Integrated Scheduling of Electric Vehicles and Demand Response Programs in a Smart Microgrid

Microgrid (MG) is one of the important blocks in the future smart distribution systems. The scheduling pattern of MGs affects distribution system operation. Also, the optimal scheduling of MGs will be result in reliable and economical operation of distribution system. In this paper, an operational planning model of a MG which considers multiple demand response (DR) programs is proposed. In the proposed approach, all types of loads can participate in demand response programs which will be considered in either energy or reserve scheduling. Also, the renewable distributed generation uncertainty is covered by reserve prepared by both DGs and loads. The novelty of this paper is the demand side participation in energy and reserve scheduling, simultaneously. Furthermore the energy and reserve scheduling is proposed for day-ahead and real-time. The proposed model was tested on a typical MG system in connected mode and the results show that running demand response programs will reduce total operation cost of MG and cause more efficient use of resources

Measurement devices allocation in distribution system using state estimation: A multi-objective approach

Optimal allocation of measurement devices is a necessity in order to carry out state estimation of a distribution system. In this paper, the placement problem of power measurement devices is modeled using a multi-objective method. The objectives of the problem are to minimize measurement devices' costs while increasing the accuracy of state estimation and improving the state estimation quality. Also, operational priorities are considered as another objective, which are based on power losses, lines' capacities, the number of lines connected to a specific line, and the change in lines' flows direction. A multi-objective evolutionary algorithm based on decomposition (MOEA/D) is used to optimize the allocation of measurement devices within the problem of distribution system state estimation. The state estimation problem is optimized by particle swarm optimization (PSO) algorithm and the Monte Carlo simulation is used to develop some conditions within the network to guarantee the robustness of the proposed method. The method is tested by simulation results on an IEEE 33-bus and IEEE 123-bus radial network

Selecting and prioritizing the electricity customers for participating in demand response programs

Demand response (DR) provides an opportunity for customers to play an important role in the operation of the electricity grid by reducing or shifting their electricity usage during peak periods. However, selecting customers to participate in DR programs is challenging. To solve this problem, typical load profiles should be characterized by data mining techniques such as clustering algorithms. Traditional clustering algorithms manually determine the centre of clusters and that the selected centre of clusters may fall into a local optimum. Here, to overcome these issues, a new clustering algorithm based on the Density Peak Clustering algorithm (DPC) and Artificial Bee Colony algorithm (ABC) which is called A-DPC, is implemented to optimally determine the representative load curves. Moreover, by introducing a new priority index, the eligible residential customers are selected for participating in DR programs. Also, to meet the sufficient load reduction in a DR event, the proposed approach suggests a plenty number of residential customers to be called. The result evidence that A-DPC has a stronger global search ability to optimally select the centre of clusters if compared to other clustering algorithms

Cloud Computing for Smart Energy Management (CC-SEM Project)

This paper describes the Cloud Computing for Smart Energy Management (CC-SEM) project, a research effort focused on building an integrated platform for smart monitoring, controlling, and planning energy consumption and generation in urban scenarios. The project integrates cutting-edge technologies (Big Data analysis, computational intelligence, Internet of Things, High Performance Computing and Cloud Computing), specific hardware for energy monitoring/controlling built within the project and explores their communication. The proposed platform considers the point of view of both citizens and administrators, providing a set of tools for controlling home devices (for end users), planning/simulating scenarios of energy generation (for energy companies and administrators), and shows some advances in communication infrastructure for transmitting the generated data.Fil: Luján, Emmanuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; ArgentinaFil: Otero, Alejandro Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Valenzuela, Sebastián. Universidad de la Republica.; UruguayFil: Mocskos, Esteban Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Steffenel, Luiz Angelo. Université de Reims Champagne ardenne; FranciaFil: Nesmachnow, Sergio. Universidad de la Republica.; Urugua