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Buildings are typically integrated with multiple distributed energy resources (DERs), enabling them to act as building prosumers engaged in both energy production and consumption. Peer-to-peer (P2P) energy trading among building prosumers is crucial to improve their benefits. However, further exploration is required to balance the benefits between building prosumers and the system operator (e.g., the integrated community energy system (ICES) operator) since they are different entities. In this context, this paper proposes a comprehensive network charge and energy sale pricing scheme for the ICES operator on heterogeneous building prosumers with P2P multi-energy trading. The interaction between the ICES operator and building prosumers is modelled as a bi-level optimization problem that belongs to the hierarchical structure, while considering the heterogeneity of thermal insulation performance of buildings. At the upper level, the ICES operator optimizes the electricity/heat network charge prices and electricity/heat sale prices to maximize its revenue. At the lower level, building prosumers with a parallel structure optimize the schedules including P2P multi-energy trading and buildings' heating loads to minimize their costs. Furthermore, to address the bi-level optimization problem with parallel and hierarchical coupling structures, an accelerated asynchronous distributed algorithm based on alternating direction method of multipliers (ADMM) is developed, integrating a warm start strategy and a dual update accelerated iteration strategy for further improving computational efficiency. Finally, case studies demonstrate that the proposed scheme can effectively benefit both the ICES operator and building prosumers in P2P multi-energy trading at the same time. Meanwhile, the feasibility and effectiveness of the proposed algorithm are validated. • An optimal pricing scheme for ICES operator on building prosumers is proposed. • A bi-level optimization is proposed for ICES operator and prosumers with P2P. • An accelerated asynchronous distributed algorithm based on ADMM is developed. • Benefits of ICES operator and prosumers through several scenarios are analyzed. [ABSTRACT FROM AUTHOR]

Integrated community energy systems (ICESs) facilitate stakeholders to collaboratively create profit by planning and operating energy devices, including generation, conversion, and storage systems. However, the coupling and complementarity of multiple energy carriers, along with the comprehensive interaction among independent stakeholders, pose significant challenges in ICES planning and operating. These challenges necessitate balancing overall economic efficiency with individual stakeholder interests. This study investigates the economic interaction between the integrated energy service provider (IESP) and the photovoltaic energy prosumers (EPs) in ICES from a cooperative perspective, focusing on both planning and operating stage. A bilateral planning and operation method for the IESP and the EPs based on Nash bargaining theory is proposed in this paper. The proposed method establishes a cooperative and mutually beneficial relationship between the IESP and EPs, instead of a hierarchical paradigm. This study also presents an analysis of the relationship between the Nash bargaining problem and the social welfare maximization problem, demonstrating that solving the Nash bargaining problem can lead to a social welfare optimum. Additionally, an accelerated privacy-preserving distributed algorithm, in comparison to the central approach, is designed to solve the Nash bargaining problem. Numerical studies based on realistic data reveal that the Nash bargaining-based planning and operation method provides more benefits for both the IESP and EPs, as compared to the non-cooperation game method. • A planning and operation method based on Nash bargaining for ICES is proposed. • The social welfare maximum is achieved through the Nash bargaining solution. • An accelerated ADMM approach is proposed to ensure the stakeholders privacy. • The proposed method increases the profitability of IESP and EPs within the ICES. [ABSTRACT FROM AUTHOR]

Low-carbon buildings (LCBs) are normally equipped with distributed energy resources (DERs) such as photovoltaic (PV) panels and batteries, thereby creating LCB prosumers that both produce and consume energy. Peer-to-peer (P2P) energy sharing among LCB prosumers could bring higher economic benefits for themselves, and facilitate better local power balance for the power grid. To fully harness the benefits of P2P energy sharing for both LCB prosumers and the power grid, a bi-level optimization method for LCB prosumers and the power grid operator is proposed in this paper. The grid operator at the upper level imposes the optimal time-varying network charge to LCB prosumers at the lower level to maximize its profit. And LCB prosumers with the objective of minimizing their costs adjust the schedules including P2P energy sharing and their heating loads to respond to the grid operator's optimal network charge prices. Additionally, to further exploit the energy resources including heating loads in LCBs, the model predictive control (MPC) approach is integrated with the bi-level optimization in the presence of uncertainties. The bi-level optimization belongs to the NP-hard category, making it challenging to solve. To reduce the computational complexity, the bi-level optimization problem is converted to the single-level programming using Karush-Kuhn-Tucker (KKT) conditions. Numerical results illustrate that the bi-level optimization method can obtain a balanced scheduling scheme between the grid operator and LCB prosumers. In addition, incorporating the thermal inertia of building heating loads can provide more operational flexibility for both the grid operator and LCB prosumers. Furthermore, the costs of LCB prosumers based on MPC approach can be reduced with the decrease of forecast error level. • A bi-level optimization for grid operator and LCB prosumers with P2P is developed. • The time-varying pricing strategy for network charge of grid operator is proposed. • The bi-level solution method integrated with MPC approach is investigated. • The utilities of bi-level optimization through comparative scenarios are analyzed. [ABSTRACT FROM AUTHOR]

• A T-VESS is considered in real time P2P energy trading. • A two-stage P2P energy trading method is proposed based on MPC. • Trading actions are implemented considering multiple trading preference grades. • The proposed method is effective and utilities for prosumers are much improved. With the increasing penetration of distributed energy resources (DER) in the electric power system, Peer-to-Peer (P2P) energy trading has become a promising paradigm for future electric power systems. Building thermal load, which is an important demand side resource, should be considered carefully in the design of a P2P trading method. In this paper, we investigate the application of building thermal energy storage capability in P2P energy trading. We aggregate and model building thermal loads as a virtual energy storage and derive a time-varying virtual energy storage system (T-VESS) model to quantify the flexibility of a building. Key parameters of T-VESS, including charging/discharging rate, energy capacity, and state of charge (SOC), are analyzed so that T-VESS can be embedded in the building prosumer model to participate effectively in electricity-oriented P2P energy trading. We propose a real time P2P energy trading method of prosumers based on model predictive control (MPC). This method consists of an energy supply and demand quantification stage and a transaction price optimization stage, which can effectively capture the time-varying nature of T-VESS. To preserve the trading preference for prosumers, we propose a distributed P2P energy trading actions implementation method based on the continuous double auction (CDA) considering multiple trading preference grades. Numerical results demonstrate that the proposed P2P energy trading method considering T-VESS can reduce the operational cost of prosumers by 3.7%, while also promoting the local integration of renewable energy by 3.1%. Furthermore, compared to existing P2P trading methods considering single preference grade, the proposed method greatly enhances the utilities of both individual prosumers and the overall community. [ABSTRACT FROM AUTHOR]

Spatiotemporal variation of PM 2.5 in 2018 and 2020 were compared to analyze the impacts of COVID-19, the spatial heterogeneity of PM 2.5 , and meteorological and socioeconomic impacts of PM 2.5 concentrations heterogeneity in China in 2020 were investigated. The results showed that the annual average PM 2.5 concentration in 2020 was 32.73 μg/m3 existing a U-shaped variation pattern, which has decreased by 6.38 μg/m3 compared to 2018. A consistent temporal pattern was found in 2018 and 2020 with significant high values in winter and low in summer. PM 2.5 declined dramatically in eastern and central China, where are densely populated and economically developed areas during the COVID-19 epidemic compared with previous years, indicating that the significantly decline of social activities had an important effect on the reduction of PM 2.5 concentrations. The lowest PM 2.5 was found in August because that precipitation had a certain dilution effect on pollutants. January was the most polluted due to centralized coal burning for heating in North China. Overall, the PM 2.5 concentrations in China were spatially agglomerated. The highly polluted contiguous zones were mainly located in northwest China and the central plains city group, while the coastal area and Inner Mongolia were areas with good air quality. Negative correlations were found between natural factors (temperature, precipitation, wind speed and relative humidity) and PM 2.5 concentrations, with precipitation has the greatest impact on PM 2.5 , which are beneficial for reducing PM 2.5 concentrations. Among the socio-economic factors, proportion of the secondary industry, number of taxis, per capita GDP, population, and industrial nitrogen oxide emissions have positive correlation effects on PM 2.5 , while the overall social electricity consumption, industrial sulfur dioxide emissions, green coverage in built-up areas, and total gas and liquefied gas supply have negative correlation effects on the PM 2.5. [Display omitted] • Temporal variation of PM 2.5 in China was affected by precipitation and coal burning. • PM 2.5 declined dramatically in eastern and central China during the COVID-19 epidemic. • Decline of social activities had an important effect on the reduction of PM 2.5. • PM 2.5 concentrations in China were spatially agglomerated. • Socioeconomic factors affecting PM 2.5 were spatially heterogeneous. [ABSTRACT FROM AUTHOR]

• A novel global and local structure preserving nonnegative subspace clustering model is designed. • Nonnegative subspace clustering can directly obtain the cluster indicators and allocate cluster members. • Efficient multiplicative updating rules are developed by nonnegative Lagrangian relaxation to optimize the model. • The data similarities and cluster indicators are learned simultaneously and can promote each other through iterative optimization. • The effectiveness of the model is demonstrated by theoretical analysis and comprehensive experiments. Most subspace clustering methods construct the similarity matrix based on self-expressive property and apply the spectral relaxation on the similarity matrix to get the final clusters. Despite the advantages of this framework, it has two limitations that are easily ignored. Firstly, the original self-expressive model only considers the global structure of data, and the ubiquitous local structure among data is not paid enough attention. Secondly, spectral relaxation is naturally suitable for 2-way clustering tasks, but when dealing with multi-way clustering tasks, the assignment of cluster members becomes indirect and requires additional steps. To overcome these problems, this paper proposes a global and local structure preserving nonnegative subspace clustering method, which learns data similarities and cluster indicators in a mutually enhanced way within a unified framework. Besides, the model is extended to kernel space to strengthen its capability of dealing with nonlinear data structures. For optimizing the objective function of the method, multiplicative updating rules based on nonnegative Lagrangian relaxation are developed, and the convergence is guaranteed in theory. Abundant experiments have shown that the proposed model is better than many advanced clustering methods in most cases. [ABSTRACT FROM AUTHOR]

Improving the computational efficiency of reliability assessment is a long‐term goal for researchers. Regarding this issue, an impact‐increment‐based decoupled reliability assessment approach (IID) is proposed in this study. First, a new formulation of reliability indices is derived by decoupling the reliability index of composite systems into two parts: generation adequacy and transmission reliability. Then, an impact‐increment‐based state enumeration reliability assessment method is implemented to both the parts, which are more efficient than tradition methods. Thereafter, a reduction technique for the higher‐order contingency states is developed for the transmission reliability evaluation. With this technique, the number of analysed contingencies can be significantly reduced, which improves the computational speed significantly. Finally, the overall framework of the proposed IID approach is demonstrated. Case studies are performed on the RTS‐79, RTS‐96 and a provincial system of China. Results indicate that the proposed method has superior performance to the traditional reliability assessment methods in terms of accuracy and computational speed, particularly on large‐scale composite systems. [ABSTRACT FROM AUTHOR]

Spectral clustering will map the data points of the original space into a low-dimensional eigen-space to make them linearly separable, so it is able to process the data with complex structures. However, spectral clustering needs to store the entire similarity matrix and requires eigen-decomposition. Both procedures will consume a lot of time and space resources, limiting the application of spectral clustering algorithm in large-scale data environment. To reduce the complexity of spectral clustering algorithm, we may use the Nyström extension technique to calculate the approximate eigenvectors by sampling a few of data points. This method sacrifices the clustering accuracy in exchange for the improvement of the algorithm efficiency. To select more representative sample points to reflect the distribution of data sets much better, this paper designs a dynamic incremental sampling method used for the Nyström spectral clustering, in which the data points are sampled according to different probability distributions and we theoretically prove that the increase of sampling times can effectively decrease the sampling error. The feasibility and effectiveness of the proposed algorithm are analyzed by the experiments on UCI machine learning data sets. [ABSTRACT FROM AUTHOR]

A two-stage robust optimization (RO) method for buildings' space heating loads (SHLs) in an integrated community energy system (ICES) is proposed. At the first stage, a bi-level optimization is deployed to formulate the hierarchical relationship between the ICES operator and consumers in buildings to enable the optimal heating pricing strategy between them. Thermal inertia of SHLs, which is modelled utilizing the Resistor-Capacitor thermal network, is used to provide heating demand response according to the optimal heating sale prices released by the ICES operator. At the second stage, the ICES operator decides the optimal energy purchase schedules from the upper energy systems after the heating sale prices are decided at the first stage. Since the day-ahead energy prices differ from the real-time ones, a RO method is adopted. The original min–max RO problem is converted into its dual problem to cooperate with the bi-level optimization. Finally, the whole optimization model is transformed into a mixed integer linear programming (MILP) based on Karush-Kuhn-Tucker conditions, duality theory, big-M method and piecewise linearization. Numerical results show that the proposed model can balance the interests between the ICES operator and consumers. And the profit of the ICES operator is 5.10% higher using RO method, compared to the deterministic optimization under energy prices uncertainties. Compared to the benchmark solution method, the computation efficiency of the final MILP model in this paper is highly improved and the convergency of the MILP model can be guaranteed. [ABSTRACT FROM AUTHOR]

In an interconnected bulk power system, wide‐area measurement system (WAMS) can provide abundant remote measurement for the coordinated control system. However, WAMS data have obvious time delays, which affect the performance of the control system and should be properly considered. Time‐delay stability margin (TDSM) is an index to reflect the maximum delay that a time‐delay system can sustain without losing its stability. To construct a coordinated control system based on WAMS data, it is crucial to accurately and rapidly determine the TDSM value. In this paper, a novel Jordan‐Taylor‐Schur (JTS) approach to determine the TDSM value of power system is proposed. It consists of three, Jordan standardization, Taylor separation and Schur simplification. With the three procedures of this method, the model dimension of time‐delay system can be reduced, and the stability criterion derivation can be rebuilt to omit some unnecessary variables so that both the computational efficiency and accuracy can be boosted. Further, it is worth highlighting that the proposed method can be applied to various Lyapunov‐based stability criteria to improve their efficiency with small errors. Finally, three power systems with time delays have been used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A new partition‐composition method for online detection of transient stability of interconnected power system with the data from wide area measurement system (WAMS) is proposed. It consists of two parts: partition and composition. In the partition step, interconnected grids are divided into several local areas according to their geographical locations or ownership. Each area is modelled as an adjoint power system (APS) to determine its own synchronism. In the composition step, transient stability of entire grid is evaluated by the APS model of entire system composed with several key parameters of local areas, which can be directly derived from local WAMS. The proposed method only uses WAMS data, hence it is independent from the offline data, such as network topology, device model and system parameters. Another outstanding feature of the proposed method is that only a few key parameters are required to be transferred from the local control centres to the global control centre. These features are helpful for the interconnected grid with different owners since exhaustive data are unnecessary for the global control centre. Case studies on the 10‐generator New England system and one of 2007 operation cases of North China Power Grid validate its correctness and effectiveness. [ABSTRACT FROM AUTHOR]

• A thermal management model of CESS is developed considering the ambient temperature. • A refined LCC model is proposed to accurately track the BESS performance. • A collaborative operation scheme of SESSs and VSCs in AC/DC hybrid system is presented. • A coordinated operation strategy of SESS and MESS is proposed under various scenarios. • A two-stage capacity allocation model of SESS and MESS in AC/DC system is established. The international mega-event, such as the Winter Olympic Game, has been considered as one of the most carbon intensive activities worldwide. The commitment of fully renewable energy accommodation and utilization while ensuring the extreme high reliability has brought significant challenges on system operation due to the stochastic nature of the renewables. The battery energy storage system (BESS) composed of stationary energy storage system (SESS) and shared mobile energy storage system (MESS) can be utilized to meet the requirements of short-term load surges, renewable accommodation and emergency power supply for important loads during the mega-event. The BESS can continue to serve the venues' electricity consumption to satisfy the carbon neutrality after the event. On the other hand, the low ambient temperature of Winter Olympic game has significant impact on BESS's degradation and performance which need to be integrated to the charging and discharging models of BESS. To this end, a joint two-stage optimal configuration method considering the ambient temperature of SESS and MESS has been developed to support the mega-event carbon reduction, to reduce redundant BESS capacity allocation and improve the system life cycle cost-benefit. Simulation results have demonstrated the rationality and effectiveness of the collaborative operation of SESS and MESS under various scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a general approach for coherency detection in bulk power systems using the projection pursuit (PP) theory. Supported by the concept of center of inertia (COI) in power systems, the PP theory is employed to model the wide-area coherency detection as an optimization problem. In the proposed method, the optimal projection direction in high dimensional orthogonal space is explored in order to detect the coherent groups via the data from synchronous phasor measurement units (PMUs). Two quantitative indices constructed with projection assessment index (PI), the objective of the optimization model, are then defined in order to determine the critical coherent group and the dominant coherent groups. The coherency detection criterion and the implementation framework for the proposed approach are also presented. Simulation data from the 16-machine 68-bus test system and China Southern power Grid (CSG), along with actual field-measurement data retrieved from WAMS database in the CSG, are employed to demonstrate the effectiveness and applicability of the proposed algorithm under different disturbances. It is shown that the proposed methodology successfully detects the dominant coherent groups of generators and buses in bulk power system via the wide-area field-measurement data. [ABSTRACT FROM PUBLISHER]

This paper proposes a continuous time Markov chain (CTMC) based sequential analytical approach for composite generation and transmission systems reliability assessment. The basic idea is to construct a CTMC model for the composite system. Based on this model, sequential analyses are performed. Various kinds of reliability indices can be obtained, including expectation, variance, frequency, duration and probability distribution. In order to reduce the dimension of the state space, traditional CTMC modeling approach is modified by merging all high order contingencies into a single state, which can be calculated by Monte Carlo simulation (MCS). Then a state mergence technique is developed to integrate all normal states to further reduce the dimension of the CTMC model. Moreover, a time discretization method is presented for the CTMC model calculation. Case studies are performed on the RBTS and a modified IEEE 300-bus test system. The results indicate that sequential reliability assessment can be performed by the proposed approach. Comparing with the traditional sequential Monte Carlo simulation method, the proposed method is more efficient, especially in small scale or very reliable power systems. [ABSTRACT FROM AUTHOR]

Conventional direct load-shedding for achieving static voltage stability lacks considerations on both customer comfort and energy efficiency, resulting in higher cost and emission. A novel security-based, optimal load-shedding strategy considering customer comfort settings is presented in this paper. A temperature priority list method is used to model the virtual generator (VG) consisting of thermostatically controlled appliances (TCAs). To illustrate the control process and performance evaluation of the proposed load-shedding scheme, a modified IEEE 6-bus test system is used. Three heat, ventilation, and air-conditioning (HVAC) groups are numerically simulated to respond to optimal load shedding signals. Reduced responsive load population, variations of temperature dead-bands, and different outdoor temperature profiles are modeled to evaluate the capacity variations of the VG and its control performance. The results demonstrate that TCAs can provide satisfactory voltage stability. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper focused on investigating the band alignment at the Cu 2 SnS 3 (CTS)/In 2 S 3 heterojunction interface by X-ray photoemission spectroscopy. An In 2 S 3 over-layer was grown on a CTS thin film, which was grown by sulfurization of vacuum thermal evaporated Sn–Cu metallic precursors in a H 2 S:N 2 atmosphere. The valence band offset (VBO) at the CTS/In 2 S 3 interface was measured to be 1.27 ± 0.10 eV. The conduction band offset (CBO) was calculated from the measured VBO, giving (0.58 ± 0.10) eV. These values show that the CBO has a spike-like behavior and the interface is a ‘type I’. [ABSTRACT FROM AUTHOR]

This paper proposes a hierarchical energy management system for multi-source multi-product (MSMP) microgrids. Traditional energy hub based scheduling method is combined with a hierarchical control structure to incorporate transient characteristics of natural gas flow and dynamics of energy converters in microgrids. The hierarchical EMS includes a supervisory control layer, an optimizing control layer, and an execution control layer. In order to efficiently accommodate the systems multi time-scale characteristics, the optimizing control layer is decomposed into three sub-layers: slow, medium and fast. Thermal, gas and electrical management systems are integrated into the slow, medium, and fast control layer, respectively. Compared with wind energy, solar energy is easier to integrate and more suitable for the microgrid environment, therefore, potential impacts of the hierarchical EMS on MSMP microgrids is investigated based on a building energy system integrating photovoltaic and microturbines. Numerical studies indicate that by using a hierarchical EMS, MSMP microgrids can be economically operated. Also, interactions among thermal, gas, and electrical system can be effectively managed. [ABSTRACT FROM AUTHOR]

Natural gas (NG) network and electric network are becoming tightly integrated by microturbines in the microgrid. Interactions between these two networks are not well captured by the traditional microturbine (MT) models. To address this issue, two improved models for single-shaft MT and split-shaft MT are proposed in this paper. In addition, dynamic models of the hybrid natural gas and electricity system (HGES) are developed for the analysis of their interactions. Dynamic behaviors of natural gas in pipes are described by partial differential equations (PDEs), while the electric network is described by differential algebraic equations (DAEs). So the overall network is a typical two-time scale dynamic system. Numerical studies indicate that the two-time scale algorithm is faster and can capture the interactions between the two networks. The results also show the HGES with a single-shaft MT is a weakly coupled system in which disturbances in the two networks mainly influence the dc link voltage of the MT, while the split-shaft MT is a strongly coupled system where the impact of an event will affect both networks. [ABSTRACT FROM PUBLISHER]

In traditional distribution system, power flow was unidirectional; thus loads were usually aggregated into the substation as an equivalent constant bus in the original risk analysis of transmission system. Detail structures of the distribution system were absolutely ignored. However, distribution system is transformed from passive to active nowadays with more and more distributed generations (DGs) being integrated into it. So, distribution system not only can be considered as loads but also can be considered as virtual power plants to supply power to local loads and even to send power back to the transmission system sometimes. In this paper, a hierarchical method is presented to evaluate the impact of role changing of the distribution system in power system risk assessment. In the study, DGs can supply critical loads in distribution system through re-dispatching after some contingency happens. Discrete probability model is employed to simulate the intermittent output of DGs. Available supply capacity (ASC) is used as a general constraint to guarantee the security level of distribution system that needs high reliability. Also, an iterative calculation between transmission system and distribution network is adopted to derive the risk indices with considering detail structures of the active distribution network. Further, impact of some factors on system risk calculation, such as DGs' capacities, dispersions, locations, and component outage probabilities, are discussed. Finally, correctness and effectiveness of the proposed method are validated by a modified IEEE RTS and RBTS system. [ABSTRACT FROM PUBLISHER]

In this paper, a new criterion to quickly evaluate the system transient stability based on wide area measurement system (WAMS) information, energy function method and adjoint power system (APS) model is proposed. APS is a two‐dimensional‐first‐order differential equation model, which can be considered as a virtual representation to the real power system. Meaning of APS is first explained by using the one machine infinite bus system. Then, a new criterion based on projection kinetic energy of APS is proposed to determine the transient stability of the original power system with multiple generators. Trajectory prediction method based on curve fitting technique is adopted to accelerate the evaluation process. The proposed method is sufficiently rapid since it does not rely on the network structure, system model, system parameters and the identification of the coherent generator groups. Therefore, it can be utilised for online transient stability assessment based on WAMS. Simulation results on the New England system and one of North China Power System validate the correctness and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A directional control method (DCM) for power flows on a set of interface lines between two regions of power system considering static voltage stability margin is developed in this paper. A surface approximation approach is firstly used to obtain the relationship between the interface flow solution and the generation direction of generator (the portion of generation variation in each participating generator to satisfy the desired power increase on the interface and the system loss). Then, an optimization model is built to determine the optimum dispatching scheme of generators. This method not only can control the total power on the interface to satisfy the power demand but also can realize the directional control of power on each interface line based on the needs of operation. The proposed DCM is further extended to determine the optimum dispatching scheme of generators for maximizing the interface flow margin (IFM), which is the active power margin of the key transmission lines between two regions of power system constrained by static voltage stability. A modified continuation power flow (MCPF) is used to show and evaluate the impacts of the DCM on the IFM. The New England 39-bus system and the IEEE 300-bus system have been employed to verify the effectiveness of the DCM. [ABSTRACT FROM AUTHOR]

As a novel clustering method, affinity propagation (AP) clustering can identify high-quality cluster centers by passing messages between data points. But its ultimate cluster number is affected by a user-defined parameter called self-confidence. When aiming at a given number of clusters due to prior knowledge, AP has to be launched many times until an appropriate setting of self-confidence is found. K-AP algorithm overcomes this disadvantage by introducing a constraint in the process of message passing to exploit the immediate results of K clusters. The key to K-AP clustering is constructing a suitable similarity matrix, which can truly reflect the intrinsic structure of the dataset. In this paper, a density-adaptive similarity measure is designed to describe the relations between data points more reasonably. Meanwhile, in order to solve the difficulties faced by K-AP algorithm in high-dimensional data sets, we use the dimension reduction method based on spectral graph theory to map the original data points to a low-dimensional eigenspace and propose a density-adaptive AP clustering algorithm based on spectral dimension reduction. Experiments show that the proposed algorithm can effectively deal with the clustering problem of datasets with complex structure and multiple scales, avoiding the singularity problem caused by the high-dimensional eigenvectors. Its clustering performance is better than AP clustering algorithm and K-AP algorithm. [ABSTRACT FROM AUTHOR]

SUMMARY This paper presents a novel methodology for constructing wide area thermal security region (WATSR) to monitor the wide area thermal security of bulk power system under ' N−1' criterion by utilizing wide area measurement system. A general cutset angle for large-scale inter-area power system is defined and developed as a measurement to assess the system thermal security so as to construct WATSR in cutset-angle space. WATSR consists of all stable operating points under ' N−1' criterion, while its boundary is a complete set of all the critical points. In this paper, the boundary of WATSR is represented via its piecewise approximation with a set of hyperplanes to enhance the calculating speed. Hyperplanes are described as a set of linear equalities, which are convenient to be determined. And, because coefficients of a hyperplane can indicate the sensitivities between various control parameters, it is beneficial to power system online security assessment and control strategy optimization. A two-step algorithm is given to apply WATSR to online monitoring for bulk power system. The first step is offline calculation. The aim of this step is to determine a minimum set of hyperplanes to properly describe the boundary of WATSR. A hyperplane is a sub-segment of the security region boundary and normally corresponds to a certain constraint. The second step is online matching. Data retrieved from wide area measurement system are used to determine the relative constraints given by the hyperplanes. And then, the stability of the system can be quickly and accurately determined by the assistance of WATSR result. Once the power system turns to critical, WATSR can help the operators to optimize their control strategy. The methodology of WATSR has been successfully implemented in the China Southern Power Grid, which is known as one of the largest alternating current/direct current parallel transmission system in the world. Feasibility and effectiveness of the presented method have been validated through intensive analyses in China Southern Power Grid. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study presents a general approach to identify dominant oscillation modes in bulk power system by using wide‐area measurement system. To automatically identify the dominant modes without artificial participation, spectral characteristic of power system oscillation mode is applied to distinguish electromechanical oscillation modes which are calculated by stochastic subspace method, and a proposed mode matching pursuit is adopted to discriminate the dominant modes from the trivial modes, then stepwise‐refinement scheme is developed to remove outliers of the dominant modes and the highly accurate dominant modes of identification are obtained. The method is implemented on the dominant modes of China Southern Power Grid which is one of the largest AC/DC paralleling grids in the world. Simulation data and field‐measurement data are used to demonstrate high accuracy and better robustness of the dominant modes identification approach. [ABSTRACT FROM AUTHOR]

Spectral clustering is a clustering method based on algebraic graph theory. It has aroused extensive attention of academia in recent years, due to its solid theoretical foundation, as well as the good performance of clustering. This paper introduces the basic concepts of graph theory and reviews main matrix representations of the graph, then compares the objective functions of typical graph cut methods and explores the nature of spectral clustering algorithm. We also summarize the latest research achievements of spectral clustering and discuss several key issues in spectral clustering, such as how to construct similarity matrix and Laplacian matrix, how to select eigenvectors, how to determine cluster number, and the applications of spectral clustering. At last, we propose several valuable research directions in light of the deficiencies of spectral clustering algorithms. [ABSTRACT FROM AUTHOR]

This study presents a detailed performance evaluation of comfort‐constrained state‐queueing model for integration of thermostatically controlled appliances (TCAs) as virtual generators (VGs). The configuration of VG models is presented. The heating mode of heating, ventilating and air‐conditioning (HVAC) unit – heat pump is used as an example TCA load to demonstrate VG operation. First, an estimated baseline of the aggregated HVAC loads is obtained based on outdoor temperature forecasts using thermal dynamic models. Then, the control method of HVAC units for intra‐hour load balancing is proposed. The effects of different comfort‐constraints, outdoor temperature profiles and thermal deadbands are modelled to evaluate the performance of TCAs as VGs. The technical functionality of the TCA‐VG is described and demonstrated by the modelling results. [ABSTRACT FROM AUTHOR]

Clustering is often considered as an unsupervised data analysis method, but making full use of the prior information in the process of clustering will significantly improve the performance of the clustering algorithm. Spectral clustering algorithm can well use the prior pairwise constraint information to cluster and has become a new hot spot of machine learning research in recent years. In this paper, we propose an effective clustering algorithm, called a semi-supervised spectral clustering algorithm based on pairwise constraints, in which the similarity matrix of data points is adjusted and optimized by pairwise constraints. The experiments on real-world data sets demonstrate the effectiveness of this algorithm. [ABSTRACT FROM AUTHOR]

One type of bifurcation named oscillation emergence bifurcation (OEB) found in time-delayed linear time invariant (abbr. LTI) systems is fully studied. The definition of OEB is initially put forward according to the eigenvalue variation. It is revealed that a real eigenvalue splits into a pair of conjugated complex eigenvalues when an OEB occurs, which means the number of the system eigenvalues will increase by one and a new oscillationmode will emerge. Next, amethod to determine OEB bifurcation in the timedelayed LTI system with single lag is developed based on LambertWfunction. A one-dimensional (1-dim) time-delayed system is firstly employed to explain the mechanism of OEB bifurcation. Then, methods to determine the OEB bifurcation in 1-dim, 2-dim, and high-dimension time-delayed LTI systems are derived. Finally, simulation results validate the correctness and effectiveness of the presented method. Since OEB bifurcation occurs with a new oscillation mode emerging, work of this paper is useful to explore the complex phenomena and the stability of time-delayed dynamic systems. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new hysteretic loop control strategy for frequency regulation is developed. [•] A statistical model to determine the capacity of BSHS is proposed. [•] Monte Carlo simulation is implemented to the statistical model. [•] The capacities of BSHS at different cumulative probability levels are determined. [•] The control strategies have close relationship with the capacity of BSHS. [ABSTRACT FROM AUTHOR]

• A new matrix factorization strategy is designed for Nyström spectral clustering. • Incomplete Cholesky decomposition is introduced to accelerate Nyström approximation. • An efficient Nyström spectral clustering algorithm called ICD-NSC is proposed. • The effectiveness of ICD-NSC is demonstrated by comprehensive experiments. Nyström method can estimate the eigenvectors of a large kernel matrix with the eigenvectors of a small sampled sub-matrix. However, we may encounter two problems when using Nyström method to speed up spectral clustering: one problem is the approximate eigenvectors generated by standard Nyström method are sub-optimal, so they may impair the performance of spectral clustering; another one is the accurate Nyström approximation needs a sufficient number of samples, which will increase the eigen-decomposition cost on the sampled sub-matrix. To solve these problems, this paper proposes an efficient Nyström spectral clustering algorithm using incomplete Cholesky decomposition, in which a new matrix factorization strategy is designed for Nyström spectral clustering to meet the orthogonal constraints, and an efficient eigensolver based on incomplete Cholesky decomposition is developed to accelerate the Nyström approximation. In this way, the obtained approximate orthogonal eigenvectors will help to improve the clustering quality, and the developed eigenvector calculation method will help to reduce the clustering complexity. The experimental results show that the proposed algorithm performs well on many challenging data sets, and it can accomplish more complex clustering tasks with limited computing resources. [ABSTRACT FROM AUTHOR]

The open-circuit fault detection and localization (FDL) technique can improve the reliability of the modular multilevel converter (MMC). However, the conventional software-based FDL methods usually have a heavy computation burden or a limited localization speed. This article proposes a simplified and fast software-based FDL approach for the grid-connected MMC. First, the errors between the measured state variables (the output current and the circulating current) and their estimated values are calculated. By comparing these errors with their threshold values, the switch fault can not only be detected but also be localized to the specific arm. Then, the capacitor voltages in this faulty arm are collected, and the submodule (SM) with the highest capacitor voltage is selected. To confirm the switch fault in this SM, a modified Pauta criterion is presented to check the abnormal voltage data. As a result, the computation burden of the proposed software-based FDL approach is significantly reduced, and the faulty SM can be localized in a short period. Simulation and experimental results verify that the proposed approach can effectively detect and localize different open-circuit faults, and it is immune to the step of power references. [ABSTRACT FROM AUTHOR]

Abstract: With the rapid development of phase measurement unit and wide area measurement system (PMU/WAMS), power system coordinated stability control has been paid more and more attention. Since there is obvious time delay in WAMS measurement, it is important to properly evaluate the impact of time delays on power system stability analysis and controller design. In this paper, influence of time delay on power system small signal stability region is deeply investigated. Small signal stability of differential algebraic equation with time delay is first reviewed as the necessary theoretical background. Then an optimal-based algorithm is given to trace boundaries of the small signal stability region with considering the time delay. Finally, two simple systems are employed to do some verification studies. It is found that time delay has significant influence to the boundary of the small signal stability region, especially when the time lag is large. This paper is helpful to properly consider the effect of time delay in power system controller design and small signal stability studies. [Copyright &y& Elsevier]

In this paper routes to chaotic oscillation in power systems and relationships between chaos and various power system instability modes are deeply studied. Firstly, using a three-bus simple system, three routes may cause power systems to chaos are illustrated and discussed. They are route of cascading period doubling bifurcation (PDB), route of torus bifurcation (TB) and route of directly initiated by a large disturbance of energy. The route of cascading PDB is a typical route to chaos, where PDB is caused by a real Floquet multiplier (FM) moving counter to the real axis and going out of the unit circle from point (−1, 0) in the complex plane. TB is caused by a couple of conjugate FMs going out of the unit circle with a nonzero imaginary part in the complex plane. Chaos caused by TB has some interesting features, such as self-organizing phenomenon, coexistence of divergent and chaotic subspaces, etc. The last route, which is directly initiated by a large disturbance of energy, is reported and studied for the first time so far as we know. Secondly, relationships between chaos and various instability modes are discussed. Also using a simple system, we illustrate the scenes that chaos leads power system to voltage collapse, angle divergence, or voltage collapse with angle divergence simultaneously when the stability conditions of the chaotic oscillation are broken. It tells us that chaos is very likely to be an intergrade existing in the transient stage after a large disturbance. In order to prevent the appearance of instability incidents effectively, it is necessary to keep up on the study of chaotic phenomena in power systems. All these studies are helpful to deeply understand the mechanism of various instability modes and to find effective anti-chaos strategies. [Copyright &y& Elsevier]

Highlights This article proposes a 6061‐T6 Al alloy and carbon fiber hybrid composite material pre‐curing and forming integrated new process, which can significantly reduce the hybrid composite material processing time, improve the production efficiency, and provide the possibility of hybrid composite materials in automotive parts applications. The process started by placing hand‐stacked carbon fiber reinforced plastic (CFRP) composites on the surface of 6061 Al alloy sheets, followed by bonding and pre‐curing of the CFRP to the Al alloy sheets through two different sets of dies in a hydraulic press, and ultimately full curing in an aging furnace. Mechanical properties of the CFRP/Al hybrid composite material were measured by flexural tests. The results show that the flexural strength of CFRP/Al hybrid composites with different layups ([90]12, [0/45/90/−45]3, and [0]12) was increased by 159%, 93.4%, and 67.9%, respectively, compared with the single 6061 Al alloy. According to the macro‐morphological observation, [90]12 lay‐up parts have the least thinning. According to the microstructure observation, no obvious cracks or voids were found in the [90]12 lay‐up parts. Elongated cracks and void defects could be found in the [0]12 lay‐up parts. For the [0/45/90/−45]3 lay‐up parts, voids are mainly exist in ±45°layups. The results of the spring‐back analysis show that the standard deviation of the formed parts obtained from the three lay‐up methods is within 1 mm, indicating that the forming effect is good. New process improves productivity and reduces production costs The flexural strength of CFRP/Al hybrid parts is greatly improved [90]°12 lay‐up parts have the least thinning and no cracks were found The forming precision of the three lay‐up parts is good [ABSTRACT FROM AUTHOR]

• Revealed the existence of GPP pools in yeast peroxisomes and mitochondria. • Engineered subcellular compartmentalization to promote sabinene production. • Uncovered novel, mitochondrial targets enhanced the compartmentalized pathways. Geranyl diphosphate (GPP) is a direct precursor in the biosynthesis of monoterpenes. Previous studies focused on the manipulation of metabolic flux to improve GPP supply in yeast. However, if the subcellular distribution of GPP with monoterpene synthase is not coordinated, the usage of GPP becomes sub-optimal. Therefore, taking sabinene production in Saccharomyces cerevisiae as an exemplar, we confirmed the location of N-truncated sabinene synthase (t34SabS1) to be primarily in the cytosol. We also revealed the existence of GPP pools in the peroxisomes and mitochondria. Combined targeting of t34SabS1 into different combinations of subcellular locations demonstrated that the highest production of sabinene was obtained when expressed simultaneously in the cytosol and mitochondria. Further overexpression of mitochondria-related genes uncovered four novel molecular targets (FIS1 , LSB3 , MBA1, and AIM25) associated with sabinene output, with AIM25 resulting in the highest production. Eventually, integrating all of these engineered genes into the host chromosome achieved a sabinene production of 154.9 mg/L, an almost 60-fold increase from our original titer. This research highlights the strategy of organelle engineering to improve precursor utilization and to enhance compartmentalized pathways. It also provides a good reference for the synthesis of other valuable monoterpenes and their derivatives, in a eukaryotic host. [ABSTRACT FROM AUTHOR]

With the high penetration of the power electronic loads in the grid, the stability of static synchronous compensator (STATCOM) devices is greatly challenged. However, the conventional control methods for the modular multilevel converter (MMC) based STATCOM only consider the stability with small signal disturbances. This article proposes a novel dual-layer back-stepping control (BSC) for the MMC-based STATCOM. In the first layer, the BSC aims to regulate the sum of the capacitor energy and the reactive output current. In the second layer, the BSC aims to control the circulating current. Therefore, the proposed method possesses a fast dynamic response and accurate tracking with the Lyapunov stability of the MMC-based STATCOM. Compared with the arm-control-based BSC for MMC-based inverters, the proposed method has a simplified structure and a reduced computation burden. Moreover, the proposed method realizes the independent control between the output current and the circulating current. The simulation and experimental results verify the effectiveness of the proposed method. In addition, its robustness toward different circuit parameters and the operation ability under unbalanced grid fault is also verified. [ABSTRACT FROM AUTHOR]

The model predictive control (MPC) methods have been widely applied due to the fast dynamic performance and multiple control targets. However, they show certain limits when applied to the multilevel converters, such as unfixed switching frequency, large computation burden, and complex weighting factors selection. This article proposes a novel modulated MPC (M2PC) method for cascaded H-bridge (CHB) converter-based static synchronous compensator (STATCOM). Instead of multitarget integration and weighting factor design, the proposed method disintegrates the voltage balancing control process from the cost function. The evaluated control sets for output current control is decided by the search step and the search range in each period. By proper design and selection of the adaptive search step for the output voltage references under α–β coordinate, the search range can be reduced, which greatly reduces the number of evaluated control sets in each control period. Therefore, the dynamic of output characteristics and the low computation burden of the MPC method are guaranteed simultaneously. In addition, with the simple and independent integration of the capacitor voltage balancing control, the proposed method can be easily applied in the CHB converter-based STATCOM application. Furthermore, this article provides a delay compensation for the prediction process, and it provides a fixed switching frequency for each H-bridge cell. Simulation and experimental results verify the effectiveness of the proposed M2PC method. [ABSTRACT FROM AUTHOR]

To solve the problem of poor formability of 7075-T6 aluminum alloy at room temperature and the degradation of properties after conventional warm forming, this paper put forward the contact heating warm forming (CHWF) process, which can make aluminum alloy blank reach the forming temperature in a very short time through the heated plates with pressure. Firstly, the uniaxial warm tensile tests with contact heating were carried out at 160–240 °C. Then the parameters of CHWF, namely forming temperature, contact pressure and holding time, were optimized based on the response surface and genetic algorithm. The U-shaped parts were obtained by using the optimal parameters. The results showed that the work hardening and dynamic recovery softening of 7075-T6 aluminum alloy at 200 °C could reach an equilibrium state, and the uniform plastic deformation ability was strong. The optimal parameters of CHWF were the forming temperature of 200 °C, the contact pressure of 10 MPa, and the holding time of 16 s. The U-shaped parts obtained by the optimal process had a small springback angle. Compared with the warm forming by using heating furnace, η′ precipitate did not grow significantly under CHWF condition and the hardness could reach 93% of T6 state. [ABSTRACT FROM AUTHOR]

With the integration of multiple types of loads and renewable generations, the number of system states significantly grows. As a result, running optimal power flow (OPF) to analyze a myriad of system states is challenging and this seriously restricts the efficiency of the state enumeration method. To address that, this paper proposes a Lagrange Multiplier based State Enumeration (LMSE) approach to accelerate the analysis without loss of accuracy. The core idea is to directly obtain the optimal load shedding of contingency states by Lagrange multiplier-based functions, rather than the time-consuming OPF algorithms. This approach can also be conveniently integrated with the impact-increment method and the clustering technique for further efficiency enhancement. Case studies are performed on the RTS-79 and IEEE 118-bus systems considering multiple types of loads, photovoltaics (PVs), and wind turbines (WTs). Results indicate that the proposed method can significantly reduce the computing time without compromising the calculation accuracy. [ABSTRACT FROM AUTHOR]

Simulating the output performance of a proposed photovoltaic plant is a necessary phase prior to the construction stage and has been intensively studied for decades. The requirement for accurate and reliable simulation needs a comprehensive analysis of the photovoltaic power generator considering environmental factors and device behaviors. Unlike other published work, this paper presents an analytical approach to simulate the output performance of photovoltaic arrays using coupled multi-physics modeling without the support of historical data. The interplay among the electrical and thermal models, device characteristics, and device topologies are deeply investigated and formulated. In the electrical model, a parameter extraction method, as well as an iterative algorithm, are utilized on the equivalent circuit model for a quick solution of the I–V characteristics of the photovoltaic module. To obtain accurate results of the output power and its interaction with the environment, irradiation, temperature, humidity, wind speed, and wind direction are analyzed along with the photovoltaic output power in the thermal model. Meanwhile, this output power is constrained to the maximum power point tracking mechanism of the inverter. Real data from a photovoltaic plant are used for comprehensive analysis in both long-term and short-term scenarios. The experiment shows that the proposed approach is competitively accurate and feasible. Compared with the state-of-the-art studies, the proposed approach improves the simulation accuracy by 7.75%. • Coupling the environment factors improved the accuracy of PV module thermal analysis. • Power output and inverter MPPT mechanism are feedback to the multi-physics model. • Applicable to the planning phase of power station, no historical data required. [ABSTRACT FROM AUTHOR]

Pulsed power loads (PPLs) are commonly incorporated in medium voltage dc microgrids on naval vessels. To mitigate their detrimental effects, dedicated energy storage systems can be installed and their converters need to have excellent disturbance rejection capability. To facilitate this objective, a moving-discretized-control-set model-predictive-control (MDCS-MPC) is proposed in this letter and applied on a dual-active-bridge converter. Fixed switching frequency is maintained, enabling easy passive components design. The proposed MDCS-MPC has a small number of calculating points in each switching period, which enables the implementation in standard commercial control platforms. The operating principle of the MDCS-MPC is introduced in development of a cost function that, on one hand, provides stiff voltage regulation; on the other hand, minimizes transformer current stress online. Theoretical claims are verified on a 20 kHz 1 kW dual active bridge. [ABSTRACT FROM AUTHOR]

• A multi-level overload relief method for transmission network is proposed. • The proposed method can capitalize on the capability from the ADN for overload relief in transmission network. • An interactive mechanism between the transmission network and ADN is proposed. With the fast development of technologies of renewable generations and distributed energy resources, the distribution grids become more and more active, and the coordinated transmission and distribution management (CTDM) becomes more important. Under the CTDM framework, this paper proposes a multi-level overload relief method for transmission network using the capability from an active distribution network (ADN). When an overload event occurs at the transmission network, an appropriate operational scheme for ADN is selected by the proposed multi-level method. Then, the ADN takes the decided operational scheme and adjusts its operational status (i.e., reconfiguring its network topology, transferring loads to Microgrids and shedding the controllable loads) to contribute to load curtailment from the ADN side and further alleviate overload in the transmission network. Numerical case studies show that the proposed method can alleviate the overload of transmission network and maintain the normal operation of the ADN, which demonstrates a promising application for CTDM. [ABSTRACT FROM AUTHOR]

• The models of responsive electric and heat load are built considering user comfort. • The bidding and control strategies of electric and heat load are proposed for IDR. • The electricity-heat coordinated retail energy market framework is proposed. • The electricity-heat coordinated double auction market model is established. Integrated demand response (IDR) is an important approach to promote renewable energy consumption and improve energy efficiency in integrated energy systems. In IDR, not only multi-energy users, but also demand-side energy stations, can become demand response resources. Hence, making full use of the flexibility of energy supply and the elasticity of electricity and heat demand is vital to the development of IDR. In this paper, based on the background of the electricity-heat integrated energy system, the precise models of typical controllable electric and heat load are constructed considering the user's requirements for comfort. Based on the model, the electricity-heat coordinated retail market framework is proposed to achieve the coordinated clearing of electric and heat load, managing the district energy generation and consumption through transactive control methods. The bidding strategies for electric and heat load are established for customers to participate in the market. The market clearing rules are defined with the aim of maximizing the net revenue of integrated energy service agency to realize the optimal energy allocation of energy station devices. Finally, the case study demonstrates that the proposed method can optimize both the resource allocation of demand-side energy stations and the energy use of customers through economic means. About 80–90% of wind power can be absorbed through participation in market bidding, and the specific consumption rate depends on the type of energy stations and users' demand. By guiding customers to consume energy reasonably during different market clearing periods, the energy expenditure can be reduced by 18.76% on average under the premise that the users' comfort is guaranteed. [ABSTRACT FROM AUTHOR]

7075 aluminum alloy is becoming an ideal material for the manufacture of vehicle body parts due to the demands for energy-saving and lighter vehicles. To solve the problem that the strength of 7075-T6 aluminum alloy decreases due to over-aging after warm forming, contact heating warm forming (CHWF) technology was proposed in this work. Contact heating is a fast and efficient heating method that can make the blank reach the target temperature in a very short time. In this work, the effect of contact heating on the mechanical properties and microstructures of 7075-T6 aluminum alloy after warm forming and paint-baking (PB) was studied. It only took about 11.5 s to heat the 2 mm 7075 aluminum alloy sheet to 200 ℃ by contact heating, and the strength and the hardness of the formed parts could reach 94% and 92.5% of T6 condition, respectively. In contrast, the heating furnace needed 690 s to heat the sheet to 200 °C, and the strength and the hardness of the formed parts were 87% and 85.4% of T6 condition, respectively. Due to the fast heating rate of contact heating (17.5 ℃/s), the atoms and vacancies in the matrix did not have time to undergo diffusion and aggregation, so that the precipitates could not coarsen obviously. As a result, most of the η' precipitates in the T6 condition were retained. However, long-term heating in the furnace led to the transformation of η' precipitates into coarse η phase and the loss of strength and hardness. [ABSTRACT FROM AUTHOR]

Qu, B.; Zhong, M.; Jia, H.; Yan, H.; Zhang, J., and Sun, Y., 2019. Power flow optimization for an offshore multi-energy system. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 585–596. Coconut Creek (Florida), ISSN 0749-0208. The increase of offshore PV and wind power installation capacity can increase the uncertainty and randomness of the power system. Therefore, it is necessary to study the offshore multi-energy system. A traditional offshore community with hybrid energy storage system (HESS) installation usually participates in demand response program to improve system economy. However, due to the high price and low discharge power of the HESS, it cannot response to the loads effectively. This paper introduces water electrolysis and on-board hydrogen storage technologies to an offshore multi-energy system, so as to effectively optimize the operation of system. Subsequently, to analyze the impact of climate change on system operation, the concept of loads matching degree is firstly defined. Finally, this paper gives a suggestion for an existed offshore multi-energy system on how to reasonably increase renewable generation system installation capacity. Results show that compared with the traditional offshore multi-energy system, the improved system can significantly reduce system operation costs and carbon emissions by 23.02% and 48.43%, respectively. Additionally, with the increase of loads matching degree, offshore system optimal operation costs, carbon emissions and the amount of hydrogen produced by water electrolysis reduce significantly. Finally, the growth of renewable energy generation capacity can lead to the reduce of emissions and increase of operating costs. [ABSTRACT FROM AUTHOR]

The digital control system of modular multilevel converters (MMCs) normally synchronizes with the switching actions. The switching frequency of an MMC is generally selected as low as possible from an efficiency point of view, which leads to an unavoidable and relatively long time delay in the control system. Such a time delay might deteriorate the MMC performance and even reduce the stability margin by introducing infinite uncertain eigenvalues into the system state functions. Without a proper time-delay model and quantitative system stability analyses, MMCs might suffer from uncertainties and unstable risks during their operations, especially when the switching frequency is low. This paper derives an MMC model considering the time delay in the digital control system. A Padé-based stability analysis method is then proposed, which consists of three steps: information integration and critical eigenvalue extraction, and trajectory visualization and critical angle (CA) calculation, and quantitative assessment and extreme value determination. By the proposed model and method, the critical eigenvalue can be effectively extracted. With the Hopf bifurcation, the time delay in the digital control system induces a pair critical conjugate eigenvalues into MMC systems, which is detected as an instability cause. The CA and the critical impact are defined to quantitatively assess the stability, while selecting the controller parameters and minimum switching frequency. The proposed method for the MMC stability assessment is experimentally verified. [ABSTRACT FROM AUTHOR]