Load-side supply and demand balance control method and system based on multivariate reliability evaluation

Abstract

The present invention belongs to the technical field of power system operation and management, and provides a load-side supply and demand balancing control method and system based on multi-dimensional reliability assessment. In terms of the access mechanism, by comprehensively evaluating key factors such as the node distribution, type, capacity and dispatchability of load-side flexibility resources, scientific and reasonable evaluation standards and access thresholds are designed, which can effectively screen out high-quality and high-reliability resources to participate in the regulation, ensure that the access resources have a good regulation basis, and greatly improve the quality of the overall regulation resources; at the same time, in the centralized control mode, a global optimal control strategy is formulated to achieve efficient and unified scheduling of resources. In the distributed autonomous mode, local information perception and modeling are used, and each distributed resource optimizes local strategies based on maximizing its own interests, which can give full play to the autonomy and flexibility of distributed resources and enhance the system's ability to respond quickly to local changes.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation and management, and in particular relates to a load-side supply and demand balancing control method and system based on multivariate reliability evaluation. Background Art

[0002] With the continuous development of the power market and the transformation of the energy structure, the balance of power supply and demand faces increasingly complex challenges. Traditional power supply relies primarily on regulation on the generation side. However, with the large-scale integration of renewable energy sources (such as wind and solar power), the uncertainty of their output has placed tremendous pressure on the power supply and demand balance. As an emerging regulatory tool, load-side flexibility resources have great potential to help achieve power supply and demand balance.

[0003] Currently, the utilization of load-side flexibility resources is still in the exploratory stage. Regarding node distribution, a lack of systematic planning and layout considerations prevents effective resource integration and coordination. Regarding types, while a variety of potential flexibility resources exist, their characteristics have yet to be thoroughly explored and categorized for utilization. Regarding capacity, a lack of clear assessment and management methods makes it difficult to determine their effective regulation capabilities under different supply and demand scenarios. Regarding dispatchability, the lack of targeted mechanism design makes it difficult to efficiently dispatch load-side flexibility resources based on the real-time needs of the power system. Furthermore, existing research and practice regarding the mechanisms for load-side flexibility resources to participate in power supply and demand balancing remain deficient. Incomplete access mechanisms hinder the selection of appropriate resources for regulation, resulting in uneven resource quality. Implementation models lack innovation and adaptability, making them difficult to effectively scale across diverse power system environments. Unreasonable pricing mechanisms fail to fully reflect the value of resources and regulation costs, undermining the incentives of resource providers. Incomplete settlement mechanisms can easily lead to disputes and inefficiencies. Summary of the Invention

[0004] In order to solve the above problems, the present invention proposes a load-side supply and demand balancing control method and system based on multi-dimensional reliability evaluation. The present invention designs scientific and reasonable evaluation standards and access thresholds by comprehensively evaluating key factors such as node distribution, type, capacity and dispatchability of load-side flexibility resources. It can effectively screen out high-quality and high-reliability resources to participate in regulation, ensure that the access resources have a good regulation basis, and greatly improve the quality of the overall regulation resources; at the same time, in the centralized control mode, a power system network model is constructed with the goal of minimizing system operating costs, and a global optimal control strategy is formulated to achieve efficient and unified scheduling of resources. In the distributed autonomous mode, with the goal of maximizing the operating benefits of local resources, local information perception and modeling are used. Each distributed resource optimizes local strategies based on maximizing its own interests, which can give full play to the autonomy and flexibility of distributed resources and enhance the system's ability to respond quickly to local changes.

[0005] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0006] In a first aspect, the present invention provides a load-side supply and demand balancing control method based on multivariate reliability evaluation, comprising:

[0007] Obtain relevant data on load-side flexibility resources;

[0008] Based on relevant data, the node distribution reliability, reliability of different resource types, capacity reliability, and dispatchability reliability of load-side flexibility resources are evaluated using a threshold comparison method. Load-side flexibility resources that meet the threshold requirements after the evaluation are admitted.

[0009] Control is performed using the accessible load-side flexibility resources, as well as the preset centralized control mode, distributed autonomous mode and hybrid mode; wherein, the distributed autonomous mode constructs a power system network model with the goal of minimizing system operating costs, and formulates a global optimal control strategy; the distributed autonomous mode maximizes the operating benefits of local resources, utilizes local information perception and modeling, and the hybrid mode combines the centralized control mode and the distributed autonomous mode.

[0010] Furthermore, the node distribution reliability is equal to the weighted sum of the node resource concentration coefficient of variation and the key node coverage factor; the node resource concentration coefficient of variation is equal to the ratio of the standard deviation of the node resource quantity to the average value of the node resource quantity; the key node coverage factor represents the degree of key node coverage; if the flexibility resource node is directly connected to the key node or connected through a short path, the higher the key node coverage factor value, otherwise the lower it is; the capacity reliability is equal to the product of the capacity margin ratio and the capacity dynamic adjustment factor; the capacity margin ratio is equal to the difference between the actual available capacity of the flexibility resource and the capacity required for power supply and demand balance regulation, divided by the capacity required for power supply and demand balance regulation.

[0011] Furthermore, the reliability of different types of resources R T and schedulability reliability R D for:

[0012]

[0013]

[0014] Where n is the number of resource types; k i is the weight of the i-th type of resource; f i (x i ) is the function expression of the relevant characteristics of the i-th type of resource, f i (x i ) including interruption time flexibility F IT and interruption time flexibility F IT ;T max is the maximum acceptable interruption time, T min is the actual interruption time; T r is the actual recovery time, T e is the expected recovery time; λ is the response time weight coefficient; T R is the response time; A R is the adjustment accuracy; R R For the adjustment range.

[0015] Furthermore, in the centralized control mode, the power dispatching center directly issues dispatching instructions, and resource providers make unified adjustments according to the instructions; the load-side flexibility resources that meet the access requirements are fully integrated to establish a resource information database; the resource information database includes the node location, type, capacity and dispatchability parameters of the resources.

[0016] Furthermore, the load-side flexibility resource nodes are connected to the grid nodes to describe the electrical relationship between the resources and the grid, and the node admittance matrix is used to represent the power system network.

[0017] Furthermore, centralized control aims to minimize system operating costs, including power generation costs, grid loss costs, and load-side flexibility resource call costs; power generation costs are thermal power costs C g (P g ), grid loss cost C loss and the load-side flexibility resource call cost C il for:

[0018]

[0019] C il =P il Q il ;

[0020] Among them, P g is the thermal power generation power, a g 、b g and c g is the cost coefficient; R ij is the resistance between node i and node j, is the current flowing through the branch; P il is the interruptible load adjustment price, Q il To interrupt power.

[0021] Furthermore, in the distributed autonomous mode, resource providers make autonomous regulation decisions based on local electricity supply and demand conditions and the overall regulation objectives of the system.

[0022] Furthermore, in the distributed autonomous mode, for distributed power sources, a power output characteristic model is used to describe the relationship between output power and grid parameters:

[0023] P dg (v, f) = a dg v 2 +b dg vf+c dg f 2 +d dg v+e dg f+f dg ;

[0024] Where v is voltage, f is frequency, a dg 、b dg 、c dg d dg 、e dg and f dg is the model coefficient;

[0025] For energy storage devices, a state of charge model is established to track power changes:

[0026]

[0027] in, is the time step; E es is the charge and discharge capacity; t is the time; P es regulating prices for energy storage;

[0028] For interruptible loads, an operating state model is established to represent the operating state of the load:

[0029]

[0030] Furthermore, with the goal of maximizing the operating efficiency of local resources, for distributed power sources, the objective function is to maximize the power generation revenue; for energy storage equipment, the objective function is to maximize the charging and discharging revenue, taking into account the peak-valley electricity price difference; for interruptible loads, the objective function is to maximize the interruption compensation revenue.

[0031] Furthermore, in the hybrid mode, a centralized control mode is adopted for large-scale industrial interruptible loads and centralized energy storage power stations, which are directly managed and dispatched by the dispatch center; a distributed autonomous mode is adopted for residential distributed power sources and small commercial energy storage, which are autonomously regulated under the conditions of meeting constraints;

[0032] Establish a price mechanism to encourage load-side flexibility resources to participate in the regulation of power supply and demand balance, and reflect the regulation cost and value; determine the price based on the actual performance of load-side flexibility resources in the regulation process, and adjust the performance price plus P A for:

[0033] P A =P B (ω1C R +ω2S RS +ω3A RP );

[0034] Among them, ω1, ω2, ω3 are weight coefficients; P B is the basic price; C R is the contribution of the regulation amount; S RS Score for response speed; A RP To adjust the accuracy index;

[0035] Taking into account the changes in electricity market supply and demand and system operating status, prices are adjusted dynamically:

[0036] The current market price P DA 、Intraday market price P ID and real-time market price P RT for:

[0037]

[0038] Among them, a1 is the price adjustment coefficient of the day before, D FTo predict the supply and demand difference, S P is the resource supply capacity; a2 is the intraday price adjustment coefficient, D R is the real-time supply and demand difference; a3 is the real-time price adjustment coefficient, E S It is an indicator of system urgency; prices are adjusted according to the supply-demand ratio. When the supply-demand ratio is less than 1, prices rise; when the supply-demand ratio is greater than 1, prices fall.

[0039] Furthermore, a settlement mechanism is established: for interruptible load providers, the remuneration is equal to the product of the interruptible load regulation price, the interruptible load regulation price and the interruption time; for energy storage equipment providers, the remuneration is equal to the product of the energy storage regulation price and the charge and discharge power.

[0040] In a second aspect, the present invention further provides a load-side supply and demand balancing control system based on multivariate reliability evaluation, comprising:

[0041] The data acquisition module is configured to: obtain relevant data of load-side flexibility resources;

[0042] The evaluation module is configured to: evaluate the node distribution reliability, reliability of different resource types, capacity reliability, and dispatchability reliability of the load-side flexibility resources based on relevant data and using a threshold comparison method, and admit the load-side flexibility resources that meet the threshold requirements after the evaluation;

[0043] The control module is configured to: utilize the allowed load-side flexibility resources, as well as the preset centralized control mode, distributed autonomous mode and hybrid mode for control; wherein, the distributed autonomous mode constructs a power system network model with the goal of minimizing system operating costs and formulates a global optimal control strategy; the distributed autonomous mode maximizes the operating benefits of local resources and utilizes local information perception and modeling; the hybrid mode combines the centralized control mode and the distributed autonomous mode.

[0044] In a third aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the load-side supply and demand balancing control method based on multivariate reliability assessment described in the first aspect.

[0045] In a fourth aspect, the present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, the steps of the load-side supply and demand balancing control method based on multivariate reliability assessment described in the first aspect are implemented.

[0046] In a fifth aspect, the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the load-side supply and demand balancing control method based on multivariate reliability assessment described in the first aspect.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. In terms of the access mechanism, the present invention designs scientific and reasonable evaluation standards and access thresholds by comprehensively evaluating key factors such as node distribution, type, capacity and dispatchability of load-side flexibility resources, which can effectively screen out high-quality and high-reliability resources to participate in regulation, ensure that the access resources have a good regulation basis, and greatly improve the quality of the overall regulation resources; at the same time, three regulation modes are adopted: centralized regulation, distributed autonomy and hybrid mode. In the centralized regulation mode, the power system network model is constructed with the goal of minimizing the system operating cost, and a global optimal regulation strategy is formulated to achieve efficient and unified scheduling of resources. In the distributed autonomy mode, with the goal of maximizing the operating efficiency of local resources, local information perception and modeling are used. Each distributed resource optimizes local strategies based on maximizing its own interests, which can give full play to the autonomy and flexibility of distributed resources and enhance the system's ability to respond quickly to local changes.

[0049] 2. In the present invention, in terms of price mechanism, a performance-based pricing model and a dynamic price adjustment strategy are combined; the performance-based pricing model determines the price markup according to factors such as the resource's regulation contribution, response speed score, and regulation accuracy index, prompting resource providers to actively improve their own regulation performance; the dynamic price adjustment strategy flexibly changes prices based on time scales and market supply and demand conditions, attracting more resources to invest in regulation when supply and demand are tight, and reasonably reducing costs when supply and demand are loose, effectively guiding the rational flow of resources, ensuring the dynamic stability of the supply and demand balance in the electricity market, and at the same time encouraging resource owners to optimize their own resource allocation according to market signals and improve resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings constituting a part of the specification of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments and descriptions of this embodiment are used to explain this embodiment and do not constitute an improper limitation on this embodiment.

[0051] Figure 1 This is a flow chart of the method of Example 1 of the present invention. DETAILED DESCRIPTION

[0052] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0053] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present application belongs.

[0054] Example 1:

[0055] This embodiment provides a load-side supply and demand balance control method based on multi-dimensional reliability assessment, aiming to provide a multi-time-scale load-side flexible resource participation regulation mechanism for the balance of power supply and demand. By scientifically designing and adjusting reliability indicators and improving the regulation mechanism (including access, implementation, price and settlement mechanisms), efficient utilization of load-side flexible resources can be achieved, the regulation accuracy and efficiency of power supply and demand balance can be improved, and the stability and economy of the power system can be enhanced.

[0056] The method in this embodiment is a multi-time-scale dynamic adjustment mechanism for the participation of load-side flexibility resources in the balance of power supply and demand based on resource characteristic differences and multi-dimensional reliability evaluation algorithms. It mainly includes the design of load-side flexibility resource adjustment reliability indicators based on resource types and characteristic differences, the design of an adjustment mechanism for the participation of load-side flexibility resources in the balance of power supply and demand based on multi-dimensional reliability evaluation and diverse control modes, and the design of a multi-time-scale dynamic price adjustment and settlement mechanism based on adjustment performance.

[0057] S1. Design of load-side flexibility resource regulation reliability indicators based on differences in resource types and characteristics:

[0058] This embodiment takes into account the complexity of power system operation and the importance of load-side flexibility resources in the regulation of power supply and demand balance. Traditional regulation reliability assessment methods are often relatively simple, and it is difficult to comprehensively and accurately measure the impact of resource characteristics in different aspects on regulation reliability. In terms of the node distribution of load-side flexibility resources, if there is a lack of reasonable planning, it may lead to uneven resource distribution, affecting its coordinated regulation effect in the system; the types are complex and diverse, and the regulation characteristics of various types of resources vary greatly, making it difficult to uniformly manage and effectively utilize them; the capacity varies and is unstable, and it is impossible to accurately match the power supply and demand balance regulation requirements; the different dispatchability may cause resources to be unable to respond to system dispatch instructions in a timely manner, reducing regulation efficiency. To solve these problems, the steps for designing load-side flexibility resource regulation reliability indicators in this embodiment are as follows:

[0059] S1.1. Design node distribution reliability indicators.

[0060] Considering the node location distribution of load-side flexibility resources in the power network, a node distribution reliability index (R NDThis indicator evaluates the reliability of the resource concentration by calculating the distribution uniformity of the flexible resources on different nodes and the coverage of key nodes. NR ) is used to measure the distribution uniformity, and the calculation formula is:

[0061] Let x i and x j are two data samples in power grid dispatching, and the local density of the samples is calculated as:

[0062]

[0063] Among them, σ NR is the standard deviation of node resources, μ NR is the average value of node resources.

[0064] For the key node coverage, the key node coverage factor (CF KN ), if the flexibility resource node is directly connected to the key node or connected through a short path, then CF KN The value is higher, and vice versa. Combining the two, we get the node distribution reliability index:

[0065] R ND =ω1CV NR +ω2CF KN (2)

[0066] Among them, ω1 and ω2 are weight coefficients, which can be determined according to the actual power system demand.

[0067] S1.2. Design reliability indicators for different types of resources.

[0068] For different types of load-side flexibility resources (such as interruptible loads, distributed power sources, energy storage equipment, etc.), the differences in their regulation characteristics are analyzed and different types of reliability indicators (R T For interruptible loads, consider the interruption time flexibility (F IT ) and recovery time determinism (D RT ), interruption time flexibility F IT The calculation formula is:

[0069]

[0070] Among them, T max is the maximum acceptable interruption time, T min The actual interruption time.

[0071] Recovery time certainty D RT The calculation formula is:

[0072]

[0073] Among them, T r is the actual recovery time, T e Expected recovery time.

[0074] For distributed power sources, considering their output stability (S OP ) and prediction accuracy (A FP ) and other factors, and determine their contribution to reliability through corresponding calculation models.

[0075] Energy storage equipment considers its charge and discharge efficiency (η), energy capacity (E C ) and self-discharge rate (D SR )wait.

[0076] Construct different types of reliability indicators:

[0077]

[0078] Where n is the number of resource types, k i is the weight of the i-th type of resource, f i (x i ) is a function expression of the relevant characteristics of the i-th type of resource.

[0079] S1.3. Design capacity reliability index.

[0080] Assess the matching degree between the capacity of load-side flexibility resources and the demand for power supply and demand balance, and design capacity reliability index (R C ). Using the capacity margin ratio (R CM ) is used to measure, and the calculation formula is:

[0081]

[0082] Among them, C a is the actual available capacity of the flexibility resource, C d The capacity required for balancing power supply and demand is adjusted. At the same time, considering the dynamic change characteristics of capacity, the capacity dynamic adjustment factor (F CD ), and its value is determined based on the charging and discharging characteristics of the resource (for energy storage equipment) or the change in load regulation capability (for interruptible loads, etc.). The final capacity reliability index is:

[0083] R C =R CM F CD (7)

[0084] S1.4. Design dispatchability reliability index. This index measures the ability of load-side flexibility resources to quickly and accurately adjust according to power system dispatch instructions.D ). By response time (T R ), adjustment accuracy (A R ) and adjustment range (R R ) and other factors. The shorter the response time, the higher the adjustment accuracy, and the wider the adjustment range, the higher the dispatchability reliability. The dispatchability reliability index can be expressed as:

[0085]

[0086] Among them, λ is the response time weight coefficient, which is determined according to the response speed requirements of the power system.

[0087] Through comprehensive indicator design, this embodiment can provide a more scientific and accurate reliability assessment basis for load-side flexibility resources to participate in the balance regulation of power supply and demand.

[0088] S2. Design of a regulation mechanism for load-side flexibility resources to participate in power supply and demand balance based on multi-dimensional reliability assessment and diverse control modes:

[0089] S2.1. Access Mechanism Design: Establish strict access standards to ensure that load-side flexibility resources participating in the regulation of power supply and demand balance have a certain degree of reliability and regulation capabilities.

[0090] S2.1.1. Resource characteristics assessment.

[0091] Optionally, a comprehensive assessment of the characteristics of the load-side flexibility resources applying for participation is conducted, including the node distribution, type, capacity, and dispatchability mentioned in step S1. Based on the assessment results, resources that meet the basic requirements are screened. Resources with node distribution that does not meet the requirements (e.g., excessive concentration in a few nodes or remote from key nodes) will not be admitted.

[0092] S2.1.2. Reliability threshold setting.

[0093] Set the thresholds of various reliability indicators. Only when the reliability indicators of resources (R ND 、R T 、R C 、R D Only when the power system's performance (e.g., performance, etc.) is above the corresponding threshold is it allowed to enter the regulation market. The threshold can be set based on the stability requirements of the power system and historical operating data.

[0094] S2.2. Design diverse implementation models to suit the characteristics of different types of load-side flexibility resources and the needs for balancing power supply and demand.

[0095] S2.2.1. Design of centralized control model.

[0096] For resources like large-scale energy storage equipment and large interruptible load aggregates, a centralized control model is adopted. The power dispatch center directly issues dispatch instructions, and resource providers make unified adjustments based on these instructions. This model enables the power dispatch center to efficiently coordinate and optimize resource allocation, ensuring maximum control effectiveness.

[0097] Comprehensively integrate load-side flexibility resources that meet the access mechanism and establish a resource information database. The database contains detailed information such as the resource's node location (expressed as geographic coordinates or electrical node number), type (such as interruptible load, distributed power source, energy storage device, etc.), capacity (in kilowatts or megawatts), and dispatchability parameters (such as minimum response time and maximum regulation rate).

[0098] Construct a power system network model and connect the load-side flexibility resource nodes with the grid nodes to accurately describe the electrical relationship between the resources and the grid. bus ) to represent the power system network, where Y ij The element represents the admittance value between node i and node j.

[0099] S2.2.2. Centralized control strategy formulation: The goal is to minimize system operating costs, including power generation costs, grid loss costs, and load-side flexibility resource deployment costs.

[0100] (1) The power generation cost can be calculated based on the cost characteristic functions of different power generation types (such as thermal power, hydropower, and renewable energy power generation), such as the thermal power cost function:

[0101]

[0102] Among them, P g is the thermal power generation power, a g 、b g 、c g is the cost coefficient).

[0103] (2) The grid loss cost is calculated based on the grid resistance and current, and the formula is:

[0104]

[0105] where R ij is the resistance between node i and node j, is the current flowing through this branch.

[0106] (3) The cost of calling the load-side flexibility resource is calculated based on the resource adjustment price and adjustment amount. For example, for interruptible loads, the call cost is:

[0107] C il =Pil Q il (11)

[0108] Among them, P il is the interruptible load adjustment price, Q il To interrupt power.

[0109] (4) The comprehensive objective function is:

[0110] min(C g +C loss +C il +…)(12)

[0111] S2.2.3. Distributed autonomous model design.

[0112] A distributed autonomy model is adopted for resources such as distributed power sources and small-scale decentralized energy storage. By establishing smart contracts or distributed control algorithms, resource providers can make autonomous regulation decisions based on local power supply and demand and overall system regulation objectives. This model fully leverages the autonomy and flexibility of distributed resources while reducing communication and control costs.

[0113] (1) Build a local resource model. For distributed power sources, use the power output characteristic model:

[0114] P dg (v, f) = a dg v 2 +b dg vf+c dg d 2 +d dg v+e dg f+f dg (13)

[0115] Where v is voltage, f is frequency, a dg 、b dg 、c dg d dg 、e dg 、f dg is the model coefficient, which describes the relationship between its output power and grid parameters.

[0116] (2) For energy storage equipment, establish a state of charge model:

[0117]

[0118] in, is the time step, used to track the change of power.

[0119] (3) For interruptible loads, establish an operating status model:

[0120]

[0121] To indicate the operating status of the load.

[0122] (4) Objective function setting.

[0123] With the goal of maximizing the operating benefits of local resources, for distributed power sources, the objective function can be to maximize the power generation revenue, that is:

[0124] max(P dg (t)P price (t)) (16)

[0125] Among them, P price (t) is the real-time electricity price.

[0126] For energy storage devices, the objective function can be to maximize the charging and discharging benefits, taking into account the difference in peak and valley electricity prices, such as:

[0127]

[0128] For interruptible loads, the objective function can be to maximize the interruption compensation benefit, that is:

[0129] max(C il T il ) (18)

[0130] Among them, C il is the interruption compensation price, T il is the interruption time).

[0131] At the same time, the operating constraints of local resources and the impact on the local power grid, such as voltage and frequency stability, need to be considered.

[0132] S2.2.4. Mixed-mode design.

[0133] Combining the advantages of centralized control and distributed autonomy, a hybrid model is adopted for some complex power system scenarios. This means that some resources are regulated under the unified command of the power dispatch center, while others make autonomous decisions based on local conditions. The two work together to achieve a balance between power supply and demand.

[0134] For large-scale load-side flexibility resources that have a greater impact on system stability (such as large industrial interruptible loads, centralized energy storage power stations, etc.), a centralized control mode is adopted, and they are directly managed and dispatched by the dispatching center.

[0135] For resources that are widely distributed and have small individual capacities (such as distributed power sources on the residential side, small commercial energy storage, etc.), a distributed autonomous model is adopted to achieve autonomous regulation under certain constraints.

[0136] S3. Design of a multi-time-scale dynamic price adjustment and settlement mechanism based on regulatory performance:

[0137] Establish a reasonable pricing mechanism to encourage load-side flexibility resources to actively participate in the regulation of electricity supply and demand balance, and reflect their regulation costs and value.

[0138] S3.1. Performance-based pricing model. Prices are determined based on the actual performance of load-side flexibility resources during the regulation process (e.g., regulation capacity, response speed, regulation accuracy, etc.). The better the regulation performance, the higher the price. For example, resources that respond quickly and accurately reach the target value are rewarded with higher prices; resources with poor regulation performance are rewarded with lower prices.

[0139] S3.1.1, Adjust performance index settings:

[0140] Define the contribution of adjustment amount (C R ), the calculation formula is:

[0141]

[0142] Where ΔQ r is the actual adjustment amount of the load-side flexibility resource, ΔQ t It is the total regulation demand of the power system.

[0143] S3.1.2. Calculate the response speed score (s RS ), the formula is:

[0144]

[0145] Among them, β is the response speed influence coefficient, T R The resource response time.

[0146] S3.1.3, determine the adjustment accuracy index (A RP ), by calculating the deviation rate between the adjustment amount and the target adjustment amount (E AP ) is measured by the formula:

[0147] A RP =1-E AP (twenty one)

[0148] S3.2. Adjust resource price calculation.

[0149] S3.2.1、Setting the basic price (P B ), determined by resource type and market supply and demand. For example, for energy storage resources with higher scarcity, the base price is relatively high.

[0150] S3.2.2, Adjust the performance price markup (P A ), the calculation formula is

[0151] P A =P B (ω1C R +ω2S RS +ω3A RP ) (twenty two)

[0152] Among them, ω1, ω2, and ω3 are weight coefficients, which are determined according to the importance the power system attaches to various aspects of regulation performance. The final resource regulation price P = P A +P B .

[0153] S3.3. Dynamic price adjustment strategies across multiple timescales. Dynamically adjust prices based on changes in power market supply and demand and system operating conditions. During periods of tight power supply and demand, prices are raised to attract more resources to participate in regulation; during periods of loose supply and demand, prices are appropriately lowered. Furthermore, different pricing strategies are developed based on regulation needs at different timescales (e.g., day-ahead, intraday, and real-time).

[0154] S3.3.1, Day-ahead market price (P DA ): Based on the power system load forecast and power generation plan, the power supply and demand balance is predicted on the day before. If the supply and demand are predicted to be tight, the day-ahead price of the load-side flexible resources is appropriately increased to attract more resources to participate in the regulation. The formula is:

[0155]

[0156] Among them, a1 is the price adjustment coefficient of the day before, D F To predict the supply and demand difference, S P To provide resource capabilities.

[0157] S3.3.2, intraday market price (p ID ): As real-time operating data is updated during the day, the power supply and demand situation is reassessed. If a sudden imbalance in supply and demand occurs, the price adjustment range will be increased. The formula is:

[0158]

[0159] Among them, a2 is the intraday price adjustment coefficient, D R The real-time supply and demand difference.

[0160] S3.3.3, Real-time market price (p RT ): In the real-time market, the price is further adjusted according to the urgency of the system (such as frequency deviation, voltage fluctuation, etc.), and the formula is:

[0161]

[0162] Among them, a3 is the real-time price adjustment coefficient, E S It is an indicator of system urgency and can be calculated based on parameters such as frequency and voltage.

[0163] S3.3.4、Design of market supply and demand price adjustment mechanism. Monitor the supply and demand of load-side flexibility resources in the market and calculate the supply-demand ratio R SD

[0164]

[0165] Among them S R is the load-side flexibility resource supply, D R is the demand for flexibility resources on the load side.

[0166] Adjust the price according to the supply and demand ratio. SD When <1 (i.e., supply exceeds demand), the price rises, and the formula is:

[0167] P=P(1+γ1(1-R SD )) (26)

[0168] When R SD When it is >1 (i.e., oversupply), the price falls, and the formula is:

[0169] P=P(1-γ3(R SD -1)) (27)

[0170] Among them, γ1 and γ2 are supply and demand price adjustment coefficients.

[0171] S3.4 Settlement Mechanism Design

[0172] Establish a fair and efficient settlement mechanism to ensure that load-side flexibility resource providers can receive compensation in a timely and accurate manner, while protecting the interests of power system operators.

[0173] S3.4.1. Measurement and accounting methods.

[0174] High-precision smart meters and sensors are used to measure the regulation of load-side flexibility resources in real time. For interruptible loads, the interruption time and amount of power interrupted are measured; for energy storage devices, the charge and discharge capacity and power are measured; and for distributed power sources, the power generation output and the amount of power connected to the grid are measured.

[0175] The price determined by the price mechanism is used to calculate the compensation that resource providers deserve in real time. For interruptible load providers, the compensation M lL for:

[0176] M IL =P IL Q IL T IL(28)

[0177] Among them, T IL is the interruptible load adjustment price, Q IL is the interruption power, T IL is the interruption time;

[0178] For energy storage equipment providers, the remuneration is:

[0179] M ES =P ES E ES (29)

[0180] Among them, P ES To adjust the price of energy storage, E ES The charge and discharge capacity.

[0181] S3.4.2. Settlement cycle setting.

[0182] Short-cycle settlement (e.g. hourly or shorter): Suitable for real-time market adjustments, promptly reflecting the adjustment contribution of resources in a short period of time, and incentivizing resource providers to quickly respond to system needs.

[0183] Long-term settlement (such as daily or monthly settlement): Comprehensively consider the overall regulatory performance of resources over a longer period of time, conduct a comprehensive assessment and settlement of resources, and ensure the long-term interests of resource providers.

[0184] S3.4.3. Selection of settlement method.

[0185] Cash settlement: Direct monetary compensation is paid to resource providers. This is simple and straightforward, suitable for most resource providers. Electricity compensation settlement: Resource providers with power generation capabilities (such as distributed power sources) can be compensated with a certain percentage of electricity, which can be used for their own electricity consumption or sold on the market, improving resource utilization efficiency. Points settlement: A points system is established, where resource providers earn points based on their regulation contributions. Points can be redeemed for benefits such as power services and equipment upgrades, enhancing the interaction between resource providers and the power system.

[0186] S3.4.4 Dispute Resolution Mechanism. Establish a comprehensive dispute resolution mechanism to handle disputes that may arise during the settlement process. This can include establishing a dedicated arbitration institution or utilizing dispute resolution clauses in smart contracts to ensure fairness and impartiality in the settlement process. If resource providers disagree with the settlement results, they can file a complaint and seek resolution through the prescribed procedures.

[0187] This embodiment, through its designed regulation mechanism, including access mechanism, implementation model, pricing mechanism, and settlement mechanism, demonstrates significant advantages in enabling load-side flexible resources to participate in regulating power supply and demand. Regarding the access mechanism, scientifically sound evaluation criteria and access thresholds are designed by comprehensively evaluating key factors such as the node distribution, type, capacity, and dispatchability of load-side flexible resources. This approach effectively selects high-quality, highly reliable resources for regulation, ensuring that the resources admitted have a sound regulatory foundation, significantly improving the overall quality of regulated resources. Regarding implementation models, innovative centralized control, distributed autonomy, and hybrid modes are proposed. In the centralized control mode, advanced resource integration and modeling technologies are leveraged to construct an accurate power system network model. An optimization algorithm is then used to develop a globally optimal control strategy, enabling efficient and unified resource scheduling. In the distributed autonomy mode, local information perception and modeling are leveraged, allowing each distributed resource to optimize its local strategy based on its own interests, fully leveraging the autonomy and flexibility of distributed resources and enhancing the system's ability to rapidly respond to local changes. Regarding the pricing mechanism, a performance-based pricing model is combined with a dynamic price adjustment strategy. The performance-based pricing model determines the price markup based on factors such as the resource's regulation contribution, response speed score, and regulation accuracy index, prompting resource providers to actively improve their own regulation performance. The dynamic price adjustment strategy flexibly changes prices based on the time scale and market supply and demand conditions, attracting more resources to invest in regulation when supply and demand are tight, and reasonably reducing costs when supply and demand are loose, effectively guiding the rational flow of resources, ensuring the dynamic stability of the supply and demand balance in the electricity market, and at the same time encouraging resource owners to optimize their own resource allocation according to market signals and improve resource utilization efficiency. In summary, the load-side flexible resource participation regulation mechanism designed in this embodiment effectively solves many problems currently faced in power system regulation, significantly improves the efficiency, reliability, and economy of power system regulation, has broad application prospects in the power industry, and can provide strong support for the stable operation and sustainable development of the power system.

[0188] Example 2:

[0189] This embodiment provides a load-side supply and demand balancing control system based on multivariate reliability evaluation, including:

[0190] The data acquisition module is configured to: obtain relevant data of load-side flexibility resources;

[0191] The evaluation module is configured to: evaluate the node distribution reliability, reliability of different resource types, capacity reliability, and dispatchability reliability of the load-side flexibility resources based on relevant data and using a threshold comparison method, and admit the load-side flexibility resources that meet the threshold requirements after the evaluation;

[0192] The control module is configured to: utilize the allowed load-side flexibility resources, as well as the preset centralized control mode, distributed autonomous mode and hybrid mode for control; wherein, the distributed autonomous mode constructs a power system network model with the goal of minimizing system operating costs and formulates a global optimal control strategy; the distributed autonomous mode maximizes the operating benefits of local resources and utilizes local information perception and modeling; the hybrid mode combines the centralized control mode and the distributed autonomous mode.

[0193] The working method of the system is the same as the load-side supply and demand balance control method based on multivariate reliability evaluation in Example 1, and will not be repeated here.

[0194] Example 3:

[0195] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the load-side supply and demand balancing control method based on multivariate reliability assessment described in Example 1 are implemented.

[0196] Example 4:

[0197] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the program, the steps of the load-side supply and demand balancing control method based on multivariate reliability assessment described in Example 1 are implemented.

[0198] Example 5:

[0199] This embodiment provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the load-side supply and demand balancing control method based on multivariate reliability assessment described in Example 1 are implemented.

[0200] The above description is merely a preferred embodiment of this embodiment and is not intended to limit this embodiment. Those skilled in the art will readily appreciate that this embodiment may be modified and varied in various ways. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this embodiment shall be within the scope of protection of this embodiment.

Claims

1. A load-side supply and demand balance control method based on multivariate reliability evaluation, characterized in that: include: Obtain relevant data on load-side flexibility resources; Based on relevant data, the node distribution reliability, reliability of different resource types, capacity reliability, and dispatchability reliability of load-side flexibility resources are evaluated using a threshold comparison method. Load-side flexibility resources that meet the threshold requirements after the evaluation are admitted. Utilizes accessible load-side flexibility resources and preset centralized control modes, distributed autonomous modes, and hybrid modes for control. The centralized control mode builds a power system network model with the goal of minimizing system operating costs and formulates a global optimal control strategy. The distributed autonomous mode maximizes the operating benefits of local resources and utilizes local information perception and modeling. The hybrid mode combines the centralized control mode and the distributed autonomous mode. The node distribution reliability is equal to the weighted sum of the node resource concentration coefficient of variation and the key node coverage factor; the node resource concentration coefficient of variation is equal to the ratio of the standard deviation of the node resource amount to the average value of the node resource amount; the key node coverage factor represents the degree of key node coverage; if the flexibility resource node is directly connected to the key node or connected through a short path, the higher the key node coverage factor value, otherwise the lower it is; the capacity reliability is equal to the product of the capacity margin ratio and the capacity dynamic adjustment factor; the capacity margin ratio is equal to the difference between the actual available capacity of the flexibility resource and the capacity required for power supply and demand balance regulation, divided by the capacity required for power supply and demand balance regulation; The reliability of different types of resources R T and schedulability reliability R D for: Where n is the number of resource types; k i is the weight of the i-th type of resource; f i (x i ) is the function expression of the relevant characteristics of the i-th type of resource, f i (x i ) including interruption time flexibility F IT and recovery time determinism D RT ;λ is the response time weight coefficient; T R is the response time; A R is the adjustment accuracy; R R For the adjustment range.

2. The load-side supply and demand balance control method based on multivariate reliability evaluation according to claim 1 is characterized in that: The interruption time flexibility F IT and recovery time determinism D rT for: Among them, T max is the maximum acceptable interruption time, T min is the actual interruption time; T r is the actual recovery time, T e Expected recovery time.

3. The load-side supply and demand balance control method based on multivariate reliability evaluation according to claim 1, characterized in that: In the centralized control mode, the power dispatching center directly issues dispatching instructions, and resource providers make unified adjustments according to the instructions; the load-side flexibility resources that meet the access requirements are fully integrated to establish a resource information database; the resource information database includes the node location, type, capacity and dispatchability parameters of the resources.

4. The load-side supply and demand balance control method based on multivariate reliability evaluation according to claim 1, characterized in that: The load-side flexibility resource nodes are connected to the grid nodes to describe the electrical relationship between the resources and the grid, and the node admittance matrix is used to represent the power system network.

5. The load-side supply and demand balance control method based on multivariate reliability evaluation according to claim 1, characterized in that: Centralized control aims to minimize system operating costs, including power generation costs, grid loss costs, and load-side flexibility resource call costs; power generation costs are thermal power costs C g (P g ), grid loss cost C loss and the load-side flexibility resource call cost C il for: C il =P il Q il ; Among them, P g is the thermal power generation power, a g 、b g and c g is the cost coefficient; R ij is the resistance between node i and node j, is the current flowing through the branch; P il is the interruptible load adjustment price, Q il To interrupt power.

6. The load-side supply and demand balance control method based on multivariate reliability evaluation according to claim 1, characterized in that: In the distributed autonomous mode, resource providers make independent adjustment decisions based on local electricity supply and demand conditions and the overall system adjustment objectives.

7. The load-side supply and demand balance control method based on multivariate reliability evaluation according to claim 1, characterized in that: In the distributed autonomous mode, for distributed power sources, the power output characteristic model is used to describe the relationship between output power and grid parameters: P dg (v,f)=a dg v 2 +b dg vf+c dg f 2 +d dg v+e dg f+f dg ; Where v is voltage, f is frequency, a dg 、b dg 、c dg d dg 、e dg and f dg is the model coefficient; For energy storage devices, a state of charge model is established to track power changes: in, is the time step; E es is the charge and discharge capacity; t is the time; P es regulating prices for energy storage; For interruptible loads, an operating state model is established to represent the operating state of the load:

8. The load-side supply and demand balance control method based on multivariate reliability evaluation according to claim 1 is characterized in that: With the goal of maximizing the operating efficiency of local resources, for distributed power sources, the objective function is to maximize the power generation revenue; for energy storage equipment, the objective function is to maximize the charging and discharging revenue, taking into account the peak-valley electricity price difference; for interruptible loads, the objective function is to maximize the interruption compensation revenue.

9. The load-side supply and demand balance control method based on multivariate reliability evaluation according to claim 1, characterized in that: In the hybrid mode, a centralized control mode is adopted for large-scale industrial interruptible loads and centralized energy storage power stations, with direct management and dispatch by the dispatch center; a distributed autonomous mode is adopted for residential distributed power sources and small commercial energy storage, with autonomous regulation under the conditions of meeting constraints; Establish a price mechanism to encourage load-side flexibility resources to participate in the regulation of power supply and demand balance, and reflect the regulation cost and value; determine the price based on the actual performance of load-side flexibility resources in the regulation process, and adjust the performance price plus P A for: P A =P B (ω1C R +ω2S RS +ω3A RP ); Among them, ω1, ω2, ω3 are weight coefficients; P B is the basic price; C R is the contribution of the regulation amount; S RS Score for response speed; A RP To adjust the accuracy index; Taking into account the changes in electricity market supply and demand and system operating status, prices are adjusted dynamically: The current market price P DA 、Intraday market price P ID and real-time market price P RT for: Among them, a1 is the price adjustment coefficient of the day before, D F To predict the supply and demand difference, S P is the resource supply capacity; a2 is the intraday price adjustment coefficient, D R is the real-time supply and demand difference; a3 is the real-time price adjustment coefficient, E S It is an indicator of system urgency; prices are adjusted according to the supply-demand ratio. When the supply-demand ratio is less than 1, prices rise; when the supply-demand ratio is greater than 1, prices fall.

10. The load-side supply and demand balance control method based on multivariate reliability evaluation according to claim 9, characterized in that: Establish a settlement mechanism: For interruptible load providers, the remuneration is equal to the product of the interruptible load adjustment price, the interruptible load adjustment price and the interruption time; for energy storage equipment providers, the remuneration is equal to the product of the energy storage adjustment price and the charge and discharge power.

11. The load-side supply and demand balance control system based on multivariate reliability evaluation is characterized by: Implementing the load-side supply and demand balance control method based on multivariate reliability evaluation as described in any one of claims 1 to 10, comprising: The data acquisition module is configured to: obtain relevant data of load-side flexibility resources; The evaluation module is configured to: evaluate the node distribution reliability, reliability of different resource types, capacity reliability, and dispatchability reliability of the load-side flexibility resources based on relevant data and using a threshold comparison method, and admit the load-side flexibility resources that meet the threshold requirements after the evaluation; The control module is configured to: utilize the allowed load-side flexibility resources, as well as the preset centralized control mode, distributed autonomous mode and hybrid mode for control; wherein, the distributed autonomous mode constructs a power system network model with the goal of minimizing system operating costs and formulates a global optimal control strategy; the distributed autonomous mode maximizes the operating benefits of local resources and utilizes local information perception and modeling; the hybrid mode combines the centralized control mode and the distributed autonomous mode.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor executes the program, the steps of the load-side supply and demand balancing control method based on multivariate reliability evaluation as described in any one of claims 1 to 10 are implemented.

13. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the load-side supply and demand balancing control method based on multivariate reliability assessment as described in any one of claims 1 to 10 are implemented.

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