Adaptive Robust Optimization with Dynamic Uncertainty Sets for Multi-Period Economic Dispatch under Significant Wind

The exceptional benefits of wind power as an environmentally responsible renewable energy resource have led to an increasing penetration of wind energy in today's power systems. This trend has started to reshape the paradigms of power system operations, as dealing with uncertainty caused by the highly intermittent and uncertain wind power becomes a significant issue. Motivated by this, we present a new framework using adaptive robust optimization for the economic dispatch of power systems with high level of wind penetration. In particular, we propose an adaptive robust optimization model for multi-period economic dispatch, and introduce the concept of dynamic uncertainty sets and methods to construct such sets to model temporal and spatial correlations of uncertainty. We also develop a simulation platform which combines the proposed robust economic dispatch model with statistical prediction tools in a rolling horizon framework. We have conducted extensive computational experiments on this platform using real wind data. The results are promising and demonstrate the benefits of our approach in terms of cost and reliability over existing robust optimization models as well as recent look-ahead dispatch models.Comment: Accepted for publication at IEEE Transactions on Power System

Predictive and Corrective Scheduling in Electric Energy Systems with Variable Resources

In the past decade, there has been sustained efforts around the globe in developing renewable energy-based generation in power systems. However, many renewables such as wind and solar are variable resources. They pose significant challenges to near real-time power system operations. This dissertation focuses on introducing and testing advanced scheduling algorithms for electric power systems with high penetration of variable resources. A novel predictive and optimal corrective look-ahead dispatch framework for real-time economic operation is proposed. This dissertation has four key parts. First, the basic framework of look-ahead dispatch is introduced. Different from conventional static economic dispatch, look-ahead dispatch is the fundamental function for future power system scheduling. Taking the whole dispatch horizon into account, look-ahead dispatch has a better economic performance in scheduling the resources in power systems. The decision-making of look-ahead dispatch is cost-effective, especially when handling with high penetration of variable resources. Second, we study the benefits of look-ahead dispatch in system security enhancement. An early detection algorithm is proposed to predict and identify potential security risks in the system. The proposed optimal corrective measures can be computed to prevent system insecurity at a minimized cost. Early awareness of such information is of vital importance to the system operators for taking timely actions with more flexible and cost-effective measures. Third, novel statistical wind power forecast models are presented, as an effort to reduce the uncertainty of renewable forecast to support the look-ahead economic dispatch and security management. The forecast models can produce more accurate forecast results by leveraging the spatio-temporal correlation in wind speed and direction data among geographically dispersed wind resources. Fourth, we propose a stochastic look-ahead dispatch (LAED-S) module to handle the high uncertainty in renewable resources. Even with state-of-the-art forecast technology, the near-real-time operational uncertainty from renewable resources cannot be eliminated. Given the uncertainty level, a conventional deterministic approach is not always the best option. The proposed LAED-S is able to judge whether a stochastic approach is preferred. The innovative computation algorithm of LAED-S leverages the progressive hedging and L-shaped method to produce good stochastic decision-making in a more efficient manner. Numerical experiments of a modified IEEE RTS system and a practical system are conducted to justify the proposed approaches in this dissertation. This framework can directly benefit the power system operation in moving from a static, passive real-time operation into a predictive and corrective paradigm

Application of Kalman Filtering for PV Power Prediction in Short-Term Economic Dispatch

The aim of this thesis is to predict the short-term power production of PhotoVoltaic (PV) power plants for the economic dispatch problem with the help of Kalman filtering. The Economic Dispatch (ED) problem in power systems is known as an optimization problem in which the cost of producing energy to reliably supply consumers is minimized, hence the power production is assigned to all the generating units that are dispatchable. Because of the generation cost of renewable energy such as PV is relatively low, it is advantageous to utilize. However, these resources are intermittent. These renewable resources bring a lot of uncertainty into the power system, their power cannot be pre-specified due to their weather dependent properties and therefore it is a big challenge to include them in the ED problem.;For this reason, the work in this thesis will focus on developing a predictive model built on Kalman Filtering for the short-term PV prediction. The model first predicts the solar irradiance and temperature based on an initial guess at each time period. Then, the Kalman filter will refine the results using sensor measurements so that the final estimated outputs from this filter can be used for better prediction in the next period. The PV electric power is then calculated since it is a function of irradiance and temperature.;The proposed methodology has been illustrated using the IEEE 24-bus reliability test system. The real data from National Renewable Energy Laboratory is used in this thesis as the actual outputs that the outputs of the predicting model should get close to. Finally, the performance of the proposed approach is obtained by comparing its results with the results from an available method called the persistent prediction method

Incorporating geostrophic wind information for improved space-time short-term wind speed forecasting

Accurate short-term wind speed forecasting is needed for the rapid development and efficient operation of wind energy resources. This is, however, a very challenging problem. Although on the large scale, the wind speed is related to atmospheric pressure, temperature, and other meteorological variables, no improvement in forecasting accuracy was found by incorporating air pressure and temperature directly into an advanced space-time statistical forecasting model, the trigonometric direction diurnal (TDD) model. This paper proposes to incorporate the geostrophic wind as a new predictor in the TDD model. The geostrophic wind captures the physical relationship between wind and pressure through the observed approximate balance between the pressure gradient force and the Coriolis acceleration due to the Earth's rotation. Based on our numerical experiments with data from West Texas, our new method produces more accurate forecasts than does the TDD model using air pressure and temperature for 1- to 6-hour-ahead forecasts based on three different evaluation criteria. Furthermore, forecasting errors can be further reduced by using moving average hourly wind speeds to fit the diurnal pattern. For example, our new method obtains between 13.9% and 22.4% overall mean absolute error reduction relative to persistence in 2-hour-ahead forecasts, and between 5.3% and 8.2% reduction relative to the best previous space-time methods in this setting.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS756 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Spatio-temporal Modeling and Analysis for Wind Energy Applications

The promising potential of wind energy as a source for carbon-free electricity is still hampered by the uncertainty and limited predictability of the wind resource. The overarching theme of this dissertation is to leverage the advancements in statistical learning for developing a set of physics-informed statistical methods that can enrich our understanding of local wind dynamics, enhance our predictions of the wind resource and associated power, and ultimately assist in making better operational decisions. At the heart of the methods proposed in this dissertation, the wind field is modeled as a stochastic spatio-temporal process. Specifically, two sets of methods are presented. The first set of methods is concerned with the statistical modeling and analysis of the transport effect of wind—a physical property related to the prevailing flow of wind in a certain dominant direction. To unearth the influence of the transport effect, a statistical tool called the spatio-temporal lens is proposed for understanding the complex spatio-temporal correlations and interactions in local wind fields. Motivated by the findings of the spatio-temporal lens, a statistical model is proposed, which takes into account the transport effect in local wind fields by characterizing the spatial and temporal dependence in tandem. Substantial improvements in the accuracy of wind speed and power forecasts are achieved relative to several existing data-driven approaches. The second part of this dissertation comprises the development of an advanced spatio-temporal statistical model, called the calibrated regime-switching model. The proposed model captures the regime-switching dynamics in wind behavior, which are often reflected in sudden power generation ramps. Tested on 11 months of data, double-digit improvements in the accuracy of wind speed and power forecasts are achieved relative to six approaches in the wind forecasting literature. This dissertation contributes to both methodology development and wind energy applications. From a methodological point of view, the contributions are relevant to the literatures on spatiotemporal statistical learning and regime-switching modeling. On the application front, these methodological innovations can minimize the uncertainty associated with the large-scale integration of wind energy in power systems, thus, ultimately boosting the economic outlook of wind energy

Wind Speed Forecasting for Power System Operation

In order to support large-scale integration of wind power into current electric energy system, accurate wind speed forecasting is essential, because the high variation and limited predictability of wind pose profound challenges to the power system operation in terms of the efficiency of the system. The goal of this dissertation is to develop advanced statistical wind speed predictive models to reduce the uncertainties in wind, especially the short-term future wind speed. Moreover, a criterion is proposed to evaluate the performance of models. Cost reduction in power system operation, as proposed, is more realistic than prevalent criteria, such as, root mean square error (RMSE) and absolute mean error (MAE). Two advanced space-time statistical models are introduced for short-term wind speed forecasting. One is a modified regime-switching, space-time wind speed fore- casting model, which allows the forecast regimes to vary according to the dominant wind direction and seasons. Thus, it avoids a subjective choice of regimes. The other one is a novel model that incorporates a new variable, geostrophic wind, which has strong influence on the surface wind, into one of the advanced space-time statistical forecasting models. This model is motivated by the lack of improvement in forecast accuracy when using air pressure and temperature directly. Using geostrophic wind in the model is not only critical, it also has a meaningful geophysical interpretation. The importance of model evaluation is emphasized in the dissertation as well. Rather than using RMSE or MAE, the performance of both wind forecasting models mentioned above are assessed by economic benefits with real wind farm data from Pacific Northwest of the U.S and West Texas. Wind forecasts are incorporated into power system economic dispatch models, and the power system operation cost is used as a loss measure for the performance of the forecasting models. From another perspective, the new criterion leads to cost-effective scheduling of system-wide wind generation with potential economic benefits arising from the system-wide generation of cost savings and ancillary services cost savings. As an illustration, the integrated forecasts and economic dispatch framework are applied to the Electric Reliability Council of Texas (ERCOT) equivalent 24- bus system. Compared with persistence and autoregressive models, the first model suggests that cost savings from integration of wind power could be on the scale of tens of millions of dollars. For the second model, numerical simulations suggest that the overall generation cost can be reduced by up to 6.6% using look-ahead dispatch coupled with spatio-temporal wind forecast as compared with dispatch with persistent wind forecast model