Short-term resource scheduling in multi-area hydrothermal power systems

Ferreira, L.A.F.M.; Andersson, Tord; Imparato, C.F.; Miller, T.E.; Pang, Chee Khiang; Svoboda, A.J.; Vojdani, Ali

doi:10.1016/0142-0615(89)90029-x

Cited by 109 publications

(35 citation statements)

References 30 publications

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“…In the short-term, a producer needs to forecast electricity prices to derive its bidding strategy in the pool and to optimally schedule its electric energy resources [5]. In a regulated environment, traditional generation scheduling of energy resources was based on cost minimization, satisfying the electricity demand and all operating constraints [6]. Therefore, the key issue was how to accurately forecast electricity demand.…”

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

359

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%

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

Catalão

Mariano

Mendes

et al. 2007

Electric Power Systems Research

359

178

View full text Add to dashboard Cite

show abstract

“…However, this method seems to take a long time to locate a feasible solution because only one m t is updated at a time. A natural extension is to simultaneously update at each iteration the m t corresponding to all the hours that the SRR is violated [3,2]. This speeds up the feasibility phase at a cost of possible overcommitment in the generating units [2].…”

Section: Phase 1: the Subgradient Algorithmmentioning

confidence: 99%

“…Among them, Lagrangian relaxation methods are now widely used approaches to solve unit commitment [2][3][4]. At PG&E, the Hydro-Thermal Optimization (HTO) program was developed almost a decade ago, based on the Lagrangian relaxation approach [3]. In our recent work, the Lagrangian relaxation-based algorithm has been extended to schedule thermal units under ramp rate constraints [5] and transmission constraints [6].…”

Section: Introductionmentioning

confidence: 99%

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99/03221 Price-based adaptive spinning reserve requirements in power system scheduling

1999

Fuel and Energy Abstracts

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In a deregulated electricity market such as the California WEPEX, spinning reserves must be explicitly identified as an ancillary service and priced. Additionally, scheduling coordinators who match suppliers and demands may either self-provide spinning reserves, or rely on the Independent System Operator (ISO) to provide reserves at the spot price. The deregulated market structure makes explicit the implicit softness that has always been recognized in the reserve constraints: additional reserves may have value even when a minimum reserve requirement has been met. In this paper we formulate the spinning reserve requirement (SRR) as a function of the endogenously determined marginal values of reserves. The spinning reserve requirement depends, according to a non-increasing response function, on a price/value signal. We present three power system scheduling algorithms in which this price/value signal is updated at each iteration of a dual optimization. Game theory is used to interpret the proposed algorithms. Numerical test results are also presented. ᭧

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“…Where the problem includes stochastic quantities such as inflows to reservoirs or energy prices, the corresponding forecasts are used. STHS is guided by specified hourly weighting factors, quantifying the energy price at each hour [3]. The goal is to maximize the value of total hydroelectric power generation throughout the time horizon, satisfying all physical and operational constraints, and consequently to maximize the profit of the hydroelectric utility from selling energy into the electric market [4].…”

Section: Introductionmentioning

confidence: 99%

Parameterisation effect on the behaviour of a head-dependent hydro chain using a nonlinear model

Catalão

Mariano

Mendes

et al. 2006

Electric Power Systems Research

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This paper is on the problem of short-term hydro scheduling (STHS), particularly concerning a head-dependent hydro chain. We use a method based on nonlinear programming (NLP), namely quadratic programming, to consider hydroelectric power generation a function of water discharge and of the head. The method has been applied successfully to solve a test case based on a realistic cascaded hydro system with a negligible computational time requirement and is also applied to show that the role played by reservoirs in the hydro chain do not depend only on their relative position. As a new contribution to earlier studies, which presented reservoir operation rules mainly for medium and long-term planning procedures, we show that the physical data defining hydro chain parameters used in the nonlinear model have an effect on the STHS, implying different optimal storage trajectories for the reservoirs accordingly not only with their position in the hydro chain but also with the new parameterisation defining the data for the hydro system. Moreover, considering head dependency in the hydroelectric power generation, usually neglected for hydro plants with a large storage capacity, provides a better short-term management of the conversion of the potential energy available in the reservoirs into electric energy, which represents a major advantage for the hydroelectric utilities in a competitive electricity market.

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Short-term resource scheduling in multi-area hydrothermal power systems

Cited by 109 publications

References 30 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

99/03221 Price-based adaptive spinning reserve requirements in power system scheduling

Parameterisation effect on the behaviour of a head-dependent hydro chain using a nonlinear model

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