Power Flow In System Research Articles

비상시 부하전환 능력 확보를 위한 배전계통 최적 계획 방안

This paper proposes an optimization problem for distribution system planning (DSP) in project aimed at ensuring load conversion capabilities during emergencies. The proposed method formulates objective functions to minimize line reinforcement costs and non-convertible capacity, and establishes constraints for system configuration, power flow, and outage-sensitive customers. Additionally, the multi-objective optimization problem for DSP is transformed into a single-objective function using the Epsilon-constraints based Multi-Objective Problem (E-MOP) technique. In a case study, to validate the effectiveness of the proposed method, simulations were conducted on a 37-bus test system. The results of the proposed method were derived a Pareto-front set of optimal DSP solutions, considering the two objective functions with inversely proportional relationships. These results could be provided an optimal solution for DSP, and supported the decision-making of the distribution system operator (DSO).

A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems

Keeping the bus voltage within acceptable limits depends on dispatching reactive power. Power quality improves as a result of creating an effective power flow system, which also helps to reduce power loss. Therefore, optimal reactive power dispatch (ORPD) studies aim at designing appropriate system configurations to enable a reliable operation of power systems. Establishment of such a configuration is handled through control variables in power systems. Various control variables, such as adjusting generator bus voltages, transformer tap locations, and switchable shunt capacitor sizes, are utilized to achieve this objective. Additionally, the integration of wind power can greatly impact power quality and mitigate power loss. In this study, the Grey Wolf Optimization (GWO) approach was applied to the ORPD issue for the first time to discover the best placement of newly installed wind power in the power system while taking into account tap changer settings, shunt capacitor sizes, and generated power levels. The main objective was to determine optimal wind placement to minimize power loss and voltage deviation, while maintaining control variables within specified limits. On the basis of IEEE 30-bus and IEEE 118-bus systems, the performance of the proposed method was investigated. The results demonstrated the superiority of GWO in multiple scenarios. In IEEE-30, GWO outperformed the PSO, GA, ABC, OGSA, HBMO, and HFA methods, reducing total loss by 10.36%, 18.03%, 9.19%, 7.13%, 5.23%, and 7.73%, respectively, and voltage deviation by 68.00%, 1.59%, 36.34%, 41.97%, 46.29%, and 71.08%, respectively. In wind integration scenarios, GWO achieved the simultaneous reduction of power loss and voltage deviation. In IEEE-118, GWO outperformed the ABC, PSO, GSA, and CFA methods, reducing power loss by approximately 19.91%, 16.83%, 14.09%, and 4.36%, respectively, and voltage deviation by 8.50%, 14.15%, 16.19%, and 7.17%, respectively. These promising results highlighted the potential of the GWO algorithm to facilitate the integration of renewable energy sources, and its role in promoting sustainable energy solutions. In addition, this study conducted an analysis to investigate site-specific wind placement by using the Weibull distribution function and commercial wind turbines.

An Extensive Critique on Smart Grid Technologies: Recent Advancements, Key Challenges, and Future Directions

Given the various aspects of climate change and the growing demand for energy, energy efficiency and environmental protection have become major concerns worldwide. If not taken care of, energy demand will become unmanageable due to technological growth in cities and nations. The solution to the global energy crisis could be an advanced two-way digital power flow system that is capable of self-healing, interoperability, and predicting conditions under various uncertainties and is equipped with cyber protections against malicious attacks. The smart grid enables the integration of renewable energy sources such as solar, wind, and energy storage into the grid. Therefore, the perception of the smart grid and the weight given to it by researchers and policymakers are of utmost importance. In this paper, the studies of many researchers on smart grids are examined in detail. Based on the literature review, various principles of smart grids, the development of smart grids, functionality of smart grids, technologies of smart grids with their characteristics, communication of smart grids, problems in the implementation of smart grids, and possible future studies proposed by various researchers have been presented.

Model and observation of the feasible region for PV integration capacity considering Wasserstein-distance-based distributionally robust chance constraints

The increasing integration scale of photovoltaic (PV) systems brings enormous challenges on distribution networks (DNs). To provide an explicit boundary of feasible PV integration capacity (PVIC) associated with each integration location, this paper proposes a novel depiction of PV hosting capability, which is the feasible region for PVIC in high-dimensional space. Next, a multi-objective optimization model based on information gap decision theory (IGDT) is proposed to observe the feasible region, where active distribution network management (ADNM) schemes with a limited budget are deployed to improve PV hosting capability. The impact of PV output uncertainty on security constraints is addressed by data-driven Wasserstein-distance-based distributionally robust chance constraints (WDRCCs). Finally, a systematic procedure is developed to solve the proposed model. Note that the exact power flow model associated with a new equivalent WDRCC reformulation method is deployed so as to guarantee the accuracy of the assessment. The effectiveness, solution accuracy, computational efficiency, and scalability of the proposed model and method are verified with the 4-bus system, the IEEE 33-bus system, and the IEEE 123-bus system. The output of the common method based on the linearized power flow deviates by more than 10% from the output of the proposed method based on the exact power flow in the 33-bus system. Each multi-location PV integration request is feasible as its locations and capacities belong to the proposed region. It implies that this region can provide guidance for PV allocation.

Reactive Power Flow in Induction Motor Drive With Inverter Output Sinusoidal Filter and Its Influence on Wide Frequency Operation

This paper presents the reactive power flow and its influence on the wide-frequency operation of sinusoidal filter-equipped induction motor (SFE-IM) drives. A sinusoidal filter, placed at the output of a voltage source inverter (VSI) to filter the pulse width modulated (PWM) voltages, unfolds a research opportunity in SFE-IM drives, as the filter capacitors also supply reactive power to the IM. The paper comprehensively investigates this reactive power flow in an SFE-IM system with filter capacitor values designed using two different methods. In continuation, with the designed capacitors, the paper analyzes issues in the wide-frequency operation, such as reactive power imbalance, abnormal escalation in inverter currents, variation in power factor seen by the VSI, and high current stress on DC-bus capacitors and VSI. Furthermore, the analysis formulates the method of filter capacitor selection for the wide-frequency operation of the SFE-IM system in terms of IM and LC filter parameters while resolving the issues mentioned above. The proposed analysis is simulated in MATLAB/Simulink and experimentally validated on a 5 hp IM drive.

Analysis of Power Transfer Characteristics of IPT System With Near Field Magnetic Coupling

This article investigates the working principle of the inductive power transfer (IPT) system from the perspectives of electromagnetic field distribution. According to the limitations of frequency and transmission distance, it is proved in this paper that the IPT system works in the near field, where the energy density of the magnetic field is much stronger than that of the electric field. Therefore, only the magnetic field in the IPT system needs to be considered in approximate power analysis. The mutual inductance model of the IPT system is an approximate and simplified form of the electromagnetic field model. The complex form of the Poynting vector of the IPT system is integrated over an intermediate infinite plane of the two coils to reveal the power transfer characteristics including the active and reactive power. When the system is in full resonance, only active power is transferred without reactive power, which is consistent with the results obtained from the circuit model. This article is useful to provide an insight into the power flow and energy distribution of the IPT system.

Multi-Area Load Frequency Control in Power System Integrated With Wind Farms Using Fuzzy Generalized Predictive Control Method

Frequency security is critical for the power systems’ stability and reliability. The integration of renewable energy resources brings new challenges to the frequency security of power systems. To provide a more robust control method for controlling the frequency and tie-line power flow of an interconnected power system integrated with wind farms (IPSWF), a load frequency control (LFC) controller of generalized predictive control (GPC) based on the Takagi-Sugeno (T–S) fuzzy model (fuzzy-GPC) is proposed in this paper. First, the T–S fuzzy model of the IPSWF is constructed. Then, the GPC is used to design the controller. Lastly, to validate the proposed scheme, an LFC strategy is established for a two-area interconnected power system integrated with a wind farm in power systems computer aided design (PSCAD). The control performance of proportional-integral, GPC, and fuzzy-GPC controller are compared under two faulty operating conditions. The simulation results show that the performance indicators and the dynamic behavior when the fuzzy-GPC controller is used are better than the performance when the PI controller and GPC controller are used. The proposed fuzzy-GPC used in the LFC of the IPSWF can regulate the frequency deviation and tie-line power deviation adaptively and achieve minimum frequency deviation and tie-line power deviation in a multi-area IPSWF.

Integrated Energy System Design of Low-carbon City Based on Multi-energy Complementary of Cooling Heating and Power

The model of urban integrated energy dispatching system is a mathematical model used to describe the distribution of energy flow in the system, which has the characteristics of high accuracy, strong nonlinearity, and non-convexity. However, in the optimal power flow and other optimization problems, the traditional numerical solution algorithm cannot be directly applied. The strong nonlinearity and non-convexity of the system power flow model bring great difficulties to the solution of the optimization problem. Based on the input and output theory of cooling, heating, and power complementary energy, this paper studies the coupling energy relationship and power flow of various energy devices in an urban multi-source microgrid. It establishes the reliability index of the system energy supply, starting from the integrated energy system structure of low-carbon cities. A bi-level optimization model considering the constraints of energy supply reliability is constructed, which provides a basis for the subsequent establishment of the optimal allocation model.

Power Flow Analysis of Electrical Network Systems Using Gauss-Seidel Method and Python

This study aims to analyze the power flow in a 150kV radial power system on the island of Bali, the reason the author uses the Bali island system is because the number of busbars in the system is not too many and the data on the system is known. The problem of this research is limited by determining the voltage, power flow, and power losses in the system. The method used in this study is the Gauss-Seidel method through simulation with the help of Visual Studio Code software and the Python 3.9.2 program. The simulation is carried out by producing a total of 8 iterations. The simulation is carried out to find the parameters of the load voltage, apparent power on the slack bus, current, power flow and power losses on the line. The simulation results show that, under a load condition of 70% of the load transformer capacity, the voltage at the load bus node experiences a voltage drop, reaching 4.48% at bus 10. So when the system operates for generation conditions, the power that can be generated is 1,275.6 MegaWatt of the total generator installed, and the power delivered to each bus node is 2,090 MVa and the power losses in the system are 3.69% of the total power distributed to the bus nodes

Frequency Regulation of Interlinked Microgrid System Using Mayfly Algorithm-Based PID Controller

The primary goal of this article is to design and implement a secondary controller with which to control the system frequency in a networked microgrid system. The proposed power system comprises of Renewable energy sources (RESs), energy-storing units (ESUs), and synchronous generator. RESs include photovoltaic (PV) and wind turbine generator (WTG) units. The ESU is composed of a flywheel and a battery. Because renewable energy sources are not constant in nature, their values fluctuate from time to time, causing an effect on system frequency and power flow variation in the tie line. The nonlinear output from the RESs is balanced with the support of ESUs. In order to address this situation, a proportional integral derivative (PID) controller based on the Mayfly algorithm (MA) is proposed and built. Comparing the responses of controllers based on the genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) technique-optimized to demonstrate the superiority of the MA-tuned controller.. The results of the validation comparisons reveal that the implemented MA-PID controller delivers and is capable of regulating system frequency under various load demand changes and renewable energy sources. A robustness analysis test was also performed in order to determine the effectiveness of the suggested optimization technique (1%, 2%, 5%, and 10%) step load perturbation (SLP) with ±25% and ±50% variation from the nominal governor and reheater time constant).

Comprehensive review on control schemes and stability investigation of hybrid AC-DC microgrid

The utility grid integration of distributed generation units is seen as one of the most accomplished through the use of microgrid. Control methods and stability analyses in the microgrid are continuously being investigated in the field of hybrid microgrid. The most reviewed control method is hierarchical control, consisting of primary, secondary, and tertiary layers. This paper reviews multiple schemes of primary control for current or voltage regulation, secondary control for voltage or current error correction, power-sharing in microgrid, and tertiary control for power flow and energy management in a hybrid microgrid system. Additionally, this study examines the benefits and drawbacks of several control architectures that function as distributed, centralized, and decentralized controls. The principle of operation and effectiveness of each microgrid architecture control method have been discussed. Specific control factors are investigated in this paper, including mode transition and coordinated control between numerous interlinking converter and energy storage systems. System stability is the main concern of microgrid for system engineer thorough knowledge of control strategies is the upmost requirement. Thus, this paper highlights control strategies, rotor angle, voltage and frequency instability in power systems, and methods for improving stability in microgrid are also reviewed.

Simultaneous Robust State Estimation, Topology Error Processing, and Outage Detection for Unbalanced Distribution Systems

This paper proposes an efficient algorithmic approach that overcomes the critical challenges in real-time unbalanced distribution system state estimation, topology error processing, and outage identification simultaneously. These challenges include (1) limited locations of measurement devices and unsynchronized measurement data as well as missing and bad data, (2) complicated mixed-phase switch actions and mutual impedances and shunt admittances, and (3) the nonlinear nature of unbalanced distribution system power flow with distributed energy resources (DERs). A single snap-shot mixed-integer quadratic programming (MIQP) optimization framework is proposed to cope with these challenges, which simultaneously identifies real-time network topology, estimates system state, and detects the outages via analytical constraints. An AC optimal power flow (ACOPF) approach is proposed to accurately model unbalanced distribution systems. In the proposed MIQP formulation, nonlinearities due to the complicated mixed-phase switch operations and the ACOPF approach are linearized. The effectiveness of the proposed approach is verified on an actual distribution feeder in Arizona. The results illustrate that the proposed model is robust, accurate, and computationally efficient for implementation in a distribution management system (DMS).

Mathematical distribution coyote optimization algorithm with crossover operator to solve optimal power flow problem of power system

The optimal power flow (OPF) of power system is to optimize the objective function such as generation cost by adjusting control variables on the premise of satisfying operation constraints and supply–demand balance. A mathematical distribution coyote optimization algorithm (MDCOA) is proposed. Crossover operation is added to the algorithm initialization process to improve the randomness and diversity of offspring. In order to improve its search efficiency and balance the process of exploration and exploitation, the step size is generated by random numbers with seven mathematical distributions, including Gaussian distribution, Uniform distribution, Exponential distribution, Beta distribution, Gamma distribution, Weibull distribution and Rayleigh distribution. Firstly, the original COA and seven improved COA were simulated under the CEC2017 functions, wCOA ranks first among these comparison algorithms and was selected to be compared with PSO, CMA-ES, MFO, HHO, HBA and WOA to verify its effectiveness. Aiming at the OPF problem, the simulation experiments are carried out on IEEE 30-bus system and IEEE 57-bus system. The generation cost, active power loss, voltage stability and bus voltage offset are selected as objective functions respectively. The proposed method can find a better solution than the standard COA and eCOA has the best comprehensive performance in all cases. Simulation are compared with ARCBBO, ECHT-DE, I-NSGA-III, MSA and other algorithms and the results show that the MDCOA can effectively solve the OPF problem.

A vertical and horizontal crossover sine cosine algorithm with pattern search for optimal power flow in power systems

Most of the energy consumption is now being used to supply human demand for electricity, which has increased the burden of power system planning to some extent, and thus, researchers proposed solutions to the optimal power flow (OPF) problem. In such a background, flexible AC transmission system (FACTS) devices are widely used in modern power systems to alleviate human power demand and improve network congestion problems. This research proposes a better-quality sine cosine algorithm (CPSCA) based on a crossover mechanism and pattern search algorithm for optimizing multiple objectives, such as system generation cost and transmission losses when we have multiple types of FACTS devices in the power system. The proposed method uses sine and cosine theory and evolutionary strategy to continuously sample and update the framework and improve the quality of the solutions. The method uses a class of vertical and horizontal crossover operators and pattern-shifting techniques to find the optimal solution set. In addition, the Weibull probability density function was used in this study to model uncertainty in wind energy, while direct costs, penalty costs, and standby costs were considered in constructing the target model. Various types of FACTS devices, such as thyristor-controlled series compensators, thyristor-controlled phase shifters, and static reactive power compensators, are also inserted into the model as power in the target optimization problem. The proposed algorithm has experimented on an IEEE30 bus test system. Based on the experimental results, CPSCA achieves an optimal total cost of 806.0225 $/h. In terms of power loss of the bus system, CPSCA reduces the total power loss value from 2.042 MW to 1.41 MW compared to the original sine cosine algorithm, which is a 31% improvement and has an optimal power loss value. Therefore, the simulation results demonstrate that the proposed algorithm is an effective technique for optimizing the optimal power flow of the entire power system and FACTS equipment.

Adaptive Neural Network Q-Learning-Based Full Recurrent Adaptive NeuroFuzzy Nonlinear Control Paradigms for Bidirectional-Interlinking Converter in a Grid-Connected Hybrid AC-DC Microgrid

The stability of a hybrid AC-DC microgrid depends mainly upon the bidirectional interlinking converter (BIC), which is responsible for power transfer, power balance, voltage solidity, frequency and transients sanity. The varying generation from renewable resources, fluctuating loads, and bidirectional power flow from the utility grid, charging station, super-capacitor, and batteries produce various stability issues on hybrid microgrids, like net active-reactive power flow on the AC-bus, frequency oscillations, total harmonic distortion (THD), and voltage variations. Therefore, the control of BIC between AC and DC buses in grid-connected hybrid microgrid power systems is of great importance for the quality/smooth operation of power flow, power sharing and stability of the whole power system. In literature, various control schemes are suggested, like conventional droop control, communication-based control, model predictive control, etc., each addressing different stability issues of hybrid AC-DC microgrids. However, model dependence, single-point-failure (SPF), communication vulnerability, complex computations, and complicated multilayer structures motivated the authors to develop online adaptive neural network (NN) Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms for BIC in a grid-connected hybrid AC-DC microgrid. The proposed strategies successfully ensure the following: (i) frequency stabilization, (ii) THD reduction, (iii) voltage normalization and (iv) negligible net active-reactive power flow on the AC-bus. Three novel adaptive NN Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms are proposed for PQ-control of BIC in a grid-connected hybrid AC-DC microgrid. The control schemes are based on NN Q-learning and full recurrent adaptive neurofuzzy identifiers. Hybrid adaptive full recurrent Legendre wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, Hybrid adaptive full recurrent Mexican hat wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, and Hybrid adaptive full recurrent Morlet wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control are modeled and tested for the control of BIC. The controllers differ from each other, based on variants used in the antecedent part (Gaussian membership function and B-Spline membership function), and consequent part (Legendre wavelet, Mexican hat wavelet, and Morlet wavelet) of the full recurrent adaptive neurofuzzy identifiers. The performance of the proposed control schemes was validated for various quality and stability parameters, using a simulation testbench in MATLAB/Simulink. The simulation results were bench-marked against an aPID controller, and each proposed control scheme, for a simulation time of a complete solar day.

Analysis of Losses in Two Different Control Approaches for S-S Wireless Power Transfer Systems for Electric Vehicle

This paper presents the study and detailed analysis of converter losses at different stages together with the series-series (S-S) compensating coils in wireless power transfer (WPT) systems, via two distinct approaches to control the power converters. The two approaches towards wireless DC–DC power flow control are termed as the Single Active High-Frequency Wireless Power Transfer (SAHFWPT) system and the Dual Active High-Frequency Wireless Power Transfer (DAHFWPT) system. The operation of converters in SAHFWPT and DAHFWPT are controlled by the extended phase shift (EPS) and dual phase shift method respectively. The general schematic of the SAHFWPT system consists of an active bridge and a passive bridge, while the schematic of the DAHFWPT system consists of both active bridges. The efficiency evolutions of ideal SAHFWPT and DAHFWPT are far away from the real ones. Moreover, this article analyzes the operation and losses of the uni-directional power flow of the WPT system, i.e., from the DC bus in the primary side to the battery load in the secondary side. The loss estimation includes high-frequency switching losses, conduction losses, hard turn on and turn off coil losses, etc. Moreover, the efficiency of the WPT system depends on operation of the converter. A 50 W–3600 W Power range system at a resonant frequency of 85 kHz is implemented in MATLAB/SIMULATION to demonstrate the validity of the proposed method.

The Use of a Regulating Transformer for Shaping Power Flow in the Power System

The current situation in world energy requires a new approach to the control of the power flow in power systems. On the one hand, regulations within the EU require the transition to renewable energy. On the other hand, there are no conventional sources available. Thus, there is a need to use known control tools in non-standard applications. One such example is the use of a regulating transformer inside the power grid to use it to shape the power flow in the system. This article presents a proposal of an algorithm for determining new locations for regulating transformers from the point of view of active power control. By analyzing the parameters of the power grid, it is possible to determine which branch of the grid is the most suitable for installing a regulating transformer in it. The use of a regulating transformer inside the grid improves the transmission capabilities of the national power system.

Power flow analysis of DC distribution system in a ship with non-electric propulsion

<span lang="EN-US">Direct current (DC) shipboard power distribution system offers higher power efficiency and voltage stability compared to the alternating current (AC) systems due to lower impedance. The implementation of DC distribution system in all-electric ship seems to be worthy since the reduction of power loss and voltage drop could overcome the drawback of DC system. However, the effectiveness of DC distribution system in ship with non-electric propulsion has not been investigated yet. Unlike in an all-electric ship, electric power flow in the distribution system of a ship with mechanic propulsion is considerably lower. The study aims to provide numerical analysis of power loss and voltage drop reduction on DC distribution system that applied to a ship with mechanic propulsion. The power flow analysis is performed on a tanker ship. Contrary to the hypothesis, the results show that the DC power distribution increase the power losses about 15% compared to AC system due to the addition of rectifier and inverter. However, the voltage drops are decreased in DC distribution system. Further investigation in the other aspects should be performed before concludes whether DC distribution system is worthy to be used in the aforementioned ship.</span>

Congestion-Aware Dynamic Optimal Traffic Power Flow in Coupled Transportation Power Systems

Electrification of vehicles is strengthening the interaction between power systems and transportation systems, formulating the coupled transportation power systems (CTPSs). A novel optimal traffic power flow (OTPF) problem is proposed to analyze the spatial and temporal congestion propagation on CTPSs, under congested roads, transmission lines, and charging stations. The traffic flow is depicted by the spatial and temporal distribution of electric vehicles (EVs) on roads and charging stations, connected by multilayer time-space networks (TSNs). This distribution is the solution to a mesoscale traffic assignment problem of EV fleets on TSNs, where the charging, discharging, routing, and origin–destination pairing can be optimized simultaneously. The power flow is captured by the optima of dynamic optimal power flow problems with security constraints. An extended alternating direction multiplier method algorithm with the convex-concave procedure is proposed to solve OTPFs. Results verify the effectiveness of the proposed scheme for congestion management on CTPSs.

A Robust Method for Distribution System State Estimation with Predator-Prey Brain Storm Optimization

In this paper, a new method is proposed for distribution system static state estimation with low redundant measurements. It is different from transmission system static state estimation because the distribution system static state estimation has low redundancy of measurements while the transmission system static state estimation has enough redundancy. This paper makes use of the nest structure of DistFlow of the distribution system power flow calculation to transform the formulation into evaluating the state variable vector at distribution substations. In other words, the distribution state estimation problem may be expressed as an optimization problem. As the optimization method, PPBSO (Predator-Prey Brain Storm Optimization) of evolutionary computation is proposed to solve the problem. The proposed method is successfully applied to a sample system.