Distributed Optimal Dispatch Method for Smart Community Demand Response Based on Machine Learning

Liu, Xingyan; Nan, Ning; Liu, Donghang; Chen, Keyang; Yuan, Jie

doi:10.1109/ceepe51765.2021.9475821

Cited by 2 publications

(4 citation statements)

References 2 publications

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“…Machine learning has recently been a prominent issue in academia. A demand response firm may better understand how customers behave by using machine learning to get insight into consumer behavior and generate better projections [67]. With the recent advances in machine learning, it is now possible to create models that are both well-suited to nonlinear data and highly accurate.…”

Section: A Machine Learningmentioning

confidence: 99%

“…Data in supervised machine learning are labeled, including comprehensive input features and outputs that correlate to those features [67]. For example, k-Nearest Neighbors, Nave Bayes, Decision Trees, Support Vector Machines, Logistic Regression, Multiplayer Perceptron, and Random Forest are examples of supervised machine learning algorithms that are extensively employed in the field.…”

Section: ) Supervised Machine Learningmentioning

confidence: 99%

“…Unsupervised machine learning tries to discover new patterns that may be utilized to enhance the decision-making process by evaluating data structure [67]. Unsupervised machine learning methods such as clustering and rule-based learning are often employed [68].…”

Section: ) Unsupervised Machine Learningmentioning

confidence: 99%

See 2 more Smart Citations

Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

et al. 2023

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In recent years, there has been a significant growth in demand response (DR) as a cost-effective technique of providing flexibility and, as a result, improving the dependability of energy systems. Although the tasks associated with demand side management (DSM) are extremely complex, the use of large-scale data and the frequent requirement for near-real-time decisions mean that Artificial Intelligence (AI) has recently emerged as a key technology for enabling DSM. Optimization algorithm methods can be used to address a variety of problems, including selecting the optimal set of consumers to respond to, learning their attributes and preferences, dynamic pricing, device scheduling, and control, as well as determining the most effective way to incentive and reward participants in DR schemes fairly and effectively. The implementation optimization algorithm needs proper selection to mitigate the cost of energy consumption. Due to that reason, this paper outlines various challenges and opportunities in developing, utilizing, controlling, and scheduling the DR scheme's optimization algorithm. In addition, several issues in applications and advantages of optimization techniques in artificial intelligence approaches are discussed. The importance of implementing demand response mechanisms in developing countries is also presented. In addition, the status of demand response optimization in demandside management solutions is also illustrated congruently.

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Section: A Machine Learningmentioning

confidence: 99%

Section: ) Supervised Machine Learningmentioning

confidence: 99%

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Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

et al. 2023

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“…They can also operate independently of the electric grid in autonomous/islanded mode. The remote islanded MGs operate in the event of a power grid breakdown to ensure the power supply of important loads (Mhankale & Thorat, 2018;Liu et al, 2021;Dashtdar et al 2022;. Economy, reliability, and energy-saving are three characteristics that must be considered in the operation of islanded-operated microgrid systems (Saeed et al, 2021b;Recalde et al, 2020;Xu et al, 2020;Pothireddy et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Demand response based microgrid's economic dispatch

Saeed,

Rana,

Kausaraahmed

et al. 2023

Int. J. Renew. Energy Dev.

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The development of energy management tools for next-generation Distributed Energy Resources (DER) based power plants, such as photovoltaic, energy storage units, and wind, helps power systems be more flexible. Microgrids are entities that coordinate DERs in a persistently more decentralized fashion, hence decreasing the operational burden on the main grid and permitting them to give their full benefits. A new power framework has emerged due to the integration of DERs-based microgrids into the conventional power system. With the rapid advancement of microgrid technology, more emphasis has been placed on maintaining the microgrids' long-term economic feasibility while ensuring security and stability. The objective of this research is to provide a multi-objective economic operation technique for microgrids containing air-conditioning clusters (ACC) taking demand response into account. A dynamic price mechanism is proposed, accurately reflecting the system's actual operational status. For economic dispatch, flexible loads and air conditioners are considered demand response resources. Then, a consumer-profit model and an AC operating cost model are developed, with a set of pragmatic constraints of consumer comfort. The generation model is then designed to reduce the generation cost. Finally, a microgrid simulation platform is developed in MATLAB/Simulink, and a case is designed to evaluate the proposed method's performance. The findings show that consumer profit increases by 69.2% while ACC operational costs decrease by 18.2%. Moreover, generation costs are reduced without sacrificing customer satisfaction.

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Distributed Optimal Dispatch Method for Smart Community Demand Response Based on Machine Learning

Cited by 2 publications

References 2 publications

Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

Demand response based microgrid's economic dispatch

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