A Nash-bargaining model for trading of electricity between aggregations of peers. (December 2020)

• Record Type: Journal Article
• Authors: Melendez, Kevin A.; Das, Tapas K.; Kwon, Changhyun
• Is Part Of: International journal of electrical power & energy systems. Volume 123(2020)
• Publication Date: 2020-12
• Subjects: Aggregation of peers -- Peer-to-peer energy trading -- Option contract -- Nash bargaining solution; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2020.106185
• Languages: English
• ISSNs: 0142-0615

Abstract

Graphical abstract: Highlights: Peer to peer energy trading will expand beyond prosumers to regular consumers. IoT and AMI is empowering end-use consumers to from aggregations. Methodology allows aggregations of consumers to schedule load and trade electivity. A generalized Nash bargaining solution model was proposed to assess optimal trading mechanisms . Offers a novel cost-sharing strategy guided by cost minimization and Nash bargaining. Abstract: In the last several years, the growth in household solar generation and the lack of success of the feed-in-tariff programs have led to the rise of peer-to-peer (P2P) energy trading schemes among prosumers. However, a change that has started more recently is the growth of smart homes and businesses, of which loads are IoT controlled and are supported by advanced metering infrastructure (AMI). This has created a new opportunity for smart homes and businesses to form aggregations (coalitions) and participate in cooperative load management and energy trading. Unlike energy trading among individual prosumers in most P2P networks, a new trading opportunity that is emerging is between aggregations of peers of smart homes and businesses and electric vehicles (EVs). In this paper, we consider one such trading scenario between two aggregations, of which one has smart homes and businesses with load consuming entities (not prosumers), and the other has EVs only. The aggregation with smart homes and businesses derive cost reduction through optimal load scheduling based on load preferences, market-based pricing of electricity, and opportunity to trade (buy) energy from the aggregation with EVs. Whereas the aggregation of EVs optimally schedules charging to meet EV needs and uses stored energy to trade (sell). A generalized Nash bargaining model is developed for obtaining optimal trading strategies in the form of plain or swing option contracts. A sample numerical problem scenario is used to show that suitable contracts can be derived that allow aggregations of peers to mutually benefit from energy trading.It is shown that there exist numerous alternative optimal solutions to the Nash bargaining problem. The solutions comprise different combinations of strike price and option value, for all of which savings to the parties remain constant. For plain option, with a contract quantity of 1 MWh, the total savings generated is equivalent to the average price of 1.62 MWh of electricity. Interactions among contract parameters (such as strike price, option value, and option quantity) and the relative market power of the aggregations are also examined.