Yuan-Yih Hsu

Bio: Yuan-Yih Hsu is an academic researcher from National Taiwan University. The author has contributed to research in topics: Electric power system & Control theory. The author has an hindex of 27, co-authored 69 publications receiving 3146 citations.

Short term load forecasting of Taiwan power system using a knowledge-based expert system

Abstract: A knowledge-based expert system was developed for the short-term load forecasting of the Taiwan power system. The developed expert system, which was implemented on a personal computer, was written in PROLOG using a 5-year database. To benefit from the expert knowledge and experience of the system operator, eleven different shapes, each with different means of load calculations, were established. With these load shapes at hand, some peculiar load characteristics pertaining to the Taiwan Power Company can be taken into account. The special load types considered by the expert system include the extremely low load levels during the week of the Chinese New Year, the special load characteristics of the days following a tropical storm or a typhoon, the partial shutdown of certain factories on Saturdays, the special event caused by a holiday on Friday or on Tuesday, etc. A characteristic feature of the knowledge-based expert system is that it is easy to add new information and new rules to the knowledge base. To illustrate the effectiveness of the system, short-term load forecasting is performed on the Taiwan power system by using both the developed algorithm and the conventional Box-Jenkins statistical method. It is found that a mean absolute error of 2.52% for one year is achieved by the expert system approach as compared to an error of 3.86% by the statistical method. >

Fuzzy dynamic programming: an application to unit commitment

Abstract: A new approach using fuzzy dynamic programming is proposed for the unit commitment of a power system. A characteristic feature of the approach is that the errors in the forecast hourly loads can be taken into account by using fuzzy set notations, making the approach superior to the conventional dynamic programming method which assumes that the hourly loads are exactly known and there exist no errors in the forecast loads. To reach an optimal commitment strategy under the fuzzy environment, a fuzzy dynamic programming model in which the hourly loads, the cost, and system security are all expressed in fuzzy set notations is developed. The effectiveness of the approach is demonstrated by the unit commitment of the Taiwan power system, which contains six nuclear units, 48 thermal units, and 44 hydro units. >

Dispatch of direct load control using dynamic programming

Abstract: An approach based on dynamic programming is presented for the dispatch of direct load control (DLC). The objective is to coordinate DLC strategies with system unit commitment such that the system production cost is minimized. To achieve this goal, the DLC dispatch is first integrated into the unit commitment problem. An optimization technique based on dynamic programming is then developed to reach the optimal DLC dispatch strategy and system generation schedule. To demonstrate the effectiveness of the approach, results from a sample study performed on the Taiwan power system are described. >

Design of a Self-Tuning PI Controller for a STATCOM Using Particle Swarm Optimization

Abstract: A self-tuning proportional-integral (PI) controller in which the controller gains are adapted using the particle swarm optimization (PSO) technique is proposed for a static synchronous compensator (STATCOM). An efficient formula for the estimation of system load impedance using real-time measurements is derived. Based on the estimated system load, a PSO algorithm, which takes the best particle gains, the best global gains, and previous change of gains into account, is employed to reach the desired controller gains. To demonstrate the effectiveness of the proposed PSO self-tuning PI controller for a STATCOM, experimental results for a system under different loading conditions are presented. Results from the self-tuning PI controller are compared with those from the fixed-gain PI controllers.

Fuzzy dynamic programming approach to reactive power/voltage control in a distribution substation

Abstract: A fuzzy dynamic programming (FDP) approach is proposed for solving the reactive power/voltage control problem in a distribution substation. The main purpose is to improve the voltage profile on the secondary bus and restrain the reactive power flow into a main transformer at the same time. To reach our objectives, the load tap changer (LTC) usually installed in a main transformer is employed to adjust the secondary voltage and the capacitor connected to the secondary bus is employed to compensate the reactive power flow for the load demands. We first forecast the real and reactive power demands of a main transformer and its primary voltages for the next day. With these forecasting data at hand, a fast LTC tap position estimation formula that takes the load models into account is derived to effectively reduce the computational burden for the proposed approach. Practical constraints on bus voltage limits, maximum allowable number switching operations in a day for the LTC and capacitor and the tolerable worst power factor for a main transformer are also considered. To demonstrate the usefulness of the proposed approach, reactive power/voltage control at a distribution substation within the service area of Taipei City District Office of Taiwan Power Company is investigated. It is found that a proper dispatching schedule for the LTC and capacitor can be reached by the proposed approach.

Neural networks for short-term load forecasting: a review and evaluation

Abstract: Load forecasting has become one of the major areas of research in electrical engineering, and most traditional forecasting models and artificial intelligence techniques have been tried out in this task. Artificial neural networks (NNs) have lately received much attention, and a great number of papers have reported successful experiments and practical tests with them. Nevertheless, some authors remain skeptical, and believe that the advantages of using NNs in forecasting have not been systematically proved yet. In order to investigate the reasons for such skepticism, this review examines a collection of papers (published between 1991 and 1999) that report the application of NNs to short-term load forecasting. Our aim is to help to clarify the issue, by critically evaluating the ways in which the NNs proposed in these papers were designed and tested.

Power System Dynamics and Stability

Abstract: 1 Introduction 2 Electromagnetic Transients 3 Synchronous Machine Modeling 4 Synchronous Machine Control Models 5 Single-Machine Dynamic Models 6 Multimachine Dynamic Models 7 Multimachine Simulation 8 Small-Signal Stability 9 Energy Function Methods Appendix A: Integral Manifolds for Model Bibliography Index

Achieving Controllability of Electric Loads

Abstract: This paper discusses conceptual frameworks for actively involving highly distributed loads in power system control actions. The context for load control is established by providing an overview of system control objectives, including economic dispatch, automatic generation control, and spinning reserve. The paper then reviews existing initiatives that seek to develop load control programs for the provision of power system services. We then discuss some of the challenges to achieving a load control scheme that balances device-level objectives with power system-level objectives. One of the central premises of the paper is that, in order to achieve full responsiveness, direct load control (as opposed to price response) is required to enable fast time scale, predictable control opportunities, especially for the provision of ancillary services such as regulation and contingency reserves. Centralized, hierarchical, and distributed control architectures are discussed along with benefits and disadvantages, especially in relation to integration with the legacy power system control architecture. Implications for the supporting communications infrastructure are also considered. Fully responsive load control is illustrated in the context of thermostatically controlled loads and plug-in electric vehicles.

Demand Side Management in Smart Grid Using Heuristic Optimization

Abstract: Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.

Energy models for demand forecasting—A review

Abstract: 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.