Fairness-driven integrated multi-prosumer load scheduling incorporating solar energy. (October 2022)

• Record Type: Journal Article
• Authors: Moradi Moghadam, Solmaz; Naghibzadeh, Mahmoud; Rouhani, Modjtaba; Wang, Lingfeng
• Is Part Of: Sustainable energy technologies and assessments. Volume 53:Part A(2022)
• Publication Date: 2022-10
• Subjects: Aggregator -- Multi-prosumer -- Fairness -- Smart appliances -- Load scheduling -- Knapsack problem; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2022.102459
• Languages: English
• ISSNs: 2213-1388

Abstract

Highlights: Multi-prosumer framework for re-scheduling smart appliances and aggregating PVs. Fairness presented as a customer satisfaction factor. A devised heuristic algorithm to overcome time-complexity and improve scalability. Outperforms two other methods in execution time and near-optimal results. Abstract: Aggregation of flexible loads and power generation of solar photo-voltaic (PV) systems is considered as a valuable power resource in residential demand response (DR). Despite the rapid growth of smart appliances, there are few practical solutions for exploiting their potentials in DR load aggregation. In this paper, we present a practical multi-prosumer framework to enable the aggregator reach a minimum bidding power and participate in the wholesale market. This is attainable through directly rescheduling a large number of smart appliances and utilizing the surplus power generation of residential PVs. An optimization model is designed which maximizes the aggregator's profit while respecting customers convenience. Fairness is a significant component of this model ensuring fair selection of appliances for shifting by the aggregator and not biased toward customers availability. We investigate the model as a hard instance of the 0–1 Knapsack problem and devise a heuristic algorithm to cope with its time complexity and to improve its scalability. The simulation results of two large-scale case studies are presented and discussed. It is demonstrated that the proposed framework is beneficial to both the aggregator and its customers, leading to a greener environment.