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Small-scale PV generation has become popular with residential customers in several jurisdictions with high solar radiation, as an alternative to improve their carbon footprint and reduce their electricity bills. However, massive deployment of such distributed generation is creating a particular and undesirable shape in the net demand, which deepens at hours of peak solar PV injections at noon and suddenly rises towards the evening, known as the “duck curve”. Hence, this paper investigates the use of pre-cooling strategies in residential households to mitigate the duck-curve effects. To this aim, appropriate thermal models and simulations of houses are first developed and carried out to demonstrate the technical feasibility of pre-cooling in a house with a typical configuration, based on the Smart Residential Load Simulator (SRLS) developed at the University of Waterloo. Then, an aggregation technique is proposed to evaluate the effects on a large grid of different penetration levels of PV, and pre-cooling approaches to manage the duck-curve in California and Texas, concluding that such techniques are capable of substantially flattening the system net demand curve. [ABSTRACT FROM AUTHOR]

This paper presents two mathematical formulations to represent uncertainties in self-scheduling models of a price-taker Compressed Air Energy Storage (CAES) facility. The proposed model is from the point of view of the plant owner participating in the energy, spinning, and idle reserve markets. The first described formulation is based on Robust Optimization (RO) and the second one is based on Affine Arithmetic (AA) techniques, which are both range arithmetic methodologies, and consider the thermodynamic characteristics of the CAES facility for a more realistic representation. The implementation of both methods are tested, validated and compared with each other and with Monte Carlo Simulations (MCS) using prices from the Ontario market. From the simulation results, it can be observed that both methods have some similarities, presenting lower computational burden compared with MCS, and demonstrate the advantage of applying the proposed models for CAES plant owners to hedge against price uncertainties. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESSs) can improve energy management in distribution grids, especially with the increasing penetration of home energy management systems (HEMSs) that schedule household appliances and render them as smart loads. A large number of uncoordinated HEMSs can result in significant changes to the aggregated load profile of the distribution system. This paper proposes a framework and mathematical model for integrating ESS in the distribution grid to minimize the operation cost of the local distribution company (LDC) and alleviate the impact of uncoordinated HEMS operation on the distribution grid. A novel neural network (NN) based state of health (SoH) estimator for a lithium-ion (Li-ion) battery based ESS is proposed, which is incorporated within the LDC's planning problem. The results show that the proposed estimation model is an accurate estimation of the SoH of the ESS. The LDC's planning decisions are also compared, considering SoH of the ESS vis-á-vis linear degradation and no-degradation models. [ABSTRACT FROM AUTHOR]

Transforming residential loads to residential energy hubs (REHs) and hence creating a flexibility in their energy consumption, can offer significant benefits to the power grid, such as peak reduction, congestion relief and capacity deferral. In this article a novel framework is proposed to simultaneously determine the optimal incentive paid by the local distribution company (LDC) to residential customers and their optimal level of participation in transforming to REHs, considering the economic benefits of both parties. The proposed framework models the relationship between customers’ participation and incentives offered by the LDC. A Monte Carlo simulation based optimization approach is employed to create a large dataset of the incentives (ω, ρInc) payable by the LDC, and a desired internal rate of return (IRR) by the residential customer, so as to cover the wide range of possible IRRs the customers may have, and the impact of the incentives (ω, ρInc) on each other. Numerical simulations considering a 33-bus distribution system show that the proposed framework is effective and can economically benefit the LDC and residential customers, while also achieving peak load reduction and improving distribution system operation. [ABSTRACT FROM AUTHOR]

This article proposes a transmission-system-level aggregated model of Battery Energy Storage Systems (BESSs) distributed through Active Distribution Networks (ADNs), to study the dynamic performance and services provided by these systems to power grids. ADNs comprise intelligent loads, local generation, particularly solar PV, and BESSs, which can provide different services to transmission grids, including voltage control, oscillation damping, frequency regulation, and active and reactive power injections. Proper equivalent models of the ADN components allow to evaluate the impact and integration of these networks on power grids. In this article, ADN’s measurements of the aggregated response of the BESSs at the boundary bus with the transmission system are used to develop an aggregated black-box model based on two Neural Networks (NNs), one for active power and another for reactive power, with their optimal topology obtained using a Genetic Algorithm (GA). Detailed simulations are performed, using a commercial-grade software for power system analysis, of multiple BESSs connected to a CIGRE benchmark ADN connected to a bus of the 9-bus WSCC benchmark transmission network; the test ADN is then replaced by the proposed black-box model, with aggregated models of the loads and PV generation, demonstrating that the proposed model can accurately reproduce the results obtained. [ABSTRACT FROM AUTHOR]

One of the main advantages of smart grids is the presence of advanced communication technologies that facilitate grid operators and local distribution companies (LDCs) to communicate directly with customers. While at one end, the end-user customers seek to optimize their energy consumption by deploying home energy management systems (HEMSs), the LDCs on the other hand, seek to improve the grid operational efficiency and reduce losses. In this paper, a two-stage optimization framework is developed wherein multiple HEMS simultaneously optimize their respective energy consumption patterns, and determines their flexibility provision, which is communicated to the LDC. The LDC aggregates the controllable demand profile and the flexibilities of each HEMS to optimize its operational performance and hence the peak reduction signals which are sent to the HEMS. Studies are carried out considering a 33-bus distribution system coordinating with 1295 houses connected at different buses, and with varying customer preferences and objectives to demonstrate the applicability of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper presents practical energy management system (EMS) models which consider the operational constraints of distributed energy resources, active-reactive power balance, unbalanced system configuration and loading, and voltage dependent loads. A novel linearization approach is proposed and validated based on the fact that, for isolated microgrids, due to the characteristics of feeders, network losses, and voltage drops across feeders are relatively small. The proposed EMS models are mixed integer quadratic programming problems, requiring less computation time and thus suitable for online applications. The practical EMS models are compared with a typical decoupled unit commitment and optimal power flow-based EMS with and without consideration of system unbalancing. The models, along with “standard” EMS models, are tested, validated, and compared using a CIGRE medium voltage benchmark system and the real isolated microgrid of Kasabonika Lake First Nation in Northern Ontario, Canada. The presented results demonstrate the effectiveness and practicability of the proposed models. [ABSTRACT FROM AUTHOR]

Oil refining is an energy-intensive industry, that is often equipped with different types of distributed generation (DG) resources. Optimal energy management of the refinery's load and its DG resources, under dynamic electricity pricing, improves electricity consumption behavior of the facility and hence reduces its energy costs. An energy management system (EMS) model is proposed in this paper for minimizing electricity consumption costs of an oil refinery facility considering on-site cogeneration capability. The energy management is based on power demand modeling of the oil refinery process. A joint electrical–thermal model is used for the cogeneration units to account for the electricity and steam production. The developed EMS model is also used as part of a demand response strategy to illustrate the impact of EMS decisions on distribution system operations. [ABSTRACT FROM AUTHOR]

With increasing environmental concerns the penetration of plug-in electric vehicles (PEVs) is expected to increase in the future. Such electrification of the transportation sector will impact the distribution grid adversely; however, PEV smart charging strategies can help mitigate the impacts. In this paper, a PEV smart charging approach is proposed where the charging loads are controlled and incentivized by the local distribution company (LDC) for every unit of energy controlled. A novel framework is proposed to determine the optimal participation of PEVs in the smart charging program and optimal incentives paid by the LDC to PEV customers, such that both parties are economically benefited. The proposed framework models the relationship between customers’ participation and incentives offered by the LDC. The relationship between the expected investment deferral and hence the economic benefits from smart charging participation are considered as well. Monte Carlo simulation is used to simulate the uncertainty of demand, electricity market price, drivers’ behavior, PEV market share, and charging level share. The proposed framework is tested on the 33-bus distribution system and the results show that the proposed approach is effective and can economically benefit both LDC and smart charging PEV participants, while also improving distribution system operation. [ABSTRACT FROM AUTHOR]

This paper describes the development of a freeware smart residential load simulator to facilitate the study of residential energy management systems in smart grids. The proposed tool is based on MATLAB-Simulink-GUIDE toolboxes and provides a complete set of user-friendly graphical interfaces to properly model and study smart thermostats, air conditioners, furnaces, water heaters, stoves, dishwashers, cloth washers, dryers, lights, pool pumps, and refrigerators, whose models are validated with actual measurements. Wind and solar power generation as well as battery sources are also modeled, and the impact of different variables, such as ambient temperature and household activity levels, which considerably contribute to energy consumption, are considered. The proposed simulator allows modeling of appliances to obtain their power demand profiles, thus helping to determine their contribution to peak demand, and allowing the calculation of their individual and total energy consumption and costs. In addition, the value and impact of generated power by residential sources can be determined for a 24-h horizon. This freeware platform is a useful tool for researchers and educators to validate and demonstrate models for energy management and optimization, and can also be used by residential customers to model and understand energy consumption profiles in households. Some simulation results are presented to demonstrate the performance and application of the proposed simulator. [ABSTRACT FROM AUTHOR]

Load characteristics play an important role in distribution systems, which are traditionally designed to supply peak load; hence, decreasing this peak can considerably reduce overall grid costs. Basic components of smart grids such as smart meters allow two-way communication between the utilities and customers; in this context, controllable smart loads are being introduced, which allow developing and implementing energy management systems for customers and distribution feeders. Therefore, this paper studies the impact of existing smart loads, in particular Peaksaver PLUS (PS+) loads in ON, Canada, to reduce summer peak loads for distribution feeders. A neural network model of controllable loads is developed and integrated into an unbalanced distribution optimal power flow (DOPF) model to optimally control tap changers and switched capacitors, as well as sent signals to programmable thermostats of air conditioners in residential buildings, in particular those associated with the PS+ program. The developed integrated DOPF is tested and validated using a practical system, demonstrating the benefits of using existing controllable loads to optimally operate distribution feeders. [ABSTRACT FROM AUTHOR]

Due to environmental concerns and depletion of fossil fuel resources, the penetration of plug-in electric vehicles (PEVs) is expected to increase in the near future. However, such electrification of the transportation sector is expected to impact the distribution grid adversely. This paper presents a novel framework to determine the appropriate level of PEV penetration that distribution systems can accommodate. Monte Carlo simulation has been used to simulate the uncertainty of typical demand, drivers’ behavior, PEV market share, and charging level share. Moreover, the maximum allowable penetration of uncontrolled and smart charging loads are determined based on the current available market data pertaining to PEV type and charging level, considering different charging scenarios. The proposed framework is examined and compared across a number of scenarios. [ABSTRACT FROM AUTHOR]

With the increasing importance of battery energy storage systems (BESS) in microgrids, accurate modeling plays a key role in understanding their behavior. This paper investigates and compares the performance of BESS models with different depths of detail. Specifically, several models are examined: an average model represented by voltage sources; an ideal dc source behind a voltage source converter; a back-to-back buck/boost and bidirectional three-phase converter, with all models sharing the same control system and parameters; and two additional proposed models where the switches are replaced by dependent sources to help analyze the differences observed in the performance of the models. All these models are developed in PSCAD and their performances are simulated and compared considering various issues such as voltage and frequency stability and total harmonic distortion in a benchmark test microgrid. It is shown through simulation results and eigenvalue studies that the proposed models can exhibit a different performance, especially when the system is heavily loaded, highlighting the need for more accurate modeling under certain microgrid conditions. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical model for representing the total charging load at an electric vehicle charging station (EVCS) in terms of controllable parameters; the load model developed using a queuing model followed by a neural network (NN). The queuing model constructs a data set of plug-in electric vehicle (PEV) charging parameters which are input to the NN to determine the controllable EVCS load model. The queuing model considers arrival of PEVs as a non-homogeneous Poisson process, while the service time is modeled considering detailed characteristics of battery. The smart EVCS load is a function of number of PEVs charging simultaneously, total charging current, arrival rate, and time; and various class of PEVs. The EVCS load is integrated within a distribution operations framework to determine the optimal operation and smart charging schedules of the EVCS. Objective functions from the perspective of the local distribution company and EVCS owner are considered for studies. A 69-bus distribution system with an EVCS at a specific bus, and smart load model is considered for the studies. The performance of a smart EVCS vis-à-vis an uncontrolled EVCS is examined to emphasize the demand response contributions of a smart EVCS and its integration into distribution operations. [ABSTRACT FROM PUBLISHER]

This paper presents a queuing analysis-based method for modeling the 24-h charging load profile of a plug-in electric vehicle (PEV) charging station. The queuing model considers the arrival of PEVs as a non-homogeneous Poisson process with different arrival rates over the day. The first PEV charging load profile assumes customer convenience as the factor that influences the hourly arrival rate of vehicles at the station, while the second profile is developed assuming that customers would respond to PEV charging prices and arrival rates are accordingly affected. One of the main contributions of this paper is to model the PEV service time considering different factors such as the state-of-charge of the vehicle battery, as well as the effect of the battery charging behavior. The impact of PEV load models on distribution systems is studied for a deterministic case, and the impact of uncertainties is examined and compared using the stochastic optimal power flow and the model predictive control approaches. [ABSTRACT FROM PUBLISHER]

In this paper, the equivalent \textCO\text{2} emission models for fossil-fuel-based distributed generator units are developed considering their individual emission characteristic and fuel consumption. These models are then integrated within a microgrid energy management system (EMS) model. Constant energy, demand shifting load models are further integrated in the EMS to examine the possible impact of demand response (DR) on the total system emissions and economics of a microgrid. Thus, the impacts of including the developed emission models on the operation of an isolated microgrid, equivalent \textCO\text{2} emissions, and costs are examined considering five different operating strategies. The proposed operating strategies are validated on a modified CIGRE medium voltage benchmark system. The results obtained highlight the effectiveness of the proposed EMS and also demonstrate the impact of DR on emissions and costs. [ABSTRACT FROM PUBLISHER]

This paper presents a mathematical model of smart loads in demand response (DR) schemes, which is integrated into centralized unit commitment (UC) with optimal power flow coupled energy management systems for isolated microgrids for optimal generation and peak load dispatch. The smart loads are modeled with a neural network (NN) load estimator as a function of the ambient temperature, time of day, time of use price, and the peak demand imposed by the microgrid operator. To develop the NN-based smart load estimator, realistic data from an actual energy hub management system is used for supervised training. Based on these, a novel microgrid energy management system (MEMS) framework based on a model predictive control approach is proposed, which yields optimal dispatch decisions of dispatchable generators, energy storage system, and peak demand for controllable loads, considering power flow and UC constraints simultaneously. To study the impact of DR on the microgrid operation with the proposed MEMS framework, a CIGRE benchmark system is used that includes distributed energy resources and renewables based generation. The results show the feasibility and benefits of the proposed models and approach. [ABSTRACT FROM PUBLISHER]

This paper presents a distributed computing architecture for solving a distribution optimal power flow (DOPF) model based on a smart grid communication middleware (SGCM) system. The system is modeled as an unbalanced three-phase distribution system, which includes different kind of loads and various components of distribution systems. In this paper, fixed loads are modeled as constant impedance, current and power loads, and neural network models of controllable smart loads are integrated into the DOPF model. A genetic algorithm is used to determine the optimal solutions for controllable devices, in particular load tap changers, switched capacitors, and smart loads in the context of an energy management system for practical feeders, accounting for the fact that smart loads consumption should not be significantly affected by network constraints. Since the number of control variables in a realistic distribution power system is large, solving the DOPF for real-time applications is computationally expensive. Hence, to reduce computational times, a decentralized system with parallel computing nodes based on an SGCM system is proposed. Using a “MapReduce” model, the SGCM system runs the DOPF model, communicates between master and worker computing nodes, and sends/receives data among different parts of parallel computing system. Compared to a centralized approach, the proposed architecture is shown to yield better optimal solutions in terms of reducing energy losses and/or energy drawn from the substation within adequate practical run-times for a realistic test feeder. [ABSTRACT FROM PUBLISHER]

This paper presents a novel framework for designing an electric vehicle charging facility (EVCF) as a smart energy microhub from the perspectives of both an investor and a local distribution company. The proposed framework includes a vehicle decision tree, a queuing model, a distribution margin assessment model, a distributed generation (DG) penetration assessment model, an economic assessment model, and a distribution operations model. Three design options for the EVCF are examined; battery energy storage systems (BESS), renewables based DG, and a microhub that incorporates both BESS and renewables-based DG with the option of exchanging power with the main grid. Test results considering a 33 bus distribution system and realistic vehicle statistics extracted from the 2009 (US) National Household Travel Survey are presented and discussed. The findings demonstrate the effectiveness of the proposed smart energy microhub design framework. [ABSTRACT FROM PUBLISHER]

This paper presents a novel framework for planning and investment studies pertaining to the implementation of system-wide conservation voltage reduction (CVR). In the CVR paradigm, optimal voltage profiles at the load buses are determined so as to yield load reductions and hence energy conservation. The system modifications required to operate at such voltages is known to be capital intensive, which is not desirable by investors. Hence, the proposed model determines the system savings and the appropriate price incentives to offer industries such that a minimum acceptable rate of return is accrued. In this model, the industrial facilities are represented by a combination of constant impedance, constant current, and constant power loads. A detailed case study for Ontario, Canada, is carried out considering that industrial loads are investing in voltage optimization to reduce their energy costs. The optimal incentives that need to be offered by the system planner, over a long-term horizon, and across various zones of Ontario, are determined using the presented mathematical model. Furthermore, a comprehensive risk analysis comprising sensitivity studies and Monte Carlo simulations is carried out considering the variations in the most uncertain model parameters. [ABSTRACT FROM PUBLISHER]

This paper presents the development of a generic optimal industrial load management (OILM) model, which can be readily incorporated into energy hub management systems (EHMSs) for industrial customers, in interaction with local distribution companies (LDCs), for automated and optimal scheduling of their processes. The mathematical models comprise an objective function to minimize the total energy costs and/or demand charges for industrial customers, and a set of equality and inequality constraints to represent the industrial process, storage units, distribution system components, operator’s requirements, and other relevant constraints. The effectiveness of the proposed OILM model is demonstrated in two industrial customers: 1) a flour mill; and 2) a water pumping facility. The results show that the proposed OILM model, in conjunction with communication and control infrastructures at the customer and LDC levels, would allow optimal operation of industrial EHMSs in smart grids. [ABSTRACT FROM PUBLISHER]

This paper presents a new modeling framework for analysis of impact and scheduling of price-responsive as well as controllable loads in a three-phase unbalanced distribution system. The price-responsive loads are assumed to be linearly or exponentially dependent on price, i.e., demand reduces as price increases and vice versa. The effect of such uncontrolled price-responsive loads on the distribution feeder is studied as customers seek to reduce their energy cost. Secondly, a novel constant energy load model, which is controllable by the local distribution company (LDC), is proposed in this paper. A controllable load is one that can be scheduled by the LDC through remote signals, demand response programs, or customer-end home energy management systems. Minimization of cost of energy drawn by LDC, feeder losses, and customers cost pertaining to the controllable component of the load are considered as objectives from the LDCs and customers’ perspective. The effect of a peak demand constraint on the controllability of the load is further examined. The proposed models are tested on two feeders: 1) the IEEE 13-node test feeder; and 2) a practical LDC feeder system. Detailed studies examine the operational aspects of price-responsive and controllable loads on the overall system. It is observed that the LDC controlled load model results in a more uniform system load profile, and that with a reduction in the peak demand cap, the energy drawn decreases, consequently reducing feeder losses and LDC’s and customers’ costs. [ABSTRACT FROM PUBLISHER]

There are many technical aspects and challenges in distributed generation (DG) that have not been properly understood and addressed so far. Distribution systems cannot be considered as balanced three-phase systems, because these are inherently unbalanced in steady-state operation. A full characterization of the unbalanced system with respect to system stability allows a better understanding of the dynamic behavior of such systems. This paper presents a comprehensive investigation of the effects of system unbalance on the stability of the distribution systems with synchronous generator (SG) and doubly-fed induction generator (DFIG) based DG units at different loading levels. Detailed steady-state and dynamic analyses of the system are performed. Based on classical voltage, small-perturbation and transient stability studies, it is demonstrated that system unbalance can significantly affect the distribution system dynamic performance, in ways that have not been discussed in the technical literature so far. A simple and effective control strategy based on an Unbalanced Voltage Stabilizer (UVS) is also proposed to improve the system control and the stability of unbalanced distribution systems with SG and DFIG. Eigenvalue analyses and time-domain simulations (TDS) demonstrate the effectiveness of the proposed UVS for unbalance conditions. [ABSTRACT FROM PUBLISHER]

This paper presents a novel modeling framework for the analysis of Plug-in Electric Vehicle (PEV) charging in unbalanced, residential, distribution systems. A Smart Distribution Power Flow (SDPF) framework is proposed to determine the controlled or smart charging schedules and hence address the shortcomings of uncontrolled charging. The effect of peak-demand constraint imposed by the Local Distribution Company (LDC) is also studied within the SDPF framework for the smart charging scenarios. Uncontrolled versus smart charging schemes are compared for various scenarios, from both the customer's and the LDC's perspective. Various objective functions, such as energy drawn by the LDC, total feeder losses, total cost of energy drawn by LDC and total cost of PEV charging are considered. Studies are carried out considering two sample systems i.e., the IEEE 13-node test feeder and a real distribution feeder. Analyses are also presented considering a probabilistic representation of the initial state of charge (SOC) and start time of charging for various scenarios to take into account the difference in customers' driving patterns. The results show that uncontrolled charging of PEVs results in increased peak demand, low node voltage levels, and increased feeder current magnitudes. On the other hand, the SDPF framework provides very satisfactory operating schedules for the overall system including smart PEV charging. [ABSTRACT FROM PUBLISHER]

This paper presents a comparative investigation of solar photovoltaic (PV) effect on system stability at different penetration levels. Three different scenarios with their relevant dynamic models are considered, namely, distributed units, and centralized farms with and without voltage regulation capabilities. Based on these models, the impact is examined through eigenvalue, voltage stability and transient stability analyses using real network data pertaining to Ontario and its neighboring systems. This impact is quantized in monetary terms based on the long run marginal cost of electricity production in Ontario. It is demonstrated that distributed solar PV generators are significantly more advantageous, from the stability point of view, than solar farms. [ABSTRACT FROM PUBLISHER]

This paper presents a novel probabilistic distribution load flow (PDLF) algorithm to study the effect of connecting a wind turbine (WT) to a distribution system. This probabilistic approach is used to capture the stochastic behavior of the generation of WTs. Three different models of WTs are developed to be embedded in the PDLF. Consequently, a probabilistic approach to evaluate the impact of wind penetration into distribution systems is developed. Futhermore, the effect of WT penetration on feeder losses, voltage profile and substation powers is presented. [ABSTRACT FROM PUBLISHER]

This paper presents mathematical optimization models of residential energy hubs which can be readily incorporated into automated decision making technologies in smart grids, and can be solved efficiently in a real-time frame to optimally control all major residential energy loads, storage and production components while properly considering the customer preferences and comfort level. Novel mathematical models for major household demand, i.e., fridge, freezer, dishwasher, washer and dryer, stove, water heater, hot tub, and pool pumps are formulated. Also, mathematical models of other components of a residential energy system including lighting, heating, and air-conditioning are developed, and generic models for solar PV panels and energy storage/generation devices are proposed. The developed mathematical models result in Mixed Integer Linear Programming (MILP) optimization problems with the objective functions of minimizing energy consumption, total cost of electricity and gas, emissions, peak load, and/or any combination of these objectives, while considering end-user preferences. Several realistic case studies are carried out to examine the performance of the mathematical model, and experimental tests are carried out to find practical procedures to determine the parameters of the model. The application of the proposed model to a real household in Ontario, Canada is presented for various objective functions. The simulation results show that savings of up to 20% on energy costs and 50% on peak demand can be achieved, while maintaining the household owner's desired comfort levels. [ABSTRACT FROM PUBLISHER]

This paper presents a generic and comprehensive distribution optimal power flow (DOPF) model that can be used by local distribution companies (LDCs) to integrate their distribution system feeders into a Smart Grid. The proposed three-phase DOPF framework incorporates detailed modeling of distribution system components and considers various operating objectives. Phase specific and voltage dependent modeling of customer loads in the three-phase DOPF model allows LDC operators to determine realistic operating strategies that can improve the overall feeder efficiency. The proposed distribution system operation objective is based on the minimization of the energy drawn from the substation while seeking to minimize the number of switching operations of load tap changers and capacitors. A novel method for solving the three-phase DOPF model by transforming the mixed-integer nonlinear programming problem to a nonlinear programming problem is proposed which reduces the computational burden and facilitates its practical implementation and application. Two practical case studies, including a real distribution feeder test case, are presented to demonstrate the features of the proposed methodology. The results illustrate the benefits of the proposed DOPF in terms of reducing energy losses while limiting the number of switching operations. [ABSTRACT FROM AUTHOR]

In , an additional affiliation for Omar Hafez was not included. The affiliation information in the first footnote should read as follows: [ABSTRACT FROM AUTHOR]