CN113315165A - Four-station integrated comprehensive energy system coordination control method and system - Google Patents

Four-station integrated comprehensive energy system coordination control method and system Download PDF

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CN113315165A
CN113315165A CN202110536469.7A CN202110536469A CN113315165A CN 113315165 A CN113315165 A CN 113315165A CN 202110536469 A CN202110536469 A CN 202110536469A CN 113315165 A CN113315165 A CN 113315165A
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station
power
formula
fusion
load
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CN113315165B (en
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唐跃中
周华
张春雁
卞世敏
窦真兰
肖楚鹏
朱亮亮
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Wuhan Energy Efficiency Evaluation Co Ltd Of State Grid Electric Power Research Institute
State Grid Corp of China SGCC
State Grid Shanghai Electric Power Co Ltd
State Grid Electric Power Research Institute
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Wuhan Energy Efficiency Evaluation Co Ltd Of State Grid Electric Power Research Institute
State Grid Corp of China SGCC
State Grid Shanghai Electric Power Co Ltd
State Grid Electric Power Research Institute
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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
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • H02J2203/20—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
    • 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
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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Abstract

The invention discloses a coordination control method and a coordination control system for a four-station integrated comprehensive energy system, wherein the four-station integrated comprehensive energy system comprises four substations which are a transformer substation, a new energy station, an energy storage station and a data center station respectively; predicting the power generation power of a new energy station and the load power of a fusion station, setting mathematical models of all sub-stations in the fusion station and setting operation constraint conditions according to predicted values, establishing a coordination control model, solving the coordination control model by adopting a particle swarm algorithm, wherein the minimum daily operation total cost of a four-station fusion system is taken as a target function, aiming at the phenomenon of light abandonment of the new energy station in the fusion station, light abandonment penalty cost is introduced to reduce the light abandonment amount so as to realize full consumption of new energy, and the safety and reliability constraint of the fusion station is established; the method ensures the economic operation of the multi-station fusion system and fully ensures the safety, reliability and environmental protection of the operation of each substation, and has certain practical application value.

Description

Four-station integrated comprehensive energy system coordination control method and system
Technical Field
The invention belongs to the technical field of comprehensive energy system coordination control, and particularly relates to a four-station integrated comprehensive energy system coordination control method and a four-station integrated comprehensive energy system coordination control system.
Background
The total energy consumption of the world is continuously increased, energy is strategic resource of the country and the region, and the energy technology revolution is an inevitable trend. In the revolution, renewable energy becomes a key, and a great revolution is generated on a fossil energy system, and the renewable energy and an information communication technology are strongly combined to jointly breed a new energy system, namely a third industrial revolution represented by 'new energy + Internet'. Under the big background of energy revolution, the energy related industries in China are actively innovated from top to bottom, the strategy of 'internet + energy' is greatly promoted, the application of digital economy in the energy field is deepened, and the high-efficiency and reasonable utilization and development of energy are promoted. China is highly concerned about the development of energy industry and is emphatically popularized with green low-carbon energy.
With the development of the power internet of things, the traditional communication technology and the computing platform cannot gradually adapt to huge data transmission and computing scale, and the 5G communication technology and the edge computing become the middle strength for supporting the construction of the power internet of things. The multi-station integration is one of important applications of the power internet of things for implementation and landing, resources such as a transformer substation, an edge data center station, a charging station and an energy storage station are gathered, urban resource configuration is optimized, data sensing and analysis operation efficiency is improved, and load consumption is carried out on the spot.
The proposal of the current 'multi-station fusion' mode also provides a new direction for the operation of the edge data center, relatively few researches on the coordinated control operation of the multi-station fusion integration are carried out, and meanwhile, the safety and the reliability of the operation are less and specifically considered on the basis of the existing researches.
Disclosure of Invention
The invention provides a four-station integrated comprehensive energy system coordination control method and a coordination control system, which can consume more renewable energy for power generation, reduce the emission of polluted gas, reduce the pollution to the environment and ensure the realization of economic, safe, reliable and environment-friendly operation of each substation under the condition of meeting the economic requirement.
In order to achieve the above object, the present invention provides a coordination control method for a four-station integrated energy system, wherein the four-station integrated energy system comprises four substations, the four substations are a transformer substation, a new energy station, an energy storage station and a data center station, and the coordination control method comprises the following steps:
s1) predicting the generating power of the new energy station and predicting the load power (namely consumed electricity, heat and cold power) of the fusion station;
s2) setting mathematical models of all the substations in the fusion station and setting operation constraint conditions according to the predicted values in the step S1);
the mathematical model comprises a transformer substation mathematical model, a new energy station mathematical model, an energy storage station mathematical model and a data center station mathematical model;
s3), establishing a coordination control model, and establishing a target function of the coordination control model according to the mathematical model in the step S2) and the sum of the electricity purchasing cost of the fusion station, the operation and maintenance cost, the power supply reliability cost and the pollutant gas emission cost in the fusion station;
s4) solving the coordination control model.
Further, in the step S2):
transformer substation mathematical model
PSL＝Pzl+Pgl+Pal+Pml (1)
In the formula, PSLThe total load of the transformer substation is obtained; pzlIs the refrigeration load size; pglIs the magnitude of the lighting load; palThe security load is; pmlThe size is the heating load;
(a) refrigeration load mathematical model
The cold load in the transformer substation is provided by an electric refrigerator and an absorption refrigerator, and mathematical models are respectively shown as formula (2) and formula (3):
(a1) mathematical model of electric refrigerator
Figure BDA0003070032970000021
In the formula (I), the compound is shown in the specification,
Figure BDA0003070032970000026
represents the output cold power of the electric refrigerator at time t; pl1Represents the electric power consumed by the electric refrigerator at time t; psiec1The refrigeration coefficient of the electric refrigerator;
(a2) mathematical model of absorption refrigerator
Figure BDA0003070032970000022
In the formula (I), the compound is shown in the specification,
Figure BDA0003070032970000027
represents the output cold power of the absorption chiller at time t;
Figure BDA0003070032970000028
represents the thermal power consumed by the absorption chiller at time t; psiac1The refrigeration coefficient of the absorption refrigerator;
(b) heating load mathematical model
The heat load in the transformer substation is heated by a heat pump and a heat storage device, and mathematical models are respectively shown as formula (4) and formula (5):
(b1) heat pump mathematical model
Figure BDA0003070032970000023
In the formula (I), the compound is shown in the specification,
Figure BDA0003070032970000029
represents the thermal power output by the heat pump in the substation at time t,
Figure BDA00030700329700000210
representing the magnitude of the electric power consumed by the heat pump at time t, ξhpThe coefficient of heating performance of the heat pump;
(b2) mathematical model of heat storage device
Figure BDA0003070032970000024
In the formula:
Figure BDA00030700329700000211
respectively representing the heat storage power and the heat release power of the thermal energy storage device at the time t,
Figure BDA00030700329700000212
respectively representing the heat storage efficiency and the heat release efficiency, Q, of a thermal energy storage deviceh,t+1、Qh,tRespectively representing the heat energy of the thermal energy storage equipment at the moment t +1 and the electric energy of the thermal energy storage equipment at the moment t; epsilon is the self-loss coefficient of the thermal energy storage equipment.
Further, in the step S2):
mathematics model of new energy station
Figure BDA0003070032970000025
In the formula, PpvThe photovoltaic power generation power is obtained; pst.maxThe maximum test power of the photovoltaic under standard experimental conditions; esIs the intensity of the illumination; es.stThe illumination intensity under standard experimental conditions; k is a power temperature coefficient; t isoIs the actual temperature, T, of the panelstIs the temperature under standard experimental conditions.
Further, in the step S2):
energy storage station mathematics model
Figure BDA0003070032970000031
In the formula, SOC (t) is the state of charge of the energy storage power station at the time t; delta is the self-discharge coefficient of the energy storage power station; pCESFor charging power, P, of energy-storage power stationsDESThe discharge power of the energy storage power station; etaCESFor charging efficiency, eta, of energy-storage power stationsDESThe discharge efficiency of the energy storage power station; esocThe rated capacity of the energy storage power station; Δ t is a scheduling time period interval;
(a) mathematical model of electric refrigerator
The cold load in the energy storage station is provided by an electric refrigerator, and the mathematical model is shown as the formula (8):
Figure BDA0003070032970000032
in the formula (I), the compound is shown in the specification,
Figure BDA0003070032970000033
representing the output cold power of the electric refrigerator in the energy storage station at time t; pl2Representing the electric power consumed by the electric refrigerator in the energy storage station at time t; psiec2Is the refrigeration coefficient of the electric refrigerator.
Further, in the step S2):
data center station mathematical model
PDC＝PIT+Pzl1+Pbat (9)
In the formula, PDCThe total load of the data center station; pITThe load size of the IT equipment; pzl1The load of refrigeration equipment of the data center station is obtained; pbatThe size of power transmission and distribution of the data center station;
(a) IT equipment mathematical model
The electric energy consumed by the servers in the IT equipment load accounts for 80% of the total power consumption, and the total power consumption of the servers is related to the number of the working servers;
Figure BDA0003070032970000034
in the formula, PNThe power consumption of a single server in a normal working state; n islP for a normally operating serverMThe power consumption of a single server in a dormant state; n islNumber of servers for normal operation, nmThe number of the working servers in the dormant state;
(b) mathematical model of absorption refrigerator
Figure BDA0003070032970000035
In the formula (I), the compound is shown in the specification,
Figure BDA0003070032970000036
representing the output cold power of the absorption chiller of the data center station at time t;
Figure BDA0003070032970000037
represents the thermal power consumed by the absorption chiller of the data center station at time t; psiac2The refrigeration coefficient of the absorption chiller of the data center station.
Further, the operation constraint conditions in step S2) are as follows:
(A) fusion station electrical balance constraints
Pnet.t+PDES.t+Ppv.t＝PCES.t+PSL.t+PDC.t (12)
In the formula: pne.ttThe interaction power of the fusion station and the power grid at the moment t is obtained; pDES.tThe discharge power of the energy storage power station at the moment t is obtained; ppv.tThe generated power of the new energy station at the moment t; pCES.tThe charging power of the energy storage station at the moment t is obtained; pSL.tFor the electric power consumed by the substation at time t, PDC.tThe electrical power consumed by the data center station at time t; and P ispv.t、PSL.t、PDC.tAll obtained by prediction in step S1);
(B) fusion station thermal balance constraints
Figure BDA0003070032970000041
In the formula (I), the compound is shown in the specification,
Figure BDA0003070032970000044
representing the thermal power output by a heat pump in the transformer substation at the time t; qh,tThe thermal power output by a thermal storage device in the transformer substation at the moment t; htThe thermal load power of the fusion station is obtained; and HtPredicted by step S1);
(C) fusion station cold balance constraint
Figure BDA0003070032970000042
In the formula (I), the compound is shown in the specification,
Figure BDA0003070032970000045
outputting cold power for the electric refrigerator at the time t in the transformer substation;
Figure BDA0003070032970000046
the output cold power of the electric refrigerator at the time t in the energy storage station is obtained;
Figure BDA0003070032970000047
outputting cold power of the absorption refrigerator at the time t in the transformer substation;
Figure BDA0003070032970000048
the output cold power of the absorption refrigerator at the time t in the data center station; cL,tThe total cold load power at the t moment in the fusion station is obtained; and CL,tPredicted by step S1);
(D) fusion station voltage current constraints
Ux.min≤Ux≤Ux.max (15)
|Ic|≤Ic.max (16)
In the formula: u shapexFor fusing the amplitude, U, of the voltage of each substation in the stationx.maxUpper limit value of node voltage in each substation, Ux.minThe lower limit value of the node voltage in each substation; i iscFor fusing the current values of the lines in the station, Ic.maxThe upper limit value of the line current in the fusion station is set;
(E) fusion station transmission line power constraints
Pnet.t≤Pmax (17)
In the formula, Pne.ttThe interactive power of the fusion station and the power grid at the moment t is obtained; pmaxMaximum power allowing interaction for transmission lines in the convergence station;
(F) operating constraints for new energy station
0≤Ppv.t≤Ppvmax (18)
In the formula, Ppv.tThe generated power of the new energy station at the moment t is obtained; ppvmaxGenerating a maximum value of electric power for the new energy station;
(G) energy storage station operation constraints
(G1) Charge and discharge state constraints
xDES+xCES≤1 (19)
(G2) Charge and discharge power constraint
Figure BDA0003070032970000043
Figure BDA0003070032970000051
(G3) Capacity constraint:
Figure BDA0003070032970000052
Esoc＝Esoc. first (23)
In the formula: x is the number ofDESRepresenting the energy storage discharge state, xCESRepresenting the energy storage charging state, and being a 0-1 state variable, wherein 1 represents the working state, and 0 represents the non-working state;
Figure BDA0003070032970000058
is the lower limit value of the discharge power of the energy storage station,
Figure BDA0003070032970000059
Is the upper limit value of the discharge power of the energy storage station,
Figure BDA00030700329700000510
Is the lower limit value of the charging power of the energy storage station,
Figure BDA00030700329700000511
The upper limit value of the charging power of the energy storage station is set;
Figure BDA00030700329700000512
respectively the upper and lower limit values of the stored energy of the energy storage station;
(H) heat pump operation constraints
Figure BDA0003070032970000053
In the formula:
Figure BDA00030700329700000513
representing the thermal power output by a heat pump in the transformer substation at the time t;
Figure BDA00030700329700000514
the maximum heat power allowed to be output by the heat pump in the transformer substation;
(I) operation constraint of electric refrigerator in transformer substation
Figure BDA0003070032970000054
In the formula:
Figure BDA00030700329700000515
representing the cold power output by the electric refrigerator in the transformer substation at the time t;
Figure BDA00030700329700000516
the maximum output cold power allowed by the electric refrigerator;
(J) operation constraint of absorption refrigerator in transformer substation
Figure BDA0003070032970000055
In the formula:
Figure BDA00030700329700000517
the cold power output by the absorption refrigerator in the transformer substation at the time t is represented;
Figure BDA00030700329700000518
the maximum output electric power allowed by the absorption refrigerator in the substation;
(K) electric refrigerator operation constraint in energy storage station
Figure BDA0003070032970000056
In the formula:
Figure BDA00030700329700000519
representing the cold power output by the electric refrigerator in the energy storage station at the moment t;
Figure BDA00030700329700000520
the maximum output cold power allowed by the electric refrigerator;
(L) absorption chiller operational constraints in data center stations
Figure BDA0003070032970000057
In the formula:
Figure BDA00030700329700000521
the cold power output by the absorption refrigerator in the data center station at the time t is represented;
Figure BDA00030700329700000522
the maximum output electric power allowed by the absorption refrigerator in the data center station;
(M) Heat storage device operation constraints
(M1) Heat accumulation and Release State constraint
yDH+yCH≤1 (29)
(M2) Heat accumulation and discharge Power constraint
Figure BDA0003070032970000061
Figure BDA0003070032970000062
(M3) capacity constraints
Figure BDA0003070032970000063
Qh. Powder＝Qh. First stage (33)
In the formula: y isDHIndicating the heat-releasing state of the heat storage device, yCHThe state of the heat storage device is represented as a 0-1 state variable, wherein 1 represents an operating state, and 0 represents a non-operating state;
Figure BDA0003070032970000066
the upper limit value of the heat-releasing power of the heat storage device,
Figure BDA0003070032970000067
The lower limit value of the heat-releasing power of the heat storage device,
Figure BDA0003070032970000068
The upper limit value of the heat storage capacity of the heat storage device,
Figure BDA0003070032970000069
The lower limit value of the heat storage power of the heat storage device;
Figure BDA00030700329700000610
the upper limit value and the lower limit value of the stored heat of the heat storage device are respectively set;
(N) Security constraints
The safety index, i.e. the voltage safety margin, determines the safety constraints of the system operation according to equation (34)
Figure BDA0003070032970000064
In the formula, Δ Ux,tThe voltage deviation amount of the node x is t time period; delta UxThe maximum node voltage deviation allowed by the node x is-8% to + 5%; xi is a threshold value of the safety margin of the multi-station fusion system; beta is the confidence level of the safety margin of the multi-station fusion system; the function phi is a 0-1 state variable, and the calculation formula is as follows:
Figure BDA0003070032970000065
(O) reliability constraints
The system power supply reliability index is the load loss probability and is determined according to the formula (36)
HLOLP＝P[(Ppv,t±εt)+(PDES,t±δt)+(Pnet,t±γt)≤PL,t±κL,t]≤1-γ (36)
In the formula, HLOLPFor a defined power supply reliability index in the fusion station, epsilontRepresenting the light abandoning amount and the disturbance amount in the new energy station at the time t; ppv,tRepresenting the generated power of the new energy station at the time t; pDES,tThe power of the energy storage station at the moment t; deltatRepresenting the disturbance quantity of the discharge power of the energy storage station at the moment t; pne,tRepresents the transmission power of the tie line at time t; gamma raytRepresenting the power disturbance quantity on the connecting line at the time t; pL,tRepresenting the total electric load size of the fusion station at the time t; kappaL,tRepresents the load fluctuation amount at time t; gamma is the confidence level of the reliability of the multi-station fusion system.
Further, the objective function of the coordinated control model in step S3) is:
minF＝min(Fg+Fom+Fre+Fgas) (37)
in the formula, F is the total cost of system operation; fgThe electricity purchase cost for the fusion station; fomThe operation and maintenance cost of the fusion station; freThe power supply reliability cost of the fusion station comprises light abandon punishment cost and load interruption cost of the new energy station; fgasCost of emission of pollution gases for the fusion station;
electricity purchase cost F of fusion stationgAs shown in equation (38):
Figure BDA0003070032970000071
in the formula, CbBuying/selling electricity price from the power grid for the time period t; pne.ttIs a signal representing the transmission power of the tie at time t;
operation and maintenance cost F of fusion stationomAs shown in formula (39):
Figure BDA0003070032970000072
in the formula, KombFor the operational maintenance factor, P, of the b-th equipment of the systemb(t) the operation power of the b-th equipment in the t time period; and P isb(t) by P in step S2l1、
Figure BDA0003070032970000077
PDES、Pl2、PIT、
Figure BDA0003070032970000076
The operating power of (c);
power supply reliability cost F of fusion stationreThe light abandonment penalty cost and the load interruption cost are formed, and the formula (40) is as follows:
Figure BDA0003070032970000073
in the formula, CpvPunishing price for unit light abandon; pdesDiscarding the light quantity for the t time period of the system; clossThe unit punishment price suffered by the system when the load is interrupted; plossThe load quantity of the interrupted load loss in the t time period of the system is obtained;
fusion station pollutant gas emission cost FgasAs shown in formula (41):
Figure BDA0003070032970000074
in the formula, λMUnit cost for mth contaminated gas; beta is aMThe discharge coefficient of the M < th > pollution gas generated per degree of electricity; pne.ttTo represent the transmission power of the tie at time t.
Further, in the step S1): predicting the power generation power of the new energy station and the load power of the fusion station by adopting a BP neural network model; the step S4): and solving the coordination control model by adopting a particle swarm algorithm.
There is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to execute the above-mentioned coordination control method when running.
Also provided is a coordination control system of a four-station integrated energy system, including:
the new energy station power generation prediction module is used for predicting the power generation power of the new energy station;
the fusion station load power prediction module is used for predicting the load power of the fusion station;
the system comprises a fusion station mathematical model setting module, a new energy station mathematical model setting module, a data center station mathematical model setting module and a power station mathematical model setting module, wherein the fusion station mathematical model setting module is used for setting four substation mathematical models which are respectively a power station mathematical model, a new energy station mathematical model, an energy storage station mathematical model and a data center station mathematical model, and setting operation constraint conditions according to predicted values;
the coordination control model setting module is used for establishing a coordination control model according to the running power and the sum of the electricity purchasing cost of the fusion station, the running maintenance cost in the fusion station, the power supply reliability cost and the pollution gas emission cost;
and the coordination control model solving module is used for solving the coordination control model by adopting a particle swarm algorithm.
Compared with the prior art, the invention has the following advantages:
1) the method takes the minimum daily running total cost of the four-station fusion system as a target function, introduces light abandoning penalty cost to reduce the light abandoning amount and realize full consumption of new energy aiming at the phenomenon of light abandoning of a new energy station in a fusion station;
2) aiming at relevant equipment in a fusion station, particularly a data center station, in order to improve the power supply reliability, load interruption punishment cost is introduced, meanwhile, the safety and reliability of the fusion station are considered, safety and reliability indexes are defined, and safety and reliability constraints are established;
3) considering that the operation of the fusion station can bring environmental problems, especially the emission of polluted gas, the pollution gas treatment cost is introduced, and the emission of the polluted gas in the operation of the fusion station is reduced;
4) the invention fully ensures the safety, reliability and environmental protection of the operation of each substation while ensuring the economic operation of the multi-station fusion system, and has certain practical application value.
Drawings
FIG. 1 is a block diagram of a four station integrated energy system architecture of the present invention;
FIG. 2 is a schematic diagram of a BP neural network prediction model according to the present invention;
FIG. 3 is a flowchart illustrating the BP neural network solution process according to the present invention;
FIG. 4 is a flow chart of the solution of the optimization algorithm of the present invention;
fig. 5 is a diagram of a coordination control system according to the present invention.
Detailed Description
In order to make the technical scheme and advantages of the implementation of the invention clearer, the invention is further described with reference to the accompanying drawings:
the four-station integrated comprehensive energy system shown in fig. 1 includes four substations, namely, the four substations are a transformer substation, a new energy station, an energy storage station and a data center station. In the fusion station, the various substations are interrelated: the energy storage station mainly provides a standby power supply for the data center station, cuts peaks and fills valleys to improve economy and carries out the function of renewable energy consumption, the data center station mainly provides data communication service for each substation in the fusion station, the new energy station power generation is mainly used for supplying power for the electric equipment of the fusion station, and redundant electric quantity is used for surfing the internet. The heat pump, the heat storage device, the electric refrigerator and the absorption refrigerator are respectively used for heating, heat storage and refrigeration in the transformer substation; the electric refrigerator in the energy storage station is used for refrigerating; an absorption chiller in the data center station is used for cooling. Aiming at the four-station integrated comprehensive energy system, a coordination control method with higher safety and reliability is established, and a new coordination control model is established by the method, so that the safety and reliability of the system are fully guaranteed under the condition of ensuring the economic operation of the four-station integrated system. The load interruption amount, the utilization rate of renewable energy sources and the like are considered in the model, safety and reliability constraints are established in the operation constraint condition, and the safety and reliability of system operation are better guaranteed while the utilization rate of the new energy sources is improved.
The coordination control method of the four-station integrated comprehensive energy system comprises S1) new energy station power generation prediction and fusion station load prediction, which are used for new energy station power generation prediction and fusion station load prediction; s2) setting mathematical models of all substations in the fusion station, wherein the mathematical models comprise mathematical models of a transformer substation, a new energy station, an energy storage station and a data center station, and operation constraint conditions of all devices; s3), establishing a coordination control model, and establishing a target function of the coordination control model according to the sum of the electricity purchasing cost of the fusion station, the operation and maintenance cost, the power supply reliability cost and the pollution gas emission cost of each device in the fusion station; s4), solving by adopting a particle swarm algorithm with high precision, high convergence speed and easy realization.
Referring to fig. 2, the specific steps of the coordination control method are as follows:
s1) predicting the generating power of the new energy station and predicting the load power (i.e. consumed electricity, heat and cold power) of the fusion station
And predicting the power generation power of the new energy station and the load power of the fusion station by adopting a BP neural network model.
(A) Predicting the generated power of the new energy station in a future time period by using historical generated data of the new energy station and meteorological parameter (temperature, radiant quantity and wind speed) data as input data;
(B) and predicting the load size of the fusion station in a future period by using the historical load data and the temperature data of the fusion station as input data.
The BP neural network model adopts a three-layer BP neural network structure as shown in figure 2, and comprises an input layer, a hidden layer and an output layer, and a solving flow chart is shown in figure 3.
S1a) normalization: carrying out normalization processing on the input new energy historical power generation data and meteorological data and then inputting the new energy historical power generation data and the meteorological data into a BP neural network model;
s1b) setting parameters of the BP neural network model: setting parameters such as maximum iteration times, learning precision, hidden layer node number, initial network weight, threshold value, learning rate and the like of the BP neural network model;
s1c) input-output value calculation: calculating the size of the input value and the output value of each layer;
s1d) error calculation: calculating the error size of a network output layer;
s1e) judging whether the error meets the precision requirement, if so, outputting a predicted value to end, and if not, returning to the step S1c) after the weight and the threshold of the correction network are not met;
s1f) outputs a predicted value that meets the error accuracy requirement.
The same method is adopted to predict the load of the fusion station, and the detailed description is omitted.
S2) setting mathematical models of the respective substations in the fusion station and setting operation constraint conditions based on the predicted values in the step S1)
The models of each substation can be selected and used according to actual requirements, any one or more of the following four models can be selected, and other known model formulas can also be selected;
(A) transformer substation mathematical model
PSL＝Pzl+Pgl+Pal+Pml (1)
In the formula, PSLThe method is characterized in that the method comprises the following steps of (1) the total load of a transformer substation is as follows: kW; pzlFor the refrigeration load size, unit: kW; pglThe unit is the size of the lighting load: kW; palFor security protection load size, unit: kW; pmlThe unit is the size of the heating load: kW.
(a) Refrigeration load mathematical model
The cold load in the transformer substation is mainly provided by an electric refrigerator and an absorption refrigerator, and mathematical models are respectively shown as a formula (2) and a formula (3).
(a1) Mathematical model of electric refrigerator
Figure BDA0003070032970000091
In the formula (I), the compound is shown in the specification,
Figure BDA0003070032970000106
represents the output cold power of the electric refrigerator at time t; pl1Represents the electric power consumed by the electric refrigerator at time t; psiec1Is the refrigeration coefficient of the electric refrigerator.
(a2) Mathematical model of absorption refrigerator
Figure BDA0003070032970000101
In the formula (I), the compound is shown in the specification,
Figure BDA0003070032970000107
represents the output cold power of the absorption chiller at time t;
Figure BDA0003070032970000108
represents the thermal power consumed by the absorption chiller at time t; psiac1The refrigeration coefficient of the absorption refrigerator.
(b) Heating load mathematical model
The heat load in the substation is mainly heated by a heat pump and a heat storage device, and mathematical models are respectively shown as a formula (4) and a formula (5).
(b1) Heat pump mathematical model
Figure BDA0003070032970000102
In the formula (I), the compound is shown in the specification,
Figure BDA0003070032970000109
represents the thermal power output by the heat pump in the substation at time t,
Figure BDA00030700329700001010
representing the magnitude of the electric power consumed by the heat pump at time t, ξhpIs the heating coefficient of performance of the heat pump.
(b2) Mathematical model of heat storage device
Figure BDA0003070032970000103
In the formula:
Figure BDA00030700329700001011
respectively representing the heat storage power and the heat release power of the thermal energy storage device at the time t,
Figure BDA00030700329700001012
respectively representing the heat storage efficiency and the heat release efficiency, Q, of a thermal energy storage deviceh,t+1、Qh,tRespectively representing the heat energy of the thermal energy storage equipment at the moment t +1 and the electric energy of the thermal energy storage equipment at the moment t; epsilon is the self-loss coefficient of the thermal energy storage equipment.
(B) Mathematics model of new energy station
Figure BDA0003070032970000104
In the formula, PpvThe unit is the photovoltaic power generation power: kW; pst.maxThe maximum test power of the photovoltaic under standard experimental conditions; esIs the intensity of the illumination; es.stThe illumination intensity under standard experimental conditions; k is a power temperature coefficient; t isoIs the actual temperature, T, of the panelstIs the temperature under standard experimental conditions.
(C) Energy storage station mathematics model
Figure BDA0003070032970000105
In the formula, SOC (t) is the state of charge of the energy storage power station at the time t; delta is the self-discharge coefficient of the energy storage power station; pCESFor charging power, P, of energy-storage power stationsDESThe unit is the discharge power of the energy storage power station: kW; etaCESFor charging efficiency, eta, of energy-storage power stationsDESFor discharging energy-storing power stationsEfficiency; esocRated capacity of the energy storage power station, unit: kWh; Δ t is the scheduling time period interval.
(a) Mathematical model of electric refrigerator
The cold load in the energy storage station is mainly provided by an electric refrigerator, and a mathematical model is shown as a formula (8).
Figure BDA00030700329700001013
In the formula (I), the compound is shown in the specification,
Figure BDA0003070032970000115
representing the output cold power of the electric refrigerator in the energy storage station at time t; pl2Representing the electric power consumed by the electric refrigerator in the energy storage station at time t; psiec2Is the refrigeration coefficient of the electric refrigerator.
(D) Data center station mathematical model
PDC＝PIT+Pzl1+Pbat (9)
In the formula, PDCThe unit is the total load of the data center station: kW; pITThe load of the IT equipment is as follows: kW; pzl1The unit is the load of refrigeration equipment of a data center station: kW; pbatThe unit is the size of power transmission and distribution of a data center station: kW.
(a) IT equipment mathematical model
The electric energy consumed by the servers in the IT equipment load accounts for about 80% of the total power consumption, and the total power consumption of the servers is related to the number of the working servers.
Figure BDA0003070032970000111
In the formula, PNThe power consumption of a single server in a normal working state, unit: kW; n islP for a normally operating serverMThe unit is the power consumption of a single server in a dormant state: kW; n islNumber of servers for normal operation, nmWorking under dormant stateAs the number of servers.
(b) Mathematical model of absorption refrigerator
Figure BDA0003070032970000112
In the formula (I), the compound is shown in the specification,
Figure BDA0003070032970000116
representing the output cold power of the absorption chiller of the data center station at time t;
Figure BDA0003070032970000117
represents the thermal power consumed by the absorption chiller of the data center station at time t; psiac2The refrigeration coefficient of the absorption chiller of the data center station.
In order to ensure the normal operation of the equipment, establishing operation constraint conditions of each equipment in the fusion station:
(A) fusion station electrical balance constraints
Pnet.t+PDES.t+Ppv.t＝PCES.t+PSL.t+PDC.t (12)
In the formula: pne.ttThe unit kW is the interaction power between the fusion station and the power grid at the moment t; pDES.tThe unit is the discharge power of the energy storage power station at the moment t: kW; ppv.tThe unit of the generated power of the new energy station at the moment t is as follows: kW; pCES.tThe charging power at the moment t of the energy storage station is represented by the unit: kW; pSL.tFor the electric power consumed by the substation at time t, the unit: kW, PDC.tFor the electrical power consumed by the data center station at time t, the unit: kW; and P ispv.t、PSL.t、PDC.tPredicted by step S1).
(B) Fusion station thermal balance constraints
Figure BDA0003070032970000113
In the formula (I), the compound is shown in the specification,
Figure BDA0003070032970000118
thermal power, unit, representing the heat pump output in the substation at time t: kW; qh,tThe unit of the thermal power output by the thermal storage device in the transformer substation at the time t is as follows: kW; htThe unit is the thermal load power of the fusion station: kW; and HtPredicted by step S1).
(C) Fusion station cold balance constraint
Figure BDA0003070032970000114
In the formula (I), the compound is shown in the specification,
Figure BDA0003070032970000119
the unit of the output cold power of the electric refrigerator at the time t in the transformer substation is as follows: kW;
Figure BDA00030700329700001110
the unit of the output cold power of the electric refrigerator at the moment t in the energy storage station is as follows: kW;
Figure BDA0003070032970000124
the unit of the output cold power of the absorption refrigerator at the time t in the transformer substation is as follows: kW;
Figure BDA0003070032970000125
the unit of the output cold power of the absorption refrigerator at the time t in the data center station is as follows: kW; cL,tThe total cooling load power at the time t in the fusion station is as follows: kW; and CL,tPredicted by step S1).
(D) Fusion station voltage current constraints
The fusion station is operated normally, and meanwhile, the voltage and the current are ensured to be within a normal working range.
Ux.min≤Ux≤Ux.max (15)
|Ic|≤Ic.max (16)
In the formula: u shapexFor each substation in a fusion stationAmplitude of the voltage, Ux.maxUpper limit value of node voltage in each substation, Ux.minIs the lower limit value of the node voltage in each substation. I iscFor fusing the current values of the lines in the station, Ic.maxThe upper limit value of the line current in the fusion station.
(E) Fusion station transmission line power constraints
Pnet.t≤Pmax (17)
In the formula, Pne.ttThe unit is the interactive power of the fusion station t moment and the power grid: kW; pmaxMaximum power for the transmission line in the fusion station to allow interaction, unit: kW.
(H) Operating constraints for new energy station
0≤Ppv.t≤Ppvmax (18)
In the formula, Ppv.tThe generated power of the new energy station at the time t is represented by the unit: kW; ppvmaxThe maximum value of the generated power of the new energy station is as follows: kW.
(I) Energy storage station operation constraints
(G1) Charge and discharge state constraints
xDES+xCES≤1 (19)
(G2) Charge and discharge power constraint
Figure BDA0003070032970000121
Figure BDA0003070032970000122
(G3) Capacity constraint:
Figure BDA0003070032970000123
Esoc＝Esoc. first (23)
In the formula: x is the number ofDESRepresenting the energy storage discharge state, xCESRepresenting the energy storage state of charge, as a 0-1 state variable (which isMiddle 1 represents working state, 0 represents non-working state);
Figure BDA0003070032970000126
is the lower limit value of the discharge power of the energy storage station,
Figure BDA0003070032970000127
Is the upper limit value of the discharge power of the energy storage station,
Figure BDA0003070032970000128
Is the lower limit value of the charging power of the energy storage station,
Figure BDA0003070032970000129
The upper limit value of the charging power of the energy storage station is set;
Figure BDA00030700329700001210
respectively the upper and lower limit values of the stored energy of the energy storage station;
(H) heat pump operation constraints
Figure BDA0003070032970000131
In the formula:
Figure BDA0003070032970000138
thermal power, unit, representing the heat pump output in the substation at time t: kW;
Figure BDA0003070032970000139
the unit of the maximum heat power allowed to be output by the heat pump in the transformer substation is as follows: kW.
(I) Operation constraint of electric refrigerator in transformer substation
Figure BDA0003070032970000132
In the formula:
Figure BDA00030700329700001310
cold power output from the electric refrigerator in the substation at time t is expressed in units of: kW;
Figure BDA00030700329700001311
the maximum output cold power allowed by the electric refrigerator is as follows: kW.
(J) Operation constraint of absorption refrigerator in transformer substation
Figure BDA0003070032970000133
In the formula:
Figure BDA00030700329700001312
the cold power output by the absorption chiller in the transformer substation at time t is represented by the unit: kW;
Figure BDA00030700329700001313
maximum output electric power allowed by an absorption chiller in a substation, unit: kW.
(K) Electric refrigerator operation constraint in energy storage station
Figure BDA0003070032970000134
In the formula:
Figure BDA00030700329700001314
represents the cold power output by the electric refrigerator in the energy storage station at time t, and the unit is: kW;
Figure BDA00030700329700001315
the maximum output cold power allowed by the electric refrigerator is as follows: kW.
(L) absorption chiller operational constraints in data center stations
Figure BDA0003070032970000135
In the formula:
Figure BDA00030700329700001316
the cold power output by the absorption refrigerator in the data center station at the time t is represented by the following unit: kW;
Figure BDA00030700329700001317
maximum output electric power level allowed by absorption chiller in data center station, unit: kW.
(M) thermal storage device operational constraints.
(M1) Heat accumulation and Release State constraint
yDH+yCH≤1 (29)
(M2) Heat accumulation and discharge Power constraint
Figure BDA0003070032970000136
Figure BDA0003070032970000137
(M3) capacity constraint:
Figure BDA00030700329700001318
Qh. powder＝Qh. First stage (33)
In the formula: y isDHIndicating the heat-releasing state of the heat storage device, yCHThe state of the heat storage device is represented as a 0-1 state variable (wherein 1 represents an operating state, and 0 represents a non-operating state);
Figure BDA0003070032970000143
the upper limit value of the heat-releasing power of the heat storage device,
Figure BDA0003070032970000144
The lower limit value of the heat-releasing power of the heat storage device,
Figure BDA0003070032970000145
The upper limit value of the heat storage capacity of the heat storage device,
Figure BDA0003070032970000146
The lower limit value of the heat storage power of the heat storage device;
Figure BDA0003070032970000147
the upper limit value and the lower limit value of the stored heat of the heat storage device are respectively set;
(N) Security constraints
The safety index, i.e. the voltage safety margin, determines the safety constraints of the system operation according to equation (34)
Figure BDA0003070032970000141
In the formula, Δ Ux,tThe voltage deviation amount of the node x is t time period; delta UxThe maximum node voltage deviation allowed by the node x is-8% to + 5%; xi is a threshold value of the safety margin of the multi-station fusion system; beta is the confidence level of the safety margin of the multi-station fusion system; the function phi is a 0-1 state variable, and the calculation formula is as follows:
Figure BDA0003070032970000142
(O) reliability constraints
The system power supply reliability index is the probability of load loss and is determined according to equation (36).
HLOLP＝P[(Ppv,t±εt)+(PDES,t±δt)+(Pnet,t±γt)≤PL,t±κL,t]≤1-γ (36)
In the formula, HLOLPFor a defined power supply reliability index in the fusion station, epsilontThe light abandoning amount and the disturbance amount in the new energy station at the time t are represented by the following unit: kW; ppv,tAnd (3) representing the generated power of the new energy station at the time t, wherein the unit is as follows: kW; pDES,tThe unit is the power of the energy storage station at the moment t: kW; deltatDisturbance quantity representing discharge power of the energy storage station at time t, unit: kW; pnet,tRepresents the transmission power of the link at time t, unit: kW; gamma raytRepresents the amount of power disturbance on the link at time t, in units: kW; pL,tAnd (3) the total electric load of the fusion station at the time t is represented by the unit: kW; kappaL,tThe load fluctuation amount at time t is expressed by unit: kW; gamma is the confidence level of the reliability of the multi-station fusion system.
S3) establishing a coordinated control model according to the mathematical model in the step S2)
The method comprises the steps of establishing a coordinated control model by taking economic operation cost as a target, and mainly considering how to consume more new energy output, how to improve and ensure the safe reliability of system operation and how to reduce the emission of pollution gas caused by the operation of a fusion station in the coordinated control model. The light abandonment amount is converted into the light abandonment punishment cost and considered in the system operation cost, the load interruption amount is converted into the load interruption punishment cost and considered in the system operation cost, and the pollution gas emission treatment cost is considered in the system operation cost.
minF＝min(Fg+Fom+Fre+Fgas) (37)
In the formula, F is the total cost of system operation; fgThe electricity purchase cost for the fusion station; fomThe operation and maintenance cost of the fusion station; freThe power supply reliability cost of the fusion station comprises light abandon punishment cost and load interruption cost of the new energy station; fgasCost of emission of pollution gases for the fusion station;
electricity purchase cost F of fusion stationgAs shown in equation (38):
Figure BDA0003070032970000151
in the formula, CbThe unit is that the price of electricity purchased/sold from the power grid in the t time period is as follows: yuan/kWh; pnet.tTo represent the transmission power of the link at time t, the unit: kW.
FusionOperating maintenance costs F of the stationomAs shown in formula (39):
Figure BDA0003070032970000152
in the formula, KombFor the operational maintenance factor, P, of the b-th equipment of the systemb(t) the operation power of the b-th equipment in the t time period; and P isb(t) by P in step S2l1、
Figure BDA0003070032970000155
P、pvPPDES、Pl2、PIT、
Figure BDA0003070032970000156
The operating power of (c).
Power supply reliability cost F of fusion stationreThe system mainly comprises a light abandoning penalty cost and a load interruption cost, and is represented by a formula (40):
Figure BDA0003070032970000153
in the formula, CpvPunishing price for unit light abandon; pdesDiscarding the light quantity for the t time period of the system; clossThe unit punishment price suffered by the system when the load is interrupted; plossThe amount of load lost for the interruption load during the t period of the system.
Fusion station pollutant gas emission cost FgasAs shown in formula (41):
Figure BDA0003070032970000154
in the formula, λMUnit cost for mth type of contaminated gas, unit: yuan/kg; beta is aMEmission coefficient of mth kind of pollution gas generated per degree of electricity, unit: yuan/kWh; pnet.tTo represent the transmission power of the link at time t, the unit:kW. Four gases NO are considered hereX、CO2、SO2、SO3。
S4) solution of coordinated control model
Considering that the particle swarm algorithm has the characteristics of high precision, high convergence speed, easy realization and the like, and has certain advantages in solving practical problems, the particle swarm algorithm is adopted for solving, the solving flow is shown in figure 4, and the solving process is as follows:
s4a) initialization parameters: the photovoltaic power generation system comprises a load, a photovoltaic output, tie line power, energy storage station charging and discharging power and the like, and the initialization power is shown in the formula (42).
P0＝Pmin+(Pmax-Pmin)α1 (42)
In the formula, P0To initialize the output power, the unit: kW; pminIs P0Lower limit of (D), PmaxIs P0Upper limit, unit: kW; alpha is alpha1Is a random number between 0 and 1.
S4b) setting the number of particles and the iteration times, generating initial particles, and initializing a local optimal solution and a global optimal solution of the particles;
s4c) calculating the objective function value of each particle;
s4d) updating the local optimal solution and the global optimal solution of the particles;
s4e) determining whether the condition for ending iteration is satisfied, if so, performing step S4g), and if not, performing step S4 f);
s4f) updating the position and velocity of the particle, and then jumping back to perform step S4 c);
s4g) outputs the calculated optimal solution set.
As shown in fig. 5, the coordination control system of the four-station integrated energy system coordination control method includes:
the new energy station power generation prediction module is used for predicting the power generation power of the new energy station;
the fusion station load power prediction module is used for predicting the load power of the fusion station;
a fusion station mathematical model setting module for setting four substation mathematical models, wherein the four substation mathematical models are respectively a power station mathematical model, a new energy station mathematical model, an energy storage station mathematical model and a data center station mathematical model, and setting an operation constraint condition according to the predicted value in the step S1);
a coordination control model setting module for establishing a coordination control model according to the operation power in the step S2) and the sum of the electricity purchasing cost of the fusion station, the operation maintenance cost, the power supply reliability cost and the pollution gas emission cost in the fusion station;
and the coordination control model solving module is used for solving the coordination control model by adopting a particle swarm algorithm.
The method takes the minimum daily running total cost of the four-station fusion system as an objective function, introduces light abandon penalty cost to reduce the light abandon amount and realize full consumption of new energy aiming at the phenomenon of light abandon of a new energy station in a fusion station. Aiming at relevant equipment in a fusion station, particularly a data center station, in order to improve the power supply reliability, load interruption punishment cost is introduced, meanwhile, the safety and reliability of the fusion station are considered, safety and reliability indexes are defined, and safety and reliability constraints are established. Considering that the operation of the fusion station can bring environmental problems, especially the emission of the polluted gas, the pollution gas treatment cost is introduced, and the emission amount of the polluted gas in the operation of the fusion station is reduced. The invention fully ensures the safety, reliability and environmental protection of the operation of each substation while ensuring the economic operation of the multi-station fusion system, and has certain practical application value.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (10)

1. A four-station integrated comprehensive energy system coordination control method comprises four substations, wherein the four substations are a transformer substation, a new energy station, an energy storage station and a data center station respectively; the method is characterized in that: the coordination control method comprises the following steps:
s1) predicting the power generation power of the new energy station and predicting the load power of the fusion station;
s2) setting mathematical models of all the substations in the fusion station and setting operation constraint conditions according to the predicted values in the step S1);
the mathematical model comprises a transformer substation mathematical model, a new energy station mathematical model, an energy storage station mathematical model and a data center station mathematical model;
s3), establishing a coordination control model, and establishing a target function of the coordination control model according to the mathematical model in the step S2) and the sum of the electricity purchasing cost of the fusion station, the operation and maintenance cost, the power supply reliability cost and the pollutant gas emission cost in the fusion station;
s4) solving the coordination control model.
2. The four-station integrated energy system coordination control method according to claim 1, characterized in that: the step S2):
transformer substation mathematical model
PSL＝Pzl+Pgl+Pal+Pml (1)
In the formula, PSLThe total load of the transformer substation is obtained; pzlIs the refrigeration load size; pglIs the magnitude of the lighting load; palThe security load is; pmlThe size is the heating load;
(a) refrigeration load mathematical model
The cold load in the transformer substation is provided by an electric refrigerator and an absorption refrigerator, and mathematical models are respectively shown as formula (2) and formula (3):
(a1) mathematical model of electric refrigerator
Figure FDA0003070032960000011
In the formula (I), the compound is shown in the specification,
Figure FDA0003070032960000012
represents the output cold power of the electric refrigerator at time t; pl1Represents the electric power consumed by the electric refrigerator at time t; psiec1The refrigeration coefficient of the electric refrigerator;
(a2) mathematical model of absorption refrigerator
Figure FDA0003070032960000013
In the formula (I), the compound is shown in the specification,
Figure FDA0003070032960000014
represents the output cold power of the absorption chiller at time t;
Figure FDA0003070032960000015
represents the thermal power consumed by the absorption chiller at time t; psiac1The refrigeration coefficient of the absorption refrigerator;
(b) heating load mathematical model
The heat load in the transformer substation is heated by a heat pump and a heat storage device, and mathematical models are respectively shown as formula (4) and formula (5):
(b1) heat pump mathematical model
Figure FDA0003070032960000021
In the formula (I), the compound is shown in the specification,
Figure FDA0003070032960000022
represents the thermal power output by the heat pump in the substation at time t,
Figure FDA0003070032960000023
representing the magnitude of the electric power consumed by the heat pump at time t, ξhpThe coefficient of heating performance of the heat pump;
(b2) mathematical model of heat storage device
Figure FDA0003070032960000024
In the formula:
Figure FDA0003070032960000025
respectively representing the heat storage power and the heat release power of the thermal energy storage device at the time t,
Figure FDA0003070032960000026
respectively representing the heat storage efficiency and the heat release efficiency, Q, of a thermal energy storage deviceh,t+1、Qh,tRespectively representing the heat energy of the thermal energy storage equipment at the moment t +1 and the electric energy of the thermal energy storage equipment at the moment t; epsilon is the self-loss coefficient of the thermal energy storage equipment.
3. The four-station integrated energy system coordination control method according to claim 1, characterized in that: the step S2):
mathematics model of new energy station
Figure FDA0003070032960000027
In the formula, PpvThe photovoltaic power generation power is obtained; pst.maxThe maximum test power of the photovoltaic under standard experimental conditions; esIs the intensity of the illumination; es.stThe illumination intensity under standard experimental conditions; k is a power temperature coefficient; t isoIs the actual temperature, T, of the panelstIs the temperature under standard experimental conditions.
4. The four-station integrated energy system coordination control method according to claim 1, characterized in that: the step S2):
energy storage station mathematics model
Figure FDA0003070032960000028
In the formula, SOC (t) is the state of charge of the energy storage power station at the time t; delta is the self-discharge coefficient of the energy storage power station; pCESFor charging power, P, of energy-storage power stationsDESThe discharge power of the energy storage power station; etaCESFor charging efficiency, eta, of energy-storage power stationsDESThe discharge efficiency of the energy storage power station; esocThe rated capacity of the energy storage power station; Δ t is a scheduling time period interval;
(a) mathematical model of electric refrigerator
The cold load in the energy storage station is provided by an electric refrigerator, and the mathematical model is shown as the formula (8):
Figure FDA0003070032960000029
in the formula (I), the compound is shown in the specification,
Figure FDA00030700329600000210
representing the output cold power of the electric refrigerator in the energy storage station at time t; pl2Representing the electric power consumed by the electric refrigerator in the energy storage station at time t; psiec2Is the refrigeration coefficient of the electric refrigerator.
5. The four-station integrated energy system coordination control method according to claim 1, characterized in that: the step S2):
data center station mathematical model
PDC＝PIT+Pzl1+Pbat (9)
In the formula, PDCThe total load of the data center station; pITThe load size of the IT equipment; pzl1The load of refrigeration equipment of the data center station is obtained; pbatThe size of power transmission and distribution of the data center station;
(a) IT equipment mathematical model
The electric energy consumed by the servers in the IT equipment load accounts for 80% of the total power consumption, and the total power consumption of the servers is related to the number of the working servers;
Figure FDA0003070032960000031
in the formula, PNThe power consumption of a single server in a normal working state; n islP for a normally operating serverMThe power consumption of a single server in a dormant state; n islNumber of servers for normal operation, nmThe number of the working servers in the dormant state;
(b) mathematical model of absorption refrigerator
Figure FDA0003070032960000032
In the formula (I), the compound is shown in the specification,
Figure FDA0003070032960000033
representing the output cold power of the absorption chiller of the data center station at time t;
Figure FDA0003070032960000034
represents the thermal power consumed by the absorption chiller of the data center station at time t; psiac2The refrigeration coefficient of the absorption chiller of the data center station.
6. The coordination control method for the four-station integrated energy system according to claim 2, 3, 4 or 5, wherein: the operation constraint conditions in the step S2) are as follows:
(A) fusion station electrical balance constraints
Pnet.t+PDES.t+Ppv.t＝PCES.t+PSL.t+PDC.t (12)
In the formula: pne.ttFor fusing the interaction work between the station and the power grid at the moment tRate; pDES.tThe discharge power of the energy storage power station at the moment t is obtained; ppv.tThe generated power of the new energy station at the moment t; pCES.tThe charging power of the energy storage station at the moment t is obtained; pSL.tFor the electric power consumed by the substation at time t, PDC.tThe electrical power consumed by the data center station at time t; and P ispv.t、PSL.t、PDC.tAll obtained by prediction in step S1);
(B) fusion station thermal balance constraints
Figure FDA0003070032960000035
In the formula (I), the compound is shown in the specification,
Figure FDA0003070032960000036
representing the thermal power output by a heat pump in the transformer substation at the time t; qh,tThe thermal power output by a thermal storage device in the transformer substation at the moment t; htThe thermal load power of the fusion station is obtained; and HtPredicted by step S1);
(C) fusion station cold balance constraint
Figure FDA0003070032960000037
In the formula (I), the compound is shown in the specification,
Figure FDA0003070032960000038
outputting cold power for the electric refrigerator at the time t in the transformer substation;
Figure FDA0003070032960000039
the output cold power of the electric refrigerator at the time t in the energy storage station is obtained;
Figure FDA00030700329600000310
outputting cold power of the absorption refrigerator at the time t in the transformer substation;
Figure FDA00030700329600000311
the output cold power of the absorption refrigerator at the time t in the data center station; cL,tThe total cold load power at the t moment in the fusion station is obtained; and CL,tPredicted by step S1);
(D) fusion station voltage current constraints
Ux.min≤Ux≤Ux.max (15)
|Ic|≤Ic.max (16)
In the formula: u shapexFor fusing the amplitude, U, of the voltage of each substation in the stationx.maxUpper limit value of node voltage in each substation, Ux.minThe lower limit value of the node voltage in each substation; i iscFor fusing the current values of the lines in the station, Ic.maxThe upper limit value of the line current in the fusion station is set;
(E) fusion station transmission line power constraints
Pnet.t≤Pmax (17)
In the formula, Pne.ttThe interactive power of the fusion station and the power grid at the moment t is obtained; pmaxMaximum power allowing interaction for transmission lines in the convergence station;
(J) operating constraints for new energy station
0≤Ppv.t≤Ppvmax (18)
In the formula, Ppv.tThe generated power of the new energy station at the moment t is obtained; ppvmaxGenerating a maximum value of electric power for the new energy station;
(K) energy storage station operation constraints
(G1) Charge and discharge state constraints
xDES+xCES≤1 (19)
(G2) Charge and discharge power constraint
Figure FDA0003070032960000041
Figure FDA0003070032960000042
(G3) Capacity constraint:
Figure FDA0003070032960000043
Esoc＝Esoc. first (23)
In the formula: x is the number ofDESRepresenting the energy storage discharge state, xCESRepresenting the energy storage charging state, and being a 0-1 state variable, wherein 1 represents the working state, and 0 represents the non-working state;
Figure FDA0003070032960000044
is the lower limit value of the discharge power of the energy storage station,
Figure FDA0003070032960000045
Is the upper limit value of the discharge power of the energy storage station,
Figure FDA0003070032960000046
Is the lower limit value of the charging power of the energy storage station,
Figure FDA0003070032960000047
The upper limit value of the charging power of the energy storage station is set;
Figure FDA0003070032960000048
respectively the upper and lower limit values of the stored energy of the energy storage station;
(H) heat pump operation constraints
Figure FDA0003070032960000049
In the formula:
Figure FDA00030700329600000410
representing the thermal power output by a heat pump in the transformer substation at the time t;
Figure FDA00030700329600000411
the maximum heat power allowed to be output by the heat pump in the transformer substation;
(I) operation constraint of electric refrigerator in transformer substation
Figure FDA00030700329600000412
In the formula:
Figure FDA0003070032960000051
representing the cold power output by the electric refrigerator in the transformer substation at the time t;
Figure FDA0003070032960000052
the maximum output cold power allowed by the electric refrigerator;
(J) operation constraint of absorption refrigerator in transformer substation
Figure FDA0003070032960000053
In the formula:
Figure FDA0003070032960000054
the cold power output by the absorption refrigerator in the transformer substation at the time t is represented;
Figure FDA0003070032960000055
the maximum output electric power allowed by the absorption refrigerator in the substation;
(K) electric refrigerator operation constraint in energy storage station
Figure FDA0003070032960000056
In the formula:
Figure FDA0003070032960000057
representing the cold power output by the electric refrigerator in the energy storage station at the moment t;
Figure FDA0003070032960000058
the maximum output cold power allowed by the electric refrigerator;
(L) absorption chiller operational constraints in data center stations
Figure FDA0003070032960000059
In the formula:
Figure FDA00030700329600000510
the cold power output by the absorption refrigerator in the data center station at the time t is represented;
Figure FDA00030700329600000511
the maximum output electric power allowed by the absorption refrigerator in the data center station;
(M) Heat storage device operation constraints
(M1) Heat accumulation and Release State constraint
yDH+yCH≤1(29)
(M2) Heat accumulation and discharge Power constraint
Figure FDA00030700329600000512
Figure FDA00030700329600000513
(M3) capacity constraints
Figure FDA00030700329600000514
Qh. Powder＝Qh. First stage(33)
In the formula: y isDHIndicating the heat-releasing state of the heat storage device, yCHThe state of the heat storage device is represented as a 0-1 state variable, wherein 1 represents an operating state, and 0 represents a non-operating state;
Figure FDA00030700329600000515
the upper limit value of the heat-releasing power of the heat storage device,
Figure FDA00030700329600000516
The lower limit value of the heat-releasing power of the heat storage device,
Figure FDA00030700329600000517
The upper limit value of the heat storage capacity of the heat storage device,
Figure FDA00030700329600000518
The lower limit value of the heat storage power of the heat storage device;
Figure FDA00030700329600000519
the upper limit value and the lower limit value of the stored heat of the heat storage device are respectively set;
(N) Security constraints
The safety index, i.e. the voltage safety margin, determines the safety constraints of the system operation according to equation (34)
Figure FDA00030700329600000520
In the formula, Δ Ux,tThe voltage deviation amount of the node x is t time period; delta UxThe maximum node voltage deviation allowed by the node x is-8% to + 5%; xi is a threshold value of the safety margin of the multi-station fusion system; beta is the confidence level of the safety margin of the multi-station fusion system; the function phi is a 0-1 state variable, and the calculation formula is as follows:
Figure FDA0003070032960000061
(O) reliability constraints
The system power supply reliability index is the load loss probability and is determined according to the formula (36)
HLOLP＝P[(Ppv,t±εt)+(PDES,t±δt)+(Pnet,t±γt)≤PL,t±κL,t]≤1-γ (36)
In the formula, HLOLPFor a defined power supply reliability index in the fusion station, epsilontRepresenting the light abandoning amount and the disturbance amount in the new energy station at the time t; ppv,tRepresenting the generated power of the new energy station at the time t; pDES,tThe power of the energy storage station at the moment t; deltatRepresenting the disturbance quantity of the discharge power of the energy storage station at the moment t;
Figure FDA0003070032960000066
represents the transmission power of the tie line at time t; gamma raytRepresenting the power disturbance quantity on the connecting line at the time t; pL,tRepresenting the total electric load size of the fusion station at the time t; kappaL,tRepresents the load fluctuation amount at time t; gamma is the confidence level of the reliability of the multi-station fusion system.
7. The four-station integrated energy system coordination control method according to claim 6, characterized in that: the objective function of the coordinated control model in step S3) is:
minF＝min(Fg+Fom+Fre+Fgas) (37)
in the formula, F is the total cost of system operation; fgThe electricity purchase cost for the fusion station; fomThe operation and maintenance cost of the fusion station; freThe power supply reliability cost of the fusion station comprises light abandon punishment cost and load interruption cost of the new energy station; fgasCost of emission of pollution gases for the fusion station;
electricity purchase cost F of fusion stationgAs shown in formula (38)：
Figure FDA0003070032960000062
In the formula, CbBuying/selling electricity price from the power grid for the time period t;
Figure FDA0003070032960000067
is a signal representing the transmission power of the tie at time t;
operation and maintenance cost F of fusion stationomAs shown in formula (39):
Figure FDA0003070032960000063
in the formula, KombFor the operational maintenance factor, P, of the b-th equipment of the systemb(t) the operation power of the b-th equipment in the t time period; and P isb(t) by P in step S2l1、
Figure FDA0003070032960000064
PDES、Pl2、PIT、
Figure FDA0003070032960000065
The operating power of (c);
power supply reliability cost F of fusion stationreThe light abandonment penalty cost and the load interruption cost are formed, and the formula (40) is as follows:
Figure FDA0003070032960000071
in the formula, CpvPunishing price for unit light abandon; pdesDiscarding the light quantity for the t time period of the system; clossThe unit punishment price suffered by the system when the load is interrupted; plossThe load quantity of the interrupted load loss in the t time period of the system is obtained;
fusion station pollutant gas emission cost FgasAs shown in formula (41):
Figure FDA0003070032960000072
in the formula, λMUnit cost for mth contaminated gas; beta is aMThe discharge coefficient of the M < th > pollution gas generated per degree of electricity; pne.ttTo represent the transmission power of the tie at time t.
8. The four-station integrated energy system coordination control method according to claim 1, characterized in that: the step S1): predicting the power generation power of the new energy station and the load power of the fusion station by adopting a BP neural network model; the step S4): and solving the coordination control model by adopting a particle swarm algorithm.
9. A storage medium, characterized by: the storage medium has stored therein a computer program, wherein the computer program is arranged to execute the coordination control method of any one of claims 1 to 8 when executed.
10. A coordinated control system of a four-station integrated energy system is characterized in that: the method comprises the following steps:
the new energy station power generation prediction module is used for predicting the power generation power of the new energy station;
the fusion station load power prediction module is used for predicting the load power of the fusion station;
the system comprises a fusion station mathematical model setting module, a new energy station mathematical model setting module, a data center station mathematical model setting module and a power station mathematical model setting module, wherein the fusion station mathematical model setting module is used for setting four substation mathematical models which are respectively a power station mathematical model, a new energy station mathematical model, an energy storage station mathematical model and a data center station mathematical model, and setting operation constraint conditions according to predicted values;
the coordination control model setting module is used for establishing a coordination control model according to the running power and the sum of the electricity purchasing cost of the fusion station, the running maintenance cost in the fusion station, the power supply reliability cost and the pollution gas emission cost;
and the coordination control model solving module is used for solving the coordination control model by adopting a particle swarm algorithm.
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