Power Flow Analysis Research Articles

Distributed security state estimation-based carbon emissions and economic cost analysis for cyber–physical power systems under hybrid attacks

Sustainable cyber–physical power systems (CPPSs) significantly reduce carbon emissions due to climate change. However, when the data exchange in CPPSs suffers from hybrid attacks, the distributed state estimation and optimal power flow (OPF) analysis will inevitably be compromised, leading to inadequate or faulty scheduling of clean energy and thermal power generations and further affecting the total carbon emissions and economic cost. To address these problems, this paper proposes a novel consensus-based distributed security state estimation (DSSE) method for CPPSs, which is used to analyze the impact of hybrid attacks on carbon emissions and economic cost. Firstly, the incomplete and non-authentic data features caused by hybrid attacks are described, and their influence on distributed state estimation model is analyzed. A new residual-based attack detection method is then constructed in each subregion, where secure and non-secure sets are employed to describe whether the subregion is attacked and the compensation mechanism is designed for the non-secure set. Secondly, considering data compensation, distributed state estimation model is reconstructed, and a distributed security state estimation method under hybrid attacks is proposed while its convergence condition is derived. Thirdly, the impacts of hybrid attacks on carbon emissions and economic costs are analyzed based on the proposed DSSE method. Finally, experimental results confirm the validity of the theoretic analysis.

Power-Flow Simulations for Integrating Renewable Distributed Generation from Biogas, Photovoltaic, and Small Wind Sources on an Underground Distribution Feeder

The rapid expansion of distributed generation leads to the integration of an increasing number of energy generation sources. However, integrating these sources into electrical distribution networks presents specific challenges to ensure that the distribution networks can effectively accommodate the associated distributed energy and power. Thus, it is crucial to evaluate the electrical effects of power along the conductors, components, and loads. Power-flow analysis is a well-established numerical methodology for assessing parameters and quantities within power systems during steady-state operation. The University of São Paulo’s Cidade Universitária “Armando de Salles Oliveira” (CUASO) campus in São Paulo, Brazil, features an underground power distribution system. The Institute of Energy and Environment (IEE) leads the integration of several distributed generation (DG) sources, including a biogas plant, photovoltaic installations, and a small wind turbine, into one of the CUASO’s feeders, referred to as “USP-105”. Load-flow simulations were conducted using the PowerWorldTM Simulator v.23, considering the interconnection of these sources. This dataset provides comprehensive information and computational files utilized in the simulations. It serves as a valuable resource for reanalysis, didactic purposes, and the dissemination of technical insights related to DG implementation.

Compensation Admittance Load Flow: A Computational Tool for the Sustainability of the Electrical Grid

Compensation Admittance Load Flow (CALF) is a power flow analysis method that was developed to enhance the sustainability of the power grid. This method has been widely used in power system planning and operation, as it provides an accurate representation of the power system and its behavior under different operating conditions. By providing a more accurate representation of the power system, it can help identify potential problems and improve the overall performance of the grid. This paper proposes a new approach to the load flow (LF) problem by introducing a linear and iterative method of solving LF equations. The aim is to obtain fast results for calculating nodal voltages while maintaining high accuracy. The proposed CALF method is fast and accurate and is suitable for the iterative calculations required by large energy utilities to solve the problem of quantifying the maximum grid acceptance capacity of new energy from renewable sources and new loads, known as hosting capacity (HC) and load capacity (LC), respectively. Speed and accuracy are achieved through a properly designed linearization of the optimization problem, which introduces the concept of compensation admittance at the node. The proposed method was validated by comparing the results obtained with those coming from state-of-the-art methods.

GPU-resident sparse direct linear solvers for alternating current optimal power flow analysis

Integrating renewable resources within the transmission grid at a wide scale poses significant challenges for economic dispatch as it requires analysis with more optimization parameters, constraints, and sources of uncertainty. This motivates the investigation of more efficient computational methods, especially those for solving the underlying linear systems, which typically take more than half of the overall computation time. In this paper, we present our work on sparse linear solvers that take advantage of hardware accelerators, such as graphical processing units (GPUs), and improve the overall performance when used within economic dispatch computations. We treat the problems as sparse, which allows for faster execution but also makes the implementation of numerical methods more challenging. We present the first GPU-native sparse direct solver that can execute on both AMD and NVIDIA GPUs. We demonstrate significant performance improvements when using high-performance linear solvers within alternating current optimal power flow (ACOPF) analysis. Furthermore, we demonstrate the feasibility of getting significant performance improvements by executing the entire computation on GPU-based hardware. Finally, we identify outstanding research issues and opportunities for even better utilization of heterogeneous systems, including those equipped with GPUs.

Real-time power flow analysis and management for a long-endurance solar UAV during continuous flight

Real-time power flow analysis and management for a long-endurance solar UAV during continuous flight

Comparative Analysis of Smart Grid Solar Integration in Urban and Rural Networks

Solar photovoltaic (PV) power, a highly promising renewable energy source, encounters challenges when integrated into smart grids. These challenges encompass voltage fluctuations, issues with voltage balance, and concerns related to power quality. This study aims to comprehensively analyze the implications of solar PV penetration in Malaysian power distribution networks predominantly found in urban and rural areas. To achieve this, we employed the OpenDSS 2022 and MATLAB 2022b software tools to conduct static power flow analyses, enabling us to assess the effects of solar PV integration over a wide area under two worst-case scenarios: peak-load and no-load periods. Our investigation considered voltage violations, power losses, and fault analysis relative to the power demand of each scenario, facilitating a comprehensive evaluation of the impacts. The findings of our study revealed crucial insights. We determined that the maximum allowable power for both urban and rural networks during no-load and peak-load situations is approximately 0.5 MW and 0.125 MW, respectively. Moreover, as the percentage of PV penetration increases, notable reductions in power losses are observed, indicating the potential benefits of higher smart grid PV integration.

Online load-loss risk assessment based on stacking ensemble learning for power systems

Power systems faces significant uncertainty during operation owing to the increased integration of renewable energy into power grids and the expansion of the scale of power systems, these factors lead to higher load-loss risks; therefore, realization of a fast online load-loss risk assessment is crucial to ensuring the operational safety and reliability of power systems. This paper presents an online load-loss risk assessment method for power systems based on stacking ensemble learning. First, a traditional load-loss risk assessment method based on power flow analysis was constructed to generate risk samples. The label of the sample is load-loss risk assessment index and the features are multiple operational variables of the power system. And the recursive feature elimination using cross validation (RFECV) was adopted for feature selection. Second, four different machine learning models, including support vector regression (SVR), extremely randomized trees (ET), extreme gradient boosting (XGBoost) and elastic network (EN) were used to form a stacking ensemble learning model for sample training. Moreover, to further improve the model performance, the particle swarm optimization (PSO) algorithms was used for parameter optimization. Finally, based on this model, the online load-loss risk assessment of a power system was realized. The application of the proposed method on IEEE test systems demonstrated that the proposed method was more accurate than methods based on individual machine learning models, from which the stacking was designed, while still maintaining a significant advantage in terms of runtime compared to the traditional risk assessment method.

Assessment of topological boundaries for microgrids in an active power distribution network during emergencies

Microgrid (MG) operation has become an intriguing possibility to enhance the sustainability, reliability, and resilience of electricity distribution systems in emergencies. However, identifying suitable physical boundaries of MGs from an active power delivery infrastructure is a highly challenging task. This study describes a novel approach for determining the topological boundary of multiple MGs to restore power supply in a power distribution system during the unavailability of grid power supply. This method tries to enhance the self-adequacy of the MGs by increasing power flow within the MG boundaries. In this context, possible demand at the nodes of the power delivery infrastructure during an emergency is anticipated by an autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) network-based time series forecast models. A modified backward-forward sweep (MBFS) power flow analysis, tailored for islanded mode, is employed to assess the power flow status in the MGs during emergencies. The MGs are supplied with locally available dispatchable distributed generators (DDGs). A particle swarm optimization (PSO) algorithm intertwined with the MBFS power flow method has been employed to determine the boundary of MGs by identifying minimum power flow line in the shortest path between the consecutive DDGs. Further, this study demonstrates the evaluation of the shortest path between paths between the consecutive DDGs with the help of a breadth-first search (BFS) algorithm. The proposed strategy has been tested on a modified 33-bus, 69-bus and 118-bus power distribution test systems. The experimented results are verified with the results obtained from similar existing approaches to explore the opportunities in the proposed methodology.

An interval power flow method for radial distribution systems based on hybrid second-order cone and linear programming

Interval power flow (IPF) analysis is a promising approach to handling the uncertainty of renewable energy sources and loads in power systems. However, most existing studies are based on standard interval arithmetic or affine arithmetic. Little work can be found that employs traditional optimization methods to obtain the interval results, especially based on convexified power flow formulation. Therefore, this paper proposes an IPF method based on a hybrid second-order cone and linear programming for radial distribution systems. First, the optimization process of IPF based on the standard power flow model is introduced. Given the non-convexity of the standard power flow model, an IPF framework based on second-order cone programming (SOCP) for radial distribution systems is proposed. Moreover, considering that the SOCP formulation can only achieve half of the whole IPF process, a revised linear DistFlow formulation is adopted, and the IPF method based on a hybrid second-order cone and linear programming is devised. Finally, the proposed IPF method is compared with the standard power flow equation-based IPF method and the Monte Carlo method. The simulation results on modified IEEE 33-node and 69-node distribution systems demonstrate the effectiveness and prospects of the proposed IPF method.

Data driven approach for the management of wind and solar energy integrated electrical distribution network with high penetration of electric vehicles

With the increased penetration of fluctuating renewables and growing population of contemporary loads such as electric vehicles, the uncertainties in the generation and demand in the electric power grids are increasing. This makes the efficient operation and management of these systems challenging. Objective of this study is to propose a real-time management system for EV charging, which maximises the renewable energy utilization. An electric power distribution network with an average and peak demands of 1.51 MW, and 3.6 MW respectively, was chosen for the study. The real time power flow through the network components were analyzed using the OpenDSS model. With a wind power density of 574.51 W/m2 and a solar insolation of 4.14 kWh/m2/day, an optimized renewable energy system consisting of a 2.3 MW wind turbine and 2.61 MWp photovoltaic power plant are proposed for the network. Models based on k-Nearest Neighbors algorithms were developed for predicting the performances of these renewable energy systems at the network area. Based on the load profile, power flow analysis, and the predicted generation from solar and wind systems, a demand side management algorithm has been developed for the charge/discharge scheduling of the electric vehicles connected within the network. The basic objective of the algorithm is to maximize the renewable energy utilization by triggering the charging cycle during the periods of excess renewable energy generation. With an annual contribution of renewables is estimated as 12.61 GWh out of which 9.33 GWh is from wind and 3.29 GWh from solar. Wind from wind and solar energy systems, the proposed scheduling algorithm could contribute 71.56 percent of the charging load demand by the EVs.

Optimal Management of Seasonal Pumped Hydro Storage System for Peak Shaving

Power demand varies on a daily and seasonal basis. Responding to changing demands over time is challenging for energy suppliers as it causes expensive power plants to operate in high-demand seasons, usually summer, increasing the cost of electricity. Peak load shaving makes the load curve flatten by reducing the peak load and shifting it to times of lower demand, hence reducing the operation of expensive power plants. Hence, there is a need for large-scale and long-term ESS to store energy in the time of low-demand seasons for future utilization in the highest-demand ones. In this work, an energy management system (EMS) is developed to optimally manage a grid-connected pumped hydro storage (PHS) for peak shaving. The proposed model incorporates a dynamic economic dispatch (DED) over a study period of one year; hence, a DC power flow analysis considering transmission constraints is utilized to ensure a fast load flow estimation and a manageable simulation time. The framework can be adopted to assess the long-term profitability of PHS-utilizing GAMS as an optimization tool. Further, to draw conclusions that would suit the characteristics of the demand pattern. This analysis is essential to motivate the construction of new seasonal PHS plants due to the high construction costs they are identified with, especially in geographical areas where this technology is not yet considered or is hard to construct. The simulation results demonstrate that integrating 1500 MWh PHS reduced the operation of expensive thermal units by 1224 MWh annually. Further, a reduction in operation costs was recorded after integrating a PHS unit that ranged from 2.6 M to 22 M USD/year, depending on the storage capacity.

Assessment of the required running capacity in weakly interconnected insular power systems

In this paper a methodology is proposed for assessing the required running capacity in weakly interconnected insular power systems. This information is of crucial importance for system operators, when deciding the operation policy of the interconnected power system. In the proposed methodology, a probabilistic analysis is initially employed to identify probable extreme stress conditions of the local oil-fired units of the insular power system and the interconnector. Then, based on the unit commitment guidelines and the technical limitations of the interconnector, a power flow analysis is proposed to be followed. Hence, useful insights can be obtained for the system operators, regarding the required running capacity and the interconnection operation. To validate the proposed methodology’s efficacy, the case of the interconnection of Crete with the Hellenic Transmission System is considered. The presented case study follows the recently proposed guidelines for unit commitment in the power system of Crete following to the interconnection, while considering the extreme scenarios derived from the probabilistic analysis. The existing oil-fired units that will be required to remain available for dispatching after the operation of the AC interconnector are identified, while the interconnection operation is characterized.

PV and QV analysis for finding the optimum location of FACTS device to investigate the effectiveness in KR power system using PSS/E

This paper presents the effect of Static Synchronous Compensator (STATCOM) device in power system for power flow and voltage stability analysis. The stability of power system is most important evaluation that is needed to be considered under various contingency states. This paper presented Power-Voltage (PV) and Reactive power-Voltage (QV) analysis which have been widely used in steady state of power system in normal operating condition and gives important details about the loading of the system and voltage stability. The shunt and series compensation can be used to increase the voltage and flow in the power system. Kurdistan Region Power System (KRPS) used to verify the effectiveness and performance of STATCOM in power system. PSS/E proposed as power system software for KRPS. The result shows that the proposed Full Newton Raphson (FNR) method spends less execution time and the optimum placement for STATCOM for enhancing static voltage stability margin and improves the power transmission capability of the line is the 'weakest bus' of the system.

A Self-Adaptive Power Flow Analysis Methodology for AC/DC Hybrid System

A self-adaptive methodology is proposed in this study to enable the power flow of an AC/DC hybrid system being uniformly analyzed in an AC framework. Firstly, the DC part of the hybrid system is transformed to its AC equivalent. The external characteristic models of various converters are then formulated as a π-shaped circuit, based on which a self-adaptive algorithm of the AC/DC hybrid power flow can be developed. Finally, the paper compares the proposed method with the unified method validated by different scale hybrid systems with different types of converters and control strategies. The results of case study show that the proposed method performs much more efficiently and reasonably.

Solving the Power Flow Problem in Bipolar DC Asymmetric Distribution Networks Using Broyden's Method.

This research addresses the power flow analysis in bipolar asymmetric direct current (DC) networks by applying Broyden's numerical method. This general successive approximations method allows for a simple Newton-based recursive formula to reach the roots of multiple nonlinear equations. The main advantage of Broyden's approach is its simple but efficient structure which can be applied to real complex nonlinear equations.The power flow problem in bipolar DC networks is still challenging, as multiple operating options must be considered, e.g., the possibility of having a solidly grounded or floating neutral wire. The main goal of this research is to contribute with a generalization of Broyden's method for the power flow solution in bipolar DC networks, with the main advantage that, under well-defined conditions, this is a numerical method equivalent to the matricial backward/forward power flow, which is equivalent to the successive approximations power flow method. Numerical results in the 21-, 33-, and 85-bus grids while considering two connections for the neutral wire (i.e., solidly grounded at any node or floating) show the effectiveness of Broyden's method in the power flow solution for bipolar asymmetric DC networks. All numerical simulations were carried out in the MATLAB programming environment.

Northern Goshawk Optimization Algorithm for Optimal Power Flow with FACTS Devices in Wind Power Integrated Electrical Networks

Power-operational problems must be resolved with flexible AC transmission systems (FACTS) and optimum power flow (OPF). The complexity of power flow analysis in modern power network models is increased by the unpredictability of renewable energy generating sources in addition to the challenge of determining the appropriate locations and ratings for FACTS devices. FACTS devices can increase the thermal capacity of power transmission lines, enabling the network to accommodate more electricity from renewable sources. The major goals of this study are to lower production costs, decrease power losses, enhance the voltage profile of the system, and enhance load capability. To solve a variety of optimization issues optimally, optimization meta-heuristics approaches have been created in response to the increased emphasis on renewable energy sources, such as wind power. Wind energy is an important component of modern power systems due to its affordability and clean nature. This article presents an OPF method for efficiently scheduling energy from wind farms using the Northern Goshawk Optimization (NGO) meta-heuristic technique. The Weibull probability distribution function is used to represent wind speed and estimate the cost of electricity produced by wind turbines based on the planned power. To validate the NGO approach and its results compared with those of other meta-heuristic optimization techniques, such as Chef-Based Optimization Algorithm, Dingo Optimization Algorithm, and Chameleon Swarm Algorithm, the IEEE system 30-bus is used to solve the OPF problem.

A Novel Adaptive Restarting Genetic Algorithm for Transmission Congestion Alleviation in the Deregulated Power Market

A key concern in a deregulated power market (DPM) scenario is congestion on electricity transmission lines, which is one of the most pressing problems. The independent system operator (ISO) manages congestion to ensure the power system framework operates reliably and securely. Considering the context of the restructured power system’s inherent uncertainties, congestion management (CM) is critical in the operation and security of DPM. CM aims to ease congestion on the transmission lines while meeting all system constraints at the lowest possible congestion cost. This study employs a novel adaptive restarting genetic algorithm (ARGA) for rescheduling the generation to alleviate transmission congestion in the energy market. This work uses the proposed ARGA CM technique to reduce congestion costs by rescheduling generators’ active power output most cost-effectively. The participation of generation units in the CM is made possible by considering the generator sensitivity factors (GSF). IEEE 57-bus and IEEE 118-bus test systems are employed to evaluate the suggested methodology’s accuracy. The acquired results are contrasted using several contemporary optimization approaches to validate the suggested technique’s validity. The proposed algorithm was tested in Python, and power flow analysis was done using the PANDAPOWER tool.

Simulation tools for electricity markets considering power flow analysis

Simulation tools for electricity markets considering power flow analysis

LTP Modeling and Analysis of Frequency Coupling in PLL-Synchronized Converters for Harmonic Power Flow Studies

As known, nonlinear loads in power systems originate harmonic distortion and power quality issues. Converter-interfaced loads exhibit a nonlinear behaviour as well and may largely contribute to increase the harmonic pollution. The nonlinearities introduced by the PLL-synchronization and power control originate, indeed, a coupling mechanism between fundamental and harmonic frequencies. These harmonic coupling effects are not captured by traditional Norton/Thevenin equivalent converter models, leading to inaccurate harmonic power flow analyses. This paper proposes a Linear Time Periodic model of a PLL-synchronized converter to be used in Harmonic Power Flow analyses. A realistic 18-bus distribution grid hosting substantial amount of power-electronic interfaced resources is used as a case study. It is revealed that, in high grid loadability condition and with distorted grid supply voltage, the harmonics are significantly amplified by the converters and the fundamental components of the buses voltages are reduced, representing a risk for the voltage stability. This phenomenon is also influenced by the tuning of the current control loop and the PLL. The accuracy of the presented analyses is validated by comparing the harmonic power flow results with time-domain simulations.

Modeling of modes of traction power supply systems equipped with renewable energy sources

The article presents the results of research aimed at developing digital models for power flow analysis of railway power supply systems (RPSS) equipped with distributed generation (DG) units based on renewable energy sources. We employed the technique of modeling in the phase reference frame, which has the following features: system-wide consistency, which enables modeling power flows in a way that takes into account the properties and characteristics of a complex RPSS and the feeding power system; versatility, which allows for modeling of traction networks of various designs; comprehensiveness, which consists in the possibility of power flow analysis of normal, emergency, and special states of a RPSS, such as those occurring during de-icing of overhead contact system wires. Three groups of models have been developed. The first one was designed for power flow analysis RPSSs with DG units based on synchronous generators driven by mini-HPP turbines. It is also possible to apply this model in the case of inverter generation that uses photovoltaic panels. The second group of models provided simulation of RPSS power flows with induction generators. The third group enabled power flow analysis in the case where there are three-phase generators connected to a single-phase traction network by means of Steinmetz converters. Active harmonic conditioners and controlled reactive power sources were used to reduce the negative impact of inferior power quality on voltage deviations and harmonic distortion. Simulation results produced by the software package Fazonord showed that the following positive effects can be obtained on the basis of the use of RES-based DG units: reduced costs of power supply; reduced imbalance on the buses of district windings of traction substations, stabilization of voltage levels on bow collectors of electric locomotives.