A Nash-bargaining model for trading of electricity between aggregations of peers
یک مدل چانه زنی نش برای تجارت برق بین تجمیع همتاها-2020
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 ve- hicles (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.
Keywords: Aggregation of peers | Peer-to-peer energy trading | Option contract | Nash bargaining solution
Learning pareto optimal solution of a multi-attribute bilateral negotiation using deep reinforcement
یادگیری راه حل بهینه پارتو یک مذاکره دوجانبه چند شاخصه با استفاده از تقویت عمیق-2020
This paper aims to design an intelligent buyer to learn how to decide in an incomplete information multiattribute bilateral simultaneous negotiation. The buyer does not know the negotiation strategy of the seller and only have access to the historical data of the previous negotiations. Using the historical data and clustering method, the type of seller is identified online during the negotiation. Then, the deep reinforcement learning method is utilized to support the buyer to learn its optimal decision. In the complete information case, we prove that the negotiation admits a unique Nash bargaining solution with possibly asymmetric negotiation powers. In comprehensive simulation studies, the efficiency of the proposed learning agent is evaluated in different scenarios and we show that the learning negotiation with incomplete information is converged to a Pareto optimal solution. Then, using the concept of the Nash bargaining solution, the negotiation power of the buyer is assessed in negotiation.
Keywords: Multi-attribute negotiation | Deep auto encoder | Actor-critic | Nash bargaining solution | Bargaining power