Power Losses In Lines Research Articles

Solving power system economic emission dispatch problem under complex constraints via dimension differential learn butterfly optimization algorithm with FDC-based

The economic emission dispatch (EED) aims to minimize the fuel and pollutant emission costs of generator units under various complex constraints. Optimizing the EED problem is of crucial importance for alleviating the current energy and environmental pressures. In this work, nearly all known complex constraints in the EED problem, including the valve-point effect, transmission line power loss, prohibited operating zones, and ramp-rate limits, are taken into account, and an enhanced version of butterfly optimization algorithm (FDCDLBOA) is proposed to solve it. First, a new adaptive fragrance is employed to optimize the instability caused by target differences and improve the convergence performance. Second, the proposed dimension differential learning strategy evolves the position of individuals with the help of superior dimensional information in the population, and this extensive learning exchange can balance global and local search, maintain diversity, and get rid of local optima. Third, the Fitness-Distance-Constraint (FDC) guide selection method is employed for the first time to handle the complex constraints of EED problems, enhancing the ability of individuals to bypass the infeasible search areas. After evaluating the proposed FDCDLBOA on CEC 2022 test suite, it is applied to solve 8 EED cases, encompassing small-, medium- and large-scale systems. Notably, the 280-generator case is the first large-scale test to exceed 200 generators. Compared with 9 representative algorithms, FDCDLBOA performs outstandingly in terms of robustness, improvement index (IF), mean constraint violation (MV), feasibility rate (FR) and Quade multiple comparison, among which IF, MV, FR, and Quade are all employed for evaluating the EED problem for the first time. The presented results confirm that the proposed method effectively enhances the robustness of high-quality solutions and the ability to handle complex constraints, demonstrating strong competitiveness and potential in solving the EED problem.

CEEMD-FFT-BiLSTM based model for power line loss prediction

Abstract Line loss rate in transmission lines is an important and inevitable metric to directly affects the economy and efficiency of power supply, which exhibits characteristics of nonlinearity, non-stationary and randomness. To improve the prediction performance of line loss rate, in this paper, a prediction model for line loss based on complete ensemble empirical mode decomposition (CEEMD), fast Fourier transform (FFT), and bidirectional long short-term memory network (BiLSTM) is proposed, denoted as CEEMD-FFT-BiLSTM. The model first performs CEEMD to decompose raw signals into various intrinsic mode functions. FFT then transforms them from the time domain to the frequency domain. BiLSTM is used to learn the long-term dependence, extract features from the frequency domain, and finally achieve a prediction. Experimental results are provided to show that the prediction accuracy of the proposed prediction method is substantially improved, as compared to other methods such as BiLSTM and FFT-BiLSTM. Therefore, the proposed prediction model has a good predictive ability and can effectively improve the prediction accuracy.

Reconfigurable topology assessment for efficient allocation of photovoltaic generators in unbalanced power delivery networks

The three-phase power delivery systems are reliable and efficient means of delivering electric power to customer node points. However, the unequal distribution of loads over the three phases leads to unbalance voltage between phases, resulting in increased power losses in distribution transformers and lines. It is highly challenging to install photovoltaic (PV) generators in unbalanced power distribution networks (PDNs) to restore power supply efficiency. This paper proposes dynamic reconfiguration (DR) in unbalanced PDNs to foster efficient integration of PV generators and improve system performance. Since the demand for electricity at residential node points and the PV-generated power varies with the season, this study proposes a seasonal DR of the PDNs. A framework with multiple objectives has been proposed for the DR-coupled PV deployment problem. A novel value-adaptive weight-aggregated (VAWA) grey-wolf optimizer (GWO) has been synthesized to contribute to system performance enhancement. The proposed strategy was validated and established through comparative investigations on an Indian 19-bus and modified IEEE 123-bus unbalanced radial distribution systems (URDSs).

ВИМІРЮВАННЯ ТА КОМПЕНСАЦІЯ НЕАКТИВНИХ ПОТУЖНОСТЕЙ ТРИФАЗНИХ СИСТЕМ ЕЛЕКТРОЖИВЛЕННЯ ЗАСОБАМИ ПАРАЛЕЛЬНОЇ АКТИВНОЇ ФІЛЬТРАЦІЇ

In this paper, the power theory of three-phase power supply systems was further developed by substantiating new formulas for calculating the unbalance power of three-phase four-wire power supply systems based on the measurement of active and reactive powers of individual phases, which can be used to form compensation signals in distributed active filtering with centralized control. A new structural scheme for controlling an autonomous shunt active filter with extended functionality and increased speed is proposed, which can be used in both three-wire and four-wire power supply systems. Computer modeling confirmed the adequacy of the new formulas for calculating the unbalance power components and the effectiveness of the proposed active filtering strategies to minimize power losses in the transmission line. References 11, figures 3, table 1.

Investigating the Impact of Interconnection Line Power Losses on Unit Commitment of Grid-Connected Microgrids

Investigating the Impact of Interconnection Line Power Losses on Unit Commitment of Grid-Connected Microgrids

Development of Mathematical Model for Determining The Relative Power Loss of Power Transmission Lines

One of the many problems faced by the power transmission and distribution network in our country is the problem of reducing the power losses during power transmission. This article presents the results of a study on technical losses as a component of intensity losses. In this study, a mathematical model was developed to determine the relative power loss, taking into account both load and conditional-constant losses. For the selected research object, the relative power loss was determined using the mathematical model developed above using MATLAB software, and the results were displayed graphically, and corresponding appropriate conclusions were drawn. It can also be seen from the calculation results that when the cross-section of the line conductor increases or the resistance of the conductor decreases, the loading current increases and the relative power loss can become a smaller value.

Collaborative optimization of reference powers and droop coefficients in DC distribution networks considering flexibility enhancement

In droop-controlled DC distribution networks (DCDNs), the uncertainties of loads and renewable energy powers worsen the system performances. This paper proposes a collaborative optimization method of reference powers and droop coefficients to optimize more objectives that including reducing line losses, improving the voltage level, and enhancing the flexibility of DCDNs. Firstly, a fast and accurate flexibility margin evaluation method is presented for DCDNs, in which the allowable power variation intervals under operational constraints are used to quantify the flexibility margins. The analytic relations of allowable power variation intervals and operational constraints are established by the linear power flow model, and the flexibility margins can be calculated quickly and directly. Secondly, the bi-level collaborative optimization method of reference powers and droop coefficients is proposed to optimize more objectives simultaneously. The reference powers are optimized to improve the voltage level and reduce line power losses in the upper-level optimization, and the droop coefficients are optimized to enhance the system flexibility in the lower-level optimization. Finally, multiple test systems with different topologies are simulated in detail. The case study demonstrates that: 1) The flexibility evaluation method can quantify the margins of operational constraints quickly and accurately. Compared with the bisection method, the calculation time of proposed flexibility evaluation method reduce 99.52% with the error of 1.68%. 2) The proposed collaborative optimization of reference powers and droop coefficients can effectively reduce line losses and the risks of voltage off-limit and VSC overload of DCDN under uncertainties.

Low power content addressable memory designing and implementation using voltage swing self adjustable match line technique

One of the essential components of computer systems is memory. A primary hindrance in this regard is the memory speed. Content Addressable Memory (CAM) speeds up transformations and table lookups in network routers and data processing systems for hardware search engines. Parallel seeks using the CAM (Content Addressable Memory) model are often used to enhance memory performance. This paper uses the voltage swing self-adjustable match line (VSSA-ML) technique to describe low-power content addressable memory design and implementation. This project decreases Match Line (ML) power loss by reducing load capacitance and ML voltage swing. A simple ML voltage detector is proposed instead of the complex, fully different detector that allows ML voltage swings near zero. This paper presents 6 T 8×8 CAM arrays using VSSA-ML Technique using Tanner tools 45-nm technology. On the other hand, this design enhances robustness in processing variations by self-adjusting voltage swings. Implementation analysis states that the described mode 6 T 8×8 CAM design utilized fewer MOSFETs than the 8 T 8×8 CAM array.

Additional Power Losses Under Non-Sinusoidal Conditions in a 22 kV Overhead Power Line

Non-sinusoidal conditions in the electrical networks cause active power losses at the fundamental frequency, corresponding to the first harmonic, and at harmonic frequencies, which are a multiple of the fundamental frequency. Non-sinusoidal currents flow through network components and create additional active power losses. One of the non-sinusoidal current sources is electronic equipment used in industrial plants to control process equipment. In Vietnam, coal mines use frequency-controlled induction motors when processing coal. The induction motors consume non-sinusoidal current, which creates additional active power losses in the supply network. The harmonic active power does not perform useful work. It causes economic damage to both the supply network and industrial enterprises. The power supply systems in industrial areas of Vietnam, where coal is mined, are characterized by low power quality. Companies engaged in coal mining and processing are forced to pay for additional losses of active power due to the low power quality under non-sinusoidal conditions, which reduces the economic efficiency of the companies' operations. The paper presents a review of the literature on the assessment of additional active power losses in overhead power lines at harmonic frequencies. An example of calculating additional losses of harmonic active power is given for a 22 kV overhead power line, through which electrical energy is supplied to the coal grading plant of the Kua Ong-Vinacomin company. Additional losses are calculated using the measurements of harmonic voltage and current at the point of the supply network connection to the 22 kV overhead power line, through which electrical energy is supplied to the coal mine and the coal grading plant. To assess the influence of the electrical equipment of the coal grading plant on the power quality in the supply network, measurements were carried out on 0.4 kV buses of electrical substation of the coal mine.

An effective gradient jellyfish search algorithm for optimal reactive power dispatch in electrical networks

AbstractAn effective optimization technique, called gradient jellyfish search optimizer (GJSO), is introduced here to address the optimal reactive power dispatch (ORPD) issue in electric networks. The ORPD problem is a complex non‐linear optimization issue involving integrated variables, aimed at achieving safe and cost‐effective operation of the system by determining optimal values for generator voltage, tap changers of transformers, and reactive power compensation. The performance of the original JSO technique is enhanced by integrating the local escaping operator into the GJSO approach. The effectiveness of the GJSO methodology is evaluated via comparison with two existing methodologies: the original jellyfish search optimizer and the equilibrium optimizer. Jellyfish search optimizer is a meta‐heuristic optimization algorithm inspired by the movement of jellyfish in the water, while equilibrium optimizer draws inspiration from game theory and equilibrium concepts. Simulations were conducted using typical IEEE‐30 bus and IEEE‐57 bus systems to validate the performance of the GJSO methodology. Two versions of the objective function are examined: minimizing line power loss and minimizing total voltage deviations at the buses. The simulation results demonstrated that the GJSO algorithm exhibited superior performance in terms of accuracy and stability compared to the standard jellyfish search optimizer and equilibrium optimizer algorithms.

Enhancing DC microgrid performance through machine learning‐optimized droop control

AbstractA machine learning‐based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication‐less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes.

The Multiobjective Control Based on Tolerance Optimization in a Multienergy System

To address the issue of multiobjective control in multienergy systems with diverse operational objectives, a two-stage optimization framework based on expected point tolerance has been proposed in this paper. In the first stage, a single objective function is used for optimization control to obtain the expected point of the multiobjective optimization problem. Then, in the second stage, by defining the allowable deviation between each optimization objective and the expected point, the original multiobjective optimization problem is transformed into a single objective optimization problem solution with tolerance measurement. Finally, in the simulation scene of a multienergy system, it is demonstrated that compared with the optimal results under each single objective method, the proposed method increases power line loss, maximum voltage deviation, new energy consumption, and economy by 2.22, 2.30, 1.02, and 2.45 times, respectively. Compared with the suboptimal results, the proposed method reduces power line loss by 22.26, 1.74, 1.09, and 0.97 times, respectively. Combining the shape of the Pareto frontier, it is demonstrated that the proposed method can comprehensively consider the needs of multiple power optimization objectives for forming a more reasonable and effective system optimization scheduling and also provide a new approach for solving multiobjective optimization problems.

Study of Reducing Transmission Losses in Java-Bali System with the Addition of Capacitors along with Optimal Capacitor Placement Methods Using Quasi-Dynamic Simulation

The Java-Bali electricity system is the largest electricity system in Indonesia which consists of 5 areas including Jakarta-Banten area, West Java area, Central Java area, East Java area and Bali area. This system is operated to meet the economic, reliability, quality, and green principles. In the case of reliability, one of the essential aims is minimization of transmission losses due to importance in system improvement to increase system operational efficiency to the possible extent. Transmission losses are an inevitable part of the electric power transfer process from generation stations to consumers. The power losses in a transmission line are inversely proportional to the square of the line voltage. This implies that lower line voltages result in higher power losses. Therefore, it is crucial to maintain optimal voltage levels to minimize transmission losses and ensure efficient energy delivery across the power system. One effective way to achieve this is by integrating capacitors into the system. In this study, we have explored two methods that aim to pinpoint the most advantageous locations for the integration of new capacitors. The goal is to optimize capacitor planning, with an emphasis not only on improving voltage levels but also on minimizing transmission losses within the Java-Bali system. The research results indicate that implementing two proposed methods can significantly reduce transmission losses in The Java-Bali system. Method-1 involves identifying the lowest voltage over the course of a year, while Method-2 focuses on the lowest voltage value within the first quartile (Q1/25%) during the same period

Prediction of performance parameters of a hermetic reciprocating compressor applying an artificial neural network

The efficiency of the compressor's thermodynamic, mechanical, and electric motors can be increased by minimizing their losses. Based on the design characteristics of the compressor, the current study proposes an artificial neural network (ANN) model to forecast the mass flow rate, cooling capacity, compression power, and discharge line loss of the hermetic reciprocating compressor as outputs. This study uses experimental and numerical results obtained by the fluid–structure interaction (FSI) method, and the whole findings are used as inputs for the machine learning method. Using 36 numerical and 3 experimental data sets, a multilayer network is created with input parameters defining mass flow rate, cooling capacity, compression power, and discharge line energy loss. The input parameters for the network model are the compressor's design parameters, which include the compressor speed, discharge valve thickness, and discharge valve length. When comparing the experimental and FSI methods for compressor speeds of 1300, 2100, and 3000 rpm, the FSI study's mass flow rate and cooling capacity parameters converged at 11.9%, 9.1%, and 9.3%, respectively, to their actual values. The convergence rate to experimental compression power data was −0.3%, 2.6%, and 11%. The numerical-experimental deviation for discharge line energy losses is 14.9%, −3.6%, and 7.8%, respectively, for 1300, 2100, and 3000 rpm. With the Levenberg–Marquardt (LM) ANN model, which is used in this study, the average squared error value is calculated as 1.75E-02 and the R2 value is calculated as 0.99976. ANN model can predict outputs with average deviation rates of less than 0.88%. It is seen that results obtained with the ANN method provide high convergence on the experimental and FSI results, while the results obtained with the regression model deviate from the compressor exhaust line results by more than ±20%, and the ANN method is more successful than regression.

Photovoltaic power generation prediction and optimization configuration model based on GPR and improved PSO algorithm

As the growing demand for energy as well as the strengthening of environmental awareness, photovoltaic power generation, as a clean and renewable energy source, has gradually attracted people's attention and attention. To facilitate the dispatching and planning of power system, this study uses historical data and meteorological data to build a photovoltaic power generation prediction and configuration optimization model on the ground of Gaussian process regression and improved particle swarm optimization algorithm. The simulation results show that the regression prediction curve of the Gaussian process regression prediction model is the closest to the real curve, and the prediction curve is stable and not easily disturbed by noise data. The Root-mean-square deviation and the average absolute proportional error of the model are small, and the disparity in the predicted value and the true value of the model is small; The integration of multi factor data has improved the accuracy of prediction data, and the regression prediction effect is good. The improved Particle swarm optimization algorithm could continuously enhance in the search for the optimal solution, and the Rate of convergence is fast. The Pareto solution can provide different solutions suitable for photovoltaic power generation optimization. Reasonable optimization configuration can effectively reduce active power line loss and voltage deviation, with the maximum reduction values reaching 132kW and 0.028, respectively. The research and design of predictive models and optimized configuration models can promote the formation of smart grids.

Machine learning-based optimal distributed generation and electric vehicle load management

The load profile of radial distribution networks (RDNs) is significantly impacted when plug-in electric vehicles (PEVs) are connected to them in large numbers. The disturbances in the load profile may lead to increased power losses in distribution lines and deterioration of the network voltage profile. The provision of distributed generation at strategic locations in the distribution network can help to compensate for the impact on the electrical network due to PEV loads. This paper proposes the use of machine learning (ML)-based models for determining the optimal location of distributed generators (DGs) in an RDN. The proposed method considered time-varying load in addition to PEV load. The suggested method determines optimal placement of DGs based on the power loss reduction index and voltage deviation index reduction index. Four distinct types of ML models were used in the proposed approach using synthesised data on the Institute of Electrical and Electronics Engineers' 33-bus RDN. The performance of the ML models was evaluated with respect to mean squared error and mean absolute percentage error and, for the ML models considered, the random forest ML model gave the best performance.

Comprehensive analysis of line losses and valuation of energy savings in optimized photovoltaic array subjected to partial shadings

In this manuscript, an attempt is made to reconfigure the conventional total cross-tied (TCT) array through a novel optimized SuDoKu (OPSDK) static reconfiguration technique. This approach helps to reduce the line losses occurring due to increased wire length and simultaneously diminishes the adverse effects of accumulated shadings in close vicinity by diffusing it across the photovoltaic array (PVA). The MATLAB/Simulink platform is used to study the variations in the performance of various existing topologies and the proposed OPSDK PVA model under various shading scenarios such as bottom-right corner, bottom-left corner, top-right corner, top-left corner and drifted-locus centre. The performance is monitored with regard to the specific characteristics viz., % fill-factor, mismatch power losses, % power loss, % efficiency, global maximum power point, maximum voltage, maximum current, local maximum power points (LMPPs), number of LMPPs, misleading power loss, current loss, voltage loss, % execution ratio, % power improvement, % thermal voltage, % power enhancement ratio, line loss and annual energy saving. Theresults show that the proposed OPSDKmethod generates 28.12852 kWh energy units per day, which is maximum when compared to other five topologies viz. SuDoKu (SDK), futoshiki SuDoKu (FSDK), skyscraper SuDoKu (SKYSDK), modified SuDoKu (MSDK), ancient chinese magic square (ACMS). Consequently, the annual income generatedby [9×9] SDK, FSDK, SKYSDK, MSDK, ACMS and OPSDK PVAs is ₹ 150,839.54, ₹ 141,514.52, ₹150,108.08, ₹ 150,751.94, ₹ 150,249.33 and ₹154,003.72, respectively, signifying that OPSDK PVA produces highest income. The extensive analysis signifies that the proposed OPSDK topology succeeds in reducing line losses by 25.62 %, 0.00 %, 37.98 %, 33.43 % and 35.49 % in comparison to SDK, FSDK, SKYSDK, MSDK and ACMS, respectively. The mismatch power losses in power output are also reduced by 643.1 W as compared to the other configurations. The OPSDK technique, thus, proves to be one of the most efficient, effective, optimised and sustainable PVA reconfiguration technique with good power savings and greater economic returns/viability.

Utilizing virtual power plants to support main grid for frequency regulation

In this paper, a new control architecture of virtual power plants (VPP) is proposed for the frequency regulation (FR) in main grid. Firstly, to address the power prediction of aggregation nodes (AN) with spatio-temporal correlation under VPP, a novel combined model based on graph convolutional networks and bi-directional long and short-term memory is established. Further, considering the effect of prediction uncertainty, a power shortage measurement method based on interval estimation is proposed to determine the effective power of AN. Secondly, a dispatch-based control strategy is introduced to enable VPP to respond to regulation commands from the main grid. Additionally, the influence of transmission line power loss on FR is considered, which can be eliminated by applying the developed power compensation design scheme based on proportional allocation. Finally, numerical simulations illustrate that the proposed method enhances prediction performance by 68.65%, taking mean absolute error as an example, while ensuring the accuracy and reliability of power prediction. Also, the adopted control strategy can effectively utilize VPP to provide FR services for the main grid. Moreover, the application results in a real scene are better than those of benchmark methods, which also demonstrates that the proposed approach has a high practical application potential in power system.

Expanding the Role of DSOs in Establishing Flexible Local Markets: A Brokerage Perspective

As more distributed energy resources are deployed into distribution networks, supply and demand matching between prosumers becomes a new challenge for distribution system operators (DSOs) due to the intermittency of DERs. Recent studies begin to expand the responsibilities of DSOs towards local generation and consumption optimization, such as establishing a flexible local market. However, the difficulty of boosting local power exchange while reducing operation costs still remains unsolved. In this paper, we propose a new way of power trading management for DSOs from a brokerage perspective. Specifically, DSOs endeavor to coordinate prosumers in the local market by setting a fair transactive price as incentive signals. Meanwhile, to compensate for the operation cost (e.g., power line loss), DSOs need to charge commission. We investigate how to compute the optimal transactive price in a privacy-preserving way by using a distributed algorithm and set a fair service fee, which attracts more power transactions in the local market as well as reduces the operation cost of DSOs. Moreover, to accelerate the distributed algorithm, we derive an optimal transactive price updating rule for DSOs, and the closed-form expression of the optimal strategy for each prosumer with respect to the transactive price. Numerical results on Southern California Edison 56-bus feeder and IEEE 123-node test feeder show that the proposed power trading management for DSOs from a brokerage perspective leads to a TRIPLE win. With the proposed mechanism, the prosumers' payoffs largely increase by as much as 192.85%. Then, the prosumers are more willing to exchange power locally, which significantly reduces the increasing transmission burden in transmission networks of power systems. Moreover, DSOs obtain compensation by charging a service commission and reduce their operating costs.

Passive filters allocation in unbalanced distribution network with high penetration of distributed generation

The purpose of this paper is the allocation of passive filters in an unbalanced power network with high penetration of distributed generation to minimize the costs of power losses in the distribution lines, including power losses caused by harmonic components and neutral conductor power losses due to unbalanced loads. The harmonic components and the fundamental current generated by the distributed generation cause a voltage increase in the generation bus, and the voltages in the network above the limit allowed by standard are reduced by allocating passive filters without cutting the active power in the distributed generation. The unbalance and reactive power compensation is performed by the ideal compensation method applied to the distribution network. The tests are for the IEEE 34 bus network, with the fourth wire in the simulation being the isolated neutral. The scatter search metaheuristic applied in the simulation tends to minimize the costs of power losses in the lines and the costs of the passive filters allocated in the medium voltage distribution network, meeting the operational constraints of the network.