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|Titel:||Long-term capacity expansion planning with variable renewable energies : enhancement of the REMix energy system modelling framework|
|Zusammenfassung:||The large-scale integration of variable renewable energies (VRE) like Photovoltaics and wind power into the power system is crucial for the transition towards a sustainable electricity supply. However, due to the inherent characteristics of VRE, i.e. the site-specific, highly variable, and unreliable power generation, as well as their low variable generation costs, the large-scale deployment of VRE causes adequacy-, grid-related, and balancing-impacts for the residual system. These impacts and the related costs need to be considered for a concerted capacity expansion planning with VRE in order to identify cost-efficient and reliable transition pathways. Traditionally applied capacity expansion planning models have limitations to consider the value of energy at its time of the delivery of VRE and their impacts on the system due to the applied low system-operational detail. Hence, new planning methods are required to ensure a successful transition towards a sustainable electricity supply. This work enhances the REMix energy system modelling framework to allow for a concerted long-term capacity expansion planning with VRE. The outcome of this is the REMix-Capacity Expansion Model (REMix-CEM). The optimization model bridges the gap between traditional long-term capacity expansion planning and short-term power system operation models. This enables the model to consider the value and the impacts of a large-scale integration of VRE into the power system accurately within capacity expansion planning. This thesis describes the challenges of long-term capacity expansion planning with VRE and presents the developed model in detail. This includes a principle description of how REMix-CEM is typically applied by DLR for a science-based consultancy of planning authorities in developing and emerging countries. To demonstrate its capabilities, the flexible formulation of the model is used to investigate two important issues within a model-based long-term capacity expansion planning with VRE - the model foresight and the applied system-operational detail. Both issues can have a significant influence on results and computational effort of the model. These correlations are investigated within two case studies for a fictitious but representative power system of a developing country. Results of the first case study indicate that the type of model foresight (single-year myopic, multi-annual rolling horizon, or perfect foresight) has a strong influence when some of the input parameters change suddenly at one point of the planning time frame, while its influence is less pronounced when parameters changes rather continuously over the period of study. Only a large model foresight enables the model to anticipate future occurrences well in advance and to adopt its investment strategies accordingly. Furthermore, the analysis shows that the larger the model foresight the higher is the competitiveness of VRE and dispatchable RE, because their advantage to produce electricity at stable costs over the lifetime can be captured more precisely. However, it is also demonstrated that a larger model foresight means also a higher computational effort to solve the capacity expansion optimization problem. In addition, a large model foresight with perfect information over the planning time frame might not fully capture the decision frame-work of real-life decision makers. To keep computational effort manageable for long-term capacity expansion planning with VRE, investment decisions are typically based on a limited number of representative dispatch periods. These dispatch periods have the aim to represent the temporal variability of load and RE resources over the year as accurate as possible. Within the second case study it is shown that the average day method, which uses average values to assign values for RE resource availability to the utilized dispatch periods, is inappropriate for capacity expansion planning with VRE. The value of energy at its time of the delivery of VRE is modeled inaccurately and system flexibility requirements, caused by the integration of VRE, are underestimated systematically. The representative day method, which uses a sample of “real” historical days instead of average values, is significantly more suitable because extreme values are not averaged. This leads to a better approximation of VRE electricity generation, which allows a more accurate consideration of the value of energy at its time of the delivery of VRE and system flexibility requirements. System flexibility requirements can be captured within capacity expansion optimization especially by considering unit commitment constraints (UCCs) of thermal generators. However, this requires a large number of integer decision variables that describes the unit commitment status. This leads to high computational complexity. Hence, UCCs are typically neglected during capacity expansion optimization. Within the second case study it is however demonstrated that neglecting UCCs within capacity expansion planning with VRE leads to an overestimation of the competitiveness of VRE and an underestimation of the need for flexible generation and storage technologies. This work shows that by a linear relaxation for UCCs system flexibility restrictions can be captured accurately during long-term capacity expansion optimization with comparably low additional computational effort.|
|Enthalten in den Sammlungen:||04 Fakultät Energie-, Verfahrens- und Biotechnik|
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