Game-theoretic approach to demand-side energy management for a smart neighbourhood in Sydney incorporating renewable resources

Fernandez, Edstan; Hossain, M. J.; Nizami, Mohammad Sohrab Hasan

doi:10.1016/j.apenergy.2018.09.171

Cited by 68 publications

(27 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The multiagent-based approach was used to monitor and control the distributed energy resources (DERs), their reverse auction process, and schedule the DER unit commitment within an hour-ahead market environment. Analytical models of local market electricity prices considering DERs and specific rules include intensive methods, shared energy calculation processes, and power-sharing negotiation process using game theory [13]. The auction modeling process considered the internal pricing mechanism, supply demand ratio (SDM), and the role of the local market operator that collects the bidding information and provides the pricing signals to the individual customers.…”

Section: A Literaturementioning

confidence: 99%

“…Figure 8 shows that the ARMA-based forecasting techniques track the nonlinear lines closely, whereas the ANN-based forecasting technique better follows the overall load profile pattern. The performance of the ARMA based forecasting technique is highly dependent on the model order, as shown in (12)(13)(14). So, a proper tuning is required to get the optimal performance of the forecasting.…”

Section: Case Studiesmentioning

confidence: 99%

“…Therefore, it is expected that the proposed peak demand management will help consumers to reduce their electricity costs, and the utilities to maintain reliable operation of their system. The proposed method is compared with the existing literature [27], [13] and [28]. Authors in [27] proposed a multi-agent based system that prevent the rebound peaks.…”

Section: Case Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Multiple Home-to-Home Energy Transactions for Peak Load Shaving

Mahmud

Nizami

Ravishankar

et al. 2020

IEEE Trans. on Ind. Applicat.

View full text Add to dashboard Cite

This paper proposes a new technique to manage the domestic peak load demand through peer-to-peer (P-P) energy transaction among multiple homes. In this process, the houses willing to sell energy are identified as the Parent, and the houses that require energy are identified as a Child. The parents having energy resources such as photovoltaics (PV), battery storage and electric vehicles (EVs) will utilize their resources to meet their peak power demand and sell the extra energy to a child. A mixed integer linear programming optimization is used to find the parent-child matching based on their energy availability, power demand and distances. After selecting the parent-child match, the power demand of a child is forecasted using two different techniques, i.e. autoregressive moving average and artificial neural networks, to identify to child's need in a day ahead of the actual operation. The proposed algorithm calculates the available energy of a parent to sell in real-time and the required energy of a child in a day-ahead, while ensuring to minimize the peak load demand. The proposed method, as confirmed by the presented analysis using data of a real Australian power distribution network, is able to significantly minimize the peak load demand, which in-turn is expected to minimize the electricity costs. The method also facilitates two agreed prosumers to transact energy between themselves without the involvement of a third party.

show abstract

Section: A Literaturementioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Multiple Home-to-Home Energy Transactions for Peak Load Shaving

Mahmud

Nizami

Ravishankar

et al. 2020

IEEE Trans. on Ind. Applicat.

View full text Add to dashboard Cite

show abstract

“…The ToU pricing decision is revealed months earlier. In this way, consumers can schedule their consumption by minimizing their use on the on‐peak hours 18 . Thus, they should react intelligently to ToU to get the maximum benefit from this DP scheme.…”

Section: Dynamic Pricingmentioning

confidence: 99%

“…In the prefect information scheme, all actors know the strategies that each actor will adopt in the game, which leads to a lack of privacy. Usually, the authors do not use this type of game, especially when privacy concerns become an essential part of the nowadays works 18 . For the imperfect information scheme, the actors do not have a vision about the strategies of others, even if they know all the sets of strategies that are present in the game.…”

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