CN111950809B - Master-slave game-based hierarchical and partitioned optimized operation method for comprehensive energy system - Google Patents

Master-slave game-based hierarchical and partitioned optimized operation method for comprehensive energy system Download PDF

Info

Publication number
CN111950809B
CN111950809B CN202010871043.2A CN202010871043A CN111950809B CN 111950809 B CN111950809 B CN 111950809B CN 202010871043 A CN202010871043 A CN 202010871043A CN 111950809 B CN111950809 B CN 111950809B
Authority
CN
China
Prior art keywords
park
energy
game
energy system
slave
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.)
Active
Application number
CN202010871043.2A
Other languages
Chinese (zh)
Other versions
CN111950809A (en
Inventor
李鹏
王子轩
郭天宇
王加浩
吴迪凡
周益斌
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.)
North China Electric Power University
Original Assignee
North China Electric Power University
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 North China Electric Power University filed Critical North China Electric Power University
Priority to CN202010871043.2A priority Critical patent/CN111950809B/en
Publication of CN111950809A publication Critical patent/CN111950809A/en
Application granted granted Critical
Publication of CN111950809B publication Critical patent/CN111950809B/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
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • General Business, Economics & Management (AREA)
  • Software Systems (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • General Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • Operations Research (AREA)
  • Primary Health Care (AREA)
  • Artificial Intelligence (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Quality & Reliability (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

A master-slave game-based hierarchical and partitioned optimization operation method for an integrated energy system comprises the following steps: establishing a master-slave game layered partition architecture of the multi-park comprehensive energy system; establishing a game main body optimization model of the multi-park comprehensive energy system and a game subordinate body optimization model of the multi-park comprehensive energy system based on a master-slave game layered partition framework of the multi-park comprehensive energy system; the game main body optimization model of the multi-park comprehensive energy system and the game slave body optimization model of the multi-park comprehensive energy system are combined to jointly form a master-slave game double-layer optimization operation model of the multi-park comprehensive energy system, and the master-slave game double-layer optimization operation model of the multi-park comprehensive energy system is solved by combining a chaotic self-adaptive particle swarm algorithm and a mixed integer linear programming method. The invention effectively exerts the complementary and cooperative advantages among various energy sources of cold, heat, electricity and gas, further reduces the comprehensive operation cost of the system and realizes the economic, flexible and cooperative optimization operation of the comprehensive energy system of multiple parks.

Description

Master-slave game-based hierarchical and partitioned optimized operation method for comprehensive energy system
Technical Field
The invention relates to an optimized operation method of a comprehensive energy system. In particular to a master-slave game-based hierarchical and partitioned optimized operation method of an integrated energy system.
Background
With the increasing exhaustion of global fossil energy and the gradual aggravation of environmental pollution problems, renewable energy sources such as wind energy, solar energy, geothermal energy and the like are more and more valued, and the world energy pattern faces a huge challenge. The comprehensive energy system is used as a natural extension of a micro-grid, integrates various distributed energy sources and traditional energy sources such as cold, heat, electricity, gas and the like, can realize the gradient high-efficiency utilization of energy, and becomes an important means for green, high-efficiency and sustainable utilization of energy at present.
A plurality of energy devices are integrated in the comprehensive energy system, different types of energy are tightly coupled and flexibly converted, and energy management and optimized operation are the key points of current comprehensive energy research. The existing literature focuses on single energy buildings or micro-grids in parks, researches on collaborative optimization operation of multiple comprehensive energy parks in the area are less, and multi-type energy interactive support among different parks is less considered. Therefore, the master-slave game-based hierarchical and partitioned optimization operation research of the multi-park comprehensive energy system has important significance.
Disclosure of Invention
The invention aims to solve the technical problem of providing a master-slave game-based comprehensive energy system layered and partitioned optimal operation method which can give full play to the energy interaction support advantage between parks and gives consideration to the benefits between the whole system and individual parks.
The technical scheme adopted by the invention is as follows: a master-slave game-based hierarchical and partitioned optimization operation method for an integrated energy system comprises the following steps:
1) establishing a master-slave game layering partition architecture of the multi-park comprehensive energy system, wherein the master-slave game layering partition architecture comprises a system layer and a park layer;
2) establishing a game main body optimization model of the multi-park comprehensive energy system and a game subordinate body optimization model of the multi-park comprehensive energy system based on the master-slave game layered partition framework of the multi-park comprehensive energy system established in the step 1);
3) combining the game main body optimization model of the multi-park comprehensive energy system established in the step 2) with the game slave body optimization model of the multi-park comprehensive energy system to jointly form a master-slave game double-layer optimization operation model of the multi-park comprehensive energy system, and solving the master-slave game double-layer optimization operation model of the multi-park comprehensive energy system by adopting a chaotic self-adaptive particle swarm algorithm and a mixed integer linear programming method.
The master-slave game-based hierarchical and partitioned optimized operation method of the comprehensive energy system has the following advantages:
1. the invention considers the interaction of various energies between gardens, can exert the interaction support advantages of different garden areas and promote the energy supply and demand balance.
2. According to the invention, by establishing the master-slave game double-layer optimization operation model of the multi-park integrated energy system, the benefits of the whole integrated energy system and the park individual can be reasonably considered, and the method is more suitable for practical application.
3. The invention can effectively exert the complementary and cooperative advantages among various energy sources of cold, heat, electricity and gas, further reduce the comprehensive operation cost of the system and realize the economic, flexible and cooperative optimized operation of the comprehensive energy system of multiple parks.
Drawings
FIG. 1 is a principal and subordinate game hierarchical partition architecture diagram of a multi-campus energy complex of the present invention;
FIG. 2 is a flow chart of a master-slave game double-layer optimization operation model solving process of the multi-park comprehensive energy system;
FIG. 3 is a block diagram of a typical example of a multi-park integrated energy system in accordance with an embodiment of the present invention
FIG. 4a is a graph illustrating energy load and wind/solar power forecast for a residential quarter in an embodiment of the present invention;
FIG. 4b is a graph illustrating energy load and wind/solar power forecast for an industrial park in accordance with an embodiment of the present invention;
FIG. 4c is a graph of energy load and wind/solar power forecast for a commercial park in accordance with an embodiment of the present invention;
FIG. 5 is a comparison graph of system flexibility margins under different scenarios in the example of the present invention
FIG. 6a is a result of scheduling for optimizing the energy flow of power supply and demand in a park according to an embodiment of the present invention;
FIG. 6b is the result of the optimized scheduling of the natural gas energy supply and demand flow in the park according to the embodiment of the present invention;
FIG. 6c is a diagram illustrating the result of the optimized scheduling of the energy flow of heat energy supply and demand in the park according to an embodiment of the present invention;
FIG. 6d is the result of scheduling optimization of energy flow for cooling energy supply and demand in the campus according to the embodiment of the present invention.
Detailed Description
The method for the layered and partitioned optimized operation of the comprehensive energy system based on the master-slave game is described in detail below with reference to the embodiments and the accompanying drawings.
The invention discloses a master-slave game-based comprehensive energy system layered and partitioned optimal operation method, which comprises the following steps of:
1) establishing a master-slave game layering partition architecture of the multi-park comprehensive energy system, wherein the master-slave game layering partition architecture comprises a system layer and a park layer; as shown in fig. 1, specifically:
the main body of the system layer is an energy scheduling center which is used as an upper leader, and the centralized scheduling of the system is realized by issuing a regulation and control instruction to each park; the main body of the park layer is a commercial park operator, an industrial park operator and a residential park operator which are used as lower-layer followers, and each park operator realizes self optimized operation by optimizing the output dispatching plan of internal equipment on the basis of an energy dispatching center instruction; the system layer and the park layer have benefit interaction to form a typical game pattern, so that the double-layer optimization problem of the multi-park integrated energy system is described as a master-slave game problem of a leader and a plurality of followers, and a master-slave game hierarchical partition architecture G of the multi-park integrated energy system is as follows:
G＝{(COi∪EDC),{SCOi},{SEDC},{BCOi},{BEDC}}
in the formula, COi and EDC respectively represent a park i operator and an energy dispatching center; sCOi、SEDCRespectively a strategy set of a park i operator and an energy dispatching center; b isCOi、BEDCRespectively for the operator of campus i and the energy dispatching center.
2) Establishing a game main body optimization model of the multi-park comprehensive energy system and a game subordinate body optimization model of the multi-park comprehensive energy system based on the master-slave game layered partition framework of the multi-park comprehensive energy system established in the step 1); wherein the content of the first and second substances,
(1) the game main body optimization model of the multi-park integrated energy system takes the minimum integrated operation cost of the multi-park integrated energy system as a target function and takes the energy purchasing constraint, the energy interaction constraint and the system flexibility constraint as constraint conditions. Wherein the content of the first and second substances,
(1.1) the objective function expression of the game subject optimization model of the multi-park integrated energy system is as follows:
Figure BDA0002651121550000031
in the formula: eEDCThe comprehensive operation cost of the comprehensive energy system of the multiple parks is reduced; including cost of energy purchase
Figure BDA0002651121550000032
Loss cost
Figure BDA0002651121550000033
And environmental protection cost
Figure BDA0002651121550000034
T is a scheduling period of 24 h;
Figure BDA0002651121550000035
and
Figure BDA0002651121550000036
respectively representing the electricity purchasing quantity, the gas purchasing quantity and the heat purchasing quantity of the park i in the time period t;
Figure BDA0002651121550000037
and
Figure BDA0002651121550000038
respectively the electricity purchase, gas purchase and heat purchase prices in the time period t; beta is ap、βgAnd betahRespectively obtaining equivalent carbon emission coefficients of electricity, gas and heat purchased from the system to an external network; alpha is the treatment cost of unit carbon emission;
Figure BDA0002651121550000039
and
Figure BDA00026511215500000310
respectively, the electric and pneumatic power interaction between the parks i and j, and
Figure BDA00026511215500000311
γp、γgthe electric and pneumatic power transmission loss coefficients are respectively.
(1.2) the constraint conditions of the game main body optimization model of the multi-park integrated energy system are specifically expressed as follows:
(1.2.1) energy purchase constraints:
Figure BDA00026511215500000312
in the formula, Pb,i,max、Gb,i,maxAnd Hb,i,maxRespectively purchasing electricity, gas and heat power upper limits of the park i; pb,max、Gb,maxAnd Hb,maxRespectively purchasing electricity, gas and heat from the system to an external network;
(1.2.2) energy interaction constraint
Figure BDA00026511215500000313
In the formula, Pij,max、Gij,maxThe upper limits of the electric power and the pneumatic power interaction between the parks i and j are respectively;
Figure BDA00026511215500000314
the power and gas power interaction directions are respectively, the power flows from the park i to the park j when the direction is positive, and the power flows from the park i to the park j when the direction is negative;
(1.2.3) System flexibility constraints
1≥IEFMt≥IEFMmin
Figure BDA00026511215500000315
In the formula, IEFMminFlexibility margins for systems IEFMtThe lower limit of (d); k represents energy type, K ═ { p, h, c, g }, p, h, c, g represent electricity, heat, cold, gas, respectively;
Figure BDA00026511215500000430
respectively the actual demand total amount and the maximum allowable supply total amount of the energy type k in the park i in the period t; m, N denote the demand side and supply side sets of units, respectively, of the campus;
Figure BDA0002651121550000041
representing the actual demand of the unit m on the energy type k in the park i in the period t;
Figure BDA0002651121550000042
represents the maximum allowable supply of unit n to energy type k in time period park i.
(2) The game slave optimization model of the multi-park integrated energy system takes the minimum running cost of the park i as an objective function and takes energy balance constraint, energy equipment constraint and park flexibility constraint as constraint conditions. Wherein the content of the first and second substances,
(2.1) the objective function expression of the game slave optimization model of the multi-park integrated energy system is as follows:
Figure BDA0002651121550000043
in the formula: eCOiFor operating costs of park i, including park i energy purchase costs
Figure BDA0002651121550000044
Park i operation and maintenance cost
Figure BDA0002651121550000045
And park i sell energy revenue
Figure BDA0002651121550000046
T is a scheduling period of 24 h;
Figure BDA0002651121550000047
and
Figure BDA0002651121550000048
respectively representing the purchase of the park i in the time period tElectricity quantity, gas purchasing quantity and heat purchasing quantity;
Figure BDA0002651121550000049
and
Figure BDA00026511215500000410
respectively the electricity purchase, gas purchase and heat purchase prices in the time period t; r is an operation and maintenance equipment set in the park i; p is a radical ofom,rThe unit power operation and maintenance cost of the equipment r;
Figure BDA00026511215500000411
the running power of equipment r in the park i at the time t;
Figure BDA00026511215500000412
and
Figure BDA00026511215500000413
respectively the electric power interaction quantity and the pneumatic power interaction quantity between the parks i and j;
Figure BDA00026511215500000414
the power interaction directions of electricity and gas are respectively;
Figure BDA00026511215500000415
Figure BDA00026511215500000416
the price of the power interaction between the park i and the park j in the time period t is respectively the price of the power interaction between the park i and the park j in the time period t.
(2.2) the constraint conditions of the game slave optimization model of the multi-park integrated energy system are specifically expressed as follows:
(2.2.1) energy balance constraints
Figure BDA00026511215500000417
In the formula (I), the compound is shown in the specification,
Figure BDA00026511215500000418
and
Figure BDA00026511215500000419
output electric power of a transformer, a photovoltaic, a fan and a micro-combustion engine in the park i at the time t respectively;
Figure BDA00026511215500000420
respectively the electric, hot, cold and air load power of the park i at the time t;
Figure BDA00026511215500000421
the power consumption of the electric refrigerator in the park i is t time period;
Figure BDA00026511215500000422
the thermal powers of the in-i heat exchanger and the micro-combustion engine in the park at the time t are respectively output;
Figure BDA00026511215500000423
the heat consumption power of the absorption refrigerator in the park i at the time period t;
Figure BDA00026511215500000424
the output cold power of the electric refrigerator and the absorption refrigerator in the park i at the time period t respectively;
Figure BDA00026511215500000425
the air consumption of the micro-combustion engine in the park i at the time t;
Figure BDA00026511215500000426
Figure BDA00026511215500000427
energy discharge power of electricity storage, heat storage, cold storage and gas storage in the park i at the time t respectively;
Figure BDA00026511215500000428
Figure BDA00026511215500000429
the charging power of electricity storage, heat storage, cold storage and gas storage in the park i at the time t is respectively.
(2.2.2) energy plant restraint
Figure BDA0002651121550000051
Figure BDA0002651121550000052
In the formula, Ppv,i,max、Pwt,i,max、Pt,i,max、Pmt,i,max、Hhe,i,max、Hac,i,maxAnd Pec,i,maxThe maximum values of the operating power of the photovoltaic, wind power, the transformer, the micro-combustion engine, the heat exchanger, the absorption refrigerator and the electric refrigerator in the park i are respectively set; subscript x represents the energy storage type;
Figure BDA0002651121550000053
respectively storing energy charging and discharging power and energy storage states in a park i at a time period t;
Figure BDA0002651121550000054
respectively storing the initial energy and the final energy of energy storage equipment in the park i; ex,i,min、Ex,i,maxRespectively the minimum and maximum energy states of the energy storage equipment in the park i; px,c,i,max、Px,d,i,maxRespectively the maximum charging and discharging power of the energy storage equipment; lambda [ alpha ]x,c,i、λx,d,iRespectively a charge state and a discharge state are 0-1 variables;
(2.2.3) park flexibility constraints
Figure BDA0002651121550000055
Figure BDA0002651121550000056
In the formula (I), the compound is shown in the specification,
Figure BDA0002651121550000057
and IEFMi,minRespectively the flexibility margin and the lower limit of the park i at the time period t; k represents energy type, K ═ { p, h, c, g }, p, h, c, g represent electricity, heat, cold, gas, respectively;
Figure BDA0002651121550000058
respectively the actual demand sum and the maximum allowed supply sum of the energy type k in the park i during the period t.
3) Combining the game main body optimization model of the multi-park comprehensive energy system established in the step 2) with the game slave body optimization model of the multi-park comprehensive energy system to jointly form a master-slave game double-layer optimization operation model of the multi-park comprehensive energy system, and solving the master-slave game double-layer optimization operation model of the multi-park comprehensive energy system by adopting a chaotic self-adaptive particle swarm algorithm and a mixed integer linear programming method. Wherein the content of the first and second substances,
(1) the master-slave game double-layer optimization operation model of the multi-park comprehensive energy system is represented as follows:
Figure BDA0002651121550000061
in the formula, EEDCThe comprehensive operation cost of the comprehensive energy system of the multiple parks is reduced; eCOiThe operating cost for park i; x is the strategy of the upper layer game main body; y isiFor policies from campus i, y-iThe strategy combination of the slave parks except the slave park i;
Figure BDA0002651121550000062
the optimal strategy of the slave park i under the condition of the strategy x of the upper-layer game main body, S (x) is the strategy set of each slave park of the lower layer,
Figure BDA0002651121550000063
the optimal strategy set of all slave parks under the condition of the strategy x of the upper layer game main body is given, namely Nash equilibrium solution of the park layer game, and
Figure BDA0002651121550000064
i＝1,2,3。
(2) the solving of the master-slave game double-layer optimization operation model of the multi-park comprehensive energy system by adopting the chaotic self-adaptive particle swarm optimization and combining the mixed integer linear programming method is as follows: the chaotic self-adaptive particle swarm algorithm and the mixed integer linear programming method are comprehensively applied, a Gurobi solver is called through a Yalmip tool box to solve a master-slave game double-layer optimization operation model of the multi-park comprehensive energy system, a layered and partitioned optimization operation scheme of the multi-park comprehensive energy system is obtained, and the solving process is shown in figure 2.
Specific examples are given below.
The invention carries out simulation analysis based on a typical example of a certain energy Internet small town multi-park comprehensive energy system, the specific structure is shown in figure 3, the system mainly comprises residential parks, industrial parks, commercial parks and town energy dispatching centers, the parks are connected through an energy pipe network, the heat/cold water transmission energy loss is large, the system only considers the electricity and gas energy interaction between the parks, and the electricity and gas transmission loss coefficients are respectively set to be 6% and 3%. Energy load and wind-solar output prediction curves of various parks are shown in fig. 4, 4b and 4c, and in order to further promote renewable energy consumption, the wind power and the photovoltaic adopt an MPPT mode. The energy prices are shown in table 1, and the energy trading price between parks is 0.05 yuan higher than the real-time energy price.
TABLE 1 energy prices
Figure BDA0002651121550000065
In order to verify the effectiveness of the master-slave game double-layer optimization operation strategy provided by the invention, the system optimization operation effect under different strategy combinations is researched in the following 4 scenes.
Scene 1: energy interaction is not considered, and each park is optimized independently.
Scene 2: and only considering the electric energy interaction, and uniformly optimizing the dispatching center.
Scene 3: and considering the interaction of various types of energy of electricity and gas, and uniformly optimizing the dispatching center.
Scene 4: and the interaction of electricity and gas with various types of energy is considered, and the master-slave game is optimized in a double-layer mode.
The system optimization results under different scenarios are shown in table 2.
TABLE 2 comparison of system optimization results under different scenarios
Figure BDA0002651121550000071
By analyzing and comparing the operation costs in different scenes in the table 1, it can be found that compared with the energy interaction between the garden intervals which is not considered in the scene 1, the comprehensive operation cost of the system can be further reduced by introducing the energy interaction strategies in the scenes 2, 3 and 4, and the economical efficiency of the scenes 3 and 4 which consider the interaction of various types of energy of electricity and gas is better than that of the scene 2 which only considers the interaction of electric energy.
Further analysis on the scenes 3 and 4 shows that the scene 4 adopts the master-slave game double-layer optimization strategy provided by the invention, the comprehensive operation cost of the scene is not much different from that of the scene 3 which performs single-layer optimization by taking the overall economic efficiency of the system as the best target, but the system flexibility margin is obviously improved in the supply and demand tension period (12:00-20:00), as shown in fig. 5. And scene 4 is based on a master-slave game model, different optimization targets and constraint conditions are respectively selected from the upper layer and the lower layer for double-layer optimization, the benefits of a master dispatching center and each slave park can be considered simultaneously, and the method is more suitable for practical application.
Specific analysis is performed on the scene 4, taking a residential park as an example, and the internal multi-energy flow optimization scheduling result is shown in fig. 6a, 6b, 6c and 6 d. In the figure, energy input is shown above the horizontal axis and energy output is shown below the horizontal axis.
It can be seen that the optimized scheduling result is the result of the joint action of multiple factors. On one hand, under the influence of the energy utilization condition and the energy price of the park, the park operator reasonably arranges the output scheduling plan of the whole day by exerting the energy flexible conversion and storage advantages of the multi-type energy equipment; on the other hand, because energy interaction exists between different parks, the energy utilization characteristics and the scheduling plan of other parks also have an influence on the optimal scheduling result of the park.
In summary, the comprehensive energy system layered and partitioned optimization operation method based on the master-slave game can give consideration to the benefits of the whole system and the individual garden, promote energy supply and demand balance, effectively reduce the comprehensive operation cost of the garden, promote energy supply and demand balance, and verify the correctness and the effectiveness of the multi-garden comprehensive energy system layered and partitioned optimization operation method based on the master-slave game.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (6)

1. A master-slave game-based hierarchical and partitioned optimization operation method of an integrated energy system is characterized by comprising the following steps:
1) establishing a master-slave game layering partition architecture of the multi-park comprehensive energy system, wherein the master-slave game layering partition architecture comprises a system layer and a park layer;
2) establishing a game main body optimization model of the multi-park comprehensive energy system and a game subordinate body optimization model of the multi-park comprehensive energy system based on the master-slave game layered partition framework of the multi-park comprehensive energy system established in the step 1); wherein the content of the first and second substances,
the game main body optimization model of the multi-park integrated energy system takes the minimum integrated operation cost of the multi-park integrated energy system as a target function and takes energy purchasing constraint, energy interaction constraint and system flexibility constraint as constraint conditions; the expression of the target function is as follows:
Figure FDA0003326341710000011
in the formula:EEDCthe comprehensive operation cost of the comprehensive energy system of the multiple parks is reduced; including cost of energy purchase
Figure FDA0003326341710000012
Loss cost
Figure FDA0003326341710000013
And environmental protection cost
Figure FDA0003326341710000014
T is a scheduling period of 24 h;
Figure FDA0003326341710000015
and
Figure FDA0003326341710000016
respectively representing the electricity purchasing quantity, the gas purchasing quantity and the heat purchasing quantity of the park i in the time period t;
Figure FDA0003326341710000017
and
Figure FDA0003326341710000018
respectively the electricity purchase, gas purchase and heat purchase prices in the time period t; beta is ap、βgAnd betahRespectively obtaining equivalent carbon emission coefficients of electricity, gas and heat purchased from the system to an external network; alpha is the treatment cost of unit carbon emission;
Figure FDA0003326341710000019
and
Figure FDA00033263417100000110
respectively, the electric and pneumatic power interaction between the parks i and j, and
Figure FDA00033263417100000111
γp、γgthe electric power transmission loss coefficient and the pneumatic power transmission loss coefficient are respectively;
the game slave optimization model of the multi-park integrated energy system takes the minimum running cost of the park i as a target function and takes energy balance constraint, energy equipment constraint and park flexibility constraint as constraint conditions; the expression of the target function is as follows:
Figure FDA00033263417100000112
in the formula: eCOiFor operating costs of park i, including park i energy purchase costs
Figure FDA00033263417100000113
Park i operation and maintenance cost
Figure FDA00033263417100000114
And park i sell energy revenue
Figure FDA00033263417100000115
T is a scheduling period of 24 h;
Figure FDA00033263417100000116
and
Figure FDA00033263417100000117
respectively representing the electricity purchasing quantity, the gas purchasing quantity and the heat purchasing quantity of the park i in the time period t;
Figure FDA0003326341710000021
and
Figure FDA0003326341710000022
respectively the electricity purchase, gas purchase and heat purchase prices in the time period t; r is an operation and maintenance equipment set in the park i; p is a radical ofom,rThe unit power operation and maintenance cost of the equipment r;
Figure FDA0003326341710000023
for the equipment r in the time zone t iThe operating power of (c);
Figure FDA0003326341710000024
and
Figure FDA0003326341710000025
respectively the electric power interaction quantity and the pneumatic power interaction quantity between the parks i and j;
Figure FDA0003326341710000026
the power interaction directions of electricity and gas are respectively;
Figure FDA0003326341710000027
respectively the electricity and gas power interaction prices between the parks i and j in the time period t;
3) combining the game main body optimization model of the multi-park comprehensive energy system established in the step 2) with the game slave body optimization model of the multi-park comprehensive energy system to jointly form a master-slave game double-layer optimization operation model of the multi-park comprehensive energy system, and solving the master-slave game double-layer optimization operation model of the multi-park comprehensive energy system by adopting a chaotic self-adaptive particle swarm algorithm and a mixed integer linear programming method.
2. The layered and partitioned optimization operation method of the comprehensive energy system based on the master-slave game as claimed in claim 1, wherein the master-slave game layered and partitioned architecture of the multi-park comprehensive energy system including the system layer and the park layer in step 1) is specifically as follows:
the main body of the system layer is an energy scheduling center which is used as an upper leader, and the centralized scheduling of the system is realized by issuing a regulation and control instruction to each park; the main body of the park layer is a commercial park operator, an industrial park operator and a residential park operator which are used as lower-layer followers, and each park operator realizes self optimized operation by optimizing the output dispatching plan of internal equipment on the basis of an energy dispatching center instruction; the master-slave game layered partition architecture G of the multi-park comprehensive energy system is as follows:
G＝{(COi∪EDC),{SCOi},{SEDC},{BCOi},{BEDC}}
in the formula, COi and EDC respectively represent a park i operator and an energy dispatching center; sCOi、SEDCRespectively a strategy set of a park i operator and an energy dispatching center; b isCOi、BEDCRespectively for the operator of campus i and the energy dispatching center.
3. The master-slave game-based hierarchical and partitioned optimization operation method of the integrated energy system, according to claim 1, wherein the constraint conditions of the game main body optimization model of the multi-park integrated energy system are specifically expressed as:
(1) energy purchase constraint:
Figure FDA0003326341710000028
in the formula, Pb,i,max、Gb,i,maxAnd Hb,i,maxRespectively purchasing electricity, gas and heat power upper limits of the park i; pb,max、Gb,maxAnd Hb,maxRespectively purchasing electricity, gas and heat from the system to an external network;
(2) energy interaction constraint
Figure FDA0003326341710000029
In the formula, Pij,max、Gij,maxThe upper limits of the electric power and the pneumatic power interaction between the parks i and j are respectively;
Figure FDA00033263417100000210
the power and gas power interaction directions are respectively, the power flows from the park i to the park j when the direction is positive, and the power flows from the park i to the park j when the direction is negative;
(3) system flexibility constraints
1≥IEFMt≥IEFMmin
Figure FDA0003326341710000031
In the formula, IEFMminFlexibility margins for systems IEFMtThe lower limit of (d); k represents energy type, K ═ { p, h, c, g }, p, h, c, g represent electricity, heat, cold, gas, respectively;
Figure FDA0003326341710000032
respectively the actual demand total amount and the maximum allowable supply total amount of the energy type k in the park i in the period t; m, N denote the demand side and supply side sets of units, respectively, of the campus;
Figure FDA0003326341710000033
representing the actual demand of the unit m on the energy type k in the park i in the period t;
Figure FDA0003326341710000034
represents the maximum allowable supply of unit n to energy type k in time period park i.
4. The master-slave game-based hierarchical and partitioned optimization operation method for the integrated energy system, according to claim 1, wherein the constraint conditions of the game slave optimization model of the multi-park integrated energy system are specifically expressed as follows:
(1) energy balance constraint
Figure FDA0003326341710000035
In the formula (I), the compound is shown in the specification,
Figure FDA0003326341710000036
and
Figure FDA0003326341710000037
respectively of transformers, photovoltaics, fans and micro-gas turbines in the park i at the time tOutputting electric power;
Figure FDA0003326341710000038
respectively the electric, hot, cold and air load power of the park i at the time t;
Figure FDA0003326341710000039
the power consumption of the electric refrigerator in the park i is t time period;
Figure FDA00033263417100000310
the thermal powers of the in-i heat exchanger and the micro-combustion engine in the park at the time t are respectively output;
Figure FDA00033263417100000311
the heat consumption power of the absorption refrigerator in the park i at the time period t;
Figure FDA00033263417100000312
the output cold power of the electric refrigerator and the absorption refrigerator in the park i at the time period t respectively;
Figure FDA00033263417100000313
the air consumption of the micro-combustion engine in the park i at the time t;
Figure FDA00033263417100000314
Figure FDA00033263417100000315
energy discharge power of electricity storage, heat storage, cold storage and gas storage in the park i at the time t respectively;
Figure FDA00033263417100000316
Figure FDA00033263417100000317
energy charging powers of electricity storage, heat storage, cold storage and gas storage in the park i at the time period t respectively;
(2) energy plant restraint
Figure FDA0003326341710000041
Figure FDA0003326341710000042
In the formula, Ppv,i,max、Pwt,i,max、Pt,i,max、Pmt,i,max、Hhe,i,max、Hac,i,maxAnd Pec,i,maxThe maximum values of the operating power of the photovoltaic, wind power, the transformer, the micro-combustion engine, the heat exchanger, the absorption refrigerator and the electric refrigerator in the park i are respectively set; subscript x represents the energy storage type;
Figure FDA0003326341710000043
respectively storing energy charging and discharging power and energy storage states in a park i at a time period t;
Figure FDA0003326341710000044
respectively storing the initial energy and the final energy of energy storage equipment in the park i; ex,i,min、Ex,i,maxRespectively the minimum and maximum energy states of the energy storage equipment in the park i; px,c,i,max、Px,d,i,maxRespectively the maximum charging and discharging power of the energy storage equipment; lambda [ alpha ]x,c,i、λx,d,iRespectively a charge state and a discharge state are 0-1 variables;
(3) park flexibility constraints
Figure FDA0003326341710000045
Figure FDA0003326341710000046
In the formula (I), the compound is shown in the specification,
Figure FDA0003326341710000047
and IEFMi,minRespectively the flexibility margin and the lower limit of the park i at the time period t; k represents energy type, K ═ { p, h, c, g }, p, h, c, g represent electricity, heat, cold, gas, respectively;
Figure FDA0003326341710000048
respectively the actual demand sum and the maximum allowed supply sum of the energy type k in the park i during the period t.
5. The method for performing hierarchical and partition optimization on the comprehensive energy system based on the master-slave game according to claim 1, wherein the master-slave game double-layer optimization operation model of the multi-park comprehensive energy system in the step 3) is represented as follows:
Figure FDA0003326341710000051
in the formula, EEDCThe comprehensive operation cost of the comprehensive energy system of the multiple parks is reduced; eCOiThe operating cost for park i; x is the strategy of the upper layer game main body; y isiFor policies from campus i, y-iThe strategy combination of the slave parks except the slave park i;
Figure FDA0003326341710000052
the optimal strategy of the slave park i under the condition of the strategy x of the upper-layer game main body, S (x) is the strategy set of each slave park of the lower layer,
Figure FDA0003326341710000053
the optimal strategy set of all slave parks under the condition of the strategy x of the upper layer game main body is given, namely Nash equilibrium solution of the park layer game, and
Figure FDA0003326341710000054
6. the method for performing layered and partitioned optimization operation on the comprehensive energy system based on the master-slave game according to claim 1, wherein the solving of the master-slave game double-layer optimization operation model of the multi-park comprehensive energy system by using the chaotic self-adaptive particle swarm algorithm and the mixed integer linear programming method in the step 3) is as follows: and comprehensively applying a chaotic self-adaptive particle swarm algorithm and a mixed integer linear programming method, and calling a Gurobi solver to solve a master-slave game double-layer optimization operation model of the multi-park comprehensive energy system through a Yalmip tool box to obtain a hierarchical and subarea optimization operation scheme of the multi-park comprehensive energy system.
CN202010871043.2A 2020-08-26 2020-08-26 Master-slave game-based hierarchical and partitioned optimized operation method for comprehensive energy system Active CN111950809B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010871043.2A CN111950809B (en) 2020-08-26 2020-08-26 Master-slave game-based hierarchical and partitioned optimized operation method for comprehensive energy system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010871043.2A CN111950809B (en) 2020-08-26 2020-08-26 Master-slave game-based hierarchical and partitioned optimized operation method for comprehensive energy system

Publications (2)

Publication Number Publication Date
CN111950809A CN111950809A (en) 2020-11-17
CN111950809B true CN111950809B (en) 2022-03-25

Family

ID=73366530

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010871043.2A Active CN111950809B (en) 2020-08-26 2020-08-26 Master-slave game-based hierarchical and partitioned optimized operation method for comprehensive energy system

Country Status (1)

Country Link
CN (1) CN111950809B (en)

Families Citing this family (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112508604A (en) * 2020-11-26 2021-03-16 国网河北省电力有限公司经济技术研究院 Optimization method, system, equipment and storage medium of integrated energy system
CN112465240B (en) * 2020-12-03 2022-08-23 上海电力大学 Cooperative game-based multi-park energy scheduling optimization method for comprehensive energy system
CN112598224A (en) * 2020-12-04 2021-04-02 国网辽宁省电力有限公司经济技术研究院 Interactive game scheduling method for park comprehensive energy system group and power grid
CN112686425B (en) * 2020-12-09 2022-03-11 南京国电南自电网自动化有限公司 Energy internet optimal scheduling method and system based on cooperative game
CN112633613B (en) * 2021-01-14 2023-05-02 南方电网数字电网研究院有限公司 Optimization method for park-level integrated energy system cluster transaction strategy
CN113553726B (en) * 2021-08-06 2022-07-05 吉林大学 Master-slave game type man-machine cooperative steering control method in ice and snow environment
CN114050571B (en) * 2021-11-22 2024-04-09 沈阳工业大学 Comprehensive energy system energy hub control method considering carbon flow
CN114374219B (en) * 2021-11-29 2023-09-15 山东大学 Distributed optimization method and system for park comprehensive energy system based on cooperative game
CN114662381B (en) * 2022-02-24 2022-12-09 上海交通大学 Layered game-based fusion operation method and system for energy traffic system of port ship
CN115526684B (en) * 2022-09-21 2023-05-02 三峡大学 Comprehensive energy system multi-main-body low-carbon operation method based on double-layer master-slave game
CN115907232B (en) * 2023-01-05 2023-06-09 中国电力科学研究院有限公司 Regional comprehensive energy system cluster collaborative optimization method, system, equipment and medium
CN115953012B (en) * 2023-03-13 2023-06-16 国网江西省电力有限公司电力科学研究院 Rural optical storage system optimal scheduling method based on multi-main-body double-layer game
CN116720879B (en) * 2023-06-27 2024-04-05 东北电力大学 Park comprehensive energy system energy pricing method based on double-layer game model
CN117689234B (en) * 2024-02-04 2024-05-03 山东科技大学 Multi-main-body double-layer game-based park comprehensive energy system scheduling method and system

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109886494A (en) * 2019-02-27 2019-06-14 华北电力大学 A kind of industrial park integrated energy system interaction optimization method and system
CN110543464A (en) * 2018-12-12 2019-12-06 广东鼎义互联科技股份有限公司 Big data platform applied to smart park and operation method

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108629470A (en) * 2017-03-17 2018-10-09 华北电力大学 System capacity management of providing multiple forms of energy to complement each other based on non-cooperative game is run with optimization
CN108876040A (en) * 2018-06-21 2018-11-23 广州供电局有限公司 The multiclass energy of garden energy internet operators is fixed a price and energy management method
CN109919371A (en) * 2019-02-27 2019-06-21 华北电力大学 The equalization methods and system in a kind of industrial park comprehensive energy market
CN110443398B (en) * 2019-05-13 2023-04-18 华北电力大学（保定） Optimal operation method of regional comprehensive energy system based on repeated game model
CN110569556A (en) * 2019-08-14 2019-12-13 上海电力大学 Master-slave game-based regional distributed energy network design and optimization method
CN111460358A (en) * 2020-03-23 2020-07-28 四川大学 Park operator energy transaction optimization decision method based on supply and demand game interaction

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110543464A (en) * 2018-12-12 2019-12-06 广东鼎义互联科技股份有限公司 Big data platform applied to smart park and operation method
CN109886494A (en) * 2019-02-27 2019-06-14 华北电力大学 A kind of industrial park integrated energy system interaction optimization method and system

Also Published As

Publication number Publication date
CN111950809A (en) 2020-11-17

Similar Documents

Publication Publication Date Title
CN111950809B (en) Master-slave game-based hierarchical and partitioned optimized operation method for comprehensive energy system
CN109919478B (en) Comprehensive energy microgrid planning method considering comprehensive energy supply reliability
CN108229025B (en) Economic optimization scheduling method for cooling, heating and power combined supply type multi-microgrid active power distribution system
CN104734168B (en) Microgrid running optimization system and method based on power and heat combined dispatching
CN111881616B (en) Operation optimization method of comprehensive energy system based on multi-main-body game
He et al. Application of game theory in integrated energy system systems: a review
Zhu et al. Optimal scheduling method for a regional integrated energy system considering joint virtual energy storage
CN109510224B (en) Capacity allocation and operation optimization method combining photovoltaic energy storage and distributed energy
Bao et al. A multi time-scale and multi energy-type coordinated microgrid scheduling solution—Part I: Model and methodology
CN108537409A (en) A kind of industrial park power distribution network collaborative planning method considering multiple-energy-source coupled characteristic
CN110889600A (en) Regional comprehensive energy system optimization scheduling method considering flexible thermal load
CN115241931B (en) Garden comprehensive energy system scheduling method based on time-varying electrical carbon factor curve
CN110163443A (en) Consider the micro- energy net Optimization Scheduling in the natural gas pressure regulating station of electric-gas integration requirement response
CN108133285B (en) Real-time scheduling method for hybrid energy system accessed to large-scale renewable energy
CN112464477A (en) Multi-energy coupling comprehensive energy operation simulation method considering demand response
CN108898265A (en) A kind of integrated energy system integration planing method
CN111404183A (en) Multi-element energy storage cooperative configuration method, program, system and application of regional comprehensive energy system
CN110598913A (en) Optimization method and system for equipment capacity configuration of park comprehensive energy system
CN113673738B (en) Comprehensive energy system optimal configuration method based on supply and demand response and adjustable scene
CN106779471A (en) A kind of multipotency interconnects alternating current-direct current mixing micro-capacitance sensor system and Optimal Configuration Method
CN112182887A (en) Comprehensive energy system planning optimization simulation method
CN112257294A (en) Energy hub subsection modeling method and system of comprehensive energy system
CN115186902A (en) Regulating and controlling method, device, terminal and storage medium of greenhouse comprehensive energy system
CN117081143A (en) Method for promoting coordination and optimization operation of park comprehensive energy system for distributed photovoltaic on-site digestion
Gu et al. Bi-level decentralized optimal economic dispatch for urban regional integrated energy system under carbon emission constraints

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
GR01 Patent grant