On-Line Optimal Charging Coordination of Plug-In Electric Vehicles in Smart Grid Environment

This PhD research proposes a new objective function for optimal on-line PEV coordination. A new enhanced on-line coordinated charging using coordinated aggregated particle swarm particle optimization (OLCC-CAPSO) has been used to solve the PEV coordination objective objection and associated constraints. The objective function provides a chance for all PEVs to start charging as quickly as possible, while customer satisfaction function is being optimized subject to network criteria including voltage profiles, generator and distribution transformer ratings

Modeling a Decentralized Market-Based Scheme for Responsive Demands

One of the major problems in the present era consists on carefully and wisely explore the resources that can be obtained throughout interaction and exploration of the present resources on our planet. As for that, it is important not only to explore resources efficiently, but also to explore them in a way that considers consistency on its exploration. A problem that energy networks are facing nowadays is the high and rising consumption level. Since not only population is growing exponentially, but also due to the fact that electricity is substituting other resources whose usage was almost electricity independent, increasing the requirements of energy and introducing problems to the network control, such as the reduction of the possibility for the network agents to find an alternative during peak hours to fulfill the energy requirements of the fleet in a scenario in which generators fail to work. To solve that problem, it would be crucial to reduce consumption during peak hours, postponing consumption to valley hours, where the network agents won't face problems such as a demand level superior the maximum accepted demand. That problem can be solved using demand response programs. Demand response programs allow end-use customers to make changes in electric usage from their usual consumption patterns, allowing them to postpone load consumption from peaks hours to valley hours. It is observable that the development of many technologies, as well as a crescent flux of information and communication, lead to a new age of smart grid. Through smart meters, demand response end-users have access to their level of consumption, as well as the price/signal data. In this scenario, customers' participation in demand response programs is more likely to happen than ever before, increasing the number and participation of responsive demands in the smart grid. Considering the difficulty for customers to directly negotiate with the independent system operator, a linking agent will be considered for managing the customers and providing individual demand response programs for each demand response end-user. On the other side, DR end-users want to have maximum satisfaction while using electricity. In a flat pricing mode, in which demand response is not considered, customers tend to use electricity at the most convenient time throughout the day, driven by their personal preferences. However, some of the electricity usage can be shiftable without causing a major impact on the demand response end-users' satisfaction. Each demand response provider attempts to form a load pattern of its demand response end-users, achieving compensation for the expenditure saving incurred to system operator due to load shaping. The DR provider motivates the end-users to adjust their electricity consumption profile by price-based or incentive-based programs. By incentivizing end-users to change their consumption scheduling, their energy usage costs will decrease, being the consumers rewarded by their price-adapted consumption. Goals In order to identificate the effectiveness of the proposed model, some case studies will be considered, being the results analyzed based on the following targets: - Investigating the billing costs of the proposed decentralized customers compared to an uncontrolled approach; - Investigating the results of the proposed model compared to a centralized aggregator-based approach, where a demand response aggregator directly purchases electricity on behalf of responsive demands in the day-ahead market