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STIPINST-Stipendiatstillinger i instituttsektoren

Stipendiatstillinger til SINTEF Energi (2017-2020)

Awarded: NOK 5.5 mill.

Magnus Askeland: Tittle on the thesis delivered for defense: Policy issues for distributed energy resources as a part of larger energy systems This thesis’s most important overarching contribution is the insight into how area-level pricing mechanisms can be designed to facilitate efficient use of energyrelated assets and flexibility at the local level while keeping the interaction with the centralised power market intact. The main contributions of this thesis can be summarised as: C1 Identification of regulatory issues: The thesis includes assessments of the regulatory framework related to distributed energy resources (DERs) on an area level. Regulatory issues that can create mismatches between stakeholder incentives and system optimality at a larger spatial scope are identified. C2 Development of modelling frameworks: Several models are developed based on the premise of decentralised decision-making in neighbourhood energy systems. These models calculate the outcomes based on decentralised decision making under various regulatory designs, which are benchmarked to the corresponding system optimal outcome. Combined, these models form a suitable framework for studying the effect of regulatory designs and pricing mechanisms on the deployment and operation of decentralised energy resources. C3 Regulatory assessments: Based on cost efficiency under decentralised decision-making and the requirement that grid pricing structures should not be excessively complicated, the research includes assessments on how the regulatory framework can be adapted to facilitate optimal solutions on a multi-stakeholder level. This includes both how to incentivise the appropriate amount and location of DERs and how to facilitate favourable operational patterns on an area level. This thesis provides an overview of the underlying motivation, research structure, methodological principles, and overarching conclusions of the research that has been carried out. Hence, the thesis aims to complement rather than repeat the content of the articles, which includes detailed descriptions of methodologies, results, and references. Per Aaslid: Tittle on the thesis delivered for defense: Optimal coordination of renewable sources and storage in energy-constrained power systems Integrating high levels of variable renewable energy sources (VRESs) in power systems imperils the security of supply. Energy storage systems (ESSs) contribute both to cost reduction through increased and improved utilization of VRESs, as well as to securing the supply in periods with low generation from VRESs. The work in this thesis has considered the modeling of microgrids (MGs), small scale power systems, with a high level of VRESs and ESSs and limited dispatchable generation capacity where VRESs and ESSs contribute to the security of supply. Although the presented work focuses on MGs, the findings are also relevant for larger systems. Since these systems rely on ESSs, where the ability to deliver power depends on a sufficiently high state-of-charge (SOC), they are vulnerable to persistent low generation from VRESs. Future generation and demand should therefore be considered in operation planning models with sufficient foresight, and for a broad range of possible scenarios. The implemented methods include: • A detailed non-linear battery optimization model representing the battery cell voltage and the converter efficiency with spline function based on empirical battery data. The model is capable of operating closer to the operational limits of the battery compared to existing simpler optimization models. • A linear multi-stage stochastic power system model using stochastic dual dynamic programming (SDDP) considering degradation due to cyclic and SOC dependent calendar degradation. The model can increase the expected lifetime of a battery by more than four years. The model results also show that it is advantageous to consider battery degradation in coherence with stochastic optimization. • A stochastic power system model using SDDP considering both short-term uncertainty within weather forecast horizon and long-term uncertainty for infinite foresight. Whereas rule-based operation and deterministic optimization causes significant load shedding in critical periods, the implemented method is superior at keeping the load shedding very low while still retaining low generation costs. The solution of stochastic dynamic programming based methods also has a useful representation with respect to valuating stored energy for systems of any size dominated by VRESs. The value of stored energy changes in time due to variations in future expected generation and demand, and it also changes with the SOC for itself and all other ESSs in the system. The value of stored energy is a useful quantity for valuating the stored energy in detailed models, for real-time operation, and for bidding into competitive markets.

Prosjektet har bidratt til forskerutdanning av to dyktige forskere som nå har fulle stillinger hos SINTEF Energi. Prosjektet har dermed bidratt til varig kompetansebygging i instituttet og arbeidet tas videre i nye prosjektforslag. I tillegg har prosjektet lagt til rette for reelt forskingssamarbeid mellom instituttet og NTNU via den felles gjennomføringen av prosjektet. Det arbeide som Prosjektleder kjenner best, nemlig arbeidet til Per Aaslid er relatert til en felles KSP søknad mellom NTNU - SINTEF og Statnett og inngår som bakgrunn i samarbeid mellom FME HydroCen og DoE HydroWires programmet i forprosjektet HydroFy om fremtidens marketdesign.

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STIPINST-Stipendiatstillinger i instituttsektoren

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