Impact of Service Sector Loads on Renewable Resource Integration

TL;DR

This paper develops a method to create synthetic service sector demand profiles and demonstrates that including these profiles significantly improves estimates of renewable energy integration potential in urban areas, with up to 33% higher estimations.

Contribution

It introduces a novel approach to generate detailed service sector demand profiles and assesses their impact on renewable resource integration in urban energy systems.

Findings

Including service sector loads improves renewable integration estimates.

In specific scenarios, estimates are up to 33% higher with service sector data.

The approach enhances urban energy system modeling accuracy.

Abstract

Urban areas consist of a mix of households and services, such as offices, shops, schools, etc. Yet most urban energy models only consider household load profiles, omitting the service sector. Realistic assessment of the potential for renewable resource integration in cities requires models that include detailed demand and generation profiles. Detailed generation profiles are available for many resources. Detailed demand profiles, however, are currently only available for households and not for the service sector. This paper addresses this gap. The paper (1) proposes a novel approach to devise synthetic service sector demand profiles based on a combination of a large number of different data sources, and (2) uses these profiles to study the impact of the service sector on the potential for renewable resource integration in urban energy systems, using the Netherlands as a case study. The importance of the service sector is addressed in a broad range of solar and wind generation scenarios, and in specific time and weather conditions (in a single scenario). Results show that including the service sector leads to statistically significantly better estimations of the potential of renewable resource integration in urban areas. In specific time and weather conditions, including the service sector results in estimations that are up to 33% higher than if only households are considered. The results can be used by researchers to improve urban energy systems models, and by decision-makers and practitioners for grid planning, operation and management}.