This paper explores how the concept of Vehicle to Grid (V2G) could bring benefits to California's electric grid, transportation sector, and environmental goals.

abstract: Substation ground system insures safety of personnel, which deserves considerable attentions. Basic substation safety requirement quantities include ground grid resistance, mesh touch potential and step potential, moreover, optimal design of a substation ground system should include both safety concerns and ground grid construction cost. In the purpose of optimal designing the ground grid in the accurate and efficient way, an application package coded in MATLAB is developed and its core algorithm and main features are introduced in this work. To ensure accuracy and personnel safety, a two-layer soil model is applied instead of the uniform soil model in this research. Some soil model parameters are needed for the two-layer soil model, namely upper-layer resistivity, lower-layer resistivity and upper-layer thickness. Since the ground grid safety requirement is considered under the earth fault, the value of fault current and fault duration time are also needed. After all these parameters are obtained, a Resistance Matrix method is applied to calculate the mutual and self resistance between conductor segments on both the horizontal and vertical direction. By using a matrix equation of the relationship of mutual and self resistance and unit current of the conductor segments, the ground grid rise can be calculated. Green's functions are applied to calculate the earth potential at a certain point produced by horizontal or vertical line of current. Furthermore, the three basic ground grid safety requirement quantities: the mesh touch potential in the worst case point can be obtained from the earth potential and ground grid rise; the step potential can be obtained from two points' earth potential difference; the grid resistance can be obtained from ground grid rise and fault current. Finally, in order to achieve ground grid optimization problem more accurate and efficient, which includes the number of meshes in the horizontal grid and the number of vertical rods, a novel two-step hybrid genetic algorithm-pattern search (GA-PS) optimization method is developed. The Genetic Algorithm (GA) is used first to search for an approximate starting point, which is used by the Pattern Search (PS) algorithm to find the final optimal result. This developed application provides an optimal grid design meeting all safety constraints. In the cause of the accuracy of the application, the touch potential, step potential, ground potential rise and grid resistance are compared with these produced by the industry standard application WinIGS and some theoretical ground grid model. In summary, the developed application can solve the ground grid optimization problem with the accurate ground grid modeling method and a hybrid two-step optimization method. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2014

Cette thèse porte sur les problèmes de sécurité sur le réseau électrique français exploité par RTE, le Gestionnaire de Réseau de Transport (GRT). Les progrès en matière d'énergie durable, d'efficacité du marché de l'électricité ou de nouveaux modes de consommation poussent les GRT à exploiter le réseau plus près de ses limites de sécurité. Pour ce faire, il est essentiel de rendre le réseau plus "intelligent". Pour s'attaquer à ce problème, ce travail explore les avantages des réseaux neuronaux artificiels. Nous proposons de nouveaux algorithmes et architectures d'apprentissage profond pour aider les opérateurs humains (dispatcheurs) à prendre des décisions que nous appelons " guided dropout ". Ceci permet de prévoir les flux électriques consécutifs à une modification volontaire ou accidentelle du réseau. Pour se faire, les données continues (productions et consommations) sont introduites de manière standard, via une couche d'entrée au réseau neuronal, tandis que les données discrètes (topologies du réseau électrique) sont encodées directement dans l'architecture réseau neuronal. L’architecture est modifiée dynamiquement en fonction de la topologie du réseau électrique en activant ou désactivant des unités cachées. Le principal avantage de cette technique réside dans sa capacité à prédire les flux même pour des topologies de réseau inédites. Le "guided dropout" atteint une précision élevée (jusqu'à 99% de précision pour les prévisions de débit) tout en allant 300 fois plus vite que des simulateurs de grille physiques basés sur les lois de Kirchoff, même pour des topologies jamais vues, sans connaissance détaillée de la structure de la grille. Nous avons également montré que le "guided dropout" peut être utilisé pour classer par ordre de gravité des évènements pouvant survenir. Dans cette application, nous avons démontré que notre algorithme permet d'obtenir le même risque que les politiques actuellement mises en œuvre tout en n'exigeant que 2 % du budget informatique. Le classement reste pertinent, même pour des cas de réseau jamais vus auparavant, et peut être utilisé pour avoir une estimation globale de la sécurité globale du réseau électrique. / This thesis addresses problems of security in the French grid operated by RTE, the French ``Transmission System Operator'' (TSO). Progress in sustainable energy, electricity market efficiency, or novel consumption patterns push TSO's to operate the grid closer to its security limits. To this end, it is essential to make the grid ``smarter''. To tackle this issue, this work explores the benefits of artificial neural networks. We propose novel deep learning algorithms and architectures to assist the decisions of human operators (TSO dispatchers) that we called “guided dropout”. This allows the predictions on power flows following of a grid willful or accidental modification. This is tackled by separating the different inputs: continuous data (productions and consumptions) are introduced in a standard way, via a neural network input layer while discrete data (grid topologies) are encoded directly in the neural network architecture. This architecture is dynamically modified based on the power grid topology by switching on or off the activation of hidden units. The main advantage of this technique lies in its ability to predict the flows even for previously unseen grid topologies. The "guided dropout" achieves a high accuracy (up to 99% of precision for flow predictions) with a 300 times speedup compared to physical grid simulators based on Kirchoff's laws even for unseen contingencies, without detailed knowledge of the grid structure. We also showed that guided dropout can be used to rank contingencies that might occur in the order of severity. In this application, we demonstrated that our algorithm obtains the same risk as currently implemented policies while requiring only 2% of today's computational budget. The ranking remains relevant even handling grid cases never seen before, and can be used to have an overall estimation of the global security of the power grid.

The challenge of managing the present and projected electricity energy needs along with targets of mitigating CO2 emissions leads to the need for energy systems to reduce reliance on fossil fuels and rely on more energy from renewable sources. The integration of more renewable energy technologies to meet present and future electricity demand leads to more challenges in matching the trade-o between economic, resilient, reliable and environmentally friendly solutions. Energy storage technologies can provide temporal resilience to energy systems by solving these challenges. Energy storage systems can improve the reliability of energy systems by reducing the mismatch between supply and demand due to the intermittency of renewable energy sources. This thesis presents a comprehensive analysis of various energy storage systems, analyzing their speci c characteristics including capital cost, e ciency, lifetime and their usefulness in di erent applications. Di erent hybrid energy systems are designed to analyze the impacts of renewable and non-renewable energy sources and energy storage systems in residential on-grid and o -grid buildings and districts. An optimization analysis is performed to determine which technology combinations provide the most economic solution to meet electric energy demands. The optimization analysis is solved using the "energy hub" model formulation which optimizes energy system operation and capacity of di erent technologies. Di erent energy systems can be optimized by using energy hub model, including multiple input energy carriers that are converted to multiple energy outputs. The analysis in this thesis employs a building simulation tool to model residential building, and real data sets to explore the di erent electricity pro le e ects on the results. The environmental e ect of hybrid energy systems comparing with base cases of conventional energy systems or grid connection are also analyzed. Results show that the feasibility of energy storage systems is a factor of di erent variables including capital cost of energy converters and energy storage systems, cost of input streams (grid electricity in on-grid systems and diesel fuel in o -grid systems, energy demand pro les and availability of renewable energy sources. The on-grid single and district buildings do not select storage technologies at current costs due to cheap grid electricity. Reduction in the cost of renewable energy technologies and/or energy storage systems (e.g. Li-ion batteries) results in more energy storage installations. In o -grid systems (single buildings and districts), Li-ion battery and pumped hydro are the main storage systems that can balance the daily and seasonal energy demands. / Graduate / 2021-03-13

In order to meet the worldwide limits on greenhouse gases emissions, a shift from a fossil fuels to a renewable energy-based electric system is required. As this process goes on, both the power generation and consumption profiles are changing in daily pattern and magnitude, so the power grid needs to become more and more flexible in order to handle this variability. At the distribution level, photo-voltaic (PV) systems are, by far, the most widespread distributed energy resource, mostly due to the recent drop in the cost at the residential level. As more and more consumers become also producers (the so called "prosumers") and the volatile solar energy production increases, a higher number of storage systems is required to both avoid grid destabilisation and minimise the CO$_2$ emissions. At the same time, since the transportation sector is responsible for a sizeable part of the total CO$_2$ emissions, electric vehicles (EVs) are bound to replace traditional internal combustion engine vehicles. However, two main issues may arise when a large number of vehicles are connected to the existing power grid at the same time. The first issue is that the electricity required to charge them needs to be renewable, while the second is that, a rapid electrification of the existing vehicles fleet could destabilise the grid. In this context, this thesis aims at partially addressing these two issues by analysing different ways to reduce the impact of both PV systems and EVs on low (LV) and medium (MV) voltage grids. After the introduction and a chapter dealing with the most closely related research work, a novel optimisation algorithm, aimed at obtaining the optimal storage capacity for each prosumer belonging to a "renewable energy community" is presented. The algorithm minimises the dependence of the community on the main grid, which is one of the main purposes of this new model, while minimising the total installed storage capacity. The algorithm is tailored to the specific case study, because it keeps track of the willingness of the users to install a battery and keeps the voltage levels between regulatory limits in the optimisation process. In the second part instead, the effects of "uncontrolled" and "smart" EV-charging the electric vehicles with the aim of reducing the power fluctuations at the MV/LV transformer level are analysed. In particular, the interaction between PV production and EV charging is investigated, while considering the grid voltage fluctuations, the distribution line losses and the transformer loading levels at the same time. The broader impact of smart charging is also analysed by performing a simplified economic and battery wear analysis. Results help in understanding if storage devices can reduce the dependence of a renewable energy community on the main grid, and to what extent it is possible and economically viable to do so. Moreover, results quantify a realistic range of EV and PV system penetration in a LV grid that still allows for a combined minimisation of their impact on the power grid.

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Grid computing is a field of research that combines many computers from distant locations to form one large computing resource. In order to be able to make use of the full potential of such a system there is a need to effectively manage resources on the Grid. There are numerous scheduling systems to perform this management for clusters of computers and a few scheduling systems for the Grid. These systems try for optimality (or close to optimality) with the goals of obtaining good throughput and minimizing job completion time. In this research, we examine issues that we believe have not been tackled in schedulers for the Grid. These issues revolve around the problem of coordinating resources belonging to separate administrative domains and scheduling in this context. In order for grid computing's vision of virtual organizations to be realized to its fullest extent, there is a need to implement and test schedulers that find resources and schedule tasks on them in a manner that is transparent to the user. These resources might be on a different administrative domain altogether and obtaining either resource or user account information on those resources might be difficult. Also, each organization might require their own policies and mechanisms to be enforced. Hence having a centralized scheduler is not feasible due to the pragmatics of the Grid. There are two basic aims to this thesis. The first aim is to design and implement a framework that takes administrative concerns into consideration during scheduling. The aim of the framework is to provide a lightweight, extensible, secure and scalable architecture under which multiple scheduling algorithms can be implemented. Second, we evaluate two prototypical of scheduling algorithms in the context of this framework. Scheduling algorithms are diverse and the applications are varied. Thus no single algorithm can obtain a good mapping for every application. We believe that different scheduling algorithms will be necessary to schedule different types of applications. In order to facilitate development of such algorithms, a framework in which it is easy to integrate other scheduling algorithms is necessary. The framework developed in this project is designed for such extensibility. / Master of Science

A Smart Grid is a digitally enabled electric power grid that integrates the computation and communication technologies from cyber world with the sensors and actuators from physical world. Due to the system complexity, typically the high cohesion of communication and power system, the Smart Grid innovation introduces new and fundamentally different security vulnerabilities and risks. In this work, two important research aspects about cyber-physical security of Smart Grid are addressed: (i) The construction, impact and countermeasure of data integrity attacks; and (ii) The design and implementation of general cyber-physical security experiment platform. For data integrity attacks: based on the system model of state estimation process in Smart Grid, firstly, a data integrity attack model is formulated, such that the attackers can generate financial benefits from the real-time electrical market operations. Then, to reduce the required knowledge about the targeted power system when launching attacks, an online attack approach is proposed, such that the attacker is able to construct the desired attacks without the network information of power system. Furthermore, a network information attacking strategy is proposed, in which the most vulnerable meters can be directly identified and the desired measurement perturbations can be achieved by strategically manipulating the network information. Besides the attacking strategies, corresponding countermeasures based on the sparsity of attack vectors and robust state estimator are provided respectively. For the experiment platform: ScorePlus, a software-hardware hybrid and federated experiment environment for Smart Grid is presented. ScorePlus incorporates both software emulator and hardware testbed, such that they all follow the same architecture, and the same Smart Grid application program can be tested on either of them without any modification; ScorePlus provides a federated environment such that multiple software emulators and hardware testbeds at different locations are able to connect and form a unified Smart Grid system; ScorePlus software is encapsulated as a resource plugin in OpenStack cloud computing platform, such that it supports massive deployments with large scale test cases in cloud infrastructure.

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