Reliability Enhancements for Real-Time Operations of Electric Power Systems

abstract: The flexibility in power system networks is not fully modeled in existing real-time contingency analysis (RTCA) and real-time security-constrained economic dispatch (RT SCED) applications. Thus, corrective transmission switching (CTS) is proposed in this dissertation to enable RTCA and RT SCED to take advantage of the flexibility in the transmission system in a practical way. RTCA is first conducted to identify critical contingencies that may cause violations. Then, for each critical contingency, CTS is performed to determine the beneficial switching actions that can reduce post-contingency violations. To reduce computational burden, fast heuristic algorithms are proposed to generate candidate switching lists. Numerical simulations performed on three large-scale realistic power systems (TVA, ERCOT, and PJM) demonstrate that CTS can significantly reduce post-contingency violations. Parallel computing can further reduce the solution time. RT SCED is to eliminate the actual overloads and potential post-contingency overloads identified by RTCA. Procedure-A, which is consistent with existing industry practices, is proposed to connect RTCA and RT SCED. As CTS can reduce post-contingency violations, higher branch limits, referred to as pseudo limits, may be available for some contingency-case network constraints. Thus, Procedure-B is proposed to take advantage of the reliability benefits provided by CTS. With the proposed Procedure-B, CTS can be modeled in RT SCED implicitly through the proposed pseudo limits for contingency-case network constraints, which requires no change to existing RT SCED tools. Numerical simulations demonstrate that the proposed Procedure-A can effectively eliminate the flow violations reported by RTCA and that the proposed Procedure-B can reduce most of the congestion cost with consideration of CTS. The system status may be inaccurately estimated due to false data injection (FDI) cyber-attacks, which may mislead operators to adjust the system improperly and cause network violations. Thus, a two-stage FDI detection (FDID) approach, along with several metrics and an alert system, is proposed in this dissertation to detect FDI attacks. The first stage is to determine whether the system is under attack and the second stage would identify the target branch. Numerical simulations demonstrate the effectiveness of the proposed two-stage FDID approach.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Improving power system static security margins by means of a real coded genetic algorithm

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. P. Martín and A. Sierra, "Improving Power System Static Security Margins by Means of a Real Coded Genetic Algorithm," in IEEE Transactions on Power Systems, vol. 31, no. 3, pp. 1915-1924, May 2016. doi: 10.1109/TPWRS.2015.2439579This paper introduces a new method of removing thermal overloads and voltage limits in an electric power system by means of the Evolution of Corrective and Preventive control Actions (ECPA). The goal is to find the minimum number of control actions that solve the identified limit violations at minimum cost. A recombination operator based on form theory allows the codification of control actions in a natural and simple way. ECPA has been tested on the IEEE 30-bus and the IEEE 118-bus systems. The limit violations are solved at minimum cost and with fewer control actions on average than alternative methods

Linear and Nonlinear Programming Methods for Dispatching Power in an Integrated AC-DC System

As the number of dc systems increases, it is natural to ask what other roles, aside that of bulk power transfer, that these systems could play in the operation of modern power systems. The objective of this research is to develop formulations and methods of solution to coordinate the dispatch of powers in an integrated ac-dc power system for purposes of minimizing transmission losses and production costs. In Section I we present an LP formulation and method of solution to minimize the ac and dc network transmission losses by coordinating the traditional reactive sources with the dispatch of the dc power transfers, taking into consideration the usual constraints on equipment ratings, line flows and bus voltage magnitudes. Results on sample test systems indicate that substantial reduction in network losses can be achieved by a coordinated dispatch involving the dc power transfers. Section II describes the mathematical formulation and method of solution for the optimal power flow problem of an integrated ac-dc power system. The method is capable of handling the network, converter tap, and control constraints of more than one multiterminal dc systems. The method uses a sequence of quadratic programming subproblems to determine the search directions. Also discussed are ways for determining the initial estimates of the Lagrange multiplier. Tests performed on modified IEEE 30 and 118 bus systems gave reasonable solution time and rate of convergence. The results obtained on the sample systems also indicate that there could be further economic advantage when the dispatch of dc powers is coordinated with the conventional controllable sources using the optimal power flow program. Section III reports on the findings from a comparative study of three methods to screen and rank severe contingencies for preventive dispatch

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

Model-Predictive Control for Alleviating Transmission Overloads and Voltage Collapse in Large-Scale Electric Power Systems

Emergency control in electric power systems requires rapid identification and implementation of corrective actions. Typically, system operators have performed this service while relying on rules-of-thumb and predetermined control sequences with limited decision support tools. Automatic control schemes offer the potential to improve this process by quickly analyzing large, complex problems to identify the most effective actions. Model-predictive control (MPC) is one such scheme which has a strong record of success in the process industry and has begun receiving attention in power systems applications. Incorporating flexibility into the MPC model using energy storage and temperature-based transmission line limits has shown promising results for relieving transmission overloads on small networks with linear active power models. Separately, MPC has demonstrated its capabilities in correcting transformer-driven voltage collapse behaviors. However, a comprehensive solution combines both aspects into a single controller formulation with knowledge of active and reactive power and voltage magnitude and angle. Additionally, most power system networks are large and result in computationally challenging problem formulations. This work considers these practical limitations and suggests techniques to enable an MPC process capable of operating reliably in the real-world. A new linear controller model is proposed which considers voltage magnitude and angle and both active and reactive power. The new model provides greater accuracy when predicting system behavior and better identifies the actual control needs of the system. The problem size is reduced by limiting the model to only those devices which are significantly affected by the emergency conditions. The new approach is shown to identify controls more rapidly and better suppresses undesirable thermal behavior on overloaded transmission lines while avoiding potential voltage collapse situations.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137120/1/jandrewm_1.pd

A reconfigurable distributed multiagent system optimized for scalability

This thesis proposes a novel solution for optimizing the size and communication overhead of a distributed multiagent system without compromising the performance. The proposed approach addresses the challenges of scalability especially when the multiagent system is large. A modified spectral clustering technique is used to partition a large network into logically related clusters. Agents are assigned to monitor dedicated clusters rather than monitor each device or node. The proposed scalable multiagent system is implemented using JADE (Java Agent Development Environment) for a large power system. The performance of the proposed topology-independent decentralized multiagent system and the scalable multiagent system is compared by comprehensively simulating different fault scenarios. The time taken for reconfiguration, the overall computational complexity, and the communication overhead incurred are computed. The results of these simulations show that the proposed scalable multiagent system uses fewer agents efficiently, makes faster decisions to reconfigure when a fault occurs, and incurs significantly less communication overhead. The proposed scalable multiagent system has been coupled with a scalable reconfiguration algorithm for an electric power system attempting to minimize the number of switch combination explored for reconfiguration. The reconfiguration algorithm reconfigures a power system while maintaining bus voltages within limits specified by constraints

A study of automatic contingency selection algorithms for steady-state security assessment of power systems and the application of parallel processing

The performance of various Contingency Selection methods has been investigated within the framework of accuracy for application to steady-state power system security assessment and suitability for execution in a real-time environment. In the study the following requirements have been considered: (a) Effectiveness: in identifying contingencies which may cause limit violations and discarding all others; (b) Adaptability: to model both permanent and temporary changes in the system; (c) Flexibility: to model any number and type of contingencies; (d) Computational efficiency: in terms of speed in selecting the sub-set of contingencies as well as in terms of storage requirements; (e) Ability: to update and augment on-line the list of contingencies given the actual system operating data. [Continues.

Optimal Transmission Investment Strategies for Sustainable Power Systems

Maintaining security and reliability in the electricity supply is fundamental to the functioning of a modern society and drives the need for adequate transmission capacity for both market participants and customers. Planning the investment in transmission has always been a complicated undertaking due to the high development costs and long lead times. Furthermore, to anticipate the future needs of customers is a task as difficult as that of cost-effective planning and construction of new facilities. Trying to find treatments for some of these issues represents a major motivation for this thesis. This thesis investigates the problem of how much reinforcement a transmission system requires when a significant proportion of wind generation is integrated into an existing transmission system. A multi-period transmission planning model is developed for determining optimal transmission capacity by balancing amortised transmission investment costs and annual generation costs subject to network security constraints, The model employs the security-constrained DC optimal power flow formulation and applies a solver (DashXpress) to obtain the results of the remaining linear large-scale optimisation problem. This thesis begins by exploring the impact of wind generation on the determination of appropriate levels of system capacity on the transmission network starting from the premise that it is no longer cost effective to invest in sufficient network capacity to accommodate simultaneous peaks from all generators. As such, a significant finding of this study is that conventional and wind generation should share network capacity. Given the acknowledged increase in uncertainty to security of supply due to difficulties in wind generation forecast this thesis also explores the optimal sourcing of generation reserve, and investigates investment in transmission capacity to exploit the cost benefits offered by standing reserve. Finally, the thesis presents and evaluates an alternative associated with transmission operation and investment level of risk and uncertainty by introducing more flexibility to the way the transmission system is operated. Application of Quadrature Boosters and Demand Side as model of corrective control, brings savings in operating costs without jeopardizing the level of system security, enables better utilisation of existing facilities and reduces the demand for new transmission investment