An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms

Aliabadi, Danial Esmaeili; Kaya, Murat; Şahin, Güvenç

doi:10.1016/j.enpol.2016.09.063

Cited by 69 publications

(32 citation statements)

References 17 publications

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“…These equations are consistent with the Folk Theorem in which the infinitely repeated game G(∞,δ) with the discount factor δ, 0 ≤ δ ≤ 1, has an equilibrium with average profit π i (a * * i , a * * −i ) for player i if δ is adequately high and close to 1 [18,23]. This equilibrium, which dominates the one-stage NE, is stable as long as the myopic player does not deviate from the CS for gaining higher short-term profit.…”

Section: Fig 1 An Example Of Prisoner's Dilemma Gamesupporting

confidence: 79%

“…Instead of the constant exploration rate (

E_{r}^{s}

), we use a decreasing one as

E_{r}^{s_{t}} \leftarrow β E_{r}^{s_{t}}

where β ∈ (0.5, 1) is a constant value and close to one for slow decaying of the exploration rate in the algorithm. Decaying of the exploration rate parameter leads to better convergence of the algorithm . The exploration rate is determined for calculating the exploration degree (

E_{s_{t}}

) through

E_{s_{t}} = E_{r}^{s_{t}} \log (n_{s_{t}})

in which

n_{s_{t}}

is the number of available actions in state s t .…”

Section: Iterative Cs Search Methods With Agent‐based Modelingmentioning

confidence: 99%

“…Learning rate expresses the sensitivity of the player to the most recent data. It decreases over time that can be action dependent as in Refs or time dependent . We design the learning rate to be exponentially time dependent as

α_{t} = α_{0} \exp (- 2 t / t_{normalmax})

where α 0 is the initial value of the learning rate and t max is the maximum iteration number of the algorithm.…”

Section: Iterative Cs Search Methods With Agent‐based Modelingmentioning

confidence: 99%

“…where β ∈ (0.5, 1) is a constant value and close to one for slow decaying of the exploration rate in the algorithm. Decaying of the exploration rate parameter leads to better convergence of the algorithm [18,27]. The exploration rate is determined for calculating the exploration degree (E st ) through (6)…”

Section: Agent-based Simulation Frameworkmentioning

confidence: 99%

“…Learning rate expresses the sensitivity of the player to the most recent data. It decreases over time that can be action dependent as in Refs [7,30,31] or time dependent [18]. We design the learning rate to be exponentially time dependent as…”

Section: Agent-based Simulation Frameworkmentioning

confidence: 99%

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An iterative method for detection of the collusive strategy in prisoner's dilemma game of electricity market

Mohtavipour

Zideh

2018

IEEJ Transactions Elec Engng

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The aim of this article is to present a method, which is called the ‘iterative collusive strategy (CS) search method’, to detect the CS in prisoner's dilemma game in which there is collusive equilibrium. We apply this method to an example of two‐player prisoner's dilemma game and a numerical duopoly example to show its effectiveness. To simulate the electricity market models, we use this method with a local optimization algorithm. Then, we employ a hybrid technique by applying an agent‐based model to the iterative CS search method for improving the results and speeding up their convergence. We simulate an electricity market example with transmission constraint to test the effectiveness of local and hybrid iterative CS search methods. Simulation results show that both local and hybrid iterative CS search methods could successfully identify the CS; however, the hybrid iterative CS search method converges to the final results with less iteration numbers. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Fig 1 An Example Of Prisoner's Dilemma Gamesupporting

confidence: 79%

“…Instead of the constant exploration rate (

E_{r}^{s}

), we use a decreasing one as

E_{r}^{s_{t}} \leftarrow β E_{r}^{s_{t}}

E_{s_{t}}

) through

E_{s_{t}} = E_{r}^{s_{t}} \log (n_{s_{t}})

in which

n_{s_{t}}

is the number of available actions in state s t .…”

Section: Iterative Cs Search Methods With Agent‐based Modelingmentioning

confidence: 99%

α_{t} = α_{0} \exp (- 2 t / t_{normalmax})

where α 0 is the initial value of the learning rate and t max is the maximum iteration number of the algorithm.…”

Section: Iterative Cs Search Methods With Agent‐based Modelingmentioning

confidence: 99%

Section: Agent-based Simulation Frameworkmentioning

confidence: 99%

Section: Agent-based Simulation Frameworkmentioning

confidence: 99%

See 3 more Smart Citations

An iterative method for detection of the collusive strategy in prisoner's dilemma game of electricity market

Mohtavipour

Zideh

2018

IEEJ Transactions Elec Engng

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Smart dispatching for energy internet with complex cyber‐physical‐social systems: A parallel dispatch perspective

Cheng

2019

Int J Energy Res

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Summary Energy internet (EI) is a complex coupled multienergy system; it is essential to investigate its multienergy dispatching optimization issues. To this end, this paper first proposes a novel conception of smart dispatching for EI with a complex cyber‐physical‐social system (CPSS) network from the perspective of parallel dispatch, called parallel dispatching robot (PDR), and investigates the implementations of PDR based on smart artificial society (SAS) modeling. First, we introduce EI and describe the dispatching issues of EI. Second, we discuss several important concepts supporting the parallel dispatch conception of EI, including knowledge automation (KA), CPSS, and parallel machine learning (PML). On the basis of these, we elaborate the concept of parallel dispatch. Moreover, we construct a large closed‐loop feedback control framework of parallel dispatch for EI integrating a CPSS network based on KA and PML. Third, we establish an experimental platform for PDR research based on the proposed parallel dispatch framework. Fourth, we develop the PML‐based SAS models of a single PDR in centralized dispatching modes and group PDRs in decentralized dispatching modes to achieve crowd wisdom emergence and performance improvement in current cyber‐physical system frameworks of EI. Moreover, we design an external global closed loop for PDR to evaluate its operation stability. Lastly, we conduct a detailed discussion on PDR and offer some prospects for its engineering implementations. The biggest innovation of this paper lies in systematically proposing the smart dispatching concept and framework for complex CPSS‐based EI from the perspective of parallel dispatch and thoroughly investigating how to use SAS modeling to implement parallel dispatching and control for EI considering human and social factors, which is a major extension and theoretical improvement to existing single smart wide area robot concept and a preliminary attempt in investigating a shift from Energy 4.0 to Energy 5.0 in China.

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The Bilateral Negotiation or Centralized Bidding? How to Choose the Transaction Mode with Power Users for Power Plants

Guan

Yue

et al. 2021

IEEJ Transactions Elec Engng

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At present, there are mainly two kinds of transaction modes in the power market: bilateral negotiation mode and centralized bidding mode. Bilateral negotiation mode means users' purchases power directly from power plants, whereas centralized bidding mode means power plants and power users conduct power transactions through a Power Trading Center. This paper adopts evolutionary game theory to model this mechanism and explores the behavioral patterns of different entities at equilibrium. This paper is organized as follows. First, we build an evolutionary game model of this mechanism. Then, we discuss the existence and stability of equilibrium outcomes. Furthermore, we compare the equilibrium results under different conditions. We find that different market supply‐demand conditions lead to different equilibrium outcomes. Besides, we find that the bilateral negotiation mode does not necessarily improve the overall profit of the power market. When the demand exceeds the supply in the centralized bidding market, introducing the bilateral negotiation mode would reduce the overall market profit. In addition, the increase of the bidding heterogeneity leads to the increase of profits of power plants in the market. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms

Cited by 69 publications

References 17 publications

An iterative method for detection of the collusive strategy in prisoner's dilemma game of electricity market

An iterative method for detection of the collusive strategy in prisoner's dilemma game of electricity market

Smart dispatching for energy internet with complex cyber‐physical‐social systems: A parallel dispatch perspective

The Bilateral Negotiation or Centralized Bidding? How to Choose the Transaction Mode with Power Users for Power Plants

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