Energy from solar photovoltaic (PV) are generated as direct current (DC) and almost all of today’s electrical loads in residential buildings, household appliances and HVAC system (Heating Ventilation and Air-conditioning) are operated on DC. For a conventional alternating current (AC) distribution system this requires the need for multiple conversion steps before the final user-stage. By switching the distribution system to DC, conversion steps between AC to DC can be avoided and, in that way, losses are reduced. Including a battery storage–the system’s losses can be reduced further and the generated PV energy is even better utilised. This thesis investigates and quantifies the energy savings when using a direct current distribution topology in a residential building together with distributed energy generation from solar photovoltaic and a battery storage. Measured load and PV generation data for a single-family house situated in Borås, Sweden is used as a case study for the analysis. Detailed and dynamic models–based on laboratory measurements of the power electronic converters and the battery–are also used to more accurately reflect the system’s dynamic performance. In this study a dynamic representation of the battery’s losses is presented which is based on laboratory measurements of the resistance and current dependency for a single lithium-ion cell based on Lithium iron phosphate (LFP). A comparative study is made with two others, commonly used, loss representations and evaluated with regards to the complete system’s performance, using the PV and load data from the single-family house. Results show that a detailed battery representation is important for a correct loss prediction when modelling the interaction between loads, PV and the battery. Four DC system topologies are also modelled and compared to an equivalent AC topology using the experimental findings from the power electronic converters and the battery measurements. Results from the quasi-dynamic modelling show that the annual energy savings potential from the suggested DC topologies ranges between 1.9–5.6%. The DC topologies also increase the PV utilisation by up to 10 percentage points, by reducing the associated losses from the inverter and the battery conversion. Results also show that the grid-tied converter is the main loss contributor and when a constant grid-tied efficiency is used, the energy savings are overestimated.

[pt] O planejamento da expansão da transmissão (PET) visa identificar novos reforços para a rede, permitindo uma conexão tecnicamente adequada entre demanda e geração de energia elétrica, ambas previstas para um determinado horizonte de planejamento. Um bom plano de expansão deve garantir o equilíbrio entre os custos de investimento e operação, mantendo um nível satisfatório de segurança no fornecimento de energia elétrica. Entretanto, a identificação de bons planos de expansão para o PET tem se tornado uma tarefa cada vez mais difícil. Isso se deve, principalmente, às características e dimensões dos sistemas atuais, a não linearidade e natureza combinatória do problema de otimização e às incertezas presentes nos dados. Os erros de previsão, a indisponibilidade de equipamentos e a disponibilidade dos recursos naturais são parâmetros que variam de forma aleatória e inserem um alto grau de incerteza nos sistemas elétricos, o qual aumenta proporcionalmente com o horizonte de planejamento. Uma das incertezas mais relevantes a ser gerenciada nas próximas décadas será a capacidade de geração oriunda de fontes renováveis, em particular as eólicas, devido à sua grande variabilidade. A utilização de dispositivos de armazenamento permitirá melhor aproveitamento dessas fontes e, portanto, torna-se necessário o desenvolvimento de ferramentas computacionais capazes de considerar tais dispositivos no problema PET. Esta dissertação apresenta uma nova metodologia de apoio ao problema PET inserindo armazenadores de energia elétrica para aumentar o aproveitamento de fontes renováveis no sistema. Isso, respeitando as restrições de segurança da rede, acompanhando à curva de demanda e levando em consideração as variáveis operativas destes dispositivos. A possibilidade de incluir sistemas de armazenamento de energia elétrica é avaliada através de uma análise custo-benefício. A metodologia proposta é aplicada a um sistema teste, submetido a diversas condições operativas, e os resultados obtidos são amplamente discutidos. / [en] The transmission expansion planning (TEP) aims at identifying new reinforcements for the network, allowing a technically adequate connection between demand and generation of electric energy, both foreseen for a given planning horizon. A good expansion plan must ensure a balance between investment and operating costs, while maintaining a satisfactory level of security of the electric energy supply. However, identifying good expansion plans for TEP has become an increasingly difficult task. This is mainly due to the characteristics and dimensions of the current systems, the nonlinearity and combinatorial nature of the optimization problem, and the uncertainties present in the data. Forecasting errors, equipment unavailability, and the availability of natural resources are parameters that vary in a random way and insert a high degree of uncertainty in the electrical system, which proportionally increases with the planning horizon. One of the most relevant uncertainties to be managed in the upcoming decades will be the generation capacity from renewable sources, particularly wind power, due to its great variability. Storage devices will allow better use of these sources and, therefore, it becomes necessary to develop computational tools capable of considering such devices in the TEP problem. This dissertation presents a new methodology to support the TEP problem by inserting electric energy storage to increase the use of renewable energy in the system, while respecting the security restrictions of the network, following the demand curve and taking into account the operational variables of these devices. The possibility of including electric energy storage systems is evaluated through a costbenefit analysis. The proposed methodology is applied to a test system, subject to various operating conditions, and the obtained results are widely discussed.

<p dir="ltr">The urgent global shift towards decarbonization necessitates the development of robust frameworks to navigate the complex technological, financial, and regulatory challenges emerging in the clean energy transition. Furthermore, the increased adoption of renewable energy sources (RES) is correlated to the exponential growth in weather data research over the last few years. This circular relationship, where big data drives renewable growth, which feeds back the data pipeline, serves as the primary focus of this study: the development of computational tools across diverse spatial and temporal scales for the optimal design and operation of renewable energy-based systems. Two scales are considered, differentiated by their primary objectives and techniques used. </p><p dir="ltr"> In the first one, the integration of probabilistic forecasts into the operations of RES microgrids (MGs) is studied in detail. It is revealed that longer scheduling horizons can reduce dispatch costs but at the expense of forecast accuracy due to increased prediction accuracy decay (PAD). To address this, a novel method that determines how to split the time horizon into timeblocks to minimize dispatch costs and maximize forecast accuracy is proposed. This forms the basis of an optimal rolling horizon strategy (ORoHS) which schedules distributed energy resources over varying prediction/execution horizons. Results offer Pareto-optimal fronts, showing the trade-offs between cost and accuracy at varying confidence levels. Solar power proved more cost-effective than wind power due to lower variability, despite wind’s higher energy output. The ORoHS strategy outperformed common scheduling methods. In the case study, it achieved a cost of \$4.68 compared to \$9.89 (greedy policy) and \$9.37 (two-hour RoHS). The second study proposes the Caribbean Energy Corridor (CEC) project, a novel, ambitious initiative that aims to achieve total grid connectivity between the Caribbean islands. The analysis makes use of thorough data procedures and optimization methods for the resource assessment and design tasks needed to build such an infrastructure. Renewable energy potentials are quantified under different temporal and spatial coverages to maximize usage. Prioritizing offshore wind development, the CEC’s could significantly surpass anticipated growth in energy demand, with an estimated installed capacity of 34 GW of clean energy upon completion. The corridor is modeled as an HVDC grid with 32 nodes and 31 links. Underwater transmission is optimized with a Submarine-Cable-Dynamic-Programming (SCDP) algorithm that determines the best routes across the bathymetry of the region. It is found that the levelized cost of electricity remains on the low end at \$0.11/kWh, despite high initial capital investments. Projected savings reach \$ 100 billion when compared with ”business-as-usual” scenarios and the current social cost of carbon. Furthermore, this infrastructure has the potential to create around 50,000 jobs in construction, policy, and research within the coming decades, while simultaneously establishing a robust and sustainable energy-water nexus in the region. Finally, the broader implications of these works are explored, highlighting their potential to address global challenges such as energy accessibility, prosperity in conflict zones, and sharing these discoveries with the upcoming generations.</p>