In previous literature, carbon reduction of -4% to 12% is estimated for Stirling engine micro-CHP (Alanne et al., 2010, Carbon Trust, 2011, Staffell and Green, 2012, Peacock and Newborough, 2005) and so the number shown here, a 4% carbon emissions saving for Stirling engine micro-CHP, is consistent with those from the literature. The main previous field trial undertaken in the UK, the one by Carbon Trust (2011), also found average carbon savings of 4% for Stirling engines, again consistent with the value here. Fuel cell emissions savings were 35%, within the 16- 40% range in the literature (Peacock and Newborough, 2005). The grid carbon intensity below which emissions savings become negative was also examined in the literature, with Hawkes et al. (2011) estimating it to be 200 gCO2/kWh for fuel cell
micro-CHP. The value here is only slightly above that estimate, at 212 gCO2/kWh.
Stirling Engine micro-CHP Fuel Cell micro-CHP
Break-even price with tariffs 1614 9799 Break-even price no export
tariff 1568 7396
Break-even price no
generation tariff 533 2533
Break-even price no export
Alanne et al. (2010) estimates the economic savings from micro-CHP to be £165, while Carbon Trust (2011) estimates similar savings of £169, with a generation reward and -£11 without a generation reward, the savings here (£209 for Stirling engines) were above both these values, but the savings were in accordance with the range suggested in another paper of £200-250 (De Paepe et al., 2006). On the whole, the results from the field trial are mostly consistent with the literature. The previously mentioned Carbon Trust (2011) field trial found, annual savings of £158, using a Stirling engine with a heat-to-power ratio of 12:1, this heat-to-power ratio is double that of the Stirling engine used here, which may explain the smaller savings. The savings from fuel cells here were £1,269, higher than the £750 suggested by the literature (Staffell and Green, 2009), though their FiT was 10 p/kWh while the one used here was 13.45 p/kWh. Using a 10 p/kWh FiT with the data in this research produces savings of around £1,000, still a higher saving. It may be that the simulated fuel cell profiles in this research used the fuel cell for more of the year, thus generating more electricity and higher monetary returns.
3.9
Chapter Summary
This chapter has provided a summary of the data used throughout the thesis. It comprises of field trial data from the CLNR project, and a simulation of fuel cell generation profiles, which are based on UK household heat demand. The field trial data is high frequency minute-scale data of: household and heat pump demand, and Stirling engine micro-CHP and solar PV generation. It is demonstrated that the use of high frequency minute-scale data provides more information than lower frequency ten-minute or half-hourly data, especially on times of peak demand. Such accurate data should be important from a grid perspective.
This chapter also examined the economic and emissions saving potential of Stirling engine and fuel cell micro-CHP. It found that Stirling engines produce savings of 4%, while fuel cells produce savings of 35%, however, both these values will decline as the grid is decarbonised, and may become negative. Long term it is probable that micro-CHP will only be a valid low-carbon heating option if it utilises a carbon neutral fuel source.
Stirling engine micro-CHP produces limited savings for the household of £209 per annum. Fuel cell micro-CHP produces considerably better savings of over £1,200,
which is also higher than the savings of either heat pumps or solar PV. However, neither technology produces enough savings to cover their current capital cost. Fuel cells would have to fall in price to £9,000 or less to become economically viable, while Stirling engines would need to fall to £1,500. But even should prices fall to a point where micro-CHP in the form studied in this paper becomes economically attractive, the ongoing decarbonisation of the electricity network will steadily reduce the emissions case for micro-CHP. A potential boost to micro-CHP would however be from an alternative low-carbon fuel supply, which in practice would mean hydrogen (the limitations of biogas were discussed in Section 2.1.2).
4 M
ETHODOLOGY OF
NETWORK ANALYSIS
A distribution network model was developed to explore the potential impacts of micro-CHP, and other micro-generation technologies, on local distribution networks. This chapter explains the choice of method, the development of a model, and the calibration and evaluation of the model.
4.1
Chapter Introduction
As mentioned in Section 2.4.2 and 2.4.3, some studies (Ackermann and Knyazkin, 2002, Acha et al., 2009) indicate that one of the main ways that micro-generation can affect distribution network operations is through reducing network losses. Others, for example Infield et al. (2007) and Thomson and Infield (2007), suggest the impacts on voltage levels will be a key concern of micro-generation, particularly voltage rise. An alternative approach by Rogers et al. (2013) focuses on the network transformer and if it is overloaded. Ideally, the chosen methods should be able to examine all three of these parameters.
Examination of the network modelling environment determined that the best way to examine these parameters was through load flow analysis. Load flow analysis uses a specific generation and demand state and network structure to solve the steady operation state to provide voltages and power flows in the electricity system (Wang et al., 2008). While load flow analysis is a steady state analysis, by performing a load
flow analysis for each time period in a set of time series data (in this study for each minute of the data), an approximation of a dynamic power system analysis can be achieved.
The chapter begins with an examination of a variety of network modelling software and justification for the choice of IPSA-Power (Section 4.2). There is then an examination of how the network is modelled within IPSA (Section 4.3) followed by an overview of the Python scripts used to perform analysis with IPSA and an outline of the scenarios (Section 4.4). The model is then tested and validated (Section 4.5), a discussion of the limitations of the model is presented (Section 4.6), and a summary of the chapter provided (Section 4.7).
4.2
The IPSA modelling environment
IPSA is a distribution network planning system in which electricity networks are defined and analysed, and which is capable of performing load flow analysis. IPSA has a complex network design system, allowing the design and testing of a wide range of networks from large meshed distribution and transmission systems to small isolated networks and anything in between (IPSA-Power, 2017).
Other power systems analysis software was examined for potential use. This software included PSS/E (SIEMENS, 2017), DIgSILENT Power Factory (DIgSILENT, 2017), ETAP (ETAP, 2017) and OpenDSS (EPRI, 2017). All of these could perform the necessary functions (i.e. load flow analysis). But only DIgSILENT, like IPSA, allowed automated analysis through the use of Python code, which the researcher was already experienced with. The fact that the models of real world distribution networks, obtained for use in this research from Northern Powergrid, were designed in IPSA made IPSA the software of choice for this research.
As mentioned above, a key advantage of IPSA for this research was its ability to interface with the Python coding software, an easy to use scripting interface which allowed powerful customisation and automation of data input and modification, and performing of load flow analyses. This automation is essential for performing load flow analyses for each data entry in a set of time series data, as discussed in the previous section (Section 4.1). TNEI also provided extensive support and training for the use of IPSA, which was also a major beneficial factor of the software. The
automation of load flow analysis through the use of Python was essential for this research. Without such interface, it would be necessary to manually run a load flow analysis for each minute of the field trial data. Instead with Python, it is possible to write code that automates the process of running load flow analyses of the field trial data.