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Profit Maximizing Control of a Microgrid with Renewable Generation and BESS Based on a Battery Cycle Life Model and Energy Price Forecasting

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  • Wenhao Zhuo

    (School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia)

  • Andrey V. Savkin

    (School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia)

Abstract
In this paper, an optimal control strategy is presented for grid-connected microgrids with renewable generation and battery energy storage systems (BESSs). In order to optimize the energy cost, the proposed approach utilizes predicted data on renewable power, electricity price, and load demand within a future period, and determines the appropriate actions of BESSs to control the actual power dispatched to the utility grid. We formulate the optimization problem as a Markov decision process and solve it with a dynamic programming algorithm under the receding horizon approach. The main contribution in this paper is a novel cost model of batteries derived from their life cycle model, which correlates the charge/discharge actions of batteries with the cost of battery life loss. Most cost models of batteries are constructed based on identifying charge–discharge cycles of batteries on different operating conditions, and the cycle counting methods used are analytical, so cannot be expressed mathematically and used in an optimization problem. As a result, the cost model proposed in this paper is a recursive and additive function over control steps that will be compatible with dynamic programming and can be included in the objective function. We test the proposed approach with actual data from a wind farm and an energy market operator.

Suggested Citation

  • Wenhao Zhuo & Andrey V. Savkin, 2019. "Profit Maximizing Control of a Microgrid with Renewable Generation and BESS Based on a Battery Cycle Life Model and Energy Price Forecasting," Energies, MDPI, vol. 12(15), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2904-:d:252470
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    References listed on IDEAS

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    1. Khalid, Muhammad & Aguilera, Ricardo P. & Savkin, Andrey V. & Agelidis, Vassilios G., 2018. "On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting," Applied Energy, Elsevier, vol. 211(C), pages 764-773.
    2. Srete Nikolovski & Hamid Reza Baghaee & Dragan Mlakić, 2018. "ANFIS-Based Peak Power Shaving/Curtailment in Microgrids Including PV Units and BESSs," Energies, MDPI, vol. 11(11), pages 1-23, October.
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    4. Khalid, M. & Savkin, A.V., 2010. "A model predictive control approach to the problem of wind power smoothing with controlled battery storage," Renewable Energy, Elsevier, vol. 35(7), pages 1520-1526.
    5. Van-Hai Bui & Akhtar Hussain & Hak-Man Kim, 2019. "Q-Learning-Based Operation Strategy for Community Battery Energy Storage System (CBESS) in Microgrid System," Energies, MDPI, vol. 12(9), pages 1-17, May.
    6. Wenhao Zhuo & Andrey V. Savkin & Ke Meng, 2019. "Decentralized Optimal Control of a Microgrid with Solar PV, BESS and Thermostatically Controlled Loads," Energies, MDPI, vol. 12(11), pages 1-15, June.
    7. Ying Ji & Jianhui Wang & Jiacan Xu & Xiaoke Fang & Huaguang Zhang, 2019. "Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning," Energies, MDPI, vol. 12(12), pages 1-21, June.
    8. Provata, Elena & Kolokotsa, Dionysia & Papantoniou, Sotiris & Pietrini, Maila & Giovannelli, Antonio & Romiti, Gino, 2015. "Development of optimization algorithms for the Leaf Community microgrid," Renewable Energy, Elsevier, vol. 74(C), pages 782-795.
    9. Xiaomin Wu & Weihua Cao & Dianhong Wang & Min Ding, 2019. "A Multi-Objective Optimization Dispatch Method for Microgrid Energy Management Considering the Power Loss of Converters," Energies, MDPI, vol. 12(11), pages 1-19, June.
    10. Khalid, M. & Savkin, A.V., 2012. "An optimal operation of wind energy storage system for frequency control based on model predictive control," Renewable Energy, Elsevier, vol. 48(C), pages 127-132.
    11. Khalid, Muhammad & Ahmadi, Abdollah & Savkin, Andrey V. & Agelidis, Vassilios G., 2016. "Minimizing the energy cost for microgrids integrated with renewable energy resources and conventional generation using controlled battery energy storage," Renewable Energy, Elsevier, vol. 97(C), pages 646-655.
    12. Wang, Dongxiao & Qiu, Jing & Reedman, Luke & Meng, Ke & Lai, Loi Lei, 2018. "Two-stage energy management for networked microgrids with high renewable penetration," Applied Energy, Elsevier, vol. 226(C), pages 39-48.
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    Cited by:

    1. Matteo Moncecchi & Claudio Brivio & Stefano Mandelli & Marco Merlo, 2020. "Battery Energy Storage Systems in Microgrids: Modeling and Design Criteria," Energies, MDPI, vol. 13(8), pages 1-18, April.
    2. Miseta, Tamás & Fodor, Attila & Vathy-Fogarassy, Ágnes, 2022. "Energy trading strategy for storage-based renewable power plants," Energy, Elsevier, vol. 250(C).
    3. Ibrahim Alotaibi & Mohammed A. Abido & Muhammad Khalid & Andrey V. Savkin, 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources," Energies, MDPI, vol. 13(23), pages 1-41, November.
    4. Fernando J. Lanas & Francisco J. Martínez-Conde & Diego Alvarado & Rodrigo Moreno & Patricio Mendoza-Araya & Guillermo Jiménez-Estévez, 2020. "Non-Strategic Capacity Withholding from Distributed Energy Storage within Microgrids Providing Energy and Reserve Services," Energies, MDPI, vol. 13(19), pages 1-14, October.
    5. Syahrul Nizam Md Saad & Adriaan Hendrik van der Weijde, 2019. "Evaluating the Potential of Hosting Capacity Enhancement Using Integrated Grid Planning modeling Methods," Energies, MDPI, vol. 12(19), pages 1-23, September.

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