Along with the introduction of renewable energy, storage batteries are being introduced. While storage batteries cannot consume fuel to supply electricity, they are capable of fast start-up, shutdown, and output control, and can store energy in ways that are not possible with conventional power sources. This feature also makes the bidding strategy for storage batteries in the electricity market different from that of conventional units. For example, in the day-ahead market phase, bids for storage batteries are often determined by reference to opportunity costs obtained by forecasting electricity prices. If the opportunity cost can be accurately predicted, the storage battery owner can maximize its own gain.

However, in a situation where bids for storage batteries, which can flexibly change bids even in the day-ahead market, are responsible for price formation in the electricity market, future prices will also change, making it very difficult to calculate an appropriate opportunity cost. Since opportunity cost is calculated based on future price forecasts, it is assumed that bids made after the opportunity cost is calculated will not change prices. However, in the actual electricity market, it will be necessary to modify the operating plan based on the assumption of bidding in the previous day's market after looking at electricity prices in the day's market. Then, the opportunity cost assumed in the storage battery bids will also need to be revised. This situation is likely to become more likely as the introduction of storage batteries expands.

Therefore, in order to analyze the impact of the increased introduction of storage batteries on electricity market price formation, it is necessary to determine what the circumstances are under which the bidding behavior of storage batteries shapes electricity prices, and to what extent the bidding behavior of storage batteries contributes to prices.

This paper describes the impact of storage battery bidding strategies and bidding behavior on electricity market price formation.

We used data from the National Electricity Market (NEM), the Australian electricity market, to determine the frequency with which each fuel type forms electricity prices and the percentage of each fuel type that influences electricity price formation, while considering the relationship between the FCAS market and electricity prices. We also analyzed how electricity prices affect the bidding behavior of storage batteries, focusing on the time points before and after characteristic price events such as extremely low prices and price spikes.

Analysis of the data shows that the ratio of time frames in which storage batteries shape the price of electricity is greater than the ratio of other power sources in situations where price spikes occur, compared to situations where electricity prices are stable at low levels.

The results indicate that in simulating electricity prices, one needs to consider bidding behavior that reflects the bidding strategy of the storage batteries.