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A computationally efficient state estimation method for lithium-ion (Li-ion) batteries is proposed based on a degradation-conscious high-fidelity electrochemical-thermal model for advanced battery management systems. The computational burden caused by the high-dimensional nonlinear nature of the battery model is effectively eased by adopting an ensemble-based state estimator using the singular evolutive interpolated Kalman filter (SEIKF). Unlike the existing schemes, it shows that the proposed algorithm intrinsically ensures mass conservation without imposing additional constraints, leading to a battery state estimator simple to tune and fast to converge. The model uncertainty caused by battery degradation and the measurement errors are properly addressed by the proposed scheme as it adaptively adjusts the error covariance matrices of the SEIKF. The performance of the proposed adaptive ensemble-based Li-ion battery state estimator is examined by comparing it with some well-established nonlinear estimation techniques that have been used previously for battery electrochemical state estimation, and the results show that excellent performance can be provided in terms of accuracy, computational speed, as well as robustness.

Existing faulted feeder identification methods in the resonant grounded distribution network are primarily based on feature extraction of the fault-generated transient currents. The reliability of these approaches is significantly compromised by the fluctuating transient signals and interfering on-off operation of the neighboring switches. To sidestep the problems, a novel method is proposed to identify the faulted feeder by consecutively tuning the arc suppression coil around the full compensation state. Once a series of steady states are reached after tuning, the trajectories of the corresponding zero-sequence currents for both the sound and the faulted feeders are obtained to formulate an adjustment trajectory matrix (ATM). With the ATM, the similarity measure of the adjustment trajectories of all feeders is then employed to identify the faulted feeder based on the selected Deng's grey relational analysis. Results show that the adjustment trajectories of the two sound lines share a high similarity degree, while the similarity between the sound and the faulted lines is much lower. The effectiveness of the proposed method is validated via simulation and some case studies are provided. The results show that the faulted feeder can be correctly identified with high reliability and robustness compared to the existing fault-generated signal-based techniques.

The installation of current sensors on lithium-ion batteries (LIBs) can be challenging due to practical constraints in specific applications like portable electronics and smart batteries. Motivated by this, our letter proposes a method for online load current and state-of-charge (SOC) coestimation, which mitigates the need of installing the current sensor for LIB management. The essence is to transform the state observation into a constrained optimization problem, which is solved numerically in a moving horizon framework to allow the online coestimation of SOC and input current. Experimental results suggest that the proposed method can coestimate the load current and SOC of LIB precisely even if the current sensor is absent. The encouraging results are insightful for reducing the structural complexity and cost of future LIB utilization.

Electric vehicles (EVs) have excellent demand response potential, but a single type of demand response is difficult to fully utilise the dispatching potential of EVs. To improve the utilisation of the demand response potential of EVs, this study proposes two kinds of dispatching strategies for electric vehicle aggregator (EVA) combined price‐based and incentive‐based demand response: long‐term contract strategy and short‐term contract strategy. The difference of the two strategies depends on the length of time for EV users to sign incentive agreements with EVA. The long‐term contract strategy assumes that some EV users sign a long‐term incentive agreement with the EVA. The short‐term contract strategy takes an EV charging process as the dispatching cycle. The objective of maximising revenues and minimising load fluctuation of EVA is optimised in two strategies. An improved multi‐objective particle swarm optimisation algorithm that combines region selection strategy and grouping strategy is used to solve the problem. The simulation results show that the two dispatching strategies can improve the comprehensive benefits of EVA as well as EV users. The two dispatching strategies can effectively improve the utilisation of dispatching potential of EVs.

The potential of variable-speed dish-Stirling (VSDS) solar-thermal generating plant in providing grid frequency support is investigated. In the proposed VSDS frequency support control scheme, the reference speed of the Stirling engine is regulated to track a deloaded power curve which is governed by the solar insolation level. The gain of a supplementary speed-frequency droop controller is then set to meet the primary frequency control requirement. Further uniqueness of the VSDS control scheme pertains to the provision of virtual inertia response by regulating the kinetic energy in the rotating mass of the engine-generator and the thermal energy in the heat absorber/receivers. Small-signal analysis shows that the frequency support scheme is inherently stable, and it will provide higher degree of damping as the penetration level of the VSDS system and/or the solar insolation level increase. The efficacy of the proposed scheme is validated by computer simulation.

Considered as one of the ultimate energy storage technologies for electrified transportation, the emerging all-solid-state batteries (ASSBs) have attracted immense attention due to their superior thermal stability, increased power and energy densities, and prolonged cycle life. To achieve the expected high performance, practical applications of ASSBs require accurate and computationally efficient models for the design and implementation of many onboard management algorithms, so that the ASSB safety, health, and cycling performance can be optimized under a wide range of operating conditions. A control-oriented modeling framework is thus established in this work by systematically simplifying a rigorous partial differential equation (PDE) based model of the ASSBs developed from underlying electrochemical principles. Specifically, partial fraction expansion and moment matching are used to obtain ordinary differential equation based reduced-order models (ROMs). By expressing the models in a canonical circuit form, excellent properties for control design such as structural simplicity and full observability are revealed. Compared to the original PDE model, the developed ROMs have demonstrated high fidelity at significantly improved computational efficiency. Extensive comparisons have also been conducted to verify its superiority to the prevailing models due to the consideration of concentration-dependent diffusion and migration. Such ROMs can thus be used for advanced control design in future intelligent management systems of ASSBs.

The development of the smart grid promotes the rapid development of distributed generation (DG), while the intermittent nature of DG and the growing demand for electricity make it difficult for the grid to maintain the reliability and stability of the grid. Moreover, the time interval between peak demand of electricity usually differs from the peak supply provided by DG, which creates a mismatch between supply and demand. An energy storage system (ESS) can be an effective solution to improve the self-consumption of electricity generated by DG. In this paper, an optimization strategy of household energy management based on DG and ESS is proposed, which makes full use of the rechargeable characteristics of ESS and EV to improve the economy and flexibility of the system in the case of uncertain output prediction of DG. The objective is to minimize the cost of electricity consumption and user comfort. The actual situation of the users is taken into account, as well as the integration of renewable energy, energy storage, demand response, and other new technologies in the smart grid. Finally, the effectiveness of the proposed model and algorithm is verified by multi-scenario analysis.

An efficient model-based state estimation method for lithium-ion (Li-ion) batteries is proposed using an extended electrochemical single particle model enhanced by electrolyte and thermal dynamics. To address the spatially distributed characteristics across the particle radius and the electrode width, an adaptive ensemble adjustment Kalman filter (AEAKF) algorithm is established to effectively reduce the computational burden caused by the high-dimensional nonlinear model for online operation. The uncertainties in the modeling and the measurement error are considered in the proposed algorithm by adaptively adjusting the error covariance matrices in the filtering scheme. The performance of the AEAKF-based distributed battery state estimator is examined via simulation by comparing it with other nonlinear estimation techniques. It is shown that the proposed method exhibits excellent performance in terms of prediction accuracy and computational speed, while the capability to signify the degradation-related internal states can be utilized for advanced health-aware battery management systems designed for various applications such as electric vehicles and smart grids.

State of charge (SOC) is an important index to ensure the safe and stable operation of vanadium redox flow battery (VRB) and its accurate estimation can protect the battery from overcharge and over-discharge. Time-varying model parameters and nonlinear estimation algorithm are the main challenges for accurate estimation of SOC. This paper proposes the SOC estimation method for VRB based on online equivalent circuit model. Firstly, the recursive least squares with forgetting factor (FRLS) is used to online identify the model parameters. Then, based on this online model, the unscented Kalman filter (UKF) is employed to online estimate SOC. Finally, a hybrid pulse current experiment is conducted to verify the proposed the estimation method on a 5kW/3kWh VRB system in the laboratory. Validated with experimental data, the online identified model parameters and the estimated SOC present excellent accuracy, the root mean square error (RMSE) of the model and SOC is 0.0165V and 0.01 respectively. The proposed SOC estimation method is capable of estimating SOC accurately.

Nowadays, a growing number of plug-in electric vehicles are charged by electric vehicle chargers (EVCs) in low-voltage (LV) residential networks. Without proper control, the increasing charge demand together with high PV penetration will lead to severe voltage issues. In this paper, a two-level coordinated voltage control scheme of EVCs is proposed for voltage regulation in LV distribution networks. The proposed control scheme includes two coordinated levels to effectively utilize EVCs. In the higher level, the distributed voltage control based on consensus algorithm achieves equal utilization among EVCs for network voltage regulation. In the lower level, the localized power allocator determines the real and reactive power outputs, considering the power limits and charging requirements of each EVC. The entire control scheme ensures that the voltages along the distribution network are within the allowable voltage ranges while avoid affecting the users’ charging demand. The proposed control method is evaluated in a LV residential distribution network under various operation conditions. The simulation results validate the effectiveness of the proposed method. Accepted version

Precise state of health (SOH) estimation for lithium-ion batteries (LIBs) can provide guidance for the rational use, reduce the failure rate of batteries, and extend the battery life. Due to the complex aging mechanism, and uncertainty of aging paths of LIB system, the model-based method needs to establish a complex mechanism model, which requires a large amount of calculation. In this paper, the Gaussian process regression (GPR) method is introduced to analyze battery experimental data and estimate battery SOH. GPR is a data-driven method with the ability to describe uncertainty. Firstly, the aging experiment of LIB was carried out. In the experiment, three LIBs of the same type were operated under three different charging and discharging current rates until the end of life. Secondly, based on the charging curve of LIB, four features reflecting battery aging were extracted. In addition, the grey relational analysis method was introduced to analyze the correlation between the extracted features and SOH. Thirdly, a SOH estimation framework based on improved GPR is proposed, and the GPR model is improved by using an explicit mean function and a combined covariance function. Finally, based on the experimental data, the proposed framework is used to estimate the SOH, and the results are analyzed.

Traditional fault indicators based on 3G and 4G cannot send out fault-generated information if the distribution lines are located in the system across remote mountainous or forest areas. Hence, power distribution systems in rural areas only rely on patrol to find faults currently, which wastes time and lacks efficiency. With the development of the Internet of things (IoT) technology, some studies have suggested combining the long-range (LoRa) and the narrowband Internet of Things (NB-IoT) technologies to increase the data transmission distance and reduce the self-built communication system operating cost. In this paper, we propose an optimal configuration scheme for novel intelligent IoT-based fault indicators. The proposed fault indicator combines LoRa and NB-IoT communication technologies with a long communication distance to achieve minimum power consumption and high-efficiency maintenance. Under this given cyber network and physical power distribution network, the whole fault location process depends on the fault indicator placement and the deployment of the communication network. The overall framework and the working principle of the fault indicators based on LoRa and NB-IoT are first illustrated to establish the optimization placement model of the proposed novel IoT-based fault indicator. Secondly, an optimization placement method has been proposed to obtain the optimal number of the acquisition and collection units of the fault indicators, as well as their locations. In the proposed method, the attenuation of the communication network and the power-supply reliability have been specially considered in the fault location process under the investment restrictions of the fault indicators. The effectiveness of the proposed method has been validated by the analysis results in an IEEE Roy Billinton Test System (IEEE-RBTS) typical system.

Accurate power estimation can ensure safe and reliable operation of vanadium redox flow energy storage system (VRB-ESS) so that the battery does not violates the safe operating limits. The parameter variation of equivalent circuit model (ECM) of VRB affects the accurate estimation of state of Power (SoP), especially when considering the aging effects of the battery. In this paper, state of charge (SoC) and state of power (SoP) are estimated respectively. Firstly, the recursive least square (RLS) method is applied for online identification of the equivalent circuit parameters of VRB, then unscented Kalman filtering (UKF) is used to predict SoC of VRB, and lastly, the charged or discharged power can be predicted according to the accurate battery terminal voltage under limiting conditions. The results show that the UKF is capable for both the SoC and SoP estimation accurately.

This study presents the results of hybrid energy storage system (HESS) and improved maximum power point tracking (MPPT) algorithm simulation analysis undertaken to improve the smoothing performance of solar photovoltaic (PV) plant installed in the ship power system. Frequency hierarchical control algorithm is used to assign high-frequency oscillation part to the supercapacitor and low-frequency oscillation part to the battery. Meanwhile, improved MPPT algorithm is also used to limit the maximum output power of PV plant, which can reduce the necessary installed capacity of HESS further. The effectiveness of the proposed method was verified under a typical ship power system model.

Vanadium redox flow battery (VRB) energy storage system has been widely utilized in renewable energy applications such as wind power integration and green buildings. An online electrical model of VRB is needed to monitor and estimate battery states by using real time data since the internal parameters of battery are varying during operation. In this paper, an improved second-order Thevenin model is proposed to describe the operating characteristics of VRB. To overcome the drawback of recursive least squares (RLS) algorithm in tracking parameter identification, this paper proposes a method based on time-varying forgetting factor recursive least Squares (TFF-RLS) algorithm to identify model parameters. According to the data collected by the pulse charging experiment, the RLS algorithm and the TFF-RLS algorithm are used to identify the model parameters. Experiments are carried out on a 5kWh/3kWh VRB experimental platform in Wuhan University of Technology. Two online identification methods are compared. The results show that the TFF-RLS method demonstrates a better performance on tracking the variation of parameters dynamically, and improving estimation accuracy compared with the RLS method.

Due to the mismatch between the residential electrical load and the output power of the rooftop photovoltaic (PV) systems, node voltages in a low voltage distribution network (LVDN) may exceed the allowable upper limit during the daytime and drop far below the lower limit at night. Existing LVDN voltage regulation methods based on reactive power control of the rooftop PV inverter usually regulate all the inverters using the same control strategy with identical control parameters, although PV inverters at different nodes have different voltage sensitivities to active and reactive power flows. In this paper, an optimized decentralized control strategy of residential PV inverters based on voltage sensitivity matrix is proposed, and different control parameters would be determined and applied to each PV inverters at different LVDN nodes. The proposed strategy provides an optimal control framework for the rooftop PV inverters to maintain voltage within the normal range under varying active power generated by the PV systems, and simulation results verify that the total reactive power output of the PV inverters can be effectively reduced.

The main shortcomings of the software-based power engineering education are a lack of physical understanding of phenomena and hands-on experience. Existing scaled-down analogous educational power system platforms cannot be widely used for experiments in universities due to the high cost, complicated operation, and huge size. An integrated power distribution system laboratory platform (PDSLP) using modular miniature physical elements is proposed in this paper. The printed circuit board (PCB) and microelectronic technology are proposed to construct each physical element. Furthermore, the constructed physical elements are used to set up an integrated PDSLP based on modular assembly technology. The size of the proposed cost-efficient PDSLP is significantly reduced, and the reliability of the proposed PDSLP can be improved greatly because the signal transmission path is shortened and a number of welding points are reduced. A PDSLP for fault location in neutral non-effectively grounded distribution systems (NGDSs) is selected as a typical experimental scenario and one scaled-down distribution network with three feeders is subsequently implemented and discussed. The measured zero-sequence currents by our proposed PDSLP when a single-phase earth fault occurred can reveal the true features of the fault-generated signals, including steady-state and transient characteristics of zero-sequence currents. They can be readily observed and used for students to design corresponding fault location algorithms. Modular renewable energy sources and other elements can be designed, implemented and integrated into the proposed platform for the laboratory education of the active distribution networks in the future.

Traditional communication technologies based on 2G and 4G cannot meet the requirements of low-power wide-area data transmission in electricity distribution network. Ubiquitous power Internet of things (UPIoT) has thus been proposed by the State Grid of China and it can be used to transmit energy information requested from any location. Narrowband Internet of things (NB-IoT) and LoRa are two promising low-power wide-area communication technologies, which are capable of providing good performance to transmit data collected from the devices installed in the distribution network. In this paper, taking the fault indicator as an example, we combine LoRa and NB-IoT to construct a wireless network to transmit the operation status of fault indicator, which can improve the transmission distance and reduce the operating cost of the system. It adopts a communication mode including a master node and a plurality of child nodes to meet the needs of large-scale information monitoring. Among them, the design of the master node uses NB-IoT and LoRa communication technologies. The design of the child node uses LoRa communication technology and sensor technology. The control chip STM32F103C8T6 is used for data transmission, and the data is finally managed by a centralized means through upper computer software.

The safe, efficient and durable utilization of a vanadium redox flow battery (VRB) requires accurate monitoring of its state of charge (SOC) and capacity decay. This paper focuses on the unbiased model parameter identification and model-based monitoring of both the SOC and capacity decay of a VRB. Specifically, a first-order resistor-capacitance (RC) model was used to simulate the dynamics of the VRB. A recursive total least squares (RTLS) method was exploited to attenuate the impact of external disturbances and accurately track the change of model parameters in realtime. The RTLS-based identification method was further integrated with an H-infinity filter (HIF)-based state estimator to monitor the SOC and capacity decay of the VRB in real-time. Experiments were carried out to validate the proposed method. The results suggested that the proposed method can achieve unbiased model parameter identification when unexpected noises corrupt the current and voltage measurements. SOC and capacity decay can also be estimated accurately in real-time without requiring additional open-circuit cells. Published version

Vanadium redox flow battery (VRB) is considered to be the most promising large-scale energy storage technology because of its high design flexibility and good reversibility. Operating characteristics of VRB are affected by various parameters including temperature, electrolyte flow rate, and current. Problems such as premature voltage cut-off and low energy efficiency may be caused by improper settings of operational parameters. Previous operation strategy only optimizes single variable, such as temperature or flow rate, neglecting the coupled effect of multi variables on battery operating characteristics. This paper proposed a dynamic multi-parameter operation strategy to improve the energy efficiency of battery systems. The parameters are such as temperature, flow rate and current density are optimized holistically. Based on relevant experiments, a 3kW/5kWh VRB electrochemical model is established for the implementation of the strategy. The real time operation objective is to reach maximum energy efficiency and genetic algorithm is applied for solving nonlinear objective. The results show that multi-parameter optimization strategy achieves high instantaneous energy efficiency compared with other optimization strategies, reaching up to 93% as the SOC increases whereas the applied current decreases. The simulation shows that the energy efficiency of the proposed strategy was improved 1% compared with that of the other strategies.

The application of lithium-ion (Li-ion) battery energy storage system (BESS) to achieve the dispatchability of a renewable power plant is examined. By taking into consideration the effects of battery cell degradation evaluated using electrochemical principles, a power flow model (PFM) of the BESS is developed specifically for use in system-level study. The PFM allows the long-term performance and lifetime of the battery be predicted as when the BESS is undertaking the power dispatch control task. Furthermore, a binary mode BESS control scheme is proposed to prevent the possible over-charge/over-discharge of the BESS due to the uncertain renewable input power. Analysis of the resulting new dispatch control scheme shows that a proposed adaptive BESS state of energy controller can guarantee the stability of the dispatch process. A particle swarm optimization algorithm is developed and is incorporated into a computational procedure for which the optimum battery capacity and power rating are determined, through minimizing the capital cost of the BESS plus the penalty cost of violating the dispatch power commitment. Results of numerical examples used to illustrate the proposed design approach show that in order to achieve hourly-constant power dispatchability of a 100-MW wind farm, the minimum-cost Li-ion BESS is rated 31-MW/22.6-MWh.

Variable-speed operation of a dish-Stirling (DS) concentrated solar-thermal power generating system can achieve higher energy conversion efficiency compared to the conventional fixed-speed operation system. However, tuning of the controllers for the existing control schemes is cumbersome due to the presence of a large number of control parameters. This paper proposes a new control system design approach for the doubly-fed induction generator (DFIG)-based DS system to achieve maximum power point tracking and constant receiver temperature regulation. Based on a developed thermo-electro-pneumatic model, a coordinated torque and mean pressure control scheme is proposed. Through steady-state analysis, the optimal torque is calculated using the measured insolation and it serves as the tracking reference for direct torque control of the DFIG. To minimize the tracking error due to temperature variation and the compressor loss of the hydrogen supply system, four optimal control parameters are determined using particle swarm optimization (PSO). Model-order reduction and the process of the pre-examination of system stability are incorporated into the PSO algorithm, and it effectively reduces the search effort for the best solution to achieve maximum power point tracking and maintain the temperature around the set-point. The results from computational simulations are presented to show the efficacy of the proposed scheme in supplying the grid system with smoothened maximum power generation as the solar irradiance varies.