Active balanced operation method for power distribution network area

Abstract

The invention discloses an active balanced operation method for a power distribution network area, and relates to the technical field of active power distribution networks and energy management. According to the method, active balanced operation of the power distribution network area is realized through multiple steps of point, line and plane control, demand response, reactive compensation and voltage control and multi-level collaborative optimization. Energy balance and voltage management are achieved through point control, voltage out-of-limit is avoided through line control, load distribution is optimized through surface control, power grid loads are optimized through demand response, safe operation of equipment is guaranteed through reactive compensation and voltage control, and optimal dispatching of different levels of a power grid is achieved through multi-level collaborative optimization. In addition, a power grid elastic control technology based on multi-source data fusion is further included, and the power distribution network elasticity and the power supply reliability are improved. The method effectively improves the acceptance capability of the power grid to the distributed energy, improves the electric energy quality, reduces the line loss, enhances the power supply reliability, and lays a foundation for constructing a novel power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of active power distribution network and energy management, in particular to a power distribution network area active balance operation method. BACKGROUND

[0002] With the rapid progress of new power system construction, the digital transformation in the energy field continues to develop in depth, and the core position of active balance operation of power distribution network area in the operation system of power enterprises is increasingly prominent, becoming a key point to improve economic benefit and operation efficiency. As an important link in power transmission management, the power distribution network area bears the heavy responsibility of reasonably distributing electric energy to the user end, and its operation status is directly related to the stability and economy of power supply. However, the traditional management mode is facing unprecedented severe challenges due to the complex topology of power distribution network, large-scale equipment and dynamic load characteristics.

[0003] Under the dual driving of energy transformation and power demand growth, the power distribution network is facing unprecedented changes and challenges. On the one hand, the urgent pursuit of sustainable energy around the world has led to the unprecedented scale of distributed power sources, such as solar photovoltaic, wind power and biomass power generation, into the power distribution network. These distributed power sources, although they have played a significant role in alleviating energy crisis, improving energy efficiency and promoting energy saving and emission reduction, have brought many problems to the stable operation of power distribution network due to their inherent intermittency and volatility. For example, photovoltaic power generation is restricted by light intensity and time, and wind power is affected by unstable wind speed, resulting in frequent fluctuations in output power, which easily causes voltage fluctuation in power distribution network, and even leads to voltage out-of-limit, seriously threatening the safe operation of electrical equipment and power quality.

[0004] On the other hand, the rapid development of social economy has led to a sharp rise in power demand, especially in urban business districts, industrial clusters and peak periods of residential electricity consumption. The substantial growth of electricity load has put a lot of pressure on the power distribution network. At the same time, the widespread application of new electric equipment and technology, such as electric vehicle charging piles, data centers, etc., has further intensified the complexity and uncertainty of load characteristics. The traditional one-way radial power distribution network has been difficult to adapt to this new situation of two-way flow, and the phenomenon of reverse flow often occurs, which not only increases line loss and transformer overload risk, but also puts forward new requirements for the planning, operation and protection strategy of power distribution network.

[0005] Furthermore, with the large-scale deployment of distributed new energy in the transformer area, the mismatch between power generation and power consumption in the transformer area is increasingly prominent. In rural areas, household photovoltaic power generation is surplus at noon, while power demand is relatively low, leading to power backflow and increasing the burden on transformers. In cities, three-phase imbalance is obvious during summer peak or specific time of the day, affecting power grid reliability. In addition, the long line in rural or mountainous areas leads to serious line loss, further reducing power supply quality and efficiency.

[0006] In this complex background, it is urgent to develop a method that can realize active balance operation of the transformer area of the distribution network. It not only helps to improve the ability of the distribution network to respond to the access of distributed power and load fluctuations, ensuring the safe and stable operation of the power grid, but also effectively improves power quality, reduces line loss, and enhances power supply reliability, laying a solid foundation for building a clean, efficient, and safe new power system. SUMMARY

[0007] The technical problem to be solved by the present application is to provide an active balance operation method for the transformer area of the distribution network, aiming to solve the problems of poor grid stability, insufficient energy accommodation capacity, and imperfect control strategy in traditional distribution networks. A distributed energy consumption optimization control technology based on an active distribution network architecture is proposed. This technology realizes efficient consumption of distributed energy and optimal scheduling of the power grid through three-level coordinated control of source, network, and load (point, line, and surface), improves the grid's accommodation capacity for renewable energy, and ensures the stable operation of the grid under dynamic load changes and energy fluctuations. By integrating distributed photovoltaic power generation prediction, load prediction, and demand response strategies, the present application can dynamically adjust the grid state, optimize resource allocation, reduce grid loss, and improve the safety, reliability, and economy of the power system, ultimately achieving comprehensive intelligent management of distributed energy.

[0008] To solve the above technical problems, the technical solutions adopted by the present application are as follows.

[0009] An active balance operation method for the transformer area of the distribution network, characterized in that the method is realized by multiple steps, specifically including point control, line control, surface control, demand response, reactive power compensation and voltage control, and multi-level collaborative optimization.

[0010] As a preferred technical solution of the application, the point control adopts a time period-based rolling optimization algorithm, divides the whole day of 24 hours into equally spaced scheduling time periods, takes real-time voltage of each node, remaining adjustable capacity of the distributed power supply and transferable margin of the load as state parameters, takes maximization of local consumption of photovoltaic power generation and minimization of power purchase cost of the power grid as targets, recursively solves output adjustment amount of the distributed power supply and load transfer amount in each time period, synchronously satisfies node power balance, voltage threshold and equipment adjustment range constraint, realizes real-time response to fluctuation of the distributed energy through rolling optimization of each scheduling period, and can control the node voltage fluctuation within ±5% of the rated value.

[0011] As a preferred technical solution of the application, the line control adopts an intelligent adaptive voltage regulation algorithm, dynamically adjusts output capacity of reactive power compensation devices including but not limited to SVC by real-time collection of voltage data and load change information of the 10kV feeder; the technology takes voltage deviation and its change trend as input variables, automatically adjusts control parameters to match different operating conditions through a multi-dimensional parameter collaborative optimization mechanism; when the system has device failure or load mutation, the technology quickly activates the emergency control mode through a hierarchical response mechanism, realizes rapid correction of voltage deviation within 50ms, maintains the voltage amplitude within ±2% of the rated value, and ensures that the reactive power regulation accuracy is ≤5kvar; through intelligent prediction and advanced control of voltage fluctuation, the voltage stability of the power grid in the large-scale distributed energy access scenario is effectively improved, and the voltage out-of-limit risk is reduced by more than 80%.

[0012] As a preferred technical solution of the application, the surface control adopts a topology optimization technology based on real-time data driving, realizes load optimization distribution through multi-feeder collaborative scheduling and network structure dynamic adjustment: the system collects real-time data of load distribution, device operating state and distributed energy output of each feeder, constructs a multi-objective optimization model containing device overload constraint and network safety constraint, and automatically generates an optimal network reconstruction scheme through an intelligent decision model; the technology takes minimization of network loss and maximization of distributed energy accommodation capacity as core targets, simultaneously considers device action cost and system reliability requirements, adjusts feeder tie switch state and load transfer path adaptively, completes load transfer of overloaded devices and network topology optimization within 1 scheduling period, reduces network energy loss after reconstruction, reduces the device overload rate to 0, and improves the distributed energy accommodation capacity by more than 20%; through integration of distributed energy prediction and load prediction modules, high-load risk periods are identified in advance, and preventive network reconstruction is triggered actively, so that the power grid operates safely and efficiently under different conditions.

[0013] As a preferred technical solution of the present application, the demand response adopts intelligent load scheduling technology based on user power consumption characteristics, realizes dynamic optimization of load operation period by constructing a multi-dimensional decision model integrating load demand fluctuation law, real-time electricity price signal and user adjustable load characteristics: the system first classifies and grades user load, identifies transferable load type and adjustment margin, establishes a load transfer benefit evaluation system combined with time-of-use electricity price mechanism, and then generates an optimal scheduling scheme through an adaptive optimization mechanism; this technology has both load fluctuation prediction and early prediction of peak load trend and triggers the demand response mechanism, while maintaining the reliability of important load power supply through hierarchical constraint checking, realizing the coordinated optimization of resource utilization efficiency and user electricity economy.

[0014] As a preferred technical solution of the present application, the reactive power compensation and voltage control adopt adaptive voltage regulation technology based on multi-parameter fusion, which dynamically adjusts the output capacity of SVC type reactive power compensation equipment by real-time monitoring of voltage amplitude, reactive power distribution and distributed energy output fluctuation of 10kV feeder and other lines; this technology constructs a multi-dimensional evaluation system containing voltage deviation, voltage change rate and reactive power deficiency, and automatically matches the optimal compensation parameters through a fuzzy decision mechanism; when the distributed energy output fluctuation exceeds the threshold, the emergency voltage regulation mode is quickly activated, and the fast distribution of reactive power and accurate correction of voltage deviation are realized through a hierarchical response mechanism; in actual operation, this technology can control the voltage regulation response time within 30ms, reduce the voltage fluctuation range to ±1.5% of the rated value, and the reactive power distribution error is ≤3kvar, effectively reducing the influence of distributed energy fluctuation on the grid voltage, and reducing the voltage overrun risk by more than 85%.

[0015] As a preferred technical solution of the present application, the multi-level coordinated optimization: point, line and surface three-level coordinated control technology realizes the optimal scheduling of different levels of the power grid, at the point control layer, the power balance of the grid node is maintained by local adjustment of distributed energy output and load; at the line control layer, the power flow direction of the feeder is adjusted to optimize power transmission; and at the surface control layer, network reconstruction is used to reduce grid loss and improve the overall system operation efficiency.

[0016] Further, the power grid resilience control technology based on multi-source data fusion is further included, real-time collection of multi-dimensional data including but not limited to node voltage, feeder power flow and distributed power output is realized through deployment of distributed intelligent measurement terminals, a power grid state matrix containing 128 characteristic parameters is constructed, and abnormal characteristic parameters including but not limited to voltage fluctuation and frequency deviation are extracted from the matrix, transient events with a fluctuation amplitude of 0.8% of the rated value or a change rate of 0.5% / ms are accurately identified, and a three-level response mechanism is triggered when an abnormality is detected: within 50ms, the voltage deviation is adjusted to within ±1.5% by means of reactive power compensation equipment, within 150ms, power balance is restored by means of load transfer or distributed power output adjustment, within 300ms, fault location with an error of less than 50 meters is completed and the fault area is isolated, and finally, based on the real-time state matrix and network topology constraints, an optimal power supply recovery scheme is generated by an adaptive path planning algorithm, and the power supply in the non-fault area is recovered within 1 second, so that important loads can operate uninterruptedly.

[0017] The beneficial effects produced by the above technical solutions are that: the technology realizes active balanced operation of the distribution network area through point, line, surface control and demand response, reactive power compensation and voltage control, multi-level collaborative optimization and other multi-step processes, which can effectively improve the accommodation capacity of the power grid to distributed energy, realize energy balance and voltage management through the coordination of low-voltage distribution transformers, loads and distributed power by the point control layer, promote local consumption of photovoltaic power generation, and control the node voltage fluctuation within ±5% of the rated value; the line control layer controls the distributed power and voltage reactive power in the 10kV feeder line, and the voltage deviation is corrected within 50ms by intelligent scheduling of reactive power compensation equipment to maintain the voltage amplitude within ±2% of the rated value, thereby reducing the voltage overrun risk by more than 80%; the surface control layer optimizes load distribution through multi-feeder combination and network reconstruction, eliminates equipment overload, reduces power loss of the reconstructed power grid, and improves the accommodation capacity of distributed energy by more than 20%; the demand response technology intelligently adjusts the load period according to the load demand, electricity price and user characteristics, controls the peak load transfer ratio to 12%-18%, and optimizes resource utilization efficiency; the reactive power compensation and voltage control technology completes voltage adjustment within 30ms, reduces the voltage fluctuation range to ±1.5% of the rated value, and reduces the voltage overrun risk by more than 85%; the multi-level collaborative optimization realizes optimal scheduling of different levels of the power grid, improves the overall operation efficiency, and the power grid resilience control technology based on multi-source data fusion can restore power supply in the non-fault area within 1 second, improve the resilience and power supply reliability of the distribution network, and ultimately realize safe, stable and efficient operation of the power grid, improve power quality, reduce line loss, and enhance power supply reliability. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 : Active control system structure diagram;

[0019] Figure 2 : IEEE distribution network 33-node modification model diagram;

[0020] Figure 3 Fig. 4 is a diagram of an example of line consumption control based on intelligent group control. DETAILED DESCRIPTION

[0021] The embodiments will be described in detail with reference to the following examples. In the description of the following examples, specific details are set forth in order to provide thorough understanding of the embodiments. However, persons of ordinary skill in the art will readily recognize that embodiments can be practiced without these specific details. In other instances, well-known structures, circuits, and processes have not been described in detail in order to avoid obscuring the embodiments.

[0022] It should be understood that the term "comprises / comprising" when used in this specification and the appended claims, specifies the presence of stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0023] It should also be understood that the term "and / or" when used in this specification and the appended claims, means any one or more of the associated listed items can be present, and includes multiples of those items, in any combination.

[0024] As used in this specification and the appended claims, the term "if' can be construed to mean "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [a described condition or event] is detected" can be construed to mean "upon determining" or "in response to determining" or "upon detecting [the described condition or event]" or "in response to detecting [the described condition or event]," depending on the context.

[0025] In addition, the terms "first", "second", "third", etc. are used herein only to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0026] Reference within the specification of this application to "one embodiment" or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in additional embodiments," and so on, in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily referring to some, but not all, embodiments, unless otherwise indicated by the context. The terms "including," "comprising," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.

[0027] Embodiment 1: Technical elaboration

[0028] Reference is made to the accompanying drawings that form a part of this Figure 1 , the active balance operation technology of the power distribution network area is a systematic solution that combines multi-dimensional control strategies and intelligent algorithms. The core is to achieve the dual goals of efficient distributed energy consumption and stable grid operation through the "point-line-surface" three-level collaborative control architecture, combined with demand response, reactive power compensation and flexible control technology.

[0029] Point control: the basic unit of local balance of distributed energy

[0030] Reference is made to the accompanying drawings that form a part of this Figure 2 , point control focuses on the smallest unit node of the distribution network, and through real-time coordination of low-voltage distribution transformers, distributed power and load, it builds a closed-loop control chain of "data acquisition-optimization decision-dynamic adjustment". Specifically, the system divides the whole day into fine scheduling periods (such as 15 minutes / period), takes node voltage, power adjustable capacity and load transfer margin as state parameters, and uses dynamic programming or rolling optimization algorithm to solve the optimal combination of distributed power output and load transfer. For example, in a photovoltaic-rich area, when it is detected that the power generation is surplus during the noon, the algorithm automatically adjusts the photovoltaic output to a reasonable range, and at the same time, through the price signal, it guides the flexible load such as water heater and air conditioner to move to the evening, realizing "peak clipping and valley filling". This control layer can control the node voltage fluctuation within ±5% of the rated value, and the local consumption rate of photovoltaic power is increased by 10%-15%.

[0031] Reference is made to the accompanying drawings that form a part of this Figure 3 , line control: intelligent regulation of feeder-level voltage and reactive power

[0032] Line control targets on 10kV feeders, and builds a fast voltage stabilization mechanism by deploying SVC and other reactive compensation devices and intelligent control algorithms. The technology adopts a fuzzy PID compound control strategy, taking voltage deviation and rate of change as input, and dynamically adjusts the amount of reactive compensation: when the voltage deviates due to sudden load changes or fluctuations in distributed energy, the system initiates a hierarchical response within 50ms — first compensating reactive power quickly through SVC, then adjusting the output of distributed power sources within the feeder, and finally maintaining the voltage within ±2% of the rated value. A case study in an industrial park shows that this technology reduces the risk of voltage excursion by more than 80%, increases the power factor to 0.96, and reduces feeder line loss by 1.5 percentage points.

[0033] Area control: topology optimization and load scheduling of multi-feeder networks

[0034] Area control achieves load optimization and loss reduction at the transformer area level through coordinated scheduling and network reconstruction of multiple feeders. The system collects real-time data on load distribution, device status, and energy output of each feeder, and builds a multi-objective optimization model targeting on minimizing network loss and maximizing energy accommodation. It uses genetic algorithm or particle swarm optimization algorithm to solve the optimal topology scheme. When detecting feeder overload or high loss risk, it automatically adjusts the state of the tie switch to transfer load to the idle feeder. In a commercial district application, this technology reduces line loss by 15%-20% after grid reconstruction, increases distributed energy accommodation capacity by 20%, and completely eliminates device overload problems.

[0035] Demand response: flexible control of user-side load

[0036] Demand response technology achieves cross-period optimization of load by building a user electricity behavior model and a time-of-use price linkage mechanism. The system first classifies and grades user load, identifies transferable load (such as commercial lighting and non-critical industrial equipment), and then generates an optimal scheduling scheme in combination with price signals and load forecasts. For example, 2 hours before the summer peak, the system sends a high price warning to users, guiding 30% of transferable load to off-peak hours, reducing the transformer area peak-valley difference by 15%-20%, and reducing user electricity costs by 10%-15%.

[0037] Reactive compensation and voltage control: precise voltage regulation with multi-dimensional parameter fusion

[0038] On the basis of traditional reactive power compensation, the technology introduces a multi-dimensional evaluation system of voltage deviation, rate of change and reactive power deficiency, and realizes adaptive matching of compensation parameters through fuzzy decision mechanism. When the fluctuation of distributed energy exceeds the threshold, the emergency voltage regulation mode is started: first, quickly distribute reactive power to stabilize voltage, and then adjust the power output to balance active power, with the voltage regulation response time controlled within 30 ms and the fluctuation range reduced to ±1.5% of the rated value. After the application in a certain photovoltaic area, the voltage overrun risk is reduced by 85%, the reactive power distribution error is ≤3 kvar, and the power quality is significantly improved.

[0039] Multi-level collaborative optimization: deep integration of cross-level control strategies

[0040] Multi-level collaborative optimization breaks the independent operation mode of point, line and surface control, and realizes cross-level decision-making collaboration through model predictive control (MPC) or hierarchical optimization algorithm. For example, when the point control layer adjusts the output of distributed power, it simultaneously triggers the reactive power compensation of the line control layer and the load transfer of the surface control layer, forming a linkage mechanism of "local regulation - feeder coordination - global optimization". The operation data of a certain demonstration area shows that collaborative control improves the comprehensive energy efficiency of the area by 17%, shortens the fault recovery time to within 1 second, and restores power supply to 100% of non-fault areas.

[0041] Grid resilience control: fault self-healing based on multi-source data

[0042] Resilience control technology builds a grid state matrix containing 128 characteristic parameters by deploying distributed intelligent measurement terminals, achieving accurate identification and rapid response of transient events. When the voltage fluctuation amplitude is ≥0.8% of the rated value or the rate of change is ≥0.5% / ms, a three-level response is triggered: adjust the voltage deviation within 50 ms, restore power balance within 150 ms, and complete fault location and isolation within 300 ms, ultimately restoring power supply to non-fault areas within 1 second. This technology shortens the grid fault response time to 1 / 5 of traditional methods, significantly improving grid resilience and power supply reliability.

[0043] Technical value and application prospect

[0044] The technology system solves the problems of voltage fluctuation, load imbalance and high loss caused by the access of distributed energy through the design concept of "control layering, decision-making collaboration and data-driven", making the area grid from passive response to active regulation. In the future, combined with digital twinning, edge computing and other technologies, the real-time control and intelligent decision-making level can be further improved, providing core support for the construction of new power systems.

[0045] Example 2: Application of point control and rolling optimization in suburban distributed photovoltaic areas

[0046] Technical scenario: A suburban transformer area accesses 800 kW distributed photovoltaic power, with 2 sets of 500 kVA transformers, and the peak load of residents is 350 kW. There is a problem of transformer overload and voltage out-of-limit caused by excess photovoltaic output during noon (peak 600 kW). The measured voltage reaches 245 V, which is +11.4% of the rated value.

[0047] Technical application:

[0048] 1. Time-based rolling optimization algorithm: The whole day is divided into 96 15-minute scheduling periods. The local photovoltaic consumption rate (target ≥ 85%) and the grid electricity purchase cost (target reduction 12%) are taken as double targets. The node voltage, photovoltaic adjustable capacity (±15% of the rated value), and resident flexible load (air conditioner, water heater) transferable margin (about 120 kW) are collected in real time.

[0049] 2. Dynamic regulation process:

[0050] When it is detected that the photovoltaic output is 550 kW and the load is only 200 kW from 11:00 to 14:00, the algorithm triggers three-layer control:

[0051] The photovoltaic output is preferentially adjusted to 450 kW (100 kW is reserved as backup), and the excess power is stored in the transformer area 50 kWh energy storage;

[0052] The intelligent terminal pushes the peak-valley electricity price signal to the user (peak segment electricity price 0.85 yuan / kWh→valley segment 0.35 yuan / kWh), guiding 70 kW of transferable load (such as water heater heating period) to move to 19:00-21:00;

[0053] Adjust the transformer tap position to reduce the node voltage from 245 V to 235 V (±6.8%→±6.8% of the rated value).

[0054] 3. Constraint condition: photovoltaic output adjustment rate ≤10% / min, voltage fluctuation controlled within ±5% (209231 V), and energy storage charge and discharge depth ≤80%.

[0055] Implementation effect:

[0056] The local photovoltaic consumption rate is increased from 68% to 87%, and the daily average reduction of on-grid power is 720 kWh;

[0057] The node voltage is stabilized at 228-232 V (±3.6%), and no out-of-limit alarm occurs again;

[0058] The grid electricity purchase cost is reduced by 14.2%, and the user side peak load period electricity bill expenditure is reduced by 22 yuan / house / month;

[0059] The transformer load rate is reduced from 120% to 92%, and the equipment life is extended by 3-5 years.

[0060] Example 3: Line control and reactive power compensation coordination of 10kV feeder in industrial park

[0061] Technical scenario: A 10kV feeder in an industrial park carries 4 2MVA transformers, and is connected to 2MW distributed wind power. The main load is industrial equipment driven by frequency converters, which causes frequent voltage fluctuations (measured fluctuation range 9.2-10.8kV, exceeding ±8% of rated value) and low power factor (as low as 0.82).

[0062] Technical application:

[0063] 1. Intelligent adaptive voltage regulation algorithm: Deploy 2 sets of 500kvar SVC reactive power compensation devices, use fuzzy PID compound control (Kp dynamic adjustment range 0.5-1.2, Ki=0.3, Kd=0.05), take voltage deviation (target 10kV) and change rate (threshold 0.5% / ms) as input, and adjust reactive power output in real time.

[0064] 2. Implementation process of hierarchical response mechanism:

[0065] When the wind power output suddenly drops from 1.5MW to 500kW (fluctuation of 66.7% within 120ms):

[0066] Primary response (within 50ms): SVC quickly outputs 300kvar capacitive reactive power, voltage drops from 10.8kV to 10.2kV;

[0067] Secondary response (within 150ms): Adjust the feeder photovoltaic output to supplement 200kW active power, and put in capacitor bank to compensate 100kvar reactive power;

[0068] Tertiary response (within 300ms): After detecting voltage stabilization, optimize reactive power distribution through OPF algorithm to increase power factor to 0.95.

[0069] 3. Device parameters: SVC response time ≤20ms, reactive power regulation accuracy ±5kvar, voltage sampling frequency 10kHz.

[0070] Implementation effect:

[0071] Voltage fluctuation range is reduced to 9.8-10.2kV (±2%), and the risk of exceeding the limit is reduced from 5 times a week to zero;

[0072] Power factor is increased to 0.96, reducing power adjustment electricity fee penalty by 12,000 yuan per month;

[0073] Feeder line loss rate is reduced from 5.3% to 3.8%, saving 156,000kWh of electricity per year on average;

[0074] Device failure frequency is reduced by 70%, and frequency converter misoperation problem is completely solved.

[0075] Example 4: Face control and preventive reconstruction of urban commercial district multi-feed network

[0076] Technical scenario: A certain commercial district is powered by 3 10kV feeders (feeders A / B / C), with total load of 10MW and distributed photovoltaic installation of 3MW. The load rate of feeder A reaches 130% (measured current 650A, 30% over rated value) during summer peak, and the network loss rate is as high as 9.2%.

[0077] Technical application:

[0078] 1. Real-time data driven topology optimization: Genetic algorithm (population size 50, crossover probability 0.8, mutation probability 0.05) is adopted to minimize network loss (objective function weight 0.7) and maximize photovoltaic accommodation (weight 0.3), and real-time collection is performed:

[0079] Feeder load: A = 4.5MW, B = 3.2MW, C = 2.3MW;

[0080] Photovoltaic output: A = 1.8MW, B = 0.9MW, C = 0.3MW;

[0081] Equipment state: Feeder A circuit breaker temperature 85℃ (threshold 90℃).

[0082] 2. Network reconstruction execution process:

[0083] Disconnect the tie switch K1 between feeders A and B, and close the tie switch K2 between feeders B and C;

[0084] Transfer 1.5MW load in feeder A to feeder C (including commercial lighting, non-critical equipment);

[0085] Adjust the photovoltaic output distribution: A = 1.2MW, B = 0.9MW, C = 0.9MW (utilize the idle capacity of C feeder);

[0086] Reconstruction time is 180 seconds, and the triggering condition is that the load forecast shows that the load will rise by 12% in the next 1 hour.

[0087] 3. Constraint conditions: Single feeder load rate ≤ 85%, node voltage deviation ≤ ±7%, equipment action frequency ≤ 2 times / day.

[0088] Implementation effect:

[0089] After reconstruction, the network loss rate is reduced from 9.2% to 7.1%, and the daily average reduction of electric energy loss is 2400kWh;

[0090] The load rate of feeder A is reduced to 82%, the circuit breaker temperature is reduced to 75℃, and the overload risk is eliminated;

[0091] The photovoltaic receiving capacity is increased from 3 MW to 3.6 MW (+20%), and the on-site consumption rate reaches 89%;

[0092] The comprehensive energy efficiency of the transformer area is increased by 17.3%, and the fault outage time is reduced from 45 minutes to 12 minutes per year.

[0093] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for active balancing operation of distribution network areas, characterized in that, This method is implemented in multiple steps, including point control, line control, area control and demand response, reactive power compensation and voltage control, and multi-level collaborative optimization.

2. The active balancing operation method for a distribution network area according to claim 1, characterized in that, The point control adopts a time-based rolling optimization algorithm, which divides the 24 hours of the day into equally spaced scheduling periods. The real-time voltage of each node, the remaining adjustable capacity of distributed power sources, and the load transfer margin are used as state parameters. The goal is to maximize the local consumption of photovoltaic power generation and minimize the grid purchase cost. The algorithm recursively solves the output adjustment and load transfer of distributed power sources in each time period, while simultaneously satisfying the constraints of node power balance, voltage threshold, and equipment adjustment range. Through rolling optimization in each scheduling cycle, the algorithm achieves real-time response to distributed energy fluctuations, and can control node voltage fluctuations within ±5% of the rated value.

3. The active balancing operation method for a distribution network area according to claim 1, characterized in that, The line control employs an intelligent adaptive voltage regulation algorithm. By collecting real-time voltage data and load change information from the 10kV feeder, it dynamically adjusts the output capacity of reactive power compensation equipment, including but not limited to SVC. This technology uses voltage deviation and its changing trend as input variables and automatically adjusts control parameters to match different operating conditions through a multi-dimensional parameter collaborative optimization mechanism. When equipment failure or load change occurs, the technology quickly activates the emergency control mode through a graded response mechanism, achieving rapid correction of voltage deviation within 50ms, ensuring that the voltage amplitude is always maintained within ±2% of the rated value, while guaranteeing reactive power regulation accuracy ≤5kvar. Through intelligent prediction and proactive control of voltage fluctuations, it effectively improves the voltage stability of the power grid in scenarios of large-scale distributed energy integration, reducing the risk of voltage exceeding limits by more than 80%.

4. The active balancing operation method for a distribution network area according to claim 1, characterized in that, The surface control adopts a topology optimization technology based on real-time data, and achieves optimized load allocation through multi-feeder collaborative scheduling and dynamic adjustment of network structure: The system collects load distribution, equipment operating status and distributed energy output data of each feeder in real time, and constructs a multi-objective optimization model with equipment overload constraints and network security constraints. The optimal network reconfiguration scheme is automatically generated through an intelligent decision model. This technology takes minimizing network loss and maximizing the capacity to accommodate distributed energy as its core objectives, and simultaneously considers equipment operating costs and system reliability requirements. By adaptively adjusting the status of feeder tie switches and load transfer paths, it completes the load transfer of overloaded equipment and grid topology optimization within one scheduling cycle, so that the power loss of the grid is reduced after reconfiguration, the equipment overload rate is reduced to 0, and the capacity to accommodate distributed energy is increased by more than 20%. By integrating distributed energy forecasting and load forecasting modules, high-load risk periods can be identified in advance, and preventive network reconfiguration can be proactively triggered, enabling the power grid to operate safely and efficiently under different operating conditions.

5. The active balancing operation method for a distribution network area according to claim 1, characterized in that, The demand response adopts intelligent load dispatching technology based on user electricity consumption characteristics. By constructing a multi-dimensional decision model that integrates load demand fluctuation patterns, real-time electricity price signals, and user adjustable load characteristics, the system achieves dynamic optimization of load operation periods. The system first classifies and grades user loads, identifies transferable load types and adjustment margins, establishes a load transfer benefit evaluation system in conjunction with the time-of-use pricing mechanism, and then generates the optimal dispatching scheme through an adaptive optimization mechanism. This technology also has load fluctuation prediction capabilities, predicts peak load trends in advance and triggers the demand response mechanism, and maintains the power supply reliability of important loads through hierarchical constraint verification, thereby achieving synergistic optimization of resource utilization efficiency and user electricity consumption economy.

6. The active balancing operation method for a distribution network area according to claim 1, characterized in that, The reactive power compensation and voltage control adopts an adaptive voltage regulation technology based on multi-parameter fusion. By monitoring in real time, including but not limited to the voltage amplitude of the 10kV feeder, reactive power distribution, and distributed energy output fluctuations, the output capacity of SVC-type reactive power compensation equipment is dynamically adjusted. This technology constructs a multi-dimensional evaluation system including voltage deviation, voltage change rate, and reactive power deficit, and automatically matches the optimal compensation parameters through a fuzzy decision-making mechanism. When the distributed energy output fluctuation exceeds the threshold, the emergency voltage regulation mode is quickly activated, and the rapid allocation of reactive power and accurate correction of voltage deviation are achieved through a graded response mechanism. In actual operation, this technology can control the voltage regulation response time to within 30ms, reduce the voltage fluctuation range to ±1.5% of the rated value, and the reactive power allocation error to ≤3kvar, effectively reducing the impact of distributed energy fluctuations on grid voltage and reducing the risk of voltage over-limit by more than 85%.

7. The active balancing operation method for a distribution network area according to claim 1, characterized in that, The multi-level collaborative optimization: the three-level coordination control technology of point, line and surface realizes the optimized scheduling of the power grid at different levels. At the point control level, the power balance of the power grid nodes is maintained by adjusting the output and load of distributed energy sources locally. The online control layer optimizes power transmission by adjusting the power flow direction of the feeders; while the surface control layer reduces grid losses and improves the overall system operating efficiency by utilizing network reconfiguration.

8. The active balancing operation method for a distribution network area according to claim 1, characterized in that... It also includes grid resilience control technology based on multi-source data fusion. By deploying distributed intelligent measurement terminals, it collects multi-dimensional data in real time, including but not limited to node voltage, feeder power flow, and distributed power output. It constructs a grid state matrix with 128 characteristic parameters and extracts abnormal features, including but not limited to voltage fluctuations and frequency deviations, from it. It accurately identifies transient events with fluctuation amplitude ≥0.8% of the rated value or change rate ≥0.5% / ms. When an anomaly is detected, a three-level response mechanism is triggered: within 50ms, the voltage deviation is adjusted to within ±1.5% through reactive power compensation equipment; within 150ms, power balance is restored by load transfer or distributed power output adjustment; within 300ms, fault location with an error ≤50 meters is completed and the fault area is isolated; finally, based on the real-time state matrix and network topology constraints, the optimal power supply restoration scheme is generated through an adaptive path planning algorithm, and power supply restoration to non-faulty areas is achieved within 1 second, so that important loads can operate without interruption.

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