Bi-level programming for the energy retrofit planning of street lighting systems

This paper addresses strategic decision making issues for the energy management of urban street lighting. We propose a hierarchical decision procedure that supports the energy manager in determining the optimal energy retrofit plan of an existing public street lighting system throughout a wide urban area. A bi-level programming model integrates several decision making units, each focusing on the energy optimization of a specific limited zone lighting system, and a central decision unit, aiming at a fair distribution of actions among these various systems, while ensuring an efficient use of public funds. We apply the technique to the case study of the city of Bari (Italy), to demonstrate the applicability and efficiency of the proposed optimization model.

[1]  Christodoulos A. Floudas,et al.  Global optimization of mixed-integer bilevel programming problems , 2005, Comput. Manag. Sci..

[2]  Leszek Kotulski,et al.  Towards Highly Energy-Efficient Roadway Lighting , 2016 .

[3]  Adam Sędziwy,et al.  A New Approach to Street Lighting Design , 2016 .

[4]  Arbab Waheed Ahmad,et al.  Energy-Efficient Intelligent Street Lighting System Using Traffic-Adaptive Control , 2016, IEEE Sensors Journal.

[5]  Hanif D. Sherali,et al.  A new reformulation-linearization technique for bilinear programming problems , 1992, J. Glob. Optim..

[6]  Philippine de T’Serclaes,et al.  The 25 IEA energy efficiency policy recommendations to the G8 Gleneagles Plan of Action , 2010 .

[7]  Ana Castillo-Martinez,et al.  A review of energy efficiency label of street lighting systems , 2017 .

[8]  Daniel Gómez-Lorente,et al.  A simple and accurate model for the design of public lighting with energy efficiency functions based on regression analysis , 2016 .

[9]  B. J. Lageweg,et al.  Analytical Evaluation of Hierarchical Planning Systems , 1981, Oper. Res..

[10]  Giuseppina Ciulla,et al.  Improvement of energy efficiency and quality of street lighting in South Italy as an action of Sustainable Energy Action Plans. The case study of Comiso (RG) , 2015 .

[11]  Javier Bajo,et al.  Intelligent system for lighting control in smart cities , 2016, Inf. Sci..

[12]  Adel Khelifi,et al.  Multiobjective Optimization of Roadway Lighting Projects , 2016 .

[13]  Mariagrazia Dotoli,et al.  ICT and optimization for the energy management of smart cities: The street lighting decision panel , 2015, 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA).

[14]  Mariagrazia Dotoli,et al.  Bi-level programming for the strategic energy management of a smart city , 2016, 2016 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS).

[15]  Mariagrazia Dotoli,et al.  A Hierarchical Decision-Making Strategy for the Energy Management of Smart Cities , 2017, IEEE Transactions on Automation Science and Engineering.

[16]  Mariagrazia Dotoli,et al.  A Decision Making Technique to Optimize a Buildings’ Stock Energy Efficiency , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[17]  Jeremy Lagorse,et al.  Sizing optimization of a stand-alone street lighting system powered by a hybrid system using fuel cell, PV and battery , 2009 .

[18]  Filippo Attivissimo,et al.  Channel Characterization of an Open Source Energy Meter , 2014, IEEE Transactions on Instrumentation and Measurement.

[19]  Gang Liu,et al.  Sustainable feasibility of solar photovoltaic powered street lighting systems , 2014 .

[20]  Nataliya I. Kalashnykova,et al.  Bilevel Programming and Applications , 2015 .