Showing papers on "Power-flow study published in 2021"

Physics-Guided Deep Neural Networks for Power Flow Analysis

Abstract: Solving power flow (PF) equations is the basis of power flow analysis, which is important in determining the best operation of existing systems, performing security analysis, etc. However, PF equations can be out-of-date or even unavailable due to system dynamics, and uncertainties, making traditional numerical approaches infeasible. To address these concerns, researchers have proposed data-driven approaches to solve the PF problem by learning the mapping rules from historical system operation data. Nevertheless, prior data-driven approaches suffer from poor performance, and generalizability, due to overly simplified assumptions of the PF problem or ignorance of physical laws governing power systems. In this paper, we propose a physics-guided neural network to solve the PF problem, with an auxiliary task to rebuild the PF model. By encoding different granularity of Kirchhoff's laws, and system topology into the rebuilt PF model, our neural-network based PF solver is regularized by the auxiliary task, and constrained by the physical laws. The simulation results show that our physics-guided neural network methods achieve better performance, and generalizability compared to existing unconstrained data-driven approaches. Furthermore, we demonstrate that the weight matrices of the proposed neural networks embody power system physics by showing their similarities with the bus admittance matrices.

Optimal sizing and allocation of renewable based distribution generation with gravity energy storage considering stochastic nature using particle swarm optimization in radial distribution network

Abstract: This paper presents an optimal sizing and allocation of a renewable energy resource (RES) based distribution generation (DG) units with gravity energy storage (GES) in the radial distribution network (DN). The optimization technique Constriction Coefficient Particle Swarm Optimization (CPSO) is utilized to reduce the total energy loss, which is subjected to equality and inequality constraints. Different DG parameters are considered and evaluated to reduce energy losses in electricity DN. To reduce search space and computational burden, a sensitivity analysis is performed to determine the candidate buses for the placement of DGs. The stochastic nature of RES (solar and wind), load, and storage unit has been handle using the probabilistic technique. The suitable penetration level is so adjusted as to restrict RES output on a certain fraction of the system load for stability consideration. The load flow analysis is performed using a backward-forward sweep algorithm embedded in the probability framework. The proposed approach has been examined on four different cases on DN consisting of 33 buses and it has been found that a notable reduction in losses with improved voltage profile is obtained by optimal sizing and placing DG units at an appropriate location. Results obtained using the CPSO technique has been validated by comparing it with the Simple Genetic Algorithm (SGA) technique. Further, the results obtained in case 3 using GES technology have compared with the battery storage system.

Current Injection Power Flow Analysis and Optimal Generation Dispatch for Bipolar DC Microgrids

Abstract: This article presents a current injection power flow analysis and optimal generation dispatch method for bipolar DC microgrids. In bipolar DC systems, despite a certain local voltage being balanced by the converter control, voltage unbalance can occur at buses that are electrically far from the balanced voltage. In addition, from the aspect of system operation, the optimization of operation cost and voltage unbalance has not been fully investigated yet. Herein, for steady-state analysis, the current injection Newton-Raphson power flow for a bipolar DC microgrid is developed using static models of the network, voltage balancer, sources, and loads. Afterwards, the optimization problem is formulated based on voltage sensitivity, which can be obtained from the Jacobian matrix, to reduce the operation cost while resolving the voltage unbalance. The problem of quadratic programming can be solved sequentially to find the final optimal solution. The case study validates that the power flow method is sufficiently accurate compared to the simulation in PSCAD/EMTDC and that optimal generation can be achieved while reducing the operation cost and mitigating voltage unbalance.

Design of fractional evolutionary processing for reactive power planning with FACTS devices.

Abstract: Reactive power dispatch is a vital problem in the operation, planning and control of power system for obtaining a fixed economic load expedition. An optimal dispatch reduces the grid congestion through the minimization of the active power loss. This strategy involves adjusting the transformer tap settings, generator voltages and reactive power sources, such as flexible alternating current transmission systems (FACTS). The optimal dispatch improves the system security, voltage profile, power transfer capability and overall network efficiency. In the present work, a fractional evolutionary approach achieves the desired objectives of reactive power planning by incorporating FACTS devices. Two compensation arrangements are possible: the shunt type compensation, through Static Var compensator (SVC) and the series compensation through the Thyristor controlled series compensator (TCSC). The fractional order Darwinian Particle Swarm Optimization (FO-DPSO) is implemented on the standard IEEE 30, IEEE 57 and IEEE 118 bus test systems. The power flow analysis is used for determining the location of TCSC, while the voltage collapse proximity indication (VCPI) method identifies the location of the SVC. The superiority of the FO-DPSO is demonstrated by comparing the results with those obtained by other techniques in terms of measure of central tendency, variation indices and time complexity.

Combining Newton-Raphson and Stochastic Gradient Descent for Power Flow Analysis

Abstract: The power flow problem is an indispensable tool to solve many of the operation and planning problems in the electric grid and has been studied for the last half-century. Currently, popular algorithms require second-order methods, which may lead to poor performance when the initialization points are poor or when the system is stressed. These conditions are becoming more common as both the generation and load profiles changes in the grid. In this paper, we present a hybrid first-order and second-order method that effectively escapes local minima that may trap existing algorithms. We demonstrate the performance of our algorithm on standard IEEE benchmarks.

Survey about impact voltage instability and transient stability for a power system with an integrated solar combined cycle plant in Iraq by using ETAP

Abstract: The Analyses for power systems are more necessary for the designing, operating phase execution control and to make sure safe network operations by sufficient protection project settings. In this article, we have prepared a sufficient scientific survey about the electrical model of a 340 MW integrated solar combined cycle system (ISCCS) located in the Iraqi southern, is developed and simulation by a program called Electrical Transient Analyzer Program (ETAP) and carry out throw this program the load flow, voltage stability and short circuit analyses for this power plant with part of the national grid in Al-Basra city in an industrial region. The effect of voltage instability for the grid on system buses (load buses) of the power system is estimated. By using load flow analysis as a case study by using the Newton-Raphson algorithm, when the load buses operating at down voltage because of instability voltage of the power grid are specified and their voltages are should to improved according to given voltage limitations that are depended on buses criticality with regard to loads. The appliance on-load tap changers of the transformer and reactive power compensation are used to improve steady-state voltage stability for any instability system. The method of the optimal position for capacitor banks placement is meaning the number of capacitor banks is proposed to adding to the weak buses by using the optimal capacitor placement module of ETAP. Energy is actually required for the expansion of our country. To sustain the generation of electric power at an adequate level power system supplies power to different types of loads that are located far away from the generating plants using transmission lines.

Monte Carlo simulation of electric vehicle loads respect to return home from work and impacts to the low voltage side of distribution network

Abstract: Usage of electrical vehicles (EV) is increasing at high rate due to their great benefits to the community well-being. However, EVs have considerable impacts to electrical power networks and especially to the low voltage side of the distribution network. In order to determine the impacts of EVs accurately, uncertain behaviors of drivers were modeled using Monte Carlo simulations. This method is proven to be a robust tool for the evaluation of stochastic processes and getting deterministic results out of it. Furthermore, real-world traffic pattern data were used to model drivers’ behaviors. Return home time of EVs was used as a charging start time, and average commute distance of drivers was used to determine the charging duration. Also, residential area was taken as a pilot network. Hourly basis transformer loading data were obtained and used to realistically reflect the base load of the pilot network. Load flow analysis was performed for non-EV and with-EVs scenarios. The results of the analysis were represented in a probabilistic approach. Violations of results were investigated according to power quality limits. Consequently, impacts of the EV charging load to the low voltage side of distribution network were analyzed in terms of voltage drops, transformers’ loadings, power losses and voltage unbalance. This study showed that with a 50% penetration rate of EVs, the probability of voltage violation increases by approximately 25%.

An Efficient Backward/Forward Sweep Algorithm for Power Flow Analysis through a Novel Tree-Like Structure for Unbalanced Distribution Networks

Abstract: The increase of distributed energy resources (DERs) in low voltage (LV) distribution networks requires the ability to perform an accurate power flow analysis (PFA) in unbalanced systems. The characteristics of a well performing power flow algorithm are the production of accurate results, robustness and quick convergence. The current study proposes an improvement to an already used backward-forward sweep (BFS) power flow algorithm for unbalanced three-phase distribution networks. The proposed power flow algorithm can be implemented in large systems producing accurate results in a small amount of time using as little computational resources as possible. In this version of the algorithm, the network is represented in a tree-like structure, instead of an incidence matrix, avoiding the use of redundant computations and the storing of unnecessary data. An implementation of the method was developed in Python programming language and tested for 3 IEEE feeder test cases (the 4 bus feeder, the 13 bus feeder and the European Low Voltage test feeder), ranging from a low (4) to a very high (907) buses number, while including a wide variety of components witnessed in LV distribution networks.

A Quasi-Oppositional Heap-Based Optimization Technique for Power Flow Analysis by Considering Large Scale Photovoltaic Generator

Abstract: Load flow analysis is an essential tool for the reliable planning and operation of interconnected power systems. The constant increase in power demand, apart from the increased intermittency in power generation due to renewable energy sources without proportionate augmentation in transmission system infrastructure, has driven the power systems to function nearer to their limits. Though the power flow (PF) solution may exist in such circumstances, the traditional Newton–Raphson based PF techniques may fail due to computational difficulties owing to the singularity of the Jacobian Matrix during critical conditions and faces difficulties in solving ill-conditioned systems. To address these problems and to assess the impact of large-scale photovoltaic generator (PVG) integration in power systems on power flow studies, a derivative-free quasi-oppositional heap-based optimization (HBO) (QOHBO) technique is proposed in the present paper. In the proposed approach, the concept of quasi-oppositional learning is applied to HBO to enhance the convergence speed. The efficacy and effectiveness of the proposed QOHBO-PF technique are verified by applying it to the standard IEEE and ill-conditioned systems. The robustness of the algorithm is validated under the maximum loadability limits and high R/X ratios, comparing the results with other well-known methods suggested in the literature. The results thus obtained show that the proposed QOHBO-PF technique has less computation time, further enhancement of reliability in the presence of PVG, and has the ability to provide multiple PF solutions that can be utilized for voltage stability analysis.

Methodology to Evaluate the Impact of Electric Vehicles on Electrical Networks Using Monte Carlo

Abstract: In preparation for the electric mobility technological transition in Colombia, an impact assessment of the electric power system is required, considering the increasing loading that must be able to be managed in the future. In this paper, a plug-in electric vehicle (PEV) charging simulation methodology is developed in order to dimension the impact of this type of load on power grids. PEV electric properties, user charging behaviors, geographic location, trip distances, and other variables of interest are modeled from empirical or known probability distributions and later evaluated in different scenarios using Monte Carlo simulation and load flow analysis. This methodology is later applied to the transmission network of Antioquia (a department of Colombia) resulting in load increases of up to 40% on transmission lines and 20% on transformers in a high PEV penetration scenario in 2030, increases that are well within the expected grid capacity for that year, avoiding the need for additional upgrades.

Steady-State Analysis of Microgrid Distributed Control Under Denial of Service Attacks

Abstract: Distributed control is an effective method to coordinate multiple components in a microgrid. It usually has mandatory requirements for communication graph topology; changing the topology unexpectedly could disrupt the operation of a distributed controller. A denial of service (DoS) attack blocks communication channels to cause variations in communication graph. Unforeseen topology changes render a controller to receive incomplete system information. Control decisions made based on incorrect information can lead to significantly reduced grid supporting capability. As a result, the vital power balance between generation and load may be challenged. Determining power balance requires steady-state power flow analysis; however, the cyber-induced steady-state analysis is not well studied in the literature. In this article, we show that a cyber–physical microgrid with unknown DoS attacks has augmented uncertain power flow equations, whose special structure creates new computational issue to determining power balance. To deal with the issue, we develop a novel cyber-induced microgrid model: the cyber–physical system is transformed into a pure physical model, where the disruption effects of DoS in the cyber layer are shown to be equivalent to the variations of the physical model parameters. With the transformed system, we develop a sufficient condition to ascertain the system power balance under unknown DoS attacks. In addition, practical insights for resilient distributed control design are shown as well.

Grid Impact Assessment of Centralized and Decentralized Photovoltaic-Based Distribution Generation: A Case Study of Power Distribution Network with High Renewable Energy Penetration

Abstract: This paper presents a grid impact assessment of a 5 MWp photovoltaic-based distribution unit on a 33 kV/23 MVA power distribution network with high penetration of renewable energy generation. The adapted network has an average load demand of 23 MVA, with a 3 MWp centralized PV system, and a number of decentralized PV systems of a capacity of 2 MWp. A grid impact assessment is done to an additional 5 MWp of PV generation as a centralized system as well as a number of decentralized systems. Power flow analysis is conducted to the grid considering different generation loading scenarios in order to study grid performance including active and reactive power flow, voltage profiles, distribution power transformers loading, transmission lines ampacity levels, and active and reactive power losses. On the other hand, the distribution of the decentralized systems is done optimally considering power distribution transformer loading and available area using the geographical information system. Finally, an economic analysis is done for both cases. Results showed that grid performance is better considering decentralized PV systems, whereas the active power losses are reduced by 13.43% and the reactive power losses are reduced by 14.48%. Moreover, the voltage of buses improved as compared to the centralized system. However, the decentralized PV systems were found to affect the power quality negatively more than the centralized system. As for the economic analysis, the decentralized PV system option is found slightly less profitable than the centralized system, whereas the simple payback period is 9 and 7 years, respectively. However, decentralized PV systems are recommended considering the technical implications of the centralized PV system.

A Three-Layer Stochastic Energy Management Approach for Electric Bus Transit Centers With PV and Energy Storage Systems

Abstract: Along with the increasing electric bus (EB) penetration, the impact of the extensive charging load on the distribution system has been aggravating. To mitigate such impact, energy storage systems (ESSs) and photovoltaic (PV) are usually installed in the EB transit centers (EBTCs). In this article, a three-layer stochastic energy management approach is proposed for EBTCs to reduce the operation cost while maintaining local voltage quality. In the first layer, a modified robust optimization over time (ROOT) approach is developed to obtain the charging/discharging margin with minimum EBTC operation cost. In the second layer, the voltage regulation impact on the local voltage quality is estimated through power flow analysis considering voltage fluctuation and line loss minimization. In the third layer, the charging/discharging strategy is optimized with dynamic programming based on a modified greedy algorithm. The performance of the proposed approach is evaluated in a case study based on the IEEE 123-bus test feeder and the real operation data obtained from St. Albert Transit in Alberta, Canada. The results indicate that the proposed approach can not only minimize the EBTC operation cost but also well maintain the local voltage quality, in comparison with existing energy management approaches.

Optimal Placement of Facts Device Considering Voltage Stability and Losses using Teaching Learning based Optimization

Abstract: In the modern power system, under heavy stress conditions, there is always the probability of line outage and resulting in voltage instability. This paper focuses on the optimal siting and sizing of Flexible AC Transmission Systems (FACTS) in the IEEE 30 bus transmission system. The optimization technique Teaching Learning Based Optimization (TLBO) algorithm is utilized to determine the optimal size of the FACT device to minimize real power loss. To reduce search space and computational burden, dv/dq index method is used to identify the weak buses for the placement of the FACTs device. The load flow analysis is performed using a Newton Raphson method. Results show that the TLBO technique is an efficient and effective method for determining the sizing of FACT devices in power system.

Optimal Allocation of Multiple Photo-Voltaic and/or Wind-Turbine Based Distributed Generations in Radial Distribution System Using Hybrid Technique with Fuzzy Logic Controller

Abstract: In this concept, an enhanced idea is proposed for solving the optimal load flow issues with uncertainties. The proposed hybrid algorithm is the combination of the fuzzy logic controller (FLC) technique and Ant-Lion Optimization Algorithm’s (ALOA). The ALO imitates the hunting mechanism of ant lions in nature. The FLC is trained based on training dataset and testing time. Based on the variation of radial distribution network parameters, the proposed hybrid technique provides the optimal allocation parameters. In this research, photo-voltaic units and wind turbine generations (PV and WT) are considering as distributed generators (DGs). Initially, the multi objective function was defined. That includes voltage deviation, minimization of power loss, and minimization of total operating cost (TOC) and improvement of voltage stability index (VSI). The effectiveness of this proposed hybrid technique was demonstrated on IEEE-33 standard radial distribution systems. With the new hybrid technique, allocation of multi-DGs at different sites, and the optimal load flow in various cases were analyzed.

Stand-Alone Direct Current Power Network Based on Photovoltaics and Lithium-Ion Batteries for Reverse Osmosis Desalination Plant

Abstract: Plummeting reserves and increasing demand of freshwater resources have culminated into a global water crisis. Desalination is a potential solution to mitigate the freshwater shortage. However, the process of desalination is expensive and energy-intensive. Due to the water-energy-climate nexus, there is an urgent need to provide sustainable low-cost electrical power for desalination that has the lowest impact on climate and related ecosystem challenges. For a large-scale reverse osmosis desalination plant, we have proposed the design and analysis of a photovoltaics and battery-based stand-alone direct current power network. The design methodology focusses on appropriate sizing, optimum tilt and temperature compensation techniques based on 10 years of irradiation data for the Carlsbad Desalination Plant in California, USA. A decision-tree approach is employed for ensuring hourly load-generation balance. The power flow analysis evaluates self-sufficient generation even during cloud cover contingencies. The primary goal of the proposed system is to maximize the utilization of generated photovoltaic power and battery energy storage with minimal conversions and transmission losses. The direct current based topology includes high-voltage transmission, on-the-spot local inversion, situational awareness and cyber security features. Lastly, economic feasibility of the proposed system is carried out for a plant lifetime of 30 years. The variable effect of utility-scale battery storage costs for 16–18 h of operation is studied. Our results show that the proposed design will provide low electricity costs ranging from 3.79 to 6.43 ¢/kWh depending on the debt rate. Without employing the concept of baseload electric power, photovoltaics and battery-based direct current power networks for large-scale desalination plants can achieve tremendous energy savings and cost reduction with negligible carbon footprint, thereby providing affordable water for all.

Technical Indicators for the Comparison of Power Network Development in Scenario Evaluations

Abstract: The problem of electric network expansion has different implications concerning the definition of criteria for the comparison of different candidate projects. Transmission expansion planning usually involves a set of economic and technical influences on market framework and on network operation over defined scenario evolutions, or even combining generation and transmission planning, although the application to real-sized networks usually implies cost-benefit analysis. In this paper, a methodology for performance analysis of a set of network development projects is proposed, including zonal market framework and load flow analysis, in order to individuate possible candidate projects and their influence on active power losses, admissible load increase and admissible renewable generation increase. Those merit indicators are compared among candidate projects by means of Analytic Hierarchy Process (AHP) method, aiming at determining the most promising solution under different weights of criteria. Moreover, the influence of network development investment cost on project selection is assessed by means of an extension of AHP. The procedure is applied to yearly operation of NREL-118 test system.