Data-oriented distributed demand response optimization with global inequality constraints based on multi-agent system

Hao, Ran; Lu, Tianguang; Ai, Qian; He, Hongyin

doi:10.1016/j.ijepes.2021.107025

Cited by 7 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the most promising areas of research right now is multi-agent systems (MAS), a sub-area of distributed artificial intelligence that provides the capability to examine these challenges. The three sub fields of MAS that were looked at in this review are automatic negotiations, cooperative/coalitional game theory, and mechanism design [87]. These three sub fields are:…”

Section: Reinforcement Learningmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Section: Reinforcement Learningmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

show abstract

“…(3) Scenario III: Under Scenario II, the performances of the proposed Adam-based method in this study, the D-PPDS method mentioned in Ref. [31], and the ADMM algorithm used in Ref. [32] were compared to prove the good convergence and high efficiency of the proposed method.…”

Section: Example Analysismentioning

confidence: 99%

“…The performance of the proposed method was compared with that of the D-PPDS [31] and ADMM algorithms [32] under the same simulation model in the following example to demonstrate the efficiency and feasibility of the Adam-based method proposed in this study.…”

Section: Scenario IIImentioning

confidence: 99%

A Cost Optimization Method Based on Adam Algorithm for Integrated Demand Response

Cheng,

2023

Electronics

Self Cite

View full text Add to dashboard Cite

Demand response has gradually evolved into integrated demand response (IDR) as energy integration technology develops in integrated energy systems (IESs). The IES has a large amount of data interaction and an increasing concern for users’ privacy protection. Based on the combined cooling, heating, and power model, our study established an IDR management model considering demand-side energy coupling, focusing on cost optimization. In terms of privacy protection in the IDR management process, an optimization method based on the Adam algorithm was proposed. Only nonsensitive data, such as gradients, were transmitted during the processing of the Adam-based method by relying on a centralized iterative computing architecture similar to federated learning. Thus, privacy protection was achieved. The final simulation results proved that the proposed IDR management model had a cost reduction of more than 9% compared with a traditional power demand response. Further simulations based on this model showed that the efficiency and accuracy of the proposed Adam-based method are better than those of other distributed computing algorithms.

show abstract

“…A reinforcement learning-based decision system is provided for the selection of electricity pricing plans, which minimize the electricity payment [3]. A method from the demand response (DR) optimization proposed in distribution markets consisting of a retailer and multiple demand response aggregators [4]. The importance of renewable energy is presented in [5].…”

Section: Introductionmentioning

confidence: 99%

An Overview on Power Quality Issues and Control Strategies for Distribution Networks With the Presence of Distributed Generation Resources

Razmi

Papari

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

Power systems based on centralized production are facing two limitations: the lack of fossil fuels and the need to reduce pollution; Therefore, the importance of distributed generation resources (DGs) has increased by connecting renewable energy systems to the network. With the increasing penetration of renewable energies in the network, power quality challenges in low voltage and medium voltage distribution systems have become one of the main fields of studies. Since most of the renewable energy systems are connected to the network with the help of electronic power converters devices and the main purpose of these converters is to connect DGs to the network in compliance with power quality standards; Therefore, if the switching frequency of inverters is not implemented properly, major power quality problems will be created. On the other hand, the reduction in power quality causes acute problems in power networks, which include lack of proper performance, reduction in useful life and efficiency of electrical and electronic devices in the network, creation of series and parallel resonance in some harmonics due to the presence of inductors and the capacitor, and as a result, more distortion in the voltage of the distribution network. This paper actually provides a road-map for researchers to investigate power quality issues. In other words, the mentioned cases show the importance of research and providing the solutions to improving power quality in distribution networks.

show abstract

Data-oriented distributed demand response optimization with global inequality constraints based on multi-agent system

Cited by 7 publications

References 33 publications

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

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

A Cost Optimization Method Based on Adam Algorithm for Integrated Demand Response

An Overview on Power Quality Issues and Control Strategies for Distribution Networks With the Presence of Distributed Generation Resources

Contact Info

Product

Resources

About