基于模型预测控制的区域电网频率控制方法

doi:10.11930/j.issn.1004-9649.202503050

摘要/Abstract

摘要：

新能源出力波动性及快速变化给新型电力系统的频率控制带来了新挑战。针对该问题，提出一种基于模型预测控制及随机优化技术的区域电网频率控制新方法。首先，构建基于预测信息的区域电网频率控制框架，将相邻区域频率波动引起的联络线功率扰动视为本区域扰动变量，从而实现本区域频率控制过程与相邻区域的有效解耦。其次，采用系统参数估计方法实现系统惯性常数的在线估计，并结合自适应在线核密度估计技术，建立机组惯性时间常数、负荷、新能源出力及相邻区域频率偏差的预测概率模型，实现各类不确定性因素的精确建模。再次，在此基础上，提出基于模型预测控制并计及多种不确定性因素的频率控制随机优化模型及快速求解算法。最后，采用修改的IEEE-39节点系统验证所提方法的有效性。算例结果表明，相较于确定性方法和仅考虑功率扰动不确定性的方法，所提方法的控制性能标准均值分别提高了14.98个百分点和11.38个百分点，区域控制偏差绝对值均值分别降低了5.30 MW和2.22 MW，证明了该新方法的有效性和先进性。所提模型及算法可为新型系统下的区域电网频率控制提供借鉴。

关键词: 频率控制, 模型预测控制, 惯性时间常数, 随机优化, 不确定性

Abstract:

The volatility and rapid variation of renewable generation pose new challenges to frequency control in new-type power systems. To address this problem, this paper proposes a novel regional grid frequency-control method that combines model predictive control with stochastic optimization techniques. First, a prediction-driven frequency-control framework is established for the regional grid, in which the tie-line power deviations caused by neighboring-area frequency fluctuations are treated as local disturbance variables, effectively decoupling the local frequency-control process from neighboring regions. Next, an online estimation scheme for system inertia constant is developed via system-parameter identification. Together with an adaptive online kernel density estimation technique, probabilistic prediction models are constructed for unit inertia time constants, load demand, renewable power output, and neighboring-area frequency deviations, enabling accurate representation of multiple uncertainties. Building on these models, a stochastic-model-predictive-control-based frequency control optimization model and its fast solution algorithm are formulated. Finally, the proposed method is validated on a modified IEEE-39-bus system. The case study results show that, compared with the deterministic strategy and the strategy that considers only power-disturbance uncertainty, the proposed method improves the mean value of the control performance standard by 14.98 percentage points and 11.38 percentage points, respectively, and reduces the mean absolute area control error by 5.30 MW and 2.22 MW, respectively, which confirms the effectiveness and superiority of the proposed method. The proposed model and algorithm offers a valuable reference for regional grid frequency control in new-type power systems with high renewable penetration.

Key words: frequency control, model predictive control, inertia time constant, stochastic optimization, uncertainty

林济铿, 石涛. 基于模型预测控制的区域电网频率控制方法[J]. 中国电力, 2025, 58(9): 124-137.

LIN Jikeng, SHI Tao. Frequency Control Method for Regional Power Grids Based on Model Predictive Control[J]. Electric Power, 2025, 58(9): 124-137.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202503050

https://www.electricpower.com.cn/CN/Y2025/V58/I9/124

图/表 15

参考文献 31

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机组编号	容量/MW	惯性时间常数/s	机组编号	容量/MW	惯性时间常数/s
G1	1000	8.53	G9	1000	6.45
G2	700	7.03	G10	1000	6.20
G3	800	7.58	W1	300	3.30
G4	800	6.86	W2	400	3.50
G5	600	6.60	W3	350	4.00
G6	800	6.48	W4	300	3.10
G7	700	7.64	BESS	100	0
G8	700	5.43

机组编号	容量/MW	惯性时间常数/s	机组编号	容量/MW	惯性时间常数/s
G1	1000	8.53	G9	1000	6.45
G2	700	7.03	G10	1000	6.20
G3	800	7.58	W1	300	3.30
G4	800	6.86	W2	400	3.50
G5	600	6.60	W3	350	4.00
G6	800	6.48	W4	300	3.10
G7	700	7.64	BESS	100	0
G8	700	5.43

机组	基准功率/ MW	最小出力/ MW	最大出力/ MW	速率/ (MW·min^–1)
G2	520.8	200	680	15.0
G3	650.0	250	790	15.0
G4	100.0	250	790	25.0
G5	650.0	240	600	12.3
G7	260.0	50	700	20.0
G10	250.0	200	950	12.3
BESS	0	–100	100	60.0

机组	基准功率/ MW	最小出力/ MW	最大出力/ MW	速率/ (MW·min^–1)
G2	520.8	200	680	15.0
G3	650.0	250	790	15.0
G4	100.0	250	790	25.0
G5	650.0	240	600	12.3
G7	260.0	50	700	20.0
G10	250.0	200	950	12.3
BESS	0	–100	100	60.0

参数	取值	参数	取值
${R_1}/({{{\rm H}{\rm z}\cdot {\rm p}}}{\text{.u}}{\text{.}}^{-1})$	0.05	${R_{\rm H1}}$	0.05
${R_2}/({{{\rm H}{\rm z}\cdot {\rm p}}}{\text{.u}}{\text{.}}^{-1})$	0.05	${T_{\rm W1}}/{\text{s}}$	1
${R_3}/({{{\rm H}{\rm z}\cdot {\rm p}}}{\text{.u}}{\text{.}}^{-1})$	0.05	${T_{\rm T1}}/{\text{s}}$	5
${R_4}/({{{\rm H}{\rm z}\cdot {\rm p}}}{\text{.u}}{\text{.}}^{-1})$	0.05	${T_{\rm G4}}/{\text{s}}$	0.2
${T_{\rm G1}}/{\text{s}}$	0.08	${T_{\rm R3}}/{\text{s}}$	5
${T_{\rm C1}}/{\text{s}}$	0.3	${R_{\rm T2}}$	0.43
${F_{\rm H1}}$	0.33	${R_{\rm H2}}$	0.05
${T_{\rm R1}}/{\text{s}}$	7	${T_{\rm W2}}/{\text{s}}$	1
${T_{\rm G2}}/{\text{s}}$	0.3	${T_{\rm V1}}/{\text{s}}$	0.2
${T_{\rm C2}}/{\text{s}}$	0.08	$D/({\text{p}}{\text{.u}}.\cdot {{\rm Hz}}^{-1})$	1
${T_{\rm G3}}/{\text{s}}$	0.2	${T_{\rm 12}}/({\text{p}}{\text{.u}}.\cdot {{\rm Hz}}^{-1})$	2
${T_{\rm R2}}/{\text{s}}$	5	$\beta /({\text{p}}{\text{.u}}.\cdot {{\rm Hz}}^{-1})$	20
${R_{\rm T1}}$	0.43