Modeling Bioenergy Supply Chains: Feedstocks Pretreatment, Integrated System Design Under Uncertainty

Biofuels have been promoted by governmental policies for reducing fossil fuel dependency and greenhouse gas emissions, as well as facilitating regional economic growth. Comprehensive model analysis is needed to assess the economic and environmental impacts of developing bioenergy production systems. For cellulosic biofuel production and supply in particular, existing studies have not accounted for the inter-dependencies between multiple participating decision makers and simultaneously incorporated uncertainties and risks associated with the linked production systems.This dissertation presents a methodology that incorporates uncertainty element to the existing integrated modeling framework specifically designed for advanced biofuel production systems using dedicated energy crops as feedstock resources. The goal of the framework is to support the bioenergy industry for infrastructure and supply chain development. The framework is flexible to adapt to different topological network structures and decision scopes based on the modeling requirements, such as on capturing the interactions between the agricultural production system and the multi-refinery bioenergy supply chain system with regards to land allocation and crop adoption patterns, which is critical for estimating feedstock supply potentials for the bioenergy industry. The methodology is also particularly designed to incorporate system uncertainties by using stochastic programming models to improve the resilience of the optimized system design.The framework is used to construct model analyses in two case studies. The results of the California biomass supply model estimate that feedstock pretreatment via combined torrefaction and pelletization reduces delivered and transportation cost for long-distance biomass shipment by 5% and 15% respectively. The Pacific Northwest hardwood biofuels application integrates full-scaled supply chain infrastructure optimization with agricultural economic modeling and estimates that bio-jet fuels can be produced at costs between 4 to 5 dollars per gallon, and identifies areas suitable for simultaneously deploying a set of biorefineries using adopted poplar as the dedicated energy crop to produce biomass feedstocks. This application specifically incorporates system uncertainties in the crop market and provides an optimal system design solution with over 17% improvement in expected total profit compared to its corresponding deterministic model

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

Integrating bio-hubs in biomass supply chains: Insights from a systematic literature review

Biomass sources are geographically scattered, and seasonal changes influence their availability. Variations in location, type, and feedstock quality impose logistical and storage challenges. Such a dispersion and variety of biomass sources, as well as the dispersion of demand points, may undermine the economies of scale and increase the risk of supply shortage. By consolidating biomass preprocessing and distribution activities in bio-hub facilities, they can contribute to the overall resilience of biomass supply chains (BSCs) and ensure a more sustainable and cost-efficient approach to bioenergy production. As such, investigating the advantages and challenges associated with bio-hub implementation can offer invaluable insights on the efficiency and sustainability of BSCs. Despite its critical role, a major part of the literature on BSCs is confined to the decision-making processes related to biomass suppliers and bioconversion facilities. To bridge this research gap, the current study conducts a systematic literature review on bio-hub implementation within BSCs in the period of the last ten years. Shortlisted papers are classified and analyzed meticulously to extract possible improvements from BSC and modeling perspectives. From the BSC viewpoint, one notable gap is the little attention to mid-term and short-term decisions of bio-hub operations such as inventory control, resource management and production planning. Furthermore, the results revealed that environmental and social aspects of bio-hub implementation require considerable attention. From the modeling perspective, findings illustrate the underutilization of integrated approaches to incorporate micro-level and macro-level information in decision-making. In this regard, a number of areas are suggested for further exploration

Investment decision-making in clean energy under uncertainties: A real options approach

International commitments on emission reduction and the deterioration of fossil energy resources have caused more research attention to clean energy production. Getting the optimal investment portfolio in infrastructure for energy supply and consumption is a minimum requirement to enable the transition towards a sustainable energy system. Due to their environmental benefits, advanced biofuel and clean power generation are expected to play an important role in the future in transportation sector and electricity sector, respectively. In this dissertation, a real options approach is adopted for valuating clean technology investment portfolios under uncertainty, exploring managerial insights, and examining policy implications. The dissertation consists three parts discussing problems on clean energy investment. Biofuel production investment, motivated by consumption volume mandates in revised Renewable Fuel Standard, is a long-term irreversible investment facing revenue uncertainty given volatile fuel market. Iowa, rich in agricultural residues like corn stover, is a major player in the fulfillment of the cellulosic biofuels mandate. In this first part, we aim to answer the question: Is now a good time for Iowa to start investing in cellulosic biofuels? Using a fast pyrolysis facility as an example, we present a real options approach for valuating the investment of a new technology for producing cellulosic biofuels subject to construction lead time and uncertain fuel price. We conduct a case study, in which the profitability of the project, optimal investment timing, and the impact of project lead time are investigated. The second part extended the previous work by incorporating supply risk and dual sourcing. While corn stover is an abundant source of feedstock for biofuels production in Iowa, there is a potential supply risk due to the following reasons: (1) lack of market; (2) low percentage of farm participation; and (3) yield uncertainty due to the changing weather conditions. The decision maker would consider investing in a land to grow his own feedstock, in addition to the investment of biofuel facility. Land option with the growing of dedicated energy crops has a value-adding effect when operating with the fast pyrolysis facility. And with dual sourcing, the impact from supply uncertainty could be mitigated. A real options approach is used to analyze the optimal investment timing and benefits of the dual sourcing. Risk-aversion has an unexpected effect on investment decision-making, which may cause the investment decision of the value-adding option can be very sensitive to the primary underlying uncertainty, and the immediate action towards land investment can no longer be described with a single fuel price threshold. Policy is deemed as one of the top decisive external factor that impacts the interest of a power producer. All energy projects are prone to policy risk, yet such eventualities are difficult to predict and therefore expensive to insure. In the third part of the study, we extend the uncertainty to the scope of government policy, in addition to considering the critical uncertainty of commodity prices. In this work, we want to examine the timing that an owner of a traditional coal-fired generator adopts in a clean technology when facing two realistic policy uncertainty cases: risk of repealing an existing policy, and risk of a policy change. The investment of a natural gas generator is considered in order to meet the load obligation while maximizing its expected long-run profit with regulated emission-related costs considered. The price uncertainties in electricity, natural gas, and carbon emission, together with policy uncertainty jointly affect profitability and decision-making of the clean technology adoption. A real options approach is applied to investigate the optimal investment decision. The producers are risk avoiding when facing uncertain future policy environment; and this reflects in delaying investment plan and creating a future investment plan that is stubborn to current carbon price. To a risk-neutral price-taking power producer, emission trading is a more effective instrument compared to carbon tax, and shifting from carbon tax to emission permits could more effectively inducing immediate investment in clean technology

Multistage decisions and risk in Markov decision processes: towards effective approximate dynamic programming architectures

The scientific domain of this thesis is optimization under uncertainty for discrete event stochastic systems. In particular, this thesis focuses on the practical implementation of the Dynamic Programming (DP) methodology to discrete event stochastic systems. Unfortunately DP in its crude form suffers from three severe computational obstacles that make its imple-mentation to such systems an impossible task. This thesis addresses these obstacles by developing and executing practical Approximate Dynamic Programming (ADP) techniques. Specifically, for the purposes of this thesis we developed the following ADP techniques. The first one is inspired from the Reinforcement Learning (RL) literature and is termed as Real Time Approximate Dynamic Programming (RTADP). The RTADP algorithm is meant for active learning while operating the stochastic system. The basic idea is that the agent while constantly interacts with the uncertain environment accumulates experience, which enables him to react more optimal in future similar situations. While the second one is an off-line ADP procedure These ADP techniques are demonstrated on a variety of discrete event stochastic systems such as: i) a three stage queuing manufacturing network with recycle, ii) a supply chain of the light aromatics of a typical refinery, iii) several stochastic shortest path instances with a single starting and terminal state and iv) a general project portfolio management problem. Moreover, this work addresses, in a systematic way, the issue of multistage risk within the DP framework by exploring the usage of intra-period and inter-period risk sensitive utility functions. In this thesis we propose a special structure for an intra-period utility and compare the derived policies in several multistage instances.Ph.D.Committee Chair: Jay H. Lee; Committee Member: Martha Grover; Committee Member: Matthew J. Realff; Committee Member: Shabbir Ahmed; Committee Member: Stylianos Kavadia

Supply and demand planning for crude oil procurement in refineries

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2006.Includes bibliographical references (leaves 70-72).The upstream petroleum supply chain is inefficient and uneconomical because of the independence of the four complex and fragmented functions which comprise it. Crude oil exploration, trading, transportation, and refining are functions which may be integrated through unified decision-making facilitated by timely information exchange. This exchange has been problematic because the four business units with their disparate activities have not been able to capture and appropriately structure the required information. How can business executives in the oil industry assemble all of the required information to achieve system-wide optimization? To remove the silos which impede system-wide optimization, there is need to analyze people, systems and issues in the upstream section of the petroleum supply chain; as a background to understanding the current challenges faced in achieving integration. Hence, the use of secondary and primary data sources was used for this research. The secondary includes the review of relevant literature while the primary data were from two sources. The first came from an on-site interview with the heads of business units of a case study, a company which is a major player in the industry.(cont.) The second is from telephone interviews with industry experts which include software providers, consultants and other major players in the industry. The findings are that on-time information exchange will maximize shareholders' value and improve process efficiency in the supply chain. This process efficiency makes the upstream supply chain more responsive to possible changes in the environment that affects its operation. This will allow supply chain managers to achieve both a reduction in the variability in price of end product will be obtained while achieving stable profit margins. This research concludes by advocating that the use of information systems that accurately support data exchange among the functions in the supply chain in a timely, coordinated fashion with minimal distortion is required to ensure consistency in optimal decision making. To achieve this, change management is necessary because it requires a shift to a holistic approach in making decisions. Finally, areas recommended for future research are stated.by Beatrice N. Nnadili.M.Eng.in Logistic

MODELING SUSTAINABILITY IN RENEWABLE ENERGY SUPPLY CHAIN SYSTEMS

This dissertation aims at modeling sustainability of renewable fuel supply chain systems against emerging challenges. In particular, the dissertation focuses on the biofuel supply chain system design, and manages to develop advanced modeling framework and corresponding solution methods in tackling challenges in sustaining biofuel supply chain systems. These challenges include: (1) to integrate \u27environmental thinking\u27 into the long-term biofuel supply chain planning; (2) to adopt multimodal transportation to mitigate seasonality in biofuel supply chain operations; (3) to provide strategies in hedging against uncertainty from conversion technology; and (4) to develop methodologies in long-term sequential planning of the biofuel supply chain under uncertainties. All models are mixed integer programs, which also involves multi-objective programming method and two-stage/multistage stochastic programming methods. In particular for the long-term sequential planning under uncertainties, to reduce the computational challenges due to the exponential expansion of the scenario tree, I also developed efficient ND-Max method which is more efficient than CPLEX and Nested Decomposition method. Through result analysis of four independent studies, it is found that the proposed modeling frameworks can effectively improve the economic performance, enhance environmental benefits and reduce risks due to systems uncertainties for the biofuel supply chain systems

Biofuel supply chain and bottom-up market equilibrium model for production and policy analysis

Renewable fuel is attracting increasing attention as a substitute for fossil based energy. The US Department of Energy (DOE) has identified pyrolysis based platforms as promising biofuel production pathways. Although the biofuel market remains in its early stage, it is expected to play an important role in climate policy in the future in the transportation sector. In this thesis, we will first propose a biofuel supply chain model to study the supply chain design and operational planning for advanced biofuel production, then a biofuel market model is developed to study the interactions between farmers, biofuel producers, blenders, and consumers along the biofuel supply chain in the market competitive setting. For the biofuel supply chain model, the focused production pathway is corn stover fast pyrolysis with upgrading to hydrocarbon gasoline equivalent fuel. The model is formulated with a Mixed Integer Linear Programming (MILP) to investigate facility locations, facility capacities at the strategic level, and feedstock flow and biofuel production decisions at the operational level. In the model, we accommodate different biomass supply and biofuel demand scenarios with supply shortage penalty and storage cost for excess biofuel production. Numerical results illustrate the supply chain design and operational planning decision making for advanced biofuel production. Unit costs for advanced biofuel under changing of scenarios are also analyzed. The case study demonstrates the economic feasibility of biofuel production at a commercial scale in Iowa. The second part of the thesis work focuses on analyzing the interaction between the key stakeholders along the supply chain. A bottom-up equilibrium model is built for biofuel market to study the competition in the advanced biofuel market, explicitly formulating the interactions between farmers, biofuel producers, blenders, and consumers. The model simulates the profit maximization of multiple market entities by incorporates their competitive decisions in farmers\u27 land allocation, biomass transportation, biofuel production, and biofuel blending. As such, the equilibrium model is capable of and appropriate for policy analysis, especially for those that have complex ramifications and result in different reactions from multiple stakeholders. For example, the model can be used to analyze the impact of biofuel policies on market outcomes, pass-through of taxes or subsidies, and consumers\u27 surplus or producers\u27 profit implications. The equilibrium model can also serve as an analytical tool to derive market prices of biomass, advanced biofuel, and the value of the Renewable Identification Numbers. Moreover, the model can be used to analyze the impact of the market structure or firms\u27 ownership setting that may arise due to oligopoly competition in the advanced biofuel market

Multi-objective optimisation under deep uncertainty

Most of the decisions in real-life problems need to be made in the absence of complete knowledge about the consequences of the decision. Furthermore, in some of these problems, the probability and/or the number of different outcomes are also unknown (named deep uncertainty). Therefore, all the probability-based approaches (such as stochastic programming) are unable to address these problems. On the other hand, involving various stakeholders with different (possibly conflicting) criteria in the problems brings additional complexity. The main aim and primary motivation for writing this thesis have been to deal with deep uncertainty in Multi-Criteria Decision-Making (MCDM) problems, especially with long-term decision-making processes such as strategic planning problems. To achieve these aims, we first introduced a two-stage scenario-based structure for dealing with deep uncertainty in Multi-Objective Optimisation (MOO)/MCDM problems. The proposed method extends the concept of two-stage stochastic programming with recourse to address the capability of dealing with deep uncertainty through the use of scenario planning rather than statistical expectation. In this research, scenarios are used as a dimension of preference (a component of what we term the meta-criteria) to avoid problems relating to the assessment and use of probabilities under deep uncertainty. Such scenario-based thinking involved a multi-objective representation of performance under different future conditions as an alternative to expectation, which fitted naturally into the broader multi-objective problem context. To aggregate these objectives of the problem, the Generalised Goal Programming (GGP) approach is used. Due to the capability of this approach to handle large numbers of objective functions/criteria, the GGP is significantly useful in the proposed framework. Identifying the goals for each criterion is the only action that the Decision Maker (DM) needs to take without needing to investigate the trade-offs between different criteria. Moreover, the proposed two-stage framework has been expanded to a three-stage structure and a moving horizon concept to handle the existing deep uncertainty in more complex problems, such as strategic planning. As strategic planning problems will deal with more than two stages and real processes are continuous, it follows that more scenarios will continuously be unfolded that may or may not be periodic. "Stages", in this study, are artificial constructs to structure thinking of an indefinite future. A suitable length of the planning window and stages in the proposed methodology are also investigated. Philosophically, the proposed two-stage structure always plans and looks one step ahead while the three-stage structure considers the conditions and consequences of two upcoming steps in advance, which fits well with our primary objective. Ignoring long-term consequences of decisions as well as likely conditions could not be a robust strategic approach. Therefore, generally, by utilising the three-stage structure, we may expect a more robust decision than with a two-stage representation. Modelling time preferences in multi-stage problems have also been introduced to solve the fundamental problem of comparability of the two proposed methodologies because of the different time horizon, as the two-stage model is ignorant of the third stage. This concept has been applied by a differential weighting in models. Importance weights, then, are primarily used to make the two- and three-stage models more directly comparable, and only secondarily as a measure of risk preference. Differential weighting can help us apply further preferences in the model and lead it to generate more preferred solutions. Expanding the proposed structure to the problems with more than three stages which usually have too many meta-scenarios may lead us to a computationally expensive model that cannot easily be solved, if it all. Moreover, extension to a planning horizon that too long will not result in an exact plan, as nothing in nature is predictable to this level of detail, and we are always surprised by new events. Therefore, beyond the expensive computation in a multi-stage structure for more than three stages, defining plausible scenarios for far stages is not logical and even impossible. Therefore, the moving horizon models in a T-stage planning window has been introduced. To be able to run and evaluate the proposed two- and three-stage moving horizon frameworks in longer planning horizons, we need to identify all plausible meta-scenarios. However, with the assumption of deep uncertainty, this identification is almost impossible. On the other hand, even with a finite set of plausible meta-scenarios, comparing and computing the results in all plausible meta-scenarios are hardly possible, because the size of the model grows exponentially by raising the length of the planning horizon. Furthermore, analysis of the solutions requires hundreds or thousands of multi-objective comparisons that are not easily conceivable, if it all. These issues motivated us to perform a Simulation-Optimisation study to simulate the reasonable number of meta-scenarios and enable evaluation, comparison and analysis of the proposed methods for the problems with a T-stage planning horizon. In this Simulation-Optimisation study, we started by setting the current scenario, the scenario that we were facing it at the beginning of the period. Then, the optimisation model was run to get the first-stage decisions which can implement immediately. Thereafter, the next scenario was randomly generated by using Monte Carlo simulation methods. In deep uncertainty, we do not have enough knowledge about the likelihood of plausible scenarios nor the probability space; therefore, to simulate the deep uncertainty we shall not use anything of scenario likelihoods in the decision models. The two- and three-stage Simulation-Optimisation algorithms were also proposed. A comparison of these algorithms showed that the solutions to the two-stage moving horizon model are feasible to the other pattern (three-stage). Also, the optimal solution to the three-stage moving horizon model is not dominated by any solutions of the other model. So, with no doubt, it must find better, or at least the same, goal achievement compared to the two-stage moving horizon model. Accordingly, the three-stage moving horizon model evaluates and compares the optimal solution of the corresponding two-stage moving horizon model to the other feasible solutions, then, if it selects anything else it must either be better in goal achievement or be robust in some future scenarios or a combination of both. However, the cost of these supremacies must be considered (as it may lead us to a computationally expensive problem), and the efficiency of applying this structure needs to be approved. Obviously, using the three-stage structure in comparison with the two-stage approach brings more complexity and calculations to the models. It is also shown that the solutions to the three-stage model would be preferred to the solutions provided by the two-stage model under most circumstances. However, by the "efficiency" of the three-stage framework in our context, we want to know that whether utilising this approach and its solutions is worth the expense of the additional complexity and computation. The experiments in this study showed that the three-stage model has advantages under most circumstances(meta-scenarios), but that the gains are quite modest. This issue is frequently observed when comparing these methods in problems with a short-term (say less than five stages) planning window. Nevertheless, analysis of the length of the planning horizon and its effects on the solutions to the proposed frameworks indicate that utilising the three-stage models is more efficient for longer periods because the differences between the solutions of the two proposed structures increase by any iteration of the algorithms in moving horizon models. Moreover, during the long-term calculations, we noticed that the two-stage algorithm failed to find the optimal solutions for some iterations while the three-stage algorithm found the optimal value in all cases. Thus, it seems that for the planning horizons with more than ten stages, the efficiency of the three-stage model be may worth the expenses of the complexity and computation. Nevertheless, if the DM prefers to not use the three-stage structure because of the complexity and/or calculations, the two-stage moving horizon model can provide us with some reasonable solutions, although they might not be as good as the solutions generated by a three-stage framework. Finally, to examine the power of the proposed methodology in real cases, the proposed two-stage structure was applied in the sugarcane industry to analyse the whole infrastructure of the sugar and bioethanol Supply Chain (SC) in such a way that all economics (Max profit), environmental (Min CO₂), and social benefits (Max job-creations) were optimised under six key uncertainties, namely sugarcane yield, ethanol and refined sugar demands and prices, and the exchange rate. Moreover, one of the critical design questions - that is, to design the optimal number and technologies as well as the best place(s) for setting up the ethanol plant(s) - was also addressed in this study. The general model for the strategic planning of sugar- bioethanol supply chains (SC) under deep uncertainty was formulated and also examined in a case study based on the South African Sugar Industry. This problem is formulated as a Scenario-Based Mixed-Integer Two-Stage Multi-Objective Optimisation problem and solved by utilising the Generalised Goal Programming Approach. To sum up, the proposed methodology is, to the best of our knowledge, a novel approach that can successfully handle the deep uncertainty in MCDM/MOO problems with both short- and long-term planning horizons. It is generic enough to use in all MCDM problems under deep uncertainty. However, in this thesis, the proposed structure only applied in Linear Problems (LP). Non-linear problems would be an important direction for future research. Different solution methods may also need to be examined to solve the non-linear problems. Moreover, many other real-world optimisation and decision-making applications can be considered to examine the proposed method in the future