Framework Convention on Climate Change

OUTPU T 29 OUTPU T 30 OUTPU T 31 OUTPU T 32 OUTPU T 25 OUTPU T 26 OUTPU T 27 OUTPU T 28 OUTPU T 21 OUTPU T 22 OUTPU T 23 OUTPU T 24 OUTPU T 17 OUTPU T 18 OUTPU T 19 OUTPU T 20 OUTPU T 13 OUTPU T 14 OUTPU T 15 OUTPU T 16 OUTPU T 9 OUTPU T 10 OUTPU T 11 OUTPU T 12 OUTPU T 5 OUTPU T 6 OUTPU T 7 OUTPU T 8 OUTPU T 1 OUTPU T 2 OUTPU T 3 OUTPU T 4 29 30 31 32 25 26 27 28 21 22 23 24 17 18 19 20 13 14 15 16 9

A User’s Guide

This paper presents a review of control strategies, stability analysis, and stabilization techniques for dc microgrids (MGs). Overall control is systematically classified into local and coordinated control levels according to respective functionalities in each level. As opposed to local control, which relies only on local measurements, some line of communication between units needs to be made available in order to achieve the coordinated control. Depending on the communication method, three basic coordinated control strategies can be distinguished, i.e., decentralized, centralized, and distributed control. Decentralized control can be regarded as an extension of the local control since it is also based exclusively on local measurements. In contrast, centralized and distributed control strategies rely on digital communication technologies. A number of approaches using these three coordinated control strategies to achieve various control objectives are reviewed in this paper. Moreover, properties of dc MG dynamics and stability are discussed. This paper illustrates that tightly regulated point-of-load converters tend to reduce the stability margins of the system since they introduce negative impedances, which can potentially oscillate with lightly damped power supply input filters. It is also demonstrated that how the stability of the whole system is defined by the relationship of the source and load impedances, referred to as the minor loop gain. Several prominent specifications for the minor loop gain are reviewed. Finally, a number of active stabilization techniques are presented.

/pdf/dc-microgrids-part-i-a-review-of-control-strategies-and-3oi6nfb2fy.pdf

DC Microgrids—Part I: A Review of Control Strategies and Stabilization Techniques

(1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Energy function analysis for power system stability

Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.

Energy models for demand forecasting—A review

Average load forecasting errors for the holidays are much higher than those for weekdays. So far, many studies on the short-term load forecasting have been made to improve the prediction accuracy using various methods such as deterministic, stochastic, artificial neural net (ANN) and neural network-fuzzy methods. In order to reduce the load forecasting error of the 24 hourly loads for the holidays, the concept of fuzzy regression analysis is employed in the short-term load forecasting problem. According to the historical load data, the same type of holiday showed a similar trend of load profile as in previous years. The fuzzy linear regression model is made from the load data of the previous three years and the coefficients of the model are found by solving the mixed linear programming problem. The proposed algorithm shows good accuracy, and the average maximum percentage error is 3.57% in the load forecasting of the holidays for the years of 1996-1997.

Short-term load forecasting for the holidays using fuzzy linear regression method

Summary form only given. Average load forecasting errors for the holidays are much higher than those for weekdays. So far, many studies on the short-term load forecasting have been made to improve the prediction accuracy using various methods such as deterministic, stochastic, artificial neural net (ANN) and neural network-fuzzy methods. In order to reduce the load forecasting error of the 24 hourly loads for the holidays, the concept of fuzzy regression analysis is employed in the short-term load forecasting problem. According to the historical load data, the same type of holiday showed a similar trend of load profile as in previous years. The fuzzy linear regression model is made from the load data of the previous three years and the coefficients of the model are found by solving the mixed linear programming problem. The proposed algorithm shows good accuracy, and the average maximum percentage error is 3.57% in the load forecasting of the holidays for the years of 1996-1997.

Foreword. Preface. Acknowledgments. Author Biographies. List of Symbols. 1 Development of HVDC Technology. 1.1 Introduction. 1.2 Advantages of HVDC Systems. 1.3 HVDC System Costs. 1.4 Overview and Organization of HVDC Systems. 1.5 Review of the HVDC System Reliability. 1.6 HVDC Characteristics and Economic Aspects. References. 2 Power Conversion. 2.1 Thyristor. 2.2 3-Phase Converter. 2.3 3-Phase Full Bridge Converter. 2.4 12-Pulse Converter. References. 3 Harmonics of HVDC and Removal. 3.1 Introduction. 3.2 Determination of Resulting Harmonic Impedance. 3.3 Active Power Filter. References. 4 Control of HVDC Converter and System. 4.1 Converter Control for an HVDC System. 4.2 Commutation Failure. 4.3 HVDC Control and Design. 4.4 HVDC Control Functions. 4.5 Reactive Power and Voltage Stability. 4.6 Summary. References. 5 Interactions between AC and DC Systems. 5.1 Definition of Short Circuit Ratio and Effective Short Circuit Ratio. 5.2 Interaction between HVDC and AC Power System. References. 6 Main Circuit Design. 6.1 Converter Circuit and Components. 6.2 Converter Transformer. 6.3 Cooling System. 6.4 HVDC Overhead Line. 6.5 HVDC Earth Electrodes. 6.6 HVDC Cable. 6.7 HVDC Telecommunications. 6.8 Current Sensors. 6.9 HVDC Noise and Vibration. References. 7 Fault Behavior and Protection of HVDC System. 7.1 Valve Protection Functions. 7.2 Protective Action of an HVDC System. 7.3 Protection by Control Actions. 7.4 Fault Analysis. References. 8 Insulation Coordination of HVDC. 8.1 Surge Arrester. 8.2 Functions of the Arresters in an HVDC Station. 8.3 Insulation Coordination of Cheju HVDC System. References. 9 A Practical Example of an HVDC System. 9.1 Introduction. 9.2 System Description. 9.3 Phase Control. References. 10 Other Converter Configurations for HVDC Transmission. 10.1 Introduction. 10.2 Voltage Source Converter (VSC). 10.3 CCC and CSCC HVDC System. 10.4 Multi-Terminal DC Transmission. References. 11 Modeling and Simulation of HVDC Systems. 11.1 Simulation Scope and Range. 11.2 Fast Methods for Accurate Simulation. 11.3 HVDC Modeling and Simulation. 11.4 Cheju Haenam HVDC Real-Time Digital Simulator. References. 12 Present and Proposed Future Installations of HVDC Systems. 12.1 USA. 12.2 Japan. 12.3 Europe. 12.4 China. 12.5 India. 12.6 Malaysia/Philippines. 12.7 Australia/New Zealand. 12.8 Brazil. 12.9 Africa. 13 Trends for HVDC Applications. 13.1 Wind Farm Technology. 13.2 Modern Voltage Source Converter (VSC) HVDC Systems. 13.3 800 kV HVDC System. References. Index.

/pdf/hvdc-transmission-power-conversion-applications-in-power-4e6sy0pww4.pdf

HVDC Transmission: Power Conversion Applications in Power Systems

Recently, many signal-processing techniques, such as fast Fourier transform, short-time Fourier transform, wavelet transform (WT), and wavelet packet transform (WPT), have been applied to detect, identify, and classify power-quality (PQ) disturbances. For research on PQ analysis, it is critical to apply the appropriate signal-processing techniques to solve PQ problems. In this paper, a new time-frequency analysis method, namely, the Gabor-Wigner transform (GWT), is introduced and applied to detect and identify PQ disturbances. Since GWT is an operational combination of the Gabor transform (GT) and the Wigner distribution function (WDF), it can overcome the disadvantages of both. GWT has two advantages which are that it has fewer cross-term problems than the WDF and higher clarity than the GT. Studies are presented which verify that the merits of GWT make it adequate for PQ analysis. In the case studies considered here, the various PQ disturbances, including voltage swell, voltage sag, harmonics, interharmonics, transients, voltage changes with multiple frequencies and voltage fluctuation, or flicker, will be thoroughly investigated by using this new time-frequency analysis method.

Time-Frequency Analysis of Power-Quality Disturbances via the Gabor–Wigner Transform

In this paper, a new energy management strategy (EMS) for a dc distribution system in buildings is being proposed. The dc distribution system is considered as a prospective system according to the increase of dc loads and dc output type distribution energy resources (DERs) such as photovoltaic (PV) systems and fuel cells. Since the dc distribution system has many advantages such as feasible connection of DERs and electric vehicles (EVs), reduction of conversion losses between dc output sources and loads, no reactive power issues, it is very suitable for industrial and commercial buildings interfaced with DERs and EVs. The establishment of an appropriate EMS based on the economic point of view can reduce energy costs of buildings and provide benefits to participants in energy management. Applicable elements for the dc distribution system are identified and the real-time decision-making-based algorithm for minimizing operating costs is proposed in this paper. The EV service model for the EMS to offer incentive to EV owners who participate in battery discharging is described. To verify the performances of the proposed algorithm, computer simulation and economic analysis are being performed where the results show that the proposed EMS reduces energy costs, motivates EV owners, and can be applied to the dc distribution buildings.

Gilsoo Jang

Papers

Short-term load forecasting for the holidays using fuzzy linear regression method

Short-term load forecasting for the holidays using fuzzy linear regression method

HVDC Transmission: Power Conversion Applications in Power Systems

Time-Frequency Analysis of Power-Quality Disturbances via the Gabor–Wigner Transform

Energy Management Strategy of the DC Distribution System in Buildings Using the EV Service Model