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For market-based procurement of low voltage (LV) flexibility, DSOs identifythe amount of flexibility needed for resolving probable distribution network(DN) voltage and thermal congestion. A framework is required to avoid over orunder procurement of flexibility in the presence of uncertainty. To this end,we propose a scenario-based robust chance-constrained (CC) day-aheadflexibility needs assessment (FNA) framework. The CC level is analogous to therisk DSO is willing to take in flexibility planning. Multi-period optimal powerflow is performed to calculate the amount of flexibility needed to avoidnetwork issues. Flexibility is defined in terms of nodal power ramp-up andramp-down and cumulative energy needs over a full day for each node. Futureuncertainties are considered as multiple scenarios generated using multivariateGaussian distribution and Cholesky decomposition. These scenarios are utilizedto solve the flexibility needs assessment optimal power flow (FNA-OPF) problem.Zonal clustering of an LV feeder is performed using electrical distance as ameasure and spatial partitioning. The FNA tool calculates ramp-up and ramp-downflexibility's power and energy requirements. Energy and power needs are oftenvalued differently in many energy markets. We identify the marginal value offlexibility associated with energy and power needs separately. From numericalresults for an LV feeder, it is observed that zonal flexibility needsassessment is more immune to uncertainty than nodal flexibility needs, makingit more useful for DSOs to evaluate day-ahead flexibility procurement. We alsopropose a Pareto optimal mechanism for selecting CC level to reduce flexibilityneeds while reducing DN congestion.

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