Solar Photovoltaic and Wind Turbine Generation based Microgrid Management Architecture Considering Battery Energy Storage Degradation and Time of Use Tariff

Document Type : Original Article


1 Department of Electrical Engineering, Odisha University of Technology and Research, Ghatikia, Kalinga Vihar

2 Department of Electrical Engineering, SOA University

3 Department of Electrical Engineering, CAPGS, Biju Patnaik University of Technology



The rapid expansion of renewable energy sources (RES), especially the combination of solar photovoltaic (PV), wind turbine generating (WTG), and battery energy storage systems (BESS), has sparked significant interest in addressing global warming and climate change issues. These energy sources offer numerous advantages, such as reduced emissions and lower operational costs, but their power output is uncertain. In order to account for fluctuating energy costs, a microgrid with diverse energy sources must schedule BESS charging optimally. The proposed method uses the Artificial Rabbit Optimisation (ARO) algorithm to optimise the charging and discharging schedule for BESS, resulting in a decrease in daily energy costs and an improvement in storage state of health (SOH). The SOH is considered an ageing coefficient for conservative BESS operation in order to extend the battery's lifespan under consistent use. To further validate the effectiveness of the energy management strategy, a fixed pricing scheme and a dynamic pricing scheme are utilised to validate its efficacy. When storage degradation and time-of-use (TOU) tariffs are accounted for, the simulation results of voltage, current, and power profiles over a 24-hour period indicate that the proposed method has the capability to maximise the profitability of a grid-connected PV and WTG-based microgrid.


  1. Elavarasan, R.M., et al., SWOT analysis: A framework for comprehensive evaluation of drivers and barriers for renewable energy development in significant countries. Energy Reports, 2020. 6: p. 1838-1864.
  2. Solaun, K. and E. Cerdá, Climate change impacts on renewable energy generation. A review of quantitative projections. Renewable and sustainable energy Reviews, 2019. 116: p. 109415.
  3. Sarwar, M., et al., Optimal selection of renewable energy–based microgrid for sustainable energy supply. International Journal of Energy Research, 2022. 46(5): p. 5828-5846.
  4. Blesslin, S.T., et al., Microgrid optimization and integration of renewable energy resources: innovation, challenges and prospects. Integration of Renewable Energy Sources with Smart Grid, 2021: p. 239-262.
  5. Ishaq, S., et al., A review on recent developments in control and optimization of micro grids. Energy Reports, 2022. 8: p. 4085-4103.
  6. Kumar, G.B., et al., Large scale renewable energy integration: Issues and solutions. Energies, 2019. 12(10): p. 1996.
  7. Shahgholian, G., A brief review on microgrids: Operation, applications, modeling, and control. International Transactions on Electrical Energy Systems, 2021. 31(6): p. e12885.
  8. Jabalameli, N. and A. Ghosh, Online centralized coordination of charging and phase switching of PEVs in unbalanced LV networks with high PV penetrations. IEEE Systems Journal, 2020. 15(1): p. 1015-1025.
  9. Kandari, R., N. Neeraj, and A. Micallef, Review on Recent Strategies for Integrating Energy Storage Systems in Microgrids. Energies, 2022. 16(1): p. 317.
  10. Zarei, A. and N. Ghaffarzadeh, Optimal Demand Response-based AC OPF Over Smart Grid Platform Considering Solar and Wind Power Plants and ESSs with Short-term Load Forecasts using LSTM. Journal of Solar Energy Research, 2023. 8(2): p. 1367-1379.
  11. Mirzakhani, A. and I. Pishkar, Finding the best configuration of an off-grid PV-Wind-Fuel cell system with battery and generator backup: a remote house in Iran. Journal of Solar Energy Research, 2023. 8(2): p. 1380-1392.
  12. Aryan Nezhad, M., Economic Impacts of Long-Term Wind Speed Changes on Optimal Planning of a Hybrid Renewable Energy System (HRES). Journal of Solar Energy Research, 2021. 6(1): p. 656-663.
  13. Ghaffarzadeh, N. and H. Faramarzi, Optimal Solar plant placement using holomorphic embedded power Flow Considering the clustering technique in uncertainty analysis. Journal of Solar Energy Research, 2022. 7(1): p. 997-1007.
  14. Theo, W.L., et al., An MILP model for cost-optimal planning of an on-grid hybrid power system for an eco-industrial park. Energy, 2016. 116: p. 1423-1441.
  15. Logesh, R., Resources, configurations, and soft computing techniques for power management and control of PV/wind hybrid system. Renewable and Sustainable Energy Reviews, 2017. 69: p. 129-143.
  16. Fathima, A.H. and K. Palanisamy, Optimization in microgrids with hybrid energy systems–A review. Renewable and Sustainable Energy Reviews, 2015. 45: p. 431-446.
  17. Gamarra, C. and J.M. Guerrero, Computational optimization techniques applied to microgrids planning: A review. Renewable and Sustainable Energy Reviews, 2015. 48: p. 413-424.
  18. Sarker, M.R., et al., Optimal operation of a battery energy storage system: Trade-off between grid economics and storage health. Electric Power Systems Research, 2017. 152: p. 342-349.
  19. Parvin, K., et al., The future energy internet for utility energy service and demand-side management in smart grid: Current practices, challenges and future directions. Sustainable Energy Technologies and Assessments, 2022. 53: p. 102648.
  20. Khezri, R. and A. Mahmoudi, Review on the state‐of‐the‐art multi‐objective optimisation of hybrid standalone/grid‐connected energy systems. IET Generation, Transmission & Distribution, 2020. 14(20): p. 4285-4300.
  21. Patnaik, S., M.R. Nayak, and M. Viswavandya, Optimal Battery Energy Storage System Management with Wind Turbine Generator in Unbalanced Low Power Distribution System. Advances in Electrical and Electronic Engineering, 2023. 20(4): p. 523-536.
  22. Kunya, A.B., A.S. Abubakar, and S.S. Yusuf, Review of economic dispatch in multi-area power system: State-of-the-art and future prospective. Electric Power Systems Research, 2023. 217: p. 109089.
  23. Zeng, B., et al., Optimal demand response resource exploitation for efficient accommodation of renewable energy sources in multi-energy systems considering correlated uncertainties. Journal of Cleaner Production, 2021. 288: p. 125666.
  24. Chen, H., L. Gao, and Z. Zhang, Multi-objective optimal scheduling of a microgrid with uncertainties of renewable power generation considering user satisfaction. International Journal of Electrical Power & Energy Systems, 2021. 131: p. 107142.
  25. Jordehi, A.R., Optimisation of demand response in electric power systems, a review. Renewable and sustainable energy reviews, 2019. 103: p. 308-319.
  26. Tian, Y., et al., Evolutionary large-scale multi-objective optimization: A survey. ACM Computing Surveys (CSUR), 2021. 54(8): p. 1-34.
  27. Adam, S.P., et al., No free lunch theorem: A review. Approximation and Optimization: Algorithms, Complexity and Applications, 2019: p. 57-82.
  28. Rodríguez-Molina, A., et al., Multi-objective meta-heuristic optimization in intelligent control: A survey on the controller tuning problem. Applied Soft Computing, 2020. 93: p. 106342.
  29. Wang, L., et al., Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 2022. 114: p. 105082.
  30. de Siqueira, L.M.S. and W. Peng, Control strategy to smooth wind power output using battery energy storage system: A review. Journal of Energy Storage, 2021. 35: p. 102252.
  31. Yang, Y., et al., Battery energy storage system size determination in renewable energy systems: A review. Renewable and Sustainable Energy Reviews, 2018. 91: p. 109-125.
  32. Nayak, C.K. and M.R. Nayak, Technoeconomic analysis of a grid-connected PV and battery energy storagesystem considering time of use pricing. Turkish Journal of Electrical Engineering and Computer Sciences, 2018. 26(1): p. 318-329.
  33. Nayak, C.K., K. Kasturi, and M.R. Nayak, Economical management of microgrid for optimal participation in electricity market. Journal of Energy Storage, 2019. 21: p. 657-664.
  34. Yazdanpanah Jahromi, M.A., S. Farahat, and S.M. Barakati, Optimal size and cost analysis of stand-alone hybrid wind/photovoltaic power-generation systems. Civil Engineering and Environmental Systems, 2014. 31(4): p. 283-303.
  35. Batteries, R.F.D.C., S-550 Spec 01. Springfield: Rolls Battery Co., 2001. 2020.
  36. Lemaire-Potteau, E., et al. Assessment of storage ageing in different types of PV systems technical and economical aspects. in 23rd European Photovoltaic Solar Energy Conference (Valencia, Spain, 2008). 2008.
  37. Delaille, A., Development of New State-of-Charge and State-of-Health Criteria for Batteries Used in Photovoltaic Systems University Pierre et Marie Curie. Ph. D Report (French), 2006.
  38. Guo, Y., et al., Failure modes of valve-regulated lead-acid batteries for electric bicycle applications in deep discharge. Journal of power sources, 2009. 191(1): p. 127-133.
  39. Bhoi, S.K. and M.R. Nayak, Optimal scheduling of battery storage with grid tied PV systems for trade-off between consumer energy cost and storage health. Microprocessors and Microsystems, 2020. 79: p. 103274.