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Effective decarbonization strategies employ both hard and soft measures to address climate change. Soft approaches can deliver carbon savings comparable to hard approaches, which are typically both infrastructure- and investment-intensive and are often postponed due to financial risks. As demonstrated in this study, the time variable can be used as a lever to reduce energy demand through an academic calendar shift that aligns the end of fall term with Thanksgiving break, thus reducing the need for redundant holiday travel among a significant population. If implemented at all undergraduate campuses of the University of California (UC) system, this strategy would produce a significant reduction (nearly 50,000 tCO2e) in the annual carbon footprint of the UC, an impact approximately equal to decarbonizing all UC-owned vehicles. This outcome is robust to many of its key assumptions and can be realized at any higher education institution that sees a significant portion of its population travel for Thanksgiving. The proposed academic calendar shift is a prime example of a soft decarbonization measure; it can be implemented within existing systems, provides numerous co-benefits, does not require new technologies, and augments ongoing hard decarbonization efforts that will lead to compounding benefits into the future.

Summary: The ever-growing installation of solar power systems imposes severe challenges on the operations of local and regional power grids due to the inherent intermittency and variability of ground-level solar irradiance. In recent decades, solar forecasting methodologies for intra-hour, intra-day and day-ahead energy markets have been extensively explored as cost-effective technologies to mitigate the negative effects on the power grids caused by solar power instability. In this work, the progress in intra-hour solar forecasting methodologies are comprehensively reviewed and concisely summarized. The theories behind the forecasting methodologies and how these theories are applied in various forecasting models are presented. The reviewed mathematical tools include regressive methods, stochastic learning methods, deep learning methods, and genetic algorithm. The reviewed forecasting methodologies include data-driven methods, local-sensing methods, hybrid forecasting methods, and application orientated methods that generate probabilistic forecasts and spatial forecasts. Furthermore, suggestions to accelerate the development of future intra-hour forecasting methods are provided.

The parameter optimisation of designed controllers for power systems is always a big concern and needs a lot of effort of researchers especially when the electricity grid becomes larger and more complex. This study proposes a control parameterisation using genetic algorithms (GAs) for power oscillation damping (POD) incorporating synchronous condensers (SCs) via software-in-the-loop simulation to enhance the damping and frequency stability for low-inertia systems. A closed-loop interfaced setup among real-time digital simulator, MATLAB, and OLE for process communication running in real time is analysed and implemented to optimise the POD parameters of a SC. Furthermore, a Prony technique based on the system measurement is applied to find out the frequency and damping ratio of the dominant oscillation mode. The POD optimal parameters are determined by the GA objective function that maximises the damping ratio of the dominant oscillation mode. The effectiveness of the proposed method in damping power oscillations and frequency stability improvement is verified through simulation results of the future western Danish power system. Simulation results demonstrate that the proposed approach offers good performance for parameter optimisation of the POD.