Abstract
For an organisation, one aspect on the path to a decarbonised future is the cost-optimal decarbonisation of their facilities’ energy systems. One method to guide the decarbonisation is internal carbon pricing. However, the design process of decarbonisation pathways, guided by internal carbon prices, can be challenging, since the energy system environment consists of many uncertainties. Despite the numerous uncertainties and existing methods to address uncertainties during the optimisation process, the optimisation of a facility’s energy system is often done by assuming perfect knowledge of all relevant input parameters (deterministic optimisation). Since real-world decisions can never be based on perfect knowledge and certain decisions might lead to path dependencies, it is important to consider the robustness of a solution in the context of developments that vary from the assumed scenarios. So far, no academic work has analysed the potential benefits of using an optimisation method that considers uncertainty about future CO2 prices and energy carrier cost as two important input parameters during the optimisation process. This publication closes the knowledge gap by optimising a real-world energy system of a manufacturing site with two-stage stochastic programming and comparing it with methods of deterministic optimisation. The results show considerably more robust results for the solutions generated by stochastic programming. The total cost deviation does not exceed 52%, while the deviation of the deterministic results reaches up to 96%. The results also indicate that organisations should not analyse their energy systems by only considering uncertain internal carbon prices, but should examine the effects together with other important but uncertain parameters.