CN114066082B - Power scheduling optimization method, electronic equipment and computer readable storage medium - Google Patents

Power scheduling optimization method, electronic equipment and computer readable storage medium Download PDF

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CN114066082B
CN114066082B CN202111397063.1A CN202111397063A CN114066082B CN 114066082 B CN114066082 B CN 114066082B CN 202111397063 A CN202111397063 A CN 202111397063A CN 114066082 B CN114066082 B CN 114066082B
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heat pump
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胡平
韩璟琳
赵辉
陈志永
张菁
李铁良
宋航程
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State Grid Corp of China SGCC
State Grid Hebei Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Hebei Electric Power Co Ltd
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State Grid Hebei Electric Power Co Ltd
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Abstract

The application is applicable to the technical field of power dispatching and provides a power dispatching optimization method, electronic equipment and a computer readable storage medium. The power dispatching optimization method comprises the following steps: acquiring an optimal scheduling scheme in future preset time of a micro-grid cluster and a heat pump cluster; carrying out tide calculation, and when the power is over the limit, initiating auxiliary service market bidding; and determining a market clearing result according to the auxiliary service price and the corresponding power, and determining an auxiliary service quotation strategy of the micro-grid cluster and the heat pump cluster and an interaction scheme in a future preset time through an auxiliary service bidding model. The optimal strategy can be found through limited times of iteration only by relying on the clear results of the auxiliary service market, the problem of short-time capacity deficiency of the power distribution network in the 'coal-to-electricity' area can be effectively solved, and technical support is provided for the promotion of the 'coal-to-electricity' engineering.

Description

Power scheduling optimization method, electronic equipment and computer readable storage medium
Technical Field
The application belongs to the technical field of power dispatching, and particularly relates to a power dispatching optimization method, a power dispatching optimization device and electronic equipment.
Background
Along with the continuous promotion of the 'coal to electricity' engineering, the electric power demand is increased rapidly, the peak-valley difference of the operation of the power grid is increased, and further the stability, the economy and the like of the operation of the power grid face a serious challenge. Especially for rural area distribution network, be in the distribution network end, grid structure is weak, often has the problem such as low, the electric energy quality is relatively poor, the power supply capacity is not enough that forms because of the seasonal load of fluctuation. The sudden load increase brought by the coal-to-electricity engineering further worsens the operation condition of the power distribution network in rural areas, and threatens the economical and reliable operation of the terminal power distribution network.
By excavating the resource endowment of renewable energy sources in rural areas, a novel rural micro-grid cluster power system based on high-proportion renewable energy sources is constructed, meanwhile, the flexible adjustment capability of heat pump loads is fully utilized, the problem that the capacity of a short-term circuit of a power distribution network is out of limit in a heating season due to 'coal power conversion' can be effectively solved, and the capacity expansion investment of the power distribution network is avoided. However, the existing power distribution network dispatching system lacks consideration of providing demand response capability for micro-grid clusters and heat pump clusters and a coordinated interaction mechanism of the micro-grid clusters and the heat pump clusters, and does not consider the policy optimality of a game main body on a plurality of time scales in auxiliary service competition.
Disclosure of Invention
In order to overcome the problems in the related art, the embodiment of the application provides a power dispatching optimization method, a device and electronic equipment.
The application is realized by the following technical scheme:
in a first aspect, an embodiment of the present application provides a power scheduling optimization method, including: acquiring an optimal scheduling scheme within a future preset time sent by a micro-grid cluster and a heat pump cluster, wherein the optimal scheduling scheme is determined based on output and load demand prediction information of a daily renewable distributed power supply; the power distribution network carries out load flow calculation according to the optimized scheduling scheme, and if the power limit exceeding situation exists, auxiliary service market bidding is initiated; the power distribution network determines a market clearing result through a service market clearing model according to auxiliary service prices and corresponding power reported by the micro-grid and the heat pump clusters, wherein the market clearing result comprises the winning electricity quantity and electricity price of the micro-grid and the heat pump clusters; and determining auxiliary service quotation strategies of the micro-grid clusters and the heat pump clusters and interaction schemes in future preset time through an auxiliary service bidding model based on multi-agent matrix game according to the market clearing result.
According to the power dispatching optimization method, each intelligent agent does not need to know bidding strategies of other intelligent agents, and the optimal strategy can be found through limited iterations only by means of the clear results of the auxiliary service market. The method can effectively solve the problem of short-time capacity shortage of the power distribution network in the coal-to-electricity area, and provides technical support for the propulsion of the coal-to-electricity engineering.
In a second aspect, an embodiment of the present application provides a power scheduling optimization apparatus, including: the acquisition module is used for acquiring an optimal scheduling scheme within a future preset time sent by the micro-grid cluster and the heat pump cluster, and the optimal scheduling scheme is determined based on the output and load demand prediction information of the daily renewable distributed power supply; the power flow calculation module is used for carrying out power flow calculation according to the optimal scheduling scheme, and if the power limit exceeding situation exists, auxiliary service market bidding is initiated; the system comprises a clearing module, a service market clearing module and a service market clearing module, wherein the clearing module is used for determining a market clearing result according to auxiliary service prices and corresponding power reported by a micro-grid and a heat pump cluster through the service market clearing module, and the market clearing result comprises the winning electricity quantity and electricity price of the micro-grid and the heat pump cluster; and the optimization module is used for determining auxiliary service quotation strategies of the micro-grid and the heat pump clusters and interaction schemes in future preset time through an auxiliary service bidding model based on multi-agent matrix game according to the market clearing result.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the power scheduling optimization method according to any one of the first aspects when executing the computer program.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the power scheduling optimization method according to any one of the first aspects.
In a fifth aspect, embodiments of the present application provide a computer program product, which when run on an electronic device, causes the electronic device to perform the power scheduling optimization method of any one of the above first aspects.
It will be appreciated that the advantages of the second to fifth aspects may be found in the relevant description of the first aspect, and are not described here again.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required for the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an application scenario schematic diagram of a power dispatching optimization method according to an embodiment of the present application;
FIG. 2 is a flow chart of a power dispatching optimization method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a two-layer coordinated optimization model of a power dispatching optimization method according to an embodiment of the present application;
FIG. 4 is an example power distribution network topology diagram of a power scheduling optimization method provided by an embodiment of the present application;
FIG. 5 is an example tie-line power diagram of a power schedule optimization method provided by an embodiment of the present application;
FIG. 6 is an example trading electricity price graph of a power dispatching optimization method provided by an embodiment of the present application;
FIG. 7 is an exemplary solution time schematic of a power schedule optimization method provided by an embodiment of the present application;
FIG. 8 is an example micro grid 2 total transaction power graph of a power dispatching optimization method provided by an embodiment of the present application;
fig. 9 is an example microgrid 2 stored energy power graph of a power dispatching optimization method provided by an embodiment of the present application;
FIG. 10 is an example micro-grid 2 energy storage SOC plot of a power dispatching optimization method provided by an embodiment of the present application;
fig. 11 is a diagram of an example heat pump cluster 1 total power of a power scheduling optimization method provided in an embodiment of the present application;
Fig. 12 is a diagram of an example heat pump cluster 2 total power of a power scheduling optimization method provided in an embodiment of the present application;
fig. 13 is a diagram of an example heat pump cluster 3 total power of a power scheduling optimization method provided in an embodiment of the present application;
fig. 14 is a schematic structural diagram of a power dispatching optimization device provided in an embodiment of the present application;
fig. 15 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
Reference in the specification to "one embodiment" or "some embodiments" or the like means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," and the like in the specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments" unless expressly specified otherwise. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
Along with the continuous promotion of the 'coal to electricity' engineering, the electric power demand is increased rapidly, the peak-valley difference of the operation of the power grid is increased, and further the stability, the economy and the like of the operation of the power grid face a serious challenge. Especially for rural area distribution network, be in the distribution network end, grid structure is weak, often has the problem such as low, the electric energy quality is relatively poor, the power supply capacity is not enough that forms because of the seasonal load of fluctuation. The sudden load increase brought by the coal-to-electricity engineering further worsens the operation condition of the power distribution network in rural areas, and threatens the economical and reliable operation of the terminal power distribution network.
By excavating the resource endowment of renewable energy sources in rural areas, a novel rural micro-grid cluster power system based on high-proportion renewable energy sources is constructed, meanwhile, the flexible adjustment capability of heat pump loads is fully utilized, the problem that the capacity of a short-term circuit of a power distribution network is out of limit in a heating season due to 'coal power conversion' can be effectively solved, and the capacity expansion investment of the power distribution network is avoided. However, the existing power distribution network dispatching system lacks consideration of providing demand response capability for micro-grid clusters and heat pump clusters and a coordinated interaction mechanism of the micro-grid clusters and the heat pump clusters, and does not consider the policy optimality of a game main body on a plurality of time scales in auxiliary service competition.
Based on the above problems, the embodiments of the present application provide a power dispatching method, which establishes a double-layer optimized dispatching architecture of a power distribution network, a micro-grid group and a heat pump group, and proposes an intra-day optimization method of the micro-grid and the heat pump group and an auxiliary service clearing method of the power distribution network; and establishing an auxiliary service bidding model based on the multi-agent matrix game, and determining the optimal bidding strategy of each agent.
By way of example, the embodiments of the present application may be applied to an exemplary scenario as shown in fig. 1. Including the distribution network 10, the micro-grid 20, and the heat pump clusters 30. The economic dispatch of the power distribution network 10 forms a dispatching architecture upper layer, minimizes the operation cost according to the auxiliary service electricity price and the power reported by the micro-grid 20 and the heat pump cluster 30, and completes the auxiliary service clearing. The micro-grid 20 and the heat pump cluster 30 economic dispatch form a lower layer of a dispatching architecture, and the strategies of electricity price and power are reported to the power distribution network 10 so as to achieve higher benefits.
For example, the micro-grid 20 and the heat pump cluster 30 perform optimized scheduling based on the day-ahead electricity prices and the forecast information in the day-ahead, achieve the day-ahead profit maximization or the cost minimization, report the trade power to the power distribution network 10, and settle the day-ahead power by the power distribution network 10. The micro grid 20 and the heat pump cluster 30 optimize the scheduling of the next 4 hours based on the day-ahead trade power and the day-ahead electricity prices. The micro-grid 20 and the heat pump cluster 30 report daily transaction power to the power distribution network 10, the power distribution network 10 calculates power flow of the next 4 hours, if the link power of the next hour is out of limit, the power distribution network 10 issues information whether the link power of the next 4 hours is out of limit, and auxiliary service bidding of the next hour is started. The micro-grid 20 and the heat pump cluster 30 report the auxiliary service electricity price and the power, then the power distribution network 10 clears the auxiliary service electricity price and the power according to the optimal power flow model, the micro-grid 20 and the heat pump cluster 30 calculate the maximum income of 4 hours in the future according to the clearing result, and the strategy of reporting the electricity price and the power is updated according to the maximum income so as to achieve higher income. And reporting and clearing are circulated until the reporting strategy is not changed any more by the micro-grid 20 and the heat pump cluster 30.
In order to better understand the solution of the present invention by those skilled in the art, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to fig. 1, and it is obvious that the described embodiment is only a part of embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The power scheduling optimization method of the present application is described in detail below with reference to fig. 1.
Fig. 2 is a schematic flowchart of a power scheduling optimization method according to an embodiment of the present application, and referring to fig. 2, the power scheduling optimization method is described in detail as follows:
in step 101, an optimized scheduling scheme within a future preset time sent by the micro grid cluster and the heat pump cluster is obtained.
The optimal scheduling scheme is a double-layer optimal scheduling framework for establishing a power distribution network, a micro-grid group and a heat pump group. Illustratively, step 101 may include: constructing a double-layer coordination optimization model, dividing a micro-grid and a heat pump cluster into a lower-layer optimization framework, and dividing a power distribution network into an upper-layer optimization framework; and (3) establishing a lower multi-agent matrix game model to obtain an intra-day optimization model of the micro-grid and the heat pump cluster.
Wherein, the double-deck coordinated optimization model structure is: the lower layer is used for solving the economic scheduling problem of micro-grid and heat pump clusters, and the maximization of future preset time benefits is realized by optimizing the reported auxiliary service electricity price and power; the upper layer is used for solving the problem of economic dispatching of the power distribution network, minimizing the operation cost according to the electricity price and the power of the auxiliary service reported by the micro-grid and the heat pump clusters, and completing the market clearing of the auxiliary service.
Referring to fig. 3, a two-layer coordinated optimization model a constructed by the power distribution network 10, the micro-grid 20, and the heat pump clusters 30 is illustrated as an example. The economic schedule of the power distribution network 10 forms an upper-layer optimization a, the economic schedule of the micro-grid 20 and the heat pump cluster 30 forms a lower-layer optimization b, and the double-layer coordination optimization model A forms an optimization scheduling scheme.
In the embodiment, the daily electricity price is optimized for the micro-grid and the heat pump cluster through the daily optimization model, and daily transaction power is obtained according to daily transaction power. The intra-day optimization model includes: a micro-grid daily optimization model and a heat pump cluster daily optimization model.
Specifically, the daily optimization model comprises a daily optimization model of a micro-grid, and the objective function of the daily optimization model of the micro-grid is as follows:
wherein t is the tone The period of degrees, t' is the starting period where auxiliary services are needed, NT is the scheduling period, Δt is the time step, pi t The basic electricity price of the power distribution network in the period t;the method comprises the steps of purchasing electricity power for the micro-grid in the day before, wherein electricity selling is positive, and electricity purchasing is negative;and->The micro-grid is the incremental electricity selling and electricity purchasing power, k, of the electricity purchasing power in the period t in the day relative to the electricity purchasing power before the day ESS For unit charge-discharge cost of energy storage unit in micro-grid, < >>And->For discharging and charging power of energy storage unit in t period of day dis And eta ch The discharging efficiency and the charging efficiency;
the constraint conditions of the micro-grid daily optimization model comprise:
SOC min ≤SOC t ≤SOC max
wherein,and->Maximum discharging power and charging power of energy storage unit, B ESS,t The binary variable is a binary variable, the value is 1 during discharging, and the value is 0 during charging; />For the rated capacity of the energy storage unit, SOC min And SOC (System on chip) max Respectively represent the minimum value and the maximum value allowed by the energy storage SOC, < >>And->The SOC at the last time of energy storage and the SOC at the corresponding time of day before optimization are respectively +.>And->Representing maximum allowable selling power and purchasing power for micro-grid and distribution networkElectric power, B MG,t The binary variable is 1 when selling electricity, 0 when purchasing electricity and P RES,t And P L,t The output power and the load power of the renewable distributed power supply in the micro-grid at the time t are obtained.
Specifically, the intra-day optimization model comprises a heat pump cluster intra-day optimization model, and an objective function of the heat pump cluster intra-day optimization model is as follows:
wherein N is s Is the number of user types in the heat pump cluster, s represents a certain type of user in the heat pump cluster, N HP,s Representing the number of users of the class s heat pump;is the heat pump power planned in the future, +.>And->The power of the s-type heat pump users is reduced and increased in the t period in the day; lambda (lambda) T,t,s Is the user subsidy cost T of the unit deviation temperature of the time period T of the s-th heat pump user in,t,s And T exp,t,s Respectively representing the indoor temperature and the expected temperature of the s-th class user in the t period;
the constraint conditions of the heat pump cluster intra-day optimization model comprise:
T in,t,s ＝T in,t-1,s +[COP s ·P HP,t,s -(T in,t-1,s -T out,t-1,s )/R s ]·Δt/C s
wherein,and->Respectively representing negative deviation and positive deviation of the indoor temperature of the s-th user in the t period from the expected temperature of the user; b (B) T,t,s A value of 1 or 0, respectively, indicating that the indoor temperature is lower or higher than the expected temperature; />And->Respectively representing the maximum values of negative deviation and positive deviation of the temperature of the s-th class user in the t period; t (T) in,t,s And T out,t,s The indoor temperature and the outdoor temperature are respectively at the end of the t period of the s-th class user; p (P) HP,t,s Heat pump electric power, COP, for class s user t period s Heating energy efficiency ratio of the heat pump; r is R s And C s House thermal resistance and heat capacity, respectively; p (P) HP,t,s Is the actual heat pump power of the s-th class user t period; />Is rated power of the heat pump of the s-type user; b (B) HP,t,s The value of 1 or 0 respectively represents that the actual power of the heat pump is lower than or higher than the daily schedule;and->The temperature at the last day of optimization and the temperature at the corresponding day before optimization of the class s user are respectively.
In step 102, the power distribution network performs power flow calculation according to an optimized scheduling scheme; if the power out-of-limit condition exists, auxiliary service market bidding is initiated.
Specifically, the power distribution network carries out power flow calculation according to daily transaction power reported by the micro-grid and the heat pump clusters, if the power of the connecting line in the next preset time period is out of limit, the power distribution network issues information of the power limit of the connecting line in the preset time period in the future, the lower micro-grid and the heat pump clusters receive the information, and auxiliary service bidding in the next hour is started.
And the upper distribution network adopts tide calculation, and the tie line power of the next preset time period is obtained according to daily transaction power reported by the lower micro-grid and the heat pump clusters. If the power of the connecting line is out of limit, issuing information of the power out of limit of the connecting line in a preset time period to a lower layer; and if the power of the connecting line is not out of limit, ending the optimal scheduling process.
For example, a newton-raphson method may be selected for load flow calculation. According to the topological structure of the whole system of the power distribution network, namely the micro-grid and the heat pump cluster, on the basis of the daily transaction power of the micro-grid and the heat pump cluster obtained in the step 101, a sparse matrix technology and an optimized numbering technology are adopted, and the power distribution network obtains the link power of the next preset time period and judges according to the link power.
Optionally, when the power of the tie line exceeds the limit, auxiliary service bidding can be performed by solving an intra-day optimization model of the micro-grid and the heat pump cluster. The auxiliary service bidding strategy may include: and constructing a game model of the micro-grid and the heat pump cluster to obtain auxiliary service electricity prices and power reported by the micro-grid and the heat pump cluster in a preset period.
Solving a micro-grid intra-day optimization model through a micro-grid game model, wherein the actions of the micro-grid game model are expressed as follows:
wherein,and->Auxiliary service electricity price and power reported by micro-grid t' period respectively, andand->The method meets the following conditions:
wherein,to assist the upper limit of the electricity price, P RES,t′ And P L,t′ Renewable distributed power source output power and load power for t' period, SOC 0 Energy storage SOC representing the initial moment, +.>And->And (5) representing the selling power and purchasing power of the micro-grid to the power distribution network in the period t'.
Solving a heat pump cluster intra-day optimization model through a heat pump cluster game model, wherein the actions of the heat pump cluster game model are expressed as follows:
wherein,and->Auxiliary service electricity price and power reported by the heat pump cluster in t' period respectively, andand->The method meets the following conditions:
in step 103, the distribution network determines a market clearing result through a market clearing model according to the auxiliary service price and corresponding power reported by the micro-grid and the heat pump clusters.
Specifically, the power distribution network generates auxiliary service demands due to the out-of-limit condition of the power of the connecting lines, and auxiliary service clearing is performed according to the acquired auxiliary service prices and corresponding power of the micro-grid and the heat pump clusters in the next period by constructing a service market clearing model, so as to acquire auxiliary service clearing electricity price and power.
The objective function of the service market clearing model is as follows:
the constraint conditions of the service market clearing model are as follows:
PF k ＝PF ij ＝V i V j (G ij cosθ ij +B ij sinθ ij )-V i 2 G ij
V i min ≤V i ≤V i max
wherein ψ is MG And psi is HP Respectively representing a set of the micro-grid and the heat pump clusters, and m and h respectively represent numbers of the micro-grid and the heat pump clusters; pi is the basic trading electricity price of electricity, And->The auxiliary service electricity prices reported by the micro-grid m and the heat pump cluster h are respectively; p (P) DN Is the power purchased from the upper power grid, < +.>Is the upper tie line power limit; />And->The auxiliary services of the micro-grid m and the heat pump cluster h respectively output clear power, and (2)>And->Auxiliary service power reported by the micro-grid m and the heat pump cluster h respectively; i. j is the node number, PG i 、QG i 、PL i And QL i Respectively power supply and negativeActive and reactive power of the load, including the power that the micro-grid and heat pump clusters have traded before and during the day; v (V) i 、V i min And V i max The voltage at node i and the minimum and maximum values thereof, respectively; g ij 、B ij And theta ij Line conductance, susceptance and phase difference, respectively; k is the line number, PF k And->The power of line k and the upper power limit, respectively.
In step 104, according to the market clearing result, the auxiliary service bidding strategy of the micro-grid cluster and the heat pump cluster and the interaction scheme in the future preset time are determined through an auxiliary service bidding model based on the multi-agent matrix game.
Specifically, the micro-grid and the heat pump cluster are used for solving by adopting a distributed Wolf-PHC algorithm according to the acquired electricity price and power of auxiliary service by constructing an auxiliary service bidding model based on multi-agent matrix game, calculating the maximum income in a preset time period in the future, and continuously updating the auxiliary service price and reporting strategy until each agent does not change the bidding strategy any more, so that the market is balanced.
Optionally, a game rewarding model of the micro-grid and the heat pump cluster can be constructed through an intra-day game model of the micro-grid and the heat pump cluster by adopting a distributed Wolf-PHC algorithm, so that the maximum gain increment of the micro-grid and the heat pump cluster in a future preset time period obtained through auxiliary service is obtained, and the auxiliary service electricity price and the reporting strategy are further updated.
The rewards of the micro-grid game model are as follows:
wherein the rewarding value of the micro-grid agentNon-availability for micro-grid through auxiliary servicesCome N T Maximum gain increment of hour, reference value is objective function value of day optimization +.> Representing total income, pi, of auxiliary service moment provided by micro-grid t′ Basic electricity price of t' period, +.>Clearing electricity price for auxiliary service market, < ->Clear power for the micro-grid in the auxiliary service market; />Representing future N T Income, χ of the rest of the time period t The power distribution network is provided with a power out-of-limit warning according to a daily transaction result, the value is 1 or 0, and whether auxiliary service requirements possibly occur in a t period or not is indicated by the power distribution network, wherein the value is 1 or 0>Representing the desirability of the micro-grid agent to offer clear electricity price to the auxiliary service in the t period;including microgrid N T The corresponding daily transaction cost, energy storage scheduling cost and daily optimization cost in the time period relate to the power distribution network auxiliary service clear electricity price +. >And output clean powerIs a function of (2).
Wherein the constraint conditions of rewards of the micro-grid game model comprise:
SOC min ≤SOC t ≤SOC max
the rewards of the heat pump cluster game model are as follows:
wherein,future N obtained for heat pump clusters through auxiliary services T The maximum income increment of the hour is about the power distribution network auxiliary service clear electricity price +.>And clear power->Is the objective function value of the heat pump cluster optimization in the day +.> Is according to the total clear power of the heat pump cluster>Optimizing the auxiliary service power provided by the s-th class user; />And->Respectively representing the power reduced and increased in the time period t of the class s user; />And->Settlement is carried out after daily optimization; />Representing the desirability of the heat pump cluster agent to service the electricity clearing price for the auxiliary service in the t period.
The constraint conditions of the heat pump cluster game model comprise:
T in,t,s ＝T in,t-1,s +[COP s ·P HP,t,s -(T in,t-1,s -T out,t-1,s )/R s ]·Δt/C s
referring to fig. 4, based on the embodiment shown in fig. 2, the power scheduling optimization method is specifically applied to a case of a power distribution network in a certain actual rural area.
Parameters of the micro-grid and heat pump clusters are shown in table 1 micro-grid parameter table and table 2 heat pump cluster parameter table. The heat pump users are divided into two types, wherein type 1 is a family mainly comprising young people; type 2 is a household containing an elderly person or child, type 2 has a relatively higher desired temperature, a smaller temperature tolerance range and a higher subsidy price. The upper limit of the power of the connecting line between the power distribution network and the upper power network is 3800kW.
TABLE 1
TABLE 2
The tie line power after settlement before day, before auxiliary service in day and after auxiliary service clearing of the distribution network is shown in fig. 5. According to the daily transaction result, the tie line power is 4212kW and 4025kW respectively in 1h and 7h, the tie line power exceeds the upper limit of the tie line power, and the daily micro-grid and the heat pump cluster reduce or transfer power by providing auxiliary service, so that the power of the distribution network does not exceed the upper limit 3800kW; the link power for 2h and 8h is increased. As shown in fig. 6, the sum of the discharged electricity price and the base electricity price of the daily auxiliary service bidding game is higher than the purchase price of electricity in Yu Ri, so that the micro grid and the heat pump cluster can obtain benefits while providing auxiliary services.
The power distribution network does not need to know an optimization model and a reward function of the micro-grid and the heat pump cluster, the micro-grid and the heat pump cluster do not need to know the running condition of the power distribution network and the optimization model and strategies of other intelligent agents, and the power distribution network, the micro-grid and the heat pump cluster interact only through electricity price and power information, so that the optimal strategy is finally converged. The total solving time of the time-by-time rolling optimization is shown in fig. 7, and the time requirement of the daily optimization can be met. Wherein 1h and 7h converge after 91 iterations and 159 iterations, respectively.
Taking the micro-grid 2 as an example, the daily optimization plan, the total transaction power before daily auxiliary service and after auxiliary service clearing are shown in fig. 8, and the corresponding energy storage power and energy storage SOC are shown in fig. 9 and 10. The micro-grid 2 moves the 1h energy storage and charging plan to 2h, and 100kW auxiliary service is provided for the power distribution network, so that no extra energy storage cost is caused. The total power of the day-ahead optimization plan, day-ahead auxiliary service and auxiliary service after clearing for the 3 heat pump clusters is shown in fig. 11, 12 and 13: the heat pump clusters 1 and 2 provide auxiliary service in 1h, and meanwhile, the electricity purchasing power of 2h is improved; the heat pump cluster 3 provides auxiliary service in 7h, and meanwhile, the electricity purchasing power of 8h is improved.
According to the power dispatching method, a double-layer optimized dispatching framework of the power distribution network, the micro-grid group and the heat pump group is established, an intra-day optimization method of the micro-grid and the heat pump group and an auxiliary service clearing method of the power distribution network are provided, an auxiliary service bidding model based on multi-agent matrix game is established, and an optimal bidding strategy of each agent is determined. Under the interactive system, each agent does not need to know bidding strategies of other agents, and the optimal strategy can be found through limited iterations only by relying on the clear results of the auxiliary service market. The method can effectively solve the problem of short-time capacity shortage of the power distribution network in the coal-to-electricity area, and provides technical support for the propulsion of the coal-to-electricity engineering.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.
Corresponding to the power scheduling optimization method described in the above embodiments, fig. 14 shows a block diagram of the power scheduling optimization apparatus provided in the embodiment of the present application, and for convenience of explanation, only the portions related to the embodiment of the present application are shown.
Referring to fig. 14, the power scheduling optimization apparatus in the embodiment of the present application may include: an acquisition module 201, a tide calculation module 202, a clearing module 203 and an optimization module 204.
The obtaining module 201 is configured to obtain an optimal scheduling scheme within a future preset time sent by the micro-grid cluster and the heat pump cluster, where the optimal scheduling scheme is determined based on output and load demand prediction information of the renewable distributed power source in a day. And the power flow calculation module 202 is configured to perform power flow calculation according to the optimal scheduling scheme, and initiate an auxiliary service market bid if a power out-of-limit condition exists. And the clearing module 203 is configured to determine a market clearing result according to the auxiliary service price and the corresponding power reported by the micro-grid and the heat pump clusters through a service market clearing model, where the market clearing result includes the winning electricity quantity and the electricity price of the micro-grid and the heat pump clusters. And the optimizing module 204 is used for determining auxiliary service quotation strategies of the micro-grid and the heat pump clusters and interaction schemes in future preset time through an auxiliary service bidding model based on multi-agent matrix game according to the market clearing result.
It should be noted that, because the content of information interaction and execution process between the above devices/units is based on the same concept as the method embodiment of the present application, specific functions and technical effects thereof may be referred to in the method embodiment section, and will not be described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
The embodiment of the application also provides an electronic device, referring to fig. 15, the electronic device 300 may include: at least one processor 310, a memory 320, and a computer program stored in the memory 320 and executable on the at least one processor 310, the processor 310, when executing the computer program, performing steps in any of the various method embodiments described above, such as steps 102-104 in the embodiment shown in fig. 2.
By way of example, a computer program may be partitioned into one or more modules/units that are stored in memory 320 and executed by processor 310 to complete the present application. The one or more modules/units may be a series of computer program segments capable of performing the specified functions, which are used to describe the execution of the computer program in the electronic device 300.
It will be appreciated by those skilled in the art that fig. 15 is merely an example of an electronic device and is not meant to be limiting and may include more or fewer components than shown, or may combine certain components, or different components, such as input-output devices, network access devices, buses, etc.
The processor 310 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 320 may be an internal memory unit of the electronic device, or may be an external memory device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), or the like. The memory 320 is used to store the computer program as well as other programs and data required by the electronic device. The memory 320 may also be used to temporarily store data that has been output or is to be output.
The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or one type of bus.
The power dispatching optimization method provided by the embodiment of the application can be applied to electronic equipment in a power distribution network, wherein the electronic equipment can be electronic equipment such as a processor, a controller, a computer, a tablet personal computer, a notebook computer, a mobile phone and the like, and the embodiment of the application does not limit the specific type of the electronic equipment.
Electronic device embodiments also provide a computer readable storage medium storing a computer program which, when executed by a processor, implements steps for implementing the embodiments of the power scheduling optimization method described above.
Embodiments of the present application provide a computer program product that, when run on a mobile terminal, causes the mobile terminal to perform steps that may be implemented in the various embodiments of the power scheduling optimization method described above.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a camera device/electronic apparatus, a recording medium, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a U-disk, removable hard disk, magnetic or optical disk, etc. In some jurisdictions, computer readable media may not be electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other manners. For example, the apparatus/network device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims (5)

1. A power scheduling optimization method, comprising:
acquiring an optimal scheduling scheme within a future preset time sent by a micro-grid cluster and a heat pump cluster, wherein the optimal scheduling scheme is determined based on output and load demand prediction information of a daily renewable distributed power supply;
Carrying out load flow calculation according to the optimized scheduling scheme, and if the power limit-off condition exists, initiating auxiliary service market bidding;
determining a market clearing result through a service market clearing model according to auxiliary service prices and corresponding power reported by the micro-grid and the heat pump clusters, wherein the market clearing result comprises the winning electricity quantity and electricity price of the micro-grid and the heat pump clusters;
determining auxiliary service quotation strategies of the micro-grid clusters and the heat pump clusters and interaction schemes in future preset time through an auxiliary service bidding model based on multi-agent matrix games according to the market clearing results;
determining the optimal scheduling scheme through an intra-day optimization model, wherein the intra-day optimization model comprises a micro-grid intra-day optimization model, and the objective function of the micro-grid intra-day optimization model is as follows:
wherein t is a scheduling period, t' is a starting period requiring auxiliary service, NT is a scheduling period, Δt is a time step, pi t The basic electricity price of the power distribution network in the period t;the method comprises the steps of purchasing electricity power for the micro-grid in the day before, wherein electricity selling is positive, and electricity purchasing is negative; />Andthe micro-grid is the incremental electricity selling and electricity purchasing power, k, of the electricity purchasing power in the period t in the day relative to the electricity purchasing power before the day ESS For unit charge-discharge cost of energy storage unit in micro-grid, < >>And->For discharging and charging power of energy storage unit in t period of day dis And eta ch The discharging efficiency and the charging efficiency;
constraints of the daily optimization model of the micro-grid include:
SOC min ≤SOC t ≤SOC max
wherein,and->Maximum discharging power and charging power of energy storage unit, B ESS,t The binary variable is a binary variable, the value is 1 during discharging, and the value is 0 during charging; />For the rated capacity of the energy storage unit, SOC min And SOC (System on chip) max Respectively represent the minimum value and the maximum value allowed by the energy storage SOC, < >>And->The SOC at the last time of energy storage and the SOC at the corresponding time of day before optimization are respectively +.>And->Representing maximum allowable selling power and purchasing power of micro-grid and distribution network, B MG,t The binary variable is 1 when selling electricity, 0 when purchasing electricity and P RES,t And P L,t The output power and the load power of the renewable distributed power supply in the micro-grid at the time t are calculated;
assisting in serving market bidding through actions of the micro-grid game model;
the actions of the micro-grid game model are expressed as follows:
wherein,and->Auxiliary service electricity price and power reported by micro-grid t' period respectively, and +.>Andthe method meets the following conditions:
wherein,to assist the upper limit of the electricity price, P RES,t′ And P L,t′ Renewable distributed power source output power and load power for t' period, SOC 0 Energy storage SOC representing the initial moment, +.>And->The method comprises the steps of representing the selling power and purchasing power of a micro-grid to a power distribution network in a t' period;
the intra-day optimization model comprises a heat pump cluster intra-day optimization model, and an objective function of the heat pump cluster intra-day optimization model is as follows:
wherein N is s Is the number of user types in the heat pump cluster, s represents a certain type of user in the heat pump cluster, N HP,s Representing the number of users of the class s heat pump;is the heat pump power planned in the future, +.>And->The power of the s-type heat pump users is reduced and increased in the t period in the day; lambda (lambda) T,t,s Is the user subsidy cost T of the unit deviation temperature of the time period T of the s-th heat pump user in,t,s And T exp,t,s Respectively representing the indoor temperature and the expected temperature of the s-th class user in the t period;
constraint conditions of the heat pump cluster intra-day optimization model include:
T in,t,s ＝T in,t-1,s +[COP s ·P HP,t,s -(T in,t-1,s -T out,t-1,s )/R s ]·Δt/C s
wherein,and->Respectively representing negative deviation and positive deviation of the indoor temperature of the s-th user in the t period from the expected temperature of the user; b (B) T,t,s A value of 1 or 0, respectively, indicating that the indoor temperature is lower or higher than the expected temperature; />And->Respectively representing the maximum values of negative deviation and positive deviation of the temperature of the s-th class user in the t period; t (T) in,t,s And T out,t,s The indoor temperature and the outdoor temperature are respectively at the end of the t period of the s-th class user; p (P) HP,t,s Heat pump electric power, COP, for class s user t period s Heating energy efficiency ratio of the heat pump; r is R s And C s House thermal resistance and heat capacity, respectively; p (P) HP,t,s Is the actual heat pump power of the s-th class user t period; />Is rated power of the heat pump of the s-type user; b (B) HP,t,s The value of 1 or 0 respectively represents that the actual power of the heat pump is lower than or higher than the daily schedule; />And->The temperature of the day optimization last moment and the temperature of the day optimization corresponding moment of the s-th class user are respectively;
assisting in serving market bidding through actions of the heat pump cluster game model;
the actions of the heat pump cluster game model are expressed as follows:
wherein,and->Auxiliary service electricity price and power reported by the heat pump cluster in t' period respectively, and +.>Andthe method meets the following conditions:
the objective function of the service market clearing model is as follows:
the constraint conditions of the service market clearing model are as follows:
PF k ＝PF ij ＝V i V j (G ij cosθ ij +B ij sinθ ij )-V i 2 G ij
V i min ≤V i ≤V i max
wherein ψ is MG And psi is HP Respectively representing a set of the micro-grid and the heat pump clusters, and m and h respectively represent numbers of the micro-grid and the heat pump clusters; pi is the basic trading electricity price of electricity,and->The auxiliary service electricity prices reported by the micro-grid m and the heat pump cluster h are respectively; p (P) DN Is the power purchased from the upper power grid, < +.>Is the upper tie line power limit; />And->The auxiliary services of the micro-grid m and the heat pump cluster h respectively output clear power, and (2)>And->Auxiliary service power reported by the micro-grid m and the heat pump cluster h respectively; i. j is the node number, PG i 、QG i 、PL i And QL i Active and reactive power of the power supply and the load respectively, including the power traded by the micro-grid and the heat pump clusters before and during the day; v (V) i 、V i min And V i max The voltage at node i and the minimum and maximum values thereof, respectively; g ij 、B ij And theta ij Line conductance, susceptance and phase difference, respectively; k is the line number, PF k And PF (physical filter) k max The power of line k and the upper power limit respectively;
the power distribution network carries out power flow calculation according to an optimal scheduling scheme; if the power out-of-limit condition exists, initiating auxiliary service market bidding, including:
the power distribution network carries out power flow calculation according to daily transaction power reported by the micro-grid and the heat pump clusters, if the power of the connecting line in the next preset time period is out of limit, the power distribution network issues information of the power of the connecting line in the preset time period in the future, the lower micro-grid and the heat pump clusters receive the information, and auxiliary service bidding in the next hour is started;
the upper distribution network adopts tide calculation, and the tie line power of the next preset time period is obtained according to daily transaction power reported by the lower micro-grid and the heat pump clusters; if the power of the connecting line is out of limit, issuing information of the power out of limit of the connecting line in a preset time period to a lower layer; and if the power of the connecting line is not out of limit, ending the optimal scheduling process.
2. The power dispatching optimization method of claim 1, wherein the auxiliary service quotation strategy of the micro-grid group is determined by rewards of the micro-grid game model;
the rewards of the micro-grid game model are as follows:
wherein the micro-gridPrize value of agentFor future N obtained through auxiliary service T Maximum gain increment of hour, reference value is objective function value of day optimization +.> Representing total income, pi, of auxiliary service moment provided by micro-grid t′ Basic electricity price of t' period, +.>Clearing electricity price for auxiliary service market, < ->Clear power for the micro-grid in the auxiliary service market; />Representing future N T Income, χ of the rest of the time period t The power distribution network is provided with a power out-of-limit warning according to a daily transaction result, the value is 1 or 0, and whether auxiliary service requirements possibly occur in a t period or not is indicated by the power distribution network, wherein the value is 1 or 0>Representing the desirability of the micro-grid agent to offer clear electricity price to the auxiliary service in the t period;including microgrid N T The corresponding daily transaction cost, energy storage scheduling cost and daily optimization cost in the time period relate to the power distribution network auxiliary service clear electricity price +.>And output clean powerIs a function of (2);
constraints for rewards of the micro-grid gaming model include:
SOC min ≤SOC t ≤SOC max
3. The power scheduling optimization method of claim 1, wherein the auxiliary service quotation strategy of the heat pump cluster is determined by rewards of a heat pump cluster game model;
the rewards of the heat pump cluster game model are as follows:
wherein,for future N obtained through auxiliary service T The maximum income increment of the hour is about the power distribution network auxiliary service clear electricity price +.>And clear power->Is the objective function value of the heat pump cluster optimization in the day +.> Is according to the total clear power of the heat pump cluster>Optimizing the auxiliary service power provided by the s-th class user; />And->Respectively representing the power reduced and increased in the time period t of the class s user; />Andsettlement is carried out after daily optimization; />Representing heat pump cluster agent pair tThe period auxiliary service obtains the desirability of the clear electricity price;
the constraint conditions of the heat pump game model comprise:
T in,t,s ＝T in,t-1,s +[COP s ·P HP,t,s -(T in,t-1,s -T out,t-1,s )/R s ]·Δt/C s
4. an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 3 when executing the computer program.
5. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method according to any one of claims 1 to 3.
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