A basic study on incentive pricing for demand response programs based on social welfare maximization

Abstract

Demand response programs (DRs) have been getting more active as one of the effective demand-side management methods (DSMs), which can contribute to the power supply-demand balancing operations in electric power grids. In particular, peak-time-rebate-DR (PTR-DR) is expected to penetrate further because it brings benefit both electric power suppliers and consumers with smaller burden on the consumer-side in comparison with the other DR types.Although several demonstrative field tests on the DRs have been promoted, there is room for discussion yet how to set the appropriate incentive payment for the cooperation of DR requirement from the power suppliers. This paper presents a pricing method of incentive payment in the DRs based on a problem framework of social optimization. In the authors' proposal, decrement of the consumers' comfort, which is caused by the DR cooperation, is converted into the price. That is, the proposed pricing method calculates the negative consumers' surplus as the appropriate incentive payment in the DRs.

An Overview of Demand Response: From its Origins to the Smart Energy Community

“…In these schemes, the pricing is flat, and the customers who cooperate during the peak load periods to reduce electricity consumption will be rewarded. On the other hand, consumers who do not reduce their electricity consumption pay the electricity price at the standard rate [76]. • Extreme Day Pricing (EDP): This scheme is similar to the TOU program, but it applies a high price rate for a limited number of days (critical) [74].…”

A generalized economic model for optimally selecting forecasted load profiles for measuring demand response in residential energy management system

“…Having defined the cost‐benefit ratio for the customer ( b c ) and the retailer ( b r ), the economic model for optimally selecting an appropriate forecasted load profile to measure the demand response of residential energy management systems was derived. Several authors model demand response programs by their ability to maximize the social welfare of the stakeholders of the program 19 ; social welfare as the weighted sum of the benefits of the stakeholders of the demand response program. Since the customer and retailer are the focus of this research the weighted sum of their benefits was expressed by Equation () 19,46 …”

“…It also involves clustering customers into groups to increase the baseline effectiveness in measuring demand response. Furthermore, demand response programs must be designed to maximize the social and economic welfare of all stakeholders; customers and retailer 19 . These general observations point to the fact that a basic economic model can be created to help select an appropriate baseline to measure the performance of residential energy management systems with consideration not only for customers but also for the retailer.…”

An Innovative Optimization Strategy for Efficient Energy Management With Day-Ahead Demand Response Signal and Energy Consumption Forecasting in Smart Grid Using Artificial Neural Network

“…The first type uses direct load control in which the utility company directly controls the load of the consumers on a short notice when required. The second type uses price-based DR program [2], where the utility company encourages users to manage their energy consumption via scheduling by home energy management controller (HEMC) in response to dayahead pricing signal.…”