不确定性条件下农业微电网与灌溉系统相结合的鲁棒优化调度

doi:10.16183/j.cnki.jsjtu.2023.035

摘要/Abstract

摘要：

农业微电网以低成本的方式为偏远农村地区的能源供应提供了一种有前景的解决方案.综合考虑风光抽水蓄能一体化农业微电网满足用电负荷和用水负荷需求,在可再生能源出力及用电负荷需求的不确定性条件下,提出包含抽水蓄能(PHS)电站的孤岛型农业微电网和灌溉系统相结合的鲁棒优化调度模型,利用农村地区水资源富足的特点和风光抽蓄补偿的优势,在最小化系统总成本的同时提高可再生能源的消纳.所提模型考虑分布式发电、用电负荷和用水负荷需求、涡轮流量和灌溉流量,具有多样性、多约束、非连续的特点.提出一种引力鲸鱼优化算法(GWOA)求解该模型,在某农业微电网上的仿真结果表明,GWOA可以获得比CPLEX求解器及其他新开发算法更具竞争力的解.另外,探究了降水量不确定性引起灌溉系统用水负荷需求变化对系统运行成本的影响以及使用PHS电站的必要性.

关键词: 农业微电网, 抽水蓄能, 灌溉系统, 不确定性条件, 引力鲸鱼优化算法

Abstract:

Agricultural microgrids offer a promising solution for energy supply in remote rural areas in a low-cost manner. In this paper, under uncertain conditions of renewable energy output and electricity load demand, a robust optimal scheduling model combined with the isolated agricultural microgrid and irrigation system containing a pumped hydro storage (PHS) power station is proposed, considering the factors that the wind-landscape pumped storage integrated agricultural microgrid can satisfy the uncertain fluctuations of power load demand and water load demand. By utilizing the abundant water resources in rural areas and the advantages of landscape drainage and storage compensation, the total cost of the system is minimized while the absorption of renewable energy is increased. Considering distributed generation, power load demand and water load demand, turbine flow, and irrigation flow, the proposed model is characterized by diversity, multi-constraint, and discontinuity. A gravitational whale optimization algorithm (GWOA) is proposed to solve the model. The simulation results of an agricultural microgrid show that the GWOA can obtain a more competitive solution than the CPLEX solver and other newly developed algorithms do. In addition, the impact of the change of water load demand caused by precipitation uncertainty on the operating cost of the irrigation system and the necessity of using PHS power station are explored.

Key words: agricultural microgrid, pumped hydro storage (PHS), irrigation system, uncertainty condition, gravitational whale optimization algorithm (GWOA)

中图分类号:

TM734

杨森, 郭宁, 张寿明. 不确定性条件下农业微电网与灌溉系统相结合的鲁棒优化调度[J]. 上海交通大学学报, 2024, 58(9): 1432-1442.

YANG Sen, GUO Ning, ZHANG Shouming. Robust Optimal Scheduling of Agricultural Microgrid Combined with Irrigation System Under Uncertainty Conditions[J]. Journal of Shanghai Jiao Tong University, 2024, 58(9): 1432-1442.

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参考文献 37

[1]	ISHRAQUE M F, SHEZAN S A, ALI M M, et al. Optimization of load dispatch strategies for an islanded microgrid connected with renewable energy sources[J]. Applied Energy, 2021, 292: 116879.
[2]	陆秋瑜, 于珍, 杨银国, 等. 考虑源荷功率不确定性的海上风力发电多微网两阶段优化调度[J]. 上海交通大学学报, 2022, 56(10): 1308-1316. doi: 10.16183/j.cnki.jsjtu.2021.409
	LU Qiuyu, YU Zhen, YANG Yinguo, et al. Two-stage optimal scheduling of offshore wind power generation in multi-microgrid considering source-load power uncertainty[J]. Journal of Shanghai Jiao Tong University, 2022, 56(10): 1308-1316.
[3]	ZHANG M Y, CHEN J J, YANG Z J, et al. Stochastic day-ahead scheduling of irrigation system integrated agricultural microgrid with pumped storage and uncertain wind power[J]. Energy, 2021, 237: 121638.
[4]	张丹, 王杰. 国内微电网项目建设及发展趋势研究[J]. 电网技术, 2016, 40(2): 451-458.
	ZHANG Dan, WANG Jie. Research on construction and development trend of micro-grid in China[J]. Power System Technology, 2016, 40(2): 451-458.
[5]	潘险险, 陈霆威, 许志恒, 等. 适应多场景的微电网一体化柔性规划方法[J]. 上海交通大学学报, 2022, 56(12): 1598-1607. doi: 10.16183/j.cnki.jsjtu.2021.402
	PAN Xianxian, CHEN Tingwei, XU Zhiheng, et al. Flexible planning method for microgrid integration adapting to multiple scenarios[J]. Journal of Shanghai Jiao Tong University, 2022, 56(12): 1598-1607.
[6]	NNAJI E C, ADGIDZI D, DIOHA M O, et al. Modelling and management of smart microgrid for rural electrification in sub-saharan Africa: The case of Nigeria[J]. The Electricity Journal, 2019, 32(10): 106672.
[7]	KAMAL M M, ASHRAF I, FERNANDEZ E. Optimal sizing of standalone rural microgrid for sustainable electrification with renewable energy resources[J]. Sustainable Cities & Society, 2023, 88: 104298.
[8]	YUAN H Z, YE H H, CHEN Y T, et al. Research on the optimal configuration of photovoltaic and energy storage in rural microgrid[J]. Energy Reports, 2022, 8: 1285-1293.
[9]	KAMAL M M, ASHRAF I, FERNANDEZ E. Planning and optimization of microgrid for rural electrification with integration of renewable energy resources[J]. Journal of Energy Storage, 2022, 52: 104782.
[10]	JAVED M S, MA T, JURASZ J, et al. Solar and wind power generation systems with pumped hydro storage: Review and future perspectives[J]. Renewable Energy, 2020, 148: 176-192.
[11]	WANG X X, VIRGUEZ E, XIAO W H, et al. Clustering and dispatching hydro, wind, and photovoltaic power resources with multiobjective optimization of power generation fluctuations: A case study in southwestern China[J]. Energy, 2019, 189: 116250.
[12]	SHYAM B, KANAKASABAPATHY P. Feasibility of floating solar PV integrated pumped storage system for a grid-connected microgrid under static time of day tariff environment: A case study from India[J]. Renewable Energy, 2022, 192: 200-215.
[13]	GHASEMI A, ENAYATZARE M. Optimal energy management of a renewable-based isolated microgrid with pumped-storage unit and demand response[J]. Renewable Energy, 2018, 123: 460-474.
[14]	荆朝霞, 胡荣兴, 袁灼新, 等. 含风/光/抽水蓄能并计及负荷响应的海岛微网优化配置[J]. 电力系统自动化, 2017, 41(1): 65-72.
	JING Zhaoxia, HU Rongxing, YUAN Zhuoxin, et al. Capacity configuration optimization for island microgrid with wind/solar/pumped storage considering demand response[J]. Automation of Electric Power Systems, 2017, 41(1): 65-72.
[15]	KHODAYAR M E. Rural electrification and expansion planning of off-grid microgrids[J]. The Electricity Journal, 2017, 30(4): 68-74.
[16]	CHAOUACHI A, KAMEL R M, ANDOULSI R, et al. Multiobjective intelligent energy management for a microgrid[J]. IEEE Transactions on Industrial Electronics, 2013, 60(4): 1688-1699.
[17]	CARDOSO G, STADLER M, SIDDIQUI A, et al. Microgrid reliability modeling and battery scheduling using stochastic linear programming[J]. Electric Power Systems Research, 2013, 103: 61-69.
[18]	HONG M G, YU X Y, YU N P, et al. An energy scheduling algorithm supporting power quality management in commercial building microgrids[J]. IEEE Transactions on Smart Grid, 2016, 7(2): 1044-1056.
[19]	HOSSAIN M A, POTA H R, SQUARTINI S, et al. Modified PSO algorithm for real-time energy management in grid-connected microgrids[J]. Renewable Energy, 2019, 136: 746-757.
[20]	LEONORI S, PASCHERO M, FRATTALE MASCIOLI F M, et al. Optimization strategies for microgrid energy management systems by genetic algorithms[J]. Applied Soft Computing, 2020, 86: 105903.
[21]	SAEED M H, WANG F Z, SALEM S, et al. Two-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources[J]. Energy Reports, 2021, 7: 8912-8928.
[22]	ZHAO X G, ZHANG Z Q, XIE Y M, et al. Economic-environmental dispatch of microgrid based on improved quantum particle swarm optimization[J]. Energy, 2020, 195: 117014.
[23]	TORKAN R, ILINCA A, GHORBANZADEH M. A genetic algorithm optimization approach for smart energy management of microgrids[J]. Renewable Energy, 2022, 197: 852-863.
[24]	NGUYEN T T, NGO T G, DAO T K, et al. Microgrid operations planning based on improving the flying sparrow search algorithm[J]. Symmetry, 2022, 14(1): 168.
[25]	LI B, DENG H S, WANG J E. Optimal scheduling of microgrid considering the interruptible load shifting based on improved biogeography-based optimization algorithm[J]. Symmetry, 2021, 13(9): 1707.
[26]	CHEN W M, SHAO Z H, WAKIL K, et al. An efficient day-ahead cost-based generation scheduling of a multi-supply microgrid using a modified krill herd algorithm[J]. Journal of Cleaner Production, 2020, 272: 122364.
[27]	YANG Q D, DONG N, ZHANG J. An enhanced adaptive bat algorithm for microgrid energy scheduling[J]. Energy, 2021, 232: 121014.
[28]	SUMAN G K, GUERRERO J M, ROY O P. Optimisation of solar/wind/bio-generator/diesel/battery based microgrids for rural areas: A PSO-GWO approach[J]. Sustainable Cities & Society, 2021, 67: 102723.
[29]	MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67.
[30]	XIAN H F, CHE J X. Unified whale optimization algorithm based multi-kernel SVR ensemble learning for wind speed forecasting[J]. Applied Soft Computing, 2022, 130: 109690.
[31]	YAN Z P, ZHANG J Z, ZENG J, et al. Three-dimensional path planning for autonomous underwater vehicles based on a whale optimization algorithm[J]. Ocean Engineering, 2022, 250: 111070.
[32]	KAUR B, RATHI S, AGRAWAL R K. Enhanced depression detection from speech using quantum whale optimization algorithm for feature selection[J]. Computers in Biology & Medicine, 2022, 150: 106122.
[33]	RASHEDI E, NEZAMABADI-POUR H, SARYAZDI S. GSA: A gravitational search algorithm[J]. Information Sciences, 2009, 179(13): 2232-2248.
[34]	吴定会, 高聪, 纪志成. 混合粒子群算法在微电网经济优化运行的应用[J]. 控制理论与应用, 2018, 35(4): 457-467.
	WU Dinghui, GAO Cong, JI Zhicheng. Economic optimization operation of the microgrid using the hybrid particle swarm optimization algorithm[J]. Control Theory & Applications, 2018, 35(4): 457-467.
[35]	GHASEMI A. Coordination of pumped-storage unit and irrigation system with intermittent wind generation for intelligent energy management of an agricultural microgrid[J]. Energy, 2018, 142: 1-13.
[36]	QAIS M H, HASANIEN H M, ALGHUWAINEM S. Enhanced whale optimization algorithm for maximum power point tracking of variable-speed wind generators[J]. Applied Soft Computing, 2020, 86: 105937.
[37]	LIU Y X, YANG S W, LI D J, et al. Improved whale optimization algorithm for solving microgrid operations planning problems[J]. Symmetry, 2022, 15(1): 36.

水池	流速上限/ (m³·h^-1)	流速下限/ (m³·h^-1)	容量上限/m³	容量下限/m³	始末规定容量/m³
上水池	60	10	450	50	350
下水池	60	10	600	150	100

算法	参数
PSO	C₁=1.5,C₂=1.5,W=0.75
ABC	L=5
WOA	b=1, p=0.5
EWOA	p=0.5
IWOA	b=1, p=0.5, α=0.01,β=1.5
GWOA	b=1, p=0.5

求解方法	最优成本/元	所用时间/s
CPLEX求解器	2254.7	0.6
WOA	2269.5	0.7
EWOA	2236.4	1.1
IWOA	2221.1	0.9
GWOA	2147.7	1.5

季节	WOA	PSO	ABC	EWOA	IWOA	GWOA
春	2256.5	2345.6	2303.4	2230.2	2224.4	2186.8
夏	2134.2	2141.3	2120.6	2065.4	2076.5	2001.7
秋	2182.8	2231.5	2151.4	2128.5	2134.2	2105.7
冬	2304.8	2408.4	2339.4	2265.2	2252.6	2222.5

场景	算法	弃风弃光率/%	弃风弃光率均值/%	最优成本/元	最优成本均值/元
含PHS	WOA	8.75	7.78	2272.9	2212.9
	PSO	8.58	7.78	2254.5	2212.9
	ABC	8.16	7.78	2228.3	2212.9
	EWOA	7.24	7.78	2188.5	2212.9
	IWOA	7.15	7.78	2196.6	2212.9
	GWOA	6.85	7.78	2136.6	2212.9
不含PHS	WOA	18.34	18.18	3564.9	3539.7
	PSO	20.56	18.18	3571.0	3539.7
	ABC	17.98	18.18	3546.9	3539.7
	EWOA	17.52	18.18	3528.4	3539.7
	IWOA	17.41	18.18	3521.0	3539.7
	GWOA	17.26	18.18	3506.2	3539.7