An Analysis of Multi Objective Energy Scheduling in PV-BESS System under Prediction Uncertainty
Energy storage systems (ESSs) are being considered to overcome issues in modern grids, caused by increasing penetration of renewable generation. Nevertheless, integration of ESS should also be supplemented with an optimal energy management framework to ensure maximum benefits from ESS. Conventional...
| Autores: | , , , , , |
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| Formato: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2021 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/261091 |
| Acesso em linha: | http://hdl.handle.net/10261/261091 |
| Access Level: | acceso abierto |
| Palavra-chave: | Model predictive control Battery management PV system Grid congestion degradation |
| Resumo: | Energy storage systems (ESSs) are being considered to overcome issues in modern grids, caused by increasing penetration of renewable generation. Nevertheless, integration of ESS should also be supplemented with an optimal energy management framework to ensure maximum benefits from ESS. Conventional energy management of battery, used with PV system, maximises self-consumption but does not mitigate grid congestion or address battery degradation. Model predictive control (MPC) can alleviate congestion, degradation while maximizing self-consumption. As such, studies will be carried out, in this work, to highlight the improvement with MPC based energy management over conventional method using simulations of one-year system behaviour. As MPC uses forecast information in decision making, the impact of forecast uncertainties will be assessed and addressing the same through constraint tightening will be presented. It is concluded that MPC provides improvement in system behaviour over multiple performance criteria. |
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