COMPLEX ENERGY NETWORKS OPTIMIZATION: PART I - DEVELOPMENT AND VALIDATION OF A SOFTWARE FOR OPTIMAL LOAD ALLOCATION

被引:0
作者
Ancona, M. A. [1 ]
Bianchi, M. [1 ]
Branchini, L. [1 ]
De Pascale, A. [1 ]
Melino, F. [1 ]
Peretto, A. [1 ]
Rosati, J. [1 ]
机构
[1] Univ Bologna, DIN Alma Mater Studiorum, Viale Risorgimento 2, I-40136 Bologna, Italy
来源
PROCEEDINGS OF THE ASME TURBO EXPO: TURBOMACHINERY TECHNICAL CONFERENCE AND EXPOSITION, VOL 8 | 2020年
关键词
scheduling optimization; non-heuristic algorithm; complex energy network management; software development; DISTRIBUTED GENERATION; MANAGEMENT-SYSTEM; PROGRAMMING-MODEL; OPTIMAL-DESIGN; FORMULATION; ALGORITHM; EVOLUTION; STRATEGY;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The growing diffusion of the distributed generation systems, due to the European and national legislations which impose the fossil fuel and greenhouse gas emissions reduction and the renewable sources exploitation, have led to an increase in the complexity of the existing energy networks. The main issue of the complex energy grids is their management, which consists in the resolution and optimization of the load allocation problem by minimizing the primary energy consumption and, thus, improving the overall efficiency. In this context, the aim of this paper is to develop and validate a non-linear algorithm suitable for the resolution of the load allocation problem. In detail, the software COMBO, which has been developed by the University of Bologna, is based on a non-heuristic algorithm and allows to optimize a complex energy network - characterized by electrical, thermal, cooling and fuel fluxes - by evaluating all the possible combinations of solutions. The objective function of the software consists in the minimization of the total cost of energy production, including not only the variable costs, but also the costs related to the environmental impact of the energy systems. In this paper the mathematical model of the algorithm at the basis of the software COMBO is presented and described in detail. Furthermore, the software has been validated by its application to a case study and comparing the results with the ones obtained with a previously developed software based on a genetic algorithm (heuristic non-linear method).
引用
收藏
页数:10
相关论文
共 41 条
[1]   Distributed generation:: a definition [J].
Ackermann, T ;
Andersson, G ;
Söder, L .
ELECTRIC POWER SYSTEMS RESEARCH, 2001, 57 (03) :195-204
[2]   Socio-technical evolution of Decentralized Energy Systems: A critical review and implications for urban planning and policy [J].
Adil, Ali M. ;
Ko, Yekang .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2016, 57 :1025-1037
[3]   MINLP Probabilistic Scheduling Model for Demand Response Programs Integrated Energy Hubs [J].
Alipour, Manijeh ;
Zare, Kazem ;
Abapour, Mehdi .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 14 (01) :79-88
[4]   Integrating distributed generation: Regulation and trends in three leading countries [J].
Anaya, Karim L. ;
Pollitt, Michael G. .
ENERGY POLICY, 2015, 85 :475-486
[5]   Complex Energy Networks Optimization for Renewables Exploitation and Efficiency Increase [J].
Ancona, M. A. ;
Branchini, L. ;
De Pascale, A. ;
Di Pietra, B. ;
Melino, F. ;
Puglisi, G. ;
Zanghirella, F. .
74TH ATI NATIONAL CONGRESS: ENERGY CONVERSION: RESEARCH, INNOVATION AND DEVELOPMENT FOR INDUSTRY AND TERRITORIES, 2019, 2191
[6]   Efficiency improvement on a cruise ship: Load allocation optimization [J].
Ancona, M. A. ;
Baldi, F. ;
Bianchi, M. ;
Branchini, L. ;
Melino, F. ;
Peretto, A. ;
Rosati, J. .
ENERGY CONVERSION AND MANAGEMENT, 2018, 164 :42-58
[7]  
Ancona M.A., 2015, P INT C ASME ATI UIT
[8]  
[Anonymous], 2009, Official Journal of the European Union, DOI DOI 10.3000/17252555.L_2009.140.ENG
[9]   A mixed integer programming model for optimal design of trigeneration in a hospital complex [J].
Arcuri, P. ;
Florio, G. ;
Fragiacomo, P. .
ENERGY, 2007, 32 (08) :1430-1447
[10]   Two phase algorithm for load balancing in heterogeneous distributed systems [J].
Attiya, G ;
Hamam, Y .
12TH EUROMICRO CONFERENCE ON PARALLEL, DISTRIBUTED AND NETWORK-BASED PROCESSING, PROCEEDINGS, 2004, :434-439