Two-stage Optimization for Building Energy Management

In recent years, the energy sector has undergone an important transformation as a result of technological progress and socioeconomic development. The continuous integration of renewable technologies drives the gradual transition from the traditional business model based on a reduced number of large power plants to a more decentralized energy production. The increasing energy demand and intermittent generation of renewable energy sources require modern control strategies to provide an uninterrupted service and guarantee high energy efficiency. Utilities and network operators permanently supervise production facilities and grids to compensate any mismatch between production and consumption. The enormous potential of local energy management contributes to grid stability and can be used to reduce the adverse effects of load variations and production fluctuations. This paper presents a building energy management which determines the optimal scheduling of all components of the local energy system. The two-stage optimization is based on a receding horizon strategy and minimizes two economic functions subject to the physical system constraints. The performance of the proposed building energy management is validated in simulations and the results are compared to the ones obtained with other energy management approaches.

[1]  Giuseppe Tommaso Costanzo,et al.  A System Architecture for Autonomous Demand Side Load Management in Smart Buildings , 2012, IEEE Transactions on Smart Grid.

[2]  Shahin Sirouspour,et al.  MILP-based rolling horizon control for microgrids with battery storage , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[3]  J. Iwaro,et al.  A review of building energy regulation and policy for energy conservation in developing countries , 2010 .

[4]  Colin Fitzpatrick,et al.  Demand side management of a domestic dishwasher: Wind energy gains, financial savings and peak-time load reduction , 2013 .

[5]  Tobias Achterberg,et al.  SCIP: solving constraint integer programs , 2009, Math. Program. Comput..

[6]  Jianhui Wang,et al.  MPC-Based Appliance Scheduling for Residential Building Energy Management Controller , 2013, IEEE Transactions on Smart Grid.

[7]  Kyung-Bin Song,et al.  An Optimal Power Scheduling Method for Demand Response in Home Energy Management System , 2013, IEEE Transactions on Smart Grid.

[8]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[9]  Thorsten Koch,et al.  Constraint Integer Programming: A New Approach to Integrate CP and MIP , 2008, CPAIOR.

[10]  Miao He,et al.  A Multi-Timescale Scheduling Approach for Stochastic Reliability in Smart Grids With Wind Generation and Opportunistic Demand , 2013, IEEE Transactions on Smart Grid.

[11]  José Luis Guzmán,et al.  Efficient building energy management using distributed model predictive control , 2014 .

[12]  Jonathan Currie,et al.  Opti: Lowering the Barrier Between Open Source Optimizers and the Industrial MATLAB User , 2012 .

[13]  Long Zhao,et al.  Job Scheduling With Uncertain Local Generation in Smart Buildings: Two-Stage Robust Approach , 2014, IEEE Transactions on Smart Grid.

[14]  Hamed Mohsenian Rad,et al.  Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments , 2010, IEEE Transactions on Smart Grid.

[15]  David Q. Mayne,et al.  Model predictive control: Recent developments and future promise , 2014, Autom..

[16]  Jianwei Huang,et al.  Demand Response Management via Real-Time Electricity Price Control in Smart Grids , 2013 .

[17]  Wei-Jen Lee,et al.  A Residential Consumer-Centered Load Control Strategy in Real-Time Electricity Pricing Environment , 2007, 2007 39th North American Power Symposium.

[18]  Tokhir Gafurov,et al.  Proactive control for energy systems in Smart Buildings , 2011, 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies.

[19]  Chin Choy Chai,et al.  Demand Bidding Program and Its Application in Hotel Energy Management , 2014, IEEE Transactions on Smart Grid.

[20]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[21]  Pedro Moura,et al.  The role of demand-side management in the grid integration of wind power , 2010 .

[22]  Alexander Schrijver,et al.  Theory of linear and integer programming , 1986, Wiley-Interscience series in discrete mathematics and optimization.

[23]  Johan Löfberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .

[24]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[25]  Edmilson Moutinho dos Santos,et al.  The risks of an energy efficiency policy for buildings based solely on the consumption evaluation of final energy , 2013 .

[26]  Kevin Tomsovic,et al.  Two-Stage Residential Energy Management Considering Network Operational Constraints , 2013, IEEE Transactions on Smart Grid.

[27]  Daniel Pérez Palomar,et al.  Noncooperative and Cooperative Optimization of Distributed Energy Generation and Storage in the Demand-Side of the Smart Grid , 2013, IEEE Transactions on Signal Processing.

[28]  Marcelo Godoy Simões,et al.  An Energy Management System for Building Structures Using a Multi-Agent Decision-Making Control Methodology , 2013 .

[29]  Paulo F. Ribeiro,et al.  Multi-agent system architecture for smart home energy management and optimization , 2013, IEEE PES ISGT Europe 2013.

[30]  Ding Li,et al.  Auctioning game based Demand Response scheduling in smart grid , 2011, 2011 IEEE Online Conference on Green Communications.