User-side distributed energy storage products

What are the challenges of user-side energy storage development?

Then the challenges of current user-side energy storage development, such as uncertainty of electricity price policy and the lack of household energy storage market, are investigated.

How does the installed capacity of distributed es affect electricity consumption?

The impact of the installed capacity of distributed ES was determined by evaluating the economic benefit of VPP aggregation and the deviation of the declared electricity consumption from the actual electricity consumption under the optimized portfolio strategy when gradually increasing the installed capacity from 4 MW to 20 MW.

Do distributed ES units have the same charging/discharging efficiency?

In this paper, it is assumed that the distributed ES units have similar charging/discharging efficiency and that the PV panels have the same conversion efficiency. Therefore, the cloud scheduling center of the VPP needs to consider only a single ES cluster and a single PV cluster when developing the scheduling plan.

What is the agreement between electricity retailers and users with Ders?

The electricity retailer and the users with DERs agree to guarantee the arbitrage revenue of the peak-valley spread of the TOU price of users with ES resources, to proportionally distribute the excess revenue to these users to acquire scheduling rights for ES resources.

Do user-side Ders yield financial benefits?

Existing literature on these user-side DERs focuses on strategies that balance power purchase costs and users' comfort , as well as participation in demand response [, , ]. However, the first approach does not directly yield financial benefits for users, and the second approach does not guarantee daily profits for them.

What is the total electric power capacity of a distributed es?

The total electric power capacity of the distributed ES was 2 MW, and the charging and discharging efficiencies were both 95 %. The first-order and second-order moments of the day-ahead price forecasting error and the predicted covariance matrix of the real-time price were obtained from the historical data over the 14 days before D day.