CN113762632A - Collaborative optimization operation method and system of electrical comprehensive energy system - Google Patents

Collaborative optimization operation method and system of electrical comprehensive energy system Download PDF

Info

Publication number
CN113762632A
CN113762632A CN202111063170.0A CN202111063170A CN113762632A CN 113762632 A CN113762632 A CN 113762632A CN 202111063170 A CN202111063170 A CN 202111063170A CN 113762632 A CN113762632 A CN 113762632A
Authority
CN
China
Prior art keywords
gas
operation model
energy
optimization
natural gas
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202111063170.0A
Other languages
Chinese (zh)
Other versions
CN113762632B (en
Inventor
雷云凯
苟竞
苏韵掣
韩宇奇
庞博
刘阳
刘嘉蔚
杜新伟
袁川
李博
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Sichuan Economic Research Institute
Original Assignee
State Grid Sichuan Economic Research Institute
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Sichuan Economic Research Institute filed Critical State Grid Sichuan Economic Research Institute
Priority to CN202111063170.0A priority Critical patent/CN113762632B/en
Publication of CN113762632A publication Critical patent/CN113762632A/en
Application granted granted Critical
Publication of CN113762632B publication Critical patent/CN113762632B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/04—Constraint-based CAD
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Development Economics (AREA)
  • Health & Medical Sciences (AREA)
  • Educational Administration (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Public Health (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Computer Hardware Design (AREA)
  • Primary Health Care (AREA)
  • General Engineering & Computer Science (AREA)
  • Geometry (AREA)
  • Water Supply & Treatment (AREA)
  • Evolutionary Computation (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a collaborative optimization operation method and a collaborative optimization operation system of an electrical comprehensive energy system, wherein the method comprises the steps of establishing an energy optimization operation model of the electrical-gas comprehensive energy system considering various standby resources, wherein the various standby resources comprise a generator standby resource, an energy storage device standby resource and an interruptible load standby resource; setting a system reserve capacity deliverable constraint considering sudden accidents according to an energy optimization operation model of the electricity-gas comprehensive energy system, wherein the system reserve capacity deliverable constraint is used as an electricity-gas comprehensive energy system robust operation constraint of the energy optimization operation model of the electricity-gas comprehensive energy system; decoupling an energy optimization operation model of the electricity-gas integrated energy system to obtain an optimization operation model of the power system and an optimization operation model of the natural gas system; and solving the model by adopting an alternating direction multiplier method based on self-adaptive penalty parameters to obtain an optimal energy optimization scheduling solution of the electricity-gas integrated energy system, so as to complete the cooperative optimization of the electricity-gas integrated energy system.

Description

Collaborative optimization operation method and system of electrical comprehensive energy system
Technical Field
The invention relates to the technical field of collaborative optimization operation of an integrated energy system, in particular to a collaborative optimization operation method and a collaborative optimization operation system of an electric integrated energy system.
Background
With the gradual reduction of global fossil energy reserves and the obvious environmental problems, many countries seek the transformation and breakthrough of the energy field, and the popularization rate of renewable energy sources in the world is rapidly increased. Traditional fossil energy sources such as coal and petroleum on the power generation side in an electric power system are gradually replaced by renewable clean energy sources, wherein the development of wind power is particularly rapid. By the end of 2020, Chinese wind power installations have reached 2.81 hundred million kilowatts. However, wind power is greatly influenced by meteorological conditions, and the output condition is difficult to predict accurately. In order to deal with the intermittent and fluctuating characteristics of renewable energy sources such as wind power and the like, the proportion of a gas turbine set with rapid climbing capability in a power system is continuously increased. The gas turbine set deepens the coupling degree of an electric power system and a natural gas system, and an electricity-gas comprehensive energy system containing high-proportion clean energy is formed.
From the current development of China, abundant and flexible adjustment resources are necessary for the operation of power systems. In order to ensure safe and stable operation of the power grid and to maximize the utilization of renewable energy, the following three solutions are adopted for the intermittency, volatility and difficult predictability of renewable energy power generation: firstly, the flexibility of power supply regulation is enhanced, namely the proportion of a gas turbine with quick climbing performance is increased; secondly, the standby adjusting capacity of the system is improved, namely energy storage equipment is reasonably configured; thirdly, peak load regulation on both supply and demand sides is relieved, namely, a demand side response means is adopted. Therefore, the electricity-gas comprehensive energy system should fully consider various spare parts in the system during operation to realize the cooperative and optimal operation of energy.
However, in practice, since the power system and the natural gas system are not usually governed by the same organization, the operation scheduling and the energy management between the two systems are independent of each other, and the power company and the natural gas company exchange only limited information to protect the data privacy. In this case, centralized optimization cannot be realized, and only a distributed optimization mode can be adopted. The stable operation of the electricity-gas integrated energy system in the face of an accident is also one of the targets of the energy cooperative operation. Therefore, considering system accidents, how to cooperatively optimize the coupled electricity-gas comprehensive energy system and realize the optimal utilization of energy under the condition of ensuring the independence of data and branch operation of the electricity system and the natural gas system is a problem to be solved urgently.
Disclosure of Invention
The invention aims to provide a collaborative optimization operation method and a collaborative optimization operation system of an electric comprehensive energy system, and provides the collaborative optimization operation method of the electric-gas comprehensive energy system considering various standby resources, which is used for solving the following two problems: firstly, the intermittent and fluctuating output of the clean energy brings difficulty to the flexible operation of the electricity-gas comprehensive energy system; second, data privacy protection of power and natural gas system operators presents difficulties for centralized solution. The method takes four standby resources of non-gas turbine unit standby, system energy storage and interruptible load into consideration, considers the deliverable capacity of the standby resources under the condition of system emergency, and improves the operation flexibility and economy of the electricity-gas comprehensive energy system on the basis of ensuring the operation safety of the system.
The invention is realized by the following technical scheme:
in a first aspect, the present invention provides a method for collaborative optimization operation of an electrical integrated energy system, the method comprising:
step 1: establishing an energy optimization operation model of the electricity-gas integrated energy system considering various standby resources, wherein the various standby resources comprise generator standby resources, energy storage equipment standby resources and interruptible load standby resources;
step 2: setting a system spare capacity deliverable constraint considering sudden accidents according to the energy optimization operation model of the electricity-gas integrated energy system, wherein the system spare capacity deliverable constraint is used as an electricity-gas integrated energy system robust operation constraint of the energy optimization operation model of the electricity-gas integrated energy system;
and step 3: decoupling the energy optimization operation model of the electricity-gas integrated energy system based on a system decoupling thought to obtain an optimization operation model of a power system and an optimization operation model of a natural gas system; and solving the optimized operation model of the power system and the optimized operation model of the natural gas system by adopting an alternating direction multiplier method based on self-adaptive punishment parameters to obtain the optimal solution of the energy optimized dispatching of the electricity-gas integrated energy system, thereby completing the cooperative optimization of the electricity-gas integrated energy system.
The working principle is as follows: the invention provides a collaborative optimization operation method of an electricity-gas comprehensive energy system considering various standby resources, and the technical key points and the protection points of the invention are as follows: firstly, the generator standby, the energy storage equipment and the interruptible load are jointly used as system standby resources, so that the generator standby load of an electricity-gas comprehensive energy system with high-proportion wind power access is reduced, the flexibility of system operation is improved, and the air abandonment quantity is reduced; secondly, the availability of the spare capacity of the system in the face of an emergency accident is considered by system operation constraint, and the safety and the stability of the system operation are ensured; thirdly, an alternating direction multiplier method with self-adaptive penalty parameters is adopted to carry out distributed collaborative optimization solving, solving efficiency is improved, data privacy among different energy operators is effectively protected, and only gas engine group data of two parties are needed in the solving process.
Further, the step 1 of establishing the energy optimization operation model of the electricity-gas integrated energy system comprises the following steps:
step 11, setting an objective function of the energy optimization operation model of the electricity-gas integrated energy system, and taking the lowest total operation cost of the system in a system operation scheduling period as an objective, wherein the total operation cost of the system comprises an energy supply cost C1Cost of abandoned wind C2System up and down spare capacity cost C3(ii) a Wherein:
the energy supply cost C1The method comprises the power generation cost of a conventional generator, the natural gas production cost, the charge and discharge cost of a storage battery and the interruption compensation cost of an interruptible load; cost of spare capacity C for up and down of the system3The up and down reserve capacity of the system is provided by the generator, battery and interruptible load together;
and 12, setting power system operation constraint, natural gas system operation constraint and system coupling constraint on the objective function.
Further, the objective function set in step 11 is as follows:
Figure BDA0003257197770000031
Figure BDA0003257197770000032
Figure BDA0003257197770000033
Figure BDA0003257197770000034
wherein, CiAnd Pi,tRespectively representing non-gas enginesCost factor and power generation capacity of the group; cgAnd Fg,tCost factor and gas yield, respectively, of the natural gas supply; csIs the unit charge-discharge cost of the accumulator, Ps,tRepresents the charging or discharging power of the storage battery;
Figure BDA0003257197770000035
and
Figure BDA0003257197770000036
is the unit power interrupt penalty cost of the interruptible load and the scheduled power of the interruptible load; cwAnd
Figure BDA0003257197770000037
respectively representing unit fine cost of the abandoned wind and the system abandoned wind volume;
Figure BDA0003257197770000038
and
Figure BDA0003257197770000039
respectively representing load shedding cost and load shedding power;
Figure BDA00032571977700000310
and
Figure BDA00032571977700000311
respectively the cost factor for the upward and downward spares,
Figure BDA00032571977700000312
and
Figure BDA00032571977700000313
upward and reserve capacity provided by a reserve source r, which may be a generator reserve or battery or interruptible load;
Figure BDA00032571977700000314
and
Figure BDA00032571977700000315
representing the cost factor of the gas turbine's up and down standby,
Figure BDA00032571977700000316
and
Figure BDA00032571977700000317
representing the amount of natural gas reserved for the gas turbine to provide upward and downward reserve capacity, NGIndicating the number of gas turbine units.
Further, the robust operation constraints of the electric-gas integrated energy system of the energy optimization operation model of the electric-gas integrated energy system in the step 2 comprise a generator shutdown constraint, a line interruption constraint and a natural gas pipeline constraint.
Further, decoupling the energy optimization operation model of the electricity-gas integrated energy system in the step 3 to obtain an optimization operation model of the power system and an optimization operation model of the natural gas system; and relaxing coupling consistency constraints of the power system and the natural gas system, and adding the punishment items into an optimized operation model of the power system and an optimized operation model of the natural gas system to obtain the optimized operation model of the power system and the optimized operation model of the natural gas system after the punishment items are added.
Specifically, the optimal operation model of the power system and the optimal operation model of the natural gas system are obtained by decoupling as follows:
Figure BDA00032571977700000318
Figure BDA0003257197770000041
wherein, minfeFor optimal operation models, minf, of electric power systemsgAn optimized operation model of the natural gas system.
Specifically, the objective functions of the optimal operation model of the power system and the optimal operation model of the natural gas system after adding the penalty term are respectively expressed as follows:
Figure BDA0003257197770000042
Figure BDA0003257197770000043
wherein, minfeFor an optimized operation model, minf, of the power system after adding penalty termsgThe method comprises the steps of (1) adding a penalty term to an optimized operation model of the natural gas system; gamma rayj,tAnd p represent the lagrangian multiplier and penalty parameters, respectively.
Further, in the step 3, solving the optimized operation model of the power system and the optimized operation model of the natural gas system by adopting an alternating direction multiplier method based on self-adaptive punishment parameters to obtain an optimal energy optimized dispatching solution of the electricity-gas integrated energy system, and finishing the cooperative optimization of the electricity-gas integrated energy system; the method specifically comprises the following substeps:
s31: initializing variables: setting iteration index n to 1, setting original and dual convergence threshold epsilonpAnd εdInitializing gas consumption of the gas turbine
Figure BDA0003257197770000044
The gas reserve reserved for the gas turbine which provides an upward and downward reserve capacity is
Figure BDA0003257197770000045
And
Figure BDA0003257197770000046
a Lagrange multiplier λ and a penalty parameter ρ;
s32: solving the optimal operation result of the power system subproblem 1: according to
Figure BDA0003257197770000047
And
Figure BDA0003257197770000048
obtaining the optimal solution result of the current power system from the initial value
Figure BDA0003257197770000049
And
Figure BDA00032571977700000410
s33: updating the auxiliary variable: order to
Figure BDA00032571977700000411
Sharing boundary variables with natural gas systems
Figure BDA00032571977700000412
And
Figure BDA00032571977700000413
s34: solving the optimal operation result of the natural gas system subproblem 2: according to
Figure BDA0003257197770000051
And
Figure BDA0003257197770000052
initial value is solved the optimum result of solving of current natural gas system
Figure BDA0003257197770000053
And
Figure BDA0003257197770000054
s35: updating the auxiliary variable: order to
Figure BDA0003257197770000055
Sharing boundary variables with power systems
Figure BDA0003257197770000056
And
Figure BDA0003257197770000057
the information of (a);
s36: calculation of boundary variables by equations (9) and (10)
Figure BDA0003257197770000058
And
Figure BDA0003257197770000059
original residual and dual residual;
s37: checking convergence: if the maximum residual satisfies the constraint conditions (11) and (12) or N > N, terminating the iterative process and outputting a solution; otherwise, go to S38; if the current iteration number N is larger than the maximum iteration limit N, the solving process is regarded as incapable of convergence;
s38: updating Lagrange multiplier through formula (13), and updating penalty parameter through formula (14) and formula (15); let n be n +1, go to S32 and repeat the iteration process.
Figure BDA00032571977700000510
Figure BDA00032571977700000511
Figure BDA00032571977700000512
Figure BDA00032571977700000513
Figure BDA00032571977700000514
Figure BDA00032571977700000515
If it is
Figure BDA00032571977700000516
Then
Figure BDA00032571977700000517
Wherein,
Figure BDA00032571977700000518
and
Figure BDA00032571977700000519
respectively representing original residual errors related to gas consumption of the gas turbine, upward standby gas consumption and downward standby gas consumption;
Figure BDA0003257197770000061
and
Figure BDA0003257197770000062
respectively representing dual residual errors related to gas consumption of the gas turbine, upward standby gas consumption and downward standby gas consumption;
Figure BDA0003257197770000063
and
Figure BDA0003257197770000064
the lagrangian parameter update values relating to gas turbine gas consumption, reserve up gas usage and reserve down gas usage are represented separately.
In a second aspect, the present invention further provides a collaborative optimal operation system of an electrical integrated energy system, which supports the collaborative optimal operation method of the electrical integrated energy system, and the system includes:
a preliminary model construction unit: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for establishing an energy optimization operation model of the electricity-gas integrated energy system considering various standby resources, and the various standby resources comprise a generator standby resource, an energy storage device standby resource and an interruptible load standby resource;
a model constraint setting unit, configured to set, according to the energy-optimized operation model of the electric-gas integrated energy system, a system spare capacity deliverable constraint considering an emergency as an electric-gas integrated energy system robust operation constraint of the energy-optimized operation model of the electric-gas integrated energy system;
the model decoupling unit is used for decoupling the energy optimization operation model of the electricity-gas integrated energy system to obtain an optimization operation model of the power system and an optimization operation model of the natural gas system;
the optimization solving unit is used for solving the optimization operation model of the power system and the optimization operation model of the natural gas system by adopting an alternating direction multiplier method based on self-adaptive punishment parameters to obtain an energy optimization scheduling optimal solution of the electricity-gas integrated energy system;
and the output unit is used for outputting the optimal energy optimization scheduling solution of the electricity-gas integrated energy system to complete the cooperative optimization of the electricity-gas integrated energy system.
Further, the establishing process of the energy optimization operation model of the electricity-gas integrated energy system in the preliminary model establishing unit is as follows:
setting an objective function of the energy optimization operation model of the electricity-gas integrated energy system, and taking the lowest total operation cost of the system in a system operation scheduling period as a target, wherein the total operation cost of the system comprises an energy supply cost C1Cost of abandoned wind C2System up and down spare capacity cost C3(ii) a Wherein: the energy supply cost C1The method comprises the power generation cost of a conventional generator, the natural gas production cost, the charge and discharge cost of a storage battery and the interruption compensation cost of an interruptible load; cost of spare capacity C for up and down of the system3The up and down reserve capacity of the system is provided by the generator, battery and interruptible load together; the set objective function is as follows:
Figure BDA0003257197770000065
Figure BDA0003257197770000066
Figure BDA0003257197770000071
Figure BDA0003257197770000072
wherein, CiAnd Pi,tRespectively representing the cost coefficient and the generating capacity of the non-gas unit; cgAnd Fg,tCost factor and gas yield, respectively, of the natural gas supply; csIs the unit charge-discharge cost of the accumulator, Ps,tRepresents the charging or discharging power of the storage battery;
Figure BDA0003257197770000073
and
Figure BDA0003257197770000074
is the unit power interrupt penalty cost of the interruptible load and the scheduled power of the interruptible load; cwRepresents the unit fine cost of the abandoned wind;
Figure BDA0003257197770000075
and
Figure BDA0003257197770000076
respectively the cost factor for the upward and downward spares,
Figure BDA0003257197770000077
and
Figure BDA0003257197770000078
upward and reserve capacity provided by a reserve source r, which may be a generator reserve or battery or interruptible load;
Figure BDA0003257197770000079
and
Figure BDA00032571977700000710
representing the cost factor of the gas turbine's up and down standby,
Figure BDA00032571977700000711
and
Figure BDA00032571977700000712
representing the amount of natural gas reserved for the gas turbine to provide upward and downward reserve capacity, NGIndicating the number of gas turbine units.
And setting power system operation constraints, natural gas system operation constraints and system coupling constraints on the objective function.
Further, in the model constraint setting unit, the robust operation constraints of the electric-gas integrated energy system of the energy optimization operation model of the electric-gas integrated energy system comprise a generator shutdown constraint, a line interruption constraint and a natural gas pipeline constraint.
Further, the model decoupling unit decouples the energy optimization operation model of the electricity-gas integrated energy system to obtain an optimization operation model of the power system and an optimization operation model of the natural gas system; and relaxing coupling consistency constraints of the power system and the natural gas system, and adding the punishment items into an optimized operation model of the power system and an optimized operation model of the natural gas system to obtain the optimized operation model of the power system and the optimized operation model of the natural gas system after the punishment items are added.
Specifically, the optimal operation model of the power system and the optimal operation model of the natural gas system are obtained by decoupling as follows:
Figure BDA00032571977700000713
Figure BDA00032571977700000714
wherein, minfeFor optimal operation models, minf, of electric power systemsgAn optimized operation model of the natural gas system.
Specifically, the objective functions of the optimal operation model of the power system and the optimal operation model of the natural gas system after adding the penalty term are respectively expressed as follows after adding the penalty term:
Figure BDA0003257197770000081
Figure BDA0003257197770000082
wherein, minfeFor an optimized operation model, minf, of the power system after adding penalty termsgThe method comprises the steps of (1) adding a penalty term to an optimized operation model of the natural gas system; gamma rayj,tAnd p represent the lagrangian multiplier and penalty parameters, respectively.
Compared with the prior art, the invention has the following advantages and beneficial effects:
compared with the prior method and system for optimizing the operation of the centralized electricity-gas comprehensive energy system, the method and the system have the beneficial effects that: standby resources such as generator standby, energy storage equipment and interruptible load are considered, so that standby burden of the generator can be effectively reduced, flexibility of system operation is improved, and waste air volume and total operation cost are reduced; the distributed collaborative optimization solving strategy based on the alternative direction multiplier method with the self-adaptive penalty parameters can effectively protect the data privacy of different energy mechanisms, and the solving process only needs gas engine group data of both parties.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
fig. 1 is a flow chart of a collaborative optimization operation method of an electrical integrated energy system according to the present invention.
Fig. 2 is a schematic diagram of a simulation topology of the electric-gas integrated energy system according to the embodiment of the invention.
FIG. 3 is a schematic diagram of a solving strategy process according to the present invention.
Fig. 4 is a block diagram of a system for collaborative optimization of an electrical integrated energy system according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
As shown in fig. 1, the present invention provides a method for collaborative optimization operation of an electrical integrated energy system, the method includes:
step 1: establishing an energy optimization operation model of the electricity-gas integrated energy system considering various standby resources, wherein the various standby resources comprise generator standby resources, energy storage equipment standby resources and interruptible load standby resources;
step 2: setting a system spare capacity deliverable constraint considering sudden accidents according to the energy optimization operation model of the electricity-gas integrated energy system, wherein the system spare capacity deliverable constraint is used as an electricity-gas integrated energy system robust operation constraint of the energy optimization operation model of the electricity-gas integrated energy system;
and step 3: decoupling the energy optimization operation model of the electricity-gas integrated energy system based on a system decoupling thought to obtain an optimization operation model of a power system and an optimization operation model of a natural gas system; and solving the optimized operation model of the power system and the optimized operation model of the natural gas system by adopting an alternating direction multiplier method based on self-adaptive punishment parameters to obtain the optimal solution of the energy optimized dispatching of the electricity-gas integrated energy system, thereby completing the cooperative optimization of the electricity-gas integrated energy system.
To further illustrate the embodiment, the step of establishing the energy optimization operation model of the electricity-gas integrated energy system considering various standby resources in step 1 comprises the following steps:
step 11, setting up the stationThe objective function of the energy optimization operation model of the electricity-gas integrated energy system aims at the lowest total operation cost of the system in the system operation scheduling period, wherein the total operation cost of the system comprises an energy supply cost C1Cost of abandoned wind C2System up and down spare capacity cost C3(ii) a Wherein:
the energy supply cost C1The method comprises the power generation cost of a conventional generator, the natural gas production cost, the charge and discharge cost of a storage battery and the interruption compensation cost of an interruptible load; cost of spare capacity C for up and down of the system3The up and down reserve capacity of the system is provided by the generator, battery and interruptible load together;
the set objective function is as follows:
Figure BDA0003257197770000091
Figure BDA0003257197770000092
Figure BDA0003257197770000093
Figure BDA0003257197770000094
wherein, CiAnd Pi,tRespectively representing the cost coefficient and the generating capacity of the non-gas unit; cgAnd Fg,tCost factor and gas yield, respectively, of the natural gas supply; csIs the unit charge-discharge cost of the accumulator, Ps,tRepresents the charging or discharging power of the storage battery;
Figure BDA0003257197770000101
and
Figure BDA0003257197770000102
is the unit power interrupt penalty cost of the interruptible load and the scheduled power of the interruptible load; cwRepresents the unit fine cost of the abandoned wind;
Figure BDA0003257197770000103
and
Figure BDA0003257197770000104
respectively the cost factor for the upward and downward spares,
Figure BDA0003257197770000105
and
Figure BDA0003257197770000106
upward and reserve capacity provided by a reserve source r, which may be a generator reserve or battery or interruptible load;
Figure BDA0003257197770000107
and
Figure BDA0003257197770000108
representing the cost factor of the gas turbine's up and down standby,
Figure BDA0003257197770000109
and
Figure BDA00032571977700001010
representing the amount of natural gas reserved for the gas turbine to provide upward and downward reserve capacity, NGIndicating the number of gas turbine units.
And 12, setting power system operation constraint, natural gas system operation constraint and system coupling constraint on the objective function. The method comprises the following specific steps:
the power system operating constraints include:
(1) and (3) charge and discharge restraint of energy storage equipment:
Figure BDA00032571977700001011
wherein E iss,tFor the amount of electricity (kWh), μ stored in the battery at time tlossIs the energy loss rate of the storage battery,
Figure BDA00032571977700001012
for the charging efficiency of the storage battery, delta t is the charging and discharging time interval (h),
Figure BDA00032571977700001013
for battery discharge efficiency, CapEIs the capacity of the battery, gammachAt maximum charge rate, γdcAt maximum discharge rate, ωs,tAnd ωr,tIs a binary variable, ω, indicating the charge and discharge state of the batterys,tWhen 1, it indicates charging.
(2) Interruptible load constraint:
Figure BDA00032571977700001014
Figure BDA00032571977700001015
wherein,
Figure BDA00032571977700001016
and
Figure BDA00032571977700001017
maximum and minimum capacities of the interruptible load connected to the ith node, respectively. u. ofk,tIs a variable from 0 to 1, indicating the operating state of the interruptible load.
Figure BDA00032571977700001018
And
Figure BDA00032571977700001019
respectively representing the minimum on-time and minimum of an interruptible loadAnd (4) interrupting time limitation.
Figure BDA00032571977700001020
And
Figure BDA00032571977700001021
respectively representing the accumulated opening time and the accumulated interruption time of the interruptible load before the time t.
(3) System power balance constraint:
Figure BDA0003257197770000111
wherein, Pi,tThe total generated power of a generator set in the system comprises a conventional generator set and a gas generator set; pw,tThe generated power of the wind power plant accessed to the system; ps,tThe discharge power provided for the storage battery in the system; l isd,e,tIs the total load of the system;
Figure BDA0003257197770000112
scheduled power, N, provided for system interruptible loadsE、Nw、NS、Ne、NintRespectively representing the number of nodes connecting the generator, the wind farm, the storage battery, the load and the interruptible load in the system.
(4) And (3) restricting the climbing speed of the generator:
Figure BDA0003257197770000113
wherein, Pi,tThe generated power of the unit i, P, at time ti,t+1Is the generating power of the unit i at the moment of t +1, riThe ramp rate of the unit i.
(5) And (3) restricting the reserve capacity of the generator:
Figure BDA0003257197770000114
wherein,
Figure BDA0003257197770000115
the upward spare capacity provided for unit i,
Figure BDA0003257197770000116
the downward spare capacity provided for unit i,
Figure BDA0003257197770000117
and the maximum generating capacity of the unit i.
(6) Generator standby response time constraints:
Figure BDA0003257197770000118
wherein, TrIs the standby response time requirement for the unit.
(7) Wind power plant output restraint:
Figure BDA0003257197770000119
wherein, Pw,tIs the actual value of the wind power output at the moment t,
Figure BDA00032571977700001110
wind power output predicted value for time t
(8) Line transmission capacity constraint:
Figure BDA00032571977700001111
Figure BDA00032571977700001112
wherein,
Figure BDA00032571977700001113
representing the maximum power of the line mTransmission limit, K is the power allocation coefficient.
(9) And (3) energy storage equipment charge and discharge power constraint:
Figure BDA0003257197770000121
wherein,
Figure BDA0003257197770000122
the maximum charging power of the storage battery.
Figure BDA0003257197770000123
Is the maximum discharge power of the battery. Omegas,tAnd ωr,tThe variable is a 0-1 variable indicating the charging and discharging state of the energy storage equipment, the storage battery is charged or discharged when the value is 1, and the storage battery is not working when the value is 0.
(10) And (4) remaining energy constraint of energy storage equipment:
Figure BDA0003257197770000124
wherein E iss,tThe energy remaining at time t for the battery.
Figure BDA0003257197770000125
And
Figure BDA0003257197770000126
the maximum energy storage amount and the minimum energy storage amount of the storage battery are respectively. When a complete operation scheduling time period is finished, the energy storage E in the systems,TWill be set to the initial stored energy Es,0。
(11) Energy storage reserve capacity constraint:
Figure BDA0003257197770000127
wherein,
Figure BDA0003257197770000128
and
Figure BDA0003257197770000129
respectively representing the up reserve capacity and down reserve capacity provided by the battery.
Figure BDA00032571977700001210
And
Figure BDA00032571977700001211
respectively representing the charge and discharge efficiency of the battery.
(12) Reserve capacity demand constraints:
the interruption of the maximum installed capacity of the system is considered to be the most serious N-1 emergency, and therefore, the spare capacity to cope with the accident is set to the maximum installed capacity of the generator. The total up and down standby constraints within the system are as follows:
Figure BDA00032571977700001212
Figure BDA00032571977700001213
wherein, betadAnd betawIs the reserve demand coefficient of load and wind energy.
Figure BDA00032571977700001214
Representing the maximum installed capacity of the genset.
The natural gas system operating constraints include:
(1) and (3) restricting the airflow of the pipeline:
Figure BDA00032571977700001215
Figure BDA00032571977700001216
wherein, CmnIs constant and depends on the characteristics of the pipe (e.g. length, diameter and temperature, etc.), FmnIs the gas flow, pi is the node gas pressure.
(2) And (3) gas output constraint:
Figure BDA0003257197770000131
wherein,
Figure BDA0003257197770000132
and
Figure BDA0003257197770000133
respectively representing the upper and lower gas output limits of a gas well.
(3) And (3) node air pressure constraint:
Figure BDA0003257197770000134
wherein,
Figure BDA0003257197770000135
and
Figure BDA0003257197770000136
respectively representing the upper and lower limits of the node gas pressure.
(4) Node airflow balance constraint:
Figure BDA0003257197770000137
wherein, Fg,tIs the natural gas output of a gas well, Ld,g,tConnecting natural gas loads to nodes, Fmn,tRepresenting the flow of gas in the pipe mn,
Figure BDA0003257197770000138
is the gas consumption of the node gas turbine.
The nonlinear non-convex natural gas pipeline gas flow equation in the natural gas network is a key factor for increasing the complexity of the model. In order to reduce the solving difficulty of the collaborative optimization model, the natural gas pipeline airflow constraint can be linearized through an incremental piecewise linearization method.
The system coupling constraints include:
using gas turbine as coupling element between power system and natural gas system, natural gas consumption of gas turbine
Figure BDA0003257197770000139
And the amount of electricity generation Pj,tUpward reserve capacity
Figure BDA00032571977700001310
And downward spare capacity
Figure BDA00032571977700001311
The relationship between them is:
Figure BDA00032571977700001312
Figure BDA00032571977700001313
Figure BDA00032571977700001314
wherein alpha isjIs the thermal conversion efficiency coefficient of the gas turbine, and is related to the unit per se; HHV is the fixed higher heating value of natural gas.
For further explanation of the embodiment, step 2 sets a system spare capacity deliverable constraint considering sudden accidents as an electric-gas integrated energy system robust operation constraint of the electric-gas integrated energy system energy optimization operation model;
it is assumed that unexpected events (generator and line outages) and prediction errors (wind power generation and load demand prediction errors) will occur in the real-time operation of the electric-gas integrated energy system, and that reserve capacity will be called to maintain power balance. In order to ensure the safety and the reliability of the operation of the electricity-gas comprehensive energy system, the deliverable constraint of the reserve capacity is considered, so that the reserve capacity can fill a system power gap in time when the system has an accident in real-time operation, and the safety of the system operation is ensured.
(1) Generator shutdown restraint
A sudden N-1 generator shutdown failure maintains power balance by planning reserve capacity, as shown in equation (39). The transmission limit of the line at this time is represented by equation (40). Equation (41) indicates that the real-time scheduled spare capacity should not exceed the total spare capacity preset by the system.
Figure BDA0003257197770000141
Figure BDA0003257197770000142
Figure BDA0003257197770000143
Where G is a power generation unit that may fail within the dispatch horizon.
(2) Line break restraint
And L represents a line in which a fault occurs within the scheduling range. The active power deficit caused by wind power fluctuations can also be addressed by scheduling the system reserve capacity, as shown in equation (42). The transmission capacity limit of the line is expressed as equation (43). Equation (44) indicates that the scheduled spare capacity should not exceed the predetermined spare capacity.
Figure BDA0003257197770000144
Figure BDA0003257197770000145
Figure BDA0003257197770000146
Where KL is the power division factor that takes into account the failure of the line L.
(3) Natural gas pipeline restraint
The gas turbine may also provide backup service in the power system. However, when the gas consumption of the gas turbine increases during operation, the natural gas pipeline may become blocked, thereby failing to meet the gas demand of the gas turbine. It is therefore necessary to verify whether the gas consumption of the gas turbine can be met by the gas network when there is an additional back-up provision requirement. The constraints are expressed as follows:
Figure BDA0003257197770000151
Figure BDA0003257197770000152
Figure BDA0003257197770000153
Figure BDA0003257197770000154
to further illustrate the present embodiment, step 3 includes the following sub-steps:
step 31, the objective function is reset.
The objective function (formula (1)) of the electric-gas integrated energy system collaborative optimization operation model can be decomposed into two sub objective functions of an electric power system and a natural gas system, which are shown in the above formulas (5) and (6).
Step 32, the coupling constraint is relaxed.
Two energy subsystems (power system, natural gas system) obey coupling element constraints and are solved in the same time
Figure BDA0003257197770000155
And
Figure BDA0003257197770000156
the optimal solution results within the two energy subsystems should remain consistent, as shown in equation (49).
Figure BDA0003257197770000157
Wherein
Figure BDA0003257197770000158
And
Figure BDA0003257197770000159
represents the optimal result of the boundary variables in the power system,
Figure BDA00032571977700001510
and
Figure BDA00032571977700001511
indicating the optimal results for the boundary variables in the natural gas system.
And relaxing the coupling consistency constraint of the two energy subsystems, and adding a penalty term into the self-optimization objective functions of the two energy subsystems. The objective functions solved by the power system and the natural gas system are expressed as formulas (7) and (8) respectively after adding penalty terms.
Step 33, solving using a distributed algorithm.
Solving an electricity-gas integrated energy system collaborative optimization operation model by using an alternating direction multiplier method (ADMM-SAP) algorithm with a self-adaptive penalty parameter, as shown in FIG. 3, specifically according to the following steps:
s31: initializing variables: setting iteration index n as 1, setting original and dual convergence threshold epsilonpAnd εdInitializing gas consumption of the gas turbine
Figure BDA00032571977700001512
The gas reserve reserved for the gas turbine which provides an upward and downward reserve capacity is
Figure BDA00032571977700001513
And
Figure BDA00032571977700001514
a Lagrange multiplier λ and a penalty parameter ρ;
s32: solving the optimal operation result of the power system subproblem 1: according to
Figure BDA00032571977700001515
And
Figure BDA00032571977700001516
obtaining the optimal solution result of the current power system from the initial value
Figure BDA00032571977700001517
And
Figure BDA00032571977700001518
s33: updating the auxiliary variable: order to
Figure BDA0003257197770000161
Sharing boundary variables with natural gas systems
Figure BDA0003257197770000162
And
Figure BDA0003257197770000163
s34: solving the optimal operation result of the natural gas system subproblem 2: according to
Figure BDA0003257197770000164
And
Figure BDA0003257197770000165
initial value is solved the optimum result of solving of current natural gas system
Figure BDA0003257197770000166
And
Figure BDA0003257197770000167
s35: updating the auxiliary variable: order to
Figure BDA0003257197770000168
Sharing boundary variables with power systems
Figure BDA0003257197770000169
And
Figure BDA00032571977700001610
the information of (a);
s36: calculation of boundary variables by equations (50) and (51)
Figure BDA00032571977700001611
And
Figure BDA00032571977700001612
original residual and dual residual;
s37: checking convergence: if the maximum residuals satisfy constraints (52) and (53) or N > N, terminating the iterative process and outputting a solution; otherwise, go to S38; if the current iteration number N is larger than the maximum iteration limit N, the solving process is regarded as incapable of convergence;
s38: updating lagrangian multiplier and penalty parameter by formula (54), and updating penalty parameter by formula (55) and formula (56); let n be n +1, go to S32 and repeat the iteration process.
Figure BDA00032571977700001613
Figure BDA00032571977700001614
Figure BDA00032571977700001615
Figure BDA00032571977700001616
Figure BDA00032571977700001617
Figure BDA00032571977700001618
If it is
Figure BDA00032571977700001619
Then
Figure BDA00032571977700001620
The invention provides a collaborative optimization operation method of an electricity-gas comprehensive energy system considering various standby resources, and the technical key points and the protection points of the invention are as follows: firstly, the generator standby, the energy storage equipment and the interruptible load are jointly used as system standby resources, so that the generator standby load of an electricity-gas comprehensive energy system with high-proportion wind power access is reduced, the flexibility of system operation is improved, and the air abandonment quantity is reduced; secondly, the availability of the spare capacity of the system in the face of an emergency accident is considered by system operation constraint, and the safety and the stability of the system operation are ensured; thirdly, an alternating direction multiplier method with self-adaptive penalty parameters is adopted to carry out distributed collaborative optimization solving, solving efficiency is improved, data privacy among different energy operators is effectively protected, and only gas engine group data of two parties are needed in the solving process.
In specific implementation, the simulation is as follows:
the invention adopts an electric-gas comprehensive energy system which is formed by coupling an improved IEEE RTS 24 node system and a natural gas 6 node system as a simulation system. The IEEE RTS 24 node system has 23 Bus nodes (namely Bus1-Bus 23 in FIG. 2), and comprises 10 conventional non-gas units G1-G10; 2 gas units G11-G12 which are respectively positioned on No. 13 and No. 23 buses; the No. 3 bus and the No. 6 bus are respectively connected with a wind power plant (W1 and W2); the Storage battery energy Storage equipment (Storage) of the system is positioned on a No. 6 bus; the No. 3 bus and the No. 10 bus are respectively connected with an Interruptible Load (IL). The natural Gas system has 6 Gas nodes (Node 1-Node 6), including two Gas wells N1 and N2, five pipelines and two natural Gas loads (Gas Load 1 and Gas Load 2). The power system and the natural gas system are coupled by two gas turbines G11 and G12. The system parameters are shown in the following table:
TABLE 1 non-gas turbine operating parameters
Generator set Maximum output (MW) Climbing rate (MW/h) Price for electricity generation ($/MW) Standby quote ($/MW)
G1 192 100 19.6 5.88
G2 192 100 19.2 5.76
G3 300 200 29.1 8.73
G4 394 280 25.4 7.62
G5 215 120 14.1 4.23
G6 155 100 14.1 4.23
G7 400 300 7.8 2.34
G8 400 300 7.8 2.34
G9 300 180 19.2 5.76
G10 310 200 14.1 4.23
TABLE 2 gas turbine operating parameters
Figure BDA0003257197770000171
TABLE 3 Battery operating parameters
Figure BDA0003257197770000181
TABLE 4 interruptible load operating parameters
Figure BDA0003257197770000182
TABLE 5 other System parameters
Parameter(s) Value taking
Natural gas price ($/kcf) 2.5
Abandon wind penalty ($/MW) 100
Load shedding penalty ($/MW) 27
Systematic prediction error (MW) Sum of 5% load and 10% wind power predicted value
Line fault set 1. Branch 3, 4, 12, 13, 23, 27
Original residual convergence threshold εp 0.5
Dual residual convergence threshold epsilond 0.5
Maximum number of iterations N 200
Initial value rho of penalty parameter 0.1
Penalty parameter maximum ρmax 1000
Scheduling period (h) 1
Total scheduling period (h) 24
The predicted values of the power load, the natural gas load and the wind power output in the system for the scheduling reference are shown in table 6.
TABLE 6 predicted values of load and wind power output in scheduling period
Figure BDA0003257197770000183
Figure BDA0003257197770000191
The model simulator was written by MATLAB 2018a and solved using the commercial solver gurobi 8.1.1.
In order to analyze the economy and robustness of the electric-gas integrated energy system scheduling under the situation, four different examples are set for comparative analysis.
Example 1: the system has no storage battery and interruptible load, and only uses non-gas turbine set, gas turbine set and wind power to provide energy source for power system, i.e. remove P and Ps,t、Rs,t、RintAnd PintThe relevant model constraints, system spare capacity, are provided by generator sparing.
Example 2: there are accumulators and interruptible loads in the system, but both participate only in power scheduling and not in system backup scheduling, i.e. remove and Rs,tAnd RintAssociated model constraints.
Example 3: there are storage batteries and interruptible loads in the system and both participate in the power scheduling and backup scheduling of the system and consider the deliverable constraints of the system backup capacity in the three emergencies of generator failure, line interruption and natural gas pipeline blockage. The basic model for the collaborative optimization scheduling of the electricity-gas integrated energy system considering various energy storages is reflected by the example.
Example 4: the spare capacity exchangeability constraint is not considered, i.e. the constraint conditions set by equations (39) to (48) in the model are removed.
The results of the solution for each example are shown in tables 7 to 8.
TABLE 7 running cost for different examples
Examples of the design Cost of energy supply/$ Wind curtailment cost/$ Spare cost/$ Total cost/$
EXAMPLE 1 841560 10091 88679 940330
EXAMPLE 2 840489 442 88251 929182
EXAMPLE 3 810964 742 97945 909651
EXAMPLE 4 804921 706 98269 903896
TABLE 8. electric quantity of each power generation equipment in electric power system in different calculation examples
Examples of the design Gas turbine/MWh Non-gas turbine unit/MWh Interruptible load/MWh Wind power/MWh
EXAMPLE 1 3215.0 43303.1 / 100.9
EXAMPLE 2 2767.9 43265.2 422.9 4.4
EXAMPLE 3 2707.4 43442.2 298.0 7.4
EXAMPLE 4 2876.6 43284.5 286.1 7.0
Compared with the prior art, the invention has the beneficial effects that: firstly, compared with the traditional electricity-gas comprehensive energy system which only depends on the standby capacity of a generator, the electricity-gas comprehensive energy system collaborative optimization operation method provided by the invention takes the standby capacity of the generator, energy storage equipment and interruptible load into consideration to be used as standby resources for system operation, so that the collaborative complementary advantages among various types of standby resources can be fully utilized, the standby supply pressure of the generator is reduced, the operation economy of the electricity-gas comprehensive energy system is improved, and the air abandonment quantity is obviously reduced; secondly, the electric-gas integrated energy system collaborative optimization operation model provided by the invention has better robustness due to the consideration of the influence of system emergencies on the reserve capacity scheduling.
Example 2
As shown in fig. 4, the present embodiment is different from embodiment 1 in that the present embodiment provides a system for collaborative optimal operation of an electric integrated energy system, which supports the method for collaborative optimal operation of an electric integrated energy system according to embodiment 1, and the system includes:
a preliminary model construction unit: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for establishing an energy optimization operation model of the electricity-gas integrated energy system considering various standby resources, and the various standby resources comprise a generator standby resource, an energy storage device standby resource and an interruptible load standby resource;
a model constraint setting unit, configured to set, according to the energy-optimized operation model of the electric-gas integrated energy system, a system spare capacity deliverable constraint considering an emergency as an electric-gas integrated energy system robust operation constraint of the energy-optimized operation model of the electric-gas integrated energy system;
the model decoupling unit is used for decoupling the energy optimization operation model of the electricity-gas integrated energy system to obtain an optimization operation model of the power system and an optimization operation model of the natural gas system;
the optimization solving unit is used for solving the optimization operation model of the power system and the optimization operation model of the natural gas system by adopting an alternating direction multiplier method based on self-adaptive punishment parameters to obtain an energy optimization scheduling optimal solution of the electricity-gas integrated energy system;
and the output unit is used for outputting the optimal energy optimization scheduling solution of the electricity-gas integrated energy system to complete the cooperative optimization of the electricity-gas integrated energy system.
The specific implementation process of each unit is implemented according to the specific steps of the collaborative optimization operation method of the electrical integrated energy system described in embodiment 1, and details are not repeated in this embodiment.
Compared with the prior method and system for optimizing the operation of the centralized electricity-gas comprehensive energy system, the method and the system have the beneficial effects that: standby resources such as generator standby, energy storage equipment and interruptible load are considered, so that standby burden of the generator can be effectively reduced, flexibility of system operation is improved, and waste air volume and total operation cost are reduced; the distributed collaborative optimization solving strategy based on the alternative direction multiplier method with the self-adaptive penalty parameters can effectively protect the data privacy of different energy mechanisms, and the solving process only needs gas engine group data of both parties.
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.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. A collaborative optimization operation method of an electric comprehensive energy system is characterized by comprising the following steps:
step 1: establishing an energy optimization operation model of the electricity-gas integrated energy system considering various standby resources, wherein the various standby resources comprise generator standby resources, energy storage equipment standby resources and interruptible load standby resources;
step 2: setting a system spare capacity deliverable constraint considering sudden accidents according to the energy optimization operation model of the electricity-gas integrated energy system, wherein the system spare capacity deliverable constraint is used as an electricity-gas integrated energy system robust operation constraint of the energy optimization operation model of the electricity-gas integrated energy system;
and step 3: decoupling the energy optimization operation model of the electricity-gas integrated energy system to obtain an optimization operation model of the power system and an optimization operation model of the natural gas system; and solving the optimized operation model of the power system and the optimized operation model of the natural gas system by adopting an alternating direction multiplier method based on self-adaptive punishment parameters to obtain the optimal solution of the energy optimized dispatching of the electricity-gas integrated energy system, thereby completing the cooperative optimization of the electricity-gas integrated energy system.
2. The method for collaborative optimization operation of an electric integrated energy system according to claim 1, wherein the step 1 of establishing the energy optimization operation model of the electric-gas integrated energy system comprises the steps of:
step 11, setting an objective function of the energy optimization operation model of the electricity-gas integrated energy system, and taking the lowest total operation cost of the system in a system operation scheduling period as an objective, wherein the total operation cost of the system comprises an energy supply cost C1Cost of abandoned wind C2System and method for controlling a power supplyUp and down spare capacity cost C3(ii) a Wherein:
the energy supply cost C1The method comprises the power generation cost of a conventional generator, the natural gas production cost, the charge and discharge cost of a storage battery and the interruption compensation cost of an interruptible load; cost of spare capacity C for up and down of the system3The up and down reserve capacity of the system is provided by the generator, battery and interruptible load together;
and 12, setting power system operation constraint, natural gas system operation constraint and system coupling constraint on the objective function.
3. The method of claim 2, wherein the objective function set in step 11 is as follows:
Figure FDA0003257197760000011
Figure FDA0003257197760000012
Figure FDA0003257197760000013
Figure FDA0003257197760000014
wherein, CiAnd Pi,tRespectively representing the cost coefficient and the generating capacity of the non-gas unit; cgAnd Fg,tCost factor and gas yield, respectively, of the natural gas supply; csIs the unit charge-discharge cost of the accumulator, Ps,tRepresents the charging or discharging power of the storage battery;
Figure FDA0003257197760000021
and
Figure FDA0003257197760000022
is the unit power interrupt penalty cost of the interruptible load and the scheduled power of the interruptible load; cwRepresents the unit fine cost of the abandoned wind;
Figure FDA0003257197760000023
and
Figure FDA0003257197760000024
respectively the cost factor for the upward and downward spares,
Figure FDA0003257197760000025
and
Figure FDA0003257197760000026
upward and reserve capacity provided by a reserve source r, which may be a generator reserve or battery or interruptible load;
Figure FDA0003257197760000027
and
Figure FDA0003257197760000028
representing the cost factor of the gas turbine's up and down standby,
Figure FDA0003257197760000029
and
Figure FDA00032571977600000210
representing the amount of natural gas reserved for the gas turbine to provide upward and downward reserve capacity, NGIndicating the number of gas turbine units.
4. The method of claim 1, wherein the robust operation constraints of the electric-gas integrated energy system energy-optimized operation model in step 2 comprise generator shutdown constraints, line break constraints, and natural gas pipeline constraints.
5. The cooperative optimization operation method of the electric integrated energy system according to claim 1, wherein the energy optimization operation model of the electric-gas integrated energy system is decoupled in step 3 to obtain an optimization operation model of an electric power system and an optimization operation model of a natural gas system; and adding the punishment items into the optimized operation model of the power system and the optimized operation model of the natural gas system to obtain the optimized operation model of the power system and the optimized operation model of the natural gas system after adding the punishment items.
6. The method of claim 5, wherein the objective functions of the optimized operation model of the power system and the optimized operation model of the natural gas system are respectively expressed as follows:
Figure FDA00032571977600000211
Figure FDA00032571977600000212
wherein, minfeFor an optimized operation model, minf, of the power system after adding penalty termsgThe method is an optimized operation model of the natural gas system after adding the penalty item.
7. The cooperative optimization operation method of the electrical integrated energy system according to claim 6, wherein in step 3, the optimal operation model of the power system and the optimal operation model of the natural gas system are solved by using an alternating direction multiplier method based on adaptive penalty parameters to obtain an optimal energy optimization scheduling solution of the electrical-gas integrated energy system, so as to complete cooperative optimization of the electrical-gas integrated energy system; the method specifically comprises the following substeps:
s31: initializing variables: setting iteration index n as 1, setting original and dual convergence threshold epsilonpAnd εdInitializing gas consumption of the gas turbine
Figure FDA00032571977600000213
The gas reserve reserved for the gas turbine that provides upward and downward reserve capacity is
Figure FDA00032571977600000214
And
Figure FDA00032571977600000215
a Lagrange multiplier λ and a penalty parameter ρ;
s32: solving the optimal operation result of the power system subproblem 1: according to
Figure FDA0003257197760000031
And
Figure FDA0003257197760000032
obtaining the optimal solution result of the current power system from the initial value
Figure FDA0003257197760000033
And
Figure FDA0003257197760000034
s33: updating the auxiliary variable: order to
Figure FDA0003257197760000035
Sharing boundary variables with natural gas systems
Figure FDA0003257197760000036
And
Figure FDA0003257197760000037
s34: solving the optimal operation result of the natural gas system subproblem 2: according to
Figure FDA0003257197760000038
And
Figure FDA0003257197760000039
initial value is solved the optimum result of solving of current natural gas system
Figure FDA00032571977600000310
And
Figure FDA00032571977600000311
s35: updating the auxiliary variable: order to
Figure FDA00032571977600000312
Sharing boundary variables with power systems
Figure FDA00032571977600000313
And
Figure FDA00032571977600000314
the information of (a);
s36: computing boundary variables
Figure FDA00032571977600000315
And
Figure FDA00032571977600000316
original residual and dual residual;
s37: checking convergence: if the maximum residual error meets the constraint condition or N is greater than N, terminating the iteration process and outputting a solution; otherwise, go to S38; if the current iteration number N is larger than the maximum iteration limit N, the solving process is regarded as incapable of convergence;
s38: and updating the Lagrange multiplier and the penalty parameter, enabling n to be n +1, and repeating the iteration after the step S32 is carried out.
8. A system for collaborative optimal operation of an electric integrated energy system, the system supporting a method for collaborative optimal operation of an electric integrated energy system according to any one of claims 1 to 7, the system comprising:
a preliminary model construction unit: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for establishing an energy optimization operation model of the electricity-gas integrated energy system considering various standby resources, and the various standby resources comprise a generator standby resource, an energy storage device standby resource and an interruptible load standby resource;
a model constraint setting unit, configured to set, according to the energy-optimized operation model of the electric-gas integrated energy system, a system spare capacity deliverable constraint considering an emergency as an electric-gas integrated energy system robust operation constraint of the energy-optimized operation model of the electric-gas integrated energy system;
the model decoupling unit is used for decoupling the energy optimization operation model of the electricity-gas integrated energy system to obtain an optimization operation model of the power system and an optimization operation model of the natural gas system;
the optimization solving unit is used for solving the optimization operation model of the power system and the optimization operation model of the natural gas system by adopting an alternating direction multiplier method based on self-adaptive punishment parameters to obtain an energy optimization scheduling optimal solution of the electricity-gas integrated energy system;
and the output unit is used for outputting the optimal energy optimization scheduling solution of the electricity-gas integrated energy system to complete the cooperative optimization of the electricity-gas integrated energy system.
9. The system of claim 8, wherein the robust electrical-gas integrated energy system operation constraints of the energy-optimized electrical-gas integrated energy system operation model in the model constraint setting unit include generator shutdown constraints, line break constraints, and natural gas pipeline constraints.
10. The system of claim 8, wherein the model decoupling unit decouples the energy-optimized operation model of the electric-gas integrated energy system to obtain an optimized operation model of an electric power system and an optimized operation model of a natural gas system; adding the punishment items into an optimized operation model of the power system and an optimized operation model of the natural gas system to obtain the optimized operation model of the power system and the optimized operation model of the natural gas system after the punishment items are added;
after adding the penalty term, the objective functions of the optimized operation model of the power system and the optimized operation model of the natural gas system are respectively expressed as follows:
Figure FDA0003257197760000041
Figure FDA0003257197760000042
wherein, minfeFor an optimized operation model, minf, of the power system after adding penalty termsgThe method comprises the steps of (1) adding a penalty term to an optimized operation model of the natural gas system; gamma rayj,tAnd p represent the lagrangian multiplier and penalty parameters, respectively.
CN202111063170.0A 2021-09-10 2021-09-10 Collaborative optimization operation method and system of electric comprehensive energy system Active CN113762632B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111063170.0A CN113762632B (en) 2021-09-10 2021-09-10 Collaborative optimization operation method and system of electric comprehensive energy system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111063170.0A CN113762632B (en) 2021-09-10 2021-09-10 Collaborative optimization operation method and system of electric comprehensive energy system

Publications (2)

Publication Number Publication Date
CN113762632A true CN113762632A (en) 2021-12-07
CN113762632B CN113762632B (en) 2024-07-02

Family

ID=78794845

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111063170.0A Active CN113762632B (en) 2021-09-10 2021-09-10 Collaborative optimization operation method and system of electric comprehensive energy system

Country Status (1)

Country Link
CN (1) CN113762632B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115619145A (en) * 2022-10-14 2023-01-17 国网江苏省电力有限公司电力科学研究院 Cooperative control method and device for comprehensive energy system and computer equipment
CN116894342A (en) * 2023-07-19 2023-10-17 天津大学 Toughness improving method for electric-gas comprehensive energy system based on natural gas network pipe storage

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107292456A (en) * 2017-08-01 2017-10-24 重庆大学 Electrical energy flow point cloth collaboration optimized calculation method based on alternating direction multiplier method
CN109980684A (en) * 2019-04-02 2019-07-05 云南电网有限责任公司电力科学研究院 A kind of distributed optimization dispatching method based on flexible interconnection micro-capacitance sensor
AU2019101317A4 (en) * 2019-10-30 2019-12-05 Southeast University A Bi-level Game-Based Planning Framework for Distribution Networks with multiple Micro-girds
CN111342452A (en) * 2020-03-16 2020-06-26 四川大学 Energy and standby distributed scheduling method for multi-region electrical comprehensive energy system
CN111342453A (en) * 2020-03-16 2020-06-26 四川大学 Electrical comprehensive energy system standby decision method considering various types of standby resources
CN112036613A (en) * 2020-08-14 2020-12-04 南方电网能源发展研究院有限责任公司 Park comprehensive energy optimization method and device based on ADMM alternating direction multiplier method
CN112217196A (en) * 2020-08-13 2021-01-12 四川大学 Long-term coordination extension planning method for gas-electricity combined system considering N-1 safety criterion and probability reliability index

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107292456A (en) * 2017-08-01 2017-10-24 重庆大学 Electrical energy flow point cloth collaboration optimized calculation method based on alternating direction multiplier method
CN109980684A (en) * 2019-04-02 2019-07-05 云南电网有限责任公司电力科学研究院 A kind of distributed optimization dispatching method based on flexible interconnection micro-capacitance sensor
AU2019101317A4 (en) * 2019-10-30 2019-12-05 Southeast University A Bi-level Game-Based Planning Framework for Distribution Networks with multiple Micro-girds
CN111342452A (en) * 2020-03-16 2020-06-26 四川大学 Energy and standby distributed scheduling method for multi-region electrical comprehensive energy system
CN111342453A (en) * 2020-03-16 2020-06-26 四川大学 Electrical comprehensive energy system standby decision method considering various types of standby resources
CN112217196A (en) * 2020-08-13 2021-01-12 四川大学 Long-term coordination extension planning method for gas-electricity combined system considering N-1 safety criterion and probability reliability index
CN112036613A (en) * 2020-08-14 2020-12-04 南方电网能源发展研究院有限责任公司 Park comprehensive energy optimization method and device based on ADMM alternating direction multiplier method

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
JING GOU 等: "Coordinated Optimization of Electricity-Gas Integrated Energy System", 《 2021 IEEE/IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA (I&CPS ASIA)》, 2 December 2021 (2021-12-02), pages 515 - 519 *
YUNFENG WEN 等: "Synergistic Operation of Electricity and Natural Gas Networks via ADMM", 《IEEE TRANSACTIONS ON SMART GRID ( VOLUME: 9, ISSUE: 5, SEPTEMBER 2018)》, vol. 9, no. 5, 2 February 2017 (2017-02-02), pages 4555 - 4565 *
乔铮 等: "电力—天然气耦合系统建模与规划运行研究综述", 《全球能源互联网》, vol. 3, no. 1, 25 January 2020 (2020-01-25), pages 14 - 26 *
刘俊勇 等: "考虑风电不确定性的电气能源系统两阶段分布鲁棒协同调度", 《电力系统自动化》, vol. 42, no. 13, 10 July 2018 (2018-07-10), pages 43 - 50 *
林舜江 等: "电-气混合系统安全约束最优能量流的分布式计算", 《华南理工大学学报（自然科学版）》, vol. 48, no. 7, 31 July 2020 (2020-07-31), pages 36 - 46 *
汪松: "电-气综合能源系统鲁棒调度控制方法研究", 中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑, no. 2021, 15 June 2021 (2021-06-15), pages 042 - 1181 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115619145A (en) * 2022-10-14 2023-01-17 国网江苏省电力有限公司电力科学研究院 Cooperative control method and device for comprehensive energy system and computer equipment
CN115619145B (en) * 2022-10-14 2024-03-19 国网江苏省电力有限公司电力科学研究院 Cooperative control method and device for comprehensive energy system and computer equipment
CN116894342A (en) * 2023-07-19 2023-10-17 天津大学 Toughness improving method for electric-gas comprehensive energy system based on natural gas network pipe storage
CN116894342B (en) * 2023-07-19 2024-03-12 天津大学 Toughness improving method for electric-gas comprehensive energy system based on natural gas network pipe storage

Also Published As

Publication number Publication date
CN113762632B (en) 2024-07-02

Similar Documents

Publication Publication Date Title
Li et al. Improving operational flexibility of integrated energy system with uncertain renewable generations considering thermal inertia of buildings
Gan et al. Security constrained co-planning of transmission expansion and energy storage
Santos et al. New multistage and stochastic mathematical model for maximizing RES hosting capacity—Part I: Problem formulation
CN110034572B (en) Energy storage configuration method for alternating current-direct current hybrid system containing multi-port power electronic transformer
Javadi et al. Optimal spinning reserve allocation in presence of electrical storage and renewable energy sources
CN113762632B (en) Collaborative optimization operation method and system of electric comprehensive energy system
Sun et al. Improving the restorability of bulk power systems with the implementation of a WF-BESS system
Zuo et al. Distributed multi-energy storage cooperative optimization control method for power grid voltage stability enhancement
CN114937990A (en) Method, system, device and storage medium for determining reserve capacity of power system
Abedini et al. Adaptive energy consumption scheduling of multi-microgrid using whale optimization algorithm
CN115425697B (en) Distributed cross-region and cross-province scheduling method and system based on alternate direction multiplier method
CN115377968A (en) Novel power distribution network sequence recovery optimization method considering renewable energy output fluctuation
Li et al. Distributed Stochastic Scheduling of Massive Backup Batteries in Cellular Networks for Operational Reserve and Frequency Support Ancillary Services
CN113346554A (en) Distributed cooperative regulation and control method for power distribution network
CN112054553A (en) Coordinated optimization operation method, system, medium and equipment for electric-heat-gas interconnection system
CN112258077A (en) Parameter determination method and related device for multi-region interconnected comprehensive energy system
CN116131365B (en) Flexible operation control management system and method for intelligent power distribution network
Wang et al. Operation Optimization of DC Distribution Network with BSS Based on GA-WDO Hybrid Algorithm
Han et al. Integrated Planning of Energy Storage Systems and Data Centers Considering Resilience Enhancement in Distribution Network
Xie et al. Optimal Sizing of Renewables and Energy Storage System in Microgrids: The Quest for Economic Uncertainty Management
Wang et al. Multi-Area Unit Commitment Model Based on Multi-Scenarios Risk Analysis
Ji et al. Optimization of Generator Dispatching Model Considering Load Demand Response Resources
Zhang et al. Nodal Invulnerability Recovery Considering Power Generation Balance: A Bi-Objective Robust Optimization Framework
Ecike Ewanga Enhacing power reliability using microgrids
Chen et al. Optimal Configuration of Multi-Energy Storage in an Electric–Thermal–Hydrogen Integrated Energy System Considering Extreme Disaster Scenarios

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant