Abstract
A new energy management system (EMS) is presented for small scale microgrids (MGs). The proposed EMS focuses on minimizing the daily cost of the energy drawn by the MG from the main electrical grid and increasing the self-consumption of local renewable energy resources (RES). This is achieved by determining the appropriate reference value for the power drawn from the main grid and forcing the MG to accurately follow this value by controlling a battery energy storage system. A mixed integer linear programming algorithm determines this reference value considering a time-of-use tariff and short-term forecasting of generation and consumption. A real-time predictive controller is used to control the battery energy storage system to follow this reference value. The results obtained show the capability of the proposed EMS to lower the daily operating costs for the MG customers. Experimental studies on a laboratory-based MG have been implemented to demonstrate that the proposed EMS can be implemented in a realistic environment.
Highlights
The growth of renewable energy sources (RES) in the electricity grid together with the increasing use of electricity for transport and heating, ventilation, and air-conditioning requires a new vision for future transmission and distribution grids
The results show that, with up to 20% increase or decrease in the system demand, the adaptive neuro-fuzzy inference systems (ANFIS) still forecasts the load for the day with nearly the same accuracy compared to the artificial neural network (ANN) that losses accuracy quickly with any load changes
The real-time predictive controller (RTPC) is a real-time controller that controls the settings of the Battery energy storage system (BESS) in real time, in a way that makes the MG follow the reference values for the power drawn from the main electric grid
Summary
The growth of renewable energy sources (RES) in the electricity grid together with the increasing use of electricity for transport and heating, ventilation, and air-conditioning requires a new vision for future transmission and distribution grids. In [27], the design and experimental validation of an adaptable MGEM were implemented in an online scheme In this case, the author aimed to minimize the operating costs and the disconnection of loads by proposing an architecture that allowed the interaction of forecasting, measurement, and optimization modules. The aims to minimize the daily cost of the energy drawn (by increasing self-consumption of locally generated energy), reduces energy costs for end-users, and by the from the main electrical grid and increaseconsumption the self-consumption of the MG’sselection renewable the MGMG consumption profile can be shaped to reduce peaks by appropriate of energy resources This is achieved by determining an appropriate reference value for the power drawn. The proposed hierarchical scheme of the small scale microgrid energy management system is values for the power drawn from the main electric grid, and it can help to overcome errors in load shown in Figure and generation prediction.
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