M. S. Fadali

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.

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.

Stability Analysis and Control of Discrete Type-1 and Type-2 TSK Fuzzy Systems: Part I. Stability Analysis

This paper introduces sufficient conditions for the exponential stability of type-1 and type-2 Takagi-Sugeno-Kang (TSK) fuzzy systems. A major advantage of the new conditions is that they do not require the existence of a common Lyapunov function and are, therefore, applicable to systems with unstable consequents. In addition, our results include two classes of type-2 TSK systems with type-1 consequents for which no stability tests are available. The use of the conditions in stability testing is demonstrated using simple numerical examples that include cases where methods that are based on a common Lyapunov function fail. The application of the stability test to develop new controller design methodologies is presented in a separate paper (i.e., Part II).

Solar Power Prediction Using Interval Type-2 TSK Modeling

The random nature of solar energy resources is one of the obstacles to their large-scale proliferation in power systems. The most practical way to predict this renewable source of energy is to use meteorological data. However, such data are usually provided in a qualitative form that cannot be exploited using traditional quantitative methods but which can be modeled using fuzzy logic. This paper proposes type-1 and interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy systems for the modeling and prediction of solar power plants. The paper considers TSK models with type-1 antecedents and crisp consequents, type-1 antecedents and consequents, and type-2 antecedents and crisp consequents. The design methodology for tuning the antecedents and consequents of each model is described. Finally, input-output data sets from a solar plant are used to obtain the three TSK models and their prediction results are compared to results from the literature. The results show that type-2 TSK models with type2 antecedents and crisp consequents provide the best performance based on the solar plant data.

Stability Analysis and Control of Discrete Type-1 and Type-2 TSK Fuzzy Systems: Part II. Control Design

This paper proposes a new control system design methodology for type-1 and type-2 Takagi-Sugeno-Kang (TSK) fuzzy systems that are based on new stability conditions. The stability conditions are discussed in a companion paper (Part I) and are used in the proofs of our main results. A major advantage of the new methodology is that it does not require a common Lyapunov function and is therefore applicable to systems with nonstabilizable consequents. Our controllers include fuzzy type-1 proportional and proportional-integral (PI) controllers, as well as constant state feedback for the same systems. The controller results in an exponentially stable system, and the designer can specify the rate of exponential convergence. The controller designs can be tested by the usage of linear matrix inequalities (LMIs). The design methodology is demonstrated by the usage of simple examples where methods that are based on a common Lyapunov function fail and physical systems where the new methodology provides better performance.

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.

TSK Fuzzy Function Approximators: Design and Accuracy Analysis

Fuzzy systems are excellent approximators of known functions or for the dynamic response of a physical system. We propose a new approach to approximate any known function by a Takagi-Sugeno-Kang fuzzy system with a guaranteed upper bound on the approximation error. The new approach is also used to approximately represent the behavior of a dynamic system from its input-output pairs using experimental data with known error bounds. We provide sufficient conditions for this class of fuzzy systems to be universal approximators with specified error bounds. The new conditions require a smaller number of membership functions than all previously published conditions. We illustrate the new results and compare them to published error bounds through numerical examples.