Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

Abstract

In recent years, taking advantage of renewable energy sources (RESs) has increased considerably due to their unique capabilities, such as a flexible nature and sustainable energy production. Prosumers, who are defined as proactive users of RESs and energy storage systems (ESSs), are deploying economic opportunities related to RESs in the electricity market. The prosumers are contracted to provide specific power for consumers in a neighborhood during daytime. This study presents optimal scheduling and operation of a prosumer owns RESs and two different types of ESSs, namely stationary battery (SB) and plugged-in electric vehicle (PHEV). Due to the intermittent nature of RESs and their dependency on weather conditions, this study introduces a weather prediction module in the energy management system (EMS) by the use of a feed-forward artificial neural network (FF-ANN). Linear regression results for predicted and real weather data have achieved 0.96, 0.988, and 0.230 for solar irradiance, temperature, and wind speed, respectively. Besides, this study considers the depreciation cost of ESSs in an objective function based on the depth of charge (DOD) reduction. To investigate the effectiveness of the proposed strategy, predicted output and the real power of RESs are deployed, and a mixed-integer linear programming (MILP) model is used to solve the presented day-ahead optimization problem. Based on the obtained results, the predicted output of RESs yields a desirable operation cost with a minor difference (US$0.031) compared to the operation cost of the system using real weather data, which shows the effectiveness of the proposed EMS in this study. Furthermore, optimum scheduling with regard to ESSs depreciation term has resulted in the reduction of operation cost of the prosumer and depreciation cost of ESS in the objective function has improved the daily operation cost of the prosumer by $0.8647.

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

“…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.…”

“…As mentioned above, time-series forecasting is interested in recent studies. Generally, time-series is a set of samples, which have been arranged in uniform time intervals [13]. In time-series forecasting approaches, a model is utilized to forecast future samples based on historical data [27].…”

“…), mean squared error (MSE), and root mean squared error (RMSE). The formulations of the considered statical criteria are shown in (10)(11)(12)(13). These criteria are most commonly used to report the accuracy of forecasting in the literature [31][32][33].…”

“…Liu et al [12] reported optimal day-ahead scheduling for the RES-based PMG, which considered intermittence and fluctuations of RESs as system uncertainties, while no analysis was performed on load demand's uncertainties. 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.…”

“… Consideration of the BSS degradation in optimal operation of PMG. The fourth novelty of this paper is developed based on future works introduced in [13]. The third term of the proposed objective function of this paper includes a model for the degradation of the BSS.…”