Parameter Optimization of PSS Based on Estimated Hessian Matrix from Trajectory Sensitivities

This paper describes the optimal tuning for the output limits of the power system stabilizer (PSS), which can improve the system damping performance immediately following a large disturbance. The non-smooth nonlinear parameters such as the saturation limits of the PSS cannot be tuned by the conventional methods based on linear approaches. To implement the systematic optimal tuning for the output limits of the PSS, a feedforward neural network (FFNN) is applied to the hybrid system model based on the differential-algebraic-impulsive-switched (DAIS) structure. The FFNN is firstly designed to identify the trajectory sensitivities obtained from the DAIS structure. Thereafter, it estimates the second-order derivatives of an objective function J, which is used during iterations of optimization process. The performance of the optimal output limits tuned by the proposed method is evaluated by applying a large disturbance to a power system

A Wide Area Hierarchical Voltage Control for Systems with High Wind Penetration and an HVDC Overlay

The modern power grid is undergoing a dramatic revolution. On the generation side, renewable resources are replacing fossil fuel in powering the system. On the transmission side, an AC-DC hybrid network has become increasingly popular to help reduce the transportation cost of electricity. Wind power, as one of the environmental friendly renewable resources, has taken a larger and larger share of the generation market. Due to the remote locations of wind plants, an HVDC overlay turns out to be attractive for transporting wind energy due to its superiority in long distance transmission of electricity. While reducing environmental concern, the increasing utilization of wind energy forces the power system to operate under a tighter operating margin. The limited reactive capability of wind turbines is insufficient to provide adequate voltage support under stressed system conditions. Moreover, the volatility of wind further aggravates the problem as it brings uncertainty to the available reactive resources and can cause undesirable voltage behavior in the system. The power electronics of the HVDC overlay may also destabilize the gird under abnormal voltage conditions. Such limitations of wind generation have undermined system security and made the power grid more vulnerable to disturbances. This dissertation proposes a Hierarchical Voltage Control (HVC) methodology to optimize the reactive reserve of a power system with high levels of wind penetration. The proposed control architecture consists of three layers. A tertiary Optimal Power Flow computes references for pilot bus voltages. Secondary voltage scheduling adjusts primary control variables to achieve the desired set points. The three levels of the proposed HVC scheme coordinate to optimize the voltage profile of the system and enhance system security. The proposed HVC is tested on an equivalent Western Electricity Coordinated Council (WECC) system modified by a multi-terminal HVDC overlay. The effectiveness of the proposed HVC is validated under a wide range of operating conditions. The capability to manage a future AC/DC hybrid network is studied to allow even higher levels of wind

Experiment design for systems biology

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 219-233).Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. Despite the growing interest in these models, a number of challenges frustrate the construction of high-quality models. First, the chemical reactions that control biochemical processes are only partially known, and multiple, mechanistically distinct models often fit all of the available data and known chemistry. We address this by providing methods for designing dynamic stimuli that can distinguish among models with different reaction mechanisms in stimulus-response experiments. We evaluated our method on models of antibody-ligand binding, mitogen-activated protein kinase phosphorylation and de-phosphorylation, and larger models of the epidermal growth factor receptor (EGFR) pathway. Inspired by these computational results, we tested the idea that pulses of EGF could help elucidate the relative contribution of different feedback loops within the EGFR network. These experimental results suggest that models from the literature do not accurately represent the relative strength of the various feedback loops in this pathway. In particular, we observed that the endocytosis and feedback loop was less strong than predicted by models, and that other feedback mechanisms were likely necessary to deactivate ERK after EGF stimulation. Second, chemical kinetic models contain many unknown parameters, at least some of which must be estimated by fitting to time-course data. We examined this question in the context of a pathway model of EGF and neuronal growth factor (NGF) signaling. Computationally, we generated a palette of experimental perturbation data that included different doses of EGF and NGF as well as single and multiple gene knockdowns and overexpressions. While no single experiment could accurately estimate all of the parameters, we identified a set of five complementary experiments that could. These results suggest that there is reason to be optimistic about the prospects for parameter estimation in even large models. Third, there is no standard formulation for chemical kinetic models of biological signaling. We propose a general and concise formulation of mass action kinetics based on sparse matrices and Kronecker products. This formulation allows any mass action model and its partial derivatives to be represented by simple matrix equations, which enabled straightforward application of several numerical methods. We show that models that use other rate laws such as MichaelisMenten can be converted to our formulation. We demonstrate this by converting a model of Escherichia coli central carbon metabolism to use only mass action kinetics. The dynamics of the new model are similar to the original model. However, we argue that because our model is based on fewer approximations it has the potential to be more accurate over a wider range of conditions. Taken together, the work presented here demonstrates that experimental design methodology can be successfully used to improve the quality of mechanism-based chemical kinetic models.by Joshua Farley Apgar.Ph.D

Optimal allocation of static and dynamic reactive power support for enhancing power system security

Power systems over the recent past few years, has undergone dramatic revolution in terms of government and private investment in various areas such as renewable generation, incorporation of smart grid to better control and operate the power grid, large scale energy storage, and fast responding reactive power sources. The ongoing growth of the electric power industry is mainly because of the deregulation of the industry and regulatory compliance which each participant of the electric power system has to comply with during planning and operational phase. Post worldwide blackouts, especially the year 2003 blackout in north-east USA, which impacted roughly 50 million people, more attention has been given to reactive power planning. At present, there is steady load growth but not enough transmission capacity to carry power to load centers. There is less transmission expansion due to high investment cost, difficulty in getting environmental clearance, and less lucrative cost recovery structure. Moreover, conventional generators close to load centers are aging or closing operation as they cannot comply with the new environmental protection agency (EPA) policies such as Cross-State Air Pollution Rule (CSAPR) and MACT. The conventional generators are getting replaced with far away renewable sources of energy. Thus, the traditional source of dynamic reactive power support close to load centers is getting retired. This has resulted in more frequently overloading of transmission network than before. These issues lead to poor power quality and power system instability. The problem gets even worse during contingencies and especially at high load levels. There is a clear need of power system static and dynamic monitoring. This can help planners and operators to clearly identify severe contingencies causing voltage acceptability problem and system instability. Also, it becomes imperative to find which buses and how much are they impacted by a severe contingency. Thus, sufficient static and dynamic reactive power resource is needed to ensure reliable operation of power system, during stressed conditions and contingencies. In this dissertation, a generic framework has been developed for filtering and ranking of severe contingency. Additionally, vulnerable buses are identified and ranked. The next task after filtering out severe contingencies is to ensure static and dynamic security of the system against them. To ensure system robustness against severe contingencies optimal location and amount of VAR support required needs to be found. Thus, optimal VAR allocation needs to be found which can ensure acceptable voltage performance against all severe contingency. The consideration of contingency in the optimization process leads to security constrained VAR allocation problem. The problem of static VAR allocation requirement is formulated as minlp. To determine optimal dynamic VAR installation requirement the problem is solved in dynamic framework and is formulated as a Mixed Integer Dynamic Optimization (MIDO). Solving the VAR allocation problem for a set of severe contingencies is a very complex problem. Thus an approach is developed in this work which reduces the overall complexity of the problem while ensuring an acceptable optimal solution. The VAR allocation optimization problem has two subparts i.e. interger part and nonlinear part. The integer part of the problem is solved by branch and bound (B&B) method. To enhance the efficiency of B&B, system based knowledge is used to customize the B&B search process. Further to reduce the complexity of B&B method, only selected candidate locations are used instead of all plausible locations in the network. The candidate locations are selected based upon the effectiveness of the location in improving the system voltage. The selected candidate locations are used during the optimization process. The optimization process is divided into two parts: static optimization and dynamic optimization. Separating the overall optimization process into two sub-parts is much more realistic and corresponds to industry practice. Immediately after the occurrence of the contingency, the system goes into transient (or dynamic) phase, which can extend from few milliseconds to a minute. During the transient phase fast acting controllers are used to restore the system. Once the transients die out, the system attains steady state which can extend for hours with the help of slow static controllers. Static optimization is used to ensure acceptable system voltage and system security during steady state. The optimal reactive power allocation as determined via static optimization is a valuable information. It\u27s valuable as during the steady state phase of the system which is a much longer phase (extending in hours), the amount of constant reactive power support needed to maintain steady system voltage is determined. The optimal locations determined during the static optimization are given preference in the dynamic optimization phase. In dynamic optimization optimal location and amount of dynamic reactive power support is determined which can ensure acceptable transient performance and security of the system. To capture the true dynamic behavior of the system, dynamic model of system components such as generator, exciter, load and reactive power source is used. The approach developed in this work can optimally allocate dynamic VAR sources. The results of this work show the effectiveness of the developed reactive power planning tool. The proposed methodology optimally allocates static and dynamic VAR sources that ensure post-contingency acceptable power quality and security of the system. The problem becomes manageable as the developed approach reduces the overall complexity of the optimization problem. We envision that the developed method will provide system planners a useful tool for optimal planning of static and dynamic reactive power support that can ensure system acceptable voltage performance and security

Novel measurement based load modeling and demand side control methods for fault induced delayed voltage recovery mitigation

The continuous increase in electric energy demand and limitations in the reinforcement of generation and transmission systems, have progressively led to a greater utilization of power systems and transmission lines. As a result, system conditions may arise where voltage collapse phenomena have a high probability to occur, either due to the accidents in the system structure, or to load becoming particularly heavy. Recently, Workshop on Residential Air Conditioner (A/C) Stalling of Department of Energy (DOE) reported that fault-induced delayed voltage recovery (FIDVR) is now a national issue since residential A/C penetration across U.S. is at an all time high and growing rapidly. The unique characteristics of air conditioner load could cause short-term voltage instability, fast voltage collapse, and delayed voltage recovery. In order to study and mitigate FIDVR problem, a systematic load modeling methodology utilizing novel parameter identification technique and an online demand side control scheme based on load shedding strategy are developed in this dissertation. As load characteristics change from traditional incandescent light bulbs to power electronics-based loads, and as the characteristics of motors change with the emergence of high-efficiency, low-inertia motor loads, it is critical to understand and model load responses to ensure stable operations of the power system during different contingencies. Developing better load models, therefore, has been an important issue for power system analysis and control. It is necessary to take advantage of the state-of-the-art techniques for load modeling and develop a systematic approach to establish accurate, aggregate load models for bulk power system stability studies. In this dissertation, a systematic methodology is provided to derive aggregate load models at the high voltage level (transmission system level) using measurement-based approach. A novel parameter identification technique via hybrid learning is also developed for deriving load model parameters accurately and efficiently. According to NERC\u27s definition, FIDVR is defined as the phenomenon whereby system voltage remains at significantly reduced levels for several seconds after a fault in transmission, subtransmission, or distribution has been cleared. Various studies have shown that FIDVR usually occurs in the areas dominated by induction motors with constant torque. These motors can stall in response to sustained low voltage and draw excessive reactive power from the power grid. Since no under voltage or stall protection is equipped with A/Cs, they can only be tripped by thermal protection which takes 3 to 20 seconds. Severe FIDVR event could lead to fast voltage collapse. In this dissertation, a novel online demand side control method utilizing motor kinetic energy is developed for disconnecting stalling motors at the transmission level to mitigate FIDVR and fast voltage collapse

Using Distributed Energy Resources to Improve Power System Stability and Voltage Unbalance

The increasing penetration of renewables has driven power systems to operate closer to their stability boundaries and makes maintaining power quality more difficult. The goals of this dissertation are to develop methods to control distributed energy resources to improve power system stability and voltage unbalance. Specifically, demand response (DR) is used to realize the former goal, and solar photovoltaic (PV) systems are used to achieve the latter. We present a new DR strategy to change the consumption of flexible loads while keeping the total load constant, improving voltage or small-signal stability without affecting frequency stability. The new loading pattern is only maintained temporarily until the generators can be re-dispatched. Additionally, an energy payback period maintains the total energy consumption of each load at its nominal value. Multiple optimization problems are proposed for determining the optimal loading pattern to improve different voltage or small-signal stability margins. The impact of different system models on the optimal solution is also investigated. To quantify voltage stability, we choose the smallest singular value (SSV) of the power flow Jacobian matrix and the distance to the closest saddle-node bifurcation (SNB) of the power flow as the stability margins. We develop an iterative linear programming (ILP) algorithm using singular value sensitivities to obtain the loading pattern with the maximum SSV. We also compare our algorithm's performance to that of an iterative nonlinear programming algorithm from the literature. Results show that our ILP algorithm is more computationally scalable. We formulate another problem to maximize the distance to the closest SNB, derive the Karush–Kuhn–Tucker conditions, and solve them using the Newton-Raphson method. We also explore the possibility of using DR to improve small-signal stability. The results indicate that DR actions can improve small-signal characteristics and sometimes achieve better performance than generation actions. Renewables can also cause power quality problems in distribution systems. To address this issue, we develop a reactive power compensation strategy that uses distributed PV systems to mitigate voltage unbalance. The proposed strategy takes advantage of Steinmetz design and is implemented via both decentralized and distributed control. We demonstrate the performance of the controllers on the IEEE 13-node feeder and a much larger feeder, considering different connections of loads and PV systems. Simulation results demonstrate the trade-offs between the controllers. It is observed that the distributed controller achieves greater voltage unbalance reduction than the decentralized controller, but requires communication infrastructure. Furthermore, we extend our distributed controller to handle inverter reactive power limits, noisy/erroneous measurements, and delayed inputs. We find that the Steinmetz controller can sometimes have adverse impacts on feeder voltages and unbalance at noncritical nodes. A centralized controller from the literature can explicitly account for these factors, but requires significantly more information from the system and longer computational times. We compare the performance of the Steinmetz controller to that of the centralized controller and propose a new controller that integrates centralized controller results into the Steinmetz controller. Results show that the integrated controller achieves better unbalance improvement compared with that of the centralized controller running infrequently. In summary, this dissertation presents two demand-side strategies to deal with the issues caused by the renewables and contributes to the growing body of literature that shows that distributed energy resources have the potential to play a key role in improving the operation of the future power system.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162969/1/mqyao_1.pd

Interpreting Horizontal Well Flow Profiles and Optimizing Well Performance by Downhole Temperature and Pressure Data

Horizontal well temperature and pressure distributions can be measured by production logging or downhole permanent sensors, such as fiber optic distributed temperature sensors (DTS). Correct interpretation of temperature and pressure data can be used to obtain downhole flow conditions, which is key information to control and optimize horizontal well production. However, the fluid flow in the reservoir is often multiphase and complex, which makes temperature and pressure interpretation very difficult. In addition, the continuous measurement provides transient temperature behavior which increases the complexity of the problem. To interpret these measured data correctly, a comprehensive model is required. In this study, an interpretation model is developed to predict flow profile of a horizontal well from downhole temperature and pressure measurement. The model consists of a wellbore model and a reservoir model. The reservoir model can handle transient, multiphase flow and it includes a flow model and a thermal model. The calculation of the reservoir flow model is based on the streamline simulation and the calculation of reservoir thermal model is based on the finite difference method. The reservoir thermal model includes thermal expansion and viscous dissipation heating which can reflect small temperature changes caused by pressure difference. We combine the reservoir model with a horizontal well flow and temperature model as the forward model. Based on this forward model, by making the forward calculated temperature and pressure match the observed data, we can inverse temperature and pressure data to downhole flow rate profiles. Two commonly used inversion methods, Levenberg- Marquardt method and Marcov chain Monte Carlo method, are discussed in the study. Field applications illustrate the feasibility of using this model to interpret the field measured data and assist production optimization. The reservoir model also reveals the relationship between temperature behavior and reservoir permeability characteristic. The measured temperature information can help us to characterize a reservoir when the reservoir modeling is done only with limited information. The transient temperature information can be used in horizontal well optimization by controlling the flow rate until favorite temperature distribution is achieved. With temperature feedback and inflow control valves (ICVs), we developed a procedure of using DTS data to optimize horizontal well performance. The synthetic examples show that this method is useful at a certain level of temperature resolution and data noise