Steady-State Power Flow Analysis of Cold-Thermal-Electric Integrated Energy System Based on Unified Power Flow Model

Abstract

The integrated energy system includes various energy forms, complex operation modes and tight coupling links, which bring challenges to its steady-state modeling and steady-state power flow calculation. In order to study the steady-state characteristics of the integrated energy system, the topological structure of the cold-thermal-electric integrated energy system is given firstly. Then, the steady-state model of the power subsystem, the thermal subsystem, the cold subsystem and the distributed energy station are established, the unified power flow model is established, and the Newton Raphson algorithm is used to solve the unified power flow model. Finally, the influence of the key technical parameters on the steady-state power flow of the integrated energy system is analyzed. Research results show that the photovoltaic power generation plays a supporting role in the voltage of each bus; with the increase of electric load power, the unit value of bus voltage decreases continuously; the water supply temperature of the source node has a greater impact on the steady-state flow in the pipeline and the water supply temperature of each node; the pipeline length of the heat network has a greater impact on the end temperature of the pipeline, the water supply temperature, and the return water temperature of each node. The analysis results can support the planning, design, and optimal operation of the integrated energy system.

Dynamic energy flow analysis of the heat-electricity integrated energy systems with a novel decomposition-iteration algorithm

“…Coupling analysis [11] TD-HL Newton-Raphson technique (I & D) × × × [13] TD-HL Newton algorithm (D) × × × [14] TD-HL Gradient descent method (I) × × × × [15] TD-HL Newton algorithm (I) × × × [16] TD-HL Individual-based method (D) × × × [17,18] TD-HL Newton-Raphson technique (I) × × × [19,20] TD-HL Newton-Raphson technique (I) × × × × [21] TD-HL Fixed-point based method (D) × × × [22] TD-HL Newton algorithm (I) × × [23] TD-HL Graph-based method (D) × × × × [24] TD-HL Ring-to-radical reduction technique (D) × × × × [25] TD-HL Industrial software integration (D)…”

“…(AC) power flow model is widely used to approximate the dynamic state information of PG, while the adopted energy flow models of HN are generally divided into two categories: 1) the simplified algebraic model considering transmission delays and heat losses of HN [11,[13][14][15][16][17][18][19][20][21][22][23][24][25][26], denoted as TD-HL model; 2) the detailed model capturing the temperature transients in pipelines [10,12,27,28], which is described by a group of partial differential equations (PDEs) derived from the law of energy conservation and can reveal more state information than the former one.…”

“…One major challenge of dynamic energy flow analysis of HE-IES lies in how to devise effective and efficient methods to solve the combined energy flow models, since the integration of PG and HN significantly enlarges the network scale and increases the solution complexity. For the energy flow model of PG, the Newton algorithm and its variant Newton-Raphson technique prove to be effective and are widely adopted, which are also employed in [11,13,15,[17][18][19][20]22] to deal with the TD-HL HN model. The difference lies in that [11] and [13] take a decoupling strategy to solve the model of HN and PG separately in each round of iteration while the rest solve the two models in an integrated way.…”

“…Step 2: Assume the initial value of x 1 to be x (0) 1 , and solve all the sub-problems in (20) iteratively until a convergent solution to x 1 is obtained. The rest variables x i (i = 2, 3, • • • , n) and x can be further derived.…”

Steady-State Analysis of the Thermal Subsystem Within Green Integrated-Energy Systems: A Novel Mathematical Modelling Approach

“…Despite the urgency of these tasks, current methods for analyzing the steady-state characteristics of thermal subsystems exhibit several limitations. For instance, oversimplified modeling processes often disregard key physical processes and parameters, leading to inaccuracies in analytical outcomes [18][19][20][21][22]. Additionally, these methods frequently lack the flexibility and precision to handle complex interactions and non-linear relationships inherent to the subsystem.…”