Power System Uncertainties Research Articles

Robust Optimization for Network Decomposition of VQC with Scatter-Search-Predator-Prey Brain Storm Optimization

In this paper, a new method is proposed for optimizing the network decomposition of Voltage and Reactive Power Control (VQC). Power system uncertainties often occur due to load variations, generation output of renewable energy, charging/discharging of ESSs and EVs, etc. As a result, it is of main concern how power system operators deal with the uncertainties appropriately. This paper focuses on Modern-Heuristics-based Robust Optimization for power system decomposition of VQC in consideration of the uncertainties. In this paper, Scatter-Search-Predator-Prey Brain Storm Optimization (SSPPBSO) is proposed to evaluate better solutions by integrating Brain Storm Optimization (BSO) of high-performance Modern Heuristics with Predator-Prey and Scatter Search strategies. Robust Optimization plays a key role to handle uncertainties of power system conditions. The effectiveness of the proposed method is tested in the IEEE 57-node system.

Data-Driven Continuous-Time Modeling of Power System Uncertainty Using a Multi-Dimensional Stochastic Differential Equation

This paper proposes a generalized continuous random process modeling framework for imposing any desired probability distribution, autocorrelation and power spectral density of measured data of power system uncertainty by developing a multi-dimension stochastic differential equation (SDE) together with a quadratic output equation. An algorithm for determining appropriate parameters of the proposed SDE model is presented. The effectiveness of derived random modeling scheme is verified by carrying out comparative simulations using actual time series data of wind speed, solar radiation, PJM dynamic regulation, and wind power. The proposed SDE model can be readily integrated in various electrical component models for reliable operation, stability assessment and control of renewable power systems.

Technoeconomic and Environmental Study of Multi-Objective Integration of PV/Wind-Based DGs Considering Uncertainty of System

For technological, economic, and environmental reasons, renewable distributed generators (RDGs) have been extensively used in distribution networks. This paper presents an effective approach for technoeconomic analysis of optimal allocation of REDGs considering the uncertainties of the system. The primary issue with renewable-based distributed generators, especially wind and photovoltaic systems, is their intermittent characteristic that results in fluctuating output power and, hence, increasing power system uncertainty. Thus, it is essential to consider the uncertainty of such resources while selecting their optimal allocation within the grid. The main contribution of this study is to figure out the optimal size and location for RDGs in radial distribution systems while considering the uncertainty of load demand and RDG output power. A Monte Carlo simulation approach and a backward reduction algorithm were used to generate a reasonable number of scenarios to reflect the uncertainties of loading and RDG output power. Manta ray foraging optimization (MRFO), an efficient technique, was used to estimate the ratings and placements of the RDGs for a multi-objective function that includes the minimization of the expected total cost, total emissions, and total system voltage deviation, in addition to enhancing predicted total voltage stability. An IEEE 118-bus network was used as a large interconnected network, along with a rural 51-bus distribution grid and the IEEE 15-bus model as a small distribution network to test the developed technique. Simulations demonstrate that the proposed optimization technique effectively addresses the optimal DG allocation problem. Furthermore, the results indicate that using the proposed method to optimally integrate wind turbines with solar-based DG decreases the expected costs, emissions, and voltage deviations while improving voltage stability by 40.27%, 62.6%, 29.33%, and 4.76%, respectively, for the IEEE 118-bus system and enhances the same parameters by 35.57%, 59.92%, 68.95%, and 11.88%, respectively, for the rural 51-bus system and by 37.74%, 61.46%, 58.39%, and 8.86%, respectively, for the 15-bus system.

Extended Equal Area Criterion Revisited: A Direct Method for Fast Transient Stability Analysis

For transient stability analysis of a multi-machine power system, the Extended Equal Area Criterion (EEAC) method applies the classic Equal Area Criterion (EAC) concept to an approximate One Machine Infinite Bus (OMIB) equivalent of the system to find the critical clearing angle. The system-critical clearing time can then be obtained by numerical integration of OMIB equations. The EEAC method was proposed in the 1980s and 1990s as a substitute for time-domain simulation for Transmission System Operators (TSOs) to provide fast, transient stability analysis with the limited computational power available those days. To ensure the secure operation of the power system, TSOs have to identify and prevent potential critical scenarios through offline analyses of a few dangerous ones. These days, due to increased uncertainties in electrical power systems, the number of these critical scenarios is increasing, substantially, calling for fast, transient stability analysis techniques once more. Among them, the EEAC is a unique approach that provides not only valuable information, but also a graphical representation of system dynamics. This paper revisits the EEAC but from a modern, functional point of view. First, the definition of the OMIB model of a multi-machine power system is redrawn in its general form. To achieve fast, transient stability analysis, EEAC relies on approximate models of the true OMIB model. These approximations are clarified, and the EAC concept is redefined with a general definition for instability, and its conditions. Based on the defined conditions and definitions, functions are developed for each EEAC building block, which are later put out together to provide a full-resolution, functional scheme. This functional scheme not only covers the previous literature on the subject, but also allows to introduce several possible new EEAC approaches and provides a detailed description of their implementation procedure. A number of approaches are applied to the French EHV network, and the approximations are examined.

A causality based feature selection approach for data-driven dynamic security assessment

The integration of renewable energy sources increases the operational uncertainty of electric power systems and can lead to more frequent dynamic phenomena. The use of classifiers from machine learning is promising to include dynamics in the security assessment of the power system. The training of these classifiers is typically performed offline on synthetically generated operating conditions (OCs) that are similar to real-time operation. However, the uncertainty in the generated OCs and the classifier’s inaccuracy is larger the longer the time between offline and real-time operation. Moving the classifier training closer to real-time operation is an important step forward to reduce inaccurate predictions and improve reliability. In this paper, a novel causality-based feature selection approach for an online dynamic security assessment (DSA) framework is proposed. The key novelty is to use the system’s physics to learn the causal structure between the features and then select the features based on this causal structure. The proposed approach results in faster computations, is more robust and more interpretable. Moreover, classifiers can be trained closer to real-time operation which enhances the predictive performance. Through a case study using transient stability on the IEEE 68-bus system, the proposed method reduces computational time by 75% in comparison to state of the art feature selection techniques. The proposed workflow showed superior performance in accuracy and robustness against uncertainty compared to conventional machine learning approaches for DSA. The computational benefit was also projected to a dataset of the French transmission system where the approach has the potential to achieve computational savings of up-to two orders of magnitudes.

An Adaptive Sliding Mode Control Based on Disturbance Observer for LFC

In the power system, the loads and nonlinearity parameters cause the system frequency deviation, which complicates the load frequency control (LFC). To deal with the above problem, an adaptive sliding mode control (SMC) based on disturbance observer is proposed to eliminate frequency deviation for interconnected power system in this paper. Firstly, the mathematical model of the power system is established, where the power exchange between the tie line is considered as the variable of the designed sliding surface. Secondly, the nonlinear disturbance observer is constructed to estimate the parameter uncertainty and load of power system. Thirdly, combined with the estimated value of the disturbance observer and integral sliding mode surface, the SMC is designed. Moreover, considering the inherent shortcoming of SMC—the chattering problem, an adaptive strategy is applied to the SMC to ensure the stability of controller. Next, the stability of the designed SMC is proved by Lyapunov stability theory. Finally, to verify the effectiveness of the proposed controller, several simulations are presented.

The Bees Algorithm Tuned Sliding Mode Control for Load Frequency Control in Two-Area Power System

This paper proposes a design of Sliding Mode Control (SMC) for Load Frequency Control (LFC) in a two-area electrical power system. The mathematical model design of the SMC is derived based on the parameters of the investigated system. In order to achieve the optimal use of the proposed controller, an optimisation tool called the Bees Algorithm (BA) is suggested in this work to tune the parameters of the SMC. The dynamic performance of the power system with SMC employed for LFC is studied by applying a load disturbance of 0.2 pu in area one. To validate the supremacy of the proposed controller, the results are compared with those of recently published works based on Fuzzy Logic Control (FLC) tuned by Teaching–Learning-Based Optimisation (TLBO) algorithm and the traditional PID optimised by Lozi map-based Chaotic Optimisation Algorithm (LCOA). Furthermore, the robustness of SMC-based BA is examined against parametric uncertainties of the electrical power system by simultaneous changes in certain parameters of the testbed system with 40% of their nominal values. Simulation results prove the superiority and the robustness of the proposed SMC as an LFC system for the investigated power system.

Efficient Robust Look-Ahead Dispatch Incorporating Critical Region Preparation in Gap Time

Robust look-ahead dispatch (RLAD) is essential to manage uncertainties in power systems. As its key step, the worst-case scenario identification (WCSI) problem is cast to a max-min program. This computationally intensive procedure has to be performed repeatedly, impairing the computational efficiency of RLAD. To address this issue, an efficient RLAD scheme incorporating critical region preparation in gap time is proposed. The computation burden is mostly transferred from the online decision stage to the gap time via a customized technique based on multi-parametric programming. The accuracy of computing the critical regions is validated by the test in a six-bus system, and the proposed method is shown to cut down 21% of total iterations and save computational time dramatically by the test in a practical-scale system in Northeastern China.

Robust mode transition control of four-wheel-drive hybrid electric vehicles based on radial basis function neural network estimation-a simulation study

Purpose The flexible mode transitions, multiple power sources and system uncertainty lead to challenges for mode transition control of four-wheel-drive hybrid powertrain. Therefore, the purpose of this paper is to improve dynamic performance and fuel economy in mode transition process for four-wheel-drive hybrid electric vehicles (HEVs), overcoming the influence of system uncertainty. Design/methodology/approach First, operation modes and transitions are analyzed and then dynamic models during mode transition process are established. Second, a robust mode transition controller based on radial basis function neural network (RBFNN) is proposed. RBFNN is designed as an uncertainty estimator to approximate lumped model uncertainty due to modeling error. Based on this estimator, a sliding mode controller (SMC) is proposed in clutch slipping phase to achieve clutch speed synchronization, despite disturbance of engine torque error, engine resistant torque and clutch torque. Finally, simulations are carried out on MATLAB/Cruise co-platform. Findings Compared with routine control and SMC, the proposed robust controller can achieve better performance in clutch slipping time, engine torque error, vehicle jerk and slipping work either in nominal system or perturbed system. Originality/value The mode transition control of four-wheel-drive HEVs is investigated, and a robust controller based on RBFNN estimation is proposed. Compared results show that the proposed controller can improve dynamic performance and fuel economy effectively in spite of the existence of uncertainty.

Analyzing impact of DER on FIDVR - comparison of EMT simulation of a combined transmission and distribution grid with aggregated positive sequence models

The increase in percentage of distributed energy resources (DERs) brings in a degree of uncertainty in bulk power system planning and operations. Further, the presence of 1−φ induction motors introduces the possibility of motor stalling and fault induced delayed voltage recovery (FIDVR). With newer versions of IEEE Std 1547™ there can be the opportunity to obtain dynamic voltage support from DERs. In this paper, the impact of DERs providing dynamic voltage on the dynamic behavior of 1−φ induction motors is examined in detail along with an exploration of the need for simulation platforms that go beyond positive sequence. The studies discussed here are carried out both in detailed electromagnetic time domain and in positive sequence domain for comparison.

Hybrid transmission expansion planning and reactive power planning considering the real network uncertainties

AbstractConventional transmission expansion planning (TEP) has evolved with the increasing influence of distributed generations (DGs) and electric vehicles (EVs). This increases uncertainty in power systems. On the other hand, reactive power plays a key role in voltage stability, so modeling reactive power planning in TEP is very important. The amount of reactive power is a determining factor regarding the stability of the network voltage and in case of shortage, occurrence of some events may endanger the voltage stability. This research aims at proposing a hybrid TEP and reactive power planning for the first time. Furthermore, the impact of wind, PV power plants, and EVs, as the common sources of uncertainties, are included. The ultimate goal of HTEP‐RPP is to find the transmission line candidates that meet the reactive power requirements. The simulation case studies on IEEE 24‐ and 118‐ buses confirm that the TEP based on DCPF may lead to some unreliable operating conditions due to violating the voltage constraints. In this case, the HTEP‐RPP results, by applying the imperialist competitive algorithm (ICA), are more comprehensive and effective than the results obtained by gravitational search algorithm (GSA), for the studied problem or even the popular TEP solutions.

Layout optimisation algorithms and reliability assessment of wind farm for microgrid integration: A comprehensive review

The paper represents a comprehensive review of the wind farm layout and reliability assessment of the wind farm integrated electrical power system. The authors have done a review on the proliferation of renewable energy which raises the uncertainties in the electrical power system. The uncertainties including wind speed and wake effect are important to deal with when an isolated microgrid is considered. The scenario becomes vigilant when the wind farms are integrated with the main grid. Due to uncertainties, the study of reliability evaluation of a wind integrated power system would become significant to analyse the electrical power system behaviour effectively. So, the paper discusses the layout optimisation methods of wind turbines considering the uncertainty parameters, mainly the wake effect. In this regard, the different wake models and optimisation methods based on a single-objective and multi-objective functions are reviewed in detail with the proper comparisons. The paper serves as a better illustration of the competency of these optimisation methods on the optimal wind turbine location on a wind farm. Furthermore, the paper extends the view on the reliability and cost assessment, and reliability improvement techniques of the wind integrated power system. This article provides comprehensive information, yields an attractive and subsequent tool for research requirements for the researchers to design the wind farm layout, and assessed the reliability of a wind integrated power system.

Interval Valued T-Spherical Fuzzy Soft Average Aggregation Operators and Their Applications in Multiple-Criteria Decision Making

This paper deals with uncertainty, asymmetric information, and risk modelling in a complex power system. The uncertainty is managed by using probability and decision theory methods. Multiple-criteria decision making (MCDM) is a very effective and well-known tool to investigate fuzzy information more effectively. However, the selection of houses cannot be done by utilizing symmetry information, because enterprises do not have complete information, so asymmetric information should be used when selecting enterprises. In this paper, the notion of soft set (SftS) and interval-valued T-spherical fuzzy set (IVT-SFS) are combined to produce a new and more effective notion called interval-valued T-spherical fuzzy soft set (IVT−SFSftS). It is a more general concept and provides more space and options to decision makers (DMs) for making their decision in the field of fuzzy set theory. Moreover, some average aggregation operators like interval-valued T-spherical fuzzy soft weighted average (IVT−SFSftWA) operator, interval-valued T-spherical fuzzy soft ordered weighted average (IVT−SFSftOWA) operator, and interval-valued T-spherical fuzzy soft hybrid average (IVT−SFSftHA) operators are explored. Furthermore, the properties of these operators are discussed in detail. An algorithm is developed and an application example is proposed to show the validity of the present work. This manuscript shows how to make a decision when there is asymmetric information about an enterprise. Further, in comparative analysis, the established work is compared with another existing method to show the advantages of the present work.

Risk Assessment of Circuit Breakers Using Influence Diagrams with Interval Probabilities

This paper deals with uncertainty, asymmetric information, and risk modelling in a complex power system. The uncertainty is managed by using probability and decision theory methods. More specifically, influence diagrams—as extended Bayesian network functions with interval probabilities represented through credal sets—were chosen for the predictive modelling scenario of replacing the most critical circuit breakers in optimal time. Namely, based on the available data on circuit breakers and other variables that affect the considered model of a complex power system, a group of experts was able to assess the situation using interval probabilities instead of crisp probabilities. Furthermore, the paper examines how the confidence interval width affects decision-making in this context and eliminates the information asymmetry of different experts. Based on the obtained results for each considered interval width separately on the action to be taken over the considered model in order to minimize the risk of the power system failure, it can be concluded that the proposed approach clearly indicates the advantages of using interval probability when making decisions in systems such as the one considered in this paper.

Adaptive Tolerant State Estimation under Model Uncertainty in Power Systems

In this paper an adaptive tolerant estimator using singular value decomposition is proposed for a distribution network under model uncertainty in power systems. The adaptive tolerant estimator was designed with adjusted parameters and adjusted weights to overcome the limitations of model uncertainty. The estimator that reduces the measurement errors is adaptive to fast parameter changes in complicated environments. The singular value decomposition method was combined into the state estimator, which extended the over-determined cases to under-determined cases under model uncertainty. The performance of the tolerant estimator was compared with the conventional adaptive estimator, and the tolerant estimator showed accurate estimations against model uncertainty in complicated measurement environments.

Load Frequency Control for Multi-Area Power Plants with Integrated Wind Resources

To provide a more practical method of controlling the frequency and tie-line power flow of a multi-area interconnected power system (MAIPS), a state observer based on sliding mode control (SOboSMC) acting under a second-order time derivative is proposed. The proposed design is used to study load frequency control against load disturbance, matched and mismatched uncertainty and parameter measurement difficulties of power systems that exist in the modern power plant, such as multi-area systems integrated with wind plants. Firstly, the state observer is designed to optimally estimate system state variables. The estimated states are applied to construct the model of the MAIPS. Secondly, a SOboSMC is designed with an integral switching surface acting on the second-order time derivative to forcefully drive the dynamic errors to zero and eliminate chattering, which can occur in the first-order approach to sliding mode control. In addition, the stability of the total power system is demonstrated with the Lyapunov stability theory based on a new linear matrix inequality (LMI) technique. To extend the validation of the proposed design control for practical purposes, it was tested in a New England system with 39 bus power against random load disturbances. The simulation results confirm the superiority of the proposed SOboSMC over other recent controllers with respect to overshoot and settling time.

A Framework for Power System Operational Planning Under Uncertainty Using Coherent Risk Measures

With the increasing integration of renewable energy sources (RESs) and the implementation of dynamic line rating (DLR), the accompanying uncertainties in power systems require intensive management to ensure reliable and secure operational planning. However, while numerous approaches and methods in the literature deal with uncertainties, they have not been analyzed axiomatically. This paper presents an analysis of risk in power system operation using coherent risk measures, elaborating on the origin of risk and the mechanisms of its management in the presence of various sources of uncertainty. To illustrate the practicality and benefits of coherent risk measures, a risk-averse asymmetry robust unit commitment (UC) model is established. It is based on coherent reformulations of the uncertain reserve and line flow constraints and is formulated in the form of a compact computationally efficient mixed-integer second-order conic program (SOCP). The overall performance of the proposed framework is verified using the updated 2019 IEEE Reliability Test System and the ACTIVSg2000 test system over a year-long period.

Research on interval multi-objective optimal power flow in AC-DC systems considering wind power fluctuation

Aiming at the influence of the uncertainty of power system operating parameters such as wind power fluctuation on AC-DC hybrid system, an interval optimal power flow calculation method based on interval and affine arithmetic is proposed in this paper. First, AC and DC interval power flow model is constructed based on the relationship between interval and affine arithmetic, and the uncertainties such as the new energy generation output of the system are expressed as interval variables; static security performance index (PI) is introduced in AC-DC multi-objective optimal power flow objective functions, which take the system’s power generation cost and network loss into account; the Pareto optimal solution set is distributed uniformly in space by using the particle swarm algorithm to solve the interval optimal power flow model. Finally, MATLAB simulation examples are used to verify that the method can optimize the system’s power generation cost, network loss and static safety index while considering wind power fluctuation.

Control method of power grid topology structure based on reinforcement learning

With the rapid development of renewable energy and power electronics technology, uncertainty, complexity and data accumulation in power system continue to increase. Traditional methods often encounter bottlenecks in solving operational optimization decision-making and control problems. The development of artificial intelligence technology provides new methods and means for solving the problem of intelligent control of power grid topology. The deep reinforcement learning (DRL) method is used to learn from the historical experience data of power grid topology control that can improve the control and decision-making and methods of system operating performance, and solve the huge variables in traditional models. Through the method in this paper, the DRL method can effectively improve the real-time optimization and control ability of the grid topology.

Operating Reserve Quantification Using Prediction Intervals of Wind Power: An Integrated Probabilistic Forecasting and Decision Methodology

Adequate reserves are urgently needed to hedge against wind power forecasting uncertainties in power systems. Traditional reserve quantification sequentially acquires statistical features of wind power and then determines reserve amounts. This paper establishes a novel integrated probabilistic forecasting and decision (IPFD) methodology to simultaneously optimize the wind power prediction intervals (PIs) and probabilistic reserve quantification. Upward and downward reserve quantities are defined to cover the wind power forecasting uncertainties within the PIs. A cost function evaluating the reserve provision payment and deficit penalty is elaborated to realize cost-benefit trade-offs of reserve decision. Nonparametric wind power PIs are constructed based on extreme learning machine, which minimizes the reserve cost function subject to eligible target coverage probability constraint. The confidence level and quantile proportions associated with wind power PIs can be jointly tuned to reduce the operational cost of reserves. Benefited from extreme learning machine, the IPFD model is reformulated as a mixed integer linear programming problem. A feasible region tightening strategy that shrinks the large constant coefficients and eliminates the redundant binary variables is proposed to accelerate model training. Numerical experiments based on actual wind power data demonstrate the remarkable cost-effective advantages of the IPFD based reserve quantification, as well as the high computational efficiency for online application.