The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.
Abstract-Electric vehicles (EVs) began to play an essential role in the public transportation system. It is a necessary part of developing and driving the economies of many countries in the future. EVs uses electric power to drive the mechanisms and other systems which must be stored electrical energy for use to drive the car move from one place to another one. However, the batteries of electric vehicles must be recharged, which requires the charging station to follow the distance traveled. Charging station and power sources for charging are an essential factor for the use of electric vehicles. This article is the study of the charging station for electric vehicles that use power sources from distribution systems and hybrid renewable energy system. In this study, photovoltaic (PV) and wind energy were the renewable energy sources to generate the electricity for charging the electric vehicles. The IEEE 33 bus system was modified and was used to simulate, and the Artificial Bee Colony (ABC) algorithm is an artificial intelligence technique to solve the problem. The simulation results show that the connection of hybrid renewable energy system to the distribution system for recharge electric vehicles provide electrical systems more reliable while being used during peak load times.
Smart energy management and control systems can improve the efficient use of electricity and maintain the balance between supply and demand. This paper proposes the modeling of a decentralized energy management system (EMS) to reduce system operation costs under renewable generation and load uncertainties. There are three stages of the proposed strategy. First, this paper applies an autoregressive moving average (ARMA) model for forecasting PV and wind generations as well as power demand. Second, an optimal generation scheduling process is designed to minimize system operating costs. The well-known algorithm of particle swarm optimization (PSO) is applied to provide optimal generation scheduling among PV and WT generation systems, fuel-based generation units, and the required power from the main grid. Third, a demand response (DR) program is introduced to shift flexible load in the microgrid system to achieve an active management system. Simulation results demonstrate the performance of the proposed method using forecast data for hourly PV and WT generations and a load profile. The simulation results show that the optimal generation scheduling can minimize the operating cost under the worst-case uncertainty. The load-shifting demand response reduced peak load by 4.3% and filled the valley load by 5% in the microgrid system. The proposed optimal scheduling system provides the minimum total operation cost with a load-shifting demand response framework.
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