Distributed Dynamic Pricing in Peer-to-Peer Transactive Energy Systems in Smart Grid

Ullah, Habib; Alseyat, Anas; Park, Jae-Do

doi:10.1109/pesgm41954.2020.9282113

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…A distributed market clearing method is proposed, which integrates coordination between various markets. Ullah et al [13] presented a privacy-preserving distributed dynamic pricing approach for P2P TEM in the smart grids with Fast Alternating Direction Method of Multipliers (F-ADMM) approach. The results show that the approach converged rapidly and simplifies easier execution.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

Shoab¹,

Alotaibi²

2022

Computers, Materials &Amp; Continua

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With the incorporation of distributed energy systems in the electric grid, transactive energy market (TEM) has become popular in balancing the demand as well as supply adaptively over the grid. The classical grid can be updated to the smart grid by the integration of Information and Communication Technology (ICT) over the grids. The TEM allows the Peerto-Peer (P2P) energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them. At the same time, there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning (DML) models. Though some of the short term load prediction techniques have existed in the literature, there is still essential to consider the intrinsic features, parameter optimization, etc. into account. In this aspect, this study devises new deep learning enabled short term load forecasting model for P2P energy trading (DLSTLF-P2P) in TEM. The proposed model involves the design of oppositional coyote optimization algorithm (OCOA) based feature selection technique in which the OCOA is derived by the integration of oppositional based learning (OBL) concept with COA for improved convergence rate. Moreover, deep belief networks (DBN) are employed for the prediction of load in the P2P energy trading systems. In order to additional improve the predictive performance of the DBN model, a hyperparameter optimizer is introduced using chicken swarm optimization (CSO) algorithm is applied for the optimal choice of DBN parameters to improve the predictive outcome. The simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training, testing, and validation accuracy of 90.17%, 87.39%, and 87.86%.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

Shoab¹,

Alotaibi²

2022

Computers, Materials &Amp; Continua

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Section: Introductionmentioning

confidence: 99%

“…Other pricing mechanisms use different optimization techniques to set the LEM prices implicitly, through dual prices of an optimal power flow model [8] or a general market clearing problem [10]. Others propose more direct pricesetting optimization algorithms, like consensus-based Alternating Method of Multipliers (ADMM) [9].…”

Section: Introductionmentioning

confidence: 99%

LCOE-based Pricing for DLT-enabled Local Energy Trading Platforms

Dynge¹,

Halden²,

Klæboe³

et al. 2022

Preprint

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Support schemes like the Feed-in-Tariff have for many years been an important driver for the deployment of distributed energy resources, and the transition from consumerism to prosumerism. This democratization and decarbonization of the energy system has led to both challenges and opportunities for the system operators, paving the way for emerging concepts like local energy markets. The Feed-in-Tariff approach has often been assumed as the lower economic bound for a prosumer's willingness to participate in such markets but is now being phased out in several countries. In this paper, a new pricing mechanism based on the Levelized Cost of Electricity is proposed, with the intention of securing profitability for the prosumers, as well as creating a transparent and fair price for all market participants. The mechanism is designed to function on a Distributed Ledger Technology-based platform and is further set up from a holistic perspective, defining the market framework as interactions in a Cyber-Physical-Social-System. Schemes based on both fixed and variable contracts with the wholesale supplier are analyzed and compared with the conventional Feed-in-Tariff, showing benefits for both prosumers and consumers.

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“…Multiple studies have proposed different strategies for both bidding processes and the actual price setting, all including different aspects to be considered in a fair market price. In general, one can categorize the different schemes proposed in the literature into auctionbased (Ghorani et al, 2018;Lin et al, 2019;Wang et al, 2020;Vieira and Zhang, 2021), optimization-based (Morstyn and McCulloch, 2019;Paudel and Gooi, 2019;Lezama et al, 2020;Morstyn et al, 2020;Ullah et al, 2020), game theory-based (Jiang et al, 2020;Fernandez et al, 2021) and cost-sharing (Long et al, 2018;Cali and Cakir, 2019;Grzanic et al, 2021) methods.…”

mentioning

confidence: 99%

“…Other pricing mechanisms use different optimization techniques to set the LEM prices implicitly, through dual prices of an optimal power flow model (Morstyn et al, 2020) or a general market clearing problem (Ullah et al, 2020). Others propose more direct price-setting optimization algorithms, like consensus-based Alternating Method of Multipliers (ADMM) (Paudel and Gooi, 2019).…”

mentioning

confidence: 99%

LCOE-Based Pricing for DLT-Enabled Local Energy Trading Platforms

et al. 2022

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Support schemes like the Feed-in-Tariff (FiT) have for many years been an important driver for the deployment of distributed energy resources, and the transition from consumerism to prosumerism. This democratization and decarbonization of the energy system has led to both challenges and opportunities for the system operators, paving the way for emerging concepts like local energy markets. The FiT approach has often been assumed as the lower economic bound for a prosumer’s willingness to participate in such markets but is now being phased out in several countries. In this paper, a new pricing mechanism based on the Levelized Cost of Electricity is proposed, with the intention of securing profitability for the prosumers, as well as creating a transparent and fair price for all market participants. The mechanism is designed to function on a Distributed Ledger Technology-based platform and is further set up from a holistic perspective, defining the market framework as interactions in a Cyber-Physical-Social-System. Schemes based on both fixed and variable contracts with the wholesale supplier are analyzed and compared with both the conventional FiT and to its proposed replacement options. The results show a cost reduction for the consumers and a slight loss in revenue for the prosumers compared to the FiT scheme. Comparing it to the actual suggested replacements to the FiT, however, it is clear that the pricing mechanism proposed in this study provides a substantial increase of benefits for both prosumers and consumers.

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Distributed Dynamic Pricing in Peer-to-Peer Transactive Energy Systems in Smart Grid

Cited by 8 publications

References 14 publications

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

LCOE-based Pricing for DLT-enabled Local Energy Trading Platforms

LCOE-Based Pricing for DLT-Enabled Local Energy Trading Platforms

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