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This paper develops a partition approach algorithm (PAA) to solve economic dispatch with non-smooth fuel cost. The PAA developed integrates the multi-section divided and interval elimination method to formulate the searching algorithm. The populations produced of PAA are model by parameter interval and divided sections to calculate the combinations of effective factors and their selected rules. The analysis of results uses a signal-to-noise ratio (S/N ratio) to aid in the determination of the offsprings. Effectiveness of the PAA is demonstrated on three example systems with a nonconvex function and compared to that of SQP, SA, GA, and EP. The results show that the proposed method are more effective than other previously developed evolutionary computation algorithms and have a great potential for other applications in power systems.

This paper proposes a decision tool for utilities to perform the optimal generation expansion planning in a deregulated electricity market. Combining the immune algorithm (IA) and Tabu search (TS) an refined immune algorithm (RIA) is developed to solve this problem. By considering the various load types (peak load, middle load, basic load) and independent power producers (IPPs) competition, the generation expansion planning model is established under the operational constraints, reliability constraints and CO 2 constraints. RIA is conducted by an improved crossover and mutation mechanism with a competition and auto-adjust scheme to avoid prematurity. Tabu lists with heuristic rules are also employed in the searching process to enhance the performance. Testing results show that RIA can offer a better tool for generation expansion planning of utilities and promoted the competition ability of company. © 2005 Elsevier B.V. All rights reserved.

A new method based on immune algorithm (IA) is presented to solve the scheduling of cogeneration plants in a deregulated market. The objective function includes fuel cost, population cost, and electricity wheeling cost, subjective to the use of mixed fuels, operational limits, emissions constraints, and transmission line flow constraints. Enhanced immune algorithm (EIA) is proposed by an improved crossover and mutation mechanism with a competition and auto-adjust scheme to avoid prematurity. Table lists with heuristic rules are also employed in the searching process to enhance the performance. EIA is also compared with the original IA. Test results verify that EIA can offer an efficient way for cogeneration plants to solve the problem of economic dispatch, environmental protection, and electricity wheeling.

A multiple-frequency three-phase load-flow model was developed in this paper. There are two new submodels including the fundamental power flow (FPF) and harmonic frequency power-flow (HPF) model. In FPF, models of electrical elements and PV buses were treated in the form of current injections in a transmission system. The standard Fourier analysis was used to deal with the harmonic loads to get injection currents. With harmonic currents as equivalent current sources, the HPF can be derived. Besides, the decoupled fast version of FPF and HPF, called DFPF and DHPF, were also proposed in this paper. Test results show that the proposed general-purpose methods are better performers than conventional power-flow solutions and are very robust.

This paper proposes two techniques for improving the system unbalance and loss reduction by means of transformer load management. First technique, a computer algorithm with linear programming, was used to derive the optimal load diversity among the various load types. Second technique, in regard to transformer phase rearrangement, dynamic programming (DP) with forward and backward procedure is employed to find out the optimal phase assignment of transformer for a feeder. Finally, a three-phase load flow program was used to compute the system losses and system unbalance on a practical feeder in cases of transformer rearrangement, optimal load diversity, and mixed-type. At last, both of the proposed techniques are verified to be valid and available.

This paper presents a multi-objective approach based on evolutionary programming to solve the economical operation of cogeneration system under emission constraints. A multi-objective function including the minimization of cost and multi-emission is formulated in this paper. The cost model includes fuels cost and tie-line energy. The emissions with CO2, SOx, and NOx were derived as a function of fuel enthalpy. All constraints including fuel mix, operational constraints, and emission constraints must be met in the optimization process. The steam output, fuel mix, and power generations will be found by considering the time-of-use dispatch between cogeneration systems and utility companies. Data of an industrial cogeneration system was used to illustrate the effectiveness of the proposed method.

This paper studies the economical operation of cogeneration systems under emission control with NOx and SOx from fossil-fueled thermal generation. The emission model is formulated as a function of fuel enthalpy dependent on the emission factor. The objective function includes fuel cost, emission cost, and tie-line energy cost, subject to the use of mixed fuels, operational limits, and emission constraints. An evolutionary programming was adopted to solve this problem. The steams, fuel mix, and generations will be found by considering the time-of-use dispatch between cogeneration systems and utility companies. A real cogeneration system is given to illustrate the proposed method.

This paper develops a software package to calculate the optimal contract capacities for industrial customers. Based on the time-of-use (TOU) rates employed by the Taiwan Power Company, the objective function is formulated, to minimize the electricity bill of industrial customers during the whole year period. Evolutionary programming (EP) was adopted to solve this problem. Users can get the optimal contract capacities for the peak load, semi-peak load, and off-peak load, respectively. Practical load consumption data were used to prove the validity of this program. Results show that the software developed in this paper can be used as a useful tool for industrial customers in selecting contract capacities to curtail the electricity bill.

This paper presents an efficient strategy for transformer planning to reduce the system losses by means of transformer rearrangement. The load patterns of residential, commercial and industrial customers are derived by load survey and then used to calculate the transformer hourly loading. The three-phase load flow is used to solve the system losses and unbalance factor according to the load patterns and transformer hourly loading. With regard to transformer rearrangement, the Dynamic Programming (DP) technique is employed to find out the phase assignment of transformers. In the DP process, a backward procedure and a forward procedure are used to search for the best phase arrangement of transformers. The objective function including the cost of loss reduction and switching is formulated in this paper. To verify the efficiency of the proposed method, a practical feeder is selected for computer simulation. By comparison of the results between without transformer rearrangement and with transformer rearrangement, the proposed method is verified to be valid and available.

This paper presents a new approach to identify fault types and phases. A transmission line fault classification method based on a radial basis function (RBF) neural network with orthogonal-least-square (OLS) learning procedure was used to identify various patterns of associated voltages and currents. The RBF neural network was also compared with the back-propagation (BP) neural network in this paper. It is shown that the RBF approach can provide a fast and precise operation for various faults. The simulation results also show that the proposed approach can be used as an effective tool for high speed relaying.

This paper presents the application of Evolutionary Programming (EP) to optimal operational strategy of cogeneration systems under Time-of-Use (TOU) rate. The fuel consumption and steam generation will first be measured and the Input–Output (I/O) curve derived using the regression method. The operational model developed also considers the connection of the cogeneration system with the utility company in terms of TOU rate and various fuel consumptions. EP was adopted to decide the optimal fuel dispatch, steam output of boiler, and generation output subjective to satisfying all the operation constraints. The Newton–Raphson based method has been implemented to show that EP does have the tendency of getting the global optimum. The proposed methodology could provide a practical model for both the utility company and the cogeneration industry to follow.

The purpose of this paper is to investigate the computer applications and analyses in industrial distribution design. First of all, we have to inquire into the design of graphic symbols and the drawing of a single-line diagram by taking advantage of AutoCAD software. Next, by employing DBASE III, the database for distribution apparatus is accomplished to integrate with the graphic information so that the distribution graphic information database is obtained. Then, we focus our attention on the applications of practical operation for a factory. Written in C language, the application programs include load flow calculation, voltage drop equation, grounding resistor selection, power factor improvement, wire selection, protective coordination display, and fault current computation. Finally, the linking of man-machine-interface and application programs can be accomplished through the modification of ACAD.MNU and ACAD.PGP of AutoCAD. Accordingly, an educational teaching software is developed to carry out a key-controlled industrial distribution system analysis.

With the aid of the Geographic Information System (GIS), an effective way is proposed to determine the substation location and its service area. The integration of GIS could produce a practical distribution plan by considering the land use, value, zoning, as well as other physical restrictions. These physical restrictions are not easy to quantify, and are awkward to model with mathematics. With a graphic terminal, a user is able to view the planning process on-line. An urban area is chosen as an example to show the effectiveness of this technology.

This paper states a distribution network information support system (DISS). The system provides an environment for building a distribution database which includes both graphic and alphanumeric data. The database can be used for inventory, facility management, engineering analysis, and map production. Facility data or network models can be retrieved from the database. The retrieved data can then be used by application programs. With a good man-machine interface (MMI), graphic output or text reports can be viewed from the graphic screen online. A good DISS could also produce a practical result for strategic decisions by considering land use, value, zoning, as well as other physical restrictions. These physical restrictions are not easy to quantify, and are awkward to model with mathematics.

Thermal power plants dominate electric power generation in Taiwan, which are also the major contributor to Green House gases (GHG). CO2 is the most important greenhouse gas that cause global warming and sea-level rising. This paper pierces the relationship between GHG reduction and power generation expansion planning (GEP) problem for the utility. Refined Immune Algorithm (RIA) is presented to determine the generation expansion planning strategy of the utility with independent power providers (IPPs). The utility has to take both the IPPs' participation and environment impact into account when a new generation unit is expanded. This problem also takes into account the GHG reduction and reliability issues, while satisfying all electrical constraints simultaneously from view of supply side. RIA was conducted by an improved crossover and mutation mechanism with a competition and auto-adjust scheme to avoid prematurity. Testing results shows that RIA can offer an efficient way in determining the generation expansion planning.

This paper proposed the use of a new constraint-preserving method (NCPM) to solve the nonconvex economic dispatch (NED) with linear constraint problems. NCPM always generates feasible solutions every time that reduces the search space (exclude the infeasible domain) to enhance the probability of obtaining the global optimum. Based on the benchmark of the same objective function, NCPM was compared with the other methods upon their search performance. Results show that the proposed algorithm outperforms the existing methods, in both global search capability and computation time.

In this paper, a novel stochastic algorithm is presented to deal with the unit commitment problems. In this algorithm, a stochastic coding scheme including a binary and an integer matrices represented all the potential combinations of units statuses during the dispatch intervals. Signal-to-Noise Ratio(SNR) technique is used to avoid the entrapment in a local minimum to obtain a better solution. The effectiveness and efficiency of the algorithm are demonstrated by using several cases and the results are compared with many previous publications. Results show that the proposed method is feasible, robust, and more effective than many other previously developed computation algorithms.

In this paper, a bidding strategy is developed from the viewpoint of utility wishing to maximize its profit in a congestion environment. The proposed algorithm is formulated as a two steps optimization problem. At the first step, a bidding strategy is solved to maximize its expected profit, and at the second step, a curtailment strategy will be perform to maximize the participant's profit when the system occurs the transmission congestion. An immune algorithm (RIA) with scale scheme system (SSS) is proposed to solve the optimization problem. It has advantages and some characteristics to show a better performance than many other algorithms. The IEEE 30 bus is selected for test. Simulation results have a good result with this standard to obtain an optimal bidding strategy for electricity suppliers under the congestion environment

In this paper, a partition approach algorithm (PAA) is developed to solve economic dispatch with valve-point effect. The PAA developed integrates the multi-section divided and interval elimination method to search the optimal problem. The populations produced of PAA are model by parameters interval and divided sections to calculate the combinations of effective factors and their selected rules. The analysis of results uses a signal-to-noise ratio (S/N ratio) to aid in the determination of the offsprings. PAA is demonstrated on an example system with valve-point effect and compared to that of SA, GA, and EP. Results can be shown that the proposed method is more effective than other evolutionary algorithms and has a best probability to guarantee a global optimal

In this paper, an improved genetic algorithm (IGA) is presented to determine the generation expansion planning of the utility in a deregulated market. The utility has to take both the IPPs' participation and environment impact into account when a new generation is expanded. The CO/sub 2/emission also took into account, while satisfying all electrical constraints simultaneously. IGA was conducted by an improved crossover and mutation mechanism with a competition and autoadjust scheme to avoid prematurity. Tabu lists with heuristic rules were also employed in the searching process to enhance the performance. Testing results shows that IGA can offer an efficient way in determining the generation expansion planning. Results can offer utilities for determining the optimal expansion planning.

In this paper, the bidding strategy problem is modeled as an optimization problem and the congestion's influence is discussed in the bidding process. An immune algorithm (IA) is presented to solve the bidding strategy problem. IA is based on the operation of human immune system. The immune system is a basic bio-defense system against viruses and disease-causing organisms. This complex defense mechanism combines genes to deal with the inbreaking antigens. Using this heuristic algorithm, IA has advantages and some characteristics to show a better performance than many other algorithms. Numerical analysis will clarify congestion's influence on price and bidding strategy.

In this paper, an effective tool is proposed to detect harmonic components by using probabilistic neural network (PNN). PNN is used to detect the harmonics from the distorted waveforms. PNN can be fast learning and recalling process, no iteration for weight regulations in the learning process, no pre-decision for the number of hidden layers and the number of hidden nodes in each layer, and adaptability for architecture changes. Many tests are conducted and the results show that PNN has advantages over other previously developed algorithm. It provides a simplifying model and shorten processing time to detect harmonics.

This paper presented an operational strategy for cogeneration plants under a deregulated market. The objective of this paper includes fuel cost, population cost, and electricity wheeling cost, subjective to the use of mixed fuels, operational limits, emissions constraints, and transmission line flow constraints. Enhanced immune algorithm (EIA) was proposed by an improved crossover and mutation mechanism with a competition and auto-adjust scheme to avoid prematurity. Results verify that EIA can offer an efficient way for cogeneration plants to solve the problem of economic dispatch, environmental protection, and electricity wheeling.

Unbalanced transformer connections cause line losses and is a reason for the possible ground current relay to trip in the distribution systems. In Taiwan, the feeder line loss introduced by transformer connections could be very serious in a practical three phase feeder, especially the open wye-open delta (OYOD) connections. If the losses and imbalance become critical, load reconnections are needed. Owing to the intrivial nature of OYOD transformers, the reconnections are not easily implemental. In this paper, load reconnection of the prime phase sequences was used. The genetic algorithms (GAs) have been implemented to solve the problem. Practical examples of Taipower Company (TPC) demonstrate the effectiveness of the proposed method.

In this paper, an effective tool is presents to perform the electrical energy management (EEM) of room air-conditioners. A practical air-conditioner is installed to measure the on-line operating information, which includes temperature, humidity, and power consumption in a room. Based on the theory of enthalpy, the training data for probabilistic neural network (PNN) is derived to decide the status of the electromagnetic valves and the operating frequency of compressor. The PNN can be fast learning and recalling process, no iteration for weight regulations in learning process, and adaptability for architecture changes. Testing results show that it provides a good tool to make better control strategies for achieving the EEM of room air-conditioners.

In this paper, an artificial neural network (ANN) is employed to control the speed of compressor for accomplishing the electrical energy management of air-conditioning systems. On the basis of "the effective temperature figure" by ASHRAE, the theory of enthalpy was utilized to obtain both comfortable temperature point and minimizing the enthalpy of air-conditioning systems. The technology of the Levenberg-Marquardt (LM) procedure and radial basis function (RBF) neural network were introduced to perform the training and learning features. A practical air-conditioner with variable frequency control was used to measure the on-line room temperature and humidity change. The simulations of the ANN network have a good result with this standard and make possible an anticipated and optimal control strategy to balance thermal comfort, energy saving, and reliability.

This paper presents a multi-objective approach bared on evolutionary programming (EP) to solve the economical operation of cogeneration system under emission constraints. A multi-objective function including the minimization of cost and emissions is formulated in this paper. The cost model includes fuels cost and tie-line energy. The emissions with CO/sub 2/, SO/sub x/, and NO/sub x/ were derived as a function of fuel enthalpy. All constraints including fuel mix, operational constraints, and emission constraints must be met in the optimization process. The steam output, fuel mix, and power generations will be found by considering the time-of-use (TOU) dispatch between cogeneration systems and utility companies. Data of an industrial cogeneration system was used to illustrate the effectiveness of the proposed method.

Presents a computerized procedure for the assessment of distribution system planning. The proposed procedure was developed on a personal computer and is good for a designated area where multiple existing substations and multiple potential sites are considered. This procedure employs load flow analysis and considers the constraints of voltage drops and line flows. Various options are provided to satisfy the load. Losses and costs are evaluated for each case. An example is shown for a designated area and a target year, where the number of new feeders, transformers, substations and associated costs and losses were found for each option. The presented procedure could be deemed as a short-range planning method. >

This paper is concerned with application of the genetic algorithm (GA) to the optimal distribution system planning (DSP). GA was used to solve the optimization of DSP problem which includes the discrete nature of substation installation and feeder routing. Several search procedures including coding and encoding the chromosome, fitness evaluated, reproduction, crossover, and mutation are examined in detail. A simple example has been implemented with the proposed method. All results show that GA can obtain a better solution.

This paper studies the economical operation of cogeneration systems under emission constraints. It attempts to control the production of atmospheric emissions such as NO/sub x/ and SO/sub x/ caused by the operation of fossil-fueled thermal generation. The objective of this paper includes fuel cost, population cost and tie-line energy cost, subject to fuel mix ratios in a boiler, operational constraints and emission condition. Evolutionary programming was adopted to solve this problem. The steam and fuel ratios of boilers and generation output will be obtained by considering the time-of-use (TOU) dispatch between cogeneration systems and utility companies. With the introduction of optimization methods, one can explore the potential for operational changes in unit commitment and dispatching to achieve minimal cost while complying with environmental standards.

This paper proposed an improved evolutionary programming (IEP) technique to minimize distribution feeder losses. In the minimization process, switches were indicated by pre-assigned integers. In this paper, the authors focused on the minimization of the real power loss, as well as on voltage and current constraints subjected to the radial network structure. Many tests were conducted to show its effectiveness.

Many utility companies of Taiwan Power Company (TPC) used the open wye-open delta (OYOD) transformers to serve various customers. This causes phase unbalance which is one of the prime reasons of ground relay tripping and extra line loss in the distribution system. Load reconnection, feeder reconfiguration and capacitor placement are common methods used to balance load or minimize power loss. The purpose of this paper is to reduce power loss by means of load reconnection of the prime phase sequence of the OYOD transformers used. Genetic algorithms (GAs) have been implemented for solving the optimal problem. Practical examples of TPC demonstrate that the proposed method is effective and available.

This paper develops an improved tabu search algorithm (ITS) for economic dispatch (ED) with noncontinuous and nonsmooth cost functions. ITS employs a flexible memory system to avoid the entrapment in a local minimum, and developed the ideal of "distance" to the fitness to accelerate optimization. The new approach extends simple tabu search algorithm (STS) to real valued optimization problem, and applies parallelism to weaken the dependence of the convergence rate of modified tabu search algorithm (MTS) on the initial condition. Effectiveness of the method was compared with many conventional methods. Results show that the proposed algorithm can provide accurate solutions with reasonable performance, and has a great potential for other applications in the power system.

This paper presents a new algorithm by integrating evolutionary programming (EP), tabu search (TS), and quadratic programming (QP) methods to solve the nonconvex economic dispatch problem (NED). A hybrid EP and TS were used for quality control and the Fletcher's quadratic programming technique was used for solving. EP and TS determine the segment of a cost curve used, which is piecewise quadratic natured. Operation constraints are modeled as linear equality or inequality equations, resulting in a typical QP problem. Fletcher's QP was chosen to enhance the performance. The fitness function is constructed from priorities without penalty terms. Numerical results show that the proposed method is more effective than other previously developed evolutionary computation algorithms.

An interactive best-compromise approach is presented, based on evolutionary programming, to solve the economical operation of cogeneration systems under emission constraints. A biobjective function including both the minimisation of cost and emission is formulated. The cost model includes fuel cost and tie-line energy cost. Emissions with SO/sub x/ and NO/sub x/ are derived as a function of fuel enthalpy. All constraints including fuel mix ratios in a boiler, operational constraints and emission constraints must be met in the search process. The steam and fuel mix is found by considering the time-of-use dispatch between cogeneration systems and utility companies. Data from a real cogeneration system are given to illustrate the effectiveness of the proposed method.

Cost of reliability of optimal distribution system planning is considered. The reliability cost model has been derived as a linear function of line flows for evaluating the outages. The objective is to minimise the total cost including the outage cost, feeder resistive loss, and fixed investment cost. Evolutionary programming was used to solve the very complicated mixed-integer, highly nonlinear and nondifferential problem. A real distribution network was modelled as the sample system for tests. There is also a higher opportunity to obtain the global optimum during the EP process.

A refined genetic algorithm for a distribution feeder reconfiguration to reduce losses is presented. The problem is optimised in a stochastic searching manner similar to that of the conventional GA. The initial population is determined by opening the switches with the lowest current in every mesh derived in the optimal power flow (OPF), with all switches closed. Solutions provided by OPF are generally the optimum or near-optimal solutions for most cases, so prematurity could occur. To avoid prematurity, the conventional crossover and mutation scheme was refined by a competition mechanism. So the dilemma of choosing a proper probability for crossover and mutation can be avoided. The two processes were also combined into one to save computation time. Tabu lists with heuristic rules were also employed in the searching process to enhance performance. The new approach provides an overall switching decision instead of a successive pattern, which tends to converge to a local optimum. Many tests were conducted and the results have shown that RGA has advantages over many other previously developed algorithms.