Bidding Strategies for Electricity Producers in a Competitive Electricity Marketplace

Gountis, V.P.; Bakirtzis, Anastasios G.

doi:10.1109/tpwrs.2003.821474

Cited by 203 publications

(124 citation statements)

References 29 publications

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“…Many papers have developed bidding strategies for generating companies [40][41][42]. Due to space limitations, the generating companies are assumed to submit supply offers that are based on their actual cost functions and expected profit margin.…”

Section: Market Clearing Proceduresmentioning

confidence: 99%

Bidding Strategy for Aggregators of Electric Vehicles in Day-Ahead Electricity Markets

et al. 2017

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Abstract:To make full use of the flexible charging and discharging capabilities of the growing number of electric vehicles (EVs), a bidding strategy for EV aggregators to participate in a day-ahead electricity energy market is proposed in this work. The proposed bidding strategy is able to reduce the operating cost of the EV aggregators and to handle the uncertainties of day-ahead market prices properly at the same time. Agreements between the EV owners and the aggregators are discussed, and a hierarchical market structure is proposed. While assuming the aggregators as economic rational entities, the bidding strategy is established based on the market prices, extra battery charging/discharging costs and the expected profits. The bidding clearing system will display the current/temporal market clearance results of the day-ahead market before the final clearance, and hence the market participants can revise their bids and mitigate the risks, to some extent, of forecasted market price forecast errors. Numerical results with a modified IEEE 30-bus system have demonstrated the feasibility and effectiveness of the proposed strategy.

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Section: Market Clearing Proceduresmentioning

confidence: 99%

Bidding Strategy for Aggregators of Electric Vehicles in Day-Ahead Electricity Markets

et al. 2017

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“…To ease the modelization, we considered λ B * it (p B it ) as a general function, not necessarily step-wise. This is a common simplified representation of the true sale bid (9) used by several authors (e.g., (Gountis and Bakirtzis, 2004)). Furthermore, it is always possible to adapt a posteriori the resulting optimal bid function to the step-wise representation (9).…”

Section: Optimal Bid Function and Equivalent Matched Energy Constraintsmentioning

confidence: 99%

“…Subsequently, it has been proved that if the proposed function (12) is bid to the day-ahead market, then the resulting matched energy will maximize the day-ahead benefit function (11) When b it = 0 (the committed unit t does not contribute to the BC covering), our problem reduces to the classical self-commitment problem discussed by several authors (Gountis and Bakirtzis, 2004;Conejo and Arroyo, 2002). In this case, it is well known that the optimal-bid strategy for a price-taker GenCo consists of an instrumental bid up to the operational minimum limit, p t , and the rest of the plant capacity at the marginal price, 2c q t p it + c l t .…”

Section: Optimal Bid Function and Equivalent Matched Energy Constraintsmentioning

confidence: 99%

“…Some researchers (Conejo and Arroyo, 2002) presented a mixed-integer programming model to optimize the production schedule of a single unit with a simple bidding strategy. Furthermore, in another study (Gountis and Bakirtzis, 2004), the approximation of step-wise bid curves by linear bid functions, based on the marginal costs was considered, although in a context without BC. Also, in an earlier study (Ni et al, 2004), the concept of price-power function, which is similar to the matched energy function defined in our study, was used to derive the optimal bid curves of a hydro-thermal system under the assumption that the spot prices for the day-ahead and reserve markets behave as a Markov Chain.…”

Section: Introductionmentioning

confidence: 99%

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A stochastic programming model for the optimal electricity market bid problem with bilateral contracts for thermal and combined cycle units

2011

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This paper developed a stochastic programming model that integrated the most recent regulation rules of the Spanish peninsular system for bilateral contracts in the day-ahead optimal bid problem. Our model allows a price-taker generation company to decide the unit commitment of the thermal and combined cycle programming units, the economic dispatch of the bilateral contracts between all the programming units and the optimal sale bid by observing the Spanish peninsular regulation. The model was solved using real data of a typical generation company and a set of scenarios for the Spanish market price. The results are reported and analyzed.

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“…The main driving force behind this radical change from the intensively monopoly to a deregulated electricity market was the fact that competition could result in an efficient utilization of resources; that leads to supplying the end customer with a cheaper but yet more reliable energy supply. Following the early attempts of Latin America, Britain, Australia, California, and the Scandinavian countries, most countries around the world have found themselves restructuring their respective electricity markets [1].…”

Section: Research Motivationmentioning

confidence: 99%

Forecasting spot electricity market prices using time series models

Mazengia

Tuấn

2008

2008 IEEE International Conference on Sustainable Energy Technologies

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The worldwide electricity industry is in an era where an overwhelming transition towards deregulation is taking place. Since its start in the early 1980s, the industry has been in a continuous change to a different atmosphere; the ultimate benefit being providing the enduse customer with a reliable but yet cheaper electricity supply. In the old monopolistic system, utilities were the only authoritative body to set the tariff based on their aggregate cost. In the contrary, as a newly emerging structure, deregulation has come-up with a new way of functioning; leading generation, transmission and distribution to be independent activities. This market is generally a customer driven market and since the early days of deregulation, price forecasting has become an important task to all the market players engaged. Unless each body is fairly aware of the market, it might result in losses to a generating company or inadequate supply to the system; resulting in a huge crisis. Hence, understanding the market behavior is of vital importance for the well-functioning of the industry and the benefit of each party.This thesis addresses the importance of electricity price forecasting in the deregulated market. After a comprehensive review of previous works in the subject, it is concluded that forecast accuracy varies depending on the forecast method used and the electricity market under study. In this thesis multiple linear regression approach are proposed to predict next day's electricity prices. The developed models are tested in the Nord Pool and the Ontario electricity markets and satisfactory results are achieved. Comparing the forecast results from the two markets, results in the Nord Pool market are significantly more accurate than the Ontario market. This arises from the fact that Ontario market is very volatile and its market prices are hardly predictable. The forecast results in this thesis work show that the proposed models perform very well with the absence of volatility. For instance, the developed models for the first week of December 2007 generated results with weekly Mean Absolute Percentage Error (MAPE) of about 5.04%. In early spring even better results are achieved where the models generated results with a daily MAPE of up to as low as 1.83% and weekly MAPE of about 2.96%. On the other hand, during summer where prices vary considerably even between hours of the same day, the models generated a relatively poor forecast. A day with a price of as low as 2.96 EURO/MWh to as high as 23.52 EURO/MWh is observed and this price fluctuation contributed for poor forecast. For instance, the models generated forecasts with a weekly MAPE of about 13% for the period from August 29 to Sept. 4, 2007. For the Ontario market, these models generated forecast results with a weekly MAPE of about 17%.

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Bidding Strategies for Electricity Producers in a Competitive Electricity Marketplace

Cited by 203 publications

References 29 publications

Bidding Strategy for Aggregators of Electric Vehicles in Day-Ahead Electricity Markets

Bidding Strategy for Aggregators of Electric Vehicles in Day-Ahead Electricity Markets

A stochastic programming model for the optimal electricity market bid problem with bilateral contracts for thermal and combined cycle units

Forecasting spot electricity market prices using time series models

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