Optimal security-constrained power scheduling by Benders decomposition

This paper presents a Benders decomposition approach to determine the optimal day-ahead power scheduling in a pool-organized power system, taking into account dispatch, network and security constraints. The study model considers the daily market and the technical constraints resolution as two different and consecutive processes. The daily market is solved in a first stage subject to economical criteria exclusively and then, the constraints solution algorithm is applied to this initial dispatch through the redispatching method. The Benders partitioning algorithm is applied to this constraints solution process to obtain an optimal secure power scheduling. The constraints solution includes a full AC network and security model to incorporate voltages magnitudes as they are a critical factor in some real power systems. The algorithm determines the active power committed to each generator so as to minimize the energy redispatch cost subject to dispatch, network and security constraints. The solution also provides the reactive power output of the generators, the value of the transformers taps and the committed voltage control devices. The model has been tested in the IEEE 24-bus Reliability Test System and in an adapted IEEE 118-bus Test System. It is programmed in GAMS mathematical modeling language. Some relevant results are reported.Publicad

Uncertainty Quantification And Economic Dispatch Models For The Power Grid

The modern power grid is constrained by several challenges, such as increased penetration of Distributed Energy Resources (DER), rising demand for Electric Vehicle (EV) integration, and the need to schedule resources in real-time accurately. To address the above challenges, this dissertation offers solutions through data-driven forecasting models, topology-aware economic dispatch models, and efficient optional power flow calculations for large scale grids. Particularly, in chapter 2, a novel microgrid decomposition scheme is proposed to divide the large scale power grids into smaller microgrids. Here, a two-stage Nearest-Generator Girvan-Newman (NGGN) algorithm, a graphicalclustering-based approach, followed by a distributed economic dispatch model, is deployed to yield a 12.64% cost savings. In chapter 3, a deep-learning-based scheduling scheme is intended for the EVs in a household community that uses forecasted demand, consumer preferences and Time-of-use (TOU) pricing scheme to reduce electricity costs for the consumers and peak shaving for the utilities. In chapter 4, a hybrid machine learning model using GLM with other methods was designed to forecast wind generation data. Finally, in chapter 5, multiple formulations for Alternating Current Optimal Power Flow (ACOPF) were designed for large scale grids in a high-performance computing environment. The ACOPF formulations, namely, power balance polar, power balance Cartesian, and current balance Cartesian, are tested on bus systems ranging from a 9-bus to 25,000. The current balance Cartesian formulation had an average of 23% faster computational time than two other formulations on a 25,000 bus system

Performance Enhancement of Power System Operation and Planning through Advanced Advisory Mechanisms

abstract: This research develops decision support mechanisms for power system operation and planning practices. Contemporary industry practices rely on deterministic approaches to approximate system conditions and handle growing uncertainties from renewable resources. The primary purpose of this research is to identify soft spots of the contemporary industry practices and propose innovative algorithms, methodologies, and tools to improve economics and reliability in power systems. First, this dissertation focuses on transmission thermal constraint relaxation practices. Most system operators employ constraint relaxation practices, which allow certain constraints to be relaxed for penalty prices, in their market models. A proper selection of penalty prices is imperative due to the influence that penalty prices have on generation scheduling and market settlements. However, penalty prices are primarily decided today based on stakeholder negotiations or system operator’s judgments. There is little to no methodology or engineered approach around the determination of these penalty prices. This work proposes new methods that determine the penalty prices for thermal constraint relaxations based on the impact overloading can have on the residual life of the line. This study evaluates the effectiveness of the proposed methods in the short-term operational planning and long-term transmission expansion planning studies. The second part of this dissertation investigates an advanced methodology to handle uncertainties associated with high penetration of renewable resources, which poses new challenges to power system reliability and calls attention to include stochastic modeling within resource scheduling applications. However, the inclusion of stochastic modeling within mathematical programs has been a challenge due to computational complexities. Moreover, market design issues due to the stochastic market environment make it more challenging. Given the importance of reliable and affordable electric power, such a challenge to advance existing deterministic resource scheduling applications is critical. This ongoing and joint research attempts to overcome these hurdles by developing a stochastic look-ahead commitment tool, which is a stand-alone advisory tool. This dissertation contributes to the derivation of a mathematical formulation for the extensive form two-stage stochastic programming model, the utilization of Progressive Hedging decomposition algorithm, and the initial implementation of the Progressive Hedging subproblem along with various heuristic strategies to enhance the computational performance.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201