Assessing the Risk to Indoor Thermal Environments on Industrial Sites Offering AHU Capacity for Demand Response

Abstract

Increasing participation in demand response within the industrial sector may be crucial to growing the levels of available flexible capacity required to reliably control national electricity grids as renewable generation increases to satisfy emission targets. This research aims to assist the uptake of demand response in the industrial sector by investigating risk to indoor thermal environments on industrial sites offering air handling unit capacity for demand response. This evaluation uses a systematic model-based approach, calibrated and validated with empirical data from a relevant case study industrial building to assess risk through a number of scenarios. The conditions investigated cover several relevant grid response times and durations, and national and international extreme external ambient temperatures in the past, present and future under a variety of temperature limits. The study demonstrated that there is very low risk to the case study site participating in demand response, with only 15 of 264 initial and 284 of 936 total scenarios triggering any risk. The major factors affecting risk levels identified were more stringent temperature limits and the influence of more extreme climates. The development and implementation of this concept has considerable potential to benefit industrial participants and the wider national electricity grids.

Advancing the Industrial Sectors Participation in Demand Response within National Electricity Grids

“…This often results in the office spaces being located in buildings with lower internal heat capacities than commercial or purpose-built office blocks, making them more interesting from an AHU DR perspective. In this case, the building housing the office space was found to have a light internal effective heat capacity classification according to ISO 52016-1 [52], which is common within the industrial sector. The particular AHU serving this area operates on a regular Monday-to-Friday schedule for normal occupancy and working hours.…”

“…In the case of the AHUs, which were identified as the most common and lowest-risk asset for inclusion, a specialised evaluation or modelling tool was required. This concept of incorporating industrial AHUs into DR programmes and demonstrating any associated risks has not yet been thoroughly evaluated outside of this study and one other study [52]; this forms the basis of the modelling evaluation. This modelling tool allows the potential DR participant to base their decision to engage on the actual demonstrated performance of a comparable representative industrial AHU.…”

“…To mitigate the challenges of field investigations on industrial sites, such as interacting with equipment outside of planned downtime, connecting to local networks or gaining access to firewall protected systems. An offline, simulation-based, systematic modelling approach was adopted to investigate the operational risks associated with office-based industrial AHUs participating in DR [52]. Using a mechanistic 1R1C air temperature model of a representative industrial building office space coupled with a simplified thermal energy model of an AHU, the complete evaluation of any risk associated with its DR participation was achieved [52].…”

“…An offline, simulation-based, systematic modelling approach was adopted to investigate the operational risks associated with office-based industrial AHUs participating in DR [52]. Using a mechanistic 1R1C air temperature model of a representative industrial building office space coupled with a simplified thermal energy model of an AHU, the complete evaluation of any risk associated with its DR participation was achieved [52]. Taking the specific building details, materials and characteristics, as well as occupancy levels, operating schedules and the actual external air temperatures for the relevant site, it was possible to simulate and investigate the impact and any associated risk of its participation in grid frequency response events.…”

“…The selected modelling approach has been shown to be the most suitable as it is the only approach designed for the industrial sector that satisfies each of the requirements, with an appropriate level of accuracy and an easily adopted framework within the Python environment. This systematic approach, combining elements of nodal networks and resistance (R) and capacitance (C) theories, was found to be the most suitable method of modelling any risks to indoor thermal environments on industrial sites participating in DR based on the building-type specifics and various other relevant influences [52]. This modelling process clearly demonstrates any potential risks on actual industrial sites participating in DR, which can be invaluable when included as part of a full risk assessment methodology.…”