Sizing Merchant Energy Storage for Maximum Revenues Considering Net Metering and Ancillary Services

Abstract

As prices for energy storage (ES) decline, merchant-owned ES units have an opportunity to be profitable if they earn revenue from multiple streams. Most papers in the literature provide a simplistic view, and do not capture practical tariff structure of commercial customers connected via load meters billed via netmetering scheme. In this paper, we present a flexible and comprehensive mathematical model to enable merchant-owned ES owners to maximize their profits by considering multiple revenue streams. The main contribution is a model that fully captures the economic picture by including displaced electricity costs, i.e., net-metering scheme, for behind-the-meter installations. It also includes operating costs, annual investment costs, and ES connected to the distribution system. The inclusion of net metering is novel, as well as simultaneously including all of ancillary services, energy costs, investment costs, net metering, and local generation. These elements can be crucial in building the business case for ES in Ontario to ensure profitability. We test our model on a large commercial customer. The load has a peak load of 1,500 kW and a solar generation capacity of 2,500 kW connected on a 13.8 kV feeder, with a limit of 5,000 kW capacity. The results show two cases. The first considers only energy arbitrage and costs $4,812,909, which is less than the cost without storage at $9,299,623. The second scenario allows for energy arbitrage and revenue via participation in local and bulk system ancillary services and yields a total benefit of $11,004,225. Both scenarios indicate benefits from purchase of storage. The second scenario is clearly more beneficial when storage investment is considered and opportunity is available. We also provide a stochastic implementation to consider uncertainty in any input parameter and demonstrate this method with energy prices.

Research on the revenue prospect for energy storage participation in electricity spot and FM auxiliary service markets —Considering capacity market mechanisms

“…An accurate prediction of energy storage strategic behaviors is essential for market efficiency and addressing market power concerns. As an enhancement, literatures [23] [25] propose a model that considers the uncertainties associated with rival generators' offering strategies and future load levels to determine the optimal size of an energy storage facility from a strategic investor's perspective； literatures [26] [27] introduce an economic energy storage investment model with considering the location, size, efficiency, and opportunity cost of the storage system itself, and the uncertainty of future energy prices, respectively, to maximize the profit of energy storage operation.…”