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Computationally efficient peer-to-peer energy trading mechanisms for autonomous agents considering network constraints and privacy preservation

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Abstract

The development of distributed energy resources (DERs) alongside the recent advances in information and communication technology has motivated electricity customers to play a more active role in network operations. The peer-to-peer (P2P) energy trading is a promising answer to this need, in which players can trade directly with each other and the main network via a two-way exchange of power and information. Therefore, this paper presents a P2P energy platform that aims to implement computationally efficient pool-based, semi-decentralized and decentralized clearing mechanisms considering network constraints and privacy preservation. To solve the newly developed pool-based and semi-decentralized/decentralized clearing mechanisms, a particle swarm optimization (PSO) algorithm and an iterative-based heuristic technique are employed, respectively. Simulations are conducted on the IEEE 11-kV, 33-bus distribution network where both dispatchable generations and renewable resources are present. Four types of consumers including agricultural, commercial, domestic, and industrial (ACDI) loads are considered for simulation purposes. Based on the results, several conclusions can be drawn: (i) even though the decentralized mechanism preserves the privacy of customers by eliminating the role of third parties, it does not guarantee network constraints; (ii) the presence of a central entity in the pool-based mechanism keeps the technical envelopes within the standard range; (iii) the semi-decentralized mechanism is the best time-consuming one, and to some extent conserves information security; and, (iv) all these devised methods are superior in terms of computational efficiency compared to a well-known primal-dual gradient method that exists in the literature.

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The data and material that has been used in this research paper is cited with references at their respective place.

Abbreviations

ADMM:

Alternative direction method of multipliers

CM:

Community manager

CO:

Constrained optimization

DDGs:

Dispatchable distributed generations

DERs:

Distributed energy resources

DR:

Demand response

DSO:

Distribution system operator

ETD-ADMM:

Energy trading distributed-alternative direction method of multipliers

F-ADMM:

Fast-ADMM

LP:

Linear programming

MILP:

Mixed integer linear programming

OC-ADMM:

Online consensus alternative direction method of multipliers.

P2P:

Peer-to-peer

PDG:

Primal-dual gradient

RDGs:

Renewable distributed generations

SP:

Stochastic programming

TE:

Transactive energy

VPPs:

Virtual power plants

\({\mathcal{B}}\) :

Set of buyers

\(b\) :

Index for buyers

\(C\) :

Cost function [$]

\({\mathcal{D}}\) :

Nodal communication matrix

\(E\) :

Energy [kJ]

\(h\) :

Index for matrix \({\mathcal{D}}\) elements

\(i,j\) :

Index for starting and ending buses for line \(l\)

\(k\) :

Index for PSO algorithm/iterative-based heuristic technique iterations

\(l\) :

Index for lines

\({\mathcal{L}}\) :

Set of lines

\(m\) :

Index for buses

\({\mathcal{N}}_{j}\) :

Set of bus \(j\)’s downstream neighbors

\(n,f\)  :

Indices for agents

\(n_{s}\) :

Total number of sellers

\(n_{b}\) :

Total number of buyers

\({\mathcal{N}}\) :

Set of all market players

\(N\) :

Number of market players

\(p_{n}\) :

Active power producing/requesting [kW]

\(p_{b}\) :

Active power requested by buyers [kW]

\(p_{s}\) :

Active power produced by sellers [kW]

\(p\) :

Vector of active power injections [kW]

\(P\) :

Vector of active power flows [kW]

\(q\) :

Vector of reactive power injections [kVAR]

\(Q\) :

Vector of reactive power line flows [kVAR]

\(R\) :

Resistance matrix [ohm]

\(s\) :

Index for sellers

\({\mathcal{S}}\) :

Set of sellers

\(T_{s}\) :

A row vector with \(n{}_{s}*1\) dimension whose all elements are -1

\(T_{b}\) :

A row vector with \(n{}_{b}*1\) dimension whose all elements are 1

\(U_{gross}\) :

Gross utility [$]

\(U_{net}\) :

Net utility [$]

\(v\) :

Vector of nodal voltages [p.u.]

\(X\) :

Reactance matrix [ohm]

\(\left| {\left\{ x \right\}} \right|\) :

The number of elements in set \(x\)

\(\alpha\) :

Cost function parameter [$/kWh2]

\(\beta\) :

Cost function parameter [$/kWh]

\(\gamma\) :

Cost function parameter [$]

\(\delta\) :

Utility function constant parameter [$/kWh2]

\(\varepsilon\) :

Acceptable error to stop the solution methodology related to the pool-based and semi-decentralized mechanisms

\(\varepsilon_{\lambda }\) :

Acceptable error for the local energy price between two iterations in the solution methodology associated with the decentralized market clearing mechanism

\(\varepsilon_{p}\) :

Acceptable error for the local power imbalance in the solution methodology pertaining to the decentralized market clearing mechanism

\(\eta\) :

Players’ willingness to cooperate in the pool-based mechanism

\(\lambda\) :

Energy price [$/kWh]

\(\mu { },{ }\mu^{^{\prime}}\) :

Inequality coefficients in the Lagrange function

\(\xi\) :

Accuracy factor of the semi-decentralized and decentralized mechanisms

\({\Pi }\) :

Set of local branches in the tree communication graph

\(\omega\) :

Buyers’ tendency to purchase energy [$/kWh]

\({\Omega }\) :

Tree graph for electric network topology

\({\mathcal{G}}\) :

Tree graph for communication network topology

\(\left| . \right|\) :

The absolute value inside the brackets

\(\left[ 1 \right]\) :

A vector whose all entries are equal to 1

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran

    Seyed Ali Sadati & Mojtaba Shivaie

  2. Faculty of Electrical Engineering, Semnan University, Semnan, Iran

    AmirAli Nazari

Authors
  1. Seyed Ali Sadati
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  2. Mojtaba Shivaie
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  3. AmirAli Nazari
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Contributions

Seyed Ali Sadati (Conceptualization, Methodology, Visualization, Software, Writing-original draft); Mojtaba Shivaie (Methodology, Formal analysis, Validation, Supervision, Writing-review & editing); and AmirAli Nazari (Methodology, Software, Validation, Resources, Writing-original draft).

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Correspondence to Mojtaba Shivaie.

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Sadati, S.A., Shivaie, M. & Nazari, A. Computationally efficient peer-to-peer energy trading mechanisms for autonomous agents considering network constraints and privacy preservation. Peer-to-Peer Netw. Appl. 16, 1088–1105 (2023). https://doi.org/10.1007/s12083-023-01456-2

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