resource and demonstrates the flexibility of RL to adapt to changes of system states, ultimately reducing the generation cost while maintaining the system security constraints.