基于分布式求解的综合能源系统运行状态估计模型

doi:10.1007/s11708-020-0687-y

Frontiers in Energy

2020, Vol. 14

Issue (4): 801-816 https://doi.org/10.1007/s11708-020-0687-y

研究论文

本期目录

基于分布式求解的综合能源系统运行状态估计模型

周登极, 马世喜, 黄大文, 张会生(

), 翁史烈

上海交通大学教育部动力机械与工程重点实验室，中国上海 200240

An operating state estimation model for integrated energy systems based on distributed solution

Dengji ZHOU, Shixi MA, Dawen HUANG, Huisheng ZHANG(

), Shilie WENG

Key Laboratory of Power Machinery and Engineering of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China

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摘要:

针对传统供能系统分散规划、分散设计、独立运行、各子系统之间非线性耦合日益突出等缺点，采用综合能源系统对多种能源的生产、输运、存储和消耗进行协调优化，提高能源和基础设施利用率，促进可再生能源消纳，确保能源供应可靠性。本文研究了电-气综合能源系统的数学模型及其状态估计方法。首先，提出了一种考虑测量方程与状态变量间非线性关系的性能仿真模型。然后，建立了多能源子系统耦合节点的状态一致性方程和约束条件，并将约束条件松弛到目标函数中，实现综合能源系统状态估计求解过程解耦。最后，结合同步交替方向乘子方法，构建了能有效估计综合能源系统状态的分布式求解框架。通过一个电-气综合能源系统的仿真模型验证了本文提出的状态估计方法的有效性和准确性。结果表明，估计的电压幅值和节点压力的平均相对误差仅为0.0132%和0.0864%，远低于拉格朗日松弛法。此外，与集中式估计方法相比，该方法节省了5.42s的计算时间。本文方法能为综合能源系统能量分配和利用决策提供更加准确、高效的运行状态估计支持。

Abstract：

In view of the disadvantages of the traditional energy supply systems, such as separate planning, separate design, independent operating mode, and the increasingly prominent nonlinear coupling between various sub-systems, the production, transmission, storage and consumption of multiple energy sources are coordinated and optimized by the integrated energy system, which improves energy and infrastructure utilization, promotes renewable energy consumption, and ensures reliability of energy supply. In this paper, the mathematical model of the electricity-gas interconnected integrated energy system and its state estimation method are studied. First, considering the nonlinearity between measurement equations and state variables, a performance simulation model is proposed. Then, the state consistency equations and constraints of the coupling nodes for multiple energy sub-systems are established, and constraints are relaxed into the objective function to decouple the integrated energy system. Finally, a distributed state estimation framework is formed by combining the synchronous alternating direction multiplier method to achieve an efficient estimation of the state of the integrated energy system. A simulation model of an electricity-gas interconnected integrated energy system verifies the efficiency and accuracy of the state estimation method proposed in this paper. The results show that the average relative errors of voltage amplitude and node pressure estimated by the proposed distributed state estimation method are only 0.0132% and 0.0864%, much lower than the estimation error by using the Lagrangian relaxation method. Besides, compared with the centralized estimation method, the proposed distributed method saves 5.42 s of computation time. The proposed method is more accurate and efficient in energy allocation and utilization.

Key words： integrated energy system state estimation electricity-gas coupling energy system nonlinear coupling distributed solution

收稿日期: 2019-07-22 出版日期: 2020-12-21

通讯作者: 张会生 E-mail: zhslm@sjtu.edu.cn

Corresponding Author(s): Huisheng ZHANG

引用本文:

周登极, 马世喜, 黄大文, 张会生, 翁史烈. 基于分布式求解的综合能源系统运行状态估计模型[J]. Frontiers in Energy, 2020, 14(4): 801-816.
Dengji ZHOU, Shixi MA, Dawen HUANG, Huisheng ZHANG, Shilie WENG. An operating state estimation model for integrated energy systems based on distributed solution. Front. Energy, 2020, 14(4): 801-816.

链接本文:

https://academic.hep.com.cn/fie/CN/10.1007/s11708-020-0687-y
https://academic.hep.com.cn/fie/CN/Y2020/V14/I4/801

Fig.1

Fig.2

Fig.3

Tab.1

Tab.2

Node	Source flow/(kg·s^–1)	Demand/(kg·s^–1)	For electricity/(kg·s^–1)	Pressure/kPa	?Pipe	$C i j$	Gas flow/(kg·s^–1)
1	36.8100	0	0	8273.8	?1	0.00878	36.8118
2	47.1900	0	0	8951.5	?2	0.00880	47.1947
3	0	0	4.9436	7132.0	?3	0.01046	7.4922
4	0	0	1.2861	7167.8	?4	0.00882	39.4075
5	0	0	7.4072	5559.3	?5	0.00847	38.3457
6	0	0	5.2893	10006.4	?6	0.00822	26.7240
7	0	0	6.8061	5557.9	?7	0.00517	30.2046
8	0	0	1.3309	10560.1	?8	0.00533	10.5008
9	0	0	15.8916	9463.1	?9	0.00505	1.2743
10	0	0	0.3410	9463.1	?10	0.00403	3.7756
11	0	0	5.1177	8795.0	?11	0.00578	5.4156
12	0	0	23.8411	9234.2	?12	0.00530	3.4334
13	0	1.3156	0	9255.6
14	0	1.6164	0	9230.8
15	0	8.8532	0	9208.0

Tab.3

Tab.4

Tab.5

Tab.6

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Tab.7

Fig.10

$x *$	Optimal estimated value
$z$	Actual measured value
$z t$	True value vector of parameters
$r$	Measured error vector
$q i$	Pipeline flow of node i
$h (x)$	System simulation model
$W a c k$	Measurement covariance matrices of power grid
$W gas m$	Measurement covariance matrices of gas networks
$J (x)$	Deviations between the measured and estimated
$x ac k$	Power gird state variable
$x gas m$	Gas network state variable
$P i j$	Active power flow of branch ij
$Q i j$	Reactive power flow of branch ij
$ε$	Convergence threshold
$p i$	Pressure of node i
$v$	Polytropic coefficient
$P i$	Active power of node i
$Q i$	Reactive power of node i
$V a c k, i$	Voltage of node i
$θ a c k, i j$	Phase of node i
$C i$	Pipeline friction resistance
$G i$	Node flow
$η$	Power generation efficiency
$H u pp, i$	Natural gas calorific value
$λ$	Lagrangian multiplier
$ρ$	Penalty coefficient
$v$	Average value of state variables
$S ¯ M$	Measurement error
$o$	Number of iterations
t	Sampling time
N	Number of samples
s	Total number of measurement point
ac	Alternating current power grid
k	kth alternating current power grid sub-system
gc	Natural gas compressor
i,j	Number of nodes
gas	Natural gas network
m	mth gas network sub-systems
pp	Power plant
cp	Coupling point

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