基于改进的混合多种群进化算法的城市能源系统供需优化方法研究

doi:10.3969/j.issn.1000-7229.2016.01.004

电力建设 ›› 2016, Vol. 37 ›› Issue (1): 23-29.doi: 10.3969/j.issn.1000-7229.2016.01.004

• 电力经济研究 ·栏目主持文福拴教授· • 上一篇下一篇

基于改进的混合多种群进化算法的城市能源系统供需优化方法研究

刘树勇1，李娜1，曾鸣2，王磊1，刘丽霞1，李春雪2，刘伟2，欧阳邵杰2

1. 国网天津市电力公司经济技术研究院，天津市300000；2. 华北电力大学经济与管理学院，北京市 102206

出版日期:2016-01-01
作者简介:刘树勇(1978)，男，硕士，高级工程师，主要从事电力市场分析预测工作；李娜(1985)，女，博士，中级经济师，主要从事能源电力经济研究工作；曾鸣(1957)，男，教授，博导，研究方向为电力技术经济及需求侧响应；王磊(1981)，男，博士，主要从事电力经济与管理工作；刘丽霞(1981)，女，硕士，高级工程师，主要从事能源与电力市场分析工作；李春雪(1991)，女，硕士研究生，研究方向为能源与电力经济；刘伟(1992)，男，硕士研究生，研究方向为能源与电力经济；欧阳邵杰(1989)，男，博士研究生，研究方向为能源与电力经济。
基金资助:
国家自然科学基金资助项目 (71271082)

Supply and Demand Optimization Method of Urban Energy System Based on Improved Multispecies Hybrid Evolutionary Algorithm

LIU Shuyong1, LI Na1, ZENG Ming2, WANG Lei1, LIU Lixia1, LI Chunxue2, LIU Wei2, OUYANG Shaojie2

1. State Grid Tianjin Economic Research Institute, Tianjin 300000, China；2. School of Economics and Management, North China Electric Power University, Beijing 102206, China

Online:2016-01-01
Supported by:
Project supported by National Natural Science Foundation of China (71271082)

摘要/Abstract

摘要：

对城市能源系统供需优化方法进行研究。首先，以能源系统总成本最小为目标，建立考虑多类能源规划约束条件的城市能源系统供需优化模型；其次，引入改进的混合多种群进化算法，确定模型的寻优流程；最后，采用终端能源消费预测模型的相关结果作为需求约束量，将模型应用于T市进行算例分析。算例结果表明，应用所构建模型获取的未来规划期内最优能源供应结果，满足城市能源相关政策文件要求，能够有效引导T市能源供应向清洁、可持续化方向发展。

关键词: 城市能源系统, 供需优化模型, 变量耦合关系, 改进的混合多种群进化算法

Abstract:

This paper studies the supply and demand optimization method of urban energy systems. Firstly, taking the minimum total cost of energy system as the target, we establish the supply and demand optimization model of urban energy system with considering multiclass energy planning constraints. Secondly, we introduce the improved multispecies hybrid evolutionary algorithm to determine the optimal process of the model. Finally, we use the related results of the terminal energy consumption forecast model as demand constraints, and apply the model to the example analysis of T city. The results show that the optimal energy supply results during the future planning period obtained in the model can meet the requirements of urban energy relative policy documents, which will guide the energy supply of T city to clean and sustainable development direction.

Key words: urban energy system, optimization model of supply and demand, coupling relation among variables, improved multispecies hybrid evolutionary algorithm

中图分类号:

TM 61
F 206

刘树勇，李娜，曾鸣，王磊，刘丽霞，李春雪，刘伟，欧阳邵杰. 基于改进的混合多种群进化算法的城市能源系统供需优化方法研究[J]. 电力建设, 2016, 37(1): 23-29.

LIU Shuyong, LI Na, ZENG Ming, WANG Lei, LIU Lixia, LI Chunxue, LIU Wei, OUYANG Shaojie. Supply and Demand Optimization Method of Urban Energy System Based on Improved Multispecies Hybrid Evolutionary Algorithm[J]. Electric Power Construction, 2016, 37(1): 23-29.

参考文献

［1］吉平, 周孝信, 宋云亭, 等. 区域可再生能源规划模型述评与展望［J］. 电网技术, 2013, 37(8): 2071-2079.
JI Ping, ZHOU Xiaoxin, SONG Yunting, et al. Review and prospect of regional renewable energy planning models［J］. Power System Technology, 2013, 37(8): 2071-2079.
［2］彭波, 陈旭, 徐乾耀, 等. 面向新能源消纳的电网规划方法初探［J］. 电网技术, 2013, 37(12): 3386-3391.
PENG Bo, CHEN Xu, XU Qianyao, et al. Preliminary research on power grid planning method aiming at accommodating new energy［J］. Power System Technology, 2013, 37(12): 3386-3391.
［3］曹军威, 杨明博, 张德华, 等．能源互联网--信息与能源的基础设施一体化［J］. 南方电网技术, 2014, 8(4): 1-10．
CAO Junwei, YANG Mingbo, ZHANG Dehua, et al. Energy internet: an infrastructure for cyber-energy integration［J］. Southern Power System Technology, 2014, 8(4): 1-10.
［4］于慎航, 孙莹, 牛晓娜, 等. 基于分布式可再生能源发电的能源互联网系统［J］. 电力自动化设备, 2010, 30(5): 104-108.
YU Shenhang, SUN Ying, NIU Xiaona, et al. Energy internet system based on distributed renewable energy generation［J］. Electric Power Automation Equipment, 2010, 30(5): 104-108．
［5］KEMAL S, WALLACE E T. Analysis of US renewable fuels policies using a modified MARKAL model［J］. Renewable Energy, 2013, 50: 701-709.
［6］TODD J L, VALERIE M T, AUDREY J L. A MARKAL model of state electricity generation［C］//International Symposium on Sustainable Systems and Technology, Arlington: IEEE, 2010: 1-5.
［7］NORMA A, GIUSEPPE M, GIOVANNI P. The biomass based energy conversion: An example of Markal application for the energy planning［C］//Industrial Technology, Vi a del Mar: IEEE, 2010: 988-993.
［8］AMORIM F, PINA A, HANA G, et al. Electricity decarbonisation pathways for 2050 in Portugal: A TIMES (the integrated MARKAL-EFOM system) based approach in closed versus open systems modeling［J］. Energy, 2014, 69: 104-112.
［9］GHADERI S F, AZADEH A, DEHGHAN S. Iran energy and environmental planning for next decade: An application of the integrated markal-EFOM system (TIMES)［C］// the 35th International Conference on Computers and Industrial Engineering, Istanbul: Istanbul Technical University, 2005: 781-786.
［10］JOAO C, SOFIA S, PATRICIA F, et al. Renewable energy sources availability under climate change scenarios-Impacts on the Portuguese energy system［C］//the 5th Electricity Market International Conference on European, Lisboa: IEEE, 2008: 1-6.
［11］MARIAM G, WAQAR A Q. Long term electricity demand forecasting in residential sector of Pakistan［C］//Power & Energy Society General Meeting, San Diego: IEEE 2012: 1-7.
［12］AUMNAD P. Integrated energy and carbon modeling with a decision support system: Policy scenarios for low-carbon city development in Bangkok［J］. Energy Policy, 2010, 38: 4808-4817.
［13］RIAHI K, EDWARD S, MARGARET T, et al. Technological learning for carbon capture and sequestration technologies［J］. Energy Economics, 2004, 26: 539-564.
［14］KLAASSEN G, RIAHI K, et al. Internalizing externalities of electricity generation: An analysis with MESSAGE-MACRO［J］．Energy Policy，2007, 35: 815-827.
［15］佟庆, 白泉, 刘滨, 等. MARKAL模型在北京中远期能源发展研究中的应用［J］. 中国能源，2004, 26(6): 37-41.
TONG Qing, BAI Quan, LIU bin, et al. MARKAL model and its application in studies on medium- and long-term energy development in Beijing［J］. Energy of China, 2004, 26(6): 37-41.
［16］常征, 潘克西. 基于LEAP模型的上海长期能源消耗及碳排放分析［J］. 当代财经, 2014(1): 98-106.
CHANG Zheng, PAN Kexi. An analysis of Shanghais long-term energy consumption and carbon emission based on lEAP model［J］. Contemporary Finance & Economics, 2014(1): 98-106.
［17］黄东风. 利用MESSAGE模型优化浙江电源结构初探［J］. 能源工程, 2006（4）: 6-9．
HUANG Dongfeng. The application of MESSAGE Model for optimizing the structure of power in Zhejiang Province［J］. Energy Engineering, 2006（4）: 6-9.
［18］陈长虹, 科林格林, 吴昌华. MARKAL模型在上海市能源结构调整与大气污染物排放中的应用［J］. 上海环境科学, 2002, 21(9): 515-519.
CHEN Changhong, KELIN Gelin, WU Changhua. Application of Markal model to energy switch and pollutant emission in Shanghai ［J］.Environmental Sciences, 2002, 21(9): 515-519.
［19］JIANG B B, CHEN W Y, YU Y F．The Future of Natural Gas Coal Consumption in Beijing, Guangdong and Shanghai: An assessment utilizing MARKAL［C］//Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh: IEEE, 2008: 1-8.
［20］杨晓鸥. 北京经济、能源、环境-安全（3E-S）系统模型研究［D］. 北京：清华大学, 2010.
YANG Xiaoou. Research on 3E-S (economy, energy, environment-security) system model for Beijing［D］. Beijing: Tsinghua University, 2010.
［21］石玉淳. 基于LEAP模型的大连市工业能源消费分析研究［D］. 大连：大连海事大学, 2014.
SHI Yuchun. Analysis of Dalian industrial energy consumption based on LEAP Model［D］. Dalian： Dalian Maritime University, 2014.

基于改进的混合多种群进化算法的城市能源系统供需优化方法研究

Supply and Demand Optimization Method of Urban Energy System Based on Improved Multispecies Hybrid Evolutionary Algorithm

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