A General Incentive Pricing Model and Its Application for Multi-type Demand-Side Resources of Load Aggregators

doi:10.12204/j.issn.1000-7229.2021.01.001

Abstract

Abstract:

A load aggregator is a professional organization to develop power demand response, which integrates demand-side adjustable resources to participate in system operation and market transactions to gain profits. It is very important for further flexible management of demand-side resources and increasing demand-response benefit to realize personalized incentive customization of multiple types of demand-response resources. It is also the core business of load aggregators. In this paper, a general model of incentive price customization for load aggregators is established according to the principle of stackelberg game. Electric-vehicle users and building air-conditioning users are selected as the research object. Considering the impact of comfort price changing with load shedding, an optimization model of building air-conditioning users with different comfort prices and a model for optimizing the charge and discharge behavior of electric vehicles are constructed under the framework of stackelberg game model. Karush-Kuhn-Tucker condition, dual theorem and linear relaxation are used to derive a two-level mixed integer linear programming model with the minimum economic loss of load aggregator. The results show that the proposed model can be effectively used to customize dynamic incentive price of building air-conditioning users and electric-vehicle users for load aggregators, and provide new ideas for demand response.

Key words: load aggregator, building air conditioning, electric vehicle, incentive, profit

CLC Number:

TM73

WU Wanlu, HAN Shuai, SUN Leping, GUO Xiaoxuan. A General Incentive Pricing Model and Its Application for Multi-type Demand-Side Resources of Load Aggregators[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(1): 1-9.

Figures/Tables 11

Fig.1 Architecture of a two-level problem

Fig.2 Framework of stackelberg game model between LA and users

Fig.3 Curve of comfort cost versus reduced load

Fig.4 Curve of comfort price versus reduced load

Table 1 Parameters of BAC

Fig.5 Forecast value of load and outdoor air temperature

Table 2 Comfort price of user

Fig.6 Wholesale price of power and retail rate

Table 3 Profit of the load aggregator in different scenarios

Table 4 Benefit of users participating in demand response

Fig.7 Simulation results under scenario 4

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