CN112036637A - Community energy Internet energy transaction scheduling method and system considering wind power consumption - Google Patents

Community energy Internet energy transaction scheduling method and system considering wind power consumption Download PDF

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CN112036637A
CN112036637A CN202010892033.7A CN202010892033A CN112036637A CN 112036637 A CN112036637 A CN 112036637A CN 202010892033 A CN202010892033 A CN 202010892033A CN 112036637 A CN112036637 A CN 112036637A
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community
energy internet
energy
wind power
internet
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胥威汀
任志超
张全明
王晞
程超
刘卉
马瑞光
汪伟
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Economic and Technological Research Institute of State Grid Sichuan Electric Power Co Ltd
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Abstract

The invention provides a community energy internet energy transaction scheduling method considering wind power consumption, wherein an information center provides an energy transaction scheduling platform for each community energy internet, each community internet considers the maximum wind power consumption and the uncertainty of wind power output according to the minimum cost principle, an electric energy bidding strategy and a scheduling request of the community are provided for the information center, and the information center schedules the electric energy of each community energy internet and a power distribution network to meet the energy load requirement of each community. Through the mode, the nearby consumption of wind power can be promoted, the situation that the power distribution network safe operation pressure is aggravated by frequent power interaction of the community energy internet and the power distribution network is avoided, and friendly interaction of the community energy internet cluster and the power distribution network is achieved. Meanwhile, when the wind power output fluctuates in the actual operation process, the real-time regulation and control cost is reduced, and the power utilization cost of the community energy internet is further reduced.

Description

Community energy Internet energy transaction scheduling method and system considering wind power consumption
Technical Field
The invention relates to the field of power grids, in particular to a community energy internet energy transaction scheduling method and system considering wind power consumption.
Background
Along with the continuous increase of community energy internet quantity and the continuous improvement of wind-electricity permeability, the frequent energy interaction of community energy internet and distribution network can produce a great deal of adverse effect to the safe and stable operation of distribution network. The large access of wind power can affect the active and reactive power flow distribution, the electric energy quality, the system network loss, the steady-state voltage distribution and the like of the power distribution network. The fluctuation and intermittence of wind power resources determine the fluctuation and intermittence of the output of wind power. When the access proportion of the wind power is small, the influence on the power distribution network is not obvious; and the wind power permeability that promotes gradually makes wind power access more obvious to the influence that the distribution network caused, may even make the system lose stability under the extreme condition.
Under the above conditions, if a traditional 'spontaneous self-use, surplus internet' energy transaction mechanism is still adopted between the community energy internet and the power distribution network, frequent energy interaction between the community energy internet and the power distribution network can bring adverse effects to normal operation of the power distribution network, and the community energy internet and the power distribution network are difficult to adapt to the trend of rapid development of future clean energy and power grids. The method has the advantages that wind power consumption is promoted, the wind abandon rate is reduced, unnecessary energy interaction between the community energy internet and the power distribution network is reduced, and adverse effects caused by large-scale wind power access to the safe and stable operation of the power distribution network are reduced, so that the method has important significance for effectively promoting the development of clean energy.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the electric quantity of electricity purchased or sold from the power distribution network by the community energy Internet cluster is reduced through electric energy transaction among different community energy Internet, the nearby consumption of wind power is promoted, and the operation cost of the community energy Internet is reduced. The invention provides a community energy internet energy transaction scheduling method considering wind power consumption for solving the problems, and provides a novel energy transaction mechanism and an implementation scheme, so that a modern power system can better support the access of various distributed intermittent power supply devices, and the friendly interaction of the community energy internet and a power distribution network is realized.
The invention is realized by the following technical scheme:
in a first aspect, a community energy internet energy transaction scheduling method considering wind power consumption is provided, and the method comprises the following steps:
s1, considering wind power maximum absorption and wind power output uncertainty factors, and formulating a robust economic dispatching strategy of the community energy Internet according to the principle of minimum operation cost and power interaction cost of the community energy Internet;
s2, the energy Internet of each community establishes bidding price and bidding electric quantity according to the bidding strategy proposed by the robust economic dispatching strategy and the non-cooperative game;
s3, according to the robust economic dispatching strategy of the community energy Internet, the proposed bidding strategy and the proposed dispatching request, the information center dispatches the electric energy of the community energy Internet and the distribution network so as to meet the load demand of each community; and the power distribution network purchases all the power left after the dispatching or supplements the power lacking after the dispatching.
Further, the model of the community energy internet is represented as:
Figure RE-GDA0002719679360000021
the model is an inner-layer robust model and an outer-layer robust model of the day-ahead economic dispatching considering wind power uncertainty, and the maximum wind power consumption is kept while the optimal operation dispatching plan is obtained;
wherein, the optimization variable theta is a day-ahead scheduling scheme; the inner layer optimization variable gamma is an execution regulation and control scheme of a day-ahead scheduling scheme theta; the wind power output uncertain parameters are obtained; x is an adjustable robust parameter; sD(theta) is the daily operation cost of the community energy Internet; sR(, γ) is the execution regulation cost of the community energy internet day-ahead scheduling scheme θ; hD(theta) ═ 0 is the energy balance constraint of the community energy internet; vD(theta) is less than or equal to 0, and is self-restraint of each operation unit of the community energy Internet; hR(theta, gamma, X) ═ 0 is the energy balance constraint of the community energy internet regulation and control layer; vRAnd (theta, gamma and X) is less than or equal to 0, which represents the regulation and control constraint of each controllable operation unit of the community energy Internet.
Further, the uncertain set of the wind power output uncertain parameters is represented as:
Figure RE-GDA0002719679360000022
wherein the content of the first and second substances,PWP(t)the output of the fan at the moment t;
Figure RE-GDA0002719679360000023
respectively the upper and lower limits of the fan output.
Further, the community energy Internet utilization adjustable parameterXWPThe method has the advantages that values of all wind power time periods are restrained, the economical efficiency of the community energy Internet scheme is ensured, and the adjustable parameters are specifically expressed as follows:
Figure RE-GDA0002719679360000024
wherein the content of the first and second substances,
Figure RE-GDA0002719679360000025
predicting wind power output for t moments respectively;PWP(t)wind power output at the time t is respectively;
Figure RE-GDA0002719679360000026
the upper and lower fluctuation ranges of the fan output at the moment t are respectively represented and are 0-1 variable.
Further, the information center takes the energy internet of each community as a transaction participant, and establishes a non-cooperative game transaction framework for the energy internet of each community, wherein the model expression is as follows:
Z＝(G1,G2,L,Gn；F1,F2,L,Fn)；
wherein the community energy Internet i minimizes its own operating cost FiBidding strategy G for targetiNash equilibrium optimal solution in model
Figure RE-GDA0002719679360000027
Satisfy the requirement of
Figure RE-GDA0002719679360000028
Wherein "oi" represents the remaining participants except the community energy internet i.
Further, the community energy internet arranges the obtained electric energy bids of other communities into a set
Figure RE-GDA0002719679360000031
And expressing the corresponding bidding electric quantity as
Figure RE-GDA0002719679360000032
When a bidding strategy is established to conduct a transaction, the bidding strategy is restricted by bidding and is expressed as follows:
Figure RE-GDA0002719679360000033
wherein, { sigmai,1t,σi,2t,…,σi,ntThe bidding price is a bidding price set of the community energy internet i at the time t; { kappa ]i,1t,κi,2t,…,κi,ntThe method comprises the steps that an electric quantity set corresponding to a bidding price at the time t is set by a community energy internet i;
Figure RE-GDA0002719679360000034
purchasing the total amount of electricity for each community energy Internet in the area at the time t; sntIs a binary auxiliary variable.
Further, the proposed bidding strategy is optimized by introducing uncertainty of wind power output into the community energy internet, and the optimization process comprises the following steps:
s301, minimizing the operation cost of the community energy Internet, constructing a max-min optimization model, solving the worst wind power output scene by using the outer max, and making an optimal strategy for coping with the uncertainty of the wind power output by using the inner min optimization decision variable in the regulation and control scheme of the worst wind power output scene;
s302, after the decision variables are optimized and solved in the step S301, under the condition that community energy Internet cluster games are considered, a day-ahead market bidding strategy is made and optimized.
Further, the objective function of the bidding strategy optimized in step S302 is represented as:
Figure RE-GDA0002719679360000035
the theta is an optimal transaction scheduling scheme meeting the requirement of minimizing the operation cost of the community energy Internet; sMTThe operating cost of the micro gas turbine; sGBThe operating cost of the gas boiler; sGThe cost of the community energy Internet trading with the power distribution network at the moment t;
Figure RE-GDA0002719679360000036
purchasing energy Internet of other communities for energy Internet of communitiesThe cost of electricity;
Figure RE-GDA0002719679360000037
for the community energy bidding profit function expression,
Figure RE-GDA0002719679360000038
for bidding lower limit of price, kappatThe competitive bidding electric quantity is obtained; v. oftbAre auxiliary variables.
Further, the scheduling of the information center includes:
the electricity purchase price of the power distribution network from the community energy Internet is less than the electricity sale price of the community to other communities;
the electricity purchasing community carries out electric energy transaction with each electricity selling community in sequence according to a bidding strategy proposed by the electricity purchasing community until the load requirement of the electricity purchasing community is met;
if the community electric energy demand can not be met after the community is scheduled, purchasing electricity from the power distribution network;
if the electric energy provided by the electricity-selling community is less than the electric energy purchased by the electricity-purchasing community in the electricity-selling community, the electricity-selling community needs to bear the temporary electricity-purchasing loss from the electricity-purchasing community to the power distribution network.
In a second aspect, a community energy internet energy transaction scheduling system considering wind power consumption is provided, which includes:
the system comprises a plurality of community energy Internet, a plurality of wind power generation and power generation control devices and a plurality of community energy Internet, wherein the community energy Internet is used for making bidding strategies participating in community energy Internet cluster transactions and optimizing the proposed bidding strategies according to the uncertainty of wind power output;
the information center is used for providing the purchasing and selling electric quantity, purchasing and selling price and purchasing and selling price information of the power distribution network of other community energy source internet for each community, and carrying out centralized transaction scheduling on electric energy of each community according to bidding strategies and requests provided by each community to meet the electric energy requirements of each community;
and the power distribution network is used for purchasing all the electric energy left by each community after scheduling and selling the electric energy to the community which can not meet the self demand through scheduling.
The invention has the following advantages and beneficial effects:
1. according to the regional community energy internet cluster robust game energy transaction mode, the electric energy resources of each community energy internet are fully utilized, the nearby consumption of wind power is promoted, the electricity utilization cost of the community energy internet is reduced, the running pressure of a power distribution network is relieved, and the friendly interaction of the multi-community energy internet and the power distribution network is realized;
2. the robust optimization method has strong capability of coping with uncertain risks, and the robust bidding scheduling scheme adopted by each community energy internet can reduce the real-time regulation and control cost when the wind power output fluctuates in the actual operation process, so that the total power consumption cost of the community energy internet is reduced;
3. due to the introduction of the non-cooperative game, the community energy internet needs to exchange low price for high electricity for the success in the game, the power price of internal transaction of the community energy internet is prevented from being high, and the good development of internal electric energy transaction of the community energy internet is promoted.
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The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a schematic diagram of a community energy Internet energy transaction scheduling system.
Fig. 2 is a schematic diagram of the bid amount in the energy internet of each community in the embodiment of the invention.
Fig. 3 is a schematic diagram of an electrical balance scheduling scheme of the community energy internet 1 according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of an electrical balance scheduling scheme of the community energy internet 2 according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of an electrical balance scheduling scheme of the community energy internet 3 according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of a heat balance scheduling scheme of the community energy internet 1 according to the embodiment of the invention.
Fig. 7 is a schematic diagram of a heat balance scheduling scheme of the community energy internet 2 according to an embodiment of the invention.
Fig. 8 is a schematic diagram of a heat balance scheduling scheme of the community energy internet 3 according to an embodiment of the present invention.
Fig. 9 is a schematic diagram of a cold balance scheduling scheme of the community energy internet 1 according to the embodiment of the invention.
Fig. 10 is a schematic diagram of a cold balance scheduling scheme of the community energy internet 2 according to an embodiment of the present invention.
Fig. 11 is a schematic diagram of a cold balance scheduling scheme of the community energy internet 3 according to the embodiment of the invention.
Fig. 12 is a schematic flow chart of a community energy internet energy transaction scheduling method of the invention.
Detailed Description
Hereinafter, the term "comprising" or "may include" used in various embodiments of the present invention indicates the presence of the invented function, operation or element, and does not limit the addition of one or more functions, operations or elements. Furthermore, as used in various embodiments of the present invention, the terms "comprises," "comprising," "includes," "including," "has," "having" and their derivatives are intended to mean that the specified features, numbers, steps, operations, elements, components, or combinations of the foregoing, are only meant to indicate that a particular feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as first excluding the existence of, or adding to the possibility of, one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
The terminology used in the various embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the various embodiments of the invention. As used herein, the singular forms are intended to include the plural forms as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present invention belong. Terms such as those defined in commonly used dictionaries will be interpreted as having a meaning that is the same as a contextual meaning in the related art and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein in various embodiments of the present invention.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the following examples and accompanying fig. 1-12, wherein the exemplary embodiments and descriptions of the present invention are only used for explaining the present invention and are not used as limitations of the present invention.
Example 1
The method for dispatching community energy internet energy trading in consideration of wind power consumption, as shown in fig. 12, includes:
s1, considering wind power maximum absorption and wind power output uncertainty factors, and formulating a robust economic dispatching strategy of the community energy Internet according to the principle of minimum operation cost and power interaction cost of the community energy Internet;
s2, the energy Internet of each community establishes bidding price and bidding electric quantity according to the bidding strategy proposed by the robust economic dispatching strategy and the non-cooperative game;
s3, according to the robust economic dispatching strategy of the community energy Internet, the proposed bidding strategy and the proposed dispatching request, the information center dispatches the electric energy of the community energy Internet and the distribution network so as to meet the load demand of each community; and the power distribution network purchases all the power left after the dispatching or supplements the power lacking after the dispatching.
The model of the community energy internet in step S1 can be expressed as:
Figure RE-GDA0002719679360000061
the model is an inner-layer robust model and an outer-layer robust model of the day-ahead economic dispatching considering wind power uncertainty, and the maximum wind power consumption is kept while the dispatching plan is operated to be optimal.
The optimization variable theta is a day-ahead scheduling scheme, and the inner-layer optimization variable gamma is an execution regulation and control scheme of the day-ahead scheduling scheme theta; in order to improve the consumption capability of the community energy internet to renewable energy, a punishment item of abandoning the renewable energy is introduced into the inner layer, the punishment item comprises uncertain parameters and is the output of a distributed power unit in the community energy internet; and X is an adjustable robust parameter.
SD(theta) is the day-ahead operation cost of the community energy Internet, including the operation cost of the micro gas turbine, the cost of trading with a power distribution network and the cost of trading in the market, and the specific expression is as follows:
Figure RE-GDA0002719679360000062
Figure RE-GDA0002719679360000063
wherein S isMTFor the operating costs of the micro gas turbine, including a cost parameter alphaMT、βMTAnd the output P of the micro gas turbine at the time tMT(t)；SGBFor the operating cost of the gas boiler, including a cost parameter alphaGB、βGBAnd the output P of the gas boiler at the time tGB(t)；SGThe cost of the community energy Internet trading with the power distribution network at the moment t comprises the electricity purchasing power at the moment t
Figure RE-GDA0002719679360000064
And selling electric power
Figure RE-GDA0002719679360000065
And the electricity purchase price at time t
Figure RE-GDA0002719679360000066
And selling price of electricity
Figure RE-GDA0002719679360000067
SR(,Gamma) executing regulation and control cost of the community energy Internet day-ahead scheduling scheme theta under the condition of considering uncertain factors, wherein the execution regulation and control cost comprises up-regulation cost and down-regulation cost of the controllable unit, and the execution regulation and control cost is specifically represented as up-regulation punishment unit price and down-regulation punishment unit price of the controllable unit; regulating and controlling layer market power interaction cost; the wind abandoning cost of the regulation layer is specifically expressed as wind abandoning punishment unit price and windThe product of the injected power of the motor group at the current moment is expressed as follows:
Figure RE-GDA0002719679360000068
Figure RE-GDA0002719679360000071
in the formula:
Figure RE-GDA0002719679360000072
and
Figure RE-GDA0002719679360000073
respectively the up-regulation cost and the down-regulation cost of the controllable unit, including the up-regulation punishment unit price of the controllable unit i
Figure RE-GDA0002719679360000074
Down-regulation punishment unit price
Figure RE-GDA0002719679360000075
And the up and down control quantity of the controllable unit i at the moment t
Figure RE-GDA0002719679360000076
SBaBalancing market power interaction cost for a regulation and control layer, wherein the market power purchase price is balanced at the moment t
Figure RE-GDA0002719679360000077
Price of electricity sold
Figure RE-GDA0002719679360000078
And the electricity purchasing power of the community energy Internet in the balance market at the moment t
Figure RE-GDA0002719679360000079
Selling electric power
Figure RE-GDA00027196793600000710
Wind abandon cost for regulation layer, including wind abandon punishment unit price
Figure RE-GDA00027196793600000711
Power injected into community energy internet by wind generating set at time t
Figure RE-GDA00027196793600000712
HDAnd (theta) ═ 0 is an energy balance constraint of the community energy internet, including an electrical balance constraint, a thermal balance constraint and a cold balance constraint, and specifically,
the electric balance being constrained to
Figure RE-GDA00027196793600000713
In the formula:
Figure RE-GDA00027196793600000714
represents the charge and discharge amount of the storage battery i at the time t; pEB(t) the electric energy consumption of the electric boiler at the time t; pEC(t) the power consumption of the electric refrigerator at time t;
Figure RE-GDA00027196793600000715
the predicted output of the wind generating set is obtained; pLoad(t) is the electric load amount at time t.
The thermal equilibrium constraints are:
QEB(t)+QGB(t)＝QLoad(t)+QAC(t)
in the formula: qEB(t) the heat generation quantity of the electric boiler at the moment t; qGB(t) the heat production of the gas boiler at the moment t; qAC(t) is the absorbed thermal power of the absorption refrigerator at time t; qLoad(t) is the thermal load at time t.
The cold balance constraints are:
CAC(t)+CEC(t)＝CLoad(t)
in the formula:CAC(t)is made of an absorption typeThe cold production capacity of the cold machine at the moment t;CEC(t)the energy production capacity of the electric refrigerator at the moment t is obtained; cLoad(t) is the amount of cold load at time t.
VD(theta) is less than or equal to 0, and is the self-restraint of each operation unit of the community energy Internet, comprising controllable unit operation restraint, energy storage restraint, interaction power restraint with a main network and community energy Internet transaction restraint in an area,
the operation constraint of the controllable unit is as follows:
Figure RE-GDA0002719679360000081
Figure RE-GDA0002719679360000082
in the formula:
Figure RE-GDA0002719679360000083
respectively is the upper and lower output limits of the controllable unit i;
Figure RE-GDA0002719679360000084
the running state of the controllable unit i at the time t is a variable of 0-1;
Figure RE-GDA0002719679360000085
and the maximum power for up-down climbing of the controllable unit i.
The energy storage constraint is:
Figure RE-GDA0002719679360000086
Figure RE-GDA0002719679360000087
Figure RE-GDA0002719679360000088
Figure RE-GDA0002719679360000089
Figure RE-GDA00027196793600000810
in the formula:
Figure RE-GDA00027196793600000811
the charge and discharge states of the storage battery i at the moment t are respectively;
Figure RE-GDA00027196793600000812
respectively representing the upper limit and the lower limit of the charge-discharge power of the storage battery i;
Figure RE-GDA00027196793600000813
the capacity of the storage battery i at the moment t;
Figure RE-GDA00027196793600000814
the self-loss rate and the charge-discharge efficiency of the storage battery are respectively;
Figure RE-GDA00027196793600000815
respectively an upper limit and a lower limit of the energy storage capacity; and T is a fixed operation period.
The interaction power constraint with the main network is:
Figure RE-GDA00027196793600000816
Figure RE-GDA00027196793600000817
Figure RE-GDA00027196793600000818
for community energy internet and large power grid interactive powerA maximum value;
Figure RE-GDA00027196793600000819
and
Figure RE-GDA00027196793600000820
and the electricity purchasing and selling states are respectively the t moment of the community energy Internet.
HRAnd (theta, gamma, X) ═ 0 is an energy balance constraint of a community energy internet regulation layer, and comprises an electric balance constraint, a heat balance constraint and a cold balance constraint.
VRAnd (theta, gamma and X) is less than or equal to 0, and represents the regulation and control constraints of each controllable operation unit of the community energy Internet, including the controllable unit adjustment constraint, the energy storage adjustment constraint, the interaction power adjustment constraint with the main network and the community energy Internet transaction adjustment constraint in the region.
The uncertain set of the wind power output uncertain parameters is represented as:
Figure RE-GDA00027196793600000821
wherein the content of the first and second substances,PWP(t)the output of the fan at the moment t;
Figure RE-GDA00027196793600000822
respectively the upper and lower limits of the fan output.
Further, in the case where multiple consecutive periods are considered, it is generally not possible for the uncertain random variables to simultaneously take the worst value. In order to prevent the optimization result from over-emphasizing robustness and neglecting economy, adjustable robust parameters are introducedXWPAnd (3) constraining values of all time periods of wind power, wherein the specific expression is as follows:
Figure RE-GDA0002719679360000091
wherein the content of the first and second substances,
Figure RE-GDA0002719679360000092
respectively for prediction of time tWind power output;PWP(t)wind power output at the time t is respectively;
Figure RE-GDA0002719679360000093
respectively representing the upper and lower fluctuation ranges of the output of the fan at the moment t;
Figure RE-GDA0002719679360000094
is a variable of 0-1 and represents the relationship between the output of the fan at the time t and the predicted output of the fan, and if the output of the fan is greater than the predicted output, the output of the fan is equal to the predicted output
Figure RE-GDA0002719679360000095
On the contrary, the method can be used for carrying out the following steps,
Figure RE-GDA0002719679360000096
considering that an optimization model introduces a regulation layer aiming at a severe wind power output scene for coping with the severe wind power output scene, and a two-order robust optimization model has a plurality of layers of optimization targets and is difficult to solve, therefore, the solving process of the model adopts a C & CG algorithm to solve, and simultaneously, a dual conversion method is used for converting the optimization problem of one layer and the optimization problem of the other layer into the same type, so that the equivalence before and after the conversion is ensured, the optimization problems are finally combined into a single-layer optimization problem, the solving difficulty is reduced, and the following models are taken as examples:
if the model expression is:
Figure RE-GDA0002719679360000097
wherein, theta is a decision variable, and V is the optimizing space of theta; q. q.sl(theta) and wk(θ) is the inequality constraint and the equality constraint of the model.
The general form of the dual problem for this model is then expressed as follows:
Figure RE-GDA0002719679360000098
wherein, λ and μ are dual decision variables, and inf { } is a lower limit function.
In the objective function of dual problem
Figure RE-GDA0002719679360000099
Is a linear expression for λ and μ, which is the lagrangian function of the original problem.
The problem conforming to the strong dual theory has a common characteristic: if both the original problem and the dual problem have an optimal solution, the optimal solutions of the original problem and the dual problem are equivalent. The equivalent conversion between the max problem and the min problem can be realized through strong dual conversion.
In addition to using a dual transformation method, a decomposition iteration method matched with a researched problem is also needed to construct a main problem and a sub problem in the solving process of the two-stage robust optimization problem, so that the mutual influence between the two stages of the robust model can be realized through multiple iterations between the two problems.
Under the condition that the number of community energy internets in a certain area is continuously increased, each community energy internet is used as a game participant, bidding strategies of other competitors need to be considered in a bidding scheme, and the community energy internet i minimizes the self-operation cost FiOptimal bidding strategy G for target decisioniThe non-cooperative game transaction framework takes the energy Internet of each community as a game participant, and the transaction model thereof is expressed as follows:
Z＝(G1,G2,L,Gn；F1,F2,L,Fn)，
wherein the community energy Internet i minimizes its own operating cost FiOptimal bidding strategy G for target decisioniNash equilibrium optimal solution in model
Figure RE-GDA0002719679360000101
Satisfy the requirement of
Figure RE-GDA0002719679360000102
Wherein "oi" represents the remaining gaming parties other than the community energy internet i.
The optimal bidding strategy is that the community energy Internet i simulates according to the load demand information of each community energy Internet in the region and the historical bidding data of other community energy internets to obtain an initial bidding strategy of a competitor
Figure RE-GDA0002719679360000103
Comprehensively considering self operation cost and the interaction cost of internal and external energy of the multi-community energy internet, and making an optimal bidding scheduling strategy
Figure RE-GDA0002719679360000104
IRAnd ISRespectively, a set of competitors and a subject participating in a bid. Community energy internetiThe objective function of (2) is as follows:
Figure RE-GDA0002719679360000105
wherein, FiFor community energy Internet i operating costs, wherein
Figure RE-GDA0002719679360000106
For the electricity purchasing cost from the community energy internet to other community energy internets,
Figure RE-GDA0002719679360000107
the competitive bidding income of the community energy Internet is realized. The specific function expression is:
Figure RE-GDA0002719679360000108
Figure RE-GDA0002719679360000109
and
Figure RE-GDA00027196793600001010
the power purchasing and selling states of the community energy Internet at the time t are respectively;
Figure RE-GDA00027196793600001011
and
Figure RE-GDA00027196793600001012
respectively the electricity price and the electricity quantity for purchasing electricity from other community energy Internet; sigmatAnd kappatRespectively bidding price and electric quantity; m is the quantity of the energy Internet of the electricity selling community; sigmant、κntAre auxiliary variables.
Because the power selling quantity and the power selling price of each community energy internet in the trading market have a mutual influence relationship and need to be purchased in sequence from low to high according to the power price to meet the self requirement, in the trading process, bidding constraints exist among the community energy internets, and the constraint expressions are as follows:
Figure RE-GDA0002719679360000111
in the formula (I), the compound is shown in the specification,
Figure RE-GDA0002719679360000112
in order to be a bidding price set,
Figure RE-GDA0002719679360000113
is a bidding electric quantity set; { sigma. }i,1t,σi,2t,…,σi,ntThe bidding price is a bidding price set of the community energy internet i at the time t; { kappa ]i,1t,κi,2t,…,κi,ntThe power set corresponding to the bidding price at the time t is the community energy internet i,
Figure RE-GDA0002719679360000114
and purchasing the total amount of electricity for each community energy Internet in the area at the time t. SntFor the introduction of auxiliary variables, binary variables, S1t+S2t+…+SntThe competitive bidding price and the electric quantity value of the community energy internet i at the time t are corresponding and unique due to the fact that the value is less than or equal to 1. The upper limit of the value of each community electricity selling quantity is the total electricity purchasing quantity of each community energy Internet at the moment tMinimum bidding price at t moment
Figure RE-GDA0002719679360000115
Corresponding electricity selling quantity
Figure RE-GDA0002719679360000116
Specifically, the method comprises the following steps:
when S is1tWhen the value is 1, indicating that the bidding price of the community energy Internet i at the time t is at the lowest price, and the upper limit of the value of the electricity selling quantity is the total electricity purchasing quantity of each community energy Internet at the time t;
when S is2tWhen the value is 1, the bidding price of the community energy Internet i at the time t is shown to be at a second low price, and the upper limit of the value of the electricity selling quantity is the sum of the electricity purchasing amount of each community energy Internet at the time t minus the lowest bidding price at the time t
Figure RE-GDA0002719679360000117
Corresponding electricity selling quantity
Figure RE-GDA0002719679360000118
When S is3tWhen the value is 1, the bidding price of the community energy Internet i at the time t is shown to be at the third low price, and the upper limit of the value of the electricity selling quantity is the sum of the electricity purchasing amount of each community energy Internet at the time t minus the lowest bidding price at the time t
Figure RE-GDA0002719679360000119
And a second lowest price
Figure RE-GDA00027196793600001110
Sum of corresponding electricity sales
Figure RE-GDA00027196793600001111
And so on.
Considering that the research objects of the embodiment are close to each other in geographic position and form an interconnection situation, and under the condition of not considering the network loss, the cold-hot power balance constraint, the controllable unit operation constraint, the energy storage operation constraint and the power grid interaction power constraint are all the same as those of the optimization model. And the interactive power constraint of the community energy Internet is as follows:
Figure RE-GDA00027196793600001112
Figure RE-GDA00027196793600001113
Figure RE-GDA00027196793600001114
the maximum value of the interaction power of the community energy internet and the rest community energy internets is set;
Figure RE-GDA00027196793600001115
and
Figure RE-GDA00027196793600001116
and the electricity purchasing and selling states are respectively the t moment of the community energy Internet.
And the decision variable of the bidding strategy made by the community energy internet i when participating in bidding is the product of two variables of the bidding price and the bidding electric quantity, so that the non-convex nonlinear optimization problem is linearized by adopting a binary extension method in the embodiment, and the specific expression after the linearization is as follows:
Figure RE-GDA0002719679360000121
Figure RE-GDA0002719679360000122
wherein the content of the first and second substances,
Figure RE-GDA0002719679360000123
and
Figure RE-GDA0002719679360000124
for biddingAn upper and lower limit; v. oftbIs an auxiliary variable; x is the number oftbIs an introduced binary variable; b isσThe number of binary variables; mσIs a sufficiently large constant, typically greater than the upper limit of the bid price.
Considering the volatility and intermittency of wind power generation, wind speed prediction technology is not mature nowadays, and prediction error is usually higher than load and other factors. Bidding strategy (sigma) made by energy Internet of communitiestκt) Except the influence of other community energy internet strategies, the risk that wind power output uncertainty brought still needs to be dealt with, therefore this embodiment adopts the two-stage robust game to compete bidding scheduling model and optimizes each community energy internet bidding strategy, makes it can deal with the risk that wind power uncertainty brought, and minimizing community energy internet running cost, the optimization process is:
s301, minimizing the operation cost of the community energy Internet, constructing a max-min optimization model, solving the worst wind power output scene by using the outer max, and making an optimal strategy for coping with the uncertainty of the wind power output by using the inner min optimization decision variable in the regulation and control scheme of the worst wind power output scene;
s302, after the decision variables are optimized and solved in the step S301, under the condition that community energy Internet cluster games are considered, a day-ahead market bidding strategy is made and optimized.
Specifically, the day-ahead scheduling layer objective function of step S302 is represented as:
Figure RE-GDA0002719679360000125
wherein S isMTThe operating cost of the micro gas turbine; sGBThe operating cost of the gas boiler; sGThe cost of the community energy Internet trading with the power distribution network at the moment t;
Figure RE-GDA0002719679360000126
the electricity purchasing cost from the community energy internet to other community energy internets is reduced;
Figure RE-GDA0002719679360000131
for the community energy bidding profit function expression,
Figure RE-GDA0002719679360000132
for bidding lower limit of price, kappatThe competitive bidding electric quantity is obtained; v. oftbAre auxiliary variables.
The step 1 of solving the theta is the optimal decision under the condition of not considering the influence of uncertain parameters
Figure RE-GDA0002719679360000133
In the stage 2, after the worst wind power output scene is introduced, the solved strategy is the established optimal strategy for dealing with the uncertainty of the wind power output:
Figure RE-GDA0002719679360000134
the constraint condition of the stage 2 is an operation regulation constraint based on the scheduling scheme of the stage 1, and the power balance regulation constraint is that the CG still meets the power balance after power adjustment under the worst wind power output condition. The uncertain regulation and control constraint of the wind turbine generator is as follows:
Figure RE-GDA0002719679360000135
wherein the content of the first and second substances,PWP(t)is an uncertain variable.
Considering that real-time regulation and control schemes of all units under actual wind power output need to be formulated on the same day, when the power balance condition under the worst condition can be met, all wind power output conditions can be met theoretically.
Example 2
The difference between the embodiment and the embodiment 1 is that the embodiment takes 3 community energy internet as a cluster research, considers the difference of specific conditions of energy internet parameters of each community, and assumes the following data as the energy parameters of each community for simulation, wherein the controllable unit parameters of each community energy internet are shown in table 1; the EES operating parameters are shown in table 2; the maximum interactive power among the community energy internet and between the community energy internet and the power grid is 300 kW.
TABLE 1 controllable Unit operation parameters of Community energy Internet 1-3
Figure RE-GDA0002719679360000141
TABLE 2 Community energy Internet 1-3 energy storage Unit parameters
Figure RE-GDA0002719679360000142
The community energy internet cluster system structure in the embodiment is shown in fig. 1 and comprises a power distribution network, an information center and a community energy internet, wherein the community energy internet is used as a basic unit of a multi-community energy internet system, a bidding strategy participating in community energy internet cluster transaction is formulated, the proposed bidding strategy is optimized according to uncertainty of wind power output and self operation and regulation and control cost, and the community energy internet cluster system structure is provided with independent wind power generation equipment, a micro gas turbine, an electric refrigerator, an electric boiler, an absorption refrigerator and electric energy storage equipment. The community energy internet meets the self load demand through modes of controllable units, wind power generation, electric energy transaction in community energy internet clusters, power purchase from a power distribution network and the like. The information center provides a platform for electric energy transaction of each community energy internet, and centralized transaction scheduling is carried out on the electric energy of each community according to bidding strategies and requests provided by each community, so that the electric energy requirements of each community are met.
In this embodiment, the value of the adjustable robust parameter is 8, the gains and the scheduling operation cost obtained by the community energy internet through bidding after 11 rounds of gaming of the constructed two-stage robust optimization game transaction model tend to be stable, each community energy internet cannot obtain more gains and reduce the operation cost of the community energy internet by changing the bidding strategy and the scheduling scheme of the community energy internet in a unilateral manner, and the bidding strategy at this time is a nash equilibrium solution which enables the operation cost of each main body to be the lowest. Competitive bidding income, day-ahead operation cost and real-time regulation and control cost of the community energy internets 1-3 when Nash equilibrium solution is achieved are shown in a table 3, competitive bidding price ratio is shown in a table 4 (only time periods with internal transactions are listed), and the bid amount of each community energy internet is shown in a figure 2
TABLE 3 optimization results under Game Balancing
Figure RE-GDA0002719679360000151
TABLE 4 Bidding price comparison
Figure RE-GDA0002719679360000152
As can be seen from table 3 and fig. 2, the community energy internet 1 has the highest profit and the largest bid amount in the electric quantity transaction in the community energy internet cluster, and occupies the absolute advantage of the three-party game; and the community energy Internet 2 has low profit and the lowest bid amount in the internal electric quantity transaction of the community energy Internet cluster, and is in disadvantage in the three-party game. The reason is that the wind power output of the community energy internet 1 all day is large, the sold electric quantity is large, and the electric quantity participating in bidding is also large; and the wind power output of the community energy internet 2 all day is small, and most of the time is in the electricity purchasing state, so that the electric quantity participating in bidding is small. From the comparative analysis of table 3 and fig. 2, it can be seen that: no person participates in competition at 6-7 points, 12-17 points and 20-24 points, so that the community energy Internet 1 successfully bids at a price 11.2% lower than the power selling price of the power grid; due to the fact that the electricity purchasing demand is large, the competition between the community energy internet 1 and the community energy internet 3 is severe, the successful bidding price of the community energy internet 1 and the community energy internet 3 is compared with the power price of a power grid, and the reduction rate of the price is 20% and 25.1% respectively. The daily transaction total electricity consumption of the community energy Internet is 3935kWh, and compared with the electricity purchase from a power grid, the electricity charge of each community energy Internet is saved by 405.08 yuan through internal transaction.
And further analyzing and demonstrating the quality relation in the three-community energy internet game environment by combining the internal unit of each community energy internet, the energy storage system and related operating parameters as follows.
As shown in fig. 3, table 1 and table 2, the community energy internet 1 has a larger energy storage system and a higher controllable unit output than the competitor. The energy storage system with large capacity enables the community energy Internet 1 to store electric energy in the time period of wind power surplus or low electricity price at night, so that the community energy Internet 1 can use low-price electric energy in the time period of load peak; and the controllable unit with higher output enables the community energy internet 1 to purchase less electricity to the power grid in the load peak period, so that the electricity utilization cost is saved. The above two reasons make the community energy internet 1 have lower operation cost and have the remaining power for sale during the peak load period, which makes it at a lower bid level in the game bidding.
As shown in fig. 4, 5 and table 1, the installed capacity of the controllable unit of the community energy internet 2 is smaller than that of the community energy internet 1 and that of the community energy internet 3, and the wind power output throughout the day is much smaller than that of the other two community energy internets, so that the self-sufficiency of the electric energy cannot be realized, a large amount of electricity needs to be purchased to the rest of the community energy internets and the power grid during the electricity consumption peak, and the operation cost is high. Meanwhile, the power selling amount of the community energy internet 2 to other community energy internets is small, so that the competitive bidding income of the community energy internet 2 in the three-party game is far lower than that of the community energy internet 1 and the community energy internet 3.
Fig. 6-8 and 9-11 are respectively a thermal balance scheduling scheme and a cold balance scheduling scheme inside the community energy internet. As can be seen from the figure, at 1-6 am, the balance of heat and cold loads is realized mainly by using an electric boiler and electric refrigeration to absorb redundant wind power. During the peak time of heat load and cold load, the running cost of the electric boiler and the electric refrigeration is increased due to insufficient wind power output, and the heat load and the cold load are respectively supplemented and satisfied by the gas boiler and the absorption refrigerator.
In order to verify the effectiveness of the two-stage robust optimization game transaction model in the embodiment, 1000 random real-time wind power scenes generated by the Monte Carlo method are compared with the economy of the robust scheme and the deterministic scheme of the community energy Internet 1, and the average values of the running total costs of the 1000 wind power scenes are counted, and the result is shown in Table 5.
TABLE 5 comparison of costs under different protocols
Figure RE-GDA0002719679360000161
Although the day-ahead operation cost of the robust scheme considering the uncertainty of the wind power is 97.06 yuan higher than that of the deterministic scheme, the real-time regulation average total cost of the uncertainty scheme is 54.25% lower than that of the deterministic scheme, and the operation total cost of the robust scheme is 4.03% lower than that of the deterministic scheme, so that the robust scheme is better in economy. The wind power uncertainty is considered in the robust scheme, and the competitive bidding scheduling scheme has strong capability of coping with the wind power uncertainty while ensuring the economy. In summary, the economy of a robust bidding solution that takes uncertainty into account is realized.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. The community energy internet energy transaction scheduling method considering wind power consumption is characterized by comprising the following steps:
s1, considering wind power maximum absorption and wind power output uncertainty factors, and formulating a robust economic dispatching strategy of the community energy Internet according to the principle of minimum operation cost and power interaction cost of the community energy Internet;
s2, the energy Internet of each community establishes bidding price and bidding electric quantity according to the bidding strategy proposed by the robust economic dispatching strategy and the non-cooperative game;
s3, according to the robust economic dispatching strategy of the community energy Internet, the proposed bidding strategy and the proposed dispatching request, the information center dispatches the electric energy of the community energy Internet and the distribution network so as to meet the load demand of each community; and the power distribution network purchases all the power left after the dispatching or supplements the power lacking after the dispatching.
2. The method for dispatching community energy internet transactions considering wind power consumption as claimed in claim 1, wherein the model of the community energy internet is represented as:
Figure RE-FDA0002719679350000011
the model is an inner-layer robust model and an outer-layer robust model of the day-ahead economic dispatching considering wind power uncertainty, and the maximum wind power consumption is kept while the optimal operation dispatching plan is obtained;
wherein, the optimization variable theta is a day-ahead scheduling scheme; the inner layer optimization variable gamma is an execution regulation and control scheme of a day-ahead scheduling scheme theta; the wind power output uncertain parameters are obtained; x is an adjustable robust parameter; sD(theta) is the daily operation cost of the community energy Internet; sR(, γ) is the execution regulation cost of the community energy internet day-ahead scheduling scheme θ; hD(theta) ═ 0 is the energy balance constraint of the community energy internet; vD(theta) is less than or equal to 0, and is self-restraint of each operation unit of the community energy Internet; hR(theta, gamma, X) ═ 0 is the energy balance constraint of the community energy internet regulation and control layer; vRAnd (theta, gamma and X) is less than or equal to 0, which represents the regulation and control constraint of each controllable operation unit of the community energy Internet.
3. The method for dispatching community energy internet energy transactions considering wind power consumption according to claim 1, wherein the uncertain set of uncertain parameters of wind power output is represented as:
Figure RE-FDA0002719679350000012
wherein, PWP(t) fan output at time t;
Figure RE-FDA0002719679350000013
respectively the upper and lower limits of the fan output.
4. The method for community energy internet energy transaction scheduling with wind power consumption in mind according to claim 1, wherein the community energy internet utilizes an adjustable parameter XWPThe method has the advantages that values of all wind power time periods are restrained, the economical efficiency of the community energy Internet scheme is ensured, and the adjustable parameters are specifically expressed as follows:
Figure RE-FDA0002719679350000014
wherein the content of the first and second substances,
Figure RE-FDA0002719679350000015
predicting wind power output for t moments respectively; pWP(t) wind power output at time t respectively;
Figure RE-FDA0002719679350000016
the upper and lower fluctuation ranges of the fan output at the moment t are respectively represented and are 0-1 variable.
5. The method for dispatching community energy internet energy trading considering wind power consumption according to claim 1, wherein the information center establishes a non-cooperative game trading framework for each community energy internet by taking each community energy internet as a trading participant, and the model expression is as follows:
Z＝(G1,G2,L,Gn；F1,F2,L,Fn)；
wherein the community energy Internet i minimizes its own operating cost FiBidding strategy G for targetiNash equilibrium optimal solution in model
Figure RE-FDA0002719679350000021
Satisfy the requirement of
Figure RE-FDA0002719679350000022
Wherein "oi" represents the remaining participants except the community energy internet i.
6. The method for community energy internet energy transaction scheduling considering wind power consumption as claimed in claim 1, wherein the community energy internet bids and arranges the obtained other community electric energy into a set
Figure RE-FDA0002719679350000023
And expressing the corresponding bidding electric quantity as
Figure RE-FDA0002719679350000024
When a bidding strategy is established to conduct a transaction, the bidding strategy is restricted by bidding and is expressed as follows:
Figure RE-FDA0002719679350000025
wherein, { sigmai,1t,σi,2t,…,σi,ntThe bidding price is a bidding price set of the community energy internet i at the time t; { kappa ]i,1t,κi,2t,…,κi,ntThe method comprises the steps that an electric quantity set corresponding to a bidding price at the time t is set by a community energy internet i;
Figure RE-FDA0002719679350000026
purchasing the total amount of electricity for each community energy Internet in the area at the time t; sntIs a binary auxiliary variable.
7. The method for dispatching the community energy internet energy trading with consideration of wind power consumption according to claim 1, wherein the community energy internet introduces wind power output uncertainty to optimize the proposed bidding strategy, and the optimization process comprises:
s301, minimizing the operation cost of the community energy Internet, constructing a max-min optimization model, solving the worst wind power output scene by using the outer max, and making an optimal strategy for coping with the uncertainty of the wind power output by using the inner min optimization decision variable in the regulation and control scheme of the worst wind power output scene;
s302, after the decision variables are optimized and solved in the step S301, under the condition that community energy Internet cluster games are considered, a day-ahead market bidding strategy is made and optimized.
8. The method for scheduling community energy internet energy trading considering wind power consumption according to claim 7, wherein the objective function of the optimized bidding strategy in the step S302 is represented as:
Figure RE-FDA0002719679350000031
the theta is an optimal transaction scheduling scheme meeting the requirement of minimizing the operation cost of the community energy Internet; sMTThe operating cost of the micro gas turbine; sGBThe operating cost of the gas boiler; sGThe cost of the community energy Internet trading with the power distribution network at the moment t;
Figure RE-FDA0002719679350000032
the electricity purchasing cost from the community energy internet to other community energy internets is reduced;
Figure RE-FDA0002719679350000033
for the community energy bidding profit function expression,
Figure RE-FDA0002719679350000034
for biddingLower valence limit,. kappatThe competitive bidding electric quantity is obtained; v. oftbAre auxiliary variables.
9. The method for dispatching community energy internet energy trading considering wind power consumption according to claim 1, wherein the dispatching of the information center comprises:
the electricity purchase price of the power distribution network from the community energy Internet is less than the electricity sale price of the community to other communities;
the electricity purchasing community carries out electric energy transaction with each electricity selling community in sequence according to a bidding strategy proposed by the electricity purchasing community until the load requirement of the electricity purchasing community is met;
if the community electric energy demand can not be met after the community is scheduled, purchasing electricity from the power distribution network;
if the electric energy provided by the electricity-selling community is less than the electric energy purchased by the electricity-purchasing community in the electricity-selling community, the electricity-selling community needs to bear the temporary electricity-purchasing loss from the electricity-purchasing community to the power distribution network.
10. Consider energy internet energy transaction dispatch system of community of wind-powered electricity generation consumption, its characterized in that includes:
the system comprises a plurality of community energy Internet, a plurality of wind power generation and power generation control devices and a plurality of community energy Internet, wherein the community energy Internet is used for making bidding strategies participating in community energy Internet cluster transactions and optimizing the proposed bidding strategies according to the uncertainty of wind power output;
the information center is used for providing the purchasing and selling electric quantity, purchasing and selling price and purchasing and selling price information of the power distribution network of other community energy source internet for each community, and carrying out centralized transaction scheduling on electric energy of each community according to bidding strategies and requests provided by each community to meet the electric energy requirements of each community;
and the power distribution network is used for purchasing all the electric energy left by each community after scheduling and selling the electric energy to the community which can not meet the self demand through scheduling.
CN202010892033.7A 2020-08-28 2020-08-28 Community energy Internet energy transaction scheduling method and system considering wind power consumption Pending CN112036637A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114069700A (en) * 2021-11-18 2022-02-18 国网江苏省电力有限公司 Regional comprehensive energy scheduling control system based on energy Internet

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114069700A (en) * 2021-11-18 2022-02-18 国网江苏省电力有限公司 Regional comprehensive energy scheduling control system based on energy Internet
CN114069700B (en) * 2021-11-18 2024-05-10 国网江苏省电力有限公司 Regional comprehensive energy scheduling control system based on energy Internet

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