Abstract
Extensive literature is available for modeling and simulating local electricity markets, often called P2P electricity markets, and for pricing local energy transactions in energy communities. Market models and pricing mechanisms provide simulation tools to better understand how these new markets behave, helping to design their main rules for real applications, and assessing the financial compensations of the internal energy transactions. As such, pricing mechanisms are often needed in energy management systems when centralized management approaches are preferred to market-based ones. First, this paper highlights the links between local electricity markets, pricing mechanisms for local electricity transactions, and other approaches to sharing the collective benefits of participating in transactive energy communities. Then, a standard nomenclature is defined to review some of the main pricing mechanisms for local energy transactions, an innovative pricing mechanism based on the economic principles of a post-delivery pool market is proposed, and other relevant approaches for local electricity market simulation such as Nash equilibrium or agent-based simulation are also revisited. The revision was based on systematic searches in common research databases and on the authors’ experience in European and national projects, including local industrial applications for the past five years. A qualitative assessment of the reviewed methods is also provided, and the research challenges are highlighted. This review is intended to serve as a practical guide to pricing mechanisms and market simulation procedures for practical designs of internal financial compensation to share the collective benefits of energy communities. © 2023 by the authors.

Abstract

Abstract
This paper proposes a three-stage model for managing energy communities for local energy sharing and providing grid flexibility services to tackle local distribution grid constraints. The first stage addresses the minimization of each prosumer’s individual energy bill by optimizing the schedules of their flexible resources. The second stage optimizes the energy bill of the whole energy community by sharing the prosumers’ energy surplus internally and re-dispatching their batteries, while guaranteeing that each prosumer’s new energy bill is always be equal to or less than the bill that results for this prosumer from stage one. This collective optimization is designed to ensure an additional collective benefit, without loss for any community member. The third stage, which can be performed by the distribution system operator (DSO), aims to solve the local grid constraints by re-dispatching the flexible resources and, if still necessary, by curtailing local generation or consumption. Stage three minimizes the impact on the schedule obtained at previous stages by minimizing the loss of profit or utility for all prosumers, which are furthermore financially compensated accordingly. This paper describes how the settlement should be performed, including the allocation coefficients to be sent to the DSO to determine the self-consumed and supplied energies of each peer. Finally, some case studies allow an assessment of the performance of the proposed methodology. Results show, among other things, the potential benefits of allowing the allocation coefficients to take negative values to increase the retail market competition; the importance of stage one or, alternatively, the need for a fair internal price to avoid unfair collective benefit sharing among the community members; or how stage three can effectively contribute to grid constraint solving, profiting first from the existing flexible resources.

Abstract

Abstract
Fundamental electricity market models tend to underestimate the real market prices because they do not properly represent the real variable production cost of the generation units, nor the strategic markup that generation companies add to their costs to price the offered energy. This markup can increase bid prices above the marginal cost of the generation units, which may leave bids out of the market, decreasing the total cleared production, but increasing the final market price. This paper proposes a simple procedure, based on the real market outcomes, to estimate these markups and improve CEVESA MIBEL market model by reducing the gap between the simulated and the real market prices.