Mycorrhizal Networks (MNs) facilitate the exchange of resources including energy, water, nutrients, and information between trees and plants in forest ecosystems. This work explored MNs as an inspiration for new market models in transactive energy networks, which similarly involve exchanges of energy and information between buildings in local communities. Specific insights from the literature on the structure and function of MNs were translated into an energy model with the aim of addressing challenges associated with the proliferation of distributed energy resources (DERs) at the grid edge and the incorporation of DER aggregations into wholesale energy markets. First, a systematic review of bio-inspired computing interventions applied to microgrids and their interactions with modern energy markets established a technical knowledge base within the context of distributed electrical systems. Second, a bio-inspired design process built on this knowledge base to yield a structural and functional blueprint for a computational mycorrhizal energy market simulation. Lastly, that computational model was implemented and simulated on a blockchain-compatible, multi-agent software platform to determine the effect that mycorrhizal strategies have on transactive energy market performance. The structural translation of a mapped ectomycorrhizal network of Douglas-firs in Oregon, USA called the 'wood-wide web' created an effective framework for the organization of a novel mycorrhizal energy market model that enabled participating buildings to redistribute percentages of their energy assets on different competing exchanges throughout a series of week-long simulations. No significant changes in functional performance –- as determined by economic, technical, and ecological metrics – were observed when the mycorrhizal results were compared to those of a baseline transactive energy community without periodic energy asset redistribution. Still, the model itself is determined to be a useful tool for further exploration of innovative, automated strategies for DER integration into modern energy market structures and electrical infrastructure in the age of Web3, especially as new science emerges to better explain trigger and feedback mechanisms for carbon exchange through MNs and how mycorrhizae adapt to changes in the environment. This dissertation concludes with a brief discussion of policy implications and an analysis applying the ecological principles of robustness, biodiversity, and altruism to the collective energy future of the human species. / Doctor of Philosophy / Beneath the forest floor, a network of fungi connects trees and plants and allows them to exchange energy and other resources. This dissertation compares this mycorrhizal network (mycorrhiza = fungus + root) to a group of solar-powered buildings generating energy and exchanging it in a local community marketplace (transactive energy markets). In the analogy, the buildings become the plants, the solar panels become the leaves, and the electrical grid represents the mycorrhizal network. Trees and plants produce their own energy through photosynthesis and then send large portions of it down to the roots, where they can trade it or send it to neighbors via the mycorrhizal network. Similarly, transactive energy markets are designed to allow buildings to sell the energy they produce on-site to neighbors, usually at better rates. This helps address a major infrastructure challenge that is arising with more people adding roof-top solar to their homes. The grid that powers our buildings is old now and it was designed to send power from a central power plant out to its edges where most homes and businesses are located. When too many homes produce solar power at the same time, there is nowhere for it to go, and it can easily overload the grid leading to fires, equipment failures, and power outages. Mycorrhizal networks solve this problem in part through local energy balancing driven by cooperative feedback patterns that have evolved over millennia to sustain forest ecosystems. This work applies scientific findings on the structure and function of mycorrhizal networks (MNs) to energy simulation methods in order to better understand the potential for building bio-inspired energy infrastructure in local communities. Specifically, the mapped structure of a MN of douglas-fir trees in Oregon, USA was adapted into a digital transactive energy market (TEM) model. This adaptation process revealed that a single building can connect to many TEMs simultaneously and that the number of connections can change over time just as symbiotic connections between organisms grow, decay, and adapt to a changing environment. The behavior of MNs in terms of when those connections are added and subtracted informed the functionality of the TEM model, which adds connections when community energy levels are high and subtracts connections when energy levels are low. The resulting 'mycorrhizal' model of the TEM was able to change how much energy each connected household traded on it by changing the number of connections (more connections mean more energy and vice versa). Though the functional performance of the mycorrhizal TEM did not change significantly from that of a typical TEM when they were the context of decentralized computer networks (blockchains) and distributed artificial intelligence. A concluding discussion addresses ways in which elements of this new model could transform energy distribution in communities and improve the resilience of local energy systems in the face of a changing climate.

Recently, due to the increasing adoption of distributed energy resource (DER) technologies including battery energy storage (BES) and electric vehicle (EV) systems, bidirectional power converters are becoming more popular. These converters are broadly utilized as interface devices and provide a bidirectional power flow in applications where the primary power supply can both supply and receive energy. A dc capacitor, called the dc-link, is an important component of such bidirectional converters. For a wide range of applications, the converter is required to control the dc-link voltage. Commonly, a proportional-integrating (PI) controller is used by the dc capacitor voltage controller to generate a set-point for the inner current controller. This approach tightly regulates the dc-link voltage to a given value. The research presented in this dissertation shows that such an approach compromises the stability margins of the converter for reverse power flow and weak grid conditions. It is shown that by allowing a small variation of dc capacitor voltage in proportion to the amount of power flowing through the converter, the stability and robustness margins are improved. This approach also simplifies the design process and can be applied to both dc/dc and dc/ac (single-phase and three-phase) converters. Moreover, it grants an inherent power sharing capability when multiple converters share the same dc-link terminals; removing the need to a communication link between parallel converters. The proposed controller is equipped with a current limiting mechanism to protect the converter during low-voltage/over-current transients. Detailed analyses, simulations, comparisons, and experimental results are included to illustrate the effectiveness of the proposed control approach. To mathematically establish the properties of the proposed method in a single-phase dc/ac application, this dissertation also derives a new and systematic modeling approach for a grid-connected bidirectional single-phase inverter controlled in stationary frame. Implementing the control system in the stationary frame has advantages over rotating frame. However, the combination of dc and ac state variables and nonlinearities make its stability analysis challenging. In the proposed model, an imaginary subsystem is properly generated and augmented to allow a full transformation to a synchronous rotating frame. The proposed modeling strategy is modular and has a closed form which facilitates further extensions. It is successfully used to demonstrate enhanced stability margins of the proposed controller.

[pt] Nos últimos anos tem se verificado um papel mais ativo do pequeno consumidor com relação ao uso da energia elétrica. O acesso mais facilitado às tecnologias de geração distribuída, principalmente produção local fotovoltaica em telhados de residências e também às novas gerações de medidores inteligentes, despertou o interesse de pequenos consumidores para gerar sua própria energia, reduzindo sua fatura com a distribuidora e também sua dependência da rede de distribuição. Nesse contexto, o objetivo da dissertação é avaliar, com base em medições inteligentes reais, o impacto nas faturas de consumidores residenciais causado pela introdução de uma Tarifa de Uso do Sistema de Distribuição, em reais/quilowatt, aplicada sobre a demanda máxima registrada. A opção pelo tema buscou atender lacunas na bibliografia nacional por tratar-se de um assunto inovador, não só no Brasil, e que está sendo estudado em diversos países no mundo simultaneamente. A metodologia pode ser dividida, basicamente, em quatro fases: (i) Apuração da receita de referência; (ii) Cálculo das tarifas de distribuição nas modalidades horária e em reais/quilowatt; (iii) Cálculo e avaliação das receitas obtidas pela aplicação das tarifas às medições dos clusters de estudo; e (iv) Cálculo e avaliação das receitas obtidas pela aplicação das tarifas às medições dos clusters de estudo, considerando geração fotovoltaica distribuída. Os resultados permitiram identificar os perfis de consumo que seriam mais impactados pela introdução de uma tarifação sobre a demanda e também os efeitos resultantes da instalação de painéis solares nas residências, assim demonstrar que essa modalidade de tarifação, ao refletir os custos da atividade de distribuição de energia, propicia estabilidade das tarifas e evita subsídios cruzados. / [en] In recent years there has been a more active role of small consumers in relation to the use of electricity. The easier access to distributed generation technologies, mainly local photovoltaic production in residential roofs and also the new generations of intelligent meters, has brought the interest of small consumers to generate their own energy, reducing their invoice with the distributor and also their dependence on the distribution network. In this context, the objective of this dissertation is to evaluate, based on real smart measurements, the impact on the invoices of residential customers caused by the introduction of a demand charge, in real/kilowatt, applied on the registered maximum demand. The option for the theme sought to address gaps in the national bibliography because it is an innovative subject, not only in Brazil, and which is being studied in several countries simultaneously. The methodology can be divided into basically four phases: (i) Calculation of the reference revenue; (ii) Calculation of distribution tariffs: hourly and based in real/kilowatt; (iii) Calculation and evaluation of the revenues obtained by applying the tariffs to the measurements from the clusters to be studied; and (iv) Calculation and evaluation of the revenues obtained by applying the tariffs to the measurements from the clusters to be studied, considering distributed photovoltaic generation. The results allowed to identify the consumption profiles, whether they would be more impacted by the introduction of a demand charge and the impacts caused by the installation of solar panels by the residences as well as demonstrating that this type of charge, reflecting the costs of distribution, provides tariff stability and avoids cross subsidies.

Les réseaux électriques actuels connaîtront un profond changement dans les années à venir. La nouvelle génération est le Smart Grid (SG) ou le réseau électrique intelligent qui se caractérise par une couche d'information et de communication qui permet aux différents composants du réseau de communiquer. Il doit considérer tous les aspects du réseau électrique, le rendant plus intelligent et flexible. Cette notion est présentée comme une réponse à l'évolution du marché de l'électricité, visant à gérer l’augmentation de la demande tout en assurant une meilleure qualité de service et plus de sécurité.Premièrement, nous présentons une formulation de programmation linéaire mixte en entier pour optimiser les systèmes de production et de consommation d'énergie dans une maison intelligente avec un déploiement efficace de plusieurs ressources énergétiques distribuées. Ensuite, à travers la conception d'expériences avec la méthode de Taguchi, divers scénarios sont introduits en faisant varier des facteurs significatifs. Par la suite, une technique heuristique est proposée pour résoudre le problème de la gestion de l'énergie résidentielle en trouvant la solution optimale globale pendant plusieurs jours consécutifs avec une réduction significative du temps d'exécution.Deuxièmement, un modèle de gestion de l'énergie est assuré grâce à des modèles mathématiques pour optimiser l’utilisation du réseau, des ressources énergétiques renouvelables, des véhicules électriques et de la batterie, ainsi que pour différents types d'appareils thermiques et électriques. Une méthode de solution exacte est mise en œuvre pour réduire le coût de l'électricité dans une maison intelligente et pour trouver des modes de fonctionnement de différentes charges. Ensuite, un algorithme d'optimisation math-heuristique est proposé pour résoudre le problème avec un temps de simulation étendu.Enfin, nous étudions le problème de gestion de l'énergie dans un microréseau constitué de plusieurs maisons intelligentes. Chacune d'elles dépose de ressources énergétiques renouvelables, d’un véhicule électrique et d’appareils intelligents. Les ressources d'énergie renouvelable injectent l’excès de l'énergie dans un système de stockage d'énergie partagé. Un modèle mathématique linéaire mixte en entier pour la gestion d'énergie est proposé pour réduire le coût total de fonctionnement du microréseau. Des comparaisons avec des scénarios conventionnels où chaque maison intelligente possède son propre système de stockage d'énergie sont effectuées pour démontrer l’efficacité de la démarche proposée. / The current electricity grids will experience a profound change in the coming years. The new generation is the Smart Grid (SG) which is characterized by information and communication layer enabling the communication between the different components of the grid. It needs to consider all sides of power grid, making it more intelligent and flexible. This notion is presented as an answer to changes in the electricity market, aiming to manage the increased demand while ensuring a better quality of service and more safety.First, we present a mixed integer linear programming formulation to optimize the energy production and consumption systems in a smart home with an effective deployment of several distributed energy resources. Then through the design of experiments with the Taguchi method, diverse scenarios are introduced by varying significant factors. Afterward, a heuristic technique is proposed to solve the problem of residential energy management by finding the global optimum solution for many consecutive days with significant reduction of execution time.Second, an energy management model is proposed thanks to mathematical models to optimize the grid, renewable energy resources, battery and electric vehicles are presented as well as for different type of thermal and electrical appliances. An exact solution method is implemented to reduce the electricity cost in a smart home and find out operation modes of different loads. Then a math-heuristic optimization algorithm is proposed to solve the problem with extended simulation time horizon.Finally, we study a microgrid energy management problem which comprises multiple smart homes. Each of them owns renewable energy resources, one electric vehicle and smart appliances. The renewable energy resources inject the excess energy in the shared energy storage system. An optimized energy management model using mixed integer linear programming is proposed to reduce the total electricity cost in the microgrid. Comparisons with conventional scenarios where each smart home has its individual small energy storage system without sharing energy with their neighbors are done to ensure that the proposed formulation is well efficient.

Les politiques entreprises dans le domaine de la production d’électricité pour lutter contre le changement climatique reposent communément sur le remplacement des moyens de production fossiles et centralisés par de nouveaux moyens de type renouvelables. Ces énergies renouvelables sont en grande partie distribuées dans les réseaux moyenne et basse tension et sont le plus souvent intermittentes (énergies éolienne et photovoltaïque principalement). Les gestionnaires de réseaux s’attentent à ce que ce changement de paradigme induise des difficultés conséquences dans leurs opérations. Les mondes de la recherche et de l’industrie se sont ainsi structurés depuis le milieu des années 2000 afin d’apporter une réponse aux problèmes anticipés. Cette réponse passe notamment par le déploiement de technologies de l’information et de la communication (TIC) dans les réseaux électriques, des centres de contrôle jusqu’au sein même des moyens de production distribués. C’est ce que l’on appelle le Smart Grid. Parmi le champ des possibles du Smart Grid, ces travaux de thèses se sont en particulier attachés à apporter une réponse aux enjeux de stabilité en fréquence du système électrique, mise en danger par la réduction anticipée de l’inertie des systèmes électriques et la raréfaction des moyens de fourniture de réserve primaire (FCR), auxquels incombent le maintien de la fréquence en temps réel. En vue de suppléer les moyens de fourniture de réserve conventionnels et centralisés, il a ainsi été élaboré un concept de coordination de charges électriques délestables distribuées, qui se déconnectent et se reconnectent de manière autonome sur le réseau au gré des variations de fréquence mesurées sur site. Ces modulations de puissance répondent à un schéma préétabli qui dépend de la consommation électrique effective de chacune des charges. Ces travaux ont été complétés d’une étude technico-économique visant à réutiliser cette infrastructure de coordination de charges délestables pour la fourniture de services systèmes ou de produits de gros complémentaires. Ce travail de thèse réalisée au sein des équipes innovation de Schneider Electric et du laboratoire de Génie Electrique de Grenoble (G2Elab), est en lien avec les projets Européens EvolvDSO et Dream, financés dans le cadre du programme FP7 de la Commission Européenne. / Climate change mitigation policies in the power generation industry lead commonly on the replacement of bulk generation assets by Renewable Energy Resources (RES-E). Such RES-E are largely distributed among the medium and low voltage grids and most of them are intermittent like photovoltaic and wind power. System Operators expect that such new power system paradigm induces significant complications in their operations. The communities of research and industry started thus to structure themselves in the mid-2000s in order to respond to these coming issues, notably through the deployment of Information and Communication Technology (ICT) in power systems assets, from the Network Operations Centers (NOCs) down to Distributed Energy Resources (DERs) units. This is the Smart Grid. Among the range of possibilities of the Smart Grid, this Ph.D work aims in priority to provide a solution to handle the issue of frequency stability of the power system that are endangered by the combined loss of inertia of the power system and the phasing-out of conventional assets which used to be in charge of the maintain of the frequency in real time through the supply of Frequency Containment Reserve (FCR). The concept developed lead on a process of coordinated modulation of the level of loads of DERs, whose evolve depending on the system frequency measured in real time on-site. The strategy of modulation of each DER follows a pattern which is determined at the scale of the portfolio of aggregation of the DER, depending on the effective level of load of the DER at normal frequency (i.e. 50Hz in Europe). This work is completed by a cost benefit analysis that assesses the opportunity of sharing of the previous infrastructure of coordinated modulation of DERs for the supply of ancillary services and wholesale products. This thesis conducted within Schneider Electric’s Innovation teams and Grenoble Electrical Engineering Laboratory (G2Elab) is linked with the European projects Dream and EvolvDSO, and funded under European Commission’s FP7 program.

Energy forms a vital input and critical infrastructure for the economic development of countries and for improving the quality of life of people. Energy is utilized in society through the operation of large socio-technical systems called energy systems. In a growing world, as the focus shifts to better access and use of modern energy sources, there is a rising demand for energy. However, certain externalities result in this demand not being met adequately, especially in developing countries. This constitutes the energy demand – supply matching problem. Load shedding is a response used by distribution utilities in developing countries, to deal with the energy demand – supply problem in the short term and to secure the grid. This response impacts the activities of consumers and entails economic losses. Given this scenario, demand – supply matching becomes a crucial decision making activity. Traditionally demand – supply matching has been carried out by increasing supply centrally in the long term or reducing demand centrally in the short term. Literature shows that these options have not been very effective in solving the demand-supply problem. Gaps in literature also show that the need of the hour is the design of alternate solutions which are tailored to a nation's specific energy service needs in a sustainable way. Microgrids using renewable and clean energy resources and demand side management can be suitable decentralized alternatives to augment the centralized grid based systems and enable demand – supply matching at a local community level. The central research question posed by this thesis is: “How can we reduce the demand – supply gap existing in a community, due to grid insufficiency, using locally available resources and the grid in an optimal way; and thereby facilitate microgrid implementation?” The overall aim of this dissertation is to solve the energy demand – supply matching problem at the community level. It is known that decisions for the creation of energy systems are influenced by several factors. This study focuses on those factors which policy-makers and stakeholders can influence. It proposes an integrated decision framework for the creation of community microgrids. The study looks at several different dimensions of the existing demand – supply problem in a holistic way. The research objectives of this study are: 1. To develop an integrated decision framework that solves the demand – supply matching problem at a community level. 2. To decompose the consumption patterns of the community into end-uses. solar thermal, solar lighting and solar pumps and a combination of these at different capacities. The options feasible for medium income consumers are solar thermal, solar pumps, municipal waste based systems and a combination of these. The options for high income consumers are municipal waste based CHP systems, solar thermal and solar pumps. Residential consumers living in multi-storied buildings also have the options of CHP, micro wind and solar. For cooking, LPG is the single most effective alternative. 3. To identify the ‗best fitting‘ distributed energy system (microgrid), based on the end-use consumption patterns of the community and locally available clean and renewable energy resources, for matching demand – supply at the community level. 4. To facilitate the implementation of microgrids by * Contextualizing the demand – supply matching problem to consider the local social and political environment or landscape, * Studying the economic impact of load shedding and incorporating it into the demand-supply matching problem, and * Presenting multiple decision scenarios, addressing the needs of different stakeholders, to enable dialogue and participative decision making. A multi-stage Integrated Decision Framework (IDF) is developed to solve the demand - supply matching problem in a sequential manner. The first stage in the IDF towards solving the problem is the identification and estimation of the energy needs / end-uses of consumers in a community. This process is called End-use Demand Decomposition (EUDD) and is accomplished by an empirical estimation of consumer electricity demand based on structural and socio-economic factors. An algorithm/ heuristic is also presented to decompose this demand into its constituent end-uses at the community level for the purpose of identifying suitable and optimal alternatives/ augments to grid based electricity. The second stage in the framework is Best Fit DES. This stage involves identifying the “best-fit‘ distributed energy system (microgrid) for the community that optimally matches the energy demand with available forms of supply and provides a schedule for the operation of these various supply options to maximize stakeholder utility. It provides the decision makers with a methodology for identifying the optimal distributed energy resource (DER) mix, capacity and annual operational schedule that “best fits” the given end-use demand profile of consumers in a community and under the constraints of that community such that it meets the needs of the stakeholders. The optimization technique developed is a Mixed Integer Linear Program and is a modification of the DER-CAM™ (Distributed Energy Resources Customer Adoption Model), which is developed by the Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory using the GAMS platform. The third stage is the Community Microgrid Implementation (CMI) stage. The CMI stage of IDF includes three steps. The first one is to contextualize the energy demand and supply for a specific region and the communities within it. This is done by the Energy Landscape Analysis (ELA). The energy landscape analysis attempts to understand the current scenario and develop a baseline for the study. It identifies the potential solutions for the demand - supply problem from a stakeholder perspective. The next step provides a rationale for the creation of community level decentralized energy systems and microgrids from a sustainability perspective. This is done by presenting a theoretical model for outage costs (or load shedding), empirically substantiating it and providing a simulation model to demonstrate the viability for distributed energy systems. Outage cost or the cost of non supply is a variable that can be used to determine the need for alternate systems in the absence/ unavailability of the grid. The final step in the CMI stage is to provide a scenario analysis for the implementation of community microgrids. The scenario analysis step in the framework enlightens decision makers about the baselines and thresholds for the solutions obtained in the “best fit‘ analysis. The first two stages of IDF, EUDD and Best Fit DES, address the problem from a bottom-up perspective. The solution obtained from these stages constitutes the optimal solution from a technical perspective. The third stage CMI is a top-down approach to the problem, which assesses the social and policy parameters. This stage provides a set of satisficing solutions/ scenarios to enable a dialogue between stakeholders to facilitate implementation of microgrids. Thus, IDF follows a hybrid approach to problem solving. The proposed IDF is then used to demonstrate the choice of microgrids for residential communities. In particular, the framework is demonstrated for a typical residential community, Vijayanagar, situated in Bangalore and the findings presented. The End-use Demand Decomposition (EUDD) stage provides the decision makers with a methodology for estimating consumer demand given their socio-economic status, fuel choice and appliance profiles. This is done by the means of a statistical analysis. For this a primary survey of 375 residential households belonging to the LT2a category of BESCOM (Bangalore Electricity Supply Company) was conducted in the Bangalore metropolitan area. The results of the current study show that consumer demand is a function of the variables family income, refrigeration, entertainment, water heating, family size, space cooling, gas use, wood use, kerosene use and space heating. The final regression model (with these variables) can effectively predict up to 60% of the variation in the electricity consumption of a household ln(ElecConsumption) = 0.2880.396*ln(Income)+0.2 66*Refri geration+ 0.708*Entertainment+0.334*WaterHeating+0.047*FamSize+ 0243*SpaceCooling.+580*GasUse+0.421*WoodUse–0.159*KeroseneUse+ 0.568*SpaceHeating ln(ElecConsumption) = 0.406*ln(Income)0.168*Ref rigeration+0.139*Entertainment+ 0.213*WaterHeating+0.114*FamSize+0.121*SpacCooling+0.171*GasUse+ 0.115*WoodUse–0.094*KeroseneUse+0.075*SpaceHeating   The next step of EUDD is to break up the demand into its constituent end-uses. The third step involves aggregating the end-uses at the community level. These two steps are to be performed using a heuristic. The Best Fit DES stage of IDF is demonstrated with data from an urban community in Bangalore. This community is located in an area called Vijayanagar in Bangalore city. Vijayanagar is a mainly a residential area with some pockets of mixed use. Since grid availability is the constraining parameter that yields varying energy availability, this constraint is taken as the criteria for evaluation of the model. The Best Fit DES model is run for different values of the grid availability parameter to study the changes in outputs obtained in DER mix, schedules and overall cost of the system and the results are tabulated. Sensitivity analysis is also performed to study the effect of changing load, price options, fuel costs and technology parameters. The results obtained from the BEST Fit DES model for Vijayanagar illustrate that microgrids and DERs can be a suitable alternative for meeting the demand – supply gap locally. The cost of implementing DERs is the optimal solution. The savings obtained from this option however is less than 1% than the base case due to the subsidized price of grid based electricity. The corresponding costs for different hours of grid availability are higher than the base case, but this is offset by the increased efficiency of the overall system and improved reliability that is obtained in the community due to availability of power 24/7 regardless of the availability of grid based power. If the price of grid power is changed to reflect the true price of electricity, it is shown that DERs continue to be the optimal solution. Also the combination of DERs chosen change with the different levels of non-supply from the grid. For the study community, Vijayanagar, Bangalore, the DERs chosen on the basis of resource availability are mainly discrete DERs. The DERs chosen are the LPG based CHP systems which run as base and intermediate generating systems. The capacity of the discrete DERs selected, depend on the end-use load of the community. Biomass based CHP systems are not chosen by the model as this technology has not reached maturity in an urban setup. Wind and hydro based systems are not selected as these resources are not available in Vijayanagar. The CMI stage of IDF demonstrates the top-down approach to the demand-supply matching problem. For the Energy Landscape Analysis (ELA), Bangalore metropolis was chosen in the study for the purpose of demonstration of the IDF framework. Bangalore consumes 25% of the state electricity supply and its per capita consumption at 1560kWh is higher than the state average of 1230kWh and is 250% more than the Indian average of 612kWh. A stakeholder workshop was conducted to ascertain the business value for clean and renewable energy technologies. From the workshop it was established that significant peak power savings could be obtained with even low penetrations of distributed energy technologies in Bangalore. The feasible options chosen by stakeholders for low income consumers are The second step of CMI is finding an economic rationale for the implementation of community microgrids. It is hypothesized that the ‘The cost of non-supply follows an s-shaped curve similar to a growth curve.’ It is moderated by the consumer income, consumer utility, and time duration of the load shedding. A pre and post event primary survey was conducted to analyze the difference in the pattern of consumer behaviour before and after the implementation of a severe load shedding program by BESCOM during 2009-10. Data was collected from 113 households during February 2009 and July 2010. The analysis proves that there is indeed a significant difference in the number of uninterrupted power systems (inverters) possessed by households. This could be attributed mainly to the power situation in Karnataka during the same period. The data also confirms the nature of the cost of non-supply curve. The third step in CMI is scenario analysis. Four categories of scenarios are developed based on potential interventions. These are business-as-usual, demand side, supply side and demand-supply side. About 21 scenarios are identified and their results compared. Comparing the four categories of scenarios, it is shown that business-as-usual scenarios may result in exacerbation of the demand-supply gap. Demand side interventions result in savings in the total costs for the community, but cannot aid communities with load shedding. Supply side interventions increase the reliability of the energy system for a small additional cost and communities have the opportunity to even meet their energy needs independent of the grid. The combination of both demand and supply side interventions are the best solution alternative for communities, as they enable communities to meet their energy needs 24/7 in a reliable manner and also do it at a lower cost. With an interactive microgrid implementation, communities have the added opportunity to sell back power to the grid for a profit. The thesis concludes with a discussion of the potential use of IDF in policy making, the potential barriers to implementation and minimization strategies. It presents policy recommendations based on the framework developed and the results obtained.

The European policy focus on smart grids implies their development as an indispensable part of the future power system. However, the definition of a smart grid is broad and vague, and the actual implementation of a smart grid can differ significantly, depending on the stakeholders involved.This work aims to inform policy makers, the electricity industry and researchers about stakeholder interests and the technical complexities involved by presenting smart grids via a techno-institutional framework. This framework takes account of the technical nature of the electricity transport and supply service as well as the institutional nature of electricity markets, stakeholder perspectives and sector regulation. In addition, this work presents potential revenues resulting from flexibility management in smart grids and proposes a way forward for smart grids and flexibility management in Europe. / <p>QC 20170925</p>

This research focuses on developing strategies for the optimal control of large-scale Combined Cooling, Heating and Power (CCHP) systems to meet electricity, heating, and cooling demands, and evaluating the cost savings potential associated with it. Optimal control of CCHP systems involves the determination of the mode of operation and set points to satisfy the specific energy requirements for each time period. It is very complex to effectively design optimal control strategies because of the stochastic behavior of energy loads and fuel prices, varying component designs and operational limitations, startup and shutdown events and many more. Also, for large-scale systems, the problem involves a large number of decision variables, both discrete and continuous, and numerous constraints along with the nonlinear performance characteristic curves of equipment. In general, the CCHP energy dispatch problem is intrinsically difficult to solve because of the non-convex, non-differentiable, multimodal and discontinuous nature of the optimization problem along with strong coupling to multiple energy components. <div><br></div><div>This work presents a solution methodology for optimizing the operation of a campus CCHP system using a detailed network energy flow model solved by a hybrid approach combining mixed-integer linear programming (MILP) and nonlinear programming (NLP) optimization techniques. In the first step, MILP optimization is applied to a plant model that includes linear models for all components and a penalty for turning on or off the boilers and steam chillers. The MILP step determines which components need to be turned on and their respective load needed to meet the campus energy demand for the chosen time period (short, medium or long term) with one-hour resolution. Based on the solution from MILP solver as a starting point, the NLP optimization determines the actual hourly state of operation of selected components based on their nonlinear performance characteristics. The optimal energy dispatch algorithm provides operational signals associated with resource allocation ensuring that the systems meet campus electricity, heating, and cooling demands. The chief benefits of this formulation are its ability to determine the optimal mix of equipment with on/off capabilities and penalties for startup and shutdown, consideration of cost from all auxiliary equipment and its applicability to large-scale energy systems with multiple heating, cooling and power generation units resulting in improved performance. </div><div><br></div><div>The case-study considered in this research work is the Wade Power Plant and the Northwest Chiller Plant (NWCP) located at the main campus of Purdue University in West Lafayette, Indiana, USA. The electricity, steam, and chilled water are produced through a CCHP system to meet the campus electricity, heating and cooling demands. The hybrid approach is validated with the plant measurements and then used with the assumption of perfect load forecasts to evaluate the economic benefits of optimal control subjected to different operational conditions and fuel prices. Example cost optimizations were performed for a 24-hour period with known cooling, heating, and electricity demand of Purdue’s main campus, and based on actual real-time prices (RTP) for purchasing electricity from utility. Three optimization cases were considered for analysis: MILP [no on/off switch penalty (SP)]; MILP [including on/off switch penalty (SP)] and NLP optimization. Around 3.5% cost savings is achievable with both MILP optimization cases while almost 10.7% cost savings is achieved using the hybrid MILP-NLP approach compared to the current plant operation. For the selected components from MILP optimization, NLP balances the equipment performance to operate at the state point where its efficiency is maximum while still meeting the demand. Using this hybrid approach, a high-quality global solution is determined when the linear model is feasible while still taking into account the nonlinear nature of the problem. </div><div><br></div><div>Simulations were extended for different seasons to examine the sensitivity of the optimization results to differences in electric, heating and cooling demand. All the optimization results suggest there are opportunities for potential cost savings across all seasons compared to the current operation of the power plant. For a large CCHP plant, this could mean significant savings for a year. The impact of choosing different time range is studied for MILP optimization because any changes in MILP outputs impact the solutions of NLP optimization. Sensitivity analysis of the optimized results to the cost of purchased electricity and natural gas were performed to illustrate the operational switch between steam and electric driven components, generation and purchasing of electricity, and usage of coal and natural gas boilers that occurs for optimal operation. Finally, a modular, generalizable, easy-to-configure optimization framework for the cost-optimal control of large-scale combined cooling, heating and power systems is developed and evaluated.</div>

Innovative Lösungsprinzipien auf andere Lebens- und Wirtschaftsbereiche zu übertragen, ist eine sich wiederholende Herausforderung in Wissenschaft und Praxis. In der vorliegenden Arbeit werden Kooperationsphänomene der Erwerbs- und Bedarfswirtschaften thematisiert. Es wird der Frage nachgegangen, ob die Gestalt und Organisation eines Kooperationsphänomens systematisch mit deskriptiv-analytischen Untersuchungsmethoden (Systemtheorie und Morphologie) beschrieben und bereichsübergreifend, unter Einbeziehung individueller Besonderheiten, übertragen werden kann. Das Vorhaben wird als Analogiebildung verstanden. Ein Schema zur Analogiebildung für Kooperationsphänomene wird entwickelt. Zur Validierung des Schemas wird als Analogiequelle das Organisationskonzept der Zwei-Ebenen-Kooperation ausgewählt, welches ursprünglich für Produktionsnetze konzipiert wurde und sich insbesondere auf Klein- und Mikrounternehmen ausrichtet. Als Empfänger wird der Bereich der dezentralen Energieversorgung festgelegt, in dem sich verstärkt kooperative Organisationsformen einer selbstbestimmten, kooperativen und bürgernahen dezentralen Energieversorgung herausbilden. Auf der Grundlage der Analogiebetrachtung aus dem Bereich der Produktionsnetze wird ein neuartiges, kooperatives Organisationskonzept für die dezentrale Energieversorgung entworfen. Die Arbeit wird damit interessant für alle, die an einer systematischen Übertragung von Wissen zur Gestalt und Organisation von Kooperationsphänomenen und der Ausgestaltung eines kooperativen Organisationskonzeptes für die dezentrale Energieversorgung interessiert sind.

The global climate and energy crisis has amplified the need for renewable energy sources, withsolar photovoltaic (PV) systems expected to play a significant role in the future energy mix. In this context, distributed energy systems (DES) are identified as part of the solution to address climate and energy challenges.With the increasing demand for photovoltaic energy sources, there is a growing requirement forefficient Business Case Tools (BCT) to analyze investments in distributed solar PV installations.A two-part model, consisting of a solar model and spot price data, was developed based onparameters such as solar radiation, location, angle, orientation, system losses, installedcapacity, and historical spot price data. The model was integrated with Power BI for investment calculations and visualization of results. The developed model provides approximations for solar PV system electricity production, which were validated against selected installations in allelectricity areas of Sweden. The validation revealed an average relative absolute error of 14.72 percent for the model. The conclusion drawn is that BCT can be utilized to analyze and visualize solar PV investments at specific locations in Sweden. The results indicate that Power BI, as a BCT, has limitations indynamic data collection but performs well in executing calculation of investments and visualizingthe results. Well-developed BCT can facilitate decision-making through real-time calculations and contribute to smoother implementation of distributed systems by providing detailed insightsinto their financial characteristics. Further research is needed to develop a model specificallytailored for distributed installations with storage capabilities. / Världen befinner sig i en global klimat- och energikris vilket ökat behovet av och efterfrågan på förnybara energikällor. Solceller förväntas utgöra en betydande del av den framtida energimixen. I kombination med detta identifieras distribuerade energisystem (DES) som endel av lösningen på klimat- och energifrågan. I takt med den ökade efterfrågan på fotovoltaiska energikällor ställs större krav på effektiva Business Case Tools (BCT) för att analysera investeringar i distribuerade solcellsanläggningar. En modell bestående av två delar, en solmodell och spotprisdata,utvecklades utifrån parametrarna solstrålning, plats, vinkel, riktning, systemförluster, installerad effekt samt historiska spotprisdata. Modellen sammankopplas med Power BI föratt utföra investeringskalkyler och visualisera resultatet. Den utvecklade modellen gerapproximationer för solcellsanläggningars elproduktion, vilket validerades mot utvaldaanläggningar i Sveriges samtliga elområden. Enligt valideringen uppgår modellens genomsnittliga relativa absoluta fel till 14,72 procent. Slutsatsen dras att BCT kan användas för att analysera och visualisera solcellsinvesteringar på specifika platser i Sverige. Resultatet visar att Power BI som BCT har brister när detkommer till dynamisk datainsamling, men genomför och visualiserar investerings kalkyler med enkelhet. Välutvecklade BCT kan användas för att underlätta beslutsfattande genomrealtidsberäkningar och kan bidra till en smidigare implementering av distribuerade systemgenom att belysa deras finansiella karaktär på ett detaljerat sätt. Fortsatt forskning krävs föratt ta fram en modell anpassad för distribuerade anläggningar med lagringsmöjligheter.