Optimised Demand Side Management for Flexible Operation of Smart Grids

In order to meet our current net-zero carbon targets, the electricity system must accommodate large volumes of intermittent renewable energy sources and new loads (e.g., electrified heating and transports). However, upgrading the capacity of the energy system to accommodate all the aforementioned technologies would be costly.

In practice, the full capacity of the energy system is only required during peak conditions,or extreme conditions, e.g., cold days in winter when heating demand is the highest or when a contingency occurs and the system operated with reduced capacity. This is because, the energy system has historically operated in a passive manner (fit-and-forget) because energy demand has been relatively predictable and stable. As this is no longer the case due to the new, sometimes intermittent, technologies being connected to the electricity grid, we need more active approaches to manage our energy systems, such as by deploying demand side management.

Demand side management, where customers actively change their energy through the use of devices (e.g., storage) or by changing their behavior (e.g., washing clothes at night), is known to provide attractive options to manage network stress and reduce needs for costly network capacity. However, the use of demand side response can introduce unintended effects on the network as customers payback their energy, e.g., increasing stress during the periods when they charge their storage, do laundry, etc. These payback effects must be properly modelled and assessed when deploying demand side response.

The aim of this research project is to provide more accurate means to capture the payback effects, and propose new methods to deploy demand side response. The specific objectives of this project include:
- Capturing the characteristics of the different resources used to provide demand side response using models that are compatible with electricity network analysis tools (e.g., ZIP models).
- Exploring different optimisation techniques that are suitable for both demand side modelling and electricity network modelling, e.g., multi-stage optimisations where the upper levels are linear.
- Producing a relevant optimization model and extending it to consider realistic multi period (rolling horizon) conditions where demand side response deployment is constantly re-optimised as better forecasts are received.
- Extend the approach to different electricity network levels, especially to the low voltage level where network imbalances tend to be critical.

The outputs of the project are meant to facilitate deployment of demand side response (e.g., by distribution network operators, aggregators, etc.) and facilitate meeting our energy decarbonisation targets in a more cost-effective manner.