Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe Sensitivity of Power System Expansion Models
120
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Power system expansion models are a widely used tool for planning powersystems, especially considering the integration of large shares of renewableresources. The backbone of these models is an optimization problem, whichdepends on a number of economic and technical parameters. Although theseparameters contain significant uncertainties, the sensitivity of power systemmodels to these uncertainties is barely investigated. In this work, we introduce a novel method to quantify the sensitivity ofpower system models to different model parameters based on measuring theadditional cost arising from misallocating generation capacities. The value ofthis method is proven by three prominent test cases: the definition of capitalcost, different weather periods and different spatial and temporal resolutions.We find that the model is most sensitive to the temporal resolution. Fur-thermore, we explain why the spatial resolution is of minor importance andwhy the underlying weather data should be chosen carefully.
rate research
Read More
The Vietnamese Power system is expected to expand considerably in upcoming decades. However, pathways towards higher shares of renewables ought to be investigated. In this work, we investigate a highly renewable Vietnamese power system by jointly optimising the expansion of renewable generation facilities and the transmission grid. We show that in the cost-optimal case, highest amounts of wind capacities are installed in southern Vietnam and solar photovoltaics (PV) in central Vietnam. In addition, we show that transmission has the potential to reduce levelised cost of electricity by approximately 10%.
Deep decarbonization of the electricity sector can be provided by a high penetration of renewable sources such as wind, solar PV and hydro power. Flexibility from hydro and storage complements the high temporal variability of wind and solar, and transmission infrastructure helps the power balancing by moving electricity in the spatial dimension. We study cost-optimal highly-renewable Chinese power systems under ambitious CO$ _2 $ emission reduction targets, by deploying a 31-node hourly-resolved techno-economic optimization model supported by a validated weather-converted 38-year-long renewable power generation and electricity demand dataset. With a new realistic reservoir hydro model, we find that if CO$_2$ emission reduction goes beyond 70%, storage facilities such as hydro, battery and hydrogen become necessary for a moderate system cost. Numerical results show that these flexibility components can lower renewable curtailment by two thirds, allow higher solar PV share by a factor of two and contribute to covering summer cooling demand. We show that expanding unidirectional high-voltage DC lines on top of the regional inter-connections is technically sufficient and more economical than ultra-high-voltage-AC-connected One-Net grid. Finally, constraining transmission volume from the optimum by up to 25% does not push total costs much higher, while the significant need for battery storage remains even with abundant interconnectivity.
A fully renewable European power system comes with a variety of problems. Most of them are linked to the intermittent nature of renewable generation from the sources of wind and photovoltaics. A possible solution to balance European generation and consumption are European hydro power with its seasonal and North African Concentrated Solar Power with its daily storage characteristics. In this paper, we investigate the interplay of hydro and CSP imports in a highly renewable European power system. We use a large weather database and historical load data to model the interplay of renewable generation, consumption and imports for Europe. We introduce and compare different hydro usage strategies and show that hydro and CSP imports must serve different purposes to maximise benefits for the total system. CSP imports should be used to cover daily deficits, whereas hydro power can cover seasonal imbalances. If hydro is used in a Hydro First strategy, only around one quarter of North African Solar Power could be exported to Europe, whereas this number increases to around 60%, if a cooperative hydro strategy is used.
We consider state-aggregation schemes for Markov chains from an information-theoretic perspective. Specifically, we consider aggregating the states of a Markov chain such that the mutual information of the aggregated states separated by T time steps is maximized. We show that for T = 1 this approach recovers the maximum-likelihood estimator of the degree-corrected stochastic block model as a particular case, thereby enabling us to explain certain features of the likelihood landscape of this popular generative network model from a dynamical lens. We further highlight how we can uncover coherent, long-range dynamical modules for which considering a time-scale T >> 1 is essential, using synthetic flows and real-world ocean currents, where we are able to recover the fundamental features of the surface currents of the oceans.
In October of 2020, China announced that it aims to start reducing its carbon dioxide (CO2) emissions before 2030 and achieve carbon neutrality before 20601. The surprise announcement came in the midst of the COVID-19 pandemic which caused a transient drop in Chinas emissions in the first half of 2020. Here, we show an unprecedented de-carbonization of Chinas power system in late 2020: although Chinas power related carbon emissions were 0.5% higher in 2020 than 2019, the majority (92.9%) of the increased power demand was met by increases in low-carbon (renewables and nuclear) generation (increased by 9.3%), as compared to only 0.4% increase for fossil fuels. Chinas low-carbon generation in the country grew in the second half of 2020, supplying a record high of 36.7% (increased by 1.9% compared to 2019) of total electricity in 2020, when the fossil production dropped to a historical low of 63.3%. Combined, the carbon intensity of Chinas power sector decreased to an historical low of 519.9 tCO2/GWh in 2020. If the fast decarbonization and slowed down power demand growth from 2019 to 2020 were to continue, by 2030, over half (50.8%) of Chinas power demand could be provided by low carbon sources. Our results thus reveal that China made progress towards its carbon neutrality target during the pandemic, and suggest the potential for substantial further decarbonization in the next few years if the latest trends persist.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
