The sites of a wind farms and transmission lines should be selected effectively during the planning of large-scale wind power generators. In this study, the sites of wind farms were selected by a portfolio selection method and transmission line expansions were optimized by a genetic algorithm. The portfolio selection method was used to determine the minimum risk for the generated power, and a genetic algorithm was used to further optimize the cost consideration. The proposed methods were able to find the appropriate wind farm locations that maximized the power generation at the minimal transmission expansion cost.

Modern technology in the service of modern operation theory is more than adequate to provide effective operation to meet the needs of any possible new structure or structures. This paper assumes that ISO (Independent System Operator) will give the next day's power purchase fee, then proposes a new effective and simple methodology for solving the IPP daily optimal operation model. The proposed operation model and optimal methods consider not only the profit/loss value of power purchase fee but also the dynamic load response constraint of the generators. The effectiveness of the proposed method is verified by a simple case simulations.

Accurate load forecasting is the key to economic dispatch and efficient operation of Multi-Energy System (MES). This paper proposes a combined load forecasting method for MES based on Bi-directional Long Short-Term Memory (BiLSTM) multi-task learning. Firstly, this paper investigates the multi-energy interaction mechanism and multi-loads characteristics and analyzes the correlation of multi-loads in different seasons. Then, a combined load forecasting method is proposed, which focuses on making full use of the coupling relationship among multiple loads. In the forecasting model, the different loads are selected combinedly as the input features according to the Maximum Information Coefficient (MIC). The multi-task learning is adopted to construct the cooling, heating and electric combined load forecasting model based on the BiLSTM algorithm, which can effectively share the coupling information among the loads. Finally, case studies verify the effectiveness and superiority of the proposed method in both learning speed and forecasting accuracy.

Peer-to-peer trading mode is one of the most potential future energy trading scheme. However, there are some issues still waiting to be solved, e.g. privacy of energy data, asynchrony of households’ desire and incomplete understanding of its operation features, etc. These issues may hinder its further application and development. To address these requirements, a research to analyze the characteristics of a typical peer-to-peer energy sharing community model is carried out. The model is built on the basis of the proof of credit consensus in blockchain technology and Shapley value in game theory. A two-level hierarchical incentive mechanism is proposed to motivate participants to obey the energy smart contract and involve in electricity peak-shifting. A series of indexes are applied to evaluate the operation performance of the proposed model, from the aspects of the economy, technique, environment and market efficiency. The validity of the incentive mechanism and the proposed model are verified, and the impacts of important factors, i.e. community scale and prosumer ratio, on the proposed model are assessed. This work would provide reference to the constructors of future peer-to-peer energy market about arranging the optimal appliance scale and distributed energy resources ratio to make it become more friendly, efficient and economical.

With the development of electric vehicle (EV), there is a huge demand for electric vehicle charging station (EVCS). The utilization of renewable energy sources (RES) in EVCS can not only decrease the energy fluctuation by participating in peakload reduction of the grid, but also reduce the pollution to the environment by cutting down the use of fossil fuels. In this paper, the optimal planning for grid-connected EVCS with RES is studied by considering EV load uncertainty. Nine scenarios are set based on a different characteristic of EV load to reveal the impact of EV load on net present cost (NPC) and to express the relationship between optimal capacity and energy flow. Moreover, since electricity price also plays an important role in EVCS planning, an economic comparison between different cases with different electricity price for peak-valley-flat period is carried out. The results reveal the economic benefits of applying RES in EVCS, and demonstrate that EV load with different characteristics would influence the capacity of each device (PV, battery, converter) in the EVCS optimal planning.

With the increasing use of sensitive loads in frequency converters and in relays in distribution networks, voltage sag has become a major power quality issue that urgently needs to be solved. For the purpose of improving the understanding of voltage sag severity in distribution net-works, a comprehensive weight‐based severity evaluation method of voltage sag is presented in this paper. First, a multi‐side index system that takes into account the combined influence of the source, network, and the load is established. A comprehensive weight method, which combines the improved analytic hierarchy process (IAHP) and the entropy method, is then adopted to determine the index weight. The weight of each index and the degree of superiority are linearly weighted to obtain the severity of voltage sag at different observation points. Finally, the effectiveness of the proposed method is verified using a distribution network model established in DIgSILENT Power‐ Factory (15.1.7, German).

Improving carbon efficiency is an effective way to save energy and reduce harmful emission for a sintering process. Optimizing carbon efficiency is an effective way to achieve that goal, and its precondition is to assess the performance of the carbon efficiency. However, there is seldom research about how to assess the carbon efficiency whether it needs to be optimized. To address this, this paper introduces a performance assessment method for evaluating the performance of the carbon efficiency. First, the sintering process and the key characteristics are analyzed, and the carbon efficiency indexes are defined. Second, the structure of the assessment method is presented. The method consists of a prediction model based on three NNs, and an assessment method based on the fuzzy synthetic evaluation method. Two-level combination strategy is proposed to improve prediction performance and assessment accuracy, with the using of bootstrap aggregating, linear combination, and majority voting. Finally, verification based on process data shows that the proposed method can assess the performance of the carbon efficiency with high accuracy. More specially, the prediction errors of the combination model for the CCR are basically in the range of [-2.738 kg/t, 3.442 kg/t], and for the CO/CO2 they are basically in the range of [-8.16 × 10−3, 4.828 × 10−3]. The combination models have better prediction performance than single NNs. Moreover, the assessment accuracy of the proposed method is 87%, which has higher accuracy than other models. This model lays the groundwork of improving the carbon efficiency for sintering process.

With the wide application of combined heat and power (CHP) and power to heat (P2H) technology, the integrated electricity and heat system (IEHS) has been attracting great attention. The hybrid stochastic/robust optimization is combined to handle the uncertainties of IEHS, in which the stochastic optimization is concentrated on the uncertainties and spatial-temporal correlativity of load and wind power, while the robust optimization is used to deal with the market electricity price uncertainty. Considering the multi-entities characteristics of IEHS, the MINLP model of the original IEHS is decoupled to the one MILP power network and one NLP heat network based on the Bregman alternating direction method of multipliers (BADMM) and the improved quantity regulation. The simulation results show that the constructed model can effectively increase the flexibility of IEHS, and the uncertainty and spatial-temporal correlativity of IEHS can affect the system state. Furthermore, the proposed distributed model based on BADMM can not only improve the convergence effectively compared with traditional ADMM, but also realize the distributed cooperative operation of IEHS.

This paper introduces an arbitrary polynomial chaos expansion method for performing probabilistic power flow analysis in power systems. The proposed method is used for uncertainty analysis, expressing the uncertainty of a system as random variables with an arbitrary output distribution based on orthogonal polynomial expansion. This method is advantageous because of its calculation speed and accuracy. This study expresses probabilistic power flow in a power system with many uncertain power sources using linear combination polynomial expansion. The orthogonal polynomial system employed is generated by moment analysis from renewable energy output data, with the polynomial coefficients derived from a collocation method. Simulation of probabilistic power flow using the proposed method is applied to a 29-bus transmission network model including three renewable energies, and the calculation speed and accuracy are evaluated by changing the expansion order of the polynomial. In addition, the influence on the polynomial coefficient is assessed when the system topology is changed due to a line fault. Therefore, since the arbitrary polynomial chaos expansion method can represent complex networks by linear combination of orthogonal polynomial sets, calculation based on it is several hundred times faster than the conventional Monte Carlo method. The results demonstrate that the proposed method is very useful for analyzing the probabilistic power distribution and that third-order expansion is practically appropriate.

In this paper, an improved optimization model is proposed for demand response in a remote off-grid microgrid local on the Dongfushan Island, China to develop the energy dispatch and economic benefits considering different electricity price under different seasonal meteorological conditions. First, the seasonal electricity pricing model is built with the power generation of renewable sources in different seasonal meteorological conditions. Second, satisfaction is evaluated by the seasonal electricity price and the power consumption pattern. Improved Pareto optimum based on a distributed learning algorithm is proposed to maximize the satisfaction so that the electricity bills of consumers are reduced and the profits of the retailer is increased. The performance of the proposed optimization model is validated in the HOMER software and Matlab. Simulation results show that the electricity bills of consumers are lower by using the proposed method. For the retailer, the generation cost saves 1216$, and the utilization of renewable energy increased by 3.9% in January 2011.

In this paper, a time series model based on hybrid-kernel least-squares support vector machine (HKLSSVM) with three processes of decomposition, classification, and reconstruction is proposed to predict short-term wind power. Firstly, on the basis of the maximal wavelet decomposition (MWD) and fuzzy C-means algorithm, a decomposition method decomposes wind power time series and classifies the decomposition time series components into three classes according to amplitude- frequency characteristics. Then, time series models on the basis of least-squares support vector machine (LSSVM) with three different kernels are established for these three classes. Non-dominated sorting genetic algorithm II optimizes the parameters of each forecasting model. Finally, outputs of forecasting models are reconstructed to obtain the forecasting power. The proposed model is compared with the empirical-mode-decomposition least-squares support vector machine (EMD-LSSVM) model and wavelet-decomposition least-squares support vector machine (WDLSSVM) model. The results of the comparison show that proposed model performs better than these benchmark models.(c) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

To reduce energy consumption and harmful emission, it is of great significance to improve carbon efficiency in sintering process, which is able to be achieved if the carbon efficiency can be accurately predicted. In this article, the ratio of CO and CO$_2$ (CO/CO$_2$) is taken as a measurement of the carbon efficiency. As CO/CO$_2$ is hard to measure, and there exist multiple working conditions, multiple variables, and nonlinearity, a hybrid CO/CO$_2$ prediction model is devised based on the aforementioned characteristics. First, the sintering process is analyzed, and the key characteristics to predict the CO/CO$_2$ are extracted. Next, the configuration of the prediction model is given based on the analysis. The model consists by two submodels, one is to predict the state variables by an improved just-in-time learning model, combining three neural network (NN) models. The other is to predict CO/CO$_2$ with semisupervised algorithm, based on deep belief network with a combination of the three NN regression methods. Then, the configurations of the two submodels are introduced in detail. The test results based on actual running data exhibit the good performance of the model.

Wind power forecasting improves the wind power trade and the wind power dispatch level. Wind speed is closely related to the accuracy of wind energy forecasting. This chapter introduces the process of wind power generation, describes an amplitude-frequency characteristic extraction method for the wind speed, and presents a hybrid-kernel least-squares support vector machine based wind power forecasting method.

This paper describes the architecture and the utilization for a facility planning optimization platform called GGOD, “Grid of Grids Optimal Designer” and applies it to expandable cluster-type micro-grid installations and operations. The expandable cluster-type micro-grid is defined as a group of micro-grids that are connected by bi-directional power transfer networks. Furthermore, power sources are also networked. Especially, by networking among power sources, powers necessary for social activities in-demand areas are secured. The proposed architecture is based on service-oriented architecture, meaning that optimization functions are executed as services. For flexibility, these services are executed by requests based on extensible mark-up language texts. The available optimizations are written in meta-data, which are accessible to end-users from the meta-data database system called clearinghouse. The meta-data are of two types, one for single optimization and the other for combined optimization. The processes in GGOD are conducted by the management function which interprets descriptions in meta-data. In meta-data, the names of optimization functions and activation orders are written. The basic executions follow sequential, branch, or loop flow processes, which execute combined optimizations, compare more than two kinds of optimization processes, and perform iterative simulations, respectively. As an application of the proposed architecture, the power generation sites and transmission networks are optimized in a geospatial integrated-resource planning scenario. In this application, a structure and a method for the combination of component functions in GGOD are exemplified. Moreover, GGOD suggests promotions of a lot of applications by effective combinations of basic optimization functions.

It becomes more and more important to consider environmental issues as well as power supply stability or economics in power system planning. In this paper effects of the CO2 emissions constraint on power source composition are analyzed through the screening curve analysis. We calculate the marginal reduction costs of CO2 emissions using shadow prices and compare them with the reduction costs resulting from introducing CCS and carbon prices.

This paper presents a new effective and simple methodology for screening transmission-line maintenance priority. Based on the failure rate of each transmission line, the degree of influence on a system is defined for a system having one-line failure. Using this system influence degree index, we present a methodology to determine the order of maintenance priority. The presented methodology is validated using the IEEE-RST-79 Test System.

Current electricity pricing menu for demand response (DR) which oriented purpose, for example TOU (Time of Use) playing more and more important role in deregulation market, especially after mass introduction of renewable energy and application of demand response mechanism in power market. However, the current studies manly focused on market mechanism and not pay attention sufficiently on the relationship between the electricity price setting menu and the actual power supply cost structure. This paper proposed a cost-based pricing scheme. First, we analyzed the cost structure of variable cost, fixed cost, and reliability loss cost of each power source based on the cost structure of the power supply configuration from long-term economic viewpoint, then conducted a basic study on the setting of electricity charges by time period considering the recovery of these costs. We use a simple example to demonstrate the applicability of this propose and concludes some useful results. The methodology proposed in this paper is one of the cost-based foundational study for actual DR pricing scheme.

In this study, GIS spatial analyst tools are used to implement an alternative method of electric transmission line (ETL) routing for a renewable energy-based microgrid system. Multiple criteria, starting points and endpoints were identified and a weighted ranking system and suitability index for geographic factors were utilized to create the cost surface. A modified method from the Electric Power Research Institute-Georgia Transmission Corporation (EPRI-GTC) is also adopted for the overhead electric transmission line siting and necessary changes were applied based on the available data and authors' review on the study area. After applying the Least Cost Path (LCP) algorithm, the results produced three alternative routes based on three perspectives: The built environment, natural environment, and engineering environment. The alternative routes are then overlaid based on a simple combined perspective to create the final route. Moreover, the alternative routes are evaluated to provide a secondary route and the routes developed by engineering perspective were nominated.

This paper evaluates the adequacy of a transmission network interconnecting a set of economic area nodes. Within an economic area, it is assumed that there is a pooled market for electrical energy exchange. In developing renewable resources, an existing transmission system might need reinforcement to facilitate interregional utilization. For this purpose, this paper constructs a general model to address the transmission expansion-planning problem. The model aims to improve social benefits.

In this paper, a method is introduced for probabilistic power flow calculations based on arbitrary polynomial chaos. For the polynomial chaos, orthogonal polynomial sets are used to represent the uncertainties of renewable power generation, and these orthogonal polynomials are generated from actual data. The aforementioned method is applied to probabilistic power flow calculations, and its applicability is confirmed in application to an actual transmission network. The calculation time and accuracy achieved using the arbitrary polynomial-chaos method are compared with those achieved using the popular Monte Carlo method. The results show that the calculation speed is 246-680 times greater than that with the direct Monte Carlo method, while the accuracy is almost same.

With the growing penetration of wind power, the wind power forecasting is fundamental in aiding the grid scheduling and electricity trading. In this paper, a numerical weather prediction wind speed error correction model based on gated recurrent unit neural networks is proposed for short-term wind power forecasting. Firstly, the standard deviation of numerical weather prediction wind speed error is extracted as weights, and these weights are rearranged according to the numerical weather prediction wind speed time series to get the weight time series. Then, the bidirectional gated recurrent unit neural networks based error correction model is proposed to correct error of numerical weather prediction wind speed with the inputs as numerical weather prediction wind speed, trend and detail terms of the weight time series. The wind power curve model is applied to forecast short-term wind power by using corrected numerical weather prediction wind speed. Finally, the effectiveness of the proposed method is compared with benchmark models by using actual data of wind farm, and the results show that the proposed model outperforms these benchmark models.

This paper proposes an architecture for a planning optimization platform called the Grid of Grids Optimal Designer (GGOD) to facilitate the optimization of cluster type micro-grid installation and operation. Micro-grid clusters are groups of micro-grids with renewable energy that operate autonomously without power supply from main grids. The GGOD simulates micro-grid optimizations based on a service-oriented architecture and comprises three layers, i.e., end user, service broker, and service supply layers. The enduser layer corresponds to the client system, and the GGOD corresponds to the service broker and service supply layers. The service broker layer is for service management, and the service supply layer comprises various optimization components. Combinable optimization components are activated based on request text in extensible markup language format from end users. By combining optimization components, different optimizations are executed for a single request, the results of the same type of optimization methods are compared, and sudden operation environment changes are considered. Geospatial integrated resource planning and micro-grid facility planning are demonstrated as examples of GGOD applications.

The purpose of this paper is to conduct a feasibility study of hybrid power system based on renewable energy resources for Miangas island, which is located on the border between Indonesia and the Philippines. It is the outermost island that situated in the North part of Sulawesi mainland which became the front porch of Indonesian territory. The prospect of PV-wind-diesel-battery hybrid system as an alternative power supply for Miangas island has been analyzed using HOMER (Hybrid Optimization Model for Electric Renewable). The predicted monthly average daily radiation in Miangas island is 5.52 kWh/m2. The wind speed varies seasonally from 3.1 m/s to 5.3 m/s. The hybrid system analysis has showed that the electricity is generated in a year is 240,068 kWh in which electricity energy sharing consist of PV, wind, diesel are 77%, 0.5% and 22.5% respectively. In addition, the simulation results for the proposed hybrid system in Miangas island community find the value of cost of energy (COE) as 0.3178 USD/kWh, Net Present Cost (NPC) as 986,356 USD and operating cost as 52,887 USD.

In this paper, we propose a model to solve the transmission expansion problem related to the large-scale introduction of renewable energies. The extensive adoption of renewable energies into electricity grids requires the expansion of transmission lines for which companies would need to invest in renewable energy generators. In our proposed model, the expenses incurred in setting up the transmission lines would be covered by the transmission fees charged for the generators. Our model involves optimizing the facility installation and determining the optimal supply-demand operations. However, supply generators would need to compete among themselves with differing cost structures based on the merit-order principle. The investment decisions place the capacity constraints at the operational level where the network may encounter congestion giving rise to different equilibrium prices at the various nodes.

This paper describes a preliminary study of the empowering remote island communities with renewable energy. This research is a case study for Talaud island which is located in the border island between Indonesia and Philipines. It is located in Kiama village Talaud Island regency of North Sulawesi province of Indonesia. Method of implementation of this program consists of several stages, namely: (1) Implementation of small-scale solar home system and a wind turbine; (2) Through technology transfer in the capacity building activities to the selected community, in order to provide the skill and knowledge to island community in Kiama Village, Talaud Islands of Indonesia. It is expected that technology implementation will be maintained in the future and sustainable. The life cycle cost analysis is done by comparing two system of pilot projects, namely solar home system and solar home system with a wind turbine. The cost of generating electrical energy with renewable energy is relatively high then it is necessary to provide strategies for the development of power infrastructure for the sake of the energy security of island communities in Indonesia. Further both monitoring and evaluation of the renewable energy system implementation should be conducted for the detail report and analysis.

This paper presents a disturbance-rejection method for a modified repetitive control system with a nonlinearity. Taking advantage of stable inversion, an improved equivalent-input-disturbance (EID) estimator that is more relaxed for system design is developed to estimate and cancel out the influence of the disturbance and nonlinearity in the low-frequency domain. The high-frequency influence is filtered owning to the low-pass nature of the linear part of the closed-loop system. To avoid the restrictive commutative condition and choose a Lyapunov function of a more general form, a new design algorithm, which takes into account the relation between the feedback control gains and the observer and improved EID estimator gains, is developed for the nonlinear system. Furthermore, comparisons with the generalized extended-state observer (GESO) and conventional EID methods are conducted. A clear relation between the developed estimator and the GESO is also clarified. Finally, simulations show the effectiveness and the advantage of the developed method.

Nowadays, the hybrid wind–diesel system is widely used on small islands. However, the operation of these systems faces a major challenge in frequency control due to their small inertia. Furthermore, it is also difficult to maintain the power balance when both wind power and load are uncertain. To solve these problems, energy storage systems (ESS) are usually installed. This paper demonstrates the effectiveness of using ESS to provide Fast Frequency Response (FFR) to ensure that the frequency criteria are met after the sudden loss of a generator. An optimal day-ahead scheduling problem is implemented to simultaneously minimize the operating cost of the system, take full advantage of the available wind power, and ensure that the ESS has enough energy to provide FFR when the wind power and demand are uncertain. The optimization problem is formulated in terms of two-stage chance-constrained programming, and solved using a Modified Sample Average Approximation (MSAA) algorithm—a combination of the traditional Sample Average Approximation (SAA) algorithm and the k-means approach. The proposed method is tested with a realistic islanded power system, and the effects of the ESS size and its response time is analyzed. Results indicate that the proposed model should perform well under real-world conditions.

An automated process for selecting sites for new wind farm installations is proposed. The region of interest is divided into a 1-km-square mesh, and geographical data such as altitude and wind speed are used to sort the mesh cells into regions that are feasible for wind farm installations. Before grouping the meshes, feasible meshes for constructing wind farms are extracted using a set of constraints. We tested two different constraints for grouping the feasible areas, either by maximizing the annual mean wind speed or by minimizing the covariance between the power outputs of each cell in the group. The first strategy is more attractive if the goal is to meet an expected level of power output each year, while the second strategy is intended to supply the most-stable power. Portfolio theory was then applied to the evaluate efficient-frontier curves of the two site-selection results from the mean and variance of the total expected power outputs. The analysis showed that grouping unit areas to maximize average wind speed most effectively suppresses variance in the expected output of an installation, and efficiently distributes the optimum wind farm locations.

In recent years, the popularity of micro-grids has been growing with the rapid growth of renewable resources. In addition, a significant part of the energy globally consumed could be attributed to the building sector. Thus, to develop the expandable micro-grid clusters (EMGCs), the authors investigate optimal facility planning in the building energy management system (BEMS), which is one of the important component of EMGCs. To optimize the total facility cost of BEMS, the hybrid optimization of multiple energy resources, i.e., HOMER, a simulation tool developed by the U.S. Department of Energy's National Renewable Energy Laboratory (NREL), is applied. A BEMS is simulated using photovoltaic generation, battery storage, a converter, and a load. The authors vary the eight types of battery capacity and investigate the correlation among the whole system for optimizing the facility configuration by minimizing the facility's lifecycle cost. The simulations reveal the optimal facility configuration that minimizes long-term costs.

An integrated method for large-scale introduction of wind farms with the associated extension of the transmission grid and connection to existing access points was investigated. Simultaneous planning is needed as the location of the wind farm plays a key role in the cost of transmission network construction, and vice versa. A common geospatial platform was introduced. Candidate wind farm locations with suitable wind speeds and topographical features were selected, taking account of the minimum power capacity required. Transmission network extensions were then mapped, to provide connections to existing access points. Optimization of wind farm siting and transmission network topology was demonstrated.

For remote island communities, the hybrid wind-diesel generation system is a very common solution to provide electricity. Nowadays, the energy storage systems have become increasingly viable. The energy storage system helps to balance the stochastic nature of renewable energy sources. Therefore, in the generation and expansion planning studies, both renewable sources and energy storage devices should be considered in a single optimization problem. This approach leads to highly complicated optimization models, especially when several uncertainty factors are included. This paper proposes a stochastic optimization model to determine optimal combinations of wind turbines and energy storage systems. The optimization takes into account the uncertainty in wind generation. The study is conducted based on an actual island power system.

The hybrid wind-diesel system is a typical configuration for the isolated power system. However, the small inertia constant of this system and the uncertain of wind power and demand causes several technical challenges. Consequently, battery energy storage system is installed to support the wind-diesel system. This paper presents optimal day-ahead scheduling of the wind-diesel system and considers the role of battery storage system in frequency response. Wind power and demand uncertainties are taken into account by using forecast error modeling. The optimization problem is formulated as a two-stage Chance-Constrained Programming solved by Sample Average Approximation (SAA) algorithm using CPLEX and YALMIP toolboxes. A simple approach is also proposed to make SAA algorithm more efficient.

In this study, we introduce an optimization design suite, referred to as the 'grid of grids optimal designer' (GGOD), as an addition to the expandable micro-grid clusters (EMGCs) for the evolving electricity generation and infrastructure sectors. EMGCs are an autonomous group of micro-grids in which clusters can generate and consume power. The GGOD is a type of simulation software suitable for Iong-term use at an electrical facility, where operation planners can plan the expansion, construction, and EMGC operation functions. One of the primary uses of the GGOD is for geospatial data, which require the execution of real world optimization planning. Here, we describe two key applications of the GGOD, including geospatial integrated resource planning for wind farm allocation and transmission configurations, as well as congestion-mitigation planning based on the nodal price approach. Moreover, a concept for the interactive use of optimization functions is also explained.

Nowadays, the wind energy is one of the fastest growing renewable energies in the world. The increasing presence of wind farms with electronic controls pose several issues for the protective relaying systems. The issues with conventional protection relays when applied to wind farm protection is due to the fact that short circuit currents from wind farm, especially those based on type III and type IV, are fundamentally different from short-circuit currents of synchronous generators. This paper investigates the performance of a typical differential protection scheme for a 110kV power transformer which connects a DFIG wind farm to the grid. The simulation results show that wind farm short circuit current might cause incorrect relay operation.

Security considerations such as ramp reserve and frequency limits are especially important in power system operation and control. However, with the increasing penetration of renewable energy such as wind power, the system frequency response in case of the power imbalance to become weaker. This paper presents a framework for unit commitment for power systems consisting of conventional generators and wind generators. The research framework also considers the role of battery storage system in maintaining the system frequency. Besides, wind power and demand uncertainties are taken into account with using forecast error modeling. The unit commitment is formulated as a Mixed Integer Linear Programming (MILP) problem and is solved using CPLEX and MATPOWER.

To suppress disturbances in uncertain tracking control systems with an input-delay, a disturbance predictor based on a high-order generalised extended-state observer is devised in this study. A smooth enough approximation of the disturbance is first made, then the approximation ahead of delay-time is predicted. The construction of the disturbance predictor is based on a truncated Taylor polynomial. To facilitate the analysis and design of the closed-loop control system, control gains for the nominal plant are designed in advance. Then the stability analysis for the closed-loop system is conducted, which yields a robust stability condition. As the disturbance predictor takes into consideration the influence of the input-delay, the presented method enables the enhancement of the disturbance-rejection performance. Finally, comparisons of the developed method with major methods in this field are conducted to validate the developed method and to demonstrate its advantages.

A robust tracking control method is presented in this paper for an uncertain plant with an unknown state delay and an exogenous disturbance. The effects of the uncertainties, the delay, and the exogenous disturbance are treated as a total disturbance
thus, the construction of the observer does not need the delay information. The system design is divided into the design of the gains of the state-feedback controller as well as the design of the gains of the observer and the improved equivalent-input-disturbance (EID) estimator. The pole-assignment method is used to design the gains of the state-feedback controller of a simplified system. A robust-stability condition in the form of a linear matrix inequality is derived to determine the gains of the observer and the improved EID estimator. Since the devised Lyapunov functional is of a more general form than those in existing EID-based methods and the restrictive commutative condition is avoided in this design, the developed design method is less conservative. Finally, comparisons of the developed method with a sliding-mode control method for a matched disturbance and a conventional EID-based method for an unmatched disturbance illustrate the validity and superiority of the developed method.

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.

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.

Unit commitment (UC) is a major problem in power system operation which determines the operation schedule of the generating units by minimizing system operation cost. Because of the uncertainty of wind power, the UC problem needs to solve as a multi-period stochastic optimization. In this stochastic problem, scenarios tree is generated and may be too large to be solved when time horizon is longer. This paper presents an approach based on Maximum Entropy principle to generate and reduce scenarios by transforming a stochastic process to a finite-state Markov chain process and finding transition probability matrix. This approach is applied to transform a wind power process modeled by ARMA(1,1) model with Stochastic Volatility. A simple stochastic unit commitment is solved in this article. Because of power system security, reserve constraints also considered.

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.

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.

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.

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.

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.

The traditional thermal and nuclear power plants are concentrated geographically. If such new power plants are planned, suitable routes which reach to nearest substations are suggested. The wind turbines in wind power plants, however, are scattered in a vast region. It's challenging to connect them and gather energy. However, there has been a few research of the method connecting wind turbines in a wind plant. In this paper, a planning method which optimizes the installation cost of power lines is developed. The target is an optimal design of power lines 'in' large wind power plants. Specifically, we speculated a situation that installs roughly 50 wind generators (single unit at 3MW) in a vast region. Also, we have taken a consideration to establish an optimal network to reduce the transmission overload when power reaches to the aggregating point. Optimal placement of towers and/or link of wind farms are out of our scope. © 2014 IEEE.

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 approach to evaluate the smoothing effect of wind power generators on power system operation based on actual wind output data in Tohoku area in Japan. Conventional LFC (Load Frequency Control) model is used and the effect of the smoothing effect on frequency control is evaluated. For evaluating the smoothing effect, spectral analysis and integration of each frequency of wind outputs are utilized. In this study, the smoothing effect in multi areas is evaluated and the decrease of the power spectrum density of individual wind generator is confirmed by comparing with the density of integrated wind generators in wide areas. Moreover, we assume many wind generators are connected to power systems, and analyze the smoothing effect of integrating wind power generators on power system operation. By using this multi-generator model, the influence of generator integration on the LFC is evaluated by investigating the change of frequency deviations. In addition, a method to suppress the frequency deviation caused by wind power fluctuations is proposed with control of batteries equipped in Electric Vehicles (EVs)’ in the power system model consists of wind generators and EVs.

Renewable energy resourses such as photovoltaic (PV) power and wind power have been installed in power networks as one of the countermeasures for global warming and depletion of fossil fuels. In particular, PV is the most promising of all renewable energy sources in Japan. However, the voltage may be risen due to the reverse power flow by connecting them to the distribution network. As one of the solutions for voltage problems due to the large scale penetration of PV generation, Voltage regulation by SVR (Step Voltage Regulator ) has been expected. In this paper, we examine the optimal allocation and planning of the SVR for stabilizing voltages when PV generators are installed to a large extent into distribution networks. As a result, the voltage deviation can be reduced 84.5% on average with introducing a SVR. The optimal allocations are depended on capacities of PV generators. In addition, the voltage deviations are decreased 2.17% on average in every PV installation plan by replacement a SVR.

Output smoothing using storage facilities for wind turbine generators has been realized typically by means of a first order system, as a low pass filter, together with compensators for keeping a level of energy storage and several limiters for energy flows; however, it is not easy to achieve an appropriate combination of the filter, the compensators and the limiters, for a desired performance of the output smoothing. To overcome this difficulty, we propose a new scheme of the output smoothing, which generates, by solving a Model Predictive Control (MPC) problem, an optimal reference signal for storage facilities so that the output smoothing is achieved and the constraints on the storage level and the energy flows are satisfied; in this scheme, trade-offs among smoothing performance and constraint fulfillment levels can be designed by selecting weighting parameters in the performance index. We perform numerical simulations, and discuss usability of the proposed scheme by comparing it with the existing output smoothing methods using fixed time-constant filter. © 2013 IEEE.

This paper addresses the hierarchical operation and control for a microgrid. In particular, the authors focus on supply and demand control of the microgrid. The microgrid has been actively introduced to distribution networks with renewables such as solar photovoltaic (PV for short) systems and wind energy conversion systems (WECS for short) [1]-[10]. Operation and control methods of the microgrid are complex because renewables have uncertain factors affected by weather. For this reason, effectual operation and control methods have not been proposed yet. In this paper, the authors have evaluated the proposed operation and control methods by the numerical simulation and a real time simulator. The simulation outcome shows the cost minimum operation and frequency stability by the proposed hierarchical operation and control.

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.

This paper presents a wind power forecasting method with confidence interval. Wind speed forecasts are calculated by regression models using GPV (Grid Point Vale) weather forecasts. The forecasts are adjusted by fuzzy inference using the latest error. The wind power forecasts are translated from the wind speed forecasts using two power-curves. They are 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 another fuzzy inference. The proposed method has been applied to actual power generators, and found that forecasting errors are better than the conventional methods. The results show the effectiveness of the proposed method. © 2011 IEEE.

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.

We are now entering a global era where many more photovoltaic (PV) generation systems will be installed. The fact that these dispersed power sources play an important role in suppressing greenhouse gases is well recognized. Conversely, there are concerns over the positive and negative impacts of too many PV systems on existing electric power systems. Even in case of a high penetration rate for decentralized generation, the distribution system is expected to maintain reliability and those PV generation systems can also contribute the proper ancillary service to the distribution system using battery control, centralized voltage control and islanding protection. This paper describes experimental studies concerning the interconnection technologies used for the high penetration of photovoltaic generation, as obtained from research conducted as part of two contracted projects: "Development of Islanding Prevention Methods under Clustered PV Conditions and Improvement of Electricity Quality" in Thailand and "Demonstrative Project on Grid-interconnection of Clustered Photovoltaic Power Generation Systems" in Ota-city, Japan. This paper outlines the methods described below and covers the proof examination of the following items: - New islanding passive and active detection methods - Voltage suppression control method using storage batteries - Centralized voltage suppression control method In particular, we will show that the unique results obtained by the proposed centralized voltage controller help to maintain a regulated voltage, while equalizing the availability in each residential PV system and maximizing total PV generation output at the same time.

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.

The electric utility industry is gradually undergoing restructuring and the main paradigm shift is the introduction of the principles of competition. With the level of future demand for electricity being unclear, power market players are reluctant to commit to long-term capital investment, with the result that construction of new large-scale power plants and also transmission and distribution infrastructure is typically being avoided. At the same time, power facilities are being upgraded in response to business strategies for bringing a profit in power markets and for keeping supply reliability for customers. Under such circumstances of competitive power markets, practical use of renewable and distributed energy generation offers an attractive alternative for power supply. The advantages include a short construction cycle as the supply can be located near to the demand, being less of a burden to the existing transmission network, and contributing to prevention of global warming through clean combustion using novel energy technologies such as natural gas co-generation, natural gas micro turbines, or fuel cells. Distributed generations using renewable energy resources such as wind and solar energy are also attracting attention. Regarding practical use of renewal energy in Japan, a new set of requirements advents. The first is the development of a set of policies for promoting power generated from renewable energy. The second is to evaluate the influence of distributed generations from renewable energy on the quality and reliability of the electricity. The third is the establishment of the technology requirements for interconnection with the power grid. The fourth is the development of future energy supply networks such as Power parks, Microgrids, and Smart grids. In implementing future energy supply networks utilizing renewable energy, power electronic devices are widely used to interface some forms of renewable energy generations and energy storages to distribution networks, and their use is likely to increase remarkably in the near future. The development of these power electronics is benefiting from the rapid advancements in the capability of power semiconductor switching devices and in the progress being made in the design and control of variable-speed drives for large motors. The most diffused application of power electronic devices is to invert the DC generated from some dispersed energy resources (e.g. photo voltaic fuel cells, micro turbines and battery storages) to existing 50/60 Hz AC. Also, power electronic devices are used to decouple rotating generates from the network and so potentially increase the efficiency of the operation of the prime mover by ensuring that they operate at their most efficient speed for the range of input power. For example, power electronics are used to support variable-speed wind turbines and are also now being proposed for some forms of small hydro-generation and energy storages during transients [1]. In this article, we will discuss the role of power electronics for renewable energy utilizations and the kinds of infrastructure that should be designed and how this should be pursued in order to maintain high reliability and quality for future energy supply networks in the restructured electricity markets. © 2006 IEEE.

This paper presents new approaches in order to solve problems related to clustered photo voltaic systems and evaluates the performance of the developed method. To cope with inappropriate phenomena caused by malfunctions of islanding detector and over voltages by reverse power flows from PV systems, we have developed a new islanding detector and two types of voltage control systems, and conducted experiments to verify the function of the developed systems. The test results obtained by this demonstrative implementation show that the proposed control system is able to maintain voltages within the regulated level and to equalize the efficiency in a PV system and also that the new method enables us to make total PV generation efficiency higher. © 2008 IEEE.

This paper discusses the stability and an interactive phenomenon of one SVC, two or more sets of SVC, an SVC and an 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. © 2007 Wiley Periodicals, Inc.

A special active filter, which can eliminate the variable harmonic currents in continuity, was investigated. A variable-frequency band-pass filter was developed for the extraction of current component of fundamental frequency. Dynamic simulations for the active filter, motor generator with a flywheel (FW-MG) and power supplies of heating devices for the experimental fusion device of LHD have been carried. We found that the harmonic currents with the amplitudes of 20% have been reduced to less than 2% in the operational frequency range from 95 Hz to 55 Hz by using this active filter. © 2005 Elsevier B.V. All rights reserved.

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 studies the impacts of physical contracts and financial contracts on the bidding strategies of GENCOs, including Physical Bilateral Contracts, Contracts for Difference (CfDs), Call Options and Put Options under discriminatory pricing mechanism. The integrated bidding decision model is applied, which has three main modules - probabilistic local marginal price simulator, market-oriented unit commitment model and multi-criteria decision system. The numerical results show that the GENCO will choose different bidding strategies if it holds different types of contract. The results also suggest that CfDs have the best performance for risk alleviation. © 2004 Elsevier Ltd. All rights reserved.

Optimal pricing for real and reactive power is a very important issue in a deregulation environment. This paper summarises the optimal pricing problem as an extended optimal power flow problem. Then, spot prices are decomposed into different components reflecting various ancillary services. The derivation of the proposed decomposition model is described in detail. Primary-Dual Interior Point method is applied to avoid 'go' 'no go' gauge. In addition, the proposed approach can be extended to cater for other types of ancillary services. © 2003 Elsevier Ltd. All rights reserved.

The LHD is an SC experimental fusion device of heliotron type. Eight sets of the helium compressors with total electric power of 3.5 MW are installed in the cryogenic system. The analytical studies of the SMES-UPS for the compressors under the deep voltage sag are reported in this paper. The amplitude and frequency of the voltage decrease gradually by the regenerating effect of the induction motors. The SMES-UPS system proposed in this report has the following functions; (1) variable frequency control, (2) regulations by ACR and AVR, and (3) rapid isolation and synchronous reconnection from the loads to grid line. We have demonstrated that SMES was useful for the large-scaled cryogenic system of the experimental fusion device. © 2003 Elsevier Science B.V. All rights reserved.

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.

This paper presents a genetic algorithm for solving network reconfiguration using three phase unbalanced load flow in distribution systems. A proper string representation for the problem is devised and a method to yield a good problem-dependent initial string population is presented. An appropriate selection method for strings is used for fast convergence and improving quality of solutions. Three phase unbalanced load flow is used for taking constant power and unbalance loads in the practical distribution network into account. The feasibility of the developed algorithm for network reconfiguration is demonstrated on distribution networks with promising 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.

The P-V Curve, Q-V Curve, or P-Q-V Curve have been widely used to analyze power system behaviors under varying loading conditions. These curves have been generated under the condition that tire constant P-Q load component of a bus (or a collection of buses) varies, with the constant current load and constant impedance load being kept indeed "constant". As such, the physical meaning of these curves can be easily explained. Motivated by the facts that load models have profound impacts on power system behaviors and that the nonlinear load model ZIP-model is popular in modeling nonlinear behaviors of loads, this paper proposes a new class of curves, called ZIP-V curves, to better trace power system steady-state stationary behavior due to load and generation variations. The ZIP-V curves encompass the traditional P-V, Q-V, P-Q-V curves (constant P-Q load), I-V curve (constant current load), Z-V curve (constant impedance load), or generalized curves such as IP-V (constant current and constant power load), ZP-V (constant impedance and constant power load) or IZ-V (constant current and constant impedance load) curve when the values of corresponding components are kept constant. A tool based on the continuation power flow (CPFLOW) method useful for generating the ZIP-V curves is developed and its application to generate ZIP-V curves of a 4561-bus interconnected power system is illustrated.

This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (Volt/Var Control: VVC) considering voltage security assessment (VSA). VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an on-line VVC strategy with continuous and discrete control variables such as automatic voltage regulator (AVR) operating values of generators, tap positions of on-load tap changer (OLTC) of transformers, and the number of reactive power compensation equipment. The feasibility of the proposed method is demonstrated and compared with reactive tabu search (RTS) and the enumeration method on practical power system models with promising results.

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.

The dynamic behaviors 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 'reverse control action for multiple TCULS' 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.

This paper presents a reactive tabu search for service restoration in electric power distribution systems. Service restoration is an emergency control in distribution control centers to restore out-of-service area as soon as possible when a fault occurs in distribution systems. Therefore, it requires fast computation time 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 proposed method is demonstrated on typical service restoration problems. It is compared favorably with conventional tabu search, genetic algorithm, and parallel simulated annealing. The results reveal the speed and effectiveness of the proposed method for solving the problem.

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.

This paper develops a parallel method for fast distribution power flow studies using multi-processors. The method belongs to the class of coarse-grain and exhibits good convergence characteristic over a wide range of r/x ratio of distribution lines. The method can be applied to radial distribution systems. It uses only active and reactive power injections at the main feeder and at laterals as state variables; thus reducing the number of state variables. Radial distribution networks are mapped into the tree structure of a multi-processor system for allocating processes. The forward/backward sweep approach is realized by communication between the root processor and leaf processors. The proposed method is evaluated on various distribution systems including an actual system with promising results. Copyright © 1996 Elsevier Science Ltd.

This paper develops a parallel method for fast distribution power flow studies using multi-processors. The method belongs to the class of coarse-grain and exhibits good convergence characteristic over wide range of r/x ratio of distribution lines. The method can be applied to radial distribution systems. It uses only active and reactive power injections at the main feeder and at laterals as state variables; thus reducing the number of state variables. Radial distribution networks are mapped into the tree structure of multi-processor system for allocating each node to processes. Forward/backward sweep approach is realized by communication between the root processor and leaf processors. The proposed method is evaluated on various distribution systems with promising results.

This paper develops a hybrid system for solving a service restoration problem in distribution systems using expert system and genetic algorithm. The main objective in service restoration procedure is to restore as many loads as possible by transferring de-energized loads via network reconfigurations to other supporting distribution feeders without violating operating and engineering constraints. The expert system determines switch operations to expand the supply margin of power sources in a case where the total power source capacity is not enough to restore the whole out-of-service areas. The genetic algorithm determines a part of out-of-service area for each power source. The feasibility of the developed algorithm for service restoration is demonstrated on a distribution network with promising results.

Power flow calculation is a basic tool for power system planning and control which includes power flow analysis, voltage control, service restoration, network reconfiguration, and other operation functions for control centers. In these applications, it is very important to solve the power flow problem as efficiently as possible. This paper presents a fast power flow using parallel processing for radial power systems. This method can be applied to secondary systems and distribution systems. Since it uses as state variables only active and reactive power injection to main lines and laterals, reduction of the number of state variables can be realized. Radical 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.

This paper presents a parallel genetic algorithm for solving the long-range generation expansion planning, which is a combinatorial optimization problem that determines the order of newly introduced generation units at each interval of the planning years. The method is implemented on a transputer that is one of the practical parallel processors. The effectiveness of the proposed method is demonstrated using a typical expansion problem with four technologies and five intervals and compared favorably with the conventional dynamic programming method and genetic algorithm. The numerical results reveal the speed and effectiveness of the proposed algorithm for solving the problem.

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.

Information gathering, processing, decision-making and dispatching in power system operation are usually involved with time delays. The time delay can be a pure time delay due to the retarded movement of discrete-type controllers (e.g. On-Load Tap Changer). In some cases, the time delay can be a substitute for a delayed response of higher order dynamical systems, when an approximate, still meaningful treatment is needed for a concise and bird's-eye view type understanding of the stability problem where gains, time constants together with the time delay play a very important role. After basic investigations of the behaviour of Ordinary Differential Equations (ODE's) with time delays, more concrete problems will be analyzed such as stability of OLTC's, coordination of steady-state and emergency-state oriented design of a controller.

Control equipment interactions pose a cumbersome and difficult-to-solve problem in a voltage-unstable environment. The authors present a simple and qualitative criterion concerning the oscillatory/nonoscillatory behavior of two OLTC units with time delays. The correspondence between the multiple-load-flow solutions and a set of stable and unstable equilibrium points is made clear. The extension and applicability of the proposed method are discussed.