Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

Long-lasting,reinforced electrical networking in a high-loading Li_(2)S cathode for high-performance lithium–sulfur batteries

Realizing a lithium sulfide(Li_(2)S)cathode with both high energy density and a long lifespan requires an innovative cathode design that maximizes electrochemical performance and resists electrode deterioration.Herein,a high-loading Li_(2)S-based cathode with micrometric Li_(2)S particles composed of two-dimensional graphene(Gr)and one-dimensional carbon nanotubes(CNTs)in a compact geometry is developed,and the role of CNTs in stable cycling of high-capacity Li–S batteries is emphasized.In a dimensionally combined carbon matrix,CNTs embedded within the Gr sheets create robust and sustainable electron diffusion pathways while suppressing the passivation of the active carbon surface.As a unique point,during the first charging process,the proposed cathode is fully activated through the direct conversion of Li_(2)S into S_(8) without inducing lithium polysulfide formation.The direct conversion of Li_(2)S into S_(8) in the composite cathode is ubiquitously investigated using the combined study of in situ Raman spectroscopy,in situ optical microscopy,and cryogenic transmission electron microscopy.The composite cathode demonstrates unprecedented electrochemical properties even with a high Li_(2)S loading of 10 mg cm^(–2);in particular,the practical and safe Li–S full cell coupled with a graphite anode shows ultra-long-term cycling stability over 800 cycles.

State Estimation Approach for Combined Heat and Electric Networks

State estimation(SE)is essential for combined heat and electric networks(CHENs)to provide a global and selfconsistent solution for multi-energy flow analysis.This paper proposes an SE approach for CHEN based on steady models of electric networks(ENs)and district heating networks(DHNs).A range of coupling components are considered.The performance of the proposed estimator is evaluated using Monte Carlo simulations and case studies.Results show that a relationship between the measurements from ENs and DHNs can improve the estimation accuracy for the entire network by using the combined SE model,especially when ENs and DHNs are strongly coupled.The coupling constraints could also provide extra redundancy to detect bad data in the boundary injection measurements of both networks.An analysis of computation time shows that the proposed method is suitable for online applications.

Global Harmonic Rate Assessment in the Electricity Distribution Network in Niamey City:Case Studies of Domestic,Industrial and Hospital Substations

Like others countries of the world, in Niger also, we are witnessing an increasing use of non-linear electric loads in the domestic, hospital and industrial sectors. However, these loads degrade the shape of the electrical signal and cause disastrous effects to the equipment of the distribution system and the devices which are connected to the network. This article highlights the presence of electric harmonics in the distribution network in Niamey city. In order to do this, measurements were taken at the secondary level of the substations using an energy quality analyze r (FLUKE 1735). By using this measuring instrument, we quantified the voltage and current Total Harmonic Distortion (THD) in the three substations. The results obtained show that, although the statutable rates set by the standards are not exceeded for phase conductors, the neutral contains a very critical percentage of distortion on the residential and hospital substations. Moreover, this assessment made it possible to observe the variation of harmonics in the presence of voltage drops.

Modeling of the Coupling of the Lightning Shock Wave Flow Circuit in the High Voltage Electrical Network

This article focuses on the aggression of lightning overload on the equipment of the electrical network of sites where storm activity is very dense;and the electrocution of people located in the direct environment of the high-voltage substation during the flow of lightning current to the ground through the ground socket. The modeling of the flow circuit of the shock wave consisting of guard wire, lightning arrester and ground socket couple to the transformer of the high voltage substations, thanks to the approach of a servo block, led to the synthesis of a PID regulator (corrector) whose action is to reject the effects of the overvoltage on the network equipment and to significantly reduce or even cancel the effects of the step or touch voltage due to the distribution of the potential around the ground socket;and thus improve the quality of service of the high-voltage transmission and distribution electricity network, especially in stormy times.

Improvement of the Voltage Quality in an Electrical Network via the Loopback: Case of the Southern Interconnected Grid (SIG) of Cameroon

Electricity being a basic national infrastructure for a country, its security, reliability and quality are the most important parameters for the network managers. Several methods are generally used to improve the voltage quality more and more. However, most of the means implemented depend on external factors independent of the network managers or require huge regular financial resources. The method used in this paper is the loopback, applied to the Southern Interconnected Grid (SIG) of Cameroon, which is the largest network in the country. The procedure used takes into account nodes experiencing huge voltage drops and network constraints. The chosen loopback scenario results in a clear improvement of the voltage plan in this network, and also a discharge of the transformers, a considerable decongestion of the lines, a reduction of the power losses and a significant reduction of the thermal placement used for improvement of the voltage profile.

Quantitative evaluation of extrinsic factors influencing electrical excitability in neuronal networks： Voltage Threshold Measurement Method(VTMM)

The electrical excitability of neural networks is influenced by different environmental factors. Effective and simple methods are required to objectively and quantitatively evaluate the influence of such factors, including variations in temperature and pharmaceutical dosage. The aim of this paper was to introduce ‘the voltage threshold measurement method＇, which is a new method using microelectrode arrays that can quantitatively evaluate the influence of different factors on the electrical excitability of neural networks. We sought to verify the feasibility and efficacy of the method by studying the effects of acetylcholine, ethanol, and temperature on hippocampal neuronal networks and hippocampal brain slices. First, we determined the voltage of the stimulation pulse signal that elicited action potentials in the two types of neural networks under normal conditions. Second, we obtained the voltage thresholds for the two types of neural networks under different concentrations of acetylcholine, ethanol, and different temperatures. Finally, we obtained the relationship between voltage threshold and the three influential factors. Our results indicated that the normal voltage thresholds of the hippocampal neuronal network and hippocampal slice preparation were 56 and 31 m V, respectively. The voltage thresholds of the two types of neural networks were inversely proportional to acetylcholine concentration, and had an exponential dependency on ethanol concentration. The curves of the voltage threshold and the temperature of the medium for the two types of neural networks were U-shaped. The hippocampal neuronal network and hippocampal slice preparations lost their excitability when the temperature of the medium decreased below 34 and 33°C or increased above 42 and 43°C, respectively. These results demonstrate that the voltage threshold measurement method is effective and simple for examining the performance/excitability of neuronal networks.

An Exact Solution of Telegraph Equations for Voltage Monitoring of Electrical Transmission Line

Telegraph equations are derived from the equations of transmission line theory. They describe the relationships between the currents and voltages on a portion of an electric line as a function of the linear constants of the conductor (resistance, conductance, inductance, capacitance). Their resolution makes it possible to determine the variation of the current and the voltage as a function of time at each point of the line. By adopting a general sinusoidal form, we propose a new exact solution to the telegraphers’ partial differential equations. Different simulations have been carried out considering the parameter of the 12/20 (24) kV Medium Voltage Cable NF C 33,220. The curves of the obtained solution better fit the real voltage curves observed in the electrical networks in operation.

Determination of Effective Load Shedding Schemes in Electric Power Plants

The ability of power system to survive the transition from preloading state to the gradual increase in load and thereafter reach an acceptable operational condition is an indication of transient stability of the system. The study analyzed load shedding scheme through the use of empirical measurement tools and load-flow simulation techniques. It was geared towards determining effective load shedding strategies to reduce unnecessary overload in order to achieve dynamic stability of the electric power network in the Export Free Trade Zone, Calabar, Nigeria. From the tests and the measurements taken, it was observed that the real and reactive powers from the generator and the mechanical power from the turbine engine were stable when the load shedding controller was switched on, as compared to when it was off. The engine speed, the bus-bar frequency and the output voltage of the generator stabilized within a shorter time (about 8 seconds) when the controller was switched on than when it was on the off condition. Also, there were noticeable fluctuations in the speed of the remaining two generators. It became stable at about 12 seconds after the loss. The variations were 0.3 per cent of the nominal speed value. The excitation voltage fluctuated from 1.2 (pu) to 4.5 (pu) when the bus voltage dipped as a result of additional load. It then came down and stabilized at 1.8 (pu) after few swings. This confirmed that the stability of power system is much enhanced when load shedding controllers are effectively configured on the network.

Evaluation of Grid-Connected Photovoltaic Plants Based on Clustering Methods

Photovoltaic(PV)systems are electric power systems designed to sup-ply usable solar power by means of photovoltaics,which is the conversion of light into electricity using semiconducting materials.PV systems have gained much attention and are a very attractive energy resource nowadays.The substantial advantage of PV systems is the usage of the most abundant and free energy from the sun.PV systems play an important role in reducing feeder losses,improving voltage profiles and providing ancillary services to local loads.However,large PV grid-connected systems may have a destructive impact on the stability of the elec-tric grid.This is due to thefluctuations of the output AC power generated from the PV systems according to the variations in the solar energy levels.Thus,the elec-trical distribution system with high penetration of PV systems is subject to perfor-mance degradation and instabilities.For that,this project attempts to enhance the integration process of PV systems into electrical grids by analyzing the impact of installing grid-connected PV plants.To accomplish this,an indicative representa-tion of solar irradiation datasets is used for planning and powerflow studies of the electric network prior to PV systems installation.Those datasets contain lengthy historical observations of solar energy data,that requires extensive analysis and simulations.To overcome that the lengthy historical datasets are reduced and clus-tered while preserving the original data characteristics.The resultant clusters can be utilized in the planning stage and simulation studies.Accordingly,studies related to PV systems integration into the electric grid are conducted in an efficient manner,avoiding computing resources and processing times with easier and practical implementation.

Optimization of the Placement and Size of Photovoltaic Source

This paper presents a new optimization study of the placement and size of a photovoltaic source(PVS)in a distribution grid,based on annual records of meteorological parameters(irradiance,temperature).Based on the recorded data,the production output as well as the daily average power(24-h vector)of the PVS is extracted over the year.When a power vector is available,it can be used as an input when searching for the optimal size of the PVS.This allows to take into account the constraint of the variation of the power generated by this source considering the variation of the power consumed by the electrical loads during the whole day.A multi-objective fitness function has been considered.The latter minimizes the active losses and maximizes the voltage stability index during the day,while considering the constraints of the system,that is,the security,technical,geographical,and meteorological constraints.This problem was solved using the Non-dominated Sorting Genetic Algorithm NSGA-II optimization technique under MATLAB 2021.It was applied to the distribution network of Ghardaïa of 59 nodes.

Many-objective optimization for coordinated operation of integrated electricity and gas network

This paper develops a many-objective optimization model, which contains objectives representing the interests of the electricity and gas networks, as well as the distributed district heating and cooling units, to coordinate the benefits of all parties participated in the integrated energy system(IES). In order to solve the many-objective optimization model efficiently, an improved objective reduction(IOR) approach is proposed, aiming at acquiring the smallest set of objectives. The IOR approach utilizes the Spearman’s rank correlation coefficient to measure the relationship between objectives based on the Pareto-optimal front captured by the multi-objective group search optimizer with adaptive covariance and Lévy flights algorithm, and adopts various strategies to reduce the number of objectives gradually. Simulation studies are conducted on an IES consisting of a modified IEEE 30-bus electricity network and a 15-node gas network. The results show that the many-objective optimization problem is transformed into a bi-objective formulation by the IOR. Furthermore,our approach improves the overall quality of dispatch solutions and alleviates the decision making burden.

A Hybrid State Estimation Approach for Integrated Heat and Electricity Networks Considering Time-scale Characteristics

State estimation(SE)usually serves as the basic function of the energy management system(EMS).In this paper,the time-scale characteristics of the integrated heat and electricity networks are studied and an SE model is established.Then,a two-stage iterative algorithm is proposed to estimate the time delay of heat power transportation in the pipeline.Meanwhile,to accommodate the measuring resolutions of the integrated network,a hybrid SE approach is developed based on the two-stage iterative algorithm.Results show that,in both steady and dynamic processes,the two-stage estimator has good accuracy and convergence.The hybrid estimator has good performance on tracking the variation of the states in the heating network,even when the available measurements are limited.

Chance-constrained Coordinated Optimization for Urban Electricity and Heat Networks

Urban electricity and heat networks(UEHN)consist of the coupling and interactions between electric power systems and district heating systems,in which the geographical and functional features of integrated energy systems are demonstrated.UEHN have been expected to provide an effective way to accommodate the intermittent and unpredictable renewable energy sources,in which the application of stochastic optimization approaches to UEHN analysis is highly desired.In this paper,we propose a chance-constrained coordinated optimization approach for UEHN considering the uncertainties in electricity loads,heat loads,and photovoltaic outputs,as well as the correlations between these uncertain sources.A solution strategy,which combines the Latin Hypercube Sampling Monte Carlo Simulation(LHSMCS)approach and a heuristic algorithm,is specifically designed to deal with the proposed chance-constrained coordinated optimization.Finally,test results on an UEHN comprised of a modified IEEE 33-bus system and a 32-node district heating system at Barry Island have verified the feasibility and effectiveness of the proposed framework.

Improved Approximate Minimum Degree Ordering Method and Its Application for Electrical Power Network Analysis and Computation

Electrical power network analysis and computation play an important role in the planning and operation of the power grid,and they are modeled mathematically as differential equations and network algebraic equations.The direct method based on Gaussian elimination theory can obtain analytical results.Two factors affect computing efficiency:the number of nonzero element fillings and the length of elimination tree.This article constructs mapping correspondence between eliminated tree nodes and quotient graph nodes through graph and quotient graph theories.The Approximate Minimum Degree(AMD)of quotient graph nodes and the length of the elimination tree nodes are composed to build an Approximate Minimum Degree and Minimum Length(AMDML)model.The quotient graph node with the minimum degree,which is also the minimum length of elimination tree node,is selected as the next ordering vector.Compared with AMD ordering method and other common methods,the proposed method further reduces the length of elimination tree without increasing the number of nonzero fillings;the length was decreased by about 10%compared with the AMD method.A testbed for experiment was built.The efficiency of the proposed method was evaluated based on different sizes of coefficient matrices of power flow cases.

Strategic Bidding in Distribution Network Electricity Market Focusing on Competition Modeling and Uncertainties

Developing the electricity market at the distribution level can facilitate the energy transactions in distribution networks with a high penetration level of distributed energy resources(DERs)and microgrids(MGs).However,the lack of comprehensive information about the marginal production cost of competitors leads to uncertainties in the optimal bidding strategy of participants.The electricity demand within the network and the price in the wholesale electricity market are two other sources of the uncertainties.In this paper,a day-ahead-market-based framework for managing the energy transactions among MGs and other participants in distribution networks is introduced.A game-theory-based method is presented to model the competition and determine the optimal bidding strategy of participants in the market.Robust optimization technique is employed to capture the uncertainties in the marginal cost of competitors.Additionally,the uncertainties in demand are modeled using a scenario-based stochastic approach.The results ob-tained from case studies reveal the merit of considering competition modeling and uncertainties.

Resistivity-temperature Characteristics of Conductive Asphalt Concrete

The changes of resistivity of conductive asphalt concrete at different temperatures were studied,and positive temperature coefficient（PTC）modelwas established to estimate the influence of temperature on the resistivity quantitatively,which eliminated the interference with conductivity evaluation brought by temperature variation.Finally,the analysis of temperature cycling test results proves that the changes of percolation network structure caused by temperature variation prompt the emergence of PTC of conductive asphalt concrete.

Potential Distribution on Random Electrical Networks

Let N = （G, c） be a random electrical network obtained by assigning a certain resistance for each edge in a random graph G ∈ G（n, p） and the potentials on the boundary vertices. In this paper, we prove that with high probability the potential distribution of all vertices of G is very close to a constant.

Electrical Properties of Medium Voltage Electricity Distribution Networks

An increasing amount of low carbon technologies(LCT)such as solar photovoltaic,wind turbines and electric vehicles are being connected at medium and low voltage levels to electric power networks.To support high-level decision-making processes,the impacts of the LCTs on large numbers of different types(e.g.,rural,suburban,urban)of distribution networks need to be fully understood and quantified.However,detailed modeling of large numbers of real-world networks is challenging for two reasons.First,access to real-world network data is limited,and second,cleaning the data requires a significant amount of time,even before modeling of the networks.This paper offers a novel systematic methodology aimed at identifying and quantifying the key electrical properties of medium-voltage level distribution networks.The methodology allows for characterizing different types(e.g.,suburban,urban)of distribution networks and obtaining'depth'dependent electrical properties of the models of the networks.Two key sets of(electrical)data were used for the study.The first set was installed capacities of distribution substations;and the second set was the conductor cross sections of the distribution lines.In the graph models of real-world networks,'nodes'represent the distribution sub-stations,switchgears,busbars and consumers locations of the network.'Links/edges'stand for the connections between the nodes through distribution lines.The results of the investigation of the real-world networks showed that,the substation capacities and the conductor cross sections could characterize the electrical properties of suburban and urban type distribution networks.The resulted probability density functions(PDF)of the electrical properties of suburban and urban type distribution networks have the potential to be directly used in generating realistic distribution network models.

Optimal allocation of solar photovoltaic distributed generation in electrical distribution networks using Archimedes optimization algorithm

This paper proposes to resolve optimal solar photovoltaic(SPV)system locations and sizes in electrical distribution networks using a novel Archimedes optimization algorithm(AOA)inspired by physical principles in order to minimize network dependence and greenhouse gas(GHG)emissions to the greatest extent possible.Loss sensitivity factors are used to predefine the search space for sites,and AOA is used to identify the optimal locations and sizes of SPV systems for reducing grid dependence and GHG emissions from conventional power plants.Experiments with composite agriculture loads on a practical Indian 22-bus agricultural feeder,a 28-bus rural feeder and an IEEE 85-bus feeder demonstrated the critical nature of optimally distributed SPV systems for minimizing grid reliance and reducing GHG emissions from conventional energy sources.Additionally,the voltage profile of the network has been enhanced,resulting in significant reductions in distribution losses.The results of AOA were compared to those of several other nature-inspired heuristic algorithms previously published in the literature,and it was observed that AOA outperformed them in terms of convergence and redundancy when solving complex,non-linear and multivariable optimization problems.