Generation scheduling in a deregulated system. The Norwegian case

Fosso, Olav Bjarte; Gjelsvik, Anders; Haugstad, A.; Mo, Birger; Wangensteen, I.

doi:10.1109/59.744487

Cited by 253 publications

(118 citation statements)

References 5 publications

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“…The hard computing techniques, where an exact model of the system is built and the solution is found using algorithms that consider the physical phenomena that govern the process, include time series models [10], auto regressive -AR models [9] and auto regressive integrated moving average -ARIMA models [12]. This approach can be very accurate, but it requires a lot of information, and the computational cost is very high [13].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the key issue was how to accurately forecast electricity demand. In a deregulated environment, since generation scheduling of energy resources, such as hydro [7] and thermal resources [8], is now based on profit maximization [9], it is an accurate price forecasting that embodies crucial information for any decision making. Consumers need short-term price forecasts for the same reasons as producers.…”

Section: Introductionmentioning

confidence: 99%

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Short-term electricity prices forecasting in a competitive market: A neural network approach

Catalão

Mariano

Mendes

et al. 2007

Electric Power Systems Research

356

178

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This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-term electricity prices forecasting in a competitive market: A neural network approach

Catalão

Mariano

Mendes

et al. 2007

Electric Power Systems Research

356

178

View full text Add to dashboard Cite

show abstract

“…Um modelo clássico de leilão hidrotérmico, adotado pela Noruega, é fornecido em (Fosso, Gjesvilk, Haugstad, Mo, & Wangensteen, 1999), no qual a função de bem estar social é maximizada em um problema monoperíodo escrito para cada intervalo de tempo, separadamente. Além disso, a transmissão não é representada neste modelo de leilão monoperíodo adotado.…”

Section: Motivação Da Pesquisaunclassified

Modelo de leilão multiperíodo para sistemas hidrotérmicos com representação da transmissão

Breda¹,

Nepomuceno²

2013

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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“…In a hydro-thermal market context, the system operating costs are typically estimated using long-term hydro-thermal scheduling (LTHTS) tools [1,2]. The LTHTS problem is dynamic since reservoir storages provide couplings between the stage-wise decisions in the scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

Detailed long‐term hydro‐thermal scheduling for expansion planning in the Nordic power system

Helseth

Henden

et al. 2017

IET Generation, Transmission & Distribution

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Abstract:The authors describe a method for long-term hydro-thermal scheduling allowing treatment of detailed large-scale hydro systems. Decisions for each week are determined by solving a two-stage stochastic linear programming problem considering uncertainty in weather and exogenous market prices. The overall scheduling problem is solved by embedding such two-stage problems in a rolling horizon simulator. The method is verified on data for the Nordic power system, studying the incremental changes in expected socio-economic surplus for expansions in both the transmission and generation systems. Comparisons are made with a widely used existing long-term hydro-thermal scheduling model. The results indicate that the model is well suited to valuate the flexibility of hydropower in systems with a high share of intermittent renewable generation. Nomenclature Index setsA set of price zones C o, t set of Benders cuts for scenario o and week t D a set of price-elastic demand steps in zone a G a set of thermal generators in zone a ℋ a set of hydropower modules in zone a K set of time steps within the week ℒ a set of interconnections connected to zone a ℳ set of exogenous markets N h set of efficiency-curve segments for module h P a set of pumps in zone a S set of N S scenarios S R reduced set of N R scenarios ω h

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Generation scheduling in a deregulated system. The Norwegian case

Cited by 253 publications

References 5 publications

Short-term electricity prices forecasting in a competitive market: A neural network approach

Short-term electricity prices forecasting in a competitive market: A neural network approach

Modelo de leilão multiperíodo para sistemas hidrotérmicos com representação da transmissão

Detailed long‐term hydro‐thermal scheduling for expansion planning in the Nordic power system

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