CN112862175B - Local optimization control method and device based on P2P power transaction - Google Patents

Local optimization control method and device based on P2P power transaction Download PDF

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CN112862175B
CN112862175B CN202110133799.1A CN202110133799A CN112862175B CN 112862175 B CN112862175 B CN 112862175B CN 202110133799 A CN202110133799 A CN 202110133799A CN 112862175 B CN112862175 B CN 112862175B
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田艳华
尤婷婷
游昌清
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Tiandaqiushi Electric Power High Technology Co ltd
Heze Power Supply Co of State Grid Shandong Electric Power Co Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0206—Price or cost determination based on market factors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/14—Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Abstract

The invention provides a local optimization control method and a local optimization control device based on P2P power trading, wherein the method comprises the following steps: according to P2P power transaction electricity price of a community micro-grid at a certain time slot and energy storage equipment conditions of community micro-grid users, a community micro-grid producer and consumer nonlinear planning production and sales mathematical model with energy storage equipment constraint conditions is established; determining a community total production and sales mathematical model according to the community micro-grid producer and consumer nonlinear programming mathematical model; performing iterative optimization according to the community micro-grid total production and marketing mathematical model to realize Nash balance, and calculating to obtain the production and marketing amount of each community micro-grid producer and consumer; and optimizing the energy storage equipment according to the output and sales volume of each community microgrid producer and consumer. Greatly improving the willingness of each of the victims to participate in the P2P power transaction. The efficiency of power distribution network management can be improved, and the electricity charge of community microgrid users is remarkably reduced or the income of producers and consumers is increased.

Description

Local optimization control method and device based on P2P power transaction
Technical Field
The invention belongs to the field of power dispatching, and particularly relates to a local optimization control method and device based on P2P power transaction.
Background
At the present stage, as more and more distributed power sources (such as rooftop photovoltaic and the like) are connected to a community microgrid system, more and more energy consumers are being converted into production consumers (producers and consumers), namely, double identities of electric energy producers and consumers are considered. The community microgrid system and the main power grid are usually provided with only one connection point. The connection point reflects the sum of the net loads of multiple producers and consumers. This summation balances the random fluctuations of the producer's distributed power and load, since the excess energy from one energy producer can be consumed by another energy consumer. If the producers and consumers in the community microgrid cooperate with each other, the total energy cost of the community microgrid may be reduced. End-to-end (P2P) power trading provides energy trading in a community micro-network to reduce economic and profit conflicts for various buyers and sellers. This is because P2P power trading creates a strong local market competition mechanism, and the behavior of the food producers in a small area can interact and act. However, it is difficult for a single person to implement the most advantageous control strategy to guide the management of the user-side elastic loads or energy storage devices based on his limited information. Therefore, developing a control algorithm responsible for managing the elastic load or energy storage device control behavior plays a crucial role during P2P power trading. The control algorithm often determines feasibility of P2P power transaction of the community microgrid.
However, the existing control method partly considers from a static point of view, partly considers from an overall point of view, does not consider the difference between individuals, and does not fully consider the enthusiasm of the producer and the consumer for changing the elastic load to obtain the maximum economic benefit, thereby reducing the participation degree of the producer and the consumer in the P2P transaction.
Disclosure of Invention
In view of this, the present invention is directed to a local optimization control method and device based on P2P power trading, so as to solve the technical problem in the prior art that the existing control algorithm reduces the participation degree of the prosumer and consumer in the P2P trading.
In one aspect, an embodiment of the present invention provides a local optimization control method based on P2P power trading, including:
according to the P2P electricity transaction price of the community microgrid at a certain time slot and the conditions of energy storage equipment of users of the community microgrid, a community microgrid producer and consumer nonlinear programming production and sales mathematical model with the constraint conditions of the energy storage equipment is established:
Figure GDA0003877663730000021
the constraint conditions are as follows:
Figure GDA0003877663730000022
Figure GDA0003877663730000023
Figure GDA0003877663730000024
wherein it is present>
Figure GDA0003877663730000025
Is the absolute value of net energy consumption by the producer or consumer. If it is not
Figure GDA0003877663730000026
Is positive or zero, then->
Figure GDA0003877663730000027
Is equal to->
Figure GDA0003877663730000028
If->
Figure GDA0003877663730000029
Is negative, then->
Figure GDA00038776637300000210
Is equal to->
Figure GDA00038776637300000211
Figure GDA00038776637300000212
And &>
Figure GDA00038776637300000213
Is the maximum discharge and charge power of the accumulator of the producer i. SOC i,min And SOC i,max Is the minimum and maximum state of charge percentages of the battery. Eta BD And η BC Is the discharge and charge efficiency of the battery. W i,N Is the nominal capacity of the battery;
determining a community micro-grid total production and sales mathematical model according to the community micro-grid producer and consumer nonlinear programming production and sales mathematical model;
performing iterative optimization according to the community micro-grid total production and marketing mathematical model to realize Nash balance, and calculating to obtain the production and marketing amount of each community micro-grid producer and consumer;
and optimizing the energy storage equipment according to the output and sales volume of each community microgrid producer and consumer.
Further, before the non-linear planning production and sales quantity mathematical model of the community microgrid producer and consumer with the energy storage device constraint condition is established according to the P2P electricity transaction price of the community microgrid at a certain time slot and the energy storage device condition of the community microgrid users, the method further comprises the following steps:
calculating the P2P power transaction electricity price of the micro-grid in the certain time slot community, wherein the P2P power transaction electricity price of the micro-grid in the certain time slot community is calculated in the following mode:
Figure GDA0003877663730000031
wherein +>
Figure GDA0003877663730000032
Is the buying and selling price of P2P power transaction between the producers and the consumers in the community microgrid of the time slot t, b i Electricity fee or income for the producer and consumer: positive numbers indicating income, negative numbers indicating electricity charge, NE i To take into account the net power consumption of the producer/consumer i after battery power.
Further, the iterative optimization according to the community microgrid total production and marketing mathematical model comprises:
and when the calculation cannot reach the Nash equilibrium, limiting the maximum value of the single change of the optimization variable by using an optimization step control method to realize the Nash equilibrium.
Further, the iterative optimization according to the community microgrid total production and marketing mathematical model comprises:
when the calculation can not reach Nash equilibrium, the variation rule in the machine learning cycle process is limited by using a process learning method so as to realize Nash equilibrium.
In another aspect, an embodiment of the present invention provides a local optimization control device based on P2P power trading, including:
the model establishing module is used for establishing a community microgrid producer and consumer nonlinear programming production and sales mathematical model with energy storage equipment constraint conditions according to P2P electricity transaction price of a certain time slot community microgrid and energy storage equipment conditions of community microgrid users:
Figure GDA0003877663730000041
the constraint conditions are as follows:
Figure GDA0003877663730000042
Figure GDA0003877663730000043
Figure GDA0003877663730000044
wherein it is present>
Figure GDA0003877663730000045
Is the absolute value of net energy consumption by the consumer i. If +>
Figure GDA0003877663730000046
Is positive or zero, then->
Figure GDA0003877663730000047
Is equal to->
Figure GDA0003877663730000048
If +>
Figure GDA0003877663730000049
Is negative, then>
Figure GDA00038776637300000410
Is equal to->
Figure GDA00038776637300000411
Figure GDA00038776637300000412
And &>
Figure GDA00038776637300000413
Is the maximum discharge and charge power of the accumulator of the producer i. SOC i,min And SOC i,max Is the minimum and maximum state of charge percentages of the battery. Eta BD And η BC Is the discharge and charge efficiency of the battery. W is a group of i,N Is the nominal capacity of the battery;
the total production and sales mathematical model determining module is used for determining a community micro-grid total production and sales mathematical model according to the community micro-grid producer and consumer nonlinear programming production and sales mathematical model;
the iteration optimization module is used for carrying out iteration optimization according to the community micro-grid total production and sales mathematical model so as to realize Nash balance and calculate the production and sales volume of the community micro-grid consumers and consumers;
and the optimization module is used for optimizing the energy storage equipment according to the output and sales volume of each community microgrid producer and consumer.
Further, the apparatus further comprises:
the electricity price calculating module is used for calculating the P2P electricity transaction electricity price of the micro-grid in the certain time slot community, and the P2P electricity transaction electricity price of the micro-grid in the certain time slot community is calculated in the following mode:
Figure GDA0003877663730000051
wherein it is present>
Figure GDA0003877663730000052
The price of buying and selling electricity for P2P electricity transaction among producers and consumers in the community microgrid of the time slot t, b i For the patients of abortionElectricity charge or income: positive numbers indicating income, negative numbers indicating electricity charge, NE i To take into account the net power consumption of the producer i after the accumulator power.
Further, the iterative optimization module includes:
and the step maximum limiting unit is used for limiting the maximum value of the single change of the optimized variable by using an optimized step control method when the calculation cannot reach the Nash balance, so as to realize the Nash balance.
Further, the iterative optimization module includes:
and the change rule limiting unit is used for limiting the change rule in the machine learning cycle process by using a process learning method when the calculation cannot reach the Nash balance so as to realize the Nash balance.
Compared with the prior art, the local optimization control method and device based on the P2P power transaction have the following advantages: according to the local optimization control method and device based on the P2P power transaction, provided by the embodiment of the invention, a community microgrid producer and consumer nonlinear planning production and sales mathematical model with energy storage equipment constraint conditions is established according to the P2P power transaction price of a certain time slot community microgrid and the energy storage equipment conditions of community microgrid users. And determining a total production and sales mathematical model of the community micro-grid according to the production and sales mathematical model, performing iterative optimization on the mathematical model, calculating the production and sales of each community micro-grid producer and consumer under the Nash equilibrium condition, and optimizing the energy storage equipment of each community micro-grid producer and consumer according to the production and sales of each community micro-grid producer and consumer. Under the condition of considering the optimality of flexible resources of each producer and the fairness among producers and consumers, a distributed energy management solution is provided for each producer and consumer in the P2P trading process, and the willingness of each producer and consumer to participate in the P2P power trading is greatly improved. The management efficiency of the power distribution network can be improved, and the electricity charge of community microgrid users is remarkably reduced or the income of producers and consumers is increased.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flowchart of a local optimization control method based on P2P power trading according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a local optimization control device based on P2P power trading according to a second embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Example one
Fig. 1 is a flowchart illustrating a local optimization control method based on P2P power trading according to a first embodiment of the present invention, and referring to fig. 1, the local optimization control method based on P2P power trading includes:
and S110, establishing a community microgrid producer and consumer nonlinear planning production and sales mathematical model with energy storage equipment constraint conditions according to the P2P power transaction price of the community microgrid at a certain time slot and the energy storage equipment conditions of community microgrid users.
In the P2P power trading process, it plays a crucial role to develop a control algorithm responsible for managing the elastic load or energy storage device control behavior. The control algorithm often determines feasibility of P2P power transaction of the community microgrid. Currently, some researches design some price mechanisms for P2P power trading, and develop some control algorithms based on these price mechanisms.
Currently, P2P pricing mechanisms can be classified into three categories: auction-based price mechanisms, competitive-based local market price mechanisms, and formulaic price mechanisms.
When these pricing mechanisms are used for P2P electricity trading, there is often an indirect link between the control actions of the producer and his economic interests. For example, when using the market ratio (SDR) method, the economic benefits of the producer and consumer are closely related to the dynamic buy and sell electricity prices, while the real-time buy and sell electricity prices depend on the market ratio. Therefore, in order to obtain the maximum economic benefit, the producer/consumer controls to change their own elastic load to change the market demand-supply ratio. The indirect link between controlling behavior and profitability is likely to be broken by some unforeseen activity of other prosumers in the community so that the prosumer does not actively change their elastic load to maximize economic benefit.
The method of using game theory can grasp conflicting interests and consider the interaction between the producers and the consumers when conducting P2P power transactions). The Stackelberg game method is currently used for energy management in micro-grids by Photovoltaic (PV) -containing producers and consumers. The P2P transaction process is divided into two processes. Price competition among sellers is modeled as a non-cooperative game. Buyer dynamics are modeled as an evolving game in the selection of sellers. The interaction between the buyer and seller is modeled as a Stackelberg game. The price of electricity depends on the excess energy that the producer and the consumer transact with the P2P transaction coordinator and the sensitivity of the producer and the consumer to the price. The disadvantage is that this method cannot ensure that every producer and consumer benefits from P2P transactions, because dynamic programming games neither consider the producer and consumer's contribution to the community microgrid nor manage the producer and consumer's control actions to reduce their own energy costs.
Thus, in the present embodiment, a local optimization control method based on P2P power trading is provided for managing elastic load or storage devices of individual victims in a guided manner. The method provides a distributed energy management solution for each prosumer and consumer in the P2P transaction process, and considers the optimality of flexible resources and the fairness among the prosumers and consumers. Enabling the abortive person to interact directly with his economic interests, allowing the abortive person to adjust his control strategy based on his own energy resources and the control activities of other abortive persons. This greatly increases the willingness of each of the victims to participate in the P2P power transaction. Consider the marginal contribution of each of the victims at different times. The method ensures fairness among producers and consumers in the cooperative game and ensures that each P2P power transaction participant can benefit from the method.
The participants in the game are autonomous individuals responsible for making their decisions to pursue the rational best results. A game is defined as a cooperative game when multiple participants can coordinate each other's actions and reach a binding agreement, producing results that cannot be achieved by individual actions.
In this embodiment, the community microgrid operator can collect the energy consumption or power generation data of each producer and consumer in real time, and calculate the power fee or income of each producer and consumer in the time slot according to the sharley value. Therefore, real-time P2P electricity buying and selling prices are obtained, and each producer and consumer carries out local optimization control according to the real-time P2P electricity prices.
Therefore, a community microgrid producer and consumer nonlinear programming production and sales mathematical model with energy storage device constraint conditions is established according to the P2P electricity transaction price of a certain time slot community microgrid and the energy storage device conditions of community microgrid users.
Specifically, the non-linear programming production and sales mathematical model of the community microgrid producer and consumer is as follows:
Figure GDA0003877663730000091
due to the nature of the energy storage device, the following constraints also exist for this formula:
Figure GDA0003877663730000092
Figure GDA0003877663730000093
Figure GDA0003877663730000094
wherein +>
Figure GDA0003877663730000095
Is the absolute value of net energy consumption by the consumer i. If it is used
Figure GDA0003877663730000096
Is positive or zero, then->
Figure GDA0003877663730000097
Is equal to +>
Figure GDA0003877663730000098
If->
Figure GDA0003877663730000099
Is negative, then->
Figure GDA00038776637300000910
Is equal to->
Figure GDA00038776637300000911
Figure GDA00038776637300000912
And &>
Figure GDA00038776637300000913
Is the maximum discharge and charge power of the accumulator of the producer i. SOC i,min And SOC i,max Is the minimum and maximum state of charge percentages of the battery. Eta BD And η BC Is the discharge and charge efficiency of the battery. W i,N Is the nominal capacity of the battery. />
Figure GDA00038776637300000914
The price of buying and selling electricity for P2P electricity transaction between producers and consumers in the community microgrid of the time slot t.
Optionally, before the non-linear planning production and sales quantity mathematical model of the community microgrid producer and consumer with the energy storage device constraint condition is established according to the P2P electricity transaction price of the community microgrid at a certain time slot and the energy storage device condition of the community microgrid user, the method further includes the following steps: and calculating the P2P power transaction electricity price of the community microgrid in a certain time slot. Calculating the electricity price of the P2P power transaction of the micro-grid in the certain time slot community by adopting the following method:
Figure GDA0003877663730000101
wherein it is present>
Figure GDA0003877663730000102
Is the buying and selling price of P2P power transaction between the producers and the consumers in the community microgrid of the time slot t, b i Electricity fee or income for the producer and consumer: positive numbers indicating revenue, negative numbers indicating electricity charge, NE i To take into account the net power consumption of the producer i after the accumulator power. Wherein +>
Figure GDA0003877663730000103
The price is the price for buying and selling the P2P power transaction between the producers and the consumers in the community microgrid in the time slot t. For convenience of calculation, in the present embodiment, the positive or negative value of the P2P buying and selling price is reserved, i.e. the buying price ^ is greater than or equal to ^ the>
Figure GDA0003877663730000104
Positive value, selling electricity price>
Figure GDA0003877663730000105
Is negative.
Sharley value is considered a fair method of revenue distribution in cooperative gaming. The present embodiment uses the sharley value for allocating electricity charges or incomes of the respective producers and consumers, and this allocation method takes into full account the marginal contribution of the respective producers and consumers. The share value method of cooperative game can express the electric charge (or income) of each producer and consumer in the time slot t as:
Figure GDA0003877663730000106
wherein
Figure GDA0003877663730000107
Representing all federations in the pool of producers N that do not contain producer i. (Ψ $ { i }) represents a new federation where federation Ψ and producer/consumer i combine. The above expression represents the average of the value function differences of the coalition of (Ψ ℃ { i }) and S, the marginal contribution of the producer/consumer i.
And S120, determining a total production and sales mathematical model of the community micro-grid according to the non-linear planning production and sales mathematical model of the community micro-grid producer and consumer.
The steps provide a nonlinear programming mathematical model of a certain producer and consumer in the community microgrid for determining a total production and sales mathematical model of the community microgrid. Therefore, the yield of each community microgrid producer and consumer can be added to obtain a mathematical model for determining the total production and sales of the community microgrid.
And S130, performing iterative optimization according to the community micro-grid total production and sales mathematical model to realize Nash balance, and calculating to obtain the production and sales volume of each community micro-grid producer and consumer.
Because a certain difference exists between the price of the power on the internet and the price of the power on the internet, the optimal strategy for the community microgrid is to consume the electric energy in the community microgrid as much as possible.
When the P2P power transaction is carried out, an iterative process exists for calculating the real-time P2P buying and selling electricity price and the electricity fee or income of each producer and consumer in the time slot t. Because the control strategy executed by the producers and consumers can change the net energy consumption of the producers and consumers, the change of the net energy consumption can influence the marginal contribution value of the producers and consumers to the community microgrid alliance, and therefore the profit of the producers and consumers in the cooperative game is influenced. The change in revenue may affect the P2P trade buy and sell electricity prices, which may affect the control strategy when the producer and consumer perform optimization. The iterative process facilitates the adjustment of the control behavior of the prenatal and xiator according to the income. Thus, the economic returns of the producers and the consumers in the cooperative game are correlated and mutually influenced.
The gaming process is achieved by local optimization of the victims changing their elastic load or the output of the energy storage device. And the system balance, namely Nash Equilibrium (Nash Equilibrium) in the game theory, is achieved through multiple loop iterations.
In this embodiment, the community microgrid total production and sales mathematical model may be iteratively optimized, for example, the community microgrid total production and sales mathematical model X may be set to 0 in the case of energy purchase, and in the case of electricity sale, a maximum value may be selected according to historical experience, and iterative operations may be performed according to the selected result. Corresponding step length can be preset for iterative optimization until each producer and consumer of the community microgrid can meet the constraint condition in the nonlinear programming production and sales mathematical model. And correspondingly obtaining the output and sales of the micro-grid producers and consumers in each community.
In this embodiment, the iterative optimization may be performed according to the community microgrid total production and marketing mathematical model, and specifically, the iterative optimization is as follows: and when the calculation cannot reach the Nash equilibrium, limiting the maximum value of the single change of the optimized variable by using an optimization step control method, and realizing the Nash equilibrium. Or when the calculation can not reach the Nash equilibrium, the variation rule in the machine learning cycle process is limited by using a process learning method so as to realize the Nash equilibrium. In most cases, the energy of the elastic load or the energy storage device is smaller than the net energy consumption of the whole community micro-area network, and Nash balance is easy to realize. However, as more and more distributed energy sources are incorporated into the community microgrid or the community microgrid including one or more large users (and the large users have larger elastic loads or energy storage devices), system misconvergence, that is, nash equilibrium is not reached, may occur. At this time, an optimization step control method or a process learning method is adopted to limit the maximum value of single change of an optimization variable or the change rule in the machine learning cycle process, so as to accelerate (or guarantee) the system to realize Nash equilibrium.
And S140, optimizing the energy storage equipment according to the output and sales volume of each community microgrid producer and consumer.
The corresponding parameters of the energy storage equipment can be determined according to the calculated output and sales volume of each community microgrid producer and consumer, the time slot electricity price and the absolute value of the producer and consumer consumption, and the energy storage equipment is optimized according to the parameters of the energy storage equipment, so that the economic profit maximization of the producer and consumer in the community microgrid is realized.
According to the local optimization control method based on the P2P power transaction, provided by the embodiment of the invention, a community microgrid producer and consumer nonlinear planning production and sales mathematical model with energy storage equipment constraint conditions is established according to the P2P power transaction price of a certain time slot community microgrid and the energy storage equipment conditions of community microgrid users. And determining a total production and sales mathematical model of the community micro-grid according to the production and sales mathematical model, performing iterative optimization on the mathematical model, calculating the production and sales of each community micro-grid producer and consumer under the Nash equilibrium condition, and optimizing the energy storage equipment of each community micro-grid producer and consumer according to the production and sales of each community micro-grid producer and consumer. Under the condition that the optimality of flexible resources of each prosumer and consummated people and the fairness among the prosumers and consummated people are considered, a distributed energy management solution is provided for each prosumer and consummated people in the P2P trading process, and willingness of each prosumer and consummated people to participate in the P2P electricity trading is greatly improved. The management efficiency of the power distribution network can be improved, and the electricity charge of community microgrid users is remarkably reduced or the income of producers and consumers is increased.
Example two
Fig. 2 is a schematic structural diagram of a local optimization control device based on P2P power trading according to a second embodiment of the present invention, referring to fig. 2, the local optimization control device for P2P power trading includes:
the model establishing module 210 is configured to establish a mathematical model of the non-linear programming production and sales volume of the community microgrid producer and consumer with energy storage device constraint conditions according to the P2P electricity transaction price of the community microgrid at a certain time slot and the energy storage device conditions of the community microgrid users:
Figure GDA0003877663730000131
the constraint conditions are as follows:
Figure GDA0003877663730000132
Figure GDA0003877663730000133
Figure GDA0003877663730000134
wherein it is present>
Figure GDA0003877663730000135
Is the absolute value of net energy consumption by the consumer i. If it is not
Figure GDA0003877663730000136
Is positive or zero, then->
Figure GDA0003877663730000137
Is equal to +>
Figure GDA0003877663730000138
If->
Figure GDA0003877663730000139
Is negative, then->
Figure GDA00038776637300001310
Is equal to->
Figure GDA00038776637300001311
Figure GDA00038776637300001312
And
Figure GDA00038776637300001313
is the maximum discharge and charge power of the accumulator of the producer i. SOC i,min And SOC i,max Is the minimum and maximum state of charge percentages of the battery. Eta BD And η BC Is the discharge and charge efficiency of the battery. W is a group of i,N Is the nominal capacity of the battery;
the total production and sales mathematical model determining module 220 is used for determining a community micro-grid total production and sales mathematical model according to the community micro-grid producer and consumer nonlinear programming production and sales mathematical model;
the iteration optimization module 230 is used for performing iteration optimization according to the community microgrid total production and marketing mathematical model to realize Nash balance and calculate the production and marketing amount of each community microgrid producer and consumer;
and the optimizing module 240 is configured to optimize the energy storage device according to the output and sales volume of each community microgrid producer and consumer.
According to the local optimization control device based on the P2P power transaction, provided by the embodiment of the invention, a community microgrid producer and consumer nonlinear planning production and sales mathematical model with energy storage equipment constraint conditions is established according to the P2P power transaction price of a certain time slot community microgrid and the energy storage equipment conditions of community microgrid users. And determining a total production and sales mathematical model of the community micro-grid according to the production and sales mathematical model, performing iterative optimization on the mathematical model, calculating the production and sales of each community micro-grid under the Nash equilibrium condition, and optimizing the energy storage equipment of the production and sales of each community micro-grid according to the production and sales of each community micro-grid. Under the condition of considering the optimality of flexible resources of each producer and the fairness among producers and consumers, a distributed energy management solution is provided for each producer and consumer in the P2P trading process, and the willingness of each producer and consumer to participate in the P2P power trading is greatly improved. The efficiency of power distribution network management can be improved, and the electricity charge of community microgrid users is remarkably reduced or the income of producers and consumers is increased.
In a preferred implementation of this embodiment, the apparatus further comprises:
the electricity price calculating module is used for calculating the P2P electricity transaction electricity price of the micro-grid in the certain time slot community, and the P2P electricity transaction electricity price of the micro-grid in the certain time slot community is calculated in the following mode:
Figure GDA0003877663730000151
wherein +>
Figure GDA0003877663730000152
The price of buying and selling electricity for P2P electricity transaction among producers and consumers in the community microgrid of the time slot t, b i Electricity fee or income for the producer and consumer: positive numbers indicating income, negative numbers indicating electricity charge, NE i To take into account the net power consumption of the producer/consumer i after battery power.
In a preferred implementation manner of this embodiment, the iterative optimization module includes:
and the step maximum limiting unit is used for limiting the maximum value of the single change of the optimized variable by using an optimized step control method when the calculation cannot reach the Nash balance, so as to realize the Nash balance.
In a preferred implementation manner of this embodiment, the iterative optimization module includes:
and the change rule limiting unit is used for limiting the change rule in the machine learning cycle process by using a process learning method when the calculation cannot reach the Nash balance so as to realize the Nash balance.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (4)

1. A local optimization control method based on P2P power trading is characterized by comprising the following steps:
according to P2P power transaction electricity price of a community micro-grid at a certain time slot and energy storage equipment conditions of community micro-grid users, a community micro-grid producer and consumer nonlinear programming production and sales mathematical model with energy storage equipment constraint conditions is established:
Figure FDA0003877663720000011
the constraint conditions are as follows:
Figure FDA0003877663720000012
Figure FDA0003877663720000013
Figure FDA0003877663720000014
wherein it is present>
Figure FDA0003877663720000015
Is the absolute value of net energy consumption of the person of either birth or death i; if->
Figure FDA0003877663720000016
Is positive or zero, then->
Figure FDA0003877663720000017
Is equal to +>
Figure FDA0003877663720000018
If->
Figure FDA0003877663720000019
Is negative, then->
Figure FDA00038776637200000110
Is equal to->
Figure FDA00038776637200000111
Figure FDA00038776637200000112
And &>
Figure FDA00038776637200000113
Maximum discharge and charge power, SOC, of a battery of a producer/consumer i,min And SOC i,max Is the minimum and maximum state of charge percentages, η, of the battery BD And η BC Is the discharge and charge efficiency, W, of the accumulator i,N Is the nominal capacity of the battery;
determining a community micro-grid total production and sales mathematical model according to the community micro-grid producer and consumer nonlinear programming production and sales mathematical model;
performing iterative optimization according to the community micro-grid total production and sales mathematical model to realize Nash balance, and calculating to obtain the production and sales volume of the consumers and the consumers of each community micro-grid;
and optimizing the energy storage equipment according to the production and sales volume of the micro-grid producers and consumers in each community.
2. The method of claim 1, wherein before the building the mathematical model of the non-linear planned output and sales of the community microgrid producer and consumer with energy storage device constraint conditions according to the P2P electricity transaction price of the community microgrid at a certain time slot and the energy storage device conditions of users of the community microgrid, the method further comprises:
calculating the P2P power transaction electricity price of the micro-grid in the certain time slot community, wherein the P2P power transaction electricity price of the micro-grid in the certain time slot community is calculated in the following mode:
Figure FDA0003877663720000021
wherein it is present>
Figure FDA0003877663720000022
The price of buying and selling electricity for P2P electricity transaction between producers and consumers in the community microgrid of the time slot t is bi, the electricity charge or income of the producers and consumers is bi, the positive number represents income, the negative number represents electricity charge, and NEi is net electricity consumption of the producers and consumers i after the electric energy of the storage battery is considered.
3. A local optimization control device based on P2P electric power transaction is characterized by comprising:
the model establishing module is used for establishing a community micro-grid producer and consumer nonlinear programming production and sales mathematical model with energy storage equipment constraint conditions according to P2P electricity transaction electricity prices of community micro-grid users at a certain time slot:
Figure FDA0003877663720000023
the constraint conditions are as follows:
Figure FDA0003877663720000024
/>
Figure FDA0003877663720000025
Figure FDA0003877663720000026
wherein +>
Figure FDA0003877663720000027
Is the absolute value of net energy consumption of the person of either birth or death i; if->
Figure FDA0003877663720000028
Is positive or zero, then->
Figure FDA0003877663720000029
Is equal to +>
Figure FDA00038776637200000210
If->
Figure FDA0003877663720000031
Is negative, then>
Figure FDA0003877663720000032
Is equal to->
Figure FDA0003877663720000033
Figure FDA0003877663720000034
And &>
Figure FDA0003877663720000035
Maximum discharge and charge power, SOC, of a battery of a producer/consumer i,min And SOC i,max Is the minimum and maximum state of charge percentages, η, of the battery BD And η BC Is the discharge and charge efficiency, W, of the accumulator i,N Is the nominal capacity of the battery;
the total production and sales mathematical model determining module is used for determining a community micro-grid total production and sales mathematical model according to the community micro-grid producer and consumer nonlinear programming production and sales mathematical model;
the iteration optimization module is used for performing iteration optimization according to the community micro-grid total production and marketing mathematical model to realize Nash balance and calculate the production and marketing amount of each community micro-grid producer and consumer;
and the optimization module is used for optimizing the energy storage equipment according to the output and sales volume of each community microgrid producer and consumer.
4. The apparatus of claim 3, further comprising:
the electricity price calculating module is used for calculating the P2P electricity transaction electricity price of the certain time slot community microgrid, and the P2P electricity transaction electricity price of the certain time slot community microgrid is calculated in the following mode:
Figure FDA0003877663720000036
wherein it is present>
Figure FDA0003877663720000037
The price of buying and selling electricity for P2P electricity transaction among producers and consumers in the community microgrid of the time slot t, b i Electricity fee or income for the producer and consumer: positive numbers indicating income, negative numbers indicating electricity charge, NE i To take into account the net power consumption of the producer i after the accumulator power. />
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