M. Etezadi-Amoli

Genetic-Algorithm-Based Optimization Approach for Energy Management

This paper proposes a new strategy to meet the controllable heating, ventilation, and air conditioning (HVAC) load with a hybrid-renewable generation and energy storage system. Historical hourly wind speed, solar irradiance, and load data are used to stochastically model the wind generation, photovoltaic generation, and load. Using fuzzy C-Means (FCM) clustering, these data are grouped into 10 clusters of days with similar data points to account for seasonal variations. In order to minimize cost and increase efficiency, we use a GA-based optimization approach together with a two-point estimate method. Minimizing the cost function guarantees minimum PV and wind generation installation as well as storage capacity selection to supply the HVAC load. Different scenarios are examined to evaluate the efficiency of the system with different percentages of load shifting. The maximum capacity of the storage system and excess energy are calculated as the most important indices for energy efficiency assessment. The cumulative distribution functions of these indices are plotted and compared. A smart-grid strategy is developed for matching renewable energy generation (solar and wind) with the HVAC load.

Rapid-Charge Electric-Vehicle Stations

Mass production of total electric vehicles capable of traveling longer distances results in a need for electric service stations that can satisfy the requirements for a significant amount of power provided in a time duration similar to that of filling a car with oil-based fuel. These vehicles would pull into the station, and need a large amount of power delivered over a short period of time for the rapid recharging of batteries which serve as their “fuel tank.” This paper investigates the effect of fast-charging electric vehicles on an existing utility company distribution system at several specified sites. This paper will cover power-flow, short-circuit, and protection studies at these sites using utility-grade software packages. In addition, the analysis of simulations of a recharging station where up to eight rapidly rechargeable vehicles come online at once will be presented.

A Framework for Optimal Placement of Energy Storage Units Within a Power System With High Wind Penetration

This paper deals with optimal placement of the energy storage units within a deregulated power system to minimize its hourly social cost. Wind generation and load are modeled probabilistically using actual data and a curve fitting approach. Based on a model of the electricity market, we minimize the hourly social cost using probabilistic optimal power flow (POPF) then use a genetic algorithm to maximize wind power utilization over a scheduling period. A business model is developed to evaluate the economics of the storage system based on the energy time-shift opportunity from wind generation. The proposed method is used to carry out simulation studies for the IEEE 24-bus system. Transmission line constraints are addressed as a bottleneck for efficient wind power integration with higher penetration levels. Distributed storage is then proposed as a solution to effectively utilize the transmission capacity and integrate the wind power more efficiently. The potential impact of distributed storage on wind utilization is also evaluated through several case studies.

Stochastic Performance Assessment and Sizing for a Hybrid Power System of Solar/Wind/Energy Storage

This paper proposes a stochastic framework for optimal sizing and reliability analysis of a hybrid power system including the renewable resources and energy storage system. Uncertainties of wind power, photovoltaic (PV) power, and load are stochastically modeled using autoregressive moving average (ARMA). A pattern search-based optimization method is used in conjunction with a sequential Monte Carlo simulation (SMCS) to minimize the system cost and satisfy the reliability requirements. The SMCS simulates the chronological behavior of the system and calculates the reliability indices from a series of simulated experiments. Load shifting strategies are proposed to provide some flexibility and reduce the mismatch between the renewable generation and heating ventilation and air conditioning loads in a hybrid power system. Different percentages of load shifting and their potential impacts on the hybrid power system reliability/cost analysis are evaluated. Using a compromise-solution method, the best compromise between the reliability and cost is realized for the hybrid power system.

Energy Storage Application for Performance Enhancement of Wind Integration

This paper proposes a stochastic framework to enhance the reliability and operability of wind integration using energy storage systems. A genetic algorithm (GA)-based optimization approach is used together with a probabilistic optimal power flow (POPF) to optimally place and adequately size the energy storage. The optimization scheme minimizes the sum of operation and interrupted-load costs over a planning period. Historical wind speed, load and equipment failure data are used to stochastically model the wind generation, load and equipment availability. Using Fuzzy C-Means (FCM) clustering, wind and load samples are grouped into 40 clusters of days with similar sample points to account for seasonal variations. The IEEE 24-bus system (RTS) is used to evaluate the performance of the proposed method and realize the maximum achievable reliability level. A cost-benefit analysis compares storage technologies and conventional gas-fired alternatives to reliably and efficiently integrate different wind penetration levels and determine the most economical design. Storage distribution and its effect on performance enhancement of wind integration are examined for higher wind penetrations.

Practical approach for sub-hourly and hourly prediction of PV power output

This paper proposes a practical and reliable approach for the prediction of photovoltaic power generation using solar irradiance as the input. Solar irradiance is modeled as the sum of a deterministic component and a Gaussian noise signal. The solar irradiance on a partly cloudy day is forecasted by Kalman filtering. The shaping filter for the Gaussian noise is calculated using spectral analysis and an autoregressive moving average (ARMA) model. The results of the two approaches are compared with the measured irradiance at a PV generating facility within an electric utility company. The results show that better estimates are obtained using spectral analysis than those obtained with the ARMA model, particularly for lower sampling rates.

A Multi-Objective Transmission Expansion Planning Framework in Deregulated Power Systems With Wind Generation

Integration of renewable energy resources into the power system has increased the financial and technical concerns for the market-based transmission expansion planning. This paper proposes a stochastic framework for transmission grid reinforcement studies in a power system with wind generation. A multi-stage multi-objective transmission network expansion planning (TNEP) methodology is developed which considers the investment cost, absorption of private investment and reliability of the system as the objective functions. A non-dominated sorting genetic algorithm (NSGA II) optimization approach is used in combination with a probabilistic optimal power flow (POPF) to determine the Pareto optimal solutions considering the power system uncertainties. Using a compromise-solution method, the best final plan is then realized based on the decision-maker preferences. The proposed methodology is applied to the IEEE 24-bus Reliability Tests System (RTS) to evaluate the feasibility and practicality of the developed planning strategy.

A Novel Method for Single and Simultaneous Fault Location in Distribution Networks

This paper introduces a novel method for single and simultaneous fault location in distribution networks by means of a sparse representation (SR) vector, Fuzzy-clustering, and machine-learning. The method requires few smart meters along the primary feeders to measure the pre- and during-fault voltages. The voltage sag values for the measured buses produce a vector whose dimension is less than the number of buses in the system. By concatenating the corresponding rows of the bus impedance matrix, an underdetermined set of equation is formed and is used to recover the fault current vector. Since the current vector ideally contains few nonzero values corresponding to fault currents at the faulted points, it is a sparse vector which can be determined by l1-norm minimization. Because the number of nonzero values in the estimated current vector often exceeds the number of fault points, we analyze the nonzero values by Fuzzy-c mean to estimate four possible faults. Furthermore, the nonzero values are processed by a new machine learning method based on the k-nearest neighborhood technique to estimate a single fault location. The performance of our algorithms is validated by their implementation on a real distribution network with noisy and noise-free measurement.

An improved modeling technique for distribution feeders with incomplete information

This paper proposes an improved technique for modeling of an electrical distribution system in the presence of incomplete information. By considering the different behavior and coincident factors of commercial and residential loads, this paper formulates a ratio factor technique to acquire an improved power-flow model. A load distribution relationship between the entire load and less than 50% of the total load on a feeder is also formulated. The technique only requires annual peak loads for each feeder and substation which are readily available. In addition, the method uses the results of the conventional technique and produces better results through a set of simple computation. It is shown that the accuracy of the new technique is significantly better than the traditional technique. >

Partial discharge pattern recognition via sparse representation and ANN

In this study, seventeen samples were created for classifying internal, surface, and corona partial discharges (PDs) in a high voltage lab. Next, PDs were measured experimentally to provide a dictionary comprising the types. Due to the huge size of the recorded dataset, a new and straightforward preprocessing method based on signal norms was used to extract the appropriate features of various samples. The new sparse representation classifier (SRC) was computed using ℓ1 and stable ℓ1-norm minimization by means of Primal-Dual Interior Point (PDIP) and Basis Pursuit De-noise (BPDN) algorithms, respectively. The pattern recognition was also performed with an artificial neural network (ANN) and compared with the sparse method. It is shown that both methods have comparable performance if training process, tuning options, and other tasks for finding the best result from ANN are not taken into account. Even with this assumption, it is shown that SRC still performs better than ANN in some cases. In addition, the SRC technique presented in this paper converges to a fixed result, while the results after training the ANN vary with every run due to random initial weights.