A description of the operative decision-making process of a power generating company on the Nordic electricity market

Scharff, Richard; Egerer, Jonas; Söder, Lennart

doi:10.1007/s12667-013-0104-2

Cited by 13 publications

(5 citation statements)

References 24 publications

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“…[1], [2]. In a liberalized power market, one typically decomposes the overall problem into three hierarchies (long-, medium-, and short-term) according to the system boundary, the level of required details in the hydro system and the representation of different stochastic variables [3], [4], [5]. In this work we consider the medium-term hydropower scheduling (MTHS) problem, aiming at maximizing a single producer's profit.…”

Section: Introductionmentioning

confidence: 99%

Nonconvex Medium-Term Hydropower Scheduling by Stochastic Dual Dynamic Integer Programming

Hjelmeland

Zou

Helseth

et al. 2019

IEEE Trans. Sustain. Energy

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Hydropower producers rely on stochastic optimization when scheduling their resources over long periods of time. Due to its computational complexity, the optimization problem is normally cast as a stochastic linear program. In a future power market with more volatile power prices, it becomes increasingly important to capture parts of the hydropower operational characteristics that are not easily linearized, e.g. unit commitment and nonconvex generation curves.Stochastic dual dynamic programming (SDDP) is a stateof-the-art algorithm for long-and medium-term hydropower scheduling with a linear problem formulation. A recently proposed extension of the SDDP method known as stochastic dual dynamic integer programming (SDDiP) has proven convergence also in the nonconvex case. We apply the SDDiP algorithm to the medium-term hydropower scheduling (MTHS) problem and elaborate on how to incorporate stagewise dependent stochastic variables on the right-hand sides and the objective of the optimization problem. Finally, we demonstrate the capability of the SDDiP algorithm on a case study for a Norwegian hydropower producer.The case study demonstrates that it is possible but timeconsuming to solve the MTHS problem to optimality. However, the case study shows that a new type of cut, known as strengthened Benders cut, significantly contributes to closing the optimality gap compared to classical Benders cuts.

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Section: Introductionmentioning

confidence: 99%

Nonconvex Medium-Term Hydropower Scheduling by Stochastic Dual Dynamic Integer Programming

Hjelmeland

Zou

Helseth

et al. 2019

IEEE Trans. Sustain. Energy

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“…A typical liberalized electricity market works on a short term basis. An auction takes place on the day before the energy is delivered, the so-called day-ahead market [18]. Further markets exist such as the reserve capacity market, intra-day market and balancing market [18,19,20].…”

Section: Introductionmentioning

confidence: 99%

“…An auction takes place on the day before the energy is delivered, the so-called day-ahead market [18]. Further markets exist such as the reserve capacity market, intra-day market and balancing market [18,19,20]. The price of electricity from one day to another is partly unknown and more unpredictable as more intermittent renewable energy technologies get into the system.…”

Section: Introductionmentioning

confidence: 99%

A novel bidding method for combined heat and power units in district heating systems

Blanco¹,

Andersen²,

Guericke³

et al. 2019

Energy Syst

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We propose a bidding method for the participation of combined heat and power (CHP) units in the day-ahead electricity market. More specifically, we consider a district heating system where heat can be produced by CHP units or heat-only units, e.g., gas or wood chip boilers. We use a mixed-integer linear program to determine the optimal operation of the portfolio of production units and storages on a daily basis. Based on the optimal production of subsets of units, we can derive the bidding prices and amounts of electricity offered by the CHP units for the day-ahead market. The novelty about our approach is that the prices are derived by iteratively replacing the production of heat-only units through CHP production. This results in an algorithm with a robust bidding strategy that does not increase the system costs even if the bids are not won. We analyze our method on a small realistic test case to illustrate our method and compare it with other bidding strategies from literature, which consider CHP units individually. The analysis shows that considering a portfolio of units in a district heating system and determining bids based on replacement of heat production of other units leads to better results.

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“…In a liberalised market context, practical hydropower scheduling is a continuous process where strategies are updated on a weekly or even daily basis using the information available at that time [22]. This process can be resembled in rolling horizon simulator scheme, as demonstrated by the authors in [23,24], combining short-term decisions and medium-term strategy computation in sequence.…”

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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A description of the operative decision-making process of a power generating company on the Nordic electricity market

Cited by 13 publications

References 24 publications

Nonconvex Medium-Term Hydropower Scheduling by Stochastic Dual Dynamic Integer Programming

Nonconvex Medium-Term Hydropower Scheduling by Stochastic Dual Dynamic Integer Programming

A novel bidding method for combined heat and power units in district heating systems

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

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