Wind Power Planning: Problems and Perceptions in Practice and Procedure

Despite the fact that the UK has highest potential in the EU to generate renewable energy from wind, it lags behind its European partners. The departure point for this study is provided by the fact that the land use planning system has been perceived by some to create difficulties in pursuit of the achievement of National Action Plan targets. In the course of a review of literature, legislation, policy and case files a number of issues emerge relating not only to operational practice but structural concerns regarding knowledge, legitimacy and ethics. These are scrutinised in an empirical investigation which provides insights into the ontologies behind how knowledge is used and abused. Concerns are highlighted regarding the tactical manipulation of knowledges and the difficulties associated with objectifying evidence so that it can be understood, validated and authenticated. The paper concludes by reflecting on the implications for the regulatory framework, the legitimisation of decisions and the ethics of the profession and how these, in turn, are conditioned by the production, use and transparency of planning knowledge.

Offshore wind power forecasting error and electricity market implications

Wind energy has been identified as key to the European Union’s 2050 low carbon economy. However, as wind is a variable resource and stochastic by nature, it is difficult to plan and schedule the power system under varying wind power generation. This paper investigates the impacts of offshore wind power forecast error on the operation and management of a pool-based electricity market in 2050. The impact of the magnitude and variance of the offshore wind power forecast error on system generation costs, emission costs, dispatch-down of wind, number of start-ups and system marginal price is analysed. The main findings of this research are that the magnitude of the offshore wind power forecast error has the largest impact on system generation costs and dispatch-down of wind, but the variance of the offshore wind power forecast error has the biggest impact on emissions costs and system marginal price. Overall offshore wind power forecast error variance results in a system marginal price increase of 9.6% in 2050.

The evolution of offshore wind power in the United Kingdom

In the United Kingdom wind power is recognised as the main source of renewable energy to achieve the European Union 2020 renewable energy targets. Currently over 50% of renewable power is generated from onshore wind with a large number of offshore wind projects in development. Recently the government has re-iterated its commitment to offshore wind power and has announced that offshore wind subsidies are to increase from £135/MWh to £140/MWh until 2019. This paper provides a detailed overview of the offshore wind power industry in the United Kingdom in terms of market growth, policy development and offshore wind farm costs. The paper clearly shows that the United Kingdom is the world leader for installed offshore wind power capacity as pro-active policies and procedures have made it the most attractive location to develop offshore wind farm arrays. The key finding is that the United Kingdom has the potential to continue to lead the world in offshore wind power as it has over 48 GW of offshore wind power projects at different stages of operation and development. The growth of offshore wind power in the United Kingdom has seen offshore wind farm costs rise and level off at approximately £3 million/MW, which are higher than onshore wind costs at £1.5–2 million/MW. Considering the recent increase in offshore wind power subsidies and plans for 48 GW of offshore wind power could see more offshore wind power becoming increasingly financially competitive with onshore wind power. Therefore offshore wind power is likely to become a significant source of electricity in the United Kingdom beyond 2020.

A long-term analysis of pumped hydro storage to firm wind power

Although pumped hydro storage is seen as a strategic key asset by grid operators, financing it is complicated in new liberalised markets. It could be argued that the optimum generation portfolio is now determined by the economic viability of generators based on a short to medium term return on investment. This has meant that capital intensive projects such as pumped hydro storage are less attractive for wholesale electricity companies because the payback periods are too long. In tandem a significant amount of wind power has entered the generation mix, which has resulted in operating and planning integration issues due to wind's inherent uncertain, varying spatial and temporal nature. These integration issues can be overcome using fast acting gas peaking plant or energy storage. Most analysis of wind power integration using storage to date has used stochastic optimisation for power system balancing or arbitrage modelling to examine techno-economic viability. In this research a deterministic dynamic programming long term generation expansion model is employed to optimise the generation mix, total system costs and total carbon dioxide emissions, and unlike other studies calculates reserve to firm wind power. The key finding of this study is that the incentive to build capital-intensive pumped hydro storage to firm wind power is limited unless exogenous market costs come very strongly into play. Furthermore it was demonstrated that reserve increases with increasing wind power showing the importance of ancillary services in future power systems. © 2014 Elsevier Ltd. All rights reserved.

Impact of offshore wind power forecast error in a carbon constraint electricity market

This paper investigates the impacts of offshore wind power forecast error on the operation and management of a pool-based electricity market in 2050. The impact from offshore wind power forecast errors of up to 2000 MW on system generation costs, emission costs, dispatch-down of wind, number of start-ups and system marginal price are analysed. The main findings of this research are an increase in system marginal prices of approximately 1% for every percentage point rise in the offshore wind power forecast error regardless of the average forecast error sign. If offshore wind power generates less than forecasted (−13%) generation costs and system marginal prices increases by 10%. However, if offshore wind power generates more than forecasted (4%) the generation costs decrease yet the system marginal prices increase by 3%. The dispatch down of large quantities of wind power highlights the need for flexible interconnector capacity. From a system operator's perspective it is more beneficial when scheduling wind ahead of the trading period to forecast less wind than will be generated.

Flexible fault ride through strategy for wind farm clusters in power systems with high wind power penetration

This paper investigates a flexible fault ride through strategy for power systems in China with high wind power penetration. The strategy comprises of adaptive fault ride through requirements and maximum power restrictions of the wind farms with weak fault ride through capabilities. The slight faults and moderate faults with high probability are the main defending objective of the strategy. The adaptive fault ride through requirement in the strategy consists of two sub fault ride through requirements, a temporary slight voltage ride through requirement corresponding to a slight fault incident, with a moderate voltage ride through requirement corresponding to a moderate fault. The temporary overloading capability of the wind farm is reflected in both requirements to enhance the capability to defend slight faults and to avoid tripping when the crowbar is disconnected after moderate faults are cleared. For those wind farms that cannot meet the adaptive fault ride through requirement, restrictions are put on the maximum power output. Simulation results show that the flexible fault ride through strategy increases the fault ride through capability of the wind farm clusters and reduces the wind power curtailment during faults.

Hybrid probabilistic wind power forecasting using temporally local Gaussian process

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Despite various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministic-probabilistic method where a temporally local ‘moving window’ technique is used in Gaussian Process to examine estimated forecasting errors. This temporally local Gaussian Process employs less measurement data while faster and better predicts wind power at two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while more likely generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.

The importance of gas infrastructure in power systems with high wind power penetrations

Gas fired generation currently plays an integral support role ensuring security of supply in power systems with high wind power penetrations due to its technical and economic attributes. However, the increase in variable wind power has affected the gas generation output profile and is pushing the boundaries of the design and operating envelope of gas infrastructure. This paper investigates the mutual dependence and interaction between electricity generation and gas systems through the first comprehensive joined-up, multi-vector energy system analysis for Ireland. Key findings reveal the high vulnerability of the Irish power system to outages on the Irish gas system. It has been shown that the economic operation of the power system can be severely impacted by gas infrastructure outages, resulting in an average system marginal price of up to €167/MWh from €67/MWh in the base case. It has also been shown that gas infrastructure outages pose problems for the location of power system reserve provision, with a 150% increase in provision across a power system transmission bottleneck. Wind forecast error was shown to be a significant cause for concern, resulting in large swings in gas demand requiring key gas infrastructure to operate at close to 100% capacity. These findings are thought to increase in prominence as the installation of wind capacity increases towards 2020, placing further stress on both power and gas systems to maintain security of supply.

Gas generation in interconnected power systems with high wind power penetration

Increasing installed capacities of wind power in an effort to achieve sustainable power systems for future generations pose problems for system operators. Volatility in generation volumes due to the adoption of stochastic wind power is increasing. Storage has been shown to act as a buffer for these stochastic energy sources, facilitating the integration of renewable energy into a historically inflexible power system. This paper examines peak and off peak benefits realised by installing a short term discharge storage unit in a system with a high penetration of wind power in 2020. A fully representative unit commitment and economic dispatch model is used to analyse two scenarios, one ‘with storage’ and one ‘without storage’. Key findings of this preliminary study show that wind curtailment can be reduced in the storage scenario, with a larger reduction in peak time ramping of gas generators is realised.

Managing Future offshore wind power variability in a European Supergrid

The future European power system will have a hierarchical structure created by layers of system control from a Supergrid via regional high-voltage transmission through to medium and low-voltage distribution. Each level will have generation sources such as large-scale offshore wind, wave, solar thermal, nuclear directly connected to this Supergrid and high levels of embedded generation, connected to the medium-voltage distribution system. It is expected that the fuel portfolio will be dominated by offshore wind in Northern Europe and PV in Southern Europe. The strategies required to manage the coordination of supply-side variability with demand-side variability will include large scale interconnection, demand side management, load aggregation and storage in the context of the Supergrid combined with the Smart Grid. The design challenge associated with this will not only include control topology, data acquisition, analysis and communications technologies, but also the selection of fuel portfolio at a macro level. This paper quantifies the amount of demand side management, storage and so-called 'back-up generation' needed to support an 80% renewable energy portfolio in Europe by 2050. © 2013 IEEE.

A review of offshore wind power development in the United Kingdom

Currently wind power is dominated by onshore wind farms. However, as the demand for power grows driven by security of energy supply issues, dwindling fossil fuel supplies and greenhouse gas emissions reduction targets, offshore wind power will develop rapidly because of the decline of viable onshore sites. The United Kingdom has a target of 21% renewable electricity by 2020 and this is expected to come mostly from wind power. Britain is the most active internationally in terms of offshore wind farm development with almost 48GW in some stage of development. In addition the Scottish Government, the Northern Ireland Executive and the Government of Ireland undertook the 'Irish-Scottish Links on Energy Study' (ISLES), which examined the feasibility of creating an offshore interconnected transmission network and subsea electricity grid based on renewable energy sources off the coast of western Scotland and the Irish Sea. The aim of this paper is to provide an appraisal of offshore wind power development with a focus on the United Kingdom. © 2013 IEEE.

Special condition wind power forecasting based on Gaussian process and similar historical data

Due to the variability of wind power, it is imperative to accurately and timely forecast the wind generation to enhance the flexibility and reliability of the operation and control of real-time power. Special events such as ramps, spikes are hard to predict with traditional methods using solely recently measured data. In this paper, a new Gaussian Process model with hybrid training data taken from both the local time and historic dataset is proposed and applied to make short-term predictions from 10 minutes to one hour ahead. A key idea is that the similar pattern data in history are properly selected and embedded in Gaussian Process model to make predictions. The results of the proposed algorithms are compared to those of standard Gaussian Process model and the persistence model. It is shown that the proposed method not only reduces magnitude error but also phase error.

What do high penetrations of wind power mean for gas generation?

Dependency on thermal generation and continued wind power growth in Europe due to renewable energy and greenhouse gas emissions targets has resulted in an interesting set of challenges for power systems. The variability of wind power impacts dispatch and balancing by grid operators, power plant operations by generating companies and market wholesale costs. This paper quantifies the effects of high wind power penetration on power systems with a dependency on gas generation using a realistic unit commitment and economic dispatch model. The test system is analyzed under two scenarios, with and without wind, over one year. The key finding of this preliminary study is that despite increased ramping requirements in the wind scenario, the unit cost of electricity due to sub-optimal operation of gas generators does not show substantial deviation from the no wind scenario.

A techno-economic feasibility study of high altitude wind power in Northern Ireland

Globally the amount of installed terrestrial wind power both onshore and offshore has grown rapidly over the last twenty years. Most large onshore and offshore wind turbines are designed to harvest winds within the atmospheric boundary layer, which can be vary variable due to terrain and weather effects. The height of the neutral atmospheric boundary layer is estimated at above 1300m. A relatively new concept is to harvest more consistent wind conditions above the atmospheric boundary layer using high altitude wind harvesting devices such as tethered kites, air foils and dirigible rotors. This paper presents a techno-economic feasibility study of high altitude wind power in Northern Ireland. First this research involved a state of the art review of the resource and the technologies proposed for high altitude wind power. Next the techno-economic analysis involving four steps is presented. In step one, the potential of high altitude wind power in Northern Ireland using online datasets (e.g. Earth System Research Laboratory) is estimated. In step two a map for easier visualisation of geographical limitations (e.g. airports, areas of scenic beauty, flight paths, military training areas, settlements etc.) that could impact on high altitude wind power is developed. In step three the actual feasible resource available is recalculated using the visualisation map to determine the ‘optimal’ high altitude wind power locations in Northern Ireland. In the last step four the list of equipment, resources and budget needed to build a demonstrator is provided in the form of a concise techno-economic appraisal using the findings of the previous three steps.

An ultra-short-term wind power prediction method using “offline classification and optimization, online model matching” based on time series features

The applicability of ultra-short-term wind power prediction (USTWPP) models is reviewed. The USTWPP method proposed extracts featrues from historical data of wind power time series (WPTS), and classifies every short WPTS into one of several different subsets well defined by stationary patterns. All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern. Every above WPTS subset needs a USTWPP model specially optimized for it offline. For on-line application, the pattern of the last short WPTS is recognized, then the corresponding prediction model is called for USTWPP. The validity of the proposed method is verified by simulations.