Juan Miguel Sánchez-Lozano

Evaluation of suitable locations for the installation of solar thermoelectric power plants

Evaluation of optimal sites to implant solar thermoelectric power plants.Combination of Soft Computing (SC) and Geographic Information Systems (GIS).Multicriteria Decision Making Method MCDM applied.Analytical Hierarchy Process (AHP) method.TOPSIS method, linguistic labels for qualitative criterion modeled by fuzzy numbers. The aim of the present paper is to determine the best location to host a solar thermoelectric power plant. We will seek to show how Geographic Information Systems (GIS) and Multi Criteria Decision Making (MCDM) such as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS method) and Analytic Hierarchy Process (AHP), are an excellent combination to solve complex locations problems. The coast of the Region of Murcia in the southeast of Spain has been chosen as the study area to carry out this evaluation.The GIS will be shown to be a very useful tool, since GIS are able to generate a database which serves as a starting point for conducting any decision support system. The posed problem will be resolved using restrictions to reduce the area of study, and the criteria that will influence the decision-making. These criteria will be of different natures; with quantitative criteria (numerical values) coexisting with qualitative criteria (labels and linguistic variables). In this article, AHP will be used to obtain the weights of the criteria, and the fuzzy TOPSIS method for the evaluation of the alternatives. In order to compare the results obtained with TOPSIS, the ELECTRE-TRI methodology will be applied.

Near-Earth object hazardous impact: A Multi-Criteria Decision Making approach

The impact of a near-Earth object (NEO) may release large amounts of energy and cause serious damage. Several NEO hazard studies conducted over the past few years provide forecasts, impact probabilities and assessment ratings, such as the Torino and Palermo scales. These high-risk NEO assessments involve several criteria, including impact energy, mass, and absolute magnitude. The main objective of this paper is to provide the first Multi-Criteria Decision Making (MCDM) approach to classify hazardous NEOs. Our approach applies a combination of two methods from a widely utilized decision making theory. Specifically, the Analytic Hierarchy Process (AHP) methodology is employed to determine the criteria weights, which influence the decision making, and the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is used to obtain a ranking of alternatives (potentially hazardous NEOs). In addition, NEO datasets provided by the NASA Near-Earth Object Program are utilized. This approach allows the classification of NEOs by descending order of their TOPSIS ratio, a single quantity that contains all of the relevant information for each object.

Spatial Analysis Using GIS for Obtaining Optimal Locations for Solar Farms— A Case Study: The Northwest of the Region of Murcia

One of the first decisions that must be taken when hosting a photovoltaic solar farm to pour the energy generated into the grid is to choose a proper location (towns, existing infrastructure, etc.). The legislative framework that is applicable must also be considered since it involves a large number of restrictions (protected areas, streams and watercourses, etc.) that will provide us with the guidelines to eliminate those unsuitable areas, as well as certain criteria (proximity to power lines, slope, solar irradiation, etc.) according to which an evaluation of the suitable areas that condition any facility will be made. It is precisely for these reasons why the management of spatial visualization tools such as Geographic Information Systems (GIS) is particularly useful. The objective of this paper is to demonstrate how the aggregation of GIS to decision procedures in the field of renewable energy can solve complex location problems. In the present case a GIS (called gvSIG) will be employed in order to obtain suitable locations to host photovoltaic solar farms in the Northwest of the Region of Murcia, in Spain.

An Analysis of Decision Criteria for the Selection of Military Training Aircrafts

The Spanish Minister of Defense needs to replace the current military training aircrafts by other models to meet current training needs in the Spanish Air Force Academy. In order to know the main features that the candidate aircrafts should have, there is a need to take into account the knowledge and experience of experts in this specific field, such as trained test pilots and flight instructors. In this way, it will be possible to recognize the main technical criteria to consider. This study shows a case study that allowed obtaining not only the preferences of an expert’s group, but also the importance of the considered criteria. Given that the criteria information provided by the experts has different nature, with qualitative criteria (human factors, flying and handling qualities, etc.) coexisting with quantitative criteria (service ceiling, stalling speed, endurance, etc.), the joint use of linguistic labels and numerical values is needed. Therefore, a survey focused on the fuzzy AHP (Analytic Hierarchy Process) methodology is proposed to extract the knowledge from the experts group and finally obtain a unique set of weights for the criteria.

Geographic Information System for Optimization and Integration of Photovoltaic Solar Energy in Agricultural Areas with Energy Deficiency and Water Scarcity

The high cost of fossil fuels and the energy dependence of agriculture, primarily for use in irrigation groundwater, has been a serious challenge to agriculture in Spain. Other factors, such as the shortage of rainfall in the Mediterranean climate, increase the problem of water scarcity for farming in these areas. In response to this scenario, a sustainable development in rural areas is necessary, seeking to optimize the water and energy resources. As a useful tool to optimize and analyze natural resources, Geographic Information Systems (GIS) are proposed. GIS allow to provide crucial information, both physical and meteorological, of the study area and help in optimizing water resources and reducing energy consumption. Furthermore, the integration of renewable energy with GIS tools offer great potential in reducing greenhouse gas emissions (mainly CO2). This can be seen in the paper with an example of optimization in the integration of photovoltaic solar facilities in the rural and agricultural sector through the use of GIS.

Technological Immersion in Industrial Engineering for a Project Management Course to Develop Dual Use Technology

Technological Immersion (TI) is defined as an approach process to current industrial activity. It is focused on improving the competitive standards of the production activity by analyzing innovation processes, which were regulated by any standard that summarizes most of the international and relevant knowledge on Innovation Management Processes (for Spain, this is the regulation UNE166000). It allows using education on Project Management as a basic foundation to develop dual use technologies for security and defense purposes and for any other industry framework as well. The aim of this case study is to compile, under a TI scope, the experiences acquired by the students of the fourth course at the University Centre of Defense at the Spanish Air Force Academy, when studying the last generation of films that may have high strategic interest for defense as well as for many industrial applications. The TI was performed through a workshop about UNE166000 (Spanish Regulation), McLuhan Tetrad and other lessons on Innovation Management, the main purpose being to teach the students how to draft a professional report about the technological and knowledge transfer opportunities that these films may generate in order to develop new industrial advantages on dual use industry.

Predictive Probabilistic Functions for Energy Prices as an Input in Monte Carlo Simulations

The continuous increase in energy costs and the volatility of energy prices are enforcing the implementation of energy efficiency measures (EEM) in companies. The choice of EEM in most cases is based on Pay-Back (PB) criteria, and in several cases on NPV and IRR criteria. In all these cases, it is necessary to estimate the price of energy in the following years so as to be able to study the profitability of the proposed EEM. Energy prices: electricity, biomass, petroleum, natural gas… change greatly throughout the period of a project, and their values are not easy to predict. If probabilistic functions are used to define the evolution of energy prices, in the period of the project, the economic parameters (PB, IRR, NPV) could also be obtained as probabilistic functions, by applying Monte Carlo Simulation Methods. This paper shows how to obtain the probabilistic functions that best describe the variation of energy prices in the period of a project, and how to apply the Monte Carlo Simulation Method to obtain a better approach to predicting future energy prices.

Obtaining the Decision Criteria and Evaluation of Optimal Sites for Renewable Energy Facilities Through a Decision Support System

In projects regarding renewable energy facilities, decision making is an essential activity that provides greater consistency and viability to the project. The first step that any promoter of such facilities should face is to select an optimal location. To do so, it is necessary to consider all the criteria that influence the decision. However, not all the criteria are equally important, which means that determining their weights is extremely important. The objective of this chapter is to obtain the weights of the decision criteria that influence the location problems of wind farms and solar photovoltaic and thermoelectric plants. For this, a Decision Support System (DSS) has been designed that allows to carry out the extraction of knowledge from an expert group by Fuzzy AHP methodology. Finally, DSS will sort the viable locations based on the importance of the criteria that influence the decision.

Applying Pareto meta-analysis in location selection for photovoltaic plant

In decision-making situations, it is common to have a set of alternatives and to choose the best among them or to establish a ranking. Multi-criteria decision methods can be applied to obtain different rankings, even though the set of alternatives and criteria are the same. Considering this, the idea of Pareto analysis is discussed to process the results obtained by different methods and reach a consensus. Those alternatives that are non-dominated will be considered the best set. That reduces the number of alternatives to be chosen because only the best set of alternatives should be taking into account.