Chuangxin Guo

A multiagent-based particle swarm optimization approach for optimal reactive power dispatch

Reactive power dispatch in power systems is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. In this paper, a solution to the reactive power dispatch problem with a novel particle swarm optimization approach based on multiagent systems (MAPSO) is presented. This method integrates the multiagent system (MAS) and the particle swarm optimization (PSO) algorithm. An agent in MAPSO represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice-like environment, with each agent fixed on a lattice point. In order to obtain optimal solution quickly, each agent competes and cooperates with its neighbors, and it can also learn by using its knowledge. Making use of these agent-agent interactions and evolution mechanism of PSO, MAPSO realizes the purpose of optimizing the value of objective function. MAPSO applied to optimal reactive power dispatch is evaluated on an IEEE 30-bus power system and a practical 118-bus power system. Simulation results show that the proposed approach converges to better solutions much faster than the earlier reported approaches. The optimization strategy is general and can be used to solve other power system optimization problems as well.

An Efficient Implementation of Automatic Differentiation in Interior Point Optimal Power Flow

This paper presents an improved implementation of automatic differentiation (AD) technique in rectangular interior point optimal power flow (OPF). Distinguished from the existing implementation of AD, the proposed implementation adds a subroutine to identify all constant first-order and second-order derivates by AD and form a list of constant derivates before the processing of iterations. At every iteration of interior point OPF algorithm, only the changing derivates are updated by AD tool. An excellent AD software-ADC-is used as a basic AD tool to finish the proposed implementation. A user-defined model interface is provided with AD technique to enhance performance and flexibility. Numerical studies on several large-scale power systems indicate that the proposed implementation of AD can compete with hand code in execution speed without loss of maintainability and flexibility of AD codes. This paper demonstrates that AD technique has an application potential in online operating environments of power systems instead of hand-coded derivates, and greatly relieves the burdens of software developers.

Fast

This letter presents a new method, the fast λ-iteration (FλI) method, to solve the economic dispatch (ED) problem considering the prohibited operating zones (POZs) and ramp rate limits of generation units. Necessary conditions for the optimal solution of the ED problem are presented and proved. The efficiency of the method has been verified on a 15-unit system and a Korea 140-unit system.

\lambda

Short term load forecasting (STLF) has an essential role in the operation of electric power systems. In recent years, artificial neural networks (ANN) are more commonly used for load forecasting. However, there still exist some difficulties in choosing the input variables and selecting an appropriate architecture of the networks. This paper presents a novel fuzzy-rough sets based ANN for STLF. The fuzzy-rough sets theory is first employed to perform input selection and determine the initial weights of ANN. In the sequel, an improved k-nearest neighbor (K-NN) method is used for the selection of similar days in history as the training set of ANN. Then ANN module is trained using historical daily load and weather data selected to perform the final forecast. To demonstrate the effectiveness of the approach, short-term load forecasting was performed on the Hang Zhou Electric Power Company in China, and the testing results show that the proposed model is feasible and promising for load forecasting

-Iteration Method for Economic Dispatch With Prohibited Operating Zones

This work presents the solution of the optimal power flow (OPF) using particle swarm optimization (PSO) technique. The main goal of this paper is to verify the viability of using PSO problem composed by the different objective functions. Incorporation of nonstationary multistage assignment penalty function in solving OFF problems can significantly improve the convergence and gain more accurate values. The proposed PSO method is demonstrated and compared with linear programming (LP) approach and genetic algorithm (GA) approach on the standard IEEE 30-bus system. The results show that the proposed PSO method is capable of obtaining higher quality solutions efficiently in OFF problem.

A new method for short-term load forecasting integrating fuzzy-rough sets with artificial neural network

We introduce emerging utility-scale energy storage (e.g., batteries) as part of the set of control measures in a corrective form of the security-constrained unit commitment (SCUC) problem. This enhanced SCUC (ESCUC) leverages utility-scale energy storage for multiple applications. In the base case, the storage units are optimally charged and discharged to realize economic operation. Immediately following a contingency, the injections of storage units are adjusted almost instantly to alleviate short-term emergency overloads, thereby avoiding potential cascading outages and giving slow ramping generating units time to adjust their output. The ESCUC is a large two-stage mixed-integer programming problem. A Benders decomposition has been developed to solve this problem. In order to achieve computational tractability, we present several acceleration techniques to improve the convergence of the proposed algorithm. Case studies on the RTS-79 and RTS-96 systems demonstrate the effectiveness of the proposed approach.

Improved particle swam optimization algorithm for OPF problems

This paper discusses how fast-response distributed battery energy storage could be used to implement post-contingency corrective control actions. Immediately after a contingency, the injections of distributed batteries could be adjusted to alleviate overloads and reduce flows below their short-term emergency rating. This ensures that the post-contingency system remains stable until the operator has redispatched the generation. Implementing this form of corrective control would allow operators to take advantage of the difference between the short- and long-term ratings of the lines and would therefore increase the available transmission capacity. This problem is formulated as a two-stage, enhanced security-constrained OPF problem, in which the first-stage optimizes the pre-contingency generation dispatch, while the second-stage minimizes the corrective actions for each contingency. Case studies based on a six-bus test system and on the RTS 96 demonstrate that the proposed method provides effective corrective actions and can guarantee operational reliability and economy.

Enhanced Security-Constrained Unit Commitment With Emerging Utility-Scale Energy Storage

This paper presents a new approach to shortterm wind speed prediction. The chaotic time series analysis method is used to capture the characteristic of complex wind behavior in which a correlation dimension method is employed to calculate embedding dimension of the time series, then a mutual information method is used to determine the time delay. Based on the embedding dimension and time delay, support vector regression (SVR) is trained to perform the prediction. The proposed method is evaluated using the real-world data collected from a wind farm. The results have demonstrated the accuracy of the proposed wind speed prediction method in comparison with that offered by an artificial neural network (ANN).

Enhanced Security-Constrained OPF With Distributed Battery Energy Storage

Regarding short-term reliability of composite power system, probability of critical event resulting in system failure within a short lead time is extremely low, which renders classical sequential Monte Carlo simulation method inefficient. In this paper, a cross-entropy-based three-stage sequential importance sampling (TSSIS) method is proposed to solve the low efficiency problem resulted from the low rate of component state transition during a fixed lead time. First, by assuming the system state transition process conforms to continuous time Markov chain, an analytical solution to optimal distorted component state transition rate to be used for sequential importance sampling is found by means of cross-entropy method. Second, TSSIS for a fixed lead time is constructed as follows: 1) acceleration of producing system state transitions; 2) enhanced learning to give optimal distorted transition rate; 3) compensation to the cost function. Case studies based on a reinforced Roy Billinton reliability test system and RTS-79 are carried out respectively for illustration of parameter settings of TSSIS as well as efficiency gain in comparison with the classical sequential Monte Carlo simulation method. The results demonstrate that given rational setting of parameters, TSSIS is of relatively high efficiency for sequential short-term reliability evaluation of composite power system.

Short-term wind speed prediction using support vector regression

Economic dispatch with valve-point effect (EDVPE) considered is presented as a more accurate model of the real problem compared to the conventional economic dispatch model. It is basically a non-convex, non-differentiable, and multi-modal optimization model with many local minima. Part I of the paper focuses on the local minimum analysis of the EDVPE. The analysis indicates that a local minimum consists of the singular points, the small convex regions, and the output of a slack unit that is dispatched to balance the load demand. Two types of local minima are identified and the second type could be ignored. To verify the rationality of the analyses, a traverse search has been performed to solve the EDVPE with and without considering the transmission loss on different test systems. All the simulation results support the analysis given in the paper. To effectively solve the EDVPE on a large-scale power system, based on the analysis presented in this paper, a new method, dimensional steepest decline method, is proposed in Part II of the paper.