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  • X Zhang

System modeling for the optimal planning and operation of polygeneration facilities

Updated: Jul 11, 2019




Abstract:

Power grids are evolving, from a centralized to a distributed configuration. This evolution supposes many benefits but also represents many challenges. In this context, polygeneration facilities emerge with a leading role. Polygeneration facilities are types of microgrids in which more than one energy vector is simultaneously produced, for example, power, heat, cold and others.


In a polygeneration facility, complex interactions take place between components and optimization is needed in the planning phase to design facilities that will achieve better economic and environmental objectives. Optimization is also needed in the operational phase to further improve economic and environmental objectives. In this paper, first, a polygeneration facility is optimally designed by minimizing an integrated savings ratio (objective function) which is a weighted sum of total annualized cost, annual fuel consumption, and annual carbon dioxide (CO2) emissions.


This optimization is performed for four operational strategies. A reference system integrated by traditional technologies is used as a baseline for comparison against the proposed system. Particle swarm optimization algorithm is used to minimize the objective function. Several operational constraints are applied.


Second, the optimally designed system is then used for day-ahead operational strategy selection to adapt the system to changing weather conditions, scheduled changes in power, heating and cooling demands, scheduled system maintenance, failures, and contingencies. Real data are used to feed the models. After the initial optimization, integrated savings ratios (ISRs) range from .1576 to .3021.


Applying the secondary optimization, ISRs were improved up to 2.5% above the initial values. We conclude that initial optimization, day-ahead, and hour-ahead operational strategy selection are key factors for improving economic and environmental objectives.


We proved that a year-long optimization can be further improved by applying a day-ahead and hour-ahead approach for operation strategy selection.



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