CN112052989B - Risk cost allocation method for comprehensive energy resource sharing community - Google Patents

Risk cost allocation method for comprehensive energy resource sharing community Download PDF

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CN112052989B
CN112052989B CN202010844437.9A CN202010844437A CN112052989B CN 112052989 B CN112052989 B CN 112052989B CN 202010844437 A CN202010844437 A CN 202010844437A CN 112052989 B CN112052989 B CN 112052989B
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郭祚刚
雷金勇
袁智勇
徐敏
王�琦
谈赢杰
唐学用
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CSG Electric Power Research Institute
China Southern Power Grid Co Ltd
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Abstract

The invention provides a risk cost sharing method for an integrated energy resource sharing community, which comprises the following steps: constructing a daily energy supply adjustment risk cost model of an energy sharing community according to the multi-energy user load and the energy spot price of the comprehensive energy system to obtain daily energy supply adjustment cost; constructing a scheduling optimization model of the energy sharing community according to the energy consumption and price of the contract and the daily energy supply adjustment cost; solving the scheduling optimization model to obtain a CVar value of the lowest energy supply total cost of the energy sharing community; and (4) carrying out risk cost allocation on the multi-energy users according to the contribution degree of the multi-energy users to the CVar value of the lowest energy supply total cost. The risk cost allocation method provided by the invention provides a double-layer scheduling optimization model with the upper layer for medium-long term decision and the lower layer for intra-day energy supply adjustment, and adopts a Sharprey value method to allocate the risk cost, so that the contribution degree of the multi-energy users to the energy supply total cost of the energy sharing community is reflected, and fair allocation is realized.

Description

Risk cost allocation method for comprehensive energy resource sharing community
Technical Field
The invention relates to the technical field of comprehensive energy systems, in particular to a risk cost sharing method, equipment and medium for a comprehensive energy sharing community.
Background
With the popularization of comprehensive energy systems with multi-energy collaborative planning operation, the coupling of various energy sources such as cold, heat, electricity and gas in production, transmission and use is gradually deepened. The traditional mutually independent single power supply and heat supply are gradually coupled in scheduling, operation and cost control. With the gradual advance of domestic energy reform, the degree of marketization of electric power and natural gas is deepened, the randomness of energy supply cost is more obvious, and the traditional scheduling optimization based on complete information is not suitable any more. The multipotent coupling and the risk of cost fluctuations present new challenges to load billing.
The existing user cost allocation technology is mostly based on complete information and fixed price, neglects the fluctuation of wholesale energy supply cost and the interference of load prediction deviation on scheduling optimization, cannot cope with the complex conditions that the multi-energy wholesale energy supply cost and load present randomness and coupling, and has great limitation.
Therefore, a user cost allocation method is urgently needed to solve the technical problem of unfair allocation caused by the fact that the contribution degree of each multi-energy user to the total energy supply risk cost of the energy sharing community is not considered.
Disclosure of Invention
The invention aims to provide a risk cost allocation method, equipment and medium of an integrated energy resource energy sharing community, so as to solve the technical problem of unfairness in allocation caused by the fact that the contribution degree of each multi-energy user to the total risk cost of energy supply of the energy sharing community is not considered.
The purpose of the invention can be realized by the following technical scheme:
a risk cost sharing method for an integrated energy and energy sharing community comprises the following steps:
constructing an energy supply adjustment risk cost model of an energy sharing community in the day according to the multi-energy user load and the energy spot price of the comprehensive energy system; the optimization target of the day function adjustment risk cost model is day energy supply adjustment cost corresponding to a day optimal adjustment strategy;
constructing a scheduling optimization model of the energy sharing community according to the energy consumption and price of the contract and the daily energy supply adjustment cost; wherein, the product of the energy consumption and the price of the contract is the medium and long term energy supply cost; the optimization target of the scheduling optimization model is a CVar value of the lowest energy supply total cost, and the energy supply total cost is the sum of the medium-long term energy supply cost and the day-to-day energy supply adjustment cost;
solving the scheduling optimization model to obtain a CVar value of the lowest energy supply total cost of the energy sharing community;
and (4) carrying out risk cost allocation on the multi-energy users according to the contribution degree of the multi-energy users to the CVar value of the lowest energy supply total cost.
Optionally, the probability density model of the multi-energy consumer electrical load is:
Figure BDA0002642561110000021
in the formula (I), the compound is shown in the specification,
Figure BDA0002642561110000022
the electrical load of the multi-energy user is represented,
Figure BDA0002642561110000023
representing the medium-and long-term predicted value, σ, of the electrical loadsRepresents an unconstrained standard deviation of the electrical load,
Figure BDA0002642561110000024
respectively represent the upper and lower limits of the power consumption.
Optionally, the probability density model of the multi-energy user heat load is:
Figure BDA0002642561110000025
in the formula (I), the compound is shown in the specification,
Figure BDA0002642561110000026
indicating the heat load of the multi-energy user,
Figure BDA0002642561110000027
represents a predicted value of the heat load, tau, for the medium and long termsRepresents the unconstrained standard deviation of the thermal load,
Figure BDA0002642561110000028
respectively representing the upper and lower limits of the thermal energy.
Optionally, the optimization objective of the energy supply adjustment risk cost model in the day is:
Figure BDA0002642561110000029
in the formula, Cadjust(XAζ) adjust costs for energy supply within a day; t denotes a period t, λt、βtRespectively representing the normal distribution, P, to which the prices of electricity and natural gas are subjectedt spotIndicating the electric quantity purchased in the spot market;
Figure BDA00026425611100000211
representing the gas purchasing quantity of the spot market; pt surRepresenting the default electric quantity of the medium and long-term contract; chi shapepunishRepresenting the default punishment of the medium-term contract unit electric quantity;
Figure BDA00026425611100000212
a thermal load indicative of an increase in demand response;
Figure BDA00026425611100000213
representing the unit demand response compensation cost.
Optionally, the scheduling optimization model specifically includes:
Figure BDA00026425611100000210
in the formula, Pt cRepresenting the electricity consumption of the medium and long term contract;
Figure BDA00026425611100000214
representing the electricity price of the medium and long term contract;
Figure BDA00026425611100000215
representing gas usage of medium and long term contracts;
Figure BDA00026425611100000216
representing gas prices for medium and long term contracts; xLRepresenting medium and long term decisions; xAAdjusting the decision for the day;
Figure BDA0002642561110000031
represents medium and long term energy supply cost;
Figure BDA0002642561110000032
representing a random variable.
Optionally, solving the scheduling optimization model to obtain a CVar value of the lowest energy supply total cost of the energy sharing community specifically includes:
searching the optimal decision of the medium and long-term stage by adopting a particle swarm algorithm;
extracting random variables by adopting a Monte Carlo sampling method to obtain a plurality of scenes and probability distribution thereof;
solving the intra-day energy supply adjustment problem corresponding to each scene by adopting a Linprog solver to obtain the CVar value of the lowest energy supply total cost of the energy sharing community.
Optionally, solving the intra-day energy supply adjustment problem corresponding to each scene by using a Linprog solver, and obtaining the CVar value of the lowest energy supply total cost of the energy sharing community specifically includes:
s11: receiving the spot price, the probability distribution information of the multi-energy load and the upper limit and the lower limit of the multi-energy load; receiving CHP equipment parameters; setting a confidence coefficient alpha; setting Monte cardLof number of samples Nc；
S12: setting the number N of particles, the iterative count iter of the particles to be 0, and the initial position of each particle N
Figure BDA0002642561110000034
Initial velocity of particles
Figure BDA0002642561110000035
Convergence criterion epsilon, acceleration constant c1、c2An inertia factor w;
s13: random variables were sampled using Monte Carlo
Figure BDA0002642561110000036
Extracting NcGroup data, obtaining theta scenes and scene probability P thereofθ(ii) a For each scene, the positions of the particles are respectively determined
Figure BDA0002642561110000037
Substituting, and forming theta energy supply adjustment problems in the step 3 for each particle;
s14: solving the problem of energy supply adjustment in each day by adopting a Linprog solver, and calculating the fitness according to the following formula
Figure BDA0002642561110000038
Figure BDA0002642561110000033
If iter is 0, the process proceeds to step S15, and if iter >0, the process proceeds to step S16;
s15: let particle n history optimal location
Figure BDA0002642561110000039
Particle n history optimal adaptation
Figure BDA00026425611100000310
If iter is equal to 0, the group history optimal fitness
Figure BDA00026425611100000311
Recording the particles corresponding to the historical optimal fitness of the population as n*Then the historical optimal location of the population
Figure BDA00026425611100000312
S16: for any particle n, if
Figure BDA00026425611100000313
Then
Figure BDA00026425611100000314
Note Fbest，0＝FbestIf, if
Figure BDA00026425611100000315
Then
Figure BDA00026425611100000316
If the convergence criterion | F is satisfiedbest，0-FbestIf the | is less than or equal to epsilon, the iteration is ended, and the step S19 is entered;
s17: updating each particle n position
Figure BDA0002642561110000042
Updating the n-velocity of each particle
Figure BDA0002642561110000043
Wherein r is1、r2To take on a value of [0,1]Random parameter in between, particle iteration count iter ═ iter + 1;
s18: repeating steps S13-S17;
s19: finishing the algorithm, and outputting the CVar value F of the group history optimal fitness, namely the lowest energy supply total cost of the energy sharing communitybest。
Optionally, the risk cost apportionment of the multi-energy users according to the contribution degree of the multi-energy users to the CVar value of the lowest total energy supply cost specifically includes:
according to the summery method, the risk cost of the ith multi-energy user is divided into:
Figure BDA0002642561110000041
in the formula (I), the compound is shown in the specification,
Figure BDA0002642561110000044
representing the risk cost shared by the ith user; ns denotes the number of multi-capable users; ns! Representing the number of energy sharing communities which are possibly formed by Ns multiple energy users;
Figure BDA0002642561110000045
and the contribution degree of the CVar value of the lowest energy supply total cost of the energy sharing community when the user i joins the energy sharing community in the j-th order is shown.
The invention also provides a risk cost allocation device of the comprehensive energy resource sharing community, which comprises the following steps:
a memory for storing a computer program;
a processor for executing the computer program to implement the method for risk cost sharing for an integrated energy sharing community.
The present invention also provides a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the method for risk cost sharing for an integrated energy sharing community.
The invention provides a risk cost sharing method, equipment and medium for an integrated energy resource sharing community, wherein the method comprises the following steps: constructing a daily energy supply adjustment risk cost model of an energy sharing community according to the multi-energy user load and the energy spot price of the comprehensive energy system to obtain daily energy supply adjustment cost; constructing a scheduling optimization model of the energy sharing community according to the energy consumption and price of the contract and the daily energy supply adjustment cost; wherein, the product of the energy consumption and the price of the contract is the medium and long term energy supply cost; the optimization target of the scheduling optimization model is a CVar value of the lowest energy supply total cost, and the energy supply total cost is the sum of the medium-long term energy supply cost and the day-to-day energy supply adjustment cost; solving the scheduling optimization model to obtain a CVar value of the lowest energy supply total cost of the energy sharing community; and (4) carrying out risk cost allocation on the multi-energy users according to the contribution degree of the multi-energy users to the CVar value of the lowest energy supply total cost.
The invention provides a risk cost allocation method of a comprehensive energy resource energy sharing community, which comprises the steps of firstly, providing a scheduling optimization model of the comprehensive energy resource energy sharing community based on a CVar theory, wherein the scheduling optimization model is a double-layer model with the upper layer for medium and long term decision and the lower layer for adjustment of energy supply in the day, and providing the optimal energy purchasing and energy supply cost considering risks; then, risk cost is shared by adopting a summer curry value method, the contribution degree of each multi-energy user to the total risk cost of the energy supply of the shared community is fully reflected, and fair sharing is achieved.
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FIG. 1 is a schematic flow chart of a method for risk cost sharing of an integrated energy sharing community according to the present invention;
FIG. 2 is a schematic flow chart of a Cvar value for solving the lowest energy supply total cost of the energy sharing community in the risk cost allocation method of the integrated energy sharing community of the present invention;
FIG. 3 is a schematic flow chart of a subroutine 2 for solving the Cvar value of the lowest energy supply total cost of the energy sharing community in the risk cost sharing method of the integrated energy sharing community of the present invention;
fig. 4 is a schematic flow diagram of risk cost sharing performed by the risk cost sharing method for the integrated energy resource sharing community according to the present invention.
Detailed Description
The embodiment of the invention provides a risk cost allocation method, equipment and medium for an integrated energy resource energy sharing community, and aims to solve the technical problem of unfairness in allocation caused by the fact that the contribution degree of each multi-energy user to the total risk cost of energy supply of the energy sharing community is not considered.
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The existing user cost allocation technology is mostly based on complete information and fixed price, neglects the interference of fluctuation of wholesale and purchase energy cost and load prediction deviation on scheduling optimization, cannot cope with the complex conditions of randomness and coupling of the multi-energy wholesale and purchase energy cost and load, and has great limitation.
The following embodiment of the risk cost allocation method for the comprehensive energy resource sharing community of the invention comprises the following steps:
s101: constructing an energy supply adjustment risk cost model of an energy sharing community in the day according to the multi-energy user load and the energy spot price of the comprehensive energy system; the optimization target of the day function adjustment risk cost model is day energy supply adjustment cost corresponding to a day optimal adjustment strategy;
s102: constructing a scheduling optimization model of the energy sharing community according to the energy consumption and price of the contract and the daily energy supply adjustment cost; wherein, the product of the energy consumption and the price of the contract is the medium and long term energy supply cost; the optimization target of the scheduling optimization model is a CVar value of the lowest energy supply total cost, and the energy supply total cost is the sum of the medium-long term energy supply cost and the day-to-day energy supply adjustment cost;
s103: solving the scheduling optimization model to obtain a CVar value of the lowest energy supply total cost of the energy sharing community;
s104: and (4) carrying out risk cost allocation on the multi-energy users according to the contribution degree of the multi-energy users to the CVar value of the lowest energy supply total cost.
Referring to fig. 1-4, before step S101, the CVar risk value is described mathematically;
CVar is a risk method based on the improvement of Var values. It is defined as the expected loss value when portfolio X is subjected to a higher risk loss value than VaR at confidence a. It is expressed mathematically as follows:
CVaRα(X)＝E[f(X,ζ)|f(X,ζ)＞VaRα] (1)
wherein f (X, ζ) is a loss function of the portfolio; ζ represents a random variable affecting portfolio loss; VaRαThe value of Var of the investment portfolio under the confidence degree alpha is shown, and the definition is shown in the formula (2).
Ψ(f(X,ζ)≥VaRα)＝α (2)
In the equation, the left function of the equation represents the probability distribution of the inequality in parentheses.
From formulas (1) - (2), CVar is of the form as formula (3):
Figure BDA0002642561110000061
wherein p (ζ) represents a probability distribution of random variables; equations (2) - (3) show that VaR can be found as long as the probability distribution of the random variables is knownαFurther, the CVaR is obtainedα(X)。
Equivalently, the optimization problem can be constructed as follows:
Figure BDA0002642561110000071
for a discretized scene, then the expression is:
Figure BDA0002642561110000072
solving the optimization problem to obtain an objective function value f (X, z) which is the CVaR to be obtainedα(X), the value of the corresponding variable z is VaRα. Where θ is a scene determined by a combination of random variable values, PθIs the probability value of the scene theta.
In step S101, the random variables mainly include the multi-energy user load and the spot energy price.
Energy sharing community by N of integrated energy systemsThe system comprises a plurality of multi-energy users, each multi-energy user is subjected to unconstrained load normal distribution in the same time period, and in addition, each multi-energy user needs to submit the upper limit and the lower limit of own energy consumption, so that the user load probability distribution presents the normal distribution of a boundary. As the pricing basis, the larger the upper and lower limit segment length is, the larger the load uncertainty is.
Multi-energy user electric load
Figure BDA0002642561110000075
The probability density function can be expressed as:
Figure BDA0002642561110000073
multipurpose user heat load
Figure BDA0002642561110000076
The probability density function can be expressed as:
Figure BDA0002642561110000074
in the formula (I), the compound is shown in the specification,
Figure BDA0002642561110000087
respectively taking the mean values of the electric load and the thermal load unconstrained random variables as medium and long term predicted values; sigmas、τsRespectively, the unconstrained standard deviation of the random variables of the electric load and the thermal load;
Figure BDA0002642561110000088
represents the upper and lower limits of the power consumption;
Figure BDA0002642561110000089
represents the upper and lower limits of the thermal energy.
Electric power spot price obeys normal distribution
Figure BDA00026425611100000810
Natural gas spot price obeying normal distribution
Figure BDA00026425611100000811
Combining the above random variables
Figure BDA00026425611100000812
Denoted as ζ.
In the day period, the electricity and heat loads of the multi-energy user
Figure BDA00026425611100000813
And spot price lambdat、βtDegradation from medium-and long-term random variables into known quantities according to probability, and medium-and long-term predicted values of electricity and heat loads
Figure BDA00026425611100000814
Can generate adjustment costs based on the day-ahead power supply scheme under known medium-and long-term power supply schemes. The optimization model for the adjustment of the energy supply scheme in the day is as follows:
Figure BDA0002642561110000081
Figure BDA0002642561110000082
Figure BDA0002642561110000083
Figure BDA0002642561110000084
Figure BDA0002642561110000085
Figure BDA0002642561110000086
wherein t represents a period t, t-1 represents a period t-1, Pt spotIndicating the electric quantity purchased in the spot market;
Figure BDA00026425611100000815
representing the gas purchasing quantity of the spot market;
Figure BDA00026425611100000816
representing medium and long term contract gas purchase amount; pt surDefault electric quantity of medium and long term contracts; chi shapepunishRepresenting the default punishment of the medium-term contract unit electric quantity;
Figure BDA00026425611100000817
a thermal load indicative of an increase in demand response;
Figure BDA00026425611100000818
representing a unit demand response compensation cost; etae、ηhRespectively representing the electrical and thermal conversion efficiency of the CHP unit; rdown、RupRespectively representing the climbing constraint of the CHP unit;
Figure BDA00026425611100000819
representing the maximum air inflow of the CHP unit; equation (8) represents the optimization objective for the adjustment of the energy supply scheme before the day; equation (9) represents the power load balancing constraint; equation (10) represents the thermodynamic load balancing constraint; the formula (11) is the climbing restriction of the CHP unit; equation (12) represents the capacity constraint of the CHP unit; equation (13) is a qualitative constraint of the adjustment strategy.
It can be easily seen that purchasing the spot electric quantity and the medium-and-long-term default electric quantity are respectively adjusting means for balancing the lower and higher power load predictions; the improvement of the thermal load demand response is an adjustment means for coping with the predicted higher thermal load; purchasing the natural gas in stock to increase CHP output may address both low power load predictions and low thermal load predictions.
The target value of the optimization problem is denoted as Cadjust(XA,ζ)，XAFor intra-day adjustment of decisions, i.e. Pt spot、
Figure BDA0002642561110000095
Pt sur、
Figure BDA0002642561110000094
In step S102, the sum of the medium-and-long-term energy supply cost and the intra-day energy supply adjustment cost is the energy supply total cost of the energy sharing community, and the table expression is as follows:
Figure BDA0002642561110000096
in the formula, Pt cRepresenting a contract electric quantity;
Figure BDA0002642561110000097
representing a contract electricity price;
Figure BDA0002642561110000098
representing a contract gas amount; beta is at cRepresenting a contract gas price; xLFor medium-long term decisions, i.e.
Figure BDA00026425611100000910
It is a known quantity in the day adjustment.
And selecting the CVar value of the total energy supply cost as a target value of scheduling optimization of the energy sharing community. The scheduling optimization problem available according to equation (5) is as follows:
Figure BDA0002642561110000091
Figure BDA0002642561110000092
Figure BDA0002642561110000093
(9)-(13)；
in the formula, alpha is the confidence coefficient of the community; θ represents a scene; m represents the number of scenes; pθA probability representing a scene theta; z is an intermediate variable, and the optimal value of the intermediate variable is the Var value of the total energy supply cost. The decision variables are medium-long term stage contract electric quantity and contract gas quantity
Figure BDA00026425611100000911
Phase of day, random variables
Figure BDA00026425611100000912
Determining a scene
Figure BDA00026425611100000913
From the formula, the intra-day decision variable XAShadow f (X)L,XAζ) C inadjust(XAζ) and its value is proportional to the objective function value, so C for each scene θ is minimizedadjust(XAζ) medium-to-long term contracts
Figure BDA0002642561110000103
Determining the day-to-day optimal adjustment strategy under certain conditions
Figure BDA0002642561110000104
So that C isadjust(XAζ), the problem in step S101.
Based on the above analysis, a two-layer optimization problem can be solved for the above problems.
The upper-layer problem is a decision problem in a medium-long period stage, so that the CVar value of the total energy supply cost is the minimum, and the specific steps are as follows:
Figure BDA0002642561110000101
s.t(16)-(17)
value of objective function f (X)L,XAζ) is the desired C VaRα(X), the value of the corresponding variable z is VaRα。
The lower-layer optimization problem is a daily energy supply adjustment problem and comprises M sub-problems, a random variable zeta in each sub-problem is a determined value, and an upper-layer optimization decision variable
Figure BDA0002642561110000105
Passed to the underlying optimization problem as a known quantity. The model is as follows:
Figure BDA0002642561110000102
s.t(9)-(13)
decision variables for upper layer optimization
Figure BDA0002642561110000106
The parameters are transmitted to the lower optimization model as constants to influence the optimization of the lower model, and simultaneously, each scene f (X) in the upper modelL,XAζ) is dependent on solving the underlying optimization model, i.e., is the target value corresponding to the underlying model.
In step S103, aiming at the double-layer optimization problem in step S102, a particle swarm algorithm is used to search for an optimal decision of the upper-layer optimization problem, and a monte carlo method is used to extract a random variable
Figure BDA0002642561110000107
Form theta scenes and give their probability distribution Pθ. Solving the theta lower-layer optimization problems in a linear programming mode by adopting a Linprog solver in a Matlab environment.
The algorithm comprises the following specific steps:
1: receiving the spot price, the probability distribution information of the multi-energy load and the upper limit and the lower limit of the multi-energy load; receiving CHP equipment parameters; setting a confidence coefficient alpha; setting the Monte Carlo method sampling number Nc。
2: setting the number N of particles, the iterative count iter of the particles to be 0, and the initial position of each particle N
Figure BDA0002642561110000108
Initial velocity of particles
Figure BDA0002642561110000109
Convergence criterion epsilon, acceleration constant c1、c2An inertia factor w.
3: random variables were sampled using Monte Carlo
Figure BDA0002642561110000111
Extracting NcGroup data, obtaining theta scenes and scene probability P thereofθ. For each scene, the positions of the particles are respectively determined
Figure BDA0002642561110000112
Instead, θ intra-day energy supply adjustment problems in step S102 are formed for each particle.
4: solving the problem of energy supply adjustment in each day by adopting a Linprog solver, and calculating the fitness according to a formula (18)
Figure BDA0002642561110000113
If iter is 0, go to step 5, and if iter >0, go to step 6.
5: let particle n history optimal location
Figure BDA0002642561110000114
Particle n history optimal fitness
Figure BDA0002642561110000115
If iter is equal to 0, the group history optimal fitness
Figure BDA0002642561110000116
Recording the particles corresponding to the historical optimal fitness of the population as n*Then the historical optimal location of the population
Figure BDA0002642561110000117
6: for any particle n, if
Figure BDA0002642561110000118
Then
Figure BDA0002642561110000119
Note Fbest，0＝FbestIf, if
Figure BDA00026425611100001110
Then
Figure BDA00026425611100001111
If the convergence criterion | F is satisfiedbest，0-FbestIf | ≦ ε, the iteration ends and step 9 is entered.
7: updating each particle n position
Figure BDA00026425611100001112
Updating the n-velocity of each particle
Figure BDA00026425611100001113
Wherein r is1、r2To take on a value of [0,1]Random parameter in between, particle iteration count iter ═ iter + 1.
8: and repeating the steps 3-7.
9: ending the algorithm and outputting the historical optimal fitness F of the groupbest。
Through the steps, the historical optimal fitness F of the group is obtainedbestNamely NsAnd the CVar value of the lowest energy supply total cost of the energy sharing community formed by the multiple multi-energy users. Will be used by the user {1,2.. NsRecording the CVar value of the lowest energy supply total cost of the energy sharing community formed by the components as CCVaR{1,2...Ns}。
In step S104, assume that NsThe permutation order of multiple multi-energy users is combined to form a set S, the element number of S is Ns| A And (4) respectively. Each ranking order corresponds to the sequence of joining the energy sharing community by the multi-energy users. With the sequential joining of the members of the multi-energy user, the CVar value C of the lowest total energy supply cost of the energy sharing communityCVaR{1,2. } also changed gradually.
For the j-th ranking order, the energy sharing community for the multi-energy user i to join at the k-th rank comprises the following steps:
Figure BDA00026425611100001114
Figure BDA00026425611100001115
and the contribution value of the CVar value of the lowest total supply cost of the energy sharing community when the user i joins the energy sharing community under the j-th order combination is shown.
According to the Sharpry value method, the CVar energy supply cost of the ith multi-energy user is divided into:
Figure BDA0002642561110000121
according to the embodiment, a CVar theory-based comprehensive energy and energy sharing community dispatching-intra-day adjustment double-layer model is constructed, and the optimal energy purchasing and energy supply cost considering risks is given. Further provides a particle swarm-Linprog combined algorithm for solving the double-layer model. And then, risk cost is shared by adopting a summer curry value method, the contribution degree of each multi-energy user to the total risk cost of the energy supply of the shared community is fully reflected, and the method has the characteristics of fairness and strong adaptability to random scenes.
The invention provides a risk cost allocation method of a comprehensive energy resource energy sharing community, which comprises the steps of firstly, providing a scheduling optimization model of the comprehensive energy resource energy sharing community based on a CVar theory, wherein the scheduling optimization model is a double-layer model with the upper layer for medium and long term decision and the lower layer for adjustment of energy supply in the day, and providing the optimal energy purchasing and energy supply cost considering risks; then, a Cvar value method is adopted to share the CVar value of the total energy supply cost, risk cost sharing is carried out according to the contribution degree of each multi-energy user to the total risk cost of energy supply of the shared community, the influence of each user on the total energy supply risk cost is fully reflected, and fair sharing is achieved.
In addition, the invention also provides a risk cost allocation device of the comprehensive energy resource sharing community, which comprises the following steps:
a memory for storing a computer program;
a processor for executing the computer program to implement the method for risk cost sharing for an integrated energy sharing community.
The present invention also provides a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the method for risk cost sharing for an integrated energy sharing community.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; 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 (7)

1. A risk cost sharing method for an integrated energy and energy sharing community is characterized by comprising the following steps:
constructing an energy supply adjustment risk cost model of an energy sharing community in the day according to the multi-energy user load and the energy spot price of the comprehensive energy system; the system comprises a plurality of energy supply adjustment risk cost models, a plurality of energy user loads and a plurality of energy user heat loads, wherein the energy user loads comprise energy user electric loads and energy user heat loads, and the optimization target of the energy supply adjustment risk cost model in the day is the energy supply adjustment cost in the day corresponding to the optimal adjustment strategy in the day;
constructing a scheduling optimization model of the energy sharing community according to the energy consumption and price of the contract and the daily energy supply adjustment cost; wherein, the product of the energy consumption and the price of the contract is the medium and long term energy supply cost; the optimization target of the scheduling optimization model is a CVar value of the lowest energy supply total cost, and the energy supply total cost is the sum of the medium-long term energy supply cost and the day-to-day energy supply adjustment cost;
the scheduling optimization model specifically comprises the following steps:
Figure FDA0003508672600000011
in the formula, XARepresenting a daily energy supply adjustment decision;
Figure FDA0003508672600000012
it is meant that the random variable is,
Figure FDA0003508672600000013
representing the electrical load of the multi-energy user;
Figure FDA0003508672600000014
representing a multi-energy user thermal load; t denotes a period t, λt、βtRespectively representing the prices of electric power and natural gas, and obeying normal distribution; pt cRepresenting the electricity consumption of the medium and long term contract;
Figure FDA0003508672600000015
representing the electricity price of the medium and long term contract;
Figure FDA0003508672600000016
representing gas usage of medium and long term contracts;
Figure FDA0003508672600000017
representing gas prices for medium and long term contracts;
Figure FDA0003508672600000018
representing medium and long term decisions;
Figure FDA0003508672600000019
represents the medium and long term energy supply cost, Cadjust(XAζ) adjust costs for energy supply within a day;
converting the scheduling optimization model into a double-layer optimization problem, wherein an upper-layer problem is a medium-long term decision problem, so that the Cvar value of the energy supply total cost is minimum, and the optimization model of the upper-layer problem is as follows:
Figure FDA00035086726000000110
where α represents the confidence of the community, θ represents the scene, PθRepresenting the probability of a scene theta, wherein z is an intermediate variable, and the optimal value of z is a Cvar value of the energy supply total cost;
the lower-layer optimization problem is a daily energy supply adjustment problem and comprises a plurality of sub-problems, a random variable zeta in each sub-problem is a determined value, and an upper-layer optimization decision variable
Figure FDA00035086726000000111
Passed to the lower optimization problem as a known quantity;
the optimization model of the lower layer optimization problem is as follows:
Figure FDA00035086726000000112
in the formula (I), the compound is shown in the specification,
Figure FDA00035086726000000113
Pt spotindicating the electric quantity purchased in the spot market;
Figure FDA00035086726000000114
representing the gas purchasing quantity of the spot market; pt surRepresenting the default electric quantity of the medium and long-term contract; chi shapepunishRepresenting the default punishment of the medium and long term contract unit electric quantity;
Figure FDA0003508672600000021
a thermal load indicative of an increase in demand response;
Figure FDA0003508672600000022
representing a unit demand response compensation cost;
solving the scheduling optimization model to obtain the CVar value of the lowest energy supply total cost of the energy sharing community, and specifically comprises the following steps:
searching the optimal decision of the medium and long-term stage by adopting a particle swarm algorithm;
extracting random variables by adopting a Monte Carlo sampling method to obtain a plurality of scenes and probability distribution thereof;
solving the intra-day energy supply adjustment problem corresponding to each scene by adopting a Linprog solver to obtain a CVar value of the lowest energy supply total cost of the energy sharing community; the intra-day energy supply adjustment strategy corresponding to the CVar value with the lowest energy supply total cost is an intra-day optimal adjustment strategy;
and (4) carrying out risk cost allocation on the multi-energy users according to the contribution degree of the multi-energy users to the CVar value of the lowest energy supply total cost.
2. The method according to claim 1, wherein the probability density model of the electrical load of the multi-energy users is:
Figure FDA0003508672600000023
in the formula (I), the compound is shown in the specification,
Figure FDA0003508672600000024
the electrical load of the multi-energy user is represented,
Figure FDA0003508672600000025
representing the medium-and long-term predicted value, σ, of the electrical loadsRepresents an unconstrained standard deviation of the electrical load,
Figure FDA0003508672600000026
respectively represent the upper and lower limits of the power consumption.
3. The method according to claim 2, wherein the probability density model of the heat load of the multi-energy users is:
Figure FDA0003508672600000027
in the formula (I), the compound is shown in the specification,
Figure FDA0003508672600000028
indicating the heat load of the multi-energy user,
Figure FDA0003508672600000029
represents a predicted value of the heat load, tau, for the medium and long termsRepresents the unconstrained standard deviation of the thermal load,
Figure FDA00035086726000000210
respectively representing the upper and lower limits of the thermal energy.
4. The method for allocating the risk cost of the integrated energy resource sharing community according to claim 3, wherein solving the intra-day energy supply adjustment problem corresponding to each scene by using a Linprog solver to obtain the CVar value of the lowest energy supply total cost of the energy resource sharing community specifically comprises:
s11: receiving the spot price, the probability distribution information of the multi-energy load and the upper limit and the lower limit of the multi-energy load; receiving CHP equipment parameters; setting a confidence coefficient alpha; setting the Monte Carlo method sampling number Nc；
S12: setting the number N of particles, the iterative count iter of the particles to be 0, and the initial position of each particle N
Figure FDA0003508672600000031
Initial velocity of particles
Figure FDA0003508672600000032
Convergence criterion epsilon, acceleration constant c1、c2An inertia factor w;
s13: random variables were sampled using Monte Carlo
Figure FDA0003508672600000033
Extracting NcGroup data, obtaining theta scenes and scene probability P thereofθ(ii) a For each scene, the positions of the particles are respectively determined
Figure FDA0003508672600000034
Substituting, and forming theta energy supply adjustment problems in the step 3 for each particle;
s14: solving the problem of energy supply adjustment in each day by adopting a Linprog solver, and calculating the fitness according to the following formula
Figure FDA0003508672600000035
Figure FDA0003508672600000036
If iter is 0, the process proceeds to step S15, and if iter >0, the process proceeds to step S16;
s15: let particle n history optimal location
Figure FDA0003508672600000037
Particle n history optimal adaptation
Figure FDA0003508672600000038
If iter is equal to 0, the group history optimal fitness
Figure FDA0003508672600000039
Recording the particles corresponding to the historical optimal fitness of the population as n*Then the historical optimal location of the population
Figure FDA00035086726000000310
S16: for any particle n, if
Figure FDA00035086726000000311
Then
Figure FDA00035086726000000312
Note Fbest，0＝FbestIf, if
Figure FDA00035086726000000313
Then
Figure FDA00035086726000000314
If the convergence criterion | F is satisfiedbest，0-FbestIf the | is less than or equal to epsilon, the iteration is ended, and the step S19 is entered;
s17: updating each particle n position
Figure FDA00035086726000000315
Updating the n-velocity of each particle
Figure FDA00035086726000000316
Wherein r is1、r2To take on a value of [0,1]Random parameter in between, particle iteration count iter ═ iter + 1;
s18: repeating steps S13-S17;
s19: finishing the algorithm, and outputting the CVar value F of the group history optimal fitness, namely the lowest energy supply total cost of the energy sharing communitybest。
5. The method for risk cost sharing of the integrated energy and energy sharing community according to claim 4, wherein the risk cost sharing of the multi-energy users according to the degree of contribution of the multi-energy users to the CVar value of the lowest total energy supply cost comprises:
according to the summery method, the risk cost of the ith multi-energy user is divided into:
Figure FDA0003508672600000041
in the formula (I), the compound is shown in the specification,
Figure FDA0003508672600000042
representing the risk cost shared by the ith user; ns denotes the number of multi-capable users; ns! Representing the number of energy sharing communities which are possibly formed by Ns multiple energy users;
Figure FDA0003508672600000043
and the contribution degree of the CVar value of the lowest energy supply total cost of the energy sharing community when the user i joins the energy sharing community in the j-th order is shown.
6. A risk cost sharing device for an integrated energy sharing community, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the method of risk cost sharing for an integrated energy sharing community of any of claims 1 to 5.
7. A computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements a risk cost sharing method of an integrated energy sharing community as claimed in any one of claims 1 to 5.
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