Evaluating a Stochastic-Programming-Based Bidding Model for a Multireservoir System

Aasgård, Ellen Krohn; Andersen, Gørild Slettjord; Fleten, Stein–Erik; Haugstvedt, Daniel

doi:10.1109/tpwrs.2014.2298311

Cited by 39 publications

(25 citation statements)

References 15 publications

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“…is a random variable which obeys the standard normal distribution , and and are the standard deviations for day-ahead and balancing prices. The energy value of an ESS is updated every day and set as the average spot price, as stated in most papers in the field of hydro-generator [29]. Parameters of ESS are listed in Table I.…”

Section: A Case Designmentioning

confidence: 99%

Optimal Offering and Operating Strategies for Wind-Storage Systems With Linear Decision Rules

Ding

Pinson

et al. 2016

IEEE Trans. Power Syst.

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Abstract-The participation of wind farm-energy storage systems (WF-ESS) in electricity markets calls for an integrated view of day-ahead offering strategies and real-time operation policies. Such an integrated strategy is proposed here by co-optimizing offering at the day-ahead stage and operation policy to be used at the balancing stage. Linear decision rules are seen as a natural approach to model and optimize the real-time operation policy. These allow enhancing profits from balancing markets based on updated information on prices and wind power generation. Our integrated strategies for WF-ESS in electricity markets are optimized under uncertainty in both wind power and price predictions. The resulting stochastic optimization problem readily yields optimal offers and linear decision rules. By adding a risk-aversion term in form of conditional value at risk into the objective function, the optimization model additionally provides flexibility in finding a trade-off between profit maximization and risk management. Uncertainty in wind power generation, as well as day-ahead and balancing prices, takes the form of scenario sets, permitting to reformulate the optimization problem as a linear program. Case studies validate the effectiveness of the strategy proposed by highlighting and quantifying benefits w.r.t. other existing strategies.

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Section: A Case Designmentioning

confidence: 99%

Optimal Offering and Operating Strategies for Wind-Storage Systems With Linear Decision Rules

Ding

Pinson

et al. 2016

IEEE Trans. Power Syst.

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“…In [16], formal optimization of bids based on stochastic programming is compared to heuristic methods currently used by the industry by simulating the operation of a reservoir system over time.…”

Section: Simulationmentioning

confidence: 99%

Hydropower Bidding Using Linearized Start-Ups

Aasgård

2017

Energies

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Hydropower producers must submit bids to electricity market auctions where they state their willingness to produce power. These bids may be determined using a mixed-integer linear stochastic program. However, for large interconnected river systems, this program may be too complex to be solved within the time limits set by current market rules. This paper investigates whether a linear approximation to start-ups can be used to reduce the computational burden without significantly degrading the solution quality. In order to investigate the trade-off of time versus solution quality, linear approximation is compared to a formulation that uses binary variables in a case study that simulates the operation of a reservoir system over time.

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“…Through the use of stochastic optimization techniques it has been shown [1], [2] that hydro power producers with serially coupled watercourses could improve their profits by improving bidding strategies. Aasgård and Andersen [1] did a comparison of stochastic bidding optimization and best industry practise using deterministic optimization, and found a profit increase of 0.6 %.…”

Section: Introductionmentioning

confidence: 99%

“…Aasgård and Andersen [1] did a comparison of stochastic bidding optimization and best industry practise using deterministic optimization, and found a profit increase of 0.6 %. This may seem insignificant, but given the large turnover, the improvement may still be large enough for investments.…”

Section: Introductionmentioning

confidence: 99%