A Stackelberg game theoretical approach for demand response in smart grid

Sivanantham, Geetha; Gopalakrishnan, Srivatsun

doi:10.1007/s00779-019-01262-9

Cited by 11 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence we follow the guidelines presented by Indian service provider [35] for selecting appliances and their needs for the purpose of simulation. The appliances listed in Table I are identical to our previous research [36]. We assumed that the customer has minimum of 10 appliances.…”

Section: Resultsmentioning

confidence: 99%

Stochastic Gradient Descent Optimization Model for Demand Response in a Connected Microgrid

2022

KSII TIIS

View full text Add to dashboard Cite

Smart power grid is a user friendly system that transforms the traditional electric grid to the one that operates in a co-operative and reliable manner. Demand Response (DR) is one of the important components of the smart grid. The DR programs enable the end user participation by which they can communicate with the electricity service provider and shape their daily energy consumption patterns and reduce their consumption costs. The increasing demands of electricity owing to growing population stresses the need for optimal usage of electricity and also to look out alternative and cheap renewable sources of electricity. The solar and wind energy are the promising sources of alternative energy at present because of renewable nature and low cost implementation. The proposed work models a smart home with renewable energy units. The random nature of the renewable sources like wind and solar energy brings an uncertainty to the model developed. A stochastic dual descent optimization method is used to bring optimality to the developed model. The proposed work is validated using the simulation results. From the results it is concluded that proposed work brings a balanced usage of the grid power and the renewable energy units. The work also optimizes the daily consumption pattern thereby reducing the consumption cost for the end users of electricity.

show abstract

Section: Resultsmentioning

confidence: 99%

Stochastic Gradient Descent Optimization Model for Demand Response in a Connected Microgrid

2022

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…It means that all actors have chosen their optimal strategies depending on others 38 . In Reference 39, the authors developed a Stackelberg game, based on real‐time pricing, between the energy providers and the consumers to minimize the peak to average ratio and the monthly electricity bills. It is worth noting that GT can be combined with other optimization techniques, which allows researchers to solve different problems 40 …”

Section: Game Theory In Sgmentioning

confidence: 99%

A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids

Alsalloum

Merghem-Boulahia

Rahim

2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary The smart grid (SG) is an enhancement of the traditional electrical grid. Its main objectives are diverse, focusing mainly on minimizing the energy cost, as well as energy consumption. The smooth integration of renewable energy balances energy production and consumption. Dynamic pricing (DP) becomes one of the important integrated solutions, which comes in line with the global efficiency and reliability of smart grids. DP incentivizes the consumer to participate in the energy scheduling decision, mainly in the real‐time decision. Consumer represents the key challenge in the SG in perspective to make the energy system more efficient. Moreover, the success of smart grids relies mostly on the evolution of the widely used optimization methods. These include measures to encourage consumers and providers to be involved in the development of the SG. The main objective of this article is to review and analyze the recent research works regarding the pricing strategies and optimization methods utilized in the context of SG. A systematic analysis is thus performed by considering twofold views. First, it comprehensively approaches the pricing majors and visualizes the game theory used in real‐time pricing. Second, it describes the optimization methods used in the smart grid context and compares them in terms of their complexity.

show abstract

“…Sivanantham and Gopalakrishnan [6] explore energy consumption patterns in smart grids, with the aim to reduce the peak load and alleviate the deviation between the demanded and supplied energy. In this context, the authors propose an optimization-based energy consumption scheme for customers in a smart grid, based on a Stackelberg game.…”

Section: Accepted Articlesmentioning

confidence: 99%

“…In Ibrahim et al [4], a case study is presented on how to determine the optimal location for indoor mobile networking base stations. In Sivanantham and Gopalakrishnan [6], the authors aim to identify peak energy usage in a smart grid to optimize energy demand and supply. An approach to identify road conditions based on the analysis of mobile phone accelerometer data recordings is presented in Varona et al [7].…”

Section: Steps Aheadmentioning

confidence: 99%