Negotiation Algorithms for Peer-to-Peer Electricity Markets: Computational Properties

Moret, Fabio; Baroche, Thomas; Sorin, Etienne

doi:10.23919/pscc.2018.8442914

Cited by 60 publications

(66 citation statements)

References 10 publications

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“…Two main drawbacks of these algorithmic approaches are listed in [31]: first, they do not take into account the strategic behaviors of the prosumers; second, they are computationally limited, which might constitute a blocking point when studying large-scale peer-to-peer networks. The latter issue is overcome in [19] with an improved consensus algorithm.…”

Section: Distributed Optimization Approachesmentioning

confidence: 99%

“…Assuming no energy waste and knowing (c n0 ) n , (c 0n ) n , (ξ n0 ) n , (ξ 0n ) n , the MO computes the nodal price at the root node by Equation (20). The nodal prices in all the other nodes of the distribution network can be inferred from λ 0 according to Equation (19). Then, for each node n ∈ N , bilateral trading prices can be computed for any node m ∈ Ω n , n = m by Equation (12) provided congestion price (ξ nm ) m>n,m∈Ωn is known 2 .…”

Section: Centralized Market Designmentioning

confidence: 99%

“…In practice, the radial structure of the distribution grid calls for hierarchical market designs, involving transmission and distribution network operators [14]. Nevertheless, various degrees of coordination can be envisaged: full coordination organized by a global market operator (transmission network operator), bilateral contract networks [21], fully decentralized market designs allowing peer-to-peer energy trading between the prosumers in a distributed fashion [19,31] or, still, within and between coalitions of prosumers, called community or hybrid peer-to-peer [20]. A community-based organization involves a community manager which organizes trades among the community and is in charge of the interactions with the rest of the market.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Peer-to-peer electricity market analysis: From variational to Generalized Nash Equilibrium

Cadre¹,

Jacquot

Wan³

et al. 2020

European Journal of Operational Research

119

134

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We consider a network of prosumers involved in peer-to-peer energy exchanges, with differentiation price preferences on the trades with their neighbors, and we analyze two market designs: (i) a centralized market, used as a benchmark, where a global market operator optimizes the flows (trades) between the nodes, local demand and flexibility activation to maximize the system overall social welfare; (ii) a distributed peer-to-peer market design where prosumers in local energy communities optimize selfishly their trades, demand, and flexibility activation.We first characterize the solution of the peer-to-peer market as a Variational Equilibrium and prove that the set of Variational Equilibria coincides with the set of social welfare optimal solutions of market design (i). We give several results that help understanding the structure of the trades at an equilibrium or at the optimum. We characterize the impact of preferences on the network line congestion and renewable energy waste under both designs. We provide a reduced example for which we give the set of all possible generalized equilibria, which enables to give an approximation of the price of anarchy. We provide a more realistic example which relies on the IEEE 14-bus network, for which we can simulate the trades under different preference prices. Our analysis shows in particular that the preferences have a large impact on the structure of the trades, but that one equilibrium (variational) is optimal.

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Section: Distributed Optimization Approachesmentioning

confidence: 99%

Section: Centralized Market Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Peer-to-peer electricity market analysis: From variational to Generalized Nash Equilibrium

Cadre¹,

Jacquot

Wan³

et al. 2020

European Journal of Operational Research

119

134

View full text Add to dashboard Cite

show abstract

“…To this end, by equating (15) to (16) and replacing the value of p c (t) with p d (t) from (17), the trading prices p c (t) and p d (t)…”

Section: Trading Of Energymentioning

confidence: 99%

“…Trading mechanisms that focus on empowering prosumers in the market are proposed in [11] and [12]. In the literature, integration of P2P trading in the energy market is also discussed via double-auction based [13], fairness based [14], consensus-based [15], negotiation-based [16], generalized Nash equilibrium [17], and orchestrator based [18] approaches respectively.…”

Section: Introductionmentioning

confidence: 99%

A coalition formation game framework for peer-to-peer energy trading

et al. 2020

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This paper studies social cooperation backed peer-to-peer energy trading technique by which prosumers can decide how they can use their batteries opportunistically for participating in the peer-to-peer trading. The objective is to achieve a solution in which the ultimate beneficiaries are the prosumers, i.e., a prosumer-centric solution. To do so, a coalition formation game is designed, which enables a prosumer to compare its benefit of participating in the peer-to-peer trading with and without using its battery and thus, allows the prosumer to form suitable social coalition groups with other similar prosumers in the network for conducting peer-to-peer trading. The properties of the formed coalitions are studied, and it is shown that 1) the coalition structure that stems from the social cooperation between participating prosumers at each time slot is both stable and optimal, and 2) the outcomes of the proposed peerto-peer trading scheme is prosumer-centric. Case studies are conducted based on real household energy usage and solar generation data to highlight how the proposed scheme can benefit prosumers through exhibiting prosumer-centric properties.

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Transaction‐based operation of electric distribution systems: A review

Küster

Aoki

Lambert‐Torres

2019

Int Trans Electr Energ Syst

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Summary The proliferation of distributed energy resources (DERs) aligned with consumer‐ level information and communication technology (ICT) propels the power systems toward a new reality. In this light, transaction‐based energy systems are emerging as a promising solution for orchestrating the growing number of dispersed prosumers and intelligent devices, each with their own objectives and value perspectives, into a resilient, secure, and efficient system. The study of market‐driven operation of distribution systems has attracted significant research attention in recent years. The design of full market‐based decentralized control of power systems has been taking many forms, according to different design philosophies and techniques. Only few of them have been implemented in pilot projects. Thus, it is not known so far which of these approaches will show to be more suitable for large‐scale adoption. This study presents a review of the state of the art of the scientific community on the application of transactive energy, market‐based control, and peer‐to‐peer energy trading, among other related concepts. A classification of the study efforts is proposed, analyzing the transaction‐based system framework with regard to motivation, enabling technologies, market types and structures, system operation issues, case studies, and valuation metrics. Two main approaches are identified, considering whether the price is determined centrally by aggregating quantity/bids from a number of agents, or if it is negotiated through direct interactions of agents between themselves. The first case is mainly referred to as “transactive energy” and explores demand‐response possibilities, whereas the second is being treated as “peer‐to‐peer energy,” highlighting the active participation of prosumers in the energy markets. The potential benefits and downsides of these main market design philosophies are evaluated, contributing to the assessment of the underlying concepts applicability to the power and energy sector.

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Negotiation Algorithms for Peer-to-Peer Electricity Markets: Computational Properties

Cited by 60 publications

References 10 publications

Peer-to-peer electricity market analysis: From variational to Generalized Nash Equilibrium

Peer-to-peer electricity market analysis: From variational to Generalized Nash Equilibrium

A coalition formation game framework for peer-to-peer energy trading

Transaction‐based operation of electric distribution systems: A review

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