CN113315165B - 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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CN113315165B
CN113315165B CN202110536469.7A CN202110536469A CN113315165B CN 113315165 B CN113315165 B CN 113315165B CN 202110536469 A CN202110536469 A CN 202110536469A CN 113315165 B CN113315165 B CN 113315165B
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CN113315165A (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, wherein the four substations 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 implementation of landing of the power internet of things, and is used for converging resources such as a transformer substation, an edge data center station, a charging station and an energy storage station, optimizing urban resource configuration, improving data sensing and analysis operation efficiency and carrying out local load absorption.
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 comprehensive energy system, wherein the four-station integrated comprehensive 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 power generation power of a new energy station and predicting the load power (namely consumed electricity, heat and cold power) of a fusion station;
s2) setting mathematical models of all 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 purchase cost of the fusion station, the operation and maintenance cost, the power supply reliability cost and the pollution gas emission cost;
and S4) solving the coordination control model.
Further, in the step S2):
transformer substation mathematical model
P SL ＝P zl +P gl +P al +P ml (1)
In the formula, P SL The total load of the transformer substation is obtained; p zl Is the refrigeration load size; p is gl Is the magnitude of the lighting load; p al The security load is the size; p ml The 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 GDA0003642566300000021
In the formula (I), the compound is shown in the specification,
Figure GDA0003642566300000022
represents the output cold power of the electric refrigerator at time t; p is l1 Represents the electric power consumed by the electric refrigerator at time t; psi ec1 The refrigeration coefficient of the electric refrigerator;
(a2) Mathematical model of absorption refrigerator
Figure GDA0003642566300000023
In the formula (I), the compound is shown in the specification,
Figure GDA0003642566300000024
represents the output cold power of the absorption chiller at time t;
Figure GDA0003642566300000025
represents the thermal power consumed by the absorption chiller at time t; psi ac1 The 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 GDA0003642566300000026
In the formula (I), the compound is shown in the specification,
Figure GDA0003642566300000027
represents the thermal power output by the heat pump in the substation at time t,
Figure GDA0003642566300000028
representing the magnitude of the electric power consumed by the heat pump at time t, ξ hp The coefficient of heating performance of the heat pump;
(b2) Mathematical model of heat storage device
Figure GDA0003642566300000029
In the formula:
Figure GDA00036425663000000210
respectively represents the heat storage power and the heat release power of the thermal energy storage device at the time t,
Figure GDA00036425663000000211
respectively representing the heat storage efficiency and the heat release efficiency, Q, of a thermal energy storage device h,t+1 、Q h,t Respectively expressed as the heat energy of the thermal energy storage equipment at the time t +1 and the electric energy of the thermal energy storage equipment at the time t; epsilon is the self-loss coefficient of the thermal energy storage equipment; Δ T is the scheduling time period interval.
Further, in the step S2):
mathematics model of new energy station
Figure GDA00036425663000000212
In the formula, P pv The magnitude of the photovoltaic power generation power; p st.max The maximum test power of the photovoltaic under standard experimental conditions; e s Is the intensity of the illumination; e s.st The illumination intensity under standard experimental conditions; k is a power temperature coefficient; t is o Is the actual temperature, T, of the panel st Is the temperature under standard experimental conditions.
Further, in the step S2):
energy storage station mathematics model
Figure GDA0003642566300000031
In the formula, SOC (t) is the state of charge of the energy storage power station at the moment t; delta is the self-discharge coefficient of the energy storage power station; p CES For charging power, P, of energy-storage power stations DES The discharge power of the energy storage power station; eta CES Charging efficiency, eta, for energy-storage power stations DES The discharge efficiency of the energy storage power station; e soc The 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 a mathematical model is shown as the formula (8):
Figure GDA0003642566300000032
in the formula (I), the compound is shown in the specification,
Figure GDA0003642566300000033
representing the output cold power of the electric refrigerator in the energy storage station at time t; p is l2 Representing the electric power consumed by the electric refrigerator in the energy storage station at time t; psi ec2 The refrigeration coefficient of the electric refrigerator.
Further, in the step S2):
data center station mathematical model
P DC ＝P IT +P zl1 +P bat (9)
In the formula, P DC The total load of the data center station; p IT The load size of the IT equipment; p zl1 The load of refrigeration equipment of the data center station is obtained; p bat The 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 GDA0003642566300000034
in the formula, P N The power consumption of a single server in a normal working state; n is a radical of an alkyl radical l P for a normally operating server M The power consumption of a single server in a dormant state; n is l Number of servers for normal operation, n m The number of the working servers in the dormant state;
(b) Mathematical model of absorption refrigerator
Figure GDA0003642566300000035
In the formula (I), the compound is shown in the specification,
Figure GDA0003642566300000036
indicating the output cold power of the absorption chiller of the data center station at time t;
Figure GDA0003642566300000037
represents the thermal power consumed by the absorption chiller of the data center station at time t; psi ac2 The 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
P net.t +P DES.t +P pv.t ＝P CES.t +P SL.t +P DC.t (12)
In the formula: p net.t The interaction power of the fusion station and the power grid at the moment t is obtained; p DES.t The discharge power of the energy storage power station at the moment t is obtained; p is pv.t The generated power of the new energy station at the moment t; p CES.t The charging power of the energy storage station at the moment t is obtained; p SL.t For the electric power consumed by the substation at time t, P DC.t The electrical power consumed by the data center station for time t; and P is pv.t 、P SL.t 、P DC.t All the data are obtained by predicting in the step S1);
(B) Fusion station thermal balance constraints
Figure GDA0003642566300000041
In the formula (I), the compound is shown in the specification,
Figure GDA0003642566300000042
the thermal power output by the heat pump in the transformer substation at the time t is represented; q h,t The thermal power output by a thermal storage device in the transformer substation at the moment t; h t The thermal load power of the fusion station is obtained; and H t Predicted by step S1);
(C) Fusion station cold balance constraint
Figure GDA0003642566300000043
In the formula (I), the compound is shown in the specification,
Figure GDA0003642566300000044
outputting cold power for the electric refrigerator at the time t in the transformer substation;
Figure GDA0003642566300000045
outputting cold power of the electric refrigerator at the t moment in the energy storage station;
Figure GDA0003642566300000046
outputting cold power of the absorption refrigerator at the time t in the transformer substation;
Figure GDA0003642566300000047
the output cold power of the absorption refrigerator at the time t in the data center station; c L,t The total cold load power at the t moment in the fusion station is obtained; and C L,t Predicted by step S1);
(D) Fusion station voltage current constraints
U x.min ≤U x ≤U x.max (15)
|I c |≤I c.max (16)
In the formula: u shape x For fusing the amplitude, U, of the voltage of each substation in the station x.max Upper limit value of node voltage in each substation, U x.min The lower limit value of the node voltage in each substation; I.C. A c For fusing the current values of the lines in the station, I c.max The upper limit value of the line current in the fusion station is set;
(E) Fusion station transmission line power constraints
P net.t ≤P max (17)
In the formula, P net.t The interactive power of the fusion station and the power grid at the moment t is obtained; p is max Maximum power allowing interaction for transmission lines in the convergence station;
(F) Operating constraints for new energy station
0≤P pv.t ≤P pvmax (18)
In the formula, P pv.t The generated power of the new energy station at the moment t is obtained; p is pvmax Generating a maximum value of electric power for the new energy station;
(G) Energy storage station operating constraints
(G1) Charge and discharge state constraints
x DES +x CES ≤1 (19)
(G2) Charge and discharge power constraint
Figure GDA0003642566300000048
Figure GDA0003642566300000051
(G3) Capacity constraint:
Figure GDA0003642566300000052
E soc ＝E soc. first (23)
In the formula: x is a radical of a fluorine atom DES Representing the energy storage discharge state, x CES Representing 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 GDA0003642566300000053
is the lower limit value of the discharge power of the energy storage station,
Figure GDA0003642566300000054
Is the upper limit value of the discharge power of the energy storage station,
Figure GDA0003642566300000055
A lower limit value of the charging power of the energy storage station,
Figure GDA0003642566300000056
An upper limit value of the charging power of the energy storage station;
Figure GDA0003642566300000057
respectively the upper and lower limit values of the stored energy of the energy storage station; e soc. first 、E soc. end Respectively scheduling the residual electric quantity of the energy storage station at the initial scheduling time and the scheduling ending time in the whole scheduling time period;
(H) Heat pump operation constraints
Figure GDA0003642566300000058
In the formula:
Figure GDA0003642566300000059
representing the thermal power output by a heat pump in the transformer substation at the time t;
Figure GDA00036425663000000510
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 GDA00036425663000000511
In the formula:
Figure GDA00036425663000000512
the cold power output by the electric refrigerator in the transformer substation at the time t is represented;
Figure GDA00036425663000000513
the maximum output cold power allowed by the electric refrigerator;
(J) Operation constraint of absorption refrigerator in transformer substation
Figure GDA00036425663000000514
In the formula:
Figure GDA00036425663000000515
the cold power output by the absorption refrigerator in the transformer substation at the time t is represented;
Figure GDA00036425663000000516
the maximum output electric power allowed by the absorption refrigerator in the substation;
(K) Electric refrigerator operation constraint in energy storage station
Figure GDA00036425663000000517
In the formula:
Figure GDA00036425663000000518
the cold power output by the electric refrigerator in the energy storage station at the time t is represented;
Figure GDA00036425663000000519
the maximum output cold power allowed by the electric refrigerator;
(L) absorption chiller operational constraints in data center stations
Figure GDA00036425663000000520
In the formula:
Figure GDA00036425663000000521
the cold power output by the absorption refrigerator in the data center station at the time t is represented;
Figure GDA00036425663000000522
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 discharge State constraint
y DH +y CH ≤1 (29)
(M2) Heat accumulation and discharge Power constraint
Figure GDA0003642566300000061
Figure GDA0003642566300000062
(M3) Capacity constraints
Figure GDA0003642566300000063
Q h. Powder ＝Q h. First stage (33)
In the formula: y is DH Indicating the heat-releasing state of the heat-storage device, y CH The 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 GDA0003642566300000064
the upper limit value of the heat-releasing power of the heat storage device,
Figure GDA0003642566300000065
The lower limit value of the heat-releasing power of the heat storage device,
Figure GDA0003642566300000066
The upper limit value of the heat storage capacity of the heat storage device,
Figure GDA0003642566300000067
The lower limit value of the heat storage power of the heat storage device;
Figure GDA0003642566300000068
upper and lower limit values of the stored heat of the heat storage device respectively; q h. First stage 、Q h. Powder Respectively scheduling the residual heat quantity of the heat storage device at the initial scheduling time and the scheduling ending time in the whole scheduling time period;
(N) Security constraints
The safety index, namely the voltage safety margin, determines the safety constraint of the system operation according to the formula (34)
Figure GDA0003642566300000069
In the formula, Δ U x,t The voltage deviation amount of the node x is t time period; delta U x The 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 expressed as:
Figure GDA00036425663000000610
(O) reliability constraints
The system power supply reliability index is the load loss probability and is determined according to the formula (36)
H LOLP ＝P[(P pv,t ±ε t )+(P DES,t ±δ t )+(P net,t ±γ t )≤P L,t ±κ L,t ]≤1-γ (36)
In the formula, H LOLP For a defined power supply reliability index in the fusion station, epsilon t The light abandoning amount and the disturbance amount in the new energy station at the moment t are shown; p pv,t Representing the generated power of the new energy station at the time t; p DES,t The power of the energy storage station at the moment t is obtained; delta t Representing the disturbance quantity of the discharge power of the energy storage station at the moment t; p net,t Representing the transmission power of the tie line at time t; gamma ray t Representing the power disturbance quantity on the connecting line at the time t; p L,t Representing the total electric load size of the fusion station at the time t; kappa L,t Represents 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:
min F＝min(F g +F om +F re +F gas ) (37)
in the formula, F is the total cost of system operation; f g The electricity purchase cost for the fusion station; f om The operation and maintenance cost of the fusion station; f re The power supply reliability cost of the fusion station comprises light abandon punishment cost and load interruption cost of the new energy station; f gas Cost of emission of pollution gases for the fusion station;
electricity purchase cost F of fusion station g As shown in equation (38):
Figure GDA0003642566300000071
in the formula, C b Buying/selling electricity price from the power grid for the time period t; p is net.t Is a signal representing the transmission power of the tie at time t;
operation maintenance cost F of fusion station om As shown in formula (39):
Figure GDA0003642566300000072
in the formula, K omb For the operational maintenance factor, P, of the system's b-th equipment b (t) the operation power of the b-th equipment in the t time period; and P is b (t) from P in step S2) l1 、
Figure GDA0003642566300000073
P pv 、P CES P DES 、P l2 、P IT 、
Figure GDA0003642566300000074
The operating power of (c);
power supply reliability cost F of fusion station re The light abandonment penalty cost and the load interruption cost are formed, and the formula (40) is as follows:
Figure GDA0003642566300000075
in the formula, C pv Punishing price for unit light abandon; p des Discarding the light quantity for the t time period of the system; c loss The unit punishment price suffered by the system when the load is interrupted; p loss The load quantity of the interrupted load loss in the t time period of the system is obtained;
contaminated gas emission cost F of fusion station gas As shown in formula (41):
Figure GDA0003642566300000076
in the formula, λ M Unit cost for mth type of pollution gas; beta is a beta M Discharge coefficient of the mth kind of pollution gas generated for each degree of electricity; p net.t To 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; in 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 coordinated 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 coordinated 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, carries out peak clipping and valley filling to improve economy and has the function of carrying out renewable energy consumption, the data center station mainly provides data communication service for each substation in the fusion station, and the new energy station power generation is mainly used for supplying power for the fusion station electric equipment, 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 the steps of S1) new energy station power generation prediction and integration station load prediction, wherein the new energy station power generation prediction and the integration station load prediction are used for new energy station power generation prediction and integration 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; and S4) solving the coordination control model by adopting a particle swarm algorithm which is high in precision, high in convergence speed and easy to realize.
Referring to fig. 2, the coordination control method includes the following steps:
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.
S1 a) 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;
s1 b) 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;
s1 c) input and output value calculation: calculating the size of the input value and the output value of each layer;
s1 d) error calculation: calculating the error size of a network output layer;
s1 e) judging whether the error meets the precision requirement, if so, outputting a predicted value, and if not, returning to the step S1 c);
s1 f) outputting a predicted value meeting the error precision 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 all sub-stations in the fusion station and setting operation constraint conditions according to the predicted values in the step S1)
The model of each substation can be selected according to actual requirements, 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
P SL ＝P zl +P gl +P al +P ml (1)
In the formula, P SL The total load of the transformer substation is as follows: kW; p zl For the refrigeration load size, unit: kW; p gl The unit is the size of the lighting load: kW; p al For security protection load size, unit: kW; p ml The 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 GDA0003642566300000101
In the formula (I), the compound is shown in the specification,
Figure GDA0003642566300000102
represents the output cold power of the electric refrigerator at time t; p l1 Represents the electric power consumed by the electric refrigerator at time t; psi ec1 Is the refrigeration coefficient of the electric refrigerator.
(a2) Mathematical model of absorption refrigerator
Figure GDA0003642566300000103
In the formula (I), the compound is shown in the specification,
Figure GDA0003642566300000104
represents the output cold power of the absorption chiller at time t;
Figure GDA0003642566300000105
represents the thermal power consumed by the absorption chiller at time t; psi ac1 The refrigeration coefficient of the absorption refrigerator.
(b) Heating load mathematical model
The heat load in the transformer 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 GDA0003642566300000106
In the formula (I), the compound is shown in the specification,
Figure GDA0003642566300000107
represents the thermal power output by the heat pump in the substation at time t,
Figure GDA0003642566300000108
representing the magnitude of the electric power consumed by the heat pump at time t, ξ hp Is the heating coefficient of performance of the heat pump.
(b2) Mathematical model of heat storage device
Figure GDA0003642566300000109
In the formula:
Figure GDA00036425663000001010
respectively represents the heat storage power and the heat release power of the thermal energy storage device at the time t,
Figure GDA00036425663000001011
respectively representing the heat storage efficiency and the heat release efficiency, Q, of a thermal energy storage device h,t+1 、Q h,t Respectively expressed as the heat energy of the thermal energy storage equipment at the time t +1 and the electric energy of the thermal energy storage equipment at the time t; epsilon is the self-loss coefficient of the thermal energy storage equipment; Δ T is the scheduling time period interval.
(B) Mathematics model of new energy station
Figure GDA00036425663000001012
In the formula, P pv The unit is the photovoltaic power generation power: kW; p is st.max The maximum test power of the photovoltaic under standard experimental conditions; e s Is the intensity of the illumination; e s.st The illumination intensity under standard experimental conditions; k is a power temperature coefficient; t is a unit of o Is the actual temperature, T, of the panel st Is the temperature under standard experimental conditions.
(C) Energy storage station mathematics model
Figure GDA00036425663000001013
In the formula, SOC (t) is the state of charge of the energy storage power station at the moment t; delta is the self-discharge coefficient of the energy storage power station; p is CES For charging power, P, of energy-storage power stations DES Discharge power of the energy storage power station, unit: kW; eta CES For charging efficiency, eta, of energy-storage power stations DES The discharge efficiency of the energy storage power station; e soc Rated 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 GDA0003642566300000111
In the formula (I), the compound is shown in the specification,
Figure GDA0003642566300000112
representing the output cold power of the electric refrigerator in the energy storage station at time t; p l2 Represents the electric power consumed by the electric refrigerator in the energy storage station at the time t; psi ec2 Is the refrigeration coefficient of the electric refrigerator.
(D) Data center station mathematical model
P DC ＝P IT +P zl1 +P bat (9)
In the formula, P DC The unit is the total load of the data center station: kW; p IT The load of the IT equipment is as follows: kW; p zl1 The unit is the load of refrigeration equipment of a data center station: kW; p bat The 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 GDA0003642566300000113
In the formula, P N The power consumption of a single server in a normal working state, unit: kW; n is l P for a Normal working Server M The unit is the power consumption of a single server in a dormant state: kW; n is a radical of an alkyl radical l Number of servers for normal operation, n m The number of servers operating in a dormant state.
(b) Mathematical model of absorption refrigerator
Figure GDA0003642566300000114
In the formula (I), the compound is shown in the specification,
Figure GDA0003642566300000115
representing the output cold power of the absorption chiller of the data center station at time t;
Figure GDA0003642566300000116
represents the thermal power consumed by the absorption chiller of the data center station at time t; psi ac2 The 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
P net.t +P DES.t +P pv.t ＝P CES.t +P SL.t +P DC.t (12)
In the formula: p net.t The unit kW is the interaction power between the fusion station and the power grid at the moment t; p DES.t The unit of the discharge power at the moment t of the energy storage power station is as follows: kW; p pv.t The unit of the generated power of the new energy station at the moment t is as follows: kW; p CES.t The charging power at the moment t of the energy storage station is represented by the unit: kW; p is SL.t For the electric power consumed by the substation at time t, the unit: kW, P DC.t For the electrical power consumed by the data center station at time t, the unit: kW; and P is pv.t 、P SL.t 、P DC.t Predicted by step S1).
(B) Fusion station thermal balance constraints
Figure GDA0003642566300000117
In the formula (I), the compound is shown in the specification,
Figure GDA0003642566300000118
thermal power, unit, representing the heat pump output in the substation at time t: kW; q h,t The unit of the thermal power output by the thermal storage device in the transformer substation at the time t is as follows: kW; h t The unit is the thermal load power of the fusion station: kW; and isH t Predicted by step S1).
(C) Fusion station cold balance constraint
Figure GDA0003642566300000121
In the formula (I), the compound is shown in the specification,
Figure GDA0003642566300000122
the unit of the output cold power of the electric refrigerator at the time t in the transformer substation is as follows: kW;
Figure GDA0003642566300000123
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 GDA0003642566300000124
the unit of the output cold power of the absorption refrigerator at the time t in the transformer substation is as follows: kW;
Figure GDA0003642566300000125
the unit of the output cold power of the absorption refrigerator at the time t in the data center station is as follows: kW; c L,t The total cooling load power at the time t in the fusion station is as follows: kW; and C L,t Predicted 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.
U x.min ≤U x ≤U x.max (15)
|I c |≤I c.max (16)
In the formula: u shape x For fusing the amplitude, U, of the voltage of each substation in the station x.max Upper limit value of node voltage, U, in each substation x.min Is the lower limit value of the node voltage in each substation. I.C. A c For fusing the current values of the lines in the station, I c.max The upper limit value of the line current in the fusion station.
(E) Fusion station transmission line power constraints
P net.t ≤P max (17)
In the formula, P net.t The unit is the interactive power of the fusion station t moment and the power grid: kW; p max Maximum power for the transmission line in the fusion station to allow interaction, unit: kW.
(H) Operating constraints for new energy station
0≤P pv.t ≤P pvmax (18)
In the formula, P pv.t The generated power of the new energy station at the time t is represented by the unit: kW; p is pvmax The 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
x DES +x CES ≤1 (19)
(G2) Charge and discharge power constraint
Figure GDA0003642566300000126
Figure GDA0003642566300000127
(G3) Capacity constraint:
Figure GDA0003642566300000128
E soc ＝E soc. first (23)
In the formula: x is the number of DES Representing the energy storage discharge state, x CES Representing 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 GDA0003642566300000131
is the lower limit value of the discharge power of the energy storage station,
Figure GDA0003642566300000132
Is the upper limit value of the discharge power of the energy storage station,
Figure GDA0003642566300000133
Is the lower limit value of the charging power of the energy storage station,
Figure GDA0003642566300000134
The upper limit value of the charging power of the energy storage station is set;
Figure GDA0003642566300000135
respectively the upper and lower limit values of the stored energy of the energy storage station; e soc. first 、E soc Respectively scheduling the residual electric quantity of the energy storage station at the initial scheduling time and the scheduling ending time in the whole scheduling time period;
(H) Heat pump operation constraints
Figure GDA0003642566300000136
In the formula:
Figure GDA0003642566300000137
thermal power that represents the heat pump output in the substation at time t, unit: kW;
Figure GDA0003642566300000138
the unit of the maximum heat power allowed to be output by the heat pump in the transformer substation is as follows: kW.
(I) Electric refrigerator operation constraint in transformer substation
Figure GDA0003642566300000139
In the formula:
Figure GDA00036425663000001310
cold power output from the electric refrigerator in the substation at time t is expressed in units: kW;
Figure GDA00036425663000001311
the maximum output cold power allowed by the electric refrigerator is as follows: kW.
(J) Operation constraint of absorption refrigerator in transformer substation
Figure GDA00036425663000001312
In the formula:
Figure GDA00036425663000001313
the cold power output by the absorption chiller in the transformer substation at time t is represented by the unit: kW;
Figure GDA00036425663000001314
maximum output electric power allowed by an absorption chiller in a substation, unit: kW.
(K) Electric refrigerator operation constraint in energy storage station
Figure GDA00036425663000001315
In the formula:
Figure GDA00036425663000001316
cold power output by the electric refrigerator in the energy storage station at time t is expressed in units: kW;
Figure GDA00036425663000001317
the maximum output cold power allowed by the electric refrigerator is as follows: kW.
(L) absorption chiller operational constraints in data center stations
Figure GDA00036425663000001318
In the formula:
Figure GDA00036425663000001319
the cold power output by the absorption chiller in the data center station at the time t is represented by the following unit: kW;
Figure GDA00036425663000001320
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 discharge State constraint
y DH +y CH ≤1 (29)
(M2) Heat accumulation and discharge Power constraint
Figure GDA00036425663000001321
Figure GDA00036425663000001322
(M3) capacity constraint:
Figure GDA0003642566300000141
Q h. powder ＝Q h. First stage (33)
In the formula: y is DH Indicating the heat-releasing state of the heat storage device, y CH The heat storage state of the heat storage device is represented as a state variable of 0-1 (wherein 1 represents an operating state, and 0 represents a non-operating state);
Figure GDA0003642566300000142
the upper limit value of the heat-releasing power of the heat storage device,
Figure GDA0003642566300000143
The lower limit value of the heat-releasing power of the heat storage device,
Figure GDA0003642566300000144
The upper limit value of the heat storage capacity of the heat storage device,
Figure GDA0003642566300000145
The lower limit value of the heat storage power of the heat storage device;
Figure GDA0003642566300000146
the upper limit value and the lower limit value of the stored heat of the heat storage device are respectively set; (ii) a Q h. First stage 、Q h. Powder Respectively scheduling the residual heat quantity of the heat storage device at the initial scheduling time and the scheduling ending time in the whole scheduling time period;
(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 GDA0003642566300000147
In the formula, Δ U x,t The voltage deviation amount of the node x in the t time period; delta U x The 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 GDA0003642566300000148
(O) reliability constraints
The system power supply reliability index is the probability of load loss and is determined according to equation (36).
H LOLP ＝P[(P pv,t ±ε t )+(P DES,t ±δ t )+(P net,t ±γ t )≤P L,t ±κ L,t ]≤1-γ (36)
In the formula, H LOLP For a defined power supply reliability index in the fusion station, epsilon t The light abandoning amount and the disturbance amount in the new energy station at the time t are represented by the following unit: kW; p pv,t And the unit of the generated power of the new energy station at the time t is as follows: kW; p is DES,t To storePower at time t of the station, unit: kW; delta t Disturbance quantity representing discharge power of the energy storage station at time t, unit: kW; p net,t Transmission power of the link at time t, unit: kW; gamma ray t Represents the amount of power disturbance on the link at time t, in units: kW; p L,t And (3) the total electric load of the fusion station at the time t is represented by the unit: kW; kappa type L,t The 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.
min F＝min(F g +F om +F re +F gas ) (37)
In the formula, F is the total cost of system operation; f g The electricity purchase cost for the fusion station; f om The operation and maintenance cost of the fusion station; f re The power supply reliability cost of the fusion station comprises light abandon punishment cost and load interruption cost of the new energy station; f gas Cost of emission of pollution gases for the fusion station;
electricity purchase cost F of fusion station g As shown in equation (38):
Figure GDA0003642566300000151
in the formula, C b The unit is that the price of electricity purchased/sold from the power grid in the t time period is as follows: yuan/kWh; p is net.t To represent the transmission power of the link at time t, the unit: kW.
Operation of a fusion stationMaintenance cost F om As shown in formula (39):
Figure GDA0003642566300000152
in the formula, K omb For the operational maintenance factor, P, of the system's b-th equipment b (t) the operation power of the b-th equipment in the t time period; and P is b (t) from P in step S2) l1 、
Figure GDA0003642566300000153
P pv 、P CES P DES 、P l2 、P IT 、
Figure GDA0003642566300000154
The operating power of (c).
Power supply reliability cost F of fusion station re The system mainly comprises a light abandoning penalty cost and a load interruption cost, and is represented by a formula (40):
Figure GDA0003642566300000155
in the formula, C pv Punishing price for unit light abandon; p des Discarding the light quantity for the t time period of the system; c loss The unit punishment price is received when the system interrupts the load; p is loss The amount of load lost for the interruption load during the t period of the system.
Fusion station pollutant gas emission cost F gas As shown in formula (41):
Figure GDA0003642566300000156
in the formula of lambda M Unit cost of mth kind of pollution gas, unit: yuan/kg; beta is a beta M Discharge coefficient of mth kind of pollution gas generated per degree of electricity, unit: yuan/kWh; p net.t To represent the transmission power of the link at time t, in units: kW. Four gases NO are considered here X 、CO 2 、SO 2 、SO 3 。
S4) solving the coordination 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:
s4 a) 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).
P 0 ＝P min +(P max -P min )α 1 (42)
In the formula, P 0 To initialize the output power, the unit: kW; p min Is P 0 Lower limit of (D), P max Is P 0 Upper limit, unit: kW; alpha (alpha) ("alpha") 1 Is a random number between 0 and 1.
S4 b) 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;
s4 c) calculating the objective function value of each particle;
s4 d) updating the local optimal solution and the global optimal solution of the particles;
s4 e) judging whether the iteration termination condition is met, if so, executing a step S4 g), and if not, executing a step S4 f);
s4 f) updating the position and velocity of the particle, and then jumping back to step S4 c);
s4 g) outputting 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;
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 the predicted values 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 pollution gas, the pollution gas treatment cost is introduced, and the emission of the pollution gas in the operation of the fusion station is reduced. The invention 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, thereby having 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 so forth) 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 (7)

1. A coordination control method of a four-station integrated comprehensive energy system 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 a new energy station and predicting the load power of a fusion station;
s2) setting mathematical models of all 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;
in the step S2):
new energy station mathematics model
Figure FDA0003763179260000011
In the formula, P pv The photovoltaic power generation power is obtained; p st.max The maximum test power of the photovoltaic under standard experimental conditions; e s Is the intensity of the illumination; e s.st The illumination intensity under standard experimental conditions; k is a power temperature coefficient; t is a unit of o Is the actual temperature, T, of the panel st Is the temperature under standard experimental conditions;
the operation constraint conditions in the step S2) are as follows:
(A) Fusion station electrical balance constraints
P net.t +P DES.t +P pv.t ＝P CES.t +P SL.t +P DC.t (12)
In the formula: p is net.t The interaction power of the fusion station and the power grid at the moment t is obtained; p is DES.t The discharge power of the energy storage power station at the moment t is obtained; p pv.t The generated power of the new energy station at the moment t; p is CES.t Charging power for the energy storage station at the moment t; p SL.t For the electric power consumed by the substation at time t, P DC.t The electrical power consumed by the data center station for time t; and P is pv.t 、P SL.t 、P DC.t Are all made ofStep S1), prediction is obtained;
(B) Fusion station thermal balance constraints
Figure FDA0003763179260000012
In the formula (I), the compound is shown in the specification,
Figure FDA0003763179260000013
the thermal power output by the heat pump in the transformer substation at the time t is represented; q h,t The thermal power output by a thermal storage device in the transformer substation at the moment t; h t The thermal load power of the fusion station is obtained; and H t Predicted by step S1);
(C) Fusion station cold balance constraint
Figure FDA0003763179260000014
In the formula (I), the compound is shown in the specification,
Figure FDA0003763179260000021
outputting cold power for the electric refrigerator at the time t in the transformer substation;
Figure FDA0003763179260000022
the output cold power of the electric refrigerator at the time t in the energy storage station is obtained;
Figure FDA0003763179260000023
outputting cold power of the absorption refrigerator at the time t in the transformer substation;
Figure FDA0003763179260000024
the output cold power of the absorption refrigerator at the time t in the data center station; c L,t The total cold load power at the t moment in the fusion station is obtained; and C L,t Predicted by step S1);
(D) Fusion station voltage current constraints
U x.min ≤U x ≤U x.max (15)
|I c |≤I c.max (16)
In the formula: u shape x For fusing the amplitude, U, of the voltage of each substation in the station x.max Upper limit value of node voltage in each substation, U x.min The lower limit value of the node voltage in each substation; i is c For fusing the current values of the lines in the station, I c.max The upper limit value of the line current in the fusion station is set;
(E) Fusion station transmission line power constraints
P net.t ≤P max (17)
In the formula, P net.t The interactive power of the fusion station and the power grid at the moment t is obtained; p max Maximum power allowing interaction for transmission lines in the convergence station;
(F) Operating constraints of new energy station
0≤P pv.t ≤P pvmax (18)
In the formula, P pv.t The generated power of the new energy station at the moment t is obtained; p pvmax Generating a maximum value of power for the new energy station;
(G) Energy storage station operation constraints
(G1) Charge and discharge state constraints
x DES +x CES ≤1 (19)
(G2) Charge and discharge power constraint
Figure FDA0003763179260000025
Figure FDA0003763179260000026
(G3) Capacity constraint:
Figure FDA0003763179260000027
E soc. end ＝E soc. first (23)
In the formula: x is a radical of a fluorine atom DES Indicating the discharge state of stored energy, x CES Representing 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 FDA0003763179260000031
is the lower limit value of the discharge power of the energy storage station,
Figure FDA0003763179260000032
Is the upper limit value of the discharge power of the energy storage station,
Figure FDA0003763179260000033
Is the lower limit value of the charging power of the energy storage station,
Figure FDA0003763179260000034
An upper limit value of the charging power of the energy storage station;
Figure FDA0003763179260000035
respectively the upper and lower limit values of the stored energy of the energy storage station;
(H) Heat pump operation constraints
Figure FDA0003763179260000036
In the formula:
Figure FDA0003763179260000037
representing the thermal power output by a heat pump in the transformer substation at the time t;
Figure FDA0003763179260000038
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 FDA0003763179260000039
In the formula:
Figure FDA00037631792600000310
representing the cold power output by the electric refrigerator in the transformer substation at the time t;
Figure FDA00037631792600000311
the maximum output cold power allowed by the electric refrigerator;
(J) Operation constraint of absorption refrigerator in transformer substation
Figure FDA00037631792600000312
In the formula:
Figure FDA00037631792600000313
the cold power output by the absorption refrigerator in the transformer substation at the time t is represented;
Figure FDA00037631792600000314
the maximum output electric power allowed by the absorption chiller in the substation;
(K) Electric refrigerator operation constraint in energy storage station
Figure FDA00037631792600000315
In the formula:
Figure FDA00037631792600000316
representing the cold power output by the electric refrigerator in the energy storage station at the moment t;
Figure FDA00037631792600000317
the maximum output cold power allowed by the electric refrigerator;
(L) absorption chiller operational constraints in data center stations
Figure FDA00037631792600000318
In the formula:
Figure FDA00037631792600000319
the cold power output by the absorption refrigerator in the data center station at the time t is represented;
Figure FDA00037631792600000320
the maximum output electric power allowed by the absorption chiller in the data center station;
(M) Heat storage device operation constraints
(M1) restraint of Heat accumulation and discharge State
y DH +y CH ≤1 (29)
(M2) Heat accumulation and discharge Power constraint
Figure FDA0003763179260000041
Figure FDA0003763179260000042
(M3) Capacity constraints
Figure FDA0003763179260000043
Q h. Powder ＝Q h. Beginning of the design (33)
In the formula: y is DH Indicating the heat-releasing state of the heat storage device, y CH The 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 FDA0003763179260000044
an upper limit value of the heat release power of the heat storage device,
Figure FDA0003763179260000045
The lower limit value of the heat-releasing power of the heat storage device,
Figure FDA0003763179260000046
The upper limit value of the heat storage capacity of the heat storage device,
Figure FDA0003763179260000047
The lower limit value of the heat storage power of the heat storage device;
Figure FDA0003763179260000048
upper and lower limit values of the stored heat of the heat storage device respectively;
(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 FDA0003763179260000049
In the formula,. DELTA.U x,t The voltage deviation amount of the node x is t time period; delta U x The 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; function(s)
Figure FDA00037631792600000410
Is a state variable from 0 to 1, and the calculation formula is expressed as follows:
Figure FDA00037631792600000411
(O) reliability constraints
The system power supply reliability index is the load loss probability and is determined according to the formula (36)
H LOLP ＝P[(P pv,t ±ε t )+(P DES,t ±δ t )+(P net,t ±γ t )≤P L,t ±κ L,t ]≤1-γ (36)
In the formula, H LOLP For a defined power supply reliability index in the fusion station, epsilon t The light abandoning amount and the disturbance amount in the new energy station at the moment t are shown; p pv,t Representing the generated power of the new energy station at the time t; p DES,t The power of the energy storage station at the moment t; delta. For the preparation of a coating t Representing the disturbance quantity of the discharge power of the energy storage station at the moment t; p net,t Represents the transmission power of the tie line at time t; gamma ray t Representing the power disturbance quantity on the connecting line at the time t; p L,t Representing the total electric load size of the fusion station at the time t; kappa type L,t Represents the load fluctuation amount at time t; gamma is the confidence level of the reliability of the multi-station fusion system;
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 purchase cost of the fusion station, the operation and maintenance cost, the power supply reliability cost and the pollution gas emission cost;
s4) solving the coordination control model;
in the step S2):
transformer substation mathematical model
P SL ＝P zl +P gl +P al +P ml (1)
In the formula, P SL The total load of the transformer substation is obtained; p zl Is the refrigeration load size; p gl Is the magnitude of the lighting load; p is al The security load is; p is ml The 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 FDA0003763179260000051
In the formula (I), the compound is shown in the specification,
Figure FDA0003763179260000052
represents the output cold power of the electric refrigerator at time t; p l1 Represents the electric power consumed by the electric refrigerator at time t; psi ec1 The refrigeration coefficient of the electric refrigerator;
(a2) Mathematical model of absorption refrigerator
Figure FDA0003763179260000053
In the formula (I), the compound is shown in the specification,
Figure FDA0003763179260000054
represents the output cold power of the absorption chiller at time t;
Figure FDA0003763179260000055
represents the thermal power consumed by the absorption chiller at time t; psi ac1 The 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 FDA0003763179260000056
In the formula (I), the compound is shown in the specification,
Figure FDA0003763179260000057
represents the thermal power output by the heat pump in the substation at time t,
Figure FDA0003763179260000058
representing the magnitude of the electric power consumed by the heat pump at time t, ξ hp The coefficient of heating performance of the heat pump;
(b2) Mathematical model of heat storage device
Figure FDA0003763179260000059
In the formula:
Figure FDA0003763179260000061
respectively representing the heat storage power and the heat release power of the thermal energy storage device at the time t,
Figure FDA0003763179260000062
respectively representing the heat storage efficiency and the heat release efficiency, Q, of a thermal energy storage device h,t+1 、Q h,t Respectively expressed as the heat energy of the thermal energy storage equipment at the time t +1 and the electric energy of the thermal energy storage equipment at the time t; epsilon is the self-loss coefficient of the thermal energy storage equipment; Δ T is the scheduling time period interval.
2. The four-station integrated energy system coordination control method according to claim 1, characterized in that: in the step S2):
mathematical model of energy storage station
Figure FDA0003763179260000063
In the formula, SOC (t) is the state of charge of the energy storage power station at the moment t; delta is the self-discharge coefficient of the energy storage power station; p CES For charging power, P, of energy-storage power stations DES The discharge power of the energy storage power station; eta CES Charging efficiency, eta, for energy-storage power stations DES The discharge efficiency of the energy storage power station; e soc The 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 a mathematical model is shown as the formula (8):
Figure FDA0003763179260000064
in the formula (I), the compound is shown in the specification,
Figure FDA0003763179260000065
representing the output cold power of the electric refrigerator in the energy storage station at time t; p is l2 Representing the electric power consumed by the electric refrigerator in the energy storage station at time t; psi ec2 The refrigeration coefficient of the electric refrigerator.
3. The coordination control method for the four-station integrated energy system according to claim 1, characterized in that: in the step S2):
data center station mathematical model
P DC ＝P IT +P zl1 +P bat (9)
In the formula, P DC The total load of the data center station; p IT The load size of the IT equipment; p is zl1 The load of refrigeration equipment of the data center station is obtained; p bat The 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 FDA0003763179260000066
in the formula, P N The power consumption of a single server in a normal working state; n is a radical of an alkyl radical l P for a normally operating server M The power consumption of a single server in a dormant state; n is l Number of servers for normal operation, n m For operating servers in a dormant stateThe number of the components;
(b) Mathematical model of absorption refrigerator
Figure FDA0003763179260000071
In the formula (I), the compound is shown in the specification,
Figure FDA0003763179260000072
representing the output cold power of the absorption chiller of the data center station at time t;
Figure FDA0003763179260000073
represents the thermal power consumed by the absorption chiller of the data center station at time t; psi ac2 The refrigeration factor of the absorption chiller of the data center station.
4. The four-station integrated energy system coordination control method according to claim 1, characterized in that: the objective function of the coordinated control model in the step S3) is:
minF＝min(F g +F om +F re +F gas ) (37)
in the formula, F is the total cost of system operation; f g The electricity purchase cost for the fusion station; f om The operation and maintenance cost of the fusion station; f re The power supply reliability cost of the fusion station comprises light abandon punishment cost and load interruption cost of the new energy station; f gas Cost of emission of pollution gas for the fusion station;
electricity purchase cost F of fusion station g As shown in equation (38):
Figure FDA0003763179260000074
in the formula, C b Buying/selling electricity price from the power grid for t time period; p is net.t Is a signal representing the transmission power of the tie at time t;
operational maintenance costs of fusion stationsF om As shown in formula (39):
Figure FDA0003763179260000075
in the formula, K omb For the operational maintenance factor, P, of the system's b-th equipment b (t) the operation power of the b-th equipment in the t time period; and P is b (t) from P in step S2) l1 、
Figure FDA0003763179260000076
P pv 、P CES P DES 、P l2 、P IT 、
Figure FDA0003763179260000077
The operating power of (c);
power supply reliability cost F of fusion station re The light abandonment penalty cost and the load interruption cost are formed, and the formula (40) is as follows:
Figure FDA0003763179260000078
in the formula, C pv Punishing price for unit light abandonment; p des Discarding the light quantity for the t time period of the system; c loss The unit punishment price suffered by the system when the load is interrupted; p is loss The load quantity of the interrupted load loss in the time period t of the system is obtained;
contaminated gas emission cost F of fusion station gas As shown in formula (41):
Figure FDA0003763179260000081
in the formula, λ M Unit cost for mth type of pollution gas; beta is a beta M Discharge coefficient of the mth kind of pollution gas generated for each degree of electricity; p net.t To represent the work of transmission of the tie-line at time tAnd (4) rate.
5. The coordination control method for the four-station integrated energy system according to claim 1, characterized in that: 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; in the step S4): and solving the coordination control model by adopting a particle swarm algorithm.
6. A storage medium, characterized by: the storage medium has stored thereon a computer program, wherein the computer program is arranged to execute the coordination control method of any of claims 1 to 5 when executed.
7. A coordination control system of a four-station integrated energy system according to the coordination control method of the four-station integrated energy system of claim 1, 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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