Virtual power plant-oriented power load response task allocation method

Abstract

This invention provides a power load response task allocation method for virtual power plants, relating to the field of power system dispatching. It categorizes incentive-based load response and price-based demand response, including time-of-use pricing equipment, real-time pricing equipment, and peak pricing equipment for price-based demand response, and direct load control equipment, interruptible load equipment, and emergency demand response equipment for incentive-based load response. Combining the characteristics of different types of incentive-based load response and price-based demand response, time-of-use pricing equipment, peak pricing equipment, and interruptible load equipment are included in the day-ahead dispatching phase when formulating the day-ahead dispatching plan. During the intraday dispatching phase, real-time pricing equipment, direct load control equipment, emergency demand response equipment, and energy storage equipment are used for deviation compensation, ensuring not only the economic efficiency of the virtual power plant but also improving its ability to cope with uncertain risks.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching, and more specifically to a method for allocating power load response tasks for virtual power plants. Background Technology

[0002] A virtual power plant is a technology for managing distributed energy resources. It aggregates geographically dispersed and diverse distributed power sources, energy storage devices, and loads within a certain area, connecting them as a whole to the power grid and participating in its operation. Typically, a virtual power plant has an internal energy management system to achieve internal energy dispatch, power generation forecasting, and participation in electricity market transactions at various levels. Demand response, short for electricity load demand response, refers to the process by which electricity users, upon receiving direct compensation notices from power suppliers inducing load reduction or signals of rising electricity prices, change their habitual electricity consumption patterns to reduce or postpone their electricity load during a specific period in response to power supply, thereby ensuring grid stability and mitigating short-term price increases. It is one of the solutions for demand-side management.

[0003] In existing technologies, load response strategies are mainly divided into two types: price-based demand response and incentive-based demand response. Price-based demand response strategies are further divided into time-of-use pricing, peak-hour pricing, and real-time pricing. Time-of-use pricing is a common pricing strategy in China, effectively reflecting the cost differences in power supply during different time periods. Its main measures include appropriately increasing prices during peak periods and appropriately decreasing prices during off-peak periods to reduce the peak-to-valley load difference, improve user electricity consumption, and achieve peak shaving and valley filling. Incentive-based demand response refers to the demand response agency formulating corresponding policies based on the power system's supply and demand situation. Users reduce their electricity demand when the system needs it or when there is power shortage, thereby obtaining direct compensation or preferential prices for other periods. This includes direct load control, interruptible load, and emergency demand response. The incentives obtained by participating users generally come in two forms: one is direct compensation independent of existing pricing policies; the other is a discount based on existing prices.

[0004] Price-based demand response (PDR) and incentive-based demand response (IPR) each have their own advantages and disadvantages. For example, PDR is economically efficient, but its power regulation timeliness is poor and its risk is high; while IPR, although less economical, has higher frequency regulation timeliness. However, current technologies often use either PDR or IPR alone in electricity market transactions, or use IPR as reserve capacity for optimized control, lacking a comprehensive consideration of both types of demand response. This fails to fully leverage the advantages of both, resulting in lower economic efficiency for virtual power plants and increased uncertainty. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the shortcomings of the above-mentioned technical solutions by providing a power load response task allocation method for virtual power plants. By classifying incentive-based load responses and price-based demand responses, and combining the characteristics of different types of incentive-based load responses and price-based demand responses, the methods are optimized and scheduled in two stages: day-ahead and intraday. This not only ensures the economic efficiency of virtual power plants, but also improves their ability to cope with uncertain risks.

[0006] To achieve the above objectives, the present invention provides a power load response task allocation method for virtual power plants, comprising the following steps:

[0007] Step (1): Classify incentive-based load response and price-based demand response, and evaluate the load response resources within the virtual power plant area; among them, price-based demand response includes time-of-use pricing equipment, real-time pricing equipment and peak pricing equipment, and incentive-based load response includes direct load control equipment, interruptible load equipment and emergency demand response equipment.

[0008] Step (2): Combine historical operating data of the virtual power plant with weather forecasts to predict the output and load data of renewable energy in the virtual power plant area;

[0009] Step (3): Develop a day-ahead scheduling plan and include time-of-use pricing equipment, peak pricing equipment and interruptible load equipment in the day-ahead scheduling phase;

[0010] Step (4): During the intraday scheduling phase, real-time electricity pricing equipment, direct load control equipment, emergency demand response equipment, and energy storage equipment are used for deviation compensation.

[0011] Preferably, the classification of incentive-based load response and price-based demand response in step (1) includes: classifying price-based demand response into time-of-use pricing equipment, real-time pricing equipment, and peak pricing equipment; and classifying incentive-based load response into direct load control equipment, interruptible load equipment, and emergency demand response equipment.

[0012] Specifically, the method for classifying price-based load response is as follows: Time-series data of user equipment is collected through smart meters to form load characteristic curves. The k-means clustering algorithm is used to classify the load characteristic curves of all users. The classified curves are evaluated with the goal of maximizing user benefits. Based on the evaluation results, customers are divided into time-of-use pricing devices, real-time pricing devices, and peak pricing devices.

[0013] Specifically, the method for classifying incentive-driven load responses is as follows: by collecting time-series data of user equipment through smart meters, and analyzing user equipment based on the discrete characteristics, time-shifting characteristics, and whether the load can be interrupted, incentive-driven load responses are classified into direct load control equipment, interruptible load equipment, and emergency demand response equipment.

[0014] It is easy to understand that, due to the inherent uncontrollability of renewable energy output, the specific steps for predicting renewable energy output within the virtual power plant area in step (2) are as follows: taking the historical wind and solar power output for the same period, and combining it with weather factors, using a grey prediction method to predict the wind and solar power output for the next day. The specific steps for predicting load data in step (2) are as follows: collecting historical user data and local weather information, calculating the correlation coefficient between load and weather, using hierarchical clustering based on weighted average distance to perform cluster analysis on historical loads, establishing prediction models for the clustering results, and predicting the load.

[0015] Preferably, in step (3), the day-ahead scheduling phase uses maximizing the operating revenue of the virtual power plant as the objective function, and the specific objective function is as follows:

[0016]

[0017] Where T represents the time period, λ pr The feed-in tariff for renewable energy during the specified period, P dg P wind P pv λ represents the output of distributed gas turbine, photovoltaic, and wind power generation during the time period, respectively. tou , λ cpp , λ il P represents the electricity price during the time period for time-of-use pricing equipment, peak pricing equipment, and interruptible load equipment, respectively. tou P cpp P il These represent the adjusted power for time-of-use pricing equipment, peak pricing equipment, and interruptible load equipment during their respective time periods.

[0018] Preferably, in step (4), the intraday scheduling phase uses minimizing the operating cost of the virtual power plant system as the objective function, and the specific objective function is as follows:

[0019]

[0020] Among them, C dg C wind C pv λ represents the operating costs of distributed gas turbine, photovoltaic, and wind power generation during the time period, respectively. sc The electricity price for charging and discharging energy storage devices during the specified time period, P dis Pchr Indicates the energy storage discharge power and charging power during the time period; λ rtp , λ dlc , λ edr P represents the electricity price for the time period of the real-time pricing equipment, direct load control equipment, and emergency demand response equipment, respectively. rtp P dlc P edr These represent the adjusted power for the real-time pricing equipment, direct load control equipment, and emergency demand response equipment during the respective time periods, C. pun This is a penalty for power deviation.

[0021] The specific constraints are as follows:

[0022] Output constraints of distributed gas turbine, photovoltaic, and wind power generation:

[0023]

[0024]

[0025]

[0026] in, These are the upper and lower limits of the output of the gas turbine unit;

[0027] These are the upper and lower limits of photovoltaic unit output; These are the upper and lower limits of wind turbine output;

[0028] Energy storage power station output constraints:

[0029]

[0030] SOC min <SOC t <SOC max

[0031]

[0032]

[0033] SOC t SOC t-1 These represent the battery SOC values ​​at time t and time t-1, respectively. η represents the battery charge and discharge capacity at time t, respectively; chr η dis Represent the battery charging efficiency and discharging efficiency at time t, respectively; SOC min SOC max These represent the minimum and maximum SOC values ​​of the battery, respectively. This represents the minimum and maximum discharge values ​​of the battery at time t; This represents the minimum and maximum charging values ​​of the battery at time t;

[0034] Price-based demand response load constraints:

[0035]

[0036]

[0037]

[0038] in, These represent the minimum and maximum dispatchable power of time-of-use pricing equipment, real-time pricing equipment, and peak-price pricing equipment at time t, respectively.

[0039] Incentive-based demand response load constraints:

[0040]

[0041]

[0042]

[0043] in, These represent the minimum and maximum dispatchable power of direct load control equipment, interruptible load equipment, and emergency demand response equipment at time t, respectively.

[0044] Based on the above technical solutions, the present invention provides a power load response task allocation method for virtual power plants, which has the following technical effects: It classifies incentive-based load response and price-based demand response, including time-of-use pricing equipment, real-time pricing equipment, and peak pricing equipment for price-based demand response, and direct load control equipment, interruptible load equipment, and emergency demand response equipment for incentive-based load response. Combining the characteristics of different types of incentive-based load response and price-based demand response, time-of-use pricing equipment, peak pricing equipment, and interruptible load equipment are included in the day-ahead scheduling phase when formulating the day-ahead scheduling plan. During the intraday scheduling phase, real-time pricing equipment, direct load control equipment, emergency demand response equipment, and energy storage equipment are used for deviation compensation, which not only ensures the economic efficiency of the virtual power plant but also improves its ability to cope with uncertain risks. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 A flowchart illustrating a power load response task allocation method for a virtual power plant, provided as an embodiment of this application;

[0047] Figure 2 This is a schematic diagram illustrating the participation of incentive-based load response and price-based demand response in the day-ahead and intraday scheduling phases, as provided in the embodiments of this application. Detailed Implementation

[0048] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0049] The following section will first combine the appendix. Figure 1 and Figure 2 The concepts involved in this application will be explained. It should be noted that the following explanation of each concept is only to make the content of this application easier to understand and does not imply any limitation on the scope of protection of this application.

[0050] According to one aspect of the invention, see the embodiment. Figure 1 This invention provides a method for allocating power load response tasks for virtual power plants, comprising the following steps:

[0051] Step (1): Classify incentive-based load response and price-based demand response, and evaluate the load response resources within the virtual power plant area; among them, price-based demand response includes time-of-use pricing equipment, real-time pricing equipment and peak pricing equipment, and incentive-based load response includes direct load control equipment, interruptible load equipment and emergency demand response equipment.

[0052] Step (2): Combine historical operating data of the virtual power plant with weather forecasts to predict the output and load data of renewable energy in the virtual power plant area;

[0053] Step (3): Develop a day-ahead scheduling plan and include time-of-use pricing equipment, peak pricing equipment and interruptible load equipment in the day-ahead scheduling phase;

[0054] Step (4): During the intraday scheduling phase, real-time electricity pricing equipment, direct load control equipment, emergency demand response equipment, and energy storage equipment are used for deviation compensation.

[0055] Preferably, the classification of incentive-based load response and price-based demand response in step (1) includes: classifying price-based demand response into time-of-use pricing equipment, real-time pricing equipment, and peak pricing equipment; and classifying incentive-based load response into direct load control equipment, interruptible load equipment, and emergency demand response equipment.

[0056] Specifically, the method for classifying price-based load response is as follows: Time-series data of user equipment is collected through smart meters to form load characteristic curves. The k-means clustering algorithm is used to classify the load characteristic curves of all users. The classified curves are evaluated with the goal of maximizing user benefits. Based on the evaluation results, customers are divided into time-of-use pricing devices, real-time pricing devices, and peak pricing devices.

[0057] Specifically, the method for classifying incentive-driven load responses is as follows: by collecting time-series data of user equipment through smart meters, and analyzing user equipment based on the discrete characteristics, time-shifting characteristics, and whether the load can be interrupted, incentive-driven load responses are classified into direct load control equipment, interruptible load equipment, and emergency demand response equipment.

[0058] It is easy to understand that, due to the inherent uncontrollability of renewable energy output, the specific steps for predicting renewable energy output within the virtual power plant area in step (2) are as follows: taking the historical wind and solar power output for the same period, and combining it with weather factors, using a grey prediction method to predict the wind and solar power output for the next day. The specific steps for predicting load data in step (2) are as follows: collecting historical user data and local weather information, calculating the correlation coefficient between load and weather, using hierarchical clustering based on weighted average distance to perform cluster analysis on historical loads, establishing prediction models for the clustering results, and predicting the load.

[0059] Preferably, see Figure 2 In step (3), time-of-use pricing equipment, peak pricing equipment, and interruptible load equipment are included in the day-ahead dispatching stage. The objective function of the day-ahead dispatching stage is to maximize the operating revenue of the virtual power plant. The specific objective function is as follows:

[0060]

[0061] Where T represents the time period, λ pr The feed-in tariff for renewable energy during the specified period, P dg P wind P pvλ represents the output of distributed gas turbine, photovoltaic, and wind power generation during the time period, respectively. tou , λ cpp , λ il P represents the electricity price during the time period for time-of-use pricing equipment, peak pricing equipment, and interruptible load equipment, respectively. tou P cpp P il These represent the adjusted power for time-of-use pricing equipment, peak pricing equipment, and interruptible load equipment during their respective time periods.

[0062] Preferably, see Figure 2 In step (4), real-time electricity pricing equipment, direct load control equipment, emergency demand response equipment, and energy storage equipment are used for deviation compensation. The objective function for the intraday scheduling phase is to minimize the operating cost of the virtual power plant system. The specific objective function is as follows:

[0063]

[0064] Among them, C dg C wind C pv λ represents the operating costs of distributed gas turbine, photovoltaic, and wind power generation during the time period, respectively. sc The electricity price for charging and discharging energy storage devices during the specified time period, P dis P chr Indicates the energy storage discharge power and charging power during the time period; λ rtp , λ dlc , λ edr P represents the electricity price for the time period of the real-time pricing equipment, direct load control equipment, and emergency demand response equipment, respectively. rtp P dlc P edr These represent the adjusted power for the real-time pricing equipment, direct load control equipment, and emergency demand response equipment during the respective time periods, C. pun This is a penalty for power deviation.

[0065] The specific constraints are as follows:

[0066] Output constraints of distributed gas turbine, photovoltaic, and wind power generation:

[0067]

[0068]

[0069]

[0070] in, These are the upper and lower limits of the output of the gas turbine unit;

[0071] These are the upper and lower limits of photovoltaic unit output; These are the upper and lower limits of wind turbine output;

[0072] Energy storage power station output constraints:

[0073]

[0074] SOC min <SOC t <SOC max

[0075]

[0076]

[0077] SOC t SOC t-1 These represent the battery SOC values ​​at time t and time t-1, respectively. η represents the battery charge and discharge capacity at time t, respectively; chr η dis Represent the battery charging efficiency and discharging efficiency at time t, respectively; SOC min SOC max These represent the minimum and maximum SOC values ​​of the battery, respectively. This represents the minimum and maximum discharge values ​​of the battery at time t; This represents the minimum and maximum charging values ​​of the battery at time t;

[0078] Price-based demand response load constraints:

[0079]

[0080]

[0081]

[0082] in, These represent the minimum and maximum dispatchable power of time-of-use pricing equipment, real-time pricing equipment, and peak-price pricing equipment at time t, respectively.

[0083] Incentive-based demand response load constraints:

[0084]

[0085]

[0086]

[0087] in, These represent the minimum and maximum dispatchable power of direct load control equipment, interruptible load equipment, and emergency demand response equipment at time t, respectively.

[0088] Based on the above technical solution, the present invention not only ensures the economic efficiency of the virtual power plant, but also improves its ability to cope with uncertain risks.

[0089] Those skilled in the art will understand that the embodiments described herein can be provided as methods, apparatus (devices), or computer program products. Therefore, this document may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. This includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0090] This document is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments herein. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] The embodiments and / or implementation methods described above are merely preferred embodiments and / or implementation methods for implementing the technology of the present invention, and are not intended to limit the implementation methods of the technology of the present invention in any way. Any person skilled in the art can make some modifications or alterations to other equivalent embodiments without departing from the scope of the technical means disclosed in the content of the present invention, but they should still be regarded as the technology or embodiments that are substantially the same as the present invention.

Claims

1. A method for allocating power load response tasks for virtual power plants, characterized in that, Includes the following steps: Step (1): Classify incentive-based load response and price-based demand response, and evaluate the load response resources within the virtual power plant area; among them, price-based demand response includes time-of-use pricing equipment, real-time pricing equipment and peak pricing equipment, and incentive-based load response includes direct load control equipment, interruptible load equipment and emergency demand response equipment. Step (2): Combine historical operating data of the virtual power plant with weather forecasts to predict the output and load data of renewable energy in the virtual power plant area; Step (3): Develop a day-ahead scheduling plan and include time-of-use pricing equipment, peak pricing equipment and interruptible load equipment in the day-ahead scheduling phase; Step (4): During the intraday scheduling phase, real-time electricity pricing equipment, direct load control equipment, emergency demand response equipment, and energy storage equipment are used for deviation compensation. The specific method for classifying price-based demand response in step (1) is as follows: collect time-series data of user equipment through smart meters and form load characteristic curves. Use the k-means clustering algorithm to classify the load characteristic curves of all users. Evaluate the classified curves with the goal of maximizing user benefits. Based on the evaluation results, classify users into time-of-use pricing devices, real-time pricing devices, and peak pricing devices. The specific method for classifying the incentive-type load response in step (1) is as follows: by collecting time-series data of user equipment through smart meters, and analyzing user equipment based on the discrete characteristics, time-shifting characteristics, and whether the load can be interrupted, incentive-type load responses are classified into direct load control equipment, interruptible load equipment, and emergency demand response equipment. In step (3), the day-ahead scheduling phase uses maximizing the operating revenue of the virtual power plant as the objective function, and the specific objective function is as follows: ； Where T represents the time period, The feed-in tariff for renewable energy during the specified period. These represent the output of distributed gas turbines, wind power, and photovoltaic power generation during the time period, respectively. These represent the electricity prices for time-of-use pricing equipment, peak-price pricing equipment, and interruptible load pricing equipment, respectively, during the specified time periods. These represent the adjusted power for time-of-use pricing equipment, peak pricing equipment, and interruptible load equipment during their respective time periods; In step (4), the intraday scheduling phase uses minimizing the operating cost of the virtual power plant system as the objective function. The specific objective function is as follows: ； in, These represent the operating costs of distributed gas turbine, wind power, and photovoltaic power generation during the respective time periods. The electricity price for charging and discharging energy storage devices during a given time period. This indicates the energy storage discharge power and charging power within a given time period; These represent the electricity prices for the time periods of real-time pricing equipment, direct load control equipment, and emergency demand response equipment, respectively. These represent the adjusted power for the real-time pricing equipment, direct load control equipment, and emergency demand response equipment during the respective time periods. This is a penalty for power deviation.

2. The power load response task allocation method for virtual power plants according to claim 1, characterized in that, The specific steps for predicting the renewable energy output within the virtual power plant area in step (2) are as follows: take the wind and solar power output of the same period in history, combine it with weather factors, and use the grey prediction method to predict the wind and solar power output for the next day.

3. The power load response task allocation method for virtual power plants according to claim 2, characterized in that, The specific steps for predicting load data in step (2) are as follows: collect historical user data and local weather information, calculate the correlation coefficient between load and weather, perform hierarchical clustering based on weighted average distance to perform cluster analysis on historical load, establish prediction models for the clustering results, and predict the load.

4. The power load response task allocation method for virtual power plants according to claim 1, characterized in that, The specific constraints are as follows: Output constraints of distributed gas turbine, photovoltaic, and wind power generation: ； ； ； in, These are the upper and lower limits of the output of the gas turbine unit; These are the upper and lower limits of the photovoltaic unit's output, respectively. These are the upper and lower limits of wind turbine output; Energy storage power station output constraints: ； ； ； ； These represent the battery SOC values ​​at time t and time t-1, respectively. These represent the battery's charging capacity and discharging capacity at time t, respectively. Let represent the battery charging efficiency and discharging efficiency at time t, respectively. These represent the minimum and maximum SOC values ​​of the battery, respectively. This represents the minimum and maximum discharge values ​​of the battery at time t; This represents the minimum and maximum charging values ​​of the battery at time t; Price-based demand response load constraints: ； in, Let represent the minimum and maximum dispatchable power of time-of-use pricing equipment at time t, respectively. Let represent the minimum and maximum dispatchable power of the real-time electricity pricing equipment at time t, respectively. Let represent the minimum and maximum dispatchable power of peak-price-type equipment at time t, respectively; Excitation-based load response load constraints: ； in, Let represent the minimum and maximum dispatchable power of the direct load control type equipment at time t, respectively. Let represent the minimum and maximum dispatchable power of interruptible load-type equipment at time t, respectively. These represent the minimum and maximum dispatchable power of emergency demand response equipment at time t, respectively.

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