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Keywords = time-ahead dynamic pricing (TADP) DRP

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23 pages, 1235 KiB  
Article
Multi-Objective Optimal Capacity Planning for 100% Renewable Energy-Based Microgrid Incorporating Cost of Demand-Side Flexibility Management
by Mark Kipngetich Kiptoo, Oludamilare Bode Adewuyi, Mohammed Elsayed Lotfy, Tomonobu Senjyu, Paras Mandal and Mamdouh Abdel-Akher
Appl. Sci. 2019, 9(18), 3855; https://doi.org/10.3390/app9183855 - 13 Sep 2019
Cited by 33 | Viewed by 4893
Abstract
The need for energy and environmental sustainability has spurred investments in renewable energy technologies worldwide. However, the flexibility needs of the power system have increased due to the intermittent nature of the energy sources. This paper investigates the prospects of interlinking short-term flexibility [...] Read more.
The need for energy and environmental sustainability has spurred investments in renewable energy technologies worldwide. However, the flexibility needs of the power system have increased due to the intermittent nature of the energy sources. This paper investigates the prospects of interlinking short-term flexibility value into long-term capacity planning towards achieving a microgrid with a high renewable energy fraction. Demand Response Programs (DRP) based on critical peak and time-ahead dynamic pricing are compared for effective demand-side flexibility management. The system components include PV, wind, and energy storages (ESS), and several optimal component-sizing scenarios are evaluated and compared using two different ESSs without and with the inclusion of DRP. To achieve this, a multi-objective problem which involves the simultaneous minimization of the loss of power supply probability (LPSP) index and total life-cycle costs is solved under each scenario to investigate the most cost-effective microgrid planning approach. The time-ahead resource forecast for DRP was implemented using the scikit-learn package in Python, and the optimization problems are solved using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm in MATLAB®. From the results, the inclusion of forecast-based DRP and PHES resulted in significant investment cost savings due to reduced system component sizing. Full article
(This article belongs to the Special Issue Standalone Renewable Energy Systems—Modeling and Controlling)
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