Multi-Input Nonlinear Programming Based Deterministic Optimization Framework for Evaluating Microgrids with Optimal Renewable-Storage Energy Mix

Yousef Alhumaid, Khalid Khan, Fahad Alismail and Muhammad Khalid
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Yousef Alhumaid: Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Khalid Khan: Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Fahad Alismail: Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Muhammad Khalid: Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

Sustainability, 2021, vol. 13, issue 11, 1-15

Abstract: Integration of renewable energy sources (RES) in a distribution network facilities the establishment of sustainable power systems. Concurrently, the incorporation of energy storage system (ESS) plays a pivotal role to maintain the economical significance as well as mitigates the technical liabilities associated with uncontrollable and fluctuating renewable output power. Nevertheless, ESS technologies require additional investments that imposes a techno-economic challenge of selection, allocation and sizing to ensure a reliable power system that is operationally optimized with reduced cost. In this paper, a deterministic cost-optimization framework is presented based on a multi-input nonlinear programming to optimally solve the sizing and allocation problem. The optimization is performed to obviate the demand-generation mismatch, that is violated with the introduction of variable renewable energy sources. The proposed optimization method is tested and validated on an IEEE 24-bus network integrated with solar and wind energy sources. The deterministic approach is solved using GAMS optimization software considering the system data of one year. Based on the optimization framework, the study also presents various different scenarios of renewable energy mix in combination with advanced ESS technologies to outline an technical as well as economical framework for ESS sizing, allocation, and selection. Based on the optimal results obtained, the optimal sizing and allocation were obtained for lead-acid, lithium-ion, nickel-cadmium and sodium-sulfur (NaS) battery energy storage system. While all these storage technologies mitigated the demand-generation mismatch with optimal size and location. However, the NaS storage technology was found to be the best among the given storage technologies for the given system minimum possible cost. Furthermore, it was observed that the cost of hybrid wind-solar mix system results in the lowest overall cost.

Keywords: battery energy storage system; nonlinear programming; renewable energy mix; optimal power flow; optimization; storage technology mix (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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