A common practice in induction machine parameter identification techniques is to use external measurements of voltage, current, speed, and/or torque. Using this approach, it has been shown that it is possible to obtain an infinite number of mathematical solutions representing the machine parameters. This paper examines the identifiability of two commonly used induction machine models, namely the T-model (the conventional per phase equivalent circuit) and the inverse Γ-model. A novel approach based on the alternating conditional expectation (ACE) algorithm is employed here for the first time to study the identifiability of the two induction machine models. The results obtained from the proposed ACE algorithm show that the parameters of the commonly employed T-model are unidentifiable, unlike the parameters of the inverse Γ-model which are uniquely identifiable from external measurements. The identifiability analysis results are experimentally verified using the measured operating characteristics of a 1.1-kW three-phase induction machine in conjunction with the Levenberg-Marquardt algorithm, which is developed and applied here for this purpose.
Unprecedented high volume of data is available with the upward growth of the advanced metering infrastructure. Because the built environment is the largest user of electricity, a deeper look at building energy consumption holds promise for helping to achieve overall optimization of the energy system. Yet, a knowledge transfer from the fusion of extensive data is under development. To overcome this limitation, in the big data era, more and more machine learning methods appear to be suitable to automatically extract, predict and optimized building electrical patterns by performing successive transformation of the data. More recently, there has been a revival of interest in deep learning methods as the most advance on-line solutions for large-scale and real databases. Enabling real-time applications from the high level of aggregation in the smart grid will put end-users in position to change their consumption patterns, offering useful benefits for the system as a whole.
The paper proposes an approximated yet reliable formula to estimate the frequency at the buses of a transmission system. Such a formula is based on the solution of a steady-state boundary value problem where boundary conditions are given by synchronous machine rotor speeds and is intended for applications in transient stability analysis. The hypotheses and assumptions to define bus frequencies are duly discussed. The rationale behind the proposed frequency divider is first illustrated through a simple 3-bus system. Then the general formulation is duly presented and tested on two real-world networks, namely a 1,479-bus model of the all-island Irish system and a 21,177-bus model of the European transmission system.
Smooth and fluctuation free power dispatch is strongly encouraged by grid operators and therefore energy storage is becoming an indispensable part in modern large scale grid connected photovoltaic (PV) systems. As a result, associated power converter topologies and energy storage interfacing technologies are currently receiving unprecedented attention. The most common energy storage interfacing technique used in grid connected PV systems is the use of an additional power electronic converter between the energy storage system and the grid connecting inverter. Taking the disadvantages of this implementation into account this paper proposes a dual inverter based battery direct integration scheme for grid connected PV systems. In this approach, the generation of proper multilevel voltage waveforms is a complicated process, particularly when the battery is charging. A modified space vector modulation method and switching strategy are proposed in this paper to address this issue. Simulation results are presented to prove the efficacy of the proposed topology and the modulation technique.
Due to the growth in the number of residential photo voltaic (PV) adoptions in the past five years, there is a need in the electricity industry for a widely-accessible model that predicts the adoption of PV based on different business and policy decisions. We analyze historical adoption patterns and find that monetary savings is the most important factor in the adoption of PV, superseding all socioeconomic factors. On the basis of the findings from our data analysis, we created an application available on Google App Engine (GAE), that allows researchers, policymakers and regulators to study the complex relationship between PV adoption, grid sustainability and utility economics. This application allows users to experiment with a variety of scenarios including different tier structures, subsidies and customer demographics. We showcase the type of analyses that are possible with this application by using it to study the impact of different policies regarding tier structures, fixed charges and PV prices.
Energy storage devices play a very important role in nowadays inter-connected power systems. They could be used for peak shaving as well as mitigating the fluctuations caused by distributed generation sources. The development of battery technologies makes it possible to integrate large-scale battery energy storage (BES) devices into power systems. In this paper, a multi-period optimal power flow (OPF) model including BES is proposed to study the effect of BES on power generation scheduling. The objective of the proposed model is to minimize the total generation cost considering charging and discharging costs of BES. State of charge (SOC) constraints and other power network constraints are taken into consideration in the model. The proposed model determines the optimal output power in each period as well as the battery charging/discharging schedule. Finally, the numerical example based on IEEE-30 system shows effectiveness and validity of the proposed model.
In power systems with a high wind power penetration, probabilistic load flow analysis is a fundamental problem for system planning and operation due to the uncertainty and fast fluctuation of wind speed. This paper proposes probabilistic collocation method (PCM) for load flow analysis with a penetration of wind farms. The orthogonal polynomials are utilized to generate the approximation of the random variable of interest as the function of uncertainty parameters. The proposed method is a computational efficient solution to provide quite an accurate approximation for the given probability distribution of system response. Therefore the method can significantly reduce the computational time compared to the traditional brute force Monte Carlo approach. Illustration examples are given on the IEEE 39 bus system to show the effectiveness of the proposed method.
In order to solve optimal technical and economic allocation of distributed generation in distribution network, a multi-objective nonlinear optimization model is built considering the uncertainty of the type, location and capacity of distributed generation. The sub-objectives in this model include the least investment cost, the highest earning, the highest environment benefits and the least loss in distribution network. Interval numbers are used to deal with the uncertainty of the property values and their weights in the actual decision-making of DG optimal allocation. A method combined by TOPSIS algorithm based on interval mathematics and genetic algorithm is proposed in this paper to solve that model. Taking a typical distribution network as a test example, the result shows the validity and practicability of the proposed method.
Based on equivalent model of power network and voltage stability index, a centralized load shedding strategy is developed for voltage stability control in this paper. Utilizing the sensitivity of voltage instability index to load power obtained from the equivalent model, the control location is decided with considering on control capability and effectiveness of load comprehensively, and the amount of the load to be shed is calculated by solving an optimization problem. Finally, a centralized load shedding algorithm and its implementation scheme is proposed to prevent voltage instability by cooperating with on-line voltage stability monitoring. Simulations conducted on typical power systems indicate that the load shedding scheme can keep voltage stability margin and voltage level effectively to prevent deterioration of power system. More, the load control algorithm adapts to on-line voltage stability control.
On-line Transient Stability Assessment (TSA) program can fast identify a list of critical contingencies. A real-time critical contingency (RTCC) detection system is to detect, on the basis of the real-time measurements provided by PMUs and the list of critical contingencies, the occurrence of a critical contingency is presented. The RTCC detection system is composed of five detectors. Each of them is designed and placed in a sequential order so as to achieve absolute capture of the occurrence of critical contingencies. The system has been evaluated in 2 test systems and one large-scale power systems with very promising results. The effectiveness of the developed critical contingency detection system is evaluated on 96 contingencies of a 10-machine, 39-bus power system, on 108 contingencies of a 50-generator, 145-bus power system and on 4588 contingencies of a 313-machine, 1648-bus power system.
This paper presents an improved optimal power flow (OPF) model in large-scale power system incorporating wind power. In order to integrate the wind uncertainty into this model, the Weibull distribution of forecasted wind speed, a nonlinear wind power curve expression and the opportunity cost that quantifies wind uncertainty into objective function are proposed. Meanwhile, considering the safety requirement, the up and down spinning reserve constraints are added into this model. The primal-dual interior point method is employed to implement this OPF model. The modified IEEE 118 system and a practical Polish 3120 system are used to verify the feasibility of the proposed model and solution method.
Voltage-source-converter (VSC) technologies present a bright opportunity in a variety of fields within the power system industry. New modular multilevel converters (MMCs) are expected to supersede two- and three-level VSC-based technologies for HVDC applications due to their recognized advantages in terms of scalability, performance, and efficiency. The computational burden introduced by detailed modeling of MMC-HVDC systems in electromagnetic-transients (EMT)-type programs complicates the study of transients especially when these systems are integrated into a large network. This paper presents a novel average-value model (AVM) for efficient and accurate representation of a detailed MMC-HVDC system. It also develops a detailed 401-level MMC-HVDC model for validating the AVM and studies the performance of both models when integrated into a large 400-kV transmission system in Europe. The results show that the AVM is significantly more efficient while maintaining its accuracy for the dynamic response of the overall system.
A generalized data preprocessing method is proposed in this paper to reduce the amount of outliers among historical data and further improve the power prediction accuracy. Historical data of wind farms are fit with an S-shape curve via Linear Regression Model. Based on this statistical curve, outliers can be identified considering different fitting error. Furthermore, the expansion of wind farm is identified through the number of outliers. Then a selection method for the allowed maximum fitting errors is recommended. The presented method has been integrated into the prediction system in Inner Mongolia of China with 36 farms. The actual application shows that the wind farm power prediction accuracy has been improved by at least 28% with this model. It is noteworthy that the proposed preprocessing method is just based on statistical analysis of historical data and thus compatible with various wind power prediction methods.
The single-machine equivalent model (SEM) is usually employed to represent the dynamics of wind farm, while the accuracy of SEM decreases with the increase of difference of the incoming wind speeds between each wind turbine in the wind farm. In order to improve the accuracy of SEM, an equivalent modeling method using power characteristics curve of the wind turbine is proposed in this paper. In this method, an equivalent maximum power curve (EMPC) is obtained, where the wake effect is taken into account. Using the EMPC, the calculation of equivalent wind speed is unnecessary during equivalent modeling of the wind farm. A wind farm with 9 wind turbines is built in Matlab, and simulations are carried out under wind fluctuation and grid fault to evaluated effectiveness of the proposed equivalent modeling method.
This paper investigates the load scheduling problem in smart grids. Due to the uncertainty of future electricity prices, the statistical knowledge is utilized in the load scheduling process. Instead of resorting to stochastic dynamic programming that is generally prohibitive to be explicitly solved, load scheduling is formulated as an optimization problem with coupling constraints. Dual decomposition and stochastic gradient are proposed to solve the optimization problem. That is, the problem is first decoupled into a series of separable subproblems, and then price uncertainty is addressed by stochastic gradient based on probability distributions of future prices. An online approach improves the performance of load scheduling by alleviating the impact of price prediction error. Numerical results are provided to validate our theoretical analysis.
In this paper, a generation expansion planning model and a transmission expansion planning model are proposed to inform investment decisions from the perspectives of generation companies and ISOs, respectively. Interactions between large-scale wind integration and transmission system planning are analyzed, and a new computational procedure of system expansion planning that coordinates generation and transmission investment is presented. The cooptimization of energy and ancillary services markets is considered to capture the impacts of wind power on the operation of the electric system. Benders decomposition is employed to reduce the overall computation time.
