SUMMARYThis paper deals with load side management strategy for (photovoltaic-wind-diesel) hybrid system for medium rural health building located in the Sahara region. The paper presents an overview about geographic and climate data of the location and develops a typical load profile of electrical energy, depending on a real investigation of daily electricity consumption. The base of the proposed management strategy is the control of the air conditioning system and the non-critical lighting of the building as they consume more than 50% of the total power consumption. The result shows that the reduction in the peak consumption is about 20% using the proposed management strategy, which has significant economical, technical, and environmental benefits.
Demand side management is considered as the next evolution of smart grid technology, it can adjust the time and the quantity of the electricity usage either by shifting the demand during the peak hours or by moving the time of energy use to off-peak periods. The objective of this paper is to develop an efficient load side management strategy for photovoltaic-wind hybrid energy system for an isolated house.The strategy aims to control the non-critical loads of the house based on an efficient analysis of the daily load profile of the house and on the climate data. The simulation result shows the effectiveness of the proposed strategy, as it improves the energy balance of the system and eliminates the energy waste.Keywords-smart grid, demand side management, hybrid power system, load profile, energy efficiency
This paper introduces an energy management strategy for an off-grid hybrid energy system. The hybrid system consists of a photovoltaic (PV) module, a LiFePO4 battery pack coupled with a Battery Management System (BMS), a hybrid solar inverter, and a load management control unit. A Long Short-Term Memory network (LSTM)-based forecasting strategy is implemented to predict the available PV and battery power. The learning data are extracted from an African country with a tropical climate, which is very suitable for PV power applications. Using LSTM as a prediction method significantly increases the efficiency of the forecasting. The main objective of the proposed strategy is to control the different loads according to the forecasted energy availability of the system and the forecasted battery state of charge (SOC). The proposed management algorithm and the system are tested using Matlab/Simulink software. A comparative study demonstrates that the reduction in the energy deficit of the system is approximately 53% compared to the system without load management. In addition to this, the reliability of the system is improved as the loss of power supply probability (LPSP) decreases from 5% to 3%.
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