Optimal Capacity and Operational Planning for Renewable Energy-Based Microgrid Considering Different Demand-Side Management Strategies

Mark Kipngetich Kiptoo (), Oludamilare Bode Adewuyi (), Harun Or Rashid Howlader (), Akito Nakadomari and Tomonobu Senjyu
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Mark Kipngetich Kiptoo: Graduate School of Science and Engineering, University of the Ryukyus, 1 Senbaru, Okinawa 903-0213, Japan
Oludamilare Bode Adewuyi: Faculty of Engineering, Information and Systems, University of Tsukuba, 1 Chome-1-1 Tennodai, Ibaraki 305-8577, Japan
Harun Or Rashid Howlader: Hawai’i Natural Energy Institute, University of Hawai’i at Manoa, Honolulu, HI 96822, USA
Akito Nakadomari: Graduate School of Science and Engineering, University of the Ryukyus, 1 Senbaru, Okinawa 903-0213, Japan
Tomonobu Senjyu: Graduate School of Science and Engineering, University of the Ryukyus, 1 Senbaru, Okinawa 903-0213, Japan

Energies, 2023, vol. 16, issue 10, 1-25

Abstract: A bi-objective joint optimization planning approach that combines component sizing and short-term operational planning into a single model with demand response strategies to realize a techno-economically feasible renewable energy-based microgrid is discussed in this paper. The system model includes a photovoltaic system, wind turbine, and battery. An enhanced demand response program with dynamic pricing devised based on instantaneous imbalances between surplus, deficit, and the battery’s power capacity is developed. A quantitative metric for assessing energy storage performance is also proposed and utilized. Emergency, critical peak pricing, and power capacity-based dynamic pricing (PCDP) demand response programs (DRPs) are comparatively analyzed to determine the most cost-effective planning approach. Four simulation scenarios to determine the most techno-economic planning approach are formulated and solved using a mixed-integer linear programming algorithm optimization solver with the epsilon constraint method in Matlab. The objective function is to minimize the total annualized costs (TACs) while satisfying the reliability criterion regarding the loss of power supply probability and energy storage dependency. The results show that including the DRP resulted in a significant reduction in TACs and system component capacities. The cost-benefit of incorporating PCDP DRP strategies in the planning model increases the overall system flexibility.

Keywords: variable renewable energy resources (VREs); demand response program (DRP); power capacity-based dynamic pricing (PCDP) DRP; loss of power supply probability (LPSP); energy storage dependency (ESD); critical peak pricing (CPP) DRP (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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