Energy System Optimization for Net-Zero Electricity
A novel and fast-converging cost minimization model using non-linear constrained mathematical programming (NLP) has been developed to optimize renewable and bioenergy generation and storage systems’ capacities for transitioning to an electricity system with net-zero greenhouse gas emissions. Running...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Digital Chemical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508122000175 |
| _version_ | 1811258014764630016 |
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| author | Jhuma Sadhukhan Sohum Sen T.M.S. Randriamahefasoa Siddharth Gadkari |
| author_facet | Jhuma Sadhukhan Sohum Sen T.M.S. Randriamahefasoa Siddharth Gadkari |
| author_sort | Jhuma Sadhukhan |
| collection | DOAJ |
| description | A novel and fast-converging cost minimization model using non-linear constrained mathematical programming (NLP) has been developed to optimize renewable and bioenergy generation and storage systems’ capacities for transitioning to an electricity system with net-zero greenhouse gas emissions. Running this temporal and spatial multi-scale model gives an in-depth understanding of realistic electricity mixes in sustainable transitioning. The model comprises three interactive modules 1) analytics and visualization of data inputs, climatic and demand time-series, and design configurations, and output results, optimal electricity mix, and storage characteristics, 2) mathematical models of renewable generation systems using non-linear climate-dependent capacity factor time-series and energy system components, and 3) NLP to minimize the total cost. Hourly and total energy balances are the crucial constraints influencing the speed and efficacy of the solution. Fast-converged solutions of the NLP model are updated considering battery energy storage with a few hours dispatch time for attainable optimum net-zero electricity (NZE) mix. The NLP optimization model is tested on the energy-intensive UK South. The feasible optimum regional solutions characterized as high renewable supply-medium-to-high-demand (South West), low-supply-medium-demand (Greater London), and high-supply-high-demand (South East) scenarios are projected to the UK national level. The inputs to the NLP model are wind speed and solar radiation with annual hourly resolutions curated from the Centre for Environmental Data Analysis, process economic parameters (investment, fixed, operating, and resource costs, weighted average cost of capital, and life in years of processes) from the LUT energy system model, and global warming potential impacts from our archived literature. 2020-2050 electricity mixes are analyzed with varying costs and demands. The NLP optimization followed by energy storage feasibility analysis gives the following attainable optimal energy mixes: wind: 55%, solar: 29%, hydro: 0.5%, geothermal: 0.4%, and bioenergy: 1% (high-supply-medium-to-high-demand); wind: 52%, solar: 32%, hydro: 0.5%, geothermal: 0.5%, and bioenergy: 1% (low-supply-medium-demand); and wind: 45%, solar: 23%, hydro: 0.7%, geothermal: 0.7%, and bioenergy: 10% (high-supply-high-demand). Energy storage (13.5 TWh in the UK South) with 13-22% contributions of load demand (80 TWh in the UK South) costs 14% of the levelized cost of electricity production, 120-190 EURO/MWh. The high-supply-medium-to-high-demand scenario, providing the UK NZE projection of wind: 40GW, solar: 21GW, bioenergy and other renewables: 5GW, nuclear: 6GW, and gas with carbon capture, utilization, storage, and sequestration (CCUS): 5GW by 2050, mirrors the government's NZE plan. The additional wind (currently at 8.65GW), solar (currently at 1.5GW), and CCUS (currently there is none) capacities require £23 billion, £4 billion, and £1 billion investment costs. |
| first_indexed | 2024-04-12T18:06:37Z |
| format | Article |
| id | doaj.art-c9ede577510148ad87f345838868c548 |
| institution | Directory Open Access Journal |
| issn | 2772-5081 |
| language | English |
| last_indexed | 2024-04-12T18:06:37Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Digital Chemical Engineering |
| spelling | doaj.art-c9ede577510148ad87f345838868c5482022-12-22T03:21:59ZengElsevierDigital Chemical Engineering2772-50812022-06-013100026Energy System Optimization for Net-Zero ElectricityJhuma Sadhukhan0Sohum Sen1T.M.S. Randriamahefasoa2Siddharth Gadkari3Centre for Environment & Sustainability, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom; Department of Chemical and Process Engineering, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom; Corresponding author.Centre for Environment & Sustainability, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom; Computer Science Department, University College London, Gower Street, London WC1E 6BT, United KingdomCentre for Environment & Sustainability, University of Surrey, Guildford, Surrey, GU2 7XH, United KingdomDepartment of Chemical and Process Engineering, University of Surrey, Guildford, Surrey, GU2 7XH, United KingdomA novel and fast-converging cost minimization model using non-linear constrained mathematical programming (NLP) has been developed to optimize renewable and bioenergy generation and storage systems’ capacities for transitioning to an electricity system with net-zero greenhouse gas emissions. Running this temporal and spatial multi-scale model gives an in-depth understanding of realistic electricity mixes in sustainable transitioning. The model comprises three interactive modules 1) analytics and visualization of data inputs, climatic and demand time-series, and design configurations, and output results, optimal electricity mix, and storage characteristics, 2) mathematical models of renewable generation systems using non-linear climate-dependent capacity factor time-series and energy system components, and 3) NLP to minimize the total cost. Hourly and total energy balances are the crucial constraints influencing the speed and efficacy of the solution. Fast-converged solutions of the NLP model are updated considering battery energy storage with a few hours dispatch time for attainable optimum net-zero electricity (NZE) mix. The NLP optimization model is tested on the energy-intensive UK South. The feasible optimum regional solutions characterized as high renewable supply-medium-to-high-demand (South West), low-supply-medium-demand (Greater London), and high-supply-high-demand (South East) scenarios are projected to the UK national level. The inputs to the NLP model are wind speed and solar radiation with annual hourly resolutions curated from the Centre for Environmental Data Analysis, process economic parameters (investment, fixed, operating, and resource costs, weighted average cost of capital, and life in years of processes) from the LUT energy system model, and global warming potential impacts from our archived literature. 2020-2050 electricity mixes are analyzed with varying costs and demands. The NLP optimization followed by energy storage feasibility analysis gives the following attainable optimal energy mixes: wind: 55%, solar: 29%, hydro: 0.5%, geothermal: 0.4%, and bioenergy: 1% (high-supply-medium-to-high-demand); wind: 52%, solar: 32%, hydro: 0.5%, geothermal: 0.5%, and bioenergy: 1% (low-supply-medium-demand); and wind: 45%, solar: 23%, hydro: 0.7%, geothermal: 0.7%, and bioenergy: 10% (high-supply-high-demand). Energy storage (13.5 TWh in the UK South) with 13-22% contributions of load demand (80 TWh in the UK South) costs 14% of the levelized cost of electricity production, 120-190 EURO/MWh. The high-supply-medium-to-high-demand scenario, providing the UK NZE projection of wind: 40GW, solar: 21GW, bioenergy and other renewables: 5GW, nuclear: 6GW, and gas with carbon capture, utilization, storage, and sequestration (CCUS): 5GW by 2050, mirrors the government's NZE plan. The additional wind (currently at 8.65GW), solar (currently at 1.5GW), and CCUS (currently there is none) capacities require £23 billion, £4 billion, and £1 billion investment costs.http://www.sciencedirect.com/science/article/pii/S2772508122000175Power system optimization (Python-Pyomo GAMS optimizer)Wind-solar-hydro-geothermal-bioenergy-nuclear-gas with CCUSTechno-economic analysis of energy systemsWind speed and solar radiation climatic analysisCapacity factor models of renewable energy systemsNet-zero electricity transitioning to build back greener |
| spellingShingle | Jhuma Sadhukhan Sohum Sen T.M.S. Randriamahefasoa Siddharth Gadkari Energy System Optimization for Net-Zero Electricity Digital Chemical Engineering Power system optimization (Python-Pyomo GAMS optimizer) Wind-solar-hydro-geothermal-bioenergy-nuclear-gas with CCUS Techno-economic analysis of energy systems Wind speed and solar radiation climatic analysis Capacity factor models of renewable energy systems Net-zero electricity transitioning to build back greener |
| title | Energy System Optimization for Net-Zero Electricity |
| title_full | Energy System Optimization for Net-Zero Electricity |
| title_fullStr | Energy System Optimization for Net-Zero Electricity |
| title_full_unstemmed | Energy System Optimization for Net-Zero Electricity |
| title_short | Energy System Optimization for Net-Zero Electricity |
| title_sort | energy system optimization for net zero electricity |
| topic | Power system optimization (Python-Pyomo GAMS optimizer) Wind-solar-hydro-geothermal-bioenergy-nuclear-gas with CCUS Techno-economic analysis of energy systems Wind speed and solar radiation climatic analysis Capacity factor models of renewable energy systems Net-zero electricity transitioning to build back greener |
| url | http://www.sciencedirect.com/science/article/pii/S2772508122000175 |
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