Power System Problems Research Articles

Comparative analysis of unidirectional and bidirectional electric vehicle charging stations (EVCS) optimal configuration in an IEEE 37-bus feeder system using Genetic Algorithm

Various power system problems and challenges may arise in the future due to the large scale of deployment of electric vehicles (EVs). Hence, the proper placement of EV charging stations (EVCS) effectively mitigates the impact of high EV loads connected to the grid. The research intends to explore and analyze differences between the regulation effectiveness of unidirectional and bidirectional charging technologies by utilizing different comparison evaluation indices. Moreover, considering their penetration level, this study tackles the impact analysis of EV and EVCS integration through time. Specifically, this paper aims to identify the optimal EVCS sites in an IEEE 37-bus test feeder system to minimize power loss brought by EV integration. Through MATLAB R2022b simulation and OpenDSS power flow analysis, the EVCS are optimally located near the supply bus. The findings show a direct relationship between the EV penetration level and system power loss. Due to the EV technology growth, there is an observed voltage profile degradation of up to 1.7094 p.u. The paper also highlights that although EV bidirectional charging technology (BCT) might reduce the load on the grid in the next few years of low penetration compared to unidirectional charging technology (UCT), it will give no significant difference due to the rapid increase of load connected during its high EV penetration.

Probabilistic spatiotemporal scenario generation method for dynamic optimal power flow in distribution networks

Due to the intricate intertwining of spatiotemporal characteristics and the substantial computational complexity caused by high dimensionality, the simultaneous consideration of both spatial and temporal correlations is a non-trivial problem in power system scheduling with variable renewable energy sources. This paper proposes a novel probabilistic spatiotemporal scenario generation (PSTSG) method for the dynamic optimal power flow problem in distribution networks, which generates probabilistic scenarios considering the spatial and temporal correlations among random variables simultaneously. First, static scenarios are generated by Latin hypercube sampling, and the probability of each static scenario is determined by copula-importance sampling theory, which is proposed to ensure the static scenarios yield the same probabilistic characteristics as the random variables. Next, a probability-based scenario reduction technique is implemented to retain effective scenarios and lower the computational burden. Then, multiple linear regression generalizes the static scenarios into dynamic ones with the same probabilities. Finally, a spatiotemporal scenario-based stochastic optimization is proposed for the dynamic optimal power flow problem, which considers uncertainties in variable renewable energy generation and aims to balance the output of controllable generators, renewable energy curtailment, and load shedding. Numerical simulations in a modified IEEE 33-node distribution network show that the proposed approach outperforms the existing methods that do not simultaneously consider spatial and temporal correlations in terms of computational efficiency and accuracy, proving that the proposed PSTSG method can efficiently capture the spatial and temporal correlation so as to make the optimal scheduling decision.

An Advanced High Dimensional Power System of Optimization Problems Algorithm

New Evolutionary Algorithm-based for High-Dimensional Power System Optimization Problems and Modern power system optimization problems face growing challenges as there are numerous decision variables and unbiased functions. Multi-objective evolutionary algorithms (MOEAs) basically are popularly applied for solving and dealing with such complicated problems in substantial power systems that have several objective functions. However, customary MOEAs works appropriately when the number of objective features comes to be less than three. Generally, the effectiveness of MOEAs begins to decrease apparently with an increase in the number of unbiased functions. This study is devoted to applying an authentic gradable evolutionary algorithm for the purpose of solving power system optimization issues which manifest numerous objective functions. In particular, this seeks an algorithm based upon NSGA-II algorithm dedicated for an efficient solution of multi-objective optimization problems. Here, we attempt to modify NSGA-II algorithm to effectively solve many target optimization problems. A novel effective grouping method is applied in the proposed algorithm in similar to the non-dominant grouping method of NSGA-II algorithm to accelerate decision convergence action in POF. The recommended algorithm is compared with modern numerous objective optimization algorithms following three test problems. The findings reveal that as the number of unbiased functions is noticeably large, the algorithm that we propose is significantly superior to other algorithms.

Data-Driven Chance-Constrained Planning for Distributed Generation: A Partial Sampling Approach

The planning of distributed energy resources has been challenged by the significant uncertainties and complexities of distribution systems. To ensure system reliability, one often employs chance-constrained programs to seek a highly likely feasible solution while minimizing certain costs. The traditional sample average approximation (SAA) is commonly used to represent uncertainties and reformulate a chance-constrained program into a deterministic optimization problem. However, the SAA introduces additional binary variables to indicate whether a scenario sample is satisfied and thus brings great computational complexity to the already challenging distributed energy resource planning problems. In this paper, we introduce a new paradigm, i.e., the partial sample average approximation (PSAA) using real data, to improve computational tractability. The innovation is that we sample only a part of the random parameters and introduce only continuous variables corresponding to the samples in the reformulation, which is a mixed-integer convex quadratic program. Our extensive experiments on the IEEE 33-Bus and 123-Bus systems show that the PSAA approach performs better than the SAA because the former provides better solutions in a shorter time in in-sample tests and provides better guaranteed probability for system reliability in out-of-sample tests. All the data used in the experiments are real data acquired from Pecan Street Inc. and ERCOT. More importantly, our proposed chance-constrained model and PSAA approach are general enough and can be applied to solve other valuable problems in power system planning and operations. Thus, this paper fits one of the journal scopes: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Distribution System Planning in Power System Planning and Implementation</i> .

Switched Observer-Based Adaptive Event-Triggered Load Frequency Control for Networked Power Systems Under Aperiodic DoS Attacks

This paper is concerned with a resilient load frequency control (LFC) problem for networked power systems against the aperiodic denial-of-service (DoS) attack. First, considering the intermittent nature of the aperiodic DoS attack and the unavailability of the partial system states simultaneously, a switched observer-based time-delay networked power model is established. Second, to save network bandwidth resources, a novel adaptive event-triggering mechanism is designed by applying a switch-adjusting threshold parameter scheme. Then, a switched observer-based PI controller is designed and a piecewise Lyapunov functional analysis approach is developed such that the exponential stability of closed-loop augmented power systems is derived with desired H∞ performance in forms of linear matrix inequality (LMI). Finally, a power system example with IEEE 39-bus is provided to illustrate the effectiveness of the designed schemes.

Solving optimal power flow problems via a constrained many-objective co-evolutionary algorithm

The optimal power flow problem in power systems is characterized by a number of complex objectives and constraints, which aim to optimize the total fuel cost, emissions, active power loss, voltage magnitude deviation, and other metrics simultaneously. These conflicting objectives and strict constraints challenge existing optimizers in balancing between active power and reactive power, along with good trade-offs among many metrics. To address these difficulties, this paper develops a co-evolutionary algorithm to solve the constrained many-objective optimization problem of optimal power flow, which evolves three populations with different selection strategies. These populations are evolved towards different parts of the huge objective space divided by large infeasible regions, and the cooperation between them renders assistance to the search for feasible and Pareto-optimal solutions. According to the experimental results on benchmark problems and the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems, the proposed algorithm is superior over peer algorithms in solving constrained many-objective optimization problems, especially the optimal power flow problems.

An energy dispatch optimization for hybrid power ship system based on improved genetic algorithm

Due to the energy crisis and environmental deterioration, the emerging hybrid energy ship power system gradually replaced the traditional ship power system to keep environmental friendliness by employing the clean energy. However, the increase of energy storage and photovoltaic generation system brings enormous challenge to the optimization scheduling of hybrid energy ship power system. For this reason, an improved genetic algorithm-based optimal scheduling strategy for the hybrid energy ship power system is developed in this paper. Firstly, a novel hybrid energy ship power system model including the diesel generator, energy storage system, propulsion system, dynamic load and photovoltaic power generation device is constructed under the constraint of energy efficiency and greenhouse gases emissions. Considering the various navigation situations that the ship may encounter, such as photovoltaic power generation limit in extreme weather and diesel generator power change in load shedding, the corresponding scheduling optimization problems for the hybrid energy ship power system are established. Under the cost and gas emission constraints, an improved genetic algorithm-based scheduling optimization algorithm is proposed. By introducing the nonlinear parameter change model in crossover and mutation operator, the performance of improved genetic algorithm can be enhanced, such as convergence speed and global optimization ability. Compared with current works, the proposed scheduling optimization strategy can achieve the lowest cost while reducing environmental impacts. Finally, simulation results under the given navigation cases demonstrate the superiority of the proposed improved genetic algorithm-based scheduling optimization strategy.

A novel honey badger algorithm based load frequency controller design of a two-area system with renewable energy sources

When it comes to the process of ensuring the stability, quality, and reliability of a power system, one of the most crucial components is known as the load frequency controller (LFC). It does this by ensuring that there is a balance between the amount of power that is produced and the amount that is consumed. This paper proposes a novel evolutionary approach, referred to as the Honey Badger Algorithm (HBA), PI/PID controllers should be configured in the best possible way in order to address the LFC problem in the electrical power system. The research takes into account a power system that is integrated between two areas and uses renewable energy sources, such as a wind system and a solar system. The utilization of renewable energy sources has the potential to yield favorable outcomes in frequency control through the provision of prompt and adaptable responses to fluctuations in system frequency, helping to maintain grid stability. The proposed HBA method is utilized to refine the controller parameter values, using a fitness function anchored on the integral of absolute error (IAE) and the integral time multiplied by absolute error (ITAE). The performance of the proposed HBA-based controller has evaluated under 5% step load perturbation (SLP) in area-1. The HBA-based controllers demonstrate greater performance in terms of settling time, overshoot, and fitness value when compared to other well-known optimization algorithms such as Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimization (GWO). According to the obtained results, the IAE-based PID controller has the best performance. The HBA-based PID controller is evaluated according to the following performance criteria; the objective function value is 0.4201, the settling time values and overshoot values for the area-1, area-2 and tie-line are 15.6, 33.7 and 27.9 s and −6.6, −0.7 and −0.0071 Hz, respectively. According to the findings, the HBA is both a dependable and effective tool for finding solutions to LFC research problems in multi-source power systems.

Dynamic Voltage Restorer as a Solution to Voltage Problems in Power Systems: Focus on Sags, Swells and Steady Fluctuations

In this study, the author presents the results of a survey on the utilisation of a dynamic voltage restorer (DVR) in power systems to alleviate voltage problems that result in sags, swells and fluctuations in voltage outside the required steady limits. A methodology based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement is adopted for conducting and reporting on the review, while the Scopus database is used to locate the relevant publications. A total of 68 publications qualify for inclusion in the survey. A bibliometric analysis covering the number of publications per annum, the top 10 most-cited journals and the top 10 most-cited publications is performed. The information from the selected publications is extracted, summarised and categorised into network scenarios for the use of DVRs, topologies and optimisation of DVRs; strategies for DVR controllers; and platforms that evaluate the feasibility of DVR topologies and controllers. Moreover, research trends and gaps are evaluated. Finally, potential areas for future research are proposed. This study provides an overview of the research on the use of a DVR to resolve voltage problems and is a resource for researchers generally interested in distributed flexible AC transmission systems (DFACTSs) and particularly interested in DVRs.

Reliable Event-Triggered Load Frequency Control of Uncertain Multiarea Power Systems With Actuator Failures

Load frequency control (LFC) is crucial for the economic operation and safety of power systems. Therefore this paper addresses the LFC problem for uncertain multi-area power systems with actuator failures. Specifically, actuator failures, uncertainties and communication bandwidth constraints appearing in multi-area power systems are taken into account simultaneously, and novel reliable event-triggered LFC schemes are proposed to cope with these troubles. The proposed schemes can ensure the asymptotical stability of the closed-loop system when only matched uncertainty exists. For the case of coexisting matched and mismatched uncertainties, the state trajectories of the closed-loop system can be controlled within a bounded set, where the size of the bounded set is only related to the mismatched uncertainty. To illustrate the theoretical results, a numerical example of three-area interconnected power system is presented. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Load frequency directly affects the quality of electric energy and is one of the main observation states of power systems, hence LFC has been widely investigated in the literature. For multi-area power systems, the system model to be controlled may be subjected to multiple unfavorable factors in practical situations, such as limited bandwidths, model uncertainties and actuator failures. To cope with these unfavorable factors, this paper is devoted to developing a unified control framework to guarantee the stability of the frequency deviation based on the event-triggered mechanism. Considering both matched and unmatched system uncertainties may exist as well as the bound of system uncertainties can be unknown, event-triggered control schemes including static event-triggered LFC and adaptive event-triggered LFC are accordingly designed to deal with aforementioned situations such that the closed-loop system is asymptotically or boundedly stable. The research outcome of this paper provides simple but effective LFC approaches that can be used to maintain the reliable and stable operation of multi-area power systems.

High‐frequency resonance in HVDC and wind systems: Root causes and solutions

AbstractThis paper presents methods to model and solve high‐frequency resonance problems in HVDC and wind power systems. Control and digital PWM delays are identified as a common root cause for such resonances. A systematic method to include such delays in small‐signal sequence impedance models is presented. The developed models are fully validated and used to characterize the effects of delay on system stability. In addition to full models that can predict the associated impedance and negative damping behaviour accurately, simplified approximate models are also presented to gain insights. The method is applied to type‐III and type‐IV turbines as well as HVDC converters based on modular multilevel converters (MMC). In addition to resonance of MMC and wind turbines with overhead transmission lines, the paper also identifies several other modes of high‐frequency resonance, including resonance between wind turbines and MMC HVDC converters, among different wind turbines, and between MMC HVDC converters and cables. Different methods to damp high‐frequency resonances are also discussed and demonstrated.

Distributed Generation Based WADC for Inter-Area Oscillations Considering Unknown Inputs

Inter-area oscillations (IAOs) are one of the main problems for contemporary power systems because of their low-damping characteristics. Additionally, the damping behaviors of IAOs might be further deteriorated by the addition of low inertial distributed generation (DG) to the power system. Therefore, a novel wide-area damping controller (WADC) for DG-integrated power systems is developed utilizing a modal linear quadratic regulator (LQR) technique that only targets essential IAOs without degrading other local modes. The states participated in linear combination from modal LQR are generally unavailable and need to be estimated. Full order observers are frequently used in literature, although they may not function if some inputs are unavailable. Hence, an unknown input observer (UIO) is suggested in this paper to estimate the states despite certain inputs being unknown. The proposed methodology is then tested on a DG-integrated 4-machine, 11-bus system and 10-machine, 39-bus system to validate the overall proposed design process. Results show that the proposed WADC successfully dampens the crucial IAO modes by 10% and 15% while not degrading other modes of the system.

Distributed fault-tolerant PI load frequency control for power system under stochastic event-triggered scheme

This article investigates the load frequency control problem of interconnected power systems by proposing a distributed fault-tolerant proportional integral (PI) control strategy. Firstly, a unified actuator fault model is established, and a distributed fault-tolerant PI control strategy is proposed on the basis of the actuator fault model. Then, a stochastic event-triggered scheme (SETS) based on stochastic sampling period is developed to relieve the redundant occupation of network communication resources. Further, an asymptotical stability criterion with H∞ performance is established by means of the Lyapunov method. Finally, illustrative examples are presented from two cases, isolated power system and interconnected power systems, to demonstrate the effectiveness of the designed control approach and the superiority of SETS compared with periodic event-triggered scheme.

A multi-area intra-day dispatch strategy for power systems under high share of renewable energy with power support capacity assessment

Long-distance power support through High-voltage Direct Current (HVDC) has provided feasible solutions for power dispatch and control problems in multi-area power systems under high share of renewable energy. In this paper, an advanced multi-area intra-day dispatch strategy for power systems with high penetration of renewable energy considering power support capacity is established to increase the accommodation of wind power and enhance the stability of the power system. At first, a novel assessment method of regional active power support capacity is carried out in this paper, which can sufficiently quantify the regional power margin (RPM), the DC dispatchable power (DCDP), and the regional power support distance (RPSD) of a power system. Subsequently, a multi-area intra-day dispatch strategy based on power support capacity analysis is proposed, where constraints on conventional units, wind farms, transmission lines, and RPSD are developed. Finally, to enable the development of the dispatch model, a model predictive control (MPC) scheme is used to update the power support capacity indicators dynamically and improve model reliability. The effectiveness and superiority of the proposed strategy are verified by case studies. Numerical results show a 7.02% cost reduction and overload risk reduction by using the proposed strategy compared to conventional global optimization methods.

Data-driven method of solving computationally expensive combined economic/emission dispatch problems in large-scale power systems: an improved kriging-assisted optimization approach

Combined economic/emission dispatch (CEED) is generally studied using analytical objective functions. However, for large-scale, high-dimension power systems, CEED problems are transformed into computationally expensive CEED (CECEED) problems, for which existing approaches are time-consuming and may not obtain satisfactory solutions. To overcome this problem, a novel data-driven surrogate-assisted method is introduced firstly. The fuel cost and emission objective functions are replaced by improved Kriging-based surrogate models. A new infilling sampling strategy for updating Kriging-based surrogate models online is proposed, which improves their fitting accuracy. Through this way, the evaluation time of the objective functions is significantly reduced. Secondly, the optimization of CECEED is executed by an improved non-dominated sorting genetic algorithm-II (NSGA-II). The above infilling sampling strategy is also used to reduce the number of evaluations for original mathematic fitness functions. To improve their local convergence ability and global search abilities, the individuals that exhibit excellent performance in a single objective are cloned and mutated. Finally, information about the Pareto front is used to guide individuals to search for better solutions. The effectiveness of this optimization method is demonstrated through simulations of IEEE 118-bus test system and IEEE 300-bus test system.

Inclusion of frequency nadir constraint in the unit commitment problem of small power systems using machine learning

As the intention is to reduce the amount of thermal generation and to increase the share of clean energy, power systems are increasingly becoming susceptible to frequency instability after outages due to reduced levels of inertia. To address this issue frequency constraints are being included in the scheduling process, which ensure a tolerable frequency deviation in case of any contingencies. In this paper, a method is proposed to integrate the non-linear frequency nadir constraint into the unit commitment problem, using machine learning. First, a synthetic training dataset is generated. Then two of the available classic machine learning methods, namely logistic regression and support vector machine, are proposed to predict the frequency nadir. To be able to compare the machine learning methods to traditional frequency constrained unit commitment approaches, simulations on the power system of La Palma island are carried out for both proposed methods as well as an analytical linearized formulation of the frequency nadir. Our results show that the unit commitment problem with a machine learning based frequency nadir constraint is solved considerably faster than with the analytical formulation, while still achieving an acceptable frequency response quality after outages.

Priority-Considered Load Shedding in Economic Dispatch: Distributed Optimization Approach

This paper studies two fundamental problems in power systems: the economic dispatch problem (EDP) and load shedding. In particular, convex optimization problems are formulated for both the EDP and the load shedding problem. For the EDP, an extension of the problem considering the transmission losses is presented. Furthermore, emphasis is placed on scheduling the load shedding when there exist some priorities on the loads. To solve the EDP and the load shedding problem in a distributed setting, we develop a method that combines the dual decomposition approach and the extragradient-based strategy. Notably, this work provides a fixed step size-based scheme for a strongly convex resource allocation problem considering general nonaffine coupled constraints. We show the proposed algorithm converges to the optimal solution under the assumption that the underlying graph is undirected. In addition, the method has an ergodic convergence rate of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(1/k)</i> in terms of the optimality residuals and the constraint violations. Simulation results are presented to demonstrate the effectiveness of the proposed optimization problems and distributed algorithm.

Adaptive Event-Triggered Model Predictive Load Frequency Control for Power Systems

This paper investigates the adaptive event-triggered (AET) output feedback model predictive control (MPC) method for the load frequency control (LFC) problem of hybrid power system involving the thermal power and the photovoltaic power. First, an AET communication mechanism, which possesses the ability to dynamically adjust the trigger threshold parameter according to an adaptive law, is addressed to reducing the communication overhead, while keeping the required control performance; Second, the output feedback model predictive controller which is composed of an off-line designed state observer and an on-line solved optimization problem, is proposed using the techniques of quadratic boundedness and S-procedure. The state observer gain is obtained by off-line maximizing the trace of a matrix which is closely related to minimal positively invariant set; While the output feedback MPC strategy is acquired by expressing the infinite horizon control moves as a free control move and a linear feedback law based on the estimated state; Third, the recursive feasibility of the on-line optimization problem and the stability of the closed-loop system are analyzed by the fact that the estimated state and estimation error will converge to the presented minimal positively invariant set. Finally, MATLAB and its linear matrix inequality (LMI) toolbox are used to solve the optimization problem, and the LFC system with load disturbance is simulated and analyzed. The simulation results show that the proposed method is feasible and effective.

Fully Parallel Optimization of Coordinated Electricity and Natural Gas Systems on High-Performance Computing

Intensified interactions between electric power and natural gas infrastructures raise significant demands to coordinate their system operations. This paper proposes a fully parallel optimization method that can achieve a rapid decision-making on the day-ahead coordinated operation of electricity and natural gas systems on the high-performance computing (HPC) platform. The proposed method can flexibly tailor decomposition strategies to solve the optimization problem according to unique features of problem models in both electric power and natural gas systems. Particularly, the operation problem of power system is split by function and time into numbers of singe-unit subproblems and single-period network subproblems, while the operation problem of natural gas system is decomposed into multiple area subproblems. All the scalable subproblems are solved and coordinated quickly in a fully parallel manner on HPC for improving computational efficiency and tractability of complex electricity-gas co-optimization problem. By optimally coordinate electricity and natural gas systems under different operating conditions, the proposed method could improve the energy economics as well as the system resilience to various outages due to extreme events. Numerical results demonstrate the effectiveness and efficiency of our proposed co-optimization method and its HPC implementation.

Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller

This paper presents a novel nature-inspired meta-heuristic optimization algorithm known as the Enhanced Whale Optimization Algorithm (EWOA), which imitates humpback whales' social behavior to solve the optimization of multi-area automatic load frequency control (LFC) problems of a stochastic renewable energy-based power system with superconducting magnetic energy storage (SMES). An EWOA algorithm is presented in response to the limitations of the conventional WOA algorithm, including its sluggish convergence time, low accuracy, and propensity to easily enter local optimum. The system model investigated includes some physical constraints such as the time delay (TD), generation rate constraint (GRC), reheat turbine (RT), and the dead band (DB). The impacts of these physical constraints on the dynamic performance of the proposed controller were investigated. The EWOA algorithm is utilized to dynamically optimize the parameters of the PID controller for optimal system performance. The effectiveness and dynamic performance of the proposed controller are compared with the conventional WOA using some performance metrics. The system model also includes superconducting magnetic energy storage (SMES) units in both areas and their impacts on the system performances are also investigated. The effects of the changes of two different parameters of the system (frequency bias parameter, B, and the governor speed regulation, R) on the frequency deviation responses and the controller's robustness are examined. It is evident from the results that the dynamic performance of the proposed controller is better than that of the conventional WOA and it is more robust and stable to changes in system loading, parameters, and step load perturbation.