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Participants in electricity markets are becoming more proactive because of the fast development of DERs and DSM, which also boosts the emergence of P2P market mechanisms. Moreover, the market is also required to operate in a real-time scheme in response to changes in generation and load to maintain power balance. Therefore, a practicable real-time P2P market mechanism is in urgent need. However, it is technically challenging to deploy P2P mechanisms in real-time, since they most often involve a heavy computation burden, while the time available for negotiation in real-time is very short. Our core contribution is to design a novel asynchronous online optimization framework to enable the real-time P2P market negotiation mechanism, which can greatly reduce the computation and communication burden from two aspects. First, a novel online consensus ADMM algorithm is proposed, which can greatly reduce the computation complexity since only one iteration is performed for each agent in every time period. Second, the market operates in an asynchronous mode so that all agents can freely trade without waiting for idle neighboring agents. The sublinear regret upper bound is proved for our online algorithm, which indicates that social welfare can be maximized in the long run on time average.

Due to the fast development of distributed energy resources and demand-side response management, agents in electricity markets are becoming more proactive, which boosts the development of peer-to-peer (P2P) market mechanisms. However, to our knowledge, none of the existing works considers clearing both energy and reserve via a P2P market mechanism in order to compensate for the uncertainty originating from renewable generation and allocate the reserve cost induced by uncertainty fairly. In this article, a novel P2P joint energy and reserve market is proposed, where each agent can negotiate with neighboring agents to determine the quantities and prices of traded energy and reserve. We model the renewable generation uncertainty by versatile distribution and determine the required reserve based on a chance-constrained optimization approach. Then, a fully decentralized P2P market based on consensus alternating direction method of multipliers (ADMM) theory is proposed. In addition, to further lower the social cost, we exploit the correlation and complementarity among uncertainties and design a renewable community-based market, where all renewable agents share uncertainty information to community manager for calculating total required reserve. Finally, simulation results show the convergence performance, fairness and scalability of our market mechanism.

With the increasing penetration of renewable energy resources and plug-in electric vehicles (PEV) in the smart grid, the problem of phase imbalance in a three-phase distribution system becomes more challenging. In this paper, we propose a phase balancing (PB) scheme performed by PEVs. With PB remunerations introduced as financial incentives, PEV owners are encouraged to assist the distribution system operator in reducing the phase imbalance. This is achieved by exploiting the charging flexibility of PEV batteries. We formulate this problem into a non-smooth non-cooperative game, where each PEV owner autonomously minimizes his/her charging cost minus the PB remuneration, considering decisions of other PEV owners. Through transforming this game into a smooth extended one, we are able to solve this challenging problem. Specifically, existence of its Nash equilibrium (NE) is demonstrated, and a distributed algorithm is developed to achieve the NE. Convergence proof of this algorithm is also provided. Simulation results validate our proposed scheme, and illustrate that both PEV owners and the distribution system operator will benefit financially. Moreover, the power quality and system reliability of the distribution network can also be improved.

Optimally scheduling power consumption of appliances is the essential feature of smart grid, which enables Demand Response Management (DRM) and helps to shape the power usage profile. This problem is often required to be solved in face of a large number of appliances and many time slots; thus the computational efficiency of solving a large scale optimal power scheduling problem with limited computational resources becomes the major concern of algorithm design. To this end, a novel algorithm is proposed based on Karush-Kuhn-Tucker (KKT) conditions to solve the optimal power scheduling problem with temporally spatially coupled constraints in a distributed manner. The proposed algorithm converts the original problem into equivalently solving an optimal KKT operator in a much lower dimension, thus the computation speed is greatly enhanced. In addition, the proposed method dose not require a step size in the iteration process, thus avoids the oscillation of numerical solution caused by problem parameter changes. Compared with the widely used conventional algorithms, e.g., interior point method and dual decomposition, the higher computational speed and less sensitivity to the problem parameter setting are observed in numerical simulations.

In order to meet the requirements of the development trend of large aperture space deployable antenna, based on the characteristics of cellular structure, a configuration scheme of support mechanism for hexagonal prism modular deployable antenna is proposed. The topological law of the module is studied, and the configuration design is carried out. Then the multi module kinematic model is established, and the numerical simulation analysis is carried out. The experiment results show that the deployable antenna support mechanism can be successfully deployed in many deployment tests, and there is no jamming fault, which indicates that the mechanism configuration principle and design structure scheme are feasible. The research results provide a reference for other types of deployable antenna mechanism.

Owing to the fast deployment of distributed energy resources (DERs) and the further development of demand-side management, small agents in electricity markets are becoming more proactive. This may boost the development of peer-to-peer (P2P) market mechanisms. Meanwhile, since actual load and power generation may substantially deviate from schedules obtained at the day-ahead (forward) market stage, it is needed to rapidly reschedule the trades among agents to maintain power balance through a real-time market mechanism, also in a P2P framework. However, it is technically challenging to develop and operate such P2P market mechanisms in real-time, since they most often involve a heavy computational burden (e.g., based on iterative distributed optimization approaches), while real-time trading demands fast calculation. Our core contribution is hence to describe and analyze a novel online optimization framework to enable the real-time P2P market. It relies on online social welfare maximization using a novel online consensus alternating direction method of multipliers (OC-ADMM) algorithm. The computational complexity is then heavily reduced since only one iteration is performed for each agent at every time step in order to satisfy real-time requirements. We derive a sublinear non-stationary regret upper bound for our algorithm, which implies that social welfare will be maximized in the long run. Simulations based on a number of case studies show that our algorithm has good convergence performance, tracking ability, and high computational efficiency.

During the re-entry of the spacecraft, it will encounter severe environmental effects such as aerodynamic heating and multi-degree-of-freedom vibration, which brings challenges to the reliability assurance of the secondary power module installed in the bulkhead. In this paper, the transient thermal shock and three-axis six-degree-of-freedom vibration conditions applied to the secondary power module are obtained based on the measured aerothermodynamics data from the flight and the thermal transfer model from the outside to the inside of the spacecraft bulkhead. Next, a finite element model of the secondary power module is established and the thermal transfer characteristics of the model are verified through thermal equilibrium tests and modal tests. Then, simulation analysis is performed under transient thermal shock, three-axis six-degree- of-freedom vibration, and transient thermal shock + three-axis six-degree-of-freedom vibration. The key components with the largest stress-strain response are identified. In addition, the failure modes and mechanisms of the comprehensive thermal effects of the key components are analyzed and the CalcePWA reliability simulation based on the failure mechanism is carried out, from which the physical laws of the failure of the key components are obtained. The thermal environmental effects and failure modes of the secondary power module during the ground development, launch, and orbit flight reveal that the failure mode of the relay’s contact resistance increases during the reentry process will become more severe. The ground test optimization and design improvements can ensure the task reliability during reentry.

As the core of the Plan, the target indicators for building a healthy China are a high degree of conciseness and summary of the development tasks and the fundamental basis for the implementation of all major tasks and major policies. With clear orientation and constraints, they are of great importance to strengthening the binding force of the Outline and ensuring the implementation of the Outline. The main targets and indicators of the Outline are supposed to closely focus on the national strategy of building a moderately prosperous society in an all-round way and achieving the “two centenary goals.” Moreover, they need to fully consider the convergence with the goals of various stages of economic and social development, and with the requirements of the UN 2030 Agenda for Sustainable Development. In this way the international comparability of indicators will be improved. The combination of taking a long-term perspective and focusing on the present must be adhered to, with consideration given to both guidance and maneuverability. It will be necessary to put an emphasis on the overall importance and representativeness of the main indicators, and to ensure that the indicators are clearly defined. With a stable source of data, the indicators will be measured, decomposed, and assessed, in order to make the annual data and provincial data accessible and to meet the requirements for the monitoring and evaluation of the Outline. Thus the binding force and maneuverability of the Outline will be guaranteed.

How to improve the reliability has been a challenging task for transient protection, which has been affected seriously by the fault initial angle (FIA) and transient resistance (TR). Based on support vector machine (SVM), a novel smart transient protection method is presented in this study, which protects a bus and two transmission lines connected to the bus. The method is based on the measurements from the two close-in ends of the two lines. Hilbert–Huang transform is used to extracting instantaneous amplitude from the measured currents. The fault area is determined from the two instantaneous amplitude-integral (IA) of the transient fault currents, the difference of the two IAs, FIA, and the TR. SVM is utilised to determine the fault area. Two IAIs, difference of IAs, FIA and TR are treated as the input to SVMs. Faults with different FIA and TR are treated as a different class of fault, respectively. Correspondingly, a different criterion of discrimination is automatically employed by SVMs for a different fault class. The influence of FIA and TR is eliminated significantly, and the reliability of protection is enhanced substantially. The performance of the method is tested using ATP/EMTP with satisfactory results.

Electromagnetic forward modeling is the cornerstone of geophysical electromagnetic inversion. During the last 50 years, numerical simulation methods have been rapidly developed and widely used in geophysical area as the computational capacity continued to increase, such as from single-core to the most modern multi-core processing cards. This paper reviews the literature of electromagnetic fields simulation, particularly focusing on the forward modeling methods include finite difference method , finite element method, integral equation method, and several hybrid methods. We also discuss the possibility of deep learning methods for EM modeling. By sorting out the work done by the predecessors, this review briefly introduces the basic principles and traces back the development of these methods. We propose a Qualitative Evaluation Model named STAMP Model and some criterias of qualitative evaluation on these methods will be discussed in this model.

From the view point of a charging station (CS), it is important to design a simple, effective and implementable algorithm that reduces the cost, improves the time efficiency and enhances operational stability. Offline algorithms built on global information, in practice, cannot be implemented to achieve the best performance, since current charging rates of existing electric vehicles (EVs) need to be determined in the absence of future information. In the context of a current electricity tariff mechanism, commonly imposed in industry, which additionally charges for peak demand, this paper proposes an online two-stage charging scheduling algorithm (OTCSA) based on observed real time information and historical data to minimize charging cost, reduce charging time, as well as lower the maximum peak power. In the first stage, charging cost is minimized with guarantee to fulfill energy demand of each EV before its departure. The additional cost that penalizes peak demand inherently contributes to flattening the load profile of the CS with the deferrability of EV charging. In the second stage, we squeeze to save more charging time for EVs given the minimal cost. Simulations further validate the three-fold benefits of the proposed approach.

The cost and stability, in terms of stable charging load, are the main concerns of a charging station (CS). To this end, an online cost optimization algorithm is proposed by assuming that all electric vehicle (EV) users are selfless, so that they will report a real demand to the CS. However, in reality, all EV users are rational and they want to complete the energy demand in the fastest time and the lowest cost. Therefore, inspired by this characteristic of EV users, an online pricing mechanism is designed based on the online algorithm. There are three-fold benefits of the proposed pricing mechanism: giving discount to the flexibility of EV users, ensuring fairness among EV users and the individual optimal strategies of EV users jointly achieve minimizing cost of the CS. Numerical results based on real data further verify the effectiveness of the proposed online algorithm and the pricing mechanism.

Marine controlled source electromagnetic (CSEM) data inversion for hydrocarbon exploration is often challenging due to high computational cost, physical memory requirement and low resolution of the obtained resistivity map. This paper aims to enhance both the speed and resolution of CSEM inversion by introducing structural geological information in the inversion algorithm. A coarse mesh is generated for Occam's inversion, where the parameters are fewer than in the fine regular mesh. This sparse mesh is defined as a coherence-based irregular (IC) sparse mesh, which is based on vertices extracted from available geological information. Inversion results on synthetic data illustrate that the IC sparse mesh has a smaller inversion computational cost compared to the regular dense (RD) mesh. It also has a higher resolution than with a regular sparse (RS) mesh for the same number of estimated parameters. In order to study how the IC sparse mesh reduces the computational time, four different meshes are generated for Occam's inversion. As a result, an IC sparse mesh can reduce the computational cost while it keeps the resolution as good as a fine regular mesh. The IC sparse mesh reduces the computational cost of the matrix operation for model updates. When the number of estimated parameters reduces to a limited value, the computational cost is independent of the number of parameters. For a testing model with two resistive layers, the inversion result using an IC sparse mesh has higher resolution in both horizontal and vertical directions. Overall, the model representing significant geological information in the IC mesh can improve the resolution of the resistivity models obtained from inversion of CSEM data.

The major disadvantage of using traditional linear or quasi-linear methods to perform magnetotelluric data inversion is that these methods heavily depend on the initial models. If a selected initial model is not suitable, these inversion algorithms may fall into a local minimum or diverge during the iterations. Meanwhile, in most geophysical inversions, it is difficult to obtain a proper initial model because of the lack of sufficient prior information. To solve this problem, a hybrid inversion algorithm based on the combined particle swarm optimization and regularized least squares is proposed to invert magnetotelluric data. Particle swarm optimization algorithms have good performances in global searching. More importantly, these algorithms are not sensitive to the initial models. Therefore, this approach can be used to preliminary search the model parameter space. Then, the searched results are used as an initial model for iterations of inversion based on the least square regularization. Both synthetic data and field data tests demonstrated that the hybrid inversion has a good performance. The numerical experiments show that by utilizing the reliable initial model generated from the particle swarm optimization, the search results of hybrid inversion are greatly refined. The hybrid algorithm offers a significant improvement in both inversion efficiency and accuracy. In particular, this algorithm is suitable for the situations that lack initial information while inverting the field magnetotelluric data.

The scheduling of appliance power consumption is one of the main tasks in demand response management in smart grids. In many scenarios, it requires us to optimally schedule a large number of appliances with limited computational resources, thus the computational efficiency becomes a major concern of algorithm design. To this end, a novel algorithm is proposed based on KKT conditions to solve the optimal power scheduling problem with temporally-spatially coupled constraints. We show the algorithm is much more efficient than conventional algorithms, e.g., dual decomposition, and less sensitive to the problem parameter setting, as verified by numerical examples.

This paper investigates the energy aware long term optimization problem of the data center network which is equipped with energy storage. The network optimizes its operation on workload distribution and energy supply to reduce the operational cost. Because of the uncertainty of workload and electricity prices, on which we do not assume any distribution, a stochastic network optimization problem is formulated. By introducing virtual queues for the batteries and constructing virtual workload queues, the problem fits into the framework of Lyapunov optimization, which leads to a dynamic and distributed algorithm. The proposed algorithm guarantees the worst case delay of the workload is no larger than a constant. Extensive simulation results verify the effectiveness of the proposed algorithm and demonstrate that we can tradeoff between the average cost and average delay.

Increasing importance has been attached to the value of information technology (IT) infrastructure in today's organizations. The development of efficacious IT infrastructure capability enhances business performance and brings sustainable competitive advantage. This study analyzed the IT infrastructure capability in a holistic way and then presented a concept model of IT capability. IT infrastructure capability was categorized into sharing capability, service capability, and flexibility. This study then empirically tested the model using a set of survey data collected from 145 firms. Three factors emerge from the factor analysis as IT flexibility, IT service capability, and IT sharing capability, which agree with those in the conceptual model built in this study.

The purpose of this paper is to investigate the short-term wind power forecasting. STWPF is a typically complex issue, because it is affected by many factors such as wind speed, wind direction, and humidity. This paper attempts to provide a reference strategy for STWPF and to solve the problems in existence. The two main contributions of this paper are as follows. (1) In data preprocessing, each encountered problem of employed real data such as irrelevant, outliers, missing value, and noisy data has been taken into account, the corresponding reasonable processing has been given, and the input variable selection and order estimation are investigated by Partial least squares technique. (2) STWPF is investigated by multiscale support vector regression (SVR) technique, and the parameters associated with SVR are optimized based on Grid-search method. In order to investigate the performance of proposed strategy, forecasting results comparison between two different forecasting models, multiscale SVR and multilayer perceptron neural network applied for power forecasts, are presented. In addition, the error evaluation demonstrates that the multiscale SVR is a robust, precise, and effective approach. Published version