A demand response modeling for residential consumers in smart grid environment using game theory based energy scheduling algorithm

Reka, S. Sofana; Ramesh, V.

doi:10.1016/j.asej.2015.12.004

Cited by 63 publications

(13 citation statements)

References 25 publications

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“…Also in Reference 91, the authors developed a meta‐heuristic algorithm to minimize the power consumption in a residential complex environment consisting of multiple homes with a variety of smart appliances. In Reference 92, a game‐theoretical approach based on non‐cooperative game is proposed to optimize the power utilization for the residential community while communicating with utility.…”

Section: Optimization Techniques For Energy Tradingmentioning

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

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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.

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Section: Optimization Techniques For Energy Tradingmentioning

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

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“…Each stage is an auction game, and every auction game pursues the Nash equilibrium. Finally, the multi-stage auction games constitute a subgame perfect Nash equilibrium [22,23].…”

Section: Figure 1 Dtn Routing Processmentioning

confidence: 99%

Routing Algorithm for Delay-Tolerant Network Based on Price Game

Lin¹,

Yang²,

Wang³

2020

ISI

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Due to the limited resource of Delay-Tolerant Network (DTN) nodes, in order to prolong their life cycles, the nodes would exhibit a "selfish" behavior, which is quite common in DTN, and will seriously affect the overall performance of the network. Targeting at these issues, to suppress such behavior and improve the overall performance of DTN, this paper proposed a DTN routing algorithm based on the auction price game, which converted the routing process into a data forwarding service auction process, and took the "prices" and the history data of the nodes as the main basis for the selection of data nodes. The experimental results showed that, compared with Epidemic algorithm and FC algorithm, the proposed algorithm showed good performance in time delay and cost ratio, which had well suppressed the "selfish" behavior of the nodes, and the proposed algorithm is a DTN routing algorithm with balanced performance.

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“…where E 1 , E 2 , and E 3 are calculated using Equation (24). The gradation of variable g is conducted in the last step; exploration and exploitation trade-off is controlled by considering value between 0 to 2 in each epoch as depicted in Equation (32).…”

Section: − − →mentioning

confidence: 99%

A Modular Framework for Optimal Load Scheduling under Price-Based Demand Response Scheme in Smart Grid

et al. 2019

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With the emergence of the smart grid (SG), real-time interaction is favorable for both residents and power companies in optimal load scheduling to alleviate electricity cost and peaks in demand. In this paper, a modular framework is introduced for efficient load scheduling. The proposed framework is comprised of four modules: power company module, forecaster module, home energy management controller (HEMC) module, and resident module. The forecaster module receives a demand response (DR), information (real-time pricing scheme (RTPS) and critical peak pricing scheme (CPPS)), and load from the power company module to forecast pricing signals and load. The HEMC module is based on our proposed hybrid gray wolf-modified enhanced differential evolutionary (HGWmEDE) algorithm using the output of the forecaster module to schedule the household load. Each appliance of the resident module receives the schedule from the HEMC module. In a smart home, all the appliances operate according to the schedule to reduce electricity cost and peaks in demand with the affordable waiting time. The simulation results validated that the proposed framework handled the uncertainties in load and supply and provided optimal load scheduling, which facilitates both residents and power companies.

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A demand response modeling for residential consumers in smart grid environment using game theory based energy scheduling algorithm

Cited by 63 publications

References 25 publications

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

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

Routing Algorithm for Delay-Tolerant Network Based on Price Game

A Modular Framework for Optimal Load Scheduling under Price-Based Demand Response Scheme in Smart Grid

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