Assessment and Simulation Tools for Sustainable Energy Systems

The leading role in the decision-making process is generally assigned to the decision maker who evaluates the various alternatives and ranks them. In some circumstances the decision is based on the use of different types of information often affected by uncertainty; thus the decision maker is not able to produce all the information necessary to make a strictly rational choice. In many cases the information can be expressed only by using linguistic labels, e.g. “very low”, “medium”, “high”, “fair”, “very high”, etc. It is not easy to precisely quantify the rating of each alternative and precision-based methods are often inadequate. Vagueness results when language is used, whether professional or not, to describe the observation or to measure the result of an experiment. This happens particularly when it is necessary to work with experts’ opinions which are translated into linguistic expressions. The use of fuzzy set theory has yielded very good results for modelling qualitative information because of their ability to handle the impreciseness that is common in rating alternatives. In this chapter a modified multicriteria method (F-PROMETHEE) that uses fuzzy sets is proposed to handle linguistic information in comparing a set of solar energy technologies using only linguistic variables.

Sustainable energy systems involve a multiplicity of stakes more or less conflicting. Multiple criteria decision analysis (MCDA) offers a broad methodological framework in which the ELECTRE-based outranking approach is suitable for searching good compromise solutions. Particularly, the RUBIS methodology offers new tools that we have used successfully for photovoltaic (PV) plants selection aid in Corsica island, a real case study from a research agreement between the University of Corsica and the Agriculture Chamber of the Haute-Corse department. This chapter will focus on the following points: outranking approaches and the choice problematic in MCDA, the RUBIS method and the RUBIS D3 web server, the insular power grid of Corsica and the studied case, the main results and their robustness, a comparison with the ELECTRE IS method.

Sustainability has gained tremendous importance and has been an important issue both for policy makers and practitioners. Realizing that the resources on the earth are limited, green energy (GE) alternatives have flourished and started to replace the conventional energy alternatives. Energy planning using different energy alternatives, for the long term becomes a vital decision. In this study, fuzzy multi criteria decision- making methodology, fuzzy analytic network process (FANP) are utilized for the ranking GE alternatives. The ANP is a multi criteria decision-making (MCDM) technique which enables feedback and replaces hierarchies of relationships with networks of relationships. In ANP technique, not only does the importance of the criteria determine the importance of the alternatives, as in a hierarchy, but also the importance of the alternatives may have impact on the importance of the criteria. Fuzzy ANP allows measuring qualitative factors by using fuzzy numbers instead of crisp numbers in order to make decisions easier and obtain more realistic results. A case study is presented for the assessment of GE alternatives in Turkey with respect to various perspectives such as; technical, economical, and environmental. According to the outcome of the BO/CR method, hydropower has the highest priority which is followed by geothermal and biomass energy sources. Though the hydropower is not the best alternative from Benefits and Opportunities viewpoint, because of low costs and risks it comes into view to be the best alternative for Turkey.

This chapter deals with the study and evaluation of decision criteria that should be considered for the optimal location of solar photovoltaic plants and solar thermal plants with high temperature and which are to be connected to the electricity distribution network. Criteria and subcriteria to be regarded will be of different nature, since environmental, geomorphologic, location, and strictly climatic criteria will all be considered, some of which are dependent on the technology being installed. Thus, we consider as possible alternatives the optimal locations and we will begin with a set of criteria, which must be evaluated for each of the possible alternatives for such a purpose, and includes both quantitative as well as qualitative information. As vaguely implied linguistic variables and numeric values have to be employed due to this disparity in the nature of the information, we will model the weights of the criteria by triangular fuzzy numbers. In order to reflect this and to carry out the extraction of knowledge a survey based on the fuzzy AHP methodology will be elaborated and sent to experts. In this way it will be possible to obtain the weights of the considered criteria for further evaluation of the alternatives.

Nowadays sustainable development is a major focus of national and international economic, social, and environmental agendas, so that a good quality of life can be enjoyed by current and future generations. The problem of climate change has caused great concerns at all levels, from the general public to national governments and international agencies. Renewable energies can be an important remedy to many environmental problems that the world faces today. In this context, some new governmental policies have been adopted to encourage the introduction of renewable energies. But the energy planning scenario has completely changed over the past two decades from and almost exclusively concern with cost minimization of supply-side options to the need of explicitly multiple and conflicting objectives. Different and numerous groups of actors, such as institutions and administration authorities, potential investors, environmental groups, get involved in the process of fossil fuel energy substitution by renewable energies. This complex environment indicates the multi-criteria character of the problem. In this chapter multi-attribute decision-making method combining cloud and utility theory is proposed in order to evaluate different locations for a wind farm in the north of Spain. Whereas utility theory allows us to use different utility curves describing different attitudes toward risk, cloud theory provides a model that facilitates transformation of uncertainty contained in both quantitative and qualitative concepts to a uniform presentation in a numerical domain. Six locations are candidate to place the wind farm according to their topography, infrastructure, land use, safety, and number of days with wind speed >=70 km/h. The results show that the location with the highest number of days with wind speed >=70 km/h and the best land use attribute is the best place to locate the wind farm for both a risk aversion decision-maker and a risk-seeking decision-maker.

Green energy (GE) refers to energy sources that have no undesired consequences such as carbon emissions from fossil fuels or hazardous waste from nuclear energy. Alternative energy sources are renewable and are thought to be “free” energy sources. These include biomass energy, wind energy, solar energy, geothermal energy, and hydroelectric energy sources. GE supply is viewed as an option for satisfying the increased energy demand with the prospect of carbon accountability. However, geographical areas have diverse GE resources and different levels of energy consumptions. Territory design is defined as the problem of grouping geographic areas into larger geographic clusters called territories in such a way that the grouping is acceptable according to the planning criteria. The aim of this study is to group geographic areas in such a way that energy requirement in a geographic cluster matches the available GE potential in the same cluster. In this way, investments may be supported through region specific policies.

Energy planning is difficult to model owing to its complex structure, with numerous decision makers, criteria, and scenarios. Fortunately, decision-making methods can be helpful for the sustainable development of energy, by the evaluation of different energy sources with regard to multiple aspects, for example, economic, environmental, political etc. In this study, a methodology based on a cumulative belief degree approach is proposed for the prioritization of energy sources. The approach enables the use of all types of evaluations, without the loss of any information. It also allows for incomplete expert evaluations which may occur in the energy sources prioritization problem. Turkey, like many countries, generates most of energy from fossil fuels, which are imported mostly from other countries. However, the enormous increase in oil prices, and an emerging energy demand, owing to economic growth and environmental issues, is forcing Turkey to improve its sustainable energy planning. Therefore, the proposed methodology is applied to the energy sources prioritization of Turkey. Results show that solar power and wind should be considered as the priori sources of energy in Turkey.

Sustainable wind energy development takes into account sociocultural variables that can be identified from citizens’ concerns about the use of this renewable energy. These concerns are included in a multicriteria decision aid process, and, expressed as postulates in this study; they are subject to a robustness analysis. The approach is described and applied to the Baie-des-Sables (Canada) Wind farm case study. While this academic post-installation assessment does not affect the current operation of the farm which started back in November 2006, we conclude that if these concerns were considered, another wind farm scenario would have got a higher rating. Robustness analysis with respect to communication tools or type of ownership of the wind farm made it possible to identify objective rules based on changes in the ranking of scenarios. This change was verified using evaluation matrices containing different, maximum and proportional values with respect to the values of the original matrix. The robustness analysis results made it possible to identify, in a conflict situation, opportunities to remove obstacles to wind farm implementation.

Technology Assessment (TA) is an approach used to evaluate and characterize technologies for multiple perspectives. Prior research used TA to model the future state of technologies (technological forecasting) or the future diffusion of technologies (technology adoption). This abstract will assess an emerging energy efficiency technology in the United States (US). A hierarchical decision model is used for the assessment. The technology of interest in this case is the heat pump water heater (HPWH). By providing much improved efficiency when compared to regular water heaters, HPWHs contribute to sustainability of the future energy supply. This approach can easily be duplicated for any other region or technology. Technology assessment results provide an insight into manufacturers as well as policy makers on what attributes to focus for faster adoption of this technology toward a sustainable future.

A vital part of sustainable development is the provision of adequate, reliable, and affordable energy, in conformity with social and environmental requirements. Energy is one of the most crucial factors that power modern economies subject to a volatility in price and supply, while at the same time it is responsible for major environmental consequences with global warming topping the list. In this chapter we develop a model that provides a general mechanism to measure the sustainability of energy sectors. Sustainability is an inherently vague concept, and for this reason the model uses fuzzy logic, which has the ability to deal with such an ambiguous, complex, and polymorphous concept. The proposed model follows the principles of SAFE (Sustainability Assessment by Fuzzy Evaluation), a model for the numerical assessment of sustainability. To consider the cumulative effects of past policies, we use exponential smoothing on sustainability data, while an imputation procedure is applied in order to overcome the problem of missing values. The model is applied to a large set of countries, which are ranked according to their sustainable energy development.

In this chapter, two of the most important artificial intelligence techniques are presented together with a variety of applications in solar energy systems. Artificial neural network (ANN) models represent a new method in system modeling and prediction. An ANN mimics mathematically the function of a human brain. They learn the relationship between the input parameters, usually collected from experiments, and the controlled and uncontrolled variables by studying previously recorded data. A genetic algorithm (GA) is a model of machine learning, which derives its behavior from a representation of the processes of evolution in nature. GAs can be used for multidimensional optimization problems in which the character string of the chromosome can be used to encode the values for the different parameters being optimized. The chapter outlines an understanding of how ANN and GA operate by way of presenting a number of problems in different solar energy systems applications, which include modeling and simulation of solar systems, prediction of the performance, and optimization of the design or operation of the systems. The systems presented include solar thermal and photovoltaic systems.

Recent advances in artificial neural networks (ANN) propose an alternative promising methodological approach to the problem of time series assessment as well as point spatial interpolation of irregularly and gridded data. In the field of wind power sustainable energy systems ANNs can be used as function approximators to estimate both the time and spatial wind speed distributions based on observational data. The first part of this work reviews the theoretical background, the mathematical formulation, the relative advantages, and limitations of ANN methodologies applicable to the field of wind speed time series and spatial modeling. The second part focuses on implementation issues and on evaluating the accuracy of the aforementioned methodologies using a set of metrics in the case of a specific region with complex terrain. A number of alternative feedforward ANN topologies have been applied in order to assess the spatial and time series wind speed prediction capabilities in different time scales. For the temporal forecasting of wind speed ANNs were trained using the Levenberg–Marquardt backpropagation algorithm with the optimum architecture being the one that minimizes the Mean Absolute Error on the validation set. For the spatial estimation of wind speed the nonlinear Radial basis function Artificial Neural Networks are compared versus the linear Multiple Linear Regression scheme.

One of the most controversial issues in the development of Life Cycle Inventory (LCI) is the allocation procedure, which consists in the partition and distribution of economic flows and environmental burdens among to each of the products of a multi-output system. Because of the use of the allocation represents a source of uncertainty in the LCI results, the authors present a new approach based on genetic algorithms (GAs) to solve the multi-output systems characterized by a rectangular matrix of technological coefficients, without using computational methods such as the allocation procedure. In this Chapter, the GAs’ approach is applied to an ancillary case study related to a cogeneration process. In detail, the authors hypothesized that there are the following multi-output processes in the case study: (1) cogeneration of electricity and heat; (2) co-production of diesel and light fuel oil; (3) co-production of copper and recycled copper. The energy and mass balances are respected by means of specific bonds that limit the space in which the GA searches the solution. The results show low differences between the inventory vector derived from the GA application and that one obtained applying the substitution method and the allocation procedure based on the energy content of the outputs. To avoid the allocation, the application of GA to calculate the LCI seems to be a promising method.

Recent advances in artificial intelligent techniques embedded into a field programmable gate array (FPGA) allowed the application of such technologies in real engineering problems (robotic, image and signal processing, control, etc.). However, the application of such technologies in the solar energy field is relatively limited. The embedded intelligent algorithm into FPGA can play a very important role in the control of solar energy systems. In this chapter, an intelligent approach based fuzzy logic and genetic algorithm (GA) is developed using a description language (VHDL standing for VHSIC Hardware Description Language), and then is implemented into FPGA-Xilinx (Virtex-II-Pro xc2v1000-4fg456) chip to track the maximal power point (MPP) in a (PV) photovoltaic module. ModelSim-based simulation results confirm the effectiveness of the designed approach in tracking the MPP. In addition, it has been demonstrated that the employed FPGA chip is largely sufficient to implement the designed approach.

As technology advances perspectives change, problems shift to reflect the new environment and situations that develop. The same goes for Renewable Energy Systems too. With each new development in a renewable energy system, new problems arise as well and these developments need to be tested before they can be applied safely. These tests can be very expensive if done in real life and that is where simulation comes into the picture. Simulation is widely used for experimentation to understand a system or make decisions about it and is very cost efficient method when compared to real life experimentation as the only requirement for modeling and analyzing complex systems is a good computer. In this chapter, different simulation techniques used in Renewable Energy Systems will be introduced and examples to how they are used will be briefly given.

Mathematical programming (MP) is the most common methodology for modeling and optimization of energy systems. Energy systems’ planning and optimization assume the knowledge of future situation, which is usually known with limited certainty. Therefore, the parameters of the model (data which assumed to be known during the modeling process) have usually a degree of uncertainty. Various methods have been proposed for dealing with this uncertainty, the most common ones being fuzzy programming, chance constrained programming, robust programming, and stochastic programming. In this work, we consider the implied uncertainty in the parameters as being of stochastic nature. Each uncertain parameter is characterized by a probability distribution. Subsequently, a Monte Carlo simulation samples the values from these distributions, and the MP models with the sampled values are solved. This process is repeated many times (1,000) in order to have an adequate sample for drawing robust conclusions. Relationships between the values of these parameters (i.e., interdependent parameters) can also be incorporated in the Monte Carlo process. The specific work is focused on the energy project portfolio selection problem where the output of each project as well as other parameters may be uncertain. In the current work, we introduce the iterative trichotomic approach (ITA) that gradually separates projects into green (selected under all circumstances), red (rejected under all circumstances), and gray sets (need further elaboration), combining Monte Carlo simulation and MP. The process output is not only the final portfolio, but also information about the certainty of participation or exclusion of every project in the final portfolio. A case study with real data from clean development mechanism (CDM) projects’ database is elaborated in order to illustrate the method.

Lean thinking is an engineering approach to avoid non-value adding tasks or processes in manufacturing. Most of the lean studies in the energy field are focused on savings in manufacturing processes. This paper suggests a future-oriented energy value stream mapping approach that aims to improve energy efficiency in small- and medium-sized manufacturing companies. Energy value stream mapping is a graphical technique that allows identifying the level of energy use and, thereby, discovering saving opportunities at each step of different processes either in production or in facility support. To analyze the possible outcomes of improvement options, future scenarios are developed using Bayesian networks. The suggested model can be used not only for diagnostic purposes but also for energy budgeting and saving measures. An application is given to demonstrate the use of energy value stream maps (E-VSMs).

The philosophy of Lean Manufacturing is to do more with less by eliminating non-value-added activities from production process. Lean manufacturing has several tools to improve lead time, cost, and quality performance as well as flexibility of systems. Therefore, the application level of Lean Manufacturing has gone through a significant evolution from shop floor to supply chain. Furthermore, Lean Manufacturing tools lead to significant effect on energy efficiency which is a vital factor for competitive advantage and environment preservation. In this chapter, a simulation-based generic framework is provided for the assessment of energy efficiency in Lean Manufacturing systems with the aim of providing contribution to the theoretical and practical studies addressing both sustainable energy and performance in manufacturing systems. Reflecting hierarchical nature of manufacturing systems, the proposed framework is illustrated in detail.

Increasing air pollution in urban areas has accelerated the interest in biodiesel and vehicles that consume biodiesel. As a caution, majority of the developed countries have started using biodiesel in transportation or determined goals and targets for the near future. Brazil has been a pioneer in the field, whereas the European Union has set the objective of utilizing 10 % of all vehicles using biodiesel by 2020. While the utilization and implementation of biodiesel-based systems severely contribute to economical and environmental savings, the antecedent production process has its own adverse effects such as the demolishment of agricultural sites. This chapter aims to analyze these effects as well as to propose a model for balancing the trade-offs by minimizing the negative consequences and maximizing the positive ones. The related model involves nonlinear constraints and objectives which are dependent of different uncertain scenarios and expectations. A particle swarm optimization (PSO) and self-organizing maps (SOMs) approach are implemented to attain appropriate solutions of the model. This proposition will also provide a new perspective for both academia and investors in the biodiesel field.