Demand Response Scheduling of Major Energy-consuming Enterprises Based on a High Proportion of Renewable Energy Power Grid
Author Bio:
LI Yuan-Zheng Associate professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers artificial intelligence and its application in smart grid, deep learning, reinforcement learning, and big data analysis
NI Zhi-Xian Master student at China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology. He received his bachelor degree in automation from Wuhan University of Technology in 2019. His research interest covers planning, optimization and scheduling of integrated power systems containing large-scale renewable energy sources
DUAN Jun-Tao Master student at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers smart grid control and scheduling, distributed control and optimization
XU Lei Ph.D. candidate at the State Laboratory of Synthetical Automation for Process Industries, Northeastern University. His research interest covers distributed control and optimization, networked systems, and Markovian jump systems
YANG Tao Professor at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. His research interest covers industrial artificial intelligence, information physical systems, and distributed cooperative control and optimization. Corresponding author of this paper
ZENG Zhi-Gang Professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers switching system control theory and application, computational intelligence, system stability, and associative memory
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摘要: 随着国家“双碳”重大战略的提出, 高比例新能源并网将成为我国电力能源转型的重要态势. 针对火电机组、配电网和需求侧关联的系列运行约束制约了电网对高比例新能源的有效消纳这一问题, 本文提出重大耗能企业这一主要电力负荷参与网需求响应(Demand response, DR)的研究思路, 通过重大耗能企业与电网协调调度促进新能源消纳, 并获得经济补偿以减少运行成本. 研究首先基于混合需求侧响应机制, 提出以重大耗能企业、新能源、火电机组为核心的协调调度方法, 并根据新能源预测值−预测误差的信息依存顺序提出了两步调度策略. 在此基础上, 进行生产过程行为建模以实现重大耗能企业需求侧响应决策描述, 并建立高比例新能源并网的重大耗能企业需求响应与电网协调调度优化模型. 最后, 基于烟台电网实际系统进行算例分析, 验证了重大耗能企业通过需求响应参与电网协调调度以及两步调度策略的有效性.Abstract: With the proposal of the national “double carbon” major strategy, the high proportion of renewable energy grid-connection becomes an important trend of China＇s power energy transformation. However, a number of operational constraints related to thermal power units, distribution networks and demand sides limit the efficient absorption of renewable energy. This paper considers the idea in which major energy-consuming enterprises, the main power load, participate in the grid demand response (DR), promotes renewable energy consumption through coordinated scheduling between major energy-consuming enterprises and the power grid, and obtains economic compensation to reduce operating costs. Based on a hybrid demand-side response mechanism, this paper proposes a coordinated scheduling method with the major energy-consuming enterprises, renewable energy and thermal power units as the core. After that, a two-step scheduling strategy is proposed according to the information dependence order of the forecast value and forecast error of renewable energy. On this basis, the production process behavior modelling is carried out to realize the description of the demand response of major energy-consuming enterprises, and the optimization model of the demand response and grid coordination scheduling of major energy-consuming enterprises with a high proportion of renewable energy grid-connection is established. Finally, an example analysis is carried out based on the actual system power grid system in Yantai, which verifies the effectiveness of the major energy-consuming enterprises participating in the coordination scheduling of the power grid through demand response and the two-step dispatch strategy.1) 1 由于所证明的命题与时段
$ t $ 无关, 因此为了简化标记将下标$ t $ 省略. -
图 1 基于混合需求侧响应的协调调度方法示意图
Fig. 1 The chart of hybrid demand response based coordination scheduling method
图 7 预测风电功率以及负荷需求信息
Fig. 7 Forecast power outputs of wind and power demand of local loads
图 11 在不同权重作用下算例3目标函数值比较图
Fig. 11 Objective values comparison of subcases mentioned in Case 3 with different weights
表 1 发电机组相关参数
Table 1 Related coefficients of thermal generators
节点 最小时间 (h) 耗量系数 启动 停止 $a$ $b$ $c$ $\alpha$ $\beta$ $\gamma$ 1 0 0 0.077 242.20 759.49 129.0 129.0 2 4 0 0 0.084 242.91 761.81 129.0 129.0 2 9 0 0 0.181 167.25 159.70 64.5 64.5 1 21 0 0 0.187 168.09 160.54 64.5 64.5 1 25 5 3 0.032 68.95 920.61 967.5 967.5 6 
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表 2 PMP网络结构参数
Table 2 Parameters of PMP system
单元编号 最大加工容量 (个) 功率 (kW/个) $M_1$ 2 48 $B_1$ 4 4 $M_2$ 2 32 $B_2$ 4 4 $M_3$ 2 12 $B_3$ 4 4 $M_4$ 2 36 $B_4$ 4 4 $M_5$ 2 32 $B_5$ 4 4 $M_6$ 2 21
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表 3 分时定价策略表
Table 3 Time-of-use price strategy
用电场景 用电时段 消耗电价($\yen$/kWh) 需求电价($\yen$/kWh) 固定费用($\yen$) 高峰期 2 am, 7 ~ 12 am, 5 ~ 7 pm 1.08296 121.26 331.66 低峰期 0 ~ 1 am, 3 ~ 6 am, 1 ~ 4 pm, 8 ~ 12 pm 0.53367 0
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表 4 三个算例对应目标函数值及相关功率
Table 4 Objective values and corresponding power information of three cases
算例 调度步骤 发电成本 ($\yen$) 生产成本 ($\yen$/单位) 风电消纳量 (MWh) 系统负荷 (MWh) 火电发电量 (MWh) 外部备用 (MWh) 算例1 ~ 3 第1步 208110.02 3528.41 2531.54 5083.35 2551.81 — 算例1 第2步 244436.30 3478.16 2738.45 5038.90 2454.23 268.00 算例2 256888.21 3528.41 2826.61 5087.74 2479.80 333.46 算例3-1 187535.17 3493.58 2528.95 5031.14 2446.30 55.89 算例3-2 187862.76 3492.16 2527.15 5032.68 2448.78 56.75 算例3-3 243154.81 3469.97 2871.31 5027.01 2445.36 401.30 算例3-4 187717.58 3492.03 2529.73 5033.88 2447.81 56.34 算例3-5 242932.61 3469.97 2871.31 5035.34 2448.59 398.39 算例3-6 242928.22 3469.97 2871.31 5035.15 2448.35 398.55
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