CN-121983961-A - Power and electricity balance method and source network charge storage boundary value calculation method in main-distribution micro collaborative development network

Abstract

The invention belongs to the technical field of electric power and discloses a power and electricity balance method in a main-auxiliary micro collaborative development network, which comprises the following steps of presetting a power balance model, inputting position data of each node of a target electric power system and historical operation data in a time period T0 before the current operation time T, calculating a net load value of all nodes in the target electric power system at each time in the time period (T-1-T0, T-1), predicting the net load value based on each node, judging whether the current operation time T is the ending time Te based on a layering and partitioning result of the current time, and performing layering partitioning and power balance calculation at the next time. The invention also discloses a source network load storage boundary value calculation method. The main beneficial technical effects are that the partitioning result is dynamically adjusted according to the real-time running state, so that the main power and micro power electric quantity balance is realized, the power generation cost can be reduced, the new energy absorbing capacity can be improved, and the stability and reliability of the power system under peak load can be effectively improved.

Inventors

• SHEN YU
• Cao Ruoshan
• CHEN WEI
• ZHOU QUAN
• HUANG HAO
• SONG ZIJIAN
• SHENG PANPAN
• CHENG LONG
• SHAO JIE
• SUN JUN
• WANG HAO
• LI BEN
• GUAN ZIRAN
• HUANG JING
• ZHENG LIAN

Assignees

• 国网湖北省电力有限公司鄂州供电公司

Dates

Publication Date

20260505

Application Date

20260126

Claims (2)

1. 1. The electric power and electricity balance method in the main-auxiliary micro cooperative development network is characterized by comprising the following steps of: presetting a power balance model, setting a primary and secondary grading self-balancing maximization of the power balance model as a primary optimization target, and setting an interaction power minimization as a secondary optimization target; inputting position data of each node of a target power system and historical operation data in a time period T0 before the current operation time T, wherein the historical operation data comprises historical load data, historical power generation data and operation constraint conditions of a novel grid-connected main body; Calculating the net load value of all nodes in a target power system at each moment in a (T-1-T0, T-1) time period, predicting the predicted net load value at the current operation time T according to the historical load data and the operation constraint condition of a novel grid-connected main body, combining the operation condition of the novel grid-connected main body in each region to ensure power supply as a target, and generating dynamic scheduling schemes of power resources in different power system regions, wherein the power system regions comprise a main network region, a distribution network region and a micro network region; Fourth, based on the predicted net load value of each node, adjusting the hierarchical partition structure of the target power system to ensure that effective cooperative control is realized among different power system areas; Fifthly, calculating a power balance result of the target power system by adopting a power balance model based on a layering and partitioning result at the current moment, and outputting a power resource distribution result under the cooperation of the main power distribution unit and the micro power distribution unit; and a sixth step of judging whether the current running time t is the ending time Te, if so, ending the operation, otherwise, updating the time t=t+1, returning to the second step, and carrying out the hierarchical partition and power balance calculation at the next time.
2. 2. The method for calculating the source network charge storage boundary value in the main-match micro-cooperative development network is characterized by covering four dimensions of the source network charge storage by taking boundary condition data as an input basis of power balance calculation and considering the power and electricity balance method in the main-match micro-cooperative development network as claimed in claim 1, and comprises the following steps: The first step, the optimization target step, comprises: (1) The primary optimization objective is that the expression of maximizing the self-balance of the main-configuration differential stage as the primary optimization objective is as follows: Wherein: Respectively representing a main network, a distribution network and a micro network; Generating power for the system k; Is the load power in system k; 1) Main and auxiliary micro-stage power balance expression: Wherein: is the power lost in system k; 2) Main-auxiliary micro-stage power balance expression, wherein the main-auxiliary micro-stage power balance expression is ; (2) Secondary optimization objective the expression for minimizing the interaction power to the secondary optimization objective is: E is an interactive connection set among a main network, a distribution network and a micro network; The interactive power between the system i and the system j comprises active power and reactive power; the second step, operation constraint conditions of the novel grid-connected main body comprise unit power upper and lower limit constraint, unit climbing constraint, photovoltaic operation constraint and energy storage operation constraint; (1) The upper and lower limits of the unit power are as follows: ; (2) And the unit climbing constraint is that: Wherein: And The power of the partition unit b at the time t and the power of the partition unit b at the time t-1 are respectively; And Respectively outputting a minimum value and a maximum value of the unit b; And The maximum climbing rising and falling rates of the unit b are respectively; (3) Photovoltaic operation constraints: the photovoltaic operation constraints are: Wherein: the actual output power of the photovoltaic at time t; the photovoltaic theoretical output power is t time; A, partitioning a photovoltaic unit set; (4) Energy storage operation constraint: ; ; ; wherein: For t time, storing energy and outputting power, discharging to be positive and charging to be negative; Maximum output power for energy storage b; And Respectively the electric quantity states of the energy storage b at the time t and the time t-1; Representing the stored energy set of partition a; the power balance model aims at minimizing the total power generation cost of the unit and the partition power imbalance, and the expression is as follows: the method comprises the steps of obtaining a partition set by layering partitions of a target power system, obtaining a specific partition of the partition set, obtaining a set of units in the partition set, obtaining a specific unit in the unit set, obtaining a cost function of the unit b, obtaining output power of the unit b at t time, obtaining a power unbalance value of the partition set at t time, and obtaining a power unbalance penalty term of the partition set.

Description

Power and electricity balance method and source network charge storage boundary value calculation method in main-distribution micro collaborative development network Technical Field The invention belongs to the technical field of power, and particularly relates to a power and electricity balance method in a main-distribution micro-cooperative development network and a source network load storage boundary value calculation method. Background The construction of a novel power system mainly based on new energy becomes a necessary choice for energy transformation in China. The transformation type positive driving power grid structure and the operation mode are deeply transformed, wherein the power grid structure is evolved from a traditional unidirectional step-by-step multi-layer system for transmission and distribution to a multi-element bidirectional mixed hierarchical structure of a main power transmission grid (main network), a medium-small power distribution grid (distribution network) and a micro power grid (micro network), and the operation mode is changed from source load real-time balance of 'source load following', integrated operation of a large power grid and collaborative operation of a multi-stage power grid of 'source load storage collaborative interaction'. Under the background, the coupling relation among the main network, the distribution network and the micro network is increasingly compact, and the operation scene is increasingly complex. In the traditional power grid, a main network is active, a distribution network is passive, a typical main-slave structure is shown, and the scale and controllable resources are relatively limited. In the novel power system, the initiative of each level of power grid is obviously enhanced, especially, along with the rapid increase of scale and controllable resources of a large number of access distribution networks and micro networks such as distributed power sources, energy storage, flexible loads and the like, the power grid structure tends to be equivalent and interactive, the influence of the lower level of power grid on the running state of the upper level of power grid is more obvious, and unprecedented higher requirements are put forward on the regulation running level. The applicant is in the region, and is currently facing the current situation that new energy installation is rapidly increased, and distributed photovoltaic, wind power and energy storage equipment are widely connected into a power distribution network. Meanwhile, the development of emerging industries such as aviation logistics, high-end manufacturing and the like makes the load characteristics more diversified, the fluctuation and the uncertainty more serious. With the construction and promotion of the economic area of the airport in the flower and lake, the regional power supply reliability, the electric energy quality and the dynamic balance capability face new challenges. The traditional main-distribution-micro network cooperative mode mainly comprising static, unidirectional and rigid connection has difficulty in adapting to complex situations of multisource, multi-load and multi-coexistence under high-proportion new energy access. With the continuous improvement of the permeability of the distributed power supply in the power distribution network, the problem of large power grid balance is increasingly remarkable, and the collaborative regulation and control functions of the secondary system of the power distribution network are urgently needed to be re-carded and planned by the system. The main-distribution micro-cooperation mode divides the power system into three layers of a main network, a distribution network and a micro network through a layering partition and unit autonomous principle to form an organic whole of vertical cooperation and horizontal interaction, and please refer to fig. 1. And the hierarchical control architecture adopts a hierarchical distributed control architecture for the main and micro coordination modes, so that the functional division and coordination of different levels are realized. (1) And the main network regulation layer is used as the highest decision level of the system, and the main network is responsible for global security and overall balance. The main functions of the method comprise the steps of making a whole-grid power generation plan, performing frequency adjustment, managing cross-region transactions and coordinating lower-level grid resources. The main network is connected with the distribution website domain layer through the power transmission network, transmits a power regulation command to the distribution network, and receives the running state information uploaded by the distribution network to form bidirectional information interaction. (2) The distribution network linkage layer, the distribution network layer is a key link of main distribution micro cooperation. The distribution network is further divided into three sub-le