M. Ghofrani

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.

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.

Smart scheduling and cost-benefit analysis of grid-enabled electric vehicles for wind power integration

This paper proposes a stochastic framework to mitigate the effects of uncertainty and enhance the predictability of wind power using the vehicle-to-grid (V2G) capabilities of electric vehicles (EVs). An Auto Regressive Moving Average (ARMA) wind speed model forecasts the wind power output. Using Fuzzy C-Means (FCM) clustering, EVs are grouped into 6 fleets of similar daily driving patterns. A Genetic Algorithm (GA) is used in combination with a Monte Carlo simulation (MCS) to optimize charging and discharging of the EVs. The optimization scheme minimizes the sum of the penalty cost associated with wind power imbalances and V2G expenses associated with purchased energy, battery wear and capital costs. The proposed method provides a collaborative strategy between the wind participants and EV owners to increase their revenues and incentives. A cost-benefit analysis assesses the economic feasibility of V2G services for wind power integration. The coordinated charging/discharging scheme optimally utilizes the V2G capacities of EVs and compensates for power imbalances due to random variations of wind power.

A novel clustering algorithm based on data transformation approaches

A new initialization technique is proposed to improve the performance of K-means.A data transformation approach is proposed to solve empty cluster problem.An efficient method is proposed to estimate the optimal number of clusters.Proposed clustering method provides more accurate clustering results. Clustering provides a knowledge acquisition method for intelligent systems. This paper proposes a novel data-clustering algorithm, by combining a new initialization technique, K-means algorithm and a new gradual data transformation approach to provide more accurate clustering results than the K-means algorithm and its variants by increasing the clusters coherence. The proposed data transformation approach solves the problem of generating empty clusters, which frequently occurs for other clustering algorithms. An efficient method based on the principal component transformation and a modified silhouette algorithm is also proposed in this paper to determine the number of clusters. Several different data sets are used to evaluate the efficacy of the proposed method to deal with the empty cluster generation problem and its accuracy and computational performance in comparison with other K-means based initialization techniques and clustering methods. The developed estimation method for determining the number of clusters is also evaluated and compared with other estimation algorithms. Significances of the proposed method include addressing the limitations of the K-means based clustering and improving the accuracy of clustering as an important method in the field of data mining and expert systems. Application of the proposed method for the knowledge acquisition in time series data such as wind, solar, electric load and stock market provides a pre-processing tool to select the most appropriate data to feed in neural networks or other estimators in use for forecasting such time series. In addition, utilization of the knowledge discovered by the proposed K-means clustering to develop rule based expert systems is one of the main impacts of the proposed method.

Mixed Derating of Distribution Transformers Under Unbalanced Supply Voltage and Nonlinear Load Conditions Using TSFEM

In this paper, a novel concept, which is called mixed derating, is introduced for the maintenance of distribution transformers against the copper and core losses increase due to the nonlinear loads and unbalanced supply voltage, respectively. Hence, the traditional equation which is utilized for the derating of transformers under nonlinear loads is modified to determine the proper rated power of a transformer under nonsinusoidal conditions. Based on the proposed concept, the harmonic loss factor as an efficient feature for defining appropriate rated power of transformers under nonsinusoidal conditions is calculated and compared with IEEE standards. The time-stepping finite-element method is utilized to simulate the distribution transformer under different nonsinusoidal conditions and evaluates transformer performance precisely. In this modeling, winding distribution, geometrical, and physical characteristics of all segments of the transformer are taken into account for accurate determination of the necessary signals and parameters to scrutinize the transformer under nonsinusoidal conditions.

Electric drive vehicle to grid synergies with large scale wind resources

Vehicle to grid (V2G) provides an opportunity for the electric vehicles (EVs) to feed back their power to the electric grid as they are connected to the grid and not in use. However, during the charging period of these vehicles, the power is drawn from the electric grid to charge the battery. This paper examines the V2G capability of the EV fleet for its potential for synergies between the storage of the fleet and the intermittent nature of wind resources. Towards this end, a distribution feeder is considered with the wind power as its primary resource. Assuming an EV with V2G for each residence, different scenarios are studied to evaluate the capability of the EV fleet as the wind electric storage. An energy management method based on an evolutionary optimization approach is proposed to minimize the cost of the conventional generation required to supplement the wind power while maximizing the utilization of wind generation. Smart grid technologies such as real-time communication, smart metering and home area networks (HANs) are proposed to enhance the V2G capability for coordinated charging and discharging of the EV fleet in a distribution feeder. Flexibility of the system is assessed for the studied scenarios and the most appropriate solution is determined based on simulation results.

A novel soft computing framework for solar radiation forecasting

Display Omitted This paper proposes a new hybrid soft computing framework to increase the solar radiation forecasting accuracy.An improved version of K-means algorithm is proposed to provide fixed, definitive clustering results.A new classification approach is developed to better characterize irregularities and variations of solar radiation.The new method is important for very short-term forecasting where the forecast horizon can be as short as a few seconds. Accurate forecasting of renewable-energy sources plays a key role in their integration into the grid. This paper proposes a novel soft computing framework using a modified clustering technique, an innovative hourly time-series classification method, a new cluster selection algorithm and a multilayer perceptron neural network (MLPNN) to increase the solar radiation forecasting accuracy. The proposed clustering method is an improved version of K-means algorithm that provides more reliable results than the K-means algorithm. The time series classification method is specifically designed for solar data to better characterize its irregularities and variations. Several different solar radiation datasets for different states of U.S. are used to evaluate the performance of the proposed forecasting model. The proposed forecasting method is also compared with the existing state-of-the-art techniques. The comparison results show the higher accuracy performance of the proposed model.

A stochastic framework for power system operation with wind generation and energy storage integration

This paper proposes a probabilistic optimal power flow (POPF) to evaluate the power system operation with renewable energy generation. Probability density functions (PDFs) are used to stochastically model the wind speed and load. A (2m+1)-point estimation considers the system uncertainties for power flow analysis. The proposed method incorporates energy storage into the POPF model to evaluate its application for wind power integration. The IEEE 24-bus system is used to evaluate the feasibility and practicality of the developed POPF framework. Different scenarios are studied to investigate the economic advantage of wind and storage co-location over the cases where the wind and storage units are installed at different buses.