Plant-level load optimal distribution based on improved artificial bee colony dynamic programming

LI Donglin; ZHU Jianhong; WANG Huaguang; HU Rongyuan; YE Jiawei; WANG Peihong

doi:10.19666/j.rlfd.202107168

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Plant-level load optimal distribution based on improved artificial bee colony dynamic programming

Thermal Power Generation Vol. 51, Issue 3, Pages: 153-158(2022)

更新时间：2025-09-28

- Plant-level load optimal distribution based on improved artificial bee colony dynamic programming
- 作者机构：
  
  1. 贵州黔西中水发电有限公司
  2. 国核电力规划设计研究院有限公司
  3. 东南大学能源与环境学院
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202107168
  CLC： TM621
- Published：2022
- 稿件说明：
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LI Donglin, ZHU Jianhong, WANG Huaguang, et al. Plant-level load optimal distribution based on improved artificial bee colony dynamic programming[J]. Thermal Power Generation, 2022, 51(3): 153-158.
DOI：

LI Donglin, ZHU Jianhong, WANG Huaguang, et al. Plant-level load optimal distribution based on improved artificial bee colony dynamic programming[J]. Thermal Power Generation, 2022, 51(3): 153-158. DOI： 10.19666/j.rlfd.202107168.

摘要

针对大规模变负荷条件下燃煤机组负荷优化问题，考虑厂级负荷优化分配的全局性及实时响应需求，提出一种利用改进人工蜂群算法，在离线状态下建立全负荷区间满足动态规划算法需要的顺序优化表，进而利用动态规划中逆序分配的方法实现在线优化分配的算法。该算法能有效完成全负荷区间的负荷优化分配计算，同时降低优化分配的计算复杂性。实例计算表明，在一定的机组负荷步长下，利用改进人工蜂群算法进行离线造表能够减少造表时间，构造的优化顺序表可有效应用于机组实时在线优化，提高机组在不同负荷下运行的经济性。

Abstract

Aiming at solving the problem of load optimization for coal-fired thermal power units under large-scale variable load conditions

and considering the global optimization and real-time response requirements of plant-level load optimal allocation

an improved artificial bee colony algorithm is proposed. The algorithm establishes a sequence optimization table for full load interval to meet the needs of dynamic programming algorithm in the offline state

and then realizes online optimal allocation algorithm realized by using the inverse sequence allocation method in dynamic programming. This improved algorithm can effectively complete the load optimal allocation calculation in full load range

and reduce the computational complexity of optimal allocation at the same time. The example shows that

under a certain unit load step

using the improved artificial bee colony algorithm for off-line tabulation can reduce the time required for tabulation. The constructed optimization sequence table can be effectively used for real-time online optimization of the unit

thus to improve the economy of the unit at different loads.

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