Chance constrained day-ahead robust flexibility needs assessment for low voltage distribution network

Abstract

For market-based procurement of low voltage (LV) flexibility, DSOs identify the amount of flexibility needed for resolving probable distribution network (DN) voltage and thermal congestion. A framework is required to avoid over or under procurement of flexibility in the presence of uncertainty. To this end, we propose a scenario-based robust chance-constrained (CC) day-ahead flexibility needs assessment (FNA) framework. The CC level is analogous to the risk DSO is willing to take in flexibility planning. Multi-period optimal power flow is performed to calculate the amount of flexibility needed to avoid network issues. Flexibility is defined in terms of nodal power ramp-up and ramp-down and cumulative energy needs over a full day for each node. Future uncertainties are considered as multiple scenarios generated using multivariate Gaussian distribution and Cholesky decomposition. These scenarios are utilized to solve the flexibility needs assessment optimal power flow (FNA-OPF) problem. Zonal clustering of an LV feeder is performed using electrical distance as a measure and spatial partitioning. The FNA tool calculates ramp-up and ramp-down flexibility's power and energy requirements. Energy and power needs are often valued differently in many energy markets. We identify the marginal value of flexibility associated with energy and power needs separately. From numerical results for an LV feeder, it is observed that zonal flexibility needs assessment is more immune to uncertainty than nodal flexibility needs, making it more useful for DSOs to evaluate day-ahead flexibility procurement. We also propose a Pareto optimal mechanism for selecting CC level to reduce flexibility needs while reducing DN congestion.

Estimation of congestion component of LMP using only local measurements for flexibility markets

“…This signal is derived using local measurements at the PCC voltage magnitude and the line loading percentages for the lines connecting a consumer. The design of FAS was first introduced in prior works [8], [12]- [14]. In this work, we focus on calculating FAS with local measurements and statistically quantifying the similarities between the OPF duals denoting the congestion component derived in Section 2.…”

“…This test example uses a single-phase equivalent DN model. More network details can be found in [14], [18]. The network details and associated load profiles are made open source for reproducibility of our research [19].…”