Energy Pricing and Management for the Integrated Energy Service Provider: A Stochastic Stackelberg Game Approach

Haibing Wang (), Chengmin Wang (), Weiqing Sun and Muhammad Qasim Khan
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Haibing Wang: Department of Electrical Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Yangpu District, Shanghai 200093, China
Chengmin Wang: Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education, 800 Dongchuan Road, Minhang District, Shanghai 200240, China
Weiqing Sun: Department of Electrical Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Yangpu District, Shanghai 200093, China
Muhammad Qasim Khan: Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education, 800 Dongchuan Road, Minhang District, Shanghai 200240, China

Energies, 2022, vol. 15, issue 19, 1-15

Abstract: As a retailer between the energy suppliers and end users, the integrated energy service provider (IESP) can effectively coordinate the energy supply end and the energy use end by setting energy prices and energy management. Because most of the current research focuses on the pricing of electricity retailers, there are few studies on IESP energy pricing and management, which are still at the initial stage. At the same time, the existing research often does not consider the impact of demand response (DR) and uncertainties, such as natural gas and electricity wholesale prices, on the pricing of IESP. It is necessary to model the DR and uncertainties in the integrated energy system. Aiming at the inadequacy of the existing research and to address the energy pricing and management of IESP, this paper develops a two-stage stochastic hierarchical framework, which comprehensively considers the DR strategy of the user end, characteristics of the electricity/gas/heat storage and the uncertainties of electricity and gas wholesale prices. The proposed hierarchical model for energy pricing and management is a two-layer model: the upper layer is the problem of maximizing the benefits of IESP, and the lower layer is the problem of minimizing the energy cost of user agents. Through the complementary transformation, the linearization method and the strong duality principle in the optimization theory, the model is transformed into a mixed-integer linear programing (MILP) problem, which can be easily solved by the off-shelf commercial solver. Finally, the simulation results are provided to demonstrate the interactive operation between the IESP and user agent through energy prices setting, DR strategy and energy management.

Keywords: integrated energy service provider; stochastic programing; Stackelberg game; demand response; interactive operation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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