The electric power grid forms the foundation for several other critical infrastructures of national importance such as public health, transportation and telecommunication systems, to thrive. The current power grid runs on the century-old technology and faces serious challenges of the 21st century - Ever-increasing demand and the need to provide a sustainable way to meet the growing demand, increased requirement of resilience against man-made and natural disasters, ability to defend against cyber attacks, increasing demand for reliable power, requirement to integrate with alternate energy generation and storage technologies. Several countries, including the United States, have realized the immediate need to modernize the grid and to pursue the goal of a smart grid. Majority of recent grid modernization efforts are directed towards the distribution systems to be able to meet these new challenges. One of the key enablers of a fully functional Smart Grid are microgrids — subsystems of the grid, utilizing small generation capacities at the distribution system level to increase the overall reliability and power quality of the local grid. It is one of the key directions recommended by national electric delivery technologies roadmap in United States as well as policy makers for electricity delivery in many countries. Microgrids have witnessed serious research activity in the past few years, especially in areas such as multi-agent system (MAS) architectures for microgrid control and auction algorithms for microgrid electricity transaction. However, most of the prior research on electricity transaction in microgrids fails to recognize and represent the true nature of the microgrid electricity market. In this research, a comprehensive microgrid electricity market has been designed, taking into account several unique characteristics of this new market place. This thesis establishes an economic rationale to the vision of wide-scale deployment of microgrids serving residential communities in near future and develops a comprehensive understanding of microgrid electricity market. A novel concept of Community Microgrids is introduced and the market and business models for electricity transaction are proposed and validated based on economic forecasts of key drivers of distributed generation. The most important contribution of this research deals with establishing a need for a trustworthy model framework for microgrid market and introducing the concept of reputation score to market participants. A framework of day-ahead energy market (DAEM) for electricity transaction, incorporating an approach of using the reputation score to incentivize the sellers in the market to be trustworthy, has been designed and implemented in MATLAB with a graphical user interface (GUI). Current implementation demonstrates a market place with two sellers and nine buyers and is easily scalable to support multiple market participants. The proposed microgrid electricity market may spur the deployment of residential microgrids, incorporating distributed generation, thereby making significant contribution to increase the overall reliability and power quality of the local grid. / Master of Science

An important part of maintaining network security is collecting and processing information about cyber threats, both from network operator's own detection tools and from third parties. A commonly used type of such information are lists of network entities (IP addresses, domains, URLs, etc.) which were identified as malicious. However, in many cases, the simple binary distinction between malicious and non-malicious entities is not sufficient. It is beneficial to keep other supplementary information for each entity, which describes its malicious activities, and also a summarizing score, which evaluates its reputation numerically. Such a score allows for quick comprehension of the level of threat the entity poses and allows to compare and sort entities. The goal of this work is to design a method for such summarization. The resulting score, called Future Maliciousness Probability (FMP score), is a value between 0 and 1, assigned to each suspicious network entity, expressing the probability that the entity will do some kind of malicious activity in a near future. Therefore, the scoring is based of prediction of future attacks. Advanced machine learning methods are used to perform the prediction. Their input is formed by previously received alerts about security events and other relevant data related to the entity. The method of computing the score is first described in a general way, usable for any kind of entity and input data. Then a more concrete version is presented for scoring IPv4 address by utilizing alerts from an alert sharing system and supplementary data from a reputation database. This variant is then evaluated on a real world dataset. In order to get enough amount and quality of data for this dataset, a part of the work is also dedicated to the area of security analysis of network data. A framework for analysis of flow data, NEMEA, and several new detection methods are designed and implemented. An open reputation database, NERD, is also implemented and described in this work. Data from these systems are then used to evaluate precision of the predictor as well as to evaluate selected use cases of the scoring method.