Bi-objective mixed integer linear programming for managing building clusters with a shared electrical energy storage

Abstract Emerging smart grid infrastructures are allowing buildings to connect to components in other buildings and utilize them in different ways. Clearly, these interconnected building clusters provide new opportunities for building operators to collaborate and help reduce their operational costs over a planning horizon. Nevertheless, since each building can be treated as an independent decision maker here, related fairness concerns have to be addressed in these collaborative environments. We address these issues on building clusters when a single electrical energy storage is shared between two buildings with deterministic demand. We introduce three energy storage sharing strategies, and develop a bi-objective mathematical formulation for each strategy. Several techniques such as piecewise McCormick relaxation are employed for linearizing non-linear terms in the proposed formulations. An extensive computational study demonstrates the efficacy of our proposed linearization techniques, and compares all three strategies in terms of fairness and freedom.

[1]  Thomas R. Stidsen,et al.  A Branch and Bound Algorithm for a Class of Biobjective Mixed Integer Programs , 2014, Manag. Sci..

[2]  Wen-Shing Lee,et al.  Optimization for ice-storage air-conditioning system using particle swarm algorithm , 2009 .

[3]  J. Apt,et al.  Economics of electric energy storage for energy arbitrage and regulation in New York , 2007 .

[4]  Heinz Isermann,et al.  The Enumeration of the Set of All Efficient Solutions for a Linear Multiple Objective Program , 1977 .

[5]  Simeng Liu,et al.  Experimental analysis of simulated reinforcement learning control for active and passive building thermal storage inventory: Part 2: Results and analysis , 2006 .

[6]  Francesco Melino,et al.  Performance analysis of an integrated CHP system with thermal and Electric Energy Storage for residential application , 2013 .

[7]  Edoardo Amaldi,et al.  A detailed MILP optimization model for combined cooling, heat and power system operation planning , 2014 .

[8]  Yang Chen,et al.  A collaborative operation decision model for distributed building clusters , 2015 .

[9]  Garth P. McCormick,et al.  Computability of global solutions to factorable nonconvex programs: Part I — Convex underestimating problems , 1976, Math. Program..

[10]  Sheng Liu,et al.  Uncertain programming of building cooling heating and power (BCHP) system based on Monte-Carlo method , 2010 .

[11]  Pedro M. Castro,et al.  Tightening piecewise McCormick relaxations for bilinear problems , 2015, Comput. Chem. Eng..

[12]  Anthony Przybylski,et al.  Multiple objective branch and bound for mixed 0-1 linear programming: Corrections and improvements for the biobjective case , 2013, Comput. Oper. Res..

[13]  M. Bhavaraju,et al.  An Economic Assessment of Battery Storage in Electric Utility Systems , 1985, IEEE Transactions on Power Apparatus and Systems.

[14]  Heejin Cho,et al.  A probability constrained multi-objective optimization model for CCHP system operation decision support , 2014 .

[15]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[16]  Peter J. Varman,et al.  Balancing fairness and efficiency in tiered storage systems with bottleneck-aware allocation , 2014, FAST.

[17]  Teresa Wu,et al.  An augmented multi-objective particle swarm optimizer for building cluster operation decisions , 2014, Appl. Soft Comput..

[18]  G. Strbac,et al.  Value of combining energy storage and wind in short-term energy and balancing markets , 2003 .

[19]  Matthias Ehrgott,et al.  A discussion of scalarization techniques for multiple objective integer programming , 2006, Ann. Oper. Res..

[20]  Pietro Belotti,et al.  Fathoming rules for biobjective mixed integer linear programs: Review and extensions , 2016 .

[21]  Haisheng Chen,et al.  Progress in electrical energy storage system: A critical review , 2009 .

[22]  Simeng Liu,et al.  Experimental analysis of simulated reinforcement learning control for active and passive building thermal storage inventory: Part 1. Theoretical foundation , 2006 .

[23]  Lothar Thiele,et al.  The Hypervolume Indicator Revisited: On the Design of Pareto-compliant Indicators Via Weighted Integration , 2007, EMO.

[24]  Boris Bellalta,et al.  A reputation-based centralized energy allocation mechanism for microgrids , 2015, 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[25]  Martin W. P. Savelsbergh,et al.  A Criterion Space Search Algorithm for Biobjective Mixed Integer Programming: The Triangle Splitting Method , 2015, INFORMS J. Comput..

[26]  Teresa Wu,et al.  Decentralized operation strategies for an integrated building energy system using a memetic algorithm , 2012, Eur. J. Oper. Res..