The Static Stability Region of an Integrated Electricity-Gas System Considering Voltage and Gas Pressure

In an integrated electricity-gas system(IEGS),load fluctuations affect not only the voltage in the power system but also the gas pressure in the natural gas system.The static voltage stability region(SVSR)method is a tool for analyzing the overall static voltage stability in a power system.However,in an IEGS,the SVSR boundary may be overly optimistic because the gas pressure may collapse before the voltage collapses.Thus,the SVSR method cannot be directly applied to an IEGS.In this paper,the concept of the SVSR is extended to the IEGS-static stability region(SSR)while considering voltage and gas pressure.First,criteria for static gas pressure stability in a natural gas system are proposed,based on the static voltage stability criteria in a power system.Then,the IEGS-SSR is defined as a set of active power injections that satisfies multi-energy flow(MEF)equations and static voltage and gas pressure stability constraints in the active power injection space of natural gas-fired generator units(NGUs).To determine the IEGSSSR,a continuation MEF(CMEF)method is employed to trace the boundary point in one specific NGU scheduling direction.A multidimensional hyperplane sampling method is also proposed to sample the NGU scheduling directions evenly.The obtained boundary points are further used to form the IEGSSSR in three-dimensional(3D)space via a Delaunay triangulation hypersurface fitting method.Finally,the numerical results of typical case studies are presented to demonstrate that the proposed method can effectively form the IEGS-SSR,providing a tool for IEGS online monitoring and dispatching.

Prospects of key technologies of integrated energy systems for rural electrification in China

Owing to increasing environmental concerns and resource scarcity, integrated energy system shave become widely used in communities. Rural energy systems, as one of the important links of the energy network in China, suffer from low energy efficiency and weak infrastructure. Therefore, it is particularly important to increase the proportion of electricity consumption and build an integrated energy system for rural electrification in China(IESREIC) with a rural distribution network as the core, in line with national conditions. In this study, by analyzing the Chinese regional differences and natural resource endowments, the development characteristics of the IESREIC are summarized. Then, according to the existing rural energy problems, key technologies are proposed for the IESREIC, such as those for planning and operation, value sharing, infrastructure, and a management and control platform. Finally, IESREIC demonstration projects and business models are introduced for agricultural production, rural industrial systems, and rural life. The purpose is to propose research concepts for the IESREIC, provide suggestions for the development of rural energy, and provide a reference for the construction of rural energy systems in countries with characteristics similar to those of China.

Fabrication of CdS/SnS Heterojunction for Photovoltaic Application

SnS/CdS heterojunction is a promising system for photovoltaic application. SnS thin films were thermally evaporated onto CdS/ITO coated glass substrates. The structure of the device was glass/ ITO/CdS/SnS/In/Ag and I-V curves of the fabricated devices were measured under dark and illuminated conditions, respectively. We discussed the relationship of the thickness and annealing temperature of CdS buffer layers with the performance of SnS/CdS heterojunctions. The optimum thickness and annealing temperature of the CdS buffer layers were 50 nm and 350°C, respectively. The best device had a conversion efficiency of 0.0025%.

Distributionally robust optimization configuration method for island microgrid considering extreme scenarios

The marine climate conditions are intricate and variable. In scenarios characterized by high proportions of wind and solar energy access, the uncertainty regarding the energy sources for island microgrid is significantly exacerbated, presenting challenges to both the economic viability and reliability of the capacity configuration for island microgrids. To address this issue, this paper proposes a distributionally robust optimization (DRO) method for island microgrids, considering extreme scenarios of wind and solar conditions. Firstly, to address the challenge of determining the probability distribution functions of wind and solar in complex island climates, a conditional generative adversarial network (CGAN) is employed to generate a scenario set for wind and solar conditions. Then, by combining k-means clustering with an extreme scenario selection method, typical scenarios and extreme scenarios are selected from the generated scenario set, forming the scenario set for the DRO model of island microgrids. On this basis, a DRO model based on multiple discrete scenarios is constructed with the objective of minimizing the sum of investment costs, operation and maintenance costs, fuel purchase costs, penalty costs of wind and solar curtailment, and penalty costs of load loss. The model is subjected to equipment operation and power balance constraints, and solved using the columns and constraints generation (CCG) algorithm. Finally, through typical examples, the effectiveness of this paper’s method in balancing the economic viability and robustness of the configuration scheme for the island microgrid, as well as reducing wind and solar curtailment and load loss, is verified.

Electricity-Heat-Based Integrated Demand Response Considering Double Auction Energy Market with Multi-Energy Storage for Interconnected Areas

With development of integrated energy systems and energy markets,transactive energy has received increasing attention from society and academia,and realization of energy distribution and integrated demand response through market transactions has become a current research hotspot.Research on optimized operation of a distributed energy station as a regional energy supply center is of great significance for improving flexibility and reliability of the system.Based on retail-side energy trading market,this study first establishes a framework of combined electric and heating energy markets and analyses a double auction market mechanism model of interconnected distributed energy stations.This study establishes a mechanism model of energy market participants,and establishes the electric heating combined market-clearing model to maximize global surplus considering multi-energy storage.Finally,in the case study,a typical user energy consumption scenario in winter is selected,showing market-clearing results and demand response effects on a typical day.Impact of transmission line constraints,energy supply equipment capacity,and other factors on clearing results and global surplus are compared and analyzed,verifying the effects of the proposed method on improving global surplus,enhancing interests of market participants and realizing coordination and optimal allocation of both supply and demand resources through energy complementarity between regions.

CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics

Lithium batteries find extensive applications in energy storage.Temperature is a crucial indicator for assessing the state of lithium-ion batteries,and numerous experiments require thermal images of lithium-ion batteries for research purposes.However,acquiring thermal imaging samples of lithium-ion battery faults is challenging due to factors such as high experimental costs and associated risks.To address this,our study proposes the utilization of a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty and Residual Network(CWGAN-GP with Residual Network)to augment the dataset of thermal images depicting lithium-ion battery faults.We employ various evaluation metrics to quantitatively analyze and compare the generated thermal images of lithium-ion batteries.Subsequently,the expanded dataset,comprising four types of thermal images depicting lithium-ion battery faults,is input into a Mask Region-based Convolutional Neural Network for training.The results demonstrate that the proposed model surpasses both traditional Generative Adversarial Network and Wasserstein Generative Adversarial Network in terms of the quality of generated thermal images of lithium-ion batteries.Moreover,the augmentation of the dataset leads to an improvement in the fault diagnosis accuracy of the Mask Region-based Convolutional Neural Network.

Successive MISOCP Algorithm for Islanded Distribution Networks with Soft Open Points

An optimal operation scheme is of great significance in islanded distribution networks to restore critical loads and has recently attracted considerable attention.In this paper,an optimal power flow(OPF)model for islanded distribution networks equipped with soft open points(SOPs)is proposed.Unlike in the grid-connected mode,the adequacy of local power generation in distribution networks is critical for islanded systems.The proposed approach utilizes the power output of local distributed generations(DGs)and the benefits of reactive power compensation provided by SOPs to allow maximum loadability.To exploit the available resources,an optimal secondary droop control strategy is introduced for the islanded distribution networks,thereby minimizing load shedding.The formulated OPF problem is essentially a mixed-integer nonlinear programming(MINLP)model.To guarantee the computation efficiency and accuracy.A successive mixed-integer second-order cone programming(SMISOCP)algorithm is proposed for handling the nonlinear islanded power flow formulations.Two case studies,incorporating a modified IEEE 33-bus system and IEEE 123-bus system,are performed to test the effectiveness of the proposed approach.

Battery Storage Configuration of AC/DC Hybrid Distribution Networks

The upscaling requirements of energy transition highlight the urgent need for ramping up renewables and boosting system efficiencies.However,the stochastic nature of excessive renewable energy resources has challenged stable and efficient operation of the power system.Battery energy storage systems(BESSs)have been identified as critical to mitigate random fluctuations,unnecessary green energy curtailment and load shedding with rapid response and flexible connection.On the other hand,an AC/DC hybrid distribution system can offer merged benefits in both AC and DC subsystems without additional losses during AC/DC power conversion.Therefore,configuring BESSs on an AC/DC distribution system is wellpositioned to meet challenges brought by carbon reductions in an efficient way.A bi-level optimization model of BESS capacity allocation for AC/DC hybrid distribution systems,considering the flexibility of voltage source converters(VSCs)and power conversion systems(PCSs),has been established in this paper to address the techno-economic issues that hindered wide implementation.The large-scale nonlinear programming problem has been solved utilizing a genetic algorithm combined with second-order cone programming.Rationality and effectiveness of the model have been verified by setting different scenarios through case studies.Simulation results have demonstrated the coordinated operation of BESS and AC/DC hybrid systems can effectively suppress voltage fluctuations and improve the cost-benefit of BESSs from a life cycle angle.

A Modified Impact-increment-based State Enumeration Method and Its Application in Power Distribution Systems

Reliable planning and operation of power distribution systems are of great significance. In this paper, the impactincrement based state enumeration(IIBSE) method is modified to adapt to the features of distribution systems. With the proposed method, the expectation, probabilistic, and duration reliability indices can be accurately obtained with a lower enumerated order of contingency states. In addition, the time-consuming optimal power flow(OPF) calculation can be replaced by a simple matrix operation for both independent and radial series failure states. Therefore, the accuracy and efficiency of the assessment process are improved comprehensively. The case of RBTS bus 6 system and IEEE 123 node test feeder system are utilized to test the performance of the modified IIBSE. The results show the superiority of the proposed method over Monte Carlo(MC) sampling and state enumeration(SE) methods in distribution systems.

Review of Key Problems Related to Integrated Energy Distribution Systems

Integrated energy distribution system(IEDS)is one of the integrated energy and power system forms,which involves electricity/gas/cold/heat and other various energy forms.The energy coupling relationship is close and complex.IEDS is the focus of regional energy internet research and development at home and abroad.Compared with the traditional power distribution system,IEDS through the multi-energy coupling link comprehensive utilization,effectively improve the distribution system economy,safety,reliability,flexibility and toughness,but also to ease the regional energy system environmental pressure.IEDS is an important direction for the future development of energy systems,and its related research and practice on China’s energy system development also has important practical and strategic significance.This paper summarizes the related researches of the IEDS and explores the energy operation characteristics and coupling mechanisms.What’s more,the integrated model of IEDS is summarized.On these bases,this paper discusses and prospects some key issues such as joint planning,optimization control and security analysis,state estimation and situational awareness and generalized demand side management.

A preventive control strategy for static voltage stability based on an efficient power plant model of electric vehicles

With the increasing integration of wind farms and electric vehicles(EVs)in power systems,voltage stability is becoming more and more serious.Based on vehicle-to-grid(V2G),an efficient power plant model of EVs(E-EPP)was developed to estimate EV charging load with available corresponding response capacity under different charging strategies.A preventive control strategy based on E-EPP was proposed to maintain the static voltage stability margin(VSM)of power system above a predefined security level.Two control modes were used including the disconnection of EV charging load(‘V1G’mode)and the discharge of stored battery energy back to power grid(‘V2G’mode).A modified IEEE 14-bus system with high penetration of wind power and EVs was used to verify the effectiveness of preventive control strategy.Simulation results showed that the proposed strategy can not only improve the static voltage stability of power system with considerable wind generation,but also guarantee the travelling comfort for EV owners.

Dynamic frequency response from electric vehicles in the Great Britain power system

With the large penetration of renewable energy,fulfilling the balance between electricity demand and supply is a challenge to the modern power system.According to the UK government,the wind power penetration will reach 30%by the year 2020.The role of electric vehicles(EVs)contributing to frequency response was investigated.A dynamic frequency control strategy which considers the comfort level of vehicle owners was developed for EVs to regulate their power consumption according to the deviation of system frequency.A simulation model of a population of EVs equipped with such controlwas implemented inMatlab/Simulink platform.In this paper,a simplified Great Britain power system model is used to study the contribution of EVs to dynamic frequency control.The case study showed that using EVs as a demand response resource can greatly reduce the frequency deviations.And the rapid response from EVs can help reduce the operation cost of conventional generators.

Load curve smoothing strategy based on unified state model of different demand side resources

Renewable energy based distributed generation(DG) has the potential to reach high penetration levels in the residential region. However, its integration at the demand side will cause rapid power fluctuations of the tieline in the residential region. The traditional generators are generally difficult to manage rapid power fluctuations due to their insufficient efficiency requirements and low responding speed. With an effective control strategy, the demand side resources(DSRs) including DGs, electric vehicles and thermostatically-controlled loads at thedemand side, are able to serve as the energy storage system to smooth the load fluctuations. However, it is a challenge to properly model different types of DSRs. To solve this problem, a unified state model is first developed to describe the characteristics of different DSRs. Then a load curve smoothing strategy is proposed to offset the load fluctuations of the tie-line of the residential region, where a control matrix deduced from the unified state model is introduced to manage the power outputs of different DSRs,considering the response order and the comfort levels.Finally, a residential region with households is used to validate the load curve smoothing strategy based on the unified state model, and the results show that the power fluctuation rate of the tie-line is significantly decreased.Meanwhile, comparative study results are shown to demonstrate the advantages of the unified state model based load curve smoothing strategy.

Estimating inter-area dominant oscillation mode in bulk power grid using multi-channel continuous wavelet transform

This paper proposes a novel continuous wavelet transform(CWT) based approach to holistically estimate the dominant oscillation using measurement data from multiple channels. CWT has been demonstrated to be effective in estimating power system oscillation modes.Using singular value decomposition(SVD) technique, the original huge phasor measurement unit(PMU) datasets are compressed to finite useful measurement data which contain critical dominant oscillation information. Further,CWT is performed on the constructed measurement signals to form wavelet coefficient matrix(WCM) at the same dilation. Then, SVD is employed to decompose the WCMs to obtain the maximum singular value and its right eigenvector. A singular value vector with the entire dilation is constructed through the maximum singular values. The right eigenvector corresponding to the maximum singular value in the singular-value vector is adopted as the input of CWT to estimate the dominant modes. Finally, the proposed approach is evaluated using the simulation data from China Southern Power Grid(CSG) as well as the actual field-measurement data retrieved from the PMUs of CSG.The simulation results demonstrate that the proposed approach performs well to holistically estimate the dominant oscillation modes in bulk power systems.

Maximum entropy based probabilistic load flow calculation for power system integrated with wind power generation

Distributed generation including wind turbine(WT) and photovoltaic panel increases very fast in recent years around the world, challenging the conventional way of probabilistic load flow(PLF) calculation. Reliable and efficient PLF method is required to take this chage into account.This paper studies the maximum entropy probabilistic density function reconstruction method based on cumulant arithmetic of linearized load flow formulation,and then develops a maximum entropy based PLF(MEPLF) calculation algorithm for power system integrated with wind power generation(WPG). Compared with traditional Gram–Charlier expansion based PLF(GC-PLF)calculation method, the proposed ME-PLF calculation algorithm can obtain more reliable and accurate probabilistic density functions(PDFs) of bus voltages and branch flows in various WT parameter scenarios. It can solve thelimitation of GC-PLF calculation method that mistakenly gains negative values in tail regions of PDFs. Linear dependence between active and reactive power injections of WPG can also be effectively considered by the modified cumulant calculation framework. Accuracy and efficiency of the proposed approach are validated with some test systems. Uncertainties yielded by the wind speed variations, WT locations, power factor fluctuations are considered.

Hierarchical and distributed demand response control strategy for thermostatically controlled appliances in smart grid

Thermostatically controlled appliances(TCAs)have great thermal storage capability and are therefore excellent demand response(DR) resources to solve the problem of power fluctuation caused by renewable energy.Traditional centralized management is affected by communication quality severely and thus usually has poor realtime control performance. To tackle this problem, a hierarchical and distributed control strategy for TCAs is established. In the proposed control strategy, target assignment has the feature of self-regulating, owing to the designed target assignment and compensating algorithm which can utilize DR resources maximally in the controlled regions and get better control effects. Besides, the model prediction strategy and customers’ responsive behavior model are integrated into the original optimal temperature regulation(OTR-O), and OTR-O will be evolved into improved optimal temperature regulation. A series of case studies have been given to demonstrate the control effectiveness of the proposed control strategy.

Peer-to-peer Electricity Transaction Decisions of the User-side Smart Energy System Based on the SARSA Reinforcement Learning

With the deep integration of advanced information technologies,such as artificial intelligence and traditional energy technologies,smart energy systems have been proposed as a method to provide the best solution for the coordination,balance,and control of the entire energy system.As a new way of energy balance and interaction in the user side energy market,a peer-to-peer(P2P)electricity transaction can effectively promote energy sharing within the user group and improve the economic benefits of users participating in the energy market.Reinforcement learning(RL)is an artificial intelligence method in which agents continuously acquire relevant experience and knowledge during the interaction with the environment,automatically update their decision-making behavior;and achieve maximum return.It is a suitable approach for P2P transaction decision analysis of small-scale users in the context of smart energy.First,this paper establishes a P2P transaction model that includes a participant model,equipment model and price model.Secondly,the transaction problem is equivalent to a Markov decision process(MDP)and each learning element model is established.Then,the MDP problem is solved and analyzed using the SARSA RL algorithm with average discrete processing.Finally,a case study of a community with multiple users is conducted to verify the effectiveness,economy,and security of the RL method in solving energy storage action selection and transaction decision problems of energy storage users.

System Adaptive AC Filter for a Line Commutated Converter High Voltage DC Transmission System

This paper proposes a novel AC filter system for a line commutated converter high voltage DC(LCC-HVDC)transmission system.Through the coordination of the hybrid active power filters(APF)and the existing reactive compensation devices,the proposed filter system can not only enhance the suppression performance for LCC-HVDC harmonics,but also optimize the AC yard layout with reduced reactive power subbanks,reducing the cost of HVDC projects.The novel filter system adopts a serial passive resonance topology obtained by careful comparison of different APFs.A proper control scheme is then designed integrating the control strategy of the APF and impedance characteristics of the HVDC system,which is able to realize harmonic suppression and dynamic reactive power support simultaneously.In addition,a novel self-adaption digital low-pass filter algorithm is presented,which is used in the APF harmonic detecting step,enhancing both high precision and fast dynamic response.On the basis of a real HVDC project,the advantages of proposed filter system in harmonic suppression,reactive power regulation,and sub-banks reduction are simulated and demonstrated.

Equivalence of stability criteria for time-delay systems

During the past few decades,there have been a number of new delay-dependent stability criteria based on linear matrix inequalities published in the literature.These criteria,to some extent,can reduce the conservativeness.In fact,some criteria are equivalence.In this paper,we aim to theoretically establish equivalence of two stability criteria.One was obtained by Sun et al.[Sun et al.In:Proceedings of IEEE Power&Energy Society General Meeting.2009,1–7]and the other was given by Xu et al.[Xu et al.IEEE Transactions on Automatic Control,2005,50(3):384–387].Also,we theoretically establish equivalence of the robust stability criterion presented by Jia et al.[Jia et al.Automation of Electric Power Systems,2010,34(3):6–11]and the one given by Xu et al.(2005).