Comprehensive evaluation of 5G+smart distribution network based on combined weighting method-cloud model

With the large-scale application of 5G technology in smart distribution networks,the operation effects of distribution networks are not clear.Herein,we propose a comprehensive evaluation model of a 5G+smart distribution network based on the combination weighting and cloud model of the improved Fuzzy Analytic Hierarchy-Entropy Weight Method(FAHP-EWM).First,we establish comprehensive evaluation indexes of a 5G+smart distribution network from five dimensions:reliable operation,economic operation,efficient interaction,technological intelligence,and green emission reduction.Second,by introducing the principle of variance minimization,we propose a combined weighting method based on the improved FAHP-EWM to calculate the comprehensive weight,so as to reduce the defects of subjective arbitrariness and promote objectivity.Finally,a comprehensive evaluation model of 5G+smart distribution network based on cloud model is proposed by considering the uncertainty of distribution network node information and equipment status information.The example analysis indicates that the overall operation of the 5G+smart distribution network project is decent,and the weight value calculated by the combined weighting method is more reasonable and accurate than that calculated by the single weighting method,which verifies the effectiveness and rationality of the proposed evaluation method.Moreover,the proposed evaluation method has a certain guiding role for the large-scale application of 5G communication technology in smart distribution networks.

Quick Hosting Capacity Evaluation Based on Distributed Dispatching for Smart Distribution Network Planning with Distributed Generation

The smart distribution network(SDN)is integrat ing increasing distributed generation(DG)and energy storage(ES).Hosting capacity evaluation is important for SDN plan ning with DG.DG and ES are usually invested by users or a third party,and they may form friendly microgrids(MGs)and operate independently.Traditional centralized dispatching meth od no longer suits for hosting capacity evaluation of SDN.A quick hosting capacity evaluation method based on distributed optimal dispatching is proposed.Firstly,a multi-objective DG hosting capacity evaluation model is established,and the host ing capacity for DG is determined by the optimal DG planning schemes.The steady-state security region method is applied to speed up the solving process of the DG hosting capacity evalua tion model.Then,the optimal dispatching models are estab lished for MG and SDN respectively to realize the operating simulation.Under the distributed dispatching strategy,the dual-side optimal operation of SDN-MGs can be realized by several iterations of power exchange requirement.Finally,an SDN with four MGs is conducted considering multiple flexible resources.It shows that the DG hosting capacity of SDN oversteps the sum of the maximum active power demand and the rated branch capacity.Besides,the annual DG electricity oversteps the maximum active power demand value.

Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart

False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad data.Existing FDIA detection methods usually employ complex neural networkmodels to detect FDIA attacks.However,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection model.To address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative confrontation.Furthermore,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal characteristics.Finally,efficient FDIA attack samples can be sequentially generated through interactive adversarial learning.Extensive simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability.

A novel PLC channel modeling method and channel characteristic analysis of a smart distribution grid

Power line carrier(PLC)technology plays an increasingly important role in the realization of cost-effective communication in a smart distribution grid.No current channel modeling method is universally applicable to more complex topologies that may emerge in smart grids,such as ring and mesh topologies.This paper presents a novel PLC channel modeling method based on the information node concept,and the universality and feasibility of the proposed method are demonstrated with applications in modeling networks with ring and mesh topologies.The factors that affect the channel characteristics of the networks and the laws that govern their behaviors for different types of topologies are analyzed.The validity and effectiveness of the proposed method are proven using simulation and laboratory tests.This paper provides the necessary theoretical basis and technical means to design the PLC modulation method for smart distribution grids.

Linear diophantine uncertain linguistic-based prospect theory approach for performance evaluation of islanded microgrid-system scenarios

Renewable-energy-based hybrid microgrids can aid in achieving one of the United Nations Sustainable Development Goals,i.e.‘Affordable and clean energy’.However,experts may be faced with the challenge of selecting the best one for the electrification of an area.To avoid the challenge and realize the ultimate goal of the United Nations,the present study,therefore,proposes a novel pros-pect theory-based decision-making approach to help experts in opting for the best microgrid scenario.The proposed decision-making framework considers the risk appetite of the decision-maker,a quintessential aspect of the process.Linear diophantine uncertain lin-guistic sets are used to model the linguistic evaluations from the experts.The information from different experts is aggregated using a linear diophantine uncertain linguistic power Einstein-weighted geometric operator.Finally,the prospect-theory-based TOmada de Decisao Interativa Multicriterio approach is employed to evaluate the performance of the available microgrid scenarios and hence opt for the best microgrid scenario.The proposed framework has been used to evaluate the performance of seven possible microgrid scenarios and hence select the best one that can be implemented for rural electrification of a remote village in Assam,India.The microgrid scenario consisting of a photovoltaic-wind turbine-fuel cell-battery converter(MG_(3))has been revealed to be the best scen-ario among the seven considered microgrid scenarios.The validity of the obtained ranking results has been adjudged through a com-prehensive evaluation regarding the attenuation factor and the weights of the criteria.Moreover,previous case studies have also been solved using the proposed methodology and the results reveal a good correlation between the obtained ranking results.

Optimal cost and feasible design for gridconnected microgrid on campus area using the robust-intelligence method

In this paper,a robust optimization and sustainable investigation are undertaken to find a feasible design for a microgrid in a campus area at minimum cost.The campus microgrid needs to be optimized with further investigation,especially to reduce the cost while considering feasibility in ensuring the continuity of energy supply.A modified combination of genetic algorithm and particle swarm optimization(MGAPSO)is applied to minimize the cost while considering the feasibility of a grid-connected photovoltaic/battery/diesel system.Then,a sustainable energy-management system is also defined to analyse the characteristics of the microgrid.The optimization results show that the MGAPSO method produces a better solution with better convergence and lower costs than conventional methods.The MGAPSO optimization reduces the system cost by up to 11.99%compared with the conventional methods.In the rest of the paper,the components that have been optimized are adjusted in a realistic scheme to discuss the energy profile and allocation characteristics.Further investigation has shown that MGAPSO can optimize the campus microgrid to be self-sustained by enhancing renewable-energy utilization.

A review on non-isolated low-power DC-DC converter topologies with high output gain for solar photovoltaic system applications

The major challenges of the high-gain DC-DC boost converters are high-voltage stress on the switch,extreme duty ratio operation,diode reverse-recovery and converter efficiency problems.There are many topologies of high-gain converters that have been widely developed to overcome those problems,especially for solar photovoltaic(PV)power-system applications.In this paper,20 high-gain and low-power DC-DC converter topologies are selected from many topologies of available literature.Then,seven prospective topologies with conversion ratios of>15 are thoroughly reviewed and compared.The selected topologies are:(i)voltage-multiplier cell,(ii)voltage doubler,(iii)coupled inductor,(iv)converter with a coupled inductor and switch capacitor,(v)converter with a switched inductor and switched capacitor,(vi)cascading techniques and(vii)voltage-lift techniques.Each topology has its advantages and disadvantages.A comparison of the seven topologies is provided in terms of the number of components,hardware complexity,maximum converter efficiency and voltage stress on the switch.These are presented in detail.So,in the future,it will be easier for researchers and policymakers to choose the right converter topologies and build them into solar PV systems based on their needs.

Use of solar PV inverters during night-time for voltage regulation and stability of the utility grid

Photovoltaic(PV)inverters are vital components for future smart grids.Although the popularity of PV-generator installations is high,their effective performance remains low.Certain inverters are designed to operate in volt-ampere reactive(VAR)mode during the night.Yet,this approach is ineffective due to the consumption of active power from the grid(as internal losses)and the regulation necessity of the direct-current(DC)bus.This paper will demonstrate the operation of a PV inverter in reactive power-injection mode when solar energy is unavailable.The primary focus is on the design of the inverter controller with respect to the synchronous rotating frame control method.The proposed novel method enables an inverter to inject the required level of reactive power to regulate the voltage levels of the utility grid within specified limits.In the process,the inverter does not absorb active power from the grid for its internal operation.The presented model has the ability to inject≤2 kVAR of reactive power at zero power factor without absorbing active power from the grid.Simulation and hardware models of the inverter were developed and tested for different reactive loads in which the hardware model represented the real-world application.The reactive power injection of the two models ran at zero power factor and produced the expected outcomes for their corresponding independent reactive loads.Henceforth,it was evident that the proposed method can enhance the efficiency of an inverter and ensure the stability of the utility grid to which it is connected.