The Impact of Social Nudge on System Cost &amp; Revenue Optimization in Local Electricity Market

Mochi, Pratik; Pandya, Kartik S.

doi:10.1109/icpee54198.2023.10060025

Cited by 4 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…s p2p j,τ represent the power purchased and sold in P2P, respectively, while Л p2p j,τ denotes the P2P price applicable to all participants. Within the P2P framework, the prosumers may take into account network losses as a central factor during the process of matching peers [18]. The power loss coefficients for power buying and selling in P2P are denoted as l buy p2p and l sell p2p , respectively.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Specifically, it enforces that prosumers can choose either to purchase or to sell power in P2P, but not both simultaneously. Constraints ( 15)-( 17) prevent a participant from engaging in simultaneous operations both in the grid and the P2P, while expression (18) signifies equilibrium between power transactions in the P2P transaction. In other words, during time period τ, a prosumer can either trade power with the grid or participate in P2P, but not both.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The system under investigation comprised 36 electricity end users in total, consisting of 30 prosumers and 6 consumers. A part of the community utilized for the study was derived from system data sourced from research [18]. These data are openly accessible, as provided in [16], for public download and utilization.…”

Section: Case Study Detailsmentioning

confidence: 99%

“…Equations (9) and (10) are the mathematical representations for assessing the cost and revenue associated with electricity buy and sell transactions in P2P for all prosumers. These calculations are subject to limits (11)(12)(13)(14)(15)(16)(17)(18).…”

Section: Mathematical Modelmentioning

confidence: 99%

See 3 more Smart Citations

Six-Segment Strategy for Prosumers’ Financial Benefit Maximization in Local Peer-to-Peer Energy Trading

Mochi,

Pandya,

Faia

et al. 2023

Mathematics

Self Cite

View full text Add to dashboard Cite

The notion of prosumers, people who create and consume energy, has emerged in the energy sector as a result of the fast integration of renewable energy sources and advances in digital technology. Platforms for peer-to-peer (P2P) energy trading have come to light as viable ways to allow prosumers to conduct direct energy transactions within small groups. Existing P2P trading models, however, frequently lack an optimized approach to maximize the advantages for prosumers. In this article, we provide a novel six-segment strategy (SSS) that is intended to increase prosumers′ ability to maximize their benefits in P2P energy trading systems. Additionally, the SSS promotes prosumers′ active market engagement by supporting community-driven energy exchanges. Through extensive mathematical modeling, simulations, and case studies, we demonstrate the effectiveness of the six-segment strategy in enhancing the economic benefits of prosumers participating in P2P energy trading. The proposed optimization strategy holds the potential to revolutionize the energy landscape by promoting more sustainable and consumer-centric energy trading. The cost savings of 12.9% are distributed among prosumers by the SSS, which is an improvement compared with previously proposed strategies.

show abstract

Section: Mathematical Modelmentioning

confidence: 99%

Section: Mathematical Modelmentioning

confidence: 99%

Section: Case Study Detailsmentioning

confidence: 99%

Section: Mathematical Modelmentioning

confidence: 99%

See 2 more Smart Citations

Six-Segment Strategy for Prosumers’ Financial Benefit Maximization in Local Peer-to-Peer Energy Trading

Mochi,

Pandya,

Faia

et al. 2023

Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The authors of the work [31] combined studies on societal support for energy policy with the idea of vocal partisanship for energy conservation. An optimization approach was proposed in [32] to model the local energy market by considering a mid-market rate tariff for prosumers. A demand response program [33] was suggested to motivate customers to participate in the energy transition.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Power Exchange Cost Considering Behavioral Intervention in Local Energy Community

et al. 2023

Self Cite

View full text Add to dashboard Cite

In order to encourage energy saving and the adoption of renewable sources, this study provides a comprehensive experimental framework that integrates socioeconomic and behavioral objectives for the local energy community. The experiment aims to find out how successfully using behavioral interventions might encourage customers to save electrical energy and encourage them to adopt renewable energy, e.g., solar photovoltaic energy, in the present case. Using this method, we can calculate the causal impact of the intervention on consumer participation in the local electricity sector. The study uses consumer data on the import and export of electrical power from retailer electricity utilities at a predetermined power exchange price and a midmarket price for local energy community power transactions. The local energy community model simulates the consumption, storage, and export of 20 residential customers who, in different scenarios, are the test subjects of an empirical experiment and embrace electricity conservation and renewable energy. We address the optimization issue of calculating the power exchange cost and revenue in various scenarios and comparing them with the base case cost. The cases are built on the customers’ behavioral interventions’ empirical response. The findings demonstrate that the interaction of socioeconomic and behavioral objectives leads to impressive cost savings of up to 19.26% for energy utility customers. The policy implication is suggested for local energy utilities.

show abstract

Prosumer Segmentation Strategies for Local Electricity Market Partaking by Monetary Reward

et al. 2023

View full text Add to dashboard Cite

The Impact of Social Nudge on System Cost & Revenue Optimization in Local Electricity Market

Cited by 4 publications

References 15 publications

Six-Segment Strategy for Prosumers’ Financial Benefit Maximization in Local Peer-to-Peer Energy Trading

Six-Segment Strategy for Prosumers’ Financial Benefit Maximization in Local Peer-to-Peer Energy Trading

Optimizing Power Exchange Cost Considering Behavioral Intervention in Local Energy Community

Prosumer Segmentation Strategies for Local Electricity Market Partaking by Monetary Reward

Contact Info

Product

Resources

About