468112 A Rolling Horizon Scenario-Based Approach for Smart House Management Under Uncertainty
This work addresses the presence of uncertainty through a discrete-time two-stage stochastic programming approach, which is combined with a rolling horizon approach (Kopanos et al., 2014) for the simultaneous management of energy supply and demand in microgrids. The proposed Mixed Integer Linear Programming (MILP) formulation contains inequality constraints corresponding to the available heat and energy generation technologies, as well as equality constraints corresponding to heat and energy balance equations that describe flows, production, storage and consumption levels. This mathematical formulation uses a scenario-based stochastic programming approach where the scenarios are associated to internal variations in the duration of the energy consumptions as well as in the overall heat demand. However, the high complexity related to the estimated weather forecast, makes the consideration of all possible external scenarios, computationally intensive. The variability in weather conditions may affect the availability and production capacity of renewable energy generators. Thereby, updating input data (i.e., wind profile, heat and energy demand) is needed to ensure the adequate quality in the obtained results. Hence, an MILP two-stage stochastic programming approach was incorporated within a rolling horizon scheme that periodically updates input data information as the uncertain input parameters are revealed or considered to be known with certainty. The main decisions to be made to maximise the profit of the microgrid includes the execution of consumptions, the amount of heat and energy to be produced or purchased, the heat and energy storage levels and the amount of energy to export to the power grid. The methodology proposed above was tested on a case study and promising results demonstrating the applicability of the methodology for decision making were obtained. This work also forms a basis for future work to address more complex problems.
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