CN113964828A - Power distribution network collaborative planning method based on interval probability statistical model - Google Patents
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Abstract
The invention provides a power distribution network collaborative planning method based on an interval probability statistical model, which comprises the following steps: step 1, respectively establishing an interval probability statistical model for predicting the output of the distributed new energy and an interval probability statistical model for predicting the interactive energy storage capacity of the electric automobile under the same confidence coefficient; step 2, carrying out optimized value taking in the prediction interval value with the maximum income of a distributed new energy investment operator and an electric vehicle charging station investment operator as targets, and respectively obtaining access places and capacities of the distributed new energy and the electric vehicle charging station; step 3, under the constraint condition, calculating a power distribution network planning scheme under the confidence coefficient; and 4, repeating the steps 2-3 until the error between the result after the optimization and the last optimization result is within a set range, and outputting the power distribution network planning scheme after the optimization. The method can obtain a more optimized distribution network planning scheme, and realizes the sufficient interaction of source load and greatly improves the reliability of distribution network planning.
Description
Technical Field
The invention belongs to the technical field of power system planning, and particularly relates to a power distribution network collaborative planning method based on an interval probability statistical model.
Background
In order to realize the purposes of carbon peak reaching and carbon neutralization and construct a novel power system taking new energy as a main body, the new energy such as wind power, photovoltaic and the like on the power supply side can be continuously and rapidly developed, and the electric energy substitution level represented by an electric automobile on the load side can be rapidly improved. After large-scale and high-proportion distributed new energy is accessed to the power distribution network, the output uncertainty, the installation place and the capacity of the distributed new energy have adverse effects on the power and electricity balance, the operation reliability and the like of the power distribution network; the electric vehicle charging station can utilize idle and intentional electric vehicles as energy storage to participate in auxiliary services such as power grid peak shaving, frequency modulation and the like, the utilization rate of power distribution network equipment can be improved, and the economical efficiency of a planning scheme is increased. The existing power distribution network planning method only considers the maximization of economic benefits, however, electric vehicle users are influenced by incentive policies, behavior habits and the like, the energy storage capacity which can participate in interaction is uncertain, the existing power distribution network planning method does not consider the uncertainty of source loads, and the source load interactivity is insufficient, so that the utilization rate of power distribution network equipment is not maximized, the economy and the reliability of the planning scheme can be further optimized, and the development of the power market is restricted.
Disclosure of Invention
In order to overcome the problems, the invention provides a power distribution network collaborative planning method based on an interval probability statistical model, which fully considers uncertainty of each party, enables a planning scheme to be more accurate, and improves equipment utilization rate and reliability on the basis of realizing economic benefit maximization.
In order to achieve the purpose, the invention adopts the following technical scheme:
a power distribution network collaborative planning method based on an interval probability statistical model comprises the following steps:
step 1, respectively establishing an interval probability statistical model for predicting the output of the distributed new energy and an interval probability statistical model for predicting the interactive energy storage capacity of the electric automobile under the same confidence coefficient;
step 2, carrying out optimized value taking on the maximum income of a distributed new energy investment operator and an electric vehicle charging station investment operator as targets, and respectively obtaining access places and capacities of the distributed new energy and the electric vehicle charging station;
step 3, under constraint conditions, taking the distributed new energy output and the access place and capacity of the electric vehicle charging station under the same confidence coefficient as input values, taking the maximum value of the investment income of the power distribution network under the confidence coefficient as a target optimization value, and calculating a power distribution network planning scheme under the confidence coefficient;
and 4, repeating the steps 2-3 until the error between the result after the optimization and the last optimization result is within a set range, and outputting the power distribution network planning scheme after the optimization.
In step 1, establishing an interval probability statistical model of the distributed new energy output prediction under a confidence coefficient, including: according to the distributed new energy output prediction and the historical data statistical rule, a new energy output interval probability statistical model under a certain confidence coefficient is established, and the probability statistical model and parameters obeyed by the prediction error are identified by adopting a maximum likelihood method, so that the interval probability statistical model of the distributed new energy output prediction under a certain confidence coefficient is obtained.
In the step 1, establishing an interval probability statistical model for predicting the interactive energy storage capacity of the electric vehicle under a confidence coefficient, including: according to the active response behavior prediction of the electric vehicle charging station user, an interval probability statistical model of the electric vehicle interaction energy storage capacity under a certain confidence coefficient is established, and the probability statistical model and parameters obeyed by the prediction error are identified by adopting a maximum likelihood method, so that the interval probability statistical model of the electric vehicle interaction energy storage capacity prediction under a certain confidence coefficient is obtained.
The invention has the beneficial effects that:
compared with the prior art, the invention provides a power distribution network collaborative planning method based on an interval probability statistical model. The method fully considers the uncertainty of the output of the distributed new energy and the uncertainty of the electric automobile participating in the power grid auxiliary service, and finally outputs an optimal planning scheme through multiple games by taking the maximum income of three main bodies of a distributed new energy investment operator, a power distribution network investment operator and an electric automobile charging station investment operator as a target. The optimal planning scheme not only meets the goal of maximizing the benefits of all parties, but also fully considers the uncertainty of all parties, so that the finally obtained planning is more reasonable and accurate.
Drawings
Fig. 1 is a schematic diagram of a power distribution network collaborative planning based on an interval probability statistical model.
Fig. 2 is a flowchart of a power distribution network collaborative planning method based on an interval probability statistical model.
Detailed Description
The following describes in detail a power distribution network collaborative planning method based on an interval probability statistical model with reference to fig. 1 and 2.
As shown in fig. 1 and fig. 2, the method for collaborative planning of a power distribution network based on an interval probability statistical model provided in this embodiment includes the following steps:
s1: and respectively establishing an interval probability statistical model for predicting the output of the distributed new energy and an interval probability statistical model for predicting the interactive energy storage capacity of the electric automobile under the same confidence coefficient.
Specifically, a new energy output interval probability statistical model under a certain confidence coefficient is established according to the distributed new energy output prediction and the historical data statistical rule; according to the active response behavior prediction of the electric vehicle charging station user, an interval probability statistical model of the electric vehicle interaction energy storage capacity under a certain confidence coefficient is established. The method comprises the steps of taking errors of new energy output and historical actual load data and historical prediction data of an electric vehicle charging station as input, identifying types and parameters obeyed by a future prediction error probability statistical model by adopting a maximum likelihood method, indicating the possibility that the data obey the type of the probability model by using a likelihood, and minimizing the error of a represented data rule when the likelihood is higher. The probability models adopted are normal distribution models and partial normal distribution models, and other models can be adopted according to specific statistical rule conditions. And superposing the prediction error probability statistical model under a certain confidence coefficient to a future predicted value to obtain an interval probability statistical model for predicting the output of the distributed new energy under a certain confidence coefficient and an interval probability statistical model for predicting the interactive energy storage capacity of the electric automobile.
The interval probability statistical model is as follows:
l is a predicted value, the interval
Is the confidence interval for L with a confidence level of 1-alpha,
respectively, at a confidence level of 1-alphaLimits and confidence limits.
The interval probability statistical model structure of the distributed new energy output prediction and the interval probability statistical model structure of the electric vehicle interactive energy storage capacity prediction are both the interval probability statistical models, and the difference is that the statistical rules are different, and the specific statistical rules are obtained according to historical data.
S2: the maximum income of a distributed new energy investment operator is taken as a target, and an interval probability statistical model of the output prediction of the distributed new energy is taken as a boundary condition to obtain an installation place and a capacity interval value of the distributed new energy under a certain confidence coefficient.
The planning model of the distributed new energy investment operator is as follows:
the maximum profit is the electricity selling profit (including subsidies), electricity abandonment cost, auxiliary service cost, investment cost, operation and maintenance cost and assessment cost.
Decision variables of the planning model are installation sites and capacities of distributed new energy, interference variables of investment operators on electric vehicle charging stations are auxiliary service benefits, and interference variables of the planning model on power distribution networks are newly built/expanded transformer substations and lines.
The planning model takes the predicted values of the distributed new energy output and the electric automobile interactive energy storage capacity as initial values to carry out iteration, and optimized values are taken in the interval probability statistical model.
The planning model takes the current distribution network as a primary iteration value, and the next iteration value is the distribution network planning scheme obtained in the previous step S4; and (4) measuring and calculating the initial value of the auxiliary service cost according to the existing market, and measuring and calculating the next iteration value according to the previous round of electric vehicle charging station planning scheme obtained in the step S3.
S3: the maximum income of an electric vehicle charging station investment operator is used as a target, and an interval probability statistical model for predicting the electric vehicle interactive energy storage capacity is used as a boundary condition to obtain an installation place and a capacity interval value of the electric vehicle charging station under a certain confidence coefficient.
The electric vehicle charging station investment operator planning model is as follows:
the maximum profit is the electricity selling profit (including subsidies), the auxiliary service profit-investment cost-operation and maintenance cost.
Decision variables of the planning model are installation places and capacities of the electric vehicle charging stations, interference variables of investment operators of the electric vehicle charging stations are auxiliary service costs, and interference variables of the distribution network are newly built/expanded transformer substations and lines.
The planning model takes the predicted values of the distributed new energy output and the electric automobile interactive energy storage capacity as initial values to carry out iteration, and optimized values are taken in the interval probability statistical model.
The planning model takes the current distribution network as a primary iteration value, and the next iteration value is the distribution network planning scheme obtained in the previous step S4; and the auxiliary service income is measured according to the demand of the distributed new energy planning scheme obtained in the step S2.
It is readily understood that there is no chronological order between the steps S2 and S3.
S4: under the constraint conditions of power supply reliability, power flow and the like, the installation place and capacity of the distributed new energy obtained in the step S2 and the installation place and capacity of the electric vehicle charging station obtained in the step S3 are used as input values, the maximum value of the investment income of the power distribution network under the confidence coefficient is used as a target optimization value, and the power distribution network planning scheme under the confidence coefficient is calculated.
The planning model of the power distribution network investment operator comprises the following steps:
the maximum profit is the profit of electricity sale, investment cost, operation and maintenance cost and network loss cost.
The model decision variables are newly built/expanded transformer substations and lines, and the interference variables to distributed new energy investment operators and electric vehicle charging station investment operators are installation sites and capacities.
It is easy to understand that the installation sites are different, the installation capacities are different, the power generation amount is different, the power generation amount determines the electricity selling profit, the installation sites determine the installation capacities, and the installation capacities correspond to the cost. Taking the installation location and capacity of the distributed new energy source obtained in step S2 and the installation location and capacity of the electric vehicle charging station obtained in step S3 as input values means that the cost converted from the corresponding installation location and capacity is calculated as an input value of the planning model.
S5: and (5) repeating the step (S2-4) until the error between the result optimized in the step (S4) and the last optimization result is within the set allowable range, and outputting a power distribution network planning scheme if each benefit subject is considered to reach a game balance state.
It should be noted that the summary and the detailed description are intended to convey the concept of the method and should not be construed as limiting the scope of the invention. Various modifications, equivalent alterations, and improvements will occur to those skilled in the art and are intended to be within the spirit and scope of the invention. Such changes and modifications are intended to be included within the scope of the appended claims.
Claims (5)
1. A power distribution network collaborative planning method based on an interval probability statistical model is characterized by comprising the following steps:
step 1, respectively establishing an interval probability statistical model for predicting the output of the distributed new energy and an interval probability statistical model for predicting the interactive energy storage capacity of the electric automobile under the same confidence coefficient;
step 2, carrying out optimized value taking on the maximum income of a distributed new energy investment operator and an electric vehicle charging station investment operator as targets, and respectively obtaining access places and capacities of the distributed new energy and the electric vehicle charging station;
step 3, under constraint conditions, taking the distributed new energy output and the access place and capacity of the electric vehicle charging station under the same confidence coefficient as input values, taking the maximum value of the investment income of the power distribution network under the confidence coefficient as a target optimization value, and calculating a power distribution network planning scheme under the confidence coefficient;
and 4, repeating the steps 2-3 until the error between the result after the optimization and the last optimization result is within a set range, and outputting the power distribution network planning scheme after the optimization.
2. The method according to claim 1, wherein in step 1, establishing a block probability statistical model of the distributed new energy contribution prediction at a confidence level comprises:
according to the distributed new energy output prediction and the historical data statistical rule, a new energy output interval probability statistical model under a certain confidence coefficient is established, and the probability statistical model and parameters obeyed by the prediction error are identified by adopting a maximum likelihood method, so that the interval probability statistical model of the distributed new energy output prediction under a certain confidence coefficient is obtained.
3. The method according to claim 1, wherein in the step 1, establishing an interval probability statistical model of the electric vehicle interaction energy storage capacity prediction at a confidence level comprises:
according to the active response behavior prediction of the electric vehicle charging station user, an interval probability statistical model of the electric vehicle interaction energy storage capacity under a certain confidence coefficient is established, and the probability statistical model and parameters obeyed by the prediction error are identified by adopting a maximum likelihood method, so that the interval probability statistical model of the electric vehicle interaction energy storage capacity prediction under a certain confidence coefficient is obtained.
4. The method of claim 1, wherein the constraints include power supply reliability, power flow constraints.
5. The method according to claim 1, wherein the step 2 specifically comprises:
the maximum income of a distributed new energy investment operator is taken as a target, and an interval probability statistical model of the output prediction of the distributed new energy is taken as a boundary condition to obtain an installation place and a capacity interval value of the distributed new energy under a certain confidence coefficient;
the maximum income of an electric vehicle charging station investment operator is used as a target, and an interval probability statistical model for predicting the electric vehicle interactive energy storage capacity is used as a boundary condition to obtain an installation place and a capacity interval value of the electric vehicle charging station under a certain confidence coefficient.
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