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

Author:

Kiptoo Mark Kipngetich1ORCID,Adewuyi Oludamilare Bode2ORCID,Howlader Harun Or Rashid3ORCID,Nakadomari Akito1ORCID,Senjyu Tomonobu1ORCID

Affiliation:

1. Graduate School of Science and Engineering, University of the Ryukyus, 1 Senbaru, Okinawa 903-0213, Japan

2. Faculty of Engineering, Information and Systems, University of Tsukuba, 1 Chome-1-1 Tennodai, Ibaraki 305-8577, Japan

3. Hawai’i Natural Energy Institute, University of Hawai’i at Manoa, Honolulu, HI 96822, USA

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.

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

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