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CN108537405B - Electric power market equilibrium optimization method, device, equipment and medium - Google Patents

Electric power market equilibrium optimization method, device, equipment and medium Download PDF

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CN108537405B
CN108537405B CN201810190293.2A CN201810190293A CN108537405B CN 108537405 B CN108537405 B CN 108537405B CN 201810190293 A CN201810190293 A CN 201810190293A CN 108537405 B CN108537405 B CN 108537405B
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朱继忠
冯禹清
禤培正
谢平平
邹金
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China South Power Grid International Co ltd
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Abstract

The invention discloses a power market equilibrium optimization method, a device, terminal equipment and a computer readable storage medium, wherein the method comprises the following steps: in a pre-established electric power market equilibrium model with parallel fixed power price and quota of renewable resources, coding control variables in the model, and optimizing by adopting an improved multi-agent particle swarm algorithm: the decision-making behavior of a power market generator is introduced to serve as each Agent unit in a multi-Agent particle swarm algorithm, decision is made under a power market equilibrium model with a fixed power price system and a quota system of renewable resources in parallel, a global optimal solution of equilibrium optimization in the power market equilibrium model is obtained through competition, cooperation operation and self-operation processes, the deep development of renewable energy is met, meanwhile, Kaldo-Hikes improvement is achieved from the economic perspective, and the conversion of the existing renewable energy power price system from the fixed power price system to the quota system is facilitated.

Description

Electric power market equilibrium optimization method, device, equipment and medium
Technical Field
The present invention relates to the field of power, and in particular, to a method and an apparatus for optimizing power market equilibrium, a terminal device, and a computer-readable storage medium.
Background
In recent years, renewable energy has gained increasing attention on a global scale. In order to ensure that the aim of fighting that non-fossil energy accounts for 15% of primary energy consumption and all renewable energy power generation machines account for 27% of all generated energy in 2020 is fulfilled, the key point for solving the problems is to establish a set of complete renewable energy system model.
In order to support the development of the renewable energy power generation industry, the current policy is to forcibly surf the internet and fix the electricity price system of renewable energy. However, the inventor finds that although this system promotes and supports the existing renewable energy power generation industry in the early stage, the problems of renewable energy power generation, internet surfing, consumption and the like cannot be solved fundamentally, and the fixed electricity price stimulates green power plant manufacturers to improve the output level, but leads to production without power and technical innovation without neglecting cost constraints, so that the fixed electricity price system is not beneficial to improving the technical progress level of renewable energy power generation in the long term.
With the development of new electricity generation in recent years, a renewable energy quota (RPS) system adapted to an economic mechanism of an electric power market is receiving more and more attention. The RPS is oriented to market competition, and the proportion of green electricity of each power generator is restricted by a matched green certificate transaction mechanism (TGC), and each power generator uses a green certificate as a medium to meet the requirement of non-large hydropower renewable energy source proportion. The implementation of quota systems enables green power plants to achieve higher profit levels by increasing revenue levels and manufacturers' technological innovations help to reduce their production costs. Therefore, quota system is beneficial to improving the technical level of renewable energy power generation, but the inventor finds that if the RPS system is introduced blindly, a series of problems such as increasing the cost of power generation manufacturers and causing the risk of power price fluctuation can be caused under the existing economic system, so that the introduction of TGC before the market is mature can face a great risk.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a method, an apparatus, a terminal device and a computer-readable storage medium for optimizing power market equilibrium, which are beneficial to realize conversion from a fixed electricity price system to a quota system in an existing renewable energy electricity price system.
In a first aspect, an embodiment of the present invention provides a power market equilibrium optimization method, including the following steps:
in a pre-established power market equilibrium model with parallel fixed power rates and quota rates of renewable resources:
l of constructed multi-agent particle swarm algorithmsize×LsizeIn the Agent environment structure, initializing the position and speed of each Agent unit in the Agent environment; wherein L issize≥1；
Constructing the obtained decision behaviors of each power generation manufacturer into each Agent unit of the agents in a multi-Agent particle swarm algorithm, and initializing the adaptive value of each Agent unit of the agents;
acquiring the local environment position of each Agent unit in each local variable; the method comprises the steps that local environment positions of all Agent units in respective local variables are obtained from the local variables formed by each Agent unit and Agent units surrounding the Agent unit, the Agent units are arranged on the upper side, the lower side, the left side, the right side and the four opposite angles of the Agent unit; wherein, let Agent have (i, j) coordinates in grid, i, j ═ 1,2sizeThen L issizeLocal variable M ofi,jIs defined as: mi,j＝{Li1,j1,Li1,j,Li1,j2,Li,j1,Li,j2,Li2,j1,Li2,j,Li2,j2Is satisfied with
Figure GDA0002385038250000021
Figure GDA0002385038250000022
And
Figure GDA0002385038250000023
in each local variable, carrying out competition and cooperation operation on each Agent unit and an adjacent Agent unit;
updating the position, the speed and the adaptive value of each Agent unit of the Agent to obtain a global extreme value;
and carrying out self-learning operation on the Agent unit of the Agent with the global extreme value to obtain the optimal value of balance optimization in the power market balance model.
In a first implementation manner of the first aspect, the power generation manufacturers include thermal power manufacturers and green power manufacturers;
the step of establishing a power market equilibrium model with the renewable resource fixed power price and quota parallel comprises:
obtaining according to the established electric power market oligopolistic balance modelThe market offers the price of electricity; wherein the market offers a price of electricity in clear
Figure GDA0002385038250000031
The market discharge price is an inverse demand function of the market price, f and g are respectively an intercept and a slope of the demand function of the market price, and q iscoal、qgreenThe yield of thermal power manufacturers and green power plants is respectively, and m and n are the number of thermal power manufacturers and green power manufacturers respectively; i. j represents a thermal power generator and a green generator set respectively.
Establishing a power generation cost model and a renewable energy quota ratio K of the thermal power manufacturer and the green power manufacturer; wherein, the power generation cost model of the thermal power manufacturer is
Figure GDA0002385038250000032
The power generation cost model of the green power manufacturer is
Figure GDA0002385038250000033
a1、b1、c1And a2、b2、c2The cost coefficients of each unit of thermal power and green power are respectively, and the renewable energy quota proportion K meets the requirement
Figure GDA0002385038250000034
Establishing a decision model of a power generator under the fixed electricity price of renewable resources according to the power generation cost models of the clear electricity price, the thermal power generator and the green power generator in the market; wherein, the decision model of the power generator under the fixed electricity price of the renewable resource
Figure GDA0002385038250000035
wgreen、wcoalThe method comprises the steps of respectively making profits for green power manufacturers and thermal power manufacturers under fixed electricity price, Q is total market demand, S is government green electricity fixed subsidy, eta is government green electricity price subsidy horizontal factor, representing no efficiency loss of government subsidy when eta is more than or equal to 1, and CENegatively influencing the cost for thermal power manufacturers, an
Figure GDA0002385038250000036
Lambda represents an ecological environment influence factor, lambda is more than or equal to 0 and less than or equal to 1, i and j respectively represent thermal power and green motor sets, and m and n respectively represent the number of the thermal power and the green motor sets;
establishing a decision model of the power generator under the renewable resource quota system according to the clear electricity price in the market, the power generation cost models of thermal power manufacturers and green power manufacturers and the renewable energy quota ratio K; wherein, the decision model of the power generator under the renewable resource quota system
Figure GDA0002385038250000037
According to the decision model of the power generator under the renewable resource fixed power price and the decision model of the power generator under the renewable resource quota system, acquiring a power market equilibrium model with the renewable resource fixed power price and the renewable resource quota in parallel; wherein the power market equilibrium model maxFALL＝aFFIT+bFTGCA and b are fixed electricity price ratio coefficients and quota ratio coefficients respectively, and a + b is equal to 1.
According to the first implementation manner of the first aspect, in a second implementation manner of the first aspect, the establishing a decision model of a power generator under a renewable resource fixed electricity price system according to the power generation cost models of the market-released electricity price, the thermal power generator and the green power generator specifically includes:
establishing a decision model of the thermal power manufacturer under the condition of renewable resource fixed electricity price according to the clear electricity price on the market and a power generation cost model of the thermal power manufacturer; wherein, the renewable resource is a decision model of thermal power manufacturers under fixed electricity price
Figure GDA0002385038250000041
Establishing a decision model of a green power manufacturer under the fixed power price of renewable resources according to the power generation cost model of the clear power price and the green power manufacturer in the market; wherein, the renewable resource is a decision model of a green power manufacturer under a fixed power price
Figure GDA0002385038250000042
s is a subsidy of the electricity price of the green electricity obtained by green power plant merchants;
establishing a decision model of a power generator manufacturer under the renewable resource fixed electricity price according to the decision model of a thermal power manufacturer under the renewable resource fixed electricity price and the decision model of a green power manufacturer under the renewable resource fixed electricity price; wherein, the decision model of the power generator under the fixed electricity price of the renewable resource
Figure GDA0002385038250000043
According to the second implementation manner of the first aspect, in a third implementation manner of the first aspect, the establishing a decision model of a power generator under a renewable resource quota system according to the market price, the power generation cost models of thermal power generators and green power generators, and the renewable energy quota ratio K specifically includes:
establishing a decision model of the thermal power manufacturer under the renewable resource quota system according to the clear electricity price on the market and a power generation cost model of the thermal power manufacturer; wherein, the decision model of the thermal power manufacturer under the renewable resource quota system
Figure GDA0002385038250000044
pcThe price of a green certificate;
establishing a decision model of the green power manufacturer under the renewable resource quota system according to the power price of the market and a power generation cost model of the green power manufacturer; wherein, the decision model of the green power supplier under the renewable resource quota system
Figure GDA0002385038250000051
Establishing a decision model of a power generator manufacturer under the renewable resource quota system according to a decision model of a thermal power manufacturer under the renewable resource quota system and a decision model of a green power manufacturer under the renewable resource quota system; wherein, the decision model of the power generator under the renewable resource quota system
Figure GDA0002385038250000052
According to a third implementation form of the first aspect, in a fourth implementation form of the first aspect, the adaptive value f (a) of each Agent unit is maxFALL＝aFFIT+bFTGC。
According to a fifth implementation manner of the first aspect, in a sixth implementation manner of the first aspect, in each local variable, the competing and cooperating operation of each Agent unit and an adjacent Agent unit is specifically:
in each local variable, acquiring a minimum adaptive value in 8 adjacent Agent units of the Agent units;
when the minimum adaptive value is larger than the adaptive value of the Agent unit of the intelligent Agent, changing the position of the Agent unit of the intelligent Agent in an Agent environment structure; wherein, the position L of the Agent unit of the Agent in the Agent environment structure is seti,j＝(l1,l2,...,ln) And the position N of the Agent unit of the Agent with the minimum adaptive value in the Agent environment structurei,j＝(n1,n2,...,nn) The position of the Agent unit of the Agent in the Agent environment structure is changed to l'k＝nk+rand(-1,1)×(nk-lk) k ═ 1, 2., n, rand (-1,1) is a random number between (-1,1), if l'k<ykmin，l'k＝ykminL 'if'k>ykmax，l'k＝ykmax，ymin＝(ykmin,ykmin,...,ynmin)、ymax＝(ykmax,ykmax,...,ynmax) Respectively, the lower and upper bounds of the fitness solution space.
In a second aspect, an embodiment of the present invention provides an electric power market equilibrium optimization apparatus, including:
in a pre-established power market equilibrium model with parallel fixed power rates and quota rates of renewable resources:
an initialization unit for L of multi-agent particle swarm algorithm under constructionsize×LsizeIn the Agent environment structure, initializing the position and speed of each Agent unit in the Agent environment; wherein L issize≥1；
The Agent unit construction unit is used for constructing the acquired decision behaviors of each power generation manufacturer into each Agent unit of the intelligent Agent in the multi-Agent particle swarm algorithm and initializing the adaptive value of each Agent unit of the intelligent Agent;
the local environment position acquisition unit is used for acquiring the local environment position of each Agent unit in each local variable; the method comprises the steps that local environment positions of all Agent units in respective local variables are obtained from the local variables formed by each Agent unit and Agent units surrounding the Agent unit, the Agent units are arranged on the upper side, the lower side, the left side, the right side and the four opposite angles of the Agent unit; wherein, let Agent have (i, j) coordinates in grid, i, j ═ 1,2sizeThen L issizeLocal variable M ofi,jIs defined as: mi,j＝{Li1,j1,Li1,j,Li1,j2,Li,j1,Li,j2,Li2,j1,Li2,j,Li2,j2Is satisfied with
Figure GDA0002385038250000061
Figure GDA0002385038250000062
And
Figure GDA0002385038250000063
the competition and cooperation operation unit is used for carrying out competition and cooperation operation on each Agent unit and adjacent Agent units in each local variable;
the global extreme value acquisition unit is used for updating the position, the speed and the adaptive value of each Agent unit of the Agent so as to acquire a global extreme value;
and the optimal value acquisition unit is used for carrying out self-learning operation on the Agent units of the intelligent agents with the global extreme values so as to acquire the optimal value of balance optimization in the power market balance model.
In a third aspect, an embodiment of the present invention provides an electric power market equilibrium optimization terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor implements the electric power market equilibrium optimization method according to any one of the first aspects when executing the computer program.
In a fourth aspect, the embodiment of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, where, when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the power market equilibrium optimization method in any one of the first aspects.
In summary, the embodiments of the present invention provide a power market equilibrium optimization method, apparatus, terminal device and computer-readable storage medium, which have the following beneficial effects:
in a pre-established electric power market equilibrium model with parallel fixed power price and quota of renewable resources, coding control variables in the model, and optimizing by adopting an improved multi-agent particle swarm algorithm: by introducing decision-making behaviors of power generation manufacturers in the power market as Agent units of each Agent in the multi-Agent particle swarm algorithm, the decision is made under the power market equilibrium model with the fixed power price and the quota system of the renewable resources in parallel, the global optimal solution of the balance optimization in the power market balance model is obtained through competition, cooperative operation and self-operation processes, the optimal solution of the balance optimization is obtained through the power market balance model with the fixed power rate system and the quota system of renewable resources in parallel, the social benefit under the environment of green power forced quota surfing is improved, the profit is balanced, and the risk caused by power price fluctuation is balanced, the Kaldo-Hikes improvement is realized from the economic perspective while the deepened development of renewable energy sources is satisfied, meanwhile, the conversion of the current renewable energy price system from a fixed price system to a quota system is facilitated.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a power market equilibrium optimization method according to a first embodiment of the present invention.
Fig. 2 is a schematic diagram of a power market equilibrium model with parallel fixed electricity prices and quota systems for renewable resources provided by a first embodiment of the present invention.
FIG. 3 is a configuration L provided by the first embodiment of the present inventionsize×LsizeSchematic diagram of the Agent environment structure of (1).
Fig. 4 is a schematic structural diagram of an electric power market equilibrium optimization apparatus according to a fourth embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a first embodiment of the present invention provides a power market equilibrium optimization method, which can be executed by a terminal device, and includes the following steps:
in a pre-established power market equilibrium model with parallel fixed power rates and quota rates of renewable resources:
s11, constructing L of multi-agent particle swarm algorithmsize×LsizeIn the Agent environment structure, initializing the position and speed of each Agent unit in the Agent environment; wherein L issize≥1。
In the embodiment of the present invention, the terminal device may be a desktop computer, a notebook, a palm computer, an intelligent tablet, a cloud server, and other computing devices.
In the embodiment of the present invention, please refer to fig. 2, fig. 2 is a power market equilibrium model with parallel fixed electricity price and quota system of renewable resources, i.e. maxFALL＝aFFIT+bFTGCIn the formula, FALLFor social benefits under the condition of coexistence of a fixed electricity price system and a quota system, a and b are the ratio coefficients of the fixed electricity price system and the quota system respectively, and a + b is 1; in particular, when a is 0 and b is 1, the model represents a power market equilibrium model under the quota system; when a is 1 and b is 0, the model represents an electric power market equilibrium model under the renewable energy fixed electricity price system, under the two systems, power generation manufacturers mutually influence in the electric power market decision process, and the solution operation relates to the process of multi-target mutual coordination under the excitation of the whole network electricity price signal and seeking the optimal solution of the multivariate nonlinear equation set.
In the embodiment of the invention, a Multi-Agent System (MAS) can coordinate the operation problems of a Multi-period and Multi-distribution System by virtue of good distributivity and autonomy, and is widely applied to researches of an active power distribution network, a micro power grid and the like, the MAS is combined with a Particle Swarm Optimization (PSO) in the embodiment of the invention, the MAS is used for carrying out Optimization calculation on a power market equilibrium model with parallel fixed power pricing and quota pricing of renewable resources by using a Multi-Agent Particle Swarm Optimization (MAPSO), the problem of uneven extreme value in the MAPSO is optimized by improving the local environment optimizing condition of each Agent, the problem of power market equilibrium scheduling of each power manufacturer under different renewable energy systems is solved, namely, the improved Multi-Agent algorithm is used for optimizing the power market equilibrium model with parallel fixed power pricing and quota resource pricing, in the process of optimizing the terminal device, please refer to fig. 3, first construct Lsize×LsizeAnd initializing the position and speed of each Agent unit in the Agent environment.
And S12, constructing the obtained decision behaviors of the power generation manufacturers into Agent units of the agents in the multi-Agent particle swarm algorithm, and initializing adaptive values of the Agent units of the agents.
In the embodiment of the invention, the terminal equipment acquires the decision behaviors of each power generation manufacturer, constructs the acquired decision behaviors of each power generation manufacturer into Agent units of each Agent in a multi-Agent particle swarm algorithm, and initializes the adaptive value of the Agent units, the MAPSO algorithm is a brand-new algorithm which applies the characteristics of MAS multiple coordination, intelligence, distribution and the like and combines the evolution mechanism of the PSO algorithm, and can well realize the operation of multilayer optimization decision in the spot-market, wherein the PSO algorithm corrects individual action strategies through information sharing among groups and individual experience summary, and finally obtains the solution of an optimization problem, in the MAPSO algorithm, each Agent is a particle in the PSO algorithm, the PSO population represents the whole PSO system, each Agent is an intelligent independent body, and the agents are located according to the environment, under the condition of meeting the condition limit, the adaptive function value is reduced to the maximum, and in the optimizing process of a power market equilibrium model with the renewable resources having a fixed power price and a quota parallel, the adaptive value of each Agent unit constructed by the decision behavior of each power generator is calculated by the following adaptive function: f (a) ═ maxFALL＝aFFIT+bFTGC。
And S13, acquiring the local environment position of each Agent unit in each local variable.
In the embodiment of the present invention, referring to fig. 3, according to the specified neighbor environment definition, the terminal device solves the local environment position of each Agent unit, and in the optimization process of the power market equilibrium model with the fixed power pricing and quota system of the renewable resource in parallel, an improved "eight-block method" is adopted in the setting of the local environment to seek local variables, that is, each Agent and the upper, lower, left, right, and four diagonal units that surround the Agent unit most recently form local variables, so that the purpose of local optimization is achieved more accurately and effectively while the calculation rate is ensured, and specifically, the local variables that each Agent unit and the Agent units that surround the Agent unit of the Agent, the upper, lower, left, right, and four diagonal units form local variablesIn measurement, acquiring local environment positions of all Agent units in respective local variables; wherein, let Agent have (i, j) coordinates in grid, i, j ═ 1,2sizeThen L issizeLocal variable M ofi,jIs defined as: mi,j＝{Li1,j1,Li1,j,Li1,j2,Li,j1,Li,j2,Li2,j1,Li2,j,Li2,j2Is satisfied with
Figure GDA0002385038250000101
And
Figure GDA0002385038250000102
it should be noted that each Agent unit corresponds to a respective local environment.
And S14, performing competition and cooperation operation on each Agent unit and adjacent Agent units in each local variable.
In the embodiment of the invention, according to the rule of a behavior policy, each Agent unit competes and cooperates with Agent units between adjacent units in respective local variables, specifically, in each local variable, the terminal device obtains a minimum adaptive value of 8 Agent units adjacent to the Agent unit, and when the minimum adaptive value is less than or equal to the adaptive value of the Agent unit, the Agent unit is a high-quality Agent unit and the position of the Agent unit in the environment is kept unchanged; when the minimum adaptive value is larger than the adaptive value of the Agent unit of the intelligent Agent, changing the position of the Agent unit of the intelligent Agent in an Agent environment structure; wherein, the position L of the Agent unit of the Agent in the Agent environment structure is seti,j＝(l1,l2,...,ln) And the position N of the Agent unit of the Agent with the minimum adaptive value in the Agent environment structurei,j＝(n1,n2,...,nn) The position of the Agent unit of the Agent in the Agent environment structure is changed to l'k＝nk+rand(-1,1)×(nk-lk) k is between 1,2, 1, n, rand (-1,1)Random number of (-1,1) < l'k<ykmin，l'k＝ykminL 'if'k>ykmax，l'k＝ykmax，ymin＝(ykmin,ykmin,...,ynmin)、ymax＝(ykmax,ykmax,...,ynmax) Respectively, the lower and upper bounds of the fitness solution space.
And S15, updating the position, the speed and the adaptive value of each Agent unit to obtain a global extreme value.
In the embodiment of the invention, the terminal equipment updates the position and the speed of each Agent unit in the Agent environment structure, then recalculates the adaptive value of each Agent unit to obtain a global extreme value, and the adaptive value of each Agent unit constructed by the decision behaviors of each power generator is calculated by the following adaptive function: f (a) ═ maxFALL＝aFFIT+bFTGCEach Agent unit updates its speed and position in the Agent environment structure according to the following formula:
Figure GDA0002385038250000111
xd+1＝xd+vd+1in the formula, the subscript d represents the number of iterations, xdRepresenting the spatial position of the particle at the d-th iteration; v. ofdDenotes the particle velocity at the d-th iteration, w is the inertia constant, j1、j2For the learning factor, rand () is a random number between (0, 10).
And S16, carrying out self-learning operation on the Agent units of the agents with the global extreme values to obtain the optimal value of the balance optimization in the power market balance model.
In the embodiment of the invention, the terminal equipment carries out self-learning operation on the Agent unit of the Agent with the global extreme value, in order to better improve the fitness precision, the terminal equipment can improve the position of the global extreme value while obtaining the global extreme value, carries out self-learning operation according to the obtained global position, further improves the convergence capability by reducing the internal environment of the global extreme value and learning the competition and cooperation behaviors among the internal environments through similar behavior processes, and thus obtains the optimal value of balanced optimization in the power market balanced model.
To sum up, the first embodiment of the present invention provides an electric power market equilibrium optimization method, in an electric power market equilibrium model with parallel fixed power price and quota system of renewable resources established in advance, encoding control variables in the model, and performing optimization by using an improved multi-agent particle swarm algorithm: by introducing decision-making behaviors of power generation manufacturers in the power market as Agent units of each Agent in the multi-Agent particle swarm algorithm, the decision is made under the power market equilibrium model with the fixed power price and the quota system of the renewable resources in parallel, the global optimal solution of the balance optimization in the power market balance model is obtained through competition, cooperative operation and self-operation processes, the optimal solution of the balance optimization is obtained through the power market balance model with the fixed power rate system and the quota system of renewable resources in parallel, the social benefit under the environment of green power forced quota surfing is improved, the profit is balanced, and the risk caused by power price fluctuation is balanced, the Kaldo-Hikes improvement is realized from the economic perspective while the deepened development of renewable energy sources is satisfied, meanwhile, the conversion of the current renewable energy price system from a fixed price system to a quota system is facilitated.
In order to facilitate an understanding of the invention, some preferred embodiments of the invention will now be described.
Second embodiment of the invention:
on the basis of the first embodiment of the invention, the power generation manufacturers comprise thermal power manufacturers and green power manufacturers.
The step of establishing a power market equilibrium model with the renewable resource fixed power price and quota parallel comprises:
acquiring market clear price according to the established power market oligopolism model; wherein the market offers a price of electricity in clear
Figure GDA0002385038250000121
The price of the clear electricity discharged from the market is the inverse demand of the market priceCalculating function, f and g are intercept and slope of demand function of market price greater than 0, qcoal、qgreenThe yield of thermal power manufacturers and green power plants is respectively, and m and n are the number of thermal power manufacturers and green power manufacturers respectively.
Establishing a power generation cost model and a renewable energy quota ratio K of the thermal power manufacturer and the green power manufacturer; wherein, the power generation cost model of the thermal power manufacturer is
Figure GDA0002385038250000122
The power generation cost model of the green power manufacturer is
Figure GDA0002385038250000123
a1、b1、c1And a2、b2、c2The cost coefficients of each unit of thermal power and green power are respectively, and the renewable energy quota proportion K meets the requirement
Figure GDA0002385038250000124
Establishing a decision model of a power generator under the fixed electricity price of renewable resources according to the power generation cost models of the clear electricity price, the thermal power generator and the green power generator in the market; wherein, the decision model of the power generator under the fixed electricity price of the renewable resource
Figure GDA0002385038250000125
wgreen、wcoalThe method comprises the steps of respectively making profits for green power manufacturers and thermal power manufacturers under fixed electricity price, Q is total market demand, S is government green electricity fixed subsidy, eta is government green electricity price subsidy horizontal factor, representing no efficiency loss of government subsidy when eta is more than or equal to 1, and CENegatively influencing the cost for thermal power manufacturers, an
Figure GDA0002385038250000126
Lambda represents an ecological environment influence factor, lambda is more than or equal to 0 and less than or equal to 1, i and j represent thermal power and green electric generator sets respectively, and m and n represent the number of the thermal power and the green electric generator sets respectively.
In the embodiments of the present invention, the following are specifically mentionedSecondly, establishing a decision model of the thermal power manufacturer under the condition of renewable resource fixed electricity price according to the clear electricity price obtained from the market and a power generation cost model of the thermal power manufacturer; wherein, the renewable resource is a decision model of thermal power manufacturers under fixed electricity price
Figure GDA0002385038250000131
Establishing a decision model of a green power manufacturer under the fixed power price of renewable resources according to the power generation cost model of the clear power price and the green power manufacturer in the market; wherein, the renewable resource is a decision model of a green power manufacturer under a fixed power price
Figure GDA0002385038250000132
s is a subsidy of the electricity price of the green electricity obtained by green power plant merchants; establishing a decision model of a power generator manufacturer under the renewable resource fixed electricity price according to the decision model of a thermal power manufacturer under the renewable resource fixed electricity price and the decision model of a green power manufacturer under the renewable resource fixed electricity price; wherein, the decision model of the power generator under the fixed electricity price of the renewable resource
Figure GDA0002385038250000133
Representing the social benefit of renewable resources under the fixed electricity price.
Establishing a decision model of the power generator under the renewable resource quota system according to the clear electricity price in the market, the power generation cost models of thermal power manufacturers and green power manufacturers and the renewable energy quota ratio K; wherein, the decision model of the power generator under the renewable resource quota system
Figure GDA0002385038250000134
In the embodiment of the invention, specifically, a decision model of a thermal power manufacturer under a renewable resource quota system is established according to the clear electricity price on the market and a power generation cost model of the thermal power manufacturer; wherein, the decision model of the thermal power manufacturer under the renewable resource quota system
Figure GDA0002385038250000135
pcThe price of a green certificate; establishing a decision model of the green power manufacturer under the renewable resource quota system according to the power price of the market and a power generation cost model of the green power manufacturer; wherein, the decision model of the green power supplier under the renewable resource quota system
Figure GDA0002385038250000136
Establishing a decision model of a power generator manufacturer under the renewable resource quota system according to a decision model of a thermal power manufacturer under the renewable resource quota system and a decision model of a green power manufacturer under the renewable resource quota system; wherein, the decision model of the power generator under the renewable resource quota system
Figure GDA0002385038250000137
Representing the social benefit under the control of renewable resource quota.
According to the decision model of the power generator under the renewable resource fixed power price and the decision model of the power generator under the renewable resource quota system, acquiring a power market equilibrium model with the renewable resource fixed power price and the renewable resource quota in parallel; wherein the power market equilibrium model maxFALL＝aFFIT+bFTGC，FALLIn order to achieve social benefits under the coexistence of two policy systems, namely a fixed electricity pricing system and a quota system, a and b are fixed electricity pricing system and quota system ratio coefficients respectively, and a + b is 1.
In the embodiment of the invention, the constructed power market equilibrium model with the fixed power price and the quota system of the renewable resources in parallel has certain significance for the current power market reform and the sustainable development of renewable energy power generation. On one hand, through correct market guidance, three major problems of renewable energy power generation, grid connection and consumption are gradually solved, and an effort is made for the industrialized development of green electric power; on the other hand, while the electric power market reform is deepened, transition from a fixed electricity price system to a green certificate market policy (namely a quota system) is carried out in parallel, and due contribution is made for relieving the environmental pressure faced by the economic development of China.
Third embodiment of the invention:
on the basis of the above embodiment, a simulation analysis process of the power market equilibrium optimization method of the present invention is provided:
assuming that 6 power generation manufacturers participate in the electricity market operation, the parameters of the cost coefficient, the upper limit and the lower limit of the installation machine and the like are shown in an attached table 1. Suppose that the first 4 are thermal power manufacturers (G1, G2, G3, G4), and the latter two are green power manufacturers (G5, G6). Each conditional market competition conforms to the gulo-balanced model. The green certificate price is calculated according to the average price (180.6 yuan/MWh) of the current green certificate trading platform, the parameters of each power generation manufacturer assume the inverse demand function coefficient (f is 15 and g is 0.0025) of the market price, the initial population number of MAPSO in the calculation example of the attached table 2 is 64 according to other parameters, and the initial inertia factor w1Is 0.9; initial social learning factor and self-learning factor c1、c2Are all 2.5; the maximum iteration number is 1000, the method is run on a computer with an Intel (R) core (TM) i5-3230 processor \4.00GB mounted memory (RAM), and optimized simulation is carried out by adopting a Matlab2014a platform:
for a clearer comparison of the analysis results, the discussion is divided into 3 scenarios. On the basis of comparing independent operation of the fixed electricity price system and the quota system of the renewable resources, an electric power market equilibrium model with the parallel fixed electricity price system and the quota system of the renewable resources is analyzed, and the result is shown in table 1. Among them, scenes 1 to 3 are power market equilibrium models in which a fixed power rate, a renewable energy quota, and a fixed power rate and a quota of renewable resources are in parallel (for clear representation of the relationship between the two systems, each bit of α and β is set to be 0.5) in the case of a chinese green power occupancy target (K15%) in 2020. As can be seen from Table 1, the total power generation amount under the condition of parallel two systems does not change obviously, and with the optimization decision among manufacturers, the benefit of the general society is increased compared with that of the prior art, the clear electricity price of the power market does not fluctuate obviously, and the optimization result meets the actual requirement.
TABLE 1
Figure GDA0002385038250000151
TABLE 2
Figure GDA0002385038250000152
Table 2 shows green electricity certificate trading markets and power market optimization strategies under different green electricity conditions, and 5 optimal balance strategies under different green electricity quotas K are shown, when K is 0, the situation that no renewable energy source participates in market share is shown in the prior art, and at this time, a single policy of the fixed electricity quotation well meets market requirements; from the social point of view, the implementation of the green electricity quota reduces the overall social benefit to some extent, but considering the reduction of negative external costs such as environment, it is advantageous for thermal power manufacturers, green electricity manufacturers and the whole society.
Referring to fig. 4, a fourth embodiment of the present invention provides an electric power market equilibrium optimization apparatus, including:
in a pre-established power market equilibrium model with parallel fixed power rates and quota rates of renewable resources:
an initialization unit 11 for L of multi-agent particle swarm algorithm under constructionsize×LsizeIn the Agent environment structure, initializing the position and speed of each Agent unit in the Agent environment; wherein L issize≥1。
And the Agent unit construction unit 12 is used for constructing the acquired decision behaviors of each power generation manufacturer into each Agent unit of the intelligent Agent in the multi-Agent particle swarm algorithm and initializing the adaptive value of each Agent unit of the intelligent Agent.
A local environment position obtaining unit 13, configured to obtain a local environment position of each Agent unit in each local variable.
And a competition and cooperation operation unit 14, configured to perform competition and cooperation operation on each Agent unit and an adjacent Agent unit in each local variable.
And the global extreme value acquisition unit 15 is configured to update the position, the speed, and the adaptive value of each Agent unit to acquire a global extreme value.
And the optimal value acquiring unit 16 is used for carrying out self-learning operation on the Agent units with the global extreme values so as to acquire the optimal value of balance optimization in the power market balance model.
In a first implementation manner of the fourth embodiment, the power generation manufacturers include thermal power manufacturers and green power manufacturers;
the step of establishing a power market equilibrium model with the renewable resource fixed power price and quota parallel comprises:
the market clear price acquisition unit is used for acquiring the market clear price according to the established electric power market oligopolism model; wherein the market offers a price of electricity in clear
Figure GDA0002385038250000171
The market discharge price is an inverse demand function of the market price, f and g are respectively an intercept and a slope of the demand function of the market price, and q iscoal、qgreenThe yield of thermal power manufacturers and green power plants is respectively, and m and n are the number of thermal power manufacturers and green power manufacturers respectively.
The cost model and quota proportion calculation unit is used for establishing a power generation cost model and a renewable energy quota proportion K of the thermal power manufacturer and the green power manufacturer; wherein, the power generation cost model of the thermal power manufacturer is
Figure GDA0002385038250000172
The power generation cost model of the green power manufacturer is
Figure GDA0002385038250000173
a1、b1、c1And a2、b2、c2The cost coefficients of each unit of thermal power and green power are respectively, and the renewable energy quota proportion K meets the requirement
Figure GDA0002385038250000174
The first decision model establishing unit is used for establishing a decision model of a power generator under the renewable resource fixed power price according to the power generation cost models of the clear power price, the thermal power generator and the green power generator in the market; wherein, the decision model of the power generator under the fixed electricity price of the renewable resource
Figure GDA0002385038250000175
wgreen、wcoalThe method comprises the steps of respectively making profits for green power manufacturers and thermal power manufacturers under fixed electricity price, Q is total market demand, S is government green electricity fixed subsidy, eta is government green electricity price subsidy horizontal factor, representing no efficiency loss of government subsidy when eta is more than or equal to 1, and CENegatively influencing the cost for thermal power manufacturers, an
Figure GDA0002385038250000176
Lambda represents an ecological environment influence factor, lambda is more than or equal to 0 and less than or equal to 1, i and j represent thermal power and green electric generator sets respectively, and m and n represent the number of the thermal power and the green electric generator sets respectively.
The second decision model establishing unit is used for establishing a decision model of the power generator under the renewable resource quota system according to the clear electricity price in the market, the power generation cost models of thermal power manufacturers and green power manufacturers and the renewable energy quota proportion K; wherein, the decision model of the power generator under the renewable resource quota system
Figure GDA0002385038250000181
The electric power market equilibrium model establishing unit is used for acquiring an electric power market equilibrium model with the fixed power price and the quota system of the renewable resources in parallel according to the decision model of the power generator under the fixed power price of the renewable resources and the decision model of the power generator under the quota system of the renewable resources; wherein the power market equilibrium model maxFALL＝aFFIT+bFTGCA and b are fixed electricity price and quota ratio coefficients respectively, and a + b is 1。
According to the first implementation manner of the fourth embodiment, in a second implementation manner of the fourth embodiment, the first decision model establishing unit specifically includes:
the first thermal power decision model establishing subunit is used for establishing a decision model of a thermal power manufacturer under the renewable resource fixed power price according to the clear power price on the market and a power generation cost model of the thermal power manufacturer; wherein, the renewable resource is a decision model of thermal power manufacturers under fixed electricity price
Figure GDA0002385038250000182
The first green electricity decision model establishing subunit is used for establishing a decision model of a green electricity manufacturer under the renewable resource fixed electricity price according to the clear electricity price in the market and a power generation cost model of the green electricity manufacturer; wherein, the renewable resource is a decision model of a green power manufacturer under a fixed power price
Figure GDA0002385038250000183
Obtaining the electricity price subsidy of unit green electricity for green power plant merchants;
the first decision model establishing subunit is used for establishing a decision model of a power generator under the renewable resource fixed electricity price according to a decision model of a thermal power generator under the renewable resource fixed electricity price and a decision model of a green power generator under the renewable resource fixed electricity price; wherein, the decision model of the power generator under the fixed electricity price of the renewable resource
Figure GDA0002385038250000184
According to the second implementation manner of the fourth embodiment, in a third implementation manner of the fourth embodiment, the second decision model establishing unit specifically includes:
the second thermal power decision model establishing subunit is used for establishing a decision model of a thermal power manufacturer under the renewable resource quota system according to the clear electricity price given by the market and a power generation cost model of the thermal power manufacturer; wherein the renewable resource quota isDecision model of thermal power manufacturer
Figure GDA0002385038250000191
pcThe price of a green certificate.
The second green electricity decision model establishing subunit is used for establishing a decision model of a green electricity manufacturer under the renewable resource quota system according to the clear electricity price in the market and a power generation cost model of the green electricity manufacturer; wherein, the decision model of the green power supplier under the renewable resource quota system
Figure GDA0002385038250000192
The second decision model establishing subunit is used for establishing a decision model of a power generator manufacturer under the renewable resource quota system according to the decision model of the thermal power manufacturer under the renewable resource quota system and the decision model of the green power manufacturer under the renewable resource quota system; wherein, the decision model of the power generator under the renewable resource quota system
Figure GDA0002385038250000193
According to a third implementation manner of the fourth embodiment, in the fourth implementation manner of the fourth embodiment, the adaptive value f (a) in each Agent unit is maxFALL＝aFFIT+bFTGC。
According to a fourth implementation manner of the fourth embodiment, in a fifth implementation manner of the fourth embodiment, the local environment position obtaining unit specifically includes:
acquiring local environment positions of all Agent units in respective local variables from local variables consisting of each Agent unit and Agent units surrounding the Agent unit, the Agent units and the Agent units on the upper, lower, left, right and four opposite angles; wherein, let Agent have (i, j) coordinates in grid, i, j ═ 1,2sizeThen L issizeLocal variable M ofi,jIs defined as: mi,j＝{Li1,j1,Li1,j,Li1,j2,Li,j1,Li,j2,Li2,j1,Li2,j,Li2,j2Is satisfied with
Figure GDA0002385038250000194
And
Figure GDA0002385038250000195
according to a fifth implementation manner of the fourth embodiment, in a sixth implementation manner of the fourth embodiment, the competition and cooperation operating unit specifically includes:
and the minimum adaptive value acquisition subunit is used for acquiring the minimum adaptive value in 8 adjacent Agent units of the Agent units in each local variable.
The position changing unit is used for changing the position of the Agent unit in the Agent environment structure when the minimum adaptive value is larger than the adaptive value of the Agent unit; wherein, the position L of the Agent unit of the Agent in the Agent environment structure is seti,j＝(l1,l2,...,ln) And the position N of the Agent unit of the Agent with the minimum adaptive value in the Agent environment structurei,j＝(n1,n2,...,nn) The position of the Agent unit of the Agent in the Agent environment structure is changed to l'k＝nk+rand(-1,1)×(nk-lk) k ═ 1, 2., n, rand (-1,1) is a random number between (-1,1), if l'k<ykmin，l'k＝ykminL 'if'k>ykmax，l'k＝ykmax，ymin＝(ykmin,ykmin,...,ynmin)、ymax＝(ykmax,ykmax,...,ynmax) Respectively, the lower and upper bounds of the fitness solution space.
The fifth embodiment of the invention provides an electric power market equilibrium optimization terminal device. The power market equilibrium optimization terminal device of the embodiment includes: a processor, a memory, and a computer program, such as a power market equilibrium optimization program, stored in the memory and executable on the processor. The processor, when executing the computer program, implements the steps in the above-mentioned various embodiments of the power market equilibrium optimization method, such as step S11 shown in fig. 1. Alternatively, the processor implements the functions of the modules/units in the above-described device embodiments, such as an optimal value obtaining unit, when executing the computer program.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of instruction segments of a computer program capable of performing specific functions, and the instruction segments are used for describing the execution process of the computer program in the power market equilibrium optimization terminal device.
The electric power market equilibrium optimization terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud server. The power market equilibrium optimization terminal device can include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of the power market equilibrium optimization terminal device, and do not constitute a limitation of the power market equilibrium optimization terminal device, and may include more or less components than the above components, or combine some components, or different components, for example, the power market equilibrium optimization terminal device may further include an input-output device, a network access device, a bus, and the like.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc., and the processor is a control center of the power market equilibrium optimization terminal device, and various interfaces and lines are used for connecting various parts of the whole power market equilibrium optimization terminal device.
The memory may be configured to store the computer program and/or the module, and the processor may implement various functions of the power market equilibrium optimization terminal device by executing or executing the computer program and/or the module stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The power market equilibrium optimization terminal device integrated module/unit can be stored in a computer readable storage medium if it is implemented in the form of a software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.

Claims (9)

1. A power market equilibrium optimization method is characterized by comprising the following steps:
in a pre-established power market equilibrium model with parallel fixed power rates and quota rates of renewable resources:
l of constructed multi-agent particle swarm algorithmsize×LsizeIn the Agent environment structure, initializing the position and speed of each Agent unit in the Agent environment; wherein L issize≥1；
Constructing the obtained decision behaviors of each power generation manufacturer into each Agent unit of the agents in a multi-Agent particle swarm algorithm, and initializing the adaptive value of each Agent unit of the agents;
acquiring the local environment position of each Agent unit in each local variable; the method comprises the steps that local environment positions of all Agent units in respective local variables are obtained from the local variables formed by each Agent unit and Agent units surrounding the Agent unit, the Agent units are arranged on the upper side, the lower side, the left side, the right side and the four opposite angles of the Agent unit; wherein, let Agent have (i, j) coordinates in grid, i, j ═ 1,2sizeThen L issizeLocal variable M ofi,jIs defined as: mi,j＝{Li1,j1,Li1,j,Li1,j2,Li,j1,Li,j2,Li2,j1,Li2,j,Li2,j2Is satisfied with
Figure FDA0002385038240000011
Figure FDA0002385038240000012
And
Figure FDA0002385038240000013
in each local variable, carrying out competition and cooperation operation on each Agent unit and an adjacent Agent unit;
updating the position, the speed and the adaptive value of each Agent unit of the Agent to obtain a global extreme value;
and carrying out self-learning operation on the Agent unit of the Agent with the global extreme value to obtain the optimal value of balance optimization in the power market balance model.
2. The power market equilibrium optimization method according to claim 1, wherein the power generation manufacturers comprise thermal power manufacturers and green power manufacturers;
the step of establishing a power market equilibrium model with the renewable resource fixed power price and quota parallel comprises:
obtaining market clear electricity price according to the established electric power market short balance model(ii) a Wherein the market offers a price of electricity in clear
Figure FDA0002385038240000021
The market discharge price is an inverse demand function of the market price, f and g are respectively an intercept and a slope of the demand function of the market price, and q iscoal、qgreenThe yield of thermal power manufacturers and green power plants is respectively, and m and n are the number of thermal power manufacturers and green power manufacturers respectively;
establishing a power generation cost model and a renewable energy quota ratio K of the thermal power manufacturer and the green power manufacturer; wherein, the power generation cost model of the thermal power manufacturer is
Figure FDA0002385038240000022
The power generation cost model of the green power manufacturer is
Figure FDA0002385038240000023
a1、b1、c1And a2、b2、c2The cost coefficients of each unit of thermal power and green power are respectively, and the renewable energy quota proportion K meets the requirement
Figure FDA0002385038240000024
Establishing a decision model of a power generator under the fixed electricity price of renewable resources according to the power generation cost models of the clear electricity price, the thermal power generator and the green power generator in the market; wherein, the decision model of the power generator under the fixed electricity price of the renewable resource
Figure FDA0002385038240000025
wgreen、wcoalThe method comprises the steps of respectively making profits for green power manufacturers and thermal power manufacturers under fixed electricity price, Q is total market demand, S is government green electricity fixed subsidy, eta is government green electricity price subsidy horizontal factor, representing no efficiency loss of government subsidy when eta is more than or equal to 1, and CENegatively influencing the cost for thermal power manufacturers, an
Figure FDA0002385038240000026
Lambda represents an ecological environment influence factor, lambda is more than or equal to 0 and less than or equal to 1, i and j respectively represent thermal power and green motor sets, and m and n respectively represent the number of the thermal power and the green motor sets;
establishing a decision model of the power generator under the renewable resource quota system according to the clear electricity price in the market, the power generation cost models of thermal power manufacturers and green power manufacturers and the renewable energy quota ratio K; wherein, the decision model of the power generator under the renewable resource quota system
Figure FDA0002385038240000027
Wherein p iscThe price of a green certificate;
according to the decision model of the power generator under the renewable resource fixed power price and the decision model of the power generator under the renewable resource quota system, acquiring a power market equilibrium model with the renewable resource fixed power price and the renewable resource quota in parallel; wherein the power market equilibrium model maxFALL＝aFFIT+bFTGCA and b are fixed electricity price ratio coefficients and quota ratio coefficients respectively, and a + b is equal to 1.
3. The power market equilibrium optimization method according to claim 2, wherein the establishing of the decision model of the power generation manufacturer under the renewable resource fixed power price according to the power generation cost models of the market-released power price, the thermal power manufacturer and the green power manufacturer specifically comprises:
establishing a decision model of the thermal power manufacturer under the condition of renewable resource fixed electricity price according to the clear electricity price on the market and a power generation cost model of the thermal power manufacturer; wherein, the renewable resource is a decision model of thermal power manufacturers under fixed electricity price
Figure FDA0002385038240000031
Establishing a decision model of a green power manufacturer under the fixed power price of renewable resources according to the power generation cost model of the clear power price and the green power manufacturer in the market;wherein, the renewable resource is a decision model of a green power manufacturer under a fixed power price
Figure FDA0002385038240000032
s is a subsidy of the electricity price of the green electricity obtained by green power plant merchants;
establishing a decision model of a power generator manufacturer under the renewable resource fixed electricity price according to the decision model of a thermal power manufacturer under the renewable resource fixed electricity price and the decision model of a green power manufacturer under the renewable resource fixed electricity price; wherein, the decision model of the power generator under the fixed electricity price of the renewable resource
Figure FDA0002385038240000033
4. The power market equilibrium optimization method according to claim 3, wherein the establishing of the decision model of the power generator under the renewable resource quota system according to the market discharge price, the power generation cost models of the thermal power generator and the green power generator, and the renewable energy quota ratio K specifically includes:
establishing a decision model of the thermal power manufacturer under the renewable resource quota system according to the clear electricity price on the market and a power generation cost model of the thermal power manufacturer; wherein, the decision model of the thermal power manufacturer under the renewable resource quota system
Figure FDA0002385038240000034
pcThe price of a green certificate;
establishing a decision model of the green power manufacturer under the renewable resource quota system according to the power price of the market and a power generation cost model of the green power manufacturer; wherein, the decision model of the green power supplier under the renewable resource quota system
Figure FDA0002385038240000035
According to a decision model of a thermal power manufacturer under the renewable resource quota system and under the renewable resource quota systemThe decision model of the green power manufacturer is used for establishing a decision model of the power generator under the renewable resource quota system; wherein, the decision model of the power generator under the renewable resource quota system
Figure FDA0002385038240000041
5. The power market equilibrium optimization method according to claim 4, wherein the adaptive value F (a) maxF for each Agent unitALL＝aFFIT+bFTGC。
6. The power market equilibrium optimization method according to claim 5, wherein in each local variable, each Agent unit and an adjacent Agent unit compete and cooperate, specifically:
in each local variable, acquiring a minimum adaptive value in 8 adjacent Agent units of the Agent units;
when the minimum adaptive value is larger than the adaptive value of the Agent unit of the intelligent Agent, changing the position of the Agent unit of the intelligent Agent in an Agent environment structure; wherein, the position L of the Agent unit of the Agent in the Agent environment structure is seti,j＝(l1,l2,...,ln) And the position N of the Agent unit of the Agent with the minimum adaptive value in the Agent environment structurei,j＝(n1,n2,...,nn) The position of the Agent unit of the Agent in the Agent environment structure is changed to l'k＝nk+rand(-1,1)×(nk-lk) k ═ 1, 2., n, rand (-1,1) is a random number between (-1,1), if l'k<ykmin，l'k＝ykminL 'if'k>ykmax，l'k＝ykmax，ymin＝(ykmin,ykmin,...,ynmin)、ymax＝(ykmax,ykmax,...,ynmax) Respectively, the lower and upper bounds of the fitness solution space.
7. An electric power market equilibrium optimization apparatus, comprising:
in a pre-established power market equilibrium model with parallel fixed power rates and quota rates of renewable resources:
an initialization unit for L of multi-agent particle swarm algorithm under constructionsize×LsizeIn the Agent environment structure, initializing the position and speed of each Agent unit in the Agent environment; wherein L issize≥1；
The Agent unit construction unit is used for constructing the acquired decision behaviors of each power generation manufacturer into each Agent unit of the intelligent Agent in the multi-Agent particle swarm algorithm and initializing the adaptive value of each Agent unit of the intelligent Agent;
the local environment position acquisition unit is used for acquiring the local environment position of each Agent unit in each local variable; the method comprises the steps that local environment positions of all Agent units in respective local variables are obtained from the local variables formed by each Agent unit and Agent units surrounding the Agent unit, the Agent units are arranged on the upper side, the lower side, the left side, the right side and the four opposite angles of the Agent unit; wherein, let Agent have (i, j) coordinates in grid, i, j ═ 1,2sizeThen L issizeLocal variable M ofi,jIs defined as: mi,j＝{Li1,j1,Li1,j,Li1,j2,Li,j1,Li,j2,Li2,j1,Li2,j,Li2,j2Is satisfied with
Figure FDA0002385038240000051
Figure FDA0002385038240000052
And
Figure FDA0002385038240000053
the competition and cooperation operation unit is used for carrying out competition and cooperation operation on each Agent unit and adjacent Agent units in each local variable;
the global extreme value acquisition unit is used for updating the position, the speed and the adaptive value of each Agent unit of the Agent so as to acquire a global extreme value;
and the optimal value acquisition unit is used for carrying out self-learning operation on the Agent units of the intelligent agents with the global extreme values so as to acquire the optimal value of balance optimization in the power market balance model.
8. An electric power market equilibrium optimization terminal device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the electric power market equilibrium optimization method according to any one of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the power market equilibrium optimization method according to any one of claims 1 to 6.
CN201810190293.2A 2018-03-07 2018-03-07 Electric power market equilibrium optimization method, device, equipment and medium Active CN108537405B (en)

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