Parallel Dispatch:A New Paradigm of Electrical Power System Dispatch

Modern power systems are evolving into sociotechnical systems with massive complexity, whose real-time operation and dispatch go beyond human capability. Thus,the need for developing and applying new intelligent power system dispatch tools are of great practical significance. In this paper, we introduce the overall business model of power system dispatch, the top level design approach of an intelligent dispatch system, and the parallel intelligent technology with its dispatch applications. We expect that a new dispatch paradigm,namely the parallel dispatch, can be established by incorporating various intelligent technologies, especially the parallel intelligent technology, to enable secure operation of complex power grids,extend system operators' capabilities, suggest optimal dispatch strategies, and to provide decision-making recommendations according to power system operational goals.

PoQ-Consensus Based Private Electricity Consumption Forecasting via Federated Learning

With the rapid development of artificial intelligence and computer technology,grid corporations have also begun to move towards comprehensive intelligence and informatization.However,data-based informatization can bring about the risk of privacy exposure of fine-grained information such as electricity consumption data.The modeling of electricity consumption data can help grid corporations to have a more thorough understanding of users’needs and their habits,providing better services for users.Nevertheless,users’electricity consumption data is sensitive and private.In order to achieve highly efficient analysis of massive private electricity consumption data without direct access,a blockchain-based federated learning method is proposed for users’electricity consumption forecasting in this paper.Specifically,a blockchain systemis established based on a proof of quality(PoQ)consensus mechanism,and a multilayer hybrid directional long short-term memory(MHD-LSTM)network model is trained for users’electricity consumption forecasting via the federal learning method.In this way,the model of the MHD-LSTM network is able to avoid suffering from severe security problems and can only share the network parameters without exchanging raw electricity consumption data,which is decentralized,secure and reliable.The experimental result shows that the proposed method has both effectiveness and high-accuracy under the premise of electricity consumption data’s privacy preservation,and can achieve better performance when compared to traditional long short-term memory(LSTM)and bidirectional LSTM(BLSTM).

Location and Capacity Determination Method of Electric Vehicle Charging Station Based on Simulated Annealing Immune Particle Swarm Optimization

As the number of electric vehicles(EVs)continues to grow and the demand for charging infrastructure is also increasing,how to improve the charging infrastructure has become a bottleneck restricting the development of EVs.In other words,reasonably planning the location and capacity of charging stations is important for development of the EV industry and the safe and stable operation of the power system.Considering the construction and maintenance of the charging station,the distribution network loss of the charging station,and the economic loss on the user side of the EV,this paper takes the node and capacity of charging station planning as control variables and the minimum cost of system comprehensive planning as objective function,and thus proposes a location and capacity planning model for the EV charging station.Based on the problems of low efficiency and insufficient global optimization ability of the current algorithm,the simulated annealing immune particle swarm optimization algorithm(SA-IPSO)is adopted in this paper.The simulated annealing algorithm is used in the global update of the particle swarm optimization(PSO),and the immune mechanism is introduced to participate in the iterative update of the particles,so as to improve the speed and efficiency of PSO.Voronoi diagram is used to divide service area of the charging station,and a joint solution process of Voronoi diagram and SA-IPSO is proposed.By example analysis,the results show that the optimal solution corresponding to the optimisation method proposed in this paper has a low overall cost,while the average charging waiting time is only 1.8 min and the charging pile utilisation rate is 75.5%.The simulation comparison verifies that the improved algorithm improves the operational efficiency by 18.1%and basically does not fall into local convergence.

A Novel Design Framework for Smart Operating Robot in Power System

This paper proposes the concept and framework of smart operating system based on the artificial intelligence(AI)techniques. The demands and the potential applications of AI technologies in power system control centers is discussed in the beginning of the paper. The discussion is based on the results of a field study in the Tianjin Power System Control Center in China. According to the study, one problem in power systems is that the power system analysis system in the control center is not fast and powerful enough to help the operators in time to deal with the incidents in the power system. Another issue in current power system control center is that the operation tickets are compiled manually by the operators, so that it is less efficient and human errors cannot be avoided. Based on these problems, a framework of the smart operating robot is proposed in this paper, which includes an intelligent power system analysis system and a smart operation ticket compiling system to solve the two problems in power system control centers. The proposed framework is mainly based on the AI techniques, especially the neural network with deep learning, since it is faster and more capable of dealing with the highly nonlinear and complex power system.

Residential PV capacity estimation and power disaggregation using net metering measurements

As the intermittency and uncertainty of photovoltaic(PV)power generation poses considerable challenges to the power system operation,accurate PV generation estimates are critical for the distribution operation,maintenance,and demand response program implementation because of the increasing usage of distributed PVs.Currently,most residential PVs are installed behind the meter,with only the net load available to the utilities.Therefore,a method for disaggregating the residential PV generation from the net load data is needed to enhance the grid-edge observability.In this study,an unsupervised PV capacity estimation method based on net metering data is proposed,for estimating the PV capacity in the customer’s premise based on the distribution characteristics of nocturnal and diurnal net load extremes.Then,the PV generation disaggregation method is presented.Based on the analysis of the correlation between the nocturnal and diurnal actual loads and the correlation between the PV capacity and their actual PV generation,the PV generation of customers is estimated by applying linear fitting of multiple typical solar exemplars and then disaggregating them into hourly-resolution power profiles.Finally,the anomalies of disaggregated PV power are calibrated and corrected using the estimated capacity.Experiment results on a real-world hourly dataset involving 260 customers show that the proposed PV capacity estimation method achieves good accuracy because of the advantages of robustness and low complexity.Compared with the state-of-the-art PV disaggregation algorithm,the proposed method exhibits a reduction of over 15%for the mean absolute percentage error and over 20%for the root mean square error.

Design of New-generation Electric Vehicle Terminal

EVT (electric vehicle terminal) has played an important role in EV (electric vehicle) operation. Based on research status of vehicle terminal, EVT brought about in the future should have the following functions: (1) fundamental functions, including real-time monitoring of batteries, guidance in station, position guidance of charging/battery-swap infrastructures, communication with OMS (operation and management system), and so on;(2) advanced functions, including but not limited to multi-media entertainment, subscribing and payment for charging/battery-swap, identification, and safety control during driving. Complete design of new-generation EVT in software structure and hardware architecture is proposed;a new idea of the application of EVT in EV industry is put forward.

Adaptive composite frequency control of power systems using reinforcement learning

With the incorporation of renewable energy,load frequency control(LFC)becomes more challenging due to uncertain power generation and changeable load demands.The electric vehicle(EV)has been a popular transportation and can also provide flexible options to play a role in frequency regulation.In this paper,a novel adaptive composite controller is designed to solve the LFC problem for the interconnected power system with electric vehicles and wind turbine.EVs are used as regulation resources to effectively compensate the power mismatch.First,the sliding mode controller is developed to reduce the random influences caused by the wind turbine generation system.Second,an auxiliary controller with reinforcement learning is proposed to produce adaptive control signals,which will be attached to the primary proportion-integration-differentiation control signal in a realtime manner.Finally,by considering random wind power,load disturbances and output constraints,the proposed scheme is verified on a two-area power system under four different cases.Simulation results demonstrate that the proposed adaptive composite frequency control scheme has a competitive performance with regard to dynamic performance.

Multi-source coordinated stochastic restoration for SOP in distribution networks with a two-stage algorithm

After suffering from a grid blackout, distributed energy resources(DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience. In this study, a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points(SOPs). Here, the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations. Owing to the uncertainty, a scenario-based stochastic optimization approach was employed,and the load restoration problem was formulated as a mixed-integer nonlinear programming model. A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods. The algorithm is organized into a two-stage structure, in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem. In the second stage, an integer programming problem is calculated to acquire the outputs of both SOPs and power resources. A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy.

Coordinated control of coastal multi-source multi-load system with desalination load: a review

Traditional seawater desalination requires high amounts of energy, with correspondingly high costs and limited benefits, hindering wider applications of the process. To further improve the comprehensive economic benefits of seawater desalination, the desalination load can be combined with renewable energy sources such as solar energy, wind energy, and ocean energy or with the power grid to ensure its effective regulation. Utilizing energy internet(EI) technology, energy balance demand of the regional power grid, and coordinated control between coastal multi-source multi-load and regional distribution network with desalination load is reviewed herein. Several key technologies, including coordinated control of coastal multi-source multi-load system with seawater desalination load, flexible interaction between seawater desalination and regional distribution network, and combined control of coastal multi-source multi-load storage system with seawater desalination load, are discussed in detail. Adoption of the flexible interaction between seawater desalination and regional distribution networks is beneficial for solving water resource problems, improving the ability to dissipate distributed renewable energy, balancing and increasing grid loads, improving the safety and economy of coastal power grids, and achieving coordinated and comprehensive application of power grids, renewable energy sources, and coastal loads.

Planning and design of a micro energy network for seawater desalination and regional energy interconnection

With the rapid developme nt of the economy, the continu ously in creasing populati on, and ongoing climate change, the shortage of freshwater resources has become an increasingly important global problem. Seawater desalination tech no logy can effectively alleviate the pressure on freshwater supplies and has bee n in vestigated in many countries. However, the majority of existing projects focus on the research and development of desalination equipment and the use of new tech no logies and pay less atte ntion to the operation optimizati on of the desalinati on process. The micro energy n etwork (MEN) designed in this study is an efficient distributed energy supply system that can be used to simultaneously supply electricity, cooling, heating, and freshwater as photovoltaic power, wind power, combined heat and power (CHP), electric cooling and heating, and a seawater desalinati on device are in teg rated into the MEN. In this study, a model for operati on optimization of a MEN for seawater desalination was developed and the influences of the electric cooling and heating ratios and the operation optimization of the seawater desalination device were studied with the aim of minimizing the life cycle cost. Based on the results of this study, MENs can reduce the operation cost of desalination devices and improve the efficiency of renewable energy sources.

Integrated energy efficiency evaluation of a multi-source multi-load desalination micro-energy network

With increasing global shortage of fresh water resources,many countries are prioritizing desalination as a means of utilizing abundantly available seawater resources.Integrated energy efficiency evaluation is a scientific method for the quantitative analysis of energy efficiency based on multiple indicators and is very useful for investment,construction,and scientific decision-making for desalination projects.In this paper,the energy efficiency evaluation of the micro energy network (MEN) of desalination for multi-source and multi-load is studied,and the basic idea of comprehensive energy efficiency evaluation is analyzed.The process includes the use of a MEN model to establish an integrated energy efficiency evaluation index system,taking into consideration energy,equipment,economic,environmental,and social factors.A combined evaluation method considering subjective and objective comprehensive weights for multi-source multi-load desalination MENs is proposed to evaluate the energy efficiency of desalination and from multiple perspectives.

Time series modeling and filtering method of electric power load stochastic noise

Stochastic noises have a great adverse effect on the prediction accuracy of electric power load.Modeling online and filtering real-time can effectively improve measurement accuracy.Firstly,pretreating and inspecting statistically the electric power load data is essential to characterize the stochastic noise of electric power load.Then,set order for the time series model by Akaike information criterion(AIC)rule and acquire model coefficients to establish ARMA(2,1)model.Next,test the applicability of the established model.Finally,Kalman filter is adopted to process the electric power load data.Simulation results of total variance demonstrate that stochastic noise is obviously decreased after Kalman filtering based on ARMA(2,1)model.Besides,variance is reduced by two orders,and every coefficient of stochastic noise is reduced by one order.The filter method based on time series model does reduce stochastic noise of electric power load,and increase measurement accuracy.

Research on operation and management muti-node model of mega city energy Internet

This paper conducts comprehensive analysis about operational characteristics, market value, stakeholders, and operating modes of a megacity energy Internet. An innovating 'one platform, two centers, and multiple nodes' operation management model is proposed. The study provides an in-depth explanation of the new operation and management model of the connotation and logical relationship. Building a comprehensive energy service platform, developing an integrated energy dispatch center, a market trading service center, communication, and other parts of the system needed to achieve benefits for all parties is including in the discussion. The proposed new model of operation and management has important guiding significance and reference for the development and construction of energy Internets in megacities.

Classification for Glass Bottles Based on Improved Selective Search Algorithm

The recycling of glass bottles can reduce the consumption of resources and contribute to environmental protection.At present,the classification of recycled glass bottles is difficult due to the many differences in specifications and models.This paper proposes a classification algorithm for glass bottles that is divided into two stages,namely the extraction of candidate regions and the classification of classifiers.In the candidate region extraction stage,aiming at the problem of the large time overhead caused by the use of the SIFT(scale-invariant feature transform)descriptor in SS(selective search),an improved feature of HLSN(Haar-like based on SPP-Net)is proposed.An integral graph is introduced to accelerate the process of forming an HBSN vector,which overcomes the problem of repeated texture feature calculation in overlapping regions by SS.In the classification stage,the improved SS algorithm is used to extract target regions.The target regions are merged using a non-maximum suppression algorithm according to the classification scores of the respective regions,and the merged regions are classified using the trained classifier.Experiments demonstrate that,compared with the original SS,the improved SS algorithm increases the calculation speed by 13.8%,and its classification accuracy is 89.4%.Additionally,the classification algorithm for glass bottles has a certain resistance to noise.

Experiments and SPICE simulations of double MgO-based perpendicular magnetic tunnel junction

We investigate properties of perpendicular anisotropy magnetic tunnel junctions(pMTJs) with a stack structure MgO/CoFeB/Ta/CoFeB/MgO as the free layer(or recording layer),and obtain the necessary device parameters from the tunneling magnetoresistance(TMR) vs.field loops and current-driven magnetization switching experiments.Based on the experimental results and device parameters,we further estimate current-driven switching performance of pMTJ including switching time and power,and their dependence on perpendicular magnetic anisotropy and damping constant of the free layer by SPICE-based circuit simulations.Our results show that the pMTJ cells exhibit a less than 1 ns switching time and write energies <1.4 pJ;meanwhile the lower perpendicular magnetic anisotropy(PMA) and damping constant can further reduce the switching time at the studied range of damping constant α <0.1.Additionally,our results demonstrate that the pMTJs with the thermal stability factor■73 can be easily transformed into spin-torque nano-oscillators from magnetic memory as microwave sources or detectors for telecommunication devices.

Research on Load Characteristic of Electrothermal Film Heating Community

In this paper, a case study of an electrothermal film heating community in Tianjin is carried out, in which the winter load characteristic and electricity use law are analyzed. In this community, every household installs two watt-hour meters, one of which is only used to measure the electrothermal heating power, so the interference factors are eliminated. The main factors influencing the residents’ power consumption are summarized, and a method for estimating the thermal load of the residents is given. The conclusions can provide important reference to generalize the electric heating technology.

A Unified Control of Super-capacitor System Based on Bi-directional DC-DC Converter for Power Smoothing in DC Microgrid

To improve the equivalent inertia of DC microgrids(DCMGs),a unified control is proposed for the first time for a bi-directional DC-DC converter based super-capacitor(SC)system,whereby power smoothing and SC terminal voltage regulation can be achieved in a DCMG simultaneously.The proposed control displays good plug-and-play features using only local measurements.For quantitative analysis and effective design of the critical parameter of unified control,two indices,equivalent power supporting time and inertia contributed by the unified controlled SC system,are introduced firstly.Then,with a simple but effective reduced-order model of a DCMG,analytical solutions are obtained for the two indices.In addition,a systematic design method is presented for the proposed unified control.Finally,to verify the proposed unified control,a switching model is developed for a typical DCMG in PSCAD/EMTDC,and theoretical analyses are conducted for different operating conditions.

Application of Al techniques in monitoring and operation of power systems

In recent years,the artificial intelligence(Al)technology is becoming more and more popular in many areas due to its amazing performance.However,the application of Al techniques in power systems is still in its infancy.Therefore,in this paper,the application potentials of Al technologies in power systems will be discussed by mainly focusing on the power system operation and monitoring.For the power system operation,the problems,the demands,and the possible applications of Al techniques in control,optimization,and decision making problems are discussed.Subsequently,the fault detection and stability analysis problems in power system monitoring are studied.At the end of the paper,a case study to use the neural network(NN)for power flow analysis is provided as a simple example to demonstrate the viability of Al techniques in solving power system problems.

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.

Multivariate Two-stage Adaptive-stacking Prediction of Regional Integrated Energy System

To reduce environmental pollution and improve the efficiency of cascaded energy utilization, regional integrated energy system(RIES) has received extensive attention. An accurate multi-energy load prediction is significant for RIES as it enables stakeholders to make effective decisions for carbon peaking and carbon neutrality goals. To this end, this paper proposes a multivariate two-stage adaptive-stacking prediction(M2ASP) framework. First, a preprocessing module based on ensemble learning is proposed. The input data are preprocessed to provide a reliable database for M2ASP, and highly correlated input variables of multi-energy load prediction are determined. Then, the load prediction results of four predictors are adaptively combined in the first stage of M2ASP to enhance generalization ability. Predictor hyper-parameters and intermediate data sets of M2ASP are trained with a metaheuristic method named collaborative atomic chaotic search(CACS) to achieve the adaptive staking of M2ASP. Finally, a prediction correction of the peak load consumption period is conducted in the second stage of M2ASP. The case studies indicate that the proposed framework has higher prediction accuracy, generalization ability, and stability than other benchmark prediction models.