Generation companies' adaptive bidding strategies using finite‐state automata in a single‐sided electricity market

Sheblé, G.B.; Gutiérrez-Alcaraz, G.

doi:10.1002/etep.605

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Although it is common to assume ζ re = [0.05,0.1] in an L R − I learning algorithm and in this sort of problems , for confidence, the test systems proposed in and the spinning tree problem (which is similar to the optimal feeder routing) are assumed. Then, percentage of the converged runs (PCR) to the expected results (or better than them) for different values of learning rate is calculated for 50 independent runs.…”

Section: Numerical Resultsmentioning

confidence: 99%

A hybrid heuristic and learning automata-based algorithm for distribution substations siting, sizing and defining the associated service areas

Mazhari

Monsef

Falaghi

2012

Int. Trans. Electr. Energ. Syst.

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This paper presents a new solution approach for optimal substation expansion planning (SEP) within electric power distribution networks. A modified fuzzy membership matrix as well as a memorable cost index vector is introduced to find the optimal substation service areas. Besides these, a Learning Automata-based algorithm is introduced for simultaneous determination of optimal service areas and capacities of the distribution substations. Electrical constraints such as voltage drops, power flow, radial flow constraints, as well as all prevalent cost indices are taken into consideration. The developed method is conducted to solve the distribution substation allocation problem for an actual distribution network with about 200 000 customers, and obtained results are compared to those of other methods. Detailed numerical results and comparisons presented in the paper show that the proposed solution approach could noticeably improve the quality of problem solutions with low computational burden and can be used as an effective tool for SEP in large distribution networks.

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Section: Numerical Resultsmentioning

confidence: 99%

A hybrid heuristic and learning automata-based algorithm for distribution substations siting, sizing and defining the associated service areas

Mazhari

Monsef

Falaghi

2012

Int. Trans. Electr. Energ. Syst.

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“…Veselka et al (2002) defined the agents' learning strategy based on Genetic Algorithm. Sheble and Gutierrez-Alcaraz (2012) used Genetic Algorithm to implement agents' adaptive bidding strategies. Learning strategy is crucial for agents to maximize their profit and reinforcement learning algorithm (Erev and Roth, 1998;Sutton and Barto, 1998) seems to be the most popular choice for designing agents' bidding strategies.…”

Section: Literature Reviewmentioning

confidence: 99%

Navigating Towards a Sustainable Electricity Supply in Indonesia

Sugianto

2014

MAS

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The realization that natural resources are limited has triggered a global sustainability movement, including in the electricity sector. Sustainable electricity supply is within the national agenda of many countries given that the demand for electricity has shown a consistent increase over many decades. An important aspect of sustainable electricity supply is the pricing of electricity. The objective of this study is to explore the effectiveness of a competitive electricity market structure in the Indonesian case. While restructuring, deregulation and privatization in the electricity industry is aimed at improving efficiency in electricity supply and ensuring competitive electricity price, experiences from other countries indicate that competitive market can lead to high price volatility and gaming behavior among market players, leading to more expensive electricity price. This study proposes to use an agent based model to simulate a competitive market. The focus of the study has been on the installed generation capacity in the region to ensure availability of supply and competitive pricing. The study features the Java-Bali grid and uses the half-hourly demand data from the region to observe the pricing dynamics.

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“…Various methods have been proposed for simulating electricity markets based on multiagent systems, some of which can be found in [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. In the majority of the efforts made, only the energy market was taken into account in the simulations and studies.…”

Section: Introductionmentioning

confidence: 99%

Multiagent-based simulation of simultaneous electricity market auctions in restructured environment

Farshad

2015

Turk J Elec Eng & Comp Sci

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In the restructured environment of the power industry, various commodities such as energy and operating reserves may be provided through simultaneous auctions. Prediction of market players' behavior in the auctions and simulation of the markets' environment can assist market decision-makers in evaluating specific policies before enforcing them in the real environment. Considering effects of the energy and varieties of reserve markets and also their interactions in the simulations is of high importance, which leads to more realistic simulation results. In this paper, an approach based on a multiagent system is proposed for simulating the simultaneous energy, spinning reserve, and replacement reserve market auctions, in which bidding of each agent is carried out based on a reinforcement learning algorithm. The proposed method is applied on a sample system, through which the impacts of considering lost opportunity cost in the payment model are examined.

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Generation companies' adaptive bidding strategies using finite‐state automata in a single‐sided electricity market

Cited by 3 publications

References 15 publications

A hybrid heuristic and learning automata-based algorithm for distribution substations siting, sizing and defining the associated service areas

A hybrid heuristic and learning automata-based algorithm for distribution substations siting, sizing and defining the associated service areas

Navigating Towards a Sustainable Electricity Supply in Indonesia

Multiagent-based simulation of simultaneous electricity market auctions in restructured environment

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