CN117436647A - Energy scheduling and trading method for interconnected micro-grid system - Google Patents

Energy scheduling and trading method for interconnected micro-grid system Download PDF

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CN117436647A
CN117436647A CN202311401102.XA CN202311401102A CN117436647A CN 117436647 A CN117436647 A CN 117436647A CN 202311401102 A CN202311401102 A CN 202311401102A CN 117436647 A CN117436647 A CN 117436647A
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王博
查中一
李怡赢
刘磊
樊慧津
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Abstract

The invention discloses an energy scheduling and trading method of an interconnected micro-grid system, which belongs to the technical field of multi-micro-grid interconnection and comprises the following steps: and introducing power trading volume, taking the declared power trading volume of the micro-grid and the actual power trading volume as constraints, dividing the energy scheduling model into two sub-models, wherein a decision variable of a first sub-model is not coupled with a decision variable of the energy trading model, solving the energy trading model on the premise that the first sub-model is optimal, and taking one of the optimal solutions of the energy trading model as the optimal solution of a second sub-model. The invention realizes decoupling between energy scheduling and transaction decision, and can improve decision accuracy; the multi-agent deep reinforcement learning algorithm and the P2P-based Nash negotiation game solution decoupled model are adopted respectively, so that the rapid solution can be realized, the requirement of real-time decision scheduling is met, the intermittent and uncertainty of renewable energy sources can be met, the privacy of a micro-grid is protected, and the fairness of transactions is ensured.

Description

Energy scheduling and trading method for interconnected micro-grid system
Technical Field
The invention belongs to the technical field of multi-microgrid interconnection, and particularly relates to an energy scheduling and trading method of an interconnection microgrid system.
Background
The distributed operation characteristics of the micro-grids based on renewable energy sources enable each micro-grid to operate independently or operate together in an interconnected micro-grid system, and energy trading is performed by utilizing the space-time difference of the energy sources. Thus, there are autonomous energy scheduling problems in the independent micro-grids and energy trading problems in the collaborative cooperation between micro-grids in the interconnected micro-grid system.
There is a decision coupling between the autonomous energy scheduling problem in the independent micro-grid and the energy trading problem in the cooperation between the micro-grids, that is, the decision coupling involves coordinating the energy scheduling decision in the sub-micro-grid and the energy trading decision between the micro-grids, the optimal solution of the autonomous energy scheduling problem in the independent micro-grid and the optimal solution of the energy trading problem in the cooperation between the micro-grids (pareto solution, by realizing pareto optimal to ensure fairness of the energy trading) are related to each other, and the pareto solution of the energy trading problem in the cooperation between the micro-grids depends on the optimal solution of the autonomous energy scheduling problem in the independent micro-grid. In the prior art, when solving one of the problems, the influence of the other problem is weakened, for example: when solving the autonomous energy scheduling problem in the independent micro-grids, the influence of the energy transaction problem in the cooperation between the micro-grids is generally ignored, or when solving the energy transaction problem in the cooperation between the micro-grids, the autonomous energy scheduling problem in the independent micro-grids is not considered, and only global optimal modeling is performed, so that the decision accuracy is lower.
In addition, when solving the problem of autonomous energy scheduling in the independent micro-grid, due to strong randomness and intermittence of renewable energy sources such as photovoltaic and wind power generation and loads, adverse effects may be generated on the safety of the power system, so that the reliability in autonomous energy scheduling in the micro-grid is not high. In the prior art, methods such as random planning and the like are generally adopted to cope with the influence of the randomness and the intermittence of renewable energy sources; however, these characteristics of renewable energy and load make it difficult to model them accurately, making decisions less efficient and accurate.
Meanwhile, when solving two problems, privacy information protection between micro-grids is also needed to be considered, in the prior art, only the data of the micro-grids are generally protected, but the protection of identity Information (ID) of the micro-grids is lacking, so that the source of the data and which micro-grid are known, the protection of the privacy information between the grids is not comprehensive enough, and the safety is lower.
Disclosure of Invention
Aiming at the defects and improvement demands of the prior art, the invention provides an energy scheduling and trading method of an interconnected micro-grid system, and aims to improve decision accuracy.
To achieve the above object, according to a first aspect of the present invention, there is provided an energy scheduling and trading method of an interconnected micro-grid system, the interconnected micro-grid system including N independently operated micro-grids, each micro-grid being connected to a distribution network, each micro-grid including a battery energy storage system, a renewable energy source and a load, the method comprising:
constructing an energy scheduling model of each micro-grid and an energy transaction model among the micro-grids;
introducing a power trade-offSaid power trade amount->Representing the sum of the transaction power between the current micro-grid n and the other micro-grids and distribution network, +.>t is the current scheduling time;
power trade amount announced by micro-grid nTrade with actual power>The consistency is taken as constraint, and the energy scheduling model is divided into two sub-models; the decision variable of the first sub-model is battery charge and discharge power of the micro-grid n-battery energy storage system +.>The objective function is to minimize the investment loss cost C of the micro-grid n-cell energy storage system B，n The method comprises the steps of carrying out a first treatment on the surface of the The second sub-model decision variable is the power sold or purchased by the micro-grid n to the distribution network +.>The objective function is to minimize the transaction cost C between the micro-grid n and the distribution network G，n ；
Solving the first sub-model, and under the condition that the first sub-model obtains the optimal solution constraint, determining the decision variable of the energy trading model as the power traded between the micro-grid n and the micro-grid m Unit price of electricity traded between micro grid n and micro grid m +.>Said power +.>n≠m；
Solving the energy transaction model, and solving the optimal solution of the energy transaction modelAs an optimal solution for the second sub-model, wherein +.>Optimal power for the micro grid n to sell or purchase to the distribution grid.
Further, solving the first sub-model includes:
s1, constructing an agent for each micro-grid n; at the current scheduling time t, the input of the intelligent agent is an independent observation quantityAction u of the agent output t Charging and discharging power for batteries of each battery energy storage system in the interconnected micro-grid; wherein the independent observation amount +.>The state variables of (a) include: />Is-> Power generated for renewable energy sources in micro-grid n,/->For the total power of the loads in the microgrid n, +.>For the state of charge of the battery energy storage system in the micro grid n +.>The time-sharing electricity buying price or the electricity selling price of the power distribution network;
s2, inputting the independent observation quantity of the current scheduling time t into the intelligent agent to obtain corresponding outputAction u t The method comprises the steps of carrying out a first treatment on the surface of the According to the action u t Calculating a prize r t With said rewards r t Updating parameters of the corresponding agent; wherein the reward r t Contrary to the objective function of the first sub-model;
And calculates the state variable of the next scheduling timeIs->At the same time, the state variable +.>AndModeling to form a normal distribution random variable, and obtaining a state variable of the next scheduling moment according to the randomness of the variable
S3, the state variable of the next scheduling time Is->And (3) inputting the parameters into the intelligent agent after parameter updating, performing training and learning of the next round until the preset training round is reached or the loss of the intelligent agent converges, and using the trained intelligent agent for battery charge and discharge power scheduling of each actual micro-grid battery energy storage system.
Further, at the current scheduling time t, according to the action u t The state of charge dynamic equation of the battery energy storage system battery in the micro-grid n obtains the state of the battery at the next momentState variable
The state variable of the next moment is obtained through time-sharing buying and selling electricity price
Further, training the intelligent agent by adopting a deep Q learning algorithm;
s2, selecting an action corresponding to the Q value as an action u output at the current scheduling time t by adopting an E-greedy method t 。
Further, the objective function of the first sub-model further includes a cost C for minimizing power fluctuation of the distribution network F 。
Further, constraints of the first sub-model, the second sub-model, and the energy transaction model further include:
At the current scheduling moment, the optimal total power provided by the renewable energy sources in the micro-grid n is the power generated by the renewable energy sources
The power trade amountThe battery charge and discharge power->The power generated by the renewable energy sourceAnd the total power of the load->And satisfies the power balance constraint.
Further, solving the energy transaction model includes:
sep1, at the current scheduling time t, according to the corresponding power transaction amount of each micro-gridIs registered as a seller i or a buyer j in the P2P market operation system, and independent identity numbers are distributed for the seller i and the buyer j; wherein, and->Representing the seller and the buyer, respectively;
the Sep2 and P2P market running system collects the purported transaction power amount of the seller and the buyer so as to establish communication between the seller and the buyer; wherein, at the current scheduling time t, the sellerThe power available is +.>Buyer->The required power is +.>
Sep3, seller and buyer participate in P2P-based Nash negotiation game to obtain transaction amount of energy output by all micro-grids serving as sellers to micro-grids serving as buyers at current scheduling time tAnd unit price of electricity for all seller transactions +.>Wherein P is i，j， t is the optimal power trade between micro grid n and micro grid m +. >π i，t For an optimal trade unit price of electricity between microgrid n and microgrid m>Obtaining optimal power sold or purchased by the micro-grid n to the distribution network according to the power balance constraint>
And the Sep4 and P2P market operation systems execute energy trading according to the agreed trading price and power.
Further, in Sep3, the seller and the buyer participate in a P2P-based nash negotiation game, including:
the seller and the buyer construct a Nash negotiation game model through P2P-based Nash negotiation game;
solving the Nash negotiation game model by adopting a sequential least square programming algorithm to obtain the trading volume of all the micro-grids serving as sellers at the current scheduling time t and outputting the micro-grids serving as buyers to the energy sources of the micro-grids serving as buyersAnd unit price of electricity for all seller transactions +.>
Wherein, the Nash negotiation game model is as follows:
wherein,and->The time-sharing electricity buying price and the electricity selling price of the distribution network are respectively; p'. i，j，t Transaction amount of power received for buyer +.>Representing the amount of electric energy sold by seller i to the distribution network, < >>Representing the energy purchased by buyer j from the distribution network; g (·) is the network loss function.
According to a second aspect of the present invention, there is provided an energy scheduling and trading system of an interconnected micro-grid system, comprising a computer readable storage medium and a processor;
The computer-readable storage medium is for storing executable instructions;
the processor is configured to read executable instructions stored in the computer readable storage medium to perform the method of any one of the first aspects.
According to a third aspect of the present invention there is provided a computer readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor implements the method according to any of the first aspects.
In general, through the above technical solutions conceived by the present invention, the following beneficial effects can be obtained:
(1) The energy scheduling and trading method of the interconnected micro-grid system enables each micro-grid to provide traded powerIntroduced as an integral variable, at the current scheduling instant t, the power trade amount declared by the current micro-gridTrade with actual power>The method comprises the steps of taking coincidence as constraint, decomposing an energy scheduling model of each micro-grid into two sub-models, wherein the first sub-model ignores the cost between the micro-grid and a power distribution network, and taking the battery charge and discharge power of a battery energy storage system of each micro-grid at the current scheduling time t as +.>The decision variables are made as univariate optimization problems, while the second sub-model focuses on the costs between the micro-grids and the distribution network, the decision variables are the power that each micro-grid sells or purchases to the large distribution network at the current scheduling instant t ∈ - >On the premise that the first sub-model is optimal, the decision variable of the energy trading model among the micro-grids is the power +.of the micro-grid n trading to the other micro-grid m at the current scheduling time t>Unit price of electricity traded between micro grid n and micro grid m +.>Power->Under the constraint, fraud is forbidden in the trade, and the power trade amount corresponding to the energy trade model among the first sub-model, the second sub-model and the micro-grid is +.>All are equal, at the moment, no coupling relation exists between the first sub-model and the decision variables of the energy transaction model between the micro-grids, so that the decoupling of the first sub-model in the energy scheduling model of each micro-grid and the energy transaction model between the micro-grids is realized, the first sub-model is solved, and the first sub-model is obtainedOptimal precondition->Solving an energy trading model among the micro-grids, and enabling one of the optimal solutions of the energy trading model among the micro-grids (optimal power of selling or buying of each micro-grid to a large distribution network at the current scheduling time t +.>) As an optimal solution for the second sub-model, to achieve energy scheduling and trading of the interconnected micro-grid system. According to the energy scheduling and trading method, in the modeling process, the problem of autonomous energy scheduling in the independent micro-grids and the problem of energy trading in the cooperative cooperation among the micro-grids are considered, so that the decision accuracy is improved.
(2) Further, when the first sub-model is solved, the first sub-model is converted into a decentralised locally observable Markov decision process, the privacy power information of each micro-grid is used as the independent observation quantity of the micro-grid as the input of the intelligent agent, and the intelligent agent corresponding to each micro-grid does not know the power dispatching quantity of other micro-grids in the energy dispatching process, so that the privacy protection of the power information among the micro-grids is realized.
At the same time, during the training process, power is generated by renewable energy sourcesAnd total load power->The historical track tau of the renewable energy source and the load power with high randomness are modeled as random variables with normal distribution, the renewable energy source and the load power state at the next moment are randomly generated according to the random variables so as to cope with the intermittence and uncertainty of the renewable energy source, the characteristic of the randomness of the renewable energy source and the load power is more met, the modeling is more accurate, and the decision accuracy is further improved.
(3) Further, the intelligent agent is trained by reinforcement learning, so that the trained intelligent agent can realize quick solution, the requirement of real-time decision scheduling is met, and the decision efficiency is improved.
(4) Further, in the method, when the energy scheduling model of the micro-grids is solved, each micro-grid drives trading according to economic benefits and according to the power trading amountThe identity and the number of the micro-grid serving as a buyer and the micro-grid serving as a seller are given positively and negatively, and only the P2P agent is told about the amount of power required to be traded in the trading process>And whether the seller or the buyer can be the seller or the buyer, both parties conduct optimal transaction pricing and energy transaction decision (realizing pareto frontier) based on Nash negotiation game, and the both parties of the transaction do not know which micro-grid the seller and the buyer correspond to, ensure identity privacy among the micro-grids of the transaction, and ensure fairness of the transaction.
(5) Preferably, the objective function of the first sub-model of the invention further comprises a cost C for minimizing power fluctuation of the distribution network F And the limitation of power fluctuation is considered in the cost function of the power distribution network, so that the safety of a power system can be ensured.
In summary, the method of the invention can solve the problem of decision coupling between energy scheduling and transaction, and improve the accuracy of energy scheduling and energy transaction decisions; intermittent and uncertain renewable energy sources can be dealt with, and the requirement of real-time decision scheduling is met; meanwhile, the energy privacy of the micro-grid and the identity information privacy of the micro-grid can be protected, the pareto frontier is realized, the benefits of all the micro-grids are ensured, and the fairness of transactions is ensured by combining a P2P transaction mechanism.
Drawings
Fig. 1 is a flow chart of an energy scheduling and trading method of the interconnected micro-grid system of the present invention.
Fig. 2 is a schematic diagram of an interconnected micro-grid system of the present invention.
Fig. 3 is a schematic diagram of a nash negotiation game in the P2P market according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
In the present invention, the terms "first," "second," and the like in the description and in the drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
As shown in fig. 1 and 2, the energy scheduling and trading method of the interconnected micro-grid system according to the present invention, wherein the interconnected micro-grid system includes N micro-grids that operate independently, each micro-grid is connected to a large distribution network and exchanges energy through a bus, each micro-grid includes a homogeneous battery energy storage system, a renewable energy source, and a load, and the renewable energy source and the corresponding load of each micro-grid may be different due to geographical conditions, etc., and the method of the present invention includes:
Constructing an energy scheduling model of each micro-grid and an energy transaction model among the micro-grids;
introducing a power trade-offThe power trade amount->Indicate->The sum of the transaction power between the individual micro-grid and all other micro-grids and the transaction power between the nth micro-grid and the distribution network;
at the current scheduling timet, the power trade amount announced by the current micro-gridTrade with actual power>The method comprises the steps of taking consistency as constraint, decomposing an energy scheduling model of each micro-grid into two sub-models, wherein a decision variable of a first sub-model is battery charge and discharge power of a battery energy storage system of each micro-grid at the current scheduling time t->The objective function is to minimize the investment loss cost C of the battery energy storage system in the micro-grid n B，n Cost C of large power distribution network power fluctuation F ；
The decision variables of the second sub-model are the power sold or purchased by each micro-grid to the large distribution network at the current scheduling time tThe objective function is to minimize the transaction cost C between the micro-grid n and the large distribution network G,n ；
Under the constraint of the first sub-model optimizationThe decision variable of the energy trading model among the micro-grids is the power of trading the micro-grid n to the other micro-grid m at the current scheduling time t >Unit price of electricity traded between micro grid n and micro grid m +.>Power->The objective function is to maximize the distance each micro grid is fromThe method has the advantages that benefits obtained in energy transaction among micro-grids are obtained;
at the current scheduling time t, the power transaction amount corresponding to the energy transaction model among the first sub-model, the second sub-model and the micro-grid is usedAll are equal to be constraint (namely, the current micro-grid announced power trading amount is consistent with the actual power trading amount is taken as constraint), a first sub-model is solved, and the first sub-model is optimized>Solving an energy transaction model among micro-grids to obtain +.> Is->And optimally solving the energy transaction model among micro-gridsIs the optimal solution for the second sub-model.
Specifically, in the embodiment of the present invention, the current scheduling time is denoted as t, the scheduling interval is denoted as Δt, and the scheduling period isAnd sets T as the last scheduling instant.
If the energy trade among the micro-grids is not considered, the objective function of the energy scheduling model of each micro-grid is to minimize the system operation cost, wherein the operation cost of each micro-grid comprises the micro-gridsInvestment and damage costs of medium-cell energy storage systemBy C B，n Trade cost C of micro-grid n and large power distribution network G，n And cost C of large distribution network power fluctuation F . As shown in equations (1) - (5).
Wherein, the formula (1) is an objective function of an energy scheduling model of each micro-grid when the energy transaction among the micro-grids is not considered; decision variablesBattery energy storage system representing nth micro-grid at current scheduling time +.>The positive sign indicates discharge and the negative sign indicates charge; />A representation; />Representing the renewable energy source of the nth microgrid at the current scheduling instant +.>The total power provided; />And the n-th micro power grid is used for selling or purchasing power to the large power distribution network at the current scheduling time t, the selling of power to the power distribution network is positive, and the purchasing of power is negative.
Formula (2) is the battery energy storage system investment loss cost C of the micro-grid n (nth micro-grid) B，n Model, C, represents the investment cost of the battery energy storage system in the nth microgrid,modeling the throughput of the battery energy storage system in the nth microgrid as shown in equation (3).
In the formula (3), the amino acid sequence of the compound,the SOC and the K represent the number of times of service life of the battery energy storage system in the nth micro-grid when the SOC approaches 1, ρ represents an empirical parameter, and CaV represents the capacity of the battery energy storage system in the nth micro-grid.
The (4) is a transaction cost model of the micro-grid n and the large power distribution network,and->And respectively obtaining the time-sharing buying and selling prices of the large distribution network at the current scheduling time t.
In order to ensure the safety of the power system, the practical situation requires that the power fluctuation of the power distribution network in adjacent time is maintained within a certain range. Therefore, the limitation of the power fluctuation is considered in the cost function of the power distribution network according to the present invention, and is expressed as the expression (5). Wherein the coefficient β represents the tolerance of the distribution network to power fluctuations.
Irrespective of the energy trade between the micro-grids, constraint conditions of the energy scheduling model of each micro-grid are as shown in the following formulas (6) - (10):
wherein, the formulas (6) and (7) are constraint conditions of battery charge and discharge power and state of charge in the micro-grid n.
Equation (8) is a dynamic equation of the state of charge of the battery in the micro-grid n at the current scheduling time t, wherein sigma and eta are the self-discharge rate and the charge-discharge efficiency of the battery respectively,and->The maximum charge and discharge power of the capacity of the battery energy storage system, respectively.
Equation (9) is the total power provided by the photovoltaic and wind (renewable energy) in the micro grid n at the current scheduling time tIs (are) restricted by>Is the sum of the actual power generation of the renewable energy sources.
Equation (10) is the power balance equation of the micro grid n during energy scheduling,is the total power of the loads in the micro-grid n.
Based on the above, and in order to maximize economic utilization of energy, the interconnected micro-grid system allows energy trading between micro-grids. Through energy trading, the difference between renewable energy sources and load distribution in micro-grids at different geographic locations can be utilized, so that the maximization of economic benefit is achieved.
Assume thatRepresenting the power traded at the current scheduling instant t on a micro grid n to another micro grid m, wherein +.>When->If yes, the micro-grid n sells electricity to the micro-grid m; when->When the energy transaction model is negative, the micro power grids n buy electricity to the micro power grids m, and each micro power grid n can only buy or sell energy at the current scheduling time t, and the energy transaction model is as follows:
wherein,representing a microgrid n-way microgrid m 1 Buying or selling electricity, +.>Representing a microgrid n-way microgrid m 2 Buying or selling electricity; considering that there is a loss in electrical energy when transferring between different micro-grids, therefore +.>Not equal toBut is related to the network loss function q (x; n, m), 0.ltoreq.q (x). Ltoreq.x, as shown in the following formula (12)>The power at which the energy transaction is carried out from the micro grid m to the micro grid n at the current scheduling instant t is represented. Therefore, after the energy transaction between the micro-grids is added, the power balance formula of the micro-grid n is rewritten by formula (10) as formula (13):
The energy transaction is considered herein to be a single-step game, usingRepresenting the unit price of electricity traded between the current scheduling instant t, micro grid n and micro grid m,/->The price per unit electricity price representing the trade between the micro-grids m and n is considered symmetrical:
then, at the current scheduling time t, the gain of the micro-grid n from the energy transaction between the micro-gridsThe method comprises the following steps:
is provided withRepresenting the objective function C after replacing the power balance constraint (10) with equation (13) O Regarding the single-step energy trading game as a multi-objective cooperative game, the objective function of the energy trading model between micro-grids is to maximize the gain of each micro-grid from the energy trading among other micro-grids, the constraint condition is formula (11) -formula (15), and the modeling is as shown in the following formula (17):
is provided withIs the pareto optimal solution of formula (17). After completion of the energy transaction, the ∈>Becomes the relaxation amount of constraint formula (13). The energy scheduling model after considering the energy transaction is:
wherein C is O′ An objective function is represented that accounts for energy scheduling issues for energy transactions.
It can be seen that, since the power balance formula (13) of the micro grid n highly couples the energy scheduling model formula (18) and the energy trading model formula (17), the optimal solution of the energy scheduling model is correlated with the pareto solution of the energy trading model, which in turn depends on the optimal solution of the energy scheduling model. Specifically, in the formula (17), when the energy trading model between the micro-grids is solved, the energy scheduling model of each micro-grid needs to be solved on the premise of optimizing; in the formula (18), when the energy scheduling model of each micro-grid is solved, the energy scheduling model between the micro-grids needs to be solved on the premise of optimizing the energy transaction model, and the decision variables of the two models are mutually coupled.
In the invention, the marginal cost of the renewable energy source is considered to be 0, so if the surplus renewable energy source can charge the battery or sell the rest of micro-grids to obtain benefits, the optimal total power provided by the renewable energy source of the nth micro-grid at the current dispatching moment t can be obtainedEquivalent to the power generated by renewable energy sources, namely:
taking the trade amount announced by the current micro-grid and the actual trade amount as constraints, and introducing the power trade amount at the current scheduling time tThe variable represents the sum of the trade power between the nth micro-grid and all other micro-grids and the trade power between the nth micro-grid and the distribution grid, that is:
after introducing additional power variables (power trade-off) Then, the energy scheduling model of the micro-grid can be decomposed into two sub-models, wherein the first sub-model is used for scheduling the battery charge and discharge power of each micro-grid battery energy storage system; the second sub-model is used to schedule power between the micro-grid and the distribution grid.
That is, the first sub-model is a univariate optimization problem that ignores the cost between the micro-grids and the distribution grid, to charge and discharge the battery of the battery energy storage system of each micro-grid at the current scheduling time t For decision variables to minimize each micro-grid +.>Investment loss cost C of medium-cell energy storage system B，n Cost C of large power distribution network power fluctuation F Is an objective function; the second sub-model then focuses on this specific cost, with the power that each micro-grid sells or purchases to the large distribution network at the current scheduling instant t +.>To minimize each micro-grid for decision variablesTrade cost C with large distribution network G，n Is an objective function.
On the premise that the first sub-model is optimal (namely at the current scheduling time t, the battery charge and discharge power of the battery energy storage system of each micro-gridIs->) The decision variables of the corresponding energy transaction model among the micro-grids comprise: the power sold or purchased by each micro-grid to the large distribution network at the current scheduling time t>The power of the micro-grid n trading against the other micro-grid m at the current scheduling instant t is +.>And the unit price of electricity traded between the current scheduling instant t microgrid n and the microgrid m +.>The objective function is to maximize the revenue each micro grid receives from energy transactions between other micro grids.
Wherein the constraint conditions of the first sub-model, the second sub-model and the energy trading model among the micro-grids further comprise the power trading amount Battery charge-discharge power +.>Renewable energy generated power +.>And the total power of the loads in the microgrid n +.>And the power balance constraint satisfied therebetween.
Specifically, the first sub-model is:
the second sub-model is:
the energy transaction model among the micro-grids at this time is as follows:
further, for the solution of the first sub-model corresponding to the formula (21), the first sub-model is converted into a decentralised locally observable Markov decision process (Dec-POMDP) and the solution is performed by reinforcement learning. Specifically, each micro grid has its private power information as its independent observationAnd transaction energy between micro-grids and between the micro-grids and the power distribution network is used as global communication state quantity s t Wherein, the model of the observation quantity and the state quantity is as follows:
wherein,representation->Or->And the time-sharing electricity buying price or the electricity selling price of the large distribution network at the current scheduling time t respectively.
The joint action at the current scheduling time t is the charge and discharge power of each battery energy storage system in the interconnected micro-grid at the time:
the objective of reinforcement learning is to maximize the prize value, in the present invention the prize function is the inverse of the minimization loss function (objective function), namely:
the power generated by the renewable energy source can be influenced by the randomness and intermittence of the renewable energy source and the load And total load power->The power generated by the renewable energy source in the present invention +.>And total load power->The historical track tau of the system is sampled, renewable energy sources and load power with high randomness are modeled as random variables with normal distribution, and the renewable energy sources and load power states at the next moment are randomly generated according to the random variables, and the method is specifically expressed as follows:
wherein Δζ PV Represents the normal distribution error delta zeta of photovoltaic power generation WT Representing the normal distribution error of wind power generation, delta zeta L A normal distribution error representing the total power of the load in the micro-grid n, Δζ being a normal distribution error of the corresponding sampled data for reflecting the possibility ofThe randomness of the renewable energy source and the load power is respectively delta zeta PV 、Δξ WT Or Δζ L ；N(μ ξ ，σ ξ ) Mean and variance are shown as mu ξ Sum sigma ξ Is taken according to experience;and->Respectively represent the average value of the corresponding sampled data, i.e +.>Power generated for renewable energy sources +.>Average value of the historical track tau sampled data, +.>Indicating total load power->An average of the historical trace τ sampled data. />
Thus, the present invention solves a first sub-model based on multi-agent reinforcement learning, comprising:
s1, constructing an agent for each micro-grid n; the input of the intelligent agent is independent observation quantity Action u of agent output t Charging and discharging power of each battery energy storage system in the interconnected micro-grid at the current scheduling time t;
s2, inputting the independent observation quantity of the current scheduling time t into the intelligent agent to obtain a corresponding output action u t ；
Action u according to current scheduling time t t And the state of charge of the battery in the micro grid n at the current scheduling instant tThe dynamic equation obtains the state variable of the next scheduling moment
Power generated by renewable energy source at current scheduling time tTotal power of load->Modeling as a normally distributed random variable, obtaining a state variable of the next scheduling moment according to the randomness of the variable>
The state variable of the next moment is obtained through time-sharing buying and selling electricity price
At the same time, according to the action u of the intelligent agent output at the current scheduling time t t Calculating a prize r t The method comprises the steps of carrying out a first treatment on the surface of the With rewards r t Updating parameters of the Q network in the corresponding agent;
s3, the state variable of the next scheduling time Is->And outputting the action of the next scheduling moment in the intelligent agent, performing the training and learning of the next round until the preset training round is reached or the loss of the intelligent agent converges, and using the trained intelligent agent for the charge and discharge power scheduling of the actual energy storage system.
Specifically, in the embodiment of the invention, the intelligent agent is trained (solved) by adopting a deep Q learning algorithm; and S2, selecting an action corresponding to the Q value by adopting an E-greedy method as an action output at the current scheduling time t.
Specifically, in the process of calculating the Q value, in order to realize privacy protection of power scheduling information among various intelligent agents, independent observation amounts are usedQ value approximates global communication state quantity s t Is a Q value of (C).
In practical application, the privacy power information of each micro-grid is used as an independent observation amount to be input into a trained intelligent body, and the intelligent body outputs an optimal economic dispatching power strategy adopted by a battery energy storage system in each micro-grid, namely optimal battery charging and discharging power
Specifically, as shown in fig. 3, the invention adopts Nash negotiation game based on P2P to solve the energy trading model between micro grids corresponding to the formula (23), and the maximization of the overall benefit between the micro grids traded by P2P trading is realized, and meanwhile, the dependence on a power distribution network is reduced. The method specifically comprises the following steps:
sep1, at the current scheduling time t, each micro-grid agent trades the amount according to the corresponding powerThe positive and negative registration of (1) is the seller i (n- > i) or the buyer j of the P2P market, and independent identity numbers are distributed to the seller i and the buyer j so as to realize anonymity of the seller and the buyer and ensure privacy protection; wherein, let micro grid n be in the current scheduling time t +.>If the value is positive, the micro-grid is seller i (n- > i), otherwise, the micro-grid is buyer j; use- >And->Representing the seller and the buyer, respectively. Wherein, on the premise of optimizing the first sub-model, according to the power trade of each micro-grid and the optimal battery charge and discharge power +.>Renewable energy generated power +.>And the total power of the loads in the microgrid n +.>The power balance constraint satisfied between the two can obtain the power trade amount of each micro-grid>
The Sep2 and P2P market running system collects the information of the purchasers and the purchasers claiming transaction energy power so as to establish communication between the sellers and the purchasers; wherein, at the current scheduling time t, the sellerThe power available is +.>Buyer's sideThe required power is +.>
Sep3, seller and buyer participate in P2P-based Nash negotiation game to obtain transaction amount of energy output by all micro-grids serving as sellers to micro-grids serving as buyers at current scheduling time tAnd unit price of electricity for all seller transactions +.>Wherein P is i，j，t That is to say that the optimum power of the microgrid n for a trade to the other microgrid m at the current scheduling instant t is +.>π i，t That is to say the optimal trade unit price of electricity +.>
The Sep4 and P2P market operation system receives the agreed trade priceAnd Power->And executing the energy transaction.
Specifically, in Sep3, the seller and the buyer participate in the nash negotiation game based on P2P, and the nash negotiation game model NBP is constructed as follows:
wherein P' i，j，t Representing the current scheduling time t, the transaction amount of the energy received by the buyer,representing the current scheduling time t, the electric energy sold by seller i to a large distribution network, < >>And the current dispatching time t is represented, and the buyer j purchases electric energy from a large power distribution network.
The energy transaction benefits of seller i and buyer j are represented asAndthe objective function represents that the profit product is maximized for both the buyer and the seller.
In the embodiment of the invention, the model is solved by adopting a sequential least squares programming algorithm to obtain the trading volume of the energy which is output to the micro-grid serving as the buyer by the micro-grid serving as the seller at the current scheduling time tUnit price of electricity for all seller transactions>
Since the total supply is not always equal to the total demand during the transaction, the redundant energy of each micro-grid will be sold to the large distribution network, and the insufficient energy will be purchased from the large distribution network to solve for P i，j，t Based on the power tradeThe satisfied relation is calculated to obtain the optimal power for selling or purchasing to the large distribution network at the current scheduling time t>
When the network transmission loss is small (the network transmission loss is smaller than the set threshold), the optimal solution obtained by NBP (optimal power sold or purchased to the large distribution network at the current scheduling time t) ) An approximately optimal value of the second sub-model (optimal power sold or purchased to the large distribution network at the current scheduling instant t +.>). In the ideal case that the network transmission loss is 0, the optimal solution through NBP is the optimal solution of the second sub-model; wherein the set threshold is selected empirically.
The energy scheduling and trading method of the interconnected micro-grid system of the invention trades power(for characterizing the power that each microgrid is able to provide for a trade) is introduced as an integral variable, at the current scheduling instant t, with the power trade declared by the current microgrid +.>Trade with actual power>The method comprises the steps of taking coincidence as constraint, decomposing an energy scheduling model of each micro-grid into two sub-models, wherein the first sub-model ignores the cost between the micro-grid and a power distribution network, and taking the battery charge and discharge power of a battery energy storage system of each micro-grid at the current scheduling time t as +.>The decision variables are made as univariate optimization problems, while the second sub-model focuses on the costs between the micro-grids and the distribution network, the decision variables are the power that each micro-grid sells or purchases to the large distribution network at the current scheduling instant t ∈ ->On the premise that the first sub-model is optimal, the decision variable of the energy trading model among the micro-grids is the power +.of the micro-grid n trading to the other micro-grid m at the current scheduling time t >Unit price of electricity traded between micro grid n and micro grid m +.>Power->
Under the constraint, fraud is forbidden in the transaction, and the first sub-model, the second sub-model and the power transaction amount corresponding to the energy transaction model among the micro-gridsAll are equal, at the moment, no coupling relation exists between the first sub-model and the decision variables of the energy transaction model between the micro-grids, so that decoupling of one sub-model in the energy scheduling model of each micro-grid and the energy transaction model between the micro-grids is realized, the first sub-model is solved first, and the conditions of optimizing the first sub-model are satisfied>Solving an energy trading model among the micro-grids, and enabling one of the optimal solutions of the energy trading model among the micro-grids (optimal power of selling or buying of each micro-grid to a large distribution network at the current scheduling time t +.>) As an optimal solution for the second sub-model, to achieve energy scheduling and trading of the interconnected micro-grid system. According to the energy scheduling and trading method, in the modeling process, the problem of autonomous energy scheduling in the independent micro-grids and the problem of energy trading in the cooperative cooperation among the micro-grids are considered, so that the decision accuracy is improved.
Further, the method of the invention converts the first sub-model into a decentralised locally observable Markov decision process when solving the first sub-model, takes the privacy electric power information of each micro-grid as the independent observation quantity as the input of the intelligent agent, and the intelligent agent corresponding to each micro-grid does not know the power adjustment quantity of other micro-grids in the energy scheduling process, thereby The privacy protection of the power information among the micro-grids is realized, and the intelligent agent is trained and solved by reinforcement learning, so that the calculation efficiency can be improved. At the same time, during the training process, power is generated by renewable energy sourcesAnd total load power->The historical track tau of the renewable energy source and the load power with high randomness are sampled, the renewable energy source and the load power with high randomness are modeled as random variables with normal distribution, the renewable energy source and the load power state at the next moment are randomly generated according to the random variables, so that the intermittence and the uncertainty of the renewable energy source are dealt with, the characteristics of the renewable energy source and the randomness of the load power are more met, the modeling is more accurate, and the decision accuracy is further improved; moreover, the trained intelligent agent can realize quick solution by adopting a reinforcement learning mode, so that the requirement of real-time decision scheduling is met, and the decision efficiency is improved.
Further, in the method, when the energy scheduling model of the micro-grids is solved, each micro-grid drives trading according to economic benefits and according to the power trading amountThe identity and the number of the micro-grid serving as a buyer and the micro-grid serving as a seller are given positively and negatively, and only the P2P agent is told about the amount of power required to be traded in the trading process >And whether the seller or the buyer can be the seller or the buyer, both parties conduct optimal transaction pricing and energy transaction decision (realizing pareto frontier) based on Nash negotiation game, and the both parties of the transaction do not know which micro-grid the seller and the buyer correspond to, ensure identity privacy among the micro-grids of the transaction, and ensure fairness of the transaction.
The invention also provides an energy scheduling and trading system of the interconnected micro-grid system, which comprises a computer readable storage medium and a processor;
the computer-readable storage medium is for storing executable instructions;
the processor is configured to read executable instructions stored in the computer readable storage medium to execute steps corresponding to the energy scheduling and transaction method of the interconnected micro grid system in the above embodiment.
The present invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps corresponding to the energy scheduling and transaction method of the interconnected micro grid system as in the above embodiments.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (10)

1. An energy scheduling and trading method of an interconnected micro-grid system, the interconnected micro-grid system comprising N independently operated micro-grids, each micro-grid being connected to a distribution network, each micro-grid comprising a battery energy storage system, a renewable energy source and a load, the method comprising:
constructing an energy scheduling model of each micro-grid and an energy transaction model among the micro-grids;
introducing a power trade-offSaid power trade amount->Representing the sum of the transaction power between the current micro-grid n and the other micro-grids and distribution network, +.>t is the current scheduling time;
power announced with micro grid nTransaction amountTrade with actual power>The consistency is taken as constraint, and the energy scheduling model is divided into two sub-models; the decision variable of the first sub-model is battery charge and discharge power of the micro-grid n-battery energy storage system +.>The objective function is to minimize the investment loss cost C of the micro-grid n-cell energy storage system B，n The method comprises the steps of carrying out a first treatment on the surface of the The second sub-model decision variable is the power sold or purchased by the micro-grid n to the distribution network +.>The objective function is to minimize the transaction cost C between the micro-grid n and the distribution network G，n ；
Solving the first sub-model, and under the condition that the first sub-model obtains the optimal solution constraint, determining the decision variable of the energy trading model as the power traded between the micro-grid n and the micro-grid m Unit price of electricity traded between micro grid n and micro grid m +.>Said power +.>
Solving the energy transaction model, and solving the optimal solution of the energy transaction modelAs an optimal solution for the second sub-model,wherein (1)>Optimal power for the micro grid n to sell or purchase to the distribution grid.
2. The method of claim 1, wherein solving the first sub-model comprises:
s1, constructing an agent for each micro-grid n; at the current scheduling time t, the input of the intelligent agent is an independent observation quantityAction u of the agent output t Charging and discharging power for batteries of each battery energy storage system in the interconnected micro-grid; wherein the independent observation amount +.>The state variables of (a) include: />Is->Power generated for renewable energy sources in micro-grid n,/->For the total power of the loads in the microgrid n, +.>For the state of charge of the battery energy storage system in the micro grid n +.>The time-sharing electricity buying price or the electricity selling price of the power distribution network;
s2, inputting independent observation quantity of current scheduling time t into the intelligent systemA body obtaining a motion u corresponding to the output t The method comprises the steps of carrying out a first treatment on the surface of the According to the action u t Calculating a prize r t With said rewards r t Updating parameters of the corresponding agent; wherein the reward r t Contrary to the objective function of the first sub-model;
and calculates the state variable of the next scheduling timeIs->At the same time, the state variable +.>And +.>Modeling to form a normal distribution random variable, and obtaining a state variable of the next scheduling moment according to the randomness of the variable
S3, the state variable of the next scheduling timeIs->And (3) inputting the parameters into the intelligent agent after parameter updating, performing training and learning of the next round until the preset training round is reached or the loss of the intelligent agent converges, and using the trained intelligent agent for battery charge and discharge power scheduling of each actual micro-grid battery energy storage system.
3. The method according to claim 2, characterized in that at the current scheduling instant t, according to said action u t Battery in micro grid nThe state of charge dynamic equation of the battery of the energy storage system obtains the state variable of the next moment
The state variable of the next moment is obtained through time-sharing buying and selling electricity price
4. A method according to claim 2 or 3, wherein the agent is trained using a deep Q learning algorithm;
s2, selecting an action corresponding to the Q value as an action u output at the current scheduling time t by adopting an E-greedy method t 。
5. The method of claim 1, wherein the objective function of the first sub-model further comprises a cost C that minimizes power fluctuation of the distribution network F 。
6. The method of claim 1, wherein constraints of the first sub-model, the second sub-model, and the energy trading model further comprise:
at the current scheduling moment, the optimal total power provided by the renewable energy sources in the micro-grid n is the power generated by the renewable energy sources
The power trade amountThe battery charge and discharge power->The renewable energy source generates power +.>And the total power of the load->And satisfies the power balance constraint.
7. A method according to any one of claims 1-3, wherein solving the energy transaction model comprises:
sep1, at the current scheduling time t, according to the corresponding power transaction amount of each micro-gridIs registered as a seller i or a buyer j in the P2P market operation system, and independent identity numbers are distributed for the seller i and the buyer j; wherein,and->Representing the seller and the buyer, respectively;
the Sep2 and P2P market running system collects the purported transaction power amount of the seller and the buyer so as to establish communication between the seller and the buyer; wherein, at the current scheduling time t, the sellerThe power available is +.>Buyer->The required power is +.>
Sep3, seller and buyer participate in P2P-based Nash negotiation game to obtain transaction amount of energy output by all micro-grids serving as sellers to micro-grids serving as buyers at current scheduling time t And unit price of electricity for all seller transactions +.>Wherein P is i，j，t Optimal power for a trade between microgrid n and microgrid m>π i，t For an optimal trade unit price of electricity between microgrid n and microgrid m>Obtaining optimal power sold or purchased by the micro-grid n to the distribution network according to the power balance constraint>
And the Sep4 and P2P market operation systems execute energy trading according to the agreed trading price and power.
8. The method of claim 7, wherein in Sep3, the sellers and buyers participate in the P2P-based nash negotiation game, comprising:
the seller and the buyer construct a Nash negotiation game model through P2P-based Nash negotiation game;
solving the Nash negotiation game model by adopting a sequential least square programming algorithm to obtain the trading volume of all the micro-grids serving as sellers at the current scheduling time t and outputting the micro-grids serving as buyers to the energy sources of the micro-grids serving as buyersAnd unit price of electricity for all seller transactions +.>
Wherein, the Nash negotiation game model is as follows:
wherein,and->The time-sharing electricity buying price and the electricity selling price of the distribution network are respectively; p'. i，j，t Transaction amount of power received for buyer +.>Representing the amount of electric energy sold by seller i to the distribution network, < >>Representing the energy purchased by buyer j from the distribution network; q (·) is the network loss function.
9. An energy scheduling and trading system for interconnecting micro-grid systems, comprising a computer readable storage medium and a processor;
the computer-readable storage medium is for storing executable instructions;
the processor is configured to read executable instructions stored in the computer readable storage medium to perform the method of any one of claims 1-8.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-8.
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