No abstract

The increased use of heat pumps to utilise low-temperature heat will undoubtedly be a part of future emission reduction measures within the heating sector. Identifying these heat sources and assessing their heat potential is essential for their utilisation. Different methods for estimating the potential of excess and natural heat sources found in the urban environment are presented in this study. The research aims to present a replicable estimation methodology which can be applied to any urban area. The methods are developed around publicly available data sources, or otherwise easily obtainable data. The research aims at producing data accurate enough to support decision-making on the district heating company or city level on utilising these heat sources. A wide range of excess and natural heat sources found in urban environments were identified in a literature review. Methods for estimating the potential of the heat sources were developed based on findings of the literature review and the expected availability of data. The developed estimation methods were applied in a case study where the potential of heat sources identified within the Turku area in Southwest Finland was estimated. The results of the case study show the potential of the heat sources within the studied area. The difficulty of obtaining raw, high-quality data are also highlighted. This emphasises the need for advanced processing of available data and insight on the related sources, e.g. building management systems or industrial processes. The methods presented in this study give an overview on how heat potential could be estimated. It can be used as a base for developing more refined methods and for detailed techno-economic assessment for utilising available excess and natural heat sources. Graphical abstract

An open-source modelling framework Predicer, standing for Predictive Decider, for multi-market day-ahead market operation purposes is described in this paper. The Predicer model uses scenario-based stochastic optimisation to obtain decision variables and bid matrixes for energy and reserve markets by maximising the risk-adjusted expected value of the profit during the model time frame. The modelled energy system structure is abstract, that is, based on basic elements such as nodes representing different energy types and processes representing flows between nodes. The abstract model structure enables user to construct arbitrary energy systems and define links between assets, commodities, energy markets and reserve markets. Predicer model can include properties such as unit ramp rates, online units, dynamic energy storages, market realisation and market bidding requirements. The aggregation of unit-based energy and reserve opportunities into a virtual power plant allows the asset owner to make optimized portfolio-level bids for different market products. The model scenarios consist of user defined forecasts for market prices, renewable energy supply, energy demand and other system related time series. Predicer is implemented in Julia programming language and uses the JuMP optimisation package.