Database
Feeder-Aware Service-Conditioned Flexibility of Grid-Interactive Buildings
arXiv cs.SY0 views
arXiv:2610.31204v1 Announce Type: new Abstract: Grid-interactive buildings can adjust demand, but building-feasible power budgets may differ from accepted feeder-head offers. This study addresses a practical question: to what extent can the adjustable electricity consumption of buildings be translated into deliverable flexibility services while satisfying feeder-head operating requirements? To enable a fair comparison, we adopt the same baseline operating schedule, equipment conditions, and post-service recovery requirements for indoor temperature and battery state of charge (SOC). Flexibility is then evaluated under two settings: one considering only building-level constraints, and the other additionally accounting for feeder-head constraints. We first use a simplified network model with explicit security margins to determine a preliminary amount of flexibility that can be committed. The resulting schedule is then validated using a more detailed network simulation via Modelica. If network losses cause the actual response to deviate from the committed response, the electricity consumption of individual buildings is rescheduled. If the required response still cannot be achieved, the committed flexibility level is reduced. The experiments cover multiple service activation times, regulation directions, and service durations. For comparative purposes, we retain the initial optimization result as the reference estimate of the maximum potentially available flexibility, rather than interpreting the failure of a particular subsequent realization attempt as evidence that a larger flexibility level is inherently infeasible. The results demonstrated that 24.51\% of nonzero flexibility contracts were rejected due to feeder-head constraints, 67.04\% of contracts were accepted with some rescheduling and building redispatch, and only 8.09\% of contracts were accepted without any changes.
Source:https://arxiv.org/abs/2610.31204PreviousConstrained Sparsest Input Selection for Structural Controllability is NP-HardNextModel Predictive Control of Wind Turbines: A Model-Oriented Review of Prediction Models, Control Objectives, and Nonlinear Aerodynamic Formulations
Related posts
Postgres LISTEN/NOTIFY vs Redis Pub/Sub: Latency, Write Cost and Failure Cases, Tested
0dev.to Node
LlamaIndex Workflows vs LangGraph: Event-Driven Architecture and Async State Overhead Showdown
0dev.to Programming

Rider and Databases: Lesser-Known IDE Features That Save Time
0dev.to Database
KivaDB v2: What's Coming and How You Can Contribute
0dev.to Database
Your LLM cost log says $0.00. The invoice says $1,320.
0dev.to Database
SQLite in Production: A Practical Setup with Litestream Backups
0dev.to Database