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<p>The advent of small-scale, distributed generators of energy has resulted in the problem of integrating them in the conventional electric power system, which is characterized by large-scale, centralized energy generators. MicroGrids have emerged as a promising solution to the integration problem and have duly received increasing research attention. Microgrids are semi-autonomous collections of controllable microsources and loads, which present themselves to the utility grid as single, controlled entities. In order to achieve the semi-autonomous and controlled nature of microgrids, especially,overcoming the challenge of balancing demand and power generation, an intelligent energy management scheme is required.</p> <p>Developing an energy management scheme is an interesting and challenging task, which provides the potential to exploit ideas from a plethora of fields like Artificial Intelligence and Machine Learning, Constrained Optimization, etc. However, testing energy management strategies on a microgrid would pose a multitude of problems,the most important of them being the unreliability and inconvenience of testing an energy management strategy, which is not optimal, on a functional microgrid. Errors in a test strategy might cause power outages and damage installed devices. Hence it is necessary to test energy management strategies on simulated microgrids.</p> <p>This thesis presents a Software Testbed of MicroGrids, specifically designed to suit the purposes of development of energy management strategies. The testbed consists of two components: Simulation Framework and Analysis Tool. The modular simulation framework enables simulation of a microgrid with microsources and loads,whose configurations can be specified by the user. The analysis tool enables visual analysis of data generated using simulations, which would enable the improvement of not only the management strategy and prediction techniques, but also the computer models used in the simulation framework. A demonstration of the software testbed's simulation and analysis capabilities is presented and possible directions for future research are suggested.</p> / Master of Science (MSc)

<p>An increasing concern over environmental impacts of fossil fuels and sustainability of energy resources is leading to significant changes in the electric power systems. Decentralized power generation, in particular, is emerging as one of the most effective and promising tools in addressing these concerns.</p> <p>Microgrids are small-scale electricity grids with elements of load, generation and storage. Microgrids have emerged as an essential building block of a future smart grid, and an enabling technology for distributed power generation and control. This thesis presents an optimization-based approach for the design and control of energy management systems (EMS) for electric microgrids. A linear programming formulation of power/energy management is proposed to minimize energy cost for a microgrid with energy storage and renewable energy generation, by taking advantage of time-of-use (TOU) pricing. The thesis also addresses the issue of sizing of the battery storage and solar power generation capacity by formulating and solving a mixed integer linear programming (MILP) problem. The aim of the optimization is to minimize the combined capital and electricity usage cost subject to applicable physical constraints. Several case scenarios are analyzed for grid-connected microgrids in residential, commercial and industrial settings, as well as a case of an islanded microgrid intended for a remote community.</p> <p>Finally, the thesis investigates circuit level control of a microgrid with EMS. A finite state machine based control logic is proposed that enables outage ride through and smooth transition between islanded and grid connected operation. Simulation results are provided to demonstrate the effectiveness of the proposed controller under various possible scenarios.</p> / Master of Applied Science (MASc)

Along with the growth of electricity demand and the penetration of intermittent renewable energy sources, electric power distribution networks will face more and more stress conditions, especially as electric vehicles (EVs) take a greater share in the personal automobile market. This may cause potential transformer overloads, feeder congestions, and undue circuit failures. Demand response (DR) is gaining attention as it can potentially relieve system stress conditions through load management. DR can possibly defer or avoid construction of large-scale power generation and transmission infrastructures by improving the electric utility load factor. This dissertation proposes to develop a planning tool for electric utilities that can provide an insight into the implementation of demand response at the end-user level. The proposed planning tool comprises control algorithms and a simulation platform that are designed to intelligently manage end-use loads to make the EV penetration transparent to an electric power distribution network. The proposed planning tool computes the demand response amount necessary at the circuit/substation level to alleviate the stress condition due to the penetration of EVs. Then, the demand response amount is allocated to the end-user as a basis for appliance scheduling and control. To accomplish the dissertation objective, electrical loads of both residential and commercial customers, as well as EV fleets, are modeled, validated, and aggregated with their control algorithms proposed at the appliance level. A multi-layer demand response model is developed that takes into account both concerns from utilities for load reduction and concerns from consumers for convenience and privacy. An analytic hierarchy process (AHP)-based approach is put forward taking into consideration opinions from all stakeholders in order to determine the priority and importance of various consumer groups. The proposed demand response strategy takes into consideration dynamic priorities of the load based on the consumers' real-time needs. Consumer comfort indices are introduced to measure the impact of demand response on consumers' life style. The proposed indices can provide electric utilities a better estimation of the customer acceptance of a DR program, and the capability of a distribution circuit to accommodate EV penetration. Research findings from this work indicate that the proposed demand response strategy can fulfill the task of peak demand reduction with different EV penetration levels while maintaining consumer comfort levels. The study shows that the higher number of EVs in the distribution circuit will result in the higher DR impacts on consumers' comfort. This indicates that when EV numbers exceed a certain threshold in an area, other measures besides demand response will have to be taken into account to tackle the peak demand growth. The proposed planning tool is expected to provide an insight into the implementation of demand response at the end-user level. It can be used to estimate demand response potentials and the benefit of implementing demand response at different DR penetration levels within a distribution circuit. The planning tool can be used by a utility to design proper incentives and encourage consumers to participate in DR programs. At the same time, the simulation results will give a better understanding of the DR impact on scheduling of electric appliances. / Ph. D.

In the context of wireless communications, low latency autonomous systems continue to grow in importance. Some applications of autonomous systems where low latency communication is essential are (i) vehicular network's safety performance depends on how recently the vehicles are updated on their neighboring vehicle's locations, (ii) updates from IoT devices need to be aggregated appropriately at the monitoring station before the information gets stale to extract temporal and spatial information from it, and (iii) sensors and controllers in a smart grid need to track the most recent state of the system to tune system parameters dynamically, etc. Each of the above-mentioned applications differs based on the connectivity between the source and the destination. First, vehicular networks involve a broadcast network where each of the vehicles broadcasts its packets to all the other vehicles. Secondly, in the case of UAV-assisted IoT networks, packets generated at multiple IoT devices are transmitted to a final destination via relays. Finally for the smart grid and generally for distributed systems, each source can have varying and unique destinations. Therefore in terms of connectivity, they can be categorized into one-to-all, all-to-one, and variable relationship between the number of sources and destinations. Additionally, some of the other major differences between the applications are the impact of mobility, the importance of a reduced AoI, centralized vs distributed manner of measuring AoI, etc. Thus the wide variety of application requirements makes it challenging to develop scheduling schemes that universally address minimizing the AoI. All these applications involve generating time-stamped status updates at a source which are then transmitted to their destination over a wireless medium. The timely reception of these updates at the destination decides the operating state of the system. This is because the fresher the information at the destination, the better its awareness of the system state for making better control decisions. This freshness of information is not the same as maximizing the throughput or minimizing the delay. While ideally throughput can be maximized by sending data as fast as possible, this may saturate the receiver resulting in queuing, contention, and other delays. On the other hand, these delays can be minimized by sending updates slowly, but this may cause high inter-arrival times. Therefore, a new metric called the Age of Information (AoI) has been proposed to measure the freshness of information that can account for many facets that influence data availability. In simple terms, AoI is measured at the destination as the time elapsed since the generation time of the most recently received update. Therefore AoI is able to incorporate both the delay and the inter-packet arrival time. This makes it a much better metric to measure end-to-end latency, and hence characterize the performance of such time-sensitive systems. These basic characteristics of AoI are explained in detail in Chapter 1. Overall, the main contribution of this dissertation is developing scheduling and resource allocation schemes targeted at improving the AoI of various autonomous systems having different types of connectivity, namely vehicular networks, UAV-assisted IoT networks, and smart grids, and then characterizing and quantifying the benefits of a reduced AoI from the application perspective. In the first contribution, we look into minimizing AoI for the case of broadcast networks having one-to-all connectivity between the source and destination devices by considering the case of vehicular networks. While vehicular networks have been studied in terms of AoI minimization, the impact of mobility and the benefit of a reduced AoI from the application perspective has not been investigated. The mobility of the vehicles is realistically modeled using the Simulation of Urban Mobility (SUMO) software to account for overtaking, lane changes, etc. We propose a safety metric that indicates the collision risk of a vehicle and do a simulation-based study on the ns3 simulator to study its relation to AoI. We see that the broadcast rate in a Dedicated Short Range Network (DSRC) that minimizes the system AoI also has the least collision risk, therefore signifying that reducing AoI improves the on-road safety of the vehicles. However, we also show that this relationship is not universally true and the mobility of the vehicles becomes a crucial aspect. Therefore, we propose a new metric called the Trackability-aware AoI (TAoI) which ensures that vehicles with unpredictable mobility broadcast at a faster rate while vehicles that are predicable are broadcasting at a reduced rate. The results obtained show that minimizing TAoI provides much better on-road safety as compared to plain AoI minimizing, which points to the importance of mobility in such applications. In the second contribution, we focus on networks with all-to-one connectivity where packets from multiple sources are transmitted to a single destination by taking an example of IoT networks. Here multiple IoT devices measure a physical phenomenon and transmit these measurements to a central base station (BS). However, under certain scenarios, the BS and IoT devices are unable to communicate directly and this necessitates the use of UAVs as relays. This creates a two-hop scenario that has not been studied for AoI minimization in UAV networks. In the first hop, the packets have to be sampled from the IoT devices to the UAV and then updated from the UAVs to the BS in the second hop. Such networks are called UAV-assisted IoT networks. We show that under ideal conditions with a generate-at-will traffic generation model and lossless wireless channels, the Maximal Age Difference (MAD) scheduler is the optimal AoI minimizing scheduler. When the ideal conditions are not applicable and more practical conditions are considered, a reinforcement learning (RL) based scheduler is desirable that can account for packet generation patterns and channel qualities. Therefore we propose to use a Deep-Q-Network (DQN)-based scheduler and it outperforms MAD and all other schedulers under general conditions. However, the DQN-based scheduler suffers from scalability issues in large networks. Therefore, another type of RL algorithm called Proximal Policy Optimization (PPO) is proposed to be used for larger networks. Additionally, the PPO-based scheduler can account for changes in the network conditions which the DQN-based scheduler was not able to do. This ensures the trained model can be deployed in environments that might be different than the trained environment. In the final contribution, AoI is studied in networks with varying connectivity between the source and destination devices. A typical example of such a distributed network is the smart grid where multiple devices exchange state information to ensure the grid operates in a stable state. To investigate AoI minimization and its impact on the smart grid, a co-simulation platform is designed where the 5G network is modeled in Python and the smart grid is modeled in PSCAD/MATLAB. In the first part of the study, the suitability of 5G in supporting smart grid operations is investigated. Based on the encouraging results that 5G can support a smart grid, we focus on the schedulers at the 5G RAN to minimize the AoI. It is seen that the AoI-based schedulers provide much better stability compared to traditional 5G schedulers like the proportional fairness and round-robin. However, the MAD scheduler which has been shown to be optimal for a variety of scenarios is no longer optimal as it cannot account for the connectivity among the devices. Additionally, distributed networks with heterogeneous sources will, in addition to the varying connectivity, have different sized packets requiring a different number of resource blocks (RB) to transmit, packet generation patterns, channel conditions, etc. This motivates an RL-based approach. Hence we propose a DQN-based scheduler that can take these factors into account and results show that the DQN-based scheduler outperforms all other schedulers in all considered conditions. / Doctor of Philosophy / Age of information (AoI) is an exciting new metric as it is able to characterize the freshness of information, where freshness means how representative the information is of the current system state. Therefore it is being actively investigated for a variety of autonomous systems that rely on having the most up-to-date information on the current state. Some examples are vehicular networks, UAV networks, and smart grids. Vehicular networks need the real-time location of their neighbor vehicles to make maneuver decisions, UAVs have to collect the most recent information from IoT devices for monitoring purposes, and devices in a smart grid need to ensure that they have the most recent information on the desired system state. From a communication point of view, each of these scenarios presents a different type of connectivity between the source and the destination. First, the vehicular network is a broadcast network where each vehicle broadcasts its packets to every other vehicle. Secondly, in the UAV network, multiple devices transmit their packets to a single destination via a relay. Finally, with the smart grid and the generally distributed networks, every source can have different and unique destinations. In these applications, AoI becomes a natural choice to measure the system performance as the fresher the information at the destination, the better its awareness of the system state which allows it to take better control decisions to reach the desired objective. Therefore in this dissertation, we use mathematical analysis and simulation-based approaches to investigate different scheduling and resource allocation policies to improve the AoI for the above-mentioned scenarios. We also show that the reduced AoI improves the system performance, i.e., better on-road safety for vehicular networks and better stability for smart grid applications. The results obtained in this dissertation show that when designing communication and networking protocols for time-sensitive applications requiring low latency, they have to be optimized to improve AoI. This is in contrast to most modern-day communication protocols that are targeted at improving the throughput or minimizing the delay.

The changing nature of the energy grid in recent years has prompted key stakeholders to think of ways to address incoming challenges. Transactive energy is an approach that promises to dynamically align active grid elements coming up in the previously inactive consumers' side to achieve a reliable and smarter grid. This work models the distribution grid structure as a combination of microgrids. A blockchain-in-the loop simulation framework is modelled and simulated for a residential microgrid using power system simulators and transactive agents. Blockchain smart contracts are used to coordinate peer-to-peer energy transactions in the microgrid. The model is used to test three market coordination schemes: a simple auction-less scheme, an auction-less scheme with a normalized sorting metric and an hour ahead single auction scheme with penalties for unfulfilled bids. Case studies are presented of a microgrid with 30 homes, at different levels of solar and energy storage penetration within the microgrid, all equipped with responsive and unresponsive appliances and transactive agents for the HVAC systems. The auction-less scheme with a normalized sorting metric is observed to provide a fairer advantage to smaller solar installations in comparison to the simple auction-less method. It is then concluded that the auction-less schemes are most beneficial to users, as they would not need sophisticated forecasting technology to reduce penalties from bid quantity inaccuracies, as long as the energy mix within the microgrid is diverse enough. / Master of Science / The legacy energy industry involved the bulk transfer of energy from huge generation plants through long transmission lines to the end consumers. However, with the onset of improved renewable energy and information technologies, energy is now being generated closer to the consumer side with appliances capable of actively participating in the energy system now widely available. Transactive energy with blockchain has been proposed in order to dynamically coordinate these systems to work towards a more reliable and smarter grid using economic value in a transparent and secure way. This work models a transactive power grid as a combination of microgrids using a blockchain network to coordinate hourly peer-to-peer energy transactions. The blockchain-in-the-loop simulation model is used to compare three different market mechanisms in a residential microgrid of 30 homes with varying levels of solar panels, batteries and transactive thermostats installed. Two auction-less schemes - one with a normalized sorting metric - and an hour ahead single auction mechanism are analyzed. While the auction-less scheme with the normalized metric is seen to be fairer than the simple auction-less scheme, it is concluded that the auction-less schemes are most beneficial to residents. This is because sophisticated forecasting technology would not be needed like in the hour ahead auction scheme, provided that the microgrid has participants with diverse energy consumption and production profiles throughout the day.

Energy systems are evolving into structures in which the role of the consumer is more and more significant. Consumers are no longer the passive entities that in the past had to be supplied in an unidirectional way (from the network to the customer), but can also supply power to the grid through renewable resources, storage capacity through the batteries of their electric vehicles or operating services through the use of their flexibility. However, when discussing on smart grids, electricity supply and consump-tion are the only considered side on many occasions, neglecting other dimensions such as natural gas, sanitary hot water or transport. In this context, this dissertation represents a novel approach to the role of consumers in the natural gas sector. While it is true that electricity consumers have been involved for years in different operation services related to the use of their flexibility (especial-ly in countries such as the United States and more recently in the European Union), the use of demand response resources in the gas sector has been so far non-existent. However, the success of demand response initiatives in electricity systems and their similarity to the gas sector, where their regulatory and technological development has been carried out in parallel in recent years, allows us to expect similar successful re-sults when implementing equivalent programs to gas networks. This dissertation highlights the huge potential that remains unexplored on the demand side of natural gas, which could be used by gas network operators for the solution of technical constraints, balance services or optimization of programming of under-ground storage. This potential is especially interesting at the moment, when the mas-sive installation of smart gas meters has started in some European countries, an infra-structure that would facilitate the use of demand response resources for the better op-eration of gas networks. The dissertation presents, firstly, an exhaustive analysis of the demand response pro-grams currently used in electrical systems around the world, identifying those services that could be equally applicable to the gas sector. The traditional structure based on which gas systems have been developed in different countries is analyzed below. In order to make better use of resources and to optimize its operation, an architecture based on the concept of smart grid is then proposed, identifying the agents that would participate in this structure and emphasizing the role that consumers would play, not only as energy demanders, but also as providers of network services. This active role of demand requires the use of adequate measurement, control and communication technologies, aspect that is also properly analyzed. Based on the results of the analysis mentioned above, this thesis proposes a new meth-odology for the development and evaluation of demand response mechanisms that allow a greater participation of gas consumers in the provision of operating services to the manager of the network, increasing the joint efficiency of the system and reducing the costs associated with such services. The proposed methodology has been successfully applied to the gas network in Italy, where the analyzed operation services have been evaluated in a town of 16,000 inhab-itants located in the central north-Italian area. In that town, consumers have been grouped by end-use, sector and size, which evidences the need to enhance the role of the aggregator for the proper use of the potential of smaller consumers, whether they receive a gas supply directly or through a distributed heat network. The results presented in this dissertation should encourage regulators to empower the use of the consumers' flexibility in order to increase the efficiency of the natural gas system, as it reduces operating costs while favoring the participation of customers in a more dynamic energy structure. / Los sistemas energéticos están evolucionando hacia estructuras en las que el papel desempeñado por el consumidor es cada vez más importante. Hoy en día, los consumidores ya no son los entes pasivos de antaño a los que había que suministrar energía de forma unidireccional (de la red al cliente), sino que también pueden suministrar energía a la red a través de recursos renovables, capacidad de almacenamiento mediante las baterías de sus vehículos eléctricos o servicios de operación a través de la utilización de su flexibilidad. Sin embargo, al hablar de redes inteligentes, en muchas ocasiones se sobreentiende únicamente lo relativo al suministro y consumo de electricidad, obviando otras dimensiones como pueden ser el gas natural, el agua caliente sanitaria o el transporte. En este marco, esta tesis supone un enfoque novedoso en lo que se refiere al papel de los consumidores en el sector del gas natural. Si bien es cierto que los consumidores de electricidad han participado desde hace años en diferentes servicios relacionados con el uso de su flexibilidad, la utilización de la respuesta de la demanda en el sector gasista ha sido hasta ahora inexistente. Sin embargo, el éxito de iniciativas de respuesta de la demanda en los sistemas eléctricos y su similitud con el sector gasista, cuyo desarrollo normativo y tecnológico se ha realizado en paralelo en los últimos años, permite esperar resultados igualmente exitosos al aplicar programas equivalentes a las redes de gas. Esta tesis pone de manifiesto el enorme potencial que permanece inexplorado en el lado de la demanda de gas natural, el cual podría ser utilizado para la solución de restricciones técnicas, servicios de balance u optimización de la programación de los almacenamientos subterráneos. Este potencial resulta especialmente interesante en estos momentos, cuando en algunos países europeos se ha comenzado la instalación masiva de contadores inteligentes de gas. La tesis presenta un análisis exhaustivo de los programas de respuesta de la demanda utilizados en la actualidad en sistemas eléctricos alrededor del mundo, identificándose aquellos servicios que podrían ser aplicables al sector gasista. A continuación se analiza la estructura tradicional en base a la que los sistemas gasistas se han desarrollado en diversos países, proponiéndose a continuación una arquitectura basada en el concepto de red inteligente, donde se identifican los agentes que participarían en esta estructura y se enfatiza el rol que los consumidores desempeñarían no sólo como demandantes de energía, sino también como proveedores de servicios de red. Este papel activo de la demanda necesita de la utilización de tecnologías de medición, control y comunicación adecuadas, aspecto que también se analiza en detalle. En base a los resultados del análisis mencionado, esta tesis propone una nueva metodología para el desarrollo y evaluación de mecanismos de respuesta de la demanda que permitan una mayor participación de los consumidores de gas en la provisión de servicios de operación al gestor de la red, aumentando la eficiencia conjunta del sistema y reduciendo los costes asociados a dichos servicios. La metodología propuesta ha sido aplicada con éxito a la red gasista de Italia, donde los servicios de operación analizados han sido evaluados en una ciudad de 16.000 habitantes, donde los consumidores han sido agrupados por uso final, sector y tamaño. Esto ha puesto de manifiesto la necesidad de potenciar el papel del agregador para valorizar el potencial de los consumidores más pequeños, tanto si reciben un suministro de gas directo o a través de una red de calor distribuido. Los resultados expuestos en esta tesis deberían impulsar a los reguladores a incentivar la utilización de la flexibilidad de los consumidores a fin de incrementar la eficiencia del sistema de gas natural, ya que reduce los costes de operación al tiempo que favorece la particip / Els sistemes energètics estan evolucionant cap a estructures en què el paper exercit pel consumidor és cada vegada més important. Avui dia, els consumidors ja no són els ens passius d'antany als quals calia subministrar energia de forma unidireccional (de la xarxa al client), sinó que també poden subministrar energia a la xarxa a través de recursos renovables, capacitat d'emmagatzematge mitjançant les bateries dels seus vehicles elèctrics o serveis d'operació a través de la utilització de la seva flexibilitat. No obstant això, en parlar de xarxes intel·ligents, en moltes ocasions se sobreentén únicament quant al subministrament i consum d'electricitat, obviant altres dimensions com poden ser el gas natural, l'aigua calenta sanitària o el transport. En aquest marc, aquesta tesi suposa un enfocament nou pel que fa al paper dels consumidors en el sector del gas natural. Si bé és cert que els consumidors d'electricitat han participat des de fa anys en diferents serveis d'operació relacionats amb l'ús de la seva flexibilitat, la utilització de la resposta de la demanda en el sector gasista ha estat fins ara inexistent. No obstant això, l'èxit d'iniciatives de resposta de la demanda en els sistemes elèctrics i la seva similitud amb el sector gasista, el desenvolupament normatiu i tecnològic s'ha realitzat en paral·lel en els últims anys, permet esperar resultats igualment reeixits en aplicar programes equivalents a les xarxes de gas. Aquesta tesi posa de manifest l'enorme potencial que roman inexplorat en el costat de la demanda de gas natural, el qual podria ser utilitzat per a la solució de restriccions tècniques, serveis de balanç o optimització de la programació dels emmagatzematges subterranis. Aquest potencial és especialment interessant en aquests moments, quan en alguns països europeus s'ha començat la instal·lació massiva de comptadors intel·ligents de gas. La tesi presenta una anàlisi exhaustiva dels programes de resposta de la demanda utilitzats en l'actualitat en sistemes elèctrics voltant del món, identificant-se aquells serveis que podrien ser aplicables al sector gasista. A continuació s'analitza l'estructura tradicional sobre la base de la qual els sistemes gasistes s'han desenvolupat en diversos països, proposant-se a continuació una arquitectura basada en el concepte de xarxa intel·ligent, on s'identifiquen els agents que participarien en aquesta estructura i s'emfatitza el paper que els consumidors exercirien no només com a demandants d'energia, sinó també com a proveïdors de serveis de xarxa. Aquest paper actiu de la demanda necessita de la utilització de tecnologies de mesurament, control i comunicació adequades, aspecte que també s'analitza en detall. En base als resultats de l'anàlisi esmentat, aquesta tesi proposa una nova metodologia per al desenvolupament i avaluació de mecanismes de resposta de la demanda que permetin una major participació dels consumidors de gas a la provisió de serveis d'operació al gestor de la xarxa, augmentant l'eficiència conjunta del sistema i reduint els costos associats a aquests serveis. La metodologia proposada ha estat aplicada amb èxit a la xarxa gasista d'Itàlia, on els serveis d'operació analitzats han estat avaluats en una ciutat de 16.000 habitants, on els consumidors han estat agrupats per ús final, sector i grandària. Això ha posat de manifest la necessitat de potenciar el paper de l'agregador per valoritzar el potencial dels consumidors més petits, tant si reben un subministrament de gas directe o mitjançant una xarxa de calor distribuïda. Els resultats exposats en aquesta tesi haurien d'impulsar els reguladors a incentivar la utilització de la flexibilitat dels consumidors a fi d'incrementar l'eficiència del sistema de gas natural, ja que redueix els costos d'operació i alhora afavoreix la participació dels clients en una estructura més dinàmica. / Montuori, L. (2017). Application of demand response strategies for the management of natural gas systems under the smart grid configuration: development of a methodology for technical, economic and environmental evaluation [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90407

To mitigate the technical challenges faced by the next-generation smart power grid, in this thesis, novel frameworks are developed for optimizing energy management and trading between power companies and grid consumers, who own renewable energy generators and storage units. The proposed frameworks explicitly account for the effect on demand-side energy management of various consumer-centric grid factors such as the stochastic renewable energy forecast, as well as the varying future valuation of stored energy. In addition, a novel approach is proposed to enhance the resilience of consumer-centric energy trading scenarios by analyzing how a power company can encourage its consumers to store energy, in order to supply the grid’s critical loads, in case of an emergency. The developed energy management mechanisms advance novel analytical tools from game theory, to capture the coupled actions and objectives of the grid actors and from the framework of prospect theory (PT), to capture the irrational behavior of consumers when faced with decision uncertainties. The studied PT and game-based solutions, obtained through analytical and algorithmic characterization, provide grid designers with key insights on the main drivers of each actor’s energy management decision. The ensuing results primarily characterize the difference in trading decisions between rational and irrational consumers, and its impact on energy management. The outcomes of this thesis will therefore allow power companies to design consumer-centric energy management programs that support the sustainable and resilient development of the smart grid by continuously matching supply and demand, and providing emergency energy reserves for critical infrastructure. / Master of Science / The next-generation smart power grid is seen as a key enabler for effectively generating, delivering, and consuming electricity in a sustainable manner. Given the increasing demand for energy and the limited nature of various energy resources, the exchange of energy between producers and consumers must be optimally managed to pave the way towards the deployment of smart grid features on a larger scale. In particular, energy management schemes must deal with a complex and dynamic smart grid composed of utility companies, traditional power sources and loads, intermittent renewable energy generators, as well as new consumer-owned devices such as energy storage units and solar panels. In this thesis, we seek to model the different entities in the smart grid and the exchange of energy between them, in order to better understand the operation and effectiveness of various energy management schemes. In addition, this thesis accounts for the non-rational behavior of consumers in the energy trading process, when faced with various sources of uncertainty in the smart grid, including the intermittent renewable energy generation and the dynamic energy price. The results of this thesis will provide utility companies with key insights, crucial to the design of consumer-centric energy management programs, aimed towards matching electricity supply and demand, and insuring power supply to critical infrastructure.