Bidding strategy with forecast technology based on support vector machine in the electricity market

Gao, Ciwei; Bompard, Ettore; Napoli, Roberto; Wan, Qiulan; Zhou, Jianyuan

doi:10.1016/j.physa.2008.02.080

Cited by 15 publications

(9 citation statements)

References 9 publications

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“…Therefore, the overall analysis of MCP, power, supplier‐side profit, and the consumer‐side profit of the IEEE 30‐bus system obtained by the proposed technique is optimal and efficient compared with existing techniques. The bidding efficiency and the solution techniques such as ABC‐PSO, ABC, PSO, SFLA, FAGSA, GSA, and traditional GSS are displayed in Table . The bidding coefficient deviation (BCD) is defined as the difference of the proposed approach average and the current method average, and the resultant value is divided by the proposed approach average.…”

Section: Resultsmentioning

confidence: 99%

“…In such manner, SVM is utilized to anticipate the MCP for the advantage of the supplier and the large consumer in a pool‐based electricity market. The SVM is an incredible machine learning technique, which can sensitively capture the evolution of the market price . Here, the bidding strategies are created, which are immovably grounded on machine learning‐based price forecast.…”

Section: Proposed Approach For Bidding Problemmentioning

confidence: 99%

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Modeling of bidding strategies in a competitive electricity market: A hybrid approach

Gayathri

Asokan

2019

Int J Numerical Modelling

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In the paper, an innovative hybrid algorithm is proposed to solve bidding strategy problem in electricity market. The proposed hybrid technique is the combination of the recurrent neural network (RNN), support vector machine (SVM), and the lightning search algorithm (LSA). The main contribution of this paper is to maximize the profits of suppliers and minimize the customer payments; it is very important to create reasonable matching rule for the electricity market. LSA optimizes bidding coefficients of both the suppliers and consumers with the consideration of available power generation limit, power demand, market-clearing price (MCP), and constraints. RNN is utilized for demand prediction at every hour, and SVM ensures the MCP based on the demand. The proposed bidding strategy ensures the maximum profit of the suppliers and consumers because of the aggregated demand model. The proposed method is tested on The Institute of Electrical and Electronics Engineers (IEEE) 30-bus system and 75-bus Indian practical system and compared with the existing techniques. The comparison results demonstrate the superiority of the proposed technique and confirm its potential to solve the problem. Also, the proposed model and the strategies are implemented in the MATLAB, and the performance will be studied under various demand environments. /journal/jnm 1 of 18 serious dangers. By bidding, GENCOs may achieve interest at a higher cost than their minimal cost. Additionally, the electricity market and the oligopoly market are same for all cases. 4 For accomplishing the maximum profit, building up an optimal bidding strategy is crucial. 5 A comprehensively utilized electricity market display is the Pool Co. 6 GENCOs make optimal bidding strategies, which contain an arrangement of price generation using the Pool Co model. The market-clearing strategy and the market-clearing price (MCP) are actualized by the International Standards Organization (ISO). 7 To accomplish profit maximization, GENCOs should offer at their marginal cost. 8 The another essential goal to perceive the potential for the man handle of market power is inquiring about bidding strategies, 9,10 particularly, the market design rules, closeness of different markets, price uncertainty, intertemporal constraints, and nonconvexity of cost curves. 11,12 In the event that the unit cannot be dispatched in at least 1 hour in the day-ahead market, three alternatives must be considered for a bidder with a negligible or close minimal unit. The unit is stopped and cool down in the first alternative. 13,14 To stop the unit, however, keep it in banking is the second alternative, and to guarantee that the unit can be dispatched to supply to construct, the bid for every one of these hours is completed in the third alternative. By utilizing a unit commitment program to represent the unit's operating constraints, the final decision can be managed. 15,16 Over the span of the latest decade on optimal bid construction, unit commitment, and payment/pricing and different choices with regard to...

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Section: Resultsmentioning

confidence: 99%

Section: Proposed Approach For Bidding Problemmentioning

confidence: 99%

Modeling of bidding strategies in a competitive electricity market: A hybrid approach

Gayathri

Asokan

2019

Int J Numerical Modelling

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“…Kang et al (2007) have assumed that each generator has complete information about its profit and the other market generators' profits and tries to estimate other competitors' biddings to maximize its profit by use of the Nash equilibrium concept. Gao et al (2008) have presented two approaches to determine market bidding strategies by support vector machine (SVM). The accuracy of the methods was examined with an example.…”

Section: Introductionmentioning

confidence: 99%

Using and comparing metaheuristic algorithms for optimizing bidding strategy viewpoint of profit maximization of generators

Mousavi¹,

Nazemi²,

Hafezalkotob

2014

J Ind Eng Int

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With the formation of the competitive electricity markets in the world, optimization of bidding strategies has become one of the main discussions in studies related to market designing. Market design is challenged by multiple objectives that need to be satisfied. The solution of those multi-objective problems is searched often over the combined strategy space, and thus requires the simultaneous optimization of multiple parameters. The problem is formulated analytically using the Nash equilibrium concept for games composed of large numbers of players having discrete and large strategy spaces. The solution methodology is based on a characterization of Nash equilibrium in terms of minima of a function and relies on a metaheuristic optimization approach to find these minima. This paper presents some metaheuristic algorithms to simulate how generators bid in the spot electricity market viewpoint of their profit maximization according to the other generators' strategies, such as genetic algorithm (GA), simulated annealing (SA) and hybrid simulated annealing genetic algorithm (HSAGA) and compares their results. As both GA and SA are generic search methods, HSAGA is also a generic search method. The model based on the actual data is implemented in a peak hour of Tehran's wholesale spot market in 2012. The results of the simulations show that GA outperforms SA and HSAGA on computing time, number of function evaluation and computing stability, as well as the results of calculated Nash equilibriums by GA are less various and different from each other than the other algorithms. Keywords Genetic algorithm (GA) Á Simulated annealing (SA) Á Hybrid simulated annealing genetic algorithm (HSAGA) Á Nash equilibrium Á Bidding strategy

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“…Then as one of the focused problems in the markets, electricity price is more complicated than the price in other kinds of the markets, due to the specialty of the electricity commodity, which need the balance of the supply and demand in real time. Therefore, analysis and forecasting of the electricity price is an essential task for market participants in electricity market [1]- [6].…”

Section: Introductionmentioning

confidence: 99%

Next day price forecasting for electricity market

Areekul

Senjyu

Toyama

et al. 2011

2011 International Conference on Advanced Power System Automation and Protection

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This paper proposes new approach to reduce the prediction error at occurrence time of the peak price, and aims to enhance the accuracy of the next day price forecasting. In the proposed method, the weekly variation data is used for input factors of the ANN at occurrence time of the peak price in order to catch the price variation. Moreover, learning data for the ANN is selected by rough sets theory at occurrence time of the peak price. From the simulation results, it is observed that the proposed method provides a more accurate and effective forecasting, which helpful for suitable bidding strategy and risk management tool for market participants in a deregulated electricity market.

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Bidding strategy with forecast technology based on support vector machine in the electricity market

Cited by 15 publications

References 9 publications

Modeling of bidding strategies in a competitive electricity market: A hybrid approach

Modeling of bidding strategies in a competitive electricity market: A hybrid approach

Using and comparing metaheuristic algorithms for optimizing bidding strategy viewpoint of profit maximization of generators

Next day price forecasting for electricity market

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