The main objective of this study is to develop a methodology which can be used to assess the economic potential for combined heat and power (CHP) systems to be employed in an effort to offset a portion or all of the conventionally supplied power and thermal energy at industrial manufacturing facilities. A methodology is developed which determines the economic considerations of proposed industrial CHP projects once the system configuration is specified. This methodology is then applied to a number of different industrial facilities in a parametric analysis in order to demonstrate how it can be used to assess the potential for success for CHP at industrial sites for a wide range of manufacturing processes. Many of the methodology inputs, such as facility operational hours, facility thermal load, etc. are then varied in order to determine how they affect the economic considerations of the corresponding project. Conclusions are subsequently made as to how each of these parameters can be indicative of project success before employing the methodology. This study focuses on industrial sites in the Southeast U.S., which historically have relatively low utility usage rates. The Southeast U.S. also lacks adequate policy applicable to CHP systems, such as net metering and interconnection standards rules, when compared to the rest of the country. It is for this reason that the methodology developed in this research assumes that a base load CHP system is the most economically viable CHP option and the current status of policy applicable to CHP at industrial facilities located in the Southeast U.S. is also investigated. The results of the parametric analysis are modified to determine if improved economics can be attained if the associated facilities engage in net metering programs. As a result, suggested net metering rates that can positively affect the economic considerations of industrial CHP projects in the Southeast U.S. are realized. Finally, a simple tool based on the methodology presented in this research was developed and can be used to calculate the project economics of an industrial facility CHP system.

The goal of this work is to develop a conceptual framework and computational tools for the optimization of utility systems in the process industries. The emphasis is devoted to the development of systematic design techniques aimed at identifying modifications to the process and the associated utility-systems to jointly optimize the process and the utility system. The following contributions describe the specific results of this work: • Development of shortcut methods for modeling and optimizing steam systems and basic thermodynamic cycles with the objective of using these methods in the optimization of combined heat and power. To enable efficient mathematical programming formulations, simple yet accurate correlations have been developed for the thermodynamic properties of steam in the utility system. • Optimization of multi-level steam system for combined process requirements and power cogeneration. A general procedure is developed to determine rigorous cogeneration targets and the optimal configuration of the system with the associated design and operating variables. • Graph theory methods are also used to optimize the pipeline layout in the plant for the distributing the utilities. • Finally, because of the nonconvex nature of much of the developed optimization formulations, a global optimization method has also been suggested by using interval analysis and simulated annealing. The techniques proposed in this work are compared to previous works and their applicabilities are presented in case studies. These techniques outperform previously suggested ones in terms of the accuracy, computational efficiency and/or optimality.

Les systèmes de cogénération (SC) sont largement définis par la production simultanée ou coïncidente de la production combinée de chaleur et d'électricité. L’idée de la cogénération revêt une importance particulière puisqu’elle est un outil de réduction des émissions à effet de serre. Comme les systèmes électriques ont été développés selon les carburants et leur utilisation énergétique, de même, les SC ont été développés afin d'utiliser l'énergie possible du carburant pour produire de l’électricité et de la chaleur. La décentralisation de la production électrique est désormais un événement existant. La favorisation maximale de l’électricité d’origine renouvelable ou des systèmes de
cogénération, a abouti à cette décentralisation formant une partie de la production électrique.
Cette thèse est appliquée au cas du système électrique libanais. Elle sert à évaluer la puissance optimale de cogénération qui doit être installée par le secteur public ou le secteur privé, ainsi que la mise en évidence des impacts économiques et environnementaux dus à l’intégration des SC et des énergies renouvelables dans le réseau. Dans ce travail de thèse, nous nous sommes intéressés à l’intégration des systèmes de cogénération dans un réseau électrique. Nous avons travaillé sur deux thèmes principaux et les avons appliqués au cas du réseau électrique libanais. Le premier thème principal est l’innovation d’une stratégie de prise de décision qui sert à trouver une puissance de cogénération respectant l’économie et l’environnement. Le second thème principal est l’optimisation et le contrôle du réseau électrique en fonction des énergies renouvelables (ER) et des SC intégrés. Les deux thèmes cités sont ensuite appliqués au cas du réseau électrique libanais pour montrer les avantages de l’intégration des SC et des ER dans ce réseau. / Cogeneration systems (CS) are largely defined by the simultaneous or coincident production of combined heat and power. The idea of cogeneration is of particular importance since it is a tool for reducing greenhouse gases emissions. As electrical systems have been developed according to the fuel and energy use, the CS have been developed to profit from the possible potential of the fuel energy to produce electricity and heat. Decentralization of power generation is considered an important fact. The maximum use of electricity from renewable sources or cogeneration systems, has leaded to the decentralization of power generation.This thesis is applied to the Lebanese electrical system. It is used to assess the optimum cogeneration power to be installed by the public sector or the private sector, as well as highlighting the economic and environmental impacts due to the integration of the CS and renewables into the grid. In this thesis, we focused on the integration of cogeneration systems into a grid. We worked on two major themes and have applied them to the case of the Lebanese electrical grid. The first main theme is the innovation of a new decision making strategy to find the cogeneration power respecting the economy and the environment. The second main theme is the optimization and the control of the electrical grid due to the integration of renewable energy (RE) and CS. The two themes cited are then applied to the case of the Lebanese electrical grid to show the benefits of the integration of RE and CS into this grid.

Road traffic dominates in domestic Swedish transportation and is highly dependent on fossil fuels, petrol and diesel. Currently, the use of renewable fuels in transportation accounts for less than 6% of the total energy use in transport. The demand for bioethanol to fuel transportation is growing and cannot be met through current domestic production alone. Lignocellulosic ethanol derived from agricultural crop residues may be a feasible alternative source of ethanol for securing a consistent regional fuel supply in Swedish climatic conditions.  This licentiate thesis focuses on regional transport fuel supply by considering local small-scale ethanol production from straw. It presents the results of investigations of regional transport fuel supply with respect to minimising regional CO2 emissions, cost estimates for transport fuel supply, and the availability of lignocellulosic resources for small-scale ethanol production. Regional transport fuel demand between the present and 2020 is also estimated. The results presented here show that significant bioethanol can be produced from the straw and Salix available in the studied regions and that this is sufficient to meet the regions’ current ethanol fuel demand.  A cost optimisation model for regional transport fuel supply is developed and applied for two cases in one study region, one when the ethanol production plant is integrated with an existing CHP plant (polygeneration), and one with a standalone ethanol production plant. The results of the optimisation model show that in both cases the changes in ethanol production costs have the biggest influence on the cost of supplying the regional passenger car fleet with transport fuel, followed by the petrol price and straw production costs.  By integrating the ethanol production process with a CHP plant, the costs of supplying regional passenger car fleet with transport fuel can be reduced by up to a third. Moreover, replacing petrol fuel with ethanol can cut regional CO2 emissions from transportation by half.

Micro-cogeneration is one of the technologies promoted as a response to the global call for the reduction of carbon emissions. Due to its recent application in the residential sector, the implications of its usage have not yet been fully explored, while at the same time, the available simulation tools are not designed for conducting research that focuses on the study of this technology. This thesis develops a virtual prototyping environment, using a dynamic multi-physics simulation tool. The model based procedure in its current form focuses on ICE based micro-CHP systems. In the process of developing the models, new approaches on general system, engine, heat exchanger, and dwelling thermal modelling are being introduced to cater for the special nature of the subject. The developed software is a unique modular simulation tool platform linking a number of independent energy generation systems, and presents a new approach in the study and design of the multi node distributed energy system (DES) with the option of further development into a real-time residential energy management system capable of reducing fuel consumption and CO2 emissions in the domestic sector. In the final chapters, the developed software is used to simulate various internal combustion engine based micro-CHP configurations in order to conclude on the system design characteristics, as well as the conditions, necessary to achieve a high technical, economic and environmental performance in the UK residential sector with the purpose of making micro- CHP a viable alternative to the conventional means of heat & power supply.

Micro-cogeneration is one of the technologies promoted as a response to the global call for the reduction of carbon emissions. Due to its recent application in the residential sector, the implications of its usage have not yet been fully explored, while at the same time, the available simulation tools are not designed for conducting research that focuses on the study of this technology. This thesis develops a virtual prototyping environment, using a dynamic multi-physics simulation tool. The model based procedure in its current form focuses on ICE based micro-CHP systems. In the process of developing the models, new approaches on general system, engine, heat exchanger, and dwelling thermal modelling are being introduced to cater for the special nature of the subject. The developed software is a unique modular simulation tool platform linking a number of independent energy generation systems, and presents a new approach in the study and design of the multi node distributed energy system (DES) with the option of further development into a real-time residential energy management system capable of reducing fuel consumption and CO2 emissions in the domestic sector. In the final chapters, the developed software is used to simulate various internal combustion engine based micro-CHP configurations in order to conclude on the system design characteristics, as well as the conditions, necessary to achieve a high technical, economic and environmental performance in the UK residential sector with the purpose of making micro- CHP a viable alternative to the conventional means of heat & power supply.

In some applications, the piston steam engine may be more suitable than the technology currently used. The thesis deals with the analysis of its advantages, weaknesses and possibilities of use, which are offered. Introduces the principles and functions of piston steam engines. It also contains a thermodynamic design based on a specific assignment and a feasibility assessment.

This master’s thesis deals with the design of a heat recovery steam generator. The introductory part of the thesis is dedicated to waste heat boilers, their division and their utilization in combined cycles gas turbine. In the following chapter, an analysis of the existing combined heat and power plant operation is performed. In the next part of the thesis, the conceptual layout of the new source is designed. Subsequently, the thermal calculation of the boiler is carried out as well as the design of individual heat exchanging surfaces. The sixth chapter deals with the strength calculation of the boiler and the outer piping, chambers and drum are designed here. At the end of the thesis there are described off-design states of the new combined cycle gas turbine.

Conventionally, to support varying power demand, the utility company must prepare to supply more electricity than actually needed, which causes inefficiency and waste. With the increasing penetration of renewable energy which is intermittent and stochastic, how to balance the power generation and demand becomes even more challenging. Demand response, which reschedules part of the elastic load in users' side, is a promising technology to increase power generation efficiency and reduce costs. However, how to coordinate all the distributed heterogeneous elastic loads efficiently is a major challenge and sparks numerous research efforts. In this thesis, we investigate different methods to provide demand response and improve power grid efficiency. First, we consider how to schedule the charging process of all the Plugged-in Hybrid Electrical Vehicles (PHEVs) so that demand peaks caused by PHEV charging are flattened. Existing solutions are either centralized which may not be scalable, or decentralized based on real-time pricing (RTP) which may not be applicable immediately for many markets. Our proposed PHEV charging approach does not need complicated, centralized control and can be executed online in a distributed manner. In addition, we extend our approach and apply it to the distribution grid to solve the bus congestion and voltage drop problems by controlling the access probability of PHEVs. One of the advantages of our algorithm is that it does not need accurate predictions on base load and future users' behaviors. Furthermore, it is deployable even when the grid size is large. Different from PHEVs, whose future arrivals are hard to predict, there is another category of elastic load, such as Heating Ventilation and Air-Conditioning (HVAC) systems, whose future status can be predicted based on the current status and control actions. How to minimize the power generation cost using this kind of elastic load is also an interesting topic to the power companies. Existing work usually used HVAC to do the load following or load shaping based on given control signals or objectives. However, optimal external control signals may not always be available. Without such control signals, how to make a tradeoff between the fluctuation of non-renewable power generation and the limited demand response potential of the elastic load, and to guarantee user comfort level, is still an open problem. To solve this problem, we first model the temperature evolution process of a room and propose an approach to estimate the key parameters of the model. Then, based on the model predictive control, a centralized and a distributed algorithm are proposed to minimize the fluctuation and maximize the user comfort level. In addition, we propose a dynamic water level adjustment algorithm to make the demand response always available in two directions. Extensive simulations based on practical data sets show that the proposed algorithms can effectively reduce the load fluctuation. Both randomized PHEV charging and HVAC control algorithms discussed above belong to direct or centralized load shaping, which has been heavily investigated. However, it is usually not clear how the users are compensated by providing load shaping services. In the last part of this thesis, we investigate indirect load shaping in a distributed manner. On one hand, we aim to reduce the users' energy cost by investigating how to fully utilize the battery pack and the water tank for the Combined Heat and Power (CHP) systems. We first formulate the queueing models for the CHP systems, and then propose an algorithm based on the Lyapunov optimization technique which does not need any statistical information about the system dynamics. The optimal control actions can be obtained by solving a non-convex optimization problem. We then discuss when it can be converted into a convex optimization problem. On the other hand, based on the users' reaction model, we propose an algorithm, with a time complexity of O(log n), to determine the RTP for the power company to effectively coordinate all the CHP systems and provide distributed load shaping services. / Graduate

This research focuses on developing strategies for the optimal control of large-scale Combined Cooling, Heating and Power (CCHP) systems to meet electricity, heating, and cooling demands, and evaluating the cost savings potential associated with it. Optimal control of CCHP systems involves the determination of the mode of operation and set points to satisfy the specific energy requirements for each time period. It is very complex to effectively design optimal control strategies because of the stochastic behavior of energy loads and fuel prices, varying component designs and operational limitations, startup and shutdown events and many more. Also, for large-scale systems, the problem involves a large number of decision variables, both discrete and continuous, and numerous constraints along with the nonlinear performance characteristic curves of equipment. In general, the CCHP energy dispatch problem is intrinsically difficult to solve because of the non-convex, non-differentiable, multimodal and discontinuous nature of the optimization problem along with strong coupling to multiple energy components. <div><br></div><div>This work presents a solution methodology for optimizing the operation of a campus CCHP system using a detailed network energy flow model solved by a hybrid approach combining mixed-integer linear programming (MILP) and nonlinear programming (NLP) optimization techniques. In the first step, MILP optimization is applied to a plant model that includes linear models for all components and a penalty for turning on or off the boilers and steam chillers. The MILP step determines which components need to be turned on and their respective load needed to meet the campus energy demand for the chosen time period (short, medium or long term) with one-hour resolution. Based on the solution from MILP solver as a starting point, the NLP optimization determines the actual hourly state of operation of selected components based on their nonlinear performance characteristics. The optimal energy dispatch algorithm provides operational signals associated with resource allocation ensuring that the systems meet campus electricity, heating, and cooling demands. The chief benefits of this formulation are its ability to determine the optimal mix of equipment with on/off capabilities and penalties for startup and shutdown, consideration of cost from all auxiliary equipment and its applicability to large-scale energy systems with multiple heating, cooling and power generation units resulting in improved performance. </div><div><br></div><div>The case-study considered in this research work is the Wade Power Plant and the Northwest Chiller Plant (NWCP) located at the main campus of Purdue University in West Lafayette, Indiana, USA. The electricity, steam, and chilled water are produced through a CCHP system to meet the campus electricity, heating and cooling demands. The hybrid approach is validated with the plant measurements and then used with the assumption of perfect load forecasts to evaluate the economic benefits of optimal control subjected to different operational conditions and fuel prices. Example cost optimizations were performed for a 24-hour period with known cooling, heating, and electricity demand of Purdue’s main campus, and based on actual real-time prices (RTP) for purchasing electricity from utility. Three optimization cases were considered for analysis: MILP [no on/off switch penalty (SP)]; MILP [including on/off switch penalty (SP)] and NLP optimization. Around 3.5% cost savings is achievable with both MILP optimization cases while almost 10.7% cost savings is achieved using the hybrid MILP-NLP approach compared to the current plant operation. For the selected components from MILP optimization, NLP balances the equipment performance to operate at the state point where its efficiency is maximum while still meeting the demand. Using this hybrid approach, a high-quality global solution is determined when the linear model is feasible while still taking into account the nonlinear nature of the problem. </div><div><br></div><div>Simulations were extended for different seasons to examine the sensitivity of the optimization results to differences in electric, heating and cooling demand. All the optimization results suggest there are opportunities for potential cost savings across all seasons compared to the current operation of the power plant. For a large CCHP plant, this could mean significant savings for a year. The impact of choosing different time range is studied for MILP optimization because any changes in MILP outputs impact the solutions of NLP optimization. Sensitivity analysis of the optimized results to the cost of purchased electricity and natural gas were performed to illustrate the operational switch between steam and electric driven components, generation and purchasing of electricity, and usage of coal and natural gas boilers that occurs for optimal operation. Finally, a modular, generalizable, easy-to-configure optimization framework for the cost-optimal control of large-scale combined cooling, heating and power systems is developed and evaluated.</div>