Ian J. Scott

TL;DR: A new methodology for adjusting the outputs of clustering algorithms is proposed that provides three advantages: the targeted level of net demand is captured, the underlying net demand shapes that define ramping challenges are accurately represented, and the relationship between annual energy and peakDemand is captured.

TL;DR: In this article, the potential of P2P energy trading under different available technologies and market paradigms, by analyzing the economic benefits for residential consumers and prosumers, given different solar generation contexts and load flexibility levels.

TL;DR: In this article, the authors investigate the importance of the approach to representing long-term uncertainty in the modelling used to evaluate different decarbonisation or renewable support policies, and find that incorporating uncertainty as individual scenarios results in wind being selected as the most cost efficient technology to respond to decarbonization policies, on average.

TL;DR: Investigation of the importance of representing a wide range of economic and physical sources of uncertainty in the modelling of the electricity market, both for investment decision making and descriptive market modelling demonstrates the difference between a deterministic and stochastic solution increases non-linearly when uncertainties across multiple inputs are combined.

TL;DR: In this paper, the authors examined how the electricity supply can evolve into the future to meet potential emission obligations for the period of 2020-2040, and generated a range of emission reduction costs that provide important benchmarks for the relatively under-studied sub-Saharan region and identify drivers of these costs specific to developing countries.