CN114123171A - Incremental power distribution network distributed optimization planning method and medium based on potential game - Google Patents

Incremental power distribution network distributed optimization planning method and medium based on potential game Download PDF

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CN114123171A
CN114123171A CN202111341010.8A CN202111341010A CN114123171A CN 114123171 A CN114123171 A CN 114123171A CN 202111341010 A CN202111341010 A CN 202111341010A CN 114123171 A CN114123171 A CN 114123171A
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distribution network
energy storage
power
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wind
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牛文娟
薛贵元
吴晨
杨颖�
王蓓蓓
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Southeast University
Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

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  • Supply And Distribution Of Alternating Current (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention discloses a distributed optimization planning method and medium for an incremental distribution network based on a potential game, wherein the distributed optimization planning method for the incremental distribution network comprises the following steps: constructing an incremental power distribution network model considering wind-solar energy storage; establishing optimization objective functions of all main bodies of the wind, light and storage; setting optimization constraints of the incremental power distribution network: constructing an incremental distribution network distributed optimization potential game model; an incremental power distribution network distributed potential game optimization planning model: and obtaining the optimal configuration scheme of the wind-solar energy storage. The planning method provided by the invention improves the overall economy and power supply reliability of the incremental power distribution network, guides the development and construction of the incremental power distribution network under the current situation, excites market activity, enhances market competitiveness and promotes the healthy development of the incremental power distribution network.

Description

Incremental power distribution network distributed optimization planning method and medium based on potential game
Technical Field
The invention relates to the technical field of power systems, in particular to a potential game-based incremental power distribution network distributed optimization planning method and medium.
Background
With the steady advance of the innovation of the power system, the incremental power distribution network is independently or jointly invested by social capital or power grid companies, the incremental power distribution network has higher autonomous planning and construction rights, and the large power grid ensures stable and reliable operation, so the pursuit of maximized investment return is a direct economic target for optimizing and planning. The wind-solar energy storage and wind-solar energy storage integrated system is applied to the incremental distribution network by paying attention to resource advantages and complementary characteristics of wind-solar energy storage and is very necessary to improve the overall economy and power supply reliability of the incremental distribution network. With diversification of investment subjects, the requirement of power distribution network planning on coordination is higher, the DG is incorporated into the incremental power distribution network, the trend direction is changed from one-way to two-way, factors such as an active management mode and the like are changed, so that the uncertainty factor of operation is increased, and certain safety and stability challenges are brought to the power selling company while the cost is reduced and the income is increased. Therefore, on the premise of ensuring the safe and stable operation of the system, a distributed optimization planning method suitable for the incremental power distribution network comprising wind, light and energy storage is needed to be researched so as to guide the development and construction of the incremental power distribution network under the current situation, stimulate market activity, enhance market competitiveness and promote the healthy development of the incremental power distribution network.
Disclosure of Invention
The invention aims to provide a distributed optimization planning method and medium for an incremental power distribution network based on a potential game, which fully consider the self-benefit and intelligence of a fan, a photovoltaic array and an energy storage device around the communication point of the incremental power distribution network and the potential game, map wind-solar storage in the incremental power distribution network into game participants, map a target function of the game participants into a revenue function of the game, map corresponding constraint conditions into a strategy space of the game, construct a potential game distributed optimization model, improve the overall economy and power supply reliability of the incremental power distribution network, guide the development and construction of the incremental power distribution network under the current situation, stimulate market activity, enhance market competitiveness and promote the healthy development of the incremental power distribution network.
The purpose of the invention can be realized by the following technical scheme:
an incremental distribution network distributed optimization planning method based on potential game comprises the following steps:
s1: constructing an incremental power distribution network model considering wind-solar energy storage: IEEE33 node distribution network structure, branch data information and node data information;
s2: based on an S1 incremental distribution network model, establishing an optimization objective function by respectively taking the maximization of the gains of all main bodies of wind, solar and photovoltaic storage as an objective;
s3: setting optimization constraints of the incremental power distribution network: inputting equipment investment, energy storage charge state, energy storage charge and discharge power, system power balance and node voltage constraint;
s4: integrating objective functions and constraint conditions of S2 and S3 to construct an incremental distribution network distributed optimization potential game model;
s5: solving the incremental power distribution network distributed potential game optimization planning model constructed by S4 by using a solver: and obtaining the optimal configuration scheme of the wind-solar energy storage.
Further, in S1, constructing an incremental distribution network model considering wind, photovoltaic and energy storage:
the IEEE33 node distribution network structure comprises a connection relation among nodes of an incremental distribution network; the branch data information specifically refers to the impedance of the network line; the node data information specifically includes the load size of each node of the incremental distribution network, the capacity of the DG device, and the installation position.
Further, in S2, establishing an optimization objective function of each main body of the wind, photovoltaic and energy storage system, the specific method includes:
wind and light are preferentially utilized to supply load power in an incremental power distribution network containing wind and light storage, an energy storage device is arranged in the incremental power distribution network to perform power smoothing, peak clipping and valley filling, load power utilization conditions are adjusted, and the whole safe and reliable operation of the system is ensured; incremental distribution network is put into operation and is built fan, photovoltaic array, energy memory, and its comprehensive cost includes that the scene stores up earlier stage construction input, fortune dimension cost, and the comprehensive profit includes the electricity selling profit to the user, the electricity generation subsidy profit that the country gave, surplus electricity income of surfing the net, then whole profit WZT objective function is as follows:
maxWZT＝Wwt+Wpv+Wba (1)
in the formula, Wwt、Wpv、WbaRespectively realizing the economic benefits of the fan, the photovoltaic array and the energy storage device;
respectively establishing an optimized objective function of the fan, the photovoltaic array and the energy storage device; the S2 specifically includes the following steps:
s21: comprehensively considering the income of power selling, subsidy income and surplus power on-line income W by using the fanS.wtEarly construction investment WI.wtAnd the operation and maintenance cost WOM.wtEstablishing an objective function of the fan, which is shown as the following formula:
Figure BDA0003351999430000031
wherein the content of the first and second substances,
Figure BDA0003351999430000032
Figure BDA0003351999430000033
Figure BDA0003351999430000034
PMAR＝PWT+PPV-(Pload+PBA) (6)
Figure BDA0003351999430000035
in the formula of UtIs a set of all moments within a one-year time scale; t is a certain time; q. q.ses.wtThe unit electricity selling price of the wind driven generator; q. q.ss.wtIs the wind power grid price; pS.WTThe wind power grid-connected electric quantity; pwtIs the total installed capacity, P, of the fanWTPower can be output for the predicted fan; pPVIs the predicted output of the photovoltaic array; x is the number ofiIs the government subsidy electricity price coefficient; pWTSIs the actual electricity sales of the fan; pMARIs the power margin of the power supply range of the distributed power supply; ploadThe load of the power supply range of the distributed power supply; pBAIs the amount of electricity stored by the energy storage system; q. q.ssg.wtIs the unit capacity fan construction investment; psg.wtActive power is output for the rated power of one fan; y iswtThe service life of the fan equipment is long; n is a radical ofi.wtThe number of the fans is set at the preset node; a is annual discount rate; q. q.som.wtThe operation and maintenance cost of power supply for a fan unit;
s22: comprehensively considering photovoltaic electricity selling income and government subsidy income WS.pvEarly construction investment cost WI.pvAnd the expense of operation and maintenance WOM.pvFor the target, an objective function is formulated as follows:
maxWpv(Ppv)＝WS.pv-WI.pv-WOM.pv (8)
wherein:
Figure BDA0003351999430000041
Figure BDA0003351999430000042
Figure BDA0003351999430000043
Figure BDA0003351999430000044
in the formula, qes.pvIs the unit electricity selling price of the photovoltaic; q. q.ss.pvIs the photoelectric internet price; pS.PVIs the photoelectric internet access electric quantity; ppvIs the total installed capacity of the photovoltaic array; pWTSIs the actual electricity sales of the fan; q. q.ssg.pvIs the unit capacity photovoltaic construction investment; psg.pvActive power is output for the rated power of a single photovoltaic generator; y ispvNamely the service life of the photoelectric equipment is long; n is a radical ofi.pvNamely, installing the number of photovoltaic cells on a preset node; q. q.som.pvThe operation and maintenance cost of providing electric energy for a photoelectric unit;
s23: energy storage power P of energy storage device at t momentBA,tThe relation between the supply and demand at the moment t-1 and t is also related to the energy state of the energy storage device at the moment t-1, namely:
PBA,t＝PBA,t-1+PWT,t-1+PPV,t-1-Pload,t-1 (13)
when the wind power and photovoltaic output is greater than the user load, judging whether the SOC meets the charging energy constraint condition, and if the SOC does not exceed the constraint upper limit, storing energy for charging; if the constraint limit of the stored energy is exceeded, the battery is in a full-charge state and cannot be charged continuously; when the wind and light output cannot meet the user requirement, judging whether the SOC reaches a constraint lower limit, and if the SOC does not exceed the constraint, storing energy and discharging; if the energy storage limit is lower than the energy storage limit lower limit, the battery is emptied and cannot continuously discharge electric energy; the charge/discharge at time t of the energy storage device is as follows:
PCS,t＝PBA,t-PBA,t-1 (14)
the energy storage system is considered as a power source in a discharging state and as a load in a charging state; comprehensively considering the electricity selling income and government subsidy income W of the energy storage systemS.baEarly construction cost WI.baAnd the operation and maintenance cost WOM.baFor the target, an objective function is formulated as follows:
maxWba(Pba)＝WS.ba-WI.ba-WOM.ba (15)
wherein:
Figure BDA0003351999430000051
Figure BDA0003351999430000052
Figure BDA0003351999430000053
Figure BDA0003351999430000054
in the formula, qes.baIs the unit electricity selling price of the energy storage system; pch/faIs the charging and discharging power of the energy storage system, P during dischargingfa>0, P at chargingch<0；RbaIs the number of times of replacement of the storage battery; q. q.ssg.baIs the construction investment of a unit capacity storage battery; pbaIs the total installed capacity of the energy storage system; y isbaIs the life cycle of the device; q. q.som.baThe expenditure of the energy storage system for unit power supply operation and maintenance is included; x is the number ofCSSA value of 1 represents discharging of the energy storage device to obtain a discharge yield, and a value of 0 represents charging of the energy storage device as a load to consume electric energy.
Further, in the S3, an incremental distribution network optimization constraint is set, and the specific method includes:
the incremental power distribution network optimization constraints comprise input equipment investment, energy storage charge state, energy storage charge and discharge power, system power balance and node voltage constraints, and the S3 comprises the following steps:
s31: fan installed capacity constraint CWT
CWT＝{Pwt:Pwt.min≤Pwt≤Pwt.max} (20)
In the formula，Pwt.min、Pwt.maxThe lower limit and the upper limit of the total installed capacity of the fan are set;
s32: photovoltaic array installed capacity constraint CPV
CPV＝{Ppv:Ppv.min≤Ppv≤Ppv.max} (21)
In the formula, Ppv.min、Ppv.maxThe lower limit and the upper limit of the total installed capacity of the photovoltaic array are set;
s33: energy storage system restraint Cba
The charge and discharge power of the lead-acid storage battery is limited to that the charge and discharge amount is less than or equal to 20% of the rated capacity in 1 hour, which is shown in the following formula:
Figure BDA0003351999430000061
in order to ensure that the energy storage system in the incremental power distribution network has enough electric quantity to support real-time scheduling or respond to the occurrence of emergency, the lower limit of the electric quantity of charge is set to be SOCmin(ii) a In order to prevent the lead-acid storage battery from being overcharged, the upper limit of the charge capacity of the lead-acid storage battery is set to be SOCmaxI.e. by
SOCmin≤SOC≤SOCmax (23)
The charge and discharge power of the storage battery constantly changes along with the charge state of the storage battery within an allowable range, and is shown as the following formula:
Figure BDA0003351999430000062
in the formula, Pch.max、Pch.minThe upper limit values of the allowable charging and discharging power are respectively greater than 0;
the constraints of the energy storage system are:
Cba＝{Pba:Pba.min≤Pba≤Pba.max} (25)
in the formula, Pba.min、Pba.maxFor the rated capacity of the energy storage batteryLower and upper limits of the amount;
s34: branch current flow restraint
Figure BDA0003351999430000071
Figure BDA0003351999430000072
In the formula, Pi.tAnd Qi.tThe active and reactive electric quantities of the i node at the moment t are respectively; u shapei.t、Uj.tThe voltage amplitudes of the i node and the j node at the time t are respectively; gij、BijRespectively the conductance and susceptance on the line between the node i and the node j;
Figure BDA0003351999430000073
i.e., the difference in voltage phase angle between node i and node j;
s35: node voltage constraint
Ui.min≤Ui.t≤Ui.max (27)
In the formula of Ui.minAnd Ui.maxRespectively, the minimum and maximum values of the i-node voltage amplitude.
Further, an incremental distribution network distributed optimization potential game model is constructed in the S4, and the specific method includes:
the modeling idea of the potential game is as follows: respectively constructing a mapping relation among game participants, the profit function and the strategy space, a fan, a photovoltaic array, an energy storage system, a target function and a constraint condition of the incremental power distribution network, further finding a potential function, constructing a distributed optimization potential game model, and obtaining a Nash equilibrium solution which is a global optimal solution of an optimization problem; wind and photovoltaic storage is abstracted into WT/PV/BA game participants, and all main economic targets and constraint limits are in one-to-one correspondence with revenue functions and decision spaces to construct an incremental distribution network distributed optimization potential game model.
Further, the S4 specifically includes the following steps:
s41: constructing a revenue function
In a potential game model of the incremental power distribution network, the power supply of wind, light and storage joint participation must meet certain load power consumption requirements; ignoring the grid loss in the incremental distribution grid, then:
Figure BDA0003351999430000074
in the formula, PiThe predicted output, P, of various power supplies of wind, light and storageloadIncrementing all load demands within the distribution network for the load forecast; lambda is the proportion of wind-solar energy storage and power supply to the total load demand;
balancing and coordinating each main body from the economic perspective, adopting a penalty function to represent the load satisfaction degree of the wind-solar energy storage, constructing a penalty function item and adding the penalty function item into the revenue function of each game participant, and then obtaining the revenue function U of the wind-solar energy storageiAs shown in the following formula:
Figure BDA0003351999430000081
Figure BDA0003351999430000082
μ＝μ0·yk-1 (31)
the income function is a function of the strategy of game participants, and the wind/light/storage strategies are respectively the installed capacities of the game participants; w of the revenue functioniCommon feature is WiOnly with the policy of the ith participant; mu is a penalty factor, and 0 < mu0Less than 1, y is more than 1, and k is more than or equal to 1; the penalty factor is rapidly increased according to an exponential rule in the interactive game process, so that the penalty function term is forced to approach 0, and the solved Nash equilibrium solution meets the requirement of safe power supply; k is iteration times and represents a game process;
in order to realize that the self benefit maximization of each main body is consistent with the optimal trend of the overall system benefit, the sum of the economic benefits of each main body is selected as a potential function x:
Figure BDA0003351999430000083
s42: formulating a policy space
Each main body strategy space is the constraint condition range, and the potential game strategy space is as follows:
Figure BDA0003351999430000084
in the formula, Pi.minAnd Pi.maxRespectively the maximum value and the minimum value of the corresponding power; h isi("coupled") characterizes the relevant state constraints.
Further, in S5, a solver is used to solve the problem of distributed potential game optimization planning of the incremental distribution network:
and (3) by combining the target functions established in S2 and S3 with the constraint conditions such as input equipment investment, energy storage charge state, energy storage charge and discharge power, system power balance, node voltage and the like, and aiming at the incremental distribution network distributed optimization potential game model established in S4, calling a CPLEX solver to obtain a wind-solar energy storage optimal configuration scheme, and realizing the optimal overall benefit.
The medium comprises the incremental distribution network distributed optimization planning method.
The invention has the beneficial effects that:
according to the method, the wind-solar storage in the incremental power distribution network is mapped into game participants, the objective function of the game participants is mapped into a gain function of the game, the corresponding constraint conditions are mapped into a strategy space of the game, a potential game distributed optimization model is constructed, the overall economy and the power supply reliability of the incremental power distribution network are improved, the development and construction of the incremental power distribution network under the current situation are guided, the market activity is stimulated, the market competitiveness is enhanced, and the healthy development of the incremental power distribution network is promoted.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a flow chart of a planning method of the present invention;
fig. 2 is a diagram of an IEEE33 node structure of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention relates to an incremental distribution network distributed optimization planning method based on potential game, which has the flow shown in figure 1, and comprises the following steps:
s1: incremental power distribution network model considering wind-solar energy storage
The incremental distribution network model comprising wind-solar energy storage considers the distribution network structure of IEEE33 nodes, branch data information and node data information. The IEEE33 node distribution network structure comprises a connection relation among nodes of an incremental distribution network; the branch data information specifically refers to the impedance of the network line; the node data information specifically includes the load size of each node of the incremental distribution network, the capacity of the DG device, and the installation position.
S2: establishing optimized objective functions of all main bodies of wind, light and energy storage
And arranging an energy storage device in the incremental power distribution network for power smoothing, peak clipping and valley filling, adjusting the load power utilization condition and ensuring the overall safe and reliable operation of the system. The integrated cost of the incremental power distribution network investment fan, the photovoltaic array and the energy storage device comprises the construction investment and the operation and maintenance cost in the early stage of wind-solar energy storage, the integrated income comprises the electricity selling income of users, the electricity generation subsidy income given by the state and the surplus electricity internet income, and the whole income W is the whole incomeZTThe objective function is as follows:
maxWZT＝Wwt+Wpv+Wba (1)
in the formula, Wwt、Wpv、WbaRespectively for the economic benefits of the fan, the photovoltaic array and the energy storage device.
And respectively establishing an optimized objective function of the fan, the photovoltaic array and the energy storage device. The S2 specifically includes:
s21: comprehensively considering the income of power selling, subsidy income and surplus power on-line income W by using the fanS.wtEarly construction investment WI.wtAnd the operation and maintenance cost WOM.wtEstablishing an objective function of the fan, which is shown as the following formula:
maxWwt(Pwt)＝WS.wt-WI.wt-WOM.wt (2)
wherein the content of the first and second substances,
Figure BDA0003351999430000101
Figure BDA0003351999430000102
Figure BDA0003351999430000111
PMAR＝PWT+PPV-(Pload+PBA) (6)
Figure BDA0003351999430000112
in the formula of UtIs a set of all moments within a one-year time scale; t is a certain time; q. q.ses.wtThe unit electricity selling price of the wind driven generator; q. q.ss.wtIs the wind power grid price; pS.WTThe wind power grid-connected electric quantity; pwtIs the total installed capacity, P, of the fanWTPower can be output for the predicted fan; pPVIs photovoltaic array predictionForce is exerted; x is the number ofiIs the government subsidy electricity price coefficient; pWTSIs the actual electricity sales of the fan; pMARIs the power margin of the power supply range of the distributed power supply; ploadThe load of the power supply range of the distributed power supply; pBAIs the amount of electricity stored by the energy storage system; q. q.ssg.wtIs the unit capacity fan construction investment; psg.wtActive power is output for the rated power of one fan; y iswtThe service life of the fan equipment is long; n is a radical ofi.wtThe number of the fans is set at the preset node; a is annual discount rate; q. q.som.wtAnd the operation and maintenance cost is supplied for the unit of the fan.
S22: comprehensively considering photovoltaic electricity selling income and government subsidy income WS.pvEarly construction investment cost WI.pvAnd the expense of operation and maintenance WOM.pvFor the target, an objective function is formulated as follows:
maxWpv(Ppv)＝WS.pv-WI.pv-WOM.pv (8)
wherein:
Figure BDA0003351999430000113
Figure BDA0003351999430000114
Figure BDA0003351999430000115
Figure BDA0003351999430000116
in the formula, qes.pvIs the unit electricity selling price of the photovoltaic; q. q.ss.pvIs the photoelectric internet price; pS.PVIs the photoelectric internet access electric quantity; ppvIs the total installed capacity of the photovoltaic array; pWTSIs the actual electricity sales of the fan; q. q.ssg.pvIs a unitBuilding investment of capacity photovoltaic; psg.pvActive power is output for the rated power of a single photovoltaic generator; y ispvNamely the service life of the photoelectric equipment is long; n is a radical ofi.pvNamely, installing the number of photovoltaic cells on a preset node; q. q.som.pvAnd the operation and maintenance cost of providing electric energy for the photoelectric unit.
S23: energy storage power P of energy storage device at t momentBA,tThe relation between the supply and demand at the moment t-1 and t is also related to the energy state of the energy storage device at the moment t-1, namely:
PBA,t＝PBA,t-1+PWT,t-1+PPV,t-1-Pload,t-1 (13)
PV is a photovoltaic point, WT is a fan point, BA is an energy storage point, when the wind power and photovoltaic output is greater than the load of a user, whether the SOC meets the constraint condition of charging energy is judged, and if the SOC does not exceed the constraint upper limit, the energy storage is used for charging; if the constraint limit of the stored energy is exceeded, the battery is in a full state and cannot be charged continuously. When the wind and light output cannot meet the user requirement, judging whether the SOC reaches a constraint lower limit, and if the SOC does not exceed the constraint, storing energy and discharging; if the energy storage limit is lower than the energy storage limit lower limit, the battery is emptied and cannot continuously discharge electric energy. The charge or discharge at the time t of the energy storage device is as follows:
PCS,t＝PBA,t-PBA,t-1 (14)
the energy storage system is considered a power source in the discharge state and a load in the charge state. Comprehensively considering the electricity selling income and government subsidy income W of the energy storage systemS.baEarly construction cost WI.baAnd the operation and maintenance cost WOM.baFor the target, an objective function is formulated as follows:
maxWba(Pba)＝WS.ba-WI.ba-WOM.ba (15)
wherein:
Figure BDA0003351999430000121
Figure BDA0003351999430000122
Figure BDA0003351999430000131
Figure BDA0003351999430000132
in the formula, qes.baIs the unit electricity selling price of the energy storage system; pch/faIs the charging and discharging power of the energy storage system, P during dischargingfa>0, P at chargingch<0；RbaIs the number of times of replacement of the storage battery; q. q.ssg.baIs the construction investment of a unit capacity storage battery; pbaIs the total installed capacity of the energy storage system; y isbaIs the life cycle of the device; q. q.som.baThe expenditure of the energy storage system for unit power supply operation and maintenance is included; x is the number ofCSSA value of 1 represents discharging of the energy storage device to obtain a discharge yield, and a value of 0 represents charging of the energy storage device as a load to consume electric energy.
S3: setting incremental distribution network optimization constraints
The incremental power distribution network optimization constraints comprise input equipment investment, energy storage charge state, energy storage charge and discharge power, system power balance and node voltage constraints, and the S3 comprises the following steps:
s31: fan installed capacity constraint CWT
CWT＝{Pwt:Pwt.min≤Pwt≤Pwt.max} (20)
In the formula, Pwt.min、Pwt.maxThe lower limit and the upper limit of the total installed capacity of the fan.
S32: photovoltaic array installed capacity constraint CPV
CPV＝{Ppv:Ppv.min≤Ppv≤Ppv.max} (21)
In the formula, Ppv.min、Ppv.maxThe lower limit and the upper limit of the total installed capacity of the photovoltaic array.
S33: energy storage system restraint Cba
The charge and discharge power of the lead-acid storage battery is limited to that the charge and discharge amount is less than or equal to 20% of the rated capacity in 1 hour, which is shown in the following formula:
Figure BDA0003351999430000133
in order to ensure that the energy storage system in the incremental power distribution network has enough electric quantity to support real-time scheduling or respond to the occurrence of emergency, the lower limit of the electric quantity of charge is set to be SOCmin(ii) a In order to prevent the lead-acid storage battery from being overcharged, the upper limit of the charge capacity of the lead-acid storage battery is set to be SOCmaxI.e. by
SOCmin≤SOC≤SOCmax (23)
The charge and discharge power of the storage battery constantly changes along with the charge state of the storage battery within an allowable range, and is shown as the following formula:
Figure BDA0003351999430000141
in the formula, Pch.max、Pch.minThe upper limit values of the allowable charging and discharging power are respectively greater than 0.
The constraints of the energy storage system are:
Cba＝{Pba:Pba.min≤Pba≤Pba.max} (25)
in the formula, Pba.min、Pba.maxThe lower limit and the upper limit of the rated capacity of the energy storage battery.
S34: branch current flow restraint
Figure BDA0003351999430000142
Figure BDA0003351999430000143
In the formula, Pi.tAnd Qi.tThe active and reactive electric quantities of the i node at the moment t are respectively; u shapei.t、Uj.tThe voltage amplitudes of the i node and the j node at the time t are respectively; gij、BijRespectively the conductance and susceptance on the line between the node i and the node j;
Figure BDA0003351999430000144
i.e., the difference in voltage phase angle between the i-node and the j-node.
Step 35: node voltage constraint
Ui.min≤Ui.t≤Ui.max (27)
In the formula of Ui.minAnd Ui.maxRespectively, the minimum and maximum values of the i-node voltage amplitude.
S4: constructing distributed optimized potential game model of incremental distribution network
Wind power, photovoltaic and energy storage in the incremental power distribution network have different benefit requirements, the wind power, the photovoltaic and the energy storage hope to maximize benefits under the limited construction, operation and maintenance cost, the service life is guaranteed, the constraint requirements are met, and the three respectively achieve respective benefit targets. Each participant maximizes the self income through continuously optimizing the self decision in the game process and promotes the optimal overall benefit of the system. The modeling idea of the potential game is as follows: and (3) respectively constructing a mapping relation among game participants, the profit function and the strategy space, a fan, a photovoltaic array, an energy storage system, a target function and a constraint condition of the incremental power distribution network, further finding a potential function, constructing a distributed optimization potential game model, and solving a Nash equilibrium solution, namely a global optimal solution of the optimization problem. Wind and photovoltaic storage is abstracted into WT/PV/BA game participants, and all main economic targets and constraint limits are in one-to-one correspondence with revenue functions and decision spaces to construct an incremental distribution network distributed optimization potential game model. The S4 includes the steps of:
s41: constructing a revenue function
In a potential game model of the incremental power distribution network, the wind, light and storage jointly participate in power supply which must meet certain load power utilization requirements. Ignoring the grid loss in the incremental distribution grid, then:
Figure BDA0003351999430000151
in the formula, PiThe predicted output, P, of various power supplies of wind, light and storageloadIncrementing all load demands within the distribution network for the load forecast; and lambda is the proportion of wind-solar energy storage and power supply to the total load demand, and the numerical value of the lambda is determined according to the distributed resource condition of the incremental power distribution network, government development planning and the like.
Balancing and coordinating each main body from the economic perspective, adopting a penalty function to represent the load satisfaction degree of the wind-solar energy storage, constructing a penalty function item and adding the penalty function item into the revenue function of each game participant, and then obtaining the revenue function U of the wind-solar energy storageiAs shown in the following formula:
Figure BDA0003351999430000152
Figure BDA0003351999430000153
μ＝μ0·yk-1 (31)
the revenue function is a function of the game participant's strategy, and the wind/light/store strategy is its installed capacity, respectively. W of the revenue functioniCommon feature is WiOnly with respect to the policy of the ith participant. Mu is a penalty factor, and 0 < mu0Less than 1, y is more than 1, and k is more than or equal to 1. The penalty factor is rapidly increased according to an exponential rule in the interactive game process, so that the penalty function term is forced to approach 0, and the solved Nash equilibrium solution meets the requirement of safe power supply. And k is iteration times and represents the game process.
In order to realize that the self benefit maximization of each main body is consistent with the optimal trend of the overall system benefit, the sum of the economic benefits of each main body is selected as a potential function x:
Figure BDA0003351999430000161
s42: formulating a policy space
Each main body strategy space is the constraint condition range, and the potential game strategy space is as follows:
Figure BDA0003351999430000162
in the formula, Pi.minAnd Pi.maxRespectively the maximum value and the minimum value of the corresponding power; h isi("coupled") characterizes the relevant state constraints.
S5: solving problem of distributed potential game optimization planning of incremental power distribution network by utilizing solver
And (3) by combining the target functions established in S2 and S3 with the constraint conditions such as input equipment investment, energy storage charge state, energy storage charge and discharge power, system power balance, node voltage and the like, and aiming at the incremental distribution network distributed optimization potential game model established in S4, calling a CPLEX solver to obtain a wind-solar energy storage optimal configuration scheme, and realizing the optimal overall benefit.
The medium comprises the incremental distribution network distributed optimization planning method.
Example of the implementation
An IEEE33 node system incremental power distribution network model considering wind energy storage and photovoltaic energy storage is constructed, the system comprises 33 branches, the total load demand is 3715+ j2300kVA, the system reference voltage is set to be 12.66kV, and the reference apparent power is set to be 5 MVA. The planning age is 10a, and the discount rate is 10%. The deviation between each node voltage and the rated voltage of the system is required to be less than 5 percent, namely Ui.min＝0.95，Ui.max1.05. The transmission line model selected was overhead line LGJ-70, which had a limited capacity of 3.86MVA at 40 ℃ to allow for thermal stability.
The wind-solar storage parameters are shown in the following tables 1-3:
TABLE 1 wind turbine-related parameters
Parameter(s) Numerical value
Single rated capacity (kW) 10
Construction investment cost (Wanyuan/kW) 1.25
Operation and maintenance cost (Yuan/kW) 0.03
Government subsidy price (Yuan/kWh) 0.19
Surplus electricity network price (Yuan/kWh) 0.29
Cut-in wind speed (m/s) 3
Rated wind speed (m/s) 8
Cut off the wind speed (m/s) 12
TABLE 2 photovoltaic array-related parameters
Parameter(s) Numerical value
Single rated capacity (kW) 10
Construction investment cost (Wanyuan/kW) 0.85
Operation and maintenance cost (Yuan/kW) 0.01
Government subsidy price (Yuan/kWh) 0.31
Surplus electricity network price (Yuan/kWh) 0.35
TABLE 3 energy storage device-related parameters
Parameter(s) Numerical value
Charge and discharge efficiency (%) 90
Initial SOC 0.5
SOCmin 0.3
SOCmax 0.9
Construction input cost (Wanyuan/kWh) 0.15
Operation and maintenance cost (Yuan/kWh) 0.009
Government subsidy price (Yuan/kWh) 1.0
Through multiple simulation analysis, a reasonable three-main-body decision variable value range is finally determined: the upper limit of wind power is 2000kW, and the lower limit is 800 kW; the upper limit of photoelectricity is 1000kW, and the lower limit is 200 kW; the upper limit of the energy storage is 800kW, and the lower limit is 200 kW. The distributed capacity optimization planning result of the incremental distribution network participated by the wind, light and storage combination is shown in the following table 4.
TABLE 4 incremental distribution network wind-solar storage distributed optimization capacity planning result
Figure BDA0003351999430000171
Figure BDA0003351999430000181
The main bodies and the overall benefits and costs of the wind-solar energy storage capacity configuration results obtained through distributed optimization are shown in the following table 5:
TABLE 5 incremental distribution network wind-solar energy storage optimization yield and cost
Figure BDA0003351999430000182
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.

Claims (8)

1. An incremental distribution network distributed optimization planning method based on potential game is characterized by comprising the following steps:
s1: constructing an incremental power distribution network model considering wind-solar energy storage: IEEE33 node distribution network structure, branch data information and node data information;
s2: based on an S1 incremental distribution network model, establishing an optimization objective function by respectively taking the maximization of the gains of all main bodies of wind, solar and photovoltaic storage as an objective;
s3: setting optimization constraints of the incremental power distribution network: inputting equipment investment, energy storage charge state, energy storage charge and discharge power, system power balance and node voltage constraint;
s4: integrating objective functions and constraint conditions of S2 and S3 to construct an incremental distribution network distributed optimization potential game model;
s5: solving the incremental power distribution network distributed potential game optimization planning model constructed by S4 by using a solver: and obtaining the optimal configuration scheme of the wind-solar energy storage.
2. The potential game based incremental distribution network distributed optimization planning method according to claim 1, wherein in S1, an incremental distribution network model considering wind, light and storage is constructed:
the IEEE33 node distribution network structure comprises a connection relation among nodes of an incremental distribution network; the branch data information specifically refers to the impedance of the network line; the node data information specifically includes the load size of each node of the incremental distribution network, the capacity of the DG device, and the installation position.
3. The incremental power distribution network distributed optimization planning method based on potential game of claim 1, wherein in S2, a wind, photovoltaic and energy storage subject optimization objective function is established, and the specific method includes:
wind and light are preferentially utilized to supply load power in an incremental power distribution network containing wind and light storage, an energy storage device is arranged in the incremental power distribution network to perform power smoothing, peak clipping and valley filling, load power utilization conditions are adjusted, and the whole safe and reliable operation of the system is ensured; incremental distribution network is put into operation and is built fan, photovoltaic array, energy memory, and its comprehensive cost includes that the scene stores up earlier stage construction input, fortune dimension cost, and the comprehensive profit includes the electricity selling profit to the user, the electricity generation subsidy profit that the country gave, surplus electricity income of surfing the net, then whole profit WZT objective function is as follows:
maxWZT＝Wwt+Wpv+Wba (1)
in the formula, Wwt、Wpv、WbaRespectively realizing the economic benefits of the fan, the photovoltaic array and the energy storage device;
respectively establishing an optimized objective function of the fan, the photovoltaic array and the energy storage device; the S2 specifically includes the following steps:
s21: comprehensively considering the income of power selling, subsidy income and surplus power on-line income W by using the fanS.wtEarly-stage constructionInvestment WI.wtAnd the operation and maintenance cost WOM.wtEstablishing an objective function of the fan, which is shown as the following formula:
maxWwt(Pwt)＝WS.wt-WI.wt-WOM.wt (2)
wherein the content of the first and second substances,
Figure FDA0003351999420000021
Figure FDA0003351999420000022
Figure FDA0003351999420000023
PMAR＝PWT+PPV-(Pload+PBA) (6)
Figure FDA0003351999420000024
in the formula of UtIs a set of all moments within a one-year time scale; t is a certain time; q. q.ses.wtThe unit electricity selling price of the wind driven generator; q. q.ss.wtIs the wind power grid price; pS.WTThe wind power grid-connected electric quantity; pwtIs the total installed capacity, P, of the fanWTPower can be output for the predicted fan; pPVIs the predicted output of the photovoltaic array; x is the number ofiIs the government subsidy electricity price coefficient; pWTSIs the actual electricity sales of the fan; pMARIs the power margin of the power supply range of the distributed power supply; ploadThe load of the power supply range of the distributed power supply; pBAIs the amount of electricity stored by the energy storage system; q. q.ssg.wtIs the unit capacity fan construction investment; psg.wtActive power is output for the rated power of one fan; y iswtThe service life of the fan equipment is long; n is a radical ofi.wtThe number of the fans is set at the preset node; a is annual discount rate; q. q.som.wtThe operation and maintenance cost of power supply for a fan unit;
s22: comprehensively considering photovoltaic electricity selling income and government subsidy income WS.pvEarly construction investment cost WI.pvAnd the expense of operation and maintenance WOM.pvFor the target, an objective function is formulated as follows:
maxWpv(Ppv)＝WS.pv-WI.pv-WOM.pv (8)
wherein:
Figure FDA0003351999420000031
Figure FDA0003351999420000032
Figure FDA0003351999420000033
Figure FDA0003351999420000034
in the formula, qes.pvIs the unit electricity selling price of the photovoltaic; q. q.ss.pvIs the photoelectric internet price; pS.PVIs the photoelectric internet access electric quantity; ppvIs the total installed capacity of the photovoltaic array; pWTSIs the actual electricity sales of the fan; q. q.ssg.pvIs the unit capacity photovoltaic construction investment; psg.pvActive power is output for the rated power of a single photovoltaic generator; y ispvNamely the service life of the photoelectric equipment is long; n is a radical ofi.pvNamely, installing the number of photovoltaic cells on a preset node; q. q.som.pvThe operation and maintenance cost of providing electric energy for a photoelectric unit;
s23: energy storage device at tEnergy storage power P of timeBA,tThe relation between the supply and demand at the moment t-1 and t is also related to the energy state of the energy storage device at the moment t-1, namely:
PBA,t＝PBA,t-1+PWT,t-1+PPV,t-1-Pload,t-1 (13)
when the wind power and photovoltaic output is greater than the user load, judging whether the SOC meets the charging energy constraint condition, and if the SOC does not exceed the constraint upper limit, storing energy for charging; if the constraint limit of the stored energy is exceeded, the battery is in a full-charge state and cannot be charged continuously; when the wind and light output cannot meet the user requirement, judging whether the SOC reaches a constraint lower limit, and if the SOC does not exceed the constraint, storing energy and discharging; if the energy storage limit is lower than the energy storage limit lower limit, the battery is emptied and cannot continuously discharge electric energy; the charge/discharge at time t of the energy storage device is as follows:
PCS,t＝PBA,t-PBA,t-1 (14)
the energy storage system is considered as a power source in a discharging state and as a load in a charging state; comprehensively considering the electricity selling income and government subsidy income W of the energy storage systemS.baEarly construction cost WI.baAnd the operation and maintenance cost WOM.baFor the target, an objective function is formulated as follows:
maxWba(Pba)＝WS.ba-WI.ba-WOM.ba (15)
wherein:
Figure FDA0003351999420000041
Figure FDA0003351999420000042
Figure FDA0003351999420000043
Figure FDA0003351999420000044
in the formula, qes.baIs the unit electricity selling price of the energy storage system; pch/faIs the charging and discharging power of the energy storage system, P during dischargingfa>0, P at chargingch<0；RbaIs the number of times of replacement of the storage battery; q. q.ssg.baIs the construction investment of a unit capacity storage battery; pbaIs the total installed capacity of the energy storage system; y isbaIs the life cycle of the device; q. q.som.baThe expenditure of the energy storage system for unit power supply operation and maintenance is included; x is the number ofCSSA value of 1 represents discharging of the energy storage device to obtain a discharge yield, and a value of 0 represents charging of the energy storage device as a load to consume electric energy.
4. The potential game-based incremental distribution network distributed optimization planning method according to claim 1, wherein incremental distribution network optimization constraints are set in S3, and the specific method is as follows:
the incremental power distribution network optimization constraints comprise input equipment investment, energy storage charge state, energy storage charge and discharge power, system power balance and node voltage constraints, and the S3 comprises the following steps:
s31: fan installed capacity constraint CWT
CWT＝{Pwt:Pwt.min≤Pwt≤Pwt.max} (20)
In the formula, Pwt.min、Pwt.maxThe lower limit and the upper limit of the total installed capacity of the fan are set;
s32: photovoltaic array installed capacity constraint CPV
CPV＝{Ppv:Ppv.min≤Ppv≤Ppv.max} (21)
In the formula, Ppv.min、Ppv.maxThe lower limit and the upper limit of the total installed capacity of the photovoltaic array are set;
s33: energy storage systemConstraint of system Cba
The charge and discharge power of the lead-acid storage battery is limited to that the charge and discharge amount is less than or equal to 20% of the rated capacity in 1 hour, which is shown in the following formula:
Figure FDA0003351999420000051
in order to ensure that the energy storage system in the incremental power distribution network has enough electric quantity to support real-time scheduling or respond to the occurrence of emergency, the lower limit of the electric quantity of charge is set to be SOCmin(ii) a In order to prevent the lead-acid storage battery from being overcharged, the upper limit of the charge capacity of the lead-acid storage battery is set to be SOCmaxI.e. by
SOCmin≤SOC≤SOCmax (23)
The charge and discharge power of the storage battery constantly changes along with the charge state of the storage battery within an allowable range, and is shown as the following formula:
Figure FDA0003351999420000052
in the formula, Pch.max、Pch.minThe upper limit values of the allowable charging and discharging power are respectively greater than 0;
the constraints of the energy storage system are:
Cba＝{Pba:Pba.min≤Pba≤Pba.max} (25)
in the formula, Pba.min、Pba.maxThe lower limit and the upper limit of the rated capacity of the energy storage battery are set;
s34: branch current flow restraint
Figure FDA0003351999420000061
Figure FDA0003351999420000062
In the formula, Pi.tAnd Qi.tThe active and reactive electric quantities of the i node at the moment t are respectively; u shapei.t、Uj.tThe voltage amplitudes of the i node and the j node at the time t are respectively; gij、BijRespectively the conductance and susceptance on the line between the node i and the node j;
Figure FDA0003351999420000063
i.e., the difference in voltage phase angle between node i and node j;
s35: node voltage constraint
Ui.min≤Ui.t≤Ui.max (27)
In the formula of Ui.minAnd Ui.maxRespectively, the minimum and maximum values of the i-node voltage amplitude.
5. The potential game-based incremental distribution network distributed optimization planning method is characterized in that an incremental distribution network distributed optimization potential game model is constructed in S4, and the method specifically comprises the following steps:
the modeling idea of the potential game is as follows: respectively constructing a mapping relation among game participants, the profit function and the strategy space, a fan, a photovoltaic array, an energy storage system, a target function and a constraint condition of the incremental power distribution network, further finding a potential function, constructing a distributed optimization potential game model, and obtaining a Nash equilibrium solution which is a global optimal solution of an optimization problem; wind and photovoltaic storage is abstracted into WT/PV/BA game participants, and all main economic targets and constraint limits are in one-to-one correspondence with revenue functions and decision spaces to construct an incremental distribution network distributed optimization potential game model.
6. The potential game-based incremental distribution network distributed optimization planning method according to claim 1 or 5, wherein the S4 specifically includes the following steps:
s41: constructing a revenue function
In the potential game model of the incremental power distribution network, the wind, light and storage jointly participate in power supply which must meet the load power demand, then:
Figure FDA0003351999420000071
in the formula, PiThe predicted output, P, of various power supplies of wind, light and storageloadIncrementing all load demands within the distribution network for the load forecast; lambda is the proportion of wind-solar energy storage and power supply to the total load demand;
balancing and coordinating each main body, adopting a penalty function to represent the load satisfaction degree of the wind-solar energy storage, constructing a penalty function item and adding the penalty function item into the revenue function of each game participant, and obtaining the revenue function U of the wind-solar energy storageiAs shown in the following formula:
Figure FDA0003351999420000072
Figure FDA0003351999420000073
μ＝μ0·yk-1 (31)
the income function is a function of the strategy of game participants, and the wind/light/storage strategies are respectively the installed capacities of the game participants; w of the revenue functioniCommon feature is WiOnly with the policy of the ith participant; mu is a penalty factor, and 0 < mu0Less than 1, y is more than 1, and k is more than or equal to 1; the penalty factor is rapidly increased according to an exponential rule in the interactive game process, so that the penalty function term is forced to approach 0, and the solved Nash equilibrium solution meets the requirement of safe power supply; k is iteration times and represents a game process;
in order to realize that the self benefit maximization of each main body is consistent with the optimal trend of the overall system benefit, the sum of the economic benefits of each main body is selected as a potential function x:
Figure FDA0003351999420000074
s42: formulating a policy space
Each main body strategy space is the constraint condition range, and the potential game strategy space is as follows:
Figure FDA0003351999420000075
in the formula, Pi.minAnd Pi.maxRespectively the maximum value and the minimum value of the corresponding power; h isi("coupled") characterizes the relevant state constraints.
7. The potential game-based incremental power distribution network distributed optimization planning method of claim 1, wherein in S5, a solver is used to solve the incremental power distribution network distributed potential game optimization planning problem:
and (3) calling a CPLEX solver to obtain an optimal wind-solar storage configuration scheme aiming at the incremental distribution network distributed optimization potential game model constructed in S4 by combining the target functions established in S2 and S3 and the constraint conditions such as input equipment investment, energy storage charge-discharge state, energy storage charge-discharge power, system power balance, node voltage and the like.
8. A medium, characterized in that the medium comprises the incremental distribution network distributed optimization planning method of claim 1.
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