Abstract
Residential users (RUs) are the vital component of terminal energy consumption. The development and application of integrated energy system (IES) and smart homes has promoted RUs to actively take part in the trading with multi-energy provider (MEP) for its preferential energy prices and services. This paper proposes a pricing strategy of MEP by using a Stackelberg game-based bi-level programming model. In the upper level model, the adjustment coefficient of electric power price is optimized by MEP to increase the trading probability with RUs. In the lower level model, an integrated demand response (IDR) program is proposed for RUs to optimize the flexible loads in home energy management system (HEMS). Specially, a HEMS is composed of a smart interactive terminal, a micro combined cooling, heating, and power (mCCHP) system and multi-energy loads. Case study shows that, on one hand, the energy optimization based on IDR can help RUs manage their multi-energy loads and reduce the expected energy cost. On the other hand, the proposed price strategy of MEP can increase the trading probability, which promote more RUs to trade with MEP, thus increasing the MEP's benefit by 12.29%. The research proves that the proposed strategy is a win-win strategy and it is efficient in the pre-decision-making progress for MEP in the energy trading market.
Highlights
The study of demand response (DR) has been paid more attention to in recent years with the development of the electricity market since the concept of demand side management (DSM) was put forward by Electrical Power Research Institute in 1996 [1]
In this paper, a Stackelberg game-based pre-decision pricing strategy is presented for multi-energy provider (MEP) in the power and natural gas trading with residential users (RUs)
A pricing strategy is proposed for MEP, in which MEP can reduce the electric power price to attract RUs and increase the trading probability
Summary
To compare with the non-cooperative way to decide the energy prices, of which the profits of consumers are neglected, the dynamic Stackelberg game-based approach is an effective method to deal with the energy trading strategy among leaders and followers [22]. The interactions among leaders and followers are formulated as a bi-level programming problem, which can be solved by the backward induction algorithm [23] To this end, this paper proposed a Stackelberg game-based bi-level programming model for the pricing strategy of MEP. With RUs. The main contributions of this study are described as follows: 1) The structure of HEMS with a smart interactive terminal, a mCCHP system and multi-energy loads is illustrated, of which each device in a RU is modelled in detail.
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