This work focuses on the development of optimization-based scheduling strategies for the coordination of microgrids. The main novelty of this work is the simultaneous management of energy production and energy demand within a reactive scheduling approach to deal with the presence of uncertainty associated to production and consumption. Delays in the nominal energy demands are allowed under associated penalty costs to tackle flexible and fluctuating demand profiles. In this study, the basic microgrid structure consists of renewable energy systems (photovoltaic panels, wind turbines) and energy storage units. Consequently, a Mixed Integer Linear Programming (MILP) formulation is presented and used within a rolling horizon scheme that periodically updates input data information.Peer ReviewedPostprint (author's final draft
This work presents a Mixed Integer Linear Programming (MILP) approach based on a combination of a rolling horizon and stochastic programming formulation. The objective of the proposed formulation is the optimal management of the supply and demand of energy and heat in microgrids under uncertainty, in order to minimise the operational cost. Delays in the starting time of energy demands are allowed within a predefined time windows to tackle flexible demand profiles. This approach uses a scenario-based stochastic programming formulation. These scenarios consider uncertainty in the wind speed forecast, the processing time of the energy tasks and the overall heat demand, to take into account all possible scenarios related to the generation and demand of energy and heat. Nevertheless, embracing all external scenarios associated with wind speed prediction makes their consideration computationally intractable. Thus, updating input information (e.g., wind speed forecast) is required to guarantee good quality and practical solutions. Hence, the two-stage stochastic MILP formulation is introduced into a rolling horizon approach that periodically updates input information.
This work is focused on the optimal management of electricity and heat generation and demand in microgrids. The objective of the proposed mathematical model is to adjust energy and heat availability profiles resulting from the use of renewable energy sources and flexible energy and heat demands. The optimisation of the resulting short-term problem is addressed through a Mixed-Integer Linear Programming (MILP) mathematical model to minimise the operational cost of the microgrid. Delays in the energy demands are allowed to tackle flexible demand profiles, under penalties in the objective function. An additional characteristic was the consideration of non-constant profiles in the considered tasks. Also, this model takes into account eventual interruptions in the tasks, applying penalties in the economic objective function. The main decisions to be made includes the schedule of tasks, as well as energy and heat generation levels, purchases from and exportation to the power grid, and storage levels.
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