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Cost-effective reliability level in 100% renewables-based standalone microgrids considering investment and expected energy not served costs

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  • Sakthivelnathan, Nallainathan
  • Arefi, Ali
  • Lund, Christopher
  • Mehrizi-Sani, Ali
  • Muyeen, S.M.

Abstract

Loss of load probability (LOLP) and expected energy not served (EENS) are commonly used in electrical power systems to evaluate reliability. LOLP defined as the probability that available generation capacity will be inadequate to supply customer demand. EENS defined as the expected amount of energy not being served to consumers by the system during the period considered due to system capacity shortages or unexpected power outages. Loss of Load Frequency (LOLF) is referred to a number of loss of load (LOL) event happened in the operation life span of the SMG. Loss of Load Reduction (LOLR) is defined as the required reduction in LOLF to obtain a specific reliability level. While power systems are designed to minimize LOLP and EENS, this is constrained by the total cost: investment cost, operation and maintenance cost, and cost of customer interruption (CCI). This research considers Standalone Microgrid (SMG), also known as Autonomous Microgrid which only operates in off-grid mode and cannot be connected to wider electrical power system. When designing a 100 % renewable energy integrated SMGs, it is crucial to determine the cost-effective reliability level (CERL). The CERL occurs when the total cost is minimum. This research proposes an approach to calculate the CERL for a fully renewable SMG. An analytical formulation is proposed to represent the LOLR needed to obtain a specific reliability level as a function of the required size of reliability improvement alternatives. The CCI is evaluated using LOLF and EENS indices. Finally, the total cost of the SMG system is evaluated for each reliability level. Consequently, the total cost of the SMG system is expressed as a function of reliability levels, and the minimum value of total cost and the corresponding reliability level are evaluated. In this research, a Monte Carlo Simulation (MCS) approach is used to find hourly LOLF, considering 25 years (219,000 h) of SMG lifespan, regression analysis is used for an analytical formulation, and mixed integer linear programming (MILP) is used for the investment decision making based on a cost minimisation approach. The result demonstrates that the CERL of the SMG system evaluated in the case study is 98.71 %.

Suggested Citation

  • Sakthivelnathan, Nallainathan & Arefi, Ali & Lund, Christopher & Mehrizi-Sani, Ali & Muyeen, S.M., 2024. "Cost-effective reliability level in 100% renewables-based standalone microgrids considering investment and expected energy not served costs," Energy, Elsevier, vol. 311(C).
    • Handle: RePEc:eee:energy:v:311:y:2024:i:c:s036054422403202x
    DOI: 10.1016/j.energy.2024.133426
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    References listed on IDEAS

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    1. Sandelic, Monika & Peyghami, Saeed & Sangwongwanich, Ariya & Blaabjerg, Frede, 2022. "Reliability aspects in microgrid design and planning: Status and power electronics-induced challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    2. Mohan Munasinghe & Mark Gellerson, 1979. "Economic Criteria for Optimizing Power System Reliability Levels," Bell Journal of Economics, The RAND Corporation, vol. 10(1), pages 353-365, Spring.
    3. Adefarati, T. & Bansal, R.C., 2017. "Reliability assessment of distribution system with the integration of renewable distributed generation," Applied Energy, Elsevier, vol. 185(P1), pages 158-171.
    4. Luo, Jianing & Li, Hangxin & Wang, Shengwei, 2022. "A quantitative reliability assessment and risk quantification method for microgrids considering supply and demand uncertainties," Applied Energy, Elsevier, vol. 328(C).
    5. Ai, B. & Yang, H. & Shen, H. & Liao, X., 2003. "Computer-aided design of PV/wind hybrid system," Renewable Energy, Elsevier, vol. 28(10), pages 1491-1512.
    6. Adefarati, T. & Bansal, R.C., 2019. "Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources," Applied Energy, Elsevier, vol. 236(C), pages 1089-1114.
    7. Nelson, D.B. & Nehrir, M.H. & Wang, C., 2006. "Unit sizing and cost analysis of stand-alone hybrid wind/PV/fuel cell power generation systems," Renewable Energy, Elsevier, vol. 31(10), pages 1641-1656.
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