Abstract
The operating cost of the consumer can be reduced in an electricity market-based environment by shifting consumption to a lower price period. This study presents the design of an advanced control strategy to be embedded in a grid-connected microgrid with renewable and energy storage capability. The objectives of the control strategy are to control the charging and discharging rates of the energy storage system to reduce the end-user operating cost through arbitrage operation of the energy storage system and to reduce the power exchange between the main and microgrid. Instead of using a forecasting-based approach, the proposed methodology takes the difference between the available renewable generation and load, state-of-charge of energy storage system and electricity market price to determine the charging and discharging rates of the energy storage system in a rolling horizon. The proposed control strategy is compared with a self-adaptive energy storage system controller and mixed-integer linear programming with the same objectives. Empirical evidence shows that the proposed controller can achieve a lower operating cost and reduce the power exchange between the main and microgrid.
Highlights
A microgrid is a small-scale power grid with local energy generation and control capability
This paper proposed a decision making fuzzy energy management system (FEMS) with three inputs and one output to simultaneously reduce the end-user electricity bill and the variation introduced by renewable energy sources while satisfying the load without any demand side management techniques
The test data used in this paper are obtained from National Renewable Energy Laboratory (NREL) [28] and wholesale electricity prices from Energy Market Company Singapore (EMCSG)
Summary
A microgrid is a small-scale power grid with local energy generation and control capability. Similar optimisation approaches and objectives are taken into consideration, e.g. maximising revenue/minimising cost, power exchange between the main grid and microgrid [14, 15] These studies focus on day-ahead and week ahead scheduling based on forecasting of renewable energies, demand and electricity pricing to reduce the end-user electricity bill or to smoothen the variation introduced by renewable energy sources [16, 25]. The fuzzy logic controller approach only considers the state-of-charge and difference between demand and renewable energy sources and did not consider arbitrage operation as one of its objective [23–25] These studies only consider a single function for the energy storage system. This paper proposed a decision making FEMS with three inputs and one output to simultaneously reduce the end-user electricity bill and the variation introduced by renewable energy sources while satisfying the load without any demand side management techniques.
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