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This paper presents a Frequency Regulation (FR) model of a large interconnected power system including Energy Storage Systems (ESSs) such as Battery Energy Storage Systems (BESSs) and Flywheel Energy Storage Systems (FESSs), considering all relevant stages in the frequency control process. Communication delays are considered in the transmission of the signals in the FR control loop and ESSs, and their State of Charge (SoC) management model is considered. The system, ESSs and SoC components are modelled in detail from a FR perspective. The model is validated using real system and ESSs data, based on a practical transient stability model of the North American Eastern Interconnection (NAEI), and the results show that the proposed model accurately represents the FR process of a large interconnected power network including ESS, and can be used for long-term FR studies. The impact of communication delays and SoC management of ESS facilities in the Area Control Error (ACE) is also studied and discussed, as well as the computational efficiency of the proposed FR model. [ABSTRACT FROM AUTHOR]

This paper presents a novel $\mathcal {H}_{2}$ filter design procedure to optimally split the Frequency Regulation (FR) signal between conventional and fast regulating Energy Storage System (ESS) assets, considering typical Communication Delays (CDs). The filter is then integrated into a previously validated FR model of the Ontario Power System (OPS) including Battery and Flywheel ESSs, which is used to analyze the impact of these ESSs, CDs, and limited regulation capacity in the FR process in a real system. The proposed methodology to split the FR signal is also compared with the existing FR process, with the results showing that the proposed $\mathcal {H}_{2}$ filter design and signal splitting strategy can improve the FR process performance significantly, in terms of reducing the Area Control Error (ACE) signal, and thus reduce the need for regulation capacity. [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]

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]

Battery energy storage systems (BESSs) can be very beneficial to power systems and microgrids for various applications. With increasing sales of electric vehicles (EV), the availability of used electric vehicle batteries (EVBs) is on the rise, which has received significant attention in recent years. The retired EVBs, after repurposing, can serve as an alternative option to new batteries in a BESS. In addition, when a microgrid operator desires to install a BESS, the optimal decisions such as installation year, energy and power size, replacement year, and the number of cycles to failure, that are normally overlooked, have to be determined. Therefore, this paper proposes a comprehensive and novel framework for planning and operation of the BESS based on repurposed EVBs. A novel linearized BESS sizing model is proposed that obtains the BESS optimal decisions regarding design and operation. Various new, modified, and linearized relationships for the BESS have been included in the planning model to ensure that the replacement year of the BESS is optimally determined. Several classes of EVs with multiple drive cycles are clustered and integrated within the proposed framework. [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]

Driven by global environmental emission issues, energy access in remote communities, and tighter requirements for system resilience and reliability, electricity production is shifting from a centralized paradigm to a decentralized one. In this context, renewable energy sources (RESs) have proliferated over the past decade, exhibiting a steadily increasing trend. Thus, today, a large number of wind turbines and photovoltaic (PV) panels are connected to medium- (1-69 kV) and low-voltage (=1 kV) grids, with traditional integrated bulk power systems becoming decentralized in the presence of active distribution networks, where the flow of power is bidirectional between generators and "prosumers." In particular, with decreasing RES s costs, these technologies are becoming attractive solutions to bring energy to remote communities and/or replace expensive fossil-fuel-based generators. However, RES s such as wind and solar are intermittent sources of energy, difficult to predict, and prone to large output fluctuations-therefore, significantly affecting system voltage and frequency. [ABSTRACT FROM PUBLISHER]

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