Carbon Intelligent Framework for Handling Uncertainty in Smart Energy Systems

Abstract

Carbon emissions are becoming a global concern responsible for climate change. The renewable energy sources (RESs) such as wind, solar, biomass are gaining importance to reduce emissions in the energy sector. However, these sources depend highly on various technical, economical, and environmental conditions and hence, a planned strategy is required to place different RESs based on their suitability. The thesis presents a detailed analysis on energy purchased/sold, costs associated with installing new RESs and the operational costs of existing electricity sources to reduce the total carbon emissions in the UK’s residential
sector.

The inherent intermittency in RESs introduces significant uncertainties, demanding robust uncertainty modelling. Most of the existing works fail to capture the dynamic fluctuations in the renewable integrated energy networks especially under extreme weather conditions. Therefore, a time-coordinated strategy is proposed to capture temporal correlations among uncertain parameters dynamically. The proposed method is compared with the traditional Markov Chain Monte Carlo technique, showing a notable shift in the probability density function under adverse weather events. Testing this approach on an IEEE 33-bus system results in an improved voltage profile and reduced power losses. Furthermore, renewable reliance and carbon emission factors are introduced to evaluate our method’s performance, and these metrics reveal the network’s sustainability and resilience.

Additionally, with the integration of RESs into modern power systems, energy consumers participate in the energy market making the entire network more complex and uncertain. Various strategies exist in the literature to address the real-time uncertainties in the distribution networks. However, the existing works do not consider the overall carbon emissions of the network under such uncertainties that need further attention. In this research, an integrated two-fold approach is proposed that combines competitive market mechanisms with cooperative strategies to enhance reliability and resilience in distribution networks under uncertainty. In the normal operational stage, a
competitive approach is adopted wherein an optimal price is determined to meet the required consumer demand, facilitating energy sharing among areas and minimising reliance on the grid, thereby reducing carbon emissions associated with conventional energy generation. To detect and identify an uncertain event within the network, a heuristic algorithm is proposed, which determines the hidden inter-dependencies among the different network input parameters. Finally, to mitigate the impact of these identified uncertainties, a cooperative approach is introduced wherein all areas leverage the battery storage facilities to ensure continuity of energy supply and minimise overall losses. During an uncertain event, a maximum reduction of 97% is observed in the carbon footprints for the proposed scheme while maintaining the overall profit.