The recent rise in demand for electric vehicles (EV) and energy storage supporting power systems has increased the demand for lithium-ion batteries (LIB), and it is expected to be more significant in near future. However, materials for LIB, such as lithium and cobalt, may face limited supply due to oligopolistic market characteristics, and this can have a significant impact on prices of LIB materials. This paper examines the dynamics of LIB raw material prices (cobalt, lithium, nickel, and manganese prices) with EV demand using the Vector Error Correction Model (VECM) method. The result shows that the EV demand is important in short-run dynamics of cobalt and lithium prices, which indicates that the recent increase in lithium and cobalt prices has been caused by increase in EV demand. In the long-run equilibrium, lithium and nickel prices move inversely with cobalt prices. The impulse response results confirm that EV demand has an immediate positive effect on cobalt price, and the effect maintains over two years. On the other hand, the EV demand shock to nickel, lithium, and manganese prices is relatively small. This study also analyses the impact of recycling policy of LIB on material prices. Finally, the paper discusses the policy implications for stabilizing material prices of LIB.

An increase in variable renewable energy sources and soaring electricity demand at peak hours undermines the efficiency and reliability of the power supply. Conventional supply-side solutions, such as additional gas turbine plants and energy storage systems, can help mitigate these problems; however, they are not cost-effective. This study highlights the potential value of electric vehicle demand response programs by analyzing three separate scenarios: electric vehicle charging based on a time-of-use tariff, smart charging controlled by an aggregator through virtual power plant networks, and smart control with vehicle-to-grid capability. The three programs are analyzed based on the stochastic form of a power system optimization model under two hypothetical power system environments in Jeju Island, Korea: one with a low share of variable renewable energy in 2019 and the other with a high share in 2030. The results show that the cost saving realized by the electric vehicle demand response program is higher in 2030 and a smart control with vehicle-to-grid capability provides the largest cost saving. When the costs of implementing an electric vehicle demand response are considered, the difference in cost saving between the scenarios is reduced; however, the benefits are still large enough to attract customers to participate.

Renewable generation sources still have not achieved economic validity in many countries including Korea, and require subsidies to support the transition to a low-carbon economy. An initial Feed-In Tariff (FIT) was adopted to support the deployment of renewable energy in Korea until 2011 and then was switched to the Renewable Portfolio Standard (RPS) to implement more market-oriented mechanisms. However, high volatilities in electricity prices and subsidies under the RPS scheme have weakened investment incentives. In this study we estimate how the multiple price volatilities under the RPS scheme affect the optimal investment decisions of energy storage projects, whose importance is increasing rapidly because they can mitigate the variability and uncertainty of solar and wind generation in the power system. We applied mathematical analysis based on real-option methods to estimate the optimal trigger price for investment in energy-storage projects with and without multiple price volatilities. We found that the optimal trigger price of subsidy called the Renewable Energy Certificate (REC) under multiple price volatilities is 10.5% higher than that under no price volatilities. If the volatility of the REC price gets doubled, the project requires a 26.6% higher optimal investment price to justify the investment against the increased risk. In the end, we propose an auction scheme that has the advantage of both RPS and FIT in order to minimize the financial burden of the subsidy program by eliminating subsidy volatility and find the minimum willingness-to-accept price for investors.

In recent years, increasing requests to reduce greenhouse gas emissions have led to renewable resources rapidly replacing conventional power sources. However, the inherent variability of renewable sources reduces the reliability of power systems. Energy storage has been proposed as a viable alternative, as it can mitigate the variability of renewable energy sources and increase the efficiency of power systems by lowering peak electricity demand. In this study, we evaluate the benefits of integrating energy storage with combined wind and solar power generation in the Korean power system through using the dynamic optimization method. Realistic wind and photovoltaic solar power generation scenarios were estimated for actual sites. The results show that the wind power-based system benefitted more from energy storage than the combined wind and solar photovoltaic power-based system. This is because the high variability of wind power was reduced when it was combined with solar power. Co-optimization for energy and reserve costs was more beneficial than optimization for energy costs alone, which suggests that the reliability offered by storage is an important cost-saving factor, in addition to the reduction of energy costs by price arbitrage. Finally, the analysis was conducted under various scenarios to determine the validity of energy storage cost effectiveness.