Optimal Incentive Strategy in Cloud-Edge Integrated Demand Response Framework for Residential Air Conditioning Loads

Jia, Qiangang; Chen, Sijie; Yan, Zheng; Li, Yiyan

doi:10.1109/tcc.2021.3118597

Cited by 21 publications

(4 citation statements)

References 30 publications

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“…The references have been categorized in terms of the application class, objective function, and building type that were described in the immediately preceding section. Cost & Comfort [103], [104] Other Academic [105] Comfort Mixed/NA [106] Other [107], [98] P2P Trading Cost [108], [109] Residential [110] EV, ES, and RG [111], [112] Mixed/NA [113] Other Residential [114], [115] Other/Mixed Cost & Comfort Commercial [116] Academic [117], [96], [118], [119], [120], [121] Residential [122] Other [123], [124] Cost [125] Mixed/NA [126] Cost & Comfort [127], [128] Cost & Load Balance [129] Other [130] P2P Trading Cost Distributed RL [131], [132] Other [138] Cost & Comfort Commercial Model Based RL [139] HVAC, Fans, WH Cost Residential Other (CARLA) [140] Cost & Comfort Commercial Other (Context. RL)…”

Section: Reinforcement Learning Algorithms In Home Energy Management ...mentioning

confidence: 99%

Reinforcement Learning: Theory and Applications in HEMS

Alani¹,

Das²

2022

Preprint

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The twin capabilities of learning from experience and learning at higher levels of abstraction, set reinforcement learning apart from other areas of machine learning and (within the broader context) all of artificial intelligence. It allows algorithmic agents to replace human beings in the real world, including in homes and buildings, in application domains that had hitherto been considered to be beyond today’s capabilities. This goal, specifically aimed at home energy automation that forms the backdrop of this article, which surveys the use of deep reinforcement learning in various HEMS applications. The article provides an overview of generic reinforcement learning. This is followed with discussions on the state-of-the-art methods for value based, policy gradient, and actor-critic methods in deep reinforcement learning. In order to make published literature in reinforcement learning more accessible to HEMS researchers, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using the same terminology as the machine learning community. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. The survey suggests that this research is still in its infancy.

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Section: Reinforcement Learning Algorithms In Home Energy Management ...mentioning

confidence: 99%

Reinforcement Learning: Theory and Applications in HEMS

Alani¹,

Das²

2022

Preprint

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“…Residents' AC DR can be divided into electricity charge sensitive DR (CSDR) and incentive-based DR (IBDR) [18][19][20]. Compared with the uncertainty of CSDR's load response, IBDR's load response uncertainty is small, and the supply and demand balance of the power grid dispatching and regulating is rapid.…”

Section: Price-based Residential Load Demand Responsementioning

confidence: 99%

Evaluation of Photovoltaic Consumption Potential of Residential Temperature-Control Load Based on ANP-Fuzzy and Research on Optimal Incentive Strategy

Lu¹,

Yan

et al. 2022

Energies

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Temperature-control loads, such as residential air conditioners (ACs) and electric water heaters (EWHs), have become important demand response resources in the power system. However, due to the impact of various factors on users’ response behavior, it has been difficult for power grid operators to accurately evaluate the response potential under complex factor relationships to derive optimal incentive strategy. Therefore, it cannot achieve a win-win economic benefit between the grid and users. In this paper, a method combining Analytic Network Process (ANP) and Fuzzy logical inference is proposed to predict the user’s willingness firstly by taking residential AC load as an example. The weight of each factor affecting users’ willingness is analyzed, and main factors are selected as inputs of fuzzy logic inference to derive the willingness of the resident to actively regulate the AC. Then, this method is applied in evaluating the response potential of certain residential area in Beijing according to the survey. By further considering users’ house size and the sacrificed comfort temperature under the incentive strategy, the power potential curve of the AC load under different incentives is obtained by using the first-order equivalent thermal parameter (ETP) model and the regulation willingness. Finally, with the objective of maximizing the consumption of the photovoltaic (PV) power, the optimal operation is achieved through the coordinated regulation of residential ACs and EWHs based on the potential curve, and the corresponding optimal incentive strategy for the flexible temperature-control loads is obtained. Simulation results show that the optimal incentive strategy proposed not only increases the PV consumption ratio to 98.35% with an increase of 24.71%, but also maximizes the economic benefits of both sides of the power grid and users. This method of deriving incentive strategy can be used as a reference for grid companies to formulate the incentive strategy to realize optimal operation, such as the maximum new energy consumption.

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“…With the widespread integration of renewable energy sources and the increasing marketization of the smart grid, the number and variety of smart grid service (SGS) at the edge of the smart grid, such as the smart distribution transformer area-typically functioning as the smallest unit of power supply management (Cen et al, 2022;Xiao, H. et al, 2023)-are expanding. These services encompass a diverse range, including Vehicle-to-Grid (V2G) (Chukwu and Mahajan, 2014;Chamola et al, 2020;Peng and Niu, 2023), micro-grid energy management and control (Mondal et al, 2022;Wu et al, 2022;Xiao et al, 2023), and demand response at the user side (Bachoumis et al, 2022;Jia et al, 2022). New demands are subsequently placed on edge computing apparatus (ECA), a key computing node at the edge side of the smart grid supporting the processing of the SGS (Li et al, 2021;.…”

Section: Introductionmentioning

confidence: 99%

Service scheduling strategy for microservice and heterogeneous multi-cores-based edge computing apparatus in smart girds with high renewable energy penetration

Hu,

Qu,

Cai

et al. 2024

Front. Energy Res.

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The microservice-based smart grid service (SGS) organization and the heterogeneous multi-cores-based computing resource supply are the development direction of edge computing in smart grid with high penetration of renewable energy sources and high market-oriented. However, their application also challenges the service schedule for edge computing apparatus (ECA), the physical carrier of edge computing. In the traditional scheduling strategy of SGS, an SGS usually corresponds to an independent application or component, and the heterogeneous multi-core computing environment is also not considered, making it difficult to cope with the above challenges. In this paper, we propose an SGS scheduling strategy for the ECA. Specifically, we first present an SGS scheduling framework of ECA and give the essential element of meeting SGS scheduling. Then, considering the deadline and importance attributes of the SGS, a microservice scheduling prioritizing module is proposed. On this basis, the inset-based method is used to allocate the microservice task to the heterogeneous multi-cores to utilize computing resources and reduce the service response time efficiently. Furthermore, we design the scheduling unit dividing module to balance the delay requirement between the service with early arrival time and the service with high importance in high concurrency scenarios. An emergency mechanism (EM) is also presented for the timely completion of urgent SGSs. Finally, the effectiveness of the proposed service scheduling strategy is verified in a typical SGS scenario in the smart distribution transformer area.

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Optimal Incentive Strategy in Cloud-Edge Integrated Demand Response Framework for Residential Air Conditioning Loads

Cited by 21 publications

References 30 publications

Reinforcement Learning: Theory and Applications in HEMS

Reinforcement Learning: Theory and Applications in HEMS

Evaluation of Photovoltaic Consumption Potential of Residential Temperature-Control Load Based on ANP-Fuzzy and Research on Optimal Incentive Strategy

Service scheduling strategy for microservice and heterogeneous multi-cores-based edge computing apparatus in smart girds with high renewable energy penetration

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