Yunhe Hou

Generalized ant colony optimization for economic dispatch of power systems

Based on the ant colony optimization (ACO) for combinatorial optimization problems, this paper presents a new versatile optimization algorithm called generalized ant colony optimization (GACO), which can be used to solve the discontinuous, nonconvex, nonlinear constrained optimization problems. This paper also studies the convergence property of GACO based on the fixed-point theorem on a complete metric space, presents a sufficient condition for convergence. This algorithm is used to solve the complicated, nonconvex, nonlinear economic dispatch (ED) problem of power systems. Several factors such as, valve-point effects of fuel cost functions, transmission capacity constraints, and system stability constraints are considered in the computation models. Numerical results show that the proposed method is feasible and efficient.

Computation of Milestones for Decision Support During System Restoration

System restoration involves status assessment, optimization of generation capability, and load pickup. The optimization problem needs to take complex constraints into consideration, and therefore, it is not practical to formulate the problem as one single optimization problem. The other critical consideration for the development of decision support tools is its generality, i.e., the tools should be portable from a system to another with minimal customization. This paper reports a practical methodology for construction of system restoration strategies. The strategy adopted by each power system differs, depending on system characteristics and policies. A new method based on the concept of “generic restoration milestones (GRMs)” is proposed. A specific restoration strategy can be synthesized by a combination of GRMs based on the actual system conditions. The decision support tool is expected to reduce the restoration time, thereby improving system reliability. The proposed decision support tool has been validated with cases based on a simplified Western Electricity Coordinating Council (WECC) 200-Bus system and Hawaiian Electric Company system.

Mitigating Voltage and Frequency Fluctuation in Microgrids Using Electric Springs

Voltage and frequency fluctuation associated with renewable integration have been well identified by power system operators and planners. At the microgrid level, a novel device for the implementation of dynamic load response, which is known as the electric springs (ES), has been developed for mitigating both active and reactive power imbalances. In this paper, a comprehensive control strategy is proposed for ES to participate in both voltage and frequency response control. It adopts the phase angle and amplitude control which respectively adjust the active power and the reactive power of the system. The proposed control strategy is validated using a model established with power system computer aided design/electro-magnetic transient in dc system. Results from the case studies show that with appropriate setting and operating strategy, ES can mitigate the voltage and frequency fluctuation caused by wind speed fluctuation, load fluctuation, and generator tripping wherever it is installed in the microgrid.

Robust Energy and Reserve Dispatch Under Variable Renewable Generation

Global warming and environmental pollution concerns have promoted dramatic integrations of renewable energy sources all over the world. Associated with benefits of environmental conservation, essentially uncertain and variable characteristics of such energy resources significantly challenge the operation of power systems. In order to implement reliable and economical operations, a robust energy and reserve dispatch (RERD) model is proposed in this paper, in which the operating decisions are divided into pre-dispatch and re-dispatch. A robust feasibility constraint set is imposed on pre-dispatch variables, such that operation constraints can be recovered by adjusting re-dispatch after wind generation realizes. The model is extended to more general dispatch decision making problems involving uncertainties in the framework of adjustable robust optimization. By revealing the convexity of the robust feasibility constraint set, a comprehensive mixed integer linear programming based oracle is presented to verify the robust feasibility of pre-dispatch decisions. A cutting plane algorithm is established to solve associated optimization problems. The proposed model and method are applied to a five-bus system as well as a realistic provincial power grid in China. Numeric experiments demonstrate that the proposed methodology is effective and efficient.

A comparative analysis of Spearman's rho and Kendall's tau in normal and contaminated normal models

This paper analyzes the performances of Spearmans rho (SR) and Kendalls tau (KT) with respect to samples drawn from bivariate normal and contaminated normal populations. Theoretical and simulation results suggest that, contrary to the opinion of equivalence between SR and KT in some literature, the behaviors of SR and KT are strikingly different in the aspects of bias effect, variance, mean square error (MSE), and asymptotic relative efficiency (ARE). The new findings revealed in this work provide not only deeper insights into the two most widely used rank-based correlation coefficients, but also a guidance for choosing which one to use under the circumstances where Pearsons product moment correlation coefficient (PPMCC) fails to apply.

An Improved Approach for AC-DC Power Flow Calculation With Multi-Infeed DC Systems

An improved approach based on sequential method for the AC-DC power flow calculation is proposed in this paper. This approach solves the convergence problem caused by voltage violations at AC buses during the power flow calculation for the DC subsystems. The convergence property can be significantly improved by adjusting the converter transformer tap position flexibly. In order to adjust the tap position of the converter transformer flexibly, three mainly modifications are proposed. Firstly, the equations for whole DC systems are decoupled into individual DC systems so as to easily figure out which DC systems tap position needs adjustment. Secondly, the tap ratio of a converter transformer is selected as an alternative state variable to replace the cosine of the control angle when necessary. Thirdly, the Newton-Raphson method is utilized to solve DC subsystems instead of the method using the linear equations. Furthermore, a theoretical analysis of the advantages of the proposed approach is also presented. Numerical simulations and practical applications show that the proposed approach meet the requirement of different system operating conditions and has advantages in terms of convergence and speed. The proposed approach has been successfully integrated into the Energy Management System (EMS) for China Southern Power Grid.

A New Recursive Dynamic Factor Analysis for Point and Interval Forecast of Electricity Price

The functional principal component analysis (FPCA) is a recent tool in multivariate statistics and it has been shown to be effective for electricity price forecasting. However, its online implementation is expensive, which requires the computation of eigen-decomposition at each update. To reduce the arithmetic complexity, we propose a recursive dynamic factor analysis (RDFA) algorithm where the PCs are recursively tracked using efficient subspace tracking algorithm while the PC scores are further tracked and predicted recursively using Kalman filter (KF). From the latter, the covariance and hence the interval of the forecasted electricity price can be estimated. Advantages of the proposed RDFA algorithm are the low online complexity, and the availability of the prediction interval thanks to the KF framework. Furthermore, a robust extension is proposed to tackle possible non-Gaussian variation. Finally, the RDFA algorithm can be extended to predict electricity price in a longer period using a multi-factor model by capturing trends in different time horizon. Experimental results on the New England and Australian datasets show that the proposed RDFA approach is able to achieve better prediction accuracy than other conventional approaches. It thus serves as an attractive alternative to other conventional approaches to forecast electricity price and other related applications because of its low complexity, efficient recursive implementation and good performance.

Constructing power system restoration strategies

System restoration is an integral part of the overall defense system against catastrophic outages. The nature of system restoration problem involves status assessment, optimization of generation capability and load pickup. The optimization problem needs to take into numerous practical considerations and, therefore, it cannot be formulated as one single optimization problem. The other critical consideration for the development of decision support tools is its generality, i.e., the tools should be portable from a system to another with minimal customization. This presentation will provide a comprehensive methodology for construction of system restoration strategies. The strategy adopted by each power system differs, depending on the system characteristics and policies. A new method based on the concept of ¿generic restoration milestones¿ and ¿generic restoration actions¿ has been developed. A specific restoration strategy can be synthesized by a combination of the milestones and actions based on the actual system conditions. The decision support tool is expected to reduce the restoration time, thereby improving the system reliability.

The Healing Touch: Tools and Challenges for Smart Grid Restoration

Effective system restoration is an important step toward a self-healing smart grid. in the future energy paradigm, with a high penetration of renewable resources and responsive demands, variability and uncertainty will affect power system operating and recovery technologies. Smart restoration provides an adaptive and optimized strategy with which to make restoration decisions, one that will reduce restoration time while maintaining system integrity. With the implementation of such decision support tools, the power grid will be better prepared and equipped to handle extreme events. They enable the streamlining of communication among all stakeholders, and they preserve knowledge and experience for future engineers.

Power system probabilistic small signal stability analysis using two point estimation method

A so-called two-point estimation (TPE) method is presented in this paper for power system probabilistic small signal stability (PSSS) analysis. With the development of power systems under open access environment, it is highly desired to investigate power system stability with uncertainties in both system parameters and operating conditions. Monte Carlo simulation (MCS) method has been widely used for this purpose. However, this method is very time-consuming. The TPE based method proposed in this paper provides a way to solve this problem to some extent. It estimate the statistical characteristics of random variables with less calculation requirement while keeping enough calculating precision. The TPE based method for the PSSS analysis is outlined. Then, the model as well as the stable indices for power system PSSS are presented. The effectiveness of the proposed method is verified by the simulation results on a 3- generator-9-node power system.