Optimal Day-Ahead Scheduling and Operation of the Prosumer by Considering Corrective Actions Based on Very Short-Term Load Forecasting

Abstract

Energy management systems (EMSs) play an important role in the optimal operation of prosumers. As an essential segment of each EMS, the load forecasting (LF) block enhances the optimal utilization of renewable energy sources (RESs) and battery energy storage systems (BESSs). In this paper, a new optimal day-ahead scheduling and operation of the prosumer is proposed based on the two-level corrective LF. The proposed two-level corrective LF actions are developed through a very precise shortterm LF. In the first level, a time-series LF is applied using multi-layer perceptron artificial neural networks (MLP-ANNs). In order to improve the accuracy of the forecasted load data at the first level, the second level corrective LF is applied using feed-forward (FF) ANNs. The second stage prediction is initiated when the LF results violate the pre-defined criteria. The proposed method is applied to a prosumer under different cases (based on the consideration of BESS operation behaviors and cost) and various scenarios (based on the accuracy of the load data). The obtained optimal day-ahead operation results illustrate the advantages of the proposed method and its corrective forecasting process. The comparison of the obtained results and those of other available ones show the effectiveness of the proposed optimal operation of the prosumers. The advantages of the proposed method are highlighted while the BESS costs are considered.

Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting

“…The solar irradiance, ambient temperature, wind speed, forecasting are implemented based on previous year historical data of Kerman province, which is located in Iran [13]. Also, the historical load data has been gathered from [14]. Fig.…”

“…As early mentioned, three types of ANFIS model, as well as MLP-ANN and RBF-ANN, have been used for forecasting time-series data. The measurement at the time (t-24) was used to forecast the weather parameters and load data at the time (t+24) [14]. For each machine learning algorithm, 70 % of all data is used for training, and the other 30 % is used for testing [24].…”

“…The advantages of machine learning algorithms have been utilized for forecasting short-term load or weather data, separately [22,23]. The time-series forecasting methods for prediction of load demand and weather conditions have received a great deal of attention [13,14,24,25]. However, most of them merely focused on one or two parameters (e.g.…”

“…The output power of the PV cell is strictly related to weather conditions such as ambient temperature and solar irradiance. The output power of the PV unit could be calculated as (14) based on the forecasted solar irradiance and ambient temperature [36]:…”

“…Although reference [13] utilized multilayer perceptron (MLP) ANN to predict day-ahead weather conditions for optimal scheduling of PMGs, the load uncertainties have been neglected. On the other hand, several studies like [14] exist, which developed load forecasting methods to concern load demand uncertainties and did not consider the uncertainties of RES-based DG units due to weather conditions. There are a few studies in the literature such as [15], which considered both weather and load uncertainties for optimal operation of PMGs.…”