| Περίληψη : | This thesis analyzes the concept of “Smart Energy Grids”. It contains a survey of this new but important scientific area. Special consideration is devoted to Load Classification, Customer Characterization and Demand-Response. The first issue is related to residential appliances and their load profile during an operation cycle. On the other hand, customer characterization is extracting distinctive features for a group of features. Finally, Demand-Response is a financial program aiming load shaping in favor of system’s efficiency and reliability. The energy sector is experiencing major changes that are dictated by economical, technological and environmental factors. Demand is constantly increasing and residential consumers are using appliances like clothes and dishwashers that soar energy consumption in a coordinated way. The current system in order to retain its reliability and efficiency is oversized with aged generation plants used in excessive demand situations. This kind of configuration is far from optimum resulting into huge operational costs and extremely unfriendly environmental behavior, especially, during peak periods – hot days of summer and cold days of winter with coordinated air-conditioning loads. In Chapter 1 a detailed presentation of the current energy market is given with sections concerning major participants, organization and economical structures. Finally, another important issue that is taken into consideration has to do with the liberalization action prevailing for the last decade.There is a number of driving forces towards an energy system restructuring process and especially the distribution network. Among these forces, the environmental concerns are of primary importance for all participants – users, generators, retailers and operators. Small decentralized power plants operating with renewable energy resources are increasingly populating the map of available resources. Wind energy and solar energy also (important technological innovations during last years in Photovoltaic panels constructions) elevated the importance of renewable energy farms and efficient usage is possible for peak load coverage. On the other hand, Power Hybrid Electric Vehicles (PHEV) technology is considered a very promising solution to the problems of costly transportation and depleted resources. This kind of vehicles they have a dual operation consuming energy while moving on the roads and providing energy to the grid while parked. However all previously mentioned alternative sources of energy are missing maturity and many design and implementation problems remain to be solved. Chapter 2 lists the cons and the pros. Smart Grids is the next step towards a reliable, efficient and sustainable energy grid. The most important aspect of this innovative structure is constant metering responsible for the provision of huge amounts of data covering the operational status of all grid components. In addition a bidirectional communication channel is implemented for conveying information from the demand side and delivering control signals from the supply side. The cornerstone for this complicated architecture is a combination of smart meters and energy gateways that introduces intelligence into residential energy management. Smart meters are connected to energy-hungry appliances and collect load data at predefined time intervals. Once per day, all prepared data sets are collected into the energy gateway that is a central processing unit for every smart home. The energy gateway is equipped with two interfaces in order to connect to Home-Area-Network and to the Internet. The Internet broadband connection is used for exchanging messages with the utility and forwarding all measured data. Chapter 3 introduces the concepts of Smart Grid and Chapter 4 contains detailed information for Smart Meters and their importance in the new architecture. The aggregated load is collected centrally at a utility managed database. Being proactive is the main reason for having a system fully aware of customer load profile. In other words, the objective is the existence for every user of data set containing all energy transactions for a length time period. This data set is used as an input to a profile extraction mechanism that allows to the utility to obtain a better understanding the habits and needs for every user. Finally, the profiles corresponding to independent users are further processed using clustering algorithms and a small number of representative groups is extracted. This kind of information is extremely helpful for the preparation of dedicated tariffs taking into consideration the distinctive characteristics for every group. Also historical data is useful for future projections and crafting escape strategies from difficult situations. Chapter 5 refers to the problem of user classification and characterization from the supplier perspective. The second part of this thesis contains Chapters 6 & 7 and is equally important since it is dealing with crucial problems that are connected to Smart Grids final acceptance. Demand-Response is a mechanism for shaping load in favor of grid efficiency, reliability, and availability. The user is exposed into financial incentives in order to shift his/ her energy consumption from peak periods to non-peak periods. The extracted load-profiles provide to the utility the necessary means to generate control signals for remote appliances control. The output of this mechanism can be a price-schedule for the next 24 hours that is forwarded into residential consumers for consideration. But there is also another finding that makes a small contradiction to the previous. All relevant surveys conclude that the consumer should take the final decision and either accept or reject the financial offers provided by the supplier. On the other hand, the data flow is so frequent and detailed that is impossible for a human being to consult all provided data. I consider very important and stimulating the design of an automated decision mechanism that is fully aware of consumer’s energy habits and the degree of acceptable shifting. This kind of knowledge will be used for the validation of any incoming control or price signal that is send by the utility. The final decision will be globally accepted since it is both consumer and supplier driven.
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