Truncated Strategy Based Dynamic Network Pricing for Energy Storage

Abstract

With the increasing penetration of local renewable energy and flexible demand, the system demand is more unpredictable and causes network overloading, resulting in costly system investment. Although the energy storage (ES) helps reduce the system peak power flow, the incentive for ES operation is not sufficient to reflect its value on the system investment deferral resulting from its operation. This paper designs a dynamic pricing signal for ES based on the truncated strategy under robust operation corresponding to the network charge reduction. Firstly, the operation strategy is designed for ES to reduce the total network investment cost considering the uncertainties of flexible load and renewable energy. These nodal uncertainties are converted into branch power flow uncertainties by the cumulant and Gram-Charlier expansion strategy. Then, a time of use (ToU) pricing scheme is designed to guide the ES operation reflecting its impact on network investment based on the long-run investment cost (LRIC) pricing scheme. The proposed ToU LRIC method allocates the investment costs averagely to network users over the potential curtailment periods, which connects the ES operation with network investment. The curtailment amount and the distribution of power flow are assessed by the truncated strategy considering the impact of uncertainties. As demonstrated in a Grid Supply Point (GSP) distribution network in the UK, the network charges at the peak time reduce more than 20% with ES operation. The proposed method is cost-reflective and ensures the fairness and efficiency of the pricing signal for ES.

Keywords

• Energy storage
• network investment
• renewable energy
• robust optimization
• uncertainty