| Title | Stochastic unit commitment in microgrids: Influence of the load forecasting error and the availability of energy storage |
|---|---|
| Authors | ALVARADO BARRIOS, LÁZARO, RODRÍGUEZ DEL NOZAL, ÁLVARO, Boza Valerino, Juan , Garcia Vera, Ignacio , Martinez-Ramos, Jose L. |
| External publication | No |
| Means | Renew. Energy |
| Scope | Article |
| Nature | Científica |
| JCR Quartile | 1 |
| SJR Quartile | 1 |
| JCR Impact | 8.001 |
| SJR Impact | 1.825 |
| Web | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070667273&doi=10.1016%2fj.renene.2019.08.032&partnerID=40&md5=ea43338f5978a2196cff1b8a9f981977 |
| Publication date | 01/02/2020 |
| ISI | 000499762300052 |
| Scopus Id | 2-s2.0-85070667273 |
| DOI | 10.1016/j.renene.2019.08.032 |
| Abstract | A Stochastic Model for the Unit Commitment (SUC) problem of a hybrid\n microgrid for a short period of 24 h is presented. The microgrid\n considered in the problem is composed of a wind turbine (WT), a\n photovoltaic plant (PV), a diesel generator (DE), a microturbine (MT)\n and a Battery Energy Storage System (BESS). The problem is addressed in\n three stages. First, based on the historical data of the demanded power\n in the microgrid, an ARMA model is used to obtain the demand prediction.\n Second, the 24-h-ahead SUC problem is solved, based on generators\'\n constraints, renewable generation and demand forecast and the\n statistical distribution of the error in the demand estimation. In this\n problem, a spinning reserve of the dispatchable units is considered,\n able to cover the uncertainties in the demand estimation. In the third\n stage, once the SUC problem has been solved, a case study is established\n in real time, in which the demand estimation error in every moment is\n known. Therefore, the objective of this stage is to select the spinning\n reserve of the units in an optimal way to minimize the cost in the\n microgrid operation. (C) 2019 Elsevier Ltd. All rights reserved. |
| Keywords | Availability; Digital storage; Distributed power generation; Energy storage; Errors; Forecasting; Stochastic systems; Textile industry; Battery energy storage systems; Micro grid; Microgrid operations; PhotoVoltaic plant; Renewable generation; Statistical distribution; Stochastic unit commitments; Unit commitment problem; Stochastic models; diesel engine; energy efficiency; energy storage; error analysis; forecasting method; power generation; stochasticity; uncertainty analysis; wind turbine |
| Universidad Loyola members |