Study with the several resources on Docsity
Earn points by helping other students or get them with a premium plan
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
The CTP Solver is an innovative linear programming tool that applies object-oriented software development practices and introduces an efficient method for handling bidirectional arcs in network flow problems. By doing so, it significantly reduces the required disk and memory allocation for fully bidirectional networks. an overview of the CTP Solver, its performance improvements, and its application to network flow problems.
Typology: Exams
1 / 136
This page cannot be seen from the preview
Don't miss anything!
Submitted to the Graduate Faculty^ A Thesis North Dakota State University^ Of the Of Agriculture and Applied Science
By Damian Lampl
In Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE
Major Department: Computer Science
July 2013
Fargo, North Dakota
North Dakota State University Graduate School Title SMART GRID OPTIMIZATION USING A CAPACITATED TRANSSHIPMENT PROBLEM SOLVER
By Damian Lampl
The Supervisory Committee certifies that this disquisition complies with North Dakota State University’s regulations and meets the accepted standards for the degree of
MASTER OF SCIENCE
Dr. Kendall Nygard Chair Dr. William Perrizo Dr. Brian Slator Dr. Samee Khan
Approved:
Date Department Chair
ABSTRACT.................................................................. iii LIST OF TABLES............................................................. xi LIST OF FIGURES........................................................... xii
xi
xiv
The creation of an autonomous, self-healing electrical grid is currently one of the most important challenges facing electrical energy providers. Such a system, known as the "smart grid", must interweave a multitude of different systems, both software and hardware, in order to form a complete solution capable of meeting the requirements outlined by the United States Department of Energy. According to the DOE, "It is a colossal task. But it is a task that must be done." [1]
This work focuses on a single aspect of those systems: optimal electrical flow through the smart grid network as determined by a cost factor. The cost factor can be a different performance metric for various optimization objectives, including values such as the distance between generators and customers, electric line repair times, or failure rates. In order to determine the best solution for multiple, different cost- related problems associated with the smart grid, the capacitated transshipment problem, or CTP, was chosen from the mathematical field of linear programming to model the smart grid network and its values. Using this model, a custom CTP Solver was developed, allowing users to easily determine the optimal network flow of a given smart grid network topology.
1.3. Organization
Four chapters describe the work performed, beginning with a short literature review, followed by the custom CTP Solver's implementation, results, and conclusion. The literature review chapter focuses on the problem definition, including an overview of the smart grid, linear programming, and how the capacitated transshipment problem is applied to the self-healing aspect of the smart grid. The implementation chapter focuses on the steps involved with creating the CTP Solver and the reasons behind its design and implementation decisions. Each step of its modified simplex algorithm is thoroughly described as to be easily followed. In addition to the details of the CTP Solver, a description of the custom network generator used in the creation of larger-scale networks is also included. In the results chapter, the CTP Solver's output is explained and its performance is compared to a couple of existing linear programming software solutions: AMPL and SAS. The accuracy as far as the minimum cost network attained for each test network is also compared against AMPL and SAS, but since they all implement optimal algorithms, the execution time of the algorithm is a more important comparison factor. Finally, the conclusion chapter sums up the work and includes recommendations as well as opportunities for future work and improvements to the CTP Solver and network generator.
capacity, made available through more than 300,000 miles of transmission lines. [2] While the current grid is considered an engineering marvel [2], it is beginning to show its age. As our electricity needs and demands increase and advance, so, too, must the electric grid providing the power. It follows, then, that "smart grid" refers to using computer-based remote control and automation in an effort to modernize the utility electricity delivery systems. [3] Among the many benefits these automated systems would help improve is the reliability of the electrical grid by dynamically rerouting power as needed in order to avoid cascading failures.
2.1.2. Self-Healing System
One of the greatest benefits of a fully-functional smart grid is the concept of self-healing. Current methods of outage detection vary and can be primitive at best, requiring customers to call the electric provider with service interruption notifications. This type of recovery solution is completely reactive, and often times much too slow to prevent catastrophic failures such as cascading outages. When a generator fails, a large system is affected and can cause overloading of other generators. As stations continue to fail, the outage spreads farther and farther throughout the network. Self-healing in the smart grid is just one aspect of a larger concept referred to as "distribution intelligence." It is concerned with the utility distribution system, or the wires, switches, and transformers that connect the utility substation to the customers. [2] Outage detection is another aspect of smart grid distribution intelligence. The CTP Solver assumes an outage has been detected and concerns itself with the optimal redistribution of power based on the current state of the smart grid network.
Figure 2 .2: Dynamic Electrical Power Rerouting. [2] 2.1.3. IEEE Test Systems
The Institute of Electrical and Electronics Engineers is the world's largest professional association dedicated to advancing technological innovation and excellence for the benefit of humanity. [4] The IEEE has multiple test systems available for the study of electrical grid networks, generally distinguished by a differing number of busses. The bus system files used in testing the CTP Solver include: 14-Bus Test System 30-Bus Test System 57-Bus Test System 118-Bus Test System 300-Bus Test System Diagrams and network data files are available on the University of Washington Electrical Engineering website for each of the IEEE test systems above. [5]
