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Modeling Annual Water Balance in The Seasonal Budyko Framework
Title
Modeling Annual Water Balance in The Seasonal Budyko Framework.
Creator
Alimohammadi, Negin, Wang, Dingbao, Hagen, Scott, Madani Larijani, Kaveh, University of Central Florida
Abstract / Description
In this thesis, the role of soil water storage change on the annual water balance is evaluated based on observations at a large number of watersheds located in a spectrum of climate regions, and an annual water balance model is developed at the seasonal scale based on Budyko hypthesis. The annual water storage change is quantified based on water balance closure given the available data of precipitation, runoff, and evaporation estimated from remote sensing data and meteorology reanalysis. The...
Show moreIn this thesis, the role of soil water storage change on the annual water balance is evaluated based on observations at a large number of watersheds located in a spectrum of climate regions, and an annual water balance model is developed at the seasonal scale based on Budyko hypthesis. The annual water storage change is quantified based on water balance closure given the available data of precipitation, runoff, and evaporation estimated from remote sensing data and meteorology reanalysis. The responses of annual runoff, evaporation, and storage change to the interannual variability of precipitation and potential evaporation are then analyzed. Both runoff and evaporation sensitivities to potential evaporation are higher under energy-limited conditions, but storage change seems to be more sensitive to potential evaporation under the conditions in which water and energy are balanced. Runoff sensitivity to precipitation is higher under energy-limited conditions; but both evaporation and storage change sensitivities to precipitation are higher under water-limited conditions. Therefore, under energy-limited conditions, most of precipitation variability is transferred to runoff variability; but under water-limited conditions, most of precipitation variability is transferred to storage change and some of precipitation variability is transferred to evaporation variability. The main finding of this part is that evaporation variability will be overestimated by assuming negligible storage change in annual water balance, particularly under water-limited conditions. Budyko framework which expresses partitioning of water supply at the mean annual scale, is adapted to be applicable in modeling water cycle in short terms i.e., seasonal and interannual scales. Seasonal aridity index is defined as the ratio of seasonal potential evaporation and the difference between precipitation and storage change. The seasonal water balance is modeled by using a Budyko-type curve with horizontal shifts which leads prediction of seasonal and annual storage changes and evaporation if precipitation, potential evaporation, and runoff data are available.
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Date Issued
2012
Identifier
CFE0004509, ucf:49283
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004509
Climate Change Impacts on Rainfed Corn Production in Malawi
Title
Climate Change Impacts on Rainfed Corn Production in Malawi.
Creator
Msowoya, Kondwani, Madani Larijani, Kaveh, Wang, Dingbao, Xanthopoulos, Petros, University of Central Florida
Abstract / Description
Agriculture is the mainstay of the economy in Malawi and accounts for 40% of the Gross Domestic Product (GDP) and 90% of the export revenues. Corn (maize) is the major cereal crop grown as staple food under rainfed conditions, covers over 92% of the total agricultural area, and contributes 54% of the caloric intake. Corn production is the principle occupation and major source of income for over 85% of the total population in Malawi. Issues of hunger and food insecurity for the entire nation...
Show moreAgriculture is the mainstay of the economy in Malawi and accounts for 40% of the Gross Domestic Product (GDP) and 90% of the export revenues. Corn (maize) is the major cereal crop grown as staple food under rainfed conditions, covers over 92% of the total agricultural area, and contributes 54% of the caloric intake. Corn production is the principle occupation and major source of income for over 85% of the total population in Malawi. Issues of hunger and food insecurity for the entire nation are associated with corn scarcity and low production. Global warming is expected to cause climate change in Malawi, including changes in temperature and precipitation amounts and patterns. These climate changes are expected to affect corn production in Malawi. This study evaluates the impacts of climate change on rainfed corn production in Malawi. Lilongwe District, with about 1,045 square miles of agriculture area, has been selected as a representative area. First, outputs of 15 General Circulation Models (GCMs) under different emission scenarios are statistically downscaled. For this purpose, a weather generator (LARS-WG) is calibrated and validated for the study area and daily precipitation as well as minimum and maximum temperature are projected for 15 GCMs for three time horizons of 2020s, 2050s and 2090s. Probability assessment of bounded range with known distributions is used to deal with the uncertainties of GCMs' outputs. These GCMs outputs are weighted by considering the ability of each model to simulate historical records. AquaCrop, a new model developed by FAO that simulates the crop yield response to water deficit conditions, is employed to assess potential rainfed corn production in the study area with and without climate change. Study results indicate an average temperature increase of 0.52 to 0.94oC, 1.26 to 2.20oC and 1.78 to 3.58oC in the near-term (2020s), mid-term (2050s) and long-term (2090s) future, respectively. The expected changes in precipitation during these periods are -17 to 11%, -26 to 0%, and -29 to -3%. Corn yields are expected to change by -8.11 to 0.53%, -7.25 to -14.33%, and -13.19 to -31.86%, during the same time periods. The study concludes with suggestion of some adaptation strategies that the Government of Malawi could consider to improve national food security under climate change.
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Date Issued
2013
Identifier
CFE0005036, ucf:50011
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005036
Virtual Interactions with Real-Agents for Sustainable Natural Resource Management
Title
Virtual Interactions with Real-Agents for Sustainable Natural Resource Management.
Creator
Pierce, Tyler, Madani Larijani, Kaveh, Wang, Dingbao, Jacques, Peter, University of Central Florida
Abstract / Description
Common pool resource management systems are complex to manage due to the absence of a clear understanding of the effects of users' behavioral characteristics. Non-cooperative decision making based on individual rationality (as opposed to group rationality) and a tendency to free ride due to lack of trust and information about other users' behavior creates externalities and can lead to tragedy of the commons without intervention by a regulator. Nevertheless, even regulatory institutions often...
Show moreCommon pool resource management systems are complex to manage due to the absence of a clear understanding of the effects of users' behavioral characteristics. Non-cooperative decision making based on individual rationality (as opposed to group rationality) and a tendency to free ride due to lack of trust and information about other users' behavior creates externalities and can lead to tragedy of the commons without intervention by a regulator. Nevertheless, even regulatory institutions often fail to sustain natural common pool resources in the absence of clear understanding of the responses of multiple heterogeneous decision makers to different regulation schemes. While modeling can help with our understanding of complex coupled human-natural systems, past research has not been able to realistically simulate these systems for two major limitations: 1) lack of computational capacity and proper mathematical models for solving distributed systems with self-optimizing agents; and 2) lack of enough information about users' characteristics in common pool resource systems due to absence of reliable monitoring information. Recently, different studies have tried to address the first limitation by developing agent-based models, which can be appropriately handled with today's computational capacity. While these models are more realistic than the social planner's models which have been traditionally used in the field, they normally rely on different heuristics for characterizing users' behavior and incorporating heterogeneity. This work is a step-forward in addressing the second limitation, suggesting an efficient method for collecting information on diverse behavioral characteristics of real agents for incorporation in distributed agent-based models. Gaming in interactive virtual environments is suggested as a reliable method for understanding different variables that promote sustainable resource use through observation of decision making and behavior of the resource system beneficiaries under various institutional frameworks and policies. A review of educational or "serious" games for environmental management was undertaken to determine an appropriate game for collecting information on real-agents and also to investigate the state of environmental management games and their potential as an educational tool. A web-based groundwater sharing simulation game(-)Irrigania(-)was selected to analyze the behavior of real agents under different common pool resource management institutions. Participants included graduate and undergraduate students from the University of Central Florida and Lund University. Information was collected on participants' resource use, behavior and mindset under different institutional settings through observation and discussion with participants. Preliminary use of water resources gaming suggests communication, cooperation, information disclosure, trust, credibility and social learning between beneficiaries as factors promoting a shift towards sustainable resource use. Additionally, Irrigania was determined to be an effective tool for complementing traditional lecture-based teaching of complex concepts related to sustainable natural resource management. The different behavioral groups identified in the study can be used for improved simulation of multi-agent groundwater management systems.
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Date Issued
2013
Identifier
CFE0005045, ucf:49953
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005045
A real-time crane service scheduling decision support system (CSS-DSS) for construction tower cranes
Title
A real-time crane service scheduling decision support system (CSS-DSS) for construction tower cranes.
Creator
Zavichi Tork, Amir, Madani Larijani, Kaveh, Oloufa, Amr, Tatari, Mehmet, Xanthopoulos, Petros, University of Central Florida
Abstract / Description
The success of construction projects depends on proper use of construction equipment and machinery to a great extent. Thus, appropriate planning and control of the activities that rely on construction equipment could have significant effects on improving the efficiency of project operations. Cranes are the largest and most conspicuous construction equipment, widely used in typical construction sites. They play a major role in relocation of materials in horizontal and vertical directions on...
Show moreThe success of construction projects depends on proper use of construction equipment and machinery to a great extent. Thus, appropriate planning and control of the activities that rely on construction equipment could have significant effects on improving the efficiency of project operations. Cranes are the largest and most conspicuous construction equipment, widely used in typical construction sites. They play a major role in relocation of materials in horizontal and vertical directions on construction sites. Given the nature of activities relying on construction cranes in various stages of a project, cranes normally have control over the critical path of the project with the potential to create schedule bottlenecks and delaying the completion of the project. This dissertation intends to improve crane operations efficiency by developing a new framework for optimizing crane service sequence schedule. The crane service sequence problem is mathematically formulated as an NP-complete optimization problem based on the well-known Travel Salesman Problem (TSP) and is solved using different optimization techniques depending on the problem's size and complexity. The proposed framework sets the basis for developing near-real time decision support tools for on-site optimization of crane operations sequence. To underline the value of the proposed crane sequence optimization methods, these methods are employed to solve several numerical examples. Results show that the proposed method can create a travel time saving of 28% on average in comparison with conventional scheduling methods such as First in First out (FIFO), Shortest Job First (SJF), and Earliest Deadline First (EDF).
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Date Issued
2013
Identifier
CFE0005078, ucf:50738
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005078
Economic Valuation of Florida Sea Turtles in Face of Sea Level Rise
Title
Economic Valuation of Florida Sea Turtles in Face of Sea Level Rise.
Creator
Hamed, Ahmed, Madani Larijani, Kaveh, VonHolle, Mary, Wright, James, Milon, Joseph, University of Central Florida
Abstract / Description
Sea level rise (SLR) is posing a great risk of flooding and inundation to coastal areas in Florida. Some coastal nesting species, including sea turtle species, have experienced diminished habitat from SLR. In an effort to assess the economic and ecosystem service loss to coastal areas with respect to sea turtles Contingent Valuation Method (CVM) and Habitat Equivalency Analysis (HEA) were used. The CVM was used to measure the economic impacts of SLR on sea turtles. Open-ended and dichotomous...
Show moreSea level rise (SLR) is posing a great risk of flooding and inundation to coastal areas in Florida. Some coastal nesting species, including sea turtle species, have experienced diminished habitat from SLR. In an effort to assess the economic and ecosystem service loss to coastal areas with respect to sea turtles Contingent Valuation Method (CVM) and Habitat Equivalency Analysis (HEA) were used. The CVM was used to measure the economic impacts of SLR on sea turtles. Open-ended and dichotomous choice CVM was used to obtain the willingness to pay (WTP) values of Florida residents to implement certain mitigation strategies which would protect Florida's east coast sea turtle nesting areas. The problem of sample selection bias was reduced by surveying residents of two cities that would potentially have varying interest in coastal conservation due to their relative distance from the coast. The hypothetical WTP of Florida households to implement policies designed to protect sea turtle habitat from development encroachment was estimated to be between $21 and $29 per year for a maximum of five years. Characteristics of respondents were found to have statistically significant impacts on their WTP. Findings include a negative correlation between the age of a respondent and the probability of an individual willing to pay the hypothetical WTP amount. Counter intuitively, it was found that WTP of an individual was not dependent on prior knowledge of the effects of SLR on sea turtle habitat. As the level of this awareness increased, the probability to pay the hypothetical WTP value decreased. The greatest indicators of whether or not an individual was willing to pay to protect sea turtle habitat were the respondents' perception regarding the importance of sea turtle population health to the ecosystem, and their confidence in the conservation methods used. Concepts of Habitat Equivalency Analysis were used in order to determine the ecosystem service lost due to SLR. The study area of Archie Carr National Wildlife Refuge (ACNWR) has a continually increasing sea turtle population due to various conservation efforts. However, how the inundation of the coastal area will injure this habitat was assessed, and if mitigation strategies to compensate for the loss are necessary. The carrying capacity (CC) of the refuge was chosen as the metric of the ecosystem service. Using the estimated area of ACNWR and the approximate area needed by a sea turtle to nest, the theoretical number of sea turtle nests possible on the refuge was calculated. This value was then projected to the year 2100 using the sea level rise scenarios provided by IPCC (2007) and NRC (2010). In order to quantify the injury caused by the decrease in the refuge's CC, the number of sea turtle nests on the refuge was projected to the year 2100 using the data obtained over the past 30 years. The analysis concludes a potential loss of service to be experienced as early as 2060's due to the carrying capacity of the refuge diminishing with the loss of the habitat due to the increase in the mean sea level.
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Date Issued
2013
Identifier
CFE0005002, ucf:50021
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005002
Developing a Group Decision Support System (GDSS) for decision making under uncertainty
Title
Developing a Group Decision Support System (GDSS) for decision making under uncertainty.
Creator
Mokhtari, Soroush, Abdel-Aty, Mohamed, Madani Larijani, Kaveh, Wang, Dingbao, Xanthopoulos, Petros, University of Central Florida
Abstract / Description
Multi-Criteria Decision Making (MCDM) problems are often associated with tradeoffs between performances of the available alternative solutions under decision making criteria. These problems become more complex when performances are associated with uncertainty. This study proposes a stochastic MCDM procedure that can handle uncertainty in MCDM problems. The proposed method coverts a stochastic MCDM problem into many deterministic ones through a Monte-Carlo (MC) selection. Each deterministic...
Show moreMulti-Criteria Decision Making (MCDM) problems are often associated with tradeoffs between performances of the available alternative solutions under decision making criteria. These problems become more complex when performances are associated with uncertainty. This study proposes a stochastic MCDM procedure that can handle uncertainty in MCDM problems. The proposed method coverts a stochastic MCDM problem into many deterministic ones through a Monte-Carlo (MC) selection. Each deterministic problem is then solved using a range of MCDM methods and the ranking order of the alternatives is established for each deterministic MCDM. The final ranking of the alternatives can be determined based on winning probabilities and ranking distribution of the alternatives. Ranking probability distributions can help the decision-maker understand the risk associated with the overall ranking of the options. Therefore, the final selection of the best alternative can be affected by the risk tolerance of the decision-makers. A Group Decision Support System (GDSS) is developed here with a user-friendly interface to facilitate the application of the proposed MC-MCDM approach in real-world multi-participant decision making for an average user. The GDSS uses a range of decision making methods to increase the robustness of the decision analysis outputs and to help understand the sensitivity of the results to level of cooperation among the decision-makers. The decision analysis methods included in the GDSS are: 1) conventional MCDM methods (Maximin, Lexicographic, TOPSIS, SAW and Dominance), appropriate when there is a high cooperation level among the decision-makers; 2) social choice rules or voting methods (Condorcet Choice, Borda scoring, Plurality, Anti-Plurality, Median Voting, Hare System of voting, Majoritarian Compromise ,and Condorcet Practical), appropriate for cases with medium cooperation level among the decision-makers; and 3) Fallback Bargaining methods (Unanimity, Q-Approval and Fallback Bargaining with Impasse), appropriate for cases with non-cooperative decision-makers. To underline the utility of the proposed method and the developed GDSS in providing valuable insights into real-world hydro-environmental group decision making, the GDSS is applied to a benchmark example, namely the California's Sacramento-San Joaquin Delta decision making problem. The implications of GDSS' outputs (winning probabilities and ranking distributions) are discussed. Findings are compared with those of previous studies, which used other methods to solve this problem, to highlight the sensitivity of the results to the choice of decision analysis methods and/or different cooperation levels among the decision-makers.
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Date Issued
2013
Identifier
CFE0004723, ucf:49821
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004723
Real-time traffic safety evaluation models and their application for variable speed limits
Title
Real-time traffic safety evaluation models and their application for variable speed limits.
Creator
Yu, Rongjie, Abdel-Aty, Mohamed, Radwan, Ahmed, Madani Larijani, Kaveh, Ahmed, Mohamed, Wang, Xuesong, University of Central Florida
Abstract / Description
Traffic safety has become the first concern in the transportation area. Crashes have cause extensive human and economic losses. With the objective of reducing crash occurrence and alleviating crash injury severity, major efforts have been dedicated to reveal the hazardous factors that affect crash occurrence at both the aggregate (targeting crash frequency per segment, intersection, etc.,) and disaggregate levels (analyzing each crash event). The aggregate traffic safety studies, mainly...
Show moreTraffic safety has become the first concern in the transportation area. Crashes have cause extensive human and economic losses. With the objective of reducing crash occurrence and alleviating crash injury severity, major efforts have been dedicated to reveal the hazardous factors that affect crash occurrence at both the aggregate (targeting crash frequency per segment, intersection, etc.,) and disaggregate levels (analyzing each crash event). The aggregate traffic safety studies, mainly developing safety performance functions (SPFs), are being conducted for the purpose of unveiling crash contributing factors for the interest locations. Results of the aggregate traffic safety studies can be used to identify crash hot spots, calculate crash modification factors (CMF), and improve geometric characteristics. Aggregate analyses mainly focus on discovering the hazardous factors that are related to the frequency of total crashes, of specific crash type, or of each crash severity level. While disaggregate studies benefit from the reliable surveillance systems which provide detailed real-time traffic and weather data. This information could help in capturing microlevel influences of the hazardous factors which might lead to a crash. The disaggregate traffic safety models, also called real-time crash risk evaluation models, can be used in monitoring crash hazardousness with the real-time field data fed in. One potential use of real-time crash risk evaluation models is to develop Variable Speed Limits (VSL) as a part of a freeway management system. Models have been developed to predict crash occurrence to proactively improve traffic safety and prevent crash occurrence.In this study, first, aggregate safety performance functions were estimated to unveil the different risk factors affecting crash occurrence for a mountainous freeway section. Then disaggregate real-time crash risk evaluation models have been developed for the total crashes with both the machine learning and hierarchical Bayesian models. Considering the need for analyzing both aggregate and disaggregate aspects of traffic safety, systematic multi-level traffic safety studies have been conducted for single- and multi-vehicle crashes, and weekday and weekend crashes. Finally, the feasibility of utilizing a VSL system to improve traffic safety on freeways has been investigated. This research was conducted based on data obtained from a 15-mile mountainous freeway section on I-70 in Colorado. The data contain historical crash data, roadway geometric characteristics, real-time weather data, and real-time traffic data. Real-time weather data were recorded by 6 weather stations installed along the freeway section, while the real-time traffic data were obtained from the Remote Traffic Microwave Sensor (RTMS) radars and Automatic Vechicle Identification (AVI) systems. Different datasets have been formulated from various data sources, and prepared for the multi-level traffic safety studies. In the aggregate traffic safety investigation, safety performance functions were developed to identify crash occurrence hazardous factors. For the first time real-time weather and traffic data were used in SPFs. Ordinary Poisson model and random effects Poisson models with Bayesian inference approach were employed to reveal the effects of weather and traffic related variables on crash occurrence. Two scenarios were considered: one seasonal based case and one crash type based case. Deviance Information Criterion (DIC) was utilized as the comparison criterion; and the correlated random effects Poisson models outperform the others. Results indicate that weather condition variables, especially precipitation, play a key role in the safety performance functions. Moreover, in order to compare with the correlated random effects Poisson model, Multivariate Poisson model and Multivariate Poisson-lognormal model have been estimated. Conclusions indicate that, instead of assuming identical random effects for the homogenous segments, considering the correlation effects between two count variables would result in better model fit. Results from the aggregate analyses shed light on the policy implication to reduce crash frequencies. For the studied roadway segment, crash occurrence in the snow season have clear trends associated with adverse weather situations (bad visibility and large amount of precipitation); weather warning systems can be employed to improve road safety during the snow season. Furthermore, different traffic management strategies should be developed according to the distinct seasonal influence factors. In particular, sites with steep slopes need more attention from the traffic management center and operators especially during snow seasons to control the excess crash occurrence. Moreover, distinct strategy of freeway management should be designed to address the differences between single- and multi-vehicle crash characteristics.In addition to developing safety performance functions with various modeling techniques, this study also investigates four different approaches of developing informative priors for the independent variables. Bayesian inference framework provides a complete and coherent way to balance the empirical data and prior expectations; merits of these informative priors have been tested along with two types of Bayesian hierarchical models (Poisson-gamma and Poisson-lognormal models). Deviance Information Criterion, R-square values, and coefficients of variance for the estimations were utilized as evaluation measures to select the best model(s). Comparisons across the models indicate that the Poisson-gamma model is superior with a better model fit and it is much more robust with the informative priors. Moreover, the two-stage Bayesian updating informative priors provided the best goodness-of-fit and coefficient estimation accuracies.In addition to the aggregate analyses, real-time crash risk evaluation models have been developed to identify crash contributing factors at the disaggregate level. Support Vector Machine (SVM), a recently proposed statistical learning model and Hierarchical Bayesian logistic regression models were introduced to evaluate real-time crash risk. Classification and regression tree (CART) model has been developed to select the most important explanatory variables. Based on the variable selection results, Bayesian logistic regression models and SVM models with different kernel functions have been developed. Model comparisons based on receiver operating curves (ROC) demonstrate that the SVM model with Radial basis kernel function outperforms the others. Results from the models demonstrated that crashes are likely to happen during congestion periods (especially when the queuing area has propagated from the downstream segment); high variation of occupancy and/or volume would increase the probability of crash occurrence.Moreover, effects of microscopic traffic, weather, and roadway geometric factors on the occurrence of specific crash types have been investigated. Crashes have been categorized as rear-end, sideswipe, and single-vehicle crashes. AVI segment average speed, real-time weather data, and roadway geometric characteristics data were utilized as explanatory variables. Conclusions from this study imply that different active traffic management (ATM) strategies should be designed for three- and two-lane roadway sections and also considering the seasonal effects. Based on the abovementioned results, real-time crash risk evaluation models have been developed separately for multi-vehicle and single-vehicle crashes, and weekday and weekend crashes. Hierarchical Bayesian logistic regression models (random effects and random parameter logistic regression models) have been introduced to address the seasonal variations, crash unit level's diversities, and unobserved heterogeneity caused by geometric characteristics. For the multi-vehicle crashes: congested conditions at downstream would contribute to an increase in the likelihood of multi-vehicle crashes; multi-vehicle crashes are more likely to occur during poor visibility conditions and if there is a turbulent area that exists downstream. Drivers who are unable to reduce their speeds timely are prone to causing rear-end crashes. While for the single-vehicle crashes: slow moving traffic platoons at the downstream detector of the crash occurrence locations would increase the probability of single-vehicle crashes; large variations of occupancy downstream would also increase the likelihood of single-vehicle crash occurrence.Substantial efforts have been dedicated to revealing the hazardous factors that affect crash occurrence from both the aggregate and disaggregate level in this study, however, findings and conclusions from these research work need to be transferred into applications for roadway design and freeway management. This study further investigates the feasibility of utilizing Variable Speed Limits (VSL) system, one key part of ATM, to improve traffic safety on freeways. A proactive traffic safety improvement VSL control algorithm has been proposed. First, an extension of the traffic flow model METANET was employed to predict traffic flow while considering VSL's impacts on the flow-density diagram; a real-time crash risk evaluation model was then estimated for the purpose of quantifying crash risk; finally, the optimal VSL control strategies were achieved by employing an optimization technique of minimizing the total predicted crash risks along the VSL implementation area. Constraints were set up to limit the increase of the average travel time and differences between posted speed limits temporarily and spatially. The proposed VSL control strategy was tested for a mountainous freeway bottleneck area in the microscopic simulation software VISSIM. Safety impacts of the VSL system were quantified as crash risk improvements and speed homogeneity improvements. Moreover, three different driver compliance levels were modeled in VISSIM to monitor the sensitivity of VSL's safety impacts on driver compliance levels. Conclusions demonstrate that the proposed VSL system could effectively improve traffic safety by decreasing crash risk, enhancing speed homogeneity, and reducing travel time under both high and moderate driver compliance levels; while the VSL system does not have significant effects on traffic safety enhancement under the low compliance scenario. Future implementations of VSL control strategies and related research topics were also discussed.
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Date Issued
2013
Identifier
CFE0005283, ucf:50556
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005283
A Systems Approach to Sustainable Energy Portfolio Development
Title
A Systems Approach to Sustainable Energy Portfolio Development.
Creator
Hadian Niasar, Saeed, Reinhart, Debra, Madani Larijani, Kaveh, Wang, Dingbao, Lee, Woo Hyoung, Pazour, Jennifer, University of Central Florida
Abstract / Description
Adequate energy supply has become one of the vital components of human development and economic growth of nations. In fact, major components of the global economy such as transportation services, communications, industrial processes, and construction activities are dependent on adequate energy resources. Even mining and extraction of energy resources, including harnessing the forces of nature to produce energy, are dependent on accessibility of sufficient energy in the appropriate form at the...
Show moreAdequate energy supply has become one of the vital components of human development and economic growth of nations. In fact, major components of the global economy such as transportation services, communications, industrial processes, and construction activities are dependent on adequate energy resources. Even mining and extraction of energy resources, including harnessing the forces of nature to produce energy, are dependent on accessibility of sufficient energy in the appropriate form at the desired location. Therefore, energy resource planning and management to provide appropriate energy in terms of both quantity and quality has become a priority at the global level. The increasing demand for energy due to growing population, higher living standards, and economic development magnifies the importance of reliable energy plans. In addition, the uneven distribution of traditional fossil fuel energy sources on the Earth and the resulting political and economic interactions are other sources of complexity within energy planning. The competition over fossil fuels that exists due to gradual depletion of such sources and the tremendous thirst of current global economic operations for these sources, as well as the sensitivity of fossil fuel supplies and prices to global conditions, all add to the complexity of effective energy planning. In addition to diversification of fossil fuel supply sources as a means of increasing national energy security, many governments are investing in non-fossil fuels, especially renewable energy sources, to combat the risks associated with adequate energy supply. Moreover, increasing the number of energy sources also adds further complication to energy planning. Global warming, resulting from concentration of greenhouse gas emissions in the atmosphere, influences energy infrastructure investments and operations management as a result of international treaty obligations and other regulations requiring that emissions be cut to sustainable levels. Burning fossil fuel, as one of the substantial driving factors of global warming and energy insecurity, is mostly impacted by such policies, pushing forward the implementation of renewable energy polices. Thus, modern energy portfolios comprise a mix of renewable energy sources and fossil fuels, with an increasing share of renewables over time. Many governments have been setting renewable energy targets that mandate increasing energy production from such sources over time. Reliance on renewable energy sources certainly helps with reduction of greenhouse gas emissions while improving national energy security. However, the growing implementation of renewable energy has some limitations. Such energy technologies are not always as cheap as fossil fuel sources, mostly due to immaturity of these energy sources in most locations as well as high prices of the materials and equipment to harness the forces of nature and transform them to usable energy. In addition, despite the fact that renewable energy sources are traditionally considered to be environmentally friendly, compared to fossil fuels, they sometimes require more natural resources such as water and land to operate and produce energy. Hence, the massive production of energy from these sources may lead to water shortage, land use change, increasing food prices, and insecurity of water supplies. In other words, the energy production from renewables might be a solution to reduce greenhouse gas emissions, but it might become a source of other problems such as scarcity of natural resources.The fact that future energy mix will rely more on renewable sources is undeniable, mostly due to depletion of fossil fuel sources over time. However, the aforementioned limitations pose a challenge to general policies that encourage immediate substitution of fossil fuels with renewables to battle climate change. In fact, such limitations should be taken into account in developing reliable energy policies that seek adequate energy supply with minimal secondary effects. Traditional energy policies have been suggesting the expansion of least cost energy options, which were mostly fossil fuels. Such sources used to be considered riskless energy options with low volatility in the absence of competitive energy markets in which various energy technologies are competing over larger market shares. Evolution of renewable energy technologies, however, complicated energy planning due to emerging risks that emanated mostly from high price volatility. Hence, energy planning began to be seen as investment problems in which the costs of energy portfolio were minimized while attempting to manage associated price risks. So, energy policies continued to rely on risky fossil fuel options and small shares of renewables with the primary goal to reduce generation costs. With emerging symptoms of climate change and the resulting consequences, the new policies accounted for the costs of carbon emissions control in addition to other costs. Such policies also encouraged the increased use of renewable energy sources. Emissions control cost is not an appropriate measure of damages because these costs are substantially less than the economic damages resulting from emissions. In addition, the effects of such policies on natural resources such as water and land is not directly taken into account. However, sustainable energy policies should be able to capture such complexities, risks, and tradeoffs within energy planning. Therefore, there is a need for adequate supply of energy while addressing issues such as global warming, energy security, economy, and environmental impacts of energy production processes. The effort in this study is to develop an energy portfolio assessment model to address the aforementioned concerns.This research utilized energy performance data, gathered from extensive review of articles and governmental institution reports. The energy performance values, namely carbon footprint, water footprint, land footprint, and cost of energy production were carefully selected in order to have the same basis for comparison purposes. If needed, adjustment factors were applied. In addition, the Energy Information Administration (EIA) energy projection scenarios were selected as the basis for estimating the share of the energy sources over the years until 2035. Furthermore, the resource availability in different states within the U.S. was obtained from publicly available governmental institutions that provide such statistics. Specifically, the carbon emissions magnitudes (metric tons per capita) for different states were extracted from EIA databases, states' freshwater withdrawals (cubic meters per capita) were found from USGS databases, states' land availability values (square kilometers) were obtained from the U.S. Census Bureau, and economic resource availability (GDP per capita) for different states were acquired from the Bureau of Economic Analysis.In this study, first, the impacts of energy production processes on global freshwater resources are investigated based on different energy projection scenarios. Considering the need for investing on energy sources with minimum environmental impacts while securing maximum efficiency, a systems approach is adopted to quantify the resource use efficiency of energy sources under sustainability indicators. The sensitivity and robustness of the resource use efficiency scores are then investigated versus existing energy performance uncertainties and varying resource availability conditions. The resource use efficiency of the energy sources is then regionalized for different resource limitation conditions in states within the U.S. Finally, a sustainable energy planning framework is developed based on Modern Portfolio Theory (MPT) and Post-Modern Portfolio Theory (PMPT) with consideration of the resource use efficiency measures and associated efficiency risks.In the energy-water nexus investigation, the energy sources are categorized into 10 major groups with distinct water footprint magnitudes and associated uncertainties. The global water footprint of energy production processes are then estimated for different EIA energy mix scenarios over the 2012-2035 period. The outcomes indicate that the water footprint of energy production increases by almost 50% depending on the scenario. In fact, growing energy production is not the only reason for increasing the energy related water footprint. Increasing the share of water intensive energy sources in the future energy mix is another driver of increasing global water footprint of energy in the future. The results of the energies' water footprint analysis demonstrate the need for a policy to reduce the water use of energy generation. Furthermore, the outcomes highlight the importance of considering the secondary impacts of energy production processes besides their carbon footprint and costs. The results also have policy implications for future energy investments in order to increase the water use efficiency of energy sources per unit of energy production, especially those with significant water footprint such as hydropower and biofuels.In the next step, substantial efforts have been dedicated to evaluating the efficiency of different energy sources from resource use perspective. For this purpose, a system of systems approach is adopted to measure the resource use efficiency of energy sources in the presence of trade-offs between independent yet interacting systems (climate, water, land, economy). Hence, a stochastic multi-criteria decision making (MCDM) framework is developed to compute the resource use efficiency scores for four sustainability assessment criteria, namely carbon footprint, water footprint, land footprint, and cost of energy production considering existing performance uncertainties. The energy sources' performances under aforementioned sustainability criteria are represented in ranges due to uncertainties that exist because of technological and regional variations. Such uncertainties are captured by the model based on Monte-Carlo selection of random values and are translated into stochastic resource use efficiency scores. As the notion of optimality is not unique, five MCDM methods are exploited in the model to counterbalance the bias toward definition of optimality. This analysis is performed under (")no resource limitation(") conditions to highlight the quality of different energy sources from a resource use perspective. The resource use efficiency is defined as a dimensionless number in scale of 0-100, with greater numbers representing a higher efficiency. The outcomes of this analysis indicate that despite increasing popularity, not all renewable energy sources are more resource use efficient than non-renewable sources. This is especially true for biofuels and different types of ethanol that demonstrate lower resource use efficiency scores compared to natural gas and nuclear energy. It is found that geothermal energy and biomass energy from miscanthus are the most and least resource use efficient energy alternatives based on the performance data available in the literature. The analysis also shows that none of the energy sources are strictly dominant or strictly dominated by other energy sources. Following the resource use efficiency analysis, sensitivity and robustness analyses are performed to determine the impacts of resource limitations and existing performance uncertainties on resource use efficiency, respectively. Sensitivity analysis indicates that geothermal energy and ethanol from sugarcane have the lowest and highest resource use efficiency sensitivity, respectively. Also, it is found that from a resource use perspective, concentrated solar power (CSP) and hydropower are respectively the most and least robust energy options with respect to the existing performance uncertainties in the literature.In addition to resource use efficiency analysis, sensitivity analysis and robustness analysis, of energy sources, this study also investigates the scheme of the energy production mix within a specific region with certain characteristics, resource limitations, and availabilities. In fact, different energy sources, especially renewables, vary in demand for natural resources (such as water and land), environmental impacts, geographic requirements, and type of infrastructure required for energy production. In fact, the efficiency of energy sources from a resource use perspective is dependent upon regional specifications, so the energy portfolio varies for different regions due to varying resource availability conditions. Hence, the resource use efficiency scores of different energy technologies are calculated based on the aforementioned sustainability criteria and regional resource availability and limitation conditions (emissions, water resources, land, and GDP) within different U.S. states, regardless of the feasibility of energy alternatives in each state. Sustainability measures are given varying weights based on the emissions cap, available economic resources, land, and water resources in each state, upon which the resource use efficiency of energy sources is calculated by utilizing the system of systems framework developed in the previous step. Efficiency scores are graphically illustrated on GIS-based maps for different states and different energy sources. The results indicate that for some states, fossil fuels such as coal and natural gas are as efficient as renewables like wind and solar energy technologies from resource use perspective. In other words, energy sources' resource use efficiency is significantly sensitive to available resources and limitations in a certain location.Moreover, energy portfolio development models have been created in order to determine the share of different energy sources of total energy production, in order to meet energy demand, maintain energy security, and address climate change with the least possible adverse impacts on the environment. In fact, the traditional (")least cost(") energy portfolios are outdated and should be replaced with (")most efficient(") ones that are not only cost-effective, but also environmentally friendly. Hence, the calculated resource use efficiency scores and associated statistical analysis outcomes for a range of renewable and nonrenewable energy sources are fed into a portfolio selection framework to choose the appropriate energy mixes associated with the risk attitudes of decision makers. For this purpose, Modern Portfolio Theory (MPT) and Post-Modern Portfolio Theory (PMPT) are both employed to illustrate how different interpretations of (")risk of return(") yield different energy portfolios. The results indicate that 2012 energy mix and projected world's 2035 energy portfolio are not sustainable in terms of resource use efficiency and could be substituted with more reliable, more effective portfolios that address energy security and global warming with minimal environmental and economic impacts.
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Date Issued
2013
Identifier
CFE0005001, ucf:50020
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005001