Mina HaghighatMehdi NiroomandHossein Dehghani TaftiChristopher D.Townsend...
1-21页
查看更多>>摘要：To maximize conversion efficiency,photovoltaic(PV)systems generally operate in the maximum power point tracking(MPPT)mode.However,due to the increasing penetra-tion level of PV systems,there is a need for more developed control functions in terms of frequency support services and voltage control to maintain the reliability and stability of the power grid.Therefore,flexible active power control is a manda-tory task for grid-connected PV systems to meet part of the grid requirements.Hence,a significant number of flexible pow-er point tracking(FPPT)algorithms have been introduced in the existing literature.The purpose of such algorithms is to real-ize a cost-effective method to provide grid support functional-ities while minimizing the reliance on energy storage systems.This paper provides a comprehensive overview of grid support functionalities that can be obtained with the FPPT control of PV systems such as frequency support and volt-var control.Each of these grid support functionalities necessitates PV sys-tems to operate under one of the three control strategies,which can be provided with FPPT algorithms.The three control strate-gies are classified as:① constant power generation control(CP-GC),② power reserve control(PRC),and ③ power ramp rate control(PRRC).A detailed discussion on available FPPT algo-rithms for each control strategy is also provided.This paper can serve as a comprehensive review of the state-of-the-art FPPT algorithms that can equip PV systems with various grid support functionalities.
查看更多>>摘要：The subsynchronous oscillations(SSOs)related to renewable generation seriously affect the stability and safety of the power systems.To realize the dynamic monitoring of SSOs by utilizing the high computational efficiency and noise-resilient features of the matrix pencil method(MPM),this paper propos-es an improved SSO parameter identification with synchropha-sors based on MPM.The MPM is enhanced by the angular fre-quency fitting equations based on the characteristic polynomial coefficients of the matrix pencil to ensure the accuracy of the identified parameters,since the existing eigenvalue solution of the MPM ignores the angular frequency conjugation constraints of the two fundamental modes and two oscillation modes.Then,the identification and recovery of bad data are proposed by uti-lizing the difference in temporal continuity of the synchropha-sors before and after noise reduction.The proposed parameter identification is verified with synthetic,simulated,and actual measured phase measurement unit(PMU)data.Compared with the existing MPM,the improved MPM achieves better accuracy for parameter identification of each component in SSOs,better real-time performance,and significantly reduces the effect of bad data.
查看更多>>摘要：This paper proposes an empirical wavelet trans-form(EWT)based method for identification and analysis of sub-synchronous oscillation(SSO)modes in the power system using phasor measurement unit(PMU)data.The phasors from PMUs are preprocessed to check for the presence of oscilla-tions.If the presence is established,the signal is decomposed us-ing EWT and the parameters of the mono-components are esti-mated through Yoshida algorithm.The superiority of the pro-posed method is tested using test signals with known parame-ters and simulated using actual SSO signals from the Hami Power Grid in Northwest China.Results show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.
查看更多>>摘要：Electric power grids are evolving into complex cy-ber-physical power systems(CPPSs)that integrate advanced in-formation and communication technologies(ICTs)but face in-creasing cyberspace threats and attacks.This study considers CPPS cyberspace security under distributed denial of service(DDoS)attacks and proposes a nonzero-sum game-theoretical model with incomplete information for appropriate allocation of defense resources based on the availability of limited resourc-es.Task time delay is applied to quantify the expected utility as CPPSs have high time requirements and incur massive damage DDoS attacks.Different resource allocation strategies are adopt-ed by attackers and defenders under the three cases of attack-free,failed attack,and successful attack,which lead to a corre-sponding consumption of resources.A multidimensional node value analysis is designed to introduce physical and cybersecuri-ty indices.Simulation experiments and numerical results dem-onstrate the effectiveness of the proposed model for the appro-priate allocation of defense resources in CPPSs under limited re-source availability.
查看更多>>摘要：Multi-area combined economic/emission dispatch(MACEED)problems are generally studied using analytical functions.However,as the scale of power systems increases,ex-isting solutions become time-consuming and may not meet oper-ational constraints.To overcome excessive computational ex-pense in high-dimensional MACEED problems,a novel data-driven surrogate-assisted method is proposed.First,a cosine-similarity-based deep belief network combined with a back-propagation(DBN+BP)neural network is utilized to replace cost and emission functions.Second,transfer learning is applied with a pretraining and fine-tuning method to improve DBN+BP regression surrogate models,thus realizing fast con-struction of surrogate models between different regional power systems.Third,a multi-objective antlion optimizer with a novel general single-dimension retention bi-objective optimization poli-cy is proposed to execute MACEED optimization to obtain scheduling decisions.The proposed method not only ensures the convergence,uniformity,and extensibility of the Pareto front,but also greatly reduces the computational time.Finally,a 4-ar-ea 40-unit test system with different constraints is employed to demonstrate the effectiveness of the proposed method.
查看更多>>摘要：Wind power prediction interval(WPPI)models in the literature have predominantly been developed for and test-ed on specific case studies.However,wind behavior and charac-teristics can vary significantly across regions.Thus,a prediction model that performs well in one case might underperform in another.To address this shortcoming,this paper proposes an en-semble WPPI framework that integrates multiple WPPI models with distinct characteristics to improve robustness.Another im-portant and often overlooked factor is the role of probabilistic wind power prediction(WPP)in quantifying wind power uncer-tainty,which should be handled by operating reserve.Operat-ing reserve in WPPI frameworks enhances the efficacy of WPP.In this regard,the proposed framework employs a novel bi-lay-er optimization approach that takes both WPPI quality and re-serve requirements into account.Comprehensive analysis with different real-world datasets and various benchmark models validates the quality of the obtained WPPIs while resulting in more optimal reserve requirements.
查看更多>>摘要：This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic(PV)power prediction that arises due to insuffi-cient data samples for new PV plants.First,a time-series gener-ative adversarial network(TimeGAN)is used to learn the distri-bution law of the original PV data samples and the temporal correlations between their features,and these are then used to generate new samples to enhance the training set.Subsequently,a hybrid network model that fuses bi-directional long-short term memory(BiLSTM)network with attention mechanism(AM)in the framework of deep & cross network(DCN)is con-structed to effectively extract deep information from the origi-nal features while enhancing the impact of important informa-tion on the prediction results.Finally,the hyperparameters in the hybrid network model are optimized using the whale optimi-zation algorithm(WOA),which prevents the network model from falling into a local optimum and gives the best prediction results.The simulation results show that after data enhance-ment by TimeGAN,the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability.
查看更多>>摘要：The operation of integrated energy systems(IESs)is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy forms.Scenario generation is one of ordinary methods to allevi-ate the system uncertainties by extracting several typical scenar-ios to represent the original high-dimensional data.This paper proposes a novel representative scenario generation method based on the feature extraction of panel data.The original high-dimensional data are represented by an aggregated indicator matrix using principal component analysis to preserve temporal variation.Then,the aggregated indicator matrix is clustered by an algorithm combining density canopy and K-medoids.Togeth-er with the proposed scenario generation method,an optimal operation model of IES is established,where the objective is to minimize the annual operation costs considering carbon trading cost.Finally,case studies based on the data of Aachen,Germa-ny in 2019 are performed.The results indicate that the adjust-ed rand index(ARI)and silhouette coef'ficient(SC)of the pro-posed method are 0.6153 and 0.6770,respectively,both higher than the traditional methods,namely K-medoids,K-means++,and density-based spatial clustering of applications with noise(DBSCAN),which means the proposed method has better accu-racy.The error between optimal operation results of the IES ob-tained by the proposed method and all-year time series bench-mark value is 0.1％,while the calculation time is reduced from 11029 s to 188 s,which verifies that the proposed method can be used to optimize operation strategy of IES with high efficien-cy without loss of accuracy.
查看更多>>摘要：With the increasing proportion of renewable ener-gy sources(RESs)in power grid,the reserve resource(RR)scarcity for correcting power deviation of RESs has become a potential issue.Consequently,the power curve of RES needs to be more rigorously assessed.The RR scarcity varies during dif-ferent time periods,so the values of assessment indicators should be dynamically adjusted.The assessment indicators in this paper include two aspects,i.e.,deviation exemption ratio and penalty price.Firstly,this paper proposes a method for dy-namically calculating the supply capacity and RR cost,primari-ly taking into account the operating status of thermal units,forecast information of RES,and load curve.Secondly,after clarifying the logical relationship between the degree of RR scarcity and the values of assessment indicators,this paper es-tablishes a mapping function between them.Based on this map-ping function,a dynamic setting method for assessment indica-tors is proposed.In the future,RES will generally be equipped with battery energy storage systems(BESSs).Reasonably utiliz-ing BESSs to reduce the power deviation of RESs can increase the expected income of RESs.Therefore,this paper proposes a power curve optimization strategy for RESs considering self-owned BESSs.The case study demonstrates that the dynamic setting method of assessment indicators can increase the reve-nue of RESs while ensuring that the penalty fees paid by RESs to the grid are sufficient to cover the RR costs.Additionally,the power curve optimization strategy can help RESs further in-crease income and fully utilize BESSs to reduce power devia-tion.
Masoume MahmoodiSeyyed Mahdi Noori Rahim AbadiAhmad AttarhaPaul Scott...
115-127页
查看更多>>摘要：Moving away from fossil fuels towards renewable sources requires system operators to determine the capacity of distribution systems to safely accommodate green and distribut-ed generation(DG).However,the DG capacity of a distribution system is often underestimated due to either overly conservative electrical demand and DG output uncertainty modelling or ne-glecting the recourse capability of the available components.To improve the accuracy of DG capacity assessment,this paper proposes a distributionally adjustable robust chance-con-strained approach that utilises uncertainty information to re-duce the conservativeness of conventional robust approaches.The proposed approach also enables fast-acting devices such as inverters to adjust to the real-time realisation of uncertainty us-ing the adjustable robust counterpart methodology.To achieve a tractable formulation,we first define uncertain chance con-straints through distributionally robust conditional value-at-risk(CVaR),which is then reformulated into convex quadratic con-straints.We subsequently solve the resulting large-scale,yet con-vex,model in a distributed fashion using the alternating direc-tion method of multipliers(ADMM).Through numerical simula-tions,we demonstrate that the proposed approach outperforms the adjustable robust and conventional distributionally robust approaches by up to 15％and 40％,respectively,in terms of to-tal installed DG capacity.
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