Pricing Strategy of Multi-Energy Provider Considering Integrated Demand Response

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-decisionmaking progress for MEP in the energy trading market.INDEX TERMS Integrated demand response (IDR), residential user (RU), multi-energy provider (MEP), Stackelberg game, pricing strategy, bi-level programing

Transaction Model Based on Stackelberg Game Method for Balancing Supply and Demand Sides of Multi-Energy Microgrid

“…Optimal Solution [23,24] × × - [25][26][27] × × - [28,29] × - [30,31] × × This paper Besides, time-of-use energy price is considered as an effective method to stimulate DR, but it also has disadvantages. For example, if all users consume energy in the same period of low price, it is possible to create new demand peaks [32].…”

“…In [28,29], IDR and carbon emissions are considered, but the benefits of users are still ignored. In [30,31], the benefits of users are considered, but the carbon emissions are not considered. Additionally, the iterative method easily causes the bilevel model to fall into the local optimal solution in Refs.…”

“…Additionally, the iterative method easily causes the bilevel model to fall into the local optimal solution in Refs. [30,31]. In addition, although in the literatures [23,24,28,29], carbon emissions are considered, only the impact of carbon emissions fines on economic benefits is considered, and carbon emissions are disregarded as the optimization objective.…”

Operation Optimization of an Integrated Energy Service Provider with Ancillary Service Provision

“…Existing publications about the IESP mainly focus on operation optimization [7,8], planning and renovation [9,10], integrated demand response (IDR) [11,12], and electricity energy market participation [13,14]. For the optimal operation of the RIES, in [7], a bidirectional flow model is established to show how the RIES enhances resilience in extreme conditions with the coordination of energy conversion and storage facilities, and a trilevel two-stage robust model is established to accommodate random outages; in [8], an operation optimization model is proposed considering the renewable energy consumption responsibility and carbon emission trading mechanism, so as to address the concern of renewable energy generation accommodation and emission reduction in the RIES operation.…”

“…For the optimal planning and renovation of the RIES, in [9], a multi-scene optimal planning and renovation method of a park-level RIES is proposed, which models the working status of the RIES and considers the costs of investment, operation, and maintenance in the planning optimization model; in [10], with the objective of minimizing investment costs and network losses, the optimal locations of integrated energy stations are determined by the kernel density of the annual energy consumption of building groups, and the distribution of energy networks is optimized by finding the shortest path with the A-star search algorithm. Regarding the IDR during IESP operation, in [11], a pricing strategy of a multi-energy provider using a Stackelberg game-based bi-level programming model is proposed, in which the IDR-based energy optimization can help residential users manage their multi-energy loads and reduce the expected energy costs; in [12], a bi-level bidding and multi-energy retail price formulation method is presented based on the unified clearing of electricity and natural gas for an IESP considering multi-energy demand elasticity, which fully exploits the potential of IDR resources in improving economic benefits. In terms of the electricity market participation strategy of IESP, in [13], the optimal decision of the IESP in trans-province electricity trading is explored considering renewable portfolio standards and consumer preferences, which can promote the coordinated development of the electricity market, integrated energy services, and the renewable energy industry; in [14], a second-order cone model of day-ahead economic dispatch for a district IESP based on the electricity-gas market environment is presented, and is used to closely study the impact of the energy market environment on the operating strategy of the IESP.…”