Optimizing the Performance of Commercial Demand Response Aggregator Using the Risk-Averse Function of Information-Gap Decision Theory

Abstract

Power systems face challenges with regard to handling the high penetration of renewable energies, including energy intermittency and fluctuations, which are not present in conventional electricity systems. Various flexibility models have been developed to address these fluctuations, including demand-side flexibility, which offers a practical solution with which to overcome these challenges in all demand sectors, including the commercial sector. This paper proposes a new structure for the participation of the commercial sector in the electricity market to integrate and coordinate the consumption of the commercial sector. Unlike previous studies that had commercial consumers participate in the electricity market individually and sometimes fail to meet the requirements for flexibility programs, this study adopts a commercial aggregator to enhance the responsiveness of commercial systems. The proposed structure includes a mathematical model for commercial systems, e.g., shopping centers, with responsive ventilation systems to achieve demand flexibility. The study also uses the information-gap decision theory to address time-based commercial demand response planning from 24 h ahead to near real time. Moreover, a multi-layered structure is proposed to integrate the flexibility of shopping centers from the demand side to the supply side through a newly invented commercial demand response aggregator. The proposed approach was implemented in the New York electricity market, and the results show that it provides demand flexibility for up to 18% of the nominal level of electricity consumption compared to the traditional system. The paper aims to present a responsive structure for commercial systems, addressing the challenges of integrating renewable energies with the electricity system.

Recent developments of demand‐side management towards flexible DER‐rich power systems: A systematic review

“…In the modified approaches (optimization based, hybrid, and other), the limitations of the conventional ones are reduced using a robust framework with a high number of operational scenarios however the computational time and the complexity are still challenging aspects. Several approaches are used for day-ahead financial planning of thermal units and RESs in references [168,214]. Others are applied to optimize both the location and capacity of ESSs in microgrids or EHs [204,208,215].…”

Optimal demand response aggregation in wholesale electricity markets: Comparative analysis of polyhedral; ellipsoidal and box methods for modeling uncertainties

“…[ 21 ], in order to change the consumption patterns of customers, a competitive model between demand response aggregators to conclude a demand response service contract with the network operator is presented. Reference [ 22 ], proposes a new structure using commercial demand response aggregators for the participation of the commercial sector in the electricity market. In this study, electricity price uncertainty is modeled by the Information-Gap Decision Theory (IGDT) method.…”