468112 A Rolling Horizon Scenario-Based Approach for Smart House Management Under Uncertainty

Wednesday, November 16, 2016: 2:05 PM
Carmel I (Hotel Nikko San Francisco)
Javier Silvente1, Georgios M. Kopanos2, Vivek Dua1 and Lazaros G. Papageorgiou3, (1)Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, London, United Kingdom, (2)School of Water, Energy & Environment, Cranfield University, Bedfordshire, United Kingdom, (3)Centre for Process Systems Engineering, Centre for Process Systems Engineering, University College London, London, United Kingdom

The development of sustainable, efficient and environmental friendly energy supply chains has led to advances in the area of Energy Systems Engineering. In this context, the growing interest in the exploitation of renewable energy sources has led to an increasing use of microgrids, which are decentralized networks able to integrate several heat/power generation systems of low/medium capacity (Hodge et al., 2011; Zhang et al., 2013), with the main purpose of distributing the generated heat/energy locally so as to reduce energy losses and improve the responsiveness to energy demand fluctuations (Kopanos et al., 2013; Tong et al., 2015). These kind of networks usually involve the presence of renewable generators. So, it is imperative that the variations in the behaviour of natural energy sources requires the consideration of uncertainty in microgrids. This variability must be considered in order to ensure the operability and the reliability of the system, since the optimal solution obtained under deterministic conditions can be sub-optimal or even infeasible. Particularly, the operations management of microgrids can be affected by several types of uncertainty, such as energy production and energy demand variations.

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.


Hodge, B.M., Huang, S., Siirola, J.D., Pekny, J.F. & Reklaitis, G.V. (2011). A multi-paradigm modeling framework for energy systems simulation and analysis. Computers & Chemical Engineering, 35, 1725-1737.

Kopanos, G.M., Georgiadis, M.C. & Pistikopoulos, E.N. (2013). Energy production planning of a network of micro combined heat and power generators. Applied Energy, 102, 1522-1534.

Kopanos, G.M. & Pistikopoulos, E.N. (2014). Reactive scheduling by a multiparametric programming rolling horizon framework: A case of a network of combined heat and power units. Industrial & Engineering Chemistry Research, 53, 4366-4386.

Tong, C.D., Palazoglu, A., El-Farra, N.H. & Yan, X.F. (2015). Energy demand management for process systems through production scheduling and control. AIChE Journal, 61, 3756-3769.

Zhang, D., Shah, N. & Papageorgiou, L.G. (2013). Efficient energy consumption and operation management in a smart building with microgrid. Energy Conversion and Management, 74, 209-222.

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See more of this Session: Energy Systems Design and Operations II
See more of this Group/Topical: Computing and Systems Technology Division