Optimal energy management of multi-carrier networked energy hubs considering efficient integration of demand response and electrical vehicles: A cooperative energy management framework

This paper presents a cooperative energy management approach for multi-carrier networked energy hubs. The proposed scheme facilitates energy hubs to exchange power for higher economic and environmental benefits. Based on the cooperative game theory, individual energy hubs cooperate to secure maximum profit, which is then fairly distributed among the hubs to ensure the economic stability of the coalition. The proposed model considers the networked system in which hubs are integrated with multi-energy resources such as electrical and natural gas supply, renewable sources, combined heat and power units (CHPs), gas boilers, electrical and thermal energy storage units to satisfy electrical and thermal demands. A scenario-based generation and reduction algorithm is employed to address the uncertain behaviour of sources. Moreover, a price-based demand response program (DR) and electrical vehicles (EVs) are integrated to make the network more flexible. To analyse the proposed cooperative energy management, the system is modelled as a mixed-integer linear programming problem. Results show that operating costs and greenhouse gas (GHG) emissions are reduced and the self reliability of the networked system improves. With cooperation and flexible resources, the coalition achieves its maximum profit, which is fairly and stably distributed among the hubs by using Shapley value.

Automated summary

This paper presents a cooperative energy management approach for multi-carrier networked energy hubs. The proposed scheme allows energy hubs to exchange power for higher economic and environmental benefits. Based on cooperative game theory, individual energy hubs collaborate to secure maximum profit, which is then distributed fairly among the hubs to ensure economic stability. The proposed model considers a networked system integrating various energy resources to meet electrical and thermal demands, and employs algorithms to address uncertain source behavior. Results show reduced operating costs and greenhouse gas emissions, as well as improved self-reliability of the networked system through cooperation and flexible resources.