Optimisation framework for the operation of battery storage within solar‐rich microgrids. Issue 4 (26th June 2019)

• Record Type: Journal Article
• Authors: Jayawardana, Asanga; Agalgaonkar, Ashish P.; Robinson, Duane A.; Fiorentini, Massimo
• Is Part Of: IET smart grid. Volume 2:Issue 4(2019)
• Page Start: 504
• Page End: 513
• Publication Date: 2019-06-26
• Subjects: predictive control -- distributed power generation -- battery storage plants -- power generation control -- power generation economics -- solar power stations; optimisation framework -- battery storage -- solar‐rich microgrids -- distributed generation -- solar photovoltaic systems -- renewable energy -- energy storage -- fixed control methods -- optimal operation -- discharge operation -- solar PV system -- model predictive control optimisation -- battery operation -- energy usage -- small scale wind turbines -- charge operation -- overall tariff; B0260 Optimisation techniques -- B8110B Power system management, operation and economics -- B8260 Other power stations and plants -- C1180 Optimisation techniques -- C1330 Optimal control -- 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.2019.0084
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : The growing trend of distributed generation, such as solar photovoltaic (PV) systems and small scale wind turbines have promoted the development of microgrids which are highly dependent on renewable energy. Due to the intermittent nature of renewable energy, these microgrids are generally equipped with energy storage, such as batteries. Batteries are generally operated using fixed control methods, often deviating from the optimal operation. This aspect has created an opportunity to gain improved outcomes for microgrid owners and operators. This research study describes a pathway for designing an optimisation framework which can be used to optimise the charge and discharge operation of battery storage within a microgrid containing a solar PV system. Optimisation is implemented in terms of gaining maximum cost benefit for microgrid owners. The advantages of using model predictive control optimisation compared to fixed control methods for this particular problem, solvers and verification procedures are highlighted. A case study is provided with results including analysis of battery operation, energy usage, and impact on overall tariff. The study describes each step of the control and optimisation platform development ensuring readers to be able to replicate the process utilised.