Benefits of spatio-temporal modelling for short term wind power forecasting at both individual and aggregated levels

The share of wind energy in total installed power capacity has grown rapidly in recent years around the world. Producing accurate and reliable forecasts of wind power production, together with a quantification of the uncertainty, is essential to optimally integrate wind energy into power systems. We build spatio-temporal models for wind power generation and obtain full probabilistic forecasts from 15 minutes to 5 hours ahead. Detailed analysis of the forecast performances on the individual wind farms and aggregated wind power are provided. We show that it is possible to improve the results of forecasting aggregated wind power by utilizing spatio-temporal correlations among individual wind farms. Furthermore, spatio-temporal models have the advantage of being able to produce spatially out-of-sample forecasts. We evaluate the predictions on a data set from wind farms in western Denmark and compare the spatio-temporal model with an autoregressive model containing a common autoregressive parameter for all wind farms, identifying the specific cases when it is important to have a spatio-temporal model instead of a temporal one. This case study demonstrates that it is possible to obtain fast and accurate forecasts of wind power generation at wind farms where data is available, but also at a larger portfolio including wind farms at new locations. The results and the methodologies are relevant for wind power forecasts across the globe as well as for spatial-temporal modelling in general

Statistical Modeling to Support Power System Planning

This dissertation focuses on data-analytic approaches that improve our understanding of power system applications to promote better decision-making. It tackles issues of risk analysis, uncertainty management, resource estimation, and the impacts of climate change. Tools of data mining and statistical modeling are used to bring new insight to a variety of complex problems facing today’s power system. The overarching goal of this research is to improve the understanding of the power system risk environment for improved operation, investment, and planning decisions. The first chapter introduces some challenges faced in planning for a sustainable power system. Chapter 2 analyzes the driving factors behind the disparity in wind energy investments among states with a goal of determining the impact that state-level policies have on incentivizing wind energy. Findings show that policy differences do not explain the disparities; physical and geographical factors are more important. Chapter 3 extends conventional wind forecasting to a risk-based focus of predicting maximum wind speeds, which are dangerous for offshore operations. Statistical models are presented that issue probabilistic predictions for the highest wind speed expected in a three-hour interval. These models achieve a high degree of accuracy and their use can improve safety and reliability in practice. Chapter 4 examines the challenges of wind power estimation for onshore wind farms. Several methods for wind power resource assessment are compared, and the weaknesses of the Jensen model are demonstrated. For two onshore farms, statistical models outperform other methods, even when very little information is known about the wind farm. Lastly, chapter 5 focuses on the power system more broadly in the context of the risks expected from tropical cyclones in a changing climate. Risks to U.S. power system infrastructure are simulated under different scenarios of tropical cyclone behavior that may result from climate change. The scenario-based approach allows me to address the deep uncertainty present by quantifying the range of impacts, identifying the most critical parameters, and assessing the sensitivity of local areas to a changing risk. Overall, this body of work quantifies the uncertainties present in several operational and planning decisions for power system applications

Accessing offshore wind turbines for maintenance : calculating access probabilities, expected delays and the associated costs using a probabilistic approach

There are ambitious plans in place for the expansion of offshore wind-power capacity in the EU and elsewhere. However, the cost of energy from offshore wind is much higher than that from land-based generation and anything between 15% and 30% of this cost is attributable to the cost of operation and maintenance (O&M). For exposed UK round three sites these costs could be higher still. The stochastic nature of the occurrence of faults, down-times due to adverse weather and sea-state and the associated losses in energy production, as well as vessel and personnel costs, all add to the potential risk to the finance of an offshore wind farm project. There is a clear need to estimate these effects and the risks associated with them when planning and financing a wind-farm. Key to all such calculations are the restrictions on safe access for maintenance associated with vessels and access methods and the consequent delays caused by adverse sea-state and weather. A computational approach has been developed at University of Strathclyde, based on an event tree and closed-form probabilistic calculations, enabling very fast estimates to be made of offshore access probabilities and expected delays using a simple spreadsheet. Examples are presented for calculations of accessibility. Turbine availability and loss of energy production are calculated based on given turbine component reliability data together with an agreed maintenance scheme. Direct maintenance cost and revenue lost due to down-time can also be calculated with suitable data on the costs of personnel, components, and vessel hire as well as electricity unit and ROC prices, and examples are given. Sensitivities to some of the key parameters are also presented

A multi-objective framework for the optimisation of life-cycle costs of wind turbines

Peer reviewedPublisher PD

Potential Climatic Impacts and Reliability of Very Large-Scale Wind Farms

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).Meeting future world energy needs while addressing climate change requires large-scale deployment of low or zero greenhouse gas (GHG) emission technologies such as wind energy. The widespread availability of wind power has fueled legitimate interest in this renewable energy source as one of the needed technologies. For very large-scale utilization of this resource, there are however potential environmental impacts, and also problems arising from its inherent intermittency, in addition to the present need to lower unit costs. To explore some of these issues, we use a threedimensional climate model to simulate the potential climate effects associated with installation of wind-powered generators over vast areas of land or coastal ocean. Using windmills to meet 10% or more of global energy demand in 2100, could cause surface warming exceeding 1oC over land installations. In contrast, surface cooling exceeding 1oC is computed over ocean installations, but the validity of simulating the impacts of windmills by simply increasing the ocean surface drag needs further study. Significant warming or cooling remote from both the land and ocean installations, and alterations of the global distributions of rainfall and clouds also occur. These results are influenced by the competing effects of increases in roughness and decreases in wind speed on near-surface turbulent heat fluxes, the differing nature of land and ocean surface friction, and the dimensions of the installations parallel and perpendicular to the prevailing winds. These results are also dependent on the accuracy of the model used, and the realism of the methods applied to simulate windmills. Additional theory and new field observations will be required for their ultimate validation. Intermittency of wind power on daily, monthly and longer time scales as computed in these simulations and inferred from meteorological observations, poses a demand for one or more options to ensure reliability, including backup generation capacity, very long distance power transmission lines, and onsite energy storage, each with specific economic and/or technological challenges.This study received support from the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsors

Economics of control reserve provision by fluctuating renewable energy sources

The delivery of control reserve by fluctuating renewable energy sources (RES) generators will be important in an energy system with high RES penetration. This paper extends a previously introduced methodology to quantify the possible additional income of different pools of fluctuating RES generators in the negative secondary and tertiary control reserve market in Germany. The updated methodology allows concluding on the ideal market conditions by comparing different pool types and years. The development of the results over a long assessment period allows extrapolating the market value of the new market participants into the future. Results show a high dependency of the possible additional income on the overall market size and the market conditions and regulations

Message Passing for Integrating and Assessing Renewable Generation in a Redundant Power Grid

A simplified model of a redundant power grid is used to study integration of fluctuating renewable generation. The grid consists of large number of generator and consumer nodes. The net power consumption is determined by the difference between the gross consumption and the level of renewable generation. The gross consumption is drawn from a narrow distribution representing the predictability of aggregated loads, and we consider two different distributions representing wind and solar resources. Each generator is connected to D consumers, and redundancy is built in by connecting R of these consumers to other generators. The lines are switchable so that at any instance each consumer is connected to a single generator. We explore the capacity of the renewable generation by determining the level of "firm" generation capacity that can be displaced for different levels of redundancy R. We also develop message-passing control algorithm for finding switch settings where no generator is overloaded.Comment: 10 pages, accepted for HICSS-4