程杉(1981),男,博士,教授,研究方向为综合能源系统、智能配用电等(E-mail:
陈诺(1999),男,硕士在读,研究方向为新能源微电网运行
徐建宇(1995),男,硕士在读,研究方向为微网需求侧管理
针对楼宇综合能源系统(residential integrated energy system, RIES)能量管理时未充分考虑影响室温因素及其对负荷建模的影响和刚性捆绑RIES、用户从未全面考虑用户舒适度和用能支出的问题,文中提出冷、热负荷参与阶梯型补贴和电负荷参与电价型综合需求响应的RIES能量管理优化模型及其求解方法。首先,综合考虑影响室温因素,得到离散化的楼宇热平衡方程,建立楼宇的柔性而非固定的冷、热、电负荷数学模型。其次,建立冷、热负荷参与的阶梯型补贴和电负荷参与的电价型综合需求响应机制。然后,考虑RIES向用户售能的收益、从外部购能的成本和支付用户的补贴费用,构建以最大化RIES运行利润为目标、计及设备和系统运行约束的能量管理优化数学模型,并采用Cplex对线性化后的模型进行求解。最后,通过算例仿真表明:计及综合需求响应的RIES能量管理优化能统筹协调供需两侧资源,提升系统与用户的经济效益。
In response to the problem that the factors affecting room temperature and its influence on load modeling and the problems of user comfort and energy consumption expenditure when rigidly bundling residential integrated energy system (RIES) and users are not fully considered in the energy management of RIES, the RIES energy management optimization model and its solution method are proposed in the paper for the participation of cooling and heating loads in tiered subsidy and electric loads in the tariff-type integrated demand response. Firstly, the discrete building heat balance equation is obtained by comprehensively considering the factors affecting the room temperature, and the mathematical model of the flexible but not fixed cold, heat and electricity loads of the building is established. Secondly, a tiered subsidy for cold and heat load participation and a tariff-type integrated demand response mechanism for electric load participation should be established. Then, taking into account of RIES's cost of purchase energy from the outside, cost of affording integrated demand response (IDR), and profit of selling energy to the users, an optimization model for optimizing energy management is established that maximizes RIES's net profit with consideration a series of operation constraints of the components and the system. The Cplex is used to solve the linearized model. Finally, a simulation example shows that the residential integrated energy system optimization strategy that takes into account the comprehensive demand response can coordinate the resources on both sides of supply and demand, thus improving the economic benefits of the system and users.
气候变化、化石能源枯竭、能源需求增长等挑战促使人们研究并推广应用具有高效的能源供应效率和灵活的系统运行方式的综合能源系统(integrated energy system, IES)[
IES根据用户的负荷需求制定合理的运行方案,且通常根据固定负荷进行调度[
建筑冷、热负荷的柔性供能也促进传统单一的电力需求响应(demand response, DR)向综合需求响应(integrated demand response, IDR)转变。IDR还可以选择消费不同形式的能源以满足用能需求,从而实现多能协调运行以降低供能压力[
以上文献在建立柔性负荷需求模型以及考虑IDR的RIES能量管理优化方面做出贡献,但主要还有2点不足:(1) 文献中多以热阻、热容模型建立制冷、制热负荷与用户室温需求的供需关系,未充分考虑影响室内温度的相关因素,不能准确反映不同种类楼宇因围护结构、热力特性、使用时间上的不同引起的柔性供需关系的差异;(2) 上述文献将用户与IES捆绑为一个刚性主体参与调度以提高IES收益,制冷、制热功率提高可提升IES利润,但会引起用户负荷增加,从而提高用户购能成本,而且过高、过低的室温会降低用户用能体验。
为此,文中首先计及围护结构传热系数、太阳辐射、遮阳系数等对建筑蓄热能力的影响,得到楼宇热平衡方程并构建楼宇柔性冷、热、电负荷数学模型,一方面可以精确描述外部环境对于管理优化模型经济性和用户舒适度的影响,另一方面可以为需求侧提供更有效的资源。在此基础上制定RIES与用户之间基于阶梯型补贴的IDR机制,避免传统将RIES与用户刚性捆绑的局限性。再者,建立以系统净利润最大为目标的RIES能量管理优化数学模型,对其进行线性化处理后采用Cplex进行求解。最后,基于仿真算例和对比分析验证RIES与用户统筹优化的能量管理数学模型的有效性和优越性,能够实现RIES与用户的双赢。
含多能流的RIES能量流如
RIES结构
Structure of RIES
GT发电产生的余热通过AR和HE输出灵活可变的冷、热功率,得到其在
式中:
HE回收GT发电余热,回收到的热功率
式中:
GT运行时还应满足运行功率约束。
式中:
GB通过燃烧天然气产生热能补充GT产热不足时的热负荷,其输出热功率
式中:
BT可以通过电功率在时间上的转移来降低用能成本,须满足式(6)的充、放电状态约束、式(7)的充、放电功率约束和式(8)、式(9)的储存能量及其约束。
式中:
式中:
式中:
在一个调度周期(1 d)始末BT蓄电量一致,如式(10)所示。
AC通过消耗电能制冷,可得其输入的电功率
式中:
AR通过GT发电所产生的余热驱动制冷,其输入的热功率
式中:
RIES不能同时向电网购电和售电,且与电网的交互功率应满足式(15)、式(16)的约束。
式中:
RIES中含有冷、热、电多种类型负荷。通过对用户负荷需求建模,设置合理的IDR补偿机制,在用户用能舒适度范围内调节能量供应,能有效提高RIES运行的经济性和可靠性。
假设楼宇制冷设备在使用时间内连续运行,则
式中:
影响
式中:
式(17)包含混合微分方程和代数方程,考虑到简化模型,对其离散化处理并联合式(18),可得离散化的楼宇热平衡方程,如式(19)所示。
由式(19)可得到室内温度与制冷功率之间的关系。为保障用户舒适度,室温应满足式(20)的室温上下限约束和式(21)、式(22)的室温波动约束。
式中:
式中:
如
式中:
为保障用户舒适度,应满足式(24)的水温上下限约束和式(25)、式(26)的水温波动约束。
式中:
式中:
基于阶梯型补贴的需求响应通过在用户舒适度范围内对室温及水温进行调控, 改变用户的柔性冷、热负荷。为避免将需求响应用户与RIES捆绑为能量管理优化的刚性主体,鼓励用户参与以经济性为导向的激励型冷、热负荷需求响应。设室温与水温的补贴系数
式中:
在阶梯型补贴机制中,实际温度与设定温度的偏离程度不同,
式中:
通过该阶梯型补贴,一方面促进用户根据激励信号主动削减负荷以降低用能成本;另一方面,通过引入温度约束和波动约束,避免因室温降低、水温升高而引起的制冷、制热负荷增加,避免因室温、水温波动过大而降低用户舒适度。
在价格型需求响应(price demand response, PDR)中,用户会根据接收到的电价信号
式中:
式中:
为确保用户正常生活不受影响,执行PDR后的负荷需求
式中:
建立以1 d的24个时段为一调度周期、以最大化运行收益的RIES能量管理优化目标函数,如式(32)所示。
式中:
模型应满足式(36)的电功率平衡约束、式(37)的冷功率平衡约束和式(38)的热负荷平衡约束。
式中:
除满足以上系统运行时的功率平衡外,各设备也要满足约束。尤其值得指出的是,式(21)和式(25)为非线性约束,对其线性化处理[
以含4栋商业建筑的RIES为例进行仿真分析。建筑热工参数和设备参数分别如
建筑参数
Parameters of the buildings
参数 | 建筑A | 建筑B | 建筑C | 建筑D |
1.092 | 0.908 | 1.146 | 0.820 | |
1 000 | 2 400 | 1 500 | 2 700 | |
2.80 | 2.75 | 2.80 | 2.50 | |
450 | 750 | 600 | 650 | |
体积/m3 | 5 400 | 24 000 | 12 000 | 30 000 |
RIES参数
Parameters of the RIES
参数 | 取值 | 参数 | 取值 | |
2.67 | 0.35 | |||
66.2 | 0.85 | |||
c | 100 | -60 | ||
50 | 60 | |||
500 | 500 | |||
0.90 | 0.95 | |||
0.02 | 0.95 | |||
80 | 0 | |||
80 | 0 | |||
60 | 4.0 | |||
360 | 1.2 | |||
500 | 400 | |||
500 | 400 | |||
1.2 | 1.0 | |||
21 | 4.2 | |||
24 | 60 | |||
100 | 80 |
RIES购、售电价格参考文献[
RIES负荷与可再生能源出力
Load of the RIES and output of PV and WT
太阳辐射功率与室外温度曲线
Solar radiation and outdoor temperature curves
4种建筑的室内热源曲线
Internal heat gains curves of the four buildings
为研究RIES内不同运行方式对调度结果的影响,设置以下5种方案进行对比说明。
方案1:冷、热负荷参与阶梯型补贴的需求响应,电负荷参与电价型需求响应;
方案2:冷、热负荷参与需求响应[
方案3:冷、热负荷参与阶梯型补贴的需求响应,电负荷不参与电价型需求响应;
方案4:冷、热负荷不参与需求响应,电负荷参与电价型需求响应;
方案5:冷、热负荷不参与需求响应,电负荷不参与电价型需求响应。
5种运行方案下运行费用组成对比如
5种方案的运行成本组成
Operating cost components of five modes
方案 | RIES售能费用 | RIES购能成本 | 补贴费用 | 用户支出 | RIES净利润 |
1 | 10 973.11 | 5 766.73 | 60.76 | 10 912.35 | 5 145.62 |
2 | 10 993.55 | 5 822.29 | 48.57 | 10 944.98 | 5 122.69 |
3 | 11 061.16 | 5 911.75 | 60.76 | 11 000.40 | 5 088.65 |
4 | 10 927.95 | 5 966.76 | 0 | 10 927.95 | 4 961.19 |
5 | 11 040.48 | 6 088.31 | 0 | 11 040.48 | 4 952.17 |
由
方案1中执行PDR后实时电价与负荷的变化情况如
价格型需求响应下实时电价与负荷变化
Real-time prices and load variation of PDR
由
电负荷平衡
Balanced state of power load
冷负荷平衡
Balanced state of cooling load
热负荷平衡
Balanced state of heating load
由
建筑室温与冷负荷
Indoor temperature of buildings and cooling load
生活热水温度与热负荷
Hot water temperature and heating load
由
文中针对含智能楼宇群的IES,计及冷、热、电负荷参与需求侧管理对系统经济调度的影响,提出考虑综合需求响应的RIES能量优化管理方法,可得以下结论:
(1) 楼宇柔性冷负荷需求数学模型综合考虑建筑围护结构、室内得热等多种热量扰动因素,可以更准确地描述不同制冷功率下的室温波动情况。
(2) 冷、热负荷参与阶梯型补贴能在楼宇冷负荷需求模型与生活热水需求模型的基础上激发用户负荷削减潜力,统筹RIES与需求侧资源,减少用户成本支出,增加RIES利润。
(3) 价格型需求响应可促进用户转移负荷以达到削峰填谷与降低用能成本的效果,是IDR的重要组成部分。
朱浩昊, 朱继忠, 李盛林, 等. 电-热综合能源系统优化调度综述[J]. 全球能源互联网, 2022, 5(4): 383-397.
ZHU Haohao, ZHU Jizhong, LI Shenglin, et al. Review of optimal scheduling of integrated electricity and heat systems[J]. Journal of Global Energy Interconnection, 2022, 5(4): 383-397.
YU Y, LI J L, CHEN D Y. Optimal dispatching method for integrated energy system based on robust economic model predictive control considering source-load power interval prediction[J]. Global Energy Interconnection, 2022, 5(5): 564-578.
贠保记, 张恩硕, 张国, 等. 考虑综合需求响应与"双碳"机制的综合能源系统优化运行[J]. 电力系统保护与控制, 2022, 50(22): 11-19.
YUN Baoji, ZHANG Enshuo, ZHANG Guo, et al. Optimal operation of an integrated energy system considering integrated demand response and a 'dual carbon' mechanism[J]. Power System Protection and Control, 2022, 50(22): 11-19.
程杉, 徐建宇, 何畅, 等. 计及不确定性的综合能源系统容量规划方法[J]. 电力系统保护与控制, 2021, 49(18): 17-24.
CHENG Shan, XU Jianyu, HE Chang, et al. Optimal capacity planning of an integrated energy system considering uncertainty[J]. Power System Protection and Control, 2021, 49(18): 17-24.
帅轩越, 王秀丽, 黄晶. 多区域综合能源系统互联下的共享储能容量优化配置[J]. 全球能源互联网, 2021, 4(4): 382-392.
SHUAI Xuanyue, WANG Xiuli, HUANG Jing. Optimal configuration of shared energy storage capacity under multiple regional integrated energy systems interconnection[J]. Journal of Global Energy Interconnection, 2021, 4(4): 382-392.
张涛, 郭玥彤, 李逸鸿, 等. 计及电气热综合需求响应的区域综合能源系统优化调度[J]. 电力系统保护与控制, 2021, 49(1): 52-61.
ZHANG Tao, GUO Yuetong, LI Yihong, et al. Optimization scheduling of regional integrated energy systems based on electric-thermal-gas integrated demand response[J]. Power System Protection and Control, 2021, 49(1): 52-61.
张涛, 黄明娟, 刘伉, 等. 计及源荷不确定性和变工况特性的区域综合能源系统优化调度[J]. 智慧电力, 2022, 50(8): 109-117.
ZHANG Tao, HUANG Mingjuan, LIU Kang, et al. Optimal scheduling of regional integrated energy system considering source-load uncertainty and variable condition characteristic[J]. Smart Power, 2022, 50(8): 109-117.
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): 2-10, 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): 2-10, 46.
郑国太, 李昊, 赵宝国, 等. 基于供需能量平衡的用户侧综合能源系统电/热储能设备综合优化配置[J]. 电力系统保护与控制, 2018, 46(16): 8-18.
ZHENG Guotai, LI Hao, ZHAO Baoguo, et al. Comprehensive optimization of electrical/thermal energy storage equipments for integrated energy system near user side based on energy supply and demand balance[J]. Power System Protection and Control, 2018, 46(16): 8-18.
TRIFONOV T O. Coordination of battery energy storage and power-to-gas in distribution systems[J]. Protection and Control of Modern Power Systems, 2017, 2(1): 1-8.
何畅, 程杉, 徐建宇, 等. 基于多时间尺度和多源储能的综合能源系统能量协调优化调度[J]. 电力系统及其自动化学报, 2020, 32(2): 77-84, 97.
HE Chang, CHENG Shan, XU Jianyu, et al. Coordinated optimal scheduling of integrated energy system considering multi-time scale and hybrid energy storage system[J]. Proceedings of the CSU-EPSA, 2020, 32(2): 77-84, 97.
许周, 孙永辉, 谢东亮, 等. 计及电/热柔性负荷的区域综合能源系统储能优化配置[J]. 电力系统自动化, 2020, 44(2): 53-59.
XU Zhou, SUN Yonghui, XIE Dongliang, et al. Optimal configuration of energy storage for integrated region energy system considering power/thermal flexible load[J]. Automation of Electric Power Systems, 2020, 44(2): 53-59.
邹云阳, 杨莉, 冯丽, 等. 考虑热负荷二维可控性的微网热电协调调度[J]. 电力系统自动化, 2017, 41(6): 13-19.
ZOU Yunyang, YANG Li, FENG Li, et al. Coordinated heat and power dispatch of microgrid considering two-dimensional controllability of heat loads[J]. Automation of Electric Power Systems, 2017, 41(6): 13-19.
仪忠凯, 李志民. 计及热网储热和供热区域热惯性的热电联合调度策略[J]. 电网技术, 2018, 42(5): 1378-1384.
YI Zhongkai, LI Zhimin. Combined heat and power dispatching strategy considering heat storage characteristics of heating network and thermal inertia in heating area[J]. Power System Technology, 2018, 42(5): 1378-1384.
郭尊, 李庚银, 周明, 等. 计及综合需求响应的商业园区能量枢纽优化运行[J]. 电网技术, 2018, 42(8): 2439-2448.
GUO Zun, LI Gengyin, ZHOU Ming, et al. Optimal operation of energy hub in business park considering integrated demand response[J]. Power System Technology, 2018, 42(8): 2439-2448.
HUANGW J, ZHANG N, KANG C Q, et al. From demand response to integrated demand response: review and prospect of research and application[J]. Protection and Control of Modern Power Systems, 2019, 4(1): 1-13.
张峰, 杨志鹏, 张利, 等. 计及多类型需求响应的孤岛型微能源网经济运行[J]. 电网技术, 2020, 44(2): 547-557.
ZHANG Feng, YANG Zhipeng, ZHANG Li, et al. Optimal operation of islanded micro energy grid with multi-type demand responses[J]. Power System Technology, 2020, 44(2): 547-557.
程杉, 魏昭彬, 黄天力, 等. 基于多能互补的热电联供型微网优化运行[J]. 电力系统保护与控制, 2020, 48(11): 160-168.
CHENG Shan, WEI Zhaobin, HUANG Tianli, et al. Multi-energy complementation based optimal operation of a microgrid with combined heat and power[J]. Power System Protection and Control, 2020, 48(11): 160-168.
刘文霞, 李征洲, 杨粤, 等. 计及需求响应不确定性的综合能源系统协同优化配置[J]. 电力系统自动化, 2020, 44(10): 41-49.
LIU Wenxia, LI Zhengzhou, YANG Yue, et al. Collaborative optimal configuration for integrated energy system considering uncertainties of demand response[J]. Automation of Electric Power Systems, 2020, 44(10): 41-49.
JIN X L, MU Y F, JIA H J, et al. Dynamic economic dispatch of a hybrid energy microgrid considering building based virtual energy storage system[J]. Applied Energy, 2017, 194: 386-398.
陈锦鹏, 胡志坚, 陈颖光, 等. 考虑阶梯式碳交易机制与电制氢的综合能源系统热电优化[J]. 电力自动化设备, 2021, 41(9): 48-55.
CHEN Jinpeng, HU Zhijian, CHEN Yingguang, et al. Thermoelectric optimization of integrated energy system considering ladder-type carbon trading mechanism and electric hydrogen production[J]. Electric Power Automation Equipment, 2021, 41(9): 48-55.
葛少云, 刘静仪, 刘洪, 等. 需求响应机制下含建筑虚拟储能的能源站经济调度[J]. 电力系统自动化, 2020, 44(4): 35-43.
GE Shaoyun, LIU Jingyi, LIU Hong, et al. Economic dispatch of energy station with building virtual energy storage in demand response mechanism[J]. Automation of Electric Power Systems, 2020, 44(4): 35-43.
程杉, 陈梓铭, 徐康仪, 等. 基于合作博弈与动态分时电价的电动汽车有序充放电方法[J]. 电力系统保护与控制, 2020, 48(21): 15-21.
CHENG Shan, CHEN Ziming, XU Kangyi, et al. An orderly charging and discharging method for electric vehicles based on a cooperative game and dynamic time-of-use price[J]. Power System Protection and Control, 2020, 48(21): 15-21.
程杉, 倪凯旋, 赵孟雨. 基于Stackelberg博弈的充换储一体化电站微电网双层协调优化调度[J]. 电力自动化设备, 2020, 40(6): 49-55, 69, 56.
CHENG Shan, NI Kaixuan, ZHAO Mengyu. Stackelberg game based bi-level coordinated optimal scheduling of microgrid accessed with charging-swapping-storage integrated station[J]. Electric Power Automation Equipment, 2020, 40(6): 49-55, 69, 56.
程杉, 钟仕凌, 尚冬冬, 等. 考虑电动汽车时空负荷分布特性的主动配电网动态重构[J]. 电力系统保护与控制, 2022, 50(17): 1-13.
CHENG Shan, ZHONG Shiling, SHANG Dongdong, et al. Dynamic reconfiguration of an active distribution network considering temporal and spatial load distribution characteristics of electric vehicles[J]. Power System Protection and Control, 2022, 50(17): 1-13.
程杉, 汪业乔, 廖玮霖, 等. 含电动汽车的新能源微电网多目标分层优化调度[J]. 电力系统保护与控制, 2022, 50(12): 63-71.
CHENG Shan, WANG Yeqiao, LIAO Weilin, et al. Bi-level multi-objective optimization of a new energy microgrid with electric vehicles[J]. Power System Protection and Control, 2022, 50(12): 63-71.