A Novel Multi-Hierarchical Bidding Strategy for Peer-to-Peer Energy Trading Among Communities

Abstract

Recently, several market types and regulations have been developed in an attempt to handle the increased carbon effect. End-users can also actively participate in the existing distribution system thanks to Peer-to-Peer (P2P) energy trading, which is one of the new emerging market types. In this paper, a novel dual bidding strategy for multi-hierarchical P2P energy trading that includes both intra community and inter communities is proposed considering uncertainties in solar irradiance and temperature. While lower-level problem consists of both optimal bids of households to the own Local Market Operators (LMO) for intra community trades and optimal bids of LMOs to the Central Market Operator (CMO) for inter community trades, profit of both the LMOs and CMO is maximized by clearing the market prices at the upper-level problem. To prove the validity of the devised model, a set of case studies are created. Moreover, the results suggest that the proposed bi-level model is robust and a remarkable amount of cost savings could be provided by integrating the model.

Cross Entropy Covariance Matrix Adaptation Evolution Strategy for Solving the Bi-Level Bidding Optimization Problem in Local Energy Markets

“…Step: 13 Execution of lower-level problem Particles' bids and offers are used to determine market clearing price (cp) through merit order mechanism. Obtain energy transacted between the agents using Equations ( 4) and (5).…”

“…In [12] a robust based bidding and offering strategy was proposed for a price-maker energy storage facility participating in a day-ahead market. In [13], a unique dual bidding technique for multi-hierarchical P2P energy trading, that incorporates both intra-and inter-community trade, while accounting for uncertainties in solar irradiance and temperature, was presented. A bi-level max-min MILP approach is used to represent the participation strategy and manage the risk of uncertainty associated with forecasted hourly generation and demand price.…”

Intelligent Bidding Strategies for Prosumers in Local Energy Markets Based on Reinforcement Learning

“…PV is considered a major source of energy during the day for most intra community and inter community energy trading. Thus, considering the uncertainties in solar irradiance and temperature can result in an optimal P2P LEM bidding strategy [6], [10]. Ref.…”

“…Ref. [6] proposed a dual bidding strategy for multi-hierarchical P2P energy trading in an LEM considering uncertainties in solar irradiance and temperature. By considering the uncertainty of renewable energy resources such as solar, wind and consumer demands in an LEM, the authors of [10] proposed a bi-level optimization model for prosumers to appropriately take advantage of their distributed energy resources in an LEM.…”

“…The agent is also responsible for selecting appropriate price for prosumers/consumers to make benefits from their electricity assets and automatically posting the bids/offers on behalf of consumer/prosumers who own the agent [3], [4]. Consequently, researchers have proposed different bidding strategies for LEM design [5], [6].…”