mlsa-iai-msu-lab/ru_sci_bench_zho_cite_reranking huggingface dataset:
Stochastic Linear Programming for Optimal Planning of Battery Storage Systems Under Unbalanced-uncertain Conditions

Battery energy storage system (BESS) has already been studied to deal with uncertain parameters of the electrical systems such as loads and renewable energies.However, the BESS have not been properly studied under unbalanced operation of power grids.This paper aims to study the modelling and operation of BESS under unbalanced-uncertain conditions in the power grids.The proposed model manages the BESS to optimize energy cost, deal with load uncertainties, and settle the unbalanced loading at the same time.The three-phase unbalanced- uncertain loads are modelled and the BESSs are utilized to produce separate charging/discharging pattern on each phase to remove the unbalanced condition.The IEEE 69-bus grid is considered as case study.The load uncertainty is developed by Gaussian probability function and the stochastic programming is adopted to tackle the uncertainties.The model is formulated as mixed-integer linear programming and solved by GAMS/ CPLEX.The results demonstrate that the model is able to deal with the unbalanced-uncertain conditions at the same time.The model also minimizes the operation cost and satisfies all security constraints of power grid.
Data-driven Transient Stability Assessment Model Considering Network Topology Changes via Mahalanobis Kernel Regression and Ensemble Learning

Transient stability assessment (TSA) is of great importance in power system operation and control.One of the usual tasks in TSA is to estimate the critical clearing time (CCT) of a given fault under the given network topology and pre-fault power flow.Data-driven methods try to obtain models describing the mapping between these factors and the CCT from a large number of samples.However, the influence of network topology on CCT is hard to be analyzed and is often ignored, which makes the models inaccurate and unpractical.In this paper, a novel data-driven TSA model combining Mahalanobis kernel regression and ensemble learning is proposed to deal with the problem.The model is a weighted sum of several sub-models.Each sub-model only uses the data of one topology to construct a kernel regressor.The weights are determined by both the topological similarity and numerical similarity between the samples.The similarities are decided by the parameters in Mahalanobis distance, and the parameters are to be trained.To reduce the model complexity, sub-models within the same topology category share the same parameters.When estimating CCT, the model uses not only the sub-model which the sample topology belongs to, but also other sub-models.Thus, it avoids the problem that there may be too few data under some topologies.It also efficiently utilizes information of data under all the topologies.Moreover, its decision-making process is clear and understandable, and an effective training algorithm is also designed.Test results on both the IEEE 10-machine 39-bus and a real system verify the effectiveness of the proposed model.
Nature-inspired Hybrid Optimization Algorithms for Load Flow Analysis of Islanded Microgrids

Load flow analysis is a significant tool for proper planning, operation, and dynamic analysis of a power system that provides the steady-state values of voltage magnitudes and angles at the fundamental frequency.However, due to the absence of a slack bus in an islanded microgrid, modified load flow algorithms should be adopted considering the system frequency as one of the solution variables.This paper proposes the application of nature-inspired hybrid optimization algorithms for solving the load flow problem of islanded microgrids.Several nature-inspired algorithms such as genetic algorithm (GA), differential evolution (DE), flower pollination algorithm (FPA), and grasshopper optimization algorithm (GOA) are separately merged with imperialistic competitive algorithm (ICA) to form four hybrid algorithms named as ICGA, ICDE, ICFPA, and ICGOA.Performances of these algorithms are tested on the 6-bus test system and the modified IEEE 37-bus test system.A comparison among the proposed algorithms is carried out in terms of statistical analysis conducted using SPSS statistics software.From the statistical analysis, it is identified that on an average, ICDE takes less number of iterations and consequently needs less execution time compared with other algorithms in solving the load flow problem of islanded microgrids.
Optimal Energy Management and Techno-economic Analysis in Microgrid with Hybrid Renewable Energy Sources

Microgrids with hybrid renewable energy sources are increasing and it is a promising solution to electrify remote areas where distribution network expansion is not feasible or not economical.Standalone microgrids with environment-friendly hybrid energy sources is a cost-effective solution that ensures system reliability and energy security.This paper determines the optimal capacity, energy dispatching and techno-economic benefits of standalone microgrid in remote area in Tamilnadu, India.Microgrids with hybrid energy sources comprising photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS) and diesel generator (DG) are considered in this paper.Various case studies are implemented with hybrid energy sources and for each case study a comparative analysis of techno-economic benefits is demonstrated.Eight different configurations of hybrid energy sources are modeled with renewable fractions of 50%, 60%, 65%, and 100%, respectively.The optimization analysis is carried out using Hybrid Optimization Model for Electric Renewable (HOMER) software.Impact of demand response is also demonstrated on energy dispatching and technoeconomic benefits.Simulation results are obtained for the optimal capacity of PV, WT, DG, converter, and BESS, charging/discharging pattern, state of charge (SOC), net present cost (NPC), cost of energy (COE), initial cost, operation cost, fuel cost, greenhouse gas emission penalty and payback period considering seasonal load variation.It is observed that PV+BESS is the most economical configuration.COE in standalone microgrid is higher than the conventional grid price.The results show that CO_2 emissions in hybrid PV+WT+DG+BESS are reduced by about 68% compared with the traditional isolated distribution system with DG.
An End-to-end Transient Recognition Method for VSC-HVDC Based on Deep Belief Network

Lightning is one of the most common transient interferences on overhead transmission lines of high-voltage direct current (HVDC) systems.Accurate and effective recognition of faults and disturbances caused by lightning strokes is crucial in transient protections such as traveling wave protection.Traditional recognition methods which adopt feature extraction and classification models rely heavily on the performance of signal processing and practical operation experiences.Misjudgments occur due to the poor generalization performance of recognition models.To improve the recognition rates and reliability of transient protection, this paper proposes a transient recognition method based on the deep belief network.The normalized line-mode components of transient currents on HVDC transmission lines are analyzed by a deep belief network which is properly designed.The feature learning process of the deep belief network can discover the inherent characteristics and improve recognition accuracy.Simulations are carried out to verify the effectiveness of the proposed method.Results demonstrate that the proposed method performs well in various scenarios and shows higher potential in practical applications than traditional machine learning based ones.
Coalition Formation of Microgrids with Distributed Energy Resources and Energy Storage in Energy Market

Power grids include entities such as home-microgrids (H-MGs), consumers, and retailers, each of which has a unique and sometimes contradictory objective compared with others while exchanging electricity and heat with other H-MGs.Therefore, there is the need for a smart structure to handle the new situation.This paper proposes a bilevel hierarchical structure for designing and planning distributed energy resources (DERs) and energy storage in H-MGs by considering the demand response (DR).In general, the upper-level structure is based on H-MG generation competition to maximize their individual and/or group income in the process of forming a coalition with other H-MGs.The upper-level problem is decomposed into a set of low-level market clearing problems.Both electricity and heat markets are simultaneously modeled in this paper.DERs, including wind turbines (WTs), combined heat and power (CHP) systems, electric boilers (EBs), electric heat pumps (EHPs), and electric energy storage systems, participate in the electricity markets.In addition, CHP systems, gas boilers (GBs), EBs, EHPs, solar thermal panels, and thermal energy storage systems participate in the heat market.Results show that the formation of a coalition among H-MGs present in one grid will not only have a significant effect on programming and regulating the value of the power generated by the generation resources, but also impact the demand consumption and behavior of consumers participating in the DR program with a cheaper market clearing price.
Mathematical Representation of WECC Composite Load Model

Composite load model of Western Electricity Coordinating Council (WECC) is a newly developed load model that has drawn great interest from the industry.To analyze its dynamic characteristics with both mathematical and engineering rigors, a detailed mathematical model is needed.Although composite load model of WECC is available in commercial software as a module and its detailed block diagrams can be found in several public reports, there is no complete mathematical representation of the full model in literature.This paper addresses a challenging problem of deriving detailed mathematical representation of composite load model of WECC from its block diagrams.In particular, we have derived the mathematical representation of the new DER_A model.The developed mathematical model is verified using both MATLAB and PSS/E to show its effectiveness in representing composite load model of WECC.The derived mathematical representation serves as an important foundation for parameter identification, order reduction and other dynamic analysis.
A Dispatching Method for Integrated Energy System Based on Dynamic Time-interval of Model Predictive Control

In integrated energy systems (IESs), traditional fixed time-interval dispatching scheme is unable to adapt to the need of dynamic properties of the transient network, demand response characteristics, dispatching time scales in energy subsystems and renewable power uncertainties.This scheme may easily result in uneconomic source-grid-load-storage operations in IES.In this paper, we propose a dispatching method for IES based on dynamic time-interval of model predictive control (MPC).We firstly build models for energy sub-systems and multi-energy loads in the power-gas-heat IES.Then, we develop an innovative optimization method leveraging trajectory deviation control, energy control, and cost control frameworks in MPC to handle the requirements and constraints over the timeinterval of dispatching.Finally, a dynamic programming algorithm is introduced to efficiently solve the proposed method.Experiments and simulation results prove the effectiveness of the method.
Review of Real-time Simulation of Power Electronics

Real-time simulation of power electronics has been recognized by the industry as an effective tool for developing power electronic devices and systems.Since there is no energy transfer during the course of the usage, real-time simulation has a lot of advantages in the process of development and experimentation.From the perspective of real-time simulation, this paper focuses on the main problems in modeling accuracy, system bandwidth and stability, limitations on communication interface and energy interface, and the cost of platform construction.Finally, we provide further research directions.
Electro-mechanical Modeling of Wind Turbine and Energy Storage Systems with Enhanced Inertial Response

In this paper, a coordinated control scheme for wind turbine generator (WTG) and supercapacitor energy storage system (ESS) is proposed for temporary frequency supports.Inertial control is designed by using generator torque limit considering the security of WTG system, while ESS releases its energy to compensate the sudden active power deficit during the recovery process of turbine rotor.WTG is modeled using the fatigue, aerodynamic, structure, turbulence (FAST) code, which identifies the mechanical loadings of the turbine and addresses electro-mechanical interactions in the wind energy system.A damping controller is augmented to the inertial control to suppress severe mechanical oscillations in the shaft and tower of the turbine during frequency supports.Furthermore, the result of small-signal stability analysis shows that the WTGESS tends to improve the stability of the whole multi-energy power grid.The major contributions of this paper will be validated by utilizing the proposed control method that combines the grid support capability and maintaining the integrity of structural design of the turbine for normal operations.
Active Dynamic Aggregation Model for Distributed Integrated Energy System as Virtual Power Plant

Distributed integrated multi-energy systems (DIMSs) can be regarded as virtual power plants to provide additional flexibility to the power system.This paper proposes a robust active dynamic aggregation model for the DIMSs to describe the maximum feasible region.The aggregation model includes the power constraints, energy constraints, and ramping constraints to aggregate different types of resources in the DIMSs.The proposed generator-like and storage-like model does not depend on the ancillary service market and can be directly incorporated into the economic dispatch model of the power system.A novel algorithm based on the column-and-constraint generation algorithm and convex-concave procedure is proposed to solve the two-stage robust optimization problem, which is more efficient than the KKT-based algorithms.Finally, a case study of an actual DIMS is developed to demonstrate the effectiveness of the proposed model.
Cascading Failure Propagation Simulation in Integrated Electricity and Natural Gas Systems

The sharp increase in the total installed capacity of natural gas generators has intensified the dynamic interaction between the electricity and natural gas systems, which could induce cascading failure propagation across the two systems that deserves intensive research.Considering the distinct time response behaviors of the two systems, this paper discusses an integrated simulation approach to simulate the cascading failure propagation process of integrated electricity and natural gas systems (IEGSs).On one hand, considering instantaneous re-distribution of power flows after the occurrence of disturbance or failure, the steady-state AC power flow model is employed.On the other hand, gas transmission dynamics are represented by dynamic model to capture the details of its transition process.The interactions between the two systems, intensified by energy coupling components (such as gas-fired generator and electricity- driven gas compressor) as well as the switching among the operation modes of compressors during the cascading failure propagation process, are studied.An IEGS composed of the IEEE 30-bus electricity system and a 14-node 15-pipeline gas system is established to illustrate the effectiveness of the proposed simulation approach, in which two energy sub-systems are coupled by compressor and gas-fired generator.Numerical results clearly demonstrate that heterogeneous interactions between electricity and gas systems would trigger the cascading failure propagation between the two coupling systems.
Virtual Difference Voltage Scheme for Fault Detection in VSC-HVDC Transmission Systems

In this paper, a fast fault detection scheme for voltage source converter based high-voltage direct current (VSCHVDC) transmission systems is proposed.Based on Bergeron model equations, the remote terminal voltage of an adopted transmission system is calculated in terms of the local measured current and voltage signals.Subsequently, the computed voltage of the remote terminal is compared with the corresponding actual measured-communicated value.Provided that the considered transmission system is functioning well, the difference between the computed and measured voltages is almost zero.However, a considerable virtual voltage arises for fault conditions.When the voltage difference exceeds a predetermined threshold, a fault condition can be detected.Although a reliable communication link is required, the delay for detecting the fault is not caused by the communication time.For evaluation purpose, a detailed simulation is developed using PSCAD/EMTDC with various fault locations, including the cases near the inside or outside of the protected transmission system.The results corroborate a fast detection scheme depending on a moderate sampling/ processing frequency level.A high security level is verified even with the worst external faults, or with the misaligned measured samples at the terminals.This corroborates the suitability of the proposed scheme for protecting multi-terminal HVDC systems.
Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

With the growing integration of distributed energy resources (DERs), flexible loads, and other emerging technologies, there are increasing complexities and uncertainties for modern power and energy systems.This brings great challenges to the operation and control.Besides, with the deployment of advanced sensor and smart meters, a large number of data are generated, which brings opportunities for novel data-driven methods to deal with complicated operation and control issues.Among them, reinforcement learning (RL) is one of the most widely promoted methods for control and optimization problems.This paper provides a comprehensive literature review of RL in terms of basic ideas, various types of algorithms, and their applications in power and energy systems.The challenges and further works are also discussed.
System Modeling and Optimal Dispatching of Multi-energy Microgrid with Energy Storage

The coordinated operation and comprehensive utilization of multi-energy sources require systematic research.A multi-energy microgrid (MEMG) is a coupling system with multiple inputs and outputs.In this paper, a system model based on unified energy flows is proposed to describe the static relationship, and an analogue energy storage model is proposed to represent the time-dependency characteristics of energy transfer processes.Then, the optimal dispatching model of an MEMG is established as a mixed-integer linear programming (MILP) problem using piecewise linear approximation and convex relaxation.Finally, the system model and optimal dispatching method are validated in an MEMG, including district electricity, natural gas and heat supply, and renewable generation.The proposed model and method provide an effective way for the energy flow analysis and optimization of MEMGs.
Power Quality Assessment of Voltage Positive Feedback Based Islanding Detection Algorithm

Islanding refers to a condition where distributed generators (DGs) inject power solely to the local load after electrical separation from power grid.Several islanding detection methods (IDMs) categorized into remote, active, and passive groups have been reported to detect this undesirable state.In active techniques, a disturbance is injected into the DG's controller to drift a local yardstick out of the permissible range.Although this disturbance leads to more effective detections even in well-balanced island, it raises the total harmonic distortion (THD) of the output current under the normal operation conditions.This paper analyzes the power quality aspect of the modified sliding mode controller as a new active IDM for grid-connected photovoltaic system (GCPVS) with a string inverter.Its performance is compared with the voltage positive feedback (VPF) method, a well-known active IDM.This evaluation is carried out for a 1 kWp GCPVS in MATLAB/Simulink platform by measuring the output current harmonics and THD as well as the efficiency under various penetration and disturbance levels.The output results demonstrate that since the proposed disturbance changes the amplitude of the output current, it does not generate harmonics/subharmonics.Thereby, it has a negligible adverse effect on power quality.It is finally concluded that the performance of the sliding mode-based IDM is reliable from the standpoints of islanding detection and power quality.
Multi-time Scale Energy Management Strategy of Aggregator Characterized by Photovoltaic Generation and Electric Vehicles

The increasing number of photovoltaic (PV) generation and electric vehicles (EVs) on the load side has necessitated an aggregator (Agg) in power system operation.In this paper, an Agg is used to manage the energy profiles of PV generation and EVs.However, the daily management of the Agg is challenged by uncertain PV fluctuations.To address this problem, a robust multi-time scale energy management strategy for the Agg is proposed.In a day-ahead phase, robust optimization is developed to determine the power schedule.In a real-time phase, a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs.A case study indicates a good scheduling performance under an uncertain PV output.Through the convexification, the solving efficiency of the real-time operation model is improved, and the over-charging and over-discharging problems of EVs can be suppressed to a certain extent.Moreover, the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient.The strategy can ensure the flexibility of the Agg for real-time operation.
Decentralized State Estimation of Combined Heat and Power System Considering Communication Packet Loss

In order to obtain an accurate state estimation of the operation in the combined heat and power system, it is necessary to carry out state estimation.Due to the limited information sharing among various energy systems, it is practical to perform state estimation in a decentralized manner.However, the possible communication packet loss is seldomly considered among various energy systems.This paper bridges this gap by proposing a relaxed alternating direction method of multiplier algorithm.It can also improve the computation efficiency compared with the conventional alternating direction of the multiplier algorithm.Case studies of two test systems are carried out to show the validity and superiority of the proposed algorithm.
Fully Distributed State Estimation for Power System with Information Propagation Algorithm

In this paper, a new fully distributed state estimation (DSE) based on weighted least square (WLS) method and graph theory is proposed for power system.The proposed method is fully distributed so that the centralized facilities, e.g., supervisory control and data acquisition (SCADA) and centralized estimators, are not required.Also, different from the existing DSE methods, the proposed method is a bus-level DSE method, in which the power system is not required to be partitioned into several areas.In order to realize the proposed fully distributed DSE method, a novel information propagation algorithm is developed in this paper.This algorithm has great potential in future applications since it is useful to broadcast the local information of the nodes to the entire system in a fully distributed network.The proposed DSE method is compared with the conventional centralized state estimation method and existing multi-area DSE method in different models in this paper.The results show that the proposed method has better performance than the traditional methods.