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Virtual power plant (VPP) has become a promising technique to facilitate distributed energy resources (DERs) to participate in the power markets. Considering the respective interests of the VPP agent and distribution system operator (DSO), a bi-level optimisation model for VPP bidding in multiple retail markets (including active power, reactive power and spinning reserve market) as price-maker is formulated. In the upper layer, taking into account various DERs, the VPP agent aims to develop hourly bidding prices and quantities of multiple market commodities to maximise its operation profits. In the lower layer, DSO conducts the retail market clearing to minimise the system operation cost considering the network constraints and bidding plans of the market participants. Moreover, the quadratic coupling constraints among different market commodities due to capacity limitation of distributed generators and branches are formulated explicitly in the proposed model. The hybrid simulated annealing-genetic algorithm with self-adaptive parameters is adopted to cope with the non-linearity and compute the economic bidding plans for VPP. The effectiveness of the proposed approach is verified under different scenarios through case studies, which indicate its superiority and great potential for implementation. [ABSTRACT FROM AUTHOR]

Power system security assessment is an important and challenging problem. Large variations in loads and power generation present increased risks to the secure operation of power systems. This study proposes a distributed deep network structure for power system security knowledge discovery based on multitask learning to monitor and control power grids more properly and effectively. First, a deep neural network structure based on the deep belief network (DBN) is designed to non‐linearly extract deep and abstract features layer‐by‐layer for total transfer capability (TTC) regression tasks. Then, a distributed training algorithm for the deep structure is developed to accelerate the training process. Furthermore, multitask learning is adopted by grouping and training‐related tasks together to improve the task performance. Finally, the accuracy and efficiency of the deep structure are evaluated using the Guangdong Power Grid in China. The simulation results demonstrate that the proposed model can outperform the existing shallow models in terms of accuracy and stability and can meet the requirements of online computing efficiency. [ABSTRACT FROM AUTHOR]

Short‐term voltage stability (SVS) is a serious issue in modern power systems. In China, the East China Power Network is especially vulnerable to short‐term voltage instability due to its increasing dependence on electrical power from external through high‐voltage direct current (HVDC) transmission lines. To study the SVS, a criterion/index is first required to evaluate the SVS of power systems. However, the currently used practical criteria cannot effectively evaluate the influence of controlling strategies (such as regulating dynamic VAR reservation) of power systems. Therefore, a practical and continuous SVS index (SVSI) based on voltage curves is proposed in this study. The proposed SVSI can quantify the extent of SVS in power systems, thus can be used in the optimisation target of preventive control for improving the SVS of power systems. Moreover, the SVSI consists of three components, and each component reflects a feature of the voltage curve in terms of SVS. The SVSI is essentially a model‐free method for assessing the SVS of power systems, so it is robust. Three verifying cases and one application case are presented to show the validity of the SVSI and the feasibility of the SVSI in practical applications. [ABSTRACT FROM AUTHOR]

To prevent the wind units from cascading trip faults, the concept of an autonomous voltage security region (AVSR) has been discussed in previous studies. However, the AVSR is sometimes not sufficiently large due to the insufficient dynamic reactive power reserve (DRPR) under normal conditions, resulting in secure operation problems and a large amount of wind curtailment. Therefore, reservation of an appropriate capacity of dynamic reactive power is a crucial issue in wind farms, so a DRPR optimisation model to enlarge the AVSR is proposed in this work. The computation of the optimal DRPR includes six steps and involves the exchange of information between wind farms and the control centre. The computation is rapid and can be applied to real wind systems online. The proposed method demonstrated good performance in a simple test case and a real system in Northern China. [ABSTRACT FROM AUTHOR]

This study describes a solution to discriminate the original sources of bad data which are concerned by the substation maintainers. Bad data caused by defects of monitoring system, such as mal‐setting of monitoring devices or time skew, are concerned here. These bad data can be identified mostly in electric power control centre by bad data identification (BDI) methods including robust state estimation; however, these methods can hardly explain the relevant original sources of bad data in substations. Here, the authors propose a data‐driven method named abductive identification (AbI) to discriminate the original sources of bad data already identified by BDI methods. In the AbI method, feature patterns of bad data, based on their residuals, are investigated. A feature pattern selection method for bad data is then proposed, in which both the absolute residual and relative residual are considered. Based on this feature selection approach, a Bayesian classifier is developed to identify the original sources of bad data. A two‐level architecture for the AbI approach is introduced to deal with diverse bad data patterns of wide‐area networks. Simulation and field test results demonstrate the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

In active distribution networks, distributed generators are integrated into microgrids or geographically distributed subsystems. They may belong to different owners and are operated independently to preserve privacy. Therefore, fully distributed economic dispatch (ED) methods are needed and most existing algorithms show first‐order convergence. This study introduces a fully distributed dynamic ED with second‐order convergence, which is based on a parallel primal–dual interior‐point algorithm with a matrix‐splitting technique. In this method, each area optimises its own problem with limited information exchanged with its neighbours, and no central coordinator is needed. Numerical tests demonstrate that the algorithm can converge at a second‐order rate. On the basis of a peer‐to‐peer communication paradigm, this method can achieve a global optimum while the privacy of each area is obliquely protected. [ABSTRACT FROM AUTHOR]

With the growing application of electric vehicles (EVs), it is of great significance to have a deep understanding of EV users driving and charging patterns for charging forecasting. However, the rapid growth scale of EV taxis with charging patterns that are closely coupled with human behaviours of temporal–spatial charging choices was not compatible with most previous coordinated strategies. Unlike the majority of existing approaches, a large volume of second‐level EV global positioning system (GPS) data was used to study the behaviour patterns of EV users. In practise, a mobile crowdsensing system that records GPS data and transmits information to the server was deployed in a fleet of electric‐taxi cabs in Shenzhen, China, making it possible to record the exact behaviour of each vehicle. Travelling and charging statuses of EVs were recorded and analysed into different characteristics of behaviour for each user. The load forecast methods proved to be more effective with more knowledge of both history and real‐time data. [ABSTRACT FROM AUTHOR]

A multivariate statistical analysis (MSA)‐based power‐grid‐partitioning method is proposed. Considering the effectiveness of automatic voltage regulation of generators, network modelling is based on the quasi‐steady‐state sensitivity of voltage with respect to reactive power. The MSA‐based power‐grid‐partitioning method contains two stages: a quantitative method for determining the appropriate number of zones and allocation of nodes into a number of zones. The proposed method is applied to an automatic voltage control system and an N − 1 robust pilot node selection method is presented. Simulation studies on IEEE 14‐bus, 39‐bus systems and a real provincial power system in China are described to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This study presents the distributed model predictive control (D‐MPC) of a wind farm equipped with fast and short‐term energy storage system (ESS) for optimal active power control using the fast gradient method via dual decomposition. The primary objective of the D‐MPC control of the wind farm is power reference tracking from system operators. Besides, by optimal distribution of the power references to individual wind turbines (WTs) and the ESS unit, the WT mechanical loads are alleviated. With the fast gradient method, the convergence rate of the D‐MPC is significantly improved which leads to a reduction of the iteration number. Accordingly, the communication burden is reduced. Case studies demonstrate that the additional ESS unit can lead to a larger WT load reduction, compared with the conventional wind farm control without ESS. Moreover, the efficiency of the developed D‐MPC algorithm is independent from the wind farm size and is suitable for the real‐time control of the wind farm with ESS. [ABSTRACT FROM AUTHOR]

Confronted with uncertainties, especially from large amounts of renewable energy sources, power flow studies need further analysis to cover the range of voltage magnitude and transferred power. To address this issue, this work proposes a novel interval power flow for the radial network by the use of an extended, simplified DistFlow formulation, which can be transformed into a set of interval linear equations. Furthermore, the Krawczyk iteration method, including an approximate inverse preconditioner using Frobenius norm minimisation, is employed to solve this problem. The approximate inverse preconditioner guarantees the convergence of the iterative method and has the potential for parallel implementation. In addition, to avoid generating a dense approximate inverse matrix in the preconditioning step, a dropping strategy is introduced to perform a sparse representation, which can significantly reduce the memory requirement and ease the matrix operation burden. The proposed methods are demonstrated on 33‐bus, 69‐bus, 123‐bus, and several large systems. A comparison with interval LU decomposition, interval Gauss elimination method, and Monte Carlo simulation verifies its effectiveness. [ABSTRACT FROM AUTHOR]

A new continuation power flow method is proposed, which is an extension of the popular backward/forward sweep power flow (BFS) for distribution network. The different reasons for the divergence of Newton's algorithm and fixed‐point iteration algorithm near the saddle node bifurcation point have been investigated extensively. Loop‐analysis‐based power flow (LBPF) is a revised version of BFS, which belong to fixed‐point iteration algorithm and its convergence remains satisfactory in a meshed network. Based on LBPF, a tailored continuous power flow method is developed, which can be used as a voltage stability analysis tool for both radial and meshed distribution networks. Numerical test results are presented to validate the proposed procedure. [ABSTRACT FROM AUTHOR]

Continuation power flow based on global parameterisations cannot overcome the Jacobian matrix singularity for some voltage instability cases with strong local characteristics and local parameterisation methods may need a forced parameter‐switching strategy at the lower part of P–V curve and a special step‐length tuning algorithm. The upper part of P–V curve can be solved through conventional newton power flow easily with load factor as parameterisation variable. Therefore this study focuses on tracing the segment near the saddle‐node bifurcation (SNB) point and the lower part of P–V curve. A new local geometric parameterisation technique is proposed, which can trace P–V curve with a fixed step‐length of parameterisation variable and no parameter‐switching strategy is needed in most cases. The Jacobian matrix singularity near the SNB is avoided by the addition of a linear equation, which is determined from the selected bus voltage magnitude and the loading factor. The method to deal with limit‐induced bifurcation problems is also considered in this study. [ABSTRACT FROM AUTHOR]