This article proposes a novel data-driven linear quadratic regulator for an interleaved boost dc/dc converter. The proposed method utilizes policy iteration and a simple fixed weight recurrent neuron network to simultaneously achieve model independent control and autonomous online optimal control gain update. Compared to the existing model-based control approaches, the proposed method is totally model free. In addition, the proposed method updates the neural network without any offline pretraining, which is a key advantage for industrial applications. The experimental results, which are obtained using the Texas Instrument C2000 series digital signal processor, are presented to demonstrate the effectiveness of the proposed method.

This paper presents a novel methodology to schedule community-level aggregator-controlled battery energy storage (BESS) to counteract the variability of distributed photovoltaic (D-PV) generation and electric vehicle (EV) demand. There is an increasing need to address the variability introduced by D-PV and EV adoption. This variability can lead to challenges like increased operation complexities and reduced efficiency of power distribution. This paper introduces a novel BESS scheduling approach tailored for community-level aggregators. The goal is to shape the grid operator’s demand profile, achieving valley filling and peak shaving without compromising operational costs. The scheduling algorithm is a convex optimisation problem, which was solved using the interior point method to intelligently decide the charging and discharging rate at each time interval. Simulator tests show significant cost reductions of up to 50%. The study shows that the proposed BESS scheduling algorithm can help to create an operator-friendly demand curve while minimising the aggregator cost.

Lithium-ion battery energy storage systems are made from sets of battery packs that are connected in series and parallel combinations depending on the application's needs for power. To achieve optimal control, advanced battery management systems (ABMSs) with health-conscious optimal control are required for highly dynamic applications where safe operation, extended battery life, and maximum performance are critical requirements. The majority of earlier research assumed that the battery cells in these energy storage systems were identical and would vary uniformly over time in terms of cell characteristics. However, in real-world situations, the battery cells might behave differently for a number of reasons. Overcharging and over-discharging are caused by an electrical imbalance that results from the cells' differences in properties and capacity. Therefore in this study, a stratified real-time control scheme was developed for the dual purposes of minimizing the capacity fade and the energy losses of a battery pack. Each of the cells in the pack is represented by a degradation-conscious physics-based reduced-order equivalent circuit model. In view of the inconsistencies between cells, the proposed control scheme uses a state estimator such that the parametric values of the circuit elements in the cell model are determined and updated in a decentralized manner. The minimization of the capacity fade and energy losses is then formulated as a multiobjective optimization problem, from which the resulting optimal control strategy is realized through the switching actions of a modular multilevel series-parallel converter which interconnects the battery pack to an external AC system. A centralized controller ensures optimal switching sequence of the converter leading to the maximum utilization of the capacity of the battery pack. Both simulation and experimental results are used to verify the proposed methodologies which aim at minimizing the battery degradation by reconfiguring the battery cells dynamically in accordance with the state of health (SOH) of the pack.

The power capability of a lithium-ion battery signifies its capacity to continuously supply or absorb energy within a given time period. For an electrified vehicle, knowing this information is critical to determining control strategies such as acceleration, power split, and regenerative braking. Unfortunately, such an indicator cannot be directly measured and is usually challenging to be inferred for today's high-energy type of batteries with thicker electrodes. In this work, we propose a novel physics-based battery power capability estimation method to prevent the battery from moving into harmful situations during its operation for its health and safety. The method incorporates a high-fidelity electrochemical-thermal battery model, with which not only the external limitations on current, voltage, and power but also the internal constraints on lithium plating and thermal runaway, can be readily taken into account. The online estimation of maximum power is accomplished by formulating and solving a constrained nonlinear optimization problem. Due to the relatively high system order, high model nonlinearity, and long prediction horizon, a scheme based on multistep nonlinear model predictive control is found to be computationally affordable and accurate.

Battery energy storage power stations (BESPS) play an important role in the power flow regulation in large-scaled micro-grids. It is proposed that the static Var compensator (SVC) be replaced by BESPS through activation of its dynamic voltage regulation (DVR) function so that the cost of SVC can be saved. An additional controller named energy storage coordination controller (ESCC) is needed to support the control algorithm of DVR and coordinate the individual battery energy storage system units. However, communication delay between the ESCC and the commercial BESS units is compounded by the difficulty of measuring the micro-grid (MG) bus voltage by ESCC. These issues should be considered in the design of the BESPS controller to avoid compromising the micro-grid operation. In this paper, we will be investigating model predictive control (MPC), Smith Predictor and PI based control schemes to deal with the communication delay issue. Kalman Filter based observer is designed to monitor the MG bus voltage as a feedback signal of the ESCC. The stability of the proposed method is further discussed and the results using the control hardware-in-the-loop (CHIL) testing are presented.

Modern industrial and commercial devices that are fed by power electronics circuits and behave text non-linearly tend to produce power quality issues in power systems including harmonics and interharmonics, swell, flicker, spikes, notches, and transient instabilities. Among them, harmonic emission is the most significant challenge to be overcome by the distribution networks. Unwanted current, overheating motors and transformers, equipment failure, and circuit breaker misoperation are some of the harmonic consequences. While it is important to employ the best methods to mitigate or suppress the harmonic distortions in power systems, it is even more essential to estimate these harmonics at the outset by developing smart, efficient, and accurate techniques. Due to their capability for learning, predicting, and identifying, researchers have turned to Artificial Intelligence technologies for harmonic estimation in distribution networks. Although the power system parameters (impedance/admittance model) and many harmonic monitors are prerequisites for traditional harmonic estimation methods, by utilizing Artificial Intelligence, these requirements are minimized. In this paper, a comprehensive review of traditional and modern (smart) harmonic estimation techniques are discussed.

This research focuses on the development of an optimisation framework for scheduling Electric Vehicles (EVs) in Vehicle-to-Grid (V2G) systems, considering the presence of substantial distributed photovoltaic (D-PV) presence in the network. A novel approach is introduced wherein EVs, and Battery Energy Storage Systems (BESS) are synergistically utilised. This dual optimisation ensures the BESS not only absorbs excess renewable energy but also seamlessly integrates EVs, leading to considerable cost efficiencies for aggregators. Utilising V2G capabilities and BESS storage, the proposed scheduling system dynamically manages EV charging and discharging. The design maximises local PV utilisation, lowers peak loads, and reduces grid dependency during high renewable time by intelligently coordinating EV activities. Simulator tests show significant cost reductions, the flexibility of EV adaptation, and improved energy storage and distribution. The study helps make high D-PV integration into V2G systems more affordable, enabling grid-friendly operations and environmentally beneficial mobility.

In recent times, wireless power transfer systems have been identified as a reliable option to supply power to medical implants. Up to now, Wireless Power Transfer Systems (WPTS) have only been used to charge batteries of low-power medical implants. However, for medical implants requiring a relatively higher power, such as a ventricular assist device, which is an implanted blood pump in the patient’s abdominal cavity, an external power supply has been used. When WPTS is used for medical implants, it increases the number of required power converter stages and hardware complexity along with the volume, which tends to reduce the overall efficiency. In addition, the existence of uncertainties in WPTS-based medical implants, such as load and mutual inductance variations, can lead to system instability or poor performance. The focus of this paper is to design a WPTS to supply power to the pump motor directly through its inverter based on the requirements of the motor drive system (MDS) without resorting to an additional DC-to-DC converter stage. To this end, the constraints that the drive system imposes upon WPTS have been identified. In addition, to make a reliable closed-loop operation, a µ-synthesis robust controller is designed to make sure the system maintains its stability and performance with respect to the system’s existing uncertainties. A number of experimental results are provided to verify the effectiveness of the adopted WPTS design approach and the corresponding closed-loop controller for WPTS. Furthermore, the experimental findings for the maximum efficiency tracking (MET) approach (to minimize WPTS coil losses) and constant DC link voltage control approach are shown and compared. According to experimental results and system efficiency analysis, the former appears to perform better. The system dynamic performance analysis, on the other hand, demonstrates the latter’s advantage.

In a dynamic distribution market environment, residential prosumers with solar power generation and battery energy storage devices can flexibly interact with the power grid via power exchange. Providing a schedule of this bidirectional power dispatch can facilitate the operational planning for the grid operator and bring additional benefits to the prosumers with some economic incentives. However, the major obstacle to achieving this win-win situation is the difficulty in 1) predicting the nonlinear behaviors of battery degradation under unknown operating conditions and 2) addressing the highly uncertain generation/load patterns, in a computationally viable way. This paper thus establishes a robust short-term dispatch framework for residential prosumers equipped with rooftop solar photovoltaic panels and household batteries. The objective is to achieve the minimum-cost operation under the dynamic distribution energy market environment with stipulated dispatch rules. A general nonlinear optimization problem is formulated, taking into consideration the operating costs due to electricity trading, battery degradation, and various operating constraints. The optimization problem is solved in real-time using a proposed ensemble nonlinear model predictive control-based economic dispatch strategy, where the uncertainty in the forecast has been addressed adequately albeit with limited local data. The effectiveness of the proposed algorithm has been validated using real-world prosumer datasets.

In recent years, the penetration of Electric Vehicles (EV) has increased owing to the advancement in battery technology and the need for cleaner transportation. This trend is transforming EVs into an integral part of our power grid ecosystem, where they can act both as a provider and consumer of energy. However, the cybersecurity risks of large fleets of EVs within our power grids remain under-explored. This paper defines and analyses a specific cybersecurity risk for EVs, which we refer to as Manipulation of Actual Demand. This attack involves coordinated charging of a large number of EVs across multiple charging stations to disrupt the power grid. We provide a detailed analysis and quantification of the impact of this unique cyber-attack on the power grid in terms of demand-side load. The findings of our analysis guide future considerations on cybersecurity risks of coordinated EV charging and their mitigation.

A bipolar high step-up Cuk-Sepic converters based on the ‘coat circuit’ is proposed, which can be used to connect single photovoltaic (PV) panel and inverters. The main advantage of the proposed converter is that it contains only one active switch as the same as the Cuk or Sepic converter, so it is easy to drive and control. The ‘coat circuit’ is introduced to increase the voltage gain of the converter and reduce the voltage stress of the devices. In addition, the proposed converter can output equalized bipolar voltage with common ground. This paper introduces the working principle and performance analysis of the proposed converter in detail. In order to verify the correctness and validity of the theoretical analysis, a 266.7W experimental prototype has been built

The transport sector is one of the leading contributors of anthropogenic climate change. Particularly, internal combustion engine (ICE) dominancy coupled with heavy private motor vehicle dependency are among the main issues that need to be addressed immediately to mitigate climate change and to avoid consequential catastrophes. As a potential solution to this issue, electric vehicle (EV) technology has been put forward and is expected to replace a sizable portion of ICE vehicles in the coming decades. Provided that the source of electricity is renewable energy resources, it is expected that the wider uptake of EVs will positively contribute to the efforts in climate change mitigation. Nonetheless, wider EV uptake also comes with important issues that could challenge urban power systems. This perspective paper advocates system-level thinking to pinpoint and address the undesired externalities of EVs on our power grids. Given that it is possible to mobilize EV batteries to act as a source of mobile-energy supporting the power grid and the paper coins, and conceptualize a novel concept of Mobile-Energy-as-a-Service (MEaaS) for system-wide integration of energy, transport, and urban infrastructures for sustainable electromobility in cities. The results of this perspective include a discussion around the issues of measuring optimal real-time power grid operability for MEaaS, transport, power, and urban engineering aspects of MEaaS, flexible incentive-based price mechanisms for MEaaS, gauging the public acceptability of MEaaS based on its desired attributes, and directions for prospective research.

With the increase of inverter-based energy resources in power grids, it is imperative that the frequency stability is maintained and inverters can be controlled to improve frequency damping by using grid-forming inverters (GFM). The microgrid that is formed by several DGs that are connected in series is called a series topology microgrid. The series topology with individual LC output filters benefits from utilizing lower and different dc-link voltages. One of the drawbacks in series topologies is that the series cascaded DG microgrids have rarely been discussed when it comes to a different configuration combinations of DGs such as dispatchable and non-dispatchable DGs. In this paper, a comprehensive study of series-cascaded microgrid configuration consisting of master and slave DGs with a storage device is done. The power circuit, control circuit, and results are shown.

To unlock the promise of electrified transportation and smart grids, emerging advanced battery management systems (BMSs) will play an important role in the health-aware monitoring, diagnosis, and control of lithium-ion (Li-ion) batteries (see “Acronyms Used in This Article”). Sophisticated physics-based battery models incorporated into BMSs can offer valuable internal battery information to achieve improved operational safety, reliability, and efficiency and to extend the battery lifetimes. However, because they are developed from fundamental electrochemical and thermodynamic principles, rigorous physics-based models are saddled with exceedingly high cognitive and computational complexity for practical applications.

In this article, a new technique of synchronized sampling to estimate the transferred power in wireless power transfer (WPT) systems is proposed. Defining demand factor as a criterion linking unknown receiver parameters to the transferred power, it can he estimated by measuring the front-end variables of the transmitter LCC compensating network. Moreover, with the use of this technique, one does not need to know how the mutual inductance or load varies. As the demand factor is directly linked to the transferred power, the estimation of the demand factor can be considered as an increasingly effective way to control the flow of power in WPT systems. To prove the validity of this approach, a WPT system equipped with an WC network to compensate the transmitter, and a series capacitor to compensate the receiver, is experimentally built and tested, and the results show a close consistency between the measured demand factor and the transferred power.

Renewable energy sources are unable to produce consistent power due to the unpredictable nature of their source. Therefore, some non-renewable energy sources are required to provide a stable power supply to the grid. Even after installing such non-renewable energy sources, they may be unable to instantaneously change their power output, requiring a storage reservoir to combat peaks and dips in demand. Li-ion battery storage systems are increasingly proposed to absorb excess re-newable energy supply and release power on demand, mitigating supply peaks and dips. This paper introduces a feasible hardware prototype using a modular multilevel series-parallel converter for a Li-ion battery energy storage system with a control mechanism for power conversion and battery management. The proposed system shows promising results with modularity, efficiency, and flexibility as its strong features.

The rapid growth in distributed energy sources on power grids leads to increasingly decentralized energy management systems for the prediction of power supply and demand and the dynamic setting of an energy price signal. Within this emerging smart grid paradigm, electric vehicles can serve as consumers, transporters, and providers of energy through two-way charging stations, which highlights a critical feedback loop between the movement patterns of these vehicles and the state of the energy grid. This article proposes a vision for an Internet of Mobile Energy (IoME), where energy and information flow seamlessly across the power and transport sectors to enhance the grid stability and end-user welfare. We identify the key challenges of trust, scalability, and privacy, particularly location and energy linking privacy for EV owners, for realizing the IoME vision. We propose an information architecture for IoME that uses scalable blockchain to provide energy data integrity and authenticity, and introduces one-time keys for public EV transactions and a verifiable anonymous trip extraction method for EV users to share their trip data while protecting their location privacy. We present an example scenario that details the seamless and closed loop information flow across the energy and transport sectors, along with a blockchain design and transaction vocabulary for trusted decentralised transactions. We finally discuss the open challenges presented by IoME that can unlock significant benefits to grid stability, innovation, and end-user welfare.

In this paper, a small-signal model of a capacitive loaded series-parallel Wireless power transfer system is presented. Dynamic analysis of a series-parallel compensated wireless power transfer system with a capacitive output filter is illustrated based on extended describing function technique. Trial and error methods are still applied to this type of converters because of their ambiguous small-signal model. Since the small-signal model might experience a significant variation due to the load change, the trial and error procedure is not a reliable option. The proposed method provides a convenient option to obtain the small-signal model of the system which would lead to a systematic controller design. The precision of the derived model is verified by comparing it with the circuit simulation in open-loop and closed-loop modes of operation.

In a typical wireless power transfer (WPT) system, the load, the mutual inductance, and the required tuning frequency can vary in a particular range. Variation of each parameter can substantially impact the dynamic characteristics of the system. Thus, they can be considered as sources of uncertainty in the system. To address this, robust control methods such as μ-synthesis are developed to deal with possible system uncertainties. This article first undertakes the generic dynamic analysis of the series–series WPT compensation network for two modes of operation, i.e., constant output voltage (COV) mode and constant output current (COC) mode, in the presence of three aforementioned uncertainty sources. Subsequently, the frequency detuning as a function of the compensation capacitor variation is explored and broadened as a technique to obtain the optimized compensation capacitor value that can lead to a plant with minimized dynamic deviations from its nominal system in the operating range between COC and COV modes. As such, this approach offers a design procedure for a least conservative robust controller, which can significantly improve the system performance. The optimized structure and the corresponding designed μ-synthesis controller are comprehensively elaborated. The theoretical achievements and experimental results show superior dynamic performance of the optimized structure and the corresponding designed controller in the presence of three uncertainty sources compared to the COC and COV modes of operation.

This article proposes a novel model-based estimator for distributed electrochemical states of lithium-ion (Li-ion) batteries. Through systematic simplifications of a high-order electrochemical–thermal coupled model consisting of partial differential-algebraic equations, a reduced-order battery model is obtained, which features an equivalent circuit form and captures local state dynamics of interest inside the battery. Based on the physics-based equivalent circuit model, a constrained ensemble Kalman filter (EnKF) is pertinently designed to detect internal variables, such as the local concentrations, overpotential, and molar flux. To address slow convergence issues due to weak observability of the battery model, the Li-ion's mass conservation is judiciously considered as a constraint in the estimation algorithm. The estimation performance is comprehensively examined under a wide operating range. It demonstrates that the proposed EnKF-based nonlinear estimator is able to accurately reproduce the physically meaningful state variables at a low computational cost and is significantly superior to its prevalent benchmarks for online applications.