Optimisation of stand-alone hybrid energy systems for power and thermal loads

Stand-alone hybrid energy systems are an attractive option for remote communities without a connection to a main power grid. However, the intermittent nature of solar and other renewable sources adversely affects the reliability with which these systems respond to load demands. Hybridisation, achieved by combining renewables with combustion-based supplementary prime movers, improves the ability to meet electric load requirements. In addition, the waste heat generated from backup Internal Combustion Engines or Micro Gas Turbines can be used to satisfy local heating and cooling loads. As a result, there is an expectation that the overall efficiency and Greenhouse Gas Emissions of stand-alone systems can be significantly improved through waste heat recovery. The aims of this PhD project are to identify how incremental increases to the hardware complexity of hybridised stand-alone energy systems affect their cost, efficiency, and CO2 footprint. The research analyses a range of systems, from those designed to meet only power requirements to others satisfying power and heating (Combined Heat and Power), or power plus both heating and cooling (Combined Cooling, Heating, and Power). The majority of methods used focus on MATLAB-based Genetic Algorithms (GAs). The modelling deployed finds the optimal selection of hardware configurations which satisfy single- or multi-objective functions (i.e. Cost of Energy, energy efficiency, and exergy efficiency). This is done in the context of highly dynamic meteorological (e.g. solar irradiation) and load data (i.e. electric, heating, and cooling). Results indicate that the type of supplementary prime movers (ICEs or MGT) and their minimum starting thresholds have insignificant effects on COE but have some effects on Renewable Penetration (RP), Life Cycle Emissions (LCE), CO2 emissions, and waste heat generation when the system is sized meeting electric load only. However, the transient start-up time of supplementary prime movers and temporal resolution have no significant effects on sizing optimisation. The type of Power Management Strategies (Following Electric Load-FEL, and Following Electric and Following Thermal Load- FEL/FTL) affect overall Combined Heating and Power (CHP) efficiency and meeting thermal demand through recovered heat for a system meeting electric and heating load with response to a specific load meeting reliability (Loss of Power Supply Probability-LPSP). However, the PMS has marginal effects on COE. The Electric to Thermal Load Ratio (ETLR) has no effects on COE for PV/Batt/ICE but strongly affects PV/Batt/MGT-based hybridised CHP systems. The higher thermal than the electric loads lead to higher efficiency and better environmental footprint. Results from this study also indicate that for a stand-alone hybridised system operating under FEL/FTL type PMS, the power only system has lower cost compared to the CHP and the Combined Cooling, Heating, and Power (CCHP) systems. This occurs at the expense of overall energy and exergy efficiencies. Additionally, the relative magnitude of heating and cooling loads have insignificant effects on COE for PV/Batt/ICE-based system configurations, however this substantially affects PV/Batt/MGT-based hybridised CCHP systems. Although there are no significant changes in the overall energy efficiency of CCHP systems in relation to variations to heating and cooling loads, systems with higher heating demand than cooling demand lead to better environmental benefits and renewable penetration at the cost of Duty Factor. Results also reveal that the choice of objective functions do not affect the system optimisation significantly

Multi-objective optimization of CCHP system with hybrid chiller under new electric load following operation strategy

The performance of combined cooling, heating and power (CCHP) system is greatly affected by its operating strategy and design. In this paper, a new electric load following (NELF) strategy was developed. It is based on the alternation between absorption cooling and electric cooling according to the building energy requirements, for hybrid chiller based CCHP systems. A comparison of the new proposed strategy with the modified electric load following (MELF) and electric load following (ELF) strategies is performed. A multi-objective optimization approach based on genetic algorithm is carried out to predict the optimal capacity of CCHP systems. Performance criteria like primary energy consumption, annual total cost and carbon dioxide emission were considered as objective functions. The performances of these CCHP systems and operation strategies were examined and compared with the separated production (SP) system for a Mosque complex located in Algiers, Algeria. Results show that hybrid chiller CCHP based NELF strategy is the best choice, which can reduce the primary energy consumption by 34.45 GWh/year, annual total cost by 0.313 million €/year and carbon dioxide emission by 8.37 kton/year. Compared to the other configurations and strategies, the hybrid CCHP based NELF achieves better energetic, economic and environmental performance under the optimized conditions

Intelligent Approaches For Modeling And Optimizing Hvac Systems

Advanced energy management control systems (EMCS), or building automation systems (BAS), offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This research will evaluate model-based optimization processes (OP) for HVAC systems utilizing MATLAB, genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. The OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. The development of several self-learning HVAC models and optimizing the process (minimizing energy use) will be tested using data collected from the HVAC system servicing the Academic building on the campus of NC A&T State University. Intelligent approaches for modeling and optimizing HVAC systems are developed and validated in this research. The optimization process (OP) including the STMs with genetic algorithms (GA) enables the ideal operation of the building’s HVAC systems when running in parallel with a building automation system (BAS). Using this proposed optimization process (OP), the optimal variable set points (OVSP), such as supply air temperature (Ts), supply duct static pressure (Ps), chilled water supply temperature (Tw), minimum outdoor ventilation, reheat (or zone supply air temperature, Tz), and chilled water differential pressure set-point (Dpw) are optimized with respect to energy use of the HVAC’s cooling side including the chiller, pump, and fan. HVAC system component models were developed and validated against both simulated and monitored real data of an existing VAV system. The optimized set point variables minimize energy use and maintain thermal comfort incorporating ASHRAE’s new ventilation standard 62.1-2013. The proposed optimization process is validated on an existing VAV system for three summer months (May, June, August). This proposed research deals primarily with: on-line, self-tuning, optimization process (OLSTOP); HVAC design principles; and control strategies within a building automation system (BAS) controller. The HVAC controller will achieve the lowest energy consumption of the cooling side while maintaining occupant comfort by performing and prioritizing the appropriate actions. Recent technological advances in computing power, sensors, and databases will influence the cost savings and scalability of the system. Improved energy efficiencies of existing Variable Air Volume (VAV) HVAC systems can be achieved by optimizing the control sequence leading to advanced BAS programming. The program’s algorithms analyze multiple variables (humidity, pressure, temperature, CO2, etc.) simultaneously at key locations throughout the HVAC system (pumps, cooling coil, chiller, fan, etc.) to reach the function’s objective, which is the lowest energy consumption while maintaining occupancy comfort

Techno-economic assessment of energy storage systems in multi-energy microgrids utilizing decomposition methodology

Renewable resources and energy storage systems integrated into microgrids are crucial in attaining sustainable energy consumption and energy cost savings. This study conducts an in-depth analysis of diverse storage systems within multi-energy microgrids, including natural gas and electricity subsystems, with a comprehensive focus on techno-economic considerations. To achieve this objective, a methodology is developed, comprising an optimization model that facilitates the determination of optimal storage system locations within microgrids. The model considers various factors, such as operating and emission costs of both gas and electricity subsystems, and incorporates a sensitivity analysis to calculate the investment and maintenance costs associated with the storage systems. Due to the incorporation of voltage and current relations in the electricity subsystem as well as gas pressure and flow considerations in the natural gas subsystem, the developed model is classified as a mixed-integer nonlinear programming model. To address the inherent complexity in solving, a decomposition approach based on Outer Approximation/Equality Relaxation/Augmented Penalty is developed. This study offers scientific insights into the costs of energy storage systems, potential operational cost savings, and technical considerations of microgrid operation. The results of the developed decomposition approach demonstrate significant advantages, including reduced solving time and a decreased number of iterations

MODELING AND OPTIMIZATION OF MICROGRID ENERGY SYSTEM FOR SHIP APPLICATIONS

Microgrid energy systems are widely used in remote communities and off-grid sites, where primary energy supplies are dominated by fuels. Limited attentions have been paid to ship applications, which require thorough and in-depth research to address their unique challenges and increasing pressure on reducing fuel consumptions. This dissertation presents comprehensive microgrid system studies for ship applications in four aspects: component modeling and study, dynamic system modeling on novel designs, novel optimization based system design framework development and investigations on two enhancement options: integrating with separate sensible and latent cooling systems, maximizing heat recovery through pinch analysis. Comprehensive component studies consist of new component models addressing unique features of ship applications. Desiccant wheels with new materials were investigated experimentally, especially under high humidity conditions for ship applications. Dynamic system modeling was conducted on several novel solar energy and waste heat powered systems, with a focus on their capabilities to reduce fuel consumptions and CO2 emissions. Results were validated against experimental data. Payload and economic studies were conducted to evaluate feasibilities of applying the designs to ship applications. A novel optimization based design framework was then developed. The framework is capable of conducting both system configuration and control strategy optimization under transient weather and load profiles, differentiating itself with current control strategy focused energy system optimization studies (Jradi and Riffat, 2014). It also extends Buoro et al. (2012)’s study on system configuration optimization to complete design from scratch with comprehensive equipment selections and integrating options. The design framework was demonstrated through a case study on container ships. Optimized systems and control strategies were found from three different scenarios: single-objective optimization, bi-objective optimization and optimization under uncertainty. Finally, two previously listed options were investigated to enhance microgrid system performance regarding thermal comfort and fuel savings. This research fills current research gaps on microgrid energy system for ship applications. It also serves as the basis for advanced microgrid system analysis framework for any applications. The dynamic system modeling platform, optimization based design framework and enhancement methods can help engineers develop and evaluate ultra-high efficiency designs, aiming to reduce energy consumptions and CO2 emissions

Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity

Whole Life Sustainability Assessment at the Building Industry and Constructed Assets, through the Whole Life Costing Assessment and Life Cycle Costing Assessment evaluating the economic and financial aspects

Els edificis d’energia neta poden ser entesos com a edificis, que durant un temps determinat generen tanta energia com consumeixen. Ja sigui des del punt de vista de l’oferta o el consum, la disponibilitat d’energia està relacionada amb alguns aspectes bàsics, com ara la font (s), la conversió, la distribució, l’ús, el malbaratament, l’optimització, l’eficiència i l’autonomia. Aquests temes revelen la complexitat del tema de l'energia i justifiquen l'atenció especial que li dóna la comunitat acadèmica. Per obtenir resultats tangibles en l'anàlisi d'aquests sistemes, en el nostre estudi ens centrem en la modelització i optimització de solucions energètiques aplicades a edificis o sistemes similars. D'altra banda, el període de temps dels objectes analitzats es va estendre fins al seu període de cicle de vida previst. Es van establir els objectius principals com: - Verificar i analitzar l’estat de la tecnologia de les energies renovables per a edificis i actius construïts i l’aplicabilitat de l’anàlisi de costos del cicle de vida a aquests temes; - Configurar models reproductibles d’edificis i les seves principals càrregues elèctriques, mitjançant eines d’enginyeria de processos assistits per ordinador, per procedir a simulacions i optimització, considerant-se com a font d’energia primària l’energia solar; - Quantificar, utilitzant estudis de casos reals i hipotètics, els beneficis de les solucions proposades, amb l'objectiu de realitzar tota l'avaluació de la sostenibilitat de la vida mitjançant la reducció de tot el cost del cicle de vida;Los edificios de energía de red cero pueden entenderse como edificios, que durante un tiempo dado generan tanta energía como consumen. O bien, desde el punto de vista del suministro o el consumo, la disponibilidad de energía está relacionada con algunos problemas básicos, como las fuentes, la conversión, la distribución, la utilización, el desperdicio, la optimización, la eficiencia y la autonomía. Estos problemas revelan la complejidad del tema de la energía y justifican la atención especial que le presta la comunidad académica. Para obtener resultados tangibles en el análisis de estos sistemas, en nuestro estudio nos centramos en el modelado y la optimización de soluciones energéticas aplicadas a edificios o sistemas similares. Por otro lado, el período de tiempo de los objetos analizados se extendió a su período de ciclo de vida esperado. Los objetivos principales se establecieron como: - Verificar y analizar el estado de la técnica de las soluciones de energía renovable para edificios y activos construidos y la aplicabilidad del análisis de costos de ciclo de vida a estas cuestiones; - Configure modelos reproducibles de edificios y sus principales cargas eléctricas, a través de herramientas de Ingeniería de Procesos Asistidos por Computadora, para proceder a simulaciones y optimización, considerando como fuente de energía primaria la energía solar;Net-zero energy buildings can be understood as buildings, that for a given time, generate as much energy as they consume. Either, from the point of view of supply or consumption, energy availability is related to some basic issues such as source (s), conversion, distribution, utilization, waste, optimization, efficiency and autonomy. These issues reveal the complexity of the subject of energy and justify the special attention given to it by the academic community. To obtain tangible results in the analysis of these systems, in our study we focus on the modelling and optimization of energy solutions applied to buildings or similar systems. On the other hand, the time frame of the analysed objects was extended to their expected life cycle period. The main objectives were stablished as: - Verify and analyse the state-of-the-art of renewable energy solutions for buildings and constructed assets and the applicability of life cycle costing analysis to these issues; - Configure reproducible models of buildings and their main electrical loads, via Computer Aided Process Engineering tools, to proceed simulations and optimization, considering as primary energy source solar energy; - Quantify, using real-life and hypothetical case studies, the benefits of the proposed solutions, aiming the whole life sustainability assessment through the reduction of the whole life cycle costing; and - Guarantee the reproducibility of the models and main general results of this study and make them public, to contribute with their applicability and further researches

分散型エネルギーシステムにおける設備保全とシステム最適化に関する研究

Owing to the continuous growth in the world\u27s energy demand, the problems of energy consumption, greenhouse gas emission, and environmental pollution have become increasingly prominent. The distributed energy resource (DER) system is a high-efficiency energy system that can promote energy-saving and decrease carbon emissions. the focus of this research is on the equipment maintenance and system optimization of DER. In the maintenance optimization stage, a maintenance priority assessment method is used to allocate maintenance management resources based on the assessment results to help managers develop reasonable maintenance strategies and reduce maintenance costs. In the system design optimization stage, the capacity and operation strategy of the system is optimized for the energy demand of users to achieve the purpose of improving economic benefits and promoting energy saving and emission reduction.北九州市立大