Optimal Allocation Method of Residential Air-Conditioners: Trade-Off Solutions Between Economic Costs and Aggregation Reliability

Han, Ruiming; Hu, Qinran; Guo, Zishan; Quan, Xiangjun; Wu, Zheng-Mao; Hu, Renjie

doi:10.1109/oajpe.2022.3151493

Cited by 8 publications

(2 citation statements)

References 31 publications

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“…In addition, due to the widespread use of AC loads worldwide, they account for a significant portion of the total urban electricity consumption [8]. During peak load periods in the summer, AC loads account for approximately 30% to 50% of the total electrical load in urban areas [9,10]. Hence, it is an interesting study to fully utilize the potential of large-scale AC loads to provide peak regulation services in power systems.…”

Section: Introductionmentioning

confidence: 99%

A hierarchical coordinated control strategy of air‐conditioning loads for peak regulation service

Wang,

Lin,

Wang

et al. 2024

IET Generation Trans & Dist

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The increased penetration of renewable energy sources and the intensification of peak‐valley differences present challenges to peak regulation in the power system. Fulfilling the peak regulation needs of the power system solely through generation‐side resources proves to be challenging. Large‐scale fixed frequency air‐conditioning (FFAC) and inverter air‐conditioning (IAC) are high‐quality flexible load resources. This paper proposes a hierarchical coordinated control strategy of air‐conditioning (AC) loads for peak regulation service. In the first layer of the control strategy, the load aggregator collaborates with multiple AC groups to ensure the completion of peak regulation tasks with specific power‐constrained requirements. In the second layer of the control strategy, the control centre of each group optimizes the temperature adjustment values for each AC load, aiming to reduce incentive costs. Finally, the proposed method is validated through simulations, demonstrating its capability to achieve coordinated control of FFAC and IAC loads under various power‐constrained requirements. Furthermore, the simulation demonstrates the effectiveness of the control strategy in reducing user discomfort and the AC's incentive costs.

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Section: Introductionmentioning

confidence: 99%

A hierarchical coordinated control strategy of air‐conditioning loads for peak regulation service

Wang,

Lin,

Wang

et al. 2024

IET Generation Trans & Dist

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show abstract

“…On the one hand, researchers hope to reduce the electricity that consumers obtain from the grid by employing distributed clean energy (Dhople, 2017;Gong et al, 2020;Chen et al, 2021). On the other hand, some research aggregates distributed resources to participate in the planning and dispatching of the power grid (Han et al, 2022;Sang et al, 2022;Zhang et al, 2022), allowing users to achieve more cost-effective and low-carbon electricity consumption goals through demand response.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning based power system optimal carbon emission flow

Qin

et al. 2022

Front. Energy Res.

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Under the strain of global warming and the constant depletion of fossil energy supplies, the power system must pursue a mode of operation and development with minimal carbon emissions. There are methods to reduce carbon emissions on both the production and consumption sides, such as using renewable energy alternatives and aggregating distributed resources. However, the issue of how to reduce carbon emissions during the transmission of electricity is ignored. Consequently, the multi-objective optimal carbon emission flow (OCEF) is proposed, which takes into account not only the economic indices in the conventional optimal power flow (OPF) but also the reduction of unnecessary carbon emissions in the electricity transmission process, i.e., carbon emission flow losses (CEFL). This paper presents a deep reinforcement learning (DRL) based multi-objective OCEF solving method that handles the generator dispatching scheme by utilizing the current power system state parameters as known quantities. The case study on the IEEE-30 system demonstrates that the DRL-based OCEF solver is more effective, efficient, and stable than traditional methods.

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An optimal bidding and scheduling method for load service entities considering demand response uncertainty

Han

Cui

et al. 2022

Applied Energy

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Optimal Allocation Method of Residential Air-Conditioners: Trade-Off Solutions Between Economic Costs and Aggregation Reliability

Abstract: This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

Cited by 8 publications

References 31 publications

A hierarchical coordinated control strategy of air‐conditioning loads for peak regulation service

A hierarchical coordinated control strategy of air‐conditioning loads for peak regulation service

Deep reinforcement learning based power system optimal carbon emission flow

An optimal bidding and scheduling method for load service entities considering demand response uncertainty

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