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

A Modeling, Optimization, and Analysis Framework for Designing Multi-Product Lignocellulosic Biorefineries

The objective of this research is to propose a methodology to develop modular decision analysis frameworks to design value chains for enterprises in the renewable fuels and chemicals sector. The decision support framework focuses on providing strategic decision support to startup and new product ventures. The tasks that are embedded in the framework include process and systems design, technology and product selection, forecasting cost and market variables, designing network capacities, and analysis of risks. The Decision support system (DSS) proposed is based on optimization modeling; systems design are carried out using integer programming with multiple sets of process and network configurations utilized as inputs. Uncertainty is incorporated using real options, which are utilized to design network processing capacity for the conversion of biomass resources. Risk analysis is carried out using Monte Carlo methods. The DSS framework is exemplified using a lignocellulosic biorefinery case study that is assumed to be located in Louisiana. The biorefinery utilizes energy crops as feedstocks and processes them into cellulosic biofuels and biobased chemicals. Optimization modeling is utilized to select an optimal network, a fractionation technology, a fermentation configuration, and optimal product recovery and purification unit operations. A decision tree is then used to design incremental capacity under uncertain market parameters. The valuation methodology proposed stresses flexibility in decision making in the face of market uncertainties as is the case with renewable fuels and chemicals. The value of flexibility, termed as “Option Value” is shown to significantly improve the net present value of the proposed biorefinery. Monte Carlo simulations are utilized to develop risk curves for alternate capacity design plans. Risk curves show a favorable risk reward ratio for the case of incremental capacity design with embedded decision options. The framework proposed here can be used by enterprises, government entities and decision makers in general to test, validate, and design technological superstructures and network processing capacities, conduct scenario analyses, and quantify the financial impacts and risks of their representative designs. We plan to further add functionality to the DSS framework and make available the tools developed to wide audience through an “open-source” software distribution model

Modular Supply Network Optimization of Renewable Ammonia and Methanol Co-production

To reduce the use of fossil fuels and other carbonaceous fuels, renewable energy sources such as solar, wind, geothermal energy have been suggested to be promising alternative energy that guarantee sustainable and clean environment. However, the availability of renewable energy has been limited due to its dependence on weather and geographical location. This challenge is intended to be solved by the utilization of the renewable energy in the production of chemical energy carriers. Hydrogen has been proposed as a potential renewable energy carrier, however, its chemical instability and high liquefaction energy makes researchers seek for other alternative energy carriers. Ammonia and methanol can serve as promising alternative energy carriers due to their chemical stability at room temperature, low liquefaction energy, high energy value. The co-production of these high energy dense energy carriers offers economic and environmental advantages since their synthesis involve the direct utilization of CO2 and common unit operations. This problem report aims to review the optimization of the co-production of methanol and ammonia from renewable energy. Form this review, research challenges and opportunities are identified in the following areas: (i) optimization of methanol and ammonia co-production under renewable and demand uncertainty, (ii) impacts of the modular exponent on the feasibility of co-production of ammonia and methanol, and (iii) development of modern computational tools for systems-based analysis

Modeling of Biorefinery Supply Chain Economic Performance with Discrete Event Simulation

As competition for fossil fuels accelerates, alternative sources of chemicals, fuels, and energy production become more appealing to researchers and the layman. Among the candidates to fill this growing niche is lignocellulosic biomass. Many researchers have examined supply chain design and optimization for biofuel and bioenergy production throughout the years. However, these models often fail to capture the variability and uncertainty inherent to the biomass supply chain. Multiple factors with high degrees of stochasticity can have major impacts on the performance of a biorefinery: weather, biomass quality, feedstock availability, and market demand for products are just a few. To begin to address this issue, a discrete event simulation model has been developed to examine the economic performance of a region specific, multifeedstock biorefinery supply chain. Probability distributions developed for product demand and feedstock supply begin to address the random nature of the supply chain. Model development is discussed in the context of a multidisciplinary framework for biorefinery supply chain design. A case study, sensitivity analysis, and scenario analysis, are utilized to examine the capabilities of the model

Social, environmental and economic impacts of alternative energy and fuel supply chains

Energy supply nowadays, being a vital element of a country’s development, has to independently meet diverse, sustainability criteria, be it economic, environmental and social. The main goal of the present research work is to present a methodological framework for the evaluation of alternative energy and fuel Supply Chains (SCs), consisting of a broad topology (representation) suggested, encompassing all the well-known energy and fuel SCs, under a unified scheme, a set of performance measures and indices as well as mathematical model development, formulated as Multi-objective Linear Programming with the extension of incorporating binary decisions as well (Multi-objective Mixed Integer-Linear programming). Basic characteristics of the current modelling approach include the adaptability of the model to be applied at different levels of energy SCs decisions, under different time frames and for multiple stakeholders. Model evaluation is carried for a set of Greek islands, located in the Aegean Archipelagos, examining both the existing energy supply options as well future, more sustainable Energy Supply Chains (ESCs) configurations. Results of the specific research work reveal the social and environmental costs which are underestimated under the traditional energy supply options' evaluation, as well as the benefits that may be produced from renewable energy based applications in terms of social security and employment

A MULTIDISCIPLINARY TECHNO-ECONOMIC DECISION SUPPORT TOOL FOR VALIDATING LONG-TERM ECONOMIC VIABILITY OF BIOREFINING PROCESSES

Increasing demand for energy and transportation fuel has motivated researchers all around the world to explore alternatives for a long-term sustainable source of energy. Biomass is one such renewable resource that can be converted into various marketable products by the process of biorefining. Currently, research is taking strides in developing conversion techniques for producing biofuels from multiple bio-based feedstocks. However, the greatest concern with emerging processes is the long-term viability as a sustainable source of energy. Hence, a framework is required that can incorporate novel and existing processes to validate their economic, environmental and social potential in satisfying present energy demands, without compromising the ability of future generations to meet their own energy needs. This research focuses on developing a framework that can incorporate fundamental research to determine its long-term viability, simultaneously providing critical techno-economic and decision support information to various stakeholders. This contribution links various simulation and optimization models to create a decision support tool, to estimate the viability of biorefining options in any given region. Multiple disciplines from the Process Systems Engineering and Supply Chain Management are integrated to develop the comprehensive framework. Process simulation models for thermochemical and biochemical processes are developed and optimized using Aspen Engineering Suite. Finally, for validation, the framework is analyzed by combining the outcomes of the process simulation with the supply chain models. The developed techno-economic model takes into account detailed variable costs and capital investments for various conversion processes. Subsequently, case studies are performed to demonstrate the applicability of the decision support tool for the Jackson Purchase region of Western Kentucky. The multidisciplinary framework is a unique contribution in the field of Process Systems Engineering as it demonstrates simulation of process optimization models and illustrates its iterative linking with the supply chain optimization models to estimate the economics of biorefinery from multi-stakeholder perspective. This informative tool not only assists in comparing modes of operation but also forecasts the effect of future scenarios, such as, utilization of marginal land for planting dedicated energy crops and incorporation of emerging enzymatic processes. The resulting framework is novel and informative in assisting investors, policy makers and other stakeholders for evaluating the impacts of biorefining. The results obtained supports the generalizability of this tool to be applied in any given region and guide stakeholders in making financial and strategic decisions

Strategic development in the petrochemical industry

Imperial Users onl

An Integrated Market for Electricity and Natural Gas Systems with Stochastic Power Producers

In energy systems with high shares of weather-driven renewable power sources, gas-fired power plants can serve as a back-up technology to ensure security of supply and provide short-term flexibility. Therefore, a tighter coordination between electricity and natural gas networks is foreseen. In this work, we examine different levels of coordination in terms of system integration and time coupling of trading floors. We propose an integrated operational model for electricity and natural gas systems under uncertain power supply by applying two-stage stochastic programming. This formulation co-optimizes day-ahead and real-time dispatch of both energy systems and aims at minimizing the total expected cost. Additionally, two deterministic models, one of an integrated energy system and one that treats the two systems independently, are presented. We utilize a formulation that considers the linepack of the natural gas system, while it results in a tractable mixed-integer linear programming (MILP) model. Our analysis demonstrates the effectiveness of the proposed model in accommodating high shares of renewables and the importance of proper natural gas system modeling in short-term operations to reveal valuable flexibility of the natural gas system. Moreover, we identify the coordination parameters between the two markets and show their impact on the system's operation and dispatch

Information requirements for strategic decision making: energy market

Over the last two decades, the electricity sector has been involved in a challenging restructuring process in which the vertical integrated structure (monopoly) is being replaced by a horizontal set of companies. The growing supply of electricity, flowing in response to free market pricing at the wellhead, led to increased competition. In the new framework of deregulation, what characterizes the electric industry is a commodity wholesale electricity marketplace. This new environment has drastically changed the objective of electricity producing companies. In the vertical integrated industry, utilities were forced to meet all the demand from customers living in a certain region at fixed rates. Then, the operation of the Generation Companies (GENCOs) was centralized and a single decision maker allocated the energy services by minimizing total production costs. Nowadays, GENCOs are involved not only in the electricity market but also in additional markets such as fuel markets or environmental markets. A gas or coal producer may have fuel contracts that define the production limit over a time horizon. Therefore, producers must observe this price levels in these other markets. This is a lesson we learned from the Electricity Crisis in California. The Californian market\u27s collapse was not the result of market decentralization but it was triggered by other decisions, such as high natural gas prices, with a direct impact in the supply-demand chain. This dissertation supports generation asset business decisions -from fuel supply concerns to wholesale trading in energy and ancillary services. The forces influencing the value chain are changing rapidly, and can become highly controversial. Through this report, the author brings an integrated and objective perspective, providing a forum to identify and address common planning and operational needs. The purpose of this dissertation is to present theories and ideas that can be applied directly in algorithms to make GENCOs decisions more efficient. This will decompose the problem into independent subproblems for each time interval. This is preferred because building a complete model in one time is practically impossible. The diverse scope of this report is unified by the importance of each topic to understanding or enhancing the profitability of generation assets. Studies of top strategic issues will assess directly the promise and limits to profitability of energy trading. Studies of ancillary services will permit companies to realistically gauge the profitability of different services, and develop bidding strategies tuned to competitive markets