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In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using artificial intelligence techniques jointly with linear-programming-based multiobjective optimization. The proposed multiobjective intelligent energy management aims to minimize the operation cost and the environmental impact of a microgrid, taking into account its preoperational variables as future availability of renewable energies and load demand (LD). An artificial neural network ensemble is developed to predict 24-h-ahead photovoltaic generation and 1-h-ahead wind power generation and LD. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. In this paper, a fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results show considerable minimization on operation cost and emission level compared to literature microgrid energy management approaches based on opportunity charging and Heuristic Flowchart (HF) battery management. [ABSTRACT FROM PUBLISHER]

This paper presents an approach for short-term load forecast problem, based on hybrid correction method. Conventional artificial neural network based short-term load forecasting techniques have limitations especially when weather changes are seasonal. Hence, we propose a load correction method by using fuzzy logic approach in which a fuzzy logic, based on similar days, corrects the neural network output to obtain the next day forecasted load. An Euclidean norm with weighted factors is used for the selection of similar days. The load correction method for the generation of new similar days is also proposed. The neural network has an advantage of dealing with the nonlinear parts of the forecasted load curves, whereas, the fuzzy rules are constructed based on the expert knowledge. Therefore, by combining these two methods, the test results show that the proposed forecasting method could provide a considerable improvement of the forecasting accuracy especially as it shows how to reduce neural network forecast error over the test period by 23 % through the application of a fuzzy logic correction. The suitability of the proposed approach is illustrated through an application to actual load data of the Okinawa Electric Power Company in Japan. [ABSTRACT FROM AUTHOR]

In a deregulated environment, system operators are required to procure certain ancillary services, which, among others, may include compensation for active-power losses. This compensation usually involves long-term energy purchases and additional short-term energy purchases to cover the daily fluctuations. The short-term energy purchases require an accurate and quick short-term forecasting method that has to be efficiently applicable in day-ahead markets. This paper presents a novel short-term active-power-loss forecast method using power-flow analysis for the forecasted day. Specifically, this includes short-term load and generation forecasts as well as network-topology forecasts, which are used for the power-flow calculations and the resulting active-power loss calculations. To minimize the forecast errors, a fuzzy-weight grouping of the different short-term load and generation forecast results is proposed. An additional step for input-data pre-processing is presented, where the fuzzy clustering considers the patterns for training the forecasting models. The proposed approach was verified by using real data for the ENTSO-E interconnection and tested for the Slovenian power system. The forecasting results demonstrate the improved accuracy of the proposed approach. [ABSTRACT FROM PUBLISHER]

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn. [ABSTRACT FROM AUTHOR]