Reachability and model prediction based system protection schemes for power systems

Interconnected power systems have been disrupted by unforeseen disturbances from time to time when millions of consumers lose power supply at a very expensive cost. System protection and emergency control to counteract power system instability play an important role in power system operation. Motivated by the industry need to mitigate the effect of disturbances on system operation and improve power system security, this dissertation develops a general framework for system protection scheme based on reachability analysis and Model Predictive Control. A systematic framework to determine switching control strategies is proposed to stabilize the system following a disturbance based on reachability analysis. The computation of the stability region of a stable equilibrium point with the purpose of power system stability analysis is proposed and the validity of discrete controls in transient stability design is studied. Model Predictive Control (MPC) is also adopted to design system protection scheme. A control strategy for maintaining voltage stability following the occurrence of a contingency is presented. Based on economic consideration and control effectiveness, a control switching strategy consisting of a sequence and amounts of shunt capacitors to switch is identified for voltage restoration. The effect of the capacitive control on voltage recovery is measured via trajectory sensitivity. In addition, voltage stability margin is an indication of how far the post-transient operating point is from the voltage collapse point. It is an index of system security. A control scheme to restore voltage following a contingency and to maintain a pre-specified amount of post-transient voltage stability margin is proposed. Moreover, dissimilar controls exist in power system for voltage control. A mixed integer programming based algorithm is presented to study the optimal coordination of the dissimilar controls to improve voltage performance following large disturbances. The developed algorithms are implemented with MATLAB and tested on the WECC system to enhance the performance of voltage and the 39 bus New England system for preventing voltage collapse

A Model Predictive Control Volt/VAr Management System for the Froan network

This work presents a Volt/VAr Management System, based on a Model Predictive Control strategy, which can be applied for managing dynamic network resilience. Dynamic network resilience, in this context, is associated with resilient microgrid operation (stable voltage profiles within admissible range) during the transition from grid-tied to island-mode operation. In addition, the predictive nature of the framework allows the systematic incorporation of forecasts on intermittent distributed renewable generation capacity and load demands which in turn can be utilized in a optimization based framework to optimally exploit network flexibility. The developed framework is validated for a migrogrid in the municipality of Frøya, Norway.acceptedVersio

Creating, Validating, and Using Synthetic Power Flow Cases: A Statistical Approach to Power System Analysis

abstract: Synthetic power system test cases offer a wealth of new data for research and development purposes, as well as an avenue through which new kinds of analyses and questions can be examined. This work provides both a methodology for creating and validating synthetic test cases, as well as a few use-cases for how access to synthetic data enables otherwise impossible analysis. First, the question of how synthetic cases may be generated in an automatic manner, and how synthetic samples should be validated to assess whether they are sufficiently ``real'' is considered. Transmission and distribution levels are treated separately, due to the different nature of the two systems. Distribution systems are constructed by sampling distributions observed in a dataset from the Netherlands. For transmission systems, only first-order statistics, such as generator limits or line ratings are sampled statistically. The task of constructing an optimal power flow case from the sample sets is left to an optimization problem built on top of the optimal power flow formulation. Secondly, attention is turned to some examples where synthetic models are used to inform analysis and modeling tasks. Co-simulation of transmission and multiple distribution systems is considered, where distribution feeders are allowed to couple transmission substations. Next, a distribution power flow method is parametrized to better account for losses. Numerical values for the parametrization can be statistically supported thanks to the ability to generate thousands of feeders on command.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Embedding OLTC nonlinearities in predictive Volt Var Control for active distribution networks

Volatile productions and consumptions generate a stochastic behavior of distribution grids and make its supervision difficult to achieve. Usually, the Distributed Generators reactive powers are adjusted to perform decentralized voltage control. Industrial controllers are generally equipped with a local affine feedback law, which settings are tuned at early stage using local data. A centralized and more efficient tuning method should aim to maximize the probability that all the node voltages of distribution grids remain within prescribed bounds. When the characteristics of the stochastic power forecasts are known, the centralized algorithm allows to update the settings on a regular time basis. However, the method requires to solve stochastic optimization problem. Assuming that stochastic variables have Gaussian distributions, a procedure is given which guarantees the convergence of the stochastic optimization. Convex problems drastically reduce the difficulty and the computational time required to reach the global minimum, compared to nonconvex optimal power flow problems. The linear controllers with optimized parameters are compared to traditional control laws using simulations of a real distribution grid model. The results show that the algorithm is reliable and moreover fast enough. Hence, the proposed method can be used to update periodically the control parameters

Online monitoring and control of voltage stability margin via machine learning-based adaptive approaches

Voltage instability or voltage collapse, observed in many blackout events, poses a significant threat to power system reliability. To prevent voltage collapse, the countermeasures suggested by the post analyses of the blackouts usually include the adoption of better online voltage stability monitoring and control tools. Recently, the variability and uncertainty imposed by the increasing penetration of renewable energy further magnifies this need. This work investigates the methodologies for online voltage stability margin (VSM) monitoring and control in the new era of smart grid and big data. It unleashes the value of online measurements and leverages the fruitful results in machine learning and demand response. An online VSM monitoring approach based on local regression and adaptive database is proposed. Considering the increasing variability and uncertainty of power system operation, this approach utilizes the locality of underlying pattern between VSM and reactive power reserve (RPR), and can adapt to the changing condition of system. LASSO (Least Absolute Shrinkage and Selection Operator) is tailored to solve the local regression problem so as to mitigate the curse of dimensionality for large-scale system. Along with the VSM prediction, its prediction interval is also estimated simultaneously in a simple but effective way, and utilized as an evidence to trigger the database updating. IEEE 30-bus system and a 60,000-bus large system are used to test and demonstrate the proposed approach. The results show that the proposed approach can be successfully employed in online voltage stability monitoring for real size systems, and the adaptivity of model and data endows the proposed approach with the advantage in the circumstances where large and unforeseen changes of system condition are inevitable. In case degenerative system conditions are identified, a control strategy is needed to steer the system back to security. A model predictive control (MPC) based framework is proposed to maintain VSM in near-real-time while minimizing the control cost. VSM is locally modeled as a linear function of RPRs based on the VSM monitoring tool, which convexifies the intricate VSM-constrained optimization problem. Thermostatically controlled loads (TCLs) are utilized through a demand response (DR) aggregator as the efficient measure to enhance voltage stability. For such an advanced application of the energy management system (EMS), plug-and-play is a necessary feature that makes the new controller really applicable in a cooperative operating environment. In this work, the cooperation is realized by a predictive interface strategy, which predicts the behaviors of relevant controllers using the simple models declared and updated by those controllers. In particular, the customer dissatisfaction, defined as the cumulative discomfort caused by DR, is explicitly constrained in respect of customers\u27 interests. This constraint maintains the applicability of the control. IEEE 30-bus system is used to demonstrate the proposed control strategy. Adaptivity and proactivity lie at the heart of the proposed approach. By making full use of real-time information, the proposed approach is competent at the task of VSM monitoring and control in a non-stationary and uncertain operating environment

Implementation and assessment of demand response and voltage/var control with distributed generators

The main topic of this research is the efficient operation of a modernized distribution grid from both the customer side and utility side. For the customer side, this dissertation discusses the planning and operation of a customer with multiple demand response programs, energy storage systems and distributed generators; for the utility side, this dissertation addresses the implementation and assessment of voltage/VAR control and conservation voltage reduction in a distribution grid with distributed generators. The objectives of this research are as follows: (1) to develop methods to assist customers to select appropriate demand response programs considering the integration of energy storage systems and DGs, and perform corresponding energy management including dispatches of loads, energy storage systems, and DGs; (2) to develop stochastic voltage/VAR control techniques for distribution grids with renewable DGs; (3) to develop optimization and validation methods for the planning of integration of renewable DGs to assist the implementation of voltage/VAR control; and (4) to develop techniques to assess load-reduction effects of voltage/VAR control and conservation voltage reduction. In this dissertation, a two-stage co-optimization method for the planning and energy management of a customer with demand response programs is proposed. The first level is to optimally select suitable demand response programs to join and integrate batteries, and the second level is to schedule the dispatches of loads, batteries and fossil-fired backup generators. The proposed method considers various demand response programs, demand scenarios and customer types. It can provide guidance to a customer to make the most beneficial decisions in an electricity market with multiple demand response programs. For the implementation of voltage/VAR control, this dissertation proposes a stochastic rolling horizon optimization-based method to conduct optimal dispatches of voltage/VAR control devices such as on-load tap changers and capacitor banks. The uncertainties of renewable DG output are taken into account by the stochastic formulation and the generated scenarios. The exponential load models are applied to capture the load behaviors of various types of customers. A new method to simultaneously consider the integration of DGs and the implementation of voltage/VAR control is also developed. The proposed method includes both solution and validation stages. The planning problem is formulated as a bi-level stochastic program. The solution stage is based on sample average approximation (SAA), and the validation stage is based on multiple replication procedure (MRP) to test the robustness of the sample average approximation solutions of the stochastic program. This research applies big data-driven analytics and load modeling techniques to propose two novel methodologies to assess the load-reduction effects of conservation voltage reduction. The proposed methods can be used to assist utilities to select preferable feeders to implement conservation voltage reduction.Ph.D

Dynamic security assessment processing system

The architecture of dynamic security assessment processing system (DSAPS) is proposed to address online dynamic security assessment (DSA) with focus of the dissertation on low-probability, high-consequence events. DSAPS upgrades current online DSA functions and adds new functions to fit into the modern power grid. Trajectory sensitivity analysis is introduced and its applications in power system are reviewed. An index is presented to assess transient voltage dips quantitatively using trajectory sensitivities. Then the framework of anticipatory computing system (ACS) for cascading defense is presented as an important function of DSAPS. ACS addresses various security problems and the uncertainties in cascading outages. Corrective control design is automated to mitigate the system stress in cascading progressions. The corrective controls introduced in the dissertation include corrective security constrained optimal power flow, a two-stage load control for severe under-frequency conditions, and transient stability constrained optimal power flow for cascading outages. With state-of-the-art computing facilities to perform high-speed extended-term time-domain simulation and optimization for large-scale systems, DSAPS/ACS efficiently addresses online DSA for low-probability, high-consequence events, which are not addressed by today\u27s industrial practice. Human interference is reduced in the computationally burdensome analysis

Optimal Voltage Control Using an Equivalent Model of a Low-Voltage Network Accommodating Inverter-Interfaced Distributed Generators

The penetration of inverter-based distributed generators (DGs), which can control their reactive power outputs, has increased for low-voltage (LV) systems. The power outputs of DGs affect the voltage and power flow of both LV and medium-voltage (MV) systems that are connected to the LV system. Therefore, the effects of DGs should be considered in the volt/var optimization (VVO) problem of LV and MV systems. However, it is inefficient to utilize a detailed LV system model in the VVO problem because the size of the VVO problem is increased owing to the detailed LV system models. Therefore, in order to formulate and solve the VVO problem in an efficient way, in this paper, a new equivalent model for an LV system including inverter-based DGs is proposed. The proposed model is developed based on an analytical approach rather than a heuristic-fitting one, and it therefore enables the VVO problem to be solved using a deterministic algorithm (e.g., interior point method). In addition, a method to utilize the proposed model for the VVO problem is presented. In the case study, the results verify that the computational burden to solve the VVO problem is significantly reduced without loss of accuracy by the proposed model.11Ysciescopu

Grid-Connected Renewable Energy Sources

The use of renewable energy sources (RESs) is a need of global society. This editorial, and its associated Special Issue “Grid-Connected Renewable Energy Sources”, offers a compilation of some of the recent advances in the analysis of current power systems that are composed after the high penetration of distributed generation (DG) with different RESs. The focus is on both new control configurations and on novel methodologies for the optimal placement and sizing of DG. The eleven accepted papers certainly provide a good contribution to control deployments and methodologies for the allocation and sizing of DG