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.

Social Energy:Mining Energy From the Society

The inherent nature of energy,i.e.,physicality,sociality and informatization,implies the inevitable and intensive interaction between energy systems and social systems.From this perspective,we define "social energy" as a complex sociotechnical system of energy systems,social systems and the derived artificial virtual systems which characterize the intense intersystem and intra-system interactions.The recent advancement in intelligent technology,including artificial intelligence and machine learning technologies,sensing and communication in Internet of Things technologies,and massive high performance computing and extreme-scale data analytics technologies,enables the possibility of substantial advancement in socio-technical system optimization,scheduling,control and management.In this paper,we provide a discussion on the nature of energy,and then propose the concept and intention of social energy systems for electrical power.A general methodology of establishing and investigating social energy is proposed,which is based on the ACP approach,i.e., "artificial systems"(A), "computational experiments"(C) and "parallel execution"(P),and parallel system methodology.A case study on the University of Denver(DU) campus grid is provided and studied to demonstrate the social energy concept.In the concluding remarks,we discuss the technical pathway,in both social and nature sciences,to social energy,and our vision on its future.

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.

Hazard Model Reliability Analysis Based on a Wind Generator Condition Monitoring System

This paper presents an application of the hazard model reliability analysis on wind generators, based on a condition monitoring system. The hazard model techniques are most widely used in the statistical analysis of the electric machine＇s lifetime data. The model can be utilized to perform appropriate maintenance decision-making based on the evaluation of the mean time to failures that occur on the wind generators due to high temperatures. The knowledge of the condition monitoring system is used to estimate the hazard failure, and survival rates, which allows the preventive maintenance approach to be performed accurately. A case study is presented to demonstrate the adequacy of the proposed method based on the condition monitoring data for two wind turbines. Such data are representative in the generator temperatures with respect to the expended operating hours of the selected wind turbines. In this context, the influence of the generator temperatures on the lifetime of the generators can be determined. The results of the study can be used to develop the predetermined maintenance program, which significantly reduces the maintenance and operation costs.

Fully Distributed AC State Estimation Method for Power System Based on Information Propagation Algorithm

This paper proposes a new distributed AC state estimation method.Different from the popular distributed state estimation(DSE)methods based on area partitioning method,the proposed method is a truly distributed method in which the power system is not required to be divided into smaller areas and a centralized state estimator in each area is not needed.In order to achieve fully DSE,the information propagation algorithm is introduced in this paper to help the distributed local state estimators share the measurement data.The information propagation algorithm is developed based on consensus protocol.The proof of the convergence of the information propagation algorithm is provided in this paper.Then,the AC state estimation method is integrated with the information propagation algorithm to realize the proposed method.The proposed method is tested in different standard power system models.The results show that the proposed method reaches the similar accuracy as the traditional centralized state estimation methods and performs faster and more accurate than the existing DSE methods.

Digital Twin Empowered PV Power Prediction

The accurate prediction of photovoltaic(PV)power generation is significant to ensure the economic and safe operation of power systems.To this end,the paper establishes a new digital twin(DT)empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power prediction.With this framework,considering potential data contamination in the collected PV data,a generative adversarial network is employed to restore the historical dataset,which offers a prerequisite to ensure accurate mapping from the physical space to the digital space.Further,a new DT-empowered PV power prediction method is proposed.Therein,we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model(i.e.,a parallel network of convolution and bidirectional long short-term memory model)for capturing the hidden spatiotemporal features.The proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model,resulting in enhanced prediction accuracy.Finally,a real dataset is conducted to assess the effectiveness of the proposed method.

Data-driven Optimal Control Strategy for Virtual Synchronous Generator via Deep Reinforcement Learning Approach

This paper aims at developing a data-driven optimal control strategy for virtual synchronous generator (VSG) in the scenario where no expert knowledge or requirement for system model is available. Firstly, the optimal and adaptive control problem for VSG is transformed into a reinforcement learning task. Specifically, the control variables, i.e., virtual inertia and damping factor, are defined as the actions. Meanwhile, the active power output, angular frequency and its derivative are considered as the observations. Moreover, the reward mechanism is designed based on three preset characteristic functions to quantify the control targets: ① maintaining the deviation of angular frequency within special limits;② preserving well-damped oscillations for both the angular frequency and active power output;③ obtaining slow frequency drop in the transient process. Next, to maximize the cumulative rewards, a decentralized deep policy gradient algorithm, which features model-free and faster convergence, is developed and employed to find the optimal control policy. With this effort, a data-driven adaptive VSG controller can be obtained. By using the proposed controller, the inverter-based distributed generator can adaptively adjust its control variables based on current observations to fulfill the expected targets in model-free fashion. Finally, simulation results validate the feasibility and effectiveness of the proposed approach.

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.

An Assessment of Flexibility Reserves in Stochastic Modeling at Multiple Timescales

Due to recent technological achievements,stochastic optimization,which inherently captures the uncertainty of intermittent resources,is being used to capture the variability and uncertainty of wind and solar resources.However,due to persistent computational limitations,it is not practical to consider all possible variable generation scenarios.As a result,a reduced number of most likely scenarios is usually considered.While this helps reduce the computational burden,it also leaves the system operator vulnerable to some risk.In order to address this issue,this paper aims at providing insight into using an explicit reserve requirement in a stochastic modeling framework in order to provide system operators with greater confidence in stochastic dispatch solutions.This is accomplished by simulating a modified version of the IEEE 118 bus system in a fully stochastic,multi-timescale framework with flexibility reserve requirements.Results show that utilizing a stochastic flexibility reserve requirement within the stochastic modeling framework offers the most reliability benefit.

Operational Reliability Modeling and Assessment of Battery Energy Storage Based on Lithium-ion Battery Lifetime Degradation

Battery energy storage(BES)systems can effectively meet the diversified needs of power system dispatching and assist in renewable energy integration.The reliability of energy storage is essential to ensure the operational safety of the power grid.However,BES systems are composed of battery cells.This suggests that BES performance depends not only on the configuration but also on the operating state over different lifetime durations.The lack of safety and reliability is the main bottleneck preventing widespread applications of BES systems.Therefore,a reliability assessment algorithm and a weak-link analytical method for BES systems are proposed while considering battery lifetime degradation.Firstly,a novel lithium-ion battery model is proposed to identify the degradation rate of solid electrolyte interphase film formation and capacity plummeting.The impacts of different operating conditions are considered in stress factor models.Then,a reliability assessment algorithm for a BES system is introduced based on a universal generating function.An innovative weak-link analytical method based on the reliability importance index is proposed that combines the evaluation results of state-oriented and state-change-oriented indexes through an entropy weight method.The model,algorithm,indexes,and the usefulness are demonstrated in case studies based on aging test data and actual bus operating data.The results demonstrate the effects of the battery status and working conditions on BES reliability.Weak-link analysis is also used to assist BES systems in avoiding short-board batteries to achieve long lifetimes and efficient operation.

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.