Název:	Optimal day-ahead self-scheduling and operation of prosumer microgrids using hybrid machine learning-based weather and load forecasting
Autoři:	Faraji, Jamal Ketabi, Abbas Hashemi Dezaki, Hamed Shafie-Khah, Miadreza Katalao, Joao P.S.
Citace zdrojového dokumentu:	FARAJI, J., KETABI, A., HASHEMI DEZAKI, H., SHAFIE-KHAH, M., KATALAO, J. P. Optimal day-ahead self-scheduling and operation of prosumer microgrids using hybrid machine learning-based weather and load forecasting. IEEE Access, 2020, roč. 8, č. 2020, s. 157284-157305. ISSN 2169-3536.
Datum vydání:	2020
Nakladatel:	Institute of Electrical and Electronics Engineers
Typ dokumentu:	článek article
URI:	2-s2.0-85091202604 http://hdl.handle.net/11025/42497
ISSN:	2169-3536
Klíčová slova v dalším jazyce:	prosumer microgrid (PMG);demand response-based energy management system;optimal scheduling and operation;hybrid machine learning-based forecasting method;load forecasting;weather forecasting
Abstrakt v dalším jazyce:	Prosumer microgrids (PMGs) are considered as active users in smart grids. These units are able to generate and sell electricity to aggregators or neighbor consumers in the prosumer market. Although the optimal scheduling and operation of PMGs have received a great deal of attention in recent studies, the challenges of PMG's uncertainties such as stochastic behavior of load data and weather conditions (solar irradiance, ambient temperature, and wind speed) and corresponding solutions have not been thoroughly investigated. In this paper, a new energy management systems (EMS) based on weather and load forecasting is proposed for PMG's optimal scheduling and operation. Developing a novel hybrid machine learning-based method using adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron (MLP) articial neural network (ANN), and radial basis function (RBF) ANN to precisely predict the load and weather data is one of the most important contributions of this article. The performance of the forecasting process is improved by using a hybrid machine learning-based forecasting method instead of conventional ones. The demand response (DR) program based on the forecasted data and considering the degradation cost of the battery storage system (BSS) are other contributions. The comparison of obtained test results with those of other existing approaches illustrates that more appropriate PMG's operation cost is achievable by applying the proposed DR-based EMS using a new hybrid machine learning forecasting method.
Práva:	© Institute of Electrical and Electronics Engineers
Vyskytuje se v kolekcích:	Články / Articles (RICE) OBD

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.