An Energy Sharing Game With Generalized Demand Bidding: Model and Properties

Chen, Yue; Mei, Shengwei; Zhou, Fengyu; Low, Steven H.; Wei, Wei; Liu, Feng

doi:10.1109/tsg.2019.2946602

Cited by 69 publications

(68 citation statements)

References 38 publications

(81 reference statements)

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“…x x x [120] x x x [121] x x x [122] x x x x [123] x x x x [124] x [125] x [81] x x x x [126] x x [127] x [128] x x [47] x x [129] x x [130] x [131] x x [132] x x x [133] x x [118] x x [134] x x [135] x x [136] x x [137] x x x [138] x x x [139] x x x [140] x x [141] x x x [142] x x x [86] x x x x [143] x x x [144] x x x [145] x x x [146] x x x [147] x x x…”

Section: E Social Aspectsmentioning

confidence: 99%

“…In Ref. [116], the DSO performs an AC PF analysis after each market clearing and informs the D R A F T x x [70] x x [94] x x x [95] x x [96] x x x x [97] x x [98] x x x [62] x x x x [99] x x x x [100] x x x x [101] x x x [102] x x x [103] x x x [104] x x [105] x x [48] x [107] x x x x [108] x x [109] x x x [110] x x x [111] x x x [112] x x x [156] x x x x [113] x x x [114] x x x [115] x x [116] x x [117] x x [118] x x [119] x x [120] x x [121] x x [122] x x [123] x x x [124] x x x [125] x x x x [81] x x x x [126] x [127] x x x [47] x [129] x x [130] x x [131] x x x [132] x x [133] x x [118] x x [134] x x x…”

Section: ) Literature Focus: Cooperative Gamesmentioning

confidence: 99%

“…A Nash game for a sharing mechanism between prosumers that utilizes auctions is proposed in Ref. [125]. It provides a proof for the Nash equilibrium of the game existing and being unique in addition to being socially optimal.…”

Section: ) Literature Focus: Cooperative Gamesmentioning

confidence: 99%

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Developments and Challenges in Local Electricity Markets: A Comprehensive Review

et al. 2021

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In recent years, power systems have undergone changes in technology and definition of the associated stakeholders. With the increase in distributed renewable generation and small-to medium-sized consumers starting to actively participate on the supply side, a suitable incorporation of decentralized agents into the power system is required. A promising scheme to support this shift is given by local electricity markets. These provide an opportunity to extend the liberal wholesale markets for electrical power found in Europe and the United States to the communal level. Compared to these more established markets, local electricity markets, however, neither have few practical implementations nor standardized frameworks. In order to fill this research gap and classify the types of local electricity markets, the presented paper therefore starts with the challenges that these markets attempt to solve. This is then extended to an analysis of the theoretical and practical background with a focus on these derived challenges. The theoretical background is provided in the form of an introduction to state-of-the-art models and the associated literature, whereas the practical background is provided in form of a summary of ongoing and recent projects on local electricity markets. As a result, this paper presents a foundation for future research and projects attempting to approach the here presented challenges in distribution of generation, integration of demand response, decentralization of markets and legal and social issues via local electricity markets.

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Section: E Social Aspectsmentioning

confidence: 99%

Section: ) Literature Focus: Cooperative Gamesmentioning

confidence: 99%

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Developments and Challenges in Local Electricity Markets: A Comprehensive Review

et al. 2021

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“…Anoh et al formulated P2P energy trading as a Stackelberg game with producers as leaders and consumers as followers and optimized both the cost for consumers and the utility for producers [24]. Chen modeled P2P energy sharing as a generalized Nash-demand bidding game and showed that Nash equilibrium occurs [25]. Cadre et al characterized a P2P energy market solution as a variational equilibrium and proved that the set of variational equilibria coincides with the set of social welfare optimal solutions of market design [26].…”

Section: B Prior Workmentioning

confidence: 99%

Peer-to-Peer Energy Transaction Mechanisms Considering Fairness in Smart Energy Communities

Son

2020

IEEE Access

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This study presents peer-to-peer (P2P) energy transaction mechanisms to maximize social welfare considering the uncertainty and profit fairness of the players. The P2P energy transaction problem is formulated as a P2P energy transaction pair matching and the determination of the P2P transaction price. To solve the problem, the optimal condition to maximize social welfare is determined using stochastic P2P energy transaction performance analysis based on the uncertainty characteristics. The analysis results show that social welfare is maximized to match the producer and consumer pairs that have similar demand characteristics; the P2P transaction price balances the profit fairness between the pair. Using these results, two centralized P2P energy transaction mechanisms are proposed by modifying the optimization problem. Moreover, a decentralized P2P energy transaction mechanism that operates in a distributed manner is suggested with the operational signal flow for the implementation of the mechanism. The simulation results show that the centralized and decentralized mechanisms have near optimal performance, with less than a 0.5% and 1% optimal gap compared to the optimal solution that requires perfect information including uncertainty, respectively. However, the decentralized mechanism is less computationally complex and uses less information than the centralized mechanisms; consequently, it can alleviate the operational burden and security and privacy problems. In addition, the results show that the performance of P2P energy transaction is related to the relative demand ratio between the producer and consumer. The optimal condition and results suggest a guide to the design of the P2P energy transaction. INDEX TERMS Demand-side management, distributed energy transaction, distributed generation, energy community, energy trading, fairness, peer-to-peer, prosumer, uncertainty.

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“…Here, a prosumer is allowed to exchange/share energy with other prosumers, and they are turning from pricetakers to price-makers. This can be done in a peer-to-peer (P2P) structure [7] or with the assistance of a platform [8]. As revealed in [9], energy sharing can be a promising direction in managing prosumers, since it can achieve a nearly social optimal solution in a distributed manner.…”

Section: Introductionmentioning

confidence: 99%

Approaching Prosumer Social Optimum via Energy Sharing With Proof of Convergence

Chen

Zhao

Low

et al. 2021

IEEE Trans. Smart Grid

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With the advent of prosumers, the traditional centralized operation may become impracticable due to computational burden, privacy concerns, and conflicting interests. In this paper, an energy sharing mechanism is proposed to accommodate prosumers' strategic decision-making on their self-production and demand in the presence of capacity constraints. Under this setting, prosumers play a generalized Nash game. We prove main properties of the game: an equilibrium exists and is partially unique; no prosumer is worse off by energy sharing and the price-of-anarchy is 1 − O(1/I) where I is the number of prosumers. In particular, the PoA tends to 1 with a growing number of prosumers, meaning that the resulting total cost under the proposed energy sharing approaches social optimum. We prove that the corresponding prosumers' strategies converge to the social optimal solution as well. Finally we propose a bidding process and prove that it converges to the energy sharing equilibrium under mild conditions. Illustrative examples are provided to validate the results. Index Terms-Energy sharing, generalized Nash equilibrium, prosumer, bidding algorithm, distributed mechanism NOMENCLATURE A. Indices, Sets, and Functions i, I Index and set of prosumers. S iAction sets of prosumer i, and S = ∏ i∈I S i .Utility function of prosumer i J i (p i , d i ) Net cost of prosumer i, which equals to f i (p i ) − u i (d i ); and J(p, d) = ∑ i∈I J i (p i , d i ). Γ i (p, d, b) Total net cost of prosumer i with sharing, which equals tosatisfies the capacity constraint. B. Parameters I Number of prosumers. p i , p i Lower/upper bound of prosumer i's production. d i , d i Lower/upper bound of prosumer i's demand. a Energy sharing market sensitivity.

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An Energy Sharing Game With Generalized Demand Bidding: Model and Properties

Cited by 69 publications

References 38 publications

Developments and Challenges in Local Electricity Markets: A Comprehensive Review

Developments and Challenges in Local Electricity Markets: A Comprehensive Review

Peer-to-Peer Energy Transaction Mechanisms Considering Fairness in Smart Energy Communities

Approaching Prosumer Social Optimum via Energy Sharing With Proof of Convergence

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