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The rapid introduction of electric vehicles (EVs) in the transportation market has initiated the concept of vehicle-to-grid (V2G) technology in smart grids. However, where V2G technology is intended to facilitate the power grid ancillary services, it could also have an adverse effect on the aging of battery packs in EVs. This is due to the instant depletion of power during the charge and discharge cycles, which could eventually impact the structural complexity and electrochemical operations in the battery pack. To address this situation, a median expectation-based regression approach is proposed for parameter estimation of vehicle batteries in V2G systems. The proposed method is built on the property of uncertainty prediction of Gaussian processes for parameter estimation while considering the cell variations as an exogenous function. First, a median expectation-based Gaussian process model is derived to predict the fused and individual cell variations of a battery pack. Second, a magnitude-squared coherence model is developed by the error matrix to detect and isolate each variation. This is obtained by extracting the cross-spectral densities for the measurements. The proposed regression-based approach is evaluated using experimental measurements collected from lithium-ion battery pack in EVs. The parametric analysis of the battery pack has been verified using D-SAT Chroma 8000ATS hardware platform. Performance evaluation shows an accurate estimation of these dynamics even in the presence of injected faults. [ABSTRACT FROM AUTHOR]

Cloud energy storage system (CESS) technology is a novel idea to eliminate the distributed energy storage systems from the consumers into a cloud service centre, where CESS acts as a virtual energy storage capacity instead of the actual devices. This combination forms a grid-forming battery-supercapacitor cloud hybrid energy storage system (CHESS) which is responsible for maintaining the voltage stability and power balance at the common DC bus of the multiple NG system. By establishing such an access to CESS, the proposed model allocates optimal shares of charge/discharge capacities for home owners, minimizes the daily operation cost of each home and grants an optimal operation of household appliances. [Extracted from the article]

With the rapid increase of renewable energy generation worldwide, real‐time information has become essential to manage such assets, especially for systems installed offshore and in remote areas. To date, there is no cost‐effective condition monitoring technique that can assess the state of renewable energy sources in real‐time and provide suitable asset management decisions to optimize the utilization of such valuable assets and avoid any full or partial blackout due to unexpected faults. Based on the Internet of Things scheme, this paper represents a new application for the Supervisory Control and Data Acquisition (SCADA) system to monitor a hybrid system comprising photovoltaic, wind, and battery energy storage systems. Electrical parameters such as voltage, current, and power are monitored in real‐time via the ThingSpeak website. Network operators can control components of the hybrid power system remotely by the proposed SCADA system. The SCADA system is interfaced with the Matlab/Simulink software tool through KEPServerEX client. For cost‐effective design, low‐cost electronic components and Arduino Integrated Development Environment ATMega2560 remote terminal unit are employed to develop a hardware prototype for experimental analysis. Simulation and experimental results attest to the feasibility of the proposed system. Compared with other existing techniques, the developed system features advantages in terms of reliability and cost‐effectiveness. [ABSTRACT FROM AUTHOR]

This paper determines the optimal capacity of solar photovoltaic (PV) and battery energy storage (BES) with novel rule-based energy management systems (EMSs) under flat and time-of-use (ToU) tariffs. Four schemes are investigated based on the combinations of flat and ToU tariffs for buying and selling the electricity: (1) Flat-Flat, (2) ToU-Flat, (3) Flat-ToU, and (4) ToU-ToU. For each scheme, two configurations are evaluated: (i) PV only, and (ii) PV-BES. The optimization of the grid-connected household is evaluated based on one-year realistic data. An uncertainty analysis is presented based on the variations of insolation, temperature, and load. Sensitivity analyses are implemented based on the average daily load, the grid constraint, and the costs of PV and BES. The operational analyses for 48 h in summer and winter are carried out to evaluate the dynamic performance of the systems for high and low solar insolation. The effectiveness of the proposed model is verified by comparing the results with that of common EMS based on the net metering scheme. It is found that the COE of the proposed EMS for a PV-BES house with ToU-Flat scheme (as the best option) is 2 ¢/kWh lower than that of the net metering scheme. [ABSTRACT FROM AUTHOR]

Summary: Modern‐day hybrid wind farm operation is fundamentally dependent on the accuracy of short‐term wind power forecasts. However, the inevitable error in wind power forecasting limits the power transfer capability to the utility grid, which calls for battery energy storage systems to furnish the deficit power. This manuscript addresses a wind forecasting based penalty cost minimization solution for hybrid wind‐battery farms. We choose six wind farm sites (three offshore and the other three onshore) to study machine intelligent forecasting based solutions and compare the performance of a wavelet‐Twin support vector regression (TSVR) based wind power forecasting model with ε‐Twin support vector regression, Random forest, and Gradient boosted machines, for penalty cost minimization. We access the penalties that arise as power imbalances along with the battery system's cost. We find that TSVR based wind power forecasting method results in a minimum global operational cost for all the wind farm sites under study. [ABSTRACT FROM AUTHOR]

This paper proposes a robust control scheme to involve the distributed Battery Energy Storage Systems (BESSs) in Load Frequency Control (LFC) through BESS aggregators with sparse communication networks. In order to cope with the uncertainties associated with system operation, a two-layer Model Predictive Control (MPC) is developed so that more efficient control signals are provided to improve the response of BESSs to make larger contribution to the LFC. The outer layer in the proposed structure produces the command signal for the aggregator based on signals which are produced by the inner layer as well as the signal provided from the actual system. These command signals are provided so as to achieve the least value of error in Area Control Error (ACE) with a minimum control effort taking a variety of operational and physical constraints into consideration. Optimization procedures are also carried out to compute the optimal value of weighting coefficients contained in the objective functions. The capability of controller to cope with uncertainties is compared with a conventional single-layer MPC. In addition, the delay caused by propagation channels in delivering control signals to BESSs is modeled, and its impact on the performance of frequency regulation is evaluated. An intelligent fuzzy coordination control is then developed to coordinate the BESS aggregator and conventional power plants to avoid extra power injection/withdrawal by the conventional power plants in case of long delays. Case studies are conducted to illustrate the effectiveness of the proposed structure in controlling distributed BESSs with diverse energy capacities, rated powers, charging/discharging coefficients and time constants; and State of Charges (SoCs). [ABSTRACT FROM AUTHOR]

This article examines the dynamic and transient performances of a battery energy storage system (BESS) connected with the output of a wind energy conversion system to smoothen the short-term fluctuations in the output power. A low-power experimental real-time testbed, using battery storage and representative power converters, is interfaced with a real-time digital simulator for the reduced-scale power hardware-in-the-loop simulation. Different configurations, where the energy storage system is integrated on the ac side via static synchronous compensator and directly at the dc link of the frequency converter using back–to–back converters, are examined under both constant and variable references. For each case, the resulting error in the net power flow, the operation of the battery, and the conduction losses in the power conversion stage are examined. The performances using the constant and variable reference of the storage device with full and reduced-order hardware stage of a storage element are demonstrated. The low-voltage transient performance of the BESS is also tested and the results are presented. The novel approach of the real-time low-power BESS testbed opens the door for in-lab testing of the energy storage devices for large wind or solar photovoltaic farms to save time and money. [ABSTRACT FROM AUTHOR]