Supply-demand side resource aggregation response and coordination power grid regulation optimization method and system

Abstract

The application discloses a power grid regulation optimization method and system for supply and demand side resource aggregation response and cooperation, relates to the technical field of power grid regulation optimization, and comprises the following steps: determining the regulation constraint boundary of a single resource based on the physical characteristics and response law of multi-form resources; performing distributed energy aggregation and joint group control based on the regulation constraint boundary, and acquiring aggregation regulation capacity and output uncertainty data; determining the cooperative control plan under multiple time scales according to the resource response speed based on the aggregation regulation capacity and output uncertainty data; and constructing a double-layer regulation optimization model according to the power distribution network loss and the economic cost of multi-form resources, and obtaining the global optimal regulation result of each period through double-layer linkage solution, with the cooperative control plan as a global constraint. The application can realize the cooperation of the power grid side and the resource side and give consideration to the internal multi-resource overall planning of supply and demand, and solves the problem that the conventional regulation technology cannot realize the cooperation of the power grid side and the resource side while giving consideration to the internal multi-resource overall planning of supply and demand.

Description

Technical Field

[0001] This application relates to the field of power grid regulation and optimization technology, specifically to a power grid regulation and optimization method and system that integrates and coordinates supply and demand-side resource aggregation response. Background Technology

[0002] Currently, new power sources such as distributed photovoltaics, energy storage systems, and electric vehicles, along with adjustable resources, are being integrated into the distribution network on a large scale. Simultaneously, the diversification and flexibility of industrial and commercial loads are becoming increasingly prominent, creating a complex supply and demand pattern with multiple resource types coexisting. While this pattern provides abundant flexible adjustment resources for distribution network operation, it also presents serious challenges to the safe and stable operation of the distribution network due to the randomness of resource output, the volatility of load demand, and the complexity of supply and demand interactions. Firstly, the intermittent output of distributed power sources can easily lead to voltage fluctuations and exceeding limits at distribution network nodes, increasing the difficulty of voltage regulation. Secondly, the lack of coordination between multiple resource types and the grid can easily increase distribution network losses and reduce energy utilization efficiency. Thirdly, the actual and planned adjustment amounts of adjustable loads are prone to deviation; without targeted cost considerations, this can affect the feasibility and economic efficiency of adjustment strategies.

[0003] To address the aforementioned issues, relevant regulation technologies propose a two-layer "planning-operation" model for distribution networks incorporating 5G base station energy storage. The planning layer aims to minimize the annual comprehensive cost of the distribution network, while the operation layer aims to optimize voltage levels. This model improves operational efficiency through the coordinated configuration of distributed energy storage and idle energy storage at 5G base stations. However, this two-layer approach fails to consider the economic costs of multiple resource types and distribution network losses, thus failing to achieve global optimization of grid operation. Alternatively, a collaborative optimization framework integrating active distribution networks and multiple microgrids can be constructed, combining carbon intensity balancing methods to address the non-convexity problem in low-carbon optimization and improve the efficiency of low-carbon economic dispatch. Furthermore, for distribution networks containing distributed photovoltaics and electric vehicles, related technologies establish reactive power optimization models that balance operating costs and system stability, reducing voltage fluctuations and network losses. However, these models cannot achieve full-dimensional inclusion and coordinated dispatch of multiple resource types, including photovoltaics, energy storage, industrial loads, commercial loads, and electric vehicles, making it difficult to fully exploit the regulation potential of various resources and adapt to the regulation needs under complex supply and demand patterns.

[0004] In summary, the relevant technologies cannot effectively solve the problems of optimizing distribution network losses and voltage deviations, and regulating and coordinating multiple resources within the supply and demand sides in scenarios with multiple forms of resource access, making it difficult to achieve precise linkage regulation of resource responses on both the supply and demand sides. Summary of the Invention

[0005] The purpose of this application is to address the problem that conventional regulation techniques cannot simultaneously coordinate the power grid and resource sides while also considering the overall planning of multiple resources within the supply and demand sides. It proposes a power grid regulation optimization method and system that integrates resource aggregation response and coordination on both the supply and demand sides. This method acquires aggregated regulation capabilities by aggregating and jointly controlling multiple resource types. Based on the supply and demand side coordinated regulation plan obtained from the aggregated regulation capabilities, a two-level optimization model is solved. Through coordinated regulation constraints and two-level optimization, optimal regulation on both sides is achieved while simultaneously considering the allocation of multiple resources within the supply and demand sides that adapt to different resource response characteristics and time scales, thereby improving the efficiency and reliability of power grid regulation optimization.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows: In a first aspect, embodiments of this application provide a power grid regulation optimization method for supply and demand side resource aggregation response and coordination, the method comprising: The adjustment constraint boundary of a single resource is determined based on the physical characteristics and response laws of multi-form resources. Based on the adjustment constraint boundary, distributed energy aggregation and joint group control are carried out to obtain data on aggregation adjustment capacity and output uncertainty. Based on aggregated regulation capacity and output uncertainty data, a coordinated regulation plan under multiple time scales is determined according to the resource response speed. Using the coordinated control plan as a global constraint, a two-layer regulation optimization model is constructed based on the distribution network loss and the economic cost of various resources. The optimal global regulation result for each time period is obtained through two-layer linkage solution.

[0007] In this scheme, different resources have inconsistent characteristics, making direct aggregation and coordination impossible. By defining the regulation constraint boundaries of each resource, the regulation boundaries and potential of each multi-form resource are clarified. Then, through aggregation and joint group control, a planned regulation capability is formed, quantifying the regulation potential of multi-form resources and transforming dispersed resources into usable cluster capabilities. This ensures that the power grid regulation can effectively cope with output fluctuations in various output scenarios, avoids the fragmentation of single-resource regulation, and guarantees the accuracy and reliability of regulation capabilities. Since different resources have different response speeds, regulation conflicts may occur during regulation. By aggregating regulation capabilities and uncertainty data, a coordinated control plan under multiple time scales is determined, adapting to the response characteristics of different resources and the regulation needs of different time periods. This avoids low power grid regulation efficiency due to time scale mismatch and improves the robustness of the regulation scheme. Furthermore, the coordinated control plan under multiple time scales is used as a global constraint of the two-level optimization model, providing a feasible regulation domain for the two-level optimization objective. This ensures that regulation tasks are accurately allocated to various resources. At the same time, it ensures effective coordination between the power grid side and the resource side, as well as effective coordination between the power grid and the resource side, while also taking into account the regulation optimization within multi-form resources, thereby achieving global optimization and improving regulation economy while ensuring the safe operation of the power grid.

[0008] Optionally, determining the adjustment constraint boundary of a single resource based on the physical characteristics and response patterns of multi-form resources includes: The uncertainty of photovoltaic output is quantified based on the photovoltaic output model, and the first adjustment constraint boundary characterizing the fluctuation range of photovoltaic output is obtained. A second adjustment constraint boundary characterizing the energy storage charge and discharge efficiency and the energy storage power limit is obtained based on the charge and discharge characteristics of a single energy storage system. The third adjustment constraint boundary is obtained based on the demand-side load type and load response pattern, taking into account the demand response of industrial load, commercial load and electric vehicle load.

[0009] Optionally, the third adjustment constraint boundary, which takes into account industrial load, commercial load, and electric vehicle load demand response, is obtained based on demand-side load type and load response patterns, including: Based on the industrial load type, establish piecewise functions of load transfer and electricity price difference, load reduction rate and electricity price to determine industrial load regulation constraints that include the regulation capacity and response cost of individual industrial loads; Based on the equivalent thermal resistance and thermal capacity-based temperature response characteristics of air conditioning operation, the controllable energy state margin is calculated to obtain the commercial load regulation constraints that characterize the air conditioning load regulation potential. A charging load model is constructed by obtaining the normal distribution of charging time and the log-normal distribution of daily mileage of electric vehicles. Based on the charging load model, the charge state boundary and controllable energy state margin of electric vehicles are determined, and the charging and discharging constraints of electric vehicle clusters are obtained. The industrial load regulation constraint, the commercial load regulation constraint, and the electric vehicle cluster charging and discharging constraint form the third regulation constraint boundary.

[0010] Optionally, the step of performing distributed energy aggregation and joint group control based on adjustment constraint boundaries to obtain aggregation adjustment capacity and output uncertainty data includes: Based on the first regulation constraint boundary and the second regulation constraint boundary, the first regulation capability of the distributed energy cluster including photovoltaic and energy storage is calculated. Simultaneously, the target output scenario is determined by Monte Carlo simulation based on the first adjustment constraint boundary, and the output probability distribution under each scenario is obtained; The second adjustment capability of the demand-side load cluster is calculated based on the third adjustment constraint boundary. Integrate the first and second adjustment capabilities to obtain the aggregated adjustment capabilities of the entire resource cluster.

[0011] Optionally, calculating the first regulation capability of the distributed energy cluster including photovoltaics and energy storage based on the first regulation constraint boundary and the second regulation constraint boundary includes: Based on the superposition of individual photovoltaic power output models, the total power output probability distribution of the photovoltaic cluster under the first adjustment constraint boundary is obtained; Based on the energy storage unit model, an adjustable power function of the energy storage cluster is constructed through coordinated control of charging and discharging states. The second adjustment constraint boundary is substituted into the function to obtain the adjustment potential of the energy storage cluster. A photovoltaic-energy storage energy management model is established with the goal of maximizing the fit between the electricity consumption curve and the photovoltaic output curve. The model is solved to obtain the first regulation capability that satisfies the probability distribution of total output and the regulation potential of the energy storage cluster.

[0012] Optionally, the step of determining the target output scenario through Monte Carlo simulation based on the first adjustment constraint boundary and obtaining the output probability distribution under each scenario includes: Based on the photovoltaic power output model, a photovoltaic power output sample was generated by Monte Carlo simulation, and the power output correlation between photovoltaic cells in different regions was corrected by the Frank-Copula function to obtain the power output sample set of the photovoltaic cluster. Using peak output, fluctuation amplitude, and peak period as clustering features, a clustering algorithm is used to cluster the output sample set to obtain target output scenarios. Each target output scenario includes an output time series curve, output probability, and fluctuation amplitude.

[0013] Optionally, calculating the second adjustment capability of the demand-side load cluster based on the third adjustment constraint boundary includes: The overall regulation capacity of the industrial load cluster is obtained by integrating the load transfer rate and load reduction rate of each individual industrial unit. By integrating the controllable energy state margin of each individual air conditioner, the total regulation capacity of the commercial air conditioning cluster can be obtained. The total charging power of the electric vehicle cluster is calculated based on the charging load model, and the allowable reverse discharge capacity is statistically analyzed. Thus, the total regulation capacity of the electric vehicle cluster is obtained based on the total charging power and the allowable reverse discharge capacity. Based on the constraints of distribution network line capacity and transformer quota, the upper limit of the regulation capacity of various load clusters is modified to obtain the second regulation capacity that characterizes the regulation capacity boundary, unit regulation cost and response speed of each load cluster on the demand side.

[0014] Optionally, the step of determining a coordinated control plan across multiple time scales based on aggregated adjustment capacity and output uncertainty data, according to resource response speed, includes: During the daytime adjustment phase, with the goal of minimizing the overall adjustment cost across all scenarios, the expected load for each time period is calculated based on the target output scenario, and adjustment tasks for each aggregated resource are allocated according to the expected load for each time period. During the hourly adjustment phase, the daily adjustment task and the real-time load deviation are compared based on the real-time output scenario, and the resource types on both the supply and demand sides are adjusted according to the real-time load deviation. During the minute-level adjustment phase, the energy storage cluster discharges and the electric vehicle cluster reverses discharge based on the output fluctuation status under the current output scenario. During the near-second-level adjustment phase, emergency adjustments to interruptible loads are made based on the output fluctuation amplitude under the current output scenario.

[0015] Optionally, the method of constructing a two-layer regulation optimization model based on the coordinated control plan as a global constraint and the economic costs of distribution network losses and various forms of resources, and obtaining the globally optimal regulation results for each time period through two-layer linkage solution, includes: With the goal of minimizing the total network loss of the distribution network, an upper-level optimization function is established based on the network loss of the distribution network system and the interaction power between each microgrid and the distribution network as variables. With the goal of minimizing the total operating cost of the microgrid, the variables to be optimized are determined based on industrial load, commercial load, electric vehicles, photovoltaics, and energy storage, and a lower-level optimization function is established. Using the aforementioned coordinated control plan as a global constraint, and combining it with the upper-level power constraint, the upper-level optimization function is solved to obtain the upper-level control result; The interaction power in the upper-level adjustment result is used as the lower-level power constraint of the lower-level optimization function. The lower-level optimization function is solved according to the lower-level power constraint and the global constraint to obtain the lower-level adjustment result. The adjustment cost in the lower-level adjustment result is used to perform feedback optimization on the upper-level adjustment result, so as to solve the global optimal adjustment result through two-level linkage iteration.

[0016] Secondly, embodiments of this application provide a power grid regulation and optimization system for supply and demand side resource aggregation response and coordination, comprising: The initial adjustment constraint module is used to determine the adjustment constraint boundary of a single resource based on the physical characteristics and response laws of multi-form resources; The aggregation and regulation estimation module is used to aggregate and jointly control distributed energy resources based on regulation constraint boundaries, and to obtain data on aggregation regulation capacity and output uncertainty. The supply and demand coordination and control module is used to determine a coordinated control plan at multiple time scales based on aggregated adjustment capacity and output uncertainty data, and according to resource response speed. The global regulation and optimization module is used to construct a two-layer regulation and optimization model based on the coordinated regulation and control plan as the global constraint, the distribution network loss and the economic cost of multiple types of resources, and obtain the global optimal regulation result for each time period through two-layer linkage solution.

[0017] The beneficial effects of this application are: 1. By clarifying the regulation boundaries of each resource through the physical characteristics and response patterns of various resources, the regulation potential of different resources can be distinguished, avoiding regulation conflicts or waste of potential due to ambiguity of resource characteristics, and laying a unified data foundation for the overall planning of supply and demand side resources. 2. By aggregating regulation capabilities, dispersed resources are qualified to participate in the overall grid optimization. Combined with the screening of uncertain photovoltaic output scenarios, predictable operating scenarios are provided for overall optimization regulation, reducing the computational complexity of subsequent regulation and avoiding regulation inaccuracies caused by uncertainty and smoothing out output fluctuations when regulating a single resource. 3. By allocating scalable capabilities to different time scales based on differences in resource response speeds, combined with aggregation capabilities and uncertain scenarios, full-time coverage of resource regulation is achieved, avoiding regulation failures caused by mismatch between resource response characteristics and time scales, and ensuring real-time adaptability of regulation. 4. By taking the coordinated control plan under multiple time scales as a global constraint and combining iterative solution of the dual-level optimization objectives, we can ensure optimal coordination between the two sides and coordinate the adjustment and allocation of multiple resources within the supply and demand, avoiding the inability to balance adjustment costs due to excessive or insufficient scheduling of a single type of resource, and achieving the coordinated goal of global power grid security and economic low-carbon development. Attached Figure Description

[0018] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings. The drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0019] Figure 1 A flowchart illustrating the power grid regulation optimization method for supply and demand side resource aggregation response and coordination provided in this application embodiment.

[0020] Figure 2 This is a schematic diagram of a target output scenario and its output probability distribution provided in an embodiment of this application.

[0021] Figure 3 This is a schematic diagram of multi-timescale relationships in a collaborative control plan provided in an embodiment of this application.

[0022] Figure 4 A schematic diagram of a power grid regulation and optimization system module for supply and demand side resource aggregation response and coordination provided in an embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely one preferred embodiment of this application and are only used to explain this application. They do not limit the scope of protection of this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Example 1: As Figure 1 As shown, a power grid regulation optimization method based on supply and demand side resource aggregation response and coordination includes steps S1-S4, wherein: S1. Determine the adjustment constraint boundary of a single resource based on the physical characteristics and response laws of multi-form resources.

[0025] In an optional embodiment, determining the adjustment constraint boundary of a single resource based on the physical characteristics and response patterns of multi-morphological resources includes: The uncertainty of photovoltaic output is quantified based on the photovoltaic output model, and the first adjustment constraint boundary characterizing the fluctuation range of photovoltaic output is obtained. A second adjustment constraint boundary characterizing the energy storage charge and discharge efficiency and the energy storage power limit is obtained based on the charge and discharge characteristics of a single energy storage system. The third adjustment constraint boundary is obtained based on the demand-side load type and load response pattern, taking into account the demand response of industrial load, commercial load and electric vehicle load.

[0026] In some embodiments, determining the adjustment constraint boundary of a single resource based on the physical characteristics and response patterns of multi-form resources includes constructing a single-unit model resource based on the physical characteristics and response patterns of each resource in supply and demand, and obtaining relevant adjustment constraint conditions based on the single-unit resource model, including but not limited to charging and discharging efficiency, power output fluctuation within the charge constraint range, etc.

[0027] In some examples, a photovoltaic (PV) output model can be constructed based on the Beta distribution function. The uncertainty in distributed energy output mainly stems from PV output, which is primarily determined by solar irradiance. The distribution pattern of irradiance over a certain time interval can be described by the Beta distribution, and the relationship between PV output and irradiance can be expressed as: (1); in, and These represent the photovoltaic output power and rated power, respectively. This indicates the rated light intensity.

[0028] In some examples, the energy storage load interacts with the grid through charging and discharging. The key parameter for charging and discharging the energy storage system is also the state of charge. Therefore, the charging and discharging power of the energy storage system is defined as positive for charging and negative for discharging. Assuming that the charging efficiency and discharging efficiency of the energy storage system remain constant during operation, a single energy storage unit model is constructed as follows: (2); in, Let be the charge / discharge efficiency coefficient of the energy storage system at time t. The charging and discharging power (kW) of the energy storage system. This represents the maximum discharge power (kW) of the energy storage system. The maximum charging power (kW) of the energy storage system. The rated capacity (kWh) of the energy storage system; The charge / discharge efficiency coefficient of the energy storage system at time t It is expressed as follows: (3); in, To improve the charging efficiency of energy storage systems. For the discharge efficiency of the energy storage system This represents the operating status of the energy storage system (charging status is 1, discharging status is -1, and no charging or discharging status is 0).

[0029] In an optional embodiment, the acquisition of the third adjustment constraint boundary, which takes into account industrial load, commercial load, and electric vehicle load demand response based on demand-side load type and load response patterns, includes: Based on the industrial load type, establish piecewise functions of load transfer and electricity price difference, load reduction rate and electricity price to determine industrial load regulation constraints that include the regulation capacity and response cost of individual industrial loads; Based on the equivalent thermal resistance and thermal capacity-based temperature response characteristics of air conditioning operation, the controllable energy state margin is calculated to obtain the commercial load regulation constraints that characterize the air conditioning load regulation potential. A charging load model is constructed by obtaining the normal distribution of charging time and the log-normal distribution of daily mileage of electric vehicles. Based on the charging load model, the charge state boundary and controllable energy state margin of electric vehicles are determined, and the charging and discharging constraints of electric vehicle clusters are obtained. The industrial load regulation constraint, the commercial load regulation constraint, and the electric vehicle cluster charging and discharging constraint form the third regulation constraint boundary.

[0030] In some embodiments, individual resource modeling can be performed for industrial load, commercial load, and electric vehicle load to obtain the corresponding load regulation constraints.

[0031] Specifically, based on the industrial load response characteristics, peak-shaving loads are divided into shiftable loads and transferable loads. Transferable loads are characterized by a constant total electricity consumption, adjustable power output within a certain range, and flexible usage time. This means electricity consumption can be shifted to periods with higher renewable energy output to improve renewable energy absorption; or peak-load periods can be shifted to off-peak periods to achieve peak shaving and valley filling. Time-of-use pricing is generally used as an incentive, informing users of pricing information one day in advance to encourage them to adjust their usage time and volume. Reduceable loads are characterized by partially or completely reduced power output, resulting in a decrease in total electricity consumption. Reduceable loads have a fast response time and can be controlled using price incentives: users are informed of the time-of-use price one day in advance and voluntarily reduce their demand during periods with higher prices. For reduceable loads, the current electricity price only affects the increase or decrease in electricity consumption at that time, and the load reduction rate characterizes the user's responsiveness to the current electricity price. In this embodiment, based on time-of-use pricing, the user's response to the price incentive level is divided into a dead zone, a linear zone, and a saturation zone. A relationship function between load transfer and price difference is established to obtain the load transfer rate, which characterizes the response of users whose load can be transferred by the price difference. A relationship function between load reduction rate and price is also established to obtain the load reduction rate. The load transfer rate is expressed as follows: (4); in, This is the coefficient of load transfer rate within the linear region as a function of peak-valley electricity price difference. This is the upper limit of the load transfer rate. For the electricity price difference, The price difference between the dead zone and the inflection point of electricity prices. The price difference at the inflection point of the linear region. This refers to the price difference at the inflection point of the saturation zone. The load reduction rate is expressed as follows: (5); in, Let be the slope of the load reduction rate as a function of electricity price within the linear zone, c be the inflection point electricity price in the dead zone, and e be the inflection point electricity price in the saturation zone. This is the upper limit for electricity prices. The upper limit of the reduction rate, This represents the electricity price for the current period. The functional expressions for load transfer rate and load reduction rate clearly define the industrial load regulation constraints, which include the individual industrial load regulation capacity and response cost.

[0032] Specifically, the commercial load is mainly composed of air conditioning load, and the air conditioning load model is used as the commercial load model. The controllable energy state margin of the air conditioning load is calculated using the model, and the controllable energy state margin is expressed as follows: (6); in, Set the temperature for the air conditioner; This refers to the threshold temperature dead zone of a fixed-frequency air conditioner. Indoor temperature; The value ranges from -1 to 1. When the indoor temperature equals the desired set temperature, the corresponding value is... The value is 0; when the indoor temperature approaches the desired set temperature, A value approaching 0 indicates a larger adjustable margin for virtual energy storage, leading to higher user satisfaction; the closer the indoor temperature is to the upper or lower temperature limits, the better. The absolute value of the value tends to 1, and the smaller the adjustable margin, the lower the user satisfaction.

[0033] Specifically, key factors in charging load modeling include the initial charging time, charging power, and charging duration. A charging load model is established based on daily mileage and charging behavior distribution. The charging power is obtained from the model, and the change in the state of charge (SOC) of the electric vehicle is determined using this power. This leads to the determination of the controllable energy state margin of the electric vehicle, which reflects the SOC's capacity boundary, ultimately yielding the charging and discharging constraints for the electric vehicle cluster. The charging load model is represented as follows: (7); (8); in, Let be the charging power of electric vehicle i at time t. The average power of electric vehicles, For the charging efficiency of electric vehicles. For the rated capacity of electric vehicles, Rated charging power, This refers to the charging / discharging time. This represents the charging status of an electric vehicle. It is 1 when charging and 0 when not charging. When the rated charging curve of the electric vehicle intersects with the upper boundary, it changes from 1 to 0. When the rated charging curve intersects with the lower boundary, it changes from 0 to 1. The controllable energy state margin of an electric vehicle is used in this embodiment to describe the charging process. The controllable energy state margin of an electric vehicle is expressed as follows: (9); in, The charge capacity for charging electric vehicles at average power. The charge capacity for electric vehicles charged using square wave charging. The size of the power dead zone is used to limit the error between the power output when disconnected from the grid and the user's target power. From the expression for the controllable energy state margin of an electric vehicle, it can be seen that... The value ranges from -1 to 1. When it approaches 0, it means that the adjustable margin is larger, the electric vehicle deviates less from the charge trajectory, and the user satisfaction is higher. When it approaches -1 or 1, it means that the electric vehicle's charge capacity dead zone is at its upper and lower limits.

[0034] S2. Based on the adjustment constraint boundary, perform distributed energy aggregation and joint group control to obtain data on aggregation adjustment capacity and output uncertainty; In an optional embodiment, step S2 includes: Based on the first regulation constraint boundary and the second regulation constraint boundary, the first regulation capability of the distributed energy cluster including photovoltaic and energy storage is calculated. Simultaneously, the target output scenario is determined by Monte Carlo simulation based on the first adjustment constraint boundary, and the output probability distribution under each scenario is obtained; The second adjustment capability of the demand-side load cluster is calculated based on the third adjustment constraint boundary. Integrate the first and second adjustment capabilities to obtain the aggregated adjustment capabilities of the entire resource cluster.

[0035] In an optional embodiment, calculating the first regulation capability of the distributed energy cluster including photovoltaics and energy storage based on the first regulation constraint boundary and the second regulation constraint boundary includes: Based on the superposition of individual photovoltaic power output models, the total power output probability distribution of the photovoltaic cluster under the first adjustment constraint boundary is obtained; Based on the energy storage unit model, an adjustable power function of the energy storage cluster is constructed through coordinated control of charging and discharging states. The second adjustment constraint boundary is substituted into the function to obtain the adjustment potential of the energy storage cluster. A photovoltaic-energy storage energy management model is established with the goal of maximizing the fit between the electricity consumption curve and the photovoltaic output curve. The model is solved to obtain the first regulation capability that satisfies the probability distribution of total output and the regulation potential of the energy storage cluster.

[0036] In some examples, the adjustable power function of an energy storage load cluster is expressed as: (10); in, To regulate the power (kW) of the preceding energy storage load group. The adjusted power of the energy storage load group (kW) is given. By substituting the historical energy storage power boundary values ​​into this function, the adjustable power value of the energy storage cluster can be obtained, and this value can be used as the adjustment potential of the energy storage cluster.

[0037] In some embodiments, photovoltaic-energy storage energy management is primarily determined based on day-ahead forecast data of photovoltaic output. However, the actual photovoltaic output and the charging and discharging of energy storage cannot be fully determined. Insufficient or excessive photovoltaic output can disrupt the power balance and electricity supply-demand balance during system operation. Therefore, the energy regulation and management objective is to maximize the fit between the actual electricity consumption curve and the planned electricity consumption curve. A photovoltaic-energy storage energy management model is constructed by comprehensively considering the power balance constraints between the output power of the photovoltaic array and the charging and discharging power of the energy storage battery within the energy regulation cycle.

[0038] Based on the photovoltaic-energy storage energy management model, the power values ​​of the photovoltaic array and the energy storage battery under power constraints are obtained. If the output power of the photovoltaic array and the charging and discharging power of the energy storage battery satisfy the probability distribution of the total output of the photovoltaic and the regulation potential of the energy storage cluster, respectively, the first regulation capability of the photovoltaic-energy storage joint cluster is determined based on the probability distribution of the total output of the photovoltaic and the regulation potential of the energy storage cluster.

[0039] The primary regulation capability includes at least the fluctuation range of photovoltaic output and the adjustable power range of the energy storage cluster (including the direction of charging and discharging).

[0040] In an optional embodiment, the step of determining the target output scenario through Monte Carlo simulation based on the first adjustment constraint boundary and obtaining the output probability distribution under each scenario includes: Based on the photovoltaic power output model, a photovoltaic power output sample was generated by Monte Carlo simulation, and the power output correlation between photovoltaic cells in different regions was corrected by the Frank-Copula function to obtain the power output sample set of the photovoltaic cluster. Using peak output, fluctuation amplitude, and peak period as clustering features, a clustering algorithm is used to cluster the output sample set to obtain target output scenarios. Each target output scenario includes an output time series curve, output probability, and fluctuation amplitude.

[0041] In some embodiments, the probability density function of photovoltaic power output for each time period is established using the nonparametric kernel density estimation method, and the joint probability distribution function of the two is established based on the Frank-Copula function. The sampled photovoltaic power output for each time period is obtained by sampling the probability distribution function of wind and solar power output using the Monte Carlo method and performing an inverse transformation on the probability distribution function of wind and solar power output.

[0042] Furthermore, the Monte Carlo method is used to sample the joint distribution function for each time period. Considering the large sampling scale, in order to balance the calculation speed and accuracy, the synchronous back-substitution elimination method is used to reduce the sampled scenarios to a specified threshold. The K-Means method is used to filter the samples to obtain the target output scenarios and calculate the probability of each scenario.

[0043] In some examples, such as Figure 2 As shown, annual photovoltaic (PV) output data for a specific region is selected, standardized, and then used to generate PV uncertainty and correlation scenarios using the aforementioned target output scenario method, with a scale of 20,000. Ultimately, four typical wind and solar power scenarios and the probability of each scenario occurring are obtained.

[0044] In an optional embodiment, calculating the second adjustment capability of the demand-side load cluster based on the third adjustment constraint boundary includes: The overall regulation capacity of the industrial load cluster is obtained by integrating the load transfer rate and load reduction rate of each individual industrial unit. By integrating the controllable energy state margin of each individual air conditioner, the total regulation capacity of the commercial air conditioning cluster can be obtained. The total charging power of the electric vehicle cluster is calculated based on the charging load model, and the allowable reverse discharge capacity is statistically analyzed. Thus, the total regulation capacity of the electric vehicle cluster is obtained based on the total charging power and the allowable reverse discharge capacity. Based on the constraints of distribution network line capacity and transformer quota, the upper limit of the regulation capacity of various load clusters is modified to obtain the second regulation capacity that characterizes the regulation capacity boundary, unit regulation cost and response speed of each load cluster on the demand side.

[0045] In some embodiments, the total regulation capacity of the industrial load cluster is obtained based on the above-mentioned load transfer rate and load reduction rate, and the total regulation capacity is expressed as follows: (11); Where M represents the total number of industrial users. Let m be the load transfer rate of the m-th industrial user. Let m be the load reduction rate of the m-th industrial user. Let m be the base load power (kW) of the m-th industrial user.

[0046] In some embodiments, the controllable energy state margin of commercial loads is aggregated to obtain the total regulation capacity of the commercial load cluster, which is expressed as follows: (12); Where R is the equivalent thermal resistance, C is the equivalent heat capacity, and K is the total number of commercial air conditioners. For air conditioner energy efficiency ratio, This represents the upper limit of acceptable temperature variation for users. To adjust the duration (i.e., adjust the duration constraint).

[0047] In some embodiments, the charging power of electric vehicles is aggregated to obtain the total charging power of the electric vehicle cluster, and the charging and discharging directions are determined based on the state of charge change to obtain the total regulation capability of the electric vehicle cluster. The total regulation capability is expressed as follows: Discharge potential: (13); Charging potential: (14); in, and These represent the minimum and maximum boundaries of the electric vehicle's charge change state, respectively. The charging and discharging duration of electric vehicles can also be used as a constraint for adjusting time periods.

[0048] S3. Based on aggregated regulation capacity and output uncertainty data, determine a coordinated regulation plan under multiple time scales according to resource response speed.

[0049] In an optional embodiment, step S3 includes: During the daytime adjustment phase, with the goal of minimizing the overall adjustment cost across all scenarios, the expected load for each time period is calculated based on the target output scenario. Adjustment tasks for each aggregated resource are then allocated based on the expected load for each time period. The adjustment tasks include allocating adjustment amounts for distributed gas turbine start-up and shutdown combinations, shifting periods for shiftable loads, price-based shiftable loads, and loads that can be reduced. During the hourly adjustment phase, the daily adjustment task and the real-time load deviation are compared based on the real-time output scenario, and the resource types on both the supply and demand sides are adjusted according to the real-time load deviation. During the minute-level adjustment phase, the energy storage cluster discharges and the electric vehicle cluster reverses discharge based on the output fluctuation status under the current output scenario. During the near-second-level adjustment phase, emergency adjustments to interruptible loads are made based on the output fluctuation amplitude under the current output scenario.

[0050] In some examples, combined Figure 3 As shown, Figure 3 This diagram illustrates the relationship between day-ahead, hourly, and minute-level adjustment scales. During the day-ahead adjustment phase, the following are determined: distributed gas turbine start-up / shutdown combinations, the shifting period for transferable loads, and the adjustment amounts for price-based transferable and load-reducible loads. In this phase, all flexible loads participate in day-ahead adjustment. Due to the slow response speed and significant uncertainty of price-based flexible loads, price-based transferable and load-reducible loads only participate in day-ahead adjustment. The dispatch center informs users of peak-valley electricity prices one day in advance so that users can plan their electricity consumption for the following day. For the hourly adjustment phase: Besides the flexible load resources determined day-ahead, all other flexible loads can participate in hourly adjustment. Day-ahead load-reducible loads no longer participate in real-time adjustment. For the minute-level adjustment phase: Real-time adjustment can mitigate fluctuations in renewable energy and the uncertainty of the output of the aforementioned flexible loads, maintaining the safe and reliable operation of the system.

[0051] Understandably, the participation of flexible loads in grid optimization and regulation is an effective means to reduce peak-valley differences, improve photovoltaic (PV) absorption, and enhance the economic efficiency of system operation. The intermittency and volatility of renewable energy bring significant uncertainty to system operation. Furthermore, the accuracy of PV and load forecasts changes with time scales; therefore, day-ahead regulation plans using a single time scale are insufficient to adapt to shorter-term operational conditions. Flexible loads, due to their varying response speeds and capacities, possess the potential to participate in multi-time-scale regulation. Moreover, the system needs sufficient reserve capacity to mitigate fluctuations outside of short-term regulation plans. Therefore, it is necessary to establish multi-time-scale regulation models to coordinate and optimize flexible loads, thereby enabling resources at different time scales to participate in power system regulation.

[0052] S4. Using the coordinated control plan as the global constraint, a two-layer regulation optimization model is constructed based on the distribution network loss and the economic cost of multiple forms of resources. The optimal global regulation result for each time period is obtained through two-layer linkage solution.

[0053] In an optional embodiment, step S4 includes: With the goal of minimizing the total network loss of the distribution network, an upper-level optimization function is established based on the network loss of the distribution network system and the interaction power between each microgrid and the distribution network as variables. With the goal of minimizing the total operating cost of the microgrid, the variables to be optimized are determined based on industrial load, commercial load, electric vehicles, photovoltaics, and energy storage, and a lower-level optimization function is established. Using the aforementioned coordinated control plan as a global constraint, and combining it with the upper-level power constraint, the upper-level optimization function is solved to obtain the upper-level control result; The interaction power in the upper-level adjustment result is used as the lower-level power constraint of the lower-level optimization function. The lower-level optimization function is solved according to the lower-level power constraint and the global constraint to obtain the lower-level adjustment result. The adjustment cost in the lower-level adjustment result is used to perform feedback optimization on the upper-level adjustment result, so as to solve the global optimal adjustment result through two-level linkage iteration.

[0054] Using the aforementioned coordinated control plan as the global control constraint, and combining the global optimal control results, the optimal control strategy for each time period is obtained.

[0055] In some embodiments, the upper-level optimization function is represented as follows: (15); (16); (17); (18); in, This represents the total network loss of the upper-level distribution network. For distribution network line losses, Transformer losses between the distribution network and the upstream power grid. Let v be the interaction power between the v-th microgrid and the distribution network. This represents the difference between the per-unit voltage value at each node in the distribution network and the standard value of the reference voltage. and These are the weight coefficients for each sub-objective. , They are respectively , Optimal time , The current value.

[0056] In some embodiments, the lower-level optimization function is represented as follows: (19); in, The total operating cost of microgrid v. These are the costs of reducing industrial and commercial loads, interruption costs, electric vehicle regulation costs, energy storage costs, micro gas turbine costs, and network loss costs in the microgrid v. The penalty cost when the planned adjustment amount of the adjustable load differs from the actual adjustment amount.

[0057] In other embodiments, the lower-level optimization objective may also consider environmental evaluation indicators, that is, constructing an objective function with the goal of minimizing carbon emissions in micro gas turbines and industrial loads.

[0058] Furthermore, upper-level power constraints include power balance constraints in the distribution network system, power transmission constraints (power transmission in the upper-level distribution network includes power transmission between microgrids and distribution networks as well as power transmission between distribution networks and the upper-level power grid, where the power transmission constraints between distribution networks and the upper-level power grid are affected by the size of transformer capacity), node voltage constraints, and transformer constraints.

[0059] Furthermore, the optimization results of the upper-level distribution network are used as the first lower-level power constraint, with industrial load, commercial load, electric vehicles, and micro gas turbines as the variables to be optimized, to carry out lower-level economic optimization and regulation. In the lower-level optimization, there are also second lower-level constraints on the variables to be optimized, including: ramping constraints and power generation constraints of micro gas turbines, power transmission constraints between microgrids, and dispatching constraints of dispatchable flexible loads (industrial load, commercial load, electric vehicles) (i.e., the second regulation capability of the aforementioned demand-side load clusters).

[0060] In this embodiment, the objectives on the grid side and the resource side are mutually constrained. If the upper-level optimization objective and the lower-level optimization objective are solved independently, problems may arise such as minimizing grid losses but causing a surge in resource-side costs, or minimizing costs but exceeding grid voltage limits. Therefore, a global optimal solution is obtained through iterative linkage between the upper and lower levels to ensure a balance between the two objectives. Specifically, the optimal results of grid loss and voltage deviation output from the upper level serve as constraints for the lower level, limiting the power boundary of resource regulation. The cost-optimal results output from the lower level are fed back to correct the upper-level boundary, avoiding excessive use of high-cost resources, achieving global optimization, and balancing safety and economy.

[0061] In this embodiment, the multi-timescale coordinated control plan provides quantified, non-redundant constraint boundaries for subsequent two-level optimization through resource-timescale adaptation. It clarifies the available resource types and regulation capacity limits for each time period; for example, only movable loads are called up at the daytime level, and only energy storage / electric vehicles are called up at the minute level. This allows the two-level optimization to focus directly on the adapted resources without traversing all resource combinations, reducing the dimensionality of decision variables. For instance, in the upper-level optimization, network loss and voltage deviation can be calculated directly based on time-segmented constraints derived from aggregated regulation capabilities, ensuring that the lower-level optimization avoids exceeding the actual regulation capacity of resources and ensuring the effectiveness and reliability of the final optimization results. Furthermore, global constraints resolve conflicts in resource calls at different timescales in advance, such as preventing excessive resource calls at the hourly level from leading to insufficient margin for smoothing fluctuations at the minute level. This reduces solution complexity, decreases the number of constraint checks during the two-level optimization iteration process, and improves solution speed.

[0062] Based on the same inventive concept, this application also provides a power grid regulation optimization system corresponding to the power grid regulation optimization method of supply and demand side resource aggregation response and coordination, such as... Figure 4 As shown, the system includes: The initial adjustment constraint module is used to determine the adjustment constraint boundary of a single resource based on the physical characteristics and response laws of multi-form resources; The aggregation and regulation estimation module is used to aggregate and jointly control distributed energy resources based on regulation constraint boundaries, and to obtain data on aggregation regulation capacity and output uncertainty. The supply and demand coordination and control module is used to determine a coordinated control plan at multiple time scales based on aggregated adjustment capacity and output uncertainty data, and according to the resource response speed. The global regulation and optimization module is used to construct a two-layer regulation and optimization model based on the coordinated regulation and control plan as the global constraint, the distribution network loss and the economic cost of multiple types of resources, and obtain the global optimal regulation result for each time period through two-layer linkage solution.

[0063] In this embodiment, different resources have inconsistent characteristics, making direct aggregation and coordination impossible. By determining the adjustment constraint boundaries of each resource, the adjustment boundaries and potential of each multi-form resource are clarified. Then, through aggregation and joint group control, a planned adjustment capability is formed, quantifying the adjustment potential of multi-form resources. This transforms dispersed resources into usable cluster capabilities, ensuring that the power grid adjustment can effectively cope with output fluctuations in various output scenarios, avoiding fragmentation of single-resource adjustment, and ensuring the accuracy and reliability of adjustment capabilities. Since different resources have different response speeds, adjustment conflicts may occur during adjustment. By aggregating adjustment capabilities and uncertainty data, a coordinated control plan under multiple time scales is determined, adapting to the response characteristics of different resources and the adjustment needs of each time period. This avoids low power grid adjustment efficiency due to time scale mismatch and improves the robustness of the adjustment scheme. Furthermore, the coordinated control plan under multiple time scales is used as a global constraint of the two-level optimization model, providing an adjustment feasible domain for the two-level optimization objective. This ensures that adjustment tasks are accurately allocated to various resources. At the same time, it ensures effective coordination between the power grid side and the resource side, as well as effective coordination between the power grid and the resource side, while also taking into account the adjustment optimization within multi-form resources, thereby achieving global optimization and improving the economic efficiency of adjustment while ensuring the safe operation of the power grid.

[0064] The above-described embodiments are preferred embodiments of this application and are not intended to limit the specific scope of this application. The scope of this application includes but is not limited to the specific embodiments described above. All equivalent changes made in accordance with the shape, structure, and method of this application are within the protection scope of this application.

Claims

1. A power grid regulation optimization method based on supply and demand side resource aggregation response and coordination, characterized by: Includes the following steps: The adjustment constraint boundary of a single resource is determined based on the physical characteristics and response laws of multi-form resources. Based on the adjustment constraint boundary, distributed energy aggregation and joint group control are carried out to obtain data on aggregation adjustment capacity and output uncertainty. Based on aggregated regulation capacity and output uncertainty data, a coordinated regulation plan under multiple time scales is determined according to the resource response speed. Using the coordinated control plan as a global constraint, a two-layer regulation optimization model is constructed based on the distribution network loss and the economic cost of various resources. The optimal global regulation result for each time period is obtained through two-layer linkage solution.

2. The power grid regulation optimization method based on supply and demand side resource aggregation response and coordination according to claim 1, characterized in that: The determination of the adjustment constraint boundary of a single resource based on the physical characteristics and response laws of multi-form resources includes: The uncertainty of photovoltaic output is quantified based on the photovoltaic output model, and the first adjustment constraint boundary characterizing the fluctuation range of photovoltaic output is obtained. A second adjustment constraint boundary characterizing the energy storage charge and discharge efficiency and the energy storage power limit is obtained based on the charge and discharge characteristics of a single energy storage system. The third adjustment constraint boundary is obtained based on the demand-side load type and load response pattern, taking into account the demand response of industrial load, commercial load and electric vehicle load.

3. The power grid regulation optimization method based on supply and demand side resource aggregation response and coordination according to claim 2, characterized in that: The third adjustment constraint boundary, which takes into account industrial load, commercial load, and electric vehicle load demand response, is obtained based on demand-side load type and load response patterns. This includes: Based on the industrial load type, establish piecewise functions of load transfer and electricity price difference, load reduction rate and electricity price to determine industrial load regulation constraints that include the regulation capacity and response cost of individual industrial loads; Based on the equivalent thermal resistance and thermal capacity-based temperature response characteristics of air conditioning operation, the controllable energy state margin is calculated to obtain the commercial load regulation constraints that characterize the air conditioning load regulation potential. A charging load model is constructed by obtaining the normal distribution of charging time and the log-normal distribution of daily mileage of electric vehicles. Based on the charging load model, the charge state boundary and controllable energy state margin of electric vehicles are determined, and the charging and discharging constraints of electric vehicle clusters are obtained. The industrial load regulation constraint, the commercial load regulation constraint, and the electric vehicle cluster charging and discharging constraint form the third regulation constraint boundary.

4. The power grid regulation optimization method based on supply and demand side resource aggregation response and coordination according to claim 2, characterized in that: The process of aggregating and jointly controlling distributed energy resources based on adjustment constraint boundaries, and obtaining data on aggregated adjustment capacity and output uncertainty, includes: Based on the first regulation constraint boundary and the second regulation constraint boundary, the first regulation capability of the distributed energy cluster including photovoltaic and energy storage is calculated. Simultaneously, the target output scenario is determined by Monte Carlo simulation based on the first adjustment constraint boundary, and the output probability distribution under each scenario is obtained; The second adjustment capability of the demand-side load cluster is calculated based on the third adjustment constraint boundary. Integrate the first and second adjustment capabilities to obtain the aggregated adjustment capabilities of the entire resource cluster.

5. The power grid regulation optimization method based on supply and demand side resource aggregation response and coordination according to claim 4, characterized in that: The calculation of the first regulation capability of the distributed energy cluster, which includes photovoltaics and energy storage, based on the first and second regulation constraint boundaries includes: Based on the superposition of individual photovoltaic power output models, the total power output probability distribution of the photovoltaic cluster under the first adjustment constraint boundary is obtained; Based on the energy storage unit model, an adjustable power function of the energy storage cluster is constructed through coordinated control of charging and discharging states. The second adjustment constraint boundary is substituted into the function to obtain the adjustment potential of the energy storage cluster. A photovoltaic-energy storage energy management model is established with the goal of maximizing the fit between the electricity consumption curve and the photovoltaic output curve. The model is solved to obtain the first regulation capability that satisfies the probability distribution of total output and the regulation potential of the energy storage cluster.

6. The power grid regulation optimization method based on supply and demand side resource aggregation response and coordination according to claim 4, characterized in that: The step of determining the target output scenario through Monte Carlo simulation based on the first adjustment constraint boundary and obtaining the output probability distribution under each scenario includes: Based on the photovoltaic power output model, a photovoltaic power output sample was generated by Monte Carlo simulation, and the power output correlation between photovoltaic cells in different regions was corrected by the Frank-Copula function to obtain the power output sample set of the photovoltaic cluster. Using peak output, fluctuation amplitude, and peak period as clustering features, a clustering algorithm is used to cluster the output sample set to obtain target output scenarios. Each target output scenario includes an output time series curve, output probability, and fluctuation amplitude.

7. The power grid regulation optimization method based on supply and demand side resource aggregation response and coordination according to claim 4, characterized in that: The calculation of the second adjustment capability of the demand-side load cluster based on the third adjustment constraint boundary includes: The overall regulation capacity of the industrial load cluster is obtained by integrating the load transfer rate and load reduction rate of each individual industrial unit. By integrating the controllable energy state margin of each individual air conditioner, the total regulation capacity of the commercial air conditioning cluster can be obtained. The total charging power of the electric vehicle cluster is calculated based on the charging load model, and the allowable reverse discharge capacity is statistically analyzed. Thus, the total regulation capacity of the electric vehicle cluster is obtained based on the total charging power and the allowable reverse discharge capacity. Based on the constraints of distribution network line capacity and transformer quota, the upper limit of the regulation capacity of various load clusters is modified to obtain the second regulation capacity that characterizes the regulation capacity boundary, unit regulation cost and response speed of each load cluster on the demand side.

8. The power grid regulation optimization method based on supply and demand side resource aggregation response and coordination according to claim 4, characterized in that: The method of determining a coordinated control plan across multiple time scales based on aggregated adjustment capacity and output uncertainty data, according to resource response speed, includes: During the daytime adjustment phase, with the goal of minimizing the overall adjustment cost across all scenarios, the expected load for each time period is calculated based on the target output scenario, and adjustment tasks for each aggregated resource are allocated according to the expected load for each time period. During the hourly adjustment phase, the daily adjustment task and the real-time load deviation are compared based on the real-time output scenario, and the resource types on both the supply and demand sides are adjusted according to the real-time load deviation. During the minute-level adjustment phase, the energy storage cluster discharges and the electric vehicle cluster reverses discharge based on the output fluctuation status under the current output scenario. During the near-second-level adjustment phase, emergency adjustments to interruptible loads are made based on the output fluctuation amplitude under the current output scenario.

9. The power grid regulation optimization method based on supply and demand side resource aggregation response and coordination according to claim 8, characterized in that: The aforementioned approach uses a coordinated control plan as a global constraint, constructs a two-layer regulation optimization model based on distribution network losses and the economic costs of various resource forms, and obtains the globally optimal regulation results for each time period through two-layer linkage solution, including: With the goal of minimizing the total network loss of the distribution network, an upper-level optimization function is established based on the network loss of the distribution network system and the interaction power between each microgrid and the distribution network as variables. With the goal of minimizing the total operating cost of the microgrid, the variables to be optimized are determined based on industrial load, commercial load, electric vehicles, photovoltaics, and energy storage, and a lower-level optimization function is established. Using the aforementioned coordinated control plan as a global constraint, and combining it with the upper-level power constraint, the upper-level optimization function is solved to obtain the upper-level control result; The interaction power in the upper-level adjustment result is used as the lower-level power constraint of the lower-level optimization function. The lower-level optimization function is solved according to the lower-level power constraint and the global constraint to obtain the lower-level adjustment result. The adjustment cost in the lower-level adjustment result is used to perform feedback optimization on the upper-level adjustment result, so as to solve the global optimal adjustment result through two-level linkage iteration.

10. A power grid regulation optimization system based on supply-demand side resource aggregation response and coordination, applicable to the power grid regulation optimization method based on supply-demand side resource aggregation response and coordination as described in any one of claims 1-9, characterized in that: include: The initial adjustment constraint module is used to determine the adjustment constraint boundary of a single resource based on the physical characteristics and response laws of multi-form resources; The aggregation and regulation estimation module is used to aggregate and jointly control distributed energy resources based on regulation constraint boundaries, and to obtain data on aggregation regulation capacity and output uncertainty. The supply and demand coordination and control module is used to determine a coordinated control plan at multiple time scales based on aggregated adjustment capacity and output uncertainty data, and according to the resource response speed. The global regulation and optimization module is used to construct a two-layer regulation and optimization model based on the coordinated regulation and control plan as the global constraint, the distribution network loss and the economic cost of multiple types of resources, and obtain the global optimal regulation result for each time period through two-layer linkage solution.

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