Decentralized Energy Management System for LV Microgrid Using Stochastic Dynamic Programming With Game Theory Approach Under Stochastic Environment

In this article, we propose a decentralized energy management system to minimize the daily operating cost of microgrid and the net charging cost of individual plug-in electric vehicles (PEVs) while maintaining comfort level and privacy. A novel approach based on stochastic dynamic programming (SDP) with N-person noncooperative game-theoretic approach is used for real-time scheduling of heterogeneous PEVs both ways-in the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) mode. We use the SDP method for full enumeration of all possible decisions while game theory provides quick convergence to Nash-equilibrium. The proposed approach considers uncertainties of renewable generation as well as load demand and offers autonomy to the microgrid operators and PEVs owners to optimize their objectives individually. The effectiveness of the proposed approach has been tested on modified LV CIGRE and modified IEEE 33 bus test networks. The results have been compared with the deterministic and stochastic bi-level optimization. The simulation results exhibit that the proposed algorithm outperforms the other two algorithms in terms of operating cost, incentive to the PEVs owner, robustness to uncertainties, and convergence. The operating cost reduction achieved for the modified CIGRE model and modified IEEE 33 bus with the proposed approach is 20.02% and 36.53%, respectively, as compared to the deterministic framework, whereas 7.64% and 16.35% are the values obtained in comparison with the bi-level approach.

Automated summary

The article proposes a decentralized energy management system that uses stochastic dynamic programming and game theory for real-time scheduling of microgrid and plug-in electric vehicles, considering uncertainties related to renewable energy and load demand, resulting in improved operating efficiency and convergence.