Toward a sustainable energy future: Peak load shaving in commercial properties to reduce cost of energy

Woodley, Tiffany Deanne

Toward a sustainable energy future: Peak load shaving in commercial properties to reduce cost of energy

Master Thesis

2022

Abstract

Transitioning from fossil fuel-based energy systems to renewable sources is a is a global environmental imperative. South Africa has a coal-based energy sector, and consumers could be incentivised to pursue renewable energy alternatives if these solutions were financially advantageous. In South Africa, commercial properties are billed per kWh and can incur an additional demand charge that often accounts for a substantial portion of the energy bill, depending on the load factor. This thesis investigates peak load shaving as a solution for commercial properties to reduce their cost of electricity while supporting the transition to a greener energy future. Of the methods proposed for peak load shaving, reinforcement learning holds the greatest promise. However, its application in practice has been limited due to the “curse of dimensionality”. To make reinforcement learning a feasible option for peak load shaving, this thesis introduces a novel approach that employs clustering the energy demand profile shapes and training separate learning agents to target specific demand shapes, thereby reducing the complexity of the problem presented to the individual agents. The reinforcement learning model was trained on historical data from a commercial shopping centre in Cape Town using a hypothetical battery. Two scenarios were considered; the first assumed the absence of solar in the energy system while the second assumed its presence. Once trained, the learning agents were tested on unfamiliar energy data from the same shopping centre, and they achieved practical peak load shaving results. In Scenario 1 when using only a battery, monthly demand was reduced by 91 kW on average. Introducing a solar system in Scenario 2 increases uncertainty in the problem. The results, only demonstrated on one cluster, show the battery most often achieved a 50 kW reduction per day. In both scenarios, a learning agent trained on particular clusters of demand profiles was able to reduce peak energy demand for all unfamiliar days. Furthermore, in Scenario 2, the agent's learning progression indicated that the agent was learning to increase the battery output during the predominant peak. This suggests that our method's efficacy would improve with increased training time. If implemented, this approach could provide a practical peak shaving solution for the commercial shopping centre in Cape Town, effectively lowering their energy demand charges. This thesis has shown that clustering techniques used in conjunction with reinforcement learning is a promising approach when considering the peak shaving problem.

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