This work presents a sequential Monte Carlo-based integrated gas and power flow (IGPF) model to quantify how different sources of uncertainty propagate within the integrated gas and electricity network (IGEN). The uncertain input parameters, i.e. photovoltaic and wind generation, and electricity and heat demand are represented by weekly probabilistic time-series profiles. The time-series profiles of photovoltaic and wind generation are determined using respective Markov chains, whereas the fluctuations in time-series profiles of electricity and heat demand are modelled to comply with respective Gaussian distributions. The goodness-of-fit of these probabilistic time-series profiles to respective historical datasets is evaluated using the Kolmogorov-Smirnov test. Subsequently, the operation of gas and electricity networks, coupled through power-To-gas technology, is simulated using the sequential Monte Carlo-based IGPF model. The effectiveness of proposed approach is assessed through a case study in a localised energy network. Finally, four test-cases are designed to investigate the impact of increasing renewable penetration levels on uncertainty propagation in IGEN.
|Title of host publication||2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Publication status||Published - Aug 2020|
|Event||2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Liege, Belgium|
Duration: 18 Aug 2020 → 21 Aug 2020
|Name||2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 - Proceedings|
|Conference||2020 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020|
|Period||18/08/20 → 21/08/20|
Bibliographical noteFunding Information:
This work was supported by EPSRC through the Supergen Energy Networks Hub project under Grant EP/S00078X/1 and EPSRC National Centre for Energy Systems Integration under Grant EP/P01173/1.
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