Implementation period: 2021-12-01 - 2023-07-31
Type: National
Division: The Division of Energy Economics
PROJECT NR: POIR.01.01.01-00-0709/21
PROJECT DESCRIPTION: Because of rising electricity prices, enterprises are making significant efforts to limit the negative impact on their operating costs. Therefore, solutions aimed at reducing the costs of acquiring electricity are being sought, for example, by utilizing their own energy sources or energy storage units. In light of the above, a market need was identified regarding the development of a dedicated tool for companies to reduce the costs of acquiring electricity through the optimization of purchasing processes, utilization of self-production, and storage. The application of such a tool will enable facility owners to optimize the operation of the system from the perspective of minimizing energy acquisition costs.
The aim of the project is to conduct R&D works that will result in the development of a tool enabling companies to optimize the process of purchasing, self-production, and storage of electricity, taking the minimization of energy acquisition costs as the optimization criterion.
The tool will conceptually be based on two categories of decision-making methods: machine learning and mathematical programming. Machine learning methods will be used for forecasting electricity prices, self-production from local sources (e.g., PV, wind), and predicting electricity consumption profiles. The results from the machine learning models will be used as input parameters for the optimization model (together with other technical-economic parameters of energy storage, generation sources, etc.) built using mathematical programming methods. The optimization criterion will be the minimization of electricity acquisition costs. The developed models will be implemented in the following computational environments: Python (for machine learning) and GAMS (for optimization).
The methodology for establishing the appropriate decisions involves iteratively selecting the values for several hundred thousand individual variables of the optimization model (depending on the model’s resolution) until the minimum value of the objective function is achieved (cost minimization within a given time horizon). As part of the project, the developed tool will be verified and tested on case studies. Pre-implementation work is also foreseen within the project.
The final result of the project —the developed tool— is primarily intended for two groups of energy consumers: companies with electricity consumption ranging from 10 MWh/month to 50 MWh/month and companies with consumption exceeding 50 MWh/month. This tool will enable industrial and service companies to increase the economic efficiency of their operations by optimizing the purchase, in-house production, and energy storage, taking the minimization of energy acquisition costs as the optimization criterion.
The project is being implemented by a consortium of two entities: TwinIO Energy Sp. z o.o. and IGSMIE PAN.
PROJECT BUDGET: 5 959 124,26 PLN
FUNDING: 4 689 045,08 PLN
PROJECT R&D MANAGER: prof. dr hab. inż. Jacek Kamiński, kontakt: kaminski@min-pan.krakow.pl
The project is funded by the National Centre for Research and Development under the Operational Programme Smart Growth 2014–2020, PRIORITY AXIS: “Support for R&D activities carried out by enterprises”, ACTIVITY: “R&D projects of enterprises”, SUB-ACTIVITY: “Industrial research and development work carried out by enterprises”, COMPETITION: 1/1.1.1/2021 – Fast track.
