Optimal Multi-Area Demand–Thermal Coordination Dispatch

With the soaring demand for electric power and the limited spinning reserve in the power system in Taiwan, the comprehensive management of both thermal power generation and load demand turns out to be a key to achieving the robustness and sustainability of the power system. This paper aims to design a demand bidding (DB) mechanism to collaborate between customers and suppliers on demand response (DR) to prevent the risks of energy shortage and realize energy conservation. The concurrent integration of the energy, transmission, and reserve capacity markets necessitates a new formulation for determining schedules and marginal prices, which is expected to enhance economic efficiency and reduce transaction costs. To dispatch energy and reserve markets concurrently, a hybrid approach of combining dynamic queuing dispatch (DQD) with direct search method (DSM) is developed to solve the extended economic dispatch (ED) problem. The effectiveness of the proposed approach is validated through three case studies of varying system scales. The impacts of tie-line congestion and area spinning reserve are fully reflected in the area marginal price, thereby facilitating the determination of optimal load reduction and spinning reserve allocation for demand-side management units. The results demonstrated that the multi-area bidding platform proposed in this paper can be used to address issues of congestion between areas, thus improving the economic efficiency and reliability of the day-ahead market system operation. Consequently, this research can serve as a valuable reference for the design of the demand bidding mechanism.

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Publication Details

Journal
Energies
Published
2025-05-22
DOI
https://doi.org/10.3390/en18112690
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.00

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article

Optimal Multi-Area Demand–Thermal Coordination Dispatch

Yu-Shan Cheng, Chun-Lung Chen, Yiyan Chen, Cheng-Ta Tsai
Energies
Smart Grid Energy Management
article

Optimal Multi-Area Demand–Thermal Coordination Dispatch

Yu-Shan Cheng, Chun-Lung Chen, Yiyan Chen, Cheng-Ta Tsai
article en

Abstract

With the soaring demand for electric power and the limited spinning reserve in the power system in Taiwan, the comprehensive management of both thermal power generation and load demand turns out to be a key to achieving the robustness and sustainability of the power system. This paper aims to design a demand bidding (DB) mechanism to collaborate between customers and suppliers on demand response (DR) to prevent the risks of energy shortage and realize energy conservation. The concurrent integration of the energy, transmission, and reserve capacity markets necessitates a new formulation for determining schedules and marginal prices, which is expected to enhance economic efficiency and reduce transaction costs. To dispatch energy and reserve markets concurrently, a hybrid approach of combining dynamic queuing dispatch (DQD) with direct search method (DSM) is developed to solve the extended economic dispatch (ED) problem. The effectiveness of the proposed approach is validated through three case studies of varying system scales. The impacts of tie-line congestion and area spinning reserve are fully reflected in the area marginal price, thereby facilitating the determination of optimal load reduction and spinning reserve allocation for demand-side management units. The results demonstrated that the multi-area bidding platform proposed in this paper can be used to address issues of congestion between areas, thus improving the economic efficiency and reliability of the day-ahead market system operation. Consequently, this research can serve as a valuable reference for the design of the demand bidding mechanism.

EnergiesVol. 18(11)
National Taiwan Ocean University (TW)
Ministry of Science and Technology, Taiwan
Openalex Percentile: Top 91%
Smart Grid Energy Management
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Optimal Multi-Area Demand–Thermal Coordination Dispatch — Yu-Shan Cheng, Chun-Lung Chen, et al. · Energies (2025) | TGRS Research Map | TGRS