Resumen:
Local energy trading between customers in energy communities (ECs) received significant interest from academia and industry as a promising approach for managing a large number of distributed energy resources (DERs) and empowering end users to take an active role in energy systems. Most of the existing literature focuses on the operation of ECs, often with an implicit assumption about the capabilities of the DERs. However, there is a noticeable lack of emphasis on the crucial aspect of optimizing the planning and integration of DERs within these ECs. Considering the high cost of DERs, there is a need to optimally size DERs of ECs' participants that maximize the benefits, minimize the expenses of DER owners, and reduce the payback period. In this paper, a linear programming model is proposed for the optimal planning and operation of DERs installed in a residential EC in Spain which includes photovoltaic (PV), battery energy storage (BES), and electric vehicles (EV). The objective is to minimize the EC's total annual costs, including investment, maintenance and operation (O&M), and operation costs (i.e., energy and contracted power costs). Furthermore, the proposed approach considers the uncertainties of load demand, PV generation, electricity prices, and EVs. The simulation results demonstrate that optimal planning reduces the annual costs by 10.75% compared to the scenario without optimal planning of PV and BES. Sensitivity analysis shows that, by decreasing the investment costs of BES, increasing the electricity prices, or decreasing the electricity selling price, it could be feasible to install BES at part of the EC houses. The scenario without optimal planning results in higher impacts on the distribution network than scenarios with optimal planning.
Resumen divulgativo:
En este artículo se propone un modelo de programación lineal para la planificación y operación óptimas de los recursos distribuidos instalados en una comunidad energética residencial en España. Los resultados de la simulación demuestran que la planificación óptima reduce los costos anuales en un 10.75% en comparación con el escenario sin planificación óptima.
Palabras clave: Local electricity market; peer-to-peer energy trading; energy community; transactive energy; optimal planning; optimal sizing.
Fecha de Registro: 02/11/2023
IIT-23-417WP
