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This work investigates the spatio-temporal patterns of EV and PV adoption,
attending to the need of informing the management of the distribution network
with spatially explicit estimations of the EV and PV adoption rates. The research
assesses the strengths and weaknesses of current modelling approaches and
finds three recurrent techniques: agent-based model (ABM), the spatial
regression (SR) and the Poisson model. The research addresses the limitations
of the current modelling approaches to characterise the different factors that drive
the decision-making for the adoption of these technologies: spatial and temporal
dependence and social dynamics. The framework addresses some of the
limitations of ABMs that use a rule or equation-based decision-making, by
adopting an aggregated agents’ definition and using artificial neural networks
(ANN) as decision-making criteria. The integrated model provides a more realistic
characterisation of the decision-making and its evolution over time, moreover, the
results can inform network operators and policymakers explicitly about the
location and pace of EV and PV adoption.
The research develops a spatio-temporally explicit ABM that accounts for: (i)
spatial and (ii) temporal dependence, (iii) peer-effect, (iv) spillover effect and (iv)
preferences towards other technologies. The development of the model follows
a sequential approach to managing the complexity of constructing this new
approach. First, two autoregressive models are developed to analyse the
adoption patterns of EV and PV patterns, using the postcode and monthly data
resolution. The temporal validation uses the Mean Absolute Percentage Error to
measure the model’s capability to replicate the time-series of the adoption rates.
The spatial validation compares the actual and estimated spatial pattern of
adoption by calculating the Moran’s I index. Besides, the results are
benchmarked against the Bass model, a commonly used tool for this purpose by
ABM experts. The results show that in most of the cases the ABM and ANN
integrated models perform better than the Bass model especially for those
months with high fluctuations in the adoption rates. These models can estimate
upmost three months with an accuracy higher than 80%, however, the models
present a significant accumulation of errors that limits the results for a longer
forecast. To reduce the error accumulation and produce a longer forecast, the
autoregressive PV model is extended by including socioeconomic variables. The
resulting model improves the performance by 5% by the incorporation of variables
including income, electricity consumption and average household size.
Lastly, the framework combines the EV and PV autoregressive models with a
view to characterising the exchange of knowledge between EVs and PVs. This
reflects the influences of owning one of those technologies on the preference for
the second technology. The exchange of knowledge improves the performance
of the model significantly with results above 80% of accuracy for eight months
into the future. Given the high spatial resolution of the model, the results may help
to design policies that recognise the socioeconomic differences within a
geographical area. The research shows how the results can inform the
management of the distribution network, by considering the worst-case scenario
where the PV generation surplus is injected to the grid, and where the entire fleet
of EVs are charged at home during the night. Also, the results of the hot spot
analysis can inform the network operators about the emergence of clusters of
EVs and PVs in the future.
The research finds that a spatio-temporally explicit ABM can characterise the EV
and PV adoption process at the aggregated level, which also accounts for social
effects. Such a model can also integrate heterogeneity amongst the population,
whilst being resilient to changes in the size of the study area. The research also
produces data-driven insights into the spatio-temporal patterns of EV and PV
adoption, and how the adaptive capabilities of the ANN address some of the
limitations of the ruled-based ABM. Lastly, the research finds that knowledge
exchange takes place between the EV and PV adoption process. These findings
are relevant to other low carbon technologies and for the modelling of other
sociotechnical systems. |
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