Advanced control strategy for an energy storage system in a grid‐connected microgrid with renewable energy generation. Issue 3 (26th September 2018)

• Record Type: Journal Article
• Authors: Teo, Tiong Teck; Logenthiran, Thillainathan; Woo, Wai Lok; Abidi, Khalid
• Is Part Of: IET smart grid. Volume 1:Issue 3(2018)
• Page Start: 96
• Page End: 103
• Publication Date: 2018-09-26
• Subjects: pricing -- linear programming -- renewable energy sources -- energy storage -- distributed power generation -- integer programming -- power grids -- power markets -- power generation control -- adaptive control; advanced control strategy -- grid‐connected microgrid -- renewable energy generation -- renewable energy storage capability -- charging discharging rates -- mixed‐integer linear programming -- electricity market price -- forecasting‐based approach -- power exchange -- end‐user operating cost reduction -- discharging rates -- electricity market‐based environment -- self‐adaptive energy storage system controller; B0260 Optimisation techniques -- B8110B Power system management, operation and economics -- C1180 Optimisation techniques -- C1340E Self‐adjusting control systems -- C3340H Control of electric power systems -- B8120K Distributed power generation; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2018.0024
• Languages: English
• ISSNs: 2515-2947

Abstract

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.