Storage or No Storage: Duopoly Competition Between Renewable Energy Suppliers in a Local Energy Market

Zhao, Dongwei; Wang, Hao; Huang, Jianwei; Lin, Xiaojun

doi:10.1109/jsac.2019.2951970

Cited by 19 publications

(7 citation statements)

References 27 publications

(98 reference statements)

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“…However, they neglect another possible transaction mode, that is, RESs suppliers also can trade directly with surrounding consumers without the intervention of others. For example, Zhao et al [16] propose a three-stage game model for RESs suppliers and local consumers, which considers the impact of investment costs for energy storage when RESs suppliers sell RESs energy. Paudel et al [17] design an M-leader and N-follower Stackelberg game approach to study the energy trading between RESs suppliers and surrounding consumers in a small community microgrid.…”

Section: Related Workmentioning

confidence: 99%

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An Adaptive Algorithm for Selling On‐Site Renewable Energy of Data Centers in Two Transaction Modes

Wangzhang

Zheng

et al. 2022

IEEJ Transactions Elec Engng

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The data center operator has diverse options to sell redundant energy generated by its on-site renewable generators to obtain payoff, where options include the grid and surrounding consumers. Different options and combinations of options mean different transaction modes for the data center operator. However, current works mainly investigate the selling of renewable energy in a fixed transaction mode, which will lead to payoff loss of data center operator. To heighten the payoff of data center operator, this paper designs two frameworks to model two transaction modes (i.e., single-channel transaction mode and dual-channel transaction mode) based on the Stackelberg game, where the Stackelberg game is used to address the conflict of interest between data center operator and electric utility company. The Stackelberg equilibrium, the optimal payoff of the data center operator, in two transaction modes is derived. Based on the difference of calculated optimal payoff in two transaction modes, an algorithm is proposed to adaptively choose the transaction mode that can bring more payoff. Simulation results show that the proposed algorithm is able to increase the payoff of the data center operator compared with that in fixed mode.

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Section: Related Workmentioning

confidence: 99%

“…For example, Zhao et al . [16] propose a three‐stage game model for RESs suppliers and local consumers, which considers the impact of investment costs for energy storage when RESs suppliers sell RESs energy. Paudel et al .…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Algorithm for Selling On‐Site Renewable Energy of Data Centers in Two Transaction Modes

Wangzhang

Zheng

et al. 2022

IEEJ Transactions Elec Engng

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“…The proposed market model consists of two stages, the inner transaction between DGs and FLs, and transferring of the residual/lacking power to/from the utility grid. The market design proposed in this paper applies the central-matching strategy from the Uber business model and the multiattribute pricing strategy from the Airbnb model [7,8]. The matching strategy of the distribution-level market is to find the point where participants in the existence of distribution-level market could gain the largest profit compared to the case without the distribution-level market.…”

Section: Market Mathematical Formulationmentioning

confidence: 99%

“…It focuses on whether fair payments are offered to flexible generators under the current electricity market design. References [5][6][7][8][9][10][11]are evaluations of flexibility value. The first three of those papers analyse flexibilities offered by specific technologies, while the remaining target at value from multi-resourced flexibilities.…”

Section: Introductionmentioning

confidence: 99%

A uber‐airbnb mixture flexibility peer‐to‐peer market in integrated TSO‐DSO architecture

Shan

2022

IET Smart Grid

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With the launch of global decarbonisation, the infiltration of low-carbon distributed energy resources (DERs) is increasing. The future transaction platforms for those flexibilities are considered to be peer-to-peer (P2P) ones conforming to resources' characteristics of small-scale and independence. This research reviews current flexibility-related topics and proposes one P2P flexibility market filling in the gaps. The contribution of this research includes: (1) it constructs a flexibility market combining pricing strategy and matching strategy of the mature and successful real-world P2P business models, accommodating the penetration of DERs. The proposed automatic trading market could mobilise the enthusiasm of market participants and satisfy individuals' needs for a convenient market. (2) it proposes a dynamic pricing strategy where prices fluctuate with the features and portfolios of market participants. More adequate market signals are provided with the promotion of market equity. (3) it discusses the segmentation tendency of the flexibility market when considering energy products as pure commodities following the disintegration from the transmission system operator to the distribution system operator. The feasibility of the proposed market under future energy scenarios is thus explored.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…According to the fact that the charging infrastructure (including charging towers) are ground-mounted and AC-powered, the infrastructure gathers energy/power sources without strict limitations. Furthermore, the charging towers can (i) share their own energy resources in order to provide reliable and efficient energy resources among them or (ii) purchase energy resources from their associated utility company (also known as external local energy market) [18][19][20][21][22]. The dynamic active energy sharing sequential decision control process is essentially required for this given problem because the energy/power prices are determined based on auction-based economic theory in the local energy market [23].…”

Section: Introductionmentioning

confidence: 99%

Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms

et al. 2021

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This paper proposes a novel coordinated multi-agent deep reinforcement learning (MADRL) algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to conduct big-data processing in a distributed manner. For realizing UAV-assisted aerial surveillance or flexible mobile cellular services, robust wireless charging mechanisms are essential for delivering energy sources from charging towers (i.e., charging infrastructure) to their associated UAVs for seamless operations of autonomous UAVs in the sky. In order to actively and intelligently manage the energy resources in charging towers, a MADRL-based coordinated energy management system is desired and proposed for energy resource sharing among charging towers. When the required energy for charging UAVs is not enough in charging towers, the energy purchase from utility company (i.e., energy source provider in local energy market) is desired, which takes high costs. Therefore, the main objective of our proposed coordinated MADRL-based energy sharing learning algorithm is minimizing energy purchase from external utility companies to minimize system-operational costs. Finally, our performance evaluation results verify that the proposed coordinated MADRL-based algorithm achieves desired performance improvements.

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Storage or No Storage: Duopoly Competition Between Renewable Energy Suppliers in a Local Energy Market

Cited by 19 publications

References 27 publications

An Adaptive Algorithm for Selling On‐Site Renewable Energy of Data Centers in Two Transaction Modes

An Adaptive Algorithm for Selling On‐Site Renewable Energy of Data Centers in Two Transaction Modes

A uber‐airbnb mixture flexibility peer‐to‐peer market in integrated TSO‐DSO architecture

Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms

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