Direct power control with common mode voltage elimination for open-winding brushless doubly-fed wind power generators

In this paper, a new open-winding control strategy is proposed for a brushless doubly-fed reluctance generator (BDFRG) applicable for wind turbines. The BDFRG control winding is fed via a dual two-level three-phase converter using a single dc bus. Direct power control based on maximum power point tracking with common mode voltage elimination is designed, which not only the active and reactive power is decoupled, but the reliability and redundancy are all improved greatly by increasing the switching modes of operation, while DC-link voltage and rating of power devices decreased by 50% comparing to the traditional three-level converter systems. Consequently its effectiveness is evaluated by simulation tests based on a 42-kW prototype generator.

Performance comparison of direct power control for brushless doubly-fed wind power generator with different control winding structure

A novel open-winding brushless doubly-fed generator (BDFG) system with two two-level bidirectional converters is proposed. This topology is equivalent to a three-level bidirectional converter connected to the typical BDFG, but solves the unbalanced-voltage-division problem of DC capacitor in the three-level converter, and has lower converter capacity, more flexible control mode, and better fault-tolerant ability. The direct power control (DPC) based on the twelve sections is adopted to implement the power tracking of the open-winding BDFG system, which is compared with the typical BDFG DPC system based on the six and twelve sections to verify the advantages of the proposed scheme.

Coal-fired Power Plants with Flexible Amine-based CCS and Co-located Wind Power: Environmental, Economic and Reliability Outcomes

Carbon Capture and Storage (CCS) technologies provide a means to significantly reduce carbon emissions from the existing fleet of fossil-fired plants, and hence can facilitate a gradual transition from conventional to more sustainable sources of electric power. This is especially relevant for coal plants that have a CO2 emission rate that is roughly two times higher than that of natural gas plants. Of the different kinds of CCS technology available, post-combustion amine based CCS is the best developed and hence more suitable for retrofitting an existing coal plant. The high costs from operating CCS could be reduced by enabling flexible operation through amine storage or allowing partial capture of CO2 during high electricity prices. This flexibility is also found to improve the power plant’s ramp capability, enabling it to offset the intermittency of renewable power sources. This thesis proposes a solution to problems associated with two promising technologies for decarbonizing the electric power system: the high costs of the energy penalty of CCS, and the intermittency and non-dispatchability of wind power. It explores the economic and technical feasibility of a hybrid system consisting of a coal plant retrofitted with a post-combustion-amine based CCS system equipped with the option to perform partial capture or amine storage, and a co-located wind farm. A techno-economic assessment of the performance of the hybrid system is carried out both from the perspective of the stakeholders (utility owners, investors, etc.) as well as that of the power system operator.

In order to perform the assessment from the perspective of the facility owners (e.g., electric power utilities, independent power producers), an optimal design and operating strategy of the hybrid system is determined for both the amine storage and partial capture configurations. A linear optimization model is developed to determine the optimal component sizes for the hybrid system and capture rates while meeting constraints on annual average emission targets of CO2, and variability of the combined power output. Results indicate that there are economic benefits of flexible operation relative to conventional CCS, and demonstrate that the hybrid system could operate as an energy storage system: providing an effective pathway for wind power integration as well as a mechanism to mute the variability of intermittent wind power.

In order to assess the performance of the hybrid system from the perspective of the system operator, a modified Unit Commitment/ Economic Dispatch model is built to consider and represent the techno-economic aspects of operation of the hybrid system within a power grid. The hybrid system is found to be effective in helping the power system meet an average CO2 emissions limit equivalent to the CO2 emission rate of a state-of-the-art natural gas plant, and to reduce power system operation costs and number of instances and magnitude of energy and reserve scarcity.

Time series wind power forecasting based on variant Gaussian process and TLBO

Due to the variability and stochastic nature of wind power system, accurate wind power forecasting has an important role in developing reliable and economic power system operation and control strategies. As wind variability is stochastic, Gaussian Process regression has recently been introduced to capture the randomness of wind energy. However, the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems. A variant Gaussian Process for time series forecasting is introduced in this study to address these issues. This new method is shown to be capable of reducing computational complexity and increasing prediction accuracy. It is further proved that the forecasting result converges as the number of available data approaches innite. Further, a teaching learning based optimization (TLBO) method is used to train the model and to accelerate
the learning rate. The proposed modelling and optimization method is applied to forecast both the wind power generation of Ireland and that from a single wind farm to show the eectiveness of the proposed method.

Damping Torque Analysis of Power Systems with DFIGs for Wind Power Generation

A grid-connected DFIG for wind power generation can affect power system small-signal angular stability in two ways: by changing the system load flow condition and dynamically interacting with synchronous generators (SGs). This paper presents the application of conventional method of damping torque analysis (DTA) to examine the effect of DFIG’s dynamic interactions with SGs on the small-signal angular stability. It shows that the effect is due to the dynamic variation of power exchange between the DFIG and power system and can be estimated approximately by the DTA. Consequently, if the DFIG is modelled as a constant power source when the effect of zero dynamic interactions is assumed, the impact of change of load flow brought about by the DFIG can be determined. Thus the total effect of DFIG can be estimated from the result of DTA added on that of constant power source model. Applications of the DTA method proposed in the paper are discussed. An example of multi-machine power systems with grid-connected DFIGs are presented to demonstrate and validate the DTA method proposed and conclusions obtained in the paper.

Statistical Analysis of Wind Power Generation in Europe

Studies have shown that large geographical spreading can reduce the wind power variability and smooth production. It is frequently assumed that storage and interconnection can manage wind power variability and are totally flexible. However, constraints do exist. In the future more and more electricity will be provided by renewable energy sources and more electricity interconnectors will be built between European Union (EU) countries, as outlines in many of the Projects of Common Interests. It is essential to understand the correlation of wind generation throughout Europe considering power system constraints. In this study the spatial and temporal correlation of wind power production across several countries is examined in order to understand how “the wind ‘travels’ across Europe”. Three years of historical hourly wind power generation from ten EU countries is analysed to investigate the geographic diversity and time scales influence on correlation of wind power variations. Results are then compared with two other studies and show similar general characteristics of correlation between EU country pairs to identify opportunities for storage optimisation, power system operations, and trading.

Management of public water supply to reduce power cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Short-term wind power forecast based on cluster analysis and artificial neural networks

[EN]In this paper an architecture for an estimator of short-term wind farm power is proposed. The estimator is made up of a Linear Machine classifier and a set of k Multilayer Perceptrons, training each one for a specific subspace of the input space. The splitting of the input dataset into the k clusters is done using a k-means technique, obtaining the equivalent Linear Machine classifier from the cluster centroids...

Techno-Economic Assessment of LNG and Diesel Production and Global Trading Based on Hybrid PV-Wind Power Plants and PtG, GtL and PtL Technologies

With growing demand for liquefied natural gas (LNG) and liquid transportation fuels, and concerns about climate change and causes of greenhouse gas emissions, this master’s thesis introduces a new value chain design for LNG and transportation fuels and respective fundamental business cases based on hybrid PV-Wind power plants. The value chains are composed of renewable electricity (RE) converted by power-to-gas (PtG), gas-to-liquids (GtL) or power-to-liquids (PtL) facilities into SNG (which is finally liquefied into LNG) or synthetic liquid fuels, mainly diesel, respectively. The RE-LNG or RE-diesel are drop-in fuels to the current energy system and can be traded everywhere in the world. The calculations for the hybrid PV-Wind power plants, electrolysis, methanation (H2tSNG), hydrogen-to-liquids (H2tL), GtL and LNG value chain are performed based on both annual full load hours (FLh) and hourly analysis. Results show that the proposed RE-LNG produced in Patagonia, as the study case, is competitive with conventional LNG in Japan for crude oil prices within a minimum price range of about 87 - 145 USD/barrel (20 – 26 USD/MBtu of LNG production cost) and the proposed RE-diesel is competitive with conventional diesel in the European Union (EU) for crude oil prices within a minimum price range of about 79 - 135 USD/barrel (0.44 – 0.75 €/l of diesel production cost), depending on the chosen specific value chain and assumptions for cost of capital, available oxygen sales and CO2 emission costs. RE-LNG or RE-diesel could become competitive with conventional fuels from an economic perspective, while removing environmental concerns. The RE-PtX value chain needs to be located at the best complementing solar and wind sites in the world combined with a de-risking strategy. This could be an opportunity for many countries to satisfy their fuel demand locally. It is also a specific business case for countries with excellent solar and wind resources to export carbon-neutral hydrocarbons, when the decrease in production cost is considerably more than the shipping cost. This is a unique opportunity to export carbon-neutral hydrocarbons around the world where the environmental limitations on conventional hydrocarbons are getting tighter.

A review on the recent history of wind power ramp forecasting.

Forecasting large and fast variations of wind power (the so called ramps) helps achieve the integration of large amounts of wind energy. This paper presents a survey on wind power ramp forecasting, reflecting the increasing interest on this topic observed since 2007. Three main aspects were identified from the literature: wind power ramp definition, ramp underlying meteorological causes and experi-ences in predicting ramps. In this framework, we additionally outline a number of recommendations and potential lines of research.

Identifying wind power ramp causes from multivariate datasets: a methodological proposal and its application to reanalysis data.

Forecasting abrupt variations in wind power generation (the so-called ramps) helps achieve large scale wind power integration. One of the main issues to be confronted when addressing wind power ramp forecasting is the way in which relevant information is identified from large datasets to optimally feed forecasting models. To this end, an innovative methodology oriented to systematically relate multivariate datasets to ramp events is presented. The methodology comprises two stages: the identification of relevant features in the data and the assessment of the dependence between these features and ramp occurrence. As a test case, the proposed methodology was employed to explore the relationships between atmospheric dynamics at the global/synoptic scales and ramp events experienced in two wind farms located in Spain. The achieved results suggested different connection degrees between these atmospheric scales and ramp occurrence. For one of the wind farms, it was found that ramp events could be partly explained from regional circulations and zonal pressure gradients. To perform a comprehensive analysis of ramp underlying causes, the proposed methodology could be applied to datasets related to other stages of the wind-topower conversion chain.

Power generation using wind energy in northwest Karnataka, India

On the backdrop of climate change scenario, there is emphasis on controlling emission of greenhouse gases such as CO2. Major thrust being seen worldwide as well as in India is for generation of electricity from renewable sources like solar and wind. Chitradurga area of Karnataka is identified as a suitable location for the production of electricity from wind turbines because of high wind-energy resource. The power generated and the performance of 18 wind turbines located in this region are studied based on the actual field data collected over the past seven years. Our study shows a good prospect for expansion of power production using wind turbines.