Real option-based network investment assessment considering energy storage systems under long-term demand uncertainties

This paper proposes a novel real option (RO)-based network investment assessment method to quantify the flexibility value of battery energy storage systems (BESS) in distribution network planning (DNP). It applied geometric Brownian motion (GBM) to simulate the long-term load growth uncertainty. Compared with commonly used stochastic models (e.g. normal probability model) that assume a constant variance, it reflects the fact that from the point of prediction, uncertainty would increase as time elapses. Hence, it avoids the bias of traditional net present value (NPV) frameworks towards lumpy investments that cannot provide strategic flexibility relative to more flexible alternatives. It is for the first time to adopt the option pricing method to evaluate the flexibility value of distribution network planning strategies. To optimize the planning scheme, this paper compares the static NPVs and flexibility values of different investment strategies. A 33-bus system is used to verify the effectiveness of the formulated model. Results indicate that flexibility values of BESS are of utmost importance to DNP under demand growth uncertainties. It provides an analytical tool to quantify the flexibility of planning measures and evaluate the well-timed investment of BESS, thus supporting network operators to facilitate flexibility services and hedge risks from the negative impact of long-term uncertainty.

Automated summary

This paper proposes a novel real option-based network investment assessment method to quantify the flexibility value of battery energy storage systems in distribution network planning. By using geometric Brownian motion to simulate long-term load growth uncertainty, the method avoids the bias of traditional net present value frameworks and evaluates the flexibility value of different investment strategies. The results show the importance of flexibility values of battery energy storage systems in distribution network planning under demand growth uncertainties.