Real option‐based network investment assessment considering energy storage systems under long‐term demand uncertainties - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IET Renewable Power Generation Année : 2022

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

Shuang Cheng
Ignacio Hernando-Gil

Résumé

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.

Dates et versions

hal-03952738 , version 1 (23-01-2023)

Identifiants

Citer

Shuang Cheng, Chenghong Gu, Ignacio Hernando-Gil, Shuangqi Li, Furong Li. Real option‐based network investment assessment considering energy storage systems under long‐term demand uncertainties. IET Renewable Power Generation, 2022, 16 (13), pp.2778-2792. ⟨10.1049/rpg2.12532⟩. ⟨hal-03952738⟩

Collections

TDS-MACS
6 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More