The proliferation of distributed energy resources (DER)s has prompted interest in the expansion of DC power systems. The technological limitations that hinder the expansion of DC power systems are the absence of DC circuit breakers and high step-up/high step-down DC converters for interconnecting DC systems. This thesis presents a transformerless high step-up DC-DC converter intended for use as an interconnect between DC systems. The converter is required to operate at medium to high voltage (>1kV) and provide high voltage gain (>5). This work details the steady state operation and dynamic model of the proposed converter. The component ratings are identified and converter design limitations are investigated. A 100V:1kV/4kW prototype is produced to verify the analytic steady state model and measure efficiency. An experimental efficiency of 90% was achieved at a step-up ratio of 1:10, however efficiency at low power is limited due to the need to circulate power.

The proliferation of distributed energy resources (DER)s has prompted interest in the expansion of DC power systems. The technological limitations that hinder the expansion of DC power systems are the absence of DC circuit breakers and high step-up/high step-down DC converters for interconnecting DC systems. This thesis presents a transformerless high step-up DC-DC converter intended for use as an interconnect between DC systems. The converter is required to operate at medium to high voltage (>1kV) and provide high voltage gain (>5). This work details the steady state operation and dynamic model of the proposed converter. The component ratings are identified and converter design limitations are investigated. A 100V:1kV/4kW prototype is produced to verify the analytic steady state model and measure efficiency. An experimental efficiency of 90% was achieved at a step-up ratio of 1:10, however efficiency at low power is limited due to the need to circulate power.

abstract: The Internet and climate change are two forces that are poised to both cause and enable changes in how we provide our energy infrastructure. The Internet has catalyzed enormous changes across many sectors by shifting the feedback and organizational structure of systems towards more decentralized users. Today’s energy systems require colossal shifts toward a more sustainable future. However, energy systems face enormous socio-technical lock-in and, thus far, have been largely unaffected by these destabilizing forces. More distributed information offers not only the ability to craft new markets, but to accelerate learning processes that respond to emerging user or prosumer centered design needs. This may include values and needs such as local reliability, transparency and accountability, integration into the built environment, and reduction of local pollution challenges. The same institutions (rules, norms and strategies) that dominated with the hierarchical infrastructure system of the twentieth century are unlikely to be good fit if a more distributed infrastructure increases in dominance. As information is produced at more distributed points, it is more difficult to coordinate and manage as an interconnected system. This research examines several aspects of these, historically dominant, infrastructure provisioning strategies to understand the implications of managing more distributed information. The first chapter experimentally examines information search and sharing strategies under different information protection rules. The second and third chapters focus on strategies to model and compare distributed energy production effects on shared electricity grid infrastructure. Finally, the fourth chapter dives into the literature of co-production, and explores connections between concepts in co-production and modularity (an engineering approach to information encapsulation) using the distributed energy resource regulations for San Diego, CA. Each of these sections highlights different aspects of how information rules offer a design space to enable a more adaptive, innovative and sustainable energy system that can more easily react to the shocks of the twenty-first century. / Dissertation/Thesis / Doctoral Dissertation Sustainability 2018

Over recent years, political, technological, environmental and economic factors have combined to increase interest in distributed energy resources (DER), and flexibility in the power system. As a resource which is both distributed and flexible, flexible demand (FD) can be considered to be particularly of interest. However, due to many facets of its nature, understanding the available flexibility, and potential value of that flexibility, is difficult. Further, understanding the effects of FD exploitation on other multi-energy system actors, given the complex nature of modern liberalised energy systems, complicates the picture further. These factors form material obstructions to the assessment of FD, for example, for the construction of business cases. To address these gaps this thesis first assesses the nature and value of various applicable current and potential markets and charging/incentive regimes, before detailing a novel multi-energy domestic demand simulation model, capable of modelling, in detail, domestic FD resources. Subsequently, a multi-commodity stochastic energy/reserve optimisation model, capable of modelling various DERs and taking into account price signals related to various energy-related commodities and services (including user utility) is specified. The separation of price components for application at different aggregation levels, which is applied in the optimisation model, also informs the described value mapping methodology, which illustrates the impacts of any, particularly demand-side, intervention on the wider multi-energy system. The power of the above detailed contributions are demonstrated through various studies, which show the physical and economic impact of various demand side interventions and of greater market participation by FD resources.

No description available.

Models and simulators have been widely used in urban contexts for many decades. The drawback of most current models is that they are normally designed for specific objectives, so the elements considered are limited and they do not take into account the potential synergies between related systems. The necessity of a framework to model complex smart city systems with a comprehensive smart city model has been remarked by many authors. Therefore, this PhD thesis presents: i) a general conceptual framework for the modelling of energy related activities in smart cities, based on determining the spheres of influence and intervention areas within the city, and on identifying agents and potential synergies among systems, and ii) the development of a holistic energy model of a smart city for the assessment of different courses of action, given its geo-location, regulatory and technical constraints, and current energy markets. This involves the creation of an optimization model that permits the optimal planning and operation of energy resources within the city. In addition, several analyses were carried out to explore different hypothesis for the smart city energy model, including: a)      an assessment of the importance of including network thermal constraints in the planning and operation of DER systems at a low voltage distribution level, b)      an analysis of aggregator’s market modelling approaches and the impact on prices due to DER aggregation levels, and c)      an analysis of synergies between different systems in a smart city context. Some of the main findings are: It is sensible to not consider network thermal constraints in the planning of DER systems. Results showed that the benefit decrement of considering network constraints was approximatively equivalent to the cost of reinforcing the network when necessary after planning without considering network constraints. The level of aggregation affects the planning and overall benefits of DER systems. Also, price-maker approaches could be more appropriate for the planning and operation of energy resources for medium to large aggregation sizes, but could be unnecessary for small sizes, with low expected impact on the market price. Synergies between different energy systems exist in an interconnected smart city context. Results showed that the overall benefits of a joint management of systems were greater than those of the independently managed systems. Lastly, the smart city energy model was applied to a case study simulating a real smart city implementation, considering five real districts in the southern area of Madrid, Spain. This analysis allowed to assess the potential benefits of the implementation of a real smart city programme, and showed how the proposed smart city energy model could be used for the planning of pilot projects. To the best of our knowledge, such a smart city energy model and modelling framework had not been developed and applied yet, and no economic results in terms of the potential benefits of such a smart city initiative had been previously reported. / <p>QC 20171010</p>

The requirement for affordable, secure and sustainable energy production is a pressing global challenge and the production of electricity with low carbon emissions is crucial. This usually entails large quantities of renewable energy generation, which is intermittent and often highly distributed throughout the electricity supply system. One of the proposed schemes to manage such generation is the smart grid, the transition to which forms the context for this research. The aim is to investigate the effect of certain psychological and social influences on the adoption of technology necessary to enable smart grids, in order to understand the implications for effective energy policy. In particular, the case of photovoltaic (PV) system adoption in the UK is studied. Empirical data detailing PV installations registered for the Feed in Tariff is analysed in order to understand rates of adoption and how they vary across both time and space. This analysis is combined with a review of policy intervention and literature from psychology to understand drivers for adoption among householders. The results from this study are then used to inform the design of an Agent Based Model of technology adoption within the smart grid context. The decision making of householders is modelled using an algorithm based on Social Cognitive Theory. The model is used to simulate different conditions and generate adoption scenarios in order to understand the potential effects of different parameters on adoption rates. In order to combine the analysis resulting from these methods, the multi-level perspective on transition in socio-technical systems is used to understand how a transition to a smart grid could be described and how adoption of PV in the UK under the Feed in Tariff incentive fits into such a transition. The results show that whilst economic incentive policies have had success in some areas adoption is also dependent on many non-financial parameters. Simulations show that the observability of adoption and the perceived inconvenience or urgency of adoption can have dramatic effects on rates of adoption, in some cases outweighing the rational economic effects of financial incentives. The implication for smart grid related policy is that non-financial factors should be taken into account as well as the more typical financial considerations in efforts to encourage adoption of necessary enabling technology by householders. The models developed could be used in further work to examine in detail adoption of other technologies such as smart home energy management systems and the interaction between adoption rates of multiple smart technologies.

Microgrids are new challenging power systems under development. This report presents a feasibility study of microgrid development. This is an essential task before implementing microgrid systems. It is extremely important to know the number and size of distributed energy resources (DERs) needed and it is necessary to compare investment costs with benefits in order to evaluate the profitability of microgrids. Under the assumption that a large number of DERs improves the reliability of microgrids an optimization problem is formulated to get the accurate mix of distributed energy resources. Uncertainty in physical and financial parameters is taken into account to model the problem considering different scenarios.  Uncertainty takes place in load demanded, renewable energy generation and electricity market price forecasts, availability of distributed energy resources and the microgrid islanding. It is modeled in a stochastic way. The optimization problem is formulated firstly as a mixed-integer programming solved via branch and bound and then it is improved formulating a two stage problem using Benders’ Decomposition which shortens the problem resolution. This optimization problem is divided in a long-term investment master problem and a short-term operation subproblem and it is solved iteratively until it reaches convergence. Bender’s Decomposition optimization problem is applied to real data from the Illinois Institute of Technology (IIT) and it gives the ideal mix of distributed energy resources for different uncertainty scenarios. These distributed energy resources are selected from an initial set. It proves the usefulness of this optimization technique which can be also applied to different microgrids and data. The different solutions obtained for different scenarios are explained and analyzed. They show the possibility of microgrid implementation and determine the most favorable scenarios to reach the microgrid implementation successfully.  Reliability is a term highly linked to the microgrid concept and one of the most important reasons of microgrid development. Thus an analysis of reliability importance is implemented using the importance index of interruption cost (  ) in order to measure the reliability improvement of developing microgrids. It shows and quantifies the reliability improvement in the system.

Hybrid Energy Resource System (HERS) is studied and applied aroundworld in recent years. Control and monitor of them are quite important in realapplication. HERS also has the equirement to integral with power grid such asdistribution grid networks. Therefore, to design and implement the informationcommunication system following IEC 61850, which is most promising standard fordesign of substation communication and automation system, is necessary. This paperpresents the design of Information Communication Technology (ICT) architectureand Unified Modeling Language (UML) models and final implementation through LabVIEW programming for Smart Energy Container. Applying design following IEC61850 series standards allow the HERS can communicate and interoperate with other IEC61850 devices and SCADA systems. The implementation is applied to SmartEnergy Container which contains wind power, solar power, battery energy storagesystem, and hydrogen energy storage system. Verification and testing results shows thedesign is qualified to control and monitor Smart Energy Container. / Smart Energy Container

The recent increase of electric cars adoption will inuence the electricity demand in the distributionnetworks which risks to be higher than the maximum power available in the grid, if not well planned. Forthis reason, it is on the DSOs and TSOs's interest to plan carefully coordinated charging of a bulk of EVsas well as assess the possibility of EVs acting as energy storages with the Vehicle-to-Grid (V2G) or Vehicleto-Building (V2B) capability. When parked and plugged into the electric grid, EVs will absorb energy andstore it, being also able to deliver electricity back to the grid/building (V2G/B system).This can be anoptimized process, performed by an aggregator, gathering multiple EVs that discharge the battery into thegrid at peak time and charge when there is low demand i.e. overnight and o-peak hours.Numerous studies have investigated the possibility of aggregating multiple EVs and optimizing theircharging and discharging schedules for peak load reduction or energy arbitrage with participation in theelectricity market. However, no study was found for optimizing a shared eet of EVs with daily reservationsfor dierent users trying to perform V2B. In this study an optimization modelling algorithm (mixed integerlinear problem - MILP) that manages the possible reservations of the shared eet of EVs, coordinates thecharging and discharging schedules, and provides V2B (Vehicle-to-Building), with the objective of minimizingenergy costs and accounting with battery ageing has been developed. A case study with real data for abuilding is carried out modelling dierent number of EVs for two dierent days in year 2017, one in Marchand other in June.Results show that the prots are higher for all cases when introducing V2B as compared to a no optimizationscenario: V2B with battery degradation (50 ore/kWh) has decreased daily variable electricity costsbetween 54 and 59% in March and 60 and 63% for June when compared without smart charging. Integrationof battery degradation cost in V2B applications is necessary and inuences signicantly the chargingand discharging strategies adopted by EV and nally the total daily costs: The total daily cost increaseby maximal 10% for the day in March and 13% for the day in June when comparing the scenario that hasstationary battery and uses only-charging model for EVs with the scenario applying V2B mode consideringa degradation cost of 80 ore/kWh. / Ö kningen av antalet elbilar kommer att påverka lasten i elnätet som riskerar att bli högre än kapacitetom det inte är väl planerat. Därför är det i elnätsföretags intresse att samordna laddningen av de flesta elbilarna samt att utvärdera möjligheterna att använda elbilar som energilager gentemot elnätet (Vehicleto-Grid,V2G) eller byggnader (Vehicle-to-Building, V2B). Vid parkering och anslutning till elnätet kommer elbilar att ladda energi och lagra den, samtidigt de kan leverera el tillbaka till elnätet eller byggnaden (V2G/V2B). Detta kan vara en optimerad process som utförs av en aggregator genom att ladda flera elbilar i låglasttimmar och ladda ur dem under höglasttimmar.Många studier har undersökt möjligheten att aggregera flera elbilar och optimera laddningsoch urladdningsplaner för topplastreduktion eller energiarbitrage på elmarknaden. Ingen studie har dock hittats för att optimera en gemensam flotta av elbilar med dagliga reservationer för olika användare som försöker utföra V2B. Denna studie har utvecklat en optimeringsmodell (blandad heltalsprogrammering MILP) som hanterar möjliga reservationer av en flotta av elbilar, koordinerar laddning och urladdning planering, och utför V2B för att minimera energikostnader med hänsyn till batteriets åldrande. En fallstudie för en byggnad genomfördes modellering av olika antal elbilar för två dagar 2017, en i mars och andra i juni.Resultaten visar att vinsten är högre i samtliga fall då man introducerar V2B jämfört med scenario utan optimering: V2B med batteriladdningskostnad 50 öre/kWh minskade dagliga rörliga elkostnader mellan 54% och 59% i mars och mellan 60% och 63% i juni jämfört med utan smart laddning. Att inkludera batteriladdningskostnaden i V2B-applikationer är nödvändigt och har en signifikant inverkan på laddningsstrategierna och de totala kostnaderna: De totala dagliga kostnaderna ökar med upp till 10% i mars och upp till 13% i juni då man jämför scenariot att bara ladda elbilar och ha stationärt batteri med scenariot V2B med hänsyntill batteriladdningskostnad 80 öre/kWh.