CN114154671A - Method and system for determining expected effect of power consumption - Google Patents

Method and system for determining expected effect of power consumption Download PDF

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CN114154671A
CN114154671A CN202111153129.2A CN202111153129A CN114154671A CN 114154671 A CN114154671 A CN 114154671A CN 202111153129 A CN202111153129 A CN 202111153129A CN 114154671 A CN114154671 A CN 114154671A
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郭庆来
李强
高昇宇
王博弘
夏天
王璇
周冬旭
朱正谊
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Tsinghua University
State Grid Information and Telecommunication Co Ltd
Nanjing Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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State Grid Information and Telecommunication Co Ltd
Nanjing Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The invention discloses a method and a system for determining expected effect of power consumption, wherein the method for determining expected effect of power consumption comprises the following steps: obtaining the average power index pi before the daytConstant retail index of electric power ptNegative imbalance real time power average index
Figure DDA0003287822420000011
Positive unbalanced real time power average index
Figure DDA0003287822420000012
Real-time power demand xtAnd market participants' day-ahead power bids
Figure DDA0003287822420000013
Six parameters; according to the obtained average index pi of the day-ahead powertConstant retail index of electric power ptNegative imbalance real time power average index
Figure DDA0003287822420000014
Positive unbalanced real time power average index
Figure DDA0003287822420000015
Real-time power demand xtAnd market participants' day-ahead power bids
Figure DDA0003287822420000016
Six parameters determine the expected yield of power consumption. The invention obtains the value of the data information to the market participants through calculation, thereby providing decision basis for the market participants, maximally improving the effect of the market participants in power consumption, and improving the operating efficiency of the power system.

Description

Method and system for determining expected effect of power consumption
Technical Field
The invention belongs to the technical field of power information, and particularly relates to a method and a system for determining expected yield of power consumption.
Background
Uncertainty is ubiquitous in power systems, particularly significant in power consumption. In the field of power generation, uncertainty of renewable energy sources is always concerned, and the increase of the permeability of the renewable energy sources has obvious influence on decision problems such as operation optimization in a power system; on the other hand, in power utilization, uncertainty of power load is also endogenous, and is affected by natural environmental factors and complex user behaviors, and is often considered in extensive demand-side management.
Data consumption is considered one of the useful ways to eliminate power uncertainty. Market participants can calculate data values from their market optimization models to help them make better decisions in the electricity market. Although the data value has been defined and explained, when the market participant's information is not completely accurate or incomplete, its nature is not clear, and it does not allow the market participant to estimate the actual data value.
The uncertainty in the power market comes from the difference between the day-ahead (DA) power forecast and the real-time (RT) power implementation. Power consumption brings economic benefits to market participants, but uncertainty leads to economic losses. Therefore, in order to optimize the yield in power consumption, market participants always need to achieve an appropriate trade-off between revenue and risk according to the result of power consumption. Therefore, it is essential to deduce the expected yield of power consumption for the decision process of market participants.
Disclosure of Invention
In view of the above problems, the present invention discloses a method for determining expected yield of power consumption, comprising the steps of:
obtaining the average power index pi before the daytConstant retail index of electric power ptNegative imbalance real time power average index
Figure BDA0003287822400000012
Positive unbalanced real time power average index
Figure BDA0003287822400000013
Real-time power demand xtAnd market participants' day-ahead power bids
Figure BDA0003287822400000011
Six parameters;
according to the obtained average index pi of the day-ahead powertConstant retail index of electric power ptNegative imbalance real time power average index
Figure BDA0003287822400000014
Positive unbalanced real time power average index
Figure BDA0003287822400000015
Real-time power demand xtAnd market participants' day-ahead power bids
Figure BDA0003287822400000021
Six parameters determine the expected yield of power consumption.
Still further, the expected yield of power consumption is a function of the probability density that the expected yield will be met based on the yield and the real-time uncertain actual power demand
Figure BDA0003287822400000027
The product of (a) and (b).
Still further, the effectiveness is determined by the following equation:
Figure BDA0003287822400000022
wherein, a1,tThe real-time power demand yield coefficient under the condition of insufficient power; a is2,tThe real-time power demand yield coefficient under the condition of power surplus; b1,tThe yield coefficient of the day-ahead power bid under the condition of insufficient power; b2,tThe yield coefficient of the day-ahead power bid under the condition of surplus power; x is the number oftReal-time power demand; xminIs the smallest possible power demand or the smallest possible power supply; xmaxThe maximum possible power demand or the maximum possible power supply.
Further, said a1,t、a2,t、b1,tAnd b2,tIs enough to satisfyThe following conditions:
b1,t＜0＜a2,t＜a1,t+b1,t＜a1,t
a1,t+b1,t＝a2,t+b2,t。
still further, the expected yield of power consumption is determined by the following equation:
Figure BDA0003287822400000023
wherein the content of the first and second substances,
Figure BDA0003287822400000024
a system for determining expected revenue from power consumption, comprising:
an acquisition unit for acquiring a day-ahead power average index pitConstant retail index of electric power ptNegative imbalance real time power average index
Figure BDA0003287822400000028
Positive unbalanced real time power average index
Figure BDA0003287822400000029
Real-time power demand xtAnd market participants' day-ahead power bids
Figure BDA0003287822400000025
Six parameters;
a determination unit for obtaining the average power index pitConstant retail index of electric power ptNegative imbalance real time power average index
Figure BDA00032878224000000210
Positive unbalanced real time power average index
Figure BDA00032878224000000211
Real-time power demand xtAnd market participants' day-ahead power bids
Figure BDA0003287822400000026
Six parameters determine the expected yield of power consumption.
Further, the determining unit may satisfy the probability density function according to the actual power demand that is not determined in real time and in terms of efficiency
Figure BDA0003287822400000035
The product of (a) determines the expected yield of power consumption.
Further, the determination unit determines the effect by the following formula:
Figure BDA0003287822400000031
wherein, a1,tThe real-time power demand yield coefficient under the condition of insufficient power; a is2,tThe real-time power demand yield coefficient under the condition of power surplus; b1,tThe yield coefficient of the day-ahead power bid under the condition of insufficient power; b2,tThe yield coefficient of the day-ahead power bid under the condition of surplus power; x is the number oftReal-time power demand; xminIs the smallest possible power demand or the smallest possible power supply; xmaxThe maximum possible power demand or the maximum possible power supply.
Further, said a1,t、a2,t、b1,tAnd b2,tThe following conditions are satisfied:
b1,t＜0＜a2,t＜a1,t+b1,t＜a1,t
a1,t+b1,t＝a2,t+b2,t。
still further, the determination unit determines the expected effect of power consumption by the following formula:
Figure BDA0003287822400000032
wherein the content of the first and second substances,
Figure BDA0003287822400000033
compared with the prior art, the invention has the beneficial effects that: by obtaining the average power index pitConstant retail index of electric power ptNegative imbalance real time power average index
Figure BDA0003287822400000037
Positive unbalanced real time power average index
Figure BDA0003287822400000036
Real-time power demand xtAnd market participants' day-ahead power bids
Figure BDA0003287822400000034
The expected effect of the power consumption is determined by the six parameters, and the value of the data information to market participants is obtained through calculation, so that a decision basis is provided for the market participants, the effect of the market participants in the power consumption is improved to the maximum extent, and the operation efficiency of a power system is improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the methods and processes particularly pointed out in the written description and claims hereof as well as the appended drawings.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 illustrates a general yield curve for a market participant with uncertain power supply/demand according to an embodiment of the invention;
FIG. 2 illustrates a power and data consumption framework diagram for a power retailer purchasing power as an end user agent and a renewable energy producer participating in a power market in two scenarios, according to an embodiment of the present invention;
FIG. 3 shows a graph of data value rates and their upper limits for different distributions, according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a method for determining expected effect of power consumption, which comprises the following steps:
obtaining the average power index pi before the daytConstant retail index of electric power ptNegative imbalance real time power average index
Figure BDA0003287822400000043
Positive unbalanced real time power average index
Figure BDA0003287822400000044
Real-time power demand xtAnd market participants' day-ahead power bids
Figure BDA0003287822400000041
Six parameters;
according to the obtained average index pi of the day-ahead powertConstant retail index of electric power ptNegative imbalance real time power average index
Figure BDA0003287822400000046
Positive unbalanced real time power average index
Figure BDA0003287822400000045
Real-time power demand xtAnd market participants' day-ahead power bids
Figure BDA0003287822400000042
Six parameters determine the expected yield of power consumption.
In order to make the market optimization model more versatile, the electric wholesale market is considered to be a fully competitive market, adopting a double-settlement market system. Thus, market participants are assumed to be index recipients and cannot influence the market index by strategic bidding. Double-settlement market systems are commonly used in real power markets, such as the united states and the iblea power market, including the day-ahead (DA) power market and the real-time (RT) power market.
In the DA electricity market, market participants submit bids to the market based on electricity forecasts. In the RT power market, market participants need to pay unbalanced costs due to power deviation, or more precisely, market participants should sell surplus power at a lower index or buy shortage power at a higher index to meet the contract of the DA power market.
Since market participants are considered index acceptors, it is important for them to reduce uncertainty as much as possible to ensure that market participants' revenues in power consumption. Therefore, based on a secondary settlement market system, the yield curve of market participants can be described, and a market optimization model of the power market is constructed.
Considering the power consumption of the power supply side and the load side, a renewable energy producer and a power retailer are respectively selected as market participants, namely participants, so that the power market analysis is more universal.
Renewable energy producers will participate in the wholesale electricity market when they need to sell the clean electricity produced to the grid. For renewable energy producers, they should bid in the day-ahead electricity market based on day-ahead electricity forecasts. Then, in the real-time electricity market, renewable energy producers will sell excess electricity at a lower index and buy insufficient electricity at a higher index to overcome the inevitable electricity imbalance.
Thus, the yield of a renewable energy producer can be expressed as a piecewise linear function as follows:
Figure BDA0003287822400000051
wherein, pitIs a day-ahead power average index;
Figure BDA0003287822400000054
negative unbalanced real-time power average index;
Figure BDA0003287822400000053
a positive unbalanced real-time power average index;
Figure BDA0003287822400000052
bidding for market participants' day-ahead power; x is the number oftIs a real-time power supply; xminIs the smallest possible power supply; xmaxIs the largest possible power supply.
The electricity retailer will participate in the electricity wholesale market under the scenario of "electricity retailer brokerage end user purchase of electricity".
For electricity retailers, they should bid in the day-ahead electricity market based on day-ahead electricity forecasts. Then, in the real-time electricity market, they sell excess electricity at a lower index and buy insufficient electricity at a higher index to overcome the inevitable electricity imbalance. Thus, the yield of the electricity retailer can also be expressed as a piecewise linear function as follows:
Figure BDA0003287822400000061
wherein p istIs a constant retail index of electricity; pitIs day aheadA power average index;
Figure BDA0003287822400000067
negative unbalanced real-time power average index;
Figure BDA0003287822400000066
a positive unbalanced real-time power average index;
Figure BDA0003287822400000062
bidding for market participants' day-ahead power; x is the number oftReal-time power demand; xminIs the minimum possible power requirement; xmaxIs the largest possible power demand.
From equations (1) and (2), it can be found that the forms of revenue of renewable energy producers and electricity retailers are similar to equations (3) and (4) below:
Figure BDA0003287822400000063
Figure BDA0003287822400000064
according to the general assumption of the double-calculation market system, the average power index pi before daytReal time power average index of imbalance with negative
Figure BDA0003287822400000069
And positive imbalance real-time power average index
Figure BDA0003287822400000068
Has the following relationship as shown in formula (5):
Figure BDA00032878224000000610
thus, the effects of renewable energy producers and electricity retailers can be summarized in general form as shown in equation (6) and fig. 1:
Figure BDA0003287822400000065
wherein, a1,tThe real-time power demand yield coefficient under the condition of insufficient power; a is2,tThe real-time power demand yield coefficient under the condition of power surplus; b1,tThe yield coefficient of the day-ahead power bid under the condition of insufficient power; b2,tThe yield coefficient of the day-ahead power bid under the condition of surplus power; x is the number oftIs a real-time power demand or a real-time power supply; xminIs the smallest possible power demand or the smallest possible power supply; xmaxThe maximum possible power demand or the maximum possible power supply.
Wherein the parameter a1,t、a2,t、b1,tAnd b2,tThe following conditions are satisfied:
b1,t＜0＜a2,t＜a1,t+b1,t＜a1,t (7)
a1,t+b1,t＝a2,t+b2,t (8)
for renewable energy producers:
Figure BDA0003287822400000071
for the electricity retailer:
Figure BDA0003287822400000072
according to the general yield curves of the renewable energy producer and the electricity retailer in the formula (6), general market optimization models of the renewable energy producer and the electricity retailer can be represented by (11) and (12), wherein market participants are considered to be risk neutral, the decision goals of the market participants are to maximize the expected yield, and the actual electricity demand is satisfied through the yield and real-time uncertaintyProbability density function of
Figure BDA0003287822400000073
The specific formula is as follows:
Figure BDA0003287822400000074
Figure BDA0003287822400000075
wherein the content of the first and second substances,
Figure BDA0003287822400000076
as desired.
Then, the decisions of the renewable energy producer and the electricity retailer can be made based on both formula (11) and formula (12). For simplicity, represented by the electricity retailer model, the data value is analyzed and its attributes are derived.
Based on the above method for determining expected effect of power consumption, the present invention provides a system for determining expected effect of power consumption, comprising:
an acquisition unit for acquiring a day-ahead power average index pitConstant retail index of electric power ptNegative imbalance real time power average index
Figure BDA0003287822400000079
Positive unbalanced real time power average index
Figure BDA0003287822400000078
Real-time power demand xtAnd market participants' day-ahead power bids
Figure BDA0003287822400000077
Six parameters;
a determination unit for obtaining the average power index pitConstant retail index of electric power ptNegative unbalance of the bodyMean index of time power
Figure BDA0003287822400000086
Positive unbalanced real time power average index
Figure BDA0003287822400000087
Real-time power demand xtAnd market participants' day-ahead power bids
Figure BDA0003287822400000081
Six parameters determine the expected yield of power consumption.
Determining a probability density function that a unit satisfies according to actual power demands of efficiency and real-time uncertainty
Figure BDA0003287822400000088
The product of (a) determines the expected yield of power consumption.
The determination unit determines the effect by the following formula:
Figure BDA0003287822400000082
wherein, a1,tThe real-time power demand yield coefficient under the condition of insufficient power; a is2,tThe real-time power demand yield coefficient under the condition of power surplus; b1,tThe yield coefficient of the day-ahead power bid under the condition of insufficient power; b2,tThe yield coefficient of the day-ahead power bid under the condition of surplus power; x is the number oftReal-time power demand; xminIs the smallest possible power demand or the smallest possible power supply; xmaxThe maximum possible power demand or the maximum possible power supply.
a1,t、a2,t、b1,tAnd b2,tThe following conditions are satisfied:
b1,t＜0＜a2,t＜a1,t+b1,t＜a1,t (7)
a1,t+b1,t＝a2,t+b2,t (8)
the determination unit determines the expected yield of power consumption by the following formula:
Figure BDA0003287822400000083
Figure BDA0003287822400000084
by obtaining the average power index pitConstant retail index of electric power ptNegative imbalance real time power average index
Figure BDA0003287822400000089
Positive unbalanced real time power average index
Figure BDA00032878224000000810
Real-time power demand xtAnd market participants' day-ahead power bids
Figure BDA0003287822400000085
The expected effect of the power consumption is determined by the six parameters, decision basis is provided for market participants, and the effect is improved to the maximum extent.
The invention provides a method for determining the maximum expected effect of a power consumption participant, which comprises the following steps:
obtaining the average power index pi before the daytConstant retail index of electric power ptUncertainty random variable X of real-time power demandtData value rate CσAnd standard deviation sigma five parameters of uncertainty random variable distribution;
according to the acquired average power index pi before the daytConstant retail index of electric power ptUncertainty random variable X of real-time power demandtData value rate CσAnd the standard deviation σ of the uncertainty random variable distribution determines the maximum expected yield of the power consuming participant.
To solve the market optimization model in equations (11) and (12), the maximum expected yield expression for the power retailer is shown in equation (13):
Figure BDA0003287822400000091
wherein the content of the first and second substances,
Figure BDA0003287822400000092
and solving to obtain the optimal day-ahead power bid according to the optimization model.
In the formula (13)
Figure BDA0003287822400000093
Can be interpreted as no risk revenue, but in equation (13)
Figure BDA0003287822400000094
And
Figure BDA0003287822400000095
which can be interpreted as a risk cost. Notably, the risk-free revenue is independent of risk, especially when the average of RT power demand μ does not fluctuate. In contrast, risk costs are risk-related and may vary with different uncertainties. To facilitate the following derivation, the desired power portion of the risk cost is extracted as follows:
Figure BDA0003287822400000096
Figure BDA0003287822400000097
wherein the content of the first and second substances,
Figure BDA0003287822400000098
an expected value of insufficient power;
Figure BDA0003287822400000099
a desired value for the surplus of power; sigma is the standard deviation of the uncertainty random variable distribution;
Figure BDA00032878224000000910
and bidding for the optimal day-ahead electric power.
Thus, the maximum expected yield of the electricity retailer is determined from the difference between the risk-free revenue and the risk cost, and can be expressed as:
Figure BDA00032878224000000911
the risk free revenue is determined by the following formula:
Figure BDA00032878224000000912
the risk cost is determined by the following formula:
Cσσ (18)
wherein, the data value rate CσIs a measure of the value of the data, calculated by:
Figure BDA0003287822400000101
standard deviation sigma of uncertainty random variable distribution and data value rate CσIs related to the dispersion of (a). Data value rate CσRepresenting the enhancement of the optimal expected yield due to a unit reduction of sigma. Data value rate C according to equation (15)σOnly on the power average index, the imbalance penalty rate and the approximate distribution of real-time power demand. Furthermore, the greater the imbalance penalty rate, the more spread the approximate distribution of real-time power demand, and the more significant the power imbalance and risk costs. Therefore, the data value rate CσAnd will be larger, matching the basic set-up of a double settlement market system.
The steps of the program for calculating the data value and the error thereof are as follows:
step 1: selecting several candidate distributions most commonly used for prediction error analysis;
step 2: determining distribution parameters by parameter estimation (e.g., least squares estimation or maximum likelihood estimation);
and step 3: measuring and judging the accuracy of distribution fitting by a Kolmogorov-Smirnov test;
and 4, step 4: acquiring estimated distribution of renewable energy power generation or comprehensive load demand on the condition of day-ahead power prediction;
and 5: calculating a data value rate according to the estimated distribution and the average real-time power average index;
step 6: by passing
Figure BDA0003287822400000102
And
Figure BDA0003287822400000103
obtaining the error of the estimated data value of the power market participant; wherein, Cσ0The approximate data value rate obtained by parameter estimation; sigma0Is an approximate standard deviation obtained by parameter estimation; m is the bias constant obtained by Kolmogorov-Smirnov test;
and 7: according to
Figure BDA0003287822400000104
The derived data value upper bound modifies the possible range of data values. Wherein the content of the first and second substances,
Figure BDA0003287822400000112
the quantile of the uncertain random variable cumulative distribution function corresponding to the optimal day-ahead bid is calculated; cσ,maxIs the upper limit of the data worth rate.
According to data value rate CσThe analytical expression of the data value rate can be derived based on a plurality of distributions, including a uniform distribution, a gaussian distribution, a logistic distribution, a laplacian distribution, an exponential distribution, and a rayleigh distribution. Data value rate CσThe analytical expressions of (a) are shown in table 1. It can be found that they are all in combination withReal-time power average index lambdatNegative imbalance real-time power average index λt -And positive imbalance real-time power average index
Figure BDA0003287822400000113
It is related.
TABLE 1 probability Density function and data Rate for different distributions
Figure BDA0003287822400000111
As shown in fig. 2, the end user/data center can provide some raw data to the data supplier, and the data supplier forms valuable data products through data processing and processing analysis, and the valuable data products can be provided to the electricity retailer/renewable energy producer to help them to make better decisions during participating in the electricity market, so as to understand the popularity of the data value. In the scenario of renewable energy producers and electricity retailers, the data usage process is similar for both. Thus, both can use the same method to evaluate the data value.
Based on the above method for determining the maximum expected yield of a power consuming participant, the present invention provides a system for determining the maximum expected yield of a power consuming participant, comprising:
an acquisition unit for acquiring a day-ahead power average index pitConstant retail index of electric power ptUncertainty random variable X of real-time power demandtData value rate CσAnd standard deviation sigma five parameters of uncertainty random variable distribution;
a determining unit for obtaining the average power index pi before the daytConstant retail index of electric power ptUncertainty random variable X of real-time power demandtData value rate CσAnd the standard deviation σ of the uncertainty random variable distribution determines the maximum expected yield of the power consuming participant.
The determining unit is configured to determine a maximum expected yield of the electricity consuming participant based on the difference between the risk-free income and the risk cost.
The determining unit determines the risk-free income by the following formula:
Figure BDA0003287822400000123
the determination unit determines the risk cost by the following formula:
Cσσ
wherein, CσIs the data value rate; σ is the standard deviation of the uncertainty random variable distribution.
The determination unit determines the data worth rate C by the following formulaσ：
Figure BDA0003287822400000121
Wherein the content of the first and second substances,
Figure BDA0003287822400000127
negative unbalanced real-time power average index;
Figure BDA0003287822400000124
a positive unbalanced real-time power average index;
Figure BDA0003287822400000125
an expected value of insufficient power;
Figure BDA0003287822400000126
is a desired value of the power surplus.
The determination unit determines the maximum expected yield of the power consuming participant by the following formula:
Figure BDA0003287822400000122
mean index of power by day-aheadtConstant retail index of electric power ptFruit of Chinese wolfberryUncertainty random variable X of time-of-day power demandtData value rate CσAnd the standard deviation sigma of the uncertainty random variable distribution determine the maximum expected yield of the power consumption participants, which not only can provide a means for evaluating the power consumption efficiency and the data application benefit for the market participants, but also can help the market participants to directly make decisions in power consumption so that the market participants obtain the maximum expected yield in the most efficient way.
Verification was performed using, as examples, smart meter power usage data for thousands of consumers of the energy regulatory Committee (CER) in ireland and average index of power data for different regions of the united states PJM. In these data sets, power consumption occurs every 30 minutes and provides an hourly system energy index. To make the time periods consistent, allocation fitting and yield settlement are performed once per hour. In order to meet the requirement of the formula (4), the penalty rates of positive and negative real-time imbalance are set to be 0.5. Constant retail index of electric power ptSet to $ 0.2/kwh.
The distribution fitting and yield settlement program was run on a personal computer equipped with an Intel Core i 71.80-GHz CPU and 16GB RAM.
From the prepared data and the theoretical results, the data value rate C can be calculatedσAnd its theoretical upper limit, as shown in figure 3. Data value rate C for all distributionsσAnd real-time power average index lambdatWill increase from 0 first and finally follow
Figure BDA0003287822400000131
Increasing from 0 to 1 and decreasing to 0. In addition, data value rate CσThe data value rate upper limit is never exceeded, shown as a black dashed line. Thus, theoretical results have proven to be true and useful.
The present invention proposes the definition of data value and the proof of two important attributes thereof, which contribute to better decision making for market participants of power and data consumption. For future work, we will use the attributes of the data value in the actual power and data consumption.
Under a double-settlement market system, integrating power consumption yield curves of power supply side and load side market participants as index acceptors into a universal form;
the upper data value rate limit is derived from the definition of data value and the Cauchy-Schwarz inequality analysis. The upper data value rate limit may tell market participants the maximum benefit their power consumption can derive from the data;
the Kolmogorov-Smirnov test (K-S test) is used to measure the accuracy of the prediction error distribution fit and deduce the possible range of data values from the statistics of the K-S test. Errors in data value can roughly tell market participants about their computational accuracy.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A method of determining an expected yield of power consumption, comprising the steps of:
obtaining the average power index pi before the daytConstant retail index of electric power ptNegative imbalance real time power average index
Figure FDA0003287822390000011
Positive unbalanced real time power average index
Figure FDA0003287822390000012
Real-time power demand xtAnd market participants' day-ahead power bids
Figure FDA0003287822390000013
Six parameters;
according to the obtained day-ahead power average indexπtConstant retail index of electric power ptNegative imbalance real time power average index
Figure FDA0003287822390000014
Positive unbalanced real time power average index
Figure FDA0003287822390000015
Real-time power demand xtAnd market participants' day-ahead power bids
Figure FDA0003287822390000016
Six parameters determine the expected yield of power consumption.
2. The method of claim 1, wherein the expected yield of power consumption is based on a probability density function of the yield and the real-time uncertain actual power demand
Figure FDA0003287822390000017
The product of (a) and (b).
3. The method of determining an expected yield of power consumption according to claim 2, wherein the yield is determined by the formula:
Figure FDA0003287822390000018
wherein, a1,tThe real-time power demand yield coefficient under the condition of insufficient power; a is2,tThe real-time power demand yield coefficient under the condition of power surplus; b1,tThe yield coefficient of the day-ahead power bid under the condition of insufficient power; b2,tThe yield coefficient of the day-ahead power bid under the condition of surplus power; x is the number oftReal-time power demand; xminIs the smallest possible power demand or the smallest possible power supply; xmaxIs the maximum possible powerDemand or maximum possible power supply.
4. The method of claim 3, wherein a is the expected yield of power consumption1,t、a2,t、b1,tAnd b2,tThe following conditions are satisfied:
b1,t＜0＜a2,t＜a1,t+b1,t＜a1,t
a1,t+b1,t＝a2,t+b2,t。
5. the method of determining expected effect on power consumption according to claim 4, wherein the expected effect on power consumption is determined by the following formula:
Figure FDA0003287822390000021
wherein the content of the first and second substances,
Figure FDA0003287822390000022
6. a system for determining expected revenue from power consumption, comprising:
an acquisition unit for acquiring a day-ahead power average index pitConstant retail index of electric power ptNegative imbalance real time power average index
Figure FDA0003287822390000023
Positive unbalanced real time power average index
Figure FDA0003287822390000024
Real-time power demand xtAnd market participants' day-ahead power bids
Figure FDA0003287822390000025
Six parameters;
a determination unit for obtaining the average power index pitConstant retail index of electric power ptNegative imbalance real time power average index
Figure FDA0003287822390000026
Positive unbalanced real time power average index
Figure FDA0003287822390000027
Real-time power demand xtAnd market participants' day-ahead power bids
Figure FDA0003287822390000028
Six parameters determine the expected yield of power consumption.
7. The system of claim 6, wherein the determining unit is configured to satisfy the expected revenue of power consumption according to a probability density function of revenue and real-time uncertain actual power demand
Figure FDA00032878223900000210
The product of (a) determines the expected yield of power consumption.
8. The system for determining expected revenue of electricity consumption of claim 7, wherein the determining unit determines revenue by the following formula:
Figure FDA0003287822390000029
wherein, a1,tThe real-time power demand yield coefficient under the condition of insufficient power; a is2,tThe real-time power demand yield coefficient under the condition of power surplus; b1,tThe yield coefficient of the day-ahead power bid under the condition of insufficient power; b2,tThe yield coefficient of the day-ahead power bid under the condition of surplus power; x is the number oftReal-time power demand; xminIs the smallest possible power demand or the smallest possible power supply; xmaxThe maximum possible power demand or the maximum possible power supply.
9. The system for determining expected revenue in accordance with claim 8, wherein a is1,t、a2,t、b1,tAnd b2,tThe following conditions are satisfied:
b1,t＜0＜a2,t＜a1,t+b1,t＜a1,t
a1,t+b1,t＝a2,t+b2,t。
10. the system of claim 9, wherein the determining unit determines the expected end of power consumption by:
Figure FDA0003287822390000031
wherein the content of the first and second substances,
Figure FDA0003287822390000032
CN202111153129.2A 2021-09-29 2021-09-29 Method and system for determining expected effect of power consumption Pending CN114154671A (en)

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