In summary, the SDS-EV case demonstrates – for a decarbonising 2035 Chinese power system – the immense benefit of dynamically incentivising EV users to optimise their charging patterns in alignment with operational requirements of the power system. A future decarbonised system with unoptimised charging patterns has a peak demand that is approximately 15% higher.

Meeting that demand with conventional generation would require significant additional investment in low-utilisation, high marginal cost peaking capacity (and most likely significant distribution grid upgrades too, which are not explicitly considered in this analysis).

Importantly, substantial investment costs are associated with building out EV infrastructure in the first place in order to enable charging of vehicles. However, this investment is considered incurred in the SDS-Inflex case as well and does not necessarily depend on the charging regime

– on the contrary, smart charging contributes to reducing required power system investment. The investment costs of enabling smart EV charging itself are comparably very low, and may to some degree ultimately be financed by EV owners and/or EV charging retailers, rather than by the power system via ratepayers; the crucial element is instead the appropriate standardisation of equipment and ICT infrastructure.

In summary, the incremental investment costs associated with enabling this flexibility option are expected to be quite low relative to the potential operational and investment-related savings, though it would be a significant institutional effort to co-ordinate such widespread transport sector integration.

A variety of actions can be considered by policy makers to realise the benefits offered by extensive smart EV charging. First and foremost, linking power sector and transport sector planning exercises can support the smart development and planning of EV charging infrastructure, enabling greater uptake of EVs through increased access to charging infrastructure, reducing distribution system build-out costs, and at the same time supporting broader VRE integration and power system transformation. Next, smart EV charging programmes must offer sufficiently attractive schemes such that EV users enrol in them and adopt smart charging behaviour. While smart charging is likely to be managed through automated processes, EV charging tariffs must nevertheless strike an appropriate balance between dynamic cost reflectivity (i.e. pricing structures that reflect the time-variable cost of power system operation in close to real time) and simplicity (i.e. pricing structures that do not unduly expose retail customers to highly variable, hard-to-understand electricity market dynamics). Finally, the regulatory system may need to allow for ratepayer-financed smart charging infrastructure, particularly at earlier development stages where its deployment may not be commercial attractive to EV charging retailers or individual customers.

Assessing portfolios of flexibility options

Understanding the value of a portfolio of DSR and EVs: SDS-DSR+EV

The value of both smart EV charging and DSR has been explored separately in previous cases. In a next step, the deployment of both sets of resources in tandem is explored to investigate synergies and trade-offs between both options.

Modelling results indicate that the simultaneous utilisation of both smart EV charging and DSR resources results in a total saving of USD 41 billion from avoided operational costs and capital investment. Summing the savings of the cases individually yields annual savings of USD 52 billion, USD 17 billion in SDS-DSR and USD 35 billion in SDS-EV. While savings are not fully additive, the joint deployment still leads to almost 80% of the summed benefits of each individual option. This indicates that both options only partially substitute each other and deploying both options does bring additional benefits.

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Understanding the value of a combined portfolio of smart EV charging, DSR and storage: SDS-Full flex

The SDS-Full flex case is used to understand the combined operational value of a portfolio of EVs, DSR and storage deployment to a decarbonising Chinese power sector in 2035. The options considered are 300 GW of residential and service-sector load contributing to DSR programmes (as detailed in the SDS-DSR case), the smart charging of 220 million EVs representing approximately 250 GW of peak EV charging load and 800 terawatt hours of total annual EV charging load (as detailed in the SDS-EV case), and a total of over 100 GW of pumped storage hydro and over 50 GW of battery energy storage (as detailed in the SDS-Storage case). These lead to the most flexible power system case considered in the analysis – hence the name SDS-Full flex.

Comparing results from the SDS-Full flex case to the SDS-Inflex case is instructive in understanding the costs, benefits and operational impacts of deploying this portfolio of flexibility measures. Operational costs in the SDS-Full flex case are approximately 11% lower than the baseline case, which is equivalent to a saving of approximately USD 32 billion per year. This saving is driven primarily by the broadly enhanced ability of the power system to utilise resources with low marginal costs (including VRE and nuclear power) at the expense of more costly generation resources (Figure 51). Reduction in emission costs is also a major source of operational savings. Total CO2 emissions in the SDS-Full flex case are 14% lower than in the SDS-Inflex case.

Figure 51. Generation patterns and demand profiles during high-stress periods, SDS-Full flex case

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The combined portfolio of flexibility measures enhances the ability of the power system to better utilise resources with low marginal costs, including during periods of high stress.

Peak demand is reduced by approximately 245 GW, a 20% reduction from the baseline SDSInflex case (Figure 52). This peak demand reduction translates into a potential annualised investment cost saving (driven by reduced investment in low-utilisation generation infrastructure) of approximately USD 32 billion per year.

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Net load (SDS-Inflex)

Net load (SDS-Full_flex)

more

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