Risk-aware tri-level optimization for smart buildings, energy communities, and distribution systems in day-ahead markets

Abstract

Coordinated operation of Smart Buildings (SBs), Energy Communities (ECs), and Distribution Systems (DSs) requires efficient market structures that preserve the privacy of participants while considering risks introduced by uncertain demand, prices, and renewable generation. Therefore, this paper proposes a decentralized risk-aware tri-level optimization framework that integrates Renewable Energy Resources (RERs) such as Photovoltaic (PV) and Wind Turbine (WT), Vehicle-to-Grid (V2G) enable Electric Vehicles (EVs) parking lots, Energy storage systems (ESSs) and Flexible Loads (FLs), enabling privacy-preserving and hierarchical scheduling across SBs, ECs, and the DS while managing uncertainties. The levels are solved sequentially, one optimization problem for each level, the results of one level feed into the problem of the next level. SBs perform day-ahead scheduling to minimize electricity costs in the first level. At the second level, ECs aggregate SBs schedules and operate in a decentralized framework. At the third level, the Distribution System Operator (DSO) integrates EC schedules into day-ahead operational planning. The risk-averse scheduling approach employs Conditional Value-at-Risk (CVaR) as a risk metric to manage the risk arising from uncertainties on generation, demand and price. The model is formulated as a Mixed-Integer Linear Programming (MILP) problem and is tested on an IEEE 33-bus distribution network under two modes: deterministic (just a single scenario) and stochastic (several scenarios at the same time). The simulation results indicate that the proposed framework can reduce SBs operation costs by up to 45.65% and increases ECs profit by 21.8% under uncertainty.