Demand Response Resource Allocation Method Using Mean-Variance Portfolio Theory for Load Aggregators in the Korean Demand Response Market

Jaeyong Chae,Sung-Kwan Joo

doi:10.3390/en10070879

Abstract

Since the demand response (DR) market was introduced in Korea, load aggregators have also been allowed to participate in the electricity market. However, a risk-management-based method for the efficient operation of demand response resources (DRRs) has not been studied from the load aggregators’ perspective. In this paper, a systematic DRR allocation method is proposed for load aggregators to operate DRRs using mean-variance portfolio theory. The proposed method is designed to determine the lowest-risk DRR portfolio for a given level of expected return using mean-variance portfolio theory from the perspective of load aggregators. The numerical results show that the proposed method can be used to reduce the risk compared to that obtained by the baseline method, in which all individual DRRs are allocated in a DRR group by maximum curtailment capability.

Highlights

The demand response (DR) market was introduced in the Korean electricity market in November2014
Load aggregators hold many demand response resources (DRRs) that have quite different characteristics such as reduction capacity, ramp period, and sustained response period. These resources would respond differently according to their business environment, and rewards for load reduction and penalties for reduction failure are offered by the market price, so the characteristics of each resource and the market price have an effect on the revenues of load aggregators
The baseline method is designed to determine a composition ratio of individual demand response resources in a portfolio based on their reduction capabilities

Summary

Introduction

The demand response (DR) market was introduced in the Korean electricity market in November2014. Load aggregators have recruited the resources of KEPCO’s customers who have participated in demand management. Markowitz’s portfolio theory, or mean-variance analysis, is a mathematical framework to assemble a portfolio of assets such that the risk is minimized for a given level of expected return, and the expected return and risk of an asset are defined to be the mean and variance (or standard deviation) of an asset’s rate of return, respectively This theory was originally developed in the finance field, but if the expected return and risk of an asset can be measured in a reasonable way, it can be applied in various fields such as a generation mix and DRRs operation. Load aggregators hold many DRRs (end use customers) that have quite different characteristics such as reduction capacity, ramp period (or response time), and sustained response period.

Methods

Results

Conclusion