Multi-objective Optimization and Sensitivity Analysis of a Cogeneration System for a Hospital Facility☆

Abstract Combined heat and power plants are recognized as very effective solutions to achieve the increasingly stringent requirements in primary energy saving. The paper addresses the use of a specifically developed methodology to conduct several analyses on the basis of the loads of a specific hospital facility and through the study of the cogeneration system-user interaction. Predictive analyses are carried out using a multi-objective approach to find optimized plant configurations that approaches the best energetic results while ensuring a reasonable profit. Optimized plant configurations and management strategies indicate primary energy savings exceeding 17%. Finally, a sensitivity analysis is carried out to evaluate the robustness of the result

Optimal integrated sizing and operation of a CHP system with Monte Carlo risk analysis for long-term uncertainty in energy demands

In this study a probabilistic approach for optimal sizing of cogeneration systems under long-term uncertainty in energy demand is proposed. A dynamic simulation framework for detailed modeling of the energy system is defined, consisting in both traditional and optimal operational strategies evaluation. A two-stage stochastic optimization algorithm is developed, adopting Monte Carlo method for the definition of a multi-objective optimization problem. An Italian hospital facility has been used as a case study and a gas internal combustion engine is considered for the cogeneration unit. The results reveal that the influence of uncertainties on both optimal size and annual total cost is significant. Optimal size obtained with the traditional deterministic approach are found to be sub-optimal (up to 30% larger) and the predicted annual cost saving is always lower when accounting for uncertainties. Pareto frontiers of different CHP configurations are presented and show the effectiveness of the proposed method as a useful tool for risk management and focused decision-making, as tradeoffs between system efficiency and system robustness

Process optimization and revamping of combined-cycle heat and power plants integrated with thermal desalination processes

Optimal revamping, sizing, and operation of an existing gas-turbine combined-cycle dual-purpose power/desalination plant – simultaneous electricity and freshwater generation – which operates with a heat recovery steam generation with one-pressure level (1P-HRSG) and a multi-stage flash desalination process, is addressed. The sizes and configurations of the gas turbine and desalination unit are kept the same as in the existing plant through the study. However, the 1P-HRSG is conveniently extended to two- or three-pressure levels with different exchanger arrangements, including steam reheating. To this end, a superstructure-based representation of the HRSG simultaneously embedding several candidate structures was proposed and a mixed-integer nonlinear programming model was derived from it. One revamping case consisted in maximizing the ratio between the freshwater production rate and the heat transfer area of HRSG, keeping unchanged the electricity generation rate (around 73 MW). It was found that the inclusion of a 3P-HRSG resulted in an increase of 13.782 kg⋅s−1 in the freshwater production, requiring 22753 m2 of heat transfer area less in the HRSG. Another revamping case consisted in maximizing the profit, contemplating the possibility to sell extra amounts of electricity and freshwater. Sale prices, for which producing extra electricity and freshwater is beneficial, were determined.Fil: Manassaldi, Juan Ignacio. Universidad Tecnológica Nacional. Regional Rosario. Centro de Aplicaciones Informáticas y Modelado en Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; ArgentinaFil: Mussati, Miguel Ceferino. Universidad Tecnológica Nacional. Regional Rosario. Centro de Aplicaciones Informáticas y Modelado en Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Scenna, Nicolas Jose. Universidad Tecnológica Nacional. Regional Rosario. Centro de Aplicaciones Informáticas y Modelado en Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; ArgentinaFil: Morosuk, Tatiana. Technishe Universitat Berlin; AlemaniaFil: Mussati, Sergio Fabian. Universidad Tecnológica Nacional. Regional Rosario. Centro de Aplicaciones Informáticas y Modelado en Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

Optimal operation of combined heat and power systems: an optimization-based control strategy

The use of decentralized Combined Heat and Power (CHP) plants is increasing since the high levels of efficiency they can achieve. Thus, to determine the optimal operation of these systems in dynamic energy-market scenarios, operational constraints and the time-varying price profiles for both electricity and the required resources should be taken into account. In order to maximize the profit during the operation of the CHP plant, this paper proposes an optimization-based controller designed according to the Economic Model Predictive Control (EMPC) approach, which uses a non-constant time step along the prediction horizon to get a shorter step size at the beginning of that horizon while a lower resolution for the far instants. Besides, a softening of related constraints to meet the market requirements related to the sale of electric power to the grid point is proposed. Simulation results show that the computational burden to solve optimization problems in real time is reduced while minimizing operational costs and satisfying the market constraints. The proposed controller is developed based on a real CHP plant installed at the ETA research factory in Darmstadt, Germany.Peer ReviewedPostprint (author's final draft

Market Power with Combined Heat and Power Production in the Nordic Energy System

The trend toward increasing energy efficiency and variable renewable energy (VRE) production has implications for combined heat and power (CHP) plants, which operate in both the price-driven power market and the district heating (DH) sector. Since CHP will be important in VRE integration, we develop a complementarity model to analyze CHP producers' roles in integrated markets. We use a Nordic case study to gain insights into (i) the effect of the link between CHP and DH on market power and (ii) market power's impact on operations in the DH sector. The results indicate that (i) the link of CHP to DH supply can increase market power and (ii) market power can induce shifts in DH production from heat-only to CHP

Regulating Greenhouse Gases from Coal Power Plants under the Clean Air Act

The Clean Air Act has assumed the central role in U.S. climate policy, directing the Environmental Protection Agency to develop regulations governing the emissions of greenhouse gases from existing coal-fired power plants. The cost and environmental effectiveness of policy options depend on abatement costs, the magnitude of emissions reduction opportunities, and the sensitivity of plant utilization. This paper examines the operation of electricity-generating units over 25 years to estimate the marginal costs and potential magnitude of emissions reductions that could result from improvements in their operating efficiency. We find that a 10 percent increase in coal prices causes a 0.3 to 0.9 percent heat rate reduction, broadly consistent with engineering assessments of abatement costs and opportunities. We also find that coal prices have a significant effect on utilization, but that will vary depending on the policy design. The results are used to compare cost-effectiveness of alternative policies.efficiency, regulation, greenhouse gas, carbon dioxide, coal, performance standards

Exergy efficiency optimization for gas turbine based cogeneration systems

Energy degradation can be calculated by the quantification of entropy and loss of work and is a common approach in power plant performance analysis. Information about the location, amount and sourc es of system deficiencies are determined by the exergy analysis, which quantifies the exergy destruction. Micro - gas turbines are prime movers that are ideally suited for cogeneration applications due to their flexibility in providing stable and reliable power. This paper presents an exergy analysis by means of a numerical simulation of a regenerative micro - gas turbine for cogeneration applications . The main objective is to study the best configuration of each system component , considering the minimization of the system irreversibilities . Each component of the system was evaluated considering the quantitative exergy balance . Subsequently the optimization procedure was applied to the mathematical model that describes the full system. The rate of irreversibility, efficiency and flaws are highlighted for each system component and for the whole system. The effect of turbine inlet temperature change on plant exergy destruction was also evaluated . The results disclose that considerable exergy destruction occurs in the combustion chamber. Also, it was revealed that the exergy efficiency is expressively dependent on the changes of the turbine inlet temperature and increases with the latter .The authors would like to express their acknowledgments for the support given by the Portuguese F01mdation for Science and Technology (FCT) through the PhD grant SFRH/BD/62287/2009. This work was financed by National Funds-Portuguese Foundation for Science and Technology, under Strategic Project and PEst-OE/EME/UI0252/2011 and also the PEst-C/EME/UI4077/2011

Multiobjective optimization of natural gas transportation networks

L'optimisation de l'exploitation d'un réseau de transport de gaz naturel (RTGN) est typiquement un problème d'optimisation multiobjectif, faisant intervenir notamment la minimisation de la consommation énergétique dans les stations de compression, la maximisation du rendement, etc. Cependant, très peu de travaux concernant l'optimisation multiobjectif des réseaux de gazoducs sont présentés dans la littérature. Ainsi, ce travail vise à fournir un cadre général de formulation et de résolution de problèmes d'optimisation multiobjectif liés aux RTGN. Dans la première partie de l'étude, le modèle du RTGN est présenté. Ensuite, diverses techniques d'optimisation multiobjectif appartenant aux deux grandes classes de méthodes par scalarisation, d'une part, et de procédures évolutionnaires, d'autre part, communément utilisées dans de nombreux domaines de l'ingénierie, sont détaillées. Sur la base d'une étude comparative menée sur deux exemples mathématiques et cinq problèmes de génie des procédés (incluant en particulier un RTGN), un algorithme génétique basé sur une variante de NSGA-II, qui surpasse les méthodes de scalarisation, de somme pondérée et d'ε-Contrainte, a été retenu pour résoudre un problème d'optimisation tricritère d'un RTGN. Tout d'abord un problème monocritère relatif à la minimisation de la consommation de fuel dans les stations de compression est résolu. Ensuite un problème bicritère, où la consommation de fuel doit être minimisée et la livraison de gaz aux points terminaux du réseau maximisée, est présenté ; l'ensemble des solutions non dominées est répresenté sur un front de Pareto. Enfin l'impact d'injection d'hydrogène dans le RTGN est analysé en introduisant un troisième critère : le pourcentage d'hydrogène injecté dans le réseau que l'on doit maximiser. Dans les deux cas multiobjectifs, des méthodes génériques d'aide à la décision multicritère sont mises en oeuvre pour déterminer les meilleures solutions parmi toutes celles déployées sur les fronts de Pareto. ABSTRACT : The optimization of a natural gas transportation network (NGTN) is typically a multiobjective optimization problem, involving for instance energy consumption minimization at the compressor stations and gas delivery maximization. However, very few works concerning multiobjective optimization of gas pipelines networks are reported in the literature. Thereby, this work aims at providing a general framework of formulation and resolution of multiobjective optimization problems related to NGTN. In the first part of the study, the NGTN model is described. Then, various multiobjective optimization techniques belonging to two main classes, scalarization and evolutionary, commonly used for engineering purposes, are presented. From a comparative study performed on two mathematical examples and on five process engineering problems (including a NGTN), a variant of the multiobjective genetic algorithm NSGA-II outmatches the classical scalararization methods, Weighted-sum and ε-Constraint. So NSGA-II has been selected for performing the triobjective optimization of a NGTN. First, the monobjective problem related to the minimization of the fuel consumption in the compression stations is solved. Then a biojective problem, where the fuel consumption has to be minimized, and the gas mass flow delivery at end-points of the network maximized, is presented. The non dominated solutions are displayed in the form of a Pareto front. Finally, the study of the impact of hydrogen injection in the NGTN is carried out by introducing a third criterion, i.e., the percentage of injected hydrogen to be maximized. In the two multiobjective cases, generic Multiple Choice Decision Making tools are implemented to identify the best solution among the ones displayed of the Pareto fronts

Improving sustainability of energy intensive sectors through multi-objective models

openGlobal energy consumption and the related carbon dioxide emissions, which represent a large share of the overall anthropogenic greenhouse gas production, are continuously increasing since most of the energy needs are still provided by fossil fuels, thus constituting one of the main issues to be addressed in the climate change mitigation agenda. To achieve the Paris Agreement’s ambitious objectives, an energy transition towards sustainable energy systems based on the new smart energy system (SES) paradigm is needed, thus integrating the various energy sources, vectors and needs within the sectors (electricity, heating, cooling, transport, etc.). However, optimal planning, design and management of complex integrated systems such as SES require to make use of proper decision support models based on multi-objective optimization techniques, since a sustainability analysis intrinsically involves environmental, economic and social aspects. Furthermore, a SES project involves several stakeholders, each driven by different and often conflicting objectives, which should be considered within such models, to remove some relevant barriers to the energy transition. Focusing on the improvement of the sustainability of the energy-intensive sectors, the main objective of this thesis is thus the development of a decision support framework based on multi-objective optimization with the aim to support the decision makers in the planning, design and management of integrated smart energy systems, while considering the different involved stakeholders. The proposed model, composed by three main phases (namely investigative, design and decision-making), has been developed by steps via its application on case studies belonging to two main topics concerning the improvement of the sustainability performance of energy-intensive sectors through the implementation of the smart energy system concept. The first main topic is representative of the context of industrial districts and concerns their sustainable energy supply based on technical solutions specifically designed for cluster of firms, allowed by geographical proximity. The other one concerns the synergic integration between industrial and urban areas, through the recovery of waste energy from industrial processes to feed municipal district heating with a carbon-free source. The case studies have been selected, within the opportunities available in the local territorial context, not only because fit for the implementation of the smart energy system concept, but also due to their suitability for the implementation of different phases of the proposed decision support system (DSS).Dottorato di ricerca in Scienze dell'ingegneria energetica e ambientaleopenCiotti, Gelli