In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(...In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.展开更多
Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power o...Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.展开更多
With the large-scale development and utilization of renewable energy,industrial flexible loads,as a kind of loadside resource with strong regulation ability,provide new opportunities for the research on renewable ener...With the large-scale development and utilization of renewable energy,industrial flexible loads,as a kind of loadside resource with strong regulation ability,provide new opportunities for the research on renewable energy consumption problem in power systems.This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning,aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy,and achieving the safe,stable and economical operation of power grids.Firstly,according to the evaluation index of renewable energy consumption characteristics of line active power,the power grid is divided into several partitions,and the interzone tie lines are taken as the optimization objects.Then,on the basis of partitioning,a two-layer active power optimization model considering the power constraints of industrial flexible loads is established.The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day;the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of theminimumoperation cost of the partition,so as to reduce the line overlimit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads.Finally,through simulation experiments,it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid.展开更多
The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonl...The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. To deal with the problem,quantum particle swarm optimization (QPSO) is firstly introduced in this paper,and according to QPSO,chaotic quantum particle swarm optimization (CQPSO) is presented,which makes use of the randomness,regularity and ergodicity of chaotic variables to improve the quantum particle swarm optimization algorithm. When the swarm is trapped in local minima,a smaller searching space chaos optimization is used to guide the swarm jumping out the local minima. So it can avoid the premature phenomenon and to trap in a local minima of QPSO. The feasibility and efficiency of the proposed algorithm are verified by the results of calculation and simulation for IEEE 14-buses and IEEE 30-buses systems.展开更多
Due to the inherent complexity, traditional ant colony optimization （ACO） algorithm is inadequate and insufficient to the reactive power optimization for distribution network. Therefore, firstly the ACO algorithm is...Due to the inherent complexity, traditional ant colony optimization （ACO） algorithm is inadequate and insufficient to the reactive power optimization for distribution network. Therefore, firstly the ACO algorithm is improved in two aspects： pheromone mutation and re-initialization strategy. Then the thought of differential evolution （DE） algorithm is proposed to be merged into ACO, and by producing new individuals with random deviation disturbance of DE, pheromone quantity left by ants is disturbed appropriately, to search the optimal path, by which the ability of search having been improved. The proposed algorithm is tested on IEEE30-hus system and actual distribution network, and the reactive power optimization results are calculated to verify the feasibility and effectiveness of the improved algorithm.展开更多
In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this stud...In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects： one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county＇s distribution network showed that the improved ACOA was practicable.展开更多
The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved p...The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind farms.First,the power flow of offshore wind farms is modeled,analyzed and calculated.To improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor.Taking the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering needs.Finally,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of SVC.Comparing the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is determined.Meanwhile,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.展开更多
The power optimization of mixed polarity Reed–Muller(MPRM)logic circuits is a classic combinatorial optimization problem.Existing optimization approaches often suffer from slow convergence and a propensity to converg...The power optimization of mixed polarity Reed–Muller(MPRM)logic circuits is a classic combinatorial optimization problem.Existing optimization approaches often suffer from slow convergence and a propensity to converge to local optima,limiting their effectiveness in achieving optimal power efficiency.First,we propose a novel multi-strategy fusion memetic algorithm(MFMA).MFMA integrates global exploration via the chimp optimization algorithm with local exploration using the coati optimization algorithm based on the optimal position learning and adaptive weight factor(COA-OLA),complemented by population management through truncation selection.Second,leveraging MFMA,we propose a power optimization approach for MPRM logic circuits that searches for the best polarity configuration to minimize circuit power.Experimental results based on Microelectronics Center of North Carolina(MCNC)benchmark circuits demonstrate significant improvements over existing power optimization approaches.MFMA achieves a maximum power saving rate of 72.30%and an average optimization rate of 43.37%;it searches for solutions faster and with higher quality,validating its effectiveness and superiority in power optimization.展开更多
The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACT...The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.展开更多
Reactive power optimization of distribution networks is traditionally addressed by physical model based methods,which often lead to locally optimal solutions and require heavy online inference time consumption.To impr...Reactive power optimization of distribution networks is traditionally addressed by physical model based methods,which often lead to locally optimal solutions and require heavy online inference time consumption.To improve the quality of the solution and reduce the inference time burden,this paper proposes a new graph attention networks based method to directly map the complex nonlinear relationship between graphs(topology and power loads)and reactive power scheduling schemes of distribution networks,from a data-driven perspective.The graph attention network is tailored specifically to this problem and incorporates several innovative features such as a self-loop in the adjacency matrix,a customized loss function,and the use of max-pooling layers.Additionally,a rulebased strategy is proposed to adjust infeasible solutions that violate constraints.Simulation results on multiple distribution networks demonstrate that the proposed method outperforms other machine learning based methods in terms of the solution quality and robustness to varying load conditions.Moreover,its online inference time is significantly faster than traditional physical model based methods,particularly for large-scale distribution networks.展开更多
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 n...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.展开更多
Ensuring the reliability of power systems in microgrids is critical,particularly under contingency conditions that can disrupt power flow and system stability.This study investigates the application of Security-Constr...Ensuring the reliability of power systems in microgrids is critical,particularly under contingency conditions that can disrupt power flow and system stability.This study investigates the application of Security-Constrained Optimal Power Flow(SCOPF)using the Line Outage Distribution Factor(LODF)to enhance resilience in a renewable energy-integrated microgrid.The research examines a 30-bus system with 14 generators and an 8669 MW load demand,optimizing both single-objective and multi-objective scenarios.The single-objective opti-mization achieves a total generation cost of$47,738,while the multi-objective approach reduces costs to$47,614 and minimizes battery power output to 165.02 kW.Under contingency conditions,failures in transmission lines 1,22,and 35 lead to complete power loss in those lines,requiring a redistribution strategy.Implementing SCOPF mitigates these disruptions by adjusting power flows,ensuring no line exceeds its capacity.Specifically,in contingency 1,power in channel 4 is reduced from 59 to 32 kW,while overall load shedding is minimized to 0.278 MW.These results demonstrate the effectiveness of SCOPF in maintaining stability and reducing economic losses.Unlike prior studies,this work integrates LODF into SCOPF for large-scale microgrid applications,offering a computationally efficient contingency management framework that enhances grid resilience and supports renewable energy adoption.展开更多
Restructuring of power market not only introduces competition but also brings complexity which increases overloading of Transmission Lines(TL).To obviate this complexity,this paper aims to mitigate the overloading and...Restructuring of power market not only introduces competition but also brings complexity which increases overloading of Transmission Lines(TL).To obviate this complexity,this paper aims to mitigate the overloading and estimate the optimal location of Static Synchronous Compensator(STATCOM) by reducing congestion for a deregulated power system.The proposed method is based on the use of Locational Marginal Price(LMP) difference technique and congestion cost.LMPs are obtained as a by-product of Optimal Power Flow(OPF),whereas Congestion Cost(CC) is a function of difference in LMP and power flows.The effiectiveness of this approach is demonstrated by reducing the CC and solution space which can identify the TLs more suitable for placement of STATCOM.Importantly,total real power loss,reactive power loss and total CC are the three main objective functions in this optimization process.The process is implemented by developing an IEEE-69 bus test system which verifies and validates the effectiveness of proposed optimization technique.Additionally,a comparative analysis is enumerated by implementing two optimization techniques:Flower Pollination Algorithm(FPA) and Particle Swarm Optimization(PSO).The comparative analysis is sufficient to demonstrate the superiority of FPA technique over PSO technique in estimating an optimal placement of a STATCOM.The results from the load-flow analysis illustrate the reduction in CC,total real and reactive power loss using FPA technique compared to PSO technique.Overall,satisfactory results are obtained without using complex calculations which verify the effectiveness of optimization techniques.展开更多
The construction of advanced metering infrastructure and the rapid evolution of artificial intelligence bring opportunities to quickly searching for the optimal dispatching strategy for reactive power optimization. Th...The construction of advanced metering infrastructure and the rapid evolution of artificial intelligence bring opportunities to quickly searching for the optimal dispatching strategy for reactive power optimization. This can be realized by mining existing prior knowledge and massive data without explicitly constructing physical models. Therefore, a novel datadriven approach is proposed for reactive power optimization of distribution networks using capsule networks(CapsNet). The convolutional layers with strong feature extraction ability are used to project the power loads to the feature space to realize the automatic extraction of key features. Furthermore, the complex relationship between input features and dispatching strategies is captured accurately by capsule layers. The back propagation algorithm is utilized to complete the training process of the CapsNet. Case studies show that the accuracy and robustness of the CapsNet are better than those of popular baselines(e.g.,convolutional neural network, multi-layer perceptron, and casebased reasoning). Besides, the computing time is much lower than that of traditional heuristic methods such as genetic algorithm, which can meet the real-time demand of reactive power optimization in distribution networks.展开更多
Dynamic Reactive Power Optimization(DRPO) is a large-scale, multi-period, and strongly coupled nonlinear mixed-integer programming problem that is difficult to solve directly. First, to handle discrete variables and s...Dynamic Reactive Power Optimization(DRPO) is a large-scale, multi-period, and strongly coupled nonlinear mixed-integer programming problem that is difficult to solve directly. First, to handle discrete variables and switching operation constraints, DRPO is formulated as a nonlinear constrained two-objective optimization problem in this paper. The first objective is to minimize the real power loss and the Total Voltage Deviations(TVDs), and the second objective is to minimize incremental system loss. Then a Filter Collaborative State Transition Algorithm(FCSTA) is presented for solving DRPO problems. Two populations corresponding to two different objectives are employed. Moreover, the filter technique is utilized to deal with constraints. Finally, the effectiveness of the proposed method is demonstrated through the results obtained for a 24-hour test on Ward & Hale 6 bus, IEEE 14 bus, and IEEE 30 bus test power systems. To substantiate the effectiveness of the proposed algorithms, the obtained results are compared with different approaches in the literature.展开更多
As high amounts of new energy and electric vehicle(EV)charging stations are connected to the distribution network,the voltage deviations are likely to occur,which will further affect the power quality.It is challengin...As high amounts of new energy and electric vehicle(EV)charging stations are connected to the distribution network,the voltage deviations are likely to occur,which will further affect the power quality.It is challenging to manage high quality voltage control of a distribution network only relying on the traditional reactive power control mode.If the reactive power regulation potentials of new energy and EVs can be tapped,it will greatly reduce the reactive power optimization pressure on the network.Keeping this in mind,our reasearch first adds EVs to the traditional distribution network model with new forms of energy,and then a multi-objective optimization model,with achieving the lowest line loss,voltage deviation,and the highest static voltage stability margin as its objectives,is constructed.Meanwihile,the corresponding model parameters are set under different climate and equipment conditions.Ultimately,the optimization model under specific scenarios is obtained.Furthermore,considering the supply and demand relation-ship of the network,an improved technique for order preference by similarity to an ideal solution decision method is proposed,which aims to judge the adaptability of different algorithms to the optimized model,so as to select a most suitable algorithm for the problem.Finally,a comparison is made between the constructed model and a model without new energy.The results reveal that the constructed model can provide a high quality reactive power regula-tion strategy.展开更多
A hierarchically correlated equilibrium Q-learning(HCEQ)algorithm for reactive power optimization that considers carbon emission on the grid-side as an optimization objective,is proposed here.Based on the multi-area d...A hierarchically correlated equilibrium Q-learning(HCEQ)algorithm for reactive power optimization that considers carbon emission on the grid-side as an optimization objective,is proposed here.Based on the multi-area decentralized collaborative framework,the controllable variables in each region are divided into several optimization layers,which is an effective method for solving the limitations posed by dimensionality.The HCEQ provides constant information on the interaction between the state-action value function matrices,as well as on the cooperative game equilibrium among agents in each region.After acquiring the optimal value function matrix in the pre-learning process,HCEQ is able to quickly achieve an optimal solution online.Simulation of the IEEE 57-bus system is performed,which demonstrates that the proposed algorithm can effectively solve multi-area decentralized collaborative reactive power optimization,with the desired global search capabilities and convergence speed.展开更多
Based on the research of population migration algorithms （PMAs）, a population migration genetic algo- rithm （PMGA） is proposed, combining a PMA with a genetic algorithm. A scheme of area and power optimization for...Based on the research of population migration algorithms （PMAs）, a population migration genetic algo- rithm （PMGA） is proposed, combining a PMA with a genetic algorithm. A scheme of area and power optimization for a ternary FPRM circuit is proposed by using the PMGA. Firstly, according to the ternary FPRM logic function expression, area and power estimation models are established. Secondly, the PMGA is used to search for the best area and power polarity. Finally, 10 MCNC Benchmark circuits are used to verify the effectiveness of the proposed method. The results show that the ternary FPRM circuits optimized by the PMGA saved 13.33% area and 20.00% power on average than the corresponding FPRM circuits optimized by a whole annealing genetic algorithm.展开更多
In this paper, we consider the power optimization problem in Orthogonal Frequency Division Multiplexing （OFDM）-based relay-enhanced device-to-device （D2D） communication. In a single cell transmission scenario, dua...In this paper, we consider the power optimization problem in Orthogonal Frequency Division Multiplexing （OFDM）-based relay-enhanced device-to-device （D2D） communication. In a single cell transmission scenario, dual- hop communication is assumed in which each D2D user re-uses the spectrum of just one Cellular User （CU）. In this work, we formulate a joint optimization scheme under a Decode-and-Forward （DF） relaying protocol to maximize the sum throughput of D2D and cellular networks via power allocation over different sub-carriers. The problem is thus transformed into a standard convex optimization, subject to individual power constraints at different transmitting nodes. We exploit the duality theory to decompose the problem into several sub-problems and use Karush-Kuhn- Tucker （KKT） conditions to solve each sub-problem. We provide simulation results to validate the performance of our proposed scheme.展开更多
The permanent magnet electromagnetic hybridmagnet (PEHM) has the advantages of low energy consumptionand a large suspension air gap. In this study, an unbalancedPEHM structure is proposed, which combines the advantage...The permanent magnet electromagnetic hybridmagnet (PEHM) has the advantages of low energy consumptionand a large suspension air gap. In this study, an unbalancedPEHM structure is proposed, which combines the advantages ofthe previous hybrid magnet structure. First, by establishing themagnetic circuit model of the new hybrid magnet structure, theinfluence of magnetic field distribution on the working magneticcircuit of the magnet is analyzed, and the method of magneticforce correction calculation of the new structure magnet isgiven. Then, the validity of the magnetic calculation method isverified by the 3D finite element method (FEM). Furthermore, theaverage suspension power force ratio is used as the optimizationgoal, and the system parameters of the hybrid magnet under aworking air gap of 6–10 mm and a load condition of 15000–20000 N are optimized by a genetic algorithm. Compared withthe previous hybrid magnet, the optimized hybrid magnet systemcan maintain lower power consumption under comprehensiveworking conditions.展开更多
基金supported by the CAS Project for Young Scientists in Basic Research under Grant YSBR-035Jiangsu Provincial Key Research and Development Program under Grant BE2021013-2.
文摘In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.
基金funded by the“Research and Application Project of Collaborative Optimization Control Technology for Distribution Station Area for High Proportion Distributed PV Consumption(4000-202318079A-1-1-ZN)”of the Headquarters of the State Grid Corporation.
文摘Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load,a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed.Firstly,the k-medoids clustering algorithm is used to divide the reduced power scene into periods.Then,the discrete variables and continuous variables are optimized in the same period of time.Finally,the number of input groups of parallel capacitor banks(CB)in multiple periods is fixed,and then the secondary static reactive power optimization correction is carried out by using the continuous reactive power output device based on the static reactive power compensation device(SVC),the new energy grid-connected inverter,and the electric vehicle charging station.According to the characteristics of the model,a hybrid optimization algorithm with a cross-feedback mechanism is used to solve different types of variables,and an improved artificial hummingbird algorithm based on tent chaotic mapping and adaptive mutation is proposed to improve the solution efficiency.The simulation results show that the proposed decoupling strategy can obtain satisfactory optimization resultswhile strictly guaranteeing the dynamic constraints of discrete variables,and the hybrid algorithm can effectively solve the mixed integer nonlinear optimization problem.
基金supported by State Grid Corporation of China Project“Research and Application of Key Technologies for Active Power Control in Regional Power Grid with High Penetration of Distributed Renewable Generation”(5108-202316044A-1-1-ZN).
文摘With the large-scale development and utilization of renewable energy,industrial flexible loads,as a kind of loadside resource with strong regulation ability,provide new opportunities for the research on renewable energy consumption problem in power systems.This paper proposes a two-layer active power optimization model based on industrial flexible loads for power grid partitioning,aiming at improving the line over-limit problem caused by renewable energy consumption in power grids with high proportion of renewable energy,and achieving the safe,stable and economical operation of power grids.Firstly,according to the evaluation index of renewable energy consumption characteristics of line active power,the power grid is divided into several partitions,and the interzone tie lines are taken as the optimization objects.Then,on the basis of partitioning,a two-layer active power optimization model considering the power constraints of industrial flexible loads is established.The upper-layer model optimizes the planned power of the inter-zone tie lines under the constraint of the minimum peak-valley difference within a day;the lower-layer model optimizes the regional source-load dispatching plan of each resource in each partition under the constraint of theminimumoperation cost of the partition,so as to reduce the line overlimit phenomenon caused by renewable energy consumption and save the electricity cost of industrial flexible loads.Finally,through simulation experiments,it is verified that the proposed model can effectively mobilize industrial flexible loads to participate in power grid operation and improve the economic stability of power grid.
基金Sponsored by the Scientific and Technological Project of Heilongjiang Province(Grant No.GD07A304)
文摘The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. To deal with the problem,quantum particle swarm optimization (QPSO) is firstly introduced in this paper,and according to QPSO,chaotic quantum particle swarm optimization (CQPSO) is presented,which makes use of the randomness,regularity and ergodicity of chaotic variables to improve the quantum particle swarm optimization algorithm. When the swarm is trapped in local minima,a smaller searching space chaos optimization is used to guide the swarm jumping out the local minima. So it can avoid the premature phenomenon and to trap in a local minima of QPSO. The feasibility and efficiency of the proposed algorithm are verified by the results of calculation and simulation for IEEE 14-buses and IEEE 30-buses systems.
文摘Due to the inherent complexity, traditional ant colony optimization （ACO） algorithm is inadequate and insufficient to the reactive power optimization for distribution network. Therefore, firstly the ACO algorithm is improved in two aspects： pheromone mutation and re-initialization strategy. Then the thought of differential evolution （DE） algorithm is proposed to be merged into ACO, and by producing new individuals with random deviation disturbance of DE, pheromone quantity left by ants is disturbed appropriately, to search the optimal path, by which the ability of search having been improved. The proposed algorithm is tested on IEEE30-hus system and actual distribution network, and the reactive power optimization results are calculated to verify the feasibility and effectiveness of the improved algorithm.
基金Supported by China Postdoctoral Science Foundation(20090460873)
文摘In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects： one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county＇s distribution network showed that the improved ACOA was practicable.
基金This work was supported by Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.,China(J2022114,Risk Assessment and Coordinated Operation of Coastal Wind Power Multi-Point Pooling Access System under Extreme Weather).
文摘The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind farms.First,the power flow of offshore wind farms is modeled,analyzed and calculated.To improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor.Taking the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering needs.Finally,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of SVC.Comparing the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is determined.Meanwhile,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.
基金Project supported by the National Natural Science Foundation of China(No.62102130)Central Government Guides Local Science and Technology Development Fund Project+4 种基金China(No.226Z0201G)Natural Science Foundation of Hebei ProvinceChina(Nos.F2020204003 and F2024204001)Science and Technology Research Projects of Higher Education Institutions in Hebei ProvinceChina(No.QN2024138)。
文摘The power optimization of mixed polarity Reed–Muller(MPRM)logic circuits is a classic combinatorial optimization problem.Existing optimization approaches often suffer from slow convergence and a propensity to converge to local optima,limiting their effectiveness in achieving optimal power efficiency.First,we propose a novel multi-strategy fusion memetic algorithm(MFMA).MFMA integrates global exploration via the chimp optimization algorithm with local exploration using the coati optimization algorithm based on the optimal position learning and adaptive weight factor(COA-OLA),complemented by population management through truncation selection.Second,leveraging MFMA,we propose a power optimization approach for MPRM logic circuits that searches for the best polarity configuration to minimize circuit power.Experimental results based on Microelectronics Center of North Carolina(MCNC)benchmark circuits demonstrate significant improvements over existing power optimization approaches.MFMA achieves a maximum power saving rate of 72.30%and an average optimization rate of 43.37%;it searches for solutions faster and with higher quality,validating its effectiveness and superiority in power optimization.
文摘The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(P_(loss,min))at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(C_(gen,min))and mean power loss values(P_(loss,min))with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study.
文摘Reactive power optimization of distribution networks is traditionally addressed by physical model based methods,which often lead to locally optimal solutions and require heavy online inference time consumption.To improve the quality of the solution and reduce the inference time burden,this paper proposes a new graph attention networks based method to directly map the complex nonlinear relationship between graphs(topology and power loads)and reactive power scheduling schemes of distribution networks,from a data-driven perspective.The graph attention network is tailored specifically to this problem and incorporates several innovative features such as a self-loop in the adjacency matrix,a customized loss function,and the use of max-pooling layers.Additionally,a rulebased strategy is proposed to adjust infeasible solutions that violate constraints.Simulation results on multiple distribution networks demonstrate that the proposed method outperforms other machine learning based methods in terms of the solution quality and robustness to varying load conditions.Moreover,its online inference time is significantly faster than traditional physical model based methods,particularly for large-scale distribution networks.
基金supported by the Deanship of Postgraduate Studies and Scientific Research at Majmaah University in Saudi Arabia under Project Number(ICR-2024-1002).
文摘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.
文摘Ensuring the reliability of power systems in microgrids is critical,particularly under contingency conditions that can disrupt power flow and system stability.This study investigates the application of Security-Constrained Optimal Power Flow(SCOPF)using the Line Outage Distribution Factor(LODF)to enhance resilience in a renewable energy-integrated microgrid.The research examines a 30-bus system with 14 generators and an 8669 MW load demand,optimizing both single-objective and multi-objective scenarios.The single-objective opti-mization achieves a total generation cost of$47,738,while the multi-objective approach reduces costs to$47,614 and minimizes battery power output to 165.02 kW.Under contingency conditions,failures in transmission lines 1,22,and 35 lead to complete power loss in those lines,requiring a redistribution strategy.Implementing SCOPF mitigates these disruptions by adjusting power flows,ensuring no line exceeds its capacity.Specifically,in contingency 1,power in channel 4 is reduced from 59 to 32 kW,while overall load shedding is minimized to 0.278 MW.These results demonstrate the effectiveness of SCOPF in maintaining stability and reducing economic losses.Unlike prior studies,this work integrates LODF into SCOPF for large-scale microgrid applications,offering a computationally efficient contingency management framework that enhances grid resilience and supports renewable energy adoption.
文摘Restructuring of power market not only introduces competition but also brings complexity which increases overloading of Transmission Lines(TL).To obviate this complexity,this paper aims to mitigate the overloading and estimate the optimal location of Static Synchronous Compensator(STATCOM) by reducing congestion for a deregulated power system.The proposed method is based on the use of Locational Marginal Price(LMP) difference technique and congestion cost.LMPs are obtained as a by-product of Optimal Power Flow(OPF),whereas Congestion Cost(CC) is a function of difference in LMP and power flows.The effiectiveness of this approach is demonstrated by reducing the CC and solution space which can identify the TLs more suitable for placement of STATCOM.Importantly,total real power loss,reactive power loss and total CC are the three main objective functions in this optimization process.The process is implemented by developing an IEEE-69 bus test system which verifies and validates the effectiveness of proposed optimization technique.Additionally,a comparative analysis is enumerated by implementing two optimization techniques:Flower Pollination Algorithm(FPA) and Particle Swarm Optimization(PSO).The comparative analysis is sufficient to demonstrate the superiority of FPA technique over PSO technique in estimating an optimal placement of a STATCOM.The results from the load-flow analysis illustrate the reduction in CC,total real and reactive power loss using FPA technique compared to PSO technique.Overall,satisfactory results are obtained without using complex calculations which verify the effectiveness of optimization techniques.
文摘The construction of advanced metering infrastructure and the rapid evolution of artificial intelligence bring opportunities to quickly searching for the optimal dispatching strategy for reactive power optimization. This can be realized by mining existing prior knowledge and massive data without explicitly constructing physical models. Therefore, a novel datadriven approach is proposed for reactive power optimization of distribution networks using capsule networks(CapsNet). The convolutional layers with strong feature extraction ability are used to project the power loads to the feature space to realize the automatic extraction of key features. Furthermore, the complex relationship between input features and dispatching strategies is captured accurately by capsule layers. The back propagation algorithm is utilized to complete the training process of the CapsNet. Case studies show that the accuracy and robustness of the CapsNet are better than those of popular baselines(e.g.,convolutional neural network, multi-layer perceptron, and casebased reasoning). Besides, the computing time is much lower than that of traditional heuristic methods such as genetic algorithm, which can meet the real-time demand of reactive power optimization in distribution networks.
基金supported by the National Natural Science Foundation of China(Nos.51767022 and 51575469)
文摘Dynamic Reactive Power Optimization(DRPO) is a large-scale, multi-period, and strongly coupled nonlinear mixed-integer programming problem that is difficult to solve directly. First, to handle discrete variables and switching operation constraints, DRPO is formulated as a nonlinear constrained two-objective optimization problem in this paper. The first objective is to minimize the real power loss and the Total Voltage Deviations(TVDs), and the second objective is to minimize incremental system loss. Then a Filter Collaborative State Transition Algorithm(FCSTA) is presented for solving DRPO problems. Two populations corresponding to two different objectives are employed. Moreover, the filter technique is utilized to deal with constraints. Finally, the effectiveness of the proposed method is demonstrated through the results obtained for a 24-hour test on Ward & Hale 6 bus, IEEE 14 bus, and IEEE 30 bus test power systems. To substantiate the effectiveness of the proposed algorithms, the obtained results are compared with different approaches in the literature.
基金supported by National Key R&D Program of China (2021ZD0111502)National Natural Science Foundation of China (51907112,U2066212)+1 种基金Natural Science Foundation of Guangdong Province of China (2019A1515011671,2021A1515011709)Scientific Research Staring Foundation of Shantou University (NTF19028,NTF20009).
文摘As high amounts of new energy and electric vehicle(EV)charging stations are connected to the distribution network,the voltage deviations are likely to occur,which will further affect the power quality.It is challenging to manage high quality voltage control of a distribution network only relying on the traditional reactive power control mode.If the reactive power regulation potentials of new energy and EVs can be tapped,it will greatly reduce the reactive power optimization pressure on the network.Keeping this in mind,our reasearch first adds EVs to the traditional distribution network model with new forms of energy,and then a multi-objective optimization model,with achieving the lowest line loss,voltage deviation,and the highest static voltage stability margin as its objectives,is constructed.Meanwihile,the corresponding model parameters are set under different climate and equipment conditions.Ultimately,the optimization model under specific scenarios is obtained.Furthermore,considering the supply and demand relation-ship of the network,an improved technique for order preference by similarity to an ideal solution decision method is proposed,which aims to judge the adaptability of different algorithms to the optimized model,so as to select a most suitable algorithm for the problem.Finally,a comparison is made between the constructed model and a model without new energy.The results reveal that the constructed model can provide a high quality reactive power regula-tion strategy.
基金supported in part by National Key Basic Research Program of China(973 Program:2013CB228205)National Natural Science Foundation of China(51177051,51477055).
文摘A hierarchically correlated equilibrium Q-learning(HCEQ)algorithm for reactive power optimization that considers carbon emission on the grid-side as an optimization objective,is proposed here.Based on the multi-area decentralized collaborative framework,the controllable variables in each region are divided into several optimization layers,which is an effective method for solving the limitations posed by dimensionality.The HCEQ provides constant information on the interaction between the state-action value function matrices,as well as on the cooperative game equilibrium among agents in each region.After acquiring the optimal value function matrix in the pre-learning process,HCEQ is able to quickly achieve an optimal solution online.Simulation of the IEEE 57-bus system is performed,which demonstrates that the proposed algorithm can effectively solve multi-area decentralized collaborative reactive power optimization,with the desired global search capabilities and convergence speed.
基金supported by the Natural Science Foundation of Zhejiang Province(No.LY13F040003)the National Natural Science Foundation of China(Nos.61234002,61306041)the K.C.Wong Magna Fund in Ningbo University
文摘Based on the research of population migration algorithms （PMAs）, a population migration genetic algo- rithm （PMGA） is proposed, combining a PMA with a genetic algorithm. A scheme of area and power optimization for a ternary FPRM circuit is proposed by using the PMGA. Firstly, according to the ternary FPRM logic function expression, area and power estimation models are established. Secondly, the PMGA is used to search for the best area and power polarity. Finally, 10 MCNC Benchmark circuits are used to verify the effectiveness of the proposed method. The results show that the ternary FPRM circuits optimized by the PMGA saved 13.33% area and 20.00% power on average than the corresponding FPRM circuits optimized by a whole annealing genetic algorithm.
文摘In this paper, we consider the power optimization problem in Orthogonal Frequency Division Multiplexing （OFDM）-based relay-enhanced device-to-device （D2D） communication. In a single cell transmission scenario, dual- hop communication is assumed in which each D2D user re-uses the spectrum of just one Cellular User （CU）. In this work, we formulate a joint optimization scheme under a Decode-and-Forward （DF） relaying protocol to maximize the sum throughput of D2D and cellular networks via power allocation over different sub-carriers. The problem is thus transformed into a standard convex optimization, subject to individual power constraints at different transmitting nodes. We exploit the duality theory to decompose the problem into several sub-problems and use Karush-Kuhn- Tucker （KKT） conditions to solve each sub-problem. We provide simulation results to validate the performance of our proposed scheme.
基金the Fundamental Research Funds for the Central Universities of China(No.2682017CX050).
文摘The permanent magnet electromagnetic hybridmagnet (PEHM) has the advantages of low energy consumptionand a large suspension air gap. In this study, an unbalancedPEHM structure is proposed, which combines the advantages ofthe previous hybrid magnet structure. First, by establishing themagnetic circuit model of the new hybrid magnet structure, theinfluence of magnetic field distribution on the working magneticcircuit of the magnet is analyzed, and the method of magneticforce correction calculation of the new structure magnet isgiven. Then, the validity of the magnetic calculation method isverified by the 3D finite element method (FEM). Furthermore, theaverage suspension power force ratio is used as the optimizationgoal, and the system parameters of the hybrid magnet under aworking air gap of 6–10 mm and a load condition of 15000–20000 N are optimized by a genetic algorithm. Compared withthe previous hybrid magnet, the optimized hybrid magnet systemcan maintain lower power consumption under comprehensiveworking conditions.
