New energy consumption transmission and distribution cooperation method and system based on electric power balance control

Abstract

The invention discloses a new energy consumption transmission and distribution cooperation method based on electric power balance control, which belongs to the technical field of electric power system operation, and comprises the following steps: collecting electric load data, thermal load data and new energy unit output prediction data of an electric power system in real time, and calculating a new energy unit output prediction result based on the prediction data; a three-layer balance control system of a balance settlement unit, a balance responsibility main body and a power distribution network management main body is constructed, rolling optimization is performed by using a preset multi-time scale supply and demand balance model, and day-ahead, intra-day and real-time scheduling strategies are generated in a coordinated manner. According to the method, the prediction precision and robustness of the system on the new energy output and load fluctuation are improved, more reliable input is provided for optimal scheduling, and the flexibility of a scheduling mode is greatly improved. According to the invention, cooperation of multi-time scale optimization and real-time control is realized, the problem of local voltage out-of-limit caused by high-proportion new energy access is solved, voltage out-of-limit and power flow blockage of a power distribution network are avoided, and safe and stable operation of a power grid is guaranteed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system operation, and particularly relates to a new energy consumption transmission and distribution coordination method and system based on power balance control. BACKGROUND

[0002] With the promotion of the "double carbon" goal, the penetration rate of intermittent and volatile renewable energy such as wind power and photovoltaic power in the power system is continuously increasing. Due to the uncertainty of its output and the anti-peaking characteristics, large-scale new energy grid connection brings great challenges to the real-time balance and safe and stable operation of the power system. The traditional power system dispatching and operation mode mainly relies on centralized control on the main grid side and the regulation capacity of conventional generating units, and it is difficult to cope with the complexity and uncertainty brought by distributed new energy.

[0003] The main problems faced by current new energy consumption include: 1. The existing dispatching mode has insufficient flexibility, and it is difficult to realize efficient coordination of source, grid, load and storage resources across layers and regions; 2. The balance responsibilities between transmission and distribution networks and between various subjects in the distribution network are not clear, and there is a lack of effective responsibility allocation and evaluation mechanism; 3. There is a lack of overall optimization of future multi-time scale uncertainty, resulting in a large deviation between the optimized dispatching result and the actual operation; 4. Local power grids, especially distribution networks, are prone to voltage out-of-limit, power flow congestion and other problems, affecting the local consumption of new energy. Therefore, there is an urgent need for an effective method that can coordinate the transmission and distribution networks, clearly define the balance responsibilities at each level, and comprehensively utilize various flexible resources to maximize the consumption of new energy SUMMARY

[0004] The purpose of the present application is to overcome the shortcomings of the prior art, and to provide a new energy consumption transmission and distribution coordination method and system based on power balance control, which solves the technical problems in the background art.

[0005] The object of the application is achieved in that a new energy consumption and transmission and distribution coordination method based on power balance control comprises the following steps: step S1, collecting real-time power system electric load data, thermal load data and new energy unit output prediction data, and optimizing the load prediction data and new energy power prediction data through a probability statistical method and a Monte Carlo simulation method to obtain prediction data meeting preset error requirements under multiple time scales; step S2, based on the prediction data, constructing a three-layer balance control system of a balance settlement unit, a balance responsibility subject and a distribution network management subject, formulating and issuing a planned operation curve of each balance responsibility subject by the distribution network management subject, and completing the sinking and decomposition of balance responsibility; step S3, according to the planned operation curve and real-time updated system state data, using a preset multi-time scale supply and demand balance model for rolling optimization to coordinate the generation of day-ahead, day-ahead and real-time scheduling strategies; through multi-element information fusion for data acquisition and residual error repair, using a multi-model series fusion algorithm for dynamic voltage adjustment, and using a distributed power grid-connected scheduling model to predict voltage fluctuation trend, automatically triggering energy storage consumption, adjusting inverter output and intelligent capacitor group reactive power compensation when voltage out-of-limit is detected; step S4, effect evaluation and feedback, the effect evaluation and feedback comprising: summarizing and statistically the output of the new energy unit and the total system electric load data in the period, evaluating the new energy consumption level, and feeding back the evaluation results to step S1 and step S3, dynamically adjusting the prediction model parameters and the scheduling strategy, forming a closed-loop control.

[0006] The probability statistical method and the Monte Carlo simulation are combined to optimize the load and new energy power prediction data, and the prediction results meeting the preset error requirements of dynamic adjustment are obtained. A new responsibility framework of a balance settlement unit, a balance responsibility subject and a distribution network management subject is constructed, and the responsibility of system balance is sunk and decomposed from the traditional transmission network level to the distribution network or even a finer granularity unit. The rolling optimization strategy based on the multi-time scale supply and demand balance model is seamlessly connected with the second-level collaborative control including the multi-model series fusion algorithm and the distributed power grid-connected scheduling model, and the closed loop from planning to execution is realized. An automatic triggering mechanism is realized at the control layer, which can automatically link energy storage, inverters, intelligent capacitors and other resources for consumption and compensation when voltage out-of-limit is detected, instead of relying on manual intervention.

[0007] The method improves the prediction accuracy and robustness of the system to new energy output and load fluctuation, provides more reliable input for optimal scheduling, greatly improves the flexibility of scheduling mode, stimulates the potential of distribution network and distributed resources to participate in system regulation, clearly defines the balance responsibility of each level, and realizes true transmission and distribution collaboration. The executable of the scheduling strategy is improved, the deviation between the plan and the actual is reduced, the multi-time scale optimization and real-time control are coordinated, the local voltage out-of-limit problem caused by high proportion of new energy access is solved through fast and automatic collaborative control, the voltage out-of-limit and power flow blockage of the distribution network are avoided, the safe and stable operation of the power grid is ensured, and the new energy consumption capacity is greatly improved.

[0008] Further, the new energy unit output prediction data in step S1 includes wind power and photovoltaic power output data; the multi-time scale includes day-ahead, intra-day and real-time scale; and the preset error requirement is dynamically adjusted according to the system operation state. The new energy prediction includes two most representative fluctuating power sources, wind power and photovoltaic power, the multi-time scale is defined as the standard scheduling sequence of the power system, and it is emphasized that the preset error requirement is dynamically adjusted, rather than a fixed value. When implemented, the new energy consumption rate is calculated periodically (such as daily and weekly), the evaluation result is compared with the target value, an adjustment instruction is generated, and the prediction and optimization modules are fed back to correct the internal parameters. The feedback principle in control theory is introduced, the output (consumption effect) of the system is fed back to the front end of the system as input, compared with the expected target, a deviation signal is generated to correct the behavior of the system, thereby reducing the deviation in future operation, realizing the stability and optimization of the system. The whole system is no longer an open-loop static system, but an intelligent system that can learn from historical operation and continuously optimize its performance. It can adapt to changes in power grid structure, growth of new energy installation and other external conditions, and maintain a high consumption level in the long term.

[0009] Further, in step S2, the balance responsibility subjects compensate for the balance deviation by interacting and adjusting internal resources, the distribution network management subject and the balance responsibility subjects perform multi-level balance deviation assessment, and the balance deviation assessment responsibility is decomposed to each balance settlement unit. Through system design (assessment) and market design (interaction), non-technical support is provided to ensure that technical balance responsibility can be effectively implemented through economic levers. It is clear that the balance responsibility subjects can compensate for the deviation by interacting and adjusting internal resources, and multi-level balance deviation assessment is introduced to bind economic incentives and responsibilities. The three-layer system provides a feasible operation mechanism and driving force, ensures the implementation of responsibility through assessment and economic means, activates the market mechanism, allows subjects to efficiently balance the deviation through transaction or coordination, and improves the overall economy.

[0010] Further, the multi-time scale supply-demand balance model in step S3 takes the minimum total system operation cost as the objective function, and considers the operation constraints of the generator units and the balance constraints of the power supply system, wherein the operation constraints of the generator units include the conventional unit power output constraints and the thermal-electric coupling constraints of the CHP units, so as to ensure the economy of the optimized dispatching strategy, promote new energy consumption, and take into account the system operation cost.

[0011] Further, the calculation method of the objective function is that the total cost in the dispatching period T is equal to the sum of the cost of each period t, and the cost of each period t is composed of the following three parts:

[0012] The sum of the fuel consumption fees of all conventional units: the fuel consumption fee f_g1,t of each conventional unit G1 in the period is calculated and accumulated;

[0013] The sum of the fuel consumption fees of all CHP units: the fuel consumption fee f_g2,t of each CHP unit G2 in the period is calculated and accumulated, and the fee depends on the electric power p_g2,t and the thermal power h_g2,t of the unit;

[0014] The sum of the start-up and shut-down fees of all units: the sum of the start-up fee uc_g,t and the shut-down fee dc_g,t of each unit G in the period is calculated and accumulated, wherein the unit includes the conventional unit and the CHP unit;

[0015] Wherein, G1, G2 and G respectively represent the number of conventional units, the number of CHP units and the total number of units.

[0016] The calculation formula of the objective function is as follows:

[0017]

[0018] Wherein, z is the total system operation cost, including the fuel consumption fee and the start-up and shut-down fee of the unit, G1 is the number of conventional units, G2 is the number of CHP units, and G is the total number of conventional units and CHP units, f_g1,t is the fuel consumption fee of the conventional unit in the tth period, f_g2,t is the fuel consumption fee of the CHP unit in the tth period, uc g,t uc_g,t is the start-up fee of the unit in the tth period, and dc g,t dc_g,t is the shut-down fee of the unit in the tth period.

[0019] Further, step S3 further comprises: constructing a chain battery energy storage power conversion system of multiple medium-voltage alternating current ports, constructing a feeder power flow regulation dynamic equation for the system, obtaining a base frequency dynamic equation frequency domain form in a dq coordinate system, performing closed-loop control on the feeder power flow through a PI controller, and realizing feeder power flow balance control through optimal control of each module. The accommodation capacity of new energy is determined by: aggregating the output of new energy units at each time period within a statistical period, combining the total electrical load within the period, calculating the new energy accommodation rate, and dynamically adjusting the accommodation target according to the renewable energy power accommodation responsibility weight index requirement.

[0020] A multi-port chain battery energy storage power conversion system is constructed by the chain energy storage system and the power flow control, and a feeder power flow regulation dynamic equation is established. The closed-loop balance control of the feeder power flow is realized through a PI controller, the problems of uneven power flow distribution and local congestion are solved, and the implementation effect of the scheme is enhanced. When implemented, the renewable energy power consumption responsibility weight is introduced as an external policy index, which is used as the basis for dynamically adjusting the internal accommodation target, realizing the linkage of technology and policy, and using the fast and flexible control capability of the power electronic converter (chain structure) to regard the energy storage system not only as an energy storage unit, but also as an advanced power flow controller. The active and reactive power flow of the distribution network feeder is directly redistributed through adjusting the output, and balance is realized. Through the flexible control of the energy storage system, the active regulation and control capability of the system on the power flow is improved, and more space is provided for new energy consumption.

[0021] A new energy consumption transmission and distribution coordination system based on power balance control is used for the above method, comprising: a data acquisition and prediction module: for real-time acquisition of electrical load data, thermal load data and new energy unit output prediction data of the power system, and optimization of the load prediction data and new energy power prediction data through probability statistical method and Monte Carlo simulation method, to obtain prediction data meeting the preset error requirement under multiple time scales;

[0022] A balance responsibility allocation module: for constructing a three-layer balance control system of a balance settlement unit, a balance responsibility subject and a distribution network management subject, formulating and issuing a planned operation curve of each balance responsibility subject by the distribution network management subject, and sinking and decomposing the balance responsibility;

[0023] A multi-time scale optimization module: for obtaining system state data and optimized multi-time scale prediction data, using a preset multi-time scale supply and demand balance model, combining rolling optimization and data feedback to coordinate the scheduling strategy under multiple time scales, and generating a scheduling strategy of the power system under multiple time scales;

[0024] The synergistic control execution module is used for executing synergistic control through the source, the net, the load and the storage synergistic control system according to the scheduling strategy, realizing second-level dynamic voltage adjustment through power distribution network operation data acquisition and residual error repair of multi-element information fusion, multi-model series fusion algorithm, and distributed power grid connection scheduling model prediction of grid connection point voltage fluctuation trend, and automatically starting energy storage consumption, adjusting inverter output and realizing reactive power compensation through an intelligent capacitor group when voltage overrun is detected;

[0025] The effect evaluation and feedback module is used for summarizing the output of the new energy unit in each period in the statistical period, evaluating the consumption capacity of the new energy in combination with the total electrical load in the period, and dynamically adjusting and optimizing the prediction model and the scheduling strategy according to the evaluation result to form a closed loop control.

[0026] Further, the ally city energy management platform in communication connection with the power distribution network management main body is used for receiving project approval and construction supervision information after the distributed new energy scale management authority is decentralized, and distributed new energy consumption space determination result, and providing the balance responsibility allocation module with the same.

[0027] The present application has the beneficial effects that: by constructing the new responsibility framework of the balance settlement unit, the balance responsibility main body and the power distribution network management main body, the responsibility of system balance is sunk and decomposed from the traditional power transmission network level to the power distribution network or even a finer granularity unit. An automatic triggering mechanism is realized at the control layer, which can automatically link energy storage, inverters, intelligent capacitors and other resources for consumption and compensation when voltage overrun is detected, instead of relying on manual intervention. The rolling optimization strategy based on the multi-time scale supply and demand balance model is seamlessly connected with the second-level synergistic control execution containing the multi-model series fusion algorithm and the distributed power grid connection scheduling model, realizing the closed loop from planning to execution.

[0028] The method of the present application improves the prediction accuracy and robustness of the system to new energy output and load fluctuation, provides more reliable input for optimization scheduling, greatly improves the flexibility of the scheduling mode, stimulates the potential of the power distribution network and distributed resources to participate in system regulation, clearly defines the balance responsibility of each level, realizes real power transmission and distribution collaboration, improves the executability of the scheduling strategy, reduces the deviation between the plan and the actual situation, realizes the collaboration of multi-time scale optimization and real-time control. Through fast and automatic synergistic control, the local voltage overrun problem caused by high proportion of new energy access is solved, voltage overrun and power flow blockage of the power distribution network are avoided, the safe and stable operation of the power grid is ensured, and the new energy consumption capacity is greatly improved. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a method flow step schematic diagram of the present application;

[0030] Figure 2 is a system architecture schematic diagram of the present application. DETAILED DESCRIPTION

[0031] The application will be described in further detail below with reference to the drawings, in which it is to be noted that all directional terms are not to be construed as limiting the application, but are merely used to explain and understand the application.

[0032] Example 1

[0033] As shown in Figure 1 and 2 , the embodiment discloses a new energy consumption and transmission and distribution coordination method based on power balance control, comprising the following steps:

[0034] Step S1, real-time collection of electric load data, thermal load data and new energy unit output prediction data of the power system, and optimization of the load prediction data and the new energy power prediction data through the probability statistical method and the Monte Carlo simulation method to obtain the prediction data meeting the preset error requirement under multiple time scales; the new energy unit output prediction data includes wind power and photovoltaic power output data; the multiple time scales include day-ahead, intra-day and real-time scales; the preset error requirement is dynamically adjusted according to the system operation state. It is clear that the new energy prediction includes two most representative fluctuating power sources, wind power and photovoltaic power, and the multiple time scales are defined as the standard scheduling sequence of the power system, which is day-ahead, intra-day and real-time, and it is emphasized that the preset error requirement is dynamically adjusted, rather than a fixed value. In implementation, the new energy consumption rate is calculated periodically (such as daily, weekly), the evaluation result is compared with the target value, the adjustment instruction is generated, and the prediction and optimization module is fed back to correct the internal parameters. The feedback principle in control theory is introduced, the output (consumption effect) of the system is fed back to the front end of the system as input, compared with the expected target, and the deviation signal is generated to correct the behavior of the system, so as to reduce the deviation in future operation and realize the stability and optimization of the system. The whole system is no longer an open-loop static system, but an intelligent system that can learn from historical operation and continuously optimize its own performance. It can adapt to changes in power grid structure, growth of new energy installation and other external conditions, and maintain a high consumption level for a long time.

[0035] Step S2, based on the prediction data, a three-layer balance control system of balance settlement unit, balance responsibility subject and distribution network management subject is constructed, the plan operation curve of each balance responsibility subject is formulated and issued by the distribution network management subject, and the sinking and decomposition of balance responsibility are completed; the balance responsibility subjects compensate for the balance deviation by interacting and adjusting internal resources, the distribution network management subject and the balance responsibility subject carry out multi-level balance deviation examination, and the balance deviation examination responsibility is decomposed to each balance settlement unit. Through system design (examination) and market design (interaction), non-technical support is provided to ensure that the technical balance responsibility can be effectively implemented through economic leverage. It is clear that the balance responsibility subjects can compensate for the deviation by interacting and adjusting internal resources, and multi-level balance deviation examination is introduced to bind economic incentives and responsibility. The three-layer system provides a feasible operation mechanism and driving force, ensures the implementation of responsibility through examination and economic means, activates the market mechanism, allows the subjects to balance the deviation efficiently through transaction or coordination, and improves the overall economy.

[0036] Step S3, according to the plan operation curve and the real-time updated system state data, a preset multi-time scale supply-demand balance model is used for rolling optimization to coordinate generation of day-ahead, day-ahead and real-time scheduling strategies; through multi-element information fusion for data acquisition and residual error repair, a multi-model series fusion algorithm is used for dynamic voltage adjustment, and a distributed power grid-connected scheduling model is used to predict voltage fluctuation trend, and when voltage overrun is detected, energy storage consumption, adjustment inverter output and intelligent capacitor bank reactive power compensation are automatically triggered; the multi-time scale supply-demand balance model takes the minimum total system operation cost as the objective function, and considers the generator set operation constraint and power supply system balance constraint, wherein the generator set operation constraint includes conventional unit power output constraint and CHP unit thermal-electric coupling constraint, to ensure the economy of the optimized scheduling strategy, promote new energy consumption while considering the system operation cost.

[0037] The calculation formula of the objective function is as follows:

[0038]

[0039] Wherein, z is the total system operation cost, including fuel consumption cost and start-stop cost of the unit, G1 is the number of conventional units, G2 is the number of CHP units, G is the total number of conventional units and CHP units, is the fuel consumption cost of the conventional unit in the tth time period, is the fuel consumption cost of the CHP generator set in the tth time period, uc g,t is the cost of starting the unit in the tth time period, dc g,t is the cost of stopping the unit in the tth time period.

[0040] The calculation method of the target function is: the total cost of all time periods within the scheduling period T is equal to the sum of the cost of each time period t, and the cost of each time period t is composed of the following three parts:

[0041] The sum of the fuel consumption costs of all conventional units: calculate the fuel consumption cost f_g1,t of all in-service conventional units G1 in this period and accumulate it;

[0042] The sum of the fuel consumption costs of all combined heat and power (CHP) units: calculate the fuel consumption cost f_g2,t of all in-service CHP units G2 in this period and accumulate it, and this cost depends on the electric output p_g2,t and the heat output h_g2,t of the unit;

[0043] The sum of the start-up and shut-down costs of all units: calculate the sum of the start-up cost uc_g,t and the shut-down cost dc_g,t of all units G in this period, including conventional units and CHP units, and accumulate it;

[0044] Wherein, G1, G2, G respectively represent the number of conventional units, the number of CHP units and the total number of units.

[0045] For better effect, a chain battery energy storage power conversion system with multiple medium-voltage alternating current ports can be constructed, a feeder power flow regulation dynamic equation is constructed for the system, a base frequency dynamic equation in frequency domain is obtained in dq coordinate system, a PI controller is used for closed-loop control of the feeder power flow, and optimal control of each module is realized to achieve balanced control of the power flow of the associated feeder. The accommodation capacity of new energy is determined by the following method: the output of new energy units at each time period within the statistical period is summarized, the total electrical load within the period is combined, the new energy accommodation rate is calculated, and the accommodation target is dynamically adjusted according to the renewable energy power consumption responsibility weight index requirement.

[0046] A chain battery energy storage power conversion system with multiple ports is constructed by a chain energy storage system and a power flow control, a feeder power flow regulation dynamic equation is established, a PI controller is used to realize closed-loop balanced control of the feeder power flow, the problem of uneven power flow distribution and local congestion is solved, and the implementation effect of the scheme is enhanced. When implemented, the renewable energy power consumption responsibility weight is introduced as an external policy index, which is used as the basis for dynamically adjusting the internal accommodation target, realizing the linkage of technology and policy. The fast and flexible control capability of the power electronic converter (chain structure) is utilized, the energy storage system is not only regarded as an energy storage unit, but also as an advanced power flow controller, the active and reactive power flow of the distribution network feeder is directly redistributed by adjusting the output of the energy storage system, and balance is realized. Through the flexible control of the energy storage system, the active regulation and control capability of the system on the power flow is improved, providing more space for new energy consumption.

[0047] Step S4, performing effect evaluation and feedback, the effect evaluation and feedback including: aggregating the output of new energy units and system total power load data in a statistical period, evaluating the new energy consumption level, and feeding back the evaluation result to step S1 and step S3, dynamically adjusting the prediction model parameters and the scheduling strategy, and forming a closed loop control.

[0048] The probability and statistics method and the Monte Carlo simulation are combined to optimize the load and new energy power prediction data, and the prediction result meeting the preset error requirement of dynamic adjustment is obtained.

[0049] The method improves the prediction accuracy and robustness of the system to new energy output and load fluctuation, provides more reliable input for optimal scheduling, greatly improves the flexibility of the scheduling mode, stimulates the potential of distribution network and distributed resources to participate in system regulation, clearly defines the balance responsibility of each level, and realizes real transmission and distribution collaboration.

[0050] A new energy consumption transmission and distribution collaborative system based on power balance control is used for the above method, comprising:

[0051] The data acquisition and prediction module is used for real-time acquisition of the electric load data, thermal load data and new energy unit output prediction data of the power system, and optimization of the load prediction data and new energy power prediction data through the probability and statistics method and the Monte Carlo simulation method, to obtain the prediction data meeting the preset error requirement under multiple time scales.

[0052] The balance responsibility allocation module is used for constructing a three-layer balance control system of balance settlement unit, balance responsibility subject and distribution network management subject, formulating and issuing the planned operation curve of each balance responsibility subject by the distribution network management subject, and sinking and decomposing the balance responsibility.

[0053] The multi-time scale optimization module is configured to generate a scheduling strategy of the power system under the multi-time scale based on the obtained system state data and the optimized multi-time scale prediction data, the preset multi-time scale supply-demand balance model, rolling optimization and data feedback.

[0054] The collaborative control execution module is configured to execute collaborative control of the source, the grid, the load and the storage according to the scheduling strategy, to realize second-level dynamic voltage adjustment through power distribution network operation data acquisition and residual error repair based on multi-element information fusion and a multi-model series fusion algorithm, to predict voltage fluctuation trends at a grid-connected point through a distributed power source grid-connected scheduling model, and to automatically start energy storage consumption, adjust inverter output and realize reactive power compensation through an intelligent capacitor bank when voltage out-of-limit is detected.

[0055] The effect evaluation and feedback module is configured to collect output of the new energy unit in each period within a statistical period, to evaluate consumption capacity of the new energy in combination with total electrical load within the period, and to dynamically adjust the optimization prediction model and the scheduling strategy according to the evaluation result to form a closed-loop control.

[0056] To achieve better effects, an alliance city energy management platform in communication connection with the power distribution network management subject can be further provided, configured to receive project approval and construction supervision information after the distributed new energy scale management authority is decentralized, to determine results of distributed new energy consumption space, and to provide the balance responsibility allocation module for use.

[0057] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can make equivalent replacements or changes to the technical solution and concept of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for power balance control-based new energy consumption and transmission and distribution coordination, characterized in that, The method comprises the following steps: Step S1, real-time collection of electric load data, thermal load data and new energy unit output prediction data of the power system, and optimization of the load prediction data and the new energy power prediction data through a probability statistical method and a Monte Carlo simulation method to obtain prediction data meeting preset error requirements under multiple time scales; Step S2, based on the prediction data, a three-layer balance control system of a balance settlement unit, a balance responsibility subject and a distribution network management subject is constructed, a planned operation curve of each balance responsibility subject is formulated and issued by the distribution network management subject, and balance responsibility is sunk and decomposed; Step S3, according to the planned operation curve and real-time updated system state data, a preset multi-time scale supply-demand balance model is used for rolling optimization to coordinate generation of day-ahead, day-ahead and real-time scheduling strategies; Through multi-element information fusion for data collection and residual error repair, a multi-model series fusion algorithm is used for dynamic voltage adjustment, and a distributed power grid connection scheduling model is used to predict voltage fluctuation trends, and when voltage overrun is detected, energy storage consumption, adjustment of inverter output and intelligent capacitor bank reactive power compensation are automatically triggered; Step S4, effect evaluation and feedback.

2. The new energy consumption and transmission coordination method based on power balance control according to claim 1, characterized in that, The effect evaluation and feedback include: summarizing and statistically analyzing the output of the new energy unit and the total electric load data in a cycle, evaluating the new energy consumption level, and feeding back the evaluation results to steps S1 and S3 to dynamically adjust the prediction model parameters and the scheduling strategy, forming a closed-loop control.

3. The new energy consumption and transmission coordination method based on power balance control according to claim 1 or 2, characterized in that, The new energy unit output prediction data in step S1 includes wind power and photovoltaic power generation output data; the multiple time scales include day-ahead, day-ahead and real-time scales; and the preset error requirements are dynamically adjusted according to the system operation state.

4. The new energy consumption and transmission coordination method based on power balance control according to claim 1 or 2, characterized in that, In step S2, the balance responsibility subjects compensate for balance deviations through interaction and adjustment of internal resources, and the distribution network management subject and the balance responsibility subjects perform multi-level balance deviation assessment and decompose the balance deviation assessment responsibility to each balance settlement unit.

5. The new energy consumption and transmission coordination method based on power balance control according to claim 1 or 2, characterized in that, In step S3, the multi-time scale supply-demand balance model takes the minimum total cost of system operation as the objective function, and considers generator unit operation constraints and power supply system balance constraints, wherein the generator unit operation constraints include conventional unit electric power output constraints and CHP unit thermal-electric coupling constraints.

6. The new energy consumption and transmission coordination method based on power balance control according to claim 5, characterized in that, The calculation method of the objective function is that the total cost in all periods within a scheduling period T is equal to the sum of the cost of each period t, and the cost of each period t is composed of the following three parts: The sum of fuel consumption fees of all conventional units: calculate and accumulate the fuel consumption fee f_g1,t of all in-service conventional units G1 in this period; The sum of fuel consumption fees of all combined heat and power CHP units: calculate and accumulate the fuel consumption fee f_g2,t of all in-service CHP units G2 in this period, and this fee depends on the electric power p_g2,t and the thermal power h_g2,t of the unit; The sum of start-up and shutdown fees of all units: calculate and accumulate the sum of the start-up fee uc_g,t and the shutdown fee dc_g,t of all units G in this period, including conventional units and CHP units; Wherein, G1, G2, G respectively represent the number of conventional units, the number of CHP units and the total number of units.

7. The new energy consumption and transmission coordination method based on power balance control according to claim 1 or 2, characterized in that, The step S3 further comprises: constructing a chain battery energy storage power conversion system of multiple medium-voltage alternating current ports, constructing a feeder power flow regulation dynamic equation for the system, obtaining a frequency domain form of a base frequency dynamic equation in a dq coordinate system, performing closed-loop control on the feeder power flow through a PI controller, and realizing balanced control of the power flow of the associated feeder through optimal control of each module. 8.The new energy consumption and transmission coordination method based on power balance control according to claim 2, characterized in that, The consumption capacity of new energy is determined by: aggregating the output of new energy units at each time period within a statistical period, combining the total electrical load within the period, calculating the new energy consumption rate, and dynamically adjusting the consumption target according to the renewable energy power consumption responsibility weight index requirement.

9. A new energy consumption and transmission and distribution coordination system based on power balance control, for the method of claim 2, characterized in that, It comprises: A data acquisition and prediction module for real-time acquisition of electrical load data, thermal load data and new energy unit output prediction data of the power system, and optimization of load prediction data and new energy power prediction data through probability statistical method and Monte Carlo simulation method to obtain prediction data meeting the preset error requirement under multiple time scales; A balance responsibility allocation module for constructing a three-layer balance control system of balance settlement unit, balance responsibility subject and distribution network management subject, formulating and issuing the planned operation curve of each balance responsibility subject by the distribution network management subject, and sinking and decomposing the balance responsibility; A multi-time scale optimization module for obtaining system state data and optimized multi-time scale prediction data, using a preset multi-time scale supply-demand balance model, combining rolling optimization and data feedback for dispatching strategy coordination under multiple time scales to generate dispatching strategies of the power system under multiple time scales; A collaborative control execution module for executing collaborative control through source, network, load and storage collaborative control system according to the dispatching strategy, realizing second-level dynamic voltage adjustment through multi-element information fusion distribution network operation data acquisition and residual error repair, multi-model series fusion algorithm, and distributed power grid-connected scheduling model prediction of grid-connected point voltage fluctuation trend, and automatically starting energy storage consumption, adjusting inverter output and realizing reactive power compensation through intelligent capacitor bank when voltage out-of-limit is detected; An effect evaluation and feedback module for aggregating the output of new energy units at each time period within a statistical period, combining the total electrical load within the period, evaluating the consumption capacity of new energy, and dynamically adjusting the optimization prediction model and dispatching strategy according to the evaluation result to form a closed-loop control.

10. The new energy consumption and transmission coordination system based on power balance control according to claim 9, characterized in that, It further comprises: An alliance city energy management platform in communication connection with the distribution network management subject, for receiving project approval and construction supervision information after the distributed new energy scale management authority is decentralized, and providing the distributed new energy consumption space determination result to the balance responsibility allocation module for use.

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