Load side peak regulation method and system based on hierarchical regulation and multi-scene generation algorithm

Abstract

The invention discloses a load side peak regulation method and system based on hierarchical regulation and a multi-scene generation algorithm, and relates to the technical field of power system operation and optimization, and the method comprises the steps: constructing a flexibility influence factor analysis model, and obtaining a flexibility index of each load at a load side; all loads on the load side are layered based on the flexibility index and the importance degree of the load side, the loads of all the layers are cooperatively adjusted in sequence till the peak regulation requirement is met or the loads of all the layers participate in adjustment, and load side peak regulation is completed; wherein a set constraint mechanism is met in the layered adjustment process; in the load coordinated regulation process of each level, different scenes are generated by adopting a multi-scene generation method, a multi-scene optimization regulation model is constructed by combining real-time monitored operation data of the new energy power system and adopting optimization regulation strategies under the different scenes, and the model is solved and calculated to obtain an optimal regulation value of each load. According to the invention, a flexible and accurate peak regulation strategy of the load side is realized and optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation and optimization, and in particular to a load-side peak regulation method and system based on hierarchical regulation and multi-scenario generation algorithm. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] In today's era, the power market is booming, and the scale of access to new energy in the power system continues to expand, which undoubtedly brings unprecedented opportunities and challenges to the operation of the power system. New energy, especially wind power and solar energy, has become a key force in energy transformation with its clean and environmentally friendly characteristics. However, its power generation process is deeply affected by natural conditions, and is intermittent and volatile. For example, the output power of wind power changes unstably with wind speed, and the photovoltaic power generation fluctuates due to the unpredictability of light intensity and duration, making it difficult to maintain the balance of power supply and demand. As an indispensable part of the power system, the load side hides huge potential for flexible adjustment. However, the existing load-side peak-shaving methods still have certain problems:

[0004] (1) Regarding the research on the factors affecting load flexibility, the existing methods are not comprehensive and lack depth, and a systematic quantitative method has not been established. For example, for industrial loads, the impact of factors such as production processes and equipment start-stop rules on the flexibility of regulation has not been accurately quantified; for residential loads, factors such as the usage habits and time distribution of different electrical equipment have not been fully taken into consideration. This makes it impossible to accurately grasp the regulation characteristics of various loads when designing peak-shaving strategies, making it difficult to fully tap the huge advantages of load-side resources.

[0005] (2) In the existing load-side peak-shaving strategies, there are obvious defects in the load regulation methods and constraint mechanisms. Some regulation methods are too simple and crude and cannot be dynamically adjusted according to the real-time status of the load. In terms of constraint mechanisms, the restrictions on load regulation capabilities are not reasonable enough, and there is a lack of comprehensive guarantees for the safe and stable operation of the system. Summary of the invention

[0006] In order to address the deficiencies of the above-mentioned prior art, the present invention provides a load-side peak-shaving method and system based on hierarchical regulation and multi-scenario generation algorithm, which realizes precise regulation and safety assurance by comprehensively and deeply analyzing the factors affecting load flexibility regulation, constructing a scientific quantitative system, and improving the regulation method and constraint mechanism. Adaptable regulation strategies are formulated for different scenarios, so that the peak-shaving plan can make adaptive adjustments in time according to the dynamic changes of different scenarios, overcome the limitations of traditional peak-shaving methods, improve the scientificity, flexibility and accuracy of the peak-shaving strategy, enhance the stability of the power grid under complex working conditions, and effectively reduce the peak-shaving cost.

[0007] In a first aspect, the present invention provides a load-side peak-shaving method based on hierarchical regulation and multi-scenario generation algorithm.

[0008] A load side peak regulation method based on hierarchical regulation and multi-scenario generation algorithm, comprising:

[0009] According to the flexibility adjustment characteristics of various loads on the load side of the new energy power system, a flexibility influencing factor analysis model is constructed to obtain the flexibility index of each load on the load side;

[0010] Based on the flexibility index and importance of each load on the load side, all loads on the load side are layered, and the loads at each layer are coordinated and adjusted in turn until the peak-shaving demand is met or all loads at all layers have participated in the adjustment, completing the load-side peak-shaving. In the layered adjustment process, the constraint mechanism based on power balance constraint, load adjustment capacity constraint, and user satisfaction constraint is met;

[0011] In the process of coordinated load regulation at each level, a multi-scenario generation method is used to generate different scenarios. Combined with the real-time monitored operation data of the new energy power system, optimization regulation strategies under different scenarios are adopted to construct a multi-scenario optimization regulation model. The model is solved and calculated to obtain the optimal regulation value of each load.

[0012] A further technical solution is to classify multiple load types according to the flexibility adjustment characteristics of multiple loads, including interruptible loads, continuously adjustable loads, and energy storage loads;

[0013] Based on the historical data of the influencing factors of various types of loads, a quantitative analysis is conducted to establish the flexibility index of various types of loads, and thus a flexibility influencing factor analysis model is constructed, including:

[0014] Establish flexibility indicators for interruptible loads based on interruption duration, interruption frequency, and recovery time;

[0015] Establish flexibility indicators for continuous load adjustment based on adjustment speed, adjustment range and user comfort;

[0016] Based on energy storage capacity, charging and discharging efficiency, and response time, flexibility indicators for energy storage loads are established.

[0017] A further technical solution is to stratify all loads on the load side based on the flexibility index and importance of each load on the load side, and coordinately adjust the loads at each level in turn, including:

[0018] Loads with high flexibility and low importance are classified into the first layer and adjusted first;

[0019] The loads with the second-highest flexibility and medium importance are divided into the second layer. When the load adjustment of the first layer cannot meet the peak load demand, the second layer load adjustment is performed.

[0020] Loads with low flexibility and high importance are divided into the third layer. When the first and second layer load adjustments cannot meet the peak-shaving demand, the third layer load adjustment is performed.

[0021] A further technical solution adopts a multi-scenario generation method to generate a variety of different renewable energy grid operation scenarios, including: renewable energy output fluctuation scenarios, load change scenarios;

[0022] An optimization and adjustment strategy for different scenarios is adopted to construct a multi-scenario optimization strategy model; the optimization and adjustment strategies for different scenarios include: an optimization strategy for new energy consumption scenarios, an optimization strategy for power supply tension scenarios, and an optimization strategy for load peak scenarios.

[0023] A further technical solution is that the new energy consumption scenario optimization strategy is a regulation strategy based on prediction error feedback, namely: based on the new energy output fluctuation scenario generation method, multiple new energy predicted outputs and corresponding prediction probabilities are obtained; the actual output of the new energy power system is monitored in real time, and the prediction error is calculated based on the actual output and the predicted output; and the charging and discharging power of different types of loads are adjusted according to the prediction error;

[0024] The optimization strategy for power supply shortage scenarios is a strategy that combines emergency demand response with orderly power consumption;

[0025] The load peak scenario optimization strategy is a pre-peak pre-regulation and peak-time coordinated control strategy.

[0026] In a second aspect, the present invention provides a load-side peak-shaving system based on hierarchical regulation and multi-scenario generation algorithm.

[0027] A load side peak regulation system based on hierarchical regulation and multi-scenario generation algorithm, comprising:

[0028] The load regulation characteristic quantification module is used to construct a flexibility influencing factor analysis model based on the flexibility regulation characteristics of various loads on the load side of the new energy power system, and obtain the flexibility index of each load on the load side;

[0029] The load-side load stratification adjustment module is used to stratify all loads on the load side based on the flexibility index and importance of each load on the load side, and coordinately adjust the loads of each level in turn until the peak-shaving demand is met or all levels of loads have participated in the adjustment, completing the load-side peak-shaving; wherein, the stratified adjustment process satisfies the constraint mechanism based on power balance constraint, load adjustment capacity constraint, and user satisfaction constraint;

[0030] The multi-scenario load regulation module is used to generate different scenarios in the process of coordinated load regulation at each level by adopting a multi-scenario generation method. It combines the real-time monitored operation data of the new energy power system, adopts the optimization regulation strategies under different scenarios, builds a multi-scenario optimization regulation model, and solves and calculates the model to obtain the optimal regulation value of each load.

[0031] In a third aspect, the present invention further provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the above-mentioned load-side peak-shaving method based on hierarchical regulation and multi-scenario generation algorithm when executing the executable instructions stored in the memory.

[0032] In a fourth aspect, the present invention further provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-mentioned load-side peak-shaving method based on hierarchical regulation and multi-scenario generation algorithm.

[0033] In a fifth aspect, the present invention also provides a computer program product, which includes executable instructions stored in a computer-readable storage medium; wherein, when the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the load-side peak-shaving method based on hierarchical regulation and multi-scenario generation algorithm is implemented.

[0034] One or more of the above technical solutions have the following beneficial effects:

[0035] 1. The present invention provides a load-side peak-shaving method and system based on hierarchical regulation and multi-scenario generation algorithm. The hierarchical regulation strategy and multi-scenario generation algorithm are adopted to realize the optimization of flexible and accurate load-side peak-shaving strategy, and solve the problem that traditional peak-shaving strategy cannot effectively tap the potential of the load side and is difficult to adapt to the new electricity supply and demand pattern. The present invention considers the regulation characteristics of various loads (such as industrial loads, commercial loads, residential loads, etc.) under different working conditions, including factors such as regulation speed, regulation range, response time, recovery ability, and correlation with user comfort and production process, comprehensively and deeply analyzes the factors affecting load flexibility regulation, constructs a comprehensive and systematic quantitative model, and accurately grasps the regulation characteristics and inherent potential of various loads; at the same time, a perfect regulation method and a reasonable constraint mechanism are designed, and a hierarchical dynamic regulation method is proposed. According to multi-dimensional factors such as load flexibility, importance, and degree of impact on users, the load is divided into multiple levels, and in actual operation, it can be based on the real-time status of the system, that is, different power grid operations. The system can accurately and dynamically adjust the loads at all levels according to the operation status (such as normal operation, load peak, large fluctuations in new energy, etc.), ensure the safe and stable operation of the power system while effectively regulating the peak, and establish a complete and reasonable constraint mechanism, covering power balance constraints, load regulation capacity constraints, and user satisfaction constraints. In this way, the stable operation of the power system and the normal order of social production and life are maintained while achieving accurate peak regulation; through accurate prediction and real-time monitoring of various scenarios such as new energy power generation and load changes, combined with advanced optimization algorithms to solve the optimal peak regulation strategy, the peak regulation plan can be adaptively adjusted in time according to the dynamic changes of different scenarios. Through the above methods, the limitations of traditional peak regulation methods can be effectively overcome, the scientificity, flexibility and accuracy of peak regulation strategies can be significantly improved, the stability of the power grid under complex working conditions can be enhanced, the utilization efficiency of power resources can be improved, and the peak regulation cost can be reduced, providing a practical solution to the problem of flexible and accurate peak regulation on the load side under the widespread access of new energy.

[0036] 2. The present invention adopts a multi-scenario generation algorithm to generate multiple scenarios such as new energy consumption scenarios, power supply shortage scenarios, and load peak scenarios, and proposes corresponding peak-shaving optimization strategies for each scenario. In the new energy consumption scenario, the synergy of energy storage loads, interruptible loads, and continuously adjustable loads is fully utilized. According to the real-time changes in the output of new energy, the consumption mode of the load is dynamically adjusted to achieve the maximum consumption of new energy, reduce the phenomenon of wind and light abandonment, and improve energy utilization efficiency; in the power supply shortage scenario, the strategy of combining emergency demand response with orderly power consumption is quickly launched to give priority to the power supply reliability of key loads, and at the same time, by reasonably reducing non-critical loads, the power supply and demand are quickly balanced to ensure the stable operation of the power grid; in the load peak scenario, the pre-peak pre-regulation and peak-time coordinated control strategies are adopted to adjust the load state in advance, reduce the load impact at the peak moment, and effectively alleviate the pressure on the power grid. Through the above-mentioned optimization measures, the disadvantages of traditional algorithms that are prone to fall into local optimality and low computational efficiency when dealing with complex load-side peak-shaving problems are effectively overcome, and the optimal peak-shaving strategy can be quickly and accurately obtained, which significantly improves the optimization efficiency and reliability of the peak-shaving strategy and reduces the operating risks and costs during the peak-shaving process.

[0037] 3. The load-side flexible and precise peak-shaving strategy optimization method proposed in the present invention not only significantly improves the utilization efficiency of load-side resources in power system peak-shaving, enhances the stability and adaptability of the power grid in complex operating environments, but also provides strong technical support for the efficient, reliable and economical operation of the power system. This solution has broad application prospects and extremely high practical value in the field of load-side peak-shaving of power systems, and can be widely used in the operation scheduling and load management of various power grids.

[0038] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0040] Figure 1 This is a flow chart of the load-side peak-shaving method based on hierarchical regulation and multi-scenario generation algorithm described in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] It should be noted that the following detailed descriptions are exemplary only, are intended to describe specific embodiments, are intended to provide further explanation of the present invention, and are not intended to limit exemplary embodiments according to the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those of ordinary skill in the art to which the present invention belongs. In addition, it should also be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0042] Considering that the access of new energy makes the load characteristics of the power system complex and changeable, it brings many difficulties to the peak-shaving work, and the traditional peak-shaving strategy cannot effectively tap the potential of the load side and is difficult to adapt to the new power supply and demand pattern. For this reason, the present invention proposes a load-side peak-shaving method based on hierarchical regulation and multi-scenario generation algorithm, deeply analyzes the factors affecting load flexibility regulation, constructs a comprehensive and systematic quantitative model, and accurately grasps the regulation characteristics of various loads; at the same time, a perfect regulation method and a reasonable constraint mechanism are designed, which can accurately and dynamically regulate the load according to the real-time state of the system in actual operation, ensuring the safe and stable operation of the power system while effectively regulating the peak; through accurate prediction and real-time monitoring of various scenarios such as new energy power generation and load changes, combined with advanced optimization algorithms to solve the optimal peak-shaving strategy, the peak-shaving plan can be adaptively adjusted in time according to the dynamic changes of different scenarios. Through the above methods, the limitations of traditional peak-shaving methods are overcome, the scientificity, flexibility and accuracy of peak-shaving strategies are improved, the stability of the power grid under complex working conditions is enhanced, the utilization efficiency of power resources is improved, and the peak-shaving cost is reduced.

[0043] Embodiment 1

[0044] This embodiment provides a load-side peak-shaving method based on hierarchical regulation and multi-scenario generation algorithm. It adopts hierarchical regulation strategy and multi-scenario generation algorithm to realize the optimization of flexible and accurate load-side peak-shaving strategy. This method is mainly reflected in the following three aspects: (1) To comprehensively and deeply analyze and systematically quantify the flexible resources on the load side, an analysis model of influencing factors based on the regulation characteristics of the flexible resources on the load side is established; (2) From multiple angles such as power balance and regulation capability, a load-side constraint mechanism based on the hierarchical regulation method is established; (3) Considering multiple application scenarios such as new energy consumption and tight power supply, a regulation strategy model based on the multi-scenario generation method is established. The method proposed in this embodiment is as follows: Figure 1 As shown, the specific steps include:

[0045] Step S1: construct a flexibility influencing factor analysis model based on the flexibility adjustment characteristics of multiple loads on the load side of the new energy power system to obtain the flexibility index of each load on the load side;

[0046] Step S2: based on the flexibility index and importance of each load on the load side, all loads on the load side are layered, and the loads at each layer are coordinated and adjusted in turn until the peak-shaving demand is met or all loads at all layers have participated in the adjustment, and the load side peak-shaving is completed; wherein, the constraint mechanism based on power balance constraint, load adjustment capability constraint, and user satisfaction constraint is satisfied during the layered adjustment process;

[0047] Step S3: In the process of coordinated load regulation at each level, a multi-scenario generation method is used to generate different scenarios. Combined with the real-time monitored operation data of the new energy power system, the optimization regulation strategy under different scenarios is adopted to construct a multi-scenario optimization regulation model. The model is solved and calculated to obtain the optimal regulation value of each load.

[0048] The following content introduces the load-side peak-shaving method based on hierarchical regulation and multi-scenario generation algorithm proposed in this embodiment in more detail.

[0049] Step S1: According to the flexibility adjustment characteristics of various loads on the load side of the new energy power system, a flexibility influencing factor analysis model is constructed to obtain the flexibility index of each load on the load side.

[0050] In this embodiment, a model for analyzing influencing factors based on different load regulation characteristics is first proposed. Specifically, considering that the existing methods are not comprehensive and in-depth enough in analyzing the influencing factors of load flexibility regulation, and lack a systematic quantitative method, it is impossible to accurately grasp the regulation characteristics of different loads when formulating peak-shaving strategies, and it is difficult to give full play to the advantages of load-side resources. To this end, this embodiment conducts an in-depth study of the flexibility regulation characteristics of various types of loads, and divides them into multiple load types according to the flexibility regulation characteristics of various loads, including: interruptible loads (such as some industrial production equipment, etc.), continuously regulated loads (such as central air-conditioning systems, etc.), energy storage loads (such as electric vehicle charging piles with energy storage functions, etc.), etc., and quantitatively analyzes the influencing factors of various types of loads based on the historical data of various types of loads, establishes flexibility indicators for various types of loads, and completes the construction of the flexibility influencing factor analysis model. The specific steps for constructing the analysis model include:

[0051] Step S1.1: For interruptible loads, focus on the impact of factors such as interruption duration, interruption frequency and recovery time on regulation flexibility, that is, establish the flexibility index of interruptible loads based on interruption duration, interruption frequency and recovery time.

[0052] Specifically, let the interruptible duration of the interruptible load i be T i,int , the interrupt frequency is f i,int , the recovery time is T i,rec , through the statistical analysis of historical data, the flexibility index is established, and the calculation formula is:

[0053]

[0054] Among them, α 1 , α 2 , α 3 is the weight coefficient, T max,int is the maximum interruptible duration of all interruptible loads, T max,rec is the maximum recovery time.

[0055] Step S1.2: For continuously adjustable loads, such as central air conditioning, analyze the influence of the adjustment speed, adjustment range and temperature change on user comfort, that is, establish the flexibility index of the continuously adjustable load based on the adjustment speed, adjustment range and user comfort.

[0056] Specifically, the adjustment speed of the central air conditioner is AC It can be measured by the rate of change of cooling capacity or heating capacity per unit time. The adjustment range is R AC The upper and lower limits of the adjustable temperature are taken into account, considering the user's comfort level C AC , establish a comprehensive flexibility index, the calculation formula is:

[0057] F AC =β 1 υ AC +β 2 R AC +β 3 (1-C AC ) (2)

[0058] Among them, β 1 , β 2 , β 3 is the weight coefficient, C AC User comfort can be calculated based on the degree and duration of temperature deviation from the set comfort temperature. For example, the temperature deviation integral index can be used. Calculate, where T(t) is the actual temperature, T set To set the comfortable temperature, [t 1 ,t 2 ] is the adjustment time period.

[0059] Step S1.3: For energy storage loads, factors such as energy storage capacity, charge and discharge efficiency, and response time are considered, that is, based on energy storage capacity, charge and discharge efficiency, and response time, a flexibility index of the energy storage load is established.

[0060] Specifically, let the energy storage capacity of energy storage load j be E j,stor , the charge and discharge efficiency is η j , the response time is T j,resp , its flexibility index is:

[0061]

[0062] Among them, γ 1 , γ 2 , γ 3 is the weight coefficient, E max,stor is the maximum energy storage capacity, T max,resp is the maximum response time.

[0063] Step S2: based on the flexibility index and importance of each load on the load side, all loads on the load side are layered, and the loads at each layer are coordinated and adjusted in turn until the peak-shaving demand is met or all layers of loads have participated in the adjustment, and the load-side peak-shaving is completed; wherein, the constraint mechanism based on power balance constraint, load adjustment capability constraint and user satisfaction constraint is met during the layered adjustment process.

[0064] In this embodiment, a load-side constraint mechanism based on a hierarchical regulation method is established, including:

[0065] Step S2.1, propose a hierarchical adjustment method, that is: according to the flexibility index and importance of each load on the load side, all loads on the load side are layered, and the loads of each layer are coordinated and adjusted in turn. Specifically, loads with high flexibility and low importance (i.e. loads with little impact on users, such as some commercial central air conditioners) are divided into the first layer and adjusted first; loads with secondary flexibility and medium importance (i.e. loads with large adjustment capacity, such as some equipment in industrial interruptible loads) are divided into the second layer; loads with low flexibility and high importance (i.e. loads that can participate in adjustment in an emergency, such as residents' interruptible loads) are divided into the third layer.

[0066] Furthermore, when the system needs to adjust the peak load, it starts from the first layer of load adjustment, according to the peak load adjustment demand of the system ΔP sys and the total regulation capacity C of the first layer load 1 , calculate the adjustment amount of the first layer load, the formula is:

[0067] ΔP 1 =min(ΔP sys ,C 1 ) (4)

[0068] If ΔP 1 =ΔP sys , the peak regulation is completed; if ΔP 1 <ΔP sys , then continue to adjust from the second layer of load, calculate the adjustment amount of the second layer of load, the formula is:

[0069] ΔP 2 =min(ΔPsys -ΔP 1 , C 2 ) (5)

[0070] Among them, C 2 is the total regulation capacity of the second layer load, and so on, until the peak regulation demand is met or all layers of loads have participated in the regulation.

[0071] Step S2.2: In the hierarchical regulation process on the load side, each level of load regulation must satisfy a constraint mechanism based on power balance constraints, load regulation capability constraints, and user satisfaction constraints. The construction of this constraint mechanism includes:

[0072] (1) Power balance constraint: During the peak load regulation process, the power balance of the system must be ensured, that is:

[0073]

[0074] Where ΔP i is the regulation amount of various loads, P gen is the power generation, P load is the original load power.

[0075] (2) Load regulation capacity constraint: For each type of load, its regulation amount must be within its allowable regulation range, where the regulation amount ΔP for the interruptible load i is i,int Need to meet:

[0076] 0≤ΔP i,int ≤P i,int,max (7)

[0077] Among them, P i,int,max Its maximum interruptible power.

[0078] For the continuous regulation of load j, the regulation amount ΔP j,cont Need to meet:

[0079] P j,cont,min ≤ΔP j,cont ≤P j,cont,max (8)

[0080] Among them, P j,cont,min and P j,cont,max Adjust the lower and upper limits of the range.

[0081] For the charging and discharging power P of the energy storage load k k,stor Need to meet:

[0082] P k,stor,min ≤P k,stor ≤P k,stor,max (9)

[0083] Among them, Pk,stor,min and P k,stor,max The lower and upper limits of its charging and discharging power.

[0084] (3) User satisfaction constraints:

[0085] In order to ensure the normal electricity demand and satisfaction of users, for continuously adjusted loads, such as central air conditioning, the user comfort level must be within an acceptable range during the adjustment process. min , ensure that the user comfort level C after adjustment satisfies: C ≥ C min ;

[0086] For interruptible loads, limit the number of interruptions per year to N. int,max Less than the set value to avoid excessive impact on users' production and life.

[0087] Step S3: In the process of coordinated load regulation at each level, a multi-scenario generation method is used to generate different scenarios. Combined with the real-time monitored operation data of the new energy power system, the optimization regulation strategy under different scenarios is adopted to construct a multi-scenario optimization regulation model. The model is solved and calculated to obtain the optimal regulation value of each load.

[0088] In this embodiment, for each level of load regulation, an optimization regulation model based on a multi-scenario generation method is established, and the optimal regulation value of each load is obtained by solving the model calculation, so as to complete the load regulation of the current level and realize the load side peak regulation, which specifically includes:

[0089] Step S3.1, adopt a multi-scenario generation method to comprehensively evaluate and optimize the flexible and precise peak-shaving strategy on the load side, consider various factors that may affect the supply and demand balance of the power system, and generate a variety of different new energy grid operation scenarios in the following ways to accurately predict various scenarios such as new energy power generation and load changes:

[0090] (1) New energy output fluctuation scenario

[0091] Based on historical renewable energy power generation data, such as wind power wind speed, photovoltaic power generation irradiance, etc., statistical analysis is performed and combined with meteorological forecast models to generate scenarios with different renewable energy output levels. Taking wind power as an example, based on the probability distribution of historical wind speed data (such as Weibull distribution), the wind turbine output under different wind speed conditions is simulated. Specifically, assuming that the wind speed ν obeys the Weibull distribution, the formula is as follows:

[0092]

[0093] Among them, k is the shape parameter and c is the scale parameter.

[0094] The relationship between fan output and wind speed is:

[0095]

[0096] Among them, v ci is the cut-in wind speed, v r is the rated wind speed, v co is the cut-out wind speed, a, b, c are coefficients determined according to the fan characteristics, P r is the rated power. By randomly sampling wind speed values ​​for multiple times, the corresponding wind turbine output is calculated to obtain the output fluctuation scenarios of different renewable energy sources.

[0097] (2) Load change scenario

[0098] Analyze the historical load data of different seasons, different date types (such as working days, holidays, etc.) and different time periods, and use time series decomposition methods (such as STL decomposition, etc.) to decompose the load data into trend terms, seasonal terms and residual terms. According to the decomposition results, predict the load change trend under different circumstances, and generate a variety of load scenarios in combination with random fluctuation factors. Specifically, for the daily load data of a certain area, the trend term T(t), seasonal term S(t) and residual term R(t) are obtained through STL decomposition, and the predicted load is:

[0099] L(t)=T(t)+S(t)+R(t)+e(t) (12)

[0100] Among them, e(t) is a random fluctuation term, which can be assumed to obey the normal distribution N(0, σ 2 ), σ is determined according to the standard deviation of historical residual data.

[0101] Based on the above load forecasting formula, different load change scenarios are generated by changing the growth rate of the trend term, the fluctuation amplitude of the seasonal term and the value of the random fluctuation term.

[0102] Step S3.2: Based on the various scenarios predicted above, combined with the real-time monitored operation data of the new energy power system, the optimization adjustment strategies under different scenarios are adopted to construct a multi-scenario optimization adjustment model, and the optimal adjustment value of the corresponding load is obtained by solving the model calculation, including:

[0103] (1) Optimization strategy for new energy consumption scenarios

[0104] In the new energy consumption scenario, since there are errors in the new energy output prediction, this embodiment adopts a regulation strategy based on prediction error feedback. First, according to the above-mentioned new energy output fluctuation scenario generation method, multiple new energy output prediction scenarios are obtained. And the corresponding predicted probability p i At the same time, real-time monitoring of the actual output of new energy Calculate the prediction error using the formula:

[0105]

[0106] Furthermore, for energy storage loads, the charging or discharging power is adjusted according to the prediction error, including:

[0107] Assume that the initial charging power plan of the energy storage load is When the prediction error When the actual output of new energy is lower than the predicted value, the charging power of the energy storage load is increased, which can be expressed as:

[0108]

[0109] Among them, α is the adjustment coefficient, which is determined according to the characteristics of the energy storage system and the grid constraints.

[0110] Then the updated charging power is:

[0111]

[0112] And must meet

[0113] When the prediction error When the actual output of new energy is higher than the predicted value, if the energy storage load has discharge capacity, the discharge power is increased:

[0114]

[0115] Among them, β is the discharge regulation coefficient.

[0116] Then the updated discharge power is:

[0117]

[0118] And at the same time must meet

[0119] Furthermore, for continuously modulating loads, such as central air conditioning systems, the cooling or heating power is adjusted according to the prediction error and user comfort constraints, including:

[0120] Assume that the initial cooling power plan of the central air conditioner is when When the central air conditioning refrigeration power is appropriately reduced, it can be expressed as:

[0121]

[0122] Among them, γ is the adjustment coefficient, which is determined by considering the characteristics of the air-conditioning system and the user's comfort requirements.

[0123] The updated cooling power is:

[0124]

[0125] And the user comfort index must be guaranteed

[0126] when When the cooling power is increased, the power consumption can be increased appropriately. The specific adjustment amount is also determined according to the adjustment coefficient and user comfort constraints.

[0127] Through this regulation strategy based on prediction error feedback, the flexibility of energy storage loads and continuously regulated loads can be fully utilized, and the regulation amount can be adjusted in time according to the actual output of new energy, thereby improving the new energy absorption capacity, reducing wind and solar power abandonment, and ensuring user comfort.

[0128] (2) Optimization strategy for power supply shortage scenarios

[0129] In the scenario of tight power supply, this embodiment adopts a strategy combining emergency demand response with orderly power consumption. Specifically, when the system has a power shortage ΔP def When (t), the emergency demand response mechanism is first activated, that is, according to the importance (or importance level) and flexibility index of the load, the load resources that can respond quickly (such as some industrial interruptible loads and commercial adjustable loads) are selected, and the set of loads that can respond quickly is set to L fast , its total regulating capacity is:

[0130]

[0131] Among them, C l is the regulating capacity of load l.

[0132] The adjustment amount that can quickly respond to the load is calculated as:

[0133] ΔP fast =min(ΔP def (t),C fast )(twenty two)

[0134] The load power is reduced in order from high to low load flexibility.

[0135] If the load adjustment amount ΔP can be quickly responded to fast <ΔP def (t), the orderly power consumption plan is initiated, that is, the power consumption priority and the amount of electricity that can be reduced for different users are determined according to the orderly power consumption agreement signed by the users and the historical power consumption data. Furthermore, the load of low-priority users is reduced first, such as non-critical production equipment of some high-energy-consuming industrial enterprises, non-essential lighting and air-conditioning loads in large commercial places, etc.; Suppose the set of loads that can be reduced for orderly power consumption is L order , the total reducible capacity is:

[0136]

[0137] Furthermore, the regulation amount in the orderly power consumption stage is calculated as:

[0138] ΔP order =min(ΔP def (t)-ΔP fast ,C order ) (twenty four)

[0139] Gradually reduce load power until the system power balance requirements are met or all loads that can be reduced are included in the regulation.

[0140] In the process of implementing the above emergency demand response and orderly power consumption, real-time monitoring of system frequency, voltage, key node power and other operating indicators, if the system operation status is found to be deteriorating, such as the frequency drops too fast or the voltage is too low, timely adjustment of the reduction strategy, giving priority to ensuring the power supply reliability of important loads, such as hospitals, transportation hubs, communication base stations and other important infrastructure power demand. Through the above emergency demand response and orderly power consumption strategy, when the power supply is tight, the load can be quickly and effectively reduced to ensure the stable operation of the system, while minimizing the impact on the production and life of users.

[0141] (3) Load peak scenario optimization strategy

[0142] In the load peak scenario, this embodiment adopts pre-peak pre-regulation and peak-time coordinated control strategies.

[0143] Specifically, according to the load forecasting scenario generation method, a variety of load forecasting scenarios are obtained, and the continuously regulated load is pre-regulated a period of time ΔT (eg, 1-2 hours in advance) before the load peak is predicted to arrive, that is, before the peak.

[0144] Taking the central air conditioning system as an example, lower the set temperature ΔT set (e.g. 1℃-2℃), increase the cooling power in advance, consume some electricity, and thus reduce the load demand at peak times. Among them, suppose the cooling power of the central air conditioner during normal operation is P AC,normal (t), the cooling power after pre-adjustment is:

[0145] P AC,pre (t) = P AC,normal (t)+ΔP AC,pre (t)(25)

[0146] In the above formula, ΔP AC,pre (t) Calculated based on the characteristic curve of the air conditioning system and the temperature set point change.

[0147] At the same time, the energy storage load is stimulated to charge before the peak, and sufficient electric energy is stored to discharge at the peak time. Specifically, the charging power of the energy storage load before the peak is P stor,ch,pre (t), according to the capacity of the energy storage system, charging efficiency and expected peak load increment ΔP peak Determine the charging power to ensure sufficient discharge power support during peak times.

[0148] At load peaks, the system load changes are monitored in real time, and the continuous adjustment load and energy storage load are coordinated and controlled. When a load peak occurs, the power of the continuous adjustment load is further adjusted according to the system power balance requirements and load growth trends. Taking central air conditioning as an example, based on factors such as indoor and outdoor temperature difference and population density, the cooling power ΔP is appropriately reduced while ensuring user comfort. AC,peak (t), subject to the following conditions:

[0149] P AC,min ≤P AC,pre (t)-ΔP AC,peak (t)≤P AC,max (26)

[0150] At the same time, the energy storage load is controlled to discharge at the maximum power P stor,dis,max Discharge to supplement the system power shortage.

[0151] Through pre-peak pre-regulation and peak-time coordinated control strategies, load peaks can be effectively reduced, the impact of peak times on the system can be reduced, the system's ability to cope with load peaks can be improved, and the safe and stable operation of the power system under high load conditions can be guaranteed.

[0152] Based on the above methods, in the scenario of new energy consumption, the synergy of energy storage load, interruptible load and continuously adjustable load is fully utilized, and the consumption mode of load is dynamically adjusted according to the real-time changes of new energy output, so as to maximize the consumption of new energy, reduce the phenomenon of wind and solar power abandonment, and improve energy utilization efficiency; in the scenario of tight power supply, the strategy of combining emergency demand response with orderly power consumption is quickly launched to give priority to the power supply reliability of key loads, and at the same time, by reasonably reducing non-critical loads, the power supply and demand are quickly balanced to ensure the stable operation of the power grid; in the scenario of load peak, the pre-peak pre-regulation and peak-time coordinated control strategies are adopted to adjust the load state in advance, reduce the load impact at peak time, and effectively alleviate the pressure on the power grid. Through the above optimization measures, the disadvantages of traditional algorithms that are prone to fall into local optimality and low computational efficiency when dealing with complex load-side peak-shaving problems are effectively overcome, and the optimal peak-shaving strategy can be obtained quickly and accurately, which significantly improves the optimization efficiency and reliability of the peak-shaving strategy and reduces the operation risk and cost during the peak-shaving process.

[0153] Embodiment 2

[0154] This embodiment provides a load-side peak regulation system based on hierarchical regulation and multi-scenario generation algorithm, including:

[0155] The load regulation characteristic quantification module is used to construct a flexibility influencing factor analysis model based on the flexibility regulation characteristics of various loads on the load side of the new energy power system, and obtain the flexibility index of each load on the load side;

[0156] The load-side load stratification adjustment module is used to stratify all loads on the load side based on the flexibility index and importance of each load on the load side, and coordinately adjust the loads of each level in turn until the peak-shaving demand is met or all levels of loads have participated in the adjustment, completing the load-side peak-shaving; wherein, the stratified adjustment process satisfies the constraint mechanism based on power balance constraint, load adjustment capacity constraint, and user satisfaction constraint;

[0157] The multi-scenario load regulation module is used to generate different scenarios in the process of coordinated load regulation at each level by adopting a multi-scenario generation method. It combines the real-time monitored operation data of the new energy power system, adopts the optimization regulation strategies under different scenarios, builds a multi-scenario optimization regulation model, and solves and calculates the model to obtain the optimal regulation value of each load.

[0158] Embodiment 3

[0159] This embodiment provides an electronic device, including: a memory, used to store executable instructions; and a processor, used to implement the above method provided in this embodiment when executing the executable instructions stored in the memory.

[0160] Embodiment 4

[0161] This embodiment also provides a computer-readable storage medium storing executable instructions. When the executable instructions are executed by a processor, the processor will be caused to execute the above method provided in this embodiment.

[0162] Embodiment 5

[0163] This embodiment provides a computer program product, which includes an executable instruction, which is a computer instruction; the executable instruction is stored in a computer-readable storage medium. When a processor of an electronic device reads the executable instruction from the computer-readable storage medium and the processor executes the executable instruction, the electronic device executes the above method provided in this embodiment.

[0164] The steps involved in the above embodiments 2 to 5 correspond to the method embodiment 1. For the specific implementation, please refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0165] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0166] The above description is only a preferred embodiment of the present invention. Although the specific implementation mode of the present invention is described in conjunction with the accompanying drawings, it is not a limitation of the protection scope of the present invention. Those skilled in the art should understand that on the basis of the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative work are still within the protection scope of the present invention.

Claims

1. A load side peak regulation method based on hierarchical regulation and multi-scenario generation algorithm, characterized in that: include: According to the flexibility adjustment characteristics of various loads on the load side of the new energy power system, a flexibility influencing factor analysis model is constructed to obtain the flexibility index of each load on the load side; Based on the flexibility index and importance of each load on the load side, all loads on the load side are layered, and the loads at each layer are coordinated and adjusted in turn until the peak-shaving demand is met or all loads at all layers have participated in the adjustment, completing the load-side peak-shaving. In the layered adjustment process, the constraint mechanism based on power balance constraint, load adjustment capacity constraint, and user satisfaction constraint is met; In the process of coordinated load regulation at each level, a multi-scenario generation method is used to generate different scenarios. Combined with the real-time monitored operation data of the new energy power system, optimization regulation strategies under different scenarios are adopted to construct a multi-scenario optimization regulation model. The model is solved and calculated to obtain the optimal regulation value of each load.

2. A load side peak regulation method based on hierarchical regulation and multi-scenario generation algorithm as claimed in claim 1, characterized in that: According to the flexible adjustment characteristics of various loads, multiple load types are divided, including interruptible loads, continuously adjustable loads, and energy storage loads; Based on the historical data of the influencing factors of various types of loads, a quantitative analysis is conducted to establish the flexibility index of various types of loads, and thus a flexibility influencing factor analysis model is constructed, including: Establish flexibility indicators for interruptible loads based on interruption duration, interruption frequency, and recovery time; Establish flexibility indicators for continuous load adjustment based on adjustment speed, adjustment range and user comfort; Based on energy storage capacity, charging and discharging efficiency, and response time, flexibility indicators for energy storage loads are established.

3. A load side peak regulation method based on hierarchical regulation and multi-scenario generation algorithm as claimed in claim 1, characterized in that: Based on the flexibility index and importance of each load on the load side, all loads on the load side are layered, and the loads at each level are coordinated and adjusted in turn, including: Loads with high flexibility and low importance are classified into the first layer and adjusted first; The loads with the second-highest flexibility and medium importance are divided into the second layer. When the load adjustment of the first layer cannot meet the peak load demand, the second layer load adjustment is performed. Loads with low flexibility and high importance are divided into the third layer. When the first and second layer load adjustments cannot meet the peak-shaving demand, the third layer load adjustment is performed.

4. The load side peak regulation method based on hierarchical regulation and multi-scenario generation algorithm according to claim 1, characterized in that: The power balance constraint is: Among them, ΔP i is the regulation amount of various loads, P gen is the power generation, P load is the original load power; The load regulation capability constraint is: For the interruptible load i, the regulation value ΔP i,int ,satisfy: 0≤ΔP i,int ≤P i,int,max ; Among them, P i,int,max is its maximum interruptible power; For the continuous regulation of load j, the regulation amount ΔP j,cont ,satisfy: P j,cont,min ≤ΔP j,cont ≤P j,cont,max ; Among them, P j,cont,min and P j,cont,max To adjust the lower and upper limits of the range; For the charging and discharging power P of the energy storage load k k,stor ,satisfy: P k,stor,min ≤P k,stor ≤P k,stor,max ; Among them, P k,stor,min and P k,stor,max It is the lower and upper limits of charge and discharge power; The user satisfaction constraint is: For continuous load regulation, the following conditions must be met: C ≥ C min ; Among them, C represents user comfort, C min Indicates the lower limit of the user comfort index; For interruptible loads, limit the annual interruption times N of the interruptible loads. int,max Less than the set value.

5. The load side peak regulation method based on hierarchical regulation and multi-scenario generation algorithm according to claim 1, characterized in that: A multi-scenario generation method is used to generate a variety of different renewable energy grid operation scenarios, including: renewable energy output fluctuation scenarios and load change scenarios; An optimization and adjustment strategy for different scenarios is adopted to construct a multi-scenario optimization strategy model; the optimization and adjustment strategies for different scenarios include: an optimization strategy for new energy consumption scenarios, an optimization strategy for power supply tension scenarios, and an optimization strategy for load peak scenarios.

6. A load side peak regulation method based on hierarchical regulation and multi-scenario generation algorithm as claimed in claim 4, characterized in that: The new energy consumption scenario optimization strategy is a regulation strategy based on prediction error feedback, that is, based on a new energy output fluctuation scenario generation method, multiple new energy predicted outputs and corresponding prediction probabilities are obtained; Monitor the actual output of the new energy power system in real time, calculate the prediction error based on the actual output and the predicted output; adjust the charging and discharging power of different types of loads based on the prediction error; The optimization strategy for power supply shortage scenarios is a strategy that combines emergency demand response with orderly power consumption; The load peak scenario optimization strategy is a pre-peak pre-regulation and peak-time coordinated control strategy.

7. A load-side peak-shaving system based on hierarchical regulation and multi-scenario generation algorithm, characterized in that: include: The load regulation characteristic quantification module is used to construct a flexibility influencing factor analysis model based on the flexibility regulation characteristics of various loads on the load side of the new energy power system, and obtain the flexibility index of each load on the load side; The load-side load stratification adjustment module is used to stratify all loads on the load side based on the flexibility index and importance of each load on the load side, and coordinately adjust the loads of each level in turn until the peak-shaving demand is met or all levels of loads have participated in the adjustment, completing the load-side peak-shaving; wherein, the stratified adjustment process satisfies the constraint mechanism based on power balance constraint, load adjustment capacity constraint, and user satisfaction constraint; The multi-scenario load regulation module is used to generate different scenarios in the process of coordinated load regulation at each level by adopting a multi-scenario generation method. It combines the real-time monitored operation data of the new energy power system, adopts the optimization regulation strategies under different scenarios, builds a multi-scenario optimization regulation model, and solves and calculates the model to obtain the optimal regulation value of each load.

8. An electronic device, characterized in that: include: A memory for storing executable instructions; The processor is used to implement the load-side peak-shaving method based on hierarchical regulation and multi-scenario generation algorithm as described in any one of claims 1 to 6 when executing the executable instructions stored in the memory.

9. A computer-readable storage medium, characterized in that: Executable instructions are stored, which are used to cause the processor to execute the executable instructions to implement the load-side peak-shaving method based on hierarchical regulation and multi-scenario generation algorithm as described in any one of claims 1-6.

10. A computer program product, characterized in that The computer program product includes executable instructions stored in a computer-readable storage medium; When the processor of the electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, the load-side peak-shaving method based on hierarchical regulation and multi-scenario generation algorithm described in any one of claims 1 to 6 is implemented.

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