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State-of-charge (SoC) imbalance and bus voltage deviation are two of the main problems in autonomous dc microgrids. Based on this concern, this paper presents an improved dual-quadrant SoC weighted control strategy and a distributed optimization control method to achieve SoC balance, ensuring accurate power-sharing and bus voltage recovery. Firstly, this paper couples the injected/released power with the current SoC and observed average SoC value to weight the droop coefficient, which is based on the charge/discharge mode for the energy storage system. Then a secondary controller is designed based on distributed optimal control to eliminate the bus voltage deviation caused by the line impedance difference. The proposed optimal control method optimizes the average bus voltage to the nominal value and achieve accurate power-sharing by constructing the correlated variables and voltage independent intermediate variables exchanged among bulk energy storage units (ESUs). Since the voltage observer cannot accurately observe the true average bus voltage under the communication delay, the proposed distributed optimal control method without the voltage observer can ensure that the average bus voltage is optimized to the nominal value, thus improving the robustness of the control system. Finally, the correctness and effectiveness of the proposed method are verified in Simulink/MATLAB. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) is the key to the performance of fuel cell / battery hybrid system. At present, reinforcement learning (RL) has been introduced into this field and has gradually become the focus of research. However, traditional EMSs only take the energy consumption into consideration when optimizing the operation economy, and ignore the cost caused by power source degradations. It would cause the problem of poor operation economy regarding Total Cost of Ownership (TCO). On the other hand, most studied RL algorithms have the disadvantages of overestimation and improper way of restricting battery SOC, which would lead to relatively poor control performance as well. To solve these problems, this paper establishes a TCO model including energy consumption, equivalent energy consumption and degradation of power sources at first, then adopt the Double Q-learning RL algorithm with state constraint and variable action space to determine the optimal EMS. Finally, using hardware-in-the-loop platform, the feasibility, superiority and generalization of proposed EMS is proved by comparing with the optimal dynamic programming and traditional RL EMS and equivalent consumption minimum strategy (ECMS) under both training and unknown operating conditions. Results prove that the proposed strategy has high global optimality and excellent SOC control ability regardless of training or unknown conditions. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear dynamic simulation model of an ultracapacitor (UC) bank and the associated control system. The control system at hand consists of two levels: the lower level controls the inverter of the UC bank, while the upper control level is responsible for providing charging/discharging active power set points to be followed by the lower control level. This paper focuses on the development of the upper control level for frequency control. Specifically, two simulation case studies are developed so as to assess the performance of the proposed control framework. In the first case study the upper control level is developed using a classical Proportional-Integral-Derivative (PID) controller. In the second case study the upper control level is devised using a Model Predictive Control (MPC) algorithm based on internal linear prediction model of a nonlinear UC bank. In both cases, a nonlinear UC bank simulation model is used. The simulation case studies are modelled and tested in Matlab/Simulink. The response of the MPC-controlled UC bank is compared to the 3 existing PID-control algorithms for frequency control. The simulation results show that the MPC algorithm outperforms the conventional PID controllers. [ABSTRACT FROM AUTHOR]

This paper presents a decomposed co-design optimization framework for an electric vehicle powertrain equipped with a Continuously Variable Transmission (CVT). We jointly design the sizes of the powertrain components and the energy management strategy, considering the design and the dynamics of the transmission. Specifically, we divide the resulting nonlinear optimization problem into separate hierarchical optimization subproblems leveraging the concept of Analytical Target Cascading (ATC). Furthermore, we compare the proposed decomposition-based strategy with a centralized approach, in which we solve the same problem, and show that the method presented in this paper can be a potential alternative to a centralized optimization formulation for solving the large-scale, complex integrated plant and control system design. Finally, we compare the resulting performance of using a CVT on the vehicle total-cost-of-ownership with that equipped with a Fixed-Gear Transmission (FGT), which is what is commonly implemented in current electric vehicles. Our results show that by jointly optimizing the design of the transmission along with the control strategy, the CVT yields a lower energy consumption and total-cost-of-ownership compared to that of the FGT. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have attracted enormous attention in recent years due to their potentials in mitigating energy crisis and air pollutions. However, the long battery charging time and lack of sufficient charging infrastructure highly restrict the popularization of EVs. In this context, it is promising to establish battery charging and swapping systems (BCSSs) based on the concept of battery swapping services. To optimally achieve the combined operation of BCSSs, this paper proposes a hybrid swapped battery charging and logistics dispatch model in continuous time domain. Identifying the special structure of the mathematical models of the two problems, this paper innovatively formulated the swapped battery charging strategy as the rectangle packing problem and the battery logistics model as the vehicle routing problem. The two models are closely linked by the delivery time of transporting the well-charged batteries from battery charging stations to battery swapping stations. A hybrid optimal operation model of BCSSs is further formulated as a mixed-integer linear programming model by incorporating the interaction between the battery charging and battery logistics. Finally, case studies are conducted on several BCSSs and numerical results validate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) charging at low temperatures will easily accelerate the aging of LIBs and reduce the useful life. This paper applies advanced multi-factors coupling aging model and bi-objective particle swarm optimization (PSO) algorithm to derive suitable charging patterns for LIBs at low temperatures. Based on the results of orthogonal experiments, which consider the effects of charging rate, charging temperature and charging cut-off voltage on battery aging, the low-temperature capacity degradation model was established. Furthermore, two important yet competing charging objectives, including battery health and charging time, are taken into account simultaneously. These two conflicting charging objectives are traded off by solving a bi-objective optimization problem based on battery electrothermal and aging behavior. Combined with PSO algorithm, the optimal low-temperature charging strategy is obtained. As a result, the three-stage constant current and constant voltage (CC-CV) charging strategy is optimized to balance various combinations of charging objectives. Different tradeoffs are compared and analyzed based on the Pareto frontiers. The verification results show that the optimized three-stage CC-CV charging strategy can effectively offer feasible health-conscious charging with desirable tradeoffs among charging speed and battery health under low-temperature charging conditions. [ABSTRACT FROM AUTHOR]

A decentralized improved I-V droop control strategy for battery-supercapacitor (SC) hybrid energy storage system (HESS) is proposed in this paper. The dynamic power sharing between battery and SC is realized by replacing the constant droop coefficient in I-V droop control with virtual impedance, i.e. virtual inductance for battery side converter and virtual resistance for SC side converter. Besides, by injecting the virtual inductance in the battery side converter, negligible DC bus voltage deviation can be achieved without extra voltage compensator. Moreover, the state-of-charge (SoC) recovery is also considered to extend the service life of the HESS. Furthermore, in the proposed regulated power system, since the power allocation, DC bus stability and SoC recovery are decoupled from each other, the design of control parameters is simple. The corresponding design guideline is demonstrated in this paper. Finally, to verify the accuracy and feasibility of the theoretical analyses, hardware in the loop simulations have been conducted. [ABSTRACT FROM AUTHOR]

While the vehicular network enables geographically distributed cooperative computation, its mature implementation has long been constrained due to the lack of an effective management platform. In this paper, employing the security and privacy attributes of blockchain, we propose a novel Blockchain-enabled Large-scale Parked Vehicular Computing (BLPVC) architecture to utilize the potential solar energy and vehicular computational resources in the outdoor parking lot. However, the uneven green power supply and random arrival time of electric vehicles compose the highly complex environment. Accordingly, in this paper, to handle the efficient utilization of the distributed resources by blockchain technology, we propose an integrated optimization framework which leverages the green energy utilization and service latency limit among the processes of block generation, task computing, and communication, whereas such a design leads to the mixed-timescale stochastic optimization problem. To this end, corresponding to the dynamic solar energy arrival, we propose a shaped deep deterministic policy gradient (DDPG) algorithm to accelerate the learning rate of computational frequency control in the short-term stage; while in the long-term stage, for the mixed-integer programming (MIP) of task offloading and blockchain parameters adjustment, a series of transformation is employed to preserve convexity. Finally, experiments are carried out on Python demonstrating that the proposed scheme achieves a balanced performance between service latency and distributed resources, while the battery depreciation cost is heavily reduced. [ABSTRACT FROM AUTHOR]

This paper presents a computation-efficient stochastic dynamic programming algorithm for solving energy storage price arbitrage considering variable charge and discharge efficiencies. We formulate the price arbitrage problem using stochastic dynamic programming and model real-time prices as a Markov process. Then we propose an analytical solution algorithm using a piecewise linear approximation of the value-to-go function. Our solution algorithm achieves extreme computation performance and solves the proposed arbitrage problem for one operating day in less than one second on a personal computer. We demonstrate our approach using historical price data from four price zones in New York Independent System Operator, with case studies comparing the performance of different stochastic models and storage settings. Our results show that the proposed method captures 50% to 90% of arbitrage profit compared to perfect price forecasts. In particular, our method captures more than 80% of arbitrage profit in three out of the four price zones when considering batteries with more than two-hour duration and realistic degradation cost. [ABSTRACT FROM AUTHOR]

The paper develops two computationally efficient models, namely a detailed equivalent model and an average-value model, with provisions for representation of the critical state of converter blocking, for modular multilevel converters with embedded storage. These models are indispensable in the simulation of dc faults. The developed detailed equivalent model provides accuracy that matches electromagnetic transient (EMT) simulations, with much reduced computational burden; the developed average-value model represents the averaged behavior of the converter by neglecting switching transients. Both models are extensively evaluated in the context of an HVDC system against benchmark results form a detailed switching model developed in PSCAD/EMTDC. The results confirm the validity and accuracy of the models for steady state, transient, and faulted operating conditions including when the converter is blocked in response to a dc fault. [ABSTRACT FROM AUTHOR]

With the widespread application of lithium-ion battery packs, it calls for efficient balancing methods to improve the performance of these battery systems. The relevant research has focused on the design of equalization circuits and control strategies while neglecting quantitative analysis and comparison of battery active equalization structures from the systematic level. In this paper, we propose an improved system-theoretic modeling approach for active equalization structures that takes into account the battery's constraints, including equalization current rate and SOC operation range. Accordingly, the mathematical models of various active equalization structures are derived that can describe their equalization dynamic behavior. Then, the analytical algorithms are developed to estimate the balancing time of active equalization structures online. Finally, extensive numerical simulations are conducted to evaluate and compare the average performance of these equalization systems in term of equalization time. It then comes up with a comprehensive comparison among them. Consequently, the Multicell-to-Multicell (MC2MC) balancing structure benefits the fast balancing speed and the lowest sensitive to initial batteries’ SOCs. The global structure has the lowest implementation cost and the series structure benefits the simplest control. Moreover, the module-based structures have faster balancing speed when each module contains about 4–6 batteries. [ABSTRACT FROM AUTHOR]

The aim of the paper is to define a limit performance of highly efficient battery electric quadricycles for urban mobility. The vehicle is employed for an energy efficiency competition in which urban concepts compete. A multi-physics (thermo-electro-mechanical) Tank To Wheels (TTW) model has been developed and validated. Given the information on the track route (track map and elevation) and on the throttle input command, the model computes the vehicle power demand and cruising speed. The model is validated by means of both indoor and outdoor experimental tests. The validated model is employed for the optimization of the transmission gear ratio and of the driving strategy to minimize the overall energy consumption on a given track. Design variables are related to the transmission gear ratio and to the throttle command profile. The algorithm aims to minimize the energy consumption, including constraints on the maximum current provided by the battery and on the maximum time available to complete the lap. Comparison with some common driving strategies confirmed the effectiveness of the proposed solution, with a foreseen 9% reduction of the vehicle overall energy demand. The single seater ultra-efficient electric vehicle, compared to other urban quadricycles, has a battery capacity ten times lower and a 50% longer driving range. [ABSTRACT FROM AUTHOR]

Regarding the continuing increase of anthropogenic carbon emissions in the power system with growing energy consumption, researchers have focused on managing the demand side to achieve low-carbon transition. However, such a low-carbon transition cannot be achieved effectively without the proper incentive scheme. Moreover, it is desired to improve the computation quality of carbon tracing tools and extend their applications by state-of-art techniques. To fulfil these existing research gaps, this paper proposes a low-carbon optimal scheduling model with demand response (DR) based carbon intensity control. It aims to reduce the dependence of customers on energy sources with high carbon intensities while decreasing reasonable energy consumption. Likewise, a data-driven approach conducted with the Bayesian interfere regression is proposed to carry out the carbon emission flow (CEF) model to cope with the drawbacks of the conventional emission calculation. Moreover, the storage emission and relevant dynamic impacts to the system are fully considered. The proposed data-driven approach is tested on a series of IEEE systems with different sizes, its high accuracy and efficiency are proved by the simulation results. Further, simulation results indicate that the proposed scheduling model can effectively achieve carbon emission mitigation. [ABSTRACT FROM AUTHOR]

Over the past decade, the high penetration of renewable-based distributed generation (DG) units has witnessed a considerable rise in electrical networks. In this context, direct current (DC) microgrids based on DGs are being preferred due to having less complexity for the establishment and control. At the same time, they offer higher efficiency and reliability compared to their alternating current (AC) counterparts. This paper proposes a new model predictive control (MPC)-trained artificial neural network (ANN) control strategy being an ANN-MPC instead of conventional cascaded-proportional-integral (PI)-trained ANN control for dynamic damping of photovoltaic (PV)-battery-based grid-connected DC microgrids. Unlike traditional controllers, the proposed control approach more rapidly attains generation-load power balancing under variable climate input (meteorological sensor data) and output (load demand), hence achieving quick DC-bus voltage damping. The proposed ANN-MPC scheme is examined under different operating conditions, and the results are compared with the ANN-based conventional PI controller. The results show the proposed control strategy's efficacy to lessen the instability issues and achieve effective attenuation of oscillations in DC microgrids. [ABSTRACT FROM AUTHOR]

Behind-the-meter battery energy storage is used to mitigate non-dispatchability of customer-owned renewable sources. While serving their owners’ objectives, their underutilized capacities can be used to provide grid services to justify their cost. However, the integration of these resources extends the size and complexity of the network operation problem, imposing a processing burden on the central control system. In this paper, an agent-based distributed control framework is proposed that enables utilizing of the underutilized behind-the-meter capacities. In this framework, while the prosumer agents initially schedule their resources for daily cost management, a local customer aggregator agent performs day-ahead active and reactive power control on the underutilized behind-the-meter batteries to provide multiple grid services. A novel grouping algorithm is developed based on voltage sensitivity and a modified $k$ -means clustering method to assign customer groups to the local agents and establish the network subsystems. Mixed-integer linear programming is used to model and solve multiple optimization problems. The proposed framework is applied to the 123-bus distribution network. The simulation results show its effectiveness in terms of complexity and optimality, compared to the central control approach in allocating the unused behind-the-meter resources for two common industry needs: network bottleneck congestion relief and grid-edge voltage support. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization methodology for sizing and operating battery energy storage systems (BESS) in distribution networks. A BESS optimal operation for both frequency regulation and energy arbitrage, constrained by battery state-of-charge (SoC) requirements, is considered in the proposed optimization algorithm. We use utility historical data as input in a case study on a real-world distribution circuit. The results indicate that a BESS generally brings high profits by participating in the frequency regulation market and should be sized to its highest allowable power capacity rather than its energy capacity. [ABSTRACT FROM AUTHOR]

Modular multilevel converters (MMCs) have been recognized as the most attractive technology for high voltage direct current (HVDC) applications during the last years. Their advantages include the ability to freely control the internally stored energy in the distributed submodules (SMs) and to independently control AC and DC powers. Recent studies showed that an MMC can take advantage of this degree of freedom to supply ancillary services with low energy requirements such as frequency response or power oscillation damping by connecting energy storage systems (ESSs) to its SM capacitors. This paper proposes a modular DC-DC converter as an interface between the energy storage element and the SM capacitor. As it can be built with switches allowing a high switching frequency, it is shown that the volume of the passive components can be reduced in comparison with interface converters known in the literature. The proposed control strategy avoids to transfer the harmonics of a SM to the ESS that prevents to drastically oversize the system. The proposed control method also provides an appropriate energy exchange without affecting the control philosophy of an MMC and it addresses balancing issues. Simulations and a downscaled prototype confirm the relevance of the proposed control. As the proposed interface converter is able to track the power reference with fast dynamics, it provides the same system-level advantages as other interface converters known in the literature but with a reduced volume. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic valuation framework to determine the opportunity value of battery capacity degradation in grid applications based on the internal degradation mechanism and utilization scenarios. The proposed framework follows a dynamic programming approach and includes a piecewise linear value function approximation solution that solves the optimization problem over a long planning horizon. The paper provides two case studies on price arbitrage and frequency regulation using real market and system data to demonstrate the broad applicability of the proposed framework. Results show that the battery lifetime value is critically dependent on both the external market environment and its internal state of health. On the grid service side, results show that second-life batteries can provide more than 50% of the value compared to new batteries, and frequency regulation provides two times more revenue than price arbitrage throughout the battery lifetime. [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]

This paper presents a stochastic framework for the optimization of microgrids that has the functionality of providing flexibility services to System Operators (SOs) considering uncertainties in the energy forecast. The methodology is developed with the aim of being applied to complex microgrids composed of different distributed energy resources and hybrid energy storage systems (ESS). The associated optimization problem is operated in two stages: the first one performs a stochastic optimization of the microgrid in order to reserve an up/down regulation capacity with which to deal with the energy forecast uncertainties of the microgrid. The different microgrid devices are optimized by considering their operational costs in order to achieve their optimal operation in the Day-Ahead Market (DM). The second stage is used to re-schedule the initial planning according to the signal request and an economic offer from the SO. The control problem is developed using Stochastic Model Predictive Control (SMPC) techniques and Mixed-Integer Quadratic Programming (MIQP), owing to the presence of logic, integer, mixed and probabilistic variables. The simulation results show that the proposed methodology reduces the risk of undergoing up/down-penalty deviations in the Regulation Service Market (RM), also being able to provide flexibility services to the SOs, despite being subject to uncertainties in the energy forecast carried out for the microgrid. [ABSTRACT FROM AUTHOR]

The future e-mobility ecosystem will be a complex structure with different stakeholders seeking to optimize their operation and benefits. In this paper, a day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling framework is proposed including electric vehicles (EVs), charging stations (CSs), and retailers. To facilitate V2G services and to avoid congestion at CSs, two types of trips, i.e., mandatory and optional trips, are defined and formulated. Also, EV drivers’ preferences are added to the model as cost/revenue threshold and extra driving distance to enhance the practical aspects of the scheduling framework. An iterative process is proposed to solve the non-cooperative Stackelberg game by determining the optimal routes and CS for each EV, optimal operation of each CS and retailers, and optimal V2G and G2V prices. Extensive simulation studies are carried out for two different e-mobility ecosystems of multiple retailers and CSs as well as numerous EVs based on real data from San Francisco, the USA. The simulation results show that the optional trips not only reduces the cost of EVs and PV curtailment by 8.8-24.2% and 26.4-28.5% on average, respectively, in different scenarios, but also mitigates congestion during specific hours while respecting EV drivers’ preferences. Moreover, the simulation results revealed the significant impact of EV drivers' preferences on the optimal solutions and cost/revenue of the stakeholders. [ABSTRACT FROM AUTHOR]

In order to effectively make up for the defect of service life of re-tired power battery echelon used in microgrids (MGs) and improve the reliability of MGs system, an energy management method based on the energy hub of the electricity heat hydrogen multi-energy storage system (EHH-MESS) is proposed the in the paper. Firstly, considering the uncertain factors such as energy output, load fluctuation, retired power battery life defects and multi energy flow impact, MGs supply-demand balance model is built. Secondly, taking the economy of energy supply as the goal, a control method based on energy hub is proposed, which considers the weight of multi-objective optimization to control MGs. Finally, the effectiveness of the proposed energy management method is verified by simulation analysis, which improves the power supply reliability and economy of the system. [ABSTRACT FROM AUTHOR]

In multi-access edge computing systems with energy harvesting (MEC-EH), the mobile devices are empowered with unstable energy harvested from renewable energy sources. To prolong the life of mobile devices, as many computation-intensive tasks as possible should be offloaded to the MEC server. However, when the system states of mobile device and MEC server are unstable, e.g. poor communication channel conditions, a great number of tasks will be executed locally, leading to a long execution time. Even worse, some tasks may be dropped due to low energy levels. To address this problem, in this paper, we propose a multi-relay assisted computation offloading framework for MEC-EH systems. In this framework, a computation task can be executed by offloading to the MEC server with the help of multiple relay nodes, such as the neighboring nodes. We introduce execution cost as a performance metric to incorporate both the task execution time and task failure. We then develop a low-complexity online algorithm, namely MRACO algorithm, to minimize the average execution cost. MRACO algorithm can select the optimal execution strategy for each task from (1) executing the task locally, (2) offloading it to the MEC server directly, (3) offloading it to the MEC server with the help of the most suitable neighboring nodes, and (4) simply dropping it. Moreover, we also develop an algorithm for selecting the suitable neighboring devices to act as relays and determining the optimal task splitting ratio between them. Finally, performance evaluation shows that the proposed MRACO algorithm greatly outperforms the benchmarks in terms of both average execution time and task drop rate. [ABSTRACT FROM AUTHOR]

Accurate and reliable capacity estimation for lithium-ion batteries adapting to uncertain environment plays a significant role in the fields of electric vehicles and renewable energy systems. This paper proposes a novel capacity fusion estimation approach based on three-parameter capacity fade model and constant current charging curves using dual unscented Kalman filters. In view of the battery cycle aging problem under variable temperature situation, a three-parameter capacity fade model is introduced to continuously estimate the battery cell capacity for unknown changes of ambient temperature. Nevertheless, the model parameters may mismatch in practical applications, so the first unscented Kalman filter views the discrete capacity based on constant current charging curves as observations to periodically update the parameters. Thereafter, the second one performs a fusion operation depending on the capacity estimated by constant current charging curves and the three-parameter capacity fade model with the corrected parameters, so as to improve the capacity estimation accuracy online. The results show that the fusion estimation errors of capacity are controlled within 2% by updating the model parameters, and the robustness of the proposed method is also validated. [ABSTRACT FROM AUTHOR]

The technology of supercapacitor is vastly examined by researchers to cope with a lower power density of battery. It is validated that a hybrid battery-supercapacitor storage (HBSS) framework can improve the overall efficiency due to taking advantage of battery's higher energy density and supercapacitor's higher power density. Despite the extensive investigations on this hybrid storage scheme in the electric vehicle (EV) industry, its experimental implementation in the power grid to cope with non-dispatchable nature of wind and solar energy is still in its initial stages. As a result, this paper concentrates on analysis of its experimental implementation on a lab-scale hybrid microgrid located at Southern Illinois University, Carbondale, IL, USA containing two wind turbines and two photovoltaic (PV) modules. The objective of this work is to promote the utilization of the experimentally validated lab-scale HBSS in power industry. [ABSTRACT FROM AUTHOR]

A high efficiency and a constant voltage at the output stage are among the major concerns in wireless charging of dynamic electric vehicles. They are usually achieved by additional converters or asymmetric switching which suffer from high cost and hard switching. To overcome these drawbacks, a variable-frequency retuning approach is proposed in this paper with LCL-Series compensation. As opposed to conventional control methods with resonance frequency as target operating frequency, this approach uses a new optimum frequency which enables ZVS. Also, it minimizes the conduction loss of the inverter switches in a wide range of load and coupling coefficients. In addition, it improves the total system efficiency up to 6% together with an increase in the power transfer capability. Using the proposed method, both sides’ currents increases up to 45% depending on the load. To find the optimum frequency in a system without resonant parameter information, an on-line maximum efficiency search method based on Perturb and Observe is employed. Extensive simulation results using PLECS, together with experimental results from a laboratory setup are presented to confirm the effectiveness of the proposed method under varying load and coupling. [ABSTRACT FROM AUTHOR]

In this paper five different flexibility options are analysed from a techno-economic perspective as alternatives to traditional grid expansion for a specific distribution grid in Germany. The options are: two reactive power control strategies with photovoltaic inverters (as a function of the power feed-in, or of the voltage at the connection point), one residential and two large scale battery storage applications (primary control reserve with autonomous reactive power control or self consumption maximisation strategy with autonomous reactive power control). For the pilot grid located in Southern Germany a photovoltaic expansion pathway is determined. The main goal of this work is to quantify the grid expansion actions that can be avoided by applying these five flexibility options for the assumed expansion pathway, focusing on large scale battery storages. It is shown that the five flexibility options increase the hosting capacity for PV systems, compared to a scenario without, by up to 45%. Furthermore, the results of the economic assessment indicate that the analysed flexibility options might be a viable alternative to traditional grid expansion as all of them show a cost reduction potential for the pilot region. These results could encourage DSOs to consider the integration of additional PV and battery storage systems not as a problem which triggers grid expansion, but as part of the solution reducing future grid expansion costs. [ABSTRACT FROM AUTHOR]

All-electric ships (AES) are regarded as a promising solution for decreasing greenhouse gas emissions since they are able to utilize clean technologies like fuel cells instead of fossil fuel. A zero-emission hybrid energy system comprising the Proton Exchange Membrane (PEM) fuel cell, battery, cold-ironing, and Recuperative organic Rankine cycle (RORC) is scrutinized in this paper. To use the waste heat produced by PEM fuel cells, a hybrid system which is composed of PEM fuel cells and RORC is suggested. In order to analyze this zero-emission all-electric ship, the real data of a ferry boat, containing the load profiles and routes is used to appraise the possibility of the suggested hybrid system. The structure of the boat and energy sources, along with mathematical models, are represented and examined. Eventually, the hourly energy management of the boat for one specific day is analyzed, and also, to optimize the power dispatch, the Deep Deterministic Policy Gradient (DDPG) is applied in actor-critic architecture. To corroborate the proposed models, the MATLAB software is employed. According to obtained results, the proposed models could ensure effective and efficient ways in marine vessels due to not only their high performances but also permissible energy cost to be a zero-emission ship. [ABSTRACT FROM AUTHOR]

With the advancements of the battery energy storage systems (BESSs), reduction of their manufacturing costs and government subsidies, the BESS uptake is likely to increase rapidly in power distribution networks. This paper investigates the effects of residential BESSs on low-voltage (LV) networks using the actual household load profiles equipped with BESS and solar-photovoltaic (PV) systems. The electricity consumption data collected via smart meters (2200 households with PV/BESS, 1950 households with PV only and 1000 households without a PV or a BESS) at different solar-PV penetration levels and network types are used to simulate real network operating scenarios. A real LV distribution network in Australia is analysed in DIgSILENT PowerFactory under different scenarios, such as, customers with and without a solar-PV/BESS, with a solar-PV but without a BESS, and without a solar-PV, by using both the power flow and quasi-dynamic simulation studies under balanced and the unbalanced network loading conditions. According to the study, customers experience large voltage excursions from solar-PV power exports, which could be resolved by the household BESS, provided that the BESS charging is coordinated with the solar-PV production. Moreover, quasi-dynamic simulation shows that the BESS could reduce the violation of the over-voltage limit during the solar peak hours (midday) by lowering the worst-case feeder voltage by 3%. Finally, extreme-event (high solar PV generation scenario) simulation shows that the implementation of the BESS controller to facilitate charging BESS during afternoon solar-PV export may reduce the negative grid impact and will assist to avoid network upgrades. [ABSTRACT FROM AUTHOR]

In this paper, a model fusion method (MFM) is proposed for online state of charge (SOC) and state of power (SOP) co-estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs). Firstly, a particle swarm optimization-genetic algorithm (PSO-GA) method is cooperated with a 2-RCCPE fractional-order model (FOM) to construct battery open-circuit voltage (OCV)-SOC curve, which only relies on a part of dynamic load profile without the prior knowledge of an initial SOC. Secondly, a dual extended Kalman filter (DEKF) algorithm based on a 1-RC model is employed to identify the model parameters and estimate battery SOC with the extracted OCV-SOC curve. Furthermore, battery polarization dynamics in a SOP prediction window is analyzed from two aspects: (1) self-recovery; and (2) current excitation. They are separately simulated using 2-RCCPE FOM and 1-RC model, and then integrated through a model fusion for online SOP estimation, which enables an analytical expression of battery peak charge/discharge current in a prediction window without weakening the nonlinear characteristic of FOM. Experimental results demonstrate the improved performance of the proposed MFM for online discharge SOP estimation, where the mean absolute error and root mean square error are only 0.288 W and 0.35 W, respectively, under the urban dynamometer driving schedule profile. [ABSTRACT FROM AUTHOR]

As one of the most successful industrial realizations of Internet of Things, a smart grid is a smart IoT system that deploys widespread smart meters to capture fine-grained data on residential power usage. Unfortunately, it always suffers diverse privacy attacks, which seriously increases the risk of violating the privacy of customers. Although some solutions have been proposed to address this privacy issue, most of them mainly rely on a trusted party and focus on the sanitization of metering masurements. Moreover, these solutions are vulnerable to advanced attacks. In this paper, we propose a decentralized mechanism for privacy-preserving computation in smart grid called DDP, which leaverages the differential privacy and extends the data sanitization from the value domain to the time domain. Specifically, we inject Laplace noise to the measurements at the end of each customer in a distributed manner, and then use a random permutation algorithm to shuffle the power measurement sequence, thereby enforcing differential privacy after aggregation and preventing the sensitive power usage mode informaton of the customers from being inferred by other parties. Extensive experiments demonstrate that DDP shows an outstanding performance in terms of privacy from the non-intrusive load monitoring (NILM) attacks and utility by using two different error analysis. [ABSTRACT FROM AUTHOR]

In this paper, a sizing method is proposed for photovoltaic (PV) and battery energy storage systems (BESSs) for buildings with demand side management capability. Three objective functions (OF) are defined, describing the self-sufficiency of the building, the net-present value of the investment in PV and BESS, and their combination in a joint expression, respectively. The latter OF provides cost-effective solutions that are potentially more profitable in case of future high increase of the electricity cost. Two analysis methods are developed to find the optimal solution under a predefined load shifting strategy. The first one applies an exhaustive search by examining all possible combinations of PV and BESS capacity. The drawback of this method is the high computational burden due to unnecessary access to the solution space. The second method is implemented with a particle swarm optimization (PSO) variant, namely the unified-PSO. Under this approach, the analysis algorithm is guided in the solution space toward efficient solutions under lower computational time. The comparative assessment of the two methods in both residential and industrial sites verifies that via the PSO algorithm the optimal solutions are found under lower execution time. [ABSTRACT FROM AUTHOR]

The digital implementation of all the control loops of a versatile buck-boost (VBB) dc–dc converter used in a stand-alone photovoltaic application is proposed in this paper to improve existing digital-analog sliding-mode-based implementations. All three control loops: maximum power point tracking (MPPT), fast input voltage regulation, and inner high-bandwidth current control, have been programmed in the same digital signal controller (DSC). A Model Predictive Control (MPC) based algorithm has satisfactorily solved the challenge of implementing the nominal 100 kHz switching frequency current loop. The MPC cost function is distributed throughout the algorithm to achieve three specific goals: the tracking of the reference current (G1), a quasi-constant steady-state switching frequency (G2), and the assurance that the duration of an interval is larger than the time required to calculate it (G3). The third goal requires the current control to toggle between peak- and valley-modes depending on the operating point. The correct fulfillment of these control objectives on the proposed MPC-based algorithm has been validated through simulations and experimental tests performed on a purpose built-prototype. [ABSTRACT FROM AUTHOR]

This paper proposes an optimized energy management strategy (EMS) for hybrid electric fuel cell/battery-based drones focusing on fuel economy while extending sources lifespans in persistent missions. An off-the-shelf drone fed by a 650 $\mathrm{W}$ fuel cell is selected as a case study, where the power splitting is conventionally managed by a simple rule-based method. Then, a multi-objective genetic algorithm is used to optimize the proposed EMS parameters considering three scenarios regarding battery state of charge, namely charge sustaining, depleting, and increasing. Therefore, advantages of rule-based strategy and genetic algorithm are combined in an online EMS to fit on drone applications. Extensive simulation results demonstrate that the proposed approach allows power sources to operate within their rated area, prolonging their service life, and leading to 5.1% of fuel consumption reduction. Thus, the autonomy will be increased depending on the carried hydrogen quantity, and the world record can be extended by about 37 min. It may also lead to benefit in the operating cost achieving 1450€ during one fuel cell stack lifecycle. In fleet tasks, the benefit can be further multiplied. [ABSTRACT FROM AUTHOR]

This paper presents a sigma-modified adaptive control algorithm to enhance the charging profile in a multi-objective electric vehicle (EV) charging installation. The present algorithm takes care of multiple parametric uncertainties and grid non-idealities to provide an instantaneous control updation in order to achieve well-regulated charging dynamics. With the support of renewable energy and battery energy storage (BES), the present algorithm also ensures an uninterrupted charging profile with controller robustness and stability for bi-directional EV charging. The sigma-mod adaptive controller provides an iterative error convergence at each clock interval of supply voltage dynamics to guarantee improved power quality operation in presence of grid distortions. To further improve the reliability of EV charging opportunities, a solar photovoltaic (PV) array in conjunction with the battery energy storage supports the ancillary services through maximum power point operation. Multivariable sliding mode control and rule-based phase-shift adaptation at different stages of power transformation assure faster convergence, parameter uncertainty and controller stability for the bi-directional EV charging operation. A 3.3 kW PV-integrated off-board charging facility is designed and developed as a laboratory prototype to validate the multi-mode charging architecture with minimal grid dependency. [ABSTRACT FROM AUTHOR]

This paper discusses battery storage formulations and analyzes the impact of the constraints on the computational performance of security constrained unit commitment (SCUC). Binary variables are in general required due to mutual exclusiveness of charging and discharging modes. We use valid inequalities to improve the SOC constraints. Adding batteries to the MISO day ahead market clearing cases reveals the impact of binary variables and the valid inequalities on SCUC solving time. Warm start and lazy constraint techniques are applied to improve the performance and make the valid inequalities more effective, reducing computation time to acceptable levels for implementation. [ABSTRACT FROM AUTHOR]

Drones have been attracting the attention of diverse industries thanks to their superior maneuverability. Logistics companies especially keep trying to utilize drones for fast delivery following the growing market size of e-commerce. Accordingly, methods for safely operating multi-drone have been researched, and many researchers have proposed various optimal or near-optimal routing methods. However, such methods have some problems that cause routing failures or huge routing computation time in a drone-dense space due to many collisions. In this paper, we propose a centralized framework that deals with enormous collisions and obtains collision-free paths rapidly. We first build a drone energy consumption model with a data-driven method using flight experiment data of a commercial drone to estimate the drone battery state-of-charge (SoC). Then, we develop a novel routing method that generates collision-free paths by considering both the congestion of the space and the SoC of each drone. The proposed method is inspired by the VLSI circuit routing method that connects all signal nets among thousands of logic components. Through numerous delivery routing simulations, we confirm that the proposed method achieves a maximum of 6 times higher routing success rate with a 10x faster runtime compared with the state-of-the-art optimal method. In addition, we validate that the proposed method is applicable to delivery routing problems with various drone battery capacities. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the possibility and condition of exploiting wireless-powered signals as a cover for conducting covert communications in unmanned aerial vehicle (UAV) networks. Specifically, we aim to design the hovering altitude of a UAV and its prior transmission probability to strike a balance between energy replenishment and covert communications. We derive the necessary condition for guaranteeing communication covertness subject to an energy replenishment constraint, under which the UAV’s optimal hovering and the prior transmission probability are determined. Our simulation shows that varying the prior transmission probability can significantly improve the achievable communication covertness under the energy replenishment constraint, relative to a baseline scheme with a fixed prior transmission probability. [ABSTRACT FROM AUTHOR]

This paper elucidates a new real time energy management framework for radial active unbalance distribution networks (UDNs) by integrating load shedding and conservation voltage reduction (CVR) techniques. In contrast with the shortsighted real time optimization strategies, the proposed technique accounts offline beneficial aspects in real time optimization platform as time coupled stochastic expressions. Those are further simplified to a mixed integer non-convex programming (MINCP) using merger of Queueing theory and Lyapunov optimization process. To solve the complex MINCP portfolio, a consecutive mixed integer linear programming (c-MILP) based solution method is proposed after adopting necessary linear approximations. After demonstrating on modified IEEE 123 bus test network, it is showed that the proposed real time strategy can provide most energy efficient, secure and reliable operation to the UDNs and can accommodate offline advantageous attributes successfully along with the real time load shedding and CVR constraints. Validating the power flow solutions at OpenDSS platform, it is proved that the proposed c-MILP approach possess fast convergence and provide near optimal power flow solutions. Further investigations certify that presence of residential consumers are more beneficial for the networks as they are more sensitive to voltage. [ABSTRACT FROM AUTHOR]

Wireless charging vehicle to grid (V2G) system is not-so-futuristic. It can maintain the power supply balance and stabilize the grid through a wireless link between vehicles using batteries based on energy supply-demand statistics. Most of the existing privacy preservation methods are based on data tampering approaches (e.g., encryption, adding noise, aggregation), which are not practicable over V2G due to complexity in billing and inaccurate state estimation. In this paper, we develop a novel adaptive demand-side energy management framework by employing federated learning-based privacy preservation for the wireless charging V2G systems. Our framework learns the temporal evolution of energy consumption of dynamic charging electric vehicles in a distributed fashion and exploits the reinforcement learning model for cost-saving and reward maximization. The convergence and the privacy preservation properties of the framework are demonstrated with extensive evaluations. [ABSTRACT FROM AUTHOR]

Ferry contributing a significant amount of greenhouse gas is one of the critical vessels to be electrified. Designing a power system for a ferry with hybrid-shaft generators is different from designing a power system for other vessels because of its fixed route. More clearly, ferries repeatedly travel between their port of origin and port of destination, and before the next voyage, the battery must be recharged to the initial state so that the optimal energy management scheme can be repeatedly applied. Furthermore, the flexibility of hybrid-shaft generators, which allow more fuel saving, increases design complexity. In this paper, a mixed-integer non-linear programming problem is first formulated, and a power management algorithm with an initialization step for fulfilling the battery recharging requirement and a refinement step for minimizing the fuel consumption is proposed. The simulation results obtained from data of an actual ferry show that the proposed power management algorithm can fully recharge the battery and consumes less fuel than a rule-based power management scheme. Simulations also reveal that fuel consumption depends on available shore power, highlighting the necessity to develop charging infrastructure for practical electrification. Because of its speed, the algorithm can support hardware sizing, e.g., battery sizing. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have been widely applied in energy conversion sectors, where effective future ageing prediction is crucial to guarantee their safety and performance. Due to the highly nonlinear ageing behaviours, developing a reliable method that could not only consider the knee point effect but also predict the future ageing trajectory with uncertainty quantification poses a formidable task. This paper derives a machine learning solution, based on the migrated Gaussian process regression (GPR), for predicting future battery two-stage ageing trajectory. Specifically, a base model is first offline identified from the easier collected accelerated-speed ageing data, through which the long life ageing information can be effectively learned. With this base model, a migrated mean function is then designed and coupled within the GPR framework for battery ageing predictions. Experimental data from three different batteries are applied for model validation and performance evaluation. Results indicate that the proposed solution leads to effective improvements in prediction accuracy and uncertainty quantification for both cases of training before and after the knee point. This is the first time to couple migration concept within GPR, paving the way to reduce experimental cost and predict battery future two-stage ageing trajectory with only a few (first 30%) data available. [ABSTRACT FROM AUTHOR]

The dilemma between battery operating revenue and aging has not been addressed appropriately by previous studies. This paper presents a co-optimization method of battery full life-cycle scheduling and lifespan for its maximal lifetime value, involving usage limits, nonlinear aging characteristics, and time-value-of-money effects. The resulting dispatch model consists of numerous large-scale non-convex nonlinear programming (NLP) problems with the nonlinearity appearing in the equality constraint, which cannot be solved efficiently through conventional methods. Thus, we design an exact and fast solution approach including the equivalent problem transformation, a modified pegging algorithm, and a novel acceleration algorithm. The proposed methods are illustrated and validated by numerical tests using real historical data from Pecan St. Project. Test results suggest that it is not the case that the longer service life, the more battery lifetime profits can be generated. The proposed scheduling method can improve the lifetime profits than the conventional methods. For a battery with a 15-year design life, the optimal scheduling and lifespan with a 5-day resolution can be obtained within half an hour. [ABSTRACT FROM AUTHOR]

This paper presents a single-objective model predictive control for a bidirectional DC-DC flying capacitor (FC) converter, which integrates a battery to the microgrid. The presence of multiple FCs facilitates the converter to interface a low-voltage battery to a high-voltage DC bus at reduced voltage stress on its power switches. However, such converter requires a controller that fulfils multiple objectives, namely DC bus and FC voltage regulations, and bidirectional power flow. The key feature of the proposed controller is that it utilizes a model predictive control with a single-objective cost function based on battery current and a redundant state selection scheme to attain these multiple objectives. Adopting such a strategy not only reduces computational burden but also eliminates the need for weighting factors and requirement of complex numerical models for its estimation. Another notable aspect is that the controller employs an improved dynamic reference model to generate appropriate battery current reference for the DC bus voltage regulation, without necessitating a secondary control loop and additional current sensors. Finally, experimental results of the proposed system for a step-response of the DC bus voltage, varying PV power, and loads are validated and compared against finite control set model predictive control. [ABSTRACT FROM AUTHOR]

Long horizon state of health (SOH) monitoring and remaining useful life (RUL) prediction are of industrial value in prognostic and health management (PHM) of lithium-ion batteries (LIBs) to ensure their reliable functionality by early detection. Machine Learning, as a data-driven health diagnostic technique, has been widely utilized in solitary and hybrid structures. However, an accurate SOH estimation and RUL prediction method with less computational burden are highly desirable for the online state prediction in an electric vehicle application. This paper evaluates nonlinear autoregressive with external input (NARX) recurrent neural network (RNN) and time delay neural network (TDNN) in their prediction precision using the NASA dataset. The superior method, NARXRNN, is employed for two different datasets to estimate the battery's SOH and predict its RUL on a broad horizon. The results reveal the outstanding performance by presenting the root mean square error within 3% and mean absolute error within 2% for unseen data. Therefore, this method is capable to accurately predict the SOH of LIBS from historical data at low computational complexity. It is a promising model for long horizon SOH and RUL prediction and practical for online applications. [ABSTRACT FROM AUTHOR]

The use of multiple low-power fuel cells (FCs), instead of a high-power one, in the powertrain of a FC-hybrid electric vehicle (FC-HEV) has recently received considerable attention. This is mainly due to the fact that this configuration can lead to higher efficiency, durability, and reliability. However, the added degrees of freedom require an advanced multi-agent energy management strategy (EMS) for an effective power distribution among power sources. This paper puts forward an EMS based on game theory (GT) for a multi-stack FC-HEV with three FCs and a battery pack. GT is a well-approved method for characterizing the interactions in multi-agent systems. Unlike the other strategies, the proposed EMS is equipped with an online identification system to constantly update the time-varying characteristics of the power sources. The performance of the suggested strategy is investigated through two case studies. Firstly, a comparative study with two other EMSs, dynamic programming (offline), and a competent rule-based strategy (online), is conducted to realize the capability of GT. Secondly, to justify the necessity of online system identification, the degradation effect of each power source on the EMS performance is examined. The carried-out studies show that the total cost (hydrogen consumption and degradation) of the proposed strategy is almost 6% better than the rule-based EMS while keeping a reasonable difference with dynamic programming. Moreover, health unawareness of power sources can increase the hydrogen consumption up to 7% in the studied system. [ABSTRACT FROM AUTHOR]

The growing popularity and penetration of electric vehicles (EVs) bring in challenges and opportunities to the power grid. They represent a huge aggregate electricity consumption from the city power grid, while it is possible to make use of their batteries for supporting vehicle-to-grid (V2G) regulation service. Besides, there is a need to coordinate a collection of EVs in an intelligent manner through the smart transportation system in a smart city. In this paper, we propose a novel smart cross-system framework to support V2G regulation service in a smart city. Specifically, by considering electric buses (EBs) as a particular group of EVs with large battery packs installed and predictable operation schedules, we jointly solve the problems of EB allocation and scheduling with V2G regulation service. First of all, by using the deep learning approach, the short-term city traffic conditions can be predicted via the gated recurrent units (GRUs) neural network. With the predicted traffic conditions, we then formulate the EB allocation and V2G scheduling as a mixed-integer quadratic programming (MIQP) problem. In order to solve this problem effectively, we utilize the Lagrangian dual decomposition method to decouple the main problem into subproblems for EBs and devise a distributed algorithm to solve each subproblem. The simulation results show that our proposed approach is effective in EB allocation and V2G scheduling for the regulation service. Through efficient EB routing strategies under accurate predicted traffic conditions, the power fluctuations of the city power grid can be well flatten by providing the V2G regulation service. [ABSTRACT FROM AUTHOR]

The use of ultracapacitors in fuel cell electric vehicles holds the potential to promote their advancement by significantly extending the fuel cell lifetime. However, the design of suitable control strategies is critical to take full advantage of the additional degree of freedom and limit the principal causes of fuel cell degradation, such as transient operation and start-up/shut-down cycles. This paper studies the energy management of fuel cell-ultracapacitor-battery hybrid vehicles, investigating different criteria for fuel cell operation to find the best trade-off between hydrogen consumption and the number of start-up/shut-down cycles. In particular, the investigated control criteria are fixed-setpoint and charge-balancing operation. A third strategy is considered as a reference for potential performance improvements of predictive energy management. The study reveals that, in fuel cell-ultracapacitor-battery vehicles, the only significant source of fuel cell degradation are start-up/shut-down cycles. Here, harmful transients are limited because ultracapacitors enable stationary fuel cell operation without compromising battery life. Moreover, the investigation shows that the ultracapacitor and battery systems can sustain short driving cycles alone, avoiding fuel cell starts to preserve its life. The simulation results indicate that the fixed-setpoint strategy yields a better trade-off between hydrogen consumption and start-up/shut-down cycles than the charge-balancing one. Additionally, the study demonstrates that a predictive energy management strategy can significantly reduce degradation without hindering system efficiency. [ABSTRACT FROM AUTHOR]

Backscatter communication is an energy-efficient communication technique for the Internet of things (IoT) devices. It enables data transmission by reflecting the incident radio signals. In this paper, we propose a communication mode selection scheme for IoT devices that can communicate using either active transmission or backscattering. In the active transmission mode, the IoT devices can transmit data over narrowband subcarriers using power-domain non-orthogonal multiple access (NOMA). In the backscattering mode, which operates over shorter distance than active transmission, nearby user equipment (UE) devices are used as relays. The UEs receive the backscattered signals from the IoT devices and forward them to the base station. We formulate a connection density maximization problem to select the communication mode used by each IoT device. We determine the IoT device pairing for active transmission mode with NOMA and UE-IoT device association for backscattering mode. The formulated problem is a binary integer programming problem. Although it can be solved optimally, the optimal algorithm incurs exponential computational complexity. Hence, we propose a low-complexity suboptimal algorithm to solve this problem. Results show that our proposed algorithm can enhance the connection density of narrowband IoT systems by up to 64% when compared with using single communication mode. [ABSTRACT FROM AUTHOR]

In heavy-duty vehicle applications, the D+ alternator with the alternator-voltage-regulator (AVR) is widely used to supply electric loads due to lower cost and wide application possibilities. The phase voltage of D+ alternator is usually connected as auxiliary output, such as techometer; however, its wiring may short to ground through the vehicle frame due to the damage of the isolation. This would result in severe thermal events and safety issues. In this paper, a novel phase fault diagnose and protection method is proposed to prevent the thermal event and safety issue for phase auxiliary output fault in D+ alternator system of heavy duty vehicles. The method detects the voltage dip from D+ terminal without extra wiring or external components. Besides, prior art issues such as complex design, high noise sensitivity, and higher cost can be solved. The analysis and LTSpice simulation of D+ voltages in normal conditions and fault conditions set the foundation and parameter design for the proposed method. The experimental platform with a three-phase D+ alternator verified that the proposed method can diagnose and protect the ground fault while the alternator is generating power with rotating speed ranging from 2000 to 6000 rpm, and the AVR can stop the alternator output to prevent the thermal event. As soon as the fault is removed, the AVR can automatically reboot the system and return to a normal operation mode. [ABSTRACT FROM AUTHOR]