Cristina Lobo

Optimal Approach for Reliability Assessment in Radial Distribution Networks

This paper proposes a new methodology for the evaluation of reliability in radial distribution networks through the identification of new investments in this kind of networks, in order to reduce the repair time and the failure rate, which leads to a reduction of the forced outage rate and, consequently, to an increase of reliability. The novelty of this research work consists in proposing an ac optimization model based on mixed-integer nonlinear programming that is developed considering the Pareto front technique, in order to achieve a reduction of repair times and of failure rates of the distribution network components, while minimizing the costs of that reduction, the power losses cost, the cost of the optimal capacitor location and size, and the maximization of reliability, which is in the form of minimization of nonsupplied energy cost. In order to estimate the outage parameters, a fuzzy set approach is used. The optimization model considers the distribution network technical constraints. A case study using a 33-bus distribution network is presented to illustrate the application of the proposed methodology.

Relaxation of non-convex problem as an initial solution of meta-heuristics for energy resource management

Energy resources management is an important topic in the context of Smart Grids (SG) and MicroGrids (MG). Virtual Power Players (VPP) emerge as aggregator entities responsible for managing energy resources in SG and MG. In this paper modern meta-heuristics are compared with their hybrid version that uses a deterministic method as initial solution. The meta-heuristics implemented are Differential Search Algorithm (DSA) and Quantum Particle Swarm Optimization (QPSO) to solve the hard combinatorial scheduling problem. The deterministic method employs a relaxation of the non-convex Mixed Integer Non-Linear Programming (MINLP) formulation in order to reduce the computational burden and the number of iterations in metaheuristics. The implemented methods are tested with a 33-bus distribution network, 1800 Electric Vehicles (EV), 15 fixed storage, 66 Distributed Generation (DG), 10 energy suppliers and 1 utility-scale wind generation facility. The results show the excellent performance of the DSA, QPSO and hybrid approaches.

Realistic traffic scenarios using a census methodology: Vila real case study

This paper presents the first phase of the redevelopment of the Electric Vehicle Scenario Simulator (EVeSSi) tool. A new methodology to generate traffic demand scenarios for the Simulation of Urban MObility (SUMO) tool for urban traffic simulation is described. This methodology is based on a Portugal census database to generate a synthetic population for a given area under study. A realistic case study of a Portuguese city, Vila Real, is assessed. For this area the road network was created along with a synthetic population and public transport. The traffic results were obtained and an electric buses fleet was evaluated assuming that the actual fleet would be replaced in a near future. The energy requirements to charge the electric fleet overnight were estimated in order to evaluate the impacts that it would cause in the local electricity network.

Increase of the delivered power probability in distribution networks using Pareto DC programming

A methodology to increase the probability of delivering power to any load point through the identification of new investments in distribution network components is proposed in this paper. The method minimizes the investment cost as well as the cost of energy not supplied in the network. A DC optimization model based on mixed integer non-linear programming is developed considering the Pareto front technique in order to identify the adequate investments in distribution networks components which allow increasing the probability of delivering power for any customer in the distribution system at the minimum possible cost for the system operator, while minimizing the energy not supplied cost. Thus, a multi-objective problem is formulated. To illustrate the application of the proposed methodology, the paper includes a case study which considers a 180 bus distribution network.

Developing Undergraduate Projects in Multinational Teams to Enhance Employability

Our society is experiencing sudden changes in work organization in part due to the growing ease with which people can collaborate. Many successful cases of peer-to-peer models of organization arise and assume leading positions in world economy replacing, in many cases, the traditional hierarchical organization. People are evolving and interacting within heterogeneous teams composed by members from many different cultural groups and with distinct skills and backgrounds. Modern economy requires engineers to excel in collaborative and communication skills at an international setting. However, these competences are not usually addressed in most engineering curricula. We believe that in such a demanding and culturally diverse environment as the labour market is today, it is essential to promote team work and communication skills at an international and intercultural level. In the Multinational Undergraduate Team Work course, MUTW, students develop their capstone project as members of an international team while working at their home institutions. MUTW projects are to be developed by teams of final-year-undergraduate students from a multinational group of higher education institutions working to solve some engineering problem. Team members are geographically spread to assure heterogeneous teams and to promote international cooperation. This paradigm can be applied in any project/internship course unit. The results from the first edition are very encouraging supporting our initial hypothesis that MUTW significantly promotes students soft skills without requiring any change to prior degree curricula.

VPP Energy Resources Management Considering Emissions: The Case of Northern Portugal 2020 to 2050

In SGs context, Distributed Generation (DG) based on renewable sources represents an alternative paradigm of energy supply and the opportunity for significant reduction in CO2 emissions. However, strict emissions regulations might impact profit seeking Virtual Power Plants (VPP) operation. This paper addresses energy management at distribution level and evaluates if electricity emissions are worth to be considered in different time horizons. A realistic case study is developed for a chosen area of the northern region of Portugal, namely one part of the distribution grid from Vila Real managed by a VPP, with estimated penetration of Electric Vehicles (EV), several Distributed Generation (DG), Demand Response (DR) and Energy Storage Systems (ESS). The considered characteristics of the case study took into account several studies and the forecasts made in the literature. For 2030 it is expected an average CO2 grid emission of 50 kgCO2/MWh in Portugal. The repository-based multi-objective Particle Swarm Optimization (MOPSO) is used to tackle the developed optimization problem. Three scenarios are evaluated for the profit seeking VPP in 2020, 2030 and 2050 perspectives.

Day-ahead distributed energy resource scheduling using differential search algorithm

The number of dispersed energy resources is growing every day, such as the use of more distributed generators. This paper deals with energy resource scheduling model in future smart grids. The methodology can be used by virtual power players (VPPs) considering day-ahead time horizon. This method considers that energy resources are managed by a VPP which establishes contracts with their owners. The full AC power flow calculation included in the model takes into account network constraints. This paper presents an application of differential search algorithm (DSA) for solving the day-ahead scheduling. DSA method is used to minimize the operation costs for the VPP providing in satisfactory execution time. Two scenarios are presented using a 33-bus distribution network, large wind farm and several distributed energy resources to illustrate the proposed methodology. These scenarios consider a contingency on the large wind farm and different forecasts regarding load demand.