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

Abstract

The invention belongs to the technical field of electric power system operation and management, and provides a load side supply and demand balance regulation and control method and system based on multivariate reliability assessment. In the aspect of an admission mechanism, key elements such as node distribution, type, capacity and schedulability of load side flexibility resources are comprehensively assessed, and the load side flexibility resources are comprehensively assessed; scientific and reasonable evaluation standards and admission thresholds are designed, high-quality and high-reliability resources can be effectively screened out to participate in adjustment, it is ensured that the admission resources have a good adjustment basis, and the quality of the overall adjustment resources is greatly improved; meanwhile, in the centralized regulation and control mode, a global optimal regulation and control strategy is formulated, efficient and unified scheduling of resources is achieved, in the distributed autonomous mode, local strategy optimization is carried out by utilizing local information perception and modeling and each distributed resource based on own benefit maximization, the autonomy and flexibility of the distributed resources can be fully exerted, and the resource utilization rate is improved. And the quick response capability of the system to local changes is enhanced.

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 mainly relies on the regulation of the power generation side, but with the large-scale access of new energy power generation (such as wind power, solar power, etc.), the uncertainty of its output has brought tremendous pressure to the balance of power supply and demand. As an emerging means of regulation, load-side flexibility resources have great potential to assist in achieving the balance of power supply and demand.

[0003] At present, the utilization of load-side flexibility resources is still in the exploratory stage. In terms of node distribution, there is a lack of systematic planning and layout considerations, which leads to the inability to effectively integrate and coordinate resources; in terms of type, although there are many potential flexibility resources, their characteristics have not been deeply explored and classified for utilization; in terms of capacity, there is no clear evaluation and management method, and it is difficult to determine its effective regulation capacity under different supply and demand scenarios; in terms of dispatchability, due to the lack of targeted mechanism design, it is difficult for load-side flexibility resources to be efficiently dispatched according to the real-time needs of the power system. In addition, in terms of the regulation mechanism of load-side flexibility resources participating in the balance of power supply and demand, there are many deficiencies in existing research and practice; the access mechanism is not sound, and it is impossible to screen out suitable resources to participate in the regulation, which may lead to uneven resource quality; the implementation model lacks innovation and adaptability, and it is difficult to effectively promote it in different power system environments. The price mechanism is unreasonable and cannot fully reflect the value and regulation cost of resources, which affects the enthusiasm of resource providers; the settlement mechanism is imperfect, which is prone to disputes and inefficiency. 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 multivariate 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 schedulability of load-side flexibility resources, 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 power system network model is constructed with the goal of minimizing the system operation cost, 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 efficiency of local resources, 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.

[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 balance 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 types of resources, capacity reliability and dispatchability reliability of load-side flexibility resources are evaluated by using the threshold comparison method, and the load-side flexibility resources that meet the threshold requirements after the evaluation are admitted;

[0009] The load-side flexibility resources that are allowed to be controlled are utilized, 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 the system operating cost, and formulates a global optimal control strategy; the distributed autonomous mode aims to maximize 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 the power supply and demand balance regulation, divided by the capacity required for the power supply and demand balance regulation.

[0011] Furthermore, the reliability of different types of resources R T and dispatchability 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 and a resource information database is established; 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, the centralized control aims to minimize the system operation cost, including power generation cost, grid loss cost and load-side flexible resource call cost; the power generation cost is the thermal power cost 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 power state 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 status model is established to represent the operating status 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, for large industrial interruptible loads and centralized energy storage power stations, a centralized control mode is adopted, which is directly managed and dispatched by the dispatch center; for residential distributed power sources and small commercial energy storage, a distributed autonomous mode is adopted, which is autonomously adjusted under the constraints.

[0032] Establish a pricing 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 (ω 1 C R +ω 2 S RS +ω 3 A RP );

[0034] Among them, ω 1 ,ω 2 ,ω 3 is the weight coefficient; P B is the basic price; C R S is the contribution of the regulation amount; RS A is the response speed score; RP To adjust the accuracy index;

[0035] Taking into account the changes in power market supply and demand and system operation 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, a 1 is the day-ahead price adjustment coefficient, D F To predict the difference between supply and demand, S P Ability to supply resources; 2 is the intraday price adjustment coefficient, D R is the real-time supply-demand difference; a 3 is the real-time price adjustment coefficient, E S It is an indicator of system urgency; the price is adjusted according to the supply-demand ratio. When the supply-demand ratio is less than 1, the price increases, and when the supply-demand ratio is greater than 1, the price decreases.

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

[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 types of resources, capacity reliability and dispatchability reliability of the load-side flexibility resources according to relevant data and by 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 the system operation cost, and formulates a global optimal control strategy; the distributed autonomous mode aims to maximize the operation efficiency of local resources, utilizes local information perception and modeling, and 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 further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein 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, the steps of the load-side supply and demand balancing control method based on multivariate reliability assessment described in the first aspect are implemented.

[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, namely centralized regulation, distributed autonomy and hybrid mode. In the centralized regulation mode, a power system network model is constructed with the goal of minimizing system operating costs, 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 benefits of local resources, 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.

[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 balance of supply and demand 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 in the specification that constitute a part of this embodiment are used to provide a further understanding of this embodiment. The schematic embodiments of this embodiment and their descriptions are used to explain this embodiment and do not constitute improper limitations on this embodiment.

[0051] Figure 1This 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 in conjunction with 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 meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0054] Embodiment 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 electricity supply and demand balance, and achieve efficient utilization of load-side flexible resources by scientifically designing and adjusting reliability indicators and improving the regulation mechanism (including access, implementation, price and settlement mechanisms), thereby improving the regulation accuracy and efficiency of electricity supply and demand balance and enhancing the stability and economy of the power system.

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

[0057] S1. Design of load-side flexibility resource regulation reliability index 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 distribution of resources, affecting their 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 capacities vary and are unstable, and cannot 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 index.

[0060] Considering the node location distribution of load-side flexibility resources in the power network, a node distribution reliability index (R ND ). This indicator evaluates the reliability of flexible resources by calculating the distribution uniformity of flexible resources on different nodes and the coverage of key nodes. The coefficient of variation of node resource concentration (CV 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 flexible 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 =ω 1 CV NR +ω 2 CF KN (2)

[0066] Among them, ω 1 and ω 2 is the weight coefficient, which can be determined according to the actual power system demand.

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

[0068] According to 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 certainty (D RT ), interruption time flexibility F IT The calculation formula is:

[0069]

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

[0071] Recovery time certainty 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 devices consider their charging and discharging 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 the 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, a capacity dynamic adjustment factor (F CD ), and its value is determined according to 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 FCD (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 the power system dispatch instructions. D ). Through the 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 flexible resources to participate in the balance and regulation of electricity 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-reliability assessment and various regulation modes:

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

[0090] S2.1.1. Resource characteristics assessment.

[0091] Optionally, a comprehensive characteristic evaluation is conducted on the load-side flexibility resources applying for participation, including the node distribution, type, capacity, and dispatchability mentioned in step S1. Based on the evaluation results, the resources that meet the basic requirements are screened out. For resources whose node distribution does not meet the requirements (such as being overly concentrated in a few nodes or far away from key nodes), they will not be allowed to enter.

[0092] S2.1.2. Reliability threshold setting.

[0093] Set the thresholds of various reliability indicators. ND , R T , R C , R D Only when the power system's power output, power generation, and power generation capacity are all above the corresponding threshold, can it be allowed to enter the regulation market. The threshold can be set based on the stability requirements of the power system and historical operation data.

[0094] S2.2. Design diversified implementation models to adapt to the characteristics of different types of load-side flexibility resources and the needs of power supply and demand balance regulation.

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

[0096] For resources such as large-scale energy storage equipment and large interruptible load aggregates, a centralized control mode is adopted. The power dispatching center directly issues dispatching instructions, and resource providers make unified adjustments according to the instructions. In this mode, the power dispatching center can achieve efficient coordination and optimal allocation of resources to ensure the maximum regulation effect.

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

[0098] The power system network model is constructed to connect the load-side flexible 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 call 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 factor).

[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 flexible resources is calculated based on the resource adjustment price and adjustment amount. For example, for interruptible loads, the call cost is:

[0107] C il =P il 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] For distributed power sources, small decentralized energy storage and other resources, a distributed autonomous model is adopted. By establishing smart contracts or distributed control algorithms, resource providers can make autonomous adjustment decisions based on local power supply and demand and the overall adjustment goals of the system. This model can give full play to the autonomy and flexibility of distributed resources and reduce 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 to describe the relationship between its output power and grid parameters.

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

[0117]

[0118] in, is the time step used to track the change in 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 benefits, 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 equipment, the objective function can be to maximize the charging and discharging benefits, taking into account the peak and valley electricity price difference, 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 for the interruption time).

[0131] At the same time, the operating constraints of local resources and the impact on the local 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 mode is adopted for some complex power system scenarios. That is, some resources are adjusted under the unified command of the power dispatching center, and some resources 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 mode is adopted to achieve autonomous regulation under certain constraints.

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

[0137] Establish a reasonable pricing mechanism to encourage load-side flexible 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. The price is determined based on the actual performance of the load-side flexibility resources during the regulation process (such as regulation amount, response speed, regulation accuracy, etc.). The better the regulation performance, the higher the price. For example, a higher price reward is given to resources that can respond quickly and accurately adjust to the target value; while for resources with poor regulation effect, their price reward is reduced.

[0139] S3.1.1, Adjustment performance index setting:

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

[0141]

[0142] Among them, 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 (PB ), determined according to resource type and market supply and demand. For example, for energy storage resources with higher scarcity, the basic price is relatively high.

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

[0151] P A =P B (ω 1 C R +ω 2 S RS +ω 3 A RP ) (twenty two)

[0152] Among them, ω 1 ,ω 2 ,ω 3 is the weight coefficient, which is determined according to the importance of the power system to various aspects of regulation performance. The final resource regulation price P = P A +P B .

[0153] S3.3. Dynamic price adjustment strategy at multiple time scales. Consider changes in the supply and demand of the electricity market and the system operation status, and adjust prices dynamically. In periods of tight electricity supply and demand, increase prices to attract more resources to participate in regulation; in periods of loose supply and demand, appropriately reduce prices. At the same time, formulate different pricing strategies based on the regulation needs of different time scales (such as day-ahead, intraday, and real-time).

[0154] S3.3.1. Day-ahead market price (P DA ): According to the power system load forecast and power generation plan, the power supply and demand balance of the day before is predicted. 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, a 1 is the day-ahead price adjustment coefficient, D F To predict the difference between supply and demand, S P To provide resource capacity.

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

[0158]

[0159] Among them, a2 is the intraday price adjustment coefficient, D R It is 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, a 3 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] Where 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 <1 (i.e., supply exceeds demand), the price rises, and the formula is:

[0167] P=P(1+γ 1 (1-R SD )) (26)

[0168] When R SD When >1 (i.e., supply exceeds demand), the price drops, and the formula is:

[0169] P=P(1-γ 3 (R SD -1)) (27)

[0170] Among them, γ 1 and γ 2 is the supply and demand price adjustment factor.

[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 adjustment amount of flexible resources on the load side in real time. For interruptible loads, the interruption time and interruption amount are measured; for energy storage equipment, the charging and discharging amount and power are measured; for distributed power sources, the power generation output and online power are measured, etc.

[0175] The price determined by the price mechanism is used to calculate the remuneration that resource providers deserve in real time. For interruptible load providers, the remuneration 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 For 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 It is the charge and discharge capacity.

[0181] S3.4.2. Settlement cycle setting.

[0182] Short-cycle settlement (such as hourly or shorter): suitable for real-time market regulation, timely reflecting the regulation contribution of resources in a short period of time, and encouraging resource providers to respond quickly to system needs.

[0183] Long-term settlement (such as daily or monthly settlement): Comprehensively consider the overall adjustment 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: Directly pay monetary compensation to resource providers. The operation is simple and direct, and is suitable for most resource providers. Electricity compensation settlement: For resource providers with power generation capabilities (such as distributed power sources), a certain proportion of electricity compensation can be given for their own electricity use or for sale in the market to improve resource utilization efficiency. Points settlement: Establish a points system, and resource providers will obtain corresponding points based on their regulation contributions. Points can be exchanged for rights and interests such as power services and equipment upgrades, enhancing the interactivity between resource providers and the power system.

[0186] S3.4.4, Dispute Resolution Mechanism. Establish a sound dispute resolution mechanism to handle disputes that may arise during the settlement process. For example, set up a special arbitration institution or adopt the dispute resolution clauses in the smart contract to ensure the fairness and justice of the settlement process. When the resource provider has objections to the settlement results, he can appeal and resolve them through the prescribed procedures.

[0187] This embodiment shows many significant advantages in the participation of load-side flexible resources in the balance regulation of power supply and demand through the designed regulation mechanism, including access mechanism, implementation mode, price mechanism and settlement mechanism. In terms of access mechanism, by comprehensively evaluating the key elements such as node distribution, type, capacity and dispatchability of load-side flexible resources, scientific and reasonable evaluation standards and access thresholds are designed. This method effectively screens out high-quality and high-reliability resources to participate in regulation, ensures that the access resources have a good regulation basis, and greatly improves the quality of the overall regulation resources. In terms of implementation mode, centralized regulation, distributed autonomy and hybrid mode are innovatively proposed. In the centralized regulation mode, with the help of advanced resource integration and modeling technology, an accurate power system network model is constructed, and the global optimal regulation strategy is formulated through optimization algorithm to achieve efficient and unified scheduling of resources. In the distributed autonomy mode, local information perception and modeling are used to optimize the local strategy of each distributed resource based on the maximization of its own interests, give full play to the autonomy and flexibility of distributed resources, and enhance the system's ability to respond quickly to local changes. In terms of price mechanism, performance-based pricing model and dynamic price adjustment strategy are combined. The performance-based pricing model determines the price markup based on factors such as the resource's contribution to regulation, 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 balance of supply and demand 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 resources designed in this embodiment participate in the regulation mechanism of power supply and demand balance, which 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] Embodiment 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 types of resources, capacity reliability and dispatchability reliability of the load-side flexibility resources according to relevant data and by 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 the system operation cost, and formulates a global optimal control strategy; the distributed autonomous mode aims to maximize the operation efficiency of local resources, utilizes local information perception and modeling, and 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] Embodiment 3:

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

[0196] Embodiment 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] Embodiment 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 evaluation described in Example 1 are implemented.

[0200] The above description is only a preferred embodiment of the present embodiment and is not intended to limit the present embodiment. For those skilled in the art, the present embodiment may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present embodiment shall be included in the protection scope of the present embodiment.

Claims

1. A load-side supply-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 types of resources, capacity reliability and dispatchability reliability of load-side flexibility resources are evaluated by using the threshold comparison method, and the load-side flexibility resources that meet the threshold requirements after the evaluation are admitted; The load-side flexibility resources that are allowed to be controlled are utilized, 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 the system operating cost, and formulates a global optimal control strategy; the distributed autonomous mode aims to maximize 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.

2. The load-side supply-demand balance control method based on multivariate reliability evaluation according to claim 1 is characterized in that: 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 flexible 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 the key node coverage factor value; 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 balancing the supply and demand of electricity, divided by the capacity required for balancing the supply and demand of electricity.

3. The load-side supply-demand balance control method based on multivariate reliability evaluation according to claim 1 is characterized in that: The reliability of different types of resources R T and dispatchability 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 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.

4. The load-side supply-demand balance control method based on multivariate reliability evaluation according to claim 1 is 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 and a resource information database is established; the resource information database includes the node location, type, capacity and dispatchability parameters of the resources.

5. The load-side supply-demand balance control method based on multivariate reliability evaluation according to claim 1 is 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.

6. The load-side supply-demand balance control method based on multivariate reliability evaluation according to claim 1 is characterized in that: Centralized control aims to minimize system operation 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.

7. The load-side supply-demand balance control method based on multivariate reliability evaluation according to claim 1 is characterized in that: 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.

8. The load-side supply-demand balance control method based on multivariate reliability evaluation according to claim 1 is 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 power state 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 status model is established to represent the operating status of the load:

9. The load-side supply-demand balance control method based on multivariate reliability evaluation according to claim 1, 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.

10. The load-side supply-demand balance control method based on multivariate reliability evaluation according to claim 1, characterized in that: In the hybrid mode, for large industrial interruptible loads and centralized energy storage power stations, a centralized control mode is adopted, which is directly managed and dispatched by the dispatch center; for residential distributed power sources and small commercial energy storage, a distributed autonomous mode is adopted, which is autonomously adjusted under the constraints. Establish a pricing 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 A is the response speed score; RP To adjust the accuracy index; Taking into account the changes in power market supply and demand and system operation 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 difference between supply and demand, S P is the resource supply capacity; a2 is the intraday price adjustment coefficient, D R is the real-time supply-demand difference; a3 is the real-time price adjustment coefficient, E S It is an indicator of system urgency; the price is adjusted according to the supply-demand ratio. When the supply-demand ratio is less than 1, the price increases, and when the supply-demand ratio is greater than 1, the price decreases.

11. The load-side supply-demand balance control method based on multivariate reliability evaluation according to claim 10, characterized in that: Establish a settlement mechanism: 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 quantity.

12. The load-side supply and demand balance control system based on multivariate reliability evaluation is characterized by: include: 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 types of resources, capacity reliability and dispatchability reliability of the load-side flexibility resources according to relevant data by 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 the system operation cost, and formulates a global optimal control strategy; the distributed autonomous mode aims to maximize the operation efficiency of local resources, utilizes local information perception and modeling, and the hybrid mode combines the centralized control mode and the distributed autonomous mode.

13. 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 assessment as described in any one of claims 1-11 are implemented.

14. 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 11 are implemented.

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