Representing Long-term Impact of Residential Building Energy Management using Stochastic Dynamic Programming

TL;DR

This paper introduces a stochastic dynamic programming model to optimize residential building energy management by accurately representing long-term impacts of current decisions under uncertain demand and weather conditions.

Contribution

The paper presents a novel SDP-based approach that models long-term effects of decision-making on peak power costs considering uncertainty, improving energy scheduling accuracy.

Findings

SDP outperforms less accurate methods by 0.3%.

The model's performance is within 3.6% of perfect information scenarios.

Uncertainty in demand and weather is effectively incorporated.

Abstract

Scheduling a residential building short-term to optimize the electricity bill can be difficult with the inclusion of capacity-based grid tariffs. Scheduling the building based on a proposed measured-peak (MP) grid tariff, which is a cost based on the highest peak power over a period, requires the user to consider the impact the current decision-making has in the future. Therefore, the authors propose a mathematical model using stochastic dynamic programming (SDP) that tries to represent the long-term impact of current decision-making. The SDP algorithm calculates non-linear expected future cost curves (EFCC) for the building based on the peak power backwards for each day over a month. The uncertainty in load demand and weather are considered using a discrete Markov chain setup. The model is applied to a case study for a Norwegian building with smart control of flexible loads, and compared against methods where the MP grid tariff is not accurately represented, and where the user has perfect information of the whole month. The results showed that the SDP algorithm performs 0.3 % better than a scenario with no accurate way of presenting future impacts, and performs 3.6 % worse compared to a scenario where the user had perfect information.