Integration of demand response and renewable resources for power generation management

Ghosh, Soumyadip; Kalagnanam, Jayant; Katz, Dmitriy; Squillante, Mark S.; Zhang, Xiaoxuan

doi:10.1109/isgt.2011.6165483

Cited by 14 publications

(5 citation statements)

References 14 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In MGs, load control is optimized by achieving the concept of mutual operation of available DGs [268,269]. The integration of optimization techniques with DR can result in system stability and maximizing economic benefits with the help of mathematical mechanisms [270,271].…”

Section: Intelligent Algorithmsmentioning

confidence: 99%

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

et al. 2022

View full text Add to dashboard Cite

In view of scarcity of traditional energy resources and environmental issues, renewable energy resources (RERs) are introduced to fulfill the electricity requirement of growing world. Moreover, the effective utilization of RERs to fulfill the varying electricity demands of customers can be achieved via demand response (DR). Furthermore, control techniques, decision variables and offered motivations are the ways to introduce DR into distribution network (DN). This categorization needs to be optimized to balance the supply and demand in DN. Therefore, intelligent algorithms are employed to achieve optimized DR. However, these algorithms are computationally restrained to handle the parametric load of uncertainty involved with RERs and power system. Henceforth, this paper focuses on the limitations of intelligent algorithms for DR. Furthermore, a comparative study of different intelligent algorithms for DR is discussed. Based on conclusions, quantum algorithms are recommended to optimize the computational burden for DR in future smartgrid.

show abstract

Section: Intelligent Algorithmsmentioning

confidence: 99%

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Its application in DR programs can maximize the economic benefits for participants and minimize the risks of instability in the system through mathematical mechanisms [96,127]. Table 7 assembles the research on DR and its optimization.…”

Section: Optimizationmentioning

confidence: 99%

A Review and Analysis of Trends Related to Demand Response

et al. 2018

View full text Add to dashboard Cite

This paper provides a review and analysis of trends related to demand response (DR). The authors have considered six different topics for the analysis of DR trends: Users, Network Services, Markets, Complementary Programs and Distributed Energy Resources (DER). A brief summary of the consulted articles is included and the behavior of the different DR trend-related topics is shown up to the year 2017 and their projections for 2020. As a result, the characterization of the main DR topics is obtained as well as its current and future trends. Based on the results of the study, it is concluded that the topic of complementary programs is a trendsetter for current trends and it is expected that there is a future change of focus towards the users and new services.

show abstract

“…The stochastic ED can be solved by imposing a set of risk constraints, in the form of chance constraints in Fu and McCalley (2001) or mean-excess constraints in Ghosh et al (2011), to balance risk of shortfalls due to uncertain generation against cost of provisioning corrective generation sources such as peakers. The stochastic ED can be solved by imposing a set of risk constraints, in the form of chance constraints in Fu and McCalley (2001) or mean-excess constraints in Ghosh et al (2011), to balance risk of shortfalls due to uncertain generation against cost of provisioning corrective generation sources such as peakers.…”

Section: Economic Dispatch With Renewable Resourcesmentioning

confidence: 99%