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As the penetration of distributed photovoltaic (PV) in the distribution network continues to increase, the problem of voltage violation is becoming increasingly prominent. PV output has strong volatility and uncertainty, leading to higher requirements on speed and accuracy of voltage control. The voltage qualification rate of the distribution network with PV is difficult to guarantee if relying on traditional measuring installation and discrete regulators. Thus, it is necessary to introduce high‐frequency sampling devices and various continuous regulators in real‐time voltage control. In this paper, a fast voltage control strategy by real‐time coordination of dynamic voltage regulators (DVRs) and PV inverters based on phasor measurement units (PMU) and remote terminal unit (RTU) information is proposed. The voltage sensitivity is calculated online based on the fast measurement information of PMU. The equivalent models of rapid voltage regulators including DVR and PV inverter are established for the online extraction of voltage optimal control gradients. According to the model predictive control (MPC) theory, a feeder‐level global voltage control strategy is proposed comprehensively considering the objectives of voltage qualification rate, control cost, and network loss, which can realize real‐time voltage control in the distribution network at the millisecond scale. The effectiveness and rapidity of the proposed global voltage control strategy are verified in the IEEE 33‐bus system and a real representative distribution system for preventing voltage violation. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time power smoothing control strategy for distribution systems with high penetration of photovoltaic (PV) based on conservation voltage reduction (CVR) technology, which employs CVR effects of feeder loads to smooth net-load power fluctuations caused by PV intermittency. CVR technology is implemented in real-time based on feedback signals provided by phasor measurement unit, and feeder voltage is under closed-loop control within statutory limits to make load demand change with PV power. Meanwhile, an online CVR model identification application based on generalized recursive least squares and a power smoothing application based on PI-Smith controller are separately designed. CVR model identification application uses well-designed M-sequence voltage disturbance to online induce CVR effects, which can accurately identify dynamic and steady-state CVR effects without being affected by color-noises. Power smoothing application combines PI controller with Smith predictor to limit the fluctuation rate of net-load without being affected by practical time-delay. The parameters of controller are automatically tuned through the identified CVR model with small computational burden, which is beneficial to engineering applications. The proposed method has been demonstrated in real 10kV distribution system in China. Simulation and field results demonstrate that proposed method has significant potential for real-time power smoothing. [ABSTRACT FROM AUTHOR]

How to consider both the influence of weather and wind power in the modeling process of probability distribution of wind power forecast error (WPFE), and to emphasize the application value of conditional modeling, is rarely studied at present. This paper proposes a novel method of conditional probability distribution modeling for WPFE. This method uses a proposed MNSGA-II-Kmeans algorithm to perform multi-objective clustering of multi-dimensional influencing factors (MDIF), including weather and wind power. It can maximize the difference between the probability distributions of each MDIF mode's WPFE while clustering, thus ensuring the application value of the conditional modeling way. Based on the clustering results, by using the versatile distribution to simulate the probability distribution of WPFE and the support vector machine to realize the recognition of MDIF modes, the specific conditional probability distribution function of WPFE can be provided to stochastic economic dispatch by identifying the forecast MDIF data. A wind plant of north China with historical data is selected for calculation. The results verify the effectiveness of the proposed method, and by comparison with the non-conditional probability distribution of WPFE that does not consider MDIF, it can effectively increase the wind power consumption of the power system. [ABSTRACT FROM AUTHOR]

This paper proposes an inertia emulation method that uses the frequency of the center of inertia (COI) as the feedback signal, aiming to slow the frequency change rate and raise the frequency nadir during frequency-dropping events. An approximated model depicting the COI frequency dynamics is first introduced, which shows the COI frequency signal has low observabilities of electromechanical modes. Thus, this feature enables the proposed inertia emulation to react quickly to steep frequency changes without suffering any adverse impacts caused by electromechanical dynamics, such as excessively saturating the controlled objects. Moreover, the electromechanical dynamics associated with small-signal stability properties will not be interrupted by the proposed inertia emulation. In addition, a simple yet effective computing procedure is proposed to configure the critical parameters, as the inertia emulation is implemented with multiple flywheel-based energy storage systems. Simulation results obtained based on two large interconnected systems verify the effectiveness of the proposed inertia emulation in terms of improving the frequency transients as well as its limited correlation with the electromechanical dynamics of the system. The signal transmission latency's impacts on the proposed inertia emulation are also investigated and discussed. [ABSTRACT FROM AUTHOR]

Online assessment of conservation voltage reduction (CVR) has great effects on the performance of CVR-based applications, especially for real-time CVR-based controls. The challenges of online CVR assessment mainly come from time-varying load composition and inevitable noise. Besides, the dynamic process resulting from motors further complicates this problem. This article proposes a micro-perturbation based load-to-voltage (LTV) process identification method to online assess the CVR effects. In particular, we construct a well-designed pseudo-random-binary-sequence voltage perturbation to excite the CVR process continually and fully. With the experimental data, the CVR factor and transfer function of LTV are estimated by the cross-correlation method, which can reflect both steady-state and dynamic CVR effects without being affected by noise. The proposed method is verified on a specific hardware-in-the-loop testbed under various cases. In addition, through the energy-saving and power smoothing applications, the impact of CVR assessment on performance of CVR-based applications is also analyzed, which further verify the validity and economic benefits of the proposed assessment method. [ABSTRACT FROM AUTHOR]

Line-commutated-converter-based HVdc (LCC-HVdc) is extensively used in modern power systems. Most of the LCC-HVdcs are implemented with the control strategy termed marginal current control method (MCCM), while their operation is sensitive to disturbance from the ac part of the system. This paper originally reveals the mechanism of two types of oscillations induced by the MCCM after ac disturbances, i.e., the current boundary induced oscillation and the current error induced oscillation. A coordinated marginal current control method (CMCCM) is proposed, aiming at suppressing the aforementioned oscillations and enhancing HVdc recovery performance. Compared to the MCCM that responds only to dc voltage, the CMCCM additionally considers ac voltage, ac current, and power factor of the converter. AC grid's parameters are also taken into account. With a more considerate design, the empirical current boundary determined by the MCCM is replaced with an analytical one to coordinately control dc current. Therefore, better performance of HVdc is achieved. Simulations in RTDS reveal that the proposed CMCCM can suppress the oscillations, speed up HVdc recovery process, and moderate transient over voltage of HVdc. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of how to effectively smooth the short-term tie-line power fluctuations for grid-connected industrial microgrids (IMGs) from demand side. A wide-area closed-loop control architecture is proposed by directly and continuously modulating load power consumption, e.g., the aluminum electrolysis load. To optimally design the load smoothing controllers, an output regulation approach combined with LQ optimal control is developed. In particular, dominant frequencies of wind power fluctuations are analyzed and incorporated into the exogenous system that models time-varying disturbances. Simulations are conducted for a real industrial microgrid in the Inner Mongolia, China, to validate the practical feasibility and effectiveness of the proposed smoothing control strategy. It is demonstrated that the demand side control based on output regulation is able to achieve not only zero steady error in rejecting the time-varying wind power disturbance but also guarantee the satisfactory dynamic performance of modulated loads for the grid-connected IMG. [ABSTRACT FROM AUTHOR]

To uncover the mechanism by which voltage-dependent loads (VDLs) influence electromechanical dynamics, this paper mathematically deduces the damping torque induced by VDLs based on a single machine infinite bus system. Moreover, by continuously regulating the terminal voltage of a typical large-scale VDL, i.e., the aluminum electrolysis load, this paper proposes a novel load damping control architecture to dynamically modulate the power consumption of controlled loads so as to eliminate interarea oscillations in power systems. Specifically, the decentralized load damping controllers (LDCs) used in this architecture are designed by a proposed multistage mixed ${{{H}}_{2}} / {{{H}}_\infty }$ control approach to guarantee robustness against uncertainties (e.g., tie-line outages and wind generation fluctuations) as well as to ensure reasonable control efforts of controlled VDLs. Simulation results on the modified New England and New York interconnected system validate the method for analysis of VDLs’ effects on interarea oscillations and show the proposed load damping control strategy can satisfactorily damp interarea oscillations over multiple operating points. [ABSTRACT FROM AUTHOR]

Frequency control of isolated power systems with high penetration of wind power is a challenging task for system operators. The difficulty results from the limited frequency response capability of thermal power units to keep up with the fluctuations of wind power. This paper develops a field verification architecture to leverage aluminum smelting loads (ASLs) to participate in primary frequency control. A new method is proposed to coordinate ASLs with thermal power units to quickly stabilize the isolated power system with significant wind power fluctuations or curtailments. Moreover, a closed-loop control system for the ASLs controller is developed and tested on a real-world isolated power system in Inner Mongolia, China. Hardware-in-the-loop simulations, as well as field tests, are conducted under various scenarios of wind power fluctuations or curtailments to verify the proposed architecture for frequency control. The highly consistent simulation results and field tests demonstrate the good frequency control capability of the ASL. The regulated power from the ASL is demonstrated to be able to reach 6% of its rated power within 4 s. Working jointly with thermal power units, the ASL helps maintain the stable operation of the isolated power system, significantly improving the frequency response characteristic. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient method that tunes wide-area power system stabilizers (WPSSs) to have probabilistic robustness for damping inter-area electromechanical oscillations in power systems with incorporated random wind power. Specifically, the efficiency of this method benefits from unique consideration and deduction of the analytic forms of cumulative distribution functions (cdfs) of two types of random variables and their derivatives with respect to tunable parameters of the conventionally structured WPSSs, based on approximations of wind power probability density functions by Gaussian mixtures. These cdfs then compose the objective function of an optimization that can rapidly solve for optimal parameters of WPSSs by a sequential quadratic programming algorithm. The optimized WPSSs are probabilistically robust because they enhance the probability of two commonly desired control effects. Simulation studies on a modified IEEE 10-machine and 39-bus system validate the superior efficiency of the proposed tuning method and the excellent performance of the derived WPSSs. [ABSTRACT FROM AUTHOR]

Optimisation of reserve purchased in advance becomes increasingly essential to hedge against the growing level of power unbalance brought by the integration of large‐scale wind power. Moreover, due to the centralised integration mode of wind farms, cross‐region reserve sharing strategy becomes quite imperative. Therefore, this study presents a dynamic reserve demand estimation (DRDE) model, which synthetically considers N − 1 contingency along with load and wind forecast error. In the proposed approach, historical data is rearranged by a two‐dimensional binning technique and fitted by the versatile probability distribution model, which assisting the model reacts to different prediction level in each dispatch period dynamically. Furthermore, a cost‐effectivity oriented reserve allocation strategy for the multi‐area system is developed based on the DRDE model. The efficiency of the proposed method is tested in both 6‐bus test system and IEEE 118‐bus test system. The results show that, whether in the scenario of self‐sufficient reserve supplying or cross‐region reserve sharing strategy, the proposed estimation model effectively reduces the expectation of both load shedding and wind curtailment. Moreover, the proposed strategy improves the economy of system operation on the premise of ensuring system reliability. [ABSTRACT FROM AUTHOR]

This paper proposes the application of information gap decision theory (IGDT) to the design of robust wide-area power system stabilizers (WPSSs) with consideration of wind farm (WF) power outputs variations and transmission line outages. According to IGDT, an optimization problem is constructed to tune WPSS parameters. Then, the derived optimal WPSSs can achieve explicit and favorable robustness to ensure the required damping control effects on the interarea oscillations over a maximum variation range of WF steady-state power outputs in normal and emergent operating conditions. Moreover, with the intent of using the excellent global searching capability of particle swarm optimization (PSO), a customized PSO algorithm is proposed to efficiently solve the resulting highly nonlinear programming problem. Finally, simulations are carried out on a modified New England (10-machine 39-bus) system to validate the efficiency of the IGDT-based design method. The derived WPSSs exhibit expected robustness with respect to the wind power variations and transmission line outages. [ABSTRACT FROM PUBLISHER]

This paper proposes a design method to coordinate dual-channel supplementary damping controllers (SDCs) of doubly-fed induction generators (DFIGs) and power system stabilizers (PSSs) for suppression of inter-area power oscillations. A dynamic performance index is introduced to measure the dynamics of the conventional synchronous generator and DFIG during the  damping control process. Hence, the proposed method designing the PSS and SDC is formulated as an optimization problem with the objective function being the sum of weighted performance indexes and the constraints indicating the requirements on the damping of the inter-area modes. Solving the optimization problem can obtain the optimal SDC and PSS, which can meet the required damping results as well as optimize dynamics of the controlled plants. Moreover, by adjusting weights in the objective function, the damping control burden can be flexibly and feasibly allocated between active and reactive power channels of DFIGs or among the damping controllers. Simulations with the modified New England and New York interconnected system prove that the proposed optimization based tuning method can not only robustly coordinate the PSS and SDC to effectively damp inter-area oscillations but also improve the dynamics of controlled plants during the damping control process over different operating conditions. [ABSTRACT FROM PUBLISHER]

Wind power representation is an integral part of stochastic economic dispatch (ED). Compared with the deterministic approach, stochastic ED allocates system reserves more effectively. This improved reserve strategy heavily relies on the wind power distribution boundary area that is not consistent with classical unbounded distribution models. To address this issue, this paper proposes a new probability distribution model named truncated versatile distribution (TVD) for wind power, which is bounded and more accurate than classical models. The fitting effect and mathematical analysis of TVD are thoroughly discussed. Then, we propose a novel look-ahead dynamic ED method with an adjustable confidence interval (ACI) based on TVD, which not only enables system operators to maintain an acceptable predefined level of risk, but also seeks the optimal level of risk to balance the system reserve strategy and risk of power unbalance. With ACI, TVD offers a reliable wind power representation to search for the optimal level of risk in the dispatch model. In addition, the employment of TVD can simplify the wind power uncertainties by its analytical forms. The results show that compared with other classical distribution models, the novel distribution is a more accurate representation of wind power. Moreover, the ED model results in improved economic performances by ACI compared with traditional ones. [ABSTRACT FROM AUTHOR]

An improved versatile distribution for wind power is proposed, with higher representation accuracy and more suitable applications in economic dispatch (ED) compared with Versatile distribution. Further, versatile mixture distribution (VMD) is proposed to represent all possible wind power distribution with customized arbitrary errors. Compared with Gaussian Mixture distribution (GMD), it has more flexible forms and can represent wind power more accurately. Its cumulative distribution function has analytical forms with higher computational efficiency. Then, real-time dynamic ED model and algorithm based on VMD with multiple wind farms (WFs) is proposed. Based on the VMD model, the probability distributions of wind power of each WF and the summation of all WFs are modeled. The ED model and algorithm solve the problem of the correlation of multiple renewable energy random variables with high computation efficiency for real-time ED. The results show that compared with GMD or unimodal distributions, VMD can represent wind power more accurately and can greatly simplify and speed up the ED model and algorithm. Compared with other methods for multiple renewable energy random variables, the proposed model and algorithm can improve economy significantly. [ABSTRACT FROM PUBLISHER]

This paper proposes a systematic approach to coordinately design probabilistically-robust wide-area power system stabilizers (WPSSs) for suppressing inter-area oscillations of power systems incorporating wind power. Specifically, the operating point of the system varies stochastically due to wind power integration and each operating point corresponds to a wind power generation scenario in the steady state. Thus, the WPSSs tuned by solving a delicately formulated optimization problem can maximize the occurrence probability of scenarios where the inter-area modes possess the acceptable damping ratios, and strictly constrain their unfavorable impacts. Multiple contingencies are also directly considered. In addition, several advanced techniques are tactfully employed for accurate and efficient evaluation of occurrence probability (objective function) during the optimization so as to ensure the proposed tuning method can deal with the highly nonlinear relationships between the system eigenvalues and the steady-state power outputs of wind farms; a customized differential evolution algorithm is proposed as well to efficiently solve the formulated optimization problem. Simulations and comparisons conducted on two classic test systems with proper modifications show the effectiveness and efficiency of the proposed control design method. [ABSTRACT FROM PUBLISHER]

Wind power ramp events (WPREs) have received increasing attention in recent years as they have the potential to impact the reliability of power grid operations. In this paper, a novel WPRE forecasting method is proposed which is able to estimate the probability distributions of three important properties of the WPREs. To do so, a neural network (NN) is first proposed to model the wind power generation (WPG) as a stochastic process so that a number of scenarios of the future WPG can be generated (or predicted). Each possible scenario of the future WPG generated in this manner contains the ramping information, and the distributions of the designated WPRE properties can be stochastically derived based on the possible scenarios. Actual wind power data from a wind power plant in the Bonneville Power Administration (BPA) were selected for testing the proposed ramp forecasting method. Results showed that the proposed method effectively forecasted the probability of ramp events. [ABSTRACT FROM PUBLISHER]

Highlights • A novel dynamic scenarios method considering power time correlation is designed. • A modified multi-objective algorithm is proposed to solve stochastic optimization. • The dynamic scenarios have smallest Brier Score for each event. • The modified multi-objective algorithm has stronger global search capability. • Voltage unbalance caused by single-phase photovoltaics has a significant decrease. Abstract With the increasing number of single-phase photovoltaics integrated into three-phase distribution network, voltage unbalance problem is becoming serious, which leads to the abnormal operation of distribution network. Therefore, in distribution network, not only energy efficiency needs to be enhanced, but also voltage unbalance needs to be decreased to ensure the security of system. This paper establishes a stochastic multi-objective optimization model for three-phase distribution network to minimize active power losses and voltage unbalance simultaneously, where the discrete decision variables are coordinated with continuous regulation of solar reactive outputs. For the purpose, the stochastic processes of solar active power are modelled in a scenarios-based framework. A novel dynamic scenarios method is designed to describe the uncertainty of solar power as well as power time correlation based on the time covariance obtained by the forgetting factor identification, which not only reflects forecast errors, but also power fluctuation. Hence, the stochastic processes are converted into a series of equivalent deterministic scenarios. In order to better solve the multi-objective problem, a modified non-dominated sorting genetic algorithm-II is proposed, in which crossover rate and mutation rate are dynamically revised by a fuzzy logic controller. Besides, a two-stage constraint handling strategy is constructed to ensure the solutions with smaller constraints deviation and better fitness have higher priority to be reserved. Finally, simulation is conducted on the modified IEEE 123 node distribution network with lots of single-phase photovoltaics. The results show that with more accurate scenarios and stronger algorithm global search capability, the multi-objective optimization gains significant decrease of active power losses and voltage unbalance. [ABSTRACT FROM AUTHOR]