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Technical advances and the decreasing costs of photovoltaic (PV) and battery (B) systems are key drivers for the consumer-prosumer transition, and crucial components in many countries' energy strategies for reducing the emission intensity in energy systems and supporting the phase-out of nuclear energy generation. The build-up and expansion of solar capacities in response to the gradual reduction of electricity generated from fossil or nuclear fuels leads to an increasingly decentralized energy supply system, which consists of many small utility- and residential-scale generators spread across a service area alongside larger centralized power plants that compensate for the intermittent nature of renewable energy sources. Consequently, the decentralization of the energy system also implies a shift from large (mostly publicly owned) investors in centralized power stations to smaller, private investors in small-scale PV systems. During the past decade, remuneration rates for PV systems in many countries have been continuously reduced in response to falling PV module costs. In some service areas, the remuneration rate is considerably less than the retail rate for energy, making it more attractive to self-consume a large share of the generated electricity. Self-consumption is defined as the share of the annually generated energy that can be directly consumed by the prosumer. The fraction that cannot be self-consumed is injected into the grid at a remuneration rate. The achieved rate of self-consumption is an important factor that influences the profitability of photovoltaic battery (PVB) systems and depends (among other factors) on how well the load profile of a household concurs with its solar production. However, the influence of the individual load profiles on the profitability of PVB systems has received little attention in the academic literature. Therefore, the first objective of this thesis is the assessment of the impact of load profile heterogeneity on the profitability of solar battery systems under various system cost scenarios. A techno-economic simulation model has been developed to account for load profile heterogeneity by processing a dataset consisting of more than 4000 real world load profiles with today's costs (without subsidies) and future cost scenarios. The simulation results for the location of Zurich suggest that the profitability of PVB systems varies considerably between households, even for households with comparable total annual demands. About 40% of the households can reach profitability under today's cost assumption (2000 €/kWp, 1000 €/kWh. Solar becomes profitable for nearly all (94%) analyzed households if costs drop to 1000 €/kWp. For battery storage to be profitable for prosumer households, battery system costs must fall to 250-500 €/kWh. Utility companies and service providers have started to offer information systems that enable households to estimate the profitability of PVB systems based on some basic inputs, such as annual demand or orientation of the solar arrays. However, these information systems omit household specific load profile data, which also contains information about the shape of the load profile. The integration of load profile data can provide more personalized and reliable estimation of profitability, optimal PV and battery size, as well as annual performance factors, like expected self-consumption and self-sufficiency rates. However, household specific smart-meter data is often not available because utilities do not store smart-meter data with sub-hourly resolution by default. Thus, the estimation of household specific profitability, optimal system sizes and expected annual performance factors need to rely on smart-meter data recorded over a time period comparable to the duration of the tendering process for a PVB system, which typically lasts a few months. At the beginning or throughout the tendering phase, smart-meter data can be collected to estimate profitability, optimal sizes and annual performance factors would allow households to make more cautious and confident investment decisions. The academic literature already provides statistical methods that help predict self-consumption or self-sufficiency from annual demand or installed solar power. However, very few existing studies include the load profile and its shape as an input factor and apply it to an unseen dataset large enough to verify its application in the tendering phase of a PVB system. Given the importance of the load profile for the personalized estimation of profitability, the second objective of the dissertation is the development and evaluation of a machine learning algorithm for the prediction of PVB profitability. The proposed machine learning algorithm uses the load profile as an exclusive input factor and predicts the PVB profitability, along with the optimal PV size, optimal battery size, achieved self-consumption and self-sufficiency rates. Good prediction accuracy was found if the smart-meter data is collected over a minimum timeframe of 30 days, which is acceptable for the prediction of profitability, optimal system sizes and annual performance factors during the tendering phase of a PVB system. Given the growing gap between the remuneration of injecting generated electricity into the grid and retail rates, one promising alternative might be electricity trading in peer-to-peer (P2P) networks. P2P energy networks may offer an alternative system in which prosumers market their surplus energy directly to consumers at a rate closer to the retail tariff that households face when they consume energy from the utility. Prosumers are therefore incentivized to maximize the share of their surplus generation that can be traded within a P2P network. However, little is known about optimal network or community configurations (i.e. number of households, prosumer share, generation and storage capacity) that reduce imports and exports out of the P2P network compared to stand-alone households. Hence, the third objective of the dissertation is the quantification of optimal P2P network configurations regarding optimal number of households, generation and storage capacity. To this end, the previously mentioned techno-economic model is extended to account for energy flows between prosumers and consumers that are part of a P2P network. Energy flows between peers lead to reductions in imports and exports at the community-level and can be compared against the sum of individual household-level imports and exports under various community configurations. The simulation results indicate that increasing the number of households within a community reduces the imports and exports out of the community, thus increasing the self-consumption and self-sufficiency rate of the entire community and enabling prosumers to market parts of their surplus generation directly to peers. However, the results also indicate that the self-consumption and self-sufficiency rate of the community already levels off with 10 participating households. Hence, adding more households to a community will only marginally increase its self-consumption and self-sufficiency rate. Therefore, larger communities are only marginally more effective in reducing imports and exports compared to many small communities consisting of at least 10 households. Prosumer-dominated communities are less effective in reducing imports and exports compared to communities with smaller prosumer shares, because the surplus generation and demand concur less frequently in communities consisting predominantly of prosumers. In a P2P community network, households should store energy only if that unit of energy cannot be simultaneously consumed elsewhere in the community. The more surplus energy generated by prosumers in a network that can be directly consumed by other households within the community, the less storage capacity is required. The simulations indicate that battery storage systems -- sized to maximize the net present value -- are not required if the prosumer ratio in the community is not larger than 25%. Even in the extreme scenario where all members of the community are prosumers, the battery units of the individual households can be reduced by 26% if the batteries cooperate to reduce imports and exports at the community-level. Overall, the results of this thesis are relevant for households making individual investment decisions as well as for utility companies to more effectively identify and approach relevant customers for the installation of PVB systems. The results indicate that large spreads in profitability will continue to persist if costs for PVB systems fall in the future. Furthermore, the dissertation develops and evaluates a prototypical machine learning algorithm for the prediction of profitability, optimal system sizes and annual performance, even if incomplete input data, such as load profiles, are available. Beyond that, the dissertation provides simple design guidelines for P2P energy networks, suggesting that small communities of 10 households already maximize the self-consumption and self-sufficiency rate by effectively reducing imports and exports compared to individual households. Given the small community size required to reduce imports and exports, communities should be embedded locally on the low-voltage grid. The results of this dissertation highlight the need to discuss and develop regulations that facilitate community building and tariff schemes that incentivize local balancing. |
