An optimal hydrothermal planning model for the New Zealand power system

Halliburton, T. S.

doi:10.1080/1448837x.2004.11464107

Cited by 5 publications

(5 citation statements)

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“…A nominal head H h 0 is used as a reference for the efficiencies η nh , and the generation is adjusted according to the actual head H h corresponding to initial reservoir volume for the current week. Since head enters the optimisation problem as a parameter and not a variable, we do only account for the immediate impact of head in (9). Pre-computed sensitivities c hk W are used in the objective (7) to consider the expected future value of head, according to the procedure in [8].…”

Section: Operational Decision Problemmentioning

confidence: 99%

“…Power balances for exogenous market areas are defined in (10). The transmission system connecting zones and markets in (9) and (10) is represented by limiting flows through maximum flow capacities and let transmission losses depend linearly on the flows. The model allows dumping of excess power d ak to avoid negative prices, and thus circumvent unlogical loading of the discharge segments in (9).…”

Section: Operational Decision Problemmentioning

confidence: 99%

“…The transmission system connecting zones and markets in (9) and (10) is represented by limiting flows through maximum flow capacities and let transmission losses depend linearly on the flows. The model allows dumping of excess power d ak to avoid negative prices, and thus circumvent unlogical loading of the discharge segments in (9). Note that the details in the modelling of the thermal market and power flows correspond with the data used in the case study, and that further constraints on the system operation, such as representation of linearised start-up costs [34] and DC power flows [35], will not be discussed here.…”

Section: Operational Decision Problemmentioning

confidence: 99%

“…The stochastic dual dynamic programming (SDDP) presented in [7] is widely used for operational scheduling, see e.g. [2,8,9]. It allows detailed representation of the hydropower in large-scale systems [10,11].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Operational Decision Problemmentioning

confidence: 99%

Section: Operational Decision Problemmentioning

confidence: 99%

Section: Operational Decision Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Gorenstin et ai, [8] recommended solution algorithm that was based on SDDP that expected to split the multi-stage stochastic problem into one stage sub-problems. In [9], Halliburton found that SDDP was expected to incorporate a strategic sampling in state space that allowed dealing with only associated regions. Investment analysis, management of system security in the presence of SDDP model is recommended.…”

Section: Introductionmentioning

confidence: 99%

Water valuation in Vietnamese electric power generation market

Duc

Singh

Ongsakul

2011

2011 International Conference &Amp; Utility Exhibition on Power and Energy Systems: Issues and Prospects for Asia (ICUE)

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This work has been devoted to water valuation regarding Vietnamese reservoirs in a competitive electricity market environment. Water value concept, which has been assumed as fuel cost of water, helped to reflect the exploiting cost of water resource in the context of hydrothermal power system. In this work, the Stochastic Dual Dynamic Programming (SDDP) approach has been suggested for modeling the water valuation problem. Further, a trade-off has been obtained between im mediate and future operating cost. Effectiveness of the developed approach has been investigated in Vietnamese electric power generation market for the period of 2011-2012. The results have been obtained in terms of the water value for major reservoirs, short-run marginal cost (SRMC) and classification of generating units as well.Moreover, optimal generation scheduling of cascading reservoirs and their respective trajectories have also been analyzed in this work.

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