Intelligent SME Energy Management and Trading with Ancillary Services

The core vision of this project is the development and pilot of a low cost, robust platform for intelligent building energy
usage that, combined with a new peer-to-peer energy market system, will facilitate localised energy trading and enable
participation of small to medium enterprises in National Grid balancing services.
The academic researchers, will work with commercial lead KiwiPower to:
(i) Develop algorithms that learn the thermal performance of the commercial building in which the building management
system is installed, enabling optimal control of the building's heating and air conditioning system during demand response
periods (where, for example, air conditioning use will be optimally curtailed to reduce peak loads without adversely affecting
the comfort of the building's occupants).
(ii) Design, develop and evaluate and effective local energy market in which autonomous trading agents representing both
individual buildings and also standalone generation and storage facilities can interact to optimally balance energy demand
against local generation and storage capacity.
The new platform and market will enable SMEs to control and monitor their production and consumption assets, to
automatically manage supply and demand at a localised level, and respond to national balancing requirements and
financial incentives to shift demand.
This project is innovative as:
(i) The novel low cost intelligent energy system, incorporating learning algorithms, overcomes existing cost barriers for
SMEs, enabling them to benefit from the latest energy optimisation algorithms to reduce costs and improve efficiency.
(ii) It will prove the technical and commercial viability of localised peer-to-peer energy markets and the ability for SMEs to
be involved in national electricity balancing services.

The technologies developed within this project will help towards the UK's legislated goal of cutting greenhouse gas
emissions by 80% by 2050. As such, it will contribute to the global effort to mitigate the worst effects of climate change
which challenges the future growth, prosperity, and political stability of all nations. Improving energy efficiency will assist in
ensuring UK energy security by reducing the need to import gas from overseas markets, and it represents an opportunity
for the UK to be at the forefront of innovation in the area of digital technologies for energy efficiency and smart grids (a
market which Siemens estimates to be worth up to £27B over the next five years).
The academic outputs from the project will be demonstrated and piloted in a low-cost intelligent building management
system, developed by KiwiPower, which will enable SMEs to control and monitor their production and consumption assets,
to automatically manage supply and demand at a localised level, and respond to national balancing requirements and
financial incentives to shift demand. The expected outcome of a successful pilot deployment are two fold:
(i) The novel low cost intelligent energy system, incorporating learning algorithms, will demonstrate that it is possible to
overcome the existing cost barriers for SMEs, enabling them to benefit from the latest energy optimisation algorithms to
reduce costs and improve efficiency.
(ii) It will prove the technical and commercial viability of localised peer-to-peer energy markets and the ability for SMEs to
be involved in national electricity balancing services.
In this respect, we will work closely with KiwiPower, to disseminate the results of the project to industry and government,
looking to create beneficiaries at a number of levels:
1) At the government level, through the demonstration of a new building management approaches, which if rolled out
across the the UK SME building stock will impact on carbon targets, energy usage and national security of supply.
2) At the building user level, through demonstrating that cost savings can be achieved without detrimental impact to internal
environmental conditions, by appropriate modeling and control of individual buildings, and by making the most effective use
of local generation within a local power market.
3) At the academic level, by generating new insights into the optimal modeling and control of commercial buildings, and the
operations of a local power market. Our findings will provide an important input to scholars, in both the artificial intelligence
and the building science literatures.
4) At the building services industry level enabling KiwiPower to develop and deploy apply new approaches to energy
management beyond traditional hard-wired control systems.