Integrated Energy System Economic Dispatch Based on Affine Adjustable Robust Optimization

Yunhui SHI; Chuangxin GUO; Xiao DING

doi:10.12096/j.2096-4528.pgt.19139

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Integrated Energy System Economic Dispatch Based on Affine Adjustable Robust Optimization

Key Technologies for Ubiquitous Power Internet of Things and Integrated Energy Systems | 更新时间：2025-07-04

- Integrated Energy System Economic Dispatch Based on Affine Adjustable Robust Optimization
- Power Generation Technology Vol. 41, Issue 2, Pages: 118-125(2020)
- 作者机构：
  
  浙江大学电气工程学院, 浙江省杭州市 310027
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(51877190)
- DOI：10.12096/j.2096-4528.pgt.19139
  CLC： TM732
- Received：21 September 2019，
  
  Published：30 April 2020
- 稿件说明：
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Yunhui SHI, Chuangxin GUO, Xiao DING. Integrated Energy System Economic Dispatch Based on Affine Adjustable Robust Optimization[J]. Power Generation Technology, 2020, 41(2): 118-125.
DOI：

Yunhui SHI, Chuangxin GUO, Xiao DING. Integrated Energy System Economic Dispatch Based on Affine Adjustable Robust Optimization[J]. Power Generation Technology, 2020, 41(2): 118-125. DOI： 10.12096/j.2096-4528.pgt.19139.

摘要

新能源和负荷的不确定性给综合能源系统（integrated energy system，IES）运行带来挑战。首先，基于线性形式的能源集线器模型，对园区IES进行了建模。其次，构建了基于仿射可调鲁棒优化的园区IES两阶段经济调度模型，通过该模型可求得机组的启停及基准出力，以满足不考虑可再生能源出力的能量平衡要求，并求得机组的参与因子，使得调度方案对可再生能源出力不确定集下的任意场景均可行。最后，将该模型转化为混合整数线性规划模型（mixed integer linear programming，MILP）进行求解。算例分析结果表明：通过可调鲁棒优化的经济调度方法所求得的调度方案较经典鲁棒优化有更好的经济性与鲁棒性。

Abstract

Uncertainties in renewable energy and loads bring challenges to the operation of integrated energy system (IES). Firstly

the general model of the industrial park's IES was established based on the linear energy hub. Then a two-stage economic dispatch model of the industrial park's IES based on the affine adjustable robust optimization was constructed. In this model

the on-off state and base power output of units were calculated to satisfy the energy balance regardless of the renewable energy output

and participation factors were obtained to ensure a feasible solution for all scenarios under the uncertainty set of the renewable energy output. Finally

the model was transformed into a mixed integer linear programming (MILP) model. The case study shows that the proposed dispatch method provides a more economical and practical scheme than the classical robust optimization.

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references

杨经纬, 张宁, 王毅, 等.面向可再生能源消纳的多能源系统:述评与展望[J].电力系统自动化, 2018, 42(4):11-24.

郑展, 张勇军.电-气-热一体化混合能源系统研究评述与展望[J].广东电力, 2018, 31(9):98-110.

李兵, 牛洪海, 陈俊, 等.多能互补综合能源系统运行优化研究[J].分布式能源, 2018, 3(2):53-57.

张治新, 陆青, 张世翔.国内综合能源服务发展趋势与策略研究[J].浙江电力, 2019, 38(2):1-6.

吴峰, 杨柳燕.孤立运行模式下海岛综合能源系统的能量控制策略[J].电网与清洁能源, 2018, 34(6):53-60.

贾宏杰, 穆云飞, 余晓丹.对我国综合能源系统发展的思考[J].电力建设, 2015, 36(1):16-25.

Zidan A, Gabbar H A. Optimal scheduling of energy hubs in interconnected multi energy systems[C]//IEEE Smart Energy Grid Engineering. Oshawa, 2016: 164-169.

Orehounig K, Evins R, Dorer V. Integration of decentralized energy systems in neighbourhoods using the energy hub approach[J]. Applied Energy, 2015, 154:277-289.

周晓倩, 余志文, 艾芊, 等.含冷热电联供的微网优化调度策略综述[J].电力自动化设备, 2017, 37(6):26-33.

Wang J, Shahidehpour M, Li Z. Security-constrained unit commitment with volatile wind power generation[J]. IEEE Transactions on Power Systems, 2008, 23(3):1319-1327.

Talari S, Yazdaninejad M, Haghifam M R. Stochasticbased scheduling of the microgrid operation including wind turbines, photovoltaic cells, energy storages and responsive loads[J]. IET Generation, Transmission&Distribution, 2015, 9(12):1498-1509.

Parisio A, Del Vecchio C, Vaccaro A. A robust optimization approach to energy hub management[J]. International Journal of Electrical Power&Energy Systems, 2012, 42(1):98-104.

丁浩, 高峰, 刘坤, 等.基于鲁棒优化的含风电工业微电网经济调度模型[J].电力系统自动化, 2015, 39(17):160-167.

Wang L, Li Q, Zhang B, et al. Robust multi-objective optimization for energy production scheduling in microgrids[J]. Engineering Optimization, 2019, 51(2):332-351.

向月, 刘俊勇, 魏震波, 等.考虑可再生能源出力不确定性的微电网能量优化鲁棒模型[J].中国电机工程学报, 2014, 34(19):3063-3072.

Jiang R, Wang J, Zhang M, et al. Two-stage minimax regretrobust unit commitment[J]. IEEE Transactions on Power Systems, 2013, 28(3):2271-2282.

Bai L Q, Li F X, Cui H T, et al. Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty[J]. Applied Energy, 2016, 167:270-279.

Ben-Tal A, Goryashko A, Guslitzer E, et al. Adjustable robust solutions of uncertain linear programs[J]. Mathematical Programming, 2004, 99(2):351-376.

Gorissen B L, Yanıkoğlu I, den Hertog D. A practical guide to robust optimization[J]. Omega-International Journal of Management Science, 2015, 53:124-137.

肖浩, 裴玮, 孔力.基于模型预测控制的微电网多时间尺度协调优化调度[J].电力系统自动化, 2016, 40(18):7-14.

Boot J C G. On sensitivity analysis in convex quadratic programming problems[J]. Operations Research, 1963, 11(5):771-786.

Dantzig G. Linear programming and extensions[M]. Princeton, USA:Princeton University Press, 2016.

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