Pilot Evaluation of the Mexican Model of Dual TVET in the State of Mexico

Since the first public announcement of the Mexican Model of Dual TVET (MMFD) in June 2013, more than 5,000 apprentices have enrolled in the programme and around 2,000 already graduated. The Ministry of Education (SEP and CONALEP), the Chambers of Commerce (i.e. COPARMEX) and the German Cooperation Agencies (i.e. CAMEXA) have been collaborating with state authorities, families, schools and companies to turn this initial idea into a significant and sustainable initiative. Although the numbers are still small, it seemed necessary to undertake a pilot evaluation study of the implementation and impact of this program on its participants to inform those responsible for this policy. We decided to focus our study on the State of Mexico because of the higher number of apprentices in this state and because of the access that the CONALEP authorities gave us to the informants. The report that you are about to read is structured in four main sections. In the first one we reviewed the international evidence on the experiences of policy transfer of Dual TVET. Transferring international good practice sin TVET is always a complex process that requires careful attention to the experiences and lessons from those that tried to do it before. In the second section, we present the main characteristics of the Mexican Model of Dual TVET and the specificities of its implementation in the State of Mexico. In a federal country like Mexico, it is important to understand that national policies may largely vary across states in terms of design and implementation. The third section outlines the methodology of the study, which is inspired by the realist evaluation principles. Realist evaluation, not only tries to measure the impact of interventions on beneficiaries, but also to understand the causal mechanisms that explain why this policy is more effective in certain contexts and with certain beneficiary populations than in others. In the final section, the results of the interviews and the survey with 25 apprentices that completed their studies under the MMFD in the State of Mexico are presented. Obviously, the reduced sample of the study limits the representativeness of our findings but it will offer some expected and unexpected results that should not be ignored by those involved in this policy in the State of Mexico and nationally

The ASSET project as a training tool for energy transition

[EN] The ASSET project aims to provide a holistic and scalable solution for research, innovation and education by creating functional networks. These networks are intended to be created between energy companies, universities, training actors, energy and environmental authorities, policy makers and, more generally, citizens who are sensitive to environmental issues and the quality of energy transition processes. The ASSET project delivers the framework and the tools to create and share knowledge and competences needed to tackle the energy transition by supporting training. As a highlight of this approach to education, a strong interdisciplinary component oriented to social sciences is added in an area with an exclusive technological vocation. This transition seeks to push towards a low-carbon society in order to make the energy sector sustainable. To reach this goal, ASSET intends to strengthen the skills of sector operators, to cultivate new talents with multidisciplinary skills, and to intensify research and network industry. Therefore, the final target is to promote innovation and strengthen understanding of the importance of reducing carbon emissions. Over the course of the project, 23 learning graph models and more than 40 educational programs are being developed, in addition to a portfolio of challenges and case studies on the subject. The actors involved will be able to search for the programs available - online and on-campus - on the ASSET website and if a search is unsuccessful, a request can be sent for the creation of content necessary for their target market. The main tools that have been developed through the ASSET project are; the Learning Graph tool, the Marketplace tool and the EMMA platform. The Learning graph tool allows for the creation and sharing of learning structures, as well as the use of existing study materials. The Marketplace tool allows the searching through the available training offer, to request courses on demand, or to offer own training programmes. Finally, the EMMA platform offers a wide range of MOOC (Massive Online Open Courses), mainly in English and with the possibility of being translated into several languages. Universitat Politècnica València (UPV) is participating in the project as one of the academic actors that is developing courses and MOOCs in the area of Energy Storage. In this way, the UPV contributes to the identification of learning needs, the application of the ASSET method and tools to its teaching material, and the delivery of this teaching material. Specifically, the course being developed is called "Hydrogen as an Energy Vector". The course provides the fundamentals of hydrogen technology, using it to store energy and further develop the concept of its use as an energy vector. The course follows the blended format, combining online elements, through a MOOC (EMMA platform) and face-to-face teaching carried out at the university facilities. In the paper, we will present the main ASSET tools, the lessons learned in the development of course materials during the lifetime project and the analysis of the results of this experience.This work was supported by the European Commission though the project A Holistic And Scalable Solution For Research, Innovation And Education In Energy Transition (European Union's Horizon 2020 research and innovation programme under grant agreement number 837854).Zúñiga Saiz, P.; Sánchez-Diaz, C. (2021). The ASSET project as a training tool for energy transition. IATED Academy. 4354-4363. https://doi.org/10.21125/inted.2021.08884354436

A Holistic Approach to Forecasting Wholesale Energy Market Prices

Electricity market price predictions enable energy market participants to shape their consumption or supply while meeting their economic and environmental objectives. By utilizing the basic properties of the supply-demand matching process performed by grid operators, known as Optimal Power Flow (OPF), we develop a methodology to recover energy market's structure and predict the resulting nodal prices by using only publicly available data, specifically grid-wide generation type mix, system load, and historical prices. Our methodology uses the latest advancements in statistical learning to cope with high dimensional and sparse real power grid topologies, as well as scarce, public market data, while exploiting structural characteristics of the underlying OPF mechanism. Rigorous validations using the Southwest Power Pool (SPP) market data reveal a strong correlation between the grid level mix and corresponding market prices, resulting in accurate day-ahead predictions of real time prices. The proposed approach demonstrates remarkable proximity to the state-of-the-art industry benchmark while assuming a fully decentralized, market-participant perspective. Finally, we recognize the limitations of the proposed and other evaluated methodologies in predicting large price spike values.Comment: 14 pages, 14 figures. Accepted for publication in IEEE Transactions on Power System

Distance education possibilities analysis for integrated innovative projects

The materials presented the possibilities development of solar and wind power plants, project development for all those who are engaged in the power studies and baseness. In this, phase of work in NTU "KPI" – studies the possibility of increasing the economic efficiency of alternative energy sources. A review of the literature and the necessary articles written on the subject: аs technologies and economies develop and become more complex, energy needs increase greatly; types and methods of alternative energy, as well as the possibility of calculating the basic set of main economic indicators are classified; identified possible areas of work in obtaining the necessary infor-mation and results. Energy is a fundamental input for economic systems. Current economic activity depends overwhelmingly on fossil fuels including oil, coal, and natural gas. These fuels are non-renewable. Renewable sources such as hydroelectric, wind, and solar power currently provide less than 10% of global energy. In just a few decide solar and wind power has developed from alternative energy sources to a new fast growing industrial branch. The history of industrial civilization is a history of energy transitions. In less developed, agrarian economies, people's basic need for food calories is provided through simple forms of agriculture, which is essentially a method of capturing solar energy for human use. As economies develop and become more complex, energy needs increase greatly

Metascheduling of HPC Jobs in Day-Ahead Electricity Markets

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market. We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.Comment: Appears in IEEE Transactions on Parallel and Distributed System

Remotely hosted services and 'cloud computing'

Emerging technologies for learning report - Article exploring potential of cloud computing to address educational issue