C. Y. Chung

A hybrid genetic algorithm-interior point method for optimal reactive power flow

By integrating a genetic algorithm (GA) with a nonlinear interior point method (IPM), a novel hybrid method for the optimal reactive power flow (ORPF) problem is proposed in this paper. The proposed method can be mainly divided into two parts. The first part is to solve the ORPF with the IPM by relaxing the discrete variables. The second part is to decompose the original ORPF into two sub-problems: continuous optimization and discrete optimization. The GA is used to solve the discrete optimization with the continuous variables being fixed, whereas the IPM solves the continuous optimization with the discrete variables being constant. The optimal solution can be obtained by solving the two sub-problems alternately. A dynamic adjustment strategy is also proposed to make the GA and the IPM to complement each other and to enhance the efficiency of the hybrid proposed method. Numerical simulations on the IEEE 30-bus, IEEE 118-bus and Chongqing 161-bus test systems illustrate that the proposed hybrid method is efficient for the ORPF problem

Quantum-Inspired Evolutionary Algorithm Approach for Unit Commitment

This paper presents a novel method for solving the unit commitment (UC) problem based on quantum-inspired evolutionary algorithm (QEA). The proposed method applies QEA to handle the unit-scheduling problem and the Lambda-iteration technique to solve the economic dispatch problem. The QEA method is based on the concept and principles of quantum computing, such as quantum bits, quantum gates and superposition of states. QEA employs quantum bit representation, which has better population diversity compared with other representations used in evolutionary algorithms, and uses quantum gate to drive the population towards the best solution. The mechanism of QEA can inherently treat the balance between exploration and exploitation and also achieve better quality of solutions, even with a small population. The proposed method is applied to systems with the number of generating units in the range of 10 to 100 in a 24-hour scheduling horizon and is compared to conventional methods in the literature. Moreover, the proposed method is extended to solve a large-scale UC problem in which 100 units are scheduled over a seven-day horizon with unit ramp-rate limits considered. The application studies have demonstrated the superior performance and feasibility of the proposed algorithm.

An Advanced Quantum-Inspired Evolutionary Algorithm for Unit Commitment

Based on a quantum-inspired evolutionary algorithm for unit commitment, this paper proposed ways to advance the efficiency and robustness of the algorithm so that its capacity for application in large-scale unit commitment problems can be significantly enhanced. The paper develops an advanced quantum-inspired evolutionary unit commitment algorithm by developing a new initialization method based on unit priority list and a special Q-bit expression for ensuring diversity in the initial search area for improving the efficiency of solution searching. Different techniques such as multi-observation, single-search, and group-search are also proposed for incorporation in the advanced algorithm. The advanced algorithm is tested and compared with the earlier quantum-inspired evolutionary algorithm and a number of known methods through its applications to test systems with up to 100 generator units for a 24-h scheduling horizon.

Robust Transmission Network Expansion Planning Method With Taguchi's Orthogonal Array Testing

This paper proposes a robust transmission network expansion planning (RTNEP) method with Taguchis orthogonal array testing (TOAT) which considers generation dispatch and operating uncertainties caused by load demand and renewable energy output. TOAT is a method which has been proven to be optimal to select representative scenarios for testing from all the possible combinations. This paper employs TOAT to determine testing scenarios in transmission network expansion planning (TNEP). A new RTNEP formulation is then proposed based on the multiple testing scenarios. The simulation results have demonstrated the effectiveness of the proposed RTNEP.

Application of plug-in electric vehicles to frequency regulation based on distributed signal acquisition via limited communication

For application of a large number of plug-in electric vehicles (PEVs) to system frequency regulation, a distributed acquisition approach based on consensus filtering is proposed, where frequency-measuring function is performed only at distribution substations. Limited communication between neighboring PEVs/distribution substations can facilitate consistent and accurate acquisition of frequency deviation signals for all PEVs. Considering the battery charging/discharging characteristics, a dynamic PEV model with feedback control is further proposed and integrated with frequency regulation based on distributed acquisition. Asymptotical stabilities of distributed acquisition and system frequency regulation are analyzed. Simulation results demonstrate that the proposed approach can provide consistent and accurate control signals for a large number of PEVs and obtain better regulation than general decentralized control in terms of eliminating noise, improving robustness and reducing device costs.

Coordinated Damping Control Design for DFIG-Based Wind Generation Considering Power Output Variation

To alleviate the impacts of stochastic wind generation on stability performance, this paper proposes a novel control design method for coordination and synthesis of damping controllers for conventional synchronous generators (SG) and double fed induction generators (DFIG)-based wind generation in multi-machine power systems. A probabilistic model of wind generation under Weibull distribution of wind speed is first introduced. Based on this model, an extended probabilistic small signal stability analysis (SSSA) incorporating wind power generation is proposed for handling probability density function (PDF) of its power output. The optimization problem for tuning of damping controllers is then formulated, considering the cumulative distribution function (CDF) of the real part and damping ratio of eigenvalues obtained by the proposed probabilistic SSSA as stability constraints. Particle swarm optimization (PSO) is used to solve the optimization problem and to determine parameters of damping controllers. The effectiveness of the proposed method is demonstrated on the modified New England power system with multi-wind farms through probabilistic SSSA and transient stability analysis, and it is compared with conventional deterministic methods. Accuracy of the proposed probabilistic SSSA is also validated by Monte Carlo simulations (MCS).

Non-Intrusive Signature Extraction for Major Residential Loads

This paper presents a technique to extract load signatures non-intrusively by using the smart meter data. Load signature extraction is different from load activity identification. It is a new and important problem to solve for the applications of non-intrusive load monitoring (NILM). For a target appliance whose signatures are to be extracted, the proposed technique first selects the candidate events that are likely to be associated with the appliance by using generic signatures and an event filtration step. It then applies a clustering algorithm to identify the authentic events of this appliance. In the third step, the operation cycles of appliances are estimated using an association algorithm. Finally, the electric signatures are extracted from these operation cycles. The results can have various applications. One is to create signature databases for the NILM applications. Another is for load condition monitoring. Validation results based on the data collected from three actual houses and a laboratory experiment have shown that the proposed method is a promising solution to the problem of load signature collection.

An Eigenstructure-Based Performance Index and Its Application to Control Design for Damping Inter-Area Oscillations in Power Systems

An eigenstructure-based performance index is proposed in this paper to measure the dynamic performance of the system as well as control efforts. Calculation of this index is based on eigenstructure of the closed loop system and the design parameters; it does not rely on control structures. Therefore, this index can be applied for solving structurally constrained control problems. A tuning scheme based on this index is proposed for coordinating power system stabilizers (PSSs) and supplementary damping controllers (SDCs) for flexible AC transmission systems (FACTS) devices to damp inter-area oscillations of systems and to optimize their control efforts under multiple operating conditions. Both PSSs and SDCs utilize control structures as a low order single-input-single-output phase lead-lag compensator. Wide-area signals are employed to upgrade their effectiveness in damping inter-area oscillations. Time delays caused by usage of wide-area signals are also considered in the tuning scheme. Results of simulation on a four-machine two-area system and the New England and New York interconnected system show that the proposed index is effective in measuring dynamic performance of the system and the coordinatedly tuned PSSs and SDCs based on this index can robustly damp inter-area oscillations of systems with optimized control efforts.

Quasi-Monte Carlo Based Probabilistic Small Signal Stability Analysis for Power Systems With Plug-In Electric Vehicle and Wind Power Integration

This paper presents a new quasi-Monte Carlo (QMC) based probabilistic small signal stability analysis (PSSSA) method to assess the dynamic effects of plug-in electric vehicles (PEVs) and wind energy conversion systems (WECSs) in power systems. The detailed dynamic model of PEVs is first proposed for stability study. To account for the stochastic behavior of PEVs and WECSs in load flow studies, the randomized model and probability density function (PDF) representing their nodal power injections are first developed, and then their stochastic injections are sampled by Sobol sequences. Finally, the distribution of system eigenvalues can be obtained by the PSSSA. The proposed QMC-based PSSSA is tested on the modified 2-area 4-machine system and New England 10-generator 39-bus system. Results showed the necessity of modeling of PEVs and WECSs, and validated the efficiency of the proposed QMC.

A Novel Modal Decomposition Control and Its Application to PSS Design for Damping Interarea Oscillations in Power Systems

The residue method has been widely used for tuning power system stabilizers (PSSs) in large power systems to improve the damping of interarea oscillations. However, an additional PSS installation may affect the performance of existing PSSs due to interactions among different modes. When contending with several interarea oscillations, compromise among different modes becomes necessary. In this paper, a novel method based on modal decomposition is proposed for tuning PSSs for damping of the concerned interarea mode, while minimizing its effect on other modes by weakening the interactions among different modes. Design considerations, PSS structure and tuning procedure are formulated. The performance of the proposed method has been validated based on a two-area four-machine system and an actual large power system, China Southern Grid.