An improved equivalent-input-disturbance (EID) approach is devised to enhance the disturbance-rejection performance for a strictly proper plant with a state delay in a modified repetitive-control system. A gain factor is introduced to construct an improved EID estimator. This increases the flexibility of system design and enables the adjustment of the dynamical performance of disturbance rejection. Moreover, the commutative condition, which is widely used for the conventional EID estimator, is avoided. Thus, it reduces the conservativeness of design by removing the constraints imposed by the commutative condition. The system is divided into two subsystems, and the separation theorem is applied to simplify the design. For one subsystem, the delay information on both the modified repetitive controller and the plant is used to reduce the conservativeness of stability condition. The resulting linear matrix inequality (LMI) is used to find the gain of the state-feedback controller. Another LMI is derived to design the gains of the state observer and the improved EID estimator for the other subsystem. A case study on a metal-cutting system validates the superiority of the developed method.

Nowadays, renewable energy has become a very viable alternative solution to provide electricity. However, the uncertainty of renewable energy changes traditional issues in operation and control of power system. This paper proposes a Stochastic Dynamic Power Flow Analysis to evaluate state variables' probability distribution at any time t. Stochastic process of renewable energy is modeled and simulated by ARMA-GARCH model. The Stochastic Response Surface Method is also applied to increase computational efficiency with the same accuracy as Monte Carlo simulation. Stochastic Dynamic Power Flow Analysis is applied to IEEE 30-bus system and time dependent probability distribution of voltage, frequency and network loss will be analyzed.

Renewable energy, such as wind and photovoltaic power generation, is an attractive energy source to help solve global environmental issues surrounding the effects of greenhouse gases. Wind power ranks the largest among all renewable energy sources and offers the highest global market opportunity. In this chapter, the status and potential of wind power energy in Japan are described for onshore, fixed offshore, and floating offshore wind power. We discuss roadmaps for increased wind power penetration in Japan using technical countermeasures.

In this study, we propose a four-dimensional geographic information system (4D-GIS) and present two of its applications. First, we introduce a mesh-type model of spatiotemporal data integration for 4D-GIS. The first application of the 4D-GIS is in the design of wind power transmission networks. In network design, 3D geospatial data and time change data are integrated as a cost surface; a combination of the genetic algorithm and a modified Prim search method generates power transmission networks that minimize the defined cost function. The second application is in operation support of clustered microgrids. The 4D-GIS is used in the visualization of power consumption changes in the microgrids. We also introduce a power information platform for processing big data, such as power consumption collected from buildings, homes, etc. The optimal configuration of power information platforms in microgrid clusters is also proposed.

Wind generated power is one of the most promising forms of renewable energy and its penetration into power grids is expected to increase in future. For the large-scale penetration of wind farms, it is important to build transmission networks dedicated to wind power generation at low costs and assess the impacts of wind power on the existing transmission networks. For solving these challenges, it is necessary to determine the appropriate capacities of the power lines to be built, and to estimate the additional power flow in the existing lines.
In this paper, we propose a current flow analysis method using stochastic power flow calculations and we apply this method to a model system that simulates the power system of Tohoku (the northeast region of Japan), First, we calculate the stochastic maximum current values of the transmission lines of the model system using this method. Next, we calculate the actual current values of the transmission lines of the model system using the measured data of the wind farms in Tohoku. Additionally, we evaluate the effectiveness of the proposed method by comparing the stochastic maximum current values with the actual current values.

Nowadays, wind energy has become a very viable alternative solution to provide electrical energy. The development of wind energy is especially relevant for the case of isolated island in the south central provinces of Vietnam, where there is great potential for wind energy. For these isolated systems, the cogeneration of diesel generators and wind turbines is a common configuration, but also poses several technical challenges. Auxiliary equipments such as battery storage, flywheel and dump loads are often needed to ensure a more stable operation, higher penetration level of wind energy. However, the cost for these auxiliary equipments can be substantial. This paper proposes an optimization framework to determine the optimal size of energy storage devices in a hybrid wind-diesel system. The optimization problem considers two main uncertain factors, which are the wind speed and the load growth rate.

Micro grids are expected to be one of the most realistic energy systems for efficient use of renewable energy sources with few adverse effects on the main electric power grids. However, it is difficult to maintain the supply-and-demand balance because distributed renewable energy generation units (DREGs), such as photovoltaic generation systems and wind turbine generation systems, generate a significant portion of electrical energy in the micro grids. Therefore, an operation planning method is needed considering the uncertainty in weather prediction in order to ensure stable micro grid operations. This paper presents an optimization method for operation plans of controllable generators in micro grids that copes with the uncertainty of DREG outputs. In the proposed method, the optimal operation plans are determined by, depending on the problem conditions, either an enumeration method or Tabu Search with preprocessing. Numerical simulations were carried out for a micro grid model in order to verify the usefulness of the proposed method. In the simulations, the daily operation plan and the modified half-hourly one were determined by the proposed method. As a result, we could obtain the optimal plans which had enough reserve margins for coping with the fluctuations caused by DREGs and demand. (C) 2014 by Wiley Periodicals, Inc.

This paper presents an online method for estimating actual load and photovoltaic generation (PV) output in distribution system with PV actual activated capacity estimation. In recent years, it is planned to introduce the renewable energy on a large scale into the power system in order to achieve the low carbon society. However, PV has a negative effect on the distribution system. When PV is introduced on a large scale into distribution system, it is difficult to know accommodating amount of load in accident restoration. Because measured load contains load in consumers and PV output. Thus, it is necessary to estimate actual load and PV output online in distribution system. This paper proposes the hybrid method of independent component analysis (ICA) and PV actual activated capacity estimating method. The former has new formulation that is inputted active power of load and insolation. The latter estimates PV actual activated capacity from results of ICA. The effectiveness of the proposed method is demonstrated using actual data of load in consumers that is regulated reactive power. The proposed method can estimate actual load and PV output more accurately than a conventional method. Moreover, the proposed method can estimate them accurately even if load is changed rapidly.

This paper describes a wind power forecasting method and its confidence interval estimation. Recently, flat control of wind power generators using various batteries has been required. In flat control, accurate wind power forecasts and their error confidence intervals are needed. In this paper, wind speed forecasts are calculated by regression models using Grid Point Value (GPV) weather forecasts. The forecasts are adjusted by fuzzy inference using the latest errors. The wind power forecasts are translated from the wind speed forecasts using two power curves. The power curves are selected or combined by fuzzy inference depending on the wind direction. The error confidence interval models are generated for each forecasting target time. Each confidence interval is combined by the other fuzzy inference. The proposed methods were applied to actual wind power generators, and it was found that the forecasting errors were smaller than in the conventional methods. Almost all of the forecasts can be within the error confidence intervals estimated by the proposed methods. The results show the effectiveness of the proposed methods. (c) 2013 Wiley Periodicals, Inc.

This paper describes a newly developed demand and supply control system for micro grid. Renewable energy is expected to reduce green house gases and becomes widely used in the world. By contrast, fluctuated power output from renewable energy may affect the power systems, especially frequency. The demand and supply control system for micro grid is expected to reduce it. The developed system consists of 4 control layers. The first layer is a planning of demand and supply every 30 minutes until next day. The second layer is an economic dispatch control (EDC) every 1 minute. The third layer is a load frequency control (LFC) every 1 second. The last layer is battery local control every 0.1 second. The developed system can control frequency stably against fast change of renewable energy output, and can operate generators economically by cooperating EDC and LFC, and using battery local control. Moreover, the battery can be operated systematically by managing battery states of charge (SOC). The developed system is verified using a real time digital simulator with promising results.

Micro grids are expected to be one of the most useful energy systems in terms of efficient use of renewable energy sources with few adverse effects on the main power grids when the micro grids are operated properly from the viewpoint of balancing the electric power supply and demand. However, it is difficult to maintain the supply-and-demand balance because distributed renewable energy generation (DREG), such as photovoltaic generation systems (PVs) and wind turbine generation systems (WTs), account for a large percentage of the generators in the micro grids. Therefore, an operation planning method considering the uncertainty of prediction is needed for ensuring a stable grids operation. This paper presents an optimization method for operating plans of controllable generators in micro grids that copes with the uncertainty of DREG's outputs. In the proposed method, the optimal operating plans are obtained by using an enumeration method or a preconditioned tabu search (TS). Numerical simulations are carried out for a micro grid model in order to verify the usefulness of the proposed method.

This paper presents an insolation forecasting method using numerical weather forecasting data and a confidence interval estimating method. It is planned to introduce photovoltaic (PV) system on a large scale into the power system in order to achieve the low carbon society. Thus, it is necessary to forecast the insolation and evaluate the confidence interval in terms of power system operating and planning. This paper proposes the insolation forecasting method by Partial Least Squares (PLS) and the confidence interval estimating method considering the forecasting error distributions. PLS handles multicollinear data among input variables in order to construct proper forecasting models. The proposed method can estimate continuous confidence intervals by linearly-approximating for representative values of some weather conditions extracted features from forecasting error distributions. The effectiveness of the proposed method is demonstrated using numerical weather forecasting data and actual data observed by Japan Meteorological Agency. The proposed method by PLS can forecast the hourly insolations more accurately than conventional multiple regression equation. Moreover, the proposed method can evaluate continuous confidence interval.

This paper describes a wind power forecasting method and its confidence interval estimation. Recently, flat control of wind power generators by various batteries is required. For the flat control, accurate wind power forecasts and their error confidence intervals are needed. In this paper, wind speed forecasts are calculated by regression models using GPV (Grid Point Vale) weather forecasts. The forecasts are adjusted by the fuzzy inference using the latest errors. The wind power forecasts are translated from the wind speed forecasts using two power-curves. The power-curves are selected or combined by fuzzy inference depending on wind direction. The error confidence interval models are generated for each forecasting target time. Each confidence interval is combined by the other fuzzy inference.<br>The proposed methods are applied to actual wind power generators, and found that forecasting errors are better than the conventional methods. The almost all of forecasts can be within error confidence intervals estimated by the proposed method. The results show the effectiveness of the proposed methods.

Deregulation in power systems has created many technological challenges encouraging the intereconnection of dispersed generators (DG) and the changes of the operation in distribution power systems. Therfore more sophiscated analysis and operation of power systems will be expected regarding the impacts and power quality in association with such situations. This article discribes the new trend of simulation methods applied for distribtion power systems.

In this paper, we present a method to determine the unit commitment schedules considering CO2 emission and cost including the frequency regulation capability of the units to mitigate fluctuation of wind power. The authors developed an extended procedure that obtains a trade-off solution of cost versus CO2 emission including a significant wind power penetration. The method has been tested on the 10-unit 24-hour model system using the estimated wind power curve derived from an actual wind farm. The results, such as the shadow prices of CO2 obtained by the trade-off analysis, may provide a basis of evaluating the equivalent cost of the wind farms and their contribution toward CO2 reduction.

This paper presents practical distribution system equipment models such as various distributed generators, voltage regulators, and loads for fast three phase unbalanced load flow calculation in distribution systems. The method can calculate voltage and current of distribution systems, in which distributed generators are introduced. The calculation time of the proposed method is about 40 times faster than that of the Newton-Raphson method. Moreover, an introduction effect evaluation tool for distributed generators using the proposed three phase unbalanced load flow calculation is presented. It provides various functions such as power system network diagram creation function and voltage profile chart display function. Therefore, the introduction effect evaluation of distributed generators in distribution systems can be evaluated quite easily.

This paper presents stability and an interactive phenomenon of one SVC, two or more sets of SVC, a SVC and a SVR in distribution systems. In particular, we focus on the saddle-node bifurcation and Hopf bifurcation points. The two local bifurcation points show a boundary of a stable domain.

This paper presents a comparative study for four modern heuristic algorithms (MHAs) to service restoration in distribution systems: reactive tabu search, tabu search, parallel simulated annealing, and genetic algorithm. Since service restoration is an emergency control in distribution control centers to restore out-of-service areas as soon as possible, it requires fast computation and high quality solutions for customers' satisfaction. The problem can be formulated as a combinatorial optimization problem to divide the out-of-service area to each power source. The effectiveness of the MHAs is compared against each other on typical service restoration problems.

Deregulation in power systems has created many technological challenges encouraging the intereconnection of dispersed generators (DG) and the changes of the operation in distribution power systems. Therfore more sophiscated analysis and operation of power systems will be expected regarding the impacts and power quality in association with such situations. This article discribes the new trend of simulation methods applied for distribtion power systems.

Service restoration in distribution systems can be formulated as a combinatorial optimization problem. It is the problem to determine power sources for each load considering various operational constraints in distribution systems. Up to now, the problem has been dealt with using conventional methods such as the branch and bound method, expert systems, neural networks, and fuzzy reasoning. Recently, modern heuristic methods such as genetic algorithms (GA), simulated annealing (SA), and tabu search (TS) have been attracting notice as efficient methods for solving large combinatorial optimization problems. Moreover, reactive tabu search (RTS) can solve the parameter tuning problem, which is recognized as the essential problem of the TS. Therefore, RTS, GA, and SA can be efficient search methods for service restoration in distribution systems. This paper develops an RTS for service restoration and compares RTS, GA, and PSA (parallel SA) for the problem. The feasibility of the proposed methods is shown and compared on a typical distribution system model with promising results.

Islanding of induction generators for wind power generation systems can cause a variety of problems and must be prevented islanding. In Japan, Ministry of International Trade and Industry (MITI) revised &ldquo;technical requirements for distributed generators on distribution systems&rdquo; in 1998. The technical requirements do not require islanding detection systems for wind power generation systems consist of induction generators. However, if reactive power of induction generators and capacitors of distribution systems are closely matched, the islanding detection by ordinary relays becomes difficult because of self-excited of induction generators. In this paper, frequency of induction generators after theirs disconnection from grids, so called the self-excited frequency, is studied by analytical procedures. The analytical procedures and related simulations, using the Electro-Magnetic Transients Program (EMTP) and Transient Network Simulator (TNS), are described. The analytical results are presented and compared with the corresponding obtained simulation results.

In the near future, many distributed generation systems, such as photovoltaic or wind power generation, will be interconnected to the electric power distribution systems of Japan. Therefore, we must make the best use of the distributed generation systems considering their influence on power quality, stability, and so on.<br> This paper investigates the influence of distributed generation systems on the voltage characteristics in distribution systems by a stochastic approach. Firstly, we calculate the node voltage fluctuations in a test distribution system using the stochastic power flow, and show that the voltage characteristics deteriorate after interconnecting distributed generation systems. Secondly, we formulate the problem of voltage characteristics improvement as the nonlinear optimization problem which has the following objective functions: 1) minimize the total voltage fluctuations throughout a distribution system; 2) regulate the node voltage at a specified value considering voltage fluctuations. These problems can be also solved using the stochastic power flow. The simulation results show that the node voltage fluctuations can be minimized by controlling the parameters regarding the reactive power of distributed generation systems, and the stochastic power flow is effective in calculating the practical value of reactive power of distributed generation systems in order to regulate the node voltage.

Service restoration in distribution systems can be formulated as a combinatorial optimization problem. It is the problem to determine power sources for each load considering various operational constraints in distribution systems. Up to now, the problem has been dealt with using conventional methods such as branch and bounds method, expert system, neural networks, and fuzzy reasoning.<br> Recently, modern heuristic methods such as Genetic Algorithms (GA), Simulated Annealing (SA), and Tabu Search (TS) are noticed as efficient methods for solving large combinatorial optimization problems. Moreover, Reactive Tabu Search (RTS) can solve the parameter tuning problem, which is recognized as the essential problem of the TS. Therefore, RTS, GA, and SA can be efficient search method for service restoration in distribution systems.<br> This paper develops a reactive tabu search (RTS) for service restoration and compares RTS, GA, and PSA (Parallel SA) for the problem. The feasibility of the proposed methods is shown and compared on a typical distribution system model with promising results.

This paper proposes a new distributed autonomous control method for tap change under load (TCUL) transformers. Using a fuzzy control scheme, we realize advantageous control characteristics which inherit both the reliability of the conventional control scheme and the optimality of the control actions of distributed controllers, where the index derived from the optimization problem is used to minimize the voltage deviations and the number of tap operations. To increase the performance of control, a voltage prediction algorithm in each controller and then the data exchange scheme among controllers is adopted to consist an adaptive control scheme. Effectiveness of the proposed method is demonstrated through numerical simulations using an equivalent system made from a real system data.

This paper presents a particle swarm optimization for reactive power and voltage control (VQC) problem formulated as a mixed integer nonlinear optimization problem (MINLP) considering voltage security assessment. The proposed method realizes loss minimization and determines VQC strategy with continuous and discrete control variables such as AYR operating values, OLTC tap positions, and amount of reactive power compensation equipment. The method also considers voltage security assessment using a continuation power flow and voltage contingency analysis technique when determining the control strategy. The feasibility of the proposed method for the problem is demonstrated and compared with reactive tabu search and enumeration method on practical power system models with promising results.

Previous voltage stability studies have shown that typical OLTC controls can cause oscillatory behaviors as wel as reverse controls in tap changing operations due to interactions among multiple OLTC controls. These phenomena are undesirable from the viewpoint of both economy and security of power system operations.<br> In this paper, to avoid the above undesirable phenomena as well as to realize suboptimal voltage profile agains voltage instability, we proposes a new control method to coordinate numerous OLTC controls existing in transmissim and distribution networks. The method is based on the autonomous decentralized control scheme. A fundamenta theory and some techniaues for this control scheme are proposed.

The dynamic behaviours of the TCUL controls are investigated from the viewpoint of the effect of control actions on voltage stability as well as on voltage regulations, taking into account the interference among the multiple control actions of TCULs. The Liapunov stability theorem is applied to a system having nonlinear voltage dependent loads to derive conditions for stability. We define the term &ldquo;reverse control action for multiple TCULs&rdquo; to indicate undesirable tap operations, where multiple controls as a whole cause the decrease in voltages of specific nodes, leading to voltage collapse, in spite of normal individual operations. A criterion to detect the phenomena is derived and then a new control strategy based on this criterion is demonstrated. In this demonstration, unsuitably acting TCULs are individually deactivated at their most effective timings to improve voltage stability. The effectiveness of the proposed criterion and its application to the deactivation control have successfully been confirmed through numerical simulations in a radial network with three cascaded TCUL transformers, where a specific area of a real system is reduced to form the example system.

Load flow calculation is a basic tool for power system planning and control which include load flow analysis, voltage control, service restoration, network reconfiguration, and etc. In these applications, it is very important to solve the load flow problem as efficiently as possible.<br> This paper presents a fast load flow using parallel processing for radial power systems. The method can be applied to secondary systems and distribution systems. Since it uses only active and reactive power injection to main lines and laterals as state variables, reduction of number of state variables can be realized. Radial networks are mapped to the tree structure of parallel processor systems in the proposed method. The forward/backward sweep approach is realized by communication from a root processor to leaf processors and vice versa. Using the mapping scheme of the proposed method, each processor has to communicate with only neighboring processors. The proposed method is evaluated on various radial systems with promising results.

Auto-parametric resonance has not been detected nor observed in a real power system, probably because the problem-oriented instrumentation network is not available today. It has been confirmed by our research group from the viewpoint of theory, digital simulations, and artificial transmission system with rotating machines that the auto-parametric resonance may come out in a power system where certain types of resonance conditions are satisfied as when the initial transient impact such as line opening, generator tripping, and a line-to-ground fault has been applied.<br>The objective of this paper is to confirm the auto-parametric resonance phenomena on a large-scale advanced-type power system simulator (APSA) which features various functions which are very close to those of a real power system, and to promote the instrumentation techniques needed to record and analyze such infrequent phenomena.