Long-term operational planning for flexible residential buildings with seasonal storage and capacity-based grid tariffs

Abstract

When performing operation of the energy system in a building, it is difficult to accurately represent the uncertain long-term objectives in the short-term window, and the correlation between long-term objectives. This work investigates a strategic modelling framework for representing long-term incentives during short-term operational planning for an energy system within flexible buildings. Multi-stage backwards Stochastic Dynamic Programming (SDP) is used to decompose the yearly operational problem into smaller stages, and create long-term cost curves that captures the cost of adjusting long-term price signals, which in this work considers seasonal thermal energy storage and recurring monthly capacity-based grid tariff costs. The proposed method is analyzed for a flexible realistic Norwegian building located in Southern Norway, where the influence both price signals have on long-term operational performance is analyzed. Results show that the framework strategically optimizes the long-term seasonal storage together with the recurring monthly grid tariff, complementing each other by using the seasonal storage to reduce peak costs during winter. With both seasonal storage and monthly demand charge, yearly operational costs are reduced by 4.3% compared to only accounting for the grid tariff. Additionally, the operational performance achieved only a 0.9% higher cost compared to yearly simulation with perfect information.