PREDICTIVE CONTROL OF POWER GRID-CONNECTED ENERGY SYSTEMS BASED ON ENERGY AND EXERGY METRICS

Building and transportation sectors account for 41% and 27% of total energy consumption in the US, respectively. Designing smart controllers for Heating, Ventilation and Air-Conditioning (HVAC) systems and Internal Combustion Engines (ICEs) can play a key role in reducing energy consumption. Exergy or availability is based on the First and Second Laws of Thermodynamics and is a more precise metric to evaluate energy systems including HVAC and ICE systems. This dissertation centers on development of exergy models and design of model-based controllers based on exergy and energy metrics for grid-connected energy systems including HVAC and ICEs. In this PhD dissertation, effectiveness of smart controllers such as Model Predictive Controller (MPC) for HVAC system in reducing energy consumption in buildings has been shown. Given the unknown and varying behavior of buildings parameters, this dissertation proposes a modeling framework for online estimation of states and unknown parameters. This method leads to a Parameter Adaptive Building (PAB) model which is used for MPC. Exergy destruction/loss in a system or process indicates the loss of work potential. In this dissertation, exergy destruction is formulated as the cost function for MPC problem. Compared to RBC, exergy-based MPC achieve 22% reduction in exergy destruction and 36% reduction in electrical energy consumption by HVAC system. In addition, the results show that exergy-based MPC outperforms energy-based MPC by 12% less energy consumption. Furthermore, the similar exergy-based approach for building is developed to control ICE operation. A detailed ICE exergy model is developed for a single cylinder engine. Then, an optimal control method based on the exergy model of the ICE is introduced for transient and steady state operations of the ICE. The proposed exergy-based controller can be applied for two applications including (i) automotive (ii) Combined Heat and Power (CHP) systems to produce electric power and thermal energy for heating purposes in buildings. The results show that using the exergy-based optimal control strategy leads to an average of 6.7% fuel saving and 8.3% exergy saving compared to commonly used FLT based combustion control. After developing thermal and exergy models for building and ICE testbeds, a framework is proposed for bilevel optimization in a system of commercial buildings integrated to smart distribution grid. The proposed framework optimizes the operation of both entities involved in the building-to-grid (B2G) integration. The framework achieves two objectives: (i) increases load penetration by maximizing the distribution system load factor and (ii) reduces energy cost for the buildings. The results show that this framework reduces commercial buildings electricity cost by 25% compared to the unoptimized case, while improving the system load factor up to 17%

Model Predictive Climate Control for Connected and Automated Vehicles

Emerging connected and automated vehicle (CAV) technologies are improving vehicle safety and energy efficiency to the next level and creating unprecedented opportunities and challenges for the control and optimization of the vehicle systems. While previous studies have been focusing on improving the fuel efficiency via powertrain optimizations, vehicle thermal management and its interaction with powertrain control in hot and cold weather conditions have not been fully explored. For light-duty vehicles, the power used by the climate control system usually represents the most significant thermal load. It has been shown that the thermal load imposed by the climate control system may lead to dramatic vehicle range reduction, especially for the vehicles with electrified powertrains. Besides its noticeable impact on vehicle range reduction, the performance of the climate control system also has a direct influence on occupant comfort and customer satisfaction. Aiming at reducing the energy consumption and improving the occupant thermal comfort (OTC) level for the automotive climate control system, this dissertation takes air conditioning (A/C) system as an example and is dedicated to developing practical A/C management strategies for electrified vehicles. In particular, the proposed strategies leverage the predictive information enabled by the CAV technologies such as the traffic and weather predictions. There are three novel MPC-based A/C management strategies developed in this dissertation, the hierarchical optimization, the precision cooling strategy (PCS), and the combined energy and comfort optimization (CECO). They can be differentiated by their OTC assumptions, robustness considerations, and implementation complexities on the testing vehicle. In the hierarchical optimization, a two-layer hierarchical MPC (H-MPC) scheme is exploited for potential integration between the A/C and the powertrain systems of an HEV. This hierarchical structure handles the timescale difference between power and thermal systems as well as the uncertainties associated with long prediction horizon. Comprehensive simulation results over different driving cycles have demonstrated the energy saving potentials of efficient A/C energy management, which is attributes to leveraging the vehicle speed sensitivity of the A/C system efficiency. In terms of the comfort metric, the average cabin air temperature is applied. In contrast to this hierarchical optimization, PCS and CECO utilize the simpler single-layer MPC structure assuming accurate predictive information. They are focusing on formulating more practical OTC metrics and the implementation on the testing vehicle. Specifically, the PCS renders the simplest control-oriented model structure and its energy benefits are validated based on an industrial-level A/C system model. The proposed PCS exploits a more practical comfort metric, DACP, which directly motivates the design of an off-line eco-cooling strategy, which coordinates the A/C operation with respect to the vehicle speed. Vehicle-level energy saving is confirmed according to repeatable vehicle experiments. Finally, the CECO strategy considers a comprehensive OTC model, PMV, and combines the energy and comfort optimizations simultaneously. Further energy saving and OTC improvement can be achieved by explicitly leveraging both traffic and weather predictive information.PHDNaval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153481/1/autowang_1.pd

Control and management of energy storage systems in microgrids

The rate of integration of the renewable energy sources in modern grids have significantly increased in the last decade. These intermittent, non-dispatchable renewable sources, though environment friendly tend to be grid unfriendly. This is precisely due to the issues pertaining to grid congestion, voltage regulation and stability of grids being reported as a result of the incorporation of renewable sources. In this scenario, the use of energy storage systems (ESS ) in electric grids is being widely proposed to overcome these issues. However, integrating energy storage systems alone will not compensate for the issue created by renewable generation. The control and management of the ESS should be done optimally so that their full capabilities are exploited to overcome the issues in the power grids and to ensure their lower cost of investment by prolonging ESS lifetime through minimising degradation. Motivated by this aspect this Ph.D work focusses on developing an efficient, optimal control and management strategy for ESS in a microgrid, especially hybrid ESS. The Ph.D work addresses this issue by proposing a hierarchical control scheme comprising of a lower power management and higher energy management stage with contributions in each stage. In the power management stage this work focusses on improving aspects of real time control of power converters interfacing ESS to grid and the microgrid system as whole. The work proposes control systems with improved dynamic behaviour for power converters based on the reset control framework. In the microgrid control the work presents a primary+secondary control scheme with improved voltage regulation performance under disturbances, using an observer. The real time power splitting strategies among hybrid ESS accounting for the ESS operating efficiencies and degradation mechanisms will also be addressed in the primary+secondary control of power management stage. The design criteria, stability and robustness analysis will be carried out, along with simulation or experimental verifications. In the higher level energy management stage, the contribution of this work involves application of an economic MPC framework for the management of ESS in microgrids. The work specifically addresses the problems of mitigating grid congestion from renewable power feed-in, minimising ESS degradation and maximising self consumption of generated renewable energy using the MPC based energy management system. A survey of the forecasting methods that can be used for MPC will be carried out and a neural network based forecasting unit for time series prediction will be developed. The practical issue of accounting for forecasting error in the decision making of MPC will be addressed and impact of the resulting conservative decision making on the system performance will be analysed. The improvement in performance with the proposed energy management scheme will be demonstrated and quantified.La integración de las fuentes de energía renovables en las redes modernas ha aumentado significativamente en la última década. Estas fuentes renovables, aunque muy convenientes para el medio ambiente son de naturaleza intermitente, y son no panificables, cosa que genera problemas en la red de distribución. Esto se debe precisamente a los problemas relacionados con la congestión de la red y la regulación del voltaje. En este escenario, el uso de sistemas de almacenamiento de energía (ESS) en redes eléctricas está siendo ampliamente propuesto para superar estos problemas. Sin embargo, la integración de sistemas de almacenamiento de energía por sí solos no compensará el problema creado por la generación renovable. El control y la gestión del ESS deben realizarse de manera óptima, de modo que se aprovechen al máximo sus capacidades para superar los problemas en las redes eléctricas, garantizar un coste de inversión razonable y prolongar la vida útil del ESS minimizando su degradación. Motivado por esta problemática, esta tesis doctoral se centra en desarrollar una estrategia de control y gestión eficiente para los ESS integrados en una microrred, especialmente cuando se trata de ESS de naturaleza. El trabajo de doctorado propone un esquema de control jerárquico compuesto por un control de bajo nivel y una parte de gestión de energía operando a más alto nivel. El trabajo realiza aportaciones en los dos campos. En el control de bajo nivel, este trabajo se centra en mejorar aspectos del control en tiempo real de los convertidores que interconectan el ESS con la red y el sistema de micro red en su conjunto. El trabajo propone sistemas de control con comportamiento dinámico mejorado para convertidores de potencia desarrollados en el marco del control de tipo reset. En el control de microrred, el trabajo presenta un esquema de control primario y uno secundario con un rendimiento de regulación de voltaje mejorado bajo perturbaciones, utilizando un observador. Además, el trabajo plantea estrategias de reparto del flujo de potencia entre los diferentes ESS. Durante el diseño de estos algoritmos de control se tienen en cuenta los mecanismos de degradación de los diferentes ESS. Los algoritmos diseñados se validarán mediante simulaciones y trabajos experimentales. En el apartado de gestión de energía, la contribución de este trabajo se centra en la aplicación del un control predictivo económico basado en modelo (EMPC) para la gestión de ESS en microrredes. El trabajo aborda específicamente los problemas de mitigar la congestión de la red a partir de la alimentación de energía renovable, minimizando la degradación de ESS y maximizando el autoconsumo de energía renovable generada. Se ha realizado una revisión de los métodos de predicción del consumo/generación que pueden usarse en el marco del EMPC y se ha desarrollado un mecanismo de predicción basado en el uso de las redes neuronales. Se ha abordado el análisis del efecto del error de predicción sobre el EMPC y el impacto que la toma de decisiones conservadoras produce en el rendimiento del sistema. La mejora en el rendimiento del esquema de gestión energética propuesto se ha cuantificado.La integració de les fonts d'energia renovables a les xarxes modernes ha augmentat significativament en l’última dècada. Aquestes fonts renovables, encara que molt convenients per al medi ambient són de naturalesa intermitent, i són no panificables, cosa que genera problemes a la xarxa de distribució. Això es deu precisament als problemes relacionats amb la congestió de la xarxa i la regulació de la tensió. En aquest escenari, l’ús de sistemes d'emmagatzematge d'energia (ESS) en xarxes elèctriques està sent àmpliament proposat per superar aquests problemes. No obstant això, la integració de sistemes d'emmagatzematge d'energia per si sols no compensarà el problema creat per la generació renovable. El control i la gestió de l'ESS s'han de fer de manera _optima, de manera que s'aprofitin al màxim les seves capacitats per superar els problemes en les xarxes elèctriques, garantir un cost d’inversió raonable i allargar la vida útil de l'ESS minimitzant la seva degradació. Motivat per aquesta problemàtica, aquesta tesi doctoral es centra a desenvolupar una estratègia de control i gestió eficient per als ESS integrats en una microxarxa, especialment quan es tracta d'ESS de natura híbrida. El treball de doctorat proposa un esquema de control jeràrquic compost per un control de baix nivell i una part de gestió d'energia operant a més alt nivell. El treball realitza aportacions en els dos camps. En el control de baix nivell, aquest treball es centra a millorar aspectes del control en temps real dels convertidors que interconnecten el ESS amb la xarxa i el sistema de microxarxa en el seu conjunt. El treball proposa sistemes de control amb comportament dinàmic millorat per a convertidors de potència desenvolupats en el marc del control de tipus reset. En el control de micro-xarxa, el treball presenta un esquema de control primari i un de secundari de regulació de voltatge millorat sota pertorbacions, utilitzant un observador. A més, el treball planteja estratègies de repartiment de el flux de potència entre els diferents ESS. Durant el disseny d'aquests algoritmes de control es tenen en compte els mecanismes de degradació dels diferents ESS. Els algoritmes dissenyats es validaran mitjanant simulacions i treballs experimentals. En l'apartat de gestió d'energia, la contribució d'aquest treball se centra en l’aplicació de l'un control predictiu econòmic basat en model (EMPC) per a la gestió d'ESS en microxarxes. El treball aborda específicament els problemes de mitigar la congestió de la xarxa a partir de l’alimentació d'energia renovable, minimitzant la degradació d'ESS i maximitzant l'autoconsum d'energia renovable generada. S'ha realitzat una revisió dels mètodes de predicció del consum/generació que poden usar-se en el marc de l'EMPC i s'ha desenvolupat un mecanisme de predicció basat en l’ús de les xarxes neuronals. S'ha abordat l’anàlisi de l'efecte de l'error de predicció sobre el EMPC i l'impacte que la presa de decisions conservadores produeix en el rendiment de el sistema. La millora en el rendiment de l'esquema de gestió energètica proposat s'ha quantificat

Multi-Objective Building Energy Management Optimization with Model Predictive Control

Today’s goals for the reduction of CO2 emissions are significantly impacting both the civil and the industrial sector. The increasing share of renewable energy sources leads to more volatile and challenging conditions for power consumption. The building sector is responsible for approximately a third of both CO2 emissions and energy consumption in Germany. At the same time, it offers the potential to adapt to the changing conditions by the intelligent use of energy storage systems. These can, e. g., be stationary batteries, electric vehicles at charging stations, heat tanks or the building itself. The control system for the power flow between these elements is called a building energy management (BEM) system. As the control strategy, Model Predictive Control (MPC) is an obvious choice. It allows optimal control while incorporating forecasts of, e. g., power demand, renewable energy production and air temperature. However, in a complex control setting such as BEM, multiple contradicting objectives are to be minimized. For example, next to the reduction of monetary costs, the building’s temperature is supposed to be kept at a comfortable level, electric vehicles have to be charged sufficiently, battery degradation should be kept low and CO2 emissions have to be reduced. To directly optimize real-world objectives such as the examples given above, Economic Model Predictive Control (EMPC) can be utilized, in which the cost function for the optimal control problem (OCP) does not need to be quadratic, but can be of arbitrary form. However, if multiple objectives have to be respected, usually this is done in form of a weighted sum. Thereby, the weights are chosen either from experience or such that all objectives are of the same magnitude. While this is a reasonably simple approach, it neglects that, especially for BEM systems, the OCP varies significantly with the volatile outer conditions. Therefore, the trade-off which is chosen by the fixed weights varies over time, too. The simultaneous optimization of contradicting objectives is called multi- objective optimization (MOO). Usually, the set of all ’optimal’ solutions is approximated and a (human) decision maker (DM) afterwards selects a solution which represents his preferences the most. This is appropriate in the case of one-time optimizations, which is usually the case in MOO. However, we want to use MOO for the permanent control of a BEM system. Therefore, we propose an extended conceptualization of dynamic MOO, which is the systematic combination of MPC and MOO. At every time step, a multi-objective OCP is formulated and an approximation of the Pareto front is derived as its solution, i. e. the set of all optimal compromises. Then, a solution is automatically chosen. To this end, we present two different options. In the metric-based automatized decision making strategy, the Pareto front is first normalized. Then, a metric is calculated for every solution and the solution with the best value is chosen. We present two normalization schemes and three metrics a DM can choose from. In the preference-based automatized decision making strategy, preferences formulated by the DM a priori are utilized. First, a knee region is determined from the normalized Pareto front to exclude solutions which are too extreme. Then, the preferences are used to construct a hyperplane with which a solution from the knee region is finally selected. The applicability of the proposed methods to the BEM problem is shown in long-term simulations. To this end, we show how the most important elements in a BEM system can be modeled while obtaining well-solvable convex optimization problems. Furthermore, we present a new method to determine an approximation of the Pareto front which is more apt for the case of dynamic MOO and its varying conditions

MODEL PREDICTIVE CONTROL OF ENERGY SYSTEMS FOR HEAT AND POWER APPLICATIONS

Building and transportation sectors together account for two-thirds of the total energy consumption in the US. There is a need to make these energy systems (i.e., buildings and vehicles) more energy efficient. One way to make grid-connected buildings more energy efficient is to integrate the heating, ventilation and air conditioning (HVAC) system of the building with a micro-scale concentrated solar power (MicroCSP) sys- tem. Additionally, one way to make vehicles driven by internal combustion engine (ICE) more energy efficient is by integrating the ICE with a waste heat recovery (WHR) system. But, both the resulting energy systems need a smart supervisory controller, such as a model predictive controller (MPC), to optimally satisfy the en- ergy demand. Consequently, this dissertation centers on development of models and design of MPCs to optimally control the combined (i) building HVAC system and the MicroCSP system, and (ii) ICE system and the WHR system. In this PhD dissertation, MPCs are designed based on the (i) First Law of Thermo- dynamics (FLT), and (ii) Second Law of Thermodynamics (SLT) for each of the two energy systems. Maximizing the FLT efficiency of an energy system will minimise energy consumption of the system. MPC designed based on FLT efficiency are de- noted as energy based MPC (EMPC). Furthermore, maximizing the SLT efficiency of the energy system will maximise the available energy for a given energy input and a given surroundings. MPC designed based on SLT efficiency are denoted as exergy based MPC (XMPC). Optimal EMPC and XMPC are designed and applied to the combined building HVAC and MicroCSP system. In order to evaluate the designed EMPC and XMPC, a com- mon rule based controller (RBC) was designed and applied to the combined building HVAC and MicroCSP system. The results show that the building energy consump- tion reduces by 38% when EMPC is applied to the combined MicroCSP and building HVAC system instead of using the RBC. XMPC applied to the combined MicroCSP and building HVAC system reduces the building energy consumption by 45%, com- pared to when RBC is applied. Optimal EMPC and XMPC are designed and applied to the combined ICE and WHR system. The results show that the fuel consumption of the ICE reduces by 4% when WHR system is added to the ICE and when RBC is applied to both ICE and WHR systems. EMPC applied to the combined ICE and WHR system reduces the fuel consumption of the ICE by 6.2%, compared to when RBC is applied to ICE without WHR system. XMPC applied to the combined ICE and WHR system reduces the fuel consumption of the ICE by 7.2%, compared to when RBC is applied to ICE without WHR system

A Framework for Flexible Loads Aggregation

L'abstract è presente nell'allegato / the abstract is in the attachmen