DC microgrid operation with hybrid energy storage considering islanding constraints and demand response coordination: A bi-level Stackelberg game approach

• Published in: Journal of energy storage 2024-11, Vol.102, p.113913, Article 113913
• Main Authors: Singh, Abhishek, Kumar, Alok, K.A., Chinmaya, Maulik, Avirup
• Format: Article
• Language: English
• Subjects: DC microgrid; Demand response; Energy management; Energy storage; Fuel cells; Hydrogen storage
• ISSN: 2352-152X
• DOI: 10.1016/j.est.2024.113913

DC microgrid (DCμG) is becoming popular for niche applications due to multiple advantages over AC microgrids (μG). However, operation of a DCμG is challenging due to uncertainties of renewable energy source (RES) generation and load demands, limited availability of controllable generation, and unintended islanding events. Sectoral coupling between electricity and hydrogen (H2), hybrid energy storage system (HESS), and demand response (DR) implementation address the challenges and enhance the techno-economic benefits of DCμG operation. Further, incorporating islanding constraints in the scheduling strategy improves the security of system operation. The objective of this paper is to develop an energy management scheme (EMS) for an electricity-H2 grid-connected DCμG with a HESS incorporating islanding constraints and DR implementation in an uncertain environment with correlated and uncorrelated input uncertainties to maximize the profit of the DCμG operator (DCμGO), minimize the electricity usage cost of consumers, and ensure secure operation after unintended islanding using bi-level optimization.DCμG network level, equipment level, and consumer’s apparatus level operating security constraints are considered in the EMS. Uncertainties of input random variables (RV) and their correlation are modelled using Copula theory and incorporated in the EMS. The DCμG consists of a gas turbine (GT), power to hydrogen (P2H), hydrogen to power (H2P), HESS (comprising battery energy storage system (BESS) and hydrogen storage system (HSS)), wind power generation (WPG), solar power generation (SPG), and consumers. The consumers have non-flexible and flexible loads (thermostatically controlled load (TCL) and plug-in hybrid electric vehicles (PHEV)). The proposed EMS is modelled using a bi-level leader–follower Stackelberg game (SG) architecture, in which the DCμGO is the leader and the consumers are followers. The DCμGO optimally schedules flexible resources within its control and sets the retail power price (RPP) to maximize the operating profit. Consumers participate in the DR program by adjusting flexible demands according to the RPP to minimize the cost of electricity use. The dynamic RPP acts as the bridge between the upper and lower-level problems. The bi-level EMS is reformulated as a single-level mixed-integer linear programming (MILP) problem by successively using Karush–Kuhn–Tucker (KKT) conditions, the big-M method, and the strong duality theory. The MILP problem is