An Approach to Harmonic State Estimation of Power System

This paper presents the harmonic state estimation (HSE) based on the Total Least Squares (TLS) through comprehensively considering the harmonic network parameter error and measurement system error. The proposed approach is tested on the IEEE 14–bus harmonic testing system. The satisfied results are obtained.

Short-term Residential Load Forecasting Based on K-shape Clustering and Domain Adversarial Transfer Network

In recent years,the expansion of the power grid has led to a continuous increase in the number of consumers within the distribution network.However,due to the scarcity of historical data for these new consumers,it has become a complex challenge to accurately forecast their electricity demands through traditional forecasting methods.This paper proposes an innovative short-term residential load forecasting method that harnesses advanced clustering,deep learning,and transfer learning technologies to address this issue.To begin,this paper leverages the domain adversarial transfer network.It employs limited data as target domain data and more abundant data as source domain data,thus enabling the utilization of source do-main insights for the forecasting task of the target domain.Moreover,a K-shape clustering method is proposed,which effectively identifies source domain data that align optimally with the target domain,and enhances the forecasting accuracy.Subsequently,a composite architecture is devised,amalgamating attention mechanism,long short-term memory network,and seq2seq network.This composite structure is integrated into the domain adversarial transfer network,bolstering the performance of feature extractor and refining the forecasting capabilities.An illustrative analysis is conducted using the residential load dataset of the Independent System Operator to validate the proposed method empirically.In the case study,the relative mean square error of the proposed method is within 30 MW,and the mean absolute percentage error is within 2%.A signifi-cant improvement in accuracy,compared with other comparative experimental results,underscores the reliability of the proposed method.The findings unequivocally demonstrate that the proposed method advocated in this paper yields superior forecasting results compared with prevailing mainstream forecast-ing methods.

A Non-Iterative Network Utilization Pricing Mechanism for P2P Energy Trading

To sustain operation and maintenance of an active distribution network(ADN),a network fee should be charged by the distribution network service provider(DNSP)for facilitating the P2P energy trading service.To this end,this paper models the interaction among the DNSP and multiple prosumers as a Stackelberg game,and then develops a non-iterative and decentralized transactive mechanism to simultaneously achieve optimal network utilization pricing and peer-to-peer(P2P)trading.Simulation results in an ADN with four prosumers connected to a common substation bus validate the effectiveness and efficiency of the proposed scheme.

A Two-stage Stochastic Mixed-integer Programming Model for Resilience Enhancement of Active Distribution Networks

Most existing distribution networks are difficult to withstand the impact of meteorological disasters. With the development of active distribution networks(ADNs), more and more upgrading and updating resources are applied to enhance the resilience of ADNs. A two-stage stochastic mixed-integer programming(SMIP) model is proposed in this paper to minimize the upgrading and operation cost of ADNs by considering random scenarios referring to different operation scenarios of ADNs caused by disastrous weather events. In the first stage, the planning decision is formulated according to the measures of hardening existing distribution lines, upgrading automatic switches, and deploying energy storage resources. The second stage is to evaluate the operation cost of ADNs by considering the cost of load shedding due to disastrous weather and optimal deployment of energy storage systems(ESSs) under normal weather condition. A novel modeling method is proposed to address the uncertainty of the operation state of distribution lines according to the canonical representation of logical constraints. The progressive hedging algorithm(PHA) is adopted to solve the SMIP model. The IEEE 33-node test system is employed to verify the feasibility and effectiveness of the proposed method. The results show that the proposed model can enhance the resilience of the ADN while ensuring economy.

Cooperation Mechanism Design for Integrated Electricity‑Heat Systems with Information Asymmetry

Cooperation between electric power networks(EPNs)and district heating networks(DHNs)has been extensively studied under the assumption that all information exchanged is authentic.However,EPNs and DHNs belonging to different entities may result in marketing fraud.This paper proposes a cooperation mechanism for integrated electricity-heat systems(IEHSs)to overcome information asymmetry.First,a fraud detection method based on multiparametric programming with guaranteed feasibility reveals the authenticity of the information.Next,all honest entities are selected to form a coalition.Furthermore,to maintain operational independence and distribute benefits fairly,Benders decomposition is enhanced to calculate Shapley values in a distributed fashion.Finally,the cooperative surplus generated by the coalition is allocated according to the marginal contribution of each entity.Numerical results show that the proposed mechanism stimulates cooperation while achieving Pareto optimality under asymmetric information.

Day-ahead Network-constrained Unit Commitment Considering Distributional Robustness and Intraday Discreteness:A Sparse Solution Approach

Quick-start generation units are critical devices and flexible resources to ensure a high penetration level of renewable energy in power systems.By considering the wind uncertainty and both binary and continuous decisions of quickstart generation units within the intraday dispatch,we develop a Wasserstein-metric-based distributionally robust optimization model for the day-ahead network-constrained unit commitment(NCUC)problem with mixed-integer recourse.We propose two feasible frameworks for solving the optimization problem.One approximates the continuous support of random wind power with a finite number of events,and the other leverages the extremal distributions instead.Both solution frameworks rely on the classic nested column-and-constraint generation(C&CG)method.It is shown that due to the sparsity of L_(1)-norm Wasserstein metric,the continuous support of wind power generation could be represented by a discrete one with a small number of events,and the rendered extremal distributions are sparse as well.With this reduction,the distributionally robust NCUC model with complicated mixed-integer recourse problems can be efficiently handled by both solution frameworks.Numerical studies are carried out,demonstrating that the model considering quick-start generation units ensures unit commitment(UC)schedules to be more robust and cost-effective,and the distributionally robust optimization method captures the wind uncertainty well in terms of out-of-sample tests.

Probabilistic Residential Load Forecasting with Sequence-to-sequence Adversarial Domain Adaptation Networks

Lately,the power demand of consumers is increasing in distribution networks,while renewable power generation keeps penetrating into the distribution networks.Insufficient data make it hard to accurately predict the new residential load or newly built apartments with volatile and changing time-series characteristics in terms of frequency and magnitude.Hence,this paper proposes a short-term probabilistic residential load forecasting scheme based on transfer learning and deep learning techniques.First,we formulate the short-term probabilistic residential load forecasting problem.Then,we propose a sequence-to-sequence(Seq2Seq)adversarial domain adaptation network and its joint training strategy to transfer generic features from the source domain(with massive consumption records of regular loads)to the target domain(with limited observations of new residential loads)and simultaneously minimize the domain difference and forecasting errors when solving the forecasting problem.For implementation,the dominant techniques or elements are used as the submodules of the Seq2Seq adversarial domain adaptation network,including the Seq2Seq recurrent neural networks(RNNs)composed of a long short-term memory(LSTM)encoder and an LSTM decoder,and quantile loss.Finally,this study conducts the case studies via multiple evaluation indices,comparative methods of classic machine learning and advanced deep learning,and various available data of the new residentical loads and other regular loads.The experimental results validate the effectiveness and stability of the proposed scheme.

An enhanced cascading failure model integrating data mining technique

An enhanced cascading failure model integrating data mining technique is proposed in this paper.In order to better simulate the process of cascading failure propagation and further analyze the relationship between failure chains,in view of a basic framework of cascading failure described in this paper,some significant improvements in emerging prevention and control measures,the subsequent failure search strategy as well as the statistical analysis for the failure chains are made elaborately.Especially,a sequential pattern mining model is employed to find out the association pertinent to the obtained failure chains.In addition,a cluster analysis model is applied to evaluate the relationship between the intermediate data and the consequence of obtained failure chain,which can provide the prediction in potential propagation path of cascading failure to reduce the risk of catastrophic events.Finally,the case studies are conducted on the IEEE 10-machine-39-bus test system as benchmark to demonstrate the validity and effectiveness of the proposed enhanced cascading failure model.Some preliminary concluding remarks and comments are drawn.

Comprehensive Evaluation of Electric Power Prediction Models Based on D-S Evidence Theory Combined with Multiple Accuracy Indicators

A comprehensive evaluation method of electric power prediction models using multiple accuracy indicators is proposed.To obtain the preferred models,this paper selects a number of accuracy indicators that can reflect the accuracy of single-point prediction and the correlation of predicted data,and carries out a comprehensive evaluation.First,according to Dempster-Shafer(D-S)evidence theory,a new accuracy indicator based on the relative error(RE)is proposed to solve the problem that RE is inconsistent with other indicators in the quantity of evaluation values and cannot be adopted at the same time.Next,a new dimensionless method is proposed,which combines the efficiency coefficient method with the extreme value method to unify the accuracy indicator into a dimensionless positive indicator,to avoid the conflict between pieces of evidence caused by the minimum value of zero.On this basis,the evidence fusion is used to obtain the comprehensive evaluation value of each model.Then,the principle and the process of consistency checking of the proposed method using the entropy method and the linear combination formula are described.Finally,the effectiveness and the superiority of the proposed method are validated by an illustrative instance.

Detection of False Data Injection Attacks on Load Frequency Control System with Renewable Energy Based on Fuzzy Logic and Neural Networks

Load frequency control(LFC)system may be destroyed by false data injection attacks(FDIAs)and consequently the security of the power system will be impacted.High-efficiency FDIA detection can reduce the damage and power loss to the power system.This paper defines various typical and hybrid FDIAs,and the influence of several FDIAs with different characteristics on the multi-area LFC system is analyzed.To detect various attacks,we introduce an improved data-driven method,which consists of fuzzy logic and neural networks.Fuzzy logic has the features of high applicability,robustness,and agility,which can make full use of samples.Further,we construct the LFC system on MATLAB/Simulink platform,and systematically simulate the experiments that FDIAs affect the LFC system by tampering with measurement data.Among them,considering the large-scale penetration of renewable energy with intermittency and volatility,we generate three simulation scenarios with or without renewable energy generation.Then,the performance for detecting FDIAs of the improved method is verified by simulation data samples.

Guest Editorial

We are very pleased to present to you the special section of the Journal of Modern Power Systems and Clean Energy on the coordinated planning,operation,and control of electricity and natural gas infrastructures.This special section aims at addressing the existing challenges in integrated planning,operation and control of natural gas and electric power systems that will enhance the resilience,economics,efficiency,reliability sustainability,and security of both infrastructures.We had invited original submissions from various countries focusing on the computational and technological aspects of the integrated natural gas and electric power systems.