The Collaborative Optimization Model of Park Energy Supply and Use Considering the Change of Supply - Demand Regulation Cost

doi:10.11930/j.issn.1004-9649.202004046

Abstract

Abstract: For an integrated energy system (IES) of park with flexible load, the production of cold, heat, electricity, and steam can be changed by adjusting energy conversion equipment, and the load can also be changed by demand-side management. All energy conversion equipment operation and demand control measures require corresponding costs. It's necessary for integrated optimization of equipment side and demand side of energy products (such as cold, heat, electricity, and steam) to reduce cost of IES. In this paper, mathematical models for the various types of energy conversion and demand response costs are established, with the goal of minimizing the overall cost of energy supply, to comprehensively optimize the energy conversion system design and demand-side management in IES. The results show that compared with simply adjusting the output of energy conversion system, the coordinated regulation of energy production and demand side of the integrated energy system has the potential to further reduce the cost of integrated energy supply and utilization system.

Key words: integrated energy system of park, energy stations, comprehensive adjustment of supply and demand, demand-side management, comprehensive optimization

LUAN Fengkui, TANG Yanmei, JIN Lu, LI Kecheng, LIU Zhiyuan. The Collaborative Optimization Model of Park Energy Supply and Use Considering the Change of Supply - Demand Regulation Cost[J]. Electric Power, 2020, 53(10): 140-148.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202004046

https://www.electricpower.com.cn/EN/Y2020/V53/I10/140

References

[1] 杜祥琬. 能源生产和消费革命战略[N]. 中国财经报, 2017-06-03(002).
[2] 杜祥琬. 能源科技发展前沿及未来方向[J]. 科学通报, 2017, 62(8): 780-784
DU Xiangwan. Frontiers and future directions of energy science and technology[J]. Chinese Science Bulletin, 2017, 62(8): 780-784
[3] 钟迪, 李启明, 周贤, 等. 多能互补能源综合利用关键技术研究现状及发展趋势[J]. 热力发电, 2018, 47(2): 1-5, 55
ZHONG Di, LI Qiming, ZHOU Xian, et al. Research status and development trends for key technologies of multi-energy complementary comprehensive utilization system[J]. Thermal Power Generation, 2018, 47(2): 1-5, 55
[4] 艾芊, 郝然. 多能互补、集成优化能源系统关键技术及挑战[J]. 电力系统自动化, 2018, 42(4): 1-10, 46
AI Qian, HAO Ran. Key technologies and challenges for multi-energy complementarity and optimization of integrated energy system[J]. Automation of Electric Power Systems, 2018, 42(4): 1-10, 46
[5] 黄子硕, 何桂雄, 闫华光, 等. 园区级综合能源系统优化模型功能综述及展望[J]. 电力自动化设备, 2020, 40(1): 10-18
HUANG Zishuo, HE Guixiong, YAN Huaguang, et al. Overview and prospect of optimization model function for community-scale integrated energy system[J]. Electric Power Automation Equipment, 2020, 40(1): 10-18
[6] BRANDONI C, RENZI M. Optimal sizing of hybrid solar micro-CHP systems for the household sector[J]. Applied Thermal Engineering, 2015, 75: 896-907.
[7] CHALKIADAKIS G, ROBU V, KOTA R, et al. Cooperatives of distributed energy resources for efficient virtual power plants[C]//The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2. Taipei, Taiwan: International Foundation for Autonomous Agents and Multiagent Systems; 2011.
[8] CEDILLOS ALVARADO D, ACHA S, SHAH N, et al. a Technology Selection and Operation (TSO) optimisation model for distributed energy systems: Mathematical formulation and case study[J]. Applied Energy, 2016, 180: 491-503.
[9] 黄子硕, 于航, 彭震伟. 广域网视角下的城市能量系统及其规划[J]. 科学通报, 2018, 63(28): 3047-3058
HUANG Zishuo, YU Hang, PENG Zhenwei. Urban energy system planning from wide area network perspective[J]. Chinese Science Bulletin, 2018, 63(28): 3047-3058
[10] CHEN X Y, KANG C Q, O'MALLEY M, et al. Increasing the flexibility of combined heat and power for wind power integration in China: modeling and implications[J]. IEEE Transactions on Power Systems, 2015, 30(4): 1848-1857.
[11] RUAN Y J, LIU Q R, LI Z W, et al. Optimization and analysis of building combined cooling, heating and power (BCHP) plants with chilled ice thermal storage system[J]. Applied Energy, 2016, 179: 738-754.
[12] GUO L, LIU W J, CAI J J, et al. A two-stage optimal planning and design method for combined cooling, heat and power microgrid system[J]. Energy Conversion and Management, 2013, 74: 433-445.
[13] UDDIN M, ROMLIE M F, ABDULLAH M F, et al. A review on peak load shaving strategies[J]. Renewable and Sustainable Energy Reviews, 2018, 82: 3323-3332.
[14] 崔鹏程, 史俊祎, 文福拴, 等. 计及综合需求侧响应的能量枢纽优化配置[J]. 电力自动化设备, 2017, 37(6): 101-109
CUI Pengcheng, SHI Junyi, WEN Fushuan, et al. Optimal energy hub configuration considering integrated demand response[J]. Electric Power Automation Equipment, 2017, 37(6): 101-109
[15] 张堙, 魏志远, 王馨, 等. 负荷特性对蓄能型建筑热电联供系统节能率的影响[J]. 工程热物理学报, 2019, 40(3): 656-660
ZHANG Yin, WEI ZhiYuan, WANG Xin, et al. Influence of load characteristics on energy saving ratio for TES-BCHP system[J]. Journal of Engineering Thermophysics, 2019, 40(3): 656-660
[16] LOCATELLI G, PALERMA E, MANCINI M. Assessing the economics of large energy storage plants with an optimisation methodology[J]. Energy, 2015, 83: 15-28.
[17] 白牧可, 唐巍, 吴聪, 等. 基于热网-电网综合潮流的用户侧微型能源站及接入网络优化规划[J]. 电力自动化设备, 2017, 37(6): 84-93
BAI Muke, TANG Wei, WU Cong, et al. Optimal planning based on integrated thermal-electric power flow for user-side micro energy station and its integrating network[J]. Electric Power Automation Equipment, 2017, 37(6): 84-93
[18] ASGHARIAN H, BANIASADI E. Experimental and numerical analyses of a cooling energy storage system using spherical capsules[J]. Applied Thermal Engineering, 2019, 149: 909-923.
[19] PFEIFER A, DOBRAVEC V, PAVLINEK L, et al. Integration of renewable energy and demand response technologies in interconnected energy systems[J]. Energy, 2018, 161: 447-455.
[20] 吴晨曦, 张杰, 张新延,等. 考虑电价影响的电动汽车削峰填谷水平评价[J]. 电力系统保护与控制, 2019, 47(17): 14-22
WU Chenxi, ZHANG Jie, ZHANG Xinyan,et al. Load shifting level evaluation of EVs in the different energy price environment[[J]. Power System Protection and Control, 2019, 47(17): 14-22
[21] SIANO P. Demand response and smart grids: a survey[J]. Renewable and Sustainable Energy Reviews, 2014, 30: 461-478.
[22] 王均, 黄琦. 基于优惠券激励的需求响应双层优化机制[J]. 电力系统保护与控制, 2019, 47(1): 108−114.
WANG Jun, HUANG Qi. Coupon incentives based customers voluntary demand response program via bilevel optimization mechanism[J]. Power System Protection and Control, 2019, 47(1): 108−114.
[23] YUSTA J M, KHODR H M, URDANETA A J. Optimal pricing of default customers in electrical distribution systems: Effect behavior performance of demand response models[J]. Electric Power Systems Research, 2007, 77(5/6): 548-558.
[24] SCHWEPPE F C, CARAMANIS M C, TABORS R D. Evaluation of spot price based electricity rates[J]. IEEE Transactions on Power Apparatus and Systems, 1985, PAS-104(7): 1644-1655.
[25] YOUSEFI S, MOGHADDAM M P, MAJD V J. Optimal real time pricing in an agent-based retail market using a comprehensive demand response model[J]. Energy, 2011, 36(9): 5716-5727.