Hierarchical aggregation method for a scalable implementation of demand side management. (August 2018)

• Record Type: Journal Article
• Authors: Amarasekara, Bhagya; Ranaweera, Chathurika; Evans, Rob; Nirmalathas, Ampalavanapillai
• Is Part Of: Computers & operations research. Volume 96(2018)
• Page Start: 188
• Page End: 199
• Publication Date: 2018-08
• Subjects: Demand side management -- Aggregation -- Residential users -- Distribution grids -- Scalability -- Optimization; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2017.10.008
• Languages: English
• ISSNs: 0305-0548

Abstract

Highlights: A scalable architecture for demand side management using aggregators is proposed. Aggregation models are formed to represent microgrids. Algorithms to distribute aggregated decisions to individuals are presented. Proposed method has a less computational time compared to the centralized method. Abstract: Demand side management (DSM) aims to efficiently manage power flow by engaging energy customers, through offering incentives via price signals to alter their consumption patterns or directly controlling their loads. However, the integration of renewable energy generators and batteries in residential premises requires new approaches for DSM as they offer more flexibility. Moreover, as there are often a large number of residential energy customers within a distribution network, it is quite challenging to accommodate all of them in the DSM. In this paper, we propose an Aggregated Method (AM) that allows the treatment of distribution grid as a composition of several microgrids, which helps to aggregate underlying energy customers' power and energy constraints and operating preferences. In addition, we provide methods for distributing the aggregated energy demand decisions among the participating energy customers. In contrast to the alternative centralized method, our approach requires less computational time to obtain decisions and hence scales well with increasing network size. Moreover, our results indicate that when using our method, energy customers receive more benefits through satisfying their energy requirements and operating conditions. Our overall analyses showed that the proposed framework can be easily adopted by the electricity market operators to create scalable DSM programs.