A Practical Pricing Approach to Smart Grid Demand Response Based on Load Classification

Abstract

Appropriate pricing schemes are of great importance in terms of managing large-scale and distributed controllable loads in smart grids. In this paper, a practical pricing scheme is proposed to encourage different consumers to participate in demand response by providing them with a list of price plans. First, a classification algorithm is employed to divide consumers into different categories based on their individual information, such as marginal cost, upper limit of load adjustment, and elasticity coefficient. Afterwards, from the perspective of load service entity, a pricing model is formulated as a nonlinear programming problem, aiming to minimize the overall operation cost. Moreover, the Bayesian discrete probability distribution function is adopted to tackle the uncertainty of consumers’ choosing behavior. Meanwhile, an incentive compatible constraint is added to capture the real private information of controllable loads, e.g., inducing them to bid as close as their marginal costs. According to the case studies on the IEEE 30-bus and 118-bus systems, the proposed pricing scheme is an effective demand response approach to managing controllable loads and achieving lower system cost.