Optimization in district heating and unit commitment: Caterina Tamburini at the Airo Young Workshop

OPTIT has renewed its support to the AIRO YOUNG workshop which took place at the University of Milan. 

The focus of this seventh edition was “Operational Research Beyond the Frontiers“. The goal is to collect contributions concerning a broad range of applications of the operations research, with an emphasis on applications to computer science and statistical data analysis.

Caterina Tamburini, senior OR Specialist in Optit, gave a presentation entitled “Optimization in the management of production with district heating: beyond the problem of unitary commitment”.

As decarbonization becomes a global priority, there is a need to increase the efficiency of energy production. Combined Heat and Power (CHP) is an energy efficient technology that generates electricity and captures the heat that would be wasted otherwise, in order to provide thermal energy, often used to feed district heating networks. Unit Commitment (UC) is a key problem in this context. The goal in UC is to determine a schedule for the machines that maximize the operative margin, satisfying a forecasted heat demand coming from a district heating network as well as functional and regulatory constraints deriving from system composition and placement. This gives rise to UC problems. 

In this work, we present some key features of the UC problem and our approach to perform the optimization of real world CHP systems, in both short and long term cases. Along with more classic and Operations Research based perspectives, we show how machine learning and data driven models can support and integrate the optimization process. 

We formulate and solve a Mixed Integer Linear Problem for the short-term optimization problem. Since the ability to predict the heat demand of the network is a relevant factor for the accuracy of the energy production plan, we developed a forecasting module that, given a series of historical data, automatically builds accurate prediction models.
Further, we present a metaheuristic algorithm for the long-term case, founded on a time-based decomposition of the problem, that leverage on a clustering module and on the above mentioned MILP for the short-term optimization. Moreover we show how the MILP for the short-term optimization can play a central role in the automatization of some complex processes, such as trading in power market sessions and managing plants where machines are operated in series.
In the first case, we have a portfolio of plants and we use the optimization model to assess cost-opportunity of alternative scenarios in order to maximize the revenues obtained trading electric power across several market sessions. In the latter case, operating temperature and water flows become important decision variables, introducing some nonlinear and nonconvex relations that make the optimization problem very hard to be solved.

AIRO Young Researchers Chapter is part of the Italian Operational Research Society (AIRO). Its aim is to foster collaboration between students and early-career researchers interested in the field of OR, and to provide them with new opportunities to advance their carreer and expand their network. It also strives to connect the demand and the offer in the OR job market, both in academia and in the industry.

The AIROYoung workshop is held each year in an Italian university. The workshop has become a reference for the young operational researchers and practitioners in Italy and abroad.


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