A multi-objective optimization approach for assessment of technical, commercial and environmental performance of microgrids

被引:26
作者
Schwaegerl, Christine [1 ]
Tao, Liang [1 ]
Mancarella, Pierluigi [2 ]
Strbac, Goran [2 ]
机构
[1] Siemens AG, ED SE PTI NC, D-91058 Erlangen, Germany
[2] Univ London Imperial Coll Sci Technol & Med, Dept Elect & Elect Engn, London SW7 2AZ, England
来源
EUROPEAN TRANSACTIONS ON ELECTRICAL POWER | 2011年 / 21卷 / 02期
基金
英国工程与自然科学研究理事会;
关键词
distributed energy resources; distributed generation; genetic algorithm; microgrid; multi-objective optimization; Pareto front; storage; unit commitment;
D O I
10.1002/etep.472
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper proposes a multi-objective optimization model to evaluate potential economic, technical and environmental benefits of Microgrid operation. Internalization of external costs such as induced by energy losses (network related) and CO2 emissions (environmentally related) is performed to address the interests of different agents within a single optimization framework. After presenting four reference operation strategies, a multi-layered scheduling algorithm based on genetic algorithm and linear/quadratic programming is introduced in detail. The algorithm is applied to a UK urban test grid with four different levels of penetration of distributed energy resources. The simulation results lead to the recommendation of a combined dispatch strategy that can simultaneously satisfy all constraints and provide a proper compromise for conflicting objectives of different stakeholders involved in energy supply chain. In particular, a Pareto front representation of the optimization results is also given to further illustrate the multi-objective nature of the problem. Copyright (C) 2010 John Wiley & Sons, Ltd.
引用
收藏
页码:1271 / 1290
页数:20
相关论文
共 17 条
[1]  
Bickel P., 2005, EXTERNE EXTERNALITIE
[2]  
BUCHHOLZ B, CIGRE 2008
[3]   THE INTRODUCTION OF NON-DISPATCHABLE TECHNOLOGIES AS DECISION VARIABLES IN LONG-TERM GENERATION EXPANSION MODELS [J].
CARAMANIS, MC ;
TABORS, RD ;
NOCHUR, KS ;
SCHWEPPE, FC .
IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS, 1982, 101 (08) :2658-2667
[4]  
CAROL C, 1995, IEEE T POWER SYSTEMS, V10, P671
[5]  
CATALAO J, 2005, 5 PSCC LIEG 2 26 AUG
[6]   Social choice, uncertainty about external costs and trade-off between intergenerational environmental impacts:: The emblematic case of gas-based energy supply decentralization [J].
Gullì, F .
ECOLOGICAL ECONOMICS, 2006, 57 (02) :282-305
[7]  
Hatziargyriou N, CIGRE 2006
[8]   Application of multi objective evolutionary programming to combined economic emission dispatch problem [J].
Jeyakumar, D. N. ;
Venkatesh, P. ;
Lee, Kwang. Y. .
2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6, 2007, :1162-+
[9]   An evolutionary programming solution to the unit commitment problem [J].
Juste, KA ;
Kita, H ;
Tanaka, E ;
Hasegawa, J .
IEEE TRANSACTIONS ON POWER SYSTEMS, 1999, 14 (04) :1452-1459
[10]  
KOEPPEL G, 2003, DISTRIBUTED GENERATI