Electric Power Systems Research最新文献

Inverter-based resources (IBRs) have become indispensable in power systems due to their numerous environmental and operational advantages, which can contribute to enhancing overall grid reliability and resilience. However, due to their stability limitations, IBRs commonly disconnect from the grid when a grid-side disturbance happens. Therefore, inverters’ extensive integration introduces complexities and challenges to power system stability, necessitating the adoption of sophisticated stability assessment tools. One practical approach for assessing large-signal stability involves delineating the system’s stability region. Traditionally, two primary methods have been employed for this purpose: time-domain simulation and direct methods. To tackle the stability assessment of IBRs and sensitivity analysis of stability region, this paper introduces a theoretical direct method based on the sum of squares (SOS) method. It provides a Hardware-in-the-Loop (HIL) simulation to verify the results. The SOS method offers a more precise and less conservative representation of the stability region. Through applying the SOS method, this paper endeavors to establish and investigate the stability region for grid-tied IBR while conducting sensitivity analysis of the inverter responses to grid-side disturbances. This sensitivity analysis specifically delves into the impacts of different load levels and different voltage-loop control parameters on the stability region of the IBR and inverter transit response. In other words, the study develops a theoretical approach using the nonlinear dynamic model and Lyapunov-based stability assessment to understand how a modification in the system can affect the stability region of the IBR and the dynamic response of the inverter for a grid-side fault. The SOS method is applied to construct an accurate Lyapunov function for the system, and the Lyapunov-based stability assessment is used to study system stability under a large disturbance. Besides the numerical sensitivity analysis of inverter dynamic response, the accuracy of the sensitivity analysis is validated through complementary techniques, including time-domain simulations of a grid-tied IBR and high-fidelity HIL simulations using a real-time digital simulation (RTDS) platform. The result proves the outcomes of numerical stability analysis and sensitivity assessment of the IBR using an SOS-based stability analysis.

This study pioneers sustainable energy solutions amidst escalating demand and fossil fuel depletion. Its primary novelty lies in proposing an integrated energy system, encompassing a concentrated solar plant, thermal energy system, and hybrid power supply within the solar energy domain. This integration substantially enhances overall system performance. The architectural design integrates various energy conversion components, including wind turbines, energy storage devices, and electric heaters, fostering synergistic collaboration. The optimization of energy usage is a key focus, utilizing heating and electricity costs as pivotal signals for participation in an Integrated Demand Response program (IRD). The demand response model, encompassing both thermal and electric loads, incorporates a sophisticated price elasticity matrix. Addressing challenges associated with renewable energy intermittency, phased carbon emissions, and the advantages of demand response, this study introduces an advanced system operation optimization model to effectively curtail operational costs. In the realm of methodology, this research breaks new ground by proposing an enhanced optimization algorithm that ingeniously combines the Whale Optimization Algorithm (WOA) with the Wavelet Transform (WT). This novel amalgamation significantly enhances the algorithm's search capabilities, providing superior performance in tackling the complexities inherent in the optimization problem. The proposed model undergoes rigorous evaluation in a comprehensive study featuring diverse energy sources and multiple scenarios. The outcomes of this evaluation distinctly showcase substantial reductions in overall operational costs compared to conventional approaches. This multifaceted contribution positions the study at the forefront of endeavors to revolutionize energy systems for enhanced sustainability and efficiency.

The three-phase two-wire distribution system is an unconventional proposal for the repowering of single wire earth return electrical networks, intended for use in remote rural areas as it allows cost reduction by using only two overhead conductors and the ground as the third phase. An intrinsic characteristic of this system is the voltage unbalance observed near the loads, resulting from impedance mismatches between overhead conductors and the ground. The main objective of this work is to explore methodologies for minimizing voltage unbalance by strategically introducing impedance elements in targeted sections of the distribution network. A novel simplified equation for line-to-line compensation based on network parameters, along with a precise computational methodology to determine optimal compensation values, are proposed as contributions to the state of the art. The simulations in MatLab/Simulink demonstrate that the arrangement in which the compensation elements are added to the system directly impacts the voltage unbalance. Moreover, the study demonstrates the efficacy of the developed computational methodology in effectively reducing unbalance within extensive distribution networks.

The traditional approach for defining the line ampacity ratings of Overhead Lines (OHLs) is static and tends to be conservative. While this approach has been valuable for many years, it may not fully capture the complexities of actual line operating conditions, such as the effect of changing environmental parameters and ambient conditions. This paper proposes a sensorless Line Rating Assessment (LRA) approach to estimate the ampacity of transmission OHL. It employs the IEEE 738 standard and the weather parameters data derived from two distinct numerical weather models, the Weather Research and Forecasting (WRF) model and the Weather Research and Forecasting Chemistry (WRF-Chem) model. The model estimations are compared with weather data collected by weather mast measurements, serving as a reference to validate the numerical weather models. The proposed strategy is applied to a section of 132kV OHL in the Dubai region. Initial investigations include sensitivity analyses to explore the impact of varying weather parameters on the line ampacity ratings. Thereafter, three performance indices are utilised to evaluate the performance of the proposed approach for ampacity estimation. The results indicate that WRF-Chem surpasses WRF by delivering estimates with greater ampacity headroom in winter and ensuring safer ratings in the summer.

This study addresses the limited research on energy storage operators as independent stakeholders in active distribution network scheduling involving multiple stakeholders. The study proposes a multi-agent Stackelberg Game Model where distribution network operators act as leaders, and energy storage operators and power users act as followers. A Bi-Level programming model is employed to solve the multi-agent game, and equilibrium strategies of each stakeholder are analyzed. Distribution network operators aims to maximize operational efficiency by formulating time-of-use price strategies, while energy storage operators and power users adjust their charging, discharging, and consumption strategies to maximize profits and minimize electricity costs. It emphasizes the importance of considering user satisfaction alongside economic costs when examining their electricity consumption. Simulation scenarios demonstrate that the proposed model enhances stakeholder benefits and societal welfare. Energy storage operators increase the depth of its own charging and discharging strategies in pursuit of maximizing benefits, resulting in a lower peak-valley transfer rate on load. Considering the interests of all stakeholders, the benefits of energy storage operators and power users are increased by 29.58 % and 20.07 % respectively, and the benefits of distribution grid companies are reduced by 18.97 %, but the total social profit is increased by 26.11 % while the cost of user satisfaction reduced by 43.78 %.© 2017 Elsevier Inc. All rights reserved.

Wind generators with integrated inertia control can autonomously regulate active power in response to frequency variations within the power system. Generator tripping is often necessary to address frequency increases during significant grid disturbances. This tripping alters the adjusLEle power range of the wind generator and frequency characteristics of the power system, impacting power regulation capacity and modifying the required generator tripping power (GTP). Precise frequency control in wind power integrated systems is challenging due to the inability to accurately quantify frequency security conditions. To address this, the synthetic power regulating speed (SPRS) is deduced to characterize regulation capability. The ranges of required GTP and SPRS are modeled under constraints including maximum frequency deviation, rate of frequency change, and the frequency regulating capacity of wind farms. A novel concept of the frequency dynamic security domain is introduced, accounting for the interplay among generator tripping, power regulation, and frequency characteristics. This approach includes identifying generator tripping locations and determining the required GTP by establishing the frequency dynamic security domain. Additionally, an emergency frequency control method that coordinates generator tripping and power regulation is proposed. Simulations show that this method minimizes generator tripping while avoiding frequency threshold exceedance, thus preserving power regulation capability.

Due to increasing the intensity and frequency of natural catastrophes, sustainable supplying of consumers is one of the main challenges of electrical distribution companies against these high-impact and low-probability (HILP) events. Recently, researches are directed to solve the mentioned challenge by various methods including optimal distributed generation and energy storage placement, micro-grid formation, structural reinforcement and etc. In addition, the uncertainty nature of the disasters, loads and renewable generations has influenced these studies. Despite the availability of different solutions considering all resilient-oriented methods and uncertain parameters, applying a proper model as long as simplifying the equations, which satisfies all the needs, are still the outstanding aspects that must be studied. This paper applies and proposes convex equations in all parts, including both operational and planning perspectives as an innovation. Mixed-integer quadratically-constrained programming (MIQCP) is adapted for formulating the problem. The proposed model is developed under GAMS environment, and its performance is evaluated by the IEEE 33-node test system under various severe fault scenarios. Comparing the results reveals that the hybridization technique against only planning technique leads to a decrease of 46.15 % and 42.45 % in load shedding and investment costs respectively. Also, it causes a decrease of 40.63 % and 25.32 % in load shedding and investment costs respectively compared to only operational techniques causes.

To facilitate the coordination between transmission and distribution system operators, accurate equivalent models of power system components are required. Towards this objective, in this paper, a black-box model is proposed for developing steady-state reduced-order equivalents of active distribution networks (ADNs) that host converter-interfaced renewable energy sources (CI-RES) and operate under various voltage control schemes. Droop-based and traditional voltage control schemes, such as on-load tap changer (OLTC) control strategies and the use of shunt capacitors, are considered. The performance of the proposed model is evaluated via simulations considering various load types and CI-RES penetration levels. Comparisons against the conventional polynomial and exponential models are also conducted, demonstrating the superior performance of the proposed black-box model.

This work presents a systematic procedure for the parameterization of a robust adaptive proportional-integral controller applied to a grid-tied inverter with an LCL filter. The controller parameterization uses the Tasmanian devil optimizer, driven by performance indexes and controller stability constraints in the formulation of the optimization problem. Simulation results considering the renewable energy conversion system subject to periodic exogenous disturbances, grid impedance variations, and system uncertainties representing critical situations of real systems are presented. The optimized controller allows current tracking in less than one grid cycle (around 9 ms) during the initial transient regime, and when the grid inductance changes around 4.33 times the nominal value (from 0.3 mH to 1.3 mH), the transient regime ends in 11 ms without significant overshoot. Two extreme scenarios were also considered: when the grid inductance increases 17.66 times (from 0.3 mH to 5.3 mH) and 34.33 times (from 0.3 mH to 10.3 mH). The duration of transient regimes was again less than one grid cycle. Moreover, there are no significant tracking errors in the transient regimes associated with current reference changes, and the tracking error tends to a residual value in steady state in all scenarios, with values on the order of 10⁻⁶.

Increased integration of inverter-based resources alters the response of large-scale power systems to contingency events. The resulting loss of control actuation and rotating inertia causes the system operating point to move substantially in a short period of time following severe disturbances. To ensure system reliability, it is essential to develop efficient global stability assessment tools. Toward this end, Lyapunov’s direct method has received considerable attention due to their rigorous mathematical foundation and fast stability screening. However, most existing approaches in this category are limited in application and cannot readily be extended to practical large-scale power systems. In this work, we propose a data-driven method based on Koopman operator theory for constructing a Lyapunov function and estimating the corresponding region of attraction (ROA). To achieve this, we employ a coordinate transformation enabled by deep neural networks. This approach addresses persistent challenges of existing direct methods in finding proper Lyapunov functions for contemporary power systems. Once the ROA is estimated, the resulting method can rapidly screen the stability of an arbitrary initial operating point without simulating the state trajectory. A numerical case study is presented using a reduced-order model of the North American Western Interconnection with battery energy storage.