Abstract
This paper proposes an energy management system (EMS) for battery storage systems in grid-connected microgrids. The battery charging/discharging power is determined such that the overall energy consumption cost is minimized, considering the variation in grid tariff, renewable power generation and load demand. The system is modeled as an economic load dispatch optimization problem over a 24 h horizon and solved using mixed integer linear programming (MILP). This formulation, therefore, requires knowledge of the expected renewable energy power production and load demand over the next 24 h. To achieve this, a long short-term memory (LSTM) network is proposed. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the EMS that benefits from using actual generation and demand data on the day. At each hour, the LSTM predicts generation and load data for the next 24 h, the dispatch problem is then solved and the battery charging or discharging command for only the first hour is applied in real-time. Real data are then used to update the LSTM input, and the process is repeated. Simulation results show that the proposed real-time strategy outperforms the offline optimization strategy, reducing the operating cost by 3.3%.
Highlights
The development of microgrid (MG) technology has provided the opportunity and the infrastructure for improving the efficiency of energy consumption [1,2]
The proposed energy management system simulation was performed in MATLAB with a 32 GB 64-bit operating system computer, dual core i7, 2.70–2.90 GHz
This paper presented an energy management system (EMS) to minimize the daily operating cost and control the charge/discharge cycle of energy storage systems (ESS) in a grid-tied microgrid, while guaranteeing the security of supply and respecting imposed constraints
Summary
The development of microgrid (MG) technology has provided the opportunity and the infrastructure for improving the efficiency of energy consumption [1,2]. Microgrid systems are typically made up of load and distributed energy resources, such as photovoltaics (PV) systems, wind turbines, biogas power plants, fuel cells and energy storage systems (ESS) [3]. The hybrid microgrid system, which comprises different distributed energy resources, has become promising as it provides an integral part of the development of smart grid systems [4,5]. This paper uses LSTM-based deep learning for predicting the load demand and the PV generation for the future, considering one year of historical data from the Ushant Island in France. To forecast the values of future time steps of the sequence, the training sequence with values shifted by one time step is specified as the response This means that at each time step of the input sequence, the LSTM network learns to predict the value of the time step. To predict the time step, the previous prediction is used as an input to the function [22]
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