Abstract
Many remote locations in Scotland and beyond rely on diesel generators to generate the required electricity and on oil boilers to provide hot water and space heating. This combination leads to high costs for the electricity and heat as well as to local pollution. In recent years more and more hybrid electricity systems which combine renewable electricity generation with energy storage and backup diesel generators are build in such locations to provide the electricity. However, due to the variability of the renewable resource and the high costs of battery energy storage, the diesel generators usually provide more than 10% of the electricity and a large fraction of the renewable electricity needs to be dumped. On top of this, the heat demand which is a large fraction of the total energy demand is still provided by fossil fuel boilers. The wasted renewable electricity increases the capital and running costs while the conventional heating and backup diesel generator have high greenhouse gas emissions. A more ambitious approach is the combination of the electricity and heating system. Such a combination can achieve a higher renewable fraction for the complete system and better utilisation of the renewable resource. This is due to the different demand profiles of electricity and heat and to the lower cost and higher capacity of thermal energy storage.
In this contribution a simulation framework for the design and optimisation of a hybrid electricity and heating system is presented and applied to a microgrid case study. This framework models a number of electricity and heat generation (solar, wind, wave, diesel generators, boilers, heat pumps, resistive heating) as well as electricity and thermal energy storage (batteries and hot water tanks) units. The framework is linked to multi-objective optimisation methods which enable the optimisation with respect to multiple and often conflicting objectives. This framework is used in a case study to optimise a hybrid electricity and heating system for a Scottish island with respect to greenhouse gas emissions, renewables fraction and levelised cost of energy. The resulting system is compared to the conventional system, i.e. diesel generators and oil boilers, and a hybrid electricity system. As expected the synergies between the electricity and heating systems lead to lower greenhouse gas emissions. On top of this, this approach can lead to lower energy costs and less wasted renewable electricity.
In this contribution a simulation framework for the design and optimisation of a hybrid electricity and heating system is presented and applied to a microgrid case study. This framework models a number of electricity and heat generation (solar, wind, wave, diesel generators, boilers, heat pumps, resistive heating) as well as electricity and thermal energy storage (batteries and hot water tanks) units. The framework is linked to multi-objective optimisation methods which enable the optimisation with respect to multiple and often conflicting objectives. This framework is used in a case study to optimise a hybrid electricity and heating system for a Scottish island with respect to greenhouse gas emissions, renewables fraction and levelised cost of energy. The resulting system is compared to the conventional system, i.e. diesel generators and oil boilers, and a hybrid electricity system. As expected the synergies between the electricity and heating systems lead to lower greenhouse gas emissions. On top of this, this approach can lead to lower energy costs and less wasted renewable electricity.
Original language | English |
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Publication status | Published - 26 Nov 2015 |
Event | UK Energy Storage Conference 2015 - University of Birmingham, Birmingham, United Kingdom Duration: 25 Nov 2015 → 27 Nov 2015 |
Conference
Conference | UK Energy Storage Conference 2015 |
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Country/Territory | United Kingdom |
City | Birmingham |
Period | 25/11/15 → 27/11/15 |
Keywords / Materials (for Non-textual outputs)
- Hybrid Energy System
- Hybrid Electricity and Thermal System
- Simulation framework
- Multi-objective optimization
- Energy storage
- Thermal energy storage