Search

This article proposes a cooperative energy transaction model for a virtual power plant (VPP) developed as a bilevel optimization program based on the Stackelberg game in the deregulated electricity markets. The VPP integrates multiple renewable energy hubs consisting of electric vehicle charging stations with photovoltaic and battery energy storage systems. The proposed model derives co-optimized strategic decisions for the VPP operator in the day-ahead (joint active power, reserve, and reactive power) markets while considering the balancing market operation. The proposed model is reformulated as a mathematical program with equilibrium constraints (MPECs) using the Karush–Kuhn–Tucker conditions and the strong duality theorem. For an efficient solution, the MPEC is approximated as a mixed-integer linear program by applying the Fortuny-Amat transform on the complementarity and slackness conditions and the binary expansion on the bilinear terms. The uncertainties of the renewables, loads, and prices are modeled using stochastic scenarios. Finally, comparative case studies of a VPP in an IEEE 33 node active distribution network show that the proposed model improves the profit by strategic cooperative energy transactions. [ABSTRACT FROM AUTHOR]

Power system substations have intelligent electronic devices (IEDs) that collect data and control other devices. As the bridge between the physical and cyber parts of the power system, IEDs capture some key system behaviors. Since adversaries can modify the system’s behavior, physical and cyber data can be used to infer characteristics about the adversary. In this article, we present alert and control logic for hardware-based power system defense using the physical data and communication status in substation IEDs for cyber threat detection, cyber-physical contingency detection and response, and physical contingency identification and response. The proposed alert and control logic routines are implemented in an industrial real-time automation controller using IEC 61131-3 in the resilient energy systems lab testbed. The goal is to help operators identify adversaries and protect the power grid in a cyber-physical environment. The effectiveness and accuracy of logic schemes are validated under different adversarial scenarios. Comparing the proposed schemes with an intrusion detection system, Snort, our results also suggest the benefits of using cyber and physical data to identify threats. The results also suggest the use of such hardware-based schemes with software algorithms in a next-generation cyber-physical energy management system, which can implement automatic control actions to protect power grids and its physical equipment against cyber threats. [ABSTRACT FROM AUTHOR]

The continuous deployment of distributed energy sources and the increase in the adoption of electric vehicles (EVs) require smart charging algorithms. The existing EV chargers offer limited flexibility and controllability and do not fully consider factors (such as EV user waiting time and the length of next trip) as well as the potential opportunities and financial benefits from using EVs to support the grid, charge from renewable energy, and deal with the negative impacts of intermittent renewable generation. The lack of adequate smart EV charging may result in high battery degradation, violation of grid control statutory limits, high greenhouse emissions, and high charging cost. In this article, a neuro-fuzzy particle swarm optimization (PSO)-based novel and advanced smart charge controller is proposed, which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (photovoltaic) output, and battery state of health. A rule-based fuzzy controller becomes complex as the number of inputs to the controller increases. In addition, it becomes difficult to achieve an optimum operation due to the conflicting nature of control requirements. To optimize the controller response, the PSO technique is proposed to provide a global optimum solution based on a predefined cost function, and to address the implementation complexity, PSO is combined with a neural network. The proposed neuro-fuzzy PSO control algorithm meets EV user requirements, works within technical constraints, and is simple to implement in real time (and requires less processing time). Simulation using MATLAB and experimental results using a dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

The escalating demand on electric vehicles (EVs) has enhanced the necessity of adequate charging infrastructure, especially in residential areas. This article proposes a smart charging approach for off-board EVs chargers in home-energy-hub (HEH) applications along with dc sources such as photovoltaic and battery storage (BS). The proposed method facilitates smart charging and discharging of EVs to obtain both vehicle-to-x and x-to-vehicle operations focusing on the domestic applications integrated with renewable and storage elements. Furthermore, the optimal state-of-charge (SOC) profiles for BS and EV in the HEHs system is defined by the extended Bellman-Ford-Moor algorithm (BFMA). This modified BFMA utilizes the forecasted data such as solar irradiation, electricity tariff, and power consumption to gain economic benefits in HEHs with respect to the user and EV requirements. Moreover, the plugging time, duration, and initial/final SOC are fluctuating at each connection due to the stochastic nature of EV conditions and user settings. This study presents a laboratory implementation of two-level hierarchical energy management system for HEHs with plug-in electric vehicles. In fact, the primary level includes power converters controller, while the proposed algorithm is implemented in the secondary level. Finally, the simulation and experimental results confirm the effectiveness of the proposed analysis regarding the interaction of HEHs and power grid with EVs behavior. [ABSTRACT FROM AUTHOR]

Over the past decade, cyberattack events on the electricity grid are rising and have severely impacted grid operation. These attacks are becoming more intelligent as they can falsify critical data and bypass existing protection protocols. This could result in the system to operate in a nonoptimal condition and suffer substantial economic losses over a prolonged period. Hence, it is crucial to develop tools to defend the system against such attacks to minimize the cost of malicious operations. This article aims to develop a novel command authentication strategy to detect and mitigate false data injection attacks (FDIAs) targeting the system centralized economic dispatch (ED) control signals. First, we describe the ED problem based on Singapore’s deregulated market context. We then perform a risk assessment and formulate a stealthy FDIA vector targeting the ED control signals. Subsequently, we propose a novel authentication technique leveraging on the multiagent system, which includes detection and mitigation algorithms to verify the ED control commands. Last, we evaluate the efficiency and latency of the proposed technique on the 5-bus, 3-generating unit, and 15-generating unit test system. Simulation and experiment results using a real-time hardware-in-the-loop system proves that the authentication technique can detect and mitigate the stealthy FDIA vector with high accuracy. [ABSTRACT FROM AUTHOR]

Time triggered communication provides fault tolerance and determinism in a distributed dependable real time systems. These systems crucially depend on fault tolerant clock synchronization. The article presents simulation of fault tolerant averaging algorithm for clock synchronization and it's modification for improved convergence. We present the simulation results of synchronization algorithm to verify fault tolerance and convergence to a single time base. By modification, we show how a two node system can also be synchronized eliminating the criteria of minimum three nodes. This article also reports a custom time triggered protocol named as deterministic fault tolerant communication protocol particularly suitable for mission critical applications or safety-critical control applications such as in automotive “by-wire” and aerospace systems. The article also presents a different approach for membership service which is based on majority voting and can be used in systems based on time triggered architecture. [ABSTRACT FROM AUTHOR]

This article presents a model predictive control (MPC) based multitime scale co-optimized dispatch for integrated electricity and natural gas system (IEGS) considering the bidirectional interactions and renewable uncertainties. In the proposed model, optimal dispatch is extended into three substages optimization problems of day-ahead, intraday, and real-time to coordinate the economy and accuracy of operations. The different optimization strategy is designed in each dispatching stage according to the operating characteristics of multienergy coupled units and high-penetration intermittent wind power. In the day-ahead stage, the unit commitment is tackled to arrange the start-stop plan of slow-response units, maintained in intraday and real-time stages to ensure full-term optimal economic operation. In the intraday stage, MPC-based rolling optimization is implemented to obtain the unit output scheme with the lowest operating cost target. Furthermore, the output scheme is rolling adjusted in the real-time stage based on the ultrashort-term forecast value, and a closed-loop feedback mechanism is introduced to achieve accurate power balance. In each stage, stochastic decision-making is executed based on the stochastic scenario method, efficiently capturing the uncertainty in IEGS. Besides, the proposed nonlinear model is transformed into a mixed-integer second-order cone programming problem using McCormick relaxation, which can be efficiently solved by commercial solvers. Simulation results on IEEE39-Gas20 and IEEE118-GAS40 test systems demonstrate the superiority of the proposed method in operational economy and wind power utilization, and also verify the effectiveness of the method to accurately track random fluctuations without obvious computational burden. [ABSTRACT FROM AUTHOR]

In this article, a hardware-assisted real-time simulation phase-domain (PD) model of a dual three-phase segmented powered linear permanent magnet synchronous machine (SP-LPMSM) used in electromagnetic drive is established. The model is able to consider the end-effect, saturation effect, coupling effect, and open-phase fault conditions, which are specially occurs in the segmented structure in SP-LPMSM. The unbalanced inductances and saturation effect caused by the segmented structure are investigated and a simplified look-up table for inductances is introduced. Additionally, to accurately evaluate the coupling effect, the permanent magnet (PM) flux linkage is decoupled into the product of unit PM flux linkage and coupling coefficient. In order to avoid the numerical impulse caused by the derivative in the progress of calculating back-EMF, sigmoid function is adopted to express the coupling coefficient. Meanwhile, to make the PD-model compatible with the open-circuit fault conditions, incidence matrices and current constraint matrices are introduced to unify the PD-models with different winding connection types and open circuit faults into one general form. The PD-model realized in field-programmable gate arrays (FPGA) based hardware is validated in comparison with finite-element analysis results. [ABSTRACT FROM AUTHOR]

Frequency is a useful parameter for ensuring the proper functioning of the power system operation, control, and protection. In this article, a fast and accurate frequency estimator (FE) is developed using a zero crossing and a peak, occurring in the buffered data, unlike the commonly used two zero crossings. Prior to this, the signal is preprocessed using the sliding Goertzel filter (SGF) that mitigates the impact of undesirable features such as harmonics, noise, etc., from the signal. The two FEs, with and without SGF, then work in tandem to form the hybrid estimator and are named as $Z_{C}P_{k}$ - ${{HFE}}$. The estimated frequency is chosen based on the minimum error computed from the two parallel estimators. The performance of the estimator is, then, validated using the simulated signals polluted with harmonics, noise, transients, etc. The article also discusses the protection-oriented applications of the proposed FE in active distribution network (ADN). Moreover, the evaluation results with the real-time field data and the IEEE-13 ADN incorporated with photovoltaic using dSPACE1104 and real-time digital simulator show its capability to estimate the real-time frequency with satisfactory output. [ABSTRACT FROM AUTHOR]

The power hardware in the loop (PHIL) is an attractive way of performing various studies for testing a non-linear power electronic converter in laboratory scale and yet get a result that resembles an actual like scenario. This approach, however promising, suffers from various stability problems arising due to the interface between the hardware and software environment required to create a PHIL. This article presents a thorough analysis of the stability problem in PHIL by individually studying the interface device that forms the ideal transformer method (ITM) interface. The model of the ITM interface is developed and verified experimentally using a frequency sweep approach. The developed model can serve as a tool to understand the factors affecting the stability in a PHIL set up. Utilizing the developed model, this article proposes a Smith predictor (SP) compensator that eliminates the effect of delay in the closed loop response of the system. The SP compensator is designed and implemented in a real time digital simulator platform and the performance of the compensator is verified through various experiments. A case study of a compensator employed resistor divider network is presented to validate a stable PHIL, both theoretically and experimentally. Further, the proposed compensator is tested to evaluate a 250 W grid connected photovoltaic inverter in a PHIL. [ABSTRACT FROM AUTHOR]

Cosimulation techniques are gaining popularity amongst the power system research community to analyze future scalable smart grid solutions. However, complications such as multiple communication protocols, uncertainty in latencies are holding up the widespread usage of these techniques for the power system analysis. These issues are even further exacerbated when applied to digital real-time simulators (DRTSs) with strict real-time constraints for power hardware-in-the-loop (PHIL) tests. In this article, we present an innovative digital real-time cosimulation infrastructure that allows interconnecting different DRTS through the Aurora 8B/10B protocol to reduce the effects of communication latency and respect real-time constraints. The proposed solution synchronizes the DRTS interconnection by means of the IEEE 1588 precision time protocol (PTP) standard to align executions and results obtained by the cosimulated scenario. The ideal transformer method interface algorithm, commonly used in PHIL applications, is used to interface the DRTS. Finally, we present time-domain and frequency-domain accuracy analyzes on the obtained experimental results to demonstrate the potential of the proposed infrastructure. With the presented setup, a time step duration down to 50 $\mu$ s is shown to be stable and accurate in running an electromagnetic transients cosimulated power grid scenario by interconnecting two commercial DRTS (i.e., RTDS NovaCor), extending the scalability of future smart grid real-time simulations. [ABSTRACT FROM AUTHOR]

This article provides a two-stage robust energy management method for a self-healing smart building that can handle contingencies that occur during real-time operation. Aside from an electrical link with the distribution network, the smart building is equipped with a diesel generator and photovoltaic solar power generating systems. The energy management system should be smart enough to plan different resources based on the situation. At first, bilevel programming identifies critical faults for affected components based on mean time to repair. After identifying major failures, the faults are described in operational scenarios, and a two-stage hybrid robust-stochastic programming technique is used to determine the bid/offer in day-ahead and real-time energy markets, in which stochastic programming is responsible for considering the uncertainty of faults, and the robust optimization approach is used to cope with the uncertainty of real-time market prices. After linearization, the final optimization is modeled as mixed-integer linear programming in the GAMS optimization package. For the studied smart building, the daily operational cost is expected to increase from $25.794 (for the deterministic case) to $ 28.097 (for the most conservative case) due to the uncertainty of real-time market prices. Due to power shortages caused by the failure of components, the total expected not-supplied load is 6.72 kW (2.53%). A comparison between naive and self-healing scheduling indicated that naive energy management will charge an additional $ 2.75 without considering the probability of components failures under the deterministic case. [ABSTRACT FROM AUTHOR]

Currently, triple phase-shift (TPS) modulation has attracted more and more attention of researchers as an advanced modulation strategy for dual active bridge converter (DAB). Since it has three degrees of freedom, it can realize better performance both in soft switching ranges and power efficiency. However, how to choose these three degrees of freedom to realize optimal power efficiency of DAB converter becomes a concern for researchers. Generally, there exist two difficulties to apply efficiency-oriented TPS modulation. The first difficulty lies in the analysis process in which the main task is to figure out the relationships between modulation parameters and power loss. The three modulation parameters in TPS bring difficulties in analysis and deduction process, which suffers from high computational burden and low accuracy. Additionally, the second difficulty lies in the real-time realization of TPS modulation. If a look-up table is applied to store the optimized modulation parameters, it is highly likely that its discrete nature will result in unsatisfactory modulation performance. Therefore, this article proposes an efficiency-oriented automatic TPS (ATPS) modulation approach, which utilizes neural network, particle swarm optimization, and fuzzy inference system, respectively, in its three stages. The proposed ATPS is able to mitigate labor in computational burden with a highly automatic fashion. Finally, this proposed ATPS has been validated with 1-kW hardware experiments. [ABSTRACT FROM AUTHOR]

Battery storage management that involves multiple revenue streams would affect customers’ monthly electricity costs. In this article, a three-level model of battery storage management is proposed for achieving various functionalities, including energy arbitrage, peak shaving, and frequency regulation. The original joint optimization problem with the horizon of one month is decomposed into three subproblems according to time scales, covering the daily battery power baseline decision, hourly regulation capacity submission, and real-time regulation response in seconds. A dynamic programming framework is adopted to integrate three submodels, featuring reduced solving complexity and the overall optimality of the proposed model. Also, the unexpected regulation capacity of customers and the uncertainty of exogenous information are considered to accurately depict the characteristics of practical scenarios. Extensive case studies for two types of customers, i.e., the pure consumer and the prosumer, are conducted to validate the effectiveness of the proposed model, which achieves at least 6% (consumer) and 19% (prosumer) benefit improvement as compared with other strategies. [ABSTRACT FROM AUTHOR]

Grid impedance has a significant impact on the small-signal stability and control of grid-connected power converters used for connecting multiple distributed energy resources and in various motor drive applications. To apply advanced control schemes, such as adaptive control, real-time grid impedance is a necessary parameter that ensures stability margins of the system during operation. For real-time grid-impedance measurement purpose, broadband frequency-response measurement techniques based on the pseudorandom binary sequence can be used. In this article, a computationally effective perturbation signal quadratic residue binary (QRB) sequence is proposed for real-time grid impedance measurement. Unlike the conventional perturbation signals, the QRB sequence has low crest factor and less perturbation duration resulting in minimal disturbance during impedance measurement. To extract the grid impedance in real-time, the QRB sequence is added to the reference d-axis and q-axis currents of the battery energy storage system (BESS). Next, perturbed voltage and current signals are extracted in the abc-frame and converted to the d-q-frame, and grid impedance is assessed. Subsequently, the system stability is analyzed using the generalized Nyquist stability criterion. Later, an adaptive control algorithm is proposed and applied to the energy storage system (ESS) to enhance stability margins of the system based on the measured grid impedance. The effectiveness of the proposed perturbation sequence for impedance measurement and stability improvement algorithm are tested on a standard CIGRE microgrid system in the MATLAB/Simulink simulation environment. Experiments are conducted on a reduced scale 3 kVA laboratory testbed and results are presented to validate the efficacy of the proposed perturbation sequence and stability improvement algorithm. [ABSTRACT FROM AUTHOR]

This article addresses the problem of optimizing voltage profiles in distribution networks. The voltage optimization is split into two stages; the former is performed offline and the latter online. First, the network is partitioned into several weakly coupled voltage control zones (VCZs) with pilot nodes (PNs). Then, the partitioning is used to optimize the voltage profiles of the distribution systems on the frame of a two time-scale-based coordinated approach. At the first level, a centralized voltage optimization problem (VOP), minimizing the distance of bus voltages at the PNs from their reference values and subject to linearized power flow equations, is solved to fix the positions of the on-load tap changer and of step-voltage regulators, and the reactive powers provided by capacitor banks. At the second level, the VOP is implemented according to a decentralized approach, in which the solution is obtained by applying a distributed algorithm based on the alternating direction method of multipliers. It optimizes in each VCZ the voltage at the PN by acting on the active and reactive powers provided by the distributed energy resources present in the VCZ; the VCZ solutions are driven to the global optimum of the whole distribution system by a limited data exchange between the PNs. The proposed approach reduces the complexity and computational burden typical when solving the VOP on a large scale system. The proposed strategy is tested on the modified IEEE 123-bus system; various load and generation scenarios are analyzed proving the effectiveness of the proposed approach in achieving the objective of voltage regulation. [ABSTRACT FROM AUTHOR]

Integration of widely distributed small-scale renewable energy sources like rooftop photovoltaic panels and emerging loads like plug-in electric vehicles would cause more volatility in total net demand of distribution networks. Utility-owned storage units and control devices like tap changers and capacitors may not be sufficient to manage the system in real-time. Exploitation of available flexibility in demand side through aggregators is a new measure that distribution system operators are interested in. In this article, we present a developed real-time management schema based on Internet of things solutions which facilitate interactions between system operators and aggregators for ancillary services like power balance at primary substation or voltage regulation at secondary substations. Two algorithms for power balance and voltage regulation are developed based on modified optimal power flow and voltage sensitivity matrix, respectively. To demonstrate the applicability of the schema, we set-up a real-time simulation-based test bed and realized the performance of this approach in a real-like environment using real data of a network with residential buildings. [ABSTRACT FROM AUTHOR]

The rapid development of distributed energy resources has posed great challenges to the real-time balance between electricity supply and demand. A suitable market design not only solves the problem of balance but also maximizes social welfare. This article compares two typical community market designs: a centralized manager-based energy market and peer-to-peer (P2P) energy trading that is based on the continuous double auction. First, both market designs and network security verification are presented. Then, in case studies, we quantitatively compare these two market designs in terms of social welfare, the total payment, and the energy trading volume. It is shown that the manager-based energy market facilitates trading energy within the community and with external communities if necessary, but this market design requires collecting information on every market participant to formulate and solve a centralized optimization problem. In contrast, for an energy self-sustaining community, P2P energy trading might be a superb design because it is decentralized, flexible, and privacy-preserving. Meanwhile, with ex post security verification, P2P energy trading can also guarantee the secure operation of the network. [ABSTRACT FROM AUTHOR]

Capital cost is a practical limitation in distributed energy resources (DER) system sizing and the optimal size may lead to high capital investment. This calls for the development of a framework that provides alternate system sizing with the ability to address capital cost constraints. A framework for sizing microgrid DER considering capital cost constraints and inclusion of manufacturer catalog ratings is introduced in this article. This framework uses a system-wide, composite, cost metric that combines capital costs and operating costs per month. This per-month optimization results are transformed to evaluate the feasible DER ratings and annual savings possible with imposed capital and operating cost constraints. The formulated framework is demonstrated using two microgrid case studies in peak shaving applications at industrial facilities located in the U.K. and USA. This article also introduces development of hardware-in-loop (HIL) system as a tool to validate microgrid sizing results demonstrating that real-time operation can provide the savings projected by the optimized assets. The HIL simulation, with a microgrid controller and the plant sized per the optimization verifies the annual savings. This HIL confirmed that the sizing did not result in violations of state of charge limits of battery energy storage. Traditional optimal sizing approaches do not verify for violations and can result in suboptimal or oversized designs. [ABSTRACT FROM AUTHOR]

Under partial shading conditions, a series-connected photovoltaic (PV) string often shows multipeak power–voltage ($P$ – $V$) characteristics, which indicate a multimodal distribution of maximum power points (MPPs). The phenomenon brings challenges to the MPP estimation since a single model usually cannot perform well in different modes. To address this problem, a real-time shading identification process is proposed to identify the shading information of the applied PV string. The measured data are divided into several fields according to the obtained number of peaks in the $P$ – $V$ curve. Multiple Gaussian process regression (M-GPR) models are then proposed to predict the MPP locus in a specific data field. Experiments are conducted to evaluate the accuracy of the proposed method. Simulation and experimental results show that the shading information enables the proposed M-GPR to obtain more accurate estimation performance compared to its single-model counterparts. [ABSTRACT FROM AUTHOR]

In this article, a robust adaptive controller based on active disturbance rejection control algorithm is proposed for interleaved dc–dc Boost converter for fuel cell application. The unknown load power and parametric variations are regarded as part of the total disturbance, which is estimated by extended-state-observer (ESO), and then canceled in the control law. Within the proposed controller, the parameter ${b_0}$ is adapted in real-time to reduce the estimation burden of ESO. It is shown that compared with the conventional controller, the proposed controller can achieve significant improvement in terms of robustness, the strong antidisturbance performance can be maintained with the varied number of active phases. Finally, simulation and experimental results validate the effectiveness and superiority of the proposed controller. [ABSTRACT FROM AUTHOR]

Situational awareness and decision support for the power grid require accurately representing all the grid components in the modeling and analysis tools. Accurate load modeling is critical to understand the impact of voltage-dependent load behavior. The traditional constant power load model is incapable of accurately considering the load behavior. Real-time data-driven power system applications can be easily realized with the availability of synchronized data at a high reporting rate from phasor measurement units (PMUs). PMU data at substation buses can be used for the accurate estimation of aggregated load model parameters. In this work, two novel algorithms, adaptive least squares assisted by variable elimination (A-LSVE) and recursive least squares with reconfiguration of covariance matrix (RLS-RC), are proposed for load parameter estimation. A-LSVE uses least-squares assisted by sensitivity-based variable elimination and adaptive selection of estimation windows. It uses the current window and two past neighboring data windows with a similar load to voltage sensitivity for parameter estimation. RLS-RC, uses recursive least squares with the sensitivity-based reconfiguration of the estimated covariance matrix. A preprocessing framework is developed for measurement data, which uses an ensemble-based bad data detector to mitigate the impact of data anomalies and a cascade filter (median filter followed by the Kalman filter) to reduce the effect of noise in PMU data in parameter estimation. Moreover, a confidence factor computation framework is proposed to give insight regarding the accuracy of estimated parameters. The developed algorithms are validated using realistic load parameter estimation data with noise and anomalies for the IEEE test system modeled using electromechanical and electromagnetic simulators. [ABSTRACT FROM AUTHOR]

This work proposes an active thermal management technology that operates ac drive inverters safely at thermal limits to maximize their current and torque capability. For that purpose, the technology exploits two fundamental opportunities: First, it monitors the coolant and junction temperature to adaptively determine the maximal peak current capability that increases at low coolant temperature. Second, it takes advantage of the thermal impedance frequency response function $Z_{\mathrm{th}}(\text {j}\omega)$ that decays with higher frequencies. Thus, larger currents are feasible at higher excitation frequencies without increasing peak junction temperature, because the associated transient losses are absorbed by the thermal capacitance of the power module. This article presents a unique active thermal management algorithm that addresses the two identified opportunities. It utilizes primarily a reduced-order electrothermal real-time model as well as negative temperature coefficient thermistor temperature information. Thus, it can be applied on most drive systems with small software modifications to adaptively maximize its current capability. To make this technology applicable in ac drives, this article also proposes torque control strategies that utilize variable current limits and discusses essential design limitation. With the introduced technology, state-of-the-art drive inverters can operate safely at up to 200% overload current and torque across a wide speed range. This supports the development of highly reliable drives with increased base speed range and power density that save space, cost, and resources. [ABSTRACT FROM AUTHOR]

This article proposes a method to estimate the impedance of lithium-ion cells by using an excitation signal. The injection signal is intended to be delivered to the battery module by the power converter used for the interface of the energy storage and using the current and voltage measurements provided by the module battery management system (BMS). The estimation method is conducted in two steps. During the system commissioning, the impedance is estimated by using a frequency-domain method known as vector fitting. Despite its high accuracy, it has an important computational burden that makes it difficult to use in real-time applications running on the embedded systems often used for battery and converter control. For that reason, during the regular operation, a recursive least squares algorithm is proposed, being the initial estimation given by the frequency-domain method employed during the commissioning process. The discussion includes not only the description and testing of the method but also the effects due to the reduced digital-domain resolution of the acquired data used for the estimation (terminal current and cell voltage), as well as the discretization method used for the digital implementation. The proposal is validated by simulation and by experimental results using the cell measurements provided by a BMS of a commercial module. [ABSTRACT FROM AUTHOR]

The modern applied research requires multidisciplinary approaches and can be effectively enriched thanks to the close collaboration of universities’ and industries’ working teams. This kind of collaboration implies sharing of hardware and software facilities, and profitable contamination of knowledge and expertise among the members of the different teams. In the power system field this kind of approach may result into a remote connection allowing the coupling (and thus, the share) of real-time simulators located in different laboratories. This article describes in detail an experimental testbed (consisting of the interconnection of two real-time simulators, located at Politecnico di Torino and Politecnico di Bari, at a geographical distance of 1000 km) in order to perform remote power hardware-in-the-loop experiments. The possibilities and limitations of this type of cosimulation are described and a case study is presented. Finally, the specific problems related to communication latency and simulation stability are analyzed and discussed. [ABSTRACT FROM AUTHOR]

This article proposes a multilevel coordinated voltage control (CVC) strategy for facilitating distributed residential demand response for network voltage optimization. The proposed CVC scheme coordinates multiple home energy management systems (HEMS) connected on a low-voltage radial distribution network through a sensitivity-based approach to minimize voltage deviations in multiple timescales owing to high photovoltaic (PV) penetrations and load variability. In order to mitigate the effects of uncertain PV output and load during actual system operation, a day-ahead two-stage robust optimization (TSRO) model has been developed, which optimizes network operation in the longer timescale, for worst-case realization of uncertainties in shorter timescales. This is sequentially followed by fully distributed hour-ahead CVC which is decomposed into network optimization and energy management subproblems through alternating direction method of multipliers. In order to counteract the effects of real-time uncertainties, local adjustments are performed by redispatching HEMS at individual households to locally adapt to unforeseen variations, thereby minimizing real-time voltage deviations. Extensive simulations are performed on the modified Dutch LV network. Simulation results demonstrate effectiveness of the proposed multilevel CVC approach to robustly handle uncertainties while ensuring reliable operation and maximum user-comfort. [ABSTRACT FROM AUTHOR]

In this article, a cost-based mathematical optimization is used to evaluate the optimal amount of imported power from the public main grid to a private microgrid (MG), that is the “LAMBDA lab MG” testbed placed at the Sapienza University of Rome. In this regard, this article considers five tests based on using different sources, including a photovoltaic (PV) array, an emergency generator set, a fuel cell, and the main grid, for load satisfaction. The “LAMBDA lab” can be considered as a “multisource multioutput energy hub” with three optional sources and both electrical and heat demands in output. This article considers PV production and load demand as indeterministic parameters and evaluates the problem under uncertainties. As a result, a stochastic programming model is defined, and a powerful optimization function is used to reach the optimal power received from the main grid. Besides, information gap decision theory is used to model the robustness of the problem against uncertainties associated with renewable generation units (PV system) and electricity loads applied on a real case for the first time. In the result section, the contribution of each source in electrical and heat load demands is presented in addition to the cost of each test by evaluating the effect of demand response of 15%. Finally, a comparison between the stochastic programming method and IGDT has been accomplished. [ABSTRACT FROM AUTHOR]

In this article, we propose a decentralized energy management system to minimize the daily operating cost of microgrid and the net charging cost of individual plug-in electric vehicles (PEVs) while maintaining comfort level and privacy. A novel approach based on stochastic dynamic programming (SDP) with N-person noncooperative game-theoretic approach is used for real-time scheduling of heterogeneous PEVs both ways—in the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) mode. We use the SDP method for full enumeration of all possible decisions while game theory provides quick convergence to Nash-equilibrium. The proposed approach considers uncertainties of renewable generation as well as load demand and offers autonomy to the microgrid operators and PEVs owners to optimize their objectives individually. The effectiveness of the proposed approach has been tested on modified LV CIGRE and modified IEEE 33 bus test networks. The results have been compared with the deterministic and stochastic bi-level optimization. The simulation results exhibit that the proposed algorithm outperforms the other two algorithms in terms of operating cost, incentive to the PEVs owner, robustness to uncertainties, and convergence. The operating cost reduction achieved for the modified CIGRE model and modified IEEE 33 bus with the proposed approach is 20.02% and 36.53%, respectively, as compared to the deterministic framework, whereas 7.64% and 16.35% are the values obtained in comparison with the bi-level approach. [ABSTRACT FROM AUTHOR]

Incentive-based demand response (IBDR) was considered as a feasible option to solve the sudden shortage of distributed energy resource in the microgrid. However, the current IBDR policies and strategies are not directly applicable to residents due to many defects. To this end, a versatile hierarchical IBDR strategy is proposed in this article. To ensure the effectiveness of IBDR in load reduction on critical period, clustering with demand response (DR) levels decision-making algorithm is proposed to obtain the reliable estimation of DR quantity. To enhance system reliability, a reliability constraint-based DR contingency management scheme is also proposed. Moreover, to avoid the additional costs cause by DR rebound effect, a systematic costs minimization algorithm is developed. To evaluate the performance of the proposed method, the actual case-based series testing was conducted. The proposed strategy is proved that is able to effectively reduce the overall cost of microgrid while the deficiency of DER occurs. [ABSTRACT FROM AUTHOR]

With the emerging of prosumers in residential level, peer-to-peer (P2P) energy trading provides a novel way to prompt the localized management of distributed energy resources (DERs). However, the work of designing an appropriate market framework, with the fair and flexible pricing strategy for each participant, is still very challenging. This article proposes a two-settlement P2P energy market framework for the joint scheduling and trading of prosumers in community microgrid. To provide price certainty and increase the localized transaction volume of DERs, a risk-implemented simultaneous game approach is adopted into the hour-ahead market. For mitigating the supply uncertainties in local, meanwhile optimizing the overall financial benefits for the entire microgrid, a BRP-based reserve services mechanism, with an auction-based pricing strategy, is implemented to a real-time market. The various case-based simulation results are presented to verify the effectiveness of the proposed two-settlement trading scheme for financial benefits enhancing and DERs consumption increasing in the P2P energy market. [ABSTRACT FROM AUTHOR]

The conservation voltage reduction (CVR) technique is a well-established technique for peak demand reduction and energy savings in the presence of voltage-sensitive loads. However, intermittent and uncertain nature of renewable energy sources such as photovoltaic generation may produce some operational issues under real-time CVR operation, which results in major problems such as undervoltage as well as increase in power losses in an active distribution network. Advanced power-electronics-based devices such as inverter-based distributed generators (IDGs) and soft open point (SOP) devices emerge as potential solutions for the aforementioned issues by providing the volt–ampere reactive (VAR) support. However, an efficient CVR methodology is much needed for proper coordination of multiple traditional volt/VAR control devices and advanced power-electronics-based devices. Therefore, this article introduces a multistage coordinated CVR methodology incorporating IDG and SOP devices for offline (i.e., day-ahead scheduling) and online (i.e., real-time dispatch) studies. For day-ahead scheduling, stochastic CVR formulation has been developed considering intermittency and uncertainties in power loads and generations. In contrast, online simulations have been performed in two stages for near-real-time operation using an inner-day rolling control model based on model-predictive control and real-time control through modified volt/VAR droop (VVD) control by optimal VVD curve selection. To validate the proposed scheme, a real-time cosimulation platform using a real-time digital simulator and a MATLAB–GAMS interface has been built. The efficacy of the proposed method has been validated on various case studies and also tested on sudden external disturbances such as sudden cloud transient, cloud cover, and substation voltage changes. Furthermore, the robustness of the proposed method has also been checked under faulty conditions. The test results demonstrate the significance of the proposed methodology on voltage violation mitigation, energy loss, energy consumption minimization, and service restoration. [ABSTRACT FROM AUTHOR]

A large number of distributed energy resources (DERs) integrate into the distribution network, which changes the power flow, increases the power fluctuations, and complicates the scheduling of the distribution network. To cope with that, a multitimescale scheduling method, which considers the demand response as well as user satisfaction, is proposed in this paper. First, in the day-ahead stage, both the generation-side and demand-side are combined to minimize the operating costs and reduce the impact of DERs. Second, in the real-time stage, the model predictive control method is introduced, smoothing the power fluctuations and maximizing the consumed renewable energy. Finally, the user comprehensive satisfaction is considered, ensuring the users’ benefit and improving the flexibility of users to participate in scheduling while shifting electricity demands. By optimizing the generation-side and demand-side on both day-ahead and real-time timescales, the proposed method can improve the operation status for the distribution network effectively while ensuring the interests of users. Simulation on an improved IEEE-33 bus distribution system verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Virtual power plants (VPPs) have become a driving force for the decentralized energy industry, due to their efficient management and control of distributed energy resources. Most of the operation strategies for VPPs are designed based on the day-ahead forecasts. However, the prediction errors of the renewable energy sources (RES) and loads in the power dispatch schedule can lead to a suboptimal operation. In this article, an adaptive and predictive energy management strategy for a real-time optimal operation of VPPs is proposed based on the model predictive control technique with a feedback correction (FC) to compensate for the prediction error. This strategy has two parts: 1) receding-horizon optimization (RHO), and 2) FC. In the first part, a hybrid prediction algorithm based on the integration of the time-series model and the Kalman filter is used to forecast the output powers of RES and the loads. Based on the prediction, the RHO model schedules the operation following the latest forecast information. In the second part, the receding schedule is adjusted based on the fast-rolling gray model's ultrashort-term error prediction. The FC is applied to minimize the adjustments for compensating the prediction error. The proposed strategy is implemented on a VPP in a real electricity distribution system in New South Wales, Australia. The simulation results demonstrate the effectiveness of the proposed strategy with a better tracking of the actual available resources and a minimal mismatch between demand and supply. [ABSTRACT FROM AUTHOR]

This article proposes a low-computation framework for on-board calculation of energy-optimal cruising speed, and uses the framework to investigate the critical parameters for energy-optimal cruising speed determination. The main features of the proposed framework are as follows: first, inclusion of all internal and external vehicle losses, particularly the accessory loads, which have a large impact on optimal cruising speed and have not been investigated in other works, second, use of accurate motor/inverter loss look-up-tables rather than approximated polynomial models, and, third, no requirement for connected data or knowledge of the future route, as the optimal cruising speed is calculated precisely for the current state of the vehicle and surroundings. To validate the framework, three electric vehicle models are developed in MATLAB/Simulink and tuned to match real-world data for a Chevrolet Bolt, Chevrolet Spark, and Tesla Model S. Furthermore, an algorithm is developed to determine near-optimal speed transitions when the optimal cruising speed changes. [ABSTRACT FROM AUTHOR]

Due to the absence of utility power grid infrastructure in remote military bases, on-site diesel generators serve as the primary sources for power demands. Increasing efficiency and preventing frequent startup/shutdown operations of on-site diesel generators are therefore becoming a critical issue for reducing fuel cost. Application of vehicle-to-grid technology in a military-based microgrid embodies potential for significant fuel economy benefits since on-board vehicle generators and energy storage units can serve as mobile power sources that provide higher flexibility for supplying power demands. In addition, energy storage system integration is considered as an alternative solution for increasing on-site diesel generators efficiency and lessening their startup/shutdown operations. This article proposes a three-stage planning procedure for identifying the optimal locations and capacities of energy storage systems, considering multiple operating scenarios via stochastic programming. Note that on-site diesel generators and on-board vehicle generators support plug-and-play functionality, meaning their startup/shutdown operations can be decided in real time. Furthermore, network-constrained ac unit commitment model is used to optimize operation of microgrids. It is assumed that in the tested microgrid systems, several tactical military vehicles with on-board generators and energy storage units are deployed as alternative power sources. The economic merits of vehicle-to-grid implementation and energy storage system integration in a military-based microgrid are validated in the numerical studies. [ABSTRACT FROM AUTHOR]

This article, based on modeling and scaling as well as simulation and experiment, provides a comprehensive discussion on a real-time real-power emulator for a medium-voltage, high-power, high-speed motor drive. A three-phase modular multilevel double-star chopper-cell (DSCC) inverter under test is connected in front-to-front (FTF) with the emulator. The DSCC inverter is the same in circuit configuration as the DSCC rectifier. This emulator is characterized by integrating mechanical dynamics of both motor and load into it. The power-electronic circuit of the emulator consists of a three-phase DSCC rectifier, three ac inductors, and a single two-winding common-mode inductor at the dc side. This article starts with modeling and scaling of a three-phase 6.6-kV 10.9-MW four-pole 9000-r/min induction motor drive. Then, the authors design, build, and test a three-phase 200-V 10-kW downscaled test bench. Experimental waveforms from the 10-kW test bench, as well as simulated waveforms from the 10.9-MW full-scale and 10-kW downscaled systems, lead to the following conclusion: The emulator can reproduce both electrical and mechanical dynamics of the three-phase 200-V, 10-kW, four-pole, 9000-r/min induction motor coupled with a centrifugal compressor. [ABSTRACT FROM AUTHOR]

This article presents a data-driven approach for the classification of faults in induction machines. The designed scheme involves newly engineered features extracted from the line current signals, which provides an improved fault discrimination. For this purpose, a topological-based fast projection technique (curvilinear component analysis) is used as a tool to reduce the dimensionality of the data and interpret the feature behavior. Consequently, a shallow convolutional neural network has been designed to classify the three-phase stator current signals. Experimental tests at different operating conditions have assessed the procedure, confirming its effectiveness and suitability for online and real-time diagnostics. [ABSTRACT FROM AUTHOR]

The high penetration rate of smart devices such as storage elements brings new challenges to the optimal power flow (OPF) problem in power systems, which is generally nonconvex and difficult to be solved in real time. This article proposes a set of two fully distributed algorithms by combining the alternating direction method of multipliers and proximal alternating minimization techniques. The first one is a basic distributed algorithm for offline scheduling of 24-h ahead OPF. The other extended one is the warm-starting algorithm by using the offline scheduling solution as the initial point, and the warm-starting algorithm can get converged faster than the basic algorithm. Both algorithms aim to provide a highly feasible solution for the nonconvex OPF problem, and simulation conducted on four radial power systems with batteries has validated the performance of these two algorithms. [ABSTRACT FROM AUTHOR]

Strategies to implement emulation of large power electronic converters with different topologies using a generic multiport decomposition technique with different levels by exploiting the parallel nature of field programmable gate array (FPGA) are explained in this article. Switches are modeled as on/off resistors and a Python-based software tool is developed to precompute the system matrices corresponding to each switch combination. Converters like three-phase five-level-flying-capacitor are modeled in C using the precomputed data, converted to VHDL and implemented on ZCU104 and ZedBoard using Vivado and verified against the corresponding MATLAB-Simulink model. Also, the real-time performance is analyzed by connecting it for the closed-loop speed control of induction motors of various specifications by changing the load torque and reference speed and found out that the latency is less than 6 μs, which is sufficient for the wide range of variable frequency drive applications. [ABSTRACT FROM AUTHOR]

With the ongoing automation driven by the push toward the smart electric grid and the advancement in associated cyber infrastructure, the interaction between physical [electric transmission and distribution (T & D) systems], cyber (communication, automation, and control), and human (grid operators and decision-makers) is increasingly becoming more complex. This creates the requirement of analyzing the effect of the transmission system on the distribution system and vice versa with consideration of the additional complexity of the cyber infrastructure. Such an integrated testbed will also help with resiliency analysis, where resiliency refers to the ability of the system to continue serving energy to the critical loads even with limited extreme contingencies. Interaction of the physical power grid with the cyber layer can be effectively modeled using real-time (RT) simulator for developing and validating various operational and control algorithms. Testbeds using RT simulators with multiple capabilities have been developed at different institutions. Still, no single existing testbed can offer full scalability while simultaneously meeting high fidelity requirements for resiliency experimentation. Co-simulating federated testbed assets can provide a scalable experimentation platform that can be leveraged for verification and validation. In this article, an architecture is developed for federated cyber-physical testbed. A local federation with two real-time simulators is developed: real-time digital simulator (RTDS) and OPAL-RT have been interfaced using VILLAS framework for end-to-end testing. Also, a real-time linear predictor is developed and integrated here to address the communication latency impact on geographically allocated federated RT simulation. Finally, resiliency analysis tools are formulated and utilized for T & D systems. As an illustrative use case, the resiliency of a T & D test system is simulated, and the results are analyzed. A 179-bus Western Electricity Coordinating Council (WECC) transmission system is developed using OPAL-RT/ HYPERSIM, and a modified IEEE 13 node feeder system is modeled in RTDS/RSCAD and interfaced for resiliency analysis. [ABSTRACT FROM AUTHOR]

Cyber-physical energy system (CPES)—complex juxtaposition of multiple energy domains and communication and automation technologies—requires a sophisticated testing framework to achieve holistic assessment and validation, especially at large scale. In this article, the real supervision, control, and data acquisition (SCADA) system of CPES is integrated with the advanced techniques—real-time simulation (RTS) and power-hardware-in-the-loop (PHIL), in a cross-infrastructure manner to create a realistic validation environment for CPES. On the one hand, the method can be applied to extend the capacity of the infrastructures as well as creating a common resource and expertise pool. On the other hand, this approach combines the realistic data and advanced SCADA services with the flexibility of the RTS platform, which provides the possibility to emulate extreme and faulty scenarios with virtual equipment and topology. The proposed approach is demonstrated via a case study of analyzing the impact of communication on advanced voltage and frequency restoration in an isolated microgrid. The case study is implemented on two remote platforms PREDIS-PRISMES (70 km apart). The validation framework comprises the RTS and PHIL platform coupled with the OPC UA SCADA system in PRISMES, the control algorithm and the communication network simulator located in PREDIS platform. [ABSTRACT FROM AUTHOR]

Electrical plug loads and appliances have evolved over time to provide sophisticated functions to occupants in a building, and they account for a big share of building's energy consumption. Despite their evolution, they are still unmanageable in commercial and industrial buildings, unlike lighting or air conditioning that are fixed infrastructures managed by an intelligent software layer, such as a building management system. The qualities of heterogeneity, numerosity, spatial irregularity, and displaceability have impeded efficient management of plug loads, unless they are appropriately identified in real time. Decades of efforts in both intrusive and nonintrusive load monitoring have not yielded any sufficiently reliable universal mechanism to identify plug loads. In this article, a practical method of uniquely identifying plug loads using a novel socket cyber-physical system is proposed. This socket/receptacle can access the metadata and operational data of plug loads in an automated fashion, thus yielding a live digital profile of plug loads connected to sockets. Based on acquired intelligence, a supervisory control system can contextually identify the plug loads to provide several digitalized services. Various benefits and use cases of the proposed smart cyber-physical system are explained, inclusive of improvement in operational energy efficiency of plug loads. [ABSTRACT FROM AUTHOR]

Future smart grids with more distributed generation and flexible demand require well-verified control and management services. This article presents a distributed multimodel cosimulation platform based on smart grid architecture model to foster general purpose services in smart grids. It aims at providing developers with support to easily set-up a test-bed environment, where they can simulate realistic scenarios to assess their algorithms and services. The proposed platform takes advantages of internet-of-things communication paradigms and protocols to enable the interoperability among different models and virtual or physical devices that compose a use case. Moreover, the integration of digital real-time simulators unlocks hardware-in-the-loop features. To test the functionality of our platform, a novel scheme of fault detection, isolation, and restoration is developed, in which communication and interoperability of different functions and devices are crucial. This service is applied on a realistic portion of a power grid in Turin, Italy, where devices communicate over the Internet. Finally, the laboratory experimental results achieved during a real-time cosimulation are discussed. [ABSTRACT FROM AUTHOR]

This article presents a real-time identification method for LCL filters used with three-phase grid converters. The method can be applied to identify both the inductance and capacitance values of the filter and the series resistance seen by the converter. As a side-product, an estimate of the grid inductance seen from the point of connection is also obtained. A wideband excitation signal is added to the converter voltage reference. During the excitation, converter current and converter voltage reference samples are used for identification. The samples are preprocessed in real time by removing dc biases and significant grid-frequency harmonics. Parameters of two discrete-time models are estimated at each sampling instant with a recursive estimation algorithm. Depending on the estimated model, the model parameter estimates are translated to either the resistance or the inductance and capacitance values of the system. The method can be embedded to a control system of pulsewidth-modulation-based converters in a plug-in manner. Only the dc-link voltage and converter currents need to be measured. Simulation and experimental results are presented for a 12.5-kVA grid converter system to evaluate the proposed method. [ABSTRACT FROM AUTHOR]

This article introduces new technologies for monitoring thermal response and life-time varying parameters of power electronic modules in real time without interrupting normal converter operation. The technologies are embedded in an adaptive thermal observer, which integrates temperature measurements and electrothermal models to estimate device losses and temperatures at reliability-critical locations of the power module. The observer structure includes a model reference adaptive system for adaptively calibrating device switching-loss models. This improves thermal monitoring accuracy, avoids time-consuming offline calibrations, and creates the opportunity for efficiency estimations during power module operation. The adaptive observer together with small-signal loss-excitation and system-identification technologies extract the thermal impedance of the power module in magnitude and phase over multiple frequency decades. This in situ thermal impedance spectroscopy has the unique feature of continuously providing information on the thermal interface of the power devices without interrupting normal inverter operation. The extracted information, i.e., life-time varying electrical and thermal parameters, enables diagnosing power module state-of-health with improved accuracy compared to prior technologies. It even allows separating different degradation effects, such as bond wire lift-off, device and base-plate solder delamination as well as deterioration of the convection process. With these new features, next generation monitoring systems can extract thermal response and degradation and thus protect power modules from severe thermal stress and overload. Furthermore, the obtained state-of-health information can be selectively used to reduce thermal stress and to schedule predictive maintenance leading to a safer and more reliable operation of future converter systems. [ABSTRACT FROM AUTHOR]

This article determines the optimal capacity of solar photovoltaic (PV) and battery energy storage (BES) for grid-connected households to minimize the net present cost of electricity. The real-time rule-based home energy management systems using actual annual data of solar insolation, ambient temperature, household electricity consumption, and electricity rates are used in the optimization process. The above-mentioned technique is applied to two system configurations—household with a solar PV and a BES. The uncertainty analysis is implemented using ten years of real data to confirm the optimal results. An accurate cash flow analysis is also presented to illustrate the customer payment in each year during the project lifetime. The sensitivity analysis is conducted by varying the cost and capacity of system components, grid constraint, average daily electricity demand, and retail price of electricity. A typical grid-connected household in South Australia is considered as the case study. A practical guideline is presented for the residential consumers in South Australia to select the optimal PV/BES based on their daily average electricity demand and the available rooftop space for PV installation. Finally, the proposed optimization method is applied to households of other Australian States and a comparison of results is presented. [ABSTRACT FROM AUTHOR]

This article presents optimal strategies in the home energy management system (HEMS) integrating solar power, energy storage, and vehicle-to-grid (V2G) capability for predetermined scenarios. The proposed system aims to address the demand response schemes, both real-time pricing and emergency load curtailment, V2G mode of operation. In the interruption event, the HEMS operates in the vehicle-to-home mode to ensure supply continuity. The control algorithms are experimentally implemented on the created hardware platform of EV and home batteries, solar power, and household loads with preset priorities. Both ac and dc charging technologies for EVs are considered. The HEMS simultaneously optimizes the scheduling of charging and discharging of EVs and home battery. Results on operation under predetermined scenarios have confirmed the validity of the proposed HEMS. [ABSTRACT FROM AUTHOR]

Proper selection of electric vehicle (EV) path will be helpful to improving the travel efficiency of EV users and alleviate their difficulties in charging during peak hours. In this context, a personalized fast-charging navigation strategy based on mutual effect of dynamic queuing is proposed. First, an effective charging navigation system is presented to enable the integration of EV information terminals, fast-charging stations (FCSs), intelligent transportation system, and information processing center (IPC). Next, by incorporating the real-time mutual effect of dynamic queuing as well as considering the priority reservation cost of the EV drivers who reserve charging priority before others, a dynamic reservation-waiting queue model is established. After that, on the basis of the above model, the coupling relations of the criteria that may affect the charging experience are investigated for the first time and the charging navigation plans in IPC are optimized for diverse EVs. Finally, the validity of the method is verified by conducting the simulation under the participation of 2250 EVs in the urban areas of a city. Results show that the proposed charging strategy will increase charging satisfaction on average by more than 10.9% in all analyzed cases. Additionally, the navigation can save the charging cost of EV owners while also improve the operation efficiency of FCSs. [ABSTRACT FROM AUTHOR]