Advanced Materials Research Vols. 860-863

Abstract: In this study, a novel prediction method for electric power demand based on markov chain model with a fuzzy probability has been developed. The model improves upon the existing prediction methods with advantages in uncertainty reflection, such as the uncertainties in electric power system which reflect the vague and ambiguous during the process of power load forecasting through allowing uncertainties expressed as fuzzy parameters and discrete intervals. The developed model is applied to predict the electric power demand of a virtual city from 2011 to 2016. Different satisfaction degrees of fuzzy parameters are considered as different levels of detail of the statistic data. The results indicate that the model can reflect the high uncertainty of long term power demand, which could support the programming and management of power system.

Abstract: Theoretically, there are two kinds of models to calculate Locational Marginal Price (LMP): single-stage optimization model and multi-stage optimization model. In the paper, two mathematical models were analyzed and compared. Economic concept of LMP was explained and LMPs calculation was derived based on optimization methods. Characteristics of LMPs for different optimization models and how marginal generators and non-marginal generators can affect LMPs were discussed. In case study, the PJM five buses system was adopted to calculate LMPs under single-stage and multi-stage optimization model. The study results show that multi-stage model can accomplish economic dispatch more effective than single-stage model. But single-stage optimization model is better competent for LMP based settlement.

Abstract: Metro Transit Railways (MTRs) provide pubic convenience, but also bring power quality impact on power grid. Based on the field measurements, this paper discusses and analyzes the power quality characteristics of MTRs in Shanghai. In the terms of harmonics, voltage flicker, voltage unbalance, voltage deviation, frequency deviation and transient events, numbers of indicators are investigated to explore the power quality characteristics and existing problems of MTRs. Although harmonics are still the main problem impacting of MTRs, other power quality indicators comply with the restriction of national standards. Besides, the line charging reactive power is a common issue and transient power quality events occurs intermittently. The paper presents the corresponding proposal, which is instructive in power quality management of MTRs in cities similar as Shanghai. .

Abstract: The framework and functions of Soft Science Laboratory about provincial electric power company operation monitoring and decision-making simulation are introduced. Based on the theory of recognizing-analysis-tracing-handling, a network topology which reflects the operation status on enterprise business flow and performance flow is put forward. Through monitoring the key indicators on the network topology, abnormal changes are detected. Combined with analyzing external policies, the Soft Science Laboratory makes a prediction and a timely warning. And it assists the leadership of the company to make corresponding decisions in order to improve the company's lean operation level.

Abstract: To introduce various kinds of information technologies and suitable modes into the power industry, is of great significance to the development of today's power enterprises. In this paper, the development of the informatization of electric power industry in China is reviewed firstly. Then, the information engineering, regulations and prospects are introduced in detail. Finally, the problems occurred in its development are analyzed and future prospects are proposed.

Abstract: A scientific, rational, and practical maintenance plan helps to improve the reliability of power system. To avoid duplication of power outages and reduce unnecessary power outages, a suitable model of the network maintenance was established , considering the importance of facilities. Then, because of the defect that the pso is easy to fall into local minimum at the late iterations, an improved algorithm agpso was proposed. Finally, by means of analyzing an example, the feasibility of the model and the correctness of the algorithm were both proved.

Abstract: Molten carbonate fuel cell/ gas turbine hybrid power system (MCFC/GT) output power should real-time response the demand of load. The auto-tuning ZieglerNichols tuned PI controller (AZNPIC) as a feedback controller for power corrective action is added to the hybrid power system. The optimal setpoints and feed forward control inputs of the controller are given by Multi-output Support Vector Machine Regression (MSVR) predictor for the hybrid system. Simulation results show that the power of hybrid system can be effectively close to the desired setpoints based on the control method.

Abstract: The supply and demand structure of Chinas coal market and electricity market is undergoing profound changes. To respond to the conflict between coal industry and power industry, coal-electricity integration has gained support from The State through relevant policies. The development of coal-electricity integration plays a vital role in balancing the energy industrial structure. This paper introduces the evolution of coal-electricity integration policy and discusses the existing pool model of Chinas coal-electricity integration. And this paper also makes an analysis of coal-electricity integrations impact on the development of power grid, providing recommendations for power enterprises facing the new situation of energy development.

Abstract: In steam power system optimal problems, uncertain parameters should be considered unless the solution will be infeasible. The uncertain parameters and constraints in steam power system optimization model are analyzed. Then the related constraints with uncertain parameters which used to be expressed by joint chance constraints are approximated, and a robust optimization model of steam power system is proposed. The simulation results illustrate the validity of the model.

Abstract: An improved radial basis function neural network is proposed that preprocessing is the key to improving the precision of short-term load forecasting. This paper presents a new model which is based on classical RBF neural network, combine the GA-optimized SVM radial basis function and RBF neural network. According to the date of the type, temperature, weather conditions and other factors ,The Application of combined GA-optimized SVM radial basis function is used to extract useful data to improve the load forecasting accuracy of RBF neural network. Spring load data of California were applied for simulation. The simulation indicates that the new method is feasible and the forecasting precision is greatly improved.