  As Internet of Things (IoT) technologies enable greater communication between
energy assets in smart cities, the operational coordination of various energy
networks in a city or district becomes more viable. Suitable tools are needed
that can harness advanced control and machine learning techniques to achieve
environmental, economic and resilience objectives. In this paper, an energy
management tool is presented that can offer optimal control, scheduling,
forecasting and coordination services to energy assets across a district,
enabling optimal decisions under user-defined objectives. The tool presented
here can coordinate different sub-systems in a district to avoid the violation
of high-level system constraints and is designed in a generic fashion to enable
transferable use across different energy sectors. The work demonstrates the
potential for a single open-source optimisation framework to be applied across
multiple energy vectors, providing local government the opportunity to manage
different assets in a coordinated fashion. This is shown through case studies
that integrate low-carbon communal heating for social housing with electric
vehicle charge-point management to achieve high-level system constraints and
local government objectives in the borough of Greenwich, London. The paper
illustrates the theoretical methodology, the software architecture and the
digital twin-based testing environment underpinning the proposed approach.
