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The role of intelligent generation control algorithms in optimizing battery energy storage systems size in microgrids: A case study from Western Australia
Edith Cowan University, Australia.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). Czech Technical University, Czech Republic.ORCID iD: 0000-0001-9051-7609
University of Technology, Iraq.
2019 (English)In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 196, p. 1335-1352Article in journal (Refereed) Published
Abstract [en]

Battery energy storage systems can play a substantial role in maintaining low-cost operation in microgrids, and therefore finding their optimal size is a key element of microgrids’ planning and design. This paper explores the optimal sizing options for batteries in microgrids that include wind turbines, solar photovoltaics, synchronous machines and a grid connection supply under various types of retail tariff schemes. The optimal size of batteries is hypothesized to be significantly related to the intelligent control rules applied to dispatch the microgrid sources. This problem can be formulated as a mixed linear integer problem and can be solved using linear/non-linear solvers depending on the complexity of the generation control plan. The main objective of this work is to apply online intelligent adaptation mechanism to tune the economic generation control (dispatch) rules of the microgrid. This tuning objectives are maintaining secure operation, maximizing profitable utilization of batteries and managing their charging life-cycles. While sizing options exploration has been formulated as a linear programming based optimization problem, Fuzzy-Logic is proposed to control the charging/discharging time and quantity for batteries. For the sake of performance comparison, various optimization techniques, i.e., Particle Swarm Optimization, Genetic Algorithm and Flower Pollination Algorithm are applied to perform the economic dispatch calculation. As a case study, a commercial type load connected to the 22 kV distribution network in south Western Australia was used in the testing and validation if the results of the proposed sizing method. The operation condition data was obtained from Western Power the distribution and transmission company in south Western Australia, the Australian Bureau Of Meteorology (BOM) and the Australian Energy Market Operator (AEMO). The results showed that employing intelligent batteries in operation can reduce the annual generation cost of microgrids. However, the decision on selecting the size of batteries depends heavily on the amount of upfront investment cost. The simulation results showed that the intelligence added to batteries’ control could achieve 6.5%, 7.6% and 11.5% of the annual generation cost in the Islanded, Grid-connected with no-export and Grid-connected with export operating modes respectively. Also, intelligent batteries operation control was proven to minimize their payback time to 2.8, 2.7 and 2.7 years in the Islanded, Grid-connected with no-export and Grid-connected with export operating modes respectively.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 196, p. 1335-1352
Keywords [en]
Battery Energy Storage System (BESS), Microgrids, Generation control, Electricity market, Generation sizing, Artificial intelligence
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-73523DOI: 10.1016/j.enconman.2019.06.045OAI: oai:DiVA.org:kau-73523DiVA, id: diva2:1335794
Available from: 2019-07-07 Created: 2019-07-07 Last updated: 2019-08-07Bibliographically approved

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Ahmed, Bestoun S.

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