Wei Jen Lee

System impact study for the interconnection of wind generation and utility system

Following in the steps of the gas industry, the traditional paradigm of the vertically integrated electric utility structure has begun to change. In the United States, the Federal Energy Regulatory Commission has issued several rules and Notices of Proposed Rulemaking to set the road map for the deregulated utility industry. The crisis in California has drawn great attention and sparked intense discussion within the utility industry. One general conclusion is to rejuvenate the idea of integrated resource planning and promote the distributed generation via traditional or renewable generation facilities for the deregulated utility systems. Wind generation is one of the most mature and cost-effective resources among different renewable energy technologies. Recently, several large-scale wind generation projects have been implemented in the U.S. and other parts of the world. Similar to other new generation facilities, the impacts of a large-scale wind generation on the system operation, voltage profile, and system security have to be investigated and studied. Remedies for possible operation issues have to be evaluated and implemented. This paper discusses the impact study of connecting a 120-MW wind farm into the transmission system of a utility company within the southwest power pool.

An Integration of ANN Wind Power Estimation Into Unit Commitment Considering the Forecasting Uncertainty

The development of wind power generation has rapidly progressed over the last decade. With the advancement in wind turbine technology, wind energy has become competitive with other fuel-based resources. The fluctuation of wind, however, makes it difficult to optimize the usage of wind power. The current practice ignores wind generation capacity in the unit commitment (UC), which discounts its usable capacity and may cause operational issues when the installation of wind generation equipment increases. To ensure system reliability, the forecasting uncertainty must be considered in the incorporation of wind power capacity into generation planning. This paper discusses the development of an artificial-neural-network-based wind power forecaster and the integration of wind forecast results into UC scheduling considering forecasting uncertainty by the probabilistic concept of confidence interval. The data from a wind farm located in Lawton City, OK, is used in this paper.

Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines

Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising.

Forecasting the Wind Generation Using a Two-Stage Network Based on Meteorological Information

This paper proposes a practical and effective model for the generation forecasting of a wind farm with an emphasis on its scheduling and trading in a wholesale electricity market. A novel forecasting model is developed based on indepth investigations of meteorological information. This model adopts a two-stage hybrid network with Bayesian clustering by dynamics and support vector regression. The proposed structure is robust with different input data types and can deal with the nonstationarity of wind speed and generation series well. Once the network is trained, we can straightforward predict the 48-h ahead wind power generation. To demonstrate the effectiveness, the model is applied and tested on a 74-MW wind farm located in the southwest Oklahoma of the United States.

Effects of nonsinusoidal voltage on the operation performance of a three-phase induction motor

This paper uses a real load test to investigate the effects of each order of harmonic from 2 to 13 under various voltage distortion factors (VDF) on the performance of a three-phase induction motor. The investigation includes input current, power factor, efficiency, temperature rise and their impacts on the consumers and utility companies. Since the life span of the motors is dramatically affected by the temperature rise, a new derating factor is proposed in this paper. Besides, the impacts of harmonics on electricity energy, consumers and the life span of a motor are also discussed, respectively. Finally, it is strongly suggested that even order harmonics and harmonics having an order below 5 should be considered in related regulations of harmonics control and limits.

Forecasting power output of photovoltaic system based on weather classification and support vector machine

Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising.

Reactive compensation techniques to improve the ride-through capability of wind turbine during disturbance

World wind energy capacity expanded at an annual rate of 25% during the 1990s. The total world wind turbine installation capacity was approximately 40 000 MW at the end of 2003. Germany has the highest installed capacity of over 10 000 MW, while Denmark, where the wind energy accounts for more than 13% of electricity consumed, has the highest wind energy level per capita. The United States is catching up in the development of wind farms, with several large-scale wind generation projects currently being materialized. Even though there is significant progress in the wind generation technology, most of the currently installed wind turbines utilize induction generators to produce the electricity. Since the induction generators do not perform voltage regulation and absorb reactive power from the utility grid, they are often the source of voltage fluctuations. It is necessary to examine their responses during the faults and possible impacts on the system stability when the percentage of the wind generation increases. This paper compares the steady-state voltage profile and the voltage ride-through capabilities of the induction-generator-based wind farms with different reactive compensation techniques.

A New Measurement Method for Power Signatures of Nonintrusive Demand Monitoring and Load Identification

Based upon the analysis of load signatures, this paper presents a nonintrusive load monitoring (NILM) technique. With a characterizing response associated with a transient energy signature, a reliable and accurate recognition result can be obtained. In this paper, artificial neural networks, in combination with turn-on transient energy analysis, are used to improve recognition accuracy and computational speed of NILM results. To minimize the distortion phenomenon in current measurements from the hysteresis of traditional current transformer (CT) iron cores, a coreless Hall CT is adopted to accurately detect nonsinusoidal waves to improve NILM accuracy. The experimental results indicate that the incorporation of turn-on transient energy algorithm into NILM significantly improve the recognition accuracy and the computational speed.

Combining the Wind Power Generation System With Energy Storage Equipment

With the advancements in wind turbine technologies, the cost of wind energy has become competitive with other fuel-based generation resources. Due to the price hike of fossil fuel and the concern of global warming, the development of wind power has rapidly progressed over the last decade. The annual growth rate has exceeded 26% since the 1990s. Many countries have set a goal for high penetration levels of wind generation. Recently, several large-scale wind generation projects have been implemented all over the world. It is economically beneficial to integrate very large amounts of wind capacity in power systems. Unlike other traditional generation facilities, using wind turbines presents technical challenges in producing continuous and controllable electric power. A distinct feature of wind energy is its nature of being ldquointermittent.rdquo Since it is difficult to predict and control the output of wind generation, its potential impacts on the electric grid are different from the traditional energy sources. At a high penetration level, an extrafast response reserve capacity is needed to cover the shortfall of generation when a sudden deficit of wind takes place. To enable a proper management of the uncertainty, this paper presents an approach to make wind power become a more reliable source on both energy and capacity by using energy storage devices. Combining the wind power generation system with energy storage will reduce fluctuation of wind power. Since it requires capital investment for the storage system, it is important to estimate the reasonable storage capacities for the desired applications. In addition, an energy storage application for reducing the output variation during the gust wind is also studied.

Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities

With the deployment of advanced metering infrastructure (AMI), an avalanche of new energy-use information became available. Better understanding of the actual power consumption patterns of customers is critical for improving load forecasting and efficient deployment of smart grid technologies to enhance operation, energy management, and planning of electric power systems. Unlike traditional aggregated system-level load forecasting, the AMI data introduces a fresh perspective to the way load forecasting is performed, ranging from very short-term load forecasting to long-term load forecasting at the system level, regional level, feeder level, or even down to the consumer level. This paper addresses the efforts involved in improving the system level intraday load forecasting by applying clustering to identify groups of customers with similar load consumption patterns from smart meters prior to performing load forecasting.