Analysis of the value for unit commitment of improved load forecasts (original) (raw)
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IEEE Transactions on Power Systems, 2000
The paper considers the use of production costing models for short term operations planning. In this period an accurate forecast of the hourly ambient temperature during the study horizon and the knowledge of the initial operating states of each generating unit are assumed to be available. Uncertainties in the production cost and marginal cost resulting from the generator availabilities and hourly loads are considered. Measures of these uncertainties are obtained from an analysis of two years' actual hourly load data. Monte Carlo simulation is used to estimate the contributions of uncertainties resulting from load and generator availabilities to the variance of production costs. It is found that ignoring the ambient temperature forecasts and correlation among hourly loads results in inaccurate prediction of costs, and that load uncertainty accounts for a significant portion of the variance of production costs. The initial states of the generating units also have an important effect on the costs. VIII. BIOGRAPHIES Jorge Valenzuela received his B.S. in Electronic Engineering from Catholic University of North in Chile in 1984. He obtained a M.S. degree in Statistics at CIENES and a M.S. degree in Industrial Engineering at Northern Illinois University in 1985 and 1996, respectively. Currently he is a Ph.D. student at the University of Pittsburgh.
Impact of Wind Power Forecasting on Unit Commitment and Dispatch
2009
The impact of wind power forecasting on unit commitment and dispatch is investigated in this paper. We present two unit commitment methods to address the variability and intermittency of wind power. The uncertainty in wind power forecasting is captured by a number of scenarios in the stochastic unit commitment approach, while a point forecast of wind power output is used in the deterministic alternative. Several cases with different wind power forecasts and reserve requirements are simulated. The preliminary results show that the quality of wind power forecasting has a great impact on unit commitment and dispatch. The stochastic method shows its value in terms of relatively lower dispatch cost. However, the dispatch results are also sensitive to the level of reserve requirement. Our results so far indicate that a deterministic method combined with an increased reserve requirement can produce results that are comparable to the stochastic case.
Quantifying the value of improved wind energy forecasts in a pool-based electricity market
Renewable Energy, 2015
This work illustrates the influence of wind forecast errors on system costs, wind curtailment and generator dispatch in a system with high wind penetration. Realistic wind forecasts of different specified accuracy levels are created using an auto-regressive moving average model and these are then used in the creation of day-ahead unit commitment schedules. The schedules are generated for a model of the 2020 Irish electricity system with 33% wind penetration using both stochastic and deterministic approaches. Improvements in wind forecast accuracy are demonstrated to deliver: (i) clear savings in total system costs for deterministic and, to a lesser extent, stochastic scheduling; (ii) a decrease in the level of wind curtailment, with close agreement between stochastic and deterministic scheduling; and (iii) a decrease in the dispatch of open cycle gas turbine generation, evident with deterministic, and to a lesser extent, with stochastic scheduling.
IEEE Transactions on Sustainable Energy, 2018
Due to their uncertain and variable nature, the large-scale integration of wind and solar power poses significant challenges to the generator scheduling process in power systems. To support this process, system operators require using repeatedly updated forecasts of the best possible quality for renewable power. Motivated by this, the present work aims to study the benefits of incorporating spatiotemporal dependence and seasonalities into probabilistic forecasts for the intra-day unit commitment (UC) process. With this purpose, a highly efficient forecasting-optimization scheme is proposed, which is composed by a detrended periodic vector autoregressive model and a technology-clustered interval UC model. The proposed approach is tested on a 120 GW power system with 210 conventional generators using real wind and solar measurements, and compared to existing deterministic and stochastic UC techniques alongside standard forecasting methods. Extensive computational experiments show that: i) the incorporation of spatiotemporal dependence and seasonalities into forecasts translates in a reduction of up to 1.55% in operational costs for a daily UC relative to standard practice; ii) the application of intra-day instead of daily UC runs further reduce operational costs in up to 1.51%; iii) the proposed forecasting-optimization scheme takes less than 10 hours to simulate a whole year. Index Terms-unit commitment, interval optimization, forecasting, autoregressive models, solar power, wind power, renewable uncertainty.
Wind power forecasting, unit commitment, and electricity market operations
IEEE Power and Energy Society General Meeting, 2011
In this paper we discuss the use of wind power forecasting in electricity market operations. In particular, we demonstrate how probabilistic forecasts can contribute to address the uncertainty and variability in wind power. We focus on efficient use of forecasts in the unit commitment problem and discuss potential implications for electricity market operations.
Modeled Impacts of Solar Forecast Error on Utility Production Cost
Solar forecast error can be represented in electric utility production cost modeling to assess impacts of uncertainty in different scenarios. We use reforecasts (a.k.a., historical forecasts, hindcasts) that were developed to be concurrent with synthetic solar generation profiles (matching each hour in the same year), along with asynchronous forecasts (i.e., with errors remapped from different hours and/or years), to explore impacts on model results. We demonstrate that production cost increases non-linearly with forecast error, and that our method for remapping asynchronous forecast errors to a solar generation profile produces results that are similar to the concurrent forecasts, but with about 20\% lower cost impacts due to forecast error.
Recent Trends in Variable Generation Forecasting and Its Value to the Power System
IEEE Transactions on Sustainable Energy, 2014
The rapid deployment of wind and solar energy generation systems has resulted in a need to better understand, predict, and manage variable generation. The uncertainty around wind and solar power forecasts is still viewed by the power industry as being quite high, and many barriers to forecast adoption by power system operators still remain. In response, the U.S. Department of Energy has sponsored, in partnership with the National Oceanic and Atmospheric Administration, public, private, and academic organizations, two projects to advance wind and solar power forecasts. Additionally, several utilities and grid operators have recognized the value of adopting variable generation forecasting and have taken great strides to enhance their usage of forecasting. In parallel, power system markets and operations are evolving to integrate greater amounts of variable generation. This paper will discuss the recent trends in wind and solar power forecasting technologies in the U.S., the role of forecasting in an evolving power system framework, and the benefits to intended forecast users.
Review and evaluation of historic electricity forecasting experience (1960-1985)
1989
Power system expansion plarning is subject to a considerable degree of uncertainty with respect to load forecast, time and cost-to-completion of new plant, fuel costs and technological innovation. Many power system planners continue to use forecasts of these planning parameters as certainty-equivalent characterizations of the future, despite the generally poor concurrence between these ex-ante forecasts and actual ex-post situations. Such disregard of uncertainty greatly enhances the prospects of future imbalances between the demand for power and the system supply capability, as well as erroneously biasing the selection of plant types to meet demand at least cost. This study focuses on load forecasts as a source of planning uncertainty by identifying the nature and extent of forecast inaccuracy per se rather than by analysis of the causes of unce-tainty. It evaluates the historic performance record of electricity sales forecasts in World Bank member countries, based on over 200 separate forecasts (1,600 annual forecasts) from 45 countries over the period 1960-1985. A comparative evaluation of U.S. forecasting experience is also included. The study identifies a strong historic bias toward overestimation which cuts across sales forecasts for all regions, vintages, system sizes and economic circumstances. Forecast accuracy deteriorated consistently with longer forecast horizons. More recent forecasts have not tended to become more accurate, despite the general increase in effort and sophistication of forecasting techniques. This appears to suggest that the scope for reducing uncertainty in load forecasting appears to be insufficient to support the deterministic approach to power system planning. The study concludes that p!anning approaches should explicitly take account of this apparently unavoidable uncertainty, and it presents some recommendations for research into such approaches. The study also investigated the relationship between forecast accuracy and
The value of day-ahead solar power forecasting improvement
Solar Energy, 2016
The value of day-ahead solar power forecasting improvements was analyzed by simulating the operation of the Independent System Operator-New England (ISO-NE) power system under a range of scenarios with varying solar power penetrations and solar power forecasting improvements. The results showed how the integration of solar power decreased operational electricity generation costs, by decreasing fuel and variable operation and maintenance costs, while decreasing start and shutdown costs of fossil fueled conventional generators. Solar power forecasting improvements changed the impacts that the uncertainty of solar power has on bulk power system operations; electricity generation from the fast start and lower efficiency power plants, ramping of all generators, start and shutdown costs, and solar power curtailment were all reduced. These impacts led to a reduction in overall operational electricity generation costs in the system that translates into an annual economic value for improving solar power forecasting. I.
Impact of forecast errors on expansion planning of power systems with a renewables target
European Journal of Operational Research, 2016
This paper analyzes the impact of production forecast errors on the expansion planning of a power system and investigates the influence of market design to facilitate the integration of renewable generation. For this purpose, we propose a stochastic programming modeling framework to determine the expansion plan that minimizes system-wide investment and operating costs, while ensuring a given share of renewable generation in the electricity supply. Unlike existing ones, this framework includes both a day-ahead and a balancing market so as to capture the impact of both production forecasts and the associated prediction errors. Within this framework, we consider two paradigmatic market designs that essentially differ in whether the day-ahead generation schedule and the subsequent balancing re-dispatch are co-optimized or not. The main features and results of the model setups are discussed using an illustrative four-node example and a more realistic 24-node case study.