A collaborative interactive intelligent control method for source, grid, load and storage resources

Abstract

The application discloses a kind of source network load storage resource collaborative interaction intelligent regulation methods, it is related to resource regulation technical field, comprising the following steps:Sa1, the real-time fluctuation of power grid system in power grid load and power grid power flow distribution are obtained, based on power grid load real-time fluctuation and new energy power generation power prediction data, the supply-demand imbalance characteristic quantity of power supply and load is generated;Sa2, according to the supply-demand imbalance characteristic quantity, a multi-objective optimization model is constructed, including energy storage system and adjustable load participation;Sa3, by solving the multi-objective optimization model, output control instruction set;Sa4, according to the control instruction set to source network load storage resource is coordinated control to realize dynamic balance.The application can solve how to regulate new energy power generation power according to power grid load real-time fluctuation to solve the problem that power supply output and load demand do not match, so as to guarantee power supply stability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of resource regulation, and particularly relates to a source network load storage resource cooperative interaction intelligent regulation method. BACKGROUND

[0002] The cooperative regulation of source network load storage resources is to integrate the multi-dimensional resources of power sources, power grids, loads and energy storage systems, utilize advanced information communication and intelligent control technologies, optimize energy distribution, and improve the operation efficiency and stability of the power system.

[0003] However, this method still faces the problem of how to effectively regulate the new energy power generation according to the real-time fluctuations of the power grid load, because new energy power generation has the characteristics of intermittency and uncertainty, and there is often a mismatch between power output and load demand in the power system. If the power generation cannot be accurately adjusted to adapt to the load changes, it may lead to unstable power supply or resource waste, which puts higher requirements on the balance and reliability of the power system. The regulation of new energy power generation according to the real-time fluctuations of the power grid load is to solve the mismatch between power output and load demand, and due to the change of power flow, local area voltage may exceed the limit or be unstable. If the voltage support cannot be accurately monitored and reasonably distributed, it will affect the normal operation state of the distribution network and the power supply quality. SUMMARY

[0004] The purpose of the present application is to provide a source network load storage resource cooperative interaction intelligent regulation method, which solves the problem of mismatch between power output and load demand, and regulates the voltage level of the distribution network according to the power flow distribution to solve the problem of local voltage exceeding the limit and instability.

[0005] The present application solves the above technical problems by the following technical solutions, which comprises the following steps:

[0006] Sa1, obtaining the real-time fluctuations of the power grid load and the power flow distribution in the power grid system, and generating the supply-demand imbalance characteristic quantity of the power source and the load based on the real-time fluctuations of the power grid load and the new energy power generation prediction data;

[0007] Sa2, constructing a multi-objective optimization model containing an energy storage system and an adjustable load according to the supply-demand imbalance characteristic quantity;

[0008] Sa3, outputting a regulation instruction set by solving the multi-objective optimization model;

[0009] Sa4, coordinating and controlling the source network load storage resources according to the regulation instruction set to realize dynamic balance.

[0010] Preferably, the generation of the supply-demand imbalance characteristic quantity of the power source and the load based on the real-time fluctuations of the power grid includes the following steps:

[0011] Obtain real-time fluctuation value P_load of power grid load and predicted value P_gen of new energy power generation;

[0012] Calculate initial power difference value ΔP_init = |P_load-P_gen| as the basic imbalance;

[0013] Adjust the feature size ΔP_final = α * ΔP_init according to the following formula, where α is the system adjustment coefficient (0 < α ≤ 1), indicating the imbalance feature adjustment strength of supply and demand;

[0014] If ΔP_final exceeds the preset maximum imbalance threshold P_threshold, limit ΔP_final =P_threshold.

[0015] Preferably, the adjustment method of the supply and demand imbalance feature is further optimized:

[0016] Collect historical load data and calculate the average load level P_avg;

[0017] Calculate the change degree of the current load state relative to the historical average level by the deviation ratio λ = |P_load-P_avg| / P_avg;

[0018] Update the system adjustment coefficient α = α * (1 + γ * λ) according to the following formula, where γ is the proportional gain factor;

[0019] Ensure that the new system adjustment coefficient α is within the range [α_min, α_max].

[0020] Preferably, the logic of dynamically evaluating the supply and demand balance feature is added:

[0021] Define the short-time sliding average time window length T_window, and calculate the average fluctuation amplitude A within the window;

[0022] Determine the recent fluctuation intensity R by the formula R = (1 / N) * Σ(|P_load_i-P_avg_i|), i ∈ {1,..., N}, N is the sampling number;

[0023] According to the current supply and demand state, if ΔP_final / R ≥ δ, it means that the imbalance is aggravated, and the response level of regulation and control needs to be improved; δ is the sensitivity coefficient;

[0024] According to the response level setting, the corresponding safety redundancy parameter is set, and the final imbalance feature quantity is regenerated.

[0025] Preferably, the state constraint of the energy storage device is considered in the characteristic variable adjustment:

[0026] The remaining capacity E_remain and the maximum supportable output / charge amount E_lim of the energy storage system are detected;

[0027] When E_remain / E_lim ≤ β, β is a safe operation ratio, 0.2 ≤ β ≤ 1, the weight of the characteristic variable is reduced to limit the control strength from being too large;

[0028] A correction factor θ is introduced to calculate the adjusted ΔP_final = θ * (E_remain / E_lim) * ΔP_init; θ represents the energy storage impact proportion coefficient;

[0029] The adjustment result ΔP_final is used in the subsequent control algorithm design to ensure stable operation of the energy storage.

[0030] Preferably, the power grid flow distribution is analyzed, including the following steps:

[0031] Sb1, determining the voltage over-limit and unstable area in the distribution network based on the analysis result of the power grid flow distribution,

[0032] Sb2, constructing an optimization control model of the source-grid-load-storage resources to generate a preliminary control strategy;

[0033] Sb3, checking and adjusting the preliminary control strategy to form a final control instruction;

[0034] Sb4, coordinating and scheduling the source-grid-load-storage resources according to the final control instruction to improve the voltage level of the distribution network.

[0035] Compared with the prior art, the beneficial effects of the present application are:

[0036] 1. The present application can solve the problem of how to control the new energy power generation according to the real-time fluctuation of the power grid load to solve the mismatch between power supply output and load demand, so as to ensure stable power supply.

[0037] 2. The present application can solve the problem of how to control the voltage level of the distribution network according to the power grid flow distribution to solve the problem of local voltage over-limit and instability, so as to ensure the normal operation of the distribution network and good power supply quality. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is the flow chart of the embodiment one of the present application;

[0039] Figure 2 is the flow chart of the embodiment two of the present application DETAILED DESCRIPTION

[0040] Embodiment one

[0041] The embodiment provides a technical solution: a source network load storage resource cooperative interaction intelligent regulation method, which regulates new energy power generation according to real-time fluctuations of power grid load to solve the mismatch between power output and load demand, and comprises the following steps:

[0042] Sa1, acquiring real-time fluctuations of power grid load and power grid power flow distribution in a power grid system, generating power supply and load supply and demand imbalance characteristic quantities based on real-time fluctuations of power grid load and new energy power generation prediction data;

[0043] Sa2, constructing a multi-objective optimization model containing an energy storage system and an adjustable load according to the supply and demand imbalance characteristic quantities;

[0044] Sa3, outputting a regulation instruction set by solving the multi-objective optimization model;

[0045] Sa4, coordinating and controlling source network load storage resources according to the regulation instruction set to realize dynamic balance.

[0046] The generation of power supply and load supply and demand imbalance characteristic quantities based on real-time fluctuations of power grid load comprises the following steps:

[0047] Acquiring a real-time fluctuation value P_load of power grid load and a new energy power generation prediction value P_gen;

[0048] Calculating an initial power difference value ΔP_init = |P_load-P_gen| as a basic imbalance value;

[0049] Adjusting the characteristic quantity size ΔP_final = α * ΔP_init according to the following formula, wherein α is a system adjustment coefficient (0 < α ≤ 1), and represents the supply and demand imbalance characteristic adjustment intensity;

[0050] If ΔP_final exceeds a preset maximum imbalance threshold P_threshold, then limit ΔP_final =P_threshold.

[0051] The adjustment mode of the supply and demand imbalance characteristic quantity is further optimized:

[0052] Collecting historical load data and calculating an average load level P_avg;

[0053] Calculating the change degree of the current load state relative to the historical average level by a deviation ratio λ = |P_load-P_avg| / P_avg;

[0054] The system adjustment coefficient a is updated according to the following formula: a = a * (1 + γ * λ), where γ is a proportional gain factor;

[0055] Ensure that the new system adjustment coefficient a is within the range [a_min, a_max].

[0056] The logic of dynamically evaluating the supply-demand balance feature is added:

[0057] Define the short-term moving average time window length T_window and calculate the average fluctuation amplitude A within the window;

[0058] Determine the recent fluctuation intensity R by the formula R = (1 / N) * Σ(|P_load_i-P_avg_i|), i ∈ {1,..., N}, where N is the number of samples;

[0059] According to the current supply-demand state, if ΔP_final / R ≥ δ, it means that the imbalance is aggravated and the control response level needs to be increased; δ is the sensitivity coefficient;

[0060] According to the response level setting, the corresponding safety redundancy parameters are set and the final imbalance feature quantity is regenerated.

[0061] Preferably, the state constraints of energy storage devices are considered in the feature quantity adjustment:

[0062] Detect the remaining capacity E_remain and the maximum supportable output / charge amount E_lim of the energy storage system;

[0063] When E_remain / E_lim ≤ β, β is a safety operation ratio, usually 0.2≤ β ≤ 1, reduce the feature variable weight to limit the control effort too large;

[0064] Introduce a correction factor θ to calculate the adjusted ΔP_final = θ * (E_remain / E_lim) * ΔP_init; θ represents the proportion coefficient of energy storage influence;

[0065] Use the adjustment result ΔP_final in the subsequent control algorithm design to ensure stable operation of the energy storage.

[0066] In this embodiment, first of all, the key steps it contains are clarified: one is to generate the supply-demand imbalance feature quantity of power supply and load; two is to build a multi-objective optimization model; three is to solve the optimization model to output the control instruction set; four is to coordinate the control of source, network, load and storage resources according to the control instruction set. This method realizes the accurate matching of power supply and load in dynamic scenarios by comprehensively analyzing various factors in the power system, such as real-time fluctuation of grid load, new energy power generation prediction data, etc.

[0067] Specifically, in the first step, the supply-demand imbalance characteristic quantity of power supply and load is generated based on real-time fluctuations in grid load and new energy power generation prediction data. This step obtains accurate information by collecting and organizing real-time grid operation data. Specifically, the current load data of the grid is collected in real time through advanced measurement instruments, monitoring platforms, and distributed sensor technology, and the future trend of new energy power generation is predicted based on weather forecast models or historical statistical data, such as the amount of electricity that a wind turbine is expected to generate in the next few hours or the power generation efficiency of a solar photovoltaic panel. Using mathematical statistical analysis, these input information is converted into variable representation for further modeling, and the deviation value between supply (actual and predicted output of power supply side) and demand (immediate demand level of user end) is formed as a key reference index, i.e. the supply-demand imbalance characteristic quantity.

[0068] Then, after obtaining the supply-demand imbalance characteristic quantity, the second step is entered. According to the characteristic quantity result, a multi-objective optimization model is established, which integrates energy storage systems and load elements with flexible adjustment capability. In order to ensure the overall efficiency of the system is optimal, the optimization model considers the goals of reducing energy waste, reducing operating costs, and maintaining power quality. Here, complex algorithms such as genetic algorithm or particle swarm optimization technology are used to achieve balance under different constraints. For example, energy storage systems can temporarily absorb excess electricity or release stored power to alleviate power supply shortages; adjustable load refers to specific devices that can temporarily reduce power consumption or delay work plans to help alleviate peak pressure. For example, there are many remotely dispatchable enterprise production devices in an industrial park, when the external power transmission network load exceeds the limit, some facilities that must be used at different times can be suspended by making corresponding measures in advance.

[0069] Then, the third step is entered, that is, the complex multi-objective optimization model is quickly solved by advanced mathematical operation tools or special software to obtain a set of final control schemes, which is called control instruction set. Each specific instruction corresponds to a list of action items to be performed at a certain time interval, such as when a pumped storage power station should pump water to convert electrical energy into potential energy for later use, or whether a number of intelligent air conditioning units in a certain area should be turned on simultaneously to enter energy-saving mode to reduce overall consumption to the optimal range value.

[0070] Finally, the entire resource joint system involving source-network node interconnection-terminal user load distribution plus various energy storage devices is actually operated to complete the dynamic balance maintenance task according to all the accurate instructions obtained above. This means that all participants act according to the preset rules and continuously monitor feedback correction to adapt to environmental changes to ensure the target is achieved.

[0071] For example, in one embodiment, imagine that one evening, a sudden drop in large-scale photovoltaic power generation leads to a power shortage in the regional distribution network. This process first identifies this widening supply-demand gap, quickly assesses its extent, and then invokes a pre-designed policy framework to organize response actions. For example, this involves discharging backup batteries to replenish the power shortage while also issuing recommended power restrictions to contracted customers. These coordinated actions promptly resolve potential issues, restore normalcy, and effectively support the robust and secure operation of the entire power grid.

[0072] Next, the method of generating a power supply and load supply and demand imbalance characteristic value based on real-time power grid load fluctuation according to the present invention includes the following steps:

[0073] First, let's list the individual steps. The first step is to obtain the real-time grid load fluctuation value P_load and the predicted renewable energy power generation value P_gen. The second step is to calculate the initial power difference ΔP_init = |P_load - P_gen| based on these values ​​and use this as the basic imbalance value. The third step is to adjust the characteristic value using the formula ΔP_final = α * ΔP_init, where the adjustment coefficient α satisfies 0 < α ≤ 1 and represents the intensity of the supply-demand imbalance characteristic adjustment. The last step is to determine if the final adjusted imbalance value ΔP_final exceeds the preset maximum imbalance threshold P_threshold. In this case, ΔP_final is forcibly limited to P_threshold.

[0074] The first step is to determine current load demand and power generation output using actual monitoring or simulation data. Grid load fluctuations reflect the dynamic power consumption of consumers within the power system over time. Meanwhile, power generation forecasts represent the projected output of renewable energy sources such as wind and photovoltaic power. These fluctuate significantly due to factors like weather, requiring real-time estimation to match demand fluctuations. Comparing these two provides first-hand data for assessing the power balance.

[0075] The second step directly compares the two parameters to obtain the difference, using absolute calculations to ensure the result is always positive. This initial power difference is used to quantify the unfilled power shortage or surplus within the system during a specific transient state. The key to this step is accurately capturing the imbalance. Therefore, it determines the prerequisites for the subsequent regulation logic.

[0076] The third step introduces a regulation coefficient a to further correct the preliminary imbalance value, in order to better adapt to the control requirements in specific scenarios. The parameter a ranges from close to zero (very slightly react to imbalance) to not more than one, and the default preferred value can be selected as 0.9. The intention of designing the formula in this way is to give different flexibility and response accuracy under different system conditions, and to avoid the situation of over-aggressive leading to ineffective regulation. Specifically, a low value of a focuses more on suppressing the risk of large-scale adjustment, while a higher value increases the sensitivity, so that small differences can be quickly fed back to the regulation unit for processing.

[0077] The fourth step aims to set a protection limit to ensure that the entire system does not operate beyond the safe load capacity range. When it is detected that the maximum imbalance threshold is exceeded, it means that an extreme situation may be triggered that requires manual intervention before being temporarily fixed at the limit state to prevent deterioration into irreversible damage. For example, during periods of strong wind but low load, if the normal allowable range is exceeded, the output must be forcibly constrained to ensure the stability of the overall structure. In one embodiment, the sudden increase in solar irradiance in a certain region during the day in a wind-solar-storage integrated scenario is specifically considered, which causes a local power surplus problem. At this time, if the initial imbalance is derived to be about twice the size of the grid carrying capacity through the previous steps, it will be suppressed to within the threshold level according to the rules to continue to observe the subsequent trend and decide whether to release excess energy into the energy storage pool or other consumption path.

[0078] Next, the further optimization of the imbalance characteristic quantity adjustment method of the present application is described: the steps include collecting historical load data, calculating the deviation ratio, updating the system regulation coefficient, and limiting the range of the system regulation coefficient.

[0079] In the first step, the average load level P_avg is obtained by collecting historical load data over a period of time. This is a representative value obtained by analyzing the power load values in a specific time interval in the past, which is used as a reference benchmark.

[0080] The second step involves quantifying the degree of change in the current load state based on the difference between the current load and the historical average level. The formula λ = |P_load-P_avg| / P_avg is used to calculate, where P_load represents the current actual load, and λ accurately reflects the degree of load change relative to the mean value. Typically, λ takes values from zero to a reasonable upper limit, which depends on the application scenario, and the optimal value will be determined based on actual operation experience to achieve stable results.

[0081] Next, in the third step, the original coefficients are dynamically adjusted using α = α * (1 + γ * λ). Here, α represents the initial control coefficient or previously set value in the system, while γ is the proportional gain factor used to adjust the degree of influence. This coefficient can be selected from a reasonable range based on the system's characteristics (for example, when λ fluctuates significantly, a relatively small value such as 0.05 can be selected to avoid excessive interference). The optimal setting is determined by experimental verification, for example, around 0.1.

[0082] Finally, after adjusting the coefficients, it is necessary to check whether the new α remains within a specified range [α_min, α_max] to avoid adverse consequences caused by being too high or too low. The boundary value setting depends on the allowable operating limits of the specific power network and equipment.

[0083] For example, in one embodiment, assume that historical data for a power grid region indicates a normal daily load level of 5 MW (P_avg = 5 MW). If this morning's load measurement suddenly increases to 8 MW, the above method can be used to quickly assess and respond. Specifically, the deviation calculation shows λ = |8 - 5| / 5 = 0.6. Then, using the set scaling factor γ = 0.1, the updated coefficient α becomes the original value multiplied by 1.06. This is then checked to see if it falls within the established range. This helps adapt to changing power conditions in real time and ensures safe and economical operation of the power system.

[0084] Next, the logic of the present invention that adds the dynamic evaluation supply and demand balance feature is described:

[0085] A short-term sliding average window length, T_window, is defined to select a recent period of power data for analysis. This operation, through a sliding average, filters out noise and the influence of short-term random fluctuations, ensuring data representativeness. The time window range can be determined by the actual application scenario. For example, in power systems, time periods ranging from minutes to hours may be optimal to accommodate the varying speeds of load characteristics.

[0086] Afterwards, the average fluctuation amplitude in the window is quantitatively analyzed by the formula R = (1 / N) * Σ(|P_load_i - P_avg_i|), i ∈ {1,..., N}, wherein R is the severity index of the load fluctuation in the window; N represents the number of sampled data points; P_load_i represents the real load power value at each time in the window; P_avg_i is the average load power calculated in the same time period T_window. The purpose of setting this formula is to measure the stability of the recent load change, thereby providing a reliable benchmark for judging future trends. Through this form of average deviation, a more smooth evaluation index can be obtained.

[0087] When the above evaluation is completed, it is necessary to further determine in combination with the current overall supply and demand relationship. If ΔP_final / R ≥ δ, it is considered that the current system imbalance is aggravated, and the regulation response level must be improved to match the supply and demand. Here, ΔP_final represents the real-time net difference (supply greater than demand or vice versa), and δ is a sensitivity coefficient set by man, which is usually obtained by experience value or test optimization to obtain a suitable value, which is used to reflect the attention of the system to the imbalance phenomenon. If the sensitivity is too high, it may trigger an overly aggressive control response, and low sensitivity may cause adjustment delay.

[0088] Finally, based on the above comprehensive evaluation results, the corresponding redundancy parameters are adjusted according to different response levels, and the final imbalance feature quantity is regenerated based on this, to ensure that all links have sufficient robustness and sensitivity when acting in coordination. In one embodiment, specifically, assuming that T_window is set to 30 minutes long, 20 groups of recent data samples are collected for evaluation, and it is found through calculation that R is large, and the absolute value of ΔP_final and the ratio of R exceed the preset limit value δ. At this time, the safety redundancy is increased to a higher level, such as increasing the additional 10% spare power capacity, and these information is input into the next decision-making process to realize the optimal scheduling strategy.

[0089] Next, the state constraints of energy storage devices in the feature quantity adjustment of the present application are described. First, each step needs to be listed and explained. These steps include: first, detecting the remaining capacity and maximum supportable output / charge of the energy storage system; second, setting a weight reduction condition according to the safe operation ratio of the energy storage system; third, introducing a correction factor to calculate the final power adjustment amount; fourth, applying the above adjustment results to the control algorithm.

[0090] Step one involves determining the real-time data of the energy storage device through testing. This step requires obtaining two key parameters: the energy storage system's remaining charge (E_remain) and its maximum allowable output or charging capacity (E_lim). Both quantities are positive real-valued, and their values ​​depend on the specific energy storage technology. This testing process can be accomplished by analyzing data from sensors within the energy storage battery.

[0091] Step two involves reducing the amount of adjustment to reduce system burden in specific situations. When the ratio of the current capacity to the upper capacity limit is less than or equal to a predetermined value, β, the limiting operation is triggered. β is defined as the safe operating ratio threshold allowed by the system. In this scheme, it is typically in the range of 0.2 to 1, with β = 0.2 being ideally selected. This is because a lower ratio means the system is operating close to the minimum pressure limit or the maximum load range, and increasing the adjustment too much may lead to irreversible changes in the system state.

[0092] Next, we move on to step three. Here, we introduce a variable called the correction factor θ, and perform the final adjustment calculation according to the formula: ΔP_final = θ * (E_remain / E_lim) * ΔP_init. Here, θ represents a coefficient that measures the impact of the overall performance of the energy storage system on global optimization. The reasonable range for θ is 0 to 1, with a recommended set point of 0.5. This allows the initial calculated power change to be appropriately scaled to meet the aforementioned safety requirements. This formula is designed to provide flexible guidance for scheduling within the entire model, taking into account the actual capacity of the energy storage units.

[0093] For the last part, which is the fourth step, it is to use the adjusted final value obtained in the previous step as the basic input factor for other related logical judgments to continue to develop in depth.

[0094] Example 2

[0095] This embodiment provides a technical solution: a method for collaboratively interacting and intelligently regulating source, grid, load, and storage resources, for regulating the distribution network voltage level based on the power grid flow distribution to address local voltage over-limit and instability, including the following steps:

[0096] Analyzing the power flow distribution of the power grid includes the following steps:

[0097] Sb1. Determine the voltage exceeding the limit and unstable areas in the distribution network based on the analysis of the power flow distribution results;

[0098] Sb2. Construct an optimization control model for source, grid, load and storage resources to generate a preliminary control strategy;

[0099] Sb3. Verify and adjust the preliminary control strategy to form the final control instructions;

[0100] Sb4, coordinating dispatching source, network, load and storage resources according to final regulation instructions to improve distribution network voltage level.

[0101] In this embodiment, first, the specific area that needs to be regulated is determined based on the power flow distribution analysis results. The key to this step is to evaluate the load distribution and line state in the current power grid and identify which parts have problems of excessively high or low voltage. For example, during some peak electricity consumption periods or load imbalance, a particular transformer may output a voltage value higher than the safe range. Accurate identification of such over-limit and instability plays a decisive role in the successful implementation of subsequent regulation strategies.

[0102] After identifying the above problems, an optimization regulation model considering power sources, power grids, loads and energy storage resources is constructed to generate a preliminary regulation strategy. Specifically, this step includes collecting various source, network, load and storage related information, such as real-time power generation capacity of generators, user end electricity demand fluctuations, actual output level of distributed new energy power generation equipment and current remaining capacity of energy storage equipment, etc. Then, these elements are linked together using mathematical modeling methods. These elements are quantitatively input into specific intelligent optimization algorithms, such as linear programming algorithms, dynamic optimization or heuristic search algorithms to develop a preliminary plan, i.e. form an initial stage of operation guidance scheme.

[0103] The preliminary regulation strategy is then further corrected and improved through a strict verification process to obtain a reliable execution instruction system. Two directions are mainly considered in the verification process: one is safety check to avoid improper configuration leading to new system risks or damage to equipment and facilities; the other is economic efficiency evaluation to ensure that the entire system operates in a state of as low cost as possible to maintain high quality service standards. If some preset schemes are found to be unable to fully consider overall benefits during the review process, the specific situation will be returned to the revision program for optimization, thereby ensuring that each decision can respond to actual demand changes in time while having long-term stability.

[0104] Finally, according to the completed adjustment, the formal published instructions are used to coordinate and command all source, network, load and storage elements to perform synchronous actions to improve the voltage quality of each point in the distribution network to within the predetermined standard range.

[0105] The above is only a preferred embodiment of the present application, which is only illustrative and not limiting. Those skilled in the art understand that many changes, modifications and even equivalents can be made within the spirit and scope of the present application as defined in the claims, but all will fall within the protection scope of the present application.

Claims

1. A collaborative interactive intelligent control method for source, grid, load and storage resources, characterized in that: The following steps are involved: Sa1. Obtain the real-time fluctuation of grid load and grid power flow distribution within the power grid system, and generate the supply and demand imbalance characteristic of power source and load based on the real-time fluctuation of grid load and the forecast data of renewable energy power generation; Sa2. Constructing a multi-objective optimization model involving an energy storage system and adjustable loads based on the supply-demand imbalance characteristic quantity; Sa3. Outputting a control instruction set by solving the multi-objective optimization model; Sa4, coordinate and control the source, grid, load and storage resources according to the control instruction set to achieve dynamic balance; The method of generating a power supply and load supply and demand imbalance characteristic value based on real-time power grid load fluctuations includes the following steps: Obtain the real-time grid load fluctuation value P_load and the predicted value of renewable energy power generation power P_gen; Calculate the initial power difference ΔP_init = |P_load- P_gen| as the basic unbalance value; Adjust the feature size according to the following formula: ΔP_final = α * ΔP_init, where α is the system adjustment coefficient 0 < α ≤ 1, indicating the intensity of the supply-demand imbalance feature adjustment; If ΔP_final exceeds the preset maximum imbalance threshold P_threshold, then limit ΔP_final = P_threshold; Further optimized the adjustment method for the supply and demand imbalance characteristic quantity: Collect historical load data and calculate the average load level P_avg; The degree of change of the current load state relative to the historical average level is calculated through the deviation ratio λ = |P_load-P_avg| / P_avg; Update the system regulation coefficient α = α * (1 + γ * λ) according to the following formula, where γ is the proportional gain factor; Make sure the new system adjustment coefficient α is within the range [α_min, α_max].

2. The method for collaborative interactive intelligent control of source, grid, load and storage resources according to claim 1, characterized in that: Added logic for dynamically evaluating supply and demand balance features: Define the short-term sliding average time window length T_window and calculate the average fluctuation amplitude A within the window; The recent volatility R is determined by the formula R = (1 / N) * Σ(|P_load_i-P_avg_i|), i ∈ {1, ..., N}, and N is the number of samples; According to the current supply and demand status, if ΔP_final / R ≥ δ, it means that the imbalance is aggravated and the control response level needs to be increased; δ is the sensitivity coefficient; The corresponding safety redundancy parameters are set according to the response level, and the final imbalance characteristic quantity is regenerated.

3. The method for collaborative interactive intelligent control of source, grid, load and storage resources according to claim 2, characterized in that: Consider the energy storage device state constraints in the characteristic quantity adjustment: Detect the remaining capacity E_remain and the maximum supported output / charge capacity E_lim of the energy storage system; When E_remain / E_lim ≤ β, β is the safe operation ratio, 0.2≤ β ≤ 1, reduce the weight of the characteristic variable to limit the excessive control intensity; The correction factor θ is introduced to calculate the adjusted ΔP_final = θ * (E_remain / E_lim) * ΔP_init; θ represents the energy storage impact coefficient; The adjustment result ΔP_final is used in the subsequent control algorithm design to ensure stable operation of energy storage.

4. The method for collaborative interactive intelligent control of source, grid, load and storage resources according to claim 1, characterized in that: Analyzing the power flow distribution of the power grid includes the following steps: Sb1. Determine the voltage exceeding the limit and unstable areas in the distribution network based on the power flow distribution analysis results. Sb2. Construct an optimization control model for source, grid, load and storage resources to generate a preliminary control strategy; Sb3. Verify and adjust the preliminary control strategy to form the final control instructions; Sb4. Coordinate and dispatch source, grid, load and storage resources according to the final control instructions to improve the distribution network voltage level.

Images