Modern Power Systems Research Articles

A multi-solver framework for co-simulation of transients in modern power systems

This paper develops a novel multi-rate, multi-solver co-simulation framework combining dynamic phasors, transient stability, base-frequency dynamic phasors for frequency-adaptive simulation of transients, and electromagnetic transient (EMT) models. This framework subdivides a given power network into several types of subsystems based on the connected devices, required accuracy in representing dynamic details, electrical distance from perturbations, and the intended purpose of the study; as such, the paper describes methods and guidelines to simulate each subsystem using the most appropriate solver and time-step size to maximize simulation efficiency and accuracy. It also addresses the tasks of multiple interfacing and solver interactions that are essential in coupling different solvers. The proposed framework is built around an industrial-grade EMT simulator, to which other solvers are interfaced, enabling access to a variety of power system models and distinct features. The accuracy and efficiency of the framework are demonstrated through co-simulations carried out on a modified version of the 118-bus network, which includes an MMC-HVDC system.

Distribution network forecasting and expansion planning with optimal location and sizing of solar photovoltaic-based distributed generation

Load forecasting is the prediction of demand for a future period, and it is a critical necessity for distribution system design and growth while also meeting all operational and technical constraints. In this paper, an analytical least squares extrapolation technique is applied to determine the optimal size and location of solar photovoltaic (SPV)-based distributed generation (DG) in the distribution system. The integration of SPV DG changes the operating characteristics of modern power systems, resulting in significant economic and technical outcomes such as minimizing losses and improving the voltage profile. The proposed work was performed on a practical Yirgalem-I distribution network in Ethiopia. The modeling and simulation of this feeder have been carried out using ETAP and MATLAB software. Simulation results show that the injection of active and reactive powers at the optimal buses reduces the active and reactive power losses by 94.03% and 96.07%, respectively, with an improved voltage profile.

Power pool‐based supervisory coordinate control strategy for DC microgrids

SummaryStandalone DC microgrids are progressively becoming explored in the modern power system. Proper voltage regulation and effective power distribution among sources are the main challenges for DC microgrid operation. A supervisory coordinated control strategy for proportionate power‐sharing and enhanced voltage regulation is presented in this paper to overcome these challenges. The formation of multiple power pools on the basis of available power from each renewable energy source (RES) is a crucial element of this approach. It modifies the power sharing between power pools and individual sources based on the amount of power available in the sources. In addition, for effective power management, it coordinates the operation of all power pools with all RESs. Also, by reducing the charging and discharging cycle of energy storage systems (ESSs), it is possible to make effective use of ESSs. The proposed control adapts to intermittent RESs and changes in the microgrid network. The proposed method's capabilities are validated by experimental results.

NSGA-III integrating eliminating strategy and dynamic constraint relaxation mechanism to solve many-objective optimal power flow problem

Increasing optimizing criteria in modern power systems promotes the birth of many-objective optimal power flow (Ma-OPF) problems in which more than three optimizing objective functions are considered. The increasing optimizing objectives as well as complex constraints of power balance and other system limits bring a decrease in selection pressure and challenges of constraint handling techniques for traditional multi-objective evaluation algorithms. Aiming at the solving difficulties of Ma-OPF problems, an improved NSGA-III integrating eliminating strategy and dynamic constraint relaxation mechanism (NSGA-III-EDR) is proposed. In the proposed approach, a new elimination strategy that eliminates the associated points having the largest angle with the corresponding reference line is employed to increase the selection pressure of the algorithm. An integrated constraint handling method, which employs a repair strategy based on assigning decision variables to feasible values and a penalty function approach together to handle power flow equality constraints, is also introduced into the NSGA-III-EDR. Aiming at lacking feasible solutions of the algorithm in the early searching stage, a strategy of relaxing constraint violations as well as a dynamic updating strategy for the tolerated threshold value of distinguishing feasible and infeasible solutions at the early evolution is proposed. Moreover, an improved domination sorting rule based on the proposed constraint handling method and the relaxing strategy to constraint violations is employed to promote the generation of feasible solutions. The effectiveness and feasibility of the proposed improved NSGA-III-EDR approach are studied and evaluated on different test cases of a famous standard IEEE 30-bus power system as well as the larger power systems of IEEE 57-bus and IEEE 118-bus. The numerical results show that the proposed NSGA-III-EDR method can provide solutions to many-objective OPF problems with tremendous potential compared with the traditional NSGA-III and other algorithms illustrated in the literature.

A State-of-the-Art Review on Recent Load Frequency Control Architectures of Various Power System Configurations

Load frequency management (LFM) work has become more difficult due to hybrid power systems. These power systems are responsible for increased power quality issues, exacerbating control problems. Frequency changes and power quality issues are the central concerns caused by these modern power systems. Frequency deviations occur mainly due to variations in generation, consumption, or both, and one of the critical reasons is the low inertia of the modern renewable penetrated hybrid power systems. So, to handle all these issues related to frequency and many more, the need for the best controller arises, which can easily handle any nonlinearities. This article comprehensively analyzes various load frequency control (LFC) architectures in various power system configurations. It focuses on the frequency management features of various power systems, including microgrids that use renewable energy and electric vehicles, with a complete understanding of current development states. Different controllers and tactics underwent comparative investigations. The detailed tabular analysis of frequency control architectures provides a deep insight into the LFC assessment of various power system configurations. The importance of better controllers for LFC planning in complicated power system domains is also emphasized in this work.

Frequency Dynamics of Power Systems with Inertial Response Support from Wind Generation

Inertial response support from wind turbine generators has become a priority requirement in most grid codes to improve the frequency response and frequency stability margins of power systems. However, the interaction between MPPT and inertial controllers may significantly degrade the power system dynamics. Therefore, there is a need to comprehensively understand the electromechanical dynamics of power systems with high penetration of wind generation. In this context, this work proposes a simplified dynamic model to assess the electromechanical dynamics of modern power systems with inertial response support from wind generation. The proposed simplified model allows simple analyses of the intrinsic and extrinsic aspects of wind generation that directly affect the system frequency dynamics and the dynamics of wind turbine generators. As a secondary contribution, this work also provides a comprehensive assessment of intrinsic and extrinsic aspects of wind generation that significantly affect the electromechanical dynamics of power systems with inertial response support from wind generation.

Frequency Support Control of Multi-Terminal Direct Current System Integrated Offshore Wind Farms Considering Direct Current Side Stability

The frequency stability of modern power systems is challenged due to widespread application of large-scale renewable energy systems, of which the offshore wind farm (OWF) is one of the dominant resources. The OWFs are usually integrated into the grid by multi-terminal direct current (MTDC) transmission systems, which makes the energy flow complicated and the frequency control design challenging. A frequency support control method of MTDC system integrated OWFs (referred to as the OWF-MTDC system) is proposed in this paper. First, the wind turbine generation system (WTGS) is controlled to reserve a certain amount of available power according to the real-time wind speed for more comprehensive frequency regulation. Then, the frequency support control of OWFs is designed, and they can release the rotor kinetic energy and reserved power to support the onshore grid frequency. In addition, the virtual inertia control of a modular multi-level converter (MMC) is designed, which can also provide frequency support in an emergency by use of the DC capacitor. To ensure that the frequency control of the OWF-MTDC system does not degrade the stability of the system, a detailed DC impedance model of the MMC-based MTDC systems is developed, considering the constant power control and DC voltage control. Based on the impedance model, the impact of the frequency control coefficients on the DC side stability of the MTDC system is analyzed. Simulation results validate the stability analysis and verify the proposed frequency control method, which can effectively provide frequency support to the onshore power grid.

Extreme scenario generation for renewable energies

AbstractWith a high penetration of renewable energies, scenario generation for wind and solar power is essential for the operation of modern power systems. Beyond the typical scenarios, extreme scenarios like full‐capacity generation for consecutive days should also be taken into account. However, developed data‐driven methods are unlikely to capture the characteristics of these extreme scenarios because of limited data. To this end, a three‐staged extreme scenario generation method is proposed for renewable energies to effectively and efficiently generate extremely high power output scenarios. First, an extreme data augmentation algorithm is designed to push the original data distribution towards the extreme case. Then, based on the extreme value theory, the tail of the augmented dataset is modelled by Generalised Pareto Distribution (GPD). Last, with the augmented dataset and the conditional labels sampled from the fitted GPD, a conditional generative adversarial network is trained with the modified loss function. A case study on a real‐world dataset shows that the authors’ proposed method has superior performance over the state‐of‐the‐art generative models in terms of extreme scenario generation. Besides, the generated samples successfully capture the temporal and spatial correlation of real scenarios of renewable energies.

Data-driven approach to fuzzy inference system tuning for static security assessment of multi-area power systems with renewable energy

Energy market deregulation and increased integration of intermittent renewable energy resources (RE) have pushed modern power systems to operate close to their security limits, which if violated could be catastrophic. To prevent power system failure and damage, a fast and accurate security assessment tool is required. In this paper, a data-driven fuzzy inference system tuned by hybrid genetic-simulated annealing (HGSA-FIS) is proposed for quick static security assessment (SSA) of a power system under a single component failure. An existing composite security index is modified to make a reliable SSA by distinguishing between severe and non-severe operating scenarios and contingencies. The prediction performance of HGSA-FIS was evaluated on an RTS-GMLC test system with operating scenarios varying with RE generation, load demand, and tie-line flows. The findings demonstrate the robust performance of HGSA-FIS in making static security assessments under varying operating scenarios, exceeding ANFIS in most and ANN in a few scenarios.

Sequence extraction-based low voltage ride-through control of grid-connected renewable energy systems

Various faults can cause voltage sag in the power grid at different voltage levels across the network. Balanced or unbalanced voltage sags lead to grid instability by tripping off a large number of wind or solar power plants from the electric power network. This is particularly problematic to maintain the stability of renewable energy-rich converter-dominated modern power systems. To mitigate the adverse effects of voltage sag, grid-connected converters (GCCs) need to be capable of operating in self-healing and fault-tolerant mode by embedding low voltage ride-through (LVRT) capability into the control system of GCCs. In order to facilitate the implementation of LVRT capabilities for unbalanced faults, fast and accurate frequency-adaptive sequence extraction of grid voltages and currents is essential. This motivated the present work of making a systematic comparison of adaptive observer-based sequence extraction techniques to provide LVRT capabilities into the control system of GCCs. In order to show the effectiveness of each observer, various comparative analyses were performed through Matlab-based numerical simulation. Different observers were benchmarked by the dynamic performance improvement during the low-voltage fault period. Experimental results using a laboratory-scale prototype GCC show that adaptive observers are a suitable choice of sequence extractors for LVRT operation of grid-connected converters in unbalanced and distorted grids. The results obtained in this work will contribute to enhancing the stability of modern power systems that are getting more and more converter-dominated.

Adaptive event‐triggering mechanism‐based N‐step predictive load frequency control for power systems with cyber attack

AbstractAlthough there have already been many works on the model predictive control (MPC) for load frequency control (LFC) of modern power systems, limited works can be found in the literature related to the output feedback MPC. This article studies an N‐step synthesis approach of output feedback MPC for LFC systems considering the problems of communication efficiency and network security. First, to improve the communication efficiency, an adaptive event‐triggering (AET) scheme involving two adaptive laws is designed to reduce the number of transmitted data packages which offers more flexible compared with existing event‐triggering schemes; Second, to handle the network security, a new model of LFC power system combining the AET scheme and random deception attack under an unified framework is established; Moreover, a synthesis approach of output feedback MPC with N‐step strategy is addressed for LFC of power system by parameterizing the infinite control moves into a series of output feedback laws. Compared with the existing predictive load frequency control methods, the present technique is shown as an useful way to improve the control performance since more degrees freedom is introduced by the N‐step strategy. Finally, the simulation experiment is carried out to verify our technique.

A Cost–Benefit Analysis Framework for Power System Resilience Enhancement Based on Optimization via Simulation Considering Climate Changes and Cascading Outages

Achieving a good level of resilience to extreme events caused by severe weather conditions is a major target for operators in modern power systems due to the increasing frequency and intensity of extreme weather phenomena. Moreover, regulatory authorities are pushing transmission and distribution operators to prepare resilience plans suitably supported by Cost–Benefit Analyses (CBAs). In this context, this paper proposes a CBA framework based on Optimization via Simulation (OvS) for the selection of the optimal portfolio of resilience enhancement measures. Starting from a comprehensive set of candidate grid hardening and operational measures, the optimal mix is identified by applying a novel two-step procedure based on an efficient application of the generalized pattern search heuristic technique. Risk indicators for the CBA are quantified, accounting for probabilistic models of climate changes. Moreover, the potential cascading outages due to multiple component failures provoked by extreme events are simulated on selected scenarios. The examples carried out on an IEEE test system show the effectiveness of the approach in identifying the best portfolio of resilience enhancement measures depending on climate change projections and costs of the measures, while the application to the model of a large portion of the Italian EHV transmission system demonstrates the practicability of the approach in real-world studies to support operators in different power system management phases, from planning to operation.

Comprehensive review of power system oscillations in large-scale power electronic-based renewable energy power plants

Recently, the large-scale integration of power electronic-based renewable energy power plants has changed the operation and response mechanism of the power system, resulting in several emerging oscillation issues that have seriously been threatening the system's stability. It helps us to recognize the similarities and differences among the triggering causes and formation mechanisms of oscillation scenarios. Following several typical oscillation events in the real world and the timescale decomposition method, this paper comprehensively reviews the wide-bandwidth oscillation study from the aspects of the analysis methods, possible cause, mechanism, and mitigation solution. The paper provides a perspective to classify the oscillations in the modern power systems on the basis of the oscillation frequency and the main oscillation module. This classification framework involves not only emerging oscillations in the power system with large-scale renewable energy sources integration but also includes typical oscillations in traditional power systems. It also systematically presents the relative relationship, development process, and inner influence between emerging oscillations and typical oscillations. Based on this review, the future research is suggested to focus on the relationship between different analytical methods or oscillation mechanisms, as well as the stability risk assessment of hybrid alternating current and direct current power systems.

Correlated Equilibrium Power Flow in Distribution Networks Using Graphical Game Theory

Modern power systems necessitate active management of distribution networks by Distribution System Operators (DSOs). A DSO can harness the flexibility capabilities of the nodes towards ensuring the safe operation of the distribution system, while optimizing a certain system objective. The objective-optimizing dispatch can be obtained by solving the Optimal Power Flow (OPF) problem. However, selfish nodes may deviate from the dispatch instruction by calculating a profitable deviation. Thus, an implementable solution needs to be not only optimal, but also an equilibrium, i.e., a point from which no node is willing to deviate. In this paper, we propose the adoption of correlated equilibrium as a solution concept that is generally more efficient than Nash and more relevant to this setting where the DSO acts as a coordinating entity. We formulate the problem of finding an efficient correlated equilibrium for distribution networks with discrete resources. The problem’s complexity is managed by exploiting the graphical structure and sparse connectivity of distribution networks, drawing on the methodology of graphical games. Simulations showcase that the proposed approach achieves an equilibrium with near-optimal efficiency, in contrast to the standard OPF approach which is shown to be unstable when each node selfishly optimizes its own objective.

Sponge Grid With Numerous Virtual Energy Storage Systems: Concept, Model, and Decentralized Control

High proportion of energy storage systems (ESSs) and flexible loads signify the main features of a modern power system. ESS with its bi-directional flow characteristic can flexibly change power network operations, thus providing a new solution for voltage regulation and control. However, since ESS resources are dispersed throughout the power system, it is necessary to design an effective aggregation scheme to achieve a concise voltage regulation. In this paper, a novel sponge grid is proposed, which is capable of both local and global tasks to offer greater flexibility and initiative in power system operations. On the one hand, it constructs the source and forms virtual energy storage (VES) systems to satisfy local demands. On the other hand, the sponge grid applies the superposition of a large number of VES systems spontaneous effects to present a collective voltage regulation behavior. Then, a message passing-based decentralized algorithm is proposed and designed for the local voltage regulation in the sponge grid. Various constraints including those representing inequality boundary, state of charge, and complex line flows, are expressed analytically and computed fast by the proposed portable projection approach. The proposed sponge grid model is tested on a modified IEEE 33-bus system to verify its effectiveness and superior performance.

Short-term Load Forecasting with Deep Learning Techniques

Short-term load forecasting is essential to the modern power system, which is the basis to guarantee the equilibrium of power supply and demand. It not only ensures the secure and stable operation of power grid, but also provides an important basis for power load dispatch, which can effectively reduce the economic loss caused by energy surplus or energy shortage in a short term. In the previous research work, traditional linear regression models, typical machine learning models and other models have been explored, but there are still problems of low accuracy or single influence factors. In this paper, a short-term forecasting model based on Temporal Convolutional Network (TCN) is proposed. Historical power load data, weather conditions, date information and their combinations are separately used to forecast the power load in different prediction models. Compared with other models, the evaluation metric of TCN model shows that its prediction accuracy is higher, and it is more suitable for short-term load forecasting.

Reactive Power Minimization for Modular Multi-Active-Bridge Converter With Whole Operating Range

Multi-port power electronic transformers (PET) are widely used in modern power systems to facilitate flexible interconnection between various AC or DC buses, power sources, and loads. However, due to the wide range of “source-load-storage” operating points, achieving efficient operation of the isolated DC-DC converter in the PET can be challenging. This paper focuses on the modular multi-active bridge (MMAB) converter in the PET, and proposes an optimization modulation scheme based on reactive power minimization (RPM) to improve efficiency. The RPM scheme utilizes a simple reactive power open-loop and active power closed-loop control structure that can be modulated online without the use of look-up tables. Importantly, this generalized method not only significantly improves efficiency, but can also be extended to any number of ports. Finally, experimental results are presented to validate the theoretical analysis and effectiveness of the RPM scheme.

The Frequency Spectrum Analysis of Wideband Oscillations of Grid-Connected Voltage Source Converter

With the widespread application of voltage source converters (VSCs) in power systems with high renewable resource penetration, the problem of wideband oscillations caused by interactions between power converters and the power grid has drawn great attention. This paper presents a new methodology for simplifying the mathematical derivations of the impedance model of a VSC. Using the new simplified model, the stability and spectrum characteristics of the performance of the VSC can be clearly analyzed. The proposed second-order d-q impedance model of a grid-connected VSC in the presence of a PLL not only mathematically demonstrates that wideband oscillatory modes always occur in conjugate pairs, but also delivers a clear vision of the physical essence of these wideband oscillations. A classic modal analysis is executed in this paper to demonstrate the stability and frequency spectral characteristics of wideband oscillations in a VSC integrated system. The impact of the control parameters of the VSC on wideband oscillatory stability is thoroughly analyzed by modal analysis using ‘DIgSILENT PowerFactory’ as well as hardware-in-the-loop experiments. This methodology can be considered a powerful tool for the control design of VSCs in the modern power system.

Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-Based Guidance

Modern power systems will have to face difficult challenges in the years to come: frequent blackouts in urban areas caused by high peaks of electricity demand, grid instability exacerbated by the intermittency of renewable generation, and climate change on a global scale amplified by increasing carbon emissions. While current practices are growingly inadequate, the pathway of artificial intelligence (AI)-based methods to widespread adoption is hindered by missing aspects of trustworthiness. The CityLearn Challenge is an exemplary opportunity for researchers from multi-disciplinary fields to investigate the potential of AI to tackle these pressing issues within the energy domain, collectively modeled as a reinforcement learning (RL) task. Multiple real-world challenges faced by contemporary RL techniques are embodied in the problem formulation. In this paper, we present a novel method using the solution function of optimization as policies to compute the actions for sequential decision-making, while notably adapting the parameters of the optimization model from online observations. Algorithmically, this is achieved by an evolutionary algorithm under a novel trajectory-based guidance scheme. Formally, the global convergence property is established. Our agent ranked first in the latest 2021 CityLearn Challenge, being able to achieve superior performance in almost all metrics while maintaining some key aspects of interpretability.

Offshore wind power generation system control using robust economic MPC scheme

With the increasing penetration of wind power in the modern power system, the economic model predictive control (EMPC) has been widely employed in the control of the offshore wind power generation system for promoting the dynamic economy. A robust EMPC strategy, aiming to minimizing damage to the turbine while maximizing the electric power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A linear model is utilized to approximate the original nonlinear behavior of the wind power generation system, while the consequent linearization error is taken as the external disturbance of the control system. A linear feedback controller with a robust control invariant set is designed to restrict the deviation between the nominal linear system and the actual nonlinear wind power generation system. Plant simulations, as well as NREL FAST code testing, are given to demonstrate the proposed controller's effectiveness throughout the overall operating region.