Boliden Tara Mines Ltd. consumed 184.7 GWh of electricity in 2014, equating to over 1% of the national demand of Ireland or approximately 35,000 homes. Ireland’s industrial electricity prices, at an average of 13 c/KWh in 2014, are amongst the most expensive in Europe. Cost effective electricity procurement is ever more pressing for businesses to remain competitive. In parallel, the proliferation of intelligent devices has led to the industrial Internet of Things paradigm becoming mainstream. As more and more devices become equipped with network connectivity, smart metering is fast becoming a means of giving energy users access to a rich array of consumption data. These modern sensor networks have facilitated the development of applications to process, analyse, and react to continuous data streams in real-time. Subsequently, future procurement and consumption decisions can be informed by a highly detailed evaluation of energy usage. With these considerations in mind, this paper uses variable energy prices from Ireland’s Single Electricity Market, along with smart meter sensor data, to simulate the scheduling of an industrial-sized underground pump station in Tara Mines. The objective is to reduce the overall energy costs whilst still functioning within the system’s operational constraints. An evaluation using real-world electricity prices and detailed sensor data for 2014 demonstrates significant savings of up to 10.72% over the year compared to the existing control systems.

The electric rail sector, like many sectors, is looking for means to reduce its energy consumption and energy cost. In this work we consider the scenario where the utility provider charges based on the maximum consumption over a period. Therefore one wishes to schedule the departure of trains such that the aggregate load is balanced across time periods while satisfying timetabling and resource restrictions. We present an approach which combines the strengths of a number of research areas such as constraint programming, linear programming, mixed-integer programming, and large neighbourhood search. The empirical performance on instances from an ongoing research challenge demonstrates the approach's ability to dramatically reduce the overall energy cost. In addition, we are able to close a number of the instances for which we prove optimality.