Dynamic participation in local energy communities with peer-to-peer trading

Abstract

Background: Energy communities and local electricity markets (e.g., as peer-to-peer trading) are on the rise due to increasingly decentralized electricity generation and favorable adjustment of the legal framework in many European countries. Methods: This work applies a bi-level optimization model for dynamic participation in peer-to-peer electricity trading to determine the optimal parameters of new participants who want to join an energy community, based on the preferences of the members of the original community (e.g., environmental, economic, or mixed preference). The upper-level problem chooses optimal parameters by minimizing an objective function that includes the prosumers' cost-saving and emission-saving preferences, while the lower level problem maximizes community welfare by optimally allocating locally generated photovoltaic (PV) electricity between members according to their willingness-to-pay. The bi-level problem is solved by transforming the lower level problem by its corresponding Karush-Kuhn-Tucker (KKT) conditions. Results: The results demonstrate that environment-oriented prosumers opt for a new prosumer with high PV capacities installed and low electricity demand, whereas profit-oriented prosumers prefer a new member with high demand but no PV system capacity, presenting a new source of income. Sensitivity analyses indicate that new prosumers' willingness-to-pay has an important influence when the community must decide between two new members. Conclusions: The added value of this work is that the proposed method can be seen as a basis for a selection process between a large number of potential new community members. Most important future work will include optimization of energy communities over the horizon several years.

A stochastic approach to dynamic participation in energy communities

“…In this work, a stochastic dynamic program is developed to optimally select new members for an energy community with peer-to-peer trading scheme. Based on previous work on energy communities by the authors in [33], where a bi-level optimization model can choose optimal parameters (PV capacity and annual electricity demand) of a new member and choose between potential new members, the present work includes scenarios of possible future developments within the energy community into the decision making process.…”

“…For the purpose of answering the research question defined above, a stochastic dynamic program with a look-ahead policy is developed. The model is based on the bi-level optimization model presented by the authors in [33], which is able to select optimal new members for an energy community while simultaneously optimally allocating the PV generation between the members considering their individual willingnessto-pay. In this paper, that model is further developed such that the decision made here and now includes a time horizon peaking into the future.…”

“…The dynamic program has to solve a lower-level problem to optimally allocate PV electricity generation within the community according to the participants' individual willingness-to-pay. The lower-level problem is adopted from [33]; therefore, a very brief overview is presented in the following. For details refer to the original publication.…”

Open energy system modelling to support the European Green Deal

“…Therefore, existing approaches need to be extended and new modelling approaches need to be developed so that the models can better represent real-world conditions and developments. This special collection on Energy Systems Modelling includes new modelling methods and datasets that address energy sufficiency policies ( Best et al , 2022 ) and peer-to-peer trading ( Perger and Auer, 2022 ) in modelling, as well as assess energy infrastructure needs ( Arduin et al, 2022 ).…”

“…Electricity generation is increasingly decentralised and therefore provides communities the opportunity to participate in the generation, storage, and trading of electricity. The research article by Perger and Auer (2022) deals with the participation of energy communities in local electricity markets via peer-to-peer trading. They apply a bi-level optimisation model to investigate how the preferences of existing community members influences the kind of new members which are allowed to join, and the impact of those new members on the system performance.…”