The impact of battery electric vehicles on the least cost electricity portfolio for South Africa

Abstract

This study has quantified the impact of future battery electric vehicle (BEV) charging on the least-cost electricity generation portfolio in South Africa (SA). This was done by performing a capacity expansion optimization of the generation portion of the SA power system for the year 2040 in the software package PLEXOS. This study assumed that there would be 2.8 million BEV’s in SA by 2040 which was informed by global adoption estimates. All existing power stations expected to be operational in the year 2040 were modelled according to their technical performance characteristics and running costs. Additionally, a suite of new technology options were configured in the model according to their expected investment and running costs. These supply options included coal, nuclear and gas-fired capacity as well as renewable energy. The 2040 electricity demand was obtained from the national Integrated Resource Plan 2016. The optimization formulation in the power system model was set to minimize total generation cost which is the sum of all new investment build decisions and their associated running costs, as well as the running cost of existing power stations while adhering to a set of constraints. Boundary condition constraints included an annual CO2 emissions limit. The installed capacity and electricity supply (energy shares) for each technology type were optimized and presented for each scenario. The resulting total generation costs as well as environmental emissions were also presented per scenario. The study looked at four main scenarios, as well as sensitivity analysis on the adoption of BEV’s. First a reference scenario, the Base Case (BC), was developed in which the model was set up without incorporating BEV’s in South Africa’s power system. The least-cost new build capacity included 34.6 GW of solar photovoltaic (PV), 38.1 GW of wind, 0.3 GW of landfill gas, 8.8 GW of combined cycle gas turbines (CCGT) and 23.2 GW of open cycle gas turbines (OCGT) in 2040 for the given input assumptions. A second scenario was then developed, the Fixed Charging (FC) scenario, with the same input assumptions as the BC scenario but with the inclusion of a 2.8 million BEV fleet (informed by global adoption estimates) in a Grid-to-Vehicle (G2V) configuration, assuming a fixed aggregated charging profile from previous literature. The BEV charging demand increased the annual electricity demand by 9 TWh (~2.5%). The least-cost optimal supply portfolio from this scenario increased the total generation cost by R9 billion compared to the BC and supplied most of the charging demand with new wind generation. A sensitivity analysis was conducted on the FC scenario whereby the adoption of BEV’s was increased to 100% of all passenger vehicles. This resulted in a BEV fleet of 8.4 million vehicles, which increased the system demand by 28 TWh and the peak demand by 5.9 GW. This additional charging demand increased the mean hourly upwards and downwards system demand gradients (ramping requirements) and thus the demand for flexible generation. The optimal supply portfolio in terms of technology type did not change for this higher BEV adoption assumption, indicating robustness in the technology choice going forward. As expected, more capacity was required in this scenario than the Base Case which resulted in an increase in the total system cost of R28 billion compared to the Base Case. A third scenario, the Optimized Charging (OC) scenario, was developed in order to test the impact of a system optimized charging profile. The model was configured to allow flexible charging which for a least-cost optimization means that the batteries are charged during periods of lowest cost supply to the power system. As expected, the optimized charging profile showed that it is least-cost to the power system to charge during off peak periods of the day. This profile also resulted in a reduction of total generation cost of R3 billion compared to the FC scenario. This equates to a savings of about R1 000 per BEV per annum. This system saving is based on the optimized charging of the whole electric vehicle fleet and thus presents the maximum possible savings to the power system. A last scenario, the V2G scenario, was developed in order to determine the impact on the least-cost supply portfolio if the BEV fleet is able to discharge back into the grid in the V2G configuration. The results from this scenario showed that further generation cost reductions could be achieved compared to the FC and OC scenarios. Both the OC and V2G scenarios built more new solar PV capacity and less wind capacity than the FC scenario, demonstrating the advantage of cheap solar PV generation during the middle of the day. For all scenarios including BEV’s, the energy share from existing coal and nuclear was reduced. This indicates a higher need for flexibility in the power system in the presence of electric vehicles. The V2G scenario represented the lowest energy share from gas-fired power which is indicative of the additional flexibility gained from the electric vehicles in the V2G configuration. It was also found that less mid-merit gas and more peaking gas was built in the OC scenarios. In conclusion, in all scenarios additional capacity was built to meet the additional charging demand in 2040. This indicates that the exclusion of BEV’s in the capacity expansion will lead to a sub optimal energy mix. Additionally, for all scenarios, the least-cost capacity investment technologies chosen by the optimization model were solar PV, wind, landfill gas, mid-merit and peaking gas-fired capacity. This finding is significant as it indicates that although the quantity and energy share of these new supply options vary per scenario, the least-cost technology choice is the same with and without the presence of BEV’s. The least-cost technology choice is therefore robust against the change in the demand profile caused by the addition of electric vehicle charging demand. The OC and V2G configurations led to lower system costs and a slightly higher energy share from solar PV relative to the FC scenario.