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An optimal planning model of the sectional switch and tie line for the distribution network is proposed considering the tradeoff between the system reliability and economy. First, the fault incidence matrices are established for the distribution network, and the explicit analytical expression of the system reliability index is realized by the algebraic operation of the fault incidence matrices and fault parameter vectors. Then, the 0–1 integer quadratically constrained optimization model is established for the sectional switch configuration considering the capacity constraint of the tie line. Finally, the sectional switches and tie lines are optimized jointly to ensure the economic investment and maximize the system reliability. The distribution network in Taiwan Power Company is used to verify the applicability of the proposed optimization method. Compared with the intelligent optimization algorithm, the mathematical programming method in this paper can always find the global optimal switch configuration quickly. In addition, the influence of the tie line capacity constraints on the sectional switch configuration is also considered, which can provide practical reference for the power network planners in the optimization design of the distribution network. [ABSTRACT FROM AUTHOR]

A fast reliability calculation method based on the fault incidence matrix (FIM) for the complex distribution system is proposed in this paper. First, the M-segment-N-tie-switch (MSNT) reliability calculation unit for the complex distribution network is established and the power supply path matrix is obtained by inversing the node branch incidence matrix of the MSNT unit. Then, three types of FIMs are constructed considering the influence type of each fault event on the load points. Finally, the system reliability indexes are calculated through the algebraic operation between the FIMs and the fault event parameter vectors. The accuracy and the efficiency of this method are verified using the IEEE RBTS bus-6 system. The reliability evaluation method proposed in this paper avoids complicated fault event enumeration and repetitive fault influence range searching, thus, saves the computation time greatly. And more importantly, the analytical expression of the reliability indexes in the matrix form can clearly show the influence of each fault event on each load interruption. So, it is convenient for the reliability sensitivity analysis and helpful for the operator with the reliability improvement planning. [ABSTRACT FROM AUTHOR]

Power system electro-magnetic transient programs (EMTP) have been popular among researchers and practitioners due to their detailed component modeling and high simulation accuracy for complex system operations. Despite broad applications in simulations with wide range of timescales, the small discretization step of these programs makes their use very time-consuming for system studies with long time span. Facing the increasingly complex power system transient characteristics and simulation demands, a multiscale algorithm that integrates the simulations of the electromagnetic and slower electromechanical transients is desirable. The multiscale simulation algorithm preserves the high fidelity of the EMTP and attains higher efficiency for the overall transient simulation. In this paper, we achieve this goal by exploiting the unique properties of the matrix exponential function. The proposed algorithm is capable of utilizing large step sizes to speed up the simulation of slow dynamics, whereas the fast transients are accurately reconstructed through efficient dense output mechanism, which is built upon the matrix exponential function computation. Numerical studies including a large-scale wind farm simulation are conducted to demonstrate the effectiveness of the proposed multiscale algorithm. [ABSTRACT FROM AUTHOR]

For a droop-controlled micro-grid, it is essential to design the droop coefficients for the inverter controllers in a rational way since they have great influences on the operation characteristics of the system. In this paper, a novel approach based on matrix perturbation theory is proposed for the coordinated optimization of the droop coefficients in inverter controllers. Rigorous perturbation analysis is applied to the droop coefficients to identify their influence on the system state matrix. Furthermore, the increments of the eigensolutions are obtained based on the matrix perturbation theory and are then utilized in the iterative parameter-optimization process. An eigenvalue-based objective function is proposed, aimed at ensuring the stability of the system, enhancing the damping characteristics, and maintaining the stability margin for a wide range of operating conditions. Finally, the effectiveness and robustness of the proposed approach are tested via a low-voltage micro-grid prototype. [ABSTRACT FROM PUBLISHER]

In this paper, a day-ahead market-clearing model for smart distribution systems is proposed. Various types of distributed energy resources (DERs), such as distributed energy storage, distributed generators, microgrids, and load aggregators, can bid into the day-ahead distribution-level electricity market. Considering system Volt/VAR control, network reconfiguration, and interactions with the wholesale market, an optimization model is built to clear the day-ahead market, through which the distribution locational marginal pricing (DLMPs) for both active power and reactive power are determined. Through derivations of the Lagrangian function and sensitivity factors, DLMPs are decomposed to five components (i.e., marginal costs for active power, reactive power, congestion, voltage support, and loss), which provide price signals to motivate DERs to contribute to congestion management and voltage support. Finally, case studies demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In order to solve the voltage violation problem caused by high penetration of photovoltaic (PV) units in distribution power networks, this paper proposes a double-layer voltage control strategy based on the distribution network partition. By optimizing the active and reactive power outputs of PV units, the minimization objective of PV active power curtailment and network active power loss can be achieved. In the basis of community detection algorithm, a novel cluster performance index based on the electrical distance and regional voltage regulation capability is presented to partition a distribution network into several clusters. The proposed double-layer voltage control strategy combines the cluster autonomous optimization and distributed intercluster coordination optimization in different time scales. The cluster autonomous optimization can eliminate intracluster voltage violation rapidly by alternately updating the intracluster optimal solution and the virtual slack bus voltage. The distributed intercluster coordination optimization with a long-time scale is based on the alternating direction method of multipliers and realizes global optimal control through finite boundary data exchange among adjacent clusters. The effectiveness and feasibility of the proposed method have been demonstrated via simulation tests on a real 10.5 kV feeder in China and the IEEE 123-bus system. [ABSTRACT FROM PUBLISHER]

The high-precision parameters in distribution networks are difficult to obtain, which brings difficulties to the model-based methods and analysis. With the widespread deployment of high-precision measurement units, data-driven methods have greater advantages in practice. In addition, with massive integration of distributed renewable generation and fast electric vehicle chargers, the fluctuations of net load increase significantly. The data-driven power flow calculation method based on the linear model becomes difficult to obtain accurate results for the low nonlinear adaptability. To improve the data-driven power flow calculation accuracy under high penetration of renewable distribution generation, this paper proposed an approach with high adaptability to the nonlinearity of power flow. Based on the thought of Koopman operator theory, the nonlinear relationship in power flow calculation is converted into a linear mapping in a higher dimension state space, which can significantly improve the calculation accuracy. Case studies on different IEEE cases have demonstrated that the proposed method can realize higher accuracy in power flow calculation with large-scale power fluctuations, compared to the existing data-driven method, in both mesh and radial networks. Finally, measurement data of a practical 10kV distribution network has been further used to verify the effectiveness of the proposed method in practical applications. [ABSTRACT FROM AUTHOR]

Based on the decentralized sliding mode control, a load frequency controller is designed in this paper for multi-area interconnected power systems with matching and unmatched uncertainties. The proportional and integral switching surface is constructed for each area to improve system dynamic performance in reaching intervals. The robust controller is proposed by the reaching law method to assure that frequency fluctuation converges to zero after a load and operation point variation. A three-area interconnected power system is studied to illustrate the effectiveness of the proposed decentralized sliding mode control scheme. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel variable-inertia emulation control (VIEC) scheme that enables voltage-source-converter based high-voltage DC (VSC-HVDC) transmission systems to flexibly support AC grid frequency stability like synchronous generators. The VIEC scheme allows us to extract the energy from the augmented DC capacitance for inertia emulation by controlling the DC voltage without affecting the stability of the AC system connected on the remote side. In particular, with the proposed VIEC scheme, the inertia time constant can be flexibly emulated and its value can be automatically adjusted according to the rate of change of grid frequency as well as grid frequency deviations. This leads to more effective inertial support across different timescales. Modal analysis is carried out to investigate the impacts of inertia and DC capacitance, and to obtain the optimal control parameters. The effectiveness and advantages of the proposed VIEC scheme are demonstrated on an IEEE benchmark system in the presence of faults and load changes. [ABSTRACT FROM AUTHOR]

Recursive projection method (RPM) is investigated to extract power system steady state and small signal stability information from time-domain simulation code (TDSC). RPM takes outputs from TDSC to identify an unstable/slow invariant subspace of the full state space. On this subspace, a Newton type of method is applied to enhance convergence for unstable/slowly-converging modes. While on the complement, fixed point iteration of TDSC is kept to evolve stable/fast-decaying modes. Dominant eigenspectrum of the integration scheme is also obtained as byproduct, from which system small signal stability features can be reconstructed. Thus, RPM can enhance convergence of fixed point iteration and enable TDSC to perform small signal stability analysis. At the same time, modeling and numerical advantages of original TDSC are preserved and extra programming costs are saved. The approach is validated by examples on WECC nine-bus, New England 39-bus, and 230-bus systems. [ABSTRACT FROM AUTHOR]

Demand-side management (DSM) increases the complexity of forecasting environment, which makes traditional forecasting methods difficult to meet the firm's need for predictive accuracy. Since deep learning can comprehensively consider various factors to improve prediction results, this paper improves the deep belief network from three aspects of input data, model and performance, and uses it to solve the short-term load forecasting problem in DSM. In the data optimization stage, the Hankel matrix is constructed to increase the input weight of DSM data, and the gray relational analysis is used to select strongly correlated data from the data set. In the model optimization stage, the Gauss-Bernoulli restricted Boltzmann machine is used as the first restricted Boltzmann machine of the deep network to convert the continuity feature of input data into binomial distribution feature. In the performance optimization stage, a pre-training method combining error constraint and unsupervised learning is proposed to provide good initial parameters, and the global fine-tuning of network parameters is realized based on the genetic algorithm. Based on the actual data of Tianjin Power Grid in China, the experimental results show that the proposed method is superior to other methods. [ABSTRACT FROM AUTHOR]

As the penetration of renewable resources and direct current (dc) nature loads increase, dc grids are getting more attraction in the future smart grid architecture. However, how to obtain the steady-state performance of a dc grid under different disturbances fast is still a challenge. To solve this problem, this study aims to derive a fast linear power flow (LPF) model instead of nonlinear algorithms for a dc grid. After analyzing the control characteristics of different converters, a generalized control equation for each dc bus is derived. Then, on the basis of the generalized control and network equations, an LPF algorithm is proposed for the strict requirement of the dc voltage deviation. Error analysis is performed on the proposed LPF algorithm, and a correction method for the LPF algorithm is developed. Case studies based on Cigre B4 DC grid test system are conducted to validate the effectiveness of the proposed LPF. Compared with the Newton–Raphson algorithm, the proposed LPF not only improves the calculating speed greatly but also preserves the accuracy of the power flow of the dc grid. [ABSTRACT FROM AUTHOR]