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Electrical energy storage (EES) can improve the flexibility and reliability of electric power systems. At the same time, they can supply different ancillary services. The profit of the energy storage operation can be maximized by deciding the best level of each service. Merchant-owned facilities require a profit-maximizing formulation for grid-connected energy storage systems with multiple ancillary services. This paperproposes anew linearprofit-maximizing formulationfor grid-connected merchant-ownedenergy storage systems operating with multiple ancillary services. All technical characteristics of EES have been modelled, including cycle life loss due to fast charge/discharge and low depth of discharge operations. A piece-wise linear model of an EES converter’s capability curve has also been included for reactive power modeling. The model was assessed considering a battery EES, a flywheel EES, and a compressed air EES, with the results demonstrating the benefits of the formulation. From case studies, it is clear that the merchant owned battery EES and the flywheel EES can generate profits, especially from voltage regulation and frequency regulation services. The case studies prove that the proposed model can be used as an optimal planning and operation tool for any type or size of EES.

A two-stage machine learning-based approach for creating synthetic phasor measurement unit (PMU) data is proposed in this article. This approach leverages generative adversarial networks (GAN) in data generation and incorporates neural ordinary differential equation (Neural ODE) to guarantee underlying physical meaning. We utilize this approach to synthetically create massive eventful PMU data, which would otherwise be difficult to obtain from the real world due to the critical energy infrastructure information (CEII) protection. To illustrate the utility of such synthetic data for subsequent data-driven methods, we specifically demonstrate the application of using synthetic PMU data for event classification by scaling up the real data set. The addition of the synthetic PMU data to a small set of real PMU data is shown to have improved the event classification accuracy by 2 to 5 percent.

Globally abundant wave energy for power generation attracts ever increasing attention. Because of non-linear dynamics and potential uncertainties in ocean energy conversion systems, generation productivity needs to be increased by applying robust control algorithms. This paper focuses on control strategies for a small ocean energy conversion system based on a direct driven permanent magnet synchronous generator (PMSG). It evaluates the performance of two kinds of control strategies, i.e., traditional field-oriented control (FOC) and robust adaptive control. The proposed adaptive control successfully achieves maximum velocity and stable power production, with reduced speed tracking error and system response time. The adaptive control also guarantees global system stability and its superiority over FOC by using a non-linear back-stepping control technique offering a better optimization solution. The robustness of the ocean energy conversion system is further enhanced by investigating the Lyapunov method and the use of a DC-DC boost converter. To overcome system complexity, turbine-generator based power take-off (PTO) is considered. A Matlab/Simulink study verifies the advantages of a non-linear control strategy for an Oscillating Water Column (OWC) based power generation system.

In the light of user-side energy power control requirements, a power control strategy for a household-level EPR based on HES droop control is proposed, focusing on the on-grid, off-grid and seamless switching process. The system operating states are divided based on the DC bus voltage information with one converter used as a slack terminal to stabilize the DC bus voltage and the other converters as power terminals. In the on-grid mode, the GCC and the HES are used as the main control unit to achieve on-grid stable operation, whereas in the off-grid mode, the PV, HES and LC are used as the main control unit at different voltages to achieve stable operation of the island network. Finally, a DC MG system based on a household-level EPR is developed using the PSCAD / EMTDC simulation platform and the results show that the control strategy can effectively adjust the output of each subunit and maintain the stability of the DC bus voltage.

In this paper, the impact of communication time delays (CTDs) on combined load frequency control (LFC) and automatic voltage regulation (AVR) of a multi-area system with hybrid generation units is addressed. Investigation reveals that CTDs have significant effect on system performance. A classical PID controller is employed as a secondary regulator and its parametric gains are optimized with a differential evolution - artificial electric field algorithm (DE-AEFA). The superior performance of the presented algorithm is established by comparing with various optimization algorithms reported in the literature. The investigation is further extended to integration of redox flow batteries (RFBs) and interline power flow controller (IPFC) with tie-lines. Analysis reveals that IPFC and RFBs coordinated control enhances system dynamic performance. Finally, the robustness of the proposed control methodology is validated by sensitivity analysis during wide variations of system parameters and load.

The modeling and multi-energy flow calculation of an integrated energy system (IES) are the bases of its operation and planning. This paper establishes the models of various energy sub-systems and the coupling equipment for an electricity-gas-thermal IES, and an integrated multi-energy flow calculation model of the IES is constructed. A simplified calculation method for the compressor model in a natural gas network, one which is not included in a loop and works in constant compression ratio mode, is also proposed based on the concept of model reduction. In addition, a numerical conversion method for dealing with the conflict between nominal value and per unit value in the multi-energy flow calculation of IES is described. A case study is given to verify the correctness and speed of the proposed method, and the electricity-gas-thermal coupling interaction characteristics among sub-systems are studied.

In this paper, a Sliding mode controller design method for frequency regulation in an interconnected power system is presented. A sliding surface having four parameters has been selected for the load frequency control (LFC) system model. In order to achieve an optimal result, the parameter of the controller is obtained by grey wolf optimization (GWO) and particle swarm optimization (PSO) techniques. The objective function for optimization has been considered as the integral of square of error of deviation in frequency and tie-line power exchange. The method has been validated through simulation of a single area as well as a multi-area power system. The performance of the Sliding mode controller has also been analyzed for parametric variation and random loading patterns. The performance of the proposed method is better than recently reported methods. The performance of the proposed Sliding mode controller via GWO has 88.91% improvement in peak value of frequency deviation over the method of Anwar and Pan in case study 1 and similar improvement has been observed over different case studies taken from the literature.

Wind power, solar power and water power are technologies that can be used as the main sources of renewable energy so that the target of decarbonisation in the energy sector can be achieved. However, when compared with conventional power plants, they have a significant difference. The share of renewable energy has made a difference and posed various challenges, especially in the power generation system. The reliability of the power system can achieve the decarbonization target but this objective often collides with several challenges and failures, such that they make achievement of the target very vulnerable, Even so, the challenges and technological solutions are still very rarely discussed in the literature. This study carried out specific investigations on various technological solutions and challenges, especially in the power system domain. The results of the review of the solution matrix and the interrelated technological challenges are the most important parts to be developed in the future. Developing a matrix with various renewable technology solutions can help solve RE challenges. The potential of the developed technological solutions is expected to be able to help and prioritize them especially cost-effective energy. In addition, technology solutions that are identified in groups can help reduce certain challenges. The categories developed in this study are used to assist in determining the specific needs and increasing transparency of the renewable energy integration process in the future.

Recent advances in distribution-level phasor measurement unit (D-PMU) technology have enabled the use of voltage phase angle measurements for direct load sharing control in distribution-level microgrid interconnections with high penetration of renewable distributed energy resources (DERs). In particular, D-PMU enabled voltage angle droop control has the potential to enhance stability and transient performance in such microgrid interconnections. However, these angle droop control designs are vulnerable to D-PMU angle measurement losses that frequently occur due to the unavailability of a GPS signal for synchronization. In the event of such measurement losses, angle droop controlled microgrid interconnections may suffer from poor performance and potentially lose stability. In this paper, we propose a novel distributed mixed voltage angle and frequency droop control (D-MAFD) framework to improve the reliability of angle droop controlled microgrid interconnections. In this framework, when the D-PMU phase angle measurement is lost at a microgrid, conventional frequency droop control is temporarily used for primary control in place of angle droop control to guarantee stability. We model the microgrid interconnection with this primary control architecture as a nonlinear switched system and design distributed secondary controllers to guarantee transient stability of the network. Further, we incorporate performance specifications such as robustness to generation-load mismatch and network topology changes in the distributed control design. We demonstrate the performance of this control framework by simulation on a test 123-feeder distribution network., Comment: 11 pages, IEEE Open Access Journal of Power and Energy (accepted)

The task of determining the phase connection of customers, known as phase identification, is crucial to obtain accurate distribution system models. This paper starts with a thorough literature review of the existing phase identification methods, which are broadly divided into three categories: hardware-based, real power-based, and voltage-based methods. This is followed by multiple case studies assessing the accuracy of six real power- and voltage-based phase identification algorithms on four realistic distribution test systems. Synthetic load profiles along with network models are used to quantify accuracy of each method for different scenarios: varying advanced metering infrastructure (AMI) coverage, number of initially mislabeled customer phases, number of available samples, and measurement noise. A case study using a real AMI data set, including field verification, is also provided. Finally, several aspects key to accurate phase identification are discussed in detail.

This paper proposes a novel distributed secondary controller for droop-controlled microgrids to regulate the frequency and voltage, and autonomously share the power mismatch. The proposed scheme is entitled generalized proportional-intergal finite-time controller (GPI-FTC). The proposed GPI-FTC is synthesized based on the control Lyapunov function method and modifying the conventional PI controller by adding a consensus term to the integrand dynamic. The proposed distributed GPI-FTC provides plug-n-play capability, scalability, and fast finite-time convergence of the system states. Moreover, a reactive power sharing (Q-sharing) method is designed to improve the sharing pattern of reactive power under exact voltage regulation. Also, a distributed voltage observer is developed for average voltage regulation. Performance of the proposed GPI-FTC is validated through numerical simulations of the detailed model of the microgrid, including small signal analysis, load change, DG outage, Q-sharing, and performance comparison.

This paper presents a novel dynamic programming (DP) technique for the determination of optimal investment decisions to improve power distribution system reliability metrics. This model is designed to select the optimal small-scale investments to protect an electrical distribution system from disruptions. The objective is to minimize distribution system reliability metrics: System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI). The primary input to this optimization model is years of recent utility historical outage data. The DP optimization technique is compared and validated against an equivalent mixed integer linear program (MILP). Through testing on synthetic and real datasets, both approaches are verified to yield equally optimal solutions. Efficiency profiles of each approach indicate that the DP algorithm is more efficient when considering wide budget ranges or a larger outage history, while the MILP model more efficiently handles larger distribution systems. The model is tested with utility data from a distribution system operator in the U.S. Results demonstrate a significant improvement in SAIDI and SAIFI metrics with the optimal small-scale investments.

High voltage direct current (HVDC) transmission is an economical option for transmitting a large amount of power over long distances. Initially, HVDC was developed using thyristor-based current source converters (CSC). With the development of semiconductor devices, a voltage source converter (VSC)-based HVDC system was introduced, and has been widely applied to integrate large-scale renewables and network interconnection. However, the VSC-based HVDC system is vulnerable to DC faults and its protection becomes ever more important with the fast growth in number of installations. In this paper, detailed characteristics of DC faults in the VSC-HVDC system are presented. The DC fault current has a large peak and steady values within a few milliseconds and thus high-speed fault detection and isolation methods are required in an HVDC grid. Therefore, development of the protection scheme for a multi-terminal VSC-based HVDC system is challenging. Various methods have been developed and this paper presents a comprehensive review of the different techniques for DC fault detection, location and isolation in both CSC and VSC-based HVDC transmission systems in two-terminal and multi-terminal network configurations.

Optimal reactive power dispatch (ORPD) is a complex and non-linear problem, and is one of the sub-problems of optimal power flow (OPF) in a power system. ORPD is formulated as a single-objective problem to minimize the active power loss in a transmission system. In this work, power from distributed generation (DG) is integrated into a conventional power system and the ORPD problem is solved to minimize transmission line power loss. It proves that the application of DG not only contributes to power loss minimization and improvement of system stability but also reduces energy consumption from the conventional sources. A recently proposed meta-heuristic algorithm known as the JAYA algorithm is applied to the standard IEEE 14, 30, 57 and 118 bus systems to solve the newly developed ORPD problem with the incorporation of DG. The simulation results prove the superiority of the JAYA algorithm over others. The respective optimal values of DG power that should be injected into the four IEEE test systems to obtain the minimum transmission line power losses are also provided.

Large interconnected power systems are usually subjected to natural oscillation (NO) and forced oscillation (FO). NO occurs due to system transient response and is characterized by several oscillation modes, while FO occurs due to external perturbations driving generation sources. Compared to NO, FO is considered a more severe threat to the safe and reliable operation of power systems. Therefore, it is important to locate the source of FO so corrective actions can be taken to ensure stable power system operation. In this paper, a novel approach based on two-step signal processing is proposed to characterize FO in terms of its frequency components, duration, nature, and the location of the source. Data recorded by the Phasor Measurement Units (PMUs) in a Wide Area Monitoring System (WAMS) is utilized for analysis. As PMU data usually contains white noise and appears as multi-frequency oscillatory signal, the first step is to de-noise the raw PMU data by decomposing it into a series of intrinsic mode functions (IMF) using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) technique. The most appropriate IMF containing the vital information is selected using the correlation technique. The second step involves various signal processing and statistical analysis tools such as segmented Power Spectrum Density (PSD), excess kurtosis, cross PSD etc. to achieve the desired objectives. The analysis performed on the simulated two-area four-machine system, reduced WECC-179 bus 29 machine system, and the real-time power system PMU data set from ISO New England, demonstrates the accuracy of the proposed method. The proposed approach is independent of complex network topologies and their characteristics, and is also robust against measurement noise usually contained in PMU data.

Large integration of doubly-fed induction generator (DFIG) based wind turbines (WTs) into power networks can have significant consequences for power system operation and the quality of the energy supplied due to their excessive sensitivity towards grid disturbances. Under voltage dips, the resulting overcurrent and overvoltage in the rotor circuit and the DC link of a DFIG, could lead to the activation of the protection system and WT disconnection. This potentially results in sudden loss of several tens/hundreds of MWs of energy, and consequently intensifying the severity of the fault. This paper aims to combine the use of a crowbar protection circuit and a robust backstepping control strategy that takes into consideration of the dynamics of the magnetic flux, to improve DFIG’s Low-Voltage Ride Through capability and fulfill the latest grid code requirements. While the power electronic interfaces are protected, the WTs also provide large reactive power during the fault to assist system voltage recovery. Simulation results using Matlab/Simulink demonstrate the effectiveness of the proposed strategy in terms of dynamic response and robustness against parametric variations.

For AC transmission lines connected to an LCC-HVDC inverter station, commutation failure can lead to the wrong operation of traditional protection. To solve the problem, this paper proposes a fast protection scheme using transient information from one end of the AC line. The boundary frequency characteristics of the AC line connected to LCC-HVDC inverter are analyzed first. This reveals the existence of significant attenuation on both high frequency signals and some specific frequency signals. Based on the boundary characteristics, a novel boundary protection principle utilizing a backward traveling wave is then proposed. A PSCAD/EMTDC simulation model of a ± 800 kV LCC-HVDC and 500 kV AC transmission system is established, and different fault cases are simulated. The simulation results prove that the novel protection principle is immune to commutation failure, fault resistance and fault type.

The electric vehicle (EV) charging station is a critical part of the infrastructure for the wide adoption of EVs. Real-time simulation of an EV station plays an essential role in testing its operation under different operating modes. However, the large numbers of high-frequency power electronic switches contained in EV chargers pose great challenges for real-time simulation. This paper proposes a compact electromagnetic transient program (C-EMTP) algorithm for FPGA-based real-time simulation of an EV station with multiple high-frequency chargers. The C-EMTP algorithm transforms the traditional EMTP algorithm into two parallel sub-tasks only consisting of simple matrix operations, to fully utilize the high parallelism of FPGA. The simulation time step can be greatly reduced compared with that of the traditional EMTP algorithm, and so the simulation accuracy for high-frequency power electronics is improved. The EV chargers can be decoupled with each other and simulated in parallel. A CPU-FPGA-based real-time simulation platform is developed and the proposed simulation of the EV station is implemented. The control strategy is simulated in a CPU with 100 μs time-step, while the EV station circuit topology is simulated in a single FPGA with a 250 ns time-step. In the case studies, the EV station consists of a two-level rectifier and five dual-active bridge (DAB) EV chargers. It is tested under different scenarios, and the real-time simulation results are validated using PSCAD/EMTDC.

Combined estimation of state and feed-back gain for optimal load frequency control is proposed. Load frequency control (LFC) addresses the problem of controlling system frequency in response to disturbance, and is one of main research areas in power system operation. A well acknowledged solution to this problem is feedback stabilization, where the Linear Quadratic Regulator (LQR) based controller computes the feedback gain K from the known system parameters and implements the control, assuming the availability of all the state variables. However, this approach restricts control to cases where the state variables are readily available and the system parameters are steady. Alternatively, by estimating the states continuously from available measurements of some of the states, it can accommodate dynamic changes in the system parameters. The paper proposes the technique of augmenting the state variables with controller gains. This introduces a non-linearity to the augmented system and thereby the estimation is performed using an Extended Kalman Filter. This results in producing controller gains that are capable of controlling the system in response to changes in load demand, system parameter variation and measurement noise.

Phasor measurement units (PMUs) are preferred for installation at weak buses in a power network. Therefore, the weak buses need to be located and the strategic locations of PMUs identified to ensure network observability. Thus, the primary aim of this work is to identify the placements of the maximum number of PMUs installed at the weak buses in the electrical network. The voltage collapse proximity indicator, line stability index, fast voltage stability index, and a new voltage stability indicator utilizing load flow measurement are used to determine the weak buses. A novel deterministic methodology based on a binary-integer linear programming model is then proposed to determine the optimal locations of PMUs. The effect of a single PMU outage considering the weak buses is also demonstrated. The effectiveness of the developed approach is tested and validated on the standard IEEE 14-, 118-, 300-, and New England 39-bus systems. The obtained results are also compared to those using different weak bus methodologies.

This paper presents a fast hybrid fault location method for active distribution networks with distributed generation (DG) and microgrids. The method uses the voltage and current data from the measurement points at the main substation, and the connection points of DG and microgrids. The data is used in a single feedforward artificial neural network (ANN) to estimate the distances to fault from all the measuring points. A k-nearest neighbors (KNN) classifier then interprets the ANN outputs and estimates a single fault location. Simulation results validate the accuracy of the fault location method under different fault conditions including fault types, fault points, and fault resistances. The performance is also validated for non-synchronized measurements and measurement errors.

PV power production is highly dependent on environmental and weather conditions, such as solar irradiance and ambient temperature. Because of the single control condition and any change in the external environment, the first step response of the converter duty cycle of the traditional MPPT incremental conductance algorithm is not accurate, resulting in misjudgment. To improve the efficiency and economy of PV systems, an improved incremental conductance algorithm of MPPT control strategy is proposed. From the traditional incremental conductance algorithm, this algorithm is simple in structure and can discriminate the instantaneous increment of current, voltage and power when the external environment changes, and so can improve tracking efficiency. MATLAB simulations are carried out under rapidly changing solar radiation level, and the results of the improved and conventional incremental conductance algorithm are compared. The results show that the proposed algorithm can effectively identify the misjudgment and avoid its occurrence. It not only optimizes the system, but also improves the efficiency, response speed and tracking efficiency of the PV system, thus ensuring the stable operation of the power grid.

To improve the robustness and performance of the dynamic response of a cage asynchronous motor, a direct torque control (DTC) based on sliding mode control (SMC) is adopted to replace traditional proportional-integral (PI) and hysteresis comparators. The combination of the proposed strategy with sinusoidal pulse width modulation (SPWM) applied to a three-level neutral point clamped (NPC) inverter brings many advantages such as a reduction in harmonics, and precise and rapid tracking of the references. Simulations are performed for a three-level inverter with SM-DTC, a two-level inverter with SM-DTC and the three-level inverter with PI-DTC-SPWM. The results show that the SM-DTC method achieves better performance in terms of reference tracking, while adoption of the three-level inverter topology can effectively reduce the ripples. Applying the SM-DTC to the three-level inverter presents the best solution for achieving efficient and robust control. In addition, the use of a sliding mode speed estimator eliminates the mechanical sensor and this increases the reliability of the system.

With the development of power electronics technology, the flexible DC grid will play a significant role in promoting the transformation and reformation of the power grid. It is immune to commutation failure and has high flexibility in power control and renewable energy grid integration. However, the protection and fault handling technology for a flexible DC grid is a big challenge because of the limited overcurrent capability of the converters. This paper summarizes the development of the flexible DC grid, and analyzes the fault characteristics in detail. Next, the applicability, advantages and disadvantages of the existing protection principle, fault isolation and recovery schemes are reviewed. Finally, the key problems and development trend of the future flexible DC grid are pointed out and forecasted respectively.

In this paper, we present a detailed procedure to determine the semiconductor losses for both structures of a shunt STATCOM (Static Compensator), STATCOM based on Current Source Inverter (CSI) and STATCOM based on Voltage Source Inverter (VSI), both used for voltage unbalance compensation. As a first step, we study the VSI-based STATCOM and the CSI-based STATCOM used in high speed railway substations. Then we analyze the design and the sizing of the unbalance compensator in order to obtain an unbalance factor that does not exceed the limits imposed by the standards or by the energy provider. Following that, we compare the performances obtained with both structures VSI-STATCOM and CSI-STATCOM, after calculating the semiconductor power losses in the STATCOM converters. Finally, we validate our approach by simulation over real data of unbalance compensation caused by the new high-speed railway in Morocco. We use the tools MATLAB / Simulink/Simpowersys for performing our simulations.

Power electronic converters will serve as the fundamental components of modern power systems. How-ever, they may suffer from poorer reliability if not properly designed, consequently affecting the overall performance of power systems. Accordingly, the converter reliability should be taken into account in design and planning of Power Elec-tronic-based Power Systems (PEPSs). Optimal decision-mak-ing in planning of PEPSs requires precise reliability modeling in converters from component up to system-level. This paper proposes model-based system-level design and maintenance strategies in PEPSs based on the reliability model of convert-ers. This will yield a reliable and economic planning of PEPSs by proper sizing of converters, cost-effective design of con-verter components, identifying and strengthening the con-verter weakest links, as well as optimal maintenance schedul-ing of converters. Numerical case studies demonstrate the ef-fectiveness of the proposed design and planning strategies for modern power systems.

The increased penetration of renewable energy resources poses challenges for grid stability. The stochastic generation of solar and wind power cannot be controlled to follow load. And, the transition away from synchronous generators is reducing the capacity to arrest and recover from frequency disturbances. Smart electric water heaters provide utilities with an appliance that can be remotely controlled and serve as a form of energy storage. They have very fast response times and make up a large amount of residential energy consumption, making them useful for load peak shifting as well as other ancillary grid services. As smart appliances become increasingly widespread, more and more devices can be brought into the utility control network and aggregated into a flexible resource on a multi-megawatt scale. This paper demonstrates the usefulness of aggregated electric water heaters for providing two ancillary services: peak shifting and frequency response. Because a large number of assets are required, emulators are developed based on observations of real devices. Emulated water heaters are then connected to an energy resource aggregator using an internet-of-things network. The aggregator uses these assets to shift consumption away from peak hours and for detecting upward frequency disturbances.

A common challenge forward-looking utilities are facing when deploying advanced applications that facilitate voltage optimization and service restoration is to provide adequate sensor data for a Distribution System State Estimator (DSSE) so that it provides sufficiently accurate estimates of the system states to enable these applications in an operational environment. We developed a stochastic method that informs telemetry and operational forecasting requirements by quantifying the DSSE performance in supporting advanced applications. The performance metric used is the $\alpha $ risk, which is the likelihood of a DSSE giving a false positive when determining if voltage and loading constraints are met. We applied this method to six real-world industrial/commercial/residential distribution circuits and evaluated $\alpha $ risk improvements provided by circuit-level sensors and operational forecasting. The results show that a combination of sensor deployment schemes was needed to reduce the $\alpha $ risk for undervoltage to effectively zero. Also, sensors deployed at large loads significantly reduce $\alpha $ risks on industrial/commercial circuits while operational forecasting consistently reduces $\alpha $ risks on all circuits. The practical method does not require advanced mathematics and can be readily used by utilities to inform grid modernization investments in sensor technologies so that advanced applications can be executed optimally and violation-free.

Supply curtailment in wind energy-rich electricity grids occurs when electric energy supply exceeds demand. Grid-level energy storage assets with the potential for storing the excess electric energy generated from wind are yet cost-prohibitive and prone to inefficiencies. An alternative for managing this excess energy is the targeted charging of available plug-in electric vehicles (PEVs). The different power requirements, load duration, and times of usage requires PEVs to be treated differently. Consequently, using a universal pricing mechanism may not lead to the maximum benefit for the utility and the consumers, especially when trying to rely on charging PEVs with wind energy. In this study, we use a data-driven approach to investigate an existing pricing mechanism for a city (Austin, Texas) in a wind energy-rich electricity grid (ERCOT) and with high projections of PEVs. The study provides a general framework for the wind energy-rich utilities to better evaluate their profitability and the benefits of the consumers. In our case study, the results indicate the need for an alternative pricing mechanism (e.g., time-of-use) dedicated to PEVs than the existing choices for maximizing the utility of available energy from wind in the absence of grid-level energy storage.

The feed-in tariff, introduced in 2012, led to a significant increase in Photovoltaics (PVs) throughout Japan. About half of PVs are three-phase PVs that are connected to low voltage or medium voltage networks. Central Research Institute of Electric Power Industry (CRIEPI) has developed root-mean square-based time-domain power system analysis tools used by all the Japanese utilities for dynamic studies following balanced and unbalanced faults for over the last thirty years. Two 10 kW three-phase PV inverter were tested in the CRIEPI’s test lab reproducing various levels of the voltage dips that come from three-phase balanced and unbalanced faults with various fault duration. The PV model was developed and validated, comparing measured responses obtained in the test lab with simulated responses obtained by the time-domain simulation tool. Sensitivities of identified parameters to the model error are carefully examined, which proves that the same model parameters may be used for balanced and unbalanced faults. Derived model parameters are further verified, comparing the simulated response of the combined two PV outputs with the measured response. The excellent match of those responses demonstrates that individually identified parameters for the two single PV inverters are also adequate for representing the combined PV dynamics.

Integration of Information and Communications Technology (ICT) into the distribution system makes today's power grid more remotely monitored and controlled than it has been. The fast increasing connectivity, however, also implies that the distribution grid today, or smart grid, is more vulnerable. Thus, research into intrusion/anomaly detection systems at the distribution level is in critical need. Current research on Intrusion Detection Systems for the power grid has been focused primarily on cyber security at the Supervisory Control And Data Acquisition, and single node levels with little attention on coordinated cyberattacks at multiple nodes. A holistic approach toward system-wide cyber security for distribution systems is yet to be developed. This paper presents a novel approach toward intrusion prevention, using a multi-agent system, at the distribution system level. Simulations of the method have been performed on the IEEE 13-Node Test Feeder, and the results compared to those obtained from existing methods. The results have validated the performance of the proposed method for protection against cyber intrusions at the distribution system level.

Modeling the common-mode behavior of power electronic systems can be a challenge. This research aims to simplify the process by developing a modeling approach based on Thévenin common-mode equivalent circuits. The Thévenin common-mode equivalent circuits prove to be both straightforward to construct and reliable in predicting worst-case common-mode behavior. In this paper, a theoretical understanding of the Thévenin-based modeling approach is first provided. Subsequently, methods to characterize the Thévenin parameters are established. The modeling approach is then validated experimentally on a dc micro-grid.

The wind turbine startup and shutdown procedures for electrical subsystems are straightforward for wind turbines featuring full-scale power electronic converters and have not required significant attentions from the research community. However, for offshore wind farms featuring series-connected dc collection systems and differential power processing capabilities, the controllable wind turbine power converters might not be rated in such a way that a full-scale output power difference can be handled within a series string. As startup and shutdown procedures can require a transition from zero to full power, a more elaborate design is required to ensure that wind turbines can still start and stop reliably under all conditions, without compromising advantages in reduced wind turbine converter ratings with differential power processing capabilities. In this paper, the startup and shutdown procedures for a series-dc wind farm featuring diode-bridge rectifiers and partial power processing converters as wind turbine converters, are investigated and constraints on converter ratings are evaluated.

In the day-ahead economic dispatch of combined heat and power systems, the electric power system is adjusted in minutes because of the short dynamic time and the requirement of real-time power balance, while the heating system is dispatched in hours due to its large inertia and long dynamic process. However, the different dispatch time scales of electricity and heat are ignored by existing synchronous dispatch methods, which limits the improvement of system efficiency. To address this challenge, in this article, the asynchronous dispatch method is proposed and instantiated by two different dispatch models featuring a short electric dispatch time scale and a long heat dispatch time scale. Case studies demonstrate the asynchronous dispatch method can overcome the efficiency and reliability problems caused by the existing synchronous dispatch methods.

Distributed Energy Resources (DER), mainly residential solar PV, are embedded deep within the power distribution network and their adoption is fast increasing globally. As more customers participate, these power generation units cause Reverse Power Flow (RPF) at the edge of the grid, directed upstream into the network, thus violating one of the traditional design principles for power networks. The effects of a single residential solar PV system is negligible, but as the adoption by end-consumers increases to high percentages, the aggregated effect is no longer negligible and must be considered in the design and configuration of power networks. This article proposes a framework that helps to predict the RPF intensity probability for any given scenario of DER penetration within the distribution network. The considered scenario parameters are the number and location of each residential DERs, their capacity and the daily net-load profiles. Classical simulation-based approach for this is not scalable as it relies on solving the load-flow equations for each individual scenario. The framework leverages machine learning techniques to make fast and precise RPF prediction within the network for each scenario. The framework enables the Distribution Network Service Providers (DNSPs) to assess DERs penetration scenarios at a granular level, derive and localise the RPF risks and assess the respective impacts on the installed assets for network planning purpose. The framework is illustrated with scenario analysis conducted on an IEEE 123 bus system and OpenDSS and shown that it can lead to multiple orders of magnitude savings in computational time while retaining an accuracy of 94% or above compared to classical brute force simulations.

Modular Multilevel Converters (MMC) are being widely considered for medium to high voltage DC applications. Designing such converters through multi-objective optimization is of interest because such an approach allows the trade-off between competing objectives (for example mass and loss) to be explicitly and quantitatively identified. In this work, an optimization based design paradigm for MMC based generator rectifier systems is proposed. Such development typically requires detailed component design and simulation models for the electric machine and converter which are computationally expensive. As an alternative, the proposed work utilizes an electric machine metamodel, inductor metamodel, and high-speed steady-state simulation model for the MMC to facilitate multi-objective optimization minimizing system metrics of interest while satisfying system constraints. A case study is undertaken to demonstrate the functionality of the proposed design paradigm.

The recent legalization of cannabis is facilitating very rapid growth in the cannabis cultivation industry, with the energy intensive indoor cultivation facilities becoming more prevalent. This presents a challenge to utilities as the high energy demand from this industry can overburden the existing utility infrastructure. Hence, from both planning and operational perspectives, it is crucial to understand the energy consumption of the rapidly growing load. This paper proposes a deterministic energy consumption model for indoor cannabis cultivation operations for the two major loads in these facilities, i.e., lighting and HVAC, over a 24-hour period based on equipment specifications and operational requirements of the facility. This model can further be used to estimate or forecast short-term and long-term energy demands and costs of indoor cannabis operation(s). The proposed model successfully simulated the environmental conditions in a real-world cannabis facility, and the model’s energy consumption output is validated using actual measurements taken from this facility as well as model output using GridLab-D.

Residential-scale distributed energy assets, like residential electric water heaters, individually present a negligible load to the power grid. When aggregated, however, these assets can impart significant effects within a balancing area; they may be dispatched en masse to provide grid services. An aggregation of water heaters may be controlled to assume generator-like functions with the ability to effectively “decrement power” through dispatch of load. This resource study examines the capabilities of a 10,000 unit water heater aggregation by subjecting the aggregate to dispatch requests of various size and duration, then analyzing how the aggregate responds to and recovers from these requests. Results show that a large-scale aggregation of electric water heaters may effectively decrement power on the scale of megawatts when the dispatch request size and duration are appropriately considered.

This paper introduces a methodology for building synthetic electric grid data sets that represent fictitious, yet realistic, combined transmission and distribution (T&D) systems. Such data sets have important applications, such as in the study of the wide-area interactions of distributed energy resources, in the validation of advanced control schemes, and in network resilience to severe events. The data sets created here are geographically located on an actual North American footprint, with the end-user load information estimated from land parcel data. The grid created to serve these fictional but realistic loads is built starting with low-voltage and medium-voltage distribution systems in full detail, connected to distribution and transmission substations. Bulk generation is added, and a high-voltage transmission grid is created. This paper explains the overall process and challenges addressed in making the combined case. An example test case, syn-austin-TDgrid-v03, is shown for a 307236-customer case located in central Texas, with 140 substations, 448 feeders, and electric line data at voltages ranging from 120 V to 230 kV. Such new combined test cases help to promote high quality in the research on large-scale systems, particularly since much actual power system data are subject to data confidentiality. The highly detailed, combined T&D data set can also facilitate the modeling and analysis of coupled infrastructures.

A benchmark distribution system is developed for investigating control and energy management of distributed generation (DG) at a residential level in the form of three single-phase microgrids. The benchmark is derived from a typical distribution network architecture with common parameters found in North-America systems including wiring specifications, line impedances and connection details for rooftop PV systems. This benchmark system can accommodate microgrids operating in both grid-connected and islanded modes. Within this benchmark, multiple single-phase DG sources located in different phases can be coordinated to form a dynamically balanced three phase system under different load and generation profiles in different phases. The coordination of DG sources in a particular phase is achieved through an intra-phase power management device, while mitigating loads and generation imbalance among all phases are done by an inter-phase power management scheme. It is expected that this benchmark system will facilitate investigation of impacts posed by proliferation of single-phase distributed generation devices and local storage systems in private residences. Three case studies have been carried out to demonstrate the versatility and effectiveness of this benchmark system.

Forecasting has been an essential part of the power and energy industry. Researchers and practitioners have contributed thousands of papers on forecasting electricity demand and prices, and renewable generation (e.g., wind and solar power). This article offers a brief review of influential energy forecasting papers; summarizes research trends; discusses importance of reproducible research and points out six valuable open data sources; makes recommendations about publishing high-quality research papers; and offers an outlook into the future of energy forecasting.

With the increasing penetration of renewable resources and the retirements of conventional coal-fired generation units, power systems are undergoing significant transformation with the instantaneous renewable generation penetration sometimes approaching over 50 percent of the demand. This transformation makes the need for comprehensive reliability assessment critical to properly account for the composite system adequacy and the sufficiency of essential reliability services (ERSs). This paper introduces a probabilistic approach to evaluate the reliability of wind power integrated power systems considering ERSs including frequency and voltage support, in conjunction with resource adequacy. To consider stochasticity in system operating conditions, the proposed approach utilizes sequential Monte-Carlo Simulation (SMCS) as the probabilistic analysis methodology and formulates probabilistic reliability metrics representing the composite system adequacy and the ERSs. The proposed approach and metrics are demonstrated on a synthetic test system. Simulation results illustrate the efficacy of the proposed approach and its importance in analyzing the impact of increasing wind power penetration as well as wind turbine generators (WTGs) providing ERSs on system reliability.

For large power systems, a continual challenge is to display wide-area data in a way that maximizes human users’ situational awareness. This paper describes a new visualization technique that draws a mosaic of colored tiles to represent multiple data fields for electric grid objects, arranged to preserve geographic context. The key problem in creating these diagrams is packing the tiles onto the display space, minimizing the total displacement while forbidding overlaps. This paper formulates that problem and presents a horizontal-packing algorithm which is able to produce a feasible, quality solution at an interactive time scale. Illustrative examples are shown for using mosaics to monitor wide-area generator status and dispatch, bus voltages, and line and transformer limits. Mosaics can be customized in numerous ways to show different aspects of the system state, providing for human users a simultaneous sense of the wide-area summary, regional trends, and prominent outliers.

This paper presents a novel methodology to control HVAC system and minimize energy cost on the premise of satisfying power system constraints. A multi-agent architecture based on game theory and reinforcement learning is developed so as to reduce the cost and computational complexity of the microgrid. The multi-agent architecture comprising agents, state variables, action variables, reward function and cost game is formulated. The paper fills the gap between multi-agent HVAC systems control and power system optimization and planning. The results and analysis indicate that the proposed algorithm is beneficial to deal with the problem of “curse of dimensionality” for multi-agent microgrid HVAC system control and speed up learning of unknown power system conditions.

This paper presents MATLAB-based programs developed for power system dynamic analysis. The programs can be used for educational purposes and research studies. With the program, time-domain simulation, system linearization, modal analysis, participation factor analysis and visualization, optimal placement of controller, feedback signal selection, frequency response analysis, and control design can be obtained. In addition to solving a power system problem, the package provides a symbolic and vectorized representation of the model in time domain and state space. The package uses the full advantages of MATLAB's powerful solvers for solving non-stiff and stiff problems. Both explicit and implicit techniques are used for solving the differential algebraic equations (DAEs). The synchronous machines are assumed to be equipped with exciter, turbine, and stabilizer. The loads can be modeled as voltage-dependent and independent loads. The test systems used in this paper are the IEEE 9-bus and 68-bus systems, and Texas's 2007-bus synthetic power system. Different types of disturbances are applied to the systems including generator-side and network-side disturbances. The results demonstrate the efficiency and educational values of the package for researchers and students.

This paper focuses on pricing Energy Storage as a Service (ESaaS) for Transmission congestion relief (TCR). We consider a merchant storage facility that competes in an electricity market to trade energy and ancillary services on a day-to-day basis. The facility also has the opportunity to provide a firm TCR service to a regional network operator under a long-term contract. Providing the additional TCR service would impose limitations on the facility's ability to fully harvest daily market trade opportunities. Thus, we model the opportunity costs associated with the TCR service and use it in a hybrid cost-value customized pricing technique to determine the risk-constrained optimal price of ESaaS for TCR. Given the long-term nature of the commitment to provide the TCR service, we use the Conditional Value at Risk (CVaR) metric to mitigate the long-term financial risks faced by the facility. The proposed pricing strategy enables the storage owner to estimate the additional financial gains and the associated risks that would likely result from adding the new service to its operation. Numerical simulations are provided to support the proposed methodology.

DC microgrids are introduced to reduce the conversion stages needed for connection of DC sources to the DC loads. They employ the droop control algorithm for managing the power flow from sources to the loads. However, the droop control functionality is affected by circuit parameters, especially line resistances. As a consequence, load sharing as the primary objective of the droop controller lacks accuracy. Parallel-connected converters have mismatched output voltages, resulting in circulating currents. This paper proposes an adaptive droop control algorithm for suppressing circulating currents in a low voltage DC microgrid. Line resistances are estimated through mathematical calculations and droop parameters are adjusted accordingly. Moreover, a distributed secondary controller is proposed to improve the load sharing accuracy and eliminate the effect of line resistances. The secondary controller shifts the droop controller voltage setpoint according to the converter current. Both of the proposed methods result in an accurate load sharing; Each of the participating converters has the rated current and consequently circulating current is suppressed. The effectiveness of the proposed method is verified through simulation and hardware-in-the-loop (HIL) setup.

This paper is related to research on parallelization methods for the simulation of electromagnetic transients (EMTs). It presents an automatic parallelization approach based on the solution of sparse matrices resulting from the formulation of network equations. Modified-augmented-nodal analysis is used to formulate network equations. The selected sparse matrix solver is parallelized and adapted to improve performance by pivot validity testing and partial refactorization. Refactorization is needed when dealing with varying topology networks and nonlinear models. The EMT solver employs a fully iterative method for nonlinear functions. Conventional computer CPU-based parallelization is achieved and does not require any user intervention for given arbitrary network topologies. The presented approach is tested on real networks with complex models, including nonlinearities and power-electronics converters for wind generator applications.

In literature, variations and distortions, and interruptions of voltages (or voltage waveforms) and currents (or current waveforms) have been considered in the framework of power quality (or voltage quality). With the massive penetration of renewable energy into power systems, variations including fluctuations and intermittences of output powers of these renewable sources are of great concerns. It is evidenced that with the high penetration of renewable energy, variations of renewable energy have increased the costs of UK's balancing markets by 39% in this spring and summer (NGESO, 2020). Therefore, there are needs to introduce a technical framework, namely, `energy quality' to (a) define the quality of power waveforms; (b) propose measures/indices to characterize the variations (fluctuations and intermittences) of powers and power flows; (c) present methods to improve energy quality. Finally, research directions of energy quality are highlighted to encourage more R&D as well as international collaborations in terms of standards and grid code developments.

Microgrid with hybrid renewable energy sources is a promising solution where the distribution network expansion is unfeasible or not economical. Integration of renewable energy sources provides energy security, substantial cost savings and reduction in greenhouse gas emissions, enabling nation to meet emission targets. Microgrid energy management is a challenging task for microgrid operator (MGO) for optimal energy utilization in microgrid with penetration of renewable energy sources, energy storage devices and demand response. In this paper, optimal energy dispatch strategy is established for grid connected and standalone microgrids integrated with photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), diesel generator (DG) and battery energy storage system (ESS). Techno-economic benefits are demonstrated for the hybrid power system. So far, microgrid energy management problem has been addressed with the aim of minimizing operating cost only. However, the issues of power losses and environment i.e., emission-related objectives need to be addressed for effective energy management of microgrid system. In this paper, microgrid energy management (MGEM) is formulated as mixed-integer linear programming and a new multi-objective solution is proposed for MGEM along with demand response program. Demand response is included in the optimization problem to demonstrate it’s impact on optimal energy dispatch and techno-commercial benefits. Fuzzy interface has been developed for optimal scheduling of ESS. Simulation results are obtained for the optimal capacity of PV, WT, DG, MT, FC, converter, BES, charging/discharging scheduling, state of charge of battery, power exchange with grid, annual net present cost, cost of energy, initial cost, operational cost, fuel cost and penalty of greenhouse gases emissions. The results show that CO2 emissions in standalone hybrid microgrid system is reduced by 51.60% compared to traditional system with grid only. Simulation results obtained with the proposed method is compared with various evolutionary algorithms to verify it’s effectiveness.