Adjustable load scheduling method based on hierarchical aggregation and related device

Abstract

The invention belongs to the field of power system automation, and discloses an adjustable load scheduling method based on hierarchical aggregation and a related device, and the method comprises the steps: collecting the adjustable load resources of each adjustable load device in a region in real time, and obtaining the adjustable load resources of a region level group according to the adjustable load resources of each adjustable load device; obtaining regional supply side power output prediction and a regional total load demand, and according to the regional supply side power output prediction, the regional total load demand and the adjustable load resources of the regional level groups, obtaining a scheduling instruction of each user level group in the region based on a multi-time scale regional level supply and demand balance scheduling model introducing user comfort constraint; and obtaining a scheduling instruction of each equipment level group in each user level group according to the scheduling instruction of each user level group in the area, and controlling each adjustable load equipment in each equipment level group according to the scheduling instruction of each equipment level group. And supply-demand balance and optimal scheduling of the system are realized, and efficient operation of the novel power system is promoted.

Description

Technical Field

[0001] The present invention belongs to the field of power system automation and relates to an adjustable load dispatching method based on hierarchical aggregation and a related device. Background Art

[0002] As the global energy system transforms towards a clean and low-carbon direction, a new power system dominated by a high proportion of renewable energy has gradually become the core trend of energy development. In the new power system, the supply side is dominated by renewable energy such as wind and solar energy, but these energies are volatile and uncertain, which poses severe challenges to the stable operation of the power system and the balance of supply and demand. At the same time, the demand side load has gradually shown the characteristics of diversification, decentralization and flexible adjustment, especially the new load subjects such as air-conditioning load, electric vehicles and mobile energy storage, which provide important regulation resources for the power system, but have not yet been fully utilized.

[0003] At present, the main challenges of the new power system include the following aspects. The first aspect is the access of a high proportion of renewable energy. For example, renewable energy such as wind power and photovoltaic power are affected by meteorological conditions, are random and intermittent, and have large output fluctuations. The dispatching mode of the traditional power system mainly relies on thermal power units to provide stable electricity, while the high proportion of access to new energy sources has significantly increased the complexity of supply and demand balance. The second aspect is the change in the characteristics of the demand-side load. For example, the proportion of loads such as air-conditioning loads and electric vehicles has gradually increased, and the peak-to-valley difference of the load has increased significantly, increasing the peak burden of the power grid. At the same time, distributed loads are decentralized and uncertain, and load response management is difficult. The third aspect is that load response resources are not fully utilized. For example, adjustable loads such as air-conditioning loads, electric vehicles, and mobile energy storage have great flexible adjustment potential, but due to the lack of effective aggregation and management mechanisms, it is difficult to play their due regulatory role. The fourth aspect is the lack of hierarchical coordination mechanism. For example, most of the existing load response technologies are concentrated at a single level (such as a single device or user), lacking a hierarchical aggregation management mechanism, and it is difficult to efficiently integrate decentralized load resources.

[0004] Although load-side response and regulation technologies have been developed to a certain extent, they still have the following shortcomings: decentralized load regulation, lack of hierarchical aggregation mechanism, imperfect interactive response mechanism, and insufficient utilization of flexible resources. Therefore, how to improve the interactive coordination between the power grid and user loads, achieve system supply and demand balance and optimize scheduling, and then promote the efficient operation and sustainable development of the new power system has become an urgent problem to be solved. Summary of the invention

[0005] The purpose of the present invention is to overcome the above-mentioned shortcomings of the prior art and provide an adjustable load scheduling method based on hierarchical aggregation and related devices.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides an adjustable load scheduling method based on hierarchical aggregation, comprising: real-time collection of adjustable load resources of each adjustable load device in a region, and obtaining adjustable load resources of a regional group according to the adjustable load resources of each adjustable load device; obtaining a regional supply-side power output forecast and a regional total load demand, and obtaining scheduling instructions for each user group in the region based on a multi-time-scale regional supply and demand balance scheduling model that introduces user comfort constraints, according to the regional supply-side power output forecast, the regional total load demand and the adjustable load resources of the regional group; obtaining scheduling instructions for each device group in each user group according to the scheduling instructions for each user group in the region, and controlling each adjustable load device in each device group according to the scheduling instructions for each device group.

[0008] Optionally, the adjustable load equipment includes air-conditioning equipment, electric vehicle equipment, energy storage equipment and distributed energy equipment.

[0009] Optionally, the multi-time-scale regional supply and demand balance scheduling model that introduces user comfort constraints takes minimizing the sum of load regulation cost, power system peak-to-valley difference optimization cost and user comfort adjustment cost as the optimization objective, and is constructed with supply and demand balance constraints, equipment regulation capacity constraints and user comfort constraints as constraints.

[0010] Optionally, the dispatching instructions for each user-level group are obtained based on the regional supply-side power output forecast, the regional total load demand and the adjustable load resources of the regional-level group, based on a multi-time-scale regional-level supply and demand balance dispatching model that introduces user comfort constraints, including: taking the historical medium-term dispatching instructions and historical long-term dispatching instructions of each user-level group as boundary conditions, and obtaining the real-time short-term dispatching instructions, real-time medium-term dispatching instructions and real-time long-term dispatching instructions for each user-level group based on the regional supply-side power output forecast, the regional total load demand and the adjustable load resources of the regional-level group, based on the multi-time-scale regional-level supply and demand balance dispatching model that introduces user comfort constraints; recording the real-time medium-term dispatching instructions and real-time long-term dispatching instructions of each user-level group and using them as the historical medium-term dispatching instructions and historical long-term dispatching instructions of each user-level group at the next dispatch; and using the real-time short-term dispatching instructions of each user-level group as the final dispatching instructions of each user-level group.

[0011] Optionally, the dispatching instruction of the user-level group is an adjustable load resource adjustment amount; the dispatching instruction of each device-level group in each user-level group is obtained according to the dispatching instructions of each user-level group in the region, including: converting the adjustable load resource adjustment amount of each user-level group into an equipment adjustment instruction of each equipment-level group in each user-level group; wherein, when the equipment-level group is an air-conditioning equipment-level group, the equipment adjustment instruction is an air-conditioning temperature setting instruction; when the equipment-level group is an electric vehicle equipment-level group, the equipment adjustment instruction is an electric vehicle charging and discharging power instruction; when the equipment-level group is a mobile energy storage equipment-level group, the equipment adjustment instruction is a charging and discharging plan of the energy storage equipment; when the equipment-level group is a distributed energy equipment-level group, the equipment adjustment instruction is an inverter output limit.

[0012] Optionally, the converting of the adjustable load resource adjustment amount of each user-level group into the equipment adjustment instructions of each equipment-level group within each user-level group includes: allocating the adjustable load resource adjustment amount of each user-level group to each equipment-level group within each user-level group in the order of the flexibility of each equipment-level group from high to low, so as to obtain the adjustable load resource adjustment amount of each equipment-level group; and converting the adjustable load resource adjustment amount of each equipment-level group into the equipment adjustment instructions of each equipment-level group within each user-level group.

[0013] Optionally, the method further includes: executing the above steps in a rolling manner at preset time intervals.

[0014] According to a second aspect of the present invention, there is provided an adjustable load dispatching system based on hierarchical aggregation, comprising: a data acquisition module for real-time acquisition of adjustable load resources of each adjustable load device in a region, and obtaining adjustable load resources of a regional group according to the adjustable load resources of each adjustable load device; a dispatching instruction determination module for obtaining a regional supply-side power output forecast and a regional total load demand, and obtaining dispatching instructions for each user group in the region based on a multi-time-scale regional supply and demand balance dispatching model that introduces user comfort constraints, based on the regional supply-side power output forecast, the regional total load demand and the adjustable load resources of the regional group; a dispatching instruction execution module for obtaining dispatching instructions for each device group in each user group according to the dispatching instructions for each user group in the region, and controlling each adjustable load device in each device group according to the dispatching instructions for each device group.

[0015] According to a third aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned adjustable load scheduling method based on hierarchical aggregation when executing the computer program.

[0016] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned adjustable load scheduling method based on hierarchical aggregation are implemented.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] The present invention is based on a hierarchical aggregation adjustable load scheduling method, based on the design of regional level groups, user level groups and equipment level groups, to achieve hierarchical management and optimized scheduling of equipment layer, user layer and regional layer, to achieve efficient aggregation and scheduling of resources between different levels, to form an overall load regulation capability, to improve the response efficiency of the system, to achieve efficient aggregation and real-time response of load resources. At the same time, through real-time data acquisition and control, the power grid dispatching center can quickly issue adjustment instructions, the equipment on the user side can achieve second-level response, effectively smooth out the fluctuation of new energy, and ensure the real-time supply and demand balance of the power system. A multi-time scale regional supply and demand balance scheduling model that introduces user comfort constraints is adopted to dynamically adjust adjustable load resources at different time scales, to provide functions such as peak and valley reduction and backup power, and to ensure that the user's electricity experience is not affected when load regulation is performed. It can provide strong support for the balance of power grid supply and demand, thereby helping to improve the stability, economy and flexibility of the power system, and promote the efficient operation and sustainable development of the new power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of an adjustable load scheduling method based on hierarchical aggregation according to an embodiment of the present invention.

[0020] Figure 2 This is a block diagram of a hierarchical aggregation architecture according to an embodiment of the present invention.

[0021] Figure 3 This is a structural block diagram of an adjustable load scheduling system based on hierarchical aggregation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0025] See also Figure 1 In one embodiment of the present invention, a hierarchical aggregation-based adjustable load scheduling method is provided to achieve dynamic balance of power system supply and demand and efficient utilization of adjustable load resources, and to achieve unified management, aggregated control and dynamic response of decentralized adjustable load resources.

[0026] Specifically, the adjustable load scheduling method based on hierarchical aggregation of the present invention includes the following steps:

[0027] S1: collect the adjustable load resources of each adjustable load device in the area in real time, and obtain the adjustable load resources of the regional group according to the adjustable load resources of each adjustable load device.

[0028] S2: Obtain the regional supply-side power output forecast and the regional total load demand, and obtain the dispatch instructions for each user-level group in the region based on the multi-time-scale regional supply and demand balance dispatch model that introduces user comfort constraints, based on the regional supply-side power output forecast, the regional total load demand and the adjustable load resources of the regional group.

[0029] S3: Obtaining the dispatching instructions of each device level group in each user level group according to the dispatching instructions of each user level group in the area, and controlling each adjustable load device in each device level group according to the dispatching instructions of each device level group.

[0030] The present invention is based on a hierarchical aggregation adjustable load scheduling method, based on the design of regional level groups, user level groups and equipment level groups, to achieve hierarchical management and optimized scheduling of equipment layer, user layer and regional layer, to achieve efficient aggregation and scheduling of resources between different levels, to form an overall load regulation capability, to improve the response efficiency of the system, to achieve efficient aggregation and real-time response of load resources. At the same time, through real-time data acquisition and control, the power grid dispatching center can quickly issue adjustment instructions, the equipment on the user side can achieve second-level response, effectively smooth out the fluctuation of new energy, and ensure the real-time supply and demand balance of the power system. A multi-time scale regional supply and demand balance scheduling model that introduces user comfort constraints is adopted to dynamically adjust adjustable load resources at different time scales, to provide functions such as peak and valley reduction and backup power, and to ensure that the user's electricity experience is not affected when load regulation is performed. It can provide strong support for the balance of power grid supply and demand, thereby helping to improve the stability, economy and flexibility of the power system, and promote the efficient operation and sustainable development of the new power system.

[0031] Explanatory, see Figure 2 , each device-level group is obtained by uniformly aggregating the adjustable load devices of each user in the area according to the device layer, each user-level group is obtained by uniformly aggregating each device-level aggregation group according to the user layer, and the regional-level group is obtained by uniformly aggregating each user-level aggregation group according to the regional layer. Specifically, the device-level group is used as the bottom-level aggregation unit to collect the real-time operating status of the adjustable load device and execute the corresponding adjustment instructions. The user-level group is used as a middle-level aggregation unit to form user-side adjustment capabilities by aggregating the device-level groups of a single user. Exemplarily, all adjustable load devices in a single user can be integrated through a home energy management system or an industrial load control platform to output the adjustable load resources of the user-level group, including the user load response curve and adjustment capability data to the regional-level group. As a top-level aggregation unit, the regional-level group provides overall load response capabilities by aggregating the adjustable load resources of multiple user units in the region, and participates in the interaction between power grid supply and demand.

[0032] In a possible implementation, the adjustable load equipment includes air conditioning equipment, electric vehicle equipment, energy storage equipment and distributed energy equipment.

[0033] Explanatory, air conditioning equipment adjusts the temperature set point through intelligent temperature control equipment to increase or decrease power. Electric vehicle equipment can achieve flexible regulation by controlling charging and discharging through V2G technology (Vehicle-to-Grid, vehicle-grid interaction technology). Energy storage equipment achieves regulation by providing power output or load reduction when needed. Distributed energy equipment achieves regulation by controlling the inverter output limit.

[0034] In one possible implementation, the multi-time-scale regional supply and demand balance scheduling model that introduces user comfort constraints takes minimizing the sum of load regulation cost, power system peak-to-valley difference optimization cost and user comfort adjustment cost as the optimization objective, and is constructed with supply and demand balance constraints, equipment regulation capacity constraints and user comfort constraints as constraints.

[0035] Explanatory, the scheduling plan is generated by a multi-time-scale regional-level supply and demand balance scheduling model. Through optimization calculation, it comprehensively considers supply and demand balance, economy and equipment operation constraints, determines the scheduling instructions of all user sides and energy resources in the region, and finally balances the supply and demand in the region.

[0036] For example, the objective function of the multi-time scale regional supply and demand balance scheduling model is:

[0037]

[0038] Among them, C DR (t) is the load regulation cost, including response cost and equipment regulation cost. E (t) is the peak-to-valley difference optimization cost of the power system. U (t) is the user comfort adjustment cost. T is the scheduling period. C is the total cost.

[0039] Load regulation cost C DR (t) includes user response costs, which are mainly related to the adjustment amount of flexible loads. User response costs are the compensation fees generated by users participating in load adjustment, such as providing rewards according to the load adjustment amount DR(i,t) of the i-th adjustable load device at time t:

[0040]

[0041] in, is the compensation rate for unit load regulation. N is the number of adjustable load devices.

[0042] Optimization cost of peak-valley difference in power system C E (t) To reduce the regulation pressure and operating costs caused by the peak-valley difference by smoothing load fluctuations, including the peak-valley load difference cost C E1 (t) and the cost of flexible resources participating in smoothing C E2 (t), where for the peak-valley load difference cost, the greater the peak-valley difference, the higher the grid regulation cost:

[0043] C E1 (t) = λ 峰谷 ·(L 峰值 -L 谷值 )

[0044] Among them, L 峰值 and L谷值 are the load peak and valley values, respectively, 峰谷 Compensation rates optimized for unit load.

[0045] The cost of flexible resource participation in smoothing means reducing the peak-to-valley difference through flexible load regulation and compensating the regulation cost:

[0046]

[0047] Among them, λ 平滑 The compensation rate for unit load smoothing.

[0048] User comfort adjustment cost C U (t) represents the cost incurred by users due to the impact on their comfort when participating in the adjustment, including the comfort cost of the temperature control equipment C U1 (t) and the response time cost C U2 (t).

[0049] Among them, the comfort cost of temperature control equipment is C U1 (t) represents the cost of adjusting the temperature beyond the user's preferred range:

[0050]

[0051] Among them, λ 温控 is the compensation rate for unit temperature control. T(i,t) is the temperature of the i-th temperature control device at time t, T 用户偏好 is the user's preferred temperature, and N1 is the number of temperature control devices.

[0052] Response time cost C U2 (t) indicates the impact of long adjustment time on users:

[0053] C U2 (t) = λ 时间 ·t 调节

[0054] Among them, λ 时间 is the compensation rate per unit time. 调节 To adjust the time.

[0055] The supply and demand balance constraint is:

[0056] P g (t)+DR(t)=L(t),t∈T

[0057] Among them, P g (t) is the regional supply-side power output forecast, DR(t) is the adjustable load output of the regional group, and L(t) is the total regional load demand.

[0058] The device regulation capability constraints are:

[0059] DR min (i)≤DR(i,t)≤DR max (i)

[0060] Among them, DR(i,t) is the adjustable load output of the i-th adjustable load device at time t, DR min (i) is the lower limit of the adjustable load output of the i-th adjustable load device, DR max (i) is the upper limit of the adjustable load output of the i-th adjustable load device.

[0061] The user comfort constraint is:

[0062] T min ≤T(i,t)≤T max

[0063] Among them, T mib is the lower limit of temperature, T max The upper temperature limit.

[0064] In a possible implementation, the dispatching instructions for each user group are obtained based on the regional supply-side power output forecast, the regional total load demand and the adjustable load resources of the regional group, based on a multi-time-scale regional supply and demand balance dispatching model that introduces user comfort constraints, including: taking the historical medium-term dispatching instructions and the historical long-term dispatching instructions of each user group as boundary conditions, according to the regional supply-side power output forecast, the regional total load demand and the adjustable load resources of the regional group, based on the multi-time-scale regional supply and demand balance dispatching model that introduces user comfort constraints, obtaining the real-time short-term dispatching instructions, real-time medium-term dispatching instructions and real-time long-term dispatching instructions of each user group; recording the real-time medium-term dispatching instructions and real-time long-term dispatching instructions of each user group and using them as the historical medium-term dispatching instructions and historical long-term dispatching instructions of each user group at the next dispatch, and using the real-time short-term dispatching instructions of each user group as the final dispatching instructions of each user group.

[0065] Explanatory, multi-time scale refers to combining scheduling decision problems in different time ranges, comprehensively considering the system demand and regulation capacity in the short term (minute level), medium term (hour level) and long term (day level or longer), so as to achieve refined scheduling. The multi-time scale optimization in the multi-time scale regional supply and demand balance scheduling model with user comfort constraints is reflected in the following aspects:

[0066] The main goal of the short-term (real-time scheduling) is to dynamically adjust the adjustable load and supply-side resources within the minute level period to meet the current supply and demand balance. The characteristic is to focus on real-time data, such as current load fluctuations, user-side equipment status, and distributed energy real-time output. Its solution is rolling optimization, updating decisions within each short time window. The main goal of the medium-term (hourly scheduling) is to optimize the scheduling plan within the hourly level period to meet the supply and demand changes in the next few hours, smooth load fluctuations and reduce peak-to-valley differences. The characteristic is to consider the fluctuations of new energy forecasts, energy storage charging and discharging plans, and load demand changes. The solution is to generate flexible load responses and output plans of supply-side resources through prediction algorithms to guide short-term scheduling optimization. The main goal of the long-term (daily and above scheduling) is to optimize long-term scheduling goals, such as reducing operating costs, reducing carbon emissions, and improving system economy. The characteristic is to focus on long-term trends, including load periodic changes (such as daily load curves) and long-term operating constraints of equipment. The solution is to generate long-term strategies based on historical data and prediction results to guide medium-term and short-term scheduling optimization.

[0067] When making each short-term decision (for example, once every 15 minutes), the medium-term (hourly) and long-term (daily) scheduling results are referenced as boundary conditions to ensure that short-term decisions meet the overall optimization goals.

[0068] In a possible implementation, the dispatching instruction of the user-level group is an adjustable load resource adjustment amount; the dispatching instruction of each device-level group in each user-level group is obtained according to the dispatching instructions of each user-level group in the region, including: converting the adjustable load resource adjustment amount of each user-level group into an equipment adjustment instruction of each equipment-level group in each user-level group; wherein, when the equipment-level group is an air-conditioning equipment-level group, the equipment adjustment instruction is an air-conditioning temperature setting instruction; when the equipment-level group is an electric vehicle equipment-level group, the equipment adjustment instruction is an electric vehicle charging and discharging power instruction; when the equipment-level group is a mobile energy storage equipment-level group, the equipment adjustment instruction is a charging and discharging plan of the energy storage equipment; when the equipment-level group is a distributed energy equipment-level group, the equipment adjustment instruction is an inverter output limit.

[0069] For example, the air conditioning temperature setting instruction is to raise the air conditioning temperature to 26°C. The control logic is that the air conditioning adjusts the temperature according to the adjustment range, and the user can confirm and modify the adjustment range through the device terminal. The electric vehicle charging and discharging power instruction is such as a discharge power of 3kW and lasting for 1 hour. The control logic is that the vehicle management system starts the charging or discharging process according to the instruction, and the user can set the priority (such as the minimum SOC limit). The charging and discharging plan of the energy storage device is such as a discharge power of 500kW and lasting for 1 hour. The control logic is that the battery management system (BMS) adjusts the working state of the energy storage device to complete the instruction. The inverter output limit is such as limiting to 70% of the rated power. The control logic is that the inverter adjusts the output power according to the instruction to prevent excess power generation.

[0070] In a possible implementation, the converting of the adjustable load resource adjustment amount of each user-level group into the equipment adjustment instructions of each equipment-level group within each user-level group includes: allocating the adjustable load resource adjustment amount of each user-level group to each equipment-level group within each user-level group in the order of the flexibility of each equipment-level group from high to low, to obtain the adjustable load resource adjustment amount of each equipment-level group; and converting the adjustable load resource adjustment amount of each equipment-level group into the equipment adjustment instructions of each equipment-level group within each user-level group.

[0071] Explanatory, priority is given to the use of highly flexible adjustable load resources on the user side, such as air-conditioning equipment and electric vehicle equipment, to maximize user responsiveness while meeting user comfort constraints.

[0072] For example, optimizing the output of distributed energy equipment (such as photovoltaic and wind power) ensures that the output meets local demand as much as possible and reduces power abandonment.

[0073] In a possible implementation, the adjustable load scheduling method based on hierarchical aggregation further includes: executing the above steps in a rolling manner at preset time intervals.

[0074] Explanatory, based on real-time data collection and rolling optimization mechanism, the latest data is continuously collected in real time, and the subsequent scheduling plan is adjusted according to the feedback results, such as reallocating response tasks or modifying equipment output plans, to achieve rolling execution scheduling optimization, and dynamically adjust the regulation strategy to achieve efficient and stable operation of the system.

[0075] Optionally, after the device makes adjustments according to the received adjustment instructions, it can also feed back the execution status to the dispatch center for comparing the actual response with the adjustment instructions to evaluate the execution deviation.

[0076] The main improvements of the scheduling method of the present invention are: 1. Hierarchical aggregation mechanism: Introduce hierarchical management of equipment layer, user layer and regional layer to improve the aggregation efficiency and response capability of adjustable load resources. 2. Adjustable load scheduling optimization: Schedule each adjustable device through a multi-time scale regional supply and demand balance scheduling model to improve the flexibility of supply and demand balance. 3. User comfort guarantee: Optimize the adjustment process by introducing user comfort constraints to ensure that the adjustment does not affect the normal user experience. 4. Real-time feedback and rolling optimization: Combine real-time data collection and dynamic feedback to form a closed-loop control mechanism to achieve efficient and stable operation of the system. In general, the present invention achieves efficient aggregation and dynamic response of adjustable loads through a hierarchical aggregation mechanism, flexible resource scheduling and grid-load interaction, solves the problems of adjustable load dispersion and low regulation efficiency in the prior art, and significantly improves the supply and demand balance capability of the power system and the utilization rate of adjustable load resources.

[0077] The adjustable load scheduling method based on hierarchical aggregation of the present invention can be widely used in: 1. Industrial park power optimization: Aggregate load resources in the industrial park, perform peak-valley difference adjustment and demand response, and achieve regional power grid supply and demand balance. Home user interactive regulation: Aggregate home adjustable loads to optimize user electricity costs. Electric vehicle group scheduling: Aggregate electric vehicle resources in the region and achieve grid load balance through V2G.

[0078] In a possible implementation manner, the adjustable load scheduling method based on hierarchical aggregation of the present invention is applied in a certain area to achieve the following technical effects:

[0079] 1. Through the hierarchical aggregation mechanism, hierarchical regulation is achieved at the device layer, user layer and regional layer, forming a considerable overall regulation capability. The response speed at the device layer is increased to seconds, realizing rapid load regulation. The load aggregation rate of a single user at the user layer is increased by 30%. The overall load response capability at the regional layer is increased by 25%-35%, forming a regional-level regulation resource pool. This effectively solves the problem that the existing adjustable load resources are dispersed, the regulation capability of a single user or device is limited, and it is difficult to form an overall regulation capability.

[0080] 2. Through dynamic response of load resources, peak load is reduced, load curve is effectively smoothed, peak load is reduced by 20%, and peak-to-valley difference of power grid is reduced by 15%-20%. Dynamic stabilization of fluctuations of new energy by demand-side resources is achieved: load imbalance rate caused by fluctuations of renewable energy output is reduced by 30%. Among them, in the industrial park scenario, the utilization rate of the regulation potential of the park load is increased to 80%, which effectively alleviates the peak pressure of the power grid. It effectively solves the problem that the volatility of renewable energy output and the uncertainty of demand load make it more difficult to balance the supply and demand of the power grid, resulting in a significant increase in the peak-to-valley difference.

[0081] 3. Achieve efficient dispatching of air-conditioning loads, electric vehicles and mobile energy storage, increase the average regulation efficiency of air-conditioning loads by 25%, and shorten the response time of power reduction to less than 30 seconds. Through V2G technology, the response rate of electric vehicles reaches 90%, and the participation of charge and discharge regulation is significantly improved. The resource utilization rate of energy storage equipment is increased by 20%-25%, achieving flexible deployment and rapid response. In extreme weather or emergencies, the backup power provided by mobile energy storage shortens the response time of power grid failures by 50%. It effectively solves the problem that the existing technology fails to fully dispatch adjustable load resources such as air-conditioning equipment, electric vehicle equipment, and mobile energy storage equipment, resulting in low resource utilization.

[0082] 4. In the process of optimizing scheduling, the user comfort constraint is taken into account, the adjustment range is kept within a reasonable threshold, the air conditioning load temperature adjustment range is controlled within ±1.5℃, the user comfort satisfaction rate is kept above 95%, the user electricity cost is reduced by 10%-15%, and the user enthusiasm is improved. This effectively solves the problem that load adjustment may affect the user's normal electricity experience, especially the problem that the temperature adjustment of the air conditioning load may cause a decrease in comfort.

[0083] 5. Through rolling optimization and feedback control mechanism, data is updated in real time and dispatch strategies are adjusted dynamically: the system dispatch response speed reaches the second level, which significantly improves the real-time supply and demand matching ability. Under the conditions of fluctuations in new energy output and random load disturbances, the supply and demand balance deviation is reduced by 30%. The system operation stability is significantly improved. Experimental data show that the system supply and demand balance error is reduced to less than 1.5%. It effectively solves the problem that the traditional dispatch method has a slow response speed and cannot respond to changes in power grid supply and demand and fluctuations in new energy in real time.

[0084] 6. Through demand-side flexible load regulation, peak load and backup power activation frequency are effectively reduced, saving about 15%-20% of backup power cost. Carbon emissions are reduced by 10%-12%. The overall operation economy is improved, and the overall operation cost of the power grid is reduced by 12%-18%, improving the economy of the system. It effectively solves the problem of needing to activate high-cost and high-carbon emission backup power when power supply is tight during peak hours.

[0085] In a possible implementation, in a certain industrial park and residential demonstration area, air-conditioning equipment, electric vehicle equipment and energy storage equipment are used as adjustable load equipment, the data collection period is set to 30 days, and the data collection frequency is set to 1 minute / time, and the adjustable load scheduling method based on hierarchical aggregation of the present invention is applied.

[0086] The application results are as follows: Peak load reduction: 20%. Peak-to-valley difference optimization: 15%-20%. Response speed: seconds. Air conditioning temperature control adjustment: within ±1.5℃, user satisfaction rate is over 95%. Electric vehicle V2G response rate: 90%. Economic cost reduction: 15%. Adjustable load resource utilization: average increase of 25%.

[0087] It can be seen that the present invention provides an efficient and stable solution for demand-side resource management in new power systems, and has broad application prospects and significant technical advantages.

[0088] The following are device embodiments of the present invention, which can be used to implement the method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0089] See also Figure 3 In another embodiment of the present invention, a hierarchical aggregation-based adjustable load scheduling system is provided, which can be used to implement the above-mentioned hierarchical aggregation-based adjustable load scheduling method. Specifically, the hierarchical aggregation-based adjustable load scheduling system includes a data acquisition module, a scheduling instruction determination module and a scheduling instruction execution module.

[0090] Among them, the data acquisition module is used to collect the adjustable load resources of each adjustable load device in the area in real time, and obtain the adjustable load resources of the regional group according to the adjustable load resources of each adjustable load device; the scheduling instruction determination module is used to obtain the regional supply-side power output forecast and the regional total load demand and obtain the scheduling instructions of each user group in the region based on the regional supply-side power output forecast, the regional total load demand and the adjustable load resources of the regional group, based on the multi-time scale regional supply and demand balance scheduling model that introduces user comfort constraints; the scheduling instruction execution module is used to obtain the scheduling instructions of each device group in each user group according to the scheduling instructions of each user group in the region, and control each adjustable load device in each device group according to the scheduling instructions of each device group.

[0091] In a possible implementation, the adjustable load equipment includes air conditioning equipment, electric vehicle equipment, energy storage equipment and distributed energy equipment.

[0092] In one possible implementation, the multi-time-scale regional supply and demand balance scheduling model that introduces user comfort constraints takes minimizing the sum of load regulation cost, power system peak-to-valley difference optimization cost and user comfort adjustment cost as the optimization objective, and is constructed with supply and demand balance constraints, equipment regulation capacity constraints and user comfort constraints as constraints.

[0093] In a possible implementation, the dispatching instructions for each user group are obtained based on the regional supply-side power output forecast, the regional total load demand and the adjustable load resources of the regional group, based on a multi-time-scale regional supply and demand balance dispatching model that introduces user comfort constraints, including: taking the historical medium-term dispatching instructions and the historical long-term dispatching instructions of each user group as boundary conditions, according to the regional supply-side power output forecast, the regional total load demand and the adjustable load resources of the regional group, based on the multi-time-scale regional supply and demand balance dispatching model that introduces user comfort constraints, obtaining the real-time short-term dispatching instructions, real-time medium-term dispatching instructions and real-time long-term dispatching instructions of each user group; recording the real-time medium-term dispatching instructions and real-time long-term dispatching instructions of each user group and using them as the historical medium-term dispatching instructions and historical long-term dispatching instructions of each user group at the next dispatch, and using the real-time short-term dispatching instructions of each user group as the final dispatching instructions of each user group.

[0094] In a possible implementation, the dispatching instruction of the user-level group is an adjustable load resource adjustment amount; the dispatching instruction of each device-level group in each user-level group is obtained according to the dispatching instructions of each user-level group in the region, including: converting the adjustable load resource adjustment amount of each user-level group into an equipment adjustment instruction of each equipment-level group in each user-level group; wherein, when the equipment-level group is an air-conditioning equipment-level group, the equipment adjustment instruction is an air-conditioning temperature setting instruction; when the equipment-level group is an electric vehicle equipment-level group, the equipment adjustment instruction is an electric vehicle charging and discharging power instruction; when the equipment-level group is a mobile energy storage equipment-level group, the equipment adjustment instruction is a charging and discharging plan of the energy storage equipment; when the equipment-level group is a distributed energy equipment-level group, the equipment adjustment instruction is an inverter output limit.

[0095] In a possible implementation, the converting of the adjustable load resource adjustment amount of each user-level group into the equipment adjustment instructions of each equipment-level group within each user-level group includes: allocating the adjustable load resource adjustment amount of each user-level group to each equipment-level group within each user-level group in the order of the flexibility of each equipment-level group from high to low, to obtain the adjustable load resource adjustment amount of each equipment-level group; and converting the adjustable load resource adjustment amount of each equipment-level group into the equipment adjustment instructions of each equipment-level group within each user-level group.

[0096] In a possible implementation, it further includes a rolling execution module, which is used to rollingly trigger the data acquisition module, the scheduling instruction determination module and the scheduling instruction execution module at a preset time interval.

[0097] All relevant contents of each step involved in the embodiment of the aforementioned adjustable load scheduling method based on hierarchical aggregation can be referred to the functional description of the functional modules corresponding to the adjustable load scheduling system based on hierarchical aggregation in the embodiment of the present invention, and will not be repeated here.

[0098] The division of modules in the embodiments of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present invention may be integrated into one processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0099] In another embodiment of the present invention, a computer device is provided, the computer device including a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the adjustable load scheduling method based on hierarchical aggregation.

[0100] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the adjustable load scheduling method based on hierarchical aggregation in the above embodiment.

[0101] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An adjustable load scheduling method based on hierarchical aggregation, characterized in that: include: Collect the adjustable load resources of each adjustable load device in the area in real time, and obtain the adjustable load resources of the regional group according to the adjustable load resources of each adjustable load device; Obtain the regional supply-side power output forecast and the regional total load demand, and obtain the dispatch instructions for each user-level group in the region based on the multi-time-scale regional supply-demand balance dispatch model that introduces user comfort constraints according to the regional supply-side power output forecast, the regional total load demand and the adjustable load resources of the regional group; According to the dispatching instructions of each user level group in the area, the dispatching instructions of each device level group in each user level group are obtained, and according to the dispatching instructions of each device level group, each adjustable load device in each device level group is controlled.

2. The adjustable load scheduling method based on hierarchical aggregation according to claim 1 is characterized in that: The adjustable load equipment includes air conditioning equipment, electric vehicle equipment, energy storage equipment and distributed energy equipment.

3. The adjustable load scheduling method based on hierarchical aggregation according to claim 1 is characterized in that: The multi-time-scale regional supply and demand balance scheduling model that introduces user comfort constraints takes minimizing the sum of load regulation cost, power system peak-to-valley difference optimization cost and user comfort adjustment cost as the optimization goal, and is constructed with supply and demand balance constraints, equipment regulation capacity constraints and user comfort constraints as constraints.

4. The adjustable load scheduling method based on hierarchical aggregation according to claim 1 is characterized in that: The dispatch instructions for each user group are obtained based on the regional supply-side power output forecast, the regional total load demand and the adjustable load resources of the regional group, and based on the multi-time scale regional supply and demand balance dispatch model with user comfort constraints: Taking the historical medium-term dispatch instructions and historical long-term dispatch instructions of each user-level group as boundary conditions, according to the regional supply-side power output forecast, regional total load demand and adjustable load resources of the regional-level group, based on the multi-time-scale regional supply and demand balance dispatch model with user comfort constraints, the real-time short-term dispatch instructions, real-time medium-term dispatch instructions and real-time long-term dispatch instructions of each user-level group are obtained; The real-time medium-term scheduling instructions and real-time long-term scheduling instructions of each user-level group are recorded and used as the historical medium-term scheduling instructions and historical long-term scheduling instructions of each user-level group during the next scheduling, and the real-time short-term scheduling instructions of each user-level group are used as the final scheduling instructions of each user-level group.

5. The adjustable load scheduling method based on hierarchical aggregation according to claim 1 is characterized in that: The dispatching instruction of the user-level group is an adjustable load resource adjustment amount; the dispatching instruction of each device-level group in each user-level group is obtained according to the dispatching instructions of each user-level group in the region, including: converting the adjustable load resource adjustment amount of each user-level group into an equipment adjustment instruction of each equipment-level group in each user-level group; wherein, when the equipment-level group is an air-conditioning equipment-level group, the equipment adjustment instruction is an air-conditioning temperature setting instruction; when the equipment-level group is an electric vehicle equipment-level group, the equipment adjustment instruction is an electric vehicle charging and discharging power instruction; when the equipment-level group is a mobile energy storage equipment-level group, the equipment adjustment instruction is a charging and discharging plan of the energy storage equipment; when the equipment-level group is a distributed energy equipment-level group, the equipment adjustment instruction is an inverter output limit.

6. The adjustable load scheduling method based on hierarchical aggregation according to claim 1 is characterized in that: The method of converting the adjustable load resource adjustment amount of each user-level group into the equipment adjustment instruction of each equipment-level group within each user-level group includes: allocating the adjustable load resource adjustment amount of each user-level group to each equipment-level group within each user-level group in the order of the flexibility of each equipment-level group from high to low, so as to obtain the adjustable load resource adjustment amount of each equipment-level group; and converting the adjustable load resource adjustment amount of each equipment-level group into the equipment adjustment instruction of each equipment-level group within each user-level group.

7. The adjustable load scheduling method based on hierarchical aggregation according to claim 1 is characterized in that: Also includes: The above steps are repeated at preset time intervals.

8. An adjustable load dispatching system based on hierarchical aggregation, characterized in that: include: A data collection module is used to collect the adjustable load resources of each adjustable load device in the area in real time, and obtain the adjustable load resources of the regional group according to the adjustable load resources of each adjustable load device; A dispatch instruction determination module is used to obtain the regional supply-side power output forecast and the regional total load demand, and obtain the dispatch instructions for each user-level group in the region based on the regional supply-side power output forecast, the regional total load demand and the adjustable load resources of the regional level group and the multi-time scale regional level supply and demand balance dispatch model that introduces user comfort constraints; The dispatch instruction execution module is used to obtain the dispatch instructions of each device level group in each user level group according to the dispatch instructions of each user level group in the area, and control each adjustable load device in each device level group according to the dispatch instructions of each device level group.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the adjustable load scheduling method based on hierarchical aggregation as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the adjustable load scheduling method based on hierarchical aggregation as claimed in any one of claims 1 to 7 are implemented.

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