A Methodology for Siting Coal Fired Thermoelectric Generating Facilities in Puerto Rico

In the past, energy facilities on the Island of Puerto Rico have not been located in the best practicable sites. This is attributable to the absence of mandatory site selection procedures. This thesis has developed and tested procedures for siting 900 MWe coal fired thermoelectric generation plants. The procedures developed here permit the placement of these facilities within the existing legal regime, with a minimum of adverse ecological and socioeconomic impact. The process has been designed to consider the entire Island of Puerto Rico for the suitability of siting a 900 MWe coal fired facility. This is accomplished through the design and use of a five-phase process. The primary goal of the process was to quickly reduce the total geographic area under siting consideration. This allowed for the identification of a number of preferred areas for a 900 MWe project. This provision allowed a majority of the effort and resources, involved in site selection, to be concentrated on those areas most suitable for facility development. This is particularly important in the case of Puerto Rico because the Island does not possess the physical or monetary resources to conduct financial and manpower intensive studies, compared to the continental United States. It is equally important that siting procedures are responsive to the Island\u27s environment. The environmental problems of Puerto Rico are particularly important due to spatial constraints. Due to its small size, the Island\u27s residents perceive environmental change quickly. The lines of cause and effect are small and can be drawn with greater clarity than those for mainland areas. The thesis has successfully designed and tested procedures that in practice will attain these goals. Ideally, the process culminates in the selection of the optimum site(s) for a 900 MWe facility. The procedures have also been designed with a high degree of general applicability. With minor alterations, the process may also benefit other Caribbean Islands in their energy development programs

Integrated Techno-Economic and Life Cycle Analyses of Biomass Utilization for Value-Added Bioproducts in the Northeastern United States

A multi-stage spatial analysis was first conducted to select locations for lignocellulosic biomass-based bioproduct facility, using Geographical Information System (GIS) spatial analysis, multi-criteria analysis ranking algorithm, and social-economic assessment. A case study was developed to determine locations for lignocellulosic biorefineries using feedstocks including forest residue biomass and three energy crops for 13 states in the northeastern United States. In the entire study area, 11.1% of the counties are high-suitable, 48.8% are medium-suitable for biorefinery siting locations. A non-parametric analysis of cross-group surveys showed that preferences on biorefinery siting are homogeneous for experts in academia and industry groups, but people in government agencies presented different opinions. With the Maximum Likelihood test, parameters of distributions and mean values were estimated for nine weighted criteria. Social asset evaluation focusing on degree of rurality and social capital index further sorted counties with higher community acceptance and economic viability. A total of 15 counties were selected with the highest potential for biorefinery sites in the region. A mixed-integer linear programming model was then developed to optimize the multiple biomass feedstock supply chains, including feedstock establishment, harvest, storage, transportation, and preprocessing. The model was applied for analyses of multiple biomass feedstocks at county level for 13 states in the northeastern United States. In the base case with a demand of 180,000 dry Mg/year of biomass, the delivered costs ranged from $67.90 to$86.97 per dry Mg with an average of $79.58 /dry Mg. The biomass delivered costs by county were from$67.90 to 150.81 per dry Mg across the northeastern U.S. Considered the entire study area, the delivered cost averaged $85.30 /dry Mg for forest residues,$84.47 /dry Mg for hybrid willow, $99.68 for switchgrass and$97.87 per dry Mg for Miscanthus. Seventy seven out of 387 counties could be able to deliver biomass at $84 per dry Mg or less a target set by US DOE by 2022. A sensitivity analysis was also conducted to evaluate the effects of feedstock availability, feedstock price, moisture content, procurement radius, and facility demand on the delivered cost. Our results showed that procurement radius, facility capacity, and forest residue availability are the most sensitive factors affecting the biomass delivered costs. An integrated life cycle and techno-economic assessment was carried out for three bioenergy products derived from multiple lignocellulosic biomass. Three cases were studied for production of pellets, biomass-based electricity, and pyrolysis bio-oil. The LCA was conducted for estimating environmental impacts on cradle-to-gate basis with functional unit of 1000 MJ for bioenergy production. Pellet production had the lowest GHG emissions, water and fossil fuels consumption, for 8.29 kg CO2 eq, 0.46 kg, and 105.42 MJ, respectively. Conversion process presented a greater environmental impact for all three bioenergy products. With producing 46,926 tons of pellets, 260,000 MWh of electricity, and 78,000 barrels of pyrolysis oil, the net present values (NPV) for all three cases indicated only pellet and biopower production cases were profitable with NPVs$1.20 million for pellet, and $81.60 million for biopower. The pellet plant and biopower plant were profitable only when discount rates are less than or equal to 10A study evaluated the environmental and economic impacts of activated carbon (AC) produced from lignocellulosic biomass was evaluated for energy storage purpose. Results indicate that overall “in-plant production” process presented the highest environmental impacts. Normalized results of life cycle impact assessment showed that the AC production had environmental impacts mainly on carcinogenics, ecotoxicity, and non-carcinogenics categories. We then further focused on life cycle analysis from raw biomass delivery to plant gate, the results showed “feedstock establishment” has the most significant environmental impact, ranging from 50.3$6.66 million, and annual operation cost was $15.46 million. The AC required selling price (RSP) was$16.79 per kg, with the discounted payback period (DPB) of 9.98 years. Alternative cases of KOH-reuse and steam processes had GHG emission of 15.4 kg CO2 eq, and 10.2 kg CO2 eq for every 1 kg activated carbon, respectively. Monte Carlo simulation showed 49.96% of the probability for an investment to be profitable in activated carbon production for supercapacitor electrodes

Solving a locational distribution problem of non-toxic solid waste on the island of Puerto Rico

The island of Puerto Rico is confronting a crisis in waste management due to inadequate management from the local government, the decreasing number of landfills available, high population density, and paucity of places for waste disposal. This research develops a least-cost model for the disposal and transportation of non-hazardous solid waste. Location-allocation (LA) and Geographic Information Systems (GIS) software are used to analyze the efficiency of the present pattern of waste allocation and to identify a near-to-optimal assignment of waste for the landfills in operation today and the landfills that will be open by 2008. The “near-to-optimal” models obtained from the LA analysis are compared to a regional system that has been proposed by the Autoridad de Desperdicios Sólidos (ADS) for the management of waste and with other waste-related infrastructure. The LA analysis revealed that the present allocation of waste is not efficiently distributed. The total cost of the present allocation of waste is 99,011.5 tons (miles) per day, while the least-cost model cost would be 83,201.5 (tons) miles per day. The least-cost model for 2008 allocated only seventy-two of the seventy-six municipios on the main island, leaving highly populated regions and 2,207.5 tons of waste generated per day out of the analysis. Most of the waste coming from the northeast would be transported to Humacao’s landfill (east). These results appear to be more economically efficient than other scenarios considered by the ADS. By 2008 most of the regions will be facing greater demands than landfill capacity. The scenario that presents the biggest savings is the LA model with twenty-seven landfills, while the model developed for 2008 provides better results than predicted by ADS, but the total distances values and cost are higher than the other scenarios evaluated. This suggests that more landfills might be needed by 2008 in order to save in operating costs. Based on these results recommendations are posed in relation to the location of waste-related infrastructure and possible regional make-ups, among others

Decentralized or centralized production : impacts to the environment, industry, and the economy

Since product take-back is mandated in Europe, and has effects for producers worldwide including the U.S., designing efficient forward and reverse supply chain networks is becoming essential for business viability. Centralizing production facilities may reduce costs but perhaps not environmental impacts. Decentralizing a supply chain may reduce transportation environmental impacts but increase capital costs. Facility location strategies of centralization or decentralization are tested for companies with supply chains that both take back and manufacture products. Decentralized and centralized production systems have different effects on the environment, industry and the economy. Decentralized production systems cluster suppliers within the geographical market region that the system serves. Centralized production systems have many suppliers spread out that meet all market demand. The point of this research is to help further the understanding of company decision-makers about impacts to the environment and costs when choosing a decentralized or centralized supply chain organizational strategy. This research explores; what degree of centralization for a supply chain makes the most financial and environmental sense for siting facilities; and which factories are in the best location to handle the financial and environmental impacts of particular processing steps needed for product manufacture. This research considered two examples of facility location for supply chains when products are taken back; the theoretical case involved shoe resoling and a real world case study considered the location of operations for a company that reclaims multiple products for use as material inputs. For the theoretical example a centralized strategy to facility location was optimal: whereas for the case study a decentralized strategy to facility location was best. In conclusion, it is not possible to say that a centralized or decentralized strategy to facility location is in general best for a company that takes back products. Each company’s specific concerns, needs, and supply chain details will determine which degree of centralization creates the optimal strategy for siting their facilities

Developing a Tool for the Location Optimization of the Alert Aircraft with Changing Threat Anticipation

The threat to the airspace is posed by the outside world in conventional terms as well as hostilities from within the airspace such as hijacked aircraft. Alert aircraft are located with the sole responsibility of responding to any incident. Different regions of the airspace may have different alert states depending on current intelligence input. Due to non-constant states of threat level, the Turkish Air Force must deploy aircraft to cover the more sensitive regions with a greater number of aircraft with a relatively short response time. This research deals with the problem by developing a tool for the location optimization of the alert aircraft. The tool can adapt to changes in threat anticipation while meeting the objectives of the alert network. Thus, a new location model with backup coverage requirements was formulated, and an interactive tool is developed that is capable of generating the aircraft locations for different user-defined threat anticipation

OR models in urban service facility location : a critical review of applications and future developments

[EN] Facility location models are well established in various application areas with more than a century of history in academia. Since the 1970s the trend has been shifting from manufacturing to service industries. Due to their nature, service industries are frequently located in or near urban areas that results in additional assumptions, objectives and constraints other than those in more traditional manufacturing location models. This survey focuses on the location of service facilities in urban areas. We studied 110 research papers across different journals and disciplines. We have analyzed these papers on two levels. On the first, we take an Operations Research perspective to investigate the papers in terms of types of decisions, location space, main assumptions, input parameters, objective functions and constraints. On the second level, we compare and contrast the papers in each of these applications categories: (a) Waste management systems (WMS), (b) Large-scale disaster (LSD), (c) Small-scale emergency (SSE), (d) General service and infrastructure (GSI), (e) Non-emergency healthcare systems (NEH) and (f) Transportation systems and their infrastructure (TSI). Each of these categories is critically analyzed in terms of application, assumptions, decision variables, input parameters, constraints, objective functions and solution techniques. Gaps, research opportunities and trends are identified within each category. Finally, some general lessons learned based on the practicality of the models is synthesized to suggest avenues of future research.Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD - Optimization of Scheduling Problems in Container Yards (No. DPI2015-65895-R) financed by FEDER funds.Farahani, RZ.; Fallah, S.; Ruiz García, R.; Hosseini, S.; Asgari, N. (2019). OR Models in Urban Service Facility Location: A Critical Review of Applications and Future Developments. European Journal of Operational Research. 276(1):1-27. https://doi.org/10.1016/j.ejor.2018.07.036S127276

Conceptual Design of Wind Farms Through Novel Multi-Objective Swarm Optimization

Wind is one of the major sources of clean and renewable energy, and global wind energy has been experiencing a steady annual growth rate of more than 20% over the past decade. In the U.S. energy market, although wind energy is one of the fastest increasing sources of electricity generation (by annual installed capacity addition), and is expected to play an important role in the future energy demographics of this country, it has also been plagued by project underperformance and concept-to-installation delays. There are various factors affecting the quality of a wind energy project, and most of these factors are strongly coupled in their influence on the socio-economic, production, and environmental objectives of a wind energy project. To develop wind farms that are profitable, reliable, and meet community acceptance, it is critical to accomplish balance between these objectives, and therefore a clean understanding of how different design and natural factors jointly impact these objectives is much needed. In this research, a Multi-objective Wind Farm Design (MOWFD) methodology is developed, which analyzes and integrates the impact of various factors on the conceptual design of wind farms. This methodology contributes three major advancements to the wind farm design paradigm: (I) provides a new understanding of the impact of key factors on the wind farm performance under the use of different wake models; (II) explores the crucial tradeoffs between energy production, cost of energy, and the quantitative role of land usage in wind farm layout optimization (WFLO); and (III) makes novel advancements on mixed-discrete particle swarm optimization algorithm through a multi-domain diversity preservation concept, to solve complex multi-objective optimization (MOO) problems. A comprehensive sensitivity analysis of the wind farm power generation is performed to understand and compare the impact of land configuration, installed capacity decisions, incoming wind speed, and ambient turbulence on the performance of conventional array layouts and optimized wind farm layouts. For array-like wind farms, the relative importance of each factor was found to vary significantly with the choice of wake models, i.e., appreciable differences in the sensitivity indices (of up to 70%) were observed across the different wake models. In contrast, for optimized wind farm layouts, the choice of wake models was observed to have no significant impact on the sensitivity indices. The MOWFD methodology is designed to explore the tradeoffs between the concerned performance objectives and simultaneously optimize the location of turbines, the type of turbines, and the land usage. More importantly, it facilitates WFLO without prescribed conditions (e.g., fixed wind farm boundaries and number of turbines), thereby allowing a more flexible exploration of the feasible layout solutions than is possible with other existing WFLO methodologies. In addition, a novel parameterization of the Pareto is performed to quantitatively explore how the best tradeoffs between energy production and land usage vary with the installed capacity decisions. The key to the various complex MO-WFLOs performed here is the unique set of capabilities offered by the new Multi-Objective Mixed-Discrete Particle Swarm Optimization (MO-MDPSO) algorithm, developed, tested and extensively used in this dissertation. The MO-MDPSO algorithm is capable of dealing with a plethora of problem complexities, namely: multiple highly nonlinear objectives, constraints, high design space dimensionality, and a mixture of continuous and discrete design variables. Prior to applying MO-MDPSO to effectively solve complex WFLO problems, this new algorithm was tested on a large and diverse suite of popular benchmark problems; the convergence and Pareto coverage offered by this algorithm was found to be competitive with some of the most popular MOO algorithms (e.g., GAs). The unique potential of the MO-MDPSO algorithm is further established through application to the following complex practical engineering problems: (I) a disc brake design problem, (II) a multi-objective wind farm layout optimization problem, simultaneously optimizing the location of turbines, the selection of turbine types, and the site orientation, and (III) simultaneously minimizing land usage and maximizing capacity factors under varying land plot availability

Optimization of Large-Scale Sustainable Renewable Energy Supply Chains in a Stochastic Environment

Due to the increasing demand of energy and environmental concern of fossil fuels, it is becoming increasingly important to find alternative renewable energy sources. Biofuels produced from lignocellulosic biomass feedstock's show enormous potential as a renewable resource. Electricity generated from the combustion of biomass is also one important type of bioenergy. Renewable resources like wind also show great potential as a resource for electricity generation. In order to deliver competitive renewable energy products to the end-market, robust renewable energy supply chains (RESCs) are essential. Research is needed in two distinct types of RESCs, namely: 1) lignocellulosic biomass-to-biofuel (LBSC); and 2) wind energy/biomass-to-electricity (WBBRESSC). LBSC is a complex system which consists of multiple uncertainties which include: 1) purchase price and availability of biomass feedstock; 2) sale price and demand of biofuels. To ensure LBSC sustainability, the following decisions need to be optimized: a) allocation of land for biomass cultivation; b) biorefinery sites selection; c) choice of biomass-to-biofuel conversion technology; and d) production capacity of biorefineries. The major uncertainty in a WBBRESC concerns wind speeds which impact the power output of wind farms. To ensure WBBRESC sustainability, the following decisions need to be optimized: a) site selection for installation of wind farms, biomass power plants (BMPPs), and grid stations; b) generation capacity of wind farms and BMPPs; and c) transmission capacity of power lines. The multiple uncertainties in RESCs if not jointly considered in the decision making process result in non-optimal (or even infeasible) solutions which generate lower profits, increased environmental pollution, and reduced social benefits. This research proposes a number of comprehensive mathematical models for the stochastic optimization of RESCs. The proposed large-scale stochastic mixed integer linear programming (SMILP) models are solved to optimality by using suitable decomposition methods (e.g. Bender's) and appropriate metaheuristic algorithms (e.g. Sample Average Approximation). Overall, the research outcomes will help to design robust RESCs focused towards sustainability in order to optimally utilize the renewable resources in the near future. The findings can be used by renewable energy producers to sustainably operate in an efficient (and cost effective) manner, boost the regional economy, and protect the environment

Algorithms and Methods for Optimizing the Spent Nuclear Fuel Allocation Strategy

Commercial nuclear power plants produce long-lasting nuclear waste, primarily in the form of spent nuclear fuel (SNF) assemblies. Spent fuel pools (SFP) and canisters or casks that sit at an independent spent fuel storage installation (ISFSI) at the reactor site store the fuel assemblies that are removed from operating reactors. The federal government has developed a plan to move the SNF from reactor sites to a Consolidated Interim Storage Facility (CISF) or a geological repository. In order to develop a predictable pick-up schedule and give utilities notice of an impending pickup from a reactor site, the federal government developed a queuing strategy based on the first-in-first-out algorithm, known as oldest fuel first (OFF). The OFF algorithm allows the federal government to remove SNF from reactor sites in the same order the assemblies came out of the reactor. While an OFF allocation strategy may result in a fair approach, it is far from the most cost-effective approach. The problem with accepting SNF using an OFF algorithm is that a handful of sites are no longer producing power and exist only to store the SNF they produced. This is an expensive process, which results in an annual cost of ~$8M [22]. Utilizing different algorithms to reduce the amount of time these shutdown reactors keep SNF on site may reduce the total system costs for the federal government. A greedy algorithm, genetic mutation algorithm, simulated annealing algorithm, and an integer programming formulation were all developed to reduce the number of years that reactors were shut down with SNF on site