Optimal Scheduling of a Fuel Cell for Domestic Combined Heat and Power Considering Variable Electricity Price

This work describes the development of a model and optimisation method to schedule a domestic fuel cell CHP device in order to maintain the internal temperature of a dwelling within defined limits, whilst minimising the cost of operation of the system. The work is carried out as there is an increasing need to find solutions for reducing greenhouse gas emissions from the heating sector. Hydrogen is amongst the contending methods for reducing GHG emissions in heating, with projects such as the Leeds City Gate scheme proposing the conversion of citywide gas networks to hydrogen. This leads to the possibility of using hydrogen powered fuel cells to produce both heat and power for domestic properties, increasing the efficiency of the fuel use. With increased penetration of generation such as solar photovoltaic onto low voltage networks, it is increasingly important to be able to actively control generation and demand on the network. The increased use of variable electricity tariffs seeks to influence consumer behaviour, whilst offering consumers the ability to reduce costs. Scheduling the output of a fuel CHP device to take advantage of electricity price variations, whilst maintaining domestic dwelling temperatures can offer advantages to both the consumer and network as a whole.
Research carried out by the University of South Wales (USW) as part of the Flexible Integrated Energy Systems project (FLEXIS) is investigating the optimal scheduling of a domestic fuel cell CHP. A model has been developed consisting of a building thermal model, fuel cell, auxiliary boiler, and battery. The model uses predicted temperature, insolation, and building activity/hot water usage to optimally schedule the operation of the fuel cell and battery over a 24 hour period with one minute resolution. A mixed integer linear optimisation model has been developed, which accounts for fuel cell minimum turndown ratio, fuel cell ramp rate limit, start up/shut down costs, and battery charging/discharging efficiency. An online optimisation method is used, where the optimal solution for the current time step is retained, and the optimisation routine then moves on to the next time step. The results show that it is possible to control the fuel cell to reduce costs whilst maintain the building temperature within limits. Figure 1 a) shows a schematic of the gas, electricity and heat flows used in the model, whilst Figure 1 b) shows the fuel cell power, boiler output and building temperature.


Figure 1: a) Schematic of gas, electricity and heat flows in model. b) Fuel cell power, boiler thermal power, and internal building temperature over 24 hours.