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Besides metering, a more transparent load with known power distribution is valuable to draft energy strategies, especially in a deregulated power market. So the usage-of-transmission can be properly considered. There are many papers published on the related subject [1]-[42], and every research tried to solve a particular part of the problem. There are three basic categories for discussing power distributions: (i) the real power distribution, (ii) the reactive power distribution, and (iii) the loss allocation. These categories were very often treated separately, while with mutual coupling terms, counter flows, and line charging largely neglected. But we know that these entities are non-separable. These are closely inter-related entities; the change of one entity is closely related to the change of every other entity. A good method should consider these entities altogether, while satisfying all electrical theories. In this paper, a method is developed to solve the above problem, where all electrical entities can be solved, and all electrical circuit theories are satisfied. With several matrix formulations, this method is capable of solving and tracing all electrical entities including the current flow, the real and reactive power, the counter flow, and the couplings between the active and reactive power. The algorithm can also allocate power distributions and loss among participants effectively. Besides, a line usage concept is proposed to allocate the loss to each generator where counter flows can be awarded. It can be integrated to the existent tariffs in a deregulated market.

In this paper, the photovoltaic generation was considered as the only one renewable power source to supply system load of a chemical factory directly. An improved genetic algorithm was applied to determine the optimal location and capacity of newly constructed PV generation systems by considering system loss minimization and improving voltage unbalance through simulation with solar power injected.

Fault occurrence or voltage disturbance, such as mismatch operations or electrical faults caused by structural changes in photovoltaic (PV) panels, local/remote faults, or heavy load operation, can disturb a PV energy conversion system (PVECS) on both the DC and AC sides. On the AC side, any serious disturbance can be isolated using power fuses, overcurrent protection and ground-fault protection devices. Therefore, the authors propose the use of fractional-order dynamic-error-based fuzzy Petri net (FPN) to detect disturbance events in a microdistribution system. PV energy conversion depends on solar radiation and temperature, and a maximum power point tracking control is used to maintain stable output power and voltage to microdistribution loads. When the desired maximum power is estimated, a bisection approach algorithm is used to regulate the output voltage of the PVECS by adjusting the duty ratios of a buck–boost converter. The maximum power drops, which are compared with meter-reading power from intelligent meters, are used to detect faults on the DC side. Then, fractional-order dynamic errors between the desired and estimated powers and a FPN are employed to detect faults. For a small-scale PVECS, computer simulations are conducted to show the effectiveness of the proposed model.

To efficiently detect non-technical loss and power blackout in micro-distribution systems, this study proposes using a cooperative game (CG) based inference mechanism under the advanced metering infrastructure technique. Fractional-order Sprott system is designed to extract specific features between the profiled usages and the measurement usages in real time analysis. The fractional-order dynamic errors are positive correlated with the changes in load usages, including normal conditions, electricity fraudulent events, and power blackout events. Then, multiple agents in a game and multiple CG based inference mechanisms are used to locate abnormalities in micro-distribution systems. For energy management applications, the proposed inference mechanism can identify the 2.5–20% irregular usages during normal demand operations. In addition, it can also identify the large changes >20% in usages, while a micro-distribution system is disconnected to operate in the islanded mode within a few hours. This function can address an outage occurrence and then quickly resume service using the service restoration strategy and distributed generations in a local grid. Using a medium-scale micro-distribution system, computer simulations are conducted to show the effectiveness of the proposed inference model.

This study proposes a maximum power tracking (MPT) controller based on chaos synchronisation (CS) for a wind-energy-conversion system. The output power conversion of a wind generator depends on the wind speed, and therefore the optimal conversion of wind energy can be obtained by a variable-speed variable-frequency model. Based on a sensorless controller, CS can express dynamic behaviours by using an incremental conductance to adjust the terminal voltage to the maximum power point. A voltage detector based on the Sprott system is used to track the desired voltage and to control the duty cycle of a boost converter. For a permanent-magnet synchronous generator, the simulation results demonstrate the effectiveness of the proposed MPT controller.

An aggregator acts as a mediator or broker to a group of customers to participate in demand responses (DRs) in an electricity market. Key green policies have encouraged green financial loans to construct distributed generations (DGs) as a virtual power plant. Hence, an innovative business market integrates load management and ancillary services into a demand and generation aggregator. This controllable entity can reduce demands with incentive and contractual controls and dispatch the ancillary power. This study proposes DR programs and ancillary services using the dynamic game model in an aggregator business market. This strategy impartially dispatches reserve energy sources with customers’ dynamic demands, such as onsite generations and storage energy. The aim is to achieve the demand reduction from the main grid, further increasing system flexibility to activate active-duty DGs. A small customer group is selected to demonstrate the effectiveness of the proposed model. Benefits for billing charges may arise.

This paper proposed an issue aiming at the goal of pierces the relationship between the emission trading scheme and dynamic economic dispatch (DED) problem for the electricity utility. A model of the CO2 emission trading market will be investigated and introduced into DED problem incorporating wind power plant and independent power providers (IPPs). Then, an accelerated particle swarm optimization (APSO) algorithm is introduced in order to avoid prematurity convergence of the original PSO and improve searching efficiency. Thus, APSO was used to determine the DED strategy of the utility with incorporation of wind power generation and contribution of IPPs. The CO2 emission trading is treated as the inner-cost, and the superfluous CO2 quotas will be resale into the market, whereas the shortage quotas can be purchased from the market.

A novel singularity control method for a fully parallel robot manipulator was proposed in this paper. The damped least-square method has frequently been used to solve the singularity problem of resolved-acceleration control schemes. It works by damping accelerations of the end-effector, so that accelerations in the degenerated directions are zero at a singular point. However, its velocities may not be zero in some situations, in those cases they will encounter fluctuations around the singular point. In this paper, the control using damped velocity, being called the damped-rate resolved-acceleration control scheme (DRRAC) is proposed to overcome this drawback. We will show that the DRRAC is asymptotically stable, and discuss its convergent properties. The main advantage of the DRRAC in the 3RPS parallel manipulator control system is not to plan its path to avoid the singular point, and could improve the workspace. Illustrative examples are given to show its effectiveness in practical applications, and experimental results are taken to verify the proposed control scheme.

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 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.

Reliability worth analysis is an important tool for distribution systems planning and operations. The interruption cost model used in the analysis directly affects the accuracy of the reliability worth evaluation. In this paper, two interruption cost models including an average or aggregated model (AAM), and a probabilistic distribution model (PDM) are proposed by using the radial basis function (RBF) neural network with orthogonal least-squares (OLS) learning method. The residential and industrial interruption costs in AAM and PDM were integrated by the proposed neural network technique. A Monte-Carlo time sequential simulation technique was adopted for worth assessment. The technique is tested by evaluating the reliability worth of a Taipower system for the installation of disconnected switches, lateral fuses, transformers, and alternative supplies. The results show that the two cost models result in very different interruption costs, and PDM may be more realistic in modeling the system.

The dynamic equations of a flat panel glass-handling robot driven by a permanent magnet synchronous motor (PMSM) are derived by use of Hamilton’s principle for the rigid and flexible models. In this paper, we adopt a new modified particle swarm optimization (MPSO) to identify all the parameters of the robot and PMSM simultaneously. This new algorithm is added with a “distance” term in the traditional PSO’s fitness function to avoid converging to a local optimum. It is found that the MPSO method can obtain optimal high-quality solutions with high calculation efficiency. In this study, the convergence characteristics of the real-coded genetic algorithm (RGA), PSO and MPSO are compared to demonstrate that the MPSO is superior to the others on identification of the dynamic flexible model.

This paper proposed an issue aiming at the goal of pierces the relationship between the emission trading scheme and dynamic economic dispatch (DED) problem for the electricity utility. A model of the CO2 emission trading market will be introduced into DED problem incorporating wind power plants and independent power providers (IPPs). The CO2 emission trading is treated as the inner-cost, and the superfluous CO2 quotas will be resale into the market, whereas the shortage quotas should be purchased from the market. The accelerated particle swarm optimization (APSO) algorithm, which avoid prematurity convergence of the original PSO and improve searching efficiency, is introduced to determine the DED strategy of the utility with incorporation of renewable power generation and contribution of IPPs.

The capability of photovoltaic (PV) panel to generate energy approximately follows the intensity of the sunlight on the panel. A dual-axis solar programmable logical controller (PLC) based automatic tracking system and its supervisory and control system was designed and implemented in this paper. The proposed automatic tracking system controls elevation and orientation angles of solar panels such that the panels always maintain perpendicular to the sunlight. The measured variables of our automatic solar tracking system were compared with those of a fixed-angle PV system. The results indicated that the automatic solar tracking system is low-cost, reliable and efficient. As a result of the experiment, the electricity generated by the proposed tracking system has an overall increase of about 8%~25% more than the fix-angle PV system.

The paper describes the IEC 61850 communication protocol based prototyping platform, and implements Generic Object Oriented Substation Event (GOOSE) interoperability tests between multi-vendor Intelligent Electronic Devices (IEDs). In addition, the proposed platform also includes human machine interface(HMI) that monitors overall IEDs via Ethernet network. The proposed prototyping platform has been completed, it can really provide the IEC 61850-based Supervision Control and Data Acquisition (SCADA) and verify the current status of GOOSE message based on available IEDs. The obvious benefits foreseen by implementing the case studies are the IEDs will not be limited to original vendor for future expansion and monitoring a system through HMI-SCADA with IEC 61850 will be as easy as integrating a new IED into the system.

Single-pole switching scheme is widely used in the Extra High Voltage system. However, the substantial negativesequence current injected to the turbine-generators imposes the electromagnetic (E/M) torque of double system- frequency components during the dead time (between single-pole clearing and line reclosing). This would induce supersynchronous resonance (SPSR) torque amplifications on low pressure turbine generator blades and even lead to fatigue damage. This paper proposes the design of a mechanical filter (MF) with natural frequency close to double-system frequency. From the simulation results, it is found that such a filter not only successfully damps the resonant effect, but also has the characteristics of feasibility and compact.

We propose an effective method to design a modified particle swarm optimization (MPSO) singularity control method for a fully parallel robot manipulator. By adopting MPSO to obtain simple and effective estimated damping values, the result automatically adjusts the damping value around a singular point and greatly improves the accuracy of system responses. This method works by damping accelerations of the end effector, so that accelerations along the degenerated directions are zero at a singular point. These velocities, however, may not be zero in some situations, in which case, fluctuations will be encountered around a singular point. To overcome this drawback, we propose a control scheme that uses both damped acceleration and damped velocity, called the hybrid damped resolved-acceleration control (HDRAC) scheme. The MPSO optimization method can immediately provide optimal damping factors when used in an online application. Our proposed approach offers such superior features as easy implementation, stable convergence characteristics, and good computational efficiency. The main advantage of the HDRAC with MPSO in the 3RPS parallel manipulator control system is that it is not necessary for the system to plan its path for avoiding the singular point; thus, the workspace can be improved. Illustrative examples are provided to show the effectiveness of this HDRAC in practical applications, and experimental results verifying the utility of the proposed control scheme are presented.

Due to a restricted right-of-way and many factors, some of the local areas in Taiwan have become increasingly difficult to build new transmission lines. Since the majority of Taiwan power loads are located in the Norton, the demand of promoting transmission power could be of great urgency owing to the large amounts of power transferring from the South to the Norton. Aimed at overcoming this problem without changing the original conductors of the double-circuit transmission lines, the six-phase transmission scheme which could be one of the best schemes is presented and has several advantages such as transient stability enhancement, minimal corona, electric and magnetic fields, and radio interferences. As a result of the successful commercial operation of six-phase transmissions, the utility planner inevitably faces with the issue for the impact of turbine-generator torsional vibrations in the initial stage of the expansion into the a six-phase network. From the simulation comparisons between the three-phase double-circuit and evolved six-phase system, the latter offers better stability characteristic without deteriorating torsional vibrations from the viewpoint of the same line voltages. For the case of the same transmission capability, it significantly reduces the torsional vibrations. These conclusions provide a constructive suggestion for the department of generation and transmission.

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.

An equivalent current injection (ECI)-based hybrid current-power optimal power flow (OPF) model is proposed in this paper, and the predictor-corrector interior point algorithm (PCIPA) is tailored to fit the OPF for solving nonlinear programming (NLP) problems. The proposed method can further decompose into two subproblems. The computational results of IEEE 9 to 300 buses have shown that the proposed algorithms can enhance the performance in terms of the number of iterations, memory storages, and CPU times.

This paper presents a current-based optimal power flow (OPF) with predictor-corrector interior point algorithm (PCIPA). The distinctive features of this OPF formulation are: i) the elements of Hessian matrix are all real, ii) the objective function and constraints are quadratic functions and such quadratic properties are explored in the development of a robust nonlinear OPF solution procedure. This paper also presents the proposed method can further decomposes into two optimal sub-problems (active and reactive). The computational results on power systems of IEEE 9 to 118 buses have shown that the proposed algorithms are very effective.

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.