Co-optimizing sales of energy and capacity in a hydropower scheduling model

Helseth, Arild; Mo, Birger; Fodstad, Marte; Hjelmeland, Martin N.

doi:10.1109/ptc.2015.7232252

Cited by 10 publications

(12 citation statements)

References 10 publications

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“…The value of R max h should be set by the modeler to realistically represent the amount of reserves required by the TSO, and will depend on the type of reserves being considered. In case of primary reserves, the capacity sold to the primary reserve market cannot exceed the physical limit dictated by the droop settings in the turbine governors, as described in [19].…”

Section: Decomposed Weekly Decision Problemmentioning

confidence: 99%

“…On the other hand, the approach depends on the assumption that inter-stage initial states and decisions cannot depend on realization of intra-stage uncertainty, and the use of SDP limits the number of reservoirs that can be included in the inter-stage part within reasonable computation time. A different and more uniform method for incorporating sales of spinning reserves in a medium-term hydropower scheduling model was presented in [19]. It extends the hybrid SDP/SDDP algorithm in [13] by allowing co-optimized sales of energy and reserve capacity, treating the capacity price as deterministic.…”

Section: Introductionmentioning

confidence: 99%

“…The novel contributions of this paper regard improvements of the model presented in [19] to more realistically address stochasticity, decision sequences and operational constraints seen in the multi-market scheduling. Firstly, the price model is updated to allow stochastic reserve capacity prices.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Medium-Term Hydropower Scheduling Considering Energy and Reserve Capacity Markets

Helseth

Fodstad

2016

IEEE Trans. Sustain. Energy

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This paper describes a method for optimal scheduling of hydropower systems for a profit maximizing, price-taking and risk neutral producer selling energy and capacity to separate and sequentially cleared markets. The method is based on a combination of stochastic dynamic programming (SDP) and stochastic dual dynamic programming (SDDP), and treats inflow to reservoirs and prices for energy and capacity as stochastic variables.The proposed method is applied in a case study for a Norwegian watercourse, quantifying the expected changes in schedules and water values when going from an energy-only market to a joint treatment of energy and reserve capacity markets.Arild Helseth (M'10) was born on Stord, Norway in 1977. He received the M.Sc. and Ph.D. degrees in electrical power engineering from the Norwegian University of Science and Technology. Currently he works at SINTEF Energy Research with hydro-thermal and hydropower scheduling models and methods.

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Section: Decomposed Weekly Decision Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Medium-Term Hydropower Scheduling Considering Energy and Reserve Capacity Markets

Helseth

Fodstad

2016

IEEE Trans. Sustain. Energy

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“…The weekly HPS subproblem is formulated as an LP problem described in (10), which is an extension of (3). The formulation is similar to that used in [33].…”

Section: B Hps Subproblemmentioning

confidence: 99%

Optimal Hydropower Maintenance Scheduling in Liberalized Markets

Helseth

Fodstad

2018

IEEE Trans. Power Syst.

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Maintenance scheduling is an important and complex task in hydropower systems. In a liberalized market, the generation company will schedule maintenance periods to maximize the expected profit. This paper describes a method for hydropower maintenance scheduling suitable for a profit maximizing, price-taking and risk neutral hydropower producer selling energy and reserve capacity to separate markets. The method uses Benders decomposition principle to coordinate the timing of power plant maintenance with the medium-term scheduling of the hydropower system, treating inflow to reservoirs and prices for energy and reserve capacity as stochastic variables.The proposed method is applied in a case study for a Norwegian watercourse, and results, in terms of maintenance schedules and computational performance, are presented and discussed.

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“…Computation times are not reported. Helseth et al (2015) propose a stochastic dual dynamic program that manages a multi-reservoir fleet of hydro plants over the course of one year in weekly time stages. The state variables of the problem record the weekly water levels of the reservoirs as well as the spot prices, while the weekly subproblems manage the commitments on the spot and the primary reserve markets.…”

Section: Introductionmentioning

confidence: 99%

A Planner-Trader Decomposition for Multi-Market Hydro Scheduling

Schindler¹,

Rujeerapaiboon²,

Kühn³

et al. 2021

Preprint

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Peak/off-peak spreads on European electricity forward and spot markets are eroding due to the ongoing nuclear phaseout and the steady growth in photovoltaic capacity. The reduced profitability of peak/offpeak arbitrage forces hydropower producers to recover part of their original profitability on the reserve markets. We propose a bi-layer stochastic programming framework for the optimal operation of a fleet of interconnected hydropower plants that sells energy on both the spot and the reserve markets. The outer layer (the planner's problem) optimizes end-of-day reservoir filling levels over one year, whereas the inner layer (the trader's problem) selects optimal hourly market bids within each day. Using an information restriction whereby the planner prescribes the end-of-day reservoir targets one day in advance, we prove that the trader's problem simplifies from an infinite-dimensional stochastic program with 25 stages to a finite twostage stochastic program with only two scenarios. Substituting this reformulation back into the outer layer and approximating the reservoir targets by affine decision rules allows us to simplify the planner's problem from an infinite-dimensional stochastic program with 365 stages to a two-stage stochastic program that can conveniently be solved via the sample average approximation. Numerical experiments based on a cascade in the Salzburg region of Austria demonstrate the effectiveness of the suggested framework.

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Co-optimizing sales of energy and capacity in a hydropower scheduling model

Cited by 10 publications

References 10 publications

Optimal Medium-Term Hydropower Scheduling Considering Energy and Reserve Capacity Markets

Optimal Medium-Term Hydropower Scheduling Considering Energy and Reserve Capacity Markets

Optimal Hydropower Maintenance Scheduling in Liberalized Markets

A Planner-Trader Decomposition for Multi-Market Hydro Scheduling

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