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This article develops and tests a controller for residential electric water heaters (EWHs). The developed controller is operated to maximize the energy stored in EWHs during off-peak-demand times, in order to reduce their power demands during peak-demand times. Desired control actions aim to adjust the minimum temperature settings of EWHs using the unit commitment (UC). In order to eliminate the need for a direct measurement of EWH power demands, the bus-split (BS) aggregation method is employed. The BS method is employed due to its ability to extract the power demands of an EWH from household power meter readings. The UC is formulated using the energy stored in a EWH as a cost function, which is to be maximized during the off-peak-demand time. The solution of the UC problem is obtained using the Lagrange relaxation method that can offer fast convergence and reduced computations. The BS-UC controller is implemented for performance testing using power meter readings that are collected from 150 residential households during the fall, winter, spring, and summer seasons. Test results demonstrate the ability of the BS-UC controller to provide accurate and effective control of EWHs, which are complimented by a minor sensitivity to the number controlled EWHs, hot water consumption, and seasonal variations in residential load power demands. [ABSTRACT FROM AUTHOR]

Domestic electric water heaters (DEWHs) can provide operational flexibility for load control due to their energy storage capacity. Load forecasting for aggregated DEWHs is important for providing information of baseline load and controlling electricity demand profile without negative impact to the normal end use. Advanced metering infrastructures nowadays provide more possibilities to further enhance forecasting with bottom-up method. This article proposes a bottom-up forecasting with Markov-based error reduction method to predict power consumption of aggregated DEWHs for multiple forecast horizons. DEWHs are randomly divided into small aggregations, whose power consumption is forecasted by independent forecast engines. In this paper, the engines are K-means and wavelet decomposition-based neural networks. After summing all forecasting of small aggregations up, a new Markov-based error reduction method is proposed to extract features in residuals and mitigate forecasting error accumulation introduced by the summation, providing opportunities to further improve forecasting accuracy for the total DEWH load. Differing from traditional Markov-based error reduction, two new compensation parameters (compensation coefficient, and compensation threshold) are proposed. They are determined by using particle swarm optimization algorithm. Experiments on real and simulated DEWH loads verified the effectiveness of the proposed forecasting method. The proposed method improved the forecast accuracy over selected benchmark algorithms by about 20% to 80%, according to four performance metrics: mean absolute error, mean absolute percentage error, root-mean-square error, normalized form RMSE. The aggregation effects on performance were also analyzed in theory and tested with simulated DEWHs, providing a good indication of the forecast dependence on the aggregation size. [ABSTRACT FROM AUTHOR]

The growing interest in optimizing the generation, distribution, and delivery of electric power have motivated the implementation of several smart grid functions in many power systems around the globe. Among such functions are the peak-load management, demand response, direct load control, and integration of distributed power generation. Nowadays, smart grid functions are being implemented for industrial, residential, and/or commercial loads. One of the key requirements for implementing smart grid functions is the accurate and reliable load aggregation. The bottom-up, coordinated, and bus-split aggregation methods have been found applicable for different load types that are included in smart grid functions. This paper reviews the methods and approaches for performing the load aggregation, and provides a discussion for the critical role of load aggregation in power systems operating and smart grid functions. In addition, this paper discusses the applicability of the load aggregation methods in smart grid functions for residential loads. [ABSTRACT FROM PUBLISHER]

This article presents the employment of battery storage systems (BSSs) to improve power system survivability. This application of BSSs is based on setting BSS charging/discharging as responses to integrating distributed generation units and/or adjusting load demands (load-side activities). The improvement of power system survivability is quantified by the survivability index $\Gamma$ , which can exceed its boundary values due to load-side activities. The improvement of power system survivability is achieved by maintaining $\Gamma$ within its boundary values post a load-side activity. The proposed approach to improve power system survivability is implemented and tested for the Barbados power system. Tests are conducted for integrating solar power generation units, and implementing demand response. Test results show that accurate BSS charging/discharging can maintain the index $\Gamma$ within its boundary values post-load-side activities. [ABSTRACT FROM AUTHOR]

This article presents a new approach for estimating the frequency variations ($Df$) due to applying smart grid functions on a load bus. The proposed approach is based on constructing a ZIP model for the power demands at a load bus that is controlled by smart grid functions.The constructed ZIP model provides a relationship between the active and reactive power demands and the frequency at any load bus. This relationship can be formulated as a set of nonlinear equations, which can be numerically solved for $Df$. The load-model approach is implemented for performance evaluation using load buses in the IEEE 30-bus power system under different loading levels. Performance results show that the proposed approach has a simple implementation, and can provide an accurate estimation of frequency variations due to slow and small changes load power demands. Furthermore, performance results reveal the insensitivity of the load-model approach to load power demands and/or seasonal changes in load power demands. [ABSTRACT FROM AUTHOR]

This paper compares the performances of protection coordination [time setting and zone selection interlocking (ZSI)] and digital modular protection, when deployed for grid-connected battery storage systems (BSSs). The comparison between these protection management methods is made in terms of their structures, functionalities, and response capabilities. These criteria are selected to demonstrate possible impacts of the system configuration and mode of operating a grid-connected BSS on protection responses. The performance comparison among the time-setting coordination, the ZSI coordination, and the digital modular protection is conducted for different grid-connected BSSs, when operated for various fault and non-fault conditions. Performance results show that the protection coordination can offer a simple structure that is set to achieve specific response (TRIP and RESTRAIN). In addition, performance results show that the digital modular protection can offer diverse responses (TRIP, RESTRAIN, and ACTIVATE), which mandate for a digital implementation. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach for detecting and identifying arc flash faults in power systems. The proposed approach is structured to extract the phases of transient frequency components present in arc flash fault currents. The desired phases can be extracted by processing fault currents using a modulated filter bank that is composed of high-pass finite impulse response (FIR) filters. These filters are designed by using the Kaiser window method to achieve linear phase responses. Extracting the phases of transient frequency components, present in arc flash fault currents, can provide signature information for accurate and fast detection and distinguish of an arc flash fault. The proposed phase-based approach is implemented for off-line testing to evaluate its performance. Test cases of parallel and series arch flash faults are conducted for supplying linear, nonlinear, and dynamic loads. Simulation and off-line results demonstrate the validity, accuracy, speed, and reliability of the phase-based approach to detect and distinguish arc flash faults with minor sensitivities to the load type and arch flash type. [ABSTRACT FROM AUTHOR]