Geostatistical Mapping of Outfall Plume Dispersion Data gathered with an autonomous underwater vehicle

The main purpose of this study was to examine the applicability of geostatistical modeling to obtain valuable information for assessing the environmental impact of sewage outfall discharges. The data set used was obtained in a monitoring campaign to S. Jacinto outfall, located off the Portuguese west coast near Aveiro region, using an AUV. The Matheron’s classical estimator was used the compute the experimental semivariogram which was fitted to three theoretical models: spherical, exponential and gaussian. The cross-validation procedure suggested the best semivariogram model and ordinary kriging was used to obtain the predictions of salinity at unknown locations. The generated map shows clearly the plume dispersion in the studied area, indicating that the effluent does not reach the near by beaches. Our study suggests that an optimal design for the AUV sampling trajectory from a geostatistical prediction point of view, can help to compute more precise predictions and hence to quantify more accurately dilution. Moreover, since accurate measurements of plume’s dilution are rare, these studies might be very helpful in the future for validation of dispersion models.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

Intelligent electric vehicle heuristic for energy resource management using simulated annealing

The smart grid concept appears as a suitable solution to guarantee the power system operation in the new electricity paradigm with electricity markets and integration of large amounts of Distributed Energy Resources (DERs). Virtual Power Player (VPP) will have a significant importance in the management of a smart grid. In the context of this new paradigm, Electric Vehicles (EVs) rise as a good available resource to be used as a DER by a VPP. This paper presents the application of the Simulated Annealing (SA) technique to solve the Energy Resource Management (ERM) of a VPP. It is also presented a new heuristic approach to intelligently handle the charge and discharge of the EVs. This heuristic process is incorporated in the SA technique, in order to improve the results of the ERM. The case study shows the results of the ERM for a 33-bus distribution network with three different EVs penetration levels, i. e., with 1000, 2000 and 3000 EVs. The results of the proposed adaptation of the SA technique are compared with a previous SA version and a deterministic technique.

Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach

This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G user´s profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

Electric vehicle scenario simulator tool for smart grid operators

This paper presents a simulator for electric vehicles in the context of smart grids and distribution networks. It aims to support network operator´s planning and operations but can be used by other entities for related studies. The paper describes the parameters supported by the current version of the Electric Vehicle Scenario Simulator (EVeSSi) tool and its current algorithm. EVeSSi enables the definition of electric vehicles scenarios on distribution networks using a built-in movement engine. The scenarios created with EVeSSi can be used by external tools (e.g., power flow) for specific analysis, for instance grid impacts. Two scenarios are briefly presented for illustration of the simulator capabilities.

Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints

Energy resource scheduling becomes increasingly important, as the use of distributed resources is intensified and massive gridable vehicle use is envisaged. The present paper proposes a methodology for dayahead energy resource scheduling for smart grids considering the intensive use of distributed generation and of gridable vehicles, usually referred as Vehicle- o-Grid (V2G). This method considers that the energy resources are managed by a Virtual Power Player (VPP) which established contracts with V2G owners. It takes into account these contracts, the user´s requirements subjected to the VPP, and several discharge price steps. Full AC power flow calculation included in the model allows taking into account network constraints. The influence of the successive day requirements on the day-ahead optimal solution is discussed and considered in the proposed model. A case study with a 33 bus distribution network and V2G is used to illustrate the good performance of the proposed method.

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Geostatistical Prediction of Ocean Outfall Plume Characteristics Based on an Autonomous Underwater Vehicle

Geostatistics has been successfully used to analyze and characterize the spatial variability of environmental properties. Besides giving estimated values at unsampled locations, it provides a measure of the accuracy of the estimate, which is a significant advantage over traditional methods used to assess pollution. In this work universal block kriging is novelty used to model and map the spatial distribution of salinity measurements gathered by an Autonomous Underwater Vehicle in a sea outfall monitoring campaign, with the aim of distinguishing the effluent plume from the receiving waters, characterizing its spatial variability in the vicinity of the discharge and estimating dilution. The results demonstrate that geostatistical methodology can provide good estimates of the dispersion of effluents that are very valuable in assessing the environmental impact and managing sea outfalls. Moreover, since accurate measurements of the plume’s dilution are rare, these studies might be very helpful in the future to validate dispersion models.

Modified particle swarm optimization for day-ahead distributed energy resources scheduling including vehicle-to-grid

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Evaluation of the Electric Vehicle Impact in the Power Demand Curve in a Smart Grid Environment

Smart grids with an intensive penetration of distributed energy resources will play an important role in future power system scenarios. The intermittent nature of renewable energy sources brings new challenges, requiring an efficient management of those sources. Additional storage resources can be beneficially used to address this problem; the massive use of electric vehicles, particularly of vehicle-to-grid (usually referred as gridable vehicles or V2G), becomes a very relevant issue. This paper addresses the impact of Electric Vehicles (EVs) in system operation costs and in power demand curve for a distribution network with large penetration of Distributed Generation (DG) units. An efficient management methodology for EVs charging and discharging is proposed, considering a multi-objective optimization problem. The main goals of the proposed methodology are: to minimize the system operation costs and to minimize the difference between the minimum and maximum system demand (leveling the power demand curve). The proposed methodology perform the day-ahead scheduling of distributed energy resources in a distribution network with high penetration of DG and a large number of electric vehicles. It is used a 32-bus distribution network in the case study section considering different scenarios of EVs penetration to analyze their impact in the network and in the other energy resources management.

Análise de um veículo todo o terreno do tipo KARTCROSS

O presente trabalho descreve a análise feita a um veículo de todo o terreno. O kartcross/buggy em estudo é usado em provas do tipo Baja, sendo estas provas longas e com traçados sinuosos. O veículo, já construído, foi testado através de softwares, a nível estrutural e ciclístico, pretendendo-se assim efetuar engenharia inversa sobre o mesmo. No decorrer da sua utilização normal o kartcross/buggy sofre vários tipos de solicitações, como sejam aceleração, travagem e força centrípta em curva. Portanto, o veículo deve ser capaz de suportar estes esforços e ter uma boa habilidade. Além dos testes em uso corrente foi analisada também a rigidez torsional do quadro do veículo e do veículo completo, podendo-se assim melhorar estes valores. A nível ciclístico foram analisados os parâmetros das suspensões como o camber, convergência/divergência, caster, entre outros. Da análise destes parâmetros e possível fazerem-se melhorias de forma a que o veículo tenha um melhor desempenho. Para validar os testes computacionais efetuados foi reproduzido experimentalmente o teste da rigidez torsional. No final, compararam-se os valores numéricos com os experimentais e aferir se o modelo se encontra bem representado.

Multi-objective Parallel Particle Swarm Optimization for Day-ahead Vehicle-To-Grid Scheduling

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Simulated Annealing Approach Applied to the Energy Resource Management Considering Demand Response for Electric Vehicle

The aggregation and management of Distributed Energy Resources (DERs) by an Virtual Power Players (VPP) is an important task in a smart grid context. The Energy Resource Management (ERM) of theses DERs can become a hard and complex optimization problem. The large integration of several DERs, including Electric Vehicles (EVs), may lead to a scenario in which the VPP needs several hours to have a solution for the ERM problem. This is the reason why it is necessary to use metaheuristic methodologies to come up with a good solution with a reasonable amount of time. The presented paper proposes a Simulated Annealing (SA) approach to determine the ERM considering an intensive use of DERs, mainly EVs. In this paper, the possibility to apply Demand Response (DR) programs to the EVs is considered. Moreover, a trip reduce DR program is implemented. The SA methodology is tested on a 32-bus distribution network with 2000 EVs, and the SA results are compared with a deterministic technique and particle swarm optimization results.