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Microgrid PV-CHP-Battery: literature review
Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting
Julio Pascual, Javier Barricarte, Pablo Sanchisa, Luis Marroyo.
This paper presents the management strategy for residential microgrid comprising of PV and wind turbines. By using forecasted data and correcting forecasting errors according to the SOC of the battery, the strategy manages to make a proper utilizaztion of the battery resulting in a better grid power profile.
-Microgrids include distributed generators, loads, energy storage which are controlled by one unit in order to exchange power with the grid. Addind renewable system to this can lower the cost and can provide better grid quality all around the world.
-Microgrids can be classified as either grid-connected or stand-alone. It will either have renewable generators, fossil fuel generators.
-In the field of stand-alone microgrids, when the only power sources are renewable energies, the ultimate goal is to manage the energy management system in order to keep the microgrid running and schedule different units in order to reduce the operating cost.
-It consists of a grid-tied microgrid with the usual electric loads for a single-family home including a heating, ventilation and air conditioning (HVAC) system.
-PV and wind turbine are installed.
Simple moving average strategy
- Two strategies have been used: SMA(Simple moving average) and CMA(Central Moving average)
-In order to make power fluctuations smooth in power exchange with the grid low pass filter can be used.
-If the power profile changes one day to another in this case the energy balance of microgrid changes and has to be compensated by battery first. This will cause SOC of the battery to drift.
Central moving average (CMA) strategy: power forecasting
- The SMA causes lag in the grid. So, CMA is used instead of SMA. This will cause lag to disappear and there will not be any need to SOC control which will result in more better grid power profile.
This results in energy management strategy for a residential microgrid to obtain a smooth power profile for the energy exchange with the grid. The strategy makes use of forecasted power profiles in order to eliminate the lag in the grid power profile.