Power resource scheduling method based on uncertainty and related equipment

Abstract

The invention discloses an uncertainty-based power resource scheduling method and related equipment. The method comprises the steps of calculating first loss according to acquired historical electricity price data and a preset value algorithm; establishing a first model according to the first loss and historical electricity price data; establishing a second model according to the obtained market income data, scene calling data and contract preset calling unit price; calculating a second loss according to a preset value algorithm and the uncertainty output data, and substituting the second loss into the second model to obtain a maximized objective function; calculating calling cost according to the obtained market participation data to obtain a third model; and substituting the current clearing electricity price prediction value into the first model, the maximization objective function and the third model for correlation calculation to obtain an optimized resource scheduling scheme. The resource utilization rate can be improved, and the reasonability of a resource scheduling scheme is improved. The method can be widely applied to the technical field of electric power.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power, and particularly relates to a power resource scheduling method based on uncertainty and related equipment. BACKGROUND

[0002] The distributed resource aggregator manages the distributed energy system, participates in the power transaction market to provide power energy, and participates in the backup auxiliary service market to provide backup capacity; however, due to the uncertainty of the output of the wind and light power sources in the distributed energy system, the distributed resource aggregator is difficult to formulate a reasonable resource scheduling scheme, thereby affecting the utilization rate of the power resources and further affecting the income of the distributed resource aggregator. SUMMARY

[0003] The main purpose of the embodiments of the present application is to provide a power resource scheduling method based on uncertainty and related equipment, which can improve the rationality of the resource scheduling scheme and improve the utilization rate of the power resources.

[0004] To achieve the above purpose, one aspect of the embodiments of the present application provides a power resource scheduling method based on uncertainty, which comprises the following steps:

[0005] obtaining a current clearing price prediction value, historical price data, market income data, scene calling data, aggregator uncertainty output data and market participation data;

[0006] calculating according to the historical price data and a preset value algorithm to determine a first loss, and establishing a model according to the first loss and the historical price data to determine a first model;

[0007] establishing a model according to the market income data, the scene calling data and a preset calling unit price to determine a second model;

[0008] calculating according to the preset value algorithm and the aggregator uncertainty output data to determine a second loss, and determining a maximization target function according to the second loss and the second model;

[0009] calculating a calling cost according to the market participation data to determine a third model, and calculating according to the current clearing price prediction value, the first model, the maximization target function and the third model to determine a target scheduling scheme.

[0010] In some embodiments, the calculating according to the historical price data and a preset value algorithm to determine a first loss specifically comprises:

[0011] analyzing the historical price data to determine historical clearing prices and price prediction data;

[0012] According to the historical clearing price and a first loss function of the preset value algorithm, actual loss data is determined by calculation;

[0013] According to the historical clearing price and price prediction data, a first probability density function is determined by statistical analysis, and a second probability density function is determined by integral calculation according to the actual loss data, the first probability density function and a first preset threshold value;

[0014] According to the second probability density function, a first preset confidence, the first preset threshold value and a first value function of the preset value algorithm, a first risk value function is determined by calculation;

[0015] According to the first risk value function, the actual loss data and a second loss function of the preset value function, an excess loss function is determined by calculation, and the first loss is determined according to the first risk value function, the first preset confidence and the excess loss function.

[0016] In some embodiments, the second model is determined by model establishment according to the market income data, the scenario calling data and a preset calling unit price, specifically including:

[0017] The scenario calling data is parsed to determine a power calling data set and a backup capacity calling data set, and the backup capacity calling data set is statistically analyzed to determine a calling probability value;

[0018] According to the preset calling unit price, the calling probability value, the power calling data set and the backup calling data set, a calling cost function is determined by calculation;

[0019] According to the market income data and the calling cost function, a net income function is determined by difference calculation, and the second model is determined by calculation according to the net income function and a preset scenario probability.

[0020] In some embodiments, the second loss is determined by calculation according to the preset value algorithm and aggregator uncertainty output data, specifically including:

[0021] The aggregator uncertainty output data is statistically analyzed to determine a third probability density function;

[0022] According to the third probability density function, a second preset threshold value and the second model, a fourth probability density function is determined by integral calculation;

[0023] According to the fourth probability density function, the second preset threshold value, a second preset confidence and a first value function of the preset value algorithm, a second risk value function is determined by calculation;

[0024] determining the first revenue data according to the second risk value function and the net revenue function, and determining the second loss according to the second risk value function, the second preset confidence, the first revenue data and a preset scenario probability.

[0025] In some embodiments, the determining the third model according to the calling cost calculation based on the market participation data specifically comprises:

[0026] analyzing the market participation data to determine market bidding data and market bidding prices;

[0027] calculating the calling cost minimization function according to the market bidding data and the market bidding prices, and analyzing a joint clearing constraint condition according to a market supply and demand balance condition, a power generation enterprise operation constraint and an aggregator operation constraint;

[0028] establishing a third model according to the calling cost minimization function and the joint clearing constraint condition.

[0029] In some embodiments, the determining the target dispatching scheme according to the current clearing price prediction value, the first model, the maximization target function and the third model specifically comprises:

[0030] substituting the current clearing price prediction value into the first model to perform a solving operation, to determine an aggregator bidding price data set, wherein the aggregator bidding price data set comprises energy market bidding price data and standby auxiliary market bidding price data of a plurality of distributed aggregators;

[0031] substituting the aggregator bidding price data set into the maximization target function to perform a calculation, to determine an aggregator reporting dispatching data set, wherein the aggregator reporting dispatching data set comprises energy market reporting dispatchable data and standby auxiliary market reporting dispatchable data of the plurality of distributed aggregators;

[0032] substituting the aggregator bidding price data set into the third model to perform an optimization calculation, to determine an optimized bidding price data set, and determining the target dispatching scheme according to the optimized bidding price data set and the reporting dispatching data set.

[0033] To achieve the above object, another aspect of the embodiment of the present application provides a power resource dispatching system based on uncertainty, which comprises:

[0034] an acquisition module, configured to acquire a current clearing price prediction value, historical price data, market revenue data, scenario calling data, aggregator uncertain output data and market participation data;

[0035] a building module configured to determine a first loss by calculating according to the historical electricity price data and a preset value algorithm, and determine a first model by modeling according to the first loss and the historical electricity price data;

[0036] an aggregating module configured to determine a second model by modeling according to the market revenue data, the scenario calling data and a preset calling unit price;

[0037] a building module configured to determine a second loss by calculating according to the preset value algorithm and the aggregator uncertainty output data, and determine a maximization target function according to the second loss and the second model;

[0038] an optimizing module configured to determine a third model by calculating calling cost according to the market participation data, and determine a target scheduling scheme by calculating according to the current clearing electricity price prediction value, the first model, the maximization target function and the third model.

[0039] To achieve the above object, another aspect of the embodiments of the present application provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the method described above when executing the computer program.

[0040] To achieve the above object, another aspect of the embodiments of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method described above.

[0041] To achieve the above object, another aspect of the embodiments of the present application provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the method described above.

[0042] The embodiments of the present application at least have the following beneficial effects: The embodiments of the present application provide a power resource scheduling method and system based on uncertainty, an electronic device, a storage medium and a program product, which calculate a first loss according to the acquired historical electricity price data and a preset value algorithm; a first model is established according to the first loss and the historical electricity price data; a second model is established according to the acquired market revenue data, scene calling data and contract preset calling unit price; a second loss is calculated according to the preset value algorithm and the uncertain output data, and the second loss is substituted into the second model to obtain a maximized target function; a calling cost is calculated according to the acquired market participation data to obtain a third model; a current clearing electricity price prediction value is substituted into the first model, the maximized target function and the third model for associated calculation to obtain an optimized resource scheduling scheme. The first model and the maximized target function are established according to the acquired data, and the loss is calculated according to the value algorithm and the acquired data, the risk loss caused by the uncertainty of the wind and light power output is quantified, the resource scheduling scheme of the resource aggregator is optimized according to the quantified risk loss and the established model, and the rationality of the scheme is improved; and the third model is established to realize market clearing, and the resource utilization efficiency is improved. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 is a flowchart of a power resource scheduling method based on uncertainty provided by the embodiments of the present application;

[0044] Figure 2 is a flowchart of step S102 in Figure 1

[0045] Figure 3 is a flowchart of step S103 in Figure 1

[0046] Figure 4 is a flowchart of step S104 in Figure 1

[0047] Figure 5 is a flowchart of step S105 in Figure 1

[0048] Figure 6 is another flowchart of step S105 in Figure 1

[0049] Figure 7 is a schematic diagram of a calculation system structure in a specific embodiment provided by the embodiments of the present application;

[0050] Figure 8 is a flowchart of a specific embodiment provided by the embodiments of the present application;

[0051] Figure 9 ​​​​​is a structural schematic diagram of an uncertain-based power resource scheduling system provided by an embodiment of the present application.

[0052] Figure 10 is a hardware structure schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. When the following description relates to the drawings, the same numerals in different drawings represent the same or similar elements unless otherwise indicated. The implementation manners described in the following exemplary embodiments do not represent all the implementation manners consistent with the embodiments of the present application, but are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0054] It can be understood that the terms "first", "second", and the like used in the present application can be used herein to describe various concepts, but unless specifically stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "when" or "when" or "in response to determining".

[0055] The terms "at least one", "multiple", "each", "any" and the like used in the present application include one, two or more than two, multiple includes two or more than two, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0057] Figure 1 is an optional flowchart of an uncertain-based power resource scheduling method provided by an embodiment of the present application, Figure 1 The method in can include but is not limited to including steps S101 to S105.

[0058] Step S101, acquiring a current clearing price prediction value, historical price data, market revenue data, scenario calling data, aggregator uncertainty output data and market participation data;

[0059] Step S102, according to the historical electricity price data and the preset value algorithm, the first loss is determined; according to the first loss and the historical electricity price data, the first model is established;

[0060] Step S103, according to the market revenue data, the scene calling data and the preset calling unit price, the second model is established;

[0061] Step S104, according to the preset value algorithm and the aggregator uncertainty output data, the second loss is determined; and according to the second loss and the second model, the maximization target function is determined;

[0062] Step S105, according to the market participation data, the calling cost is calculated, the third model is determined; and according to the current clearing price prediction value, the first model, the maximization target function and the third model, the target scheduling scheme is determined.

[0063] The steps S101 to S105 shown in the embodiments of the application, by collecting the clearing price, the market revenue, the scene calling data, the uncertainty output data and the market participation data of the distributed resource aggregator participating in the energy market and the reserve auxiliary service market, the bidding strategy model, the revenue maximization model and the joint clearing model of the resource aggregator participating in the power trading market are constructed, and the algorithm of the conditional value at risk is used to quantify the risk loss caused by the uncertainty characteristics of the distributed energy such as the wind and light power source of the resource aggregator; the quantified risk loss is substituted into the constructed bidding strategy model, the revenue maximization model and the joint clearing model, the constructed bidding strategy model, the revenue maximization model and the joint clearing model are solved, and the corresponding bidding strategy and the resource scheduling strategy of the uncertainty scene are obtained; the resource aggregator participates in the power trading market through the obtained bidding strategy and the resource scheduling strategy, and the revenue maximization and the resource scheduling cost minimization can be realized.

[0064] Please refer to Figure 2 In some embodiments, step S102 can include but is not limited to steps S201 to S205:

[0065] Step S201, the historical electricity price data is parsed, the historical clearing price and the electricity price prediction data are determined;

[0066] Step S202, according to the historical clearing price and the first loss function of the preset value algorithm, the actual loss data is determined;

[0067] Step S203, according to the historical clearing price and the electricity price prediction data, the statistical analysis is performed, the first probability density function is determined; and according to the actual loss data, the first probability density function and the first preset threshold, the integral calculation is performed, the second probability density function is determined;

[0068] Step S204, according to the second probability density function, the first preset confidence, the first preset threshold and the first value function of the preset value algorithm, a first risk value function is determined by calculation;

[0069] Step S205, according to the first risk value function, the actual loss data and the second loss function of the preset value function, an excess loss function is determined by calculation; and according to the first risk value function, the first preset confidence and the excess loss function, a first loss is determined by calculation.

[0070] In step S201 of some embodiments, the obtained historical electricity price data is parsed, and the historical clearing price of the resource aggregator participating in the electricity trading market and the corresponding electricity price prediction data, i.e. the clearing price prediction data, are extracted; and a bidding model of the bidding price of the resource aggregator participating in the market is constructed according to the historical clearing price.

[0071] In step S202 of some embodiments, the actual loss of the resource aggregator participating in the energy market at a certain moment is obtained by calculation according to the obtained historical clearing price and the loss function in the preset value algorithm; and the risk loss caused by the uncertainty scenario is quantified according to the calculated actual loss; in this embodiment, the actual loss is calculated according to the following loss function:

[0072]

[0073] Wherein, is the actual loss of the ith distributed resource aggregator at time t, y t is the clearing price of the energy market at time t, is the energy market bidding price of the ith distributed resource aggregator at time t.

[0074] In step S203 of some embodiments, the difference between the clearing price and the corresponding electricity price prediction value is obtained by calculation according to the obtained historical clearing price and the electricity price prediction data; the calculated difference is statistically analyzed to obtain the corresponding first probability density function, which represents the probability distribution of different differences between the clearing price and the prediction value of the resource aggregator participating in the energy market at time t; the second probability density function is obtained by integral operation according to the obtained first probability density function, the calculated actual loss of the distributed resource aggregator and the set threshold, which represents the probability distribution of the actual loss of the distributed resource aggregator not exceeding the set threshold; in this embodiment, the formula of the second probability density function is as follows:

[0075]

[0076] Wherein, is a second probability density function, is a first probability density function, is a preset threshold value, is a difference between a predicted value of a bidding price and a clearing price of the i-th distributed resource aggregator at the t time.

[0077] In step S204 of some embodiments, the risk value of the distributed resource aggregator under the set confidence level is obtained according to the obtained second probability density function, the set first confidence level, the set threshold value and the value function in the conditional risk value; in this embodiment, the risk value is obtained by the following formula:

[0078]

[0079] wherein, is the risk value of the i-th distributed resource aggregator at the t time and under the confidence level β, is a second probability density function, is a preset threshold value, and β is the set confidence level.

[0080] In step S205 of some embodiments, the excess loss is obtained according to the calculated risk value, the actual loss and the loss function in the conditional risk value algorithm; the excess loss can further quantify the loss of the distributed resource aggregator caused by the uncertainty of the energy market clearing price; in this embodiment, the excess loss is obtained by the following formula:

[0081]

[0082] wherein, is the excess loss of the i-th distributed resource aggregator at the t time and under the clearing price y t , is the actual loss of the i-th distributed resource aggregator at the t time, is the risk value of the i-th distributed resource aggregator at the t time and under the confidence level β. After the excess loss is calculated, the conditional risk value of the energy market clearing price uncertainty at a certain time to the distributed resource aggregator is obtained according to the obtained excess loss, the risk value and the preset confidence level, so as to quantify the risk loss caused by the energy market clearing price uncertainty; in this embodiment, the conditional risk value is calculated by the following formula:

[0083]

[0084] wherein,

[0085] ​β is the confidence level set by the i th distributed resource aggregator at time t, β is the confidence level set by the i th distributed resource aggregator at time t, β is the confidence level set by the i th distributed resource aggregator at time t, t β is the confidence level set by the i th distributed resource aggregator at time t, β is the confidence level set by the i th distributed resource aggregator at time t, β is the confidence level set by the i th distributed resource aggregator at time t, t β is the confidence level set by the i th distributed resource aggregator at time t.

[0086] Please refer to Figure 3 In some embodiments, step S103 can include but is not limited to steps S301-S303:

[0087] Step S301, the scene calling data is parsed, the power calling data set and the standby capacity calling data set are determined, and the standby capacity calling data set is statistically analyzed to determine the calling probability value;

[0088] Step S302, according to the preset calling unit price, the calling probability value, the power calling data set and the standby calling data set, the calling cost function is calculated;

[0089] Step S303, according to the market income data and the calling cost function, the difference is calculated to determine the net income function; and according to the net income function and the preset scene probability, the second model is determined.

[0090] In step S301 of some embodiments, the obtained scene calling data is parsed to determine the power calling data of wind and light power sources under different scenes, and the standby capacity calling data of wind and light power sources under different uncertain scenes; the statistical probability of the parsed standby capacity calling data is approximated to obtain the calling probability value of the standby capacity at a certain time, and the calling probability value is applied to different scenes at the time, that is, at the time, the calling probability of different scenes to standby capacity is the same.

[0091] In step S302 of some embodiments, according to the calling probability value obtained by statistical analysis, the unit calling price specified in the contract signed by the owner and the resource aggregator of wind and light power sources and other uncertain resource handling resources, the power calling data and standby capacity calling data when wind and light power sources and other uncertain resource handling resources are output are calculated to obtain the market net income of the distributed resource aggregator under the uncertain resource output scene; in this embodiment, the market net income is calculated by the following formula:

[0092]

[0093] wherein C s,i is the market net revenue of the ith distributed resource aggregator in the uncertain output scenario s, are the unit invocation prices of wind power, photovoltaic power, curtailed load, energy storage, and electric vehicle in the contract signed by the ith distributed resource aggregator with the distributed resource owner, in sequence, are the power and reserve capacity reported by the ith distributed resource aggregator, respectively, are the bidding prices of the energy market and the reserve auxiliary service market reported by the ith distributed resource aggregator, respectively, are the scheduled powers of wind power, photovoltaic power, curtailed load, energy storage, and electric vehicle of the ith distributed resource aggregator in the energy market at time t in the uncertain output scenario s, respectively, are the reserved reserve capacities of wind power, photovoltaic power, curtailed load, energy storage, and electric vehicle provided by the ith distributed resource aggregator at time t in the uncertain output scenario s, respectively, and γ t is the invocation probability at time t, and Δt is the invocation period.

[0094] In step S303 of some embodiments, after the cost data of invoking different types of distributed resources of a certain distributed resource aggregator in an uncertain output scenario is calculated, the invocation cost is removed from the revenue data of the distributed aggregator to obtain the net revenue of the distributed resource aggregator participating in the energy market; then, the occurrence probability of the uncertain output scenario and the type of the uncertain output scenario obtained through statistical analysis are calculated to obtain the market expected net revenue of the distributed resource aggregator, which serves as the second model; in this embodiment, the formula of the market expected net revenue of the distributed resource aggregator is as follows:

[0095]

[0096] wherein C i is the market expected net revenue of the ith distributed resource aggregator, π s,i is the probability of the occurrence of the uncertain output scenario of the ith distributed resource aggregator, C s,i is the market net revenue of the ith distributed resource aggregator in the uncertain output scenario s.

[0097] Please refer to Figure 4 In some embodiments, step S104 can include but is not limited to steps S401 to S404:

[0098] Step S401, statistically analyze the aggregator uncertain output data to determine a third probability density function;

[0099] Step S402, according to the third probability density function, the second preset threshold and the second model, integral calculation is carried out to determine the fourth probability density function;

[0100] Step S403, according to the fourth probability density function, the second preset threshold, the second preset confidence and the first value function of the preset value algorithm, calculation is carried out to determine the second risk value function;

[0101] Step S404, according to the second risk value function and the net income function, calculation is carried out to determine the first income data; and according to the second risk value function, the second preset confidence, the first income data and the preset scene probability, calculation is carried out to determine the second loss.

[0102] In step S401 of some embodiments, the uncertainty output data in the distributed resource collected by the distributed aggregator is statistically analyzed to determine the probability distribution of the sum of the uncertainty outputs of different types of distributed resources, i.e. the third probability density function; and subsequently, the third probability density function is calculated to determine the probability distribution of the expected market net income of the distributed resource aggregator under different sums of uncertainty outputs not exceeding the preset threshold.

[0103] In step S402 of some embodiments, according to the third probability density function obtained by statistical analysis, the second threshold and the second model established in the foregoing, calculation is carried out to determine the probability distribution of the expected market net income of the distributed resource aggregator under different sums of uncertainty outputs not exceeding the preset threshold, i.e. the fourth probability density function; in this embodiment, the fourth probability density function is calculated by the following formula:

[0104]

[0105] Wherein, is the probability density function of the expected market net income C i of the i th distributed resource aggregator under different sums of uncertainty outputs not exceeding the preset threshold , G i is the sum of the wind and solar power uncertainty outputs of the i th distributed resource aggregator.

[0106] In step S403 of some embodiments, according to the calculated fourth probability density function, the set threshold, and the value function in the conditional risk value algorithm, calculation is carried out to determine the risk value of the distributed resource aggregator under the set confidence; in this embodiment, the risk value is calculated by the following formula:

[0107]

[0108] Wherein, the value at risk of the ith distributed resource aggregator at the confidence level β, the expected market net revenue C of the ith distributed resource aggregator at the sum of different uncertainty outputs i not more than a preset threshold the probability density function of x i is the decision variable geometry of the ith distributed resource aggregator, i.e., the distributed new energy available for dispatching in the energy market and the reserve service market.

[0109] In step S404 of some embodiments, the excess revenue of the market net revenue of the distributed resource aggregator exceeding the value at risk is calculated as first revenue data according to the calculated market net revenue function of the distributed resource aggregator and the calculated value at risk; then, the conditional value at risk of the wind-solar power output uncertainty to the distributed resource aggregator is calculated as second loss according to the first revenue data, the set confidence level, the value at risk of the distributed resource aggregator at the set confidence level, and the scene occurrence probability of the uncertainty output; in this embodiment, the second loss is calculated by the following formula:

[0110]

[0111] wherein, the conditional value at risk of the wind-solar power output uncertainty to the ith distributed resource aggregator, the value at risk of the ith distributed resource aggregator at the confidence level β, π s,i the probability of the uncertainty output scene of the ith distributed resource aggregator, τ s,i the excess revenue of the market net revenue of the ith distributed resource aggregator exceeding the value at risk in the uncertainty output scene s.

[0112] In this embodiment, after obtaining the maximized objective function, the constraint condition of the maximized objective function is established according to the operation conditions of the energy market and the reserve auxiliary service market, and the operation constraints of the distributed resources such as wind-solar power; in this embodiment, the constructed constraint conditions include:

[0113] Balancing constraint of bidding power and reserve capacity:

[0114]

[0115] wherein, respectively, the power and reserve capacity reported by the distributed resource aggregator, respectively, the dispatching power of wind power, solar power, curtailed load, energy storage, and electric vehicle of the ith distributed resource aggregator for the energy market at time t in the uncertainty output scene s, respectively are the reserved reserve capacity provided by the wind power and the photovoltaic power of the i th distributed resource aggregator at time t under the uncertain output scenario s;

[0116] Wind-photovoltaic power operation constraints:

[0117]

[0118] wherein, respectively are the scheduled power of the wind power and the photovoltaic power of the i th distributed resource aggregator for the energy market at time t under the uncertain output scenario s, respectively are the reserved reserve capacity provided by the wind power and the photovoltaic power of the i th distributed resource aggregator at time t under the uncertain output scenario s, respectively are the predicted values of the maximum output of the wind power and the photovoltaic power of the i th distributed resource aggregator at time t under the uncertain output scenario s;

[0119] Load curtailment constraints:

[0120]

[0121] wherein, is the total power of the load curtailment of the i th distributed resource aggregator at time t under the uncertain output scenario s, is the maximum load curtailment of the i th distributed resource aggregator at time t, and k is the maximum curtailment ratio;

[0122] Energy storage operation constraints:

[0123]

[0124] wherein, respectively are 0-1 variables of the discharging and charging states of the energy storage of the i th distributed resource aggregator at time t under the uncertain output scenario s, is the discharging amount of the energy storage of the i th distributed resource aggregator at time t under the uncertain output scenario s, is the charging amount of the energy storage of the i th distributed resource aggregator at time t under the uncertain output scenario s, respectively are the upper and lower limits of the discharging power of the energy storage of the i th distributed resource aggregator, respectively are the upper and lower limits of the charging power of the energy storage of the i th distributed resource aggregator, E s,i,t is the capacity state of the energy storage in the i th distributed resource aggregator at time t under the uncertain output scenario s, respectively are the charging and discharging efficiency coefficients of the energy storage in the i th distributed resource aggregator, and the value ranges thereof are Ei , are the upper and lower limits of the capacity of the i-th distributed resource aggregator considering the life of energy storage and other factors, respectively;

[0125] Electric vehicle operation constraints:

[0126]

[0127] wherein, are 0-1 variables of the discharging and charging state of the electric vehicle of the i-th distributed resource aggregator at time t under the uncertain output scenario s, are the discharging and charging capacity of the electric vehicle of the i-th distributed resource aggregator at time t under the uncertain output scenario s, PV i d , are the upper and lower limits of the discharging power of the electric vehicle of the i-th distributed resource aggregator, respectively, PV i c , are the upper and lower limits of the charging power of the electric vehicle of the i-th distributed resource aggregator, respectively.

[0128] Referring to Figure 5 In some embodiments, step S105 can include but is not limited to steps S501 to S503:

[0129] Step S501, the market participation data is parsed to determine the market bid data and the market bid price;

[0130] Step S502, according to the market bid data and the market bid price, the cost minimization function is determined by calculation; according to the market supply and demand balance condition, the power generation enterprise operation constraint and the aggregator operation constraint, the joint clearing constraint condition is determined by analysis;

[0131] Step S503, according to the calling cost minimization function and the joint clearing constraint condition, the third model is determined by model establishment.

[0132] In step S501 of some embodiments, the collected market participation data of the distributed resource aggregator is parsed to determine the bid data and the bid price of participating in the energy market and the reserve auxiliary service market, including the bid power data and the reserve capacity data of the power generation enterprise in the energy market and the reserve auxiliary service market, respectively, the bid price of the power generation enterprise in the energy market and the reserve auxiliary service market, respectively, the bid power data and the reserve capacity data of the distributed resource aggregator in the energy market and the reserve auxiliary service market, respectively, and the bid price of the distributed resource aggregator in the energy market and the reserve auxiliary service market, respectively;

[0133] In step S502 of some embodiments, according to the parsed winning data and the bidding price, a calling cost minimization function of the power generation enterprises and the distributed resource aggregators participating in the energy market and the reserve auxiliary service market is determined, which specifically includes the cost of the power generation enterprises and the distributed resource aggregators providing electric energy and the cost of providing reserve capacity; in the present embodiment, the calling cost minimization function is as follows:

[0134]

[0135] wherein, are the bidding prices of the jth power generation enterprise participating in the energy market and the reserve auxiliary service market at t time, respectively, are the winning power and reserve capacity of the jth power generation enterprise at t time, respectively, are the winning power and reserve capacity of the ith distributed resource aggregator at t time, respectively, are the bidding prices of the ith distributed resource aggregator participating in the energy market and the reserve auxiliary service market at t time, respectively.

[0136] In step S503 of some embodiments, after the calling cost minimization function is constructed, according to the balance condition of market participation, the operation condition of the power generation enterprise and the operation condition of the distributed resource aggregator, the constraint condition of the calling cost minimization function is constructed, and the calling cost minimization function and the corresponding constraint condition are taken as a joint clearing model, i.e., a third model; in the present embodiment, the constraint conditions established include:

[0137] Winning power and reserve capacity supply-demand balance constraint:

[0138]

[0139] wherein, are the winning power and reserve capacity of the jth power generation enterprise at t time, respectively, are the winning power and reserve capacity of the ith distributed resource aggregator at t time, respectively, are the power generation demand and reserve capacity demand of the system at t time, respectively.

[0140] Please refer to Figure 6 In some embodiments, step S105 can further include but is not limited to steps S601 to S603:

[0141] Step S601, substituting the current clearing price prediction value into the first model to perform solving operation, to determine the aggregator bidding price data set; wherein the aggregator bidding price data set includes the energy market bidding price data and the reserve auxiliary market bidding price data of a plurality of distributed aggregators;

[0142] Step S602, the aggregator bidding price dataset is substituted into the maximization objective function for calculation to determine the aggregator reporting scheduling dataset; wherein the aggregator reporting scheduling dataset includes energy market reporting dispatchable data and standby auxiliary market reporting dispatchable data of a plurality of distributed aggregators;

[0143] Step S603, the aggregator bidding price dataset is substituted into the third model for optimization calculation to determine the optimized bidding price dataset; the target scheduling scheme is determined according to the optimized bidding price dataset and the reporting scheduling dataset.

[0144] In step S601 of some embodiments, the predicted value of the clearing price of the participating energy market and standby auxiliary service market at the current time is obtained and substituted into the constructed first model, the first model calculates the difference between the bidding price of the participating market and the predicted value of the clearing price according to the input clearing price prediction value, that is:

[0145]

[0146] Among them, is the difference between the clearing price and its predicted value of the i th distributed resource aggregator participating in the energy market at t time, is the bidding price of the i th distributed resource aggregator participating in the energy market at t time, is the predicted value of the clearing price of the i th distributed resource aggregator participating in the energy market at t time; the difference is substituted into the first probability density function for analysis and calculation to determine the corresponding clearing price; the obtained clearing price is substituted into the loss function to calculate the actual loss value, and substituted into the second probability density function for calculation to determine the probability that the corresponding loss function does not exceed the preset threshold, and into the risk value function for calculation to obtain the risk value of the current distributed resource aggregator under the set confidence, and the calculated risk value and actual loss value are compared and calculated to determine the corresponding excess loss; according to the calculated excess calculation, the difference between the clearing price and the predicted value of the clearing price, and the corresponding risk value, the conditional risk value function is used for calculation to obtain the corresponding conditional risk value, and the conditional risk value and the risk aversion coefficient of the distributed resource aggregator are calculated to determine the bidding price of the distributed resource aggregator participating in the energy market and standby auxiliary service market; in this embodiment, the expression of the bidding price is as follows:

[0147]

[0148] Among them, is the bidding price of the i th distributed resource aggregator participating in the energy market at t time, λ i is the risk aversion coefficient of the i th distributed resource aggregator, The conditional risk value of the uncertainty of the energy market clearing price brought to the ith distributed resource aggregator at time t.

[0149] In step S602 of some embodiments, the risk loss brought by the uncertain output scenarios of the distributed resource aggregator is calculated by substituting the obtained bidding price into the maximized target function, and the expected market net income of the distributed resource aggregator participating in the market transaction is calculated according to the calculated bidding price, the calculated risk loss and the expected market net transaction are substituted into the constructed expected net income maximized target function, and the schedulable power and the provided reserve capacity of the distributed resource aggregator participating in the energy market and the reserve auxiliary service market are obtained, that is, the resource scheduling decision of the distributed resource aggregator.

[0150] In step S603 of some embodiments, the obtained bidding price strategy and resource scheduling strategy of the distributed resource aggregator participating in the market are substituted into the constructed third model for calculation to determine the scheduling cost of the current bidding price strategy and resource scheduling strategy, and the resource scheduling strategy and the bidding price strategy are optimized according to the constraint condition and the scheduling cost minimization function, to obtain the optimal market clearing scheme of the distributed resource aggregator, including the bidding price in the energy market and the reserve auxiliary service market, and the reported power and reserve capacity in the energy market and the reserve auxiliary service market.

[0151] Next, the scheme of the embodiments of the present application will be described and explained in detail in combination with specific application examples:

[0152] Please refer to Figure 7 , Figure 7 is a computing system applying the uncertain-based power resource scheduling method provided by the present application, comprising an establishing module, an aggregating module, a constructing module and an optimizing module; the computing system is provided with a processor and a memory, the memory stores the computer program capable of realizing the uncertain-based power resource scheduling method provided by the present application, and the program is executed by the processor; the computing system executes the computing process as shown in Figure 8 , realizes the uncertain-based power resource scheduling method provided by the present application, the system quantifies the risk loss brought by the uncertainty by using the conditional risk value, obtains the bidding model of the distributed resource aggregator in the energy market and the reserve auxiliary service market, then introduces the risk loss caused by the uncertain output of the wind and light power into the market expected net income function, constructs the target function of the distributed resource scheduling decision, then constructs the constraint condition of the distributed resource scheduling decision, and establishes the joint clearing model and the constraint condition of the energy and reserve auxiliary service market, and finally optimizes the market transaction strategy of the distributed resource aggregator based on the constructed risk-quantified energy reserve auxiliary service market power transaction decision method.

[0153] The embodiments of the present application at least have the following beneficial effects: the embodiments of the present application provide a power resource scheduling method and system based on uncertainty, an electronic device, a storage medium and a program product, the scheme calculates a first loss according to the acquired historical electricity price data and a preset value algorithm; and establishes a first model according to the first loss and the historical electricity price data; establishes a second model according to acquired market income data, scene calling data and a preset calling unit price; calculates a second loss according to the preset value algorithm and uncertain output data, and substitutes the second loss into the second model to obtain a maximized target function; calculates a calling cost according to acquired market participation data to obtain a third model; substitutes a current clearing electricity price prediction value into the first model, the maximized target function and the third model for associated calculation to obtain an optimized resource scheduling scheme. The first model and the maximized target function are respectively established according to the acquired data, and the loss is calculated according to the value algorithm and the acquired data, the risk loss caused by the uncertainty of wind and light power output is quantified, the resource scheduling scheme of the resource aggregator is optimized according to the quantified risk loss and the established model, and the rationality of the scheme is improved; and the third model is established to realize market clearing, and the resource utilization efficiency is improved.

[0154] Please refer to Figure 9 The embodiments of the present application also provide a power resource scheduling system based on uncertainty, which can implement the above method, and the system comprises:

[0155] The acquisition module is configured to acquire a current clearing electricity price prediction value, historical electricity price data, market income data, scene calling data, aggregator uncertain output data and market participation data.

[0156] The establishment module is configured to calculate a first loss according to the historical electricity price data and a preset value algorithm; and establish a first model according to the first loss and the historical electricity price data.

[0157] The aggregation module is configured to establish a second model according to the market income data, the scene calling data and a preset calling unit price.

[0158] The construction module is configured to calculate a second loss according to the preset value algorithm and the aggregator uncertain output data; and determine a maximized target function according to the second loss and the second model.

[0159] The optimization module is configured to calculate a third model according to a calling cost of the market participation data; and determine a target scheduling scheme according to the current clearing electricity price prediction value, the first model, the maximized target function and the third model.

[0160] It can be understood that the contents in the above method embodiments are all applicable to the present system embodiment, the present device embodiment specifically implements the same functions as the above method embodiments, and achieves the same beneficial effects as the above method embodiments.

[0161] The present application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor implements the above method when executing the computer program. The electronic device can be any smart terminal, such as a tablet computer or a vehicle-mounted computer.

[0162] It can be understood that the contents in the above method embodiments are all applicable to the present device embodiment, the present device embodiment specifically implements the same functions as the above method embodiments, and achieves the same beneficial effects as the above method embodiments.

[0163] Please refer to Figure 10 , Figure 10 The hardware structure of the electronic device of another embodiment is illustrated, which includes:

[0164] The processor 1001 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is used to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0165] The memory 1002 can be implemented in the form of a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory). The memory 1002 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 1002 and are called and executed by the processor 1001 to implement the above method of the present application.

[0166] The input / output interface 1003 is used to realize information input and output.

[0167] The communication interface 1004 is used to realize the communication interaction between the device and other devices. The communication can be realized by a wired manner (such as a USB, a network cable, etc.) or a wireless manner (such as a mobile network, WIFI, Bluetooth, etc.).

[0168] a bus 1005 for communicating information among the various components (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004) of the device;

[0169] The processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004 are communicatively connected to each other within the device through the bus 1005.

[0170] The computer program is executed by the processor to implement the method described above.

[0171] It can be understood that the contents in the above method embodiments are all applicable to the present storage medium embodiment, the present storage medium embodiment specifically implements the same functions as the above method embodiments, and achieves the same beneficial effects as the above method embodiments.

[0172] The computer program product includes a computer program, which is executed by the processor to implement the method described above.

[0173] It can be understood that the contents in the above method embodiments are all applicable to the present program product embodiment, the present program product embodiment specifically implements the same functions as the above method embodiments, and achieves the same beneficial effects as the above method embodiments.

[0174] The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory disposed remotely with respect to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0175] The embodiments described in the present application are used to more clearly illustrate the technical solutions of the present application, and do not constitute a limitation on the technical solutions provided by the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the present application are also applicable to similar technical problems.

[0176] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation to the embodiments of the present application, and can include more or fewer steps than the figures, or combine certain steps, or different steps.

[0177] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separated, that is, can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0178] Those skilled in the art can understand that all or some steps in the above disclosed method, functions of the modules / units in the system and the device can be implemented as software, firmware, hardware and appropriate combinations thereof.

[0179] The terms "first", "second", "third", "fourth" and the like in the description of the present application and the above-mentioned figures (if any) are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0180] It should be understood that in the present application, "at least one" means one or more, and "multiple" means two or more. "And / or" is used to describe the association between the associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0181] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented by other manners. For example, the apparatus embodiments described above are merely illustrative, for example, the division of the above units is merely a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interfaces, apparatuses or units, and can be electrical, mechanical or other forms.

[0182] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e. they can be located in one place or distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0183] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0184] If the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that makes a contribution or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method of each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program storage media.

[0185] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, but this does not limit the scope of the rights of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the rights of the embodiments of the present application.

Claims

1. A power resource scheduling method based on uncertainty, characterized in that, The method includes: Obtain current clearing price forecasts, historical electricity price data, market revenue data, scenario call data, aggregator uncertainty output data, and market participation data; The first loss is determined by calculating based on the historical electricity price data and a preset value algorithm; a first model is established based on the first loss and the historical electricity price data. A model is established based on the market revenue data, the scenario call data, and the preset call unit price to determine the second model; The second loss is determined by calculating based on the preset value algorithm and the aggregator's uncertain output data; and the objective function is determined by maximizing the objective function based on the second loss and the second model. The third model is determined by calculating the call cost based on the market participation data; and the target scheduling scheme is determined by calculating the target scheduling scheme based on the current clearing price forecast, the first model, the maximization objective function, and the third model.

2. The method according to claim 1, characterized in that, The step of calculating and determining the first loss based on the historical electricity price data and a preset value algorithm specifically includes: The historical electricity price data is analyzed to determine the historical clearing electricity price and the electricity price forecast data; The actual loss data is determined by calculating based on the historical clearing electricity price and the first loss function of the preset value algorithm. A first probability density function is determined by statistical analysis based on the historical clearing electricity price and electricity price forecast data; and a second probability density function is determined by integral calculation based on the actual loss data, the first probability density function and the first preset threshold. The first risk value function is determined by calculating based on the second probability density function, the first preset confidence level, the first preset threshold, and the first value function of the preset value algorithm. The excess loss function is determined by calculating the second loss function based on the first risk value function, the actual loss data, and the preset value function; and the first loss is determined by calculating the first risk value function, the first preset confidence level, and the excess loss function.

3. The method according to claim 1, characterized in that, The step of establishing a model based on the market revenue data, the scenario call data, and the preset call unit price, and determining the second model, specifically includes: The scenario call data is parsed to determine the power call dataset and the standby capacity call dataset; and the standby capacity call dataset is statistically analyzed to determine the call probability value. The call cost function is determined by calculating based on the preset call unit price, the call probability value, the power call dataset, and the backup call dataset. The net revenue function is determined by calculating the difference between the market revenue data and the call cost function; and the second model is determined by calculating the difference between the net revenue function and the preset scenario probability.

4. The method according to claim 1, characterized in that, The step of calculating and determining the second loss based on the preset value algorithm and the aggregator's uncertain output data specifically includes: Statistical analysis is performed on the uncertain output data of the aggregator to determine the third probability density function; The fourth probability density function is determined by integral calculation based on the third probability density function, the second preset threshold, and the second model. The second risk value function is determined by calculating based on the fourth probability density function, the second preset threshold, the second preset confidence level, and the first value function of the preset value algorithm. The first return data is determined by calculating based on the second value-at-risk function and the net return function; and the second loss is determined by calculating based on the second value-at-risk function, the second preset confidence level, the first return data, and the preset scenario probability.

5. The method according to claim 1, characterized in that, The step of calculating the call cost based on the market participation data to determine the third model specifically includes: The market participation data is analyzed to determine the market winning bid data and market bid prices; The cost minimization function is determined by calculating the market winning bid data and the market bid price; the joint clearing constraints are determined by analyzing the market supply and demand balance conditions, the operating constraints of power generation enterprises and aggregators. The model is established based on the function for minimizing the call cost and the joint clearing constraints, and the third model is determined.

6. The method according to claim 1, characterized in that, The step of determining the target scheduling scheme based on the current cleared electricity price forecast, the first model, the maximized objective function, and the third model specifically includes: The current clearing price forecast is substituted into the first model for calculation to determine the aggregator bid price dataset; wherein, the aggregator bid price dataset includes energy market bid price data and standby auxiliary market bid price data of several distributed aggregators; The aggregater bid price dataset is substituted into the maximization objective function for calculation to determine the aggregater-reported scheduling dataset; wherein, the aggregater-reported scheduling dataset includes the energy market-reported available scheduling data and the standby auxiliary market-reported available scheduling data of several distributed aggregaters; The aggregator bid price dataset is substituted into the third model for optimization calculation to determine the optimized bid price dataset; the target scheduling scheme is determined based on the optimized bid price dataset and the reported scheduling dataset.

7. A power resource dispatching system based on uncertainty, characterized in that, include: The acquisition module is used to acquire the current clearing price forecast, historical electricity price data, market revenue data, scenario call data, aggregator uncertainty output data, and market participation data; A model building module is used to calculate and determine the first loss based on the historical electricity price data and a preset value algorithm; and to build a model based on the first loss and the historical electricity price data to determine the first model. The aggregation module is used to build a model based on the market revenue data, the scenario call data, and the preset call unit price, and to determine the second model; The construction module is used to calculate and determine the second loss based on the preset value algorithm and the aggregator's uncertain output data; and to determine the maximization objective function based on the second loss and the second model. The optimization module is used to calculate the call cost based on the market participation data, determine the third model, and calculate and determine the target scheduling scheme based on the current clearing price forecast, the first model, the maximization objective function and the third model.

8. An electronic device, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

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