(540c) Decarbonization of Electricity Grids: A Multi-Scale Challenge

Sharifzadeh Mahdi[1] , Shah Nilay.

Centre for process Systems Engineering (CPSE), Chemical Engineering Department, South Kensington Campus, Imperial College London, SW7 2AZ, UK.

Abstract - Electricity power consumption in a country has a direct relation to the quality of life and economic growth. Nevertheless, about a quarter of global greenhouse gas emissions is associated with electricity and heat production, [1]. Therefore, decarbonisation of electricity grids is one of the major environmental impact pathways. The main methods for the desired decarbonization include carbon capture from exiting fossil-fired power generation stations and substituting fossil fuels with renewable or cleaner resources. This presentation covers a part of our recent research activities for design and operation of low carbon electricity grids.

In the first part of the presentation, we review our research for integrated design and operation of electricity grids. We proposed a comprehensive framework in which design and operation of electricity grid is considered simultaneously using stochastic mixed integer optimization programming. The formulated mathematical model is comprehensive and include conventional members of the electricity grid, the potentials for including renewable generators such as wind and solar energies, energy storage and the transmission network. The proposed method is demonstrated for the case of retrofitting the UK electricity grid to include 50% penetration of the renewables energies. Five years of historical data was applied for generating the representative scenarios regarding availability of wind and solar power and fluctuations in demand. The features of the interest include the configuration of the electricity grid after retrofit, investment in renewable power generators and the operational scheduling for power generation under various stochastic scenarios, [2].

Electricity grids are subject to high degrees of uncertainties. Examples of uncertainties in electricity grid include the stochastic behaviours of the solar and wind energies and fluctuation in electricity demand. The key observation is that future electricity grids will consist of a heterogeneous portfolio of energy conversion technologies and will be associated with a large degree of uncertainties. Such operational uncertainties can be accommodated using standby power generation capacitates, often provided by â??fast-actingâ? grid members such as gas-fired power plants or by investing in energy storage. However, there is a much cheaper third option and that is to enhance the â??smartnessâ? of the grid. In this part of our research, we investigate the application of artificial intelligence (AI) for enhancing the predictability of electricity demand, in addition to wind speed and solar radiance. Various AI methods are incorporated and compared and the benefits of grid intelligence are quantified in economic terms, [3].

Currently, 80% of the household energy consumption is associated with heating and is supplied by fossil fuels. While electrification of residential heating systems can deliver an important environmental impact, electricity grids, unlike natural gas grids, are not resilient enough to accommodate the uncertainties in the heat demand. The third and last part of this presentation will study the potentials for decarbonisation of urban energy systems using combined heat and power systems integrated with carbon capture and storage. The feature of interest is the overall network flexibility and is demonstrated on the case of the urban energy system in London, [4].

Keywords

Smart grid, renewable energy, optimization, Big-data, Uncertainty, Combined Heat and Power (CHP) generation

References

[1] EPA, Global Emissions by Economic Sector, accessed online:

https://www3.epa.gov/climatechange/ghgemissions/global.html

[2] Sharifzadeh M, Lubiano H, Shah N. Optimal design and operation of integrated renewable energy systems. Under review.

[3] Sharifzadeh M, Sikinioti-Lock, A., Lubiano H, Shah N. The economic implications of enhance electricity grid smartness. In preparation.

[4] Sharifzadeh M, Shah N. Decarbonization of urbane energy using combined heat and energy cycles. In preparation.