Multi-agent Collaboration for Conflict Management in Residential Demand Response

Abstract

Balancing electricity supply and consumption improves stability and performance of an electricity Grid. Demand-Response (DR) mechanisms are used to optimize energy consumption patterns by shifting non-critical electrical energy demand to times of low electricity demand (off-peak). Market penetration of electrical loads from Electrical Vehicles (EVs) has significantly increased residential demand, with a direct impact on the grid’s performance and effectiveness. By using multi-agent planning and scheduling algorithms such as Parallel Monte-Carlo Tree Search (P-MCTS) in DR, EVs can coordinate their actions and reschedule their consumption pattern. P-MCTS has been used to decentralize consumption planning, scheduling the optimum consumption pattern for each EV. However, a lack of coordination and collaboration limits its reliability in emergent situations, since agents’ sub-optimal solutions are not guaranteed to aggregate to an optimized overall grid solution.This paper describes Collaborative P-MCTS (CP-MCTS), which enables EVs to actively affect the planning process and resolve their conflicts via negotiation and optimizes the final consumption pattern using collective knowledge obtained during the negotiation. The negotiation algorithm supports agents to actively participate in collaboration, arguing about their stance and making new proposals. The results obtained show a significant load-shifting in peak times, a smoother load curve, and improved charging fairness and flexibility.