Author
Listed:
- Mohammad Masih Sediqi
(Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan)
- Akito Nakadomari
(Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan)
- Alexey Mikhaylov
(Financial Research Institute of Ministry of Finance of the Russian Federation, 127006 Moscow, Russia)
- Narayanan Krishnan
(Department of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, India)
- Mohammed Elsayed Lotfy
(Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan
Electrical Power & Machines Department, Zagazig University, Zagazig 44519, Egypt)
- Atsushi Yona
(Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan)
- Tomonobu Senjyu
(Faculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan)
Abstract
Like most developing countries, Afghanistan still employs the traditional philosophy of supplying all its load demands whenever they happen. However, to have a reliable and cost-effective system, the new approach proposes to keep the variations of demand at the lowest possible level. The power system infrastructure requires massive capital investment; demand response (DR) is one of the economic options for running the system according to the new scheme. DR has become the intention of many researchers in developed countries. However, very limited works have investigated the employment of appropriate DR programs for developing nations, particularly considering renewable energy sources (RESs). In this paper, as two-stage programming, the effect of the time-of-use demand response (TOU-DR) program on optimal operation of Afghanistan real power system in the presence of RESs and pumped hydropower storage (PHS) system in the day-ahead power market is analyzed. Using the concept of price elasticity, first, an economic model indicating the behaviour of customers involved in TOU-DR program is developed. A genetic algorithm (GA) coded in MATLAB software is used accordingly to schedule energy and reserve so that the total operation cost of the system is minimized. Two simulation cases are considered to verify the effectiveness of the suggested scheme. The first stage programming approach leads case 2 with TOU-DR program to 35 MW (811 MW − 776 MW), $16,235 ($528,825 − $512,590), and 64 MW reductions in the peak load, customer bill and peak to valley distance, respectively compared to case 1 without TOU-DR program. Also, the simulation results for stage 2 show that by employing the TOU-DR program, the system’s total cost can be reduced from $317,880 to $302,750, which indicates a significant reduction in thermal units’ operation cost, import power tariffs and reserve cost.
Suggested Citation
Mohammad Masih Sediqi & Akito Nakadomari & Alexey Mikhaylov & Narayanan Krishnan & Mohammed Elsayed Lotfy & Atsushi Yona & Tomonobu Senjyu, 2022.
"Impact of Time-of-Use Demand Response Program on Optimal Operation of Afghanistan Real Power System,"
Energies, MDPI, vol. 15(1), pages 1-21, January.
Handle:
RePEc:gam:jeners:v:15:y:2022:i:1:p:296-:d:716278
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Citations
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Cited by:
- Tayyab, Qudratullah & Qani, Nazir Ahmad & Elkholy, M.H. & Ahmed, Shoaib & Yona, Atsushi & Senjyu, Tomonobu, 2024.
"Techno-economic configuration of an optimized resident microgrid: A case study for Afghanistan,"
Renewable Energy, Elsevier, vol. 224(C).
- Irshad, Ahmad Shah & Samadi, Wais Khan & Fazli, Agha Mohammad & Noori, Abdul Ghani & Amin, Ahmad Shah & Zakir, Mohammad Naseer & Bakhtyal, Irfan Ahmad & Karimi, Bashir Ahmad & Ludin, Gul Ahmad & Senjy, 2023.
"Resilience and reliable integration of PV-wind and hydropower based 100% hybrid renewable energy system without any energy storage system for inaccessible area electrification,"
Energy, Elsevier, vol. 282(C).
- Zheng, Xidong & Zhou, Sheng & Jin, Tao, 2023.
"A new machine learning-based approach for cross-region coupled wind-storage integrated systems identification considering electricity demand response and data integration: A new provincial perspective,"
Energy, Elsevier, vol. 283(C).
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