Network Pricing for Multienergy Systems Under Long-Term Load Growth Uncertainty

Abstract

The long-term uncertainty of multi-energy demand poses significant challenges to the coordinated pricing of multiple energy systems (MES). This paper proposes an integrated network pricing methodology for MES based on the long-run-incremental cost (LRIC) to recover network investment costs, affecting the siting and sizing of future distributed energy resources (DERs) and incentivizing the efficient utilization of MES. The stochasticity of multi-energy demand growth is captured by the Geometric Brownian Motion (GBM)-based model. Then, it is integrated with a system operation model to minimize operation costs, considering low-carbon targets and flexible demand. Thereafter, the kernel density estimation (KDE) method is used to perform the probabilistic optimal energy flow (POEF) to obtain energy flows under uncertain load conditions. Based on the probability density functions (PDFs) of energy flows, an LRIC-based network pricing model is designed, where Tail Value at Risk (TVaR) is used to model the risks of loading levels of branches and pipelines. The performance of the proposed methodology is validated on a typical MES. The proposed pricing method can stimulate cost-effective planning and utilization of MES infrastructures under long-term uncertainty, thus helping reduce low-carbon transition costs.

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

“…Conventional methods may cause a considerable portion of idle capacity and inefficient utilization of network infrastructure [2]. To hedge the risks in distribution network planning, alternative smart technologies can be adopted to enhance planning flexibility by deferring expensive traditional solutions until uncertainty unfolds over time [3].…”

“…Any attempt to assess the flexibility value (i.e. deferral option value) embedded in strategic DNI portfolios naturally leads to the concept of RO [3]. Borrowed from financial mathematics [19], an RO can be regarded as the option to defer and adjust investment decisions to solve uncertainty.…”

“…To hedge the risks in distribution network planning, alternative smart technologies can be adopted to enhance planning flexibility by deferring expensive traditional solutions until uncertainty unfolds over time [3]. Battery energy storage systems (BESS) can reduce system peak load and hence are essential to enable networks to integrate more renewable energy and volatile demand [4].…”