CN112016825A - Centralized transaction optimization decision method of regional comprehensive energy system - Google Patents

Centralized transaction optimization decision method of regional comprehensive energy system Download PDF

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CN112016825A
CN112016825A CN202010863413.8A CN202010863413A CN112016825A CN 112016825 A CN112016825 A CN 112016825A CN 202010863413 A CN202010863413 A CN 202010863413A CN 112016825 A CN112016825 A CN 112016825A
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胥威汀
邓盈盈
唐权
任志超
王海燕
汪伟
徐浩
陈博
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Economic and Technological Research Institute of State Grid Sichuan Electric Power Co Ltd
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Abstract

The invention discloses a centralized transaction optimization decision method of a regional comprehensive energy system, which comprises the following steps: step S1, establishing a master-slave game mathematical model of each sub-energy system in the intermediate agent and the regional comprehensive energy system; step S2, constructing a benefit model of an intermediate agent, a benefit model of a sub-energy system, operation constraints of the sub-energy system and a balanced solution form of a master-slave game based on a master-slave game mathematical model; and step S3, solving the equilibrium solution of the master-slave game by adopting a two-layer iterative optimization method. The invention improves the transaction desire of each sub-energy system, thereby enhancing the energy mutual-aid capability of the regional comprehensive energy system, reducing the unbalanced electric quantity of the transaction between the intermediate agent and the power grid and ensuring that the power grid is safer and more stable.

Description

Centralized transaction optimization decision method of regional comprehensive energy system
Technical Field
The invention belongs to the technical field of electric energy transaction assistance of regional comprehensive energy systems, and particularly relates to a centralized transaction optimization decision method of a regional comprehensive energy system.
Background
With the rapid development of society, the globalization of economy and the acceleration of the industrialization process of all countries in the world, the energy demand is greatly increased, but the problem of shortage of non-renewable energy sources such as coal, petroleum and the like is aggravated, and the problem of energy environment also becomes a great challenge for human beings. In order to solve the problem of energy environment, renewable energy technology is correspondingly developed, and a new energy utilization system of energy internet appears. The regional integrated energy system is an important component of an energy internet, and is internally provided with a plurality of distributed renewable energy power generation units, distributed energy storage equipment, energy conversion equipment and loads in various energy forms. With the development of the regional comprehensive energy system, a plurality of sub energy systems are arranged in the regional comprehensive energy system, and each sub energy system coordinates and controls the operation condition of each device, so that the energy utilization rate can be improved, and the operation cost can be reduced. Each sub-energy system of the regional total energy system can belong to different benefit subjects, and the electric energy transaction is an effective means for coordinating different benefit subjects and improving the energy utilization rate of the regional comprehensive energy system.
At present, research aiming at electric energy transaction aid decision is mainly analyzed on the surface of a microgrid, and the method cannot be applied to a regional comprehensive energy system; and for the comprehensive energy system which is gradually developed, the research is mainly focused on energy flow analysis in a single comprehensive energy system, and the research on the optimization of the electric energy transaction among a plurality of comprehensive energy systems is less.
Disclosure of Invention
In order to overcome the defect that the conventional electric energy transaction auxiliary decision-making technology cannot be applied to a regional comprehensive energy system, the invention provides a centralized transaction optimization decision-making method of the regional comprehensive energy system. The invention can provide an effective auxiliary optimization decision-making means for improving the energy utilization rate of the regional comprehensive energy system, effectively utilize the capacity redundancy of the comprehensive energy system containing high-proportion renewable energy and the low-cost electric quantity of some comprehensive energy systems, and support the electricity-shortage comprehensive energy system with useful energy demand in the region.
The invention is realized by the following technical scheme:
a centralized transaction optimization decision-making method of a regional integrated energy system comprises the following steps:
step S1, establishing a master-slave game mathematical model of each sub-energy system in the intermediate agent and the regional comprehensive energy system;
step S2, constructing a benefit model of an intermediate agent, a benefit model of a sub-energy system, operation constraints of the sub-energy system and a balanced solution form of a master-slave game based on a master-slave game mathematical model;
and step S3, solving the equilibrium solution of the master-slave game by adopting a two-layer iterative optimization method.
The invention provides a centralized transaction mechanism consisting of a plurality of sub energy systems and a Middle Agent (MA) by establishing an energy transaction framework of a regional energy system so as to fully realize the mutual energy assistance of the sub energy systems, and solves an MA optimal electricity price strategy and an operation strategy of each sub energy system by establishing a master-slave game model, wherein the MA optimal electricity price strategy can improve the income of each sub energy system and a power grid.
Optionally, the master-slave game mathematical model established in step S1 of the present invention is:
G＝{{(MA∪SR∪IES)},{pMA,s},{pMA,b}
,{R1,...,Ri,...,RN},{PRO},{U1,...,Ui,...,UN}}
wherein, (MA U.G MG) is both sides of the game, the intermediate agent is taken as the leader, the sub energy system is taken as the follower; p is a radical ofMA,s、pMA,bThe power selling price and the power purchasing price strategy set is MA; PRO is the yield of MA; u shapeiIs the benefit of sub energy system i; riThe set of operating strategies for sub-energy system i is represented by the following equation:
Figure BDA0002648935870000021
Figure BDA0002648935870000022
in the formula (I), the compound is shown in the specification,
Figure BDA0002648935870000023
for the set of operating strategies for the sub-energy system i at time t,
Figure BDA0002648935870000024
is the electrical power output by the gas turbine,
Figure BDA0002648935870000025
is the thermal power output by the waste heat boiler,
Figure BDA0002648935870000026
is the thermal power output by the gas-fired boiler,
Figure BDA0002648935870000027
in order to absorb the cold power output by the refrigerator,
Figure BDA0002648935870000028
is the cold power output by the electric refrigerator,
Figure BDA0002648935870000029
the storage capacity of the energy storage device.
Optionally, step S2 of the present invention specifically includes:
step S21, an intermediate agent benefit model is established, expressed as:
Figure BDA00026489358700000210
Figure BDA00026489358700000211
Figure BDA00026489358700000212
Figure BDA00026489358700000213
Figure BDA00026489358700000214
in the formula, PROtThe income of the intermediate agent at the time t;
Figure BDA00026489358700000215
respectively representing the purchase and sale of electric quantity of the sub-energy system i; n is a radical ofb/NsThe number of the electronic energy purchasing/selling systems is represented; delta PtFor the intermediate agent's total net charge in/out at time t, when Δ PtWhen the electric quantity is more than 0, the intermediate agent and each sub-energy system electric quantity transaction is finished, and finally, the net flow of delta P is realizedtWhen delta P is used for selling to a large power gridtLess than 0 indicates a net flow of Δ PtThe electric quantity of (2) needs to buy delta P from a large power gridtThe amount of electricity of;
Figure BDA0002648935870000031
respectively selling electricity and purchasing electricity prices of the intermediate agent at the time t;
step S22, establishing a sub-energy system benefit model expressed as:
Ui＝-Mi＝-(Mg,i+Me,i+Mc,i)
in the formula, Mg,iRepresents a cost of gas; me,iRepresenting a transaction fee with the MA; mc,iRepresents a carbon emission cost;
step S23, establishing operation constraints of the sub-energy system, including energy conversion element constraints, energy storage equipment constraints and energy balance constraints;
step S24, according to the benefit model of the intermediate agent, the benefit function is related to the electricity price strategy and the transaction electric quantity, and the transaction electric quantity is influenced by the operation strategy of each sub-energy system, so the benefit function of the intermediate agent is simplified as follows:
PRO＝PRO(pMA,s,pMA,b,R1,...,Ri,...,RN)
for the sub-energy system i, the benefit function is related to the self-operation strategy and the electricity price strategy formulated by the intermediate agent, so that the benefit function of the sub-system is simplified as follows:
Ui＝Ui(pMA,s,pMA,b,R1,...,Ri,...,RN)
strategy p of gameMA,s，pMA,b，R1,...,Ri,...,RNThe game is a solution set of the master-slave game;
when the electricity purchasing/selling price strategy of the intermediate agent is an optimal strategy obtained based on the optimal operation strategy of the sub-energy system, and the operation strategy of the sub-energy system is also the optimal strategy under the electricity purchasing/selling price strategy, the optimal solution set is obtained and recorded as:
Figure BDA0002648935870000032
Figure BDA0002648935870000033
it satisfies the following relation:
Figure BDA0002648935870000034
Figure BDA0002648935870000035
Figure BDA0002648935870000036
optionally, in step S22 of the present invention, the gas cost is expressed as:
Figure BDA0002648935870000037
in the formula, CngThe price of each ton of natural gas; qLHVLow calorific value per ton of natural gas;
Figure BDA0002648935870000038
the equivalent power value of natural gas consumed by a gas turbine and a gas boiler of the sub-energy system i at the moment t;
the transaction fee with the intermediate agent is expressed as:
Figure BDA0002648935870000041
the carbon emission cost is expressed as:
Figure BDA0002648935870000042
in the formula, betae、βg(kg/(kW. h)) represents the equivalent CO of purchased electric power or gas2A discharge coefficient; (element/kg) denotes CO2Per unit processing cost.
Optionally, the constraining of the energy conversion element in step S23 of the present invention includes:
gas turbine converts natural gas into electrical energy
Figure BDA0002648935870000043
And the residual heat of the flue gas
Figure BDA0002648935870000044
Figure BDA0002648935870000045
Figure BDA0002648935870000046
In the formula etaGTE,i、ηGTH,iThe power generation efficiency and the thermal efficiency coefficient of the gas turbine of the sub-energy system i are respectively;
Figure BDA0002648935870000047
the equivalent power value of the natural gas consumed by the gas turbine at the moment t of the sub-energy system i;
exhaust-heat boiler generates heat power by using waste of gas turbine
Figure BDA0002648935870000048
Figure BDA0002648935870000049
In the formula etaWH,iThe thermal efficiency coefficient of the waste heat boiler of the sub energy system i;
heat production quantity for converting natural gas into heat energy by gas boiler
Figure BDA00026489358700000410
And coefficient of heat generation efficiency etaGB,iThe following steps are involved:
Figure BDA00026489358700000411
the absorption refrigerator absorbs heat to refrigerate:
Figure BDA00026489358700000412
in the formula (I), the compound is shown in the specification,
Figure BDA00026489358700000413
the absorption type of the sub energy system i is the cold power output by the refrigerator at the moment t; cOPAC,iThe refrigeration coefficient of the absorption refrigerator of the sub energy system i;
Figure BDA00026489358700000414
inputting the thermal power of the absorption refrigerator for the sub-energy system i at the moment t;
the electric refrigerator is a main device for converting electric energy into cold energy:
Figure BDA00026489358700000415
in the formula (I), the compound is shown in the specification,
Figure BDA00026489358700000416
the cold power output by the electric refrigerator of the sub energy system i at the moment t; cOPEC,iThe refrigeration coefficient of the electric refrigerator of the sub energy system i;
Figure BDA00026489358700000417
the electric power of the electric refrigerator is input to the sub-energy system i at the time t.
Optionally, the energy storage device constraint in step S23 of the present invention includes:
establishing a general mathematical model of the cold, heat and electricity energy storage equipment:
Figure BDA0002648935870000051
Figure BDA0002648935870000052
Figure BDA0002648935870000053
Figure BDA0002648935870000054
Figure BDA0002648935870000055
in the formula, subscript x represents energy storage equipment, and the storage battery, the heat storage tank and the ice storage air conditioner are represented by BAT, HS and CS respectively;x,ithe energy loss rate of the energy storage equipment;
Figure BDA0002648935870000056
respectively storing and discharging power; etaxc,i、ηxd,iRespectively storing and releasing energy efficiency of the energy storage equipment;
Figure BDA0002648935870000057
a variable representing the charge/discharge state value of 0/1 represents charge when 0 is taken and represents charge when 1 is taken.
Optionally, the energy balance constraint in step S23 of the present invention is:
Figure BDA0002648935870000058
Figure BDA0002648935870000059
Figure BDA00026489358700000510
in the formula etaT,iRepresenting the transmission efficiency of the transformer with the distribution network;
Figure BDA00026489358700000511
representing a wind-solar output prediction;
Figure BDA00026489358700000512
representing an electrical load;
Figure BDA00026489358700000513
represents the thermal load;
Figure BDA00026489358700000514
indicating the cooling load.
Optionally, the solving process of step S24 in the present invention specifically includes:
for the intermediate agent, the need is to solve the following problem:
Figure BDA00026489358700000515
in the formula, T belongs to [1,2]T is the time of a scheduling cycle;
Figure BDA00026489358700000516
the prices of selling electricity and purchasing electricity of the MA at the time t are respectively;
Figure BDA00026489358700000517
respectively selling electricity and purchasing electricity prices of the power grid at the time t;
for sub-energy system i, the demand solves the following linear programming problem:
Figure BDA00026489358700000518
wherein f (x) is a target function of the sub energy system, and x is a decision variable; a represents a decision variable coefficient matrix, and B represents a constant matrix related to model equality constraint; x is the number ofmin/xmaxRepresents the minimum/maximum value that the decision variable may assume;
the solution of the intermediate agent and the solution of the sub-energy system are mutually influenced, and the optimization targets of the intermediate agent and the sub-energy system are inconsistent, so that a two-layer iterative optimization method is adopted for solving.
Optionally, the solution of the two-layer iterative optimization method of the present invention specifically includes:
the bottom layer is the operation optimization solution of the sub energy system, and on the basis of the electricity price strategy given by the leader, the CPLEX is adopted to solve the linear problem according to the operation model;
and the upper layer is used for solving the electricity price strategy of the intermediate agent, the electricity price constraint condition is considered, the electricity price strategy is used as a particle, the income under the electricity price is used as the fitness of the particle, and the PSO optimization algorithm is used for searching the upper layer optimal electricity price strategy, so that the optimal game solution set is obtained.
The invention has the following advantages and beneficial effects:
1. the invention establishes a centralized energy trading framework of a master and a plurality of slaves of a regional comprehensive energy system, constructs a master-slave game model for obtaining an optimal electricity price strategy of an intermediate agent and an operation strategy of each sub-energy system, and solves the optimal electricity price strategy by adopting a two-layer iterative optimization method.
2. After the two-layer iterative optimization based on the master-slave game model, compared with the direct adoption of power grid price trading, the method improves the income of intermediate agents and reduces the operation cost of each sub-energy system.
3. The invention improves the transaction desire of each sub-energy system, thereby enhancing the energy mutual-aid capability of the regional comprehensive energy system, reducing the unbalanced electric quantity of the transaction between the intermediate agent and the power grid and ensuring that the power grid is safer and more stable.
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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 schematic flow chart of the method of the present invention.
Fig. 2 is an energy trading framework of the regional integrated energy system of the present invention.
Fig. 3 is a schematic diagram of the primary-secondary game solving process of the present invention.
FIG. 4 is a wind power and photovoltaic power generation unit output curve of the present invention.
Fig. 5 is a cooling, heating and power load curve of the present invention.
Fig. 6 is an iterative convergence process of the MA benefit of the present invention.
Fig. 7 is a comparison of the MA optimal electricity rate strategy of the present invention with the grid electricity rates.
Fig. 8 shows the unbalanced charge of the MA of the present invention in trading with the grid.
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
The embodiment provides a centralized transaction optimization decision method for a regional integrated energy system. The embodiment firstly establishes an energy trading framework of the regional comprehensive energy system. Considering that the sub energy systems are interconnected through the power distribution network, the power distribution network participates in the power distribution network in the MA identity mode, and forms a main multi-slave energy trading relationship with the sub energy systems.
Secondly, aiming at the transaction relation, a master-slave game mathematical model of the MA and each sub energy system is established. As one party of operation and transaction, the MA has the authority of preferentially establishing the price, so that the MA establishes the electricity price for purchase and sale by using the identity of a leader, and the sub-energy system is used as a follower to respond to the electricity price, so that the MA and the sub-energy system can form a Stackelberg master-slave game.
And then developing based on a master-slave game model, researching MA electricity price optimization and sub-energy system operation optimization, establishing an MA benefit model, comprehensively considering the energy flow relation and the transaction relation of the sub-energy system, establishing the benefit model of the sub-energy system, and providing a balanced solution form of the master-slave game.
And finally, solving the equilibrium solution of the master-slave game by adopting a two-layer iterative optimization method, optimizing the operation of the sub-energy system at the bottom layer, and solving the linear problem by adopting CPLEX according to an operation model on the basis of the electricity price strategy given by the leader. And the upper layer is an MA electricity price strategy solution, the electricity price constraint condition is considered, the electricity price strategy is used as a particle, the income under the electricity price is used as the fitness of the particle, and the PSO optimization algorithm is used for searching the upper layer optimal electricity price strategy.
Specifically, as shown in fig. 1, the optimization decision method of the present embodiment includes steps 1 to 4:
energy trading framework for 1-region comprehensive energy system
After the energy management and distribution of the plurality of sub-energy system main bodies in the regional integrated energy system are completed, the electric energy trading framework of the regional integrated energy system needs to be researched for further mining the benefits of the sub-energy systems and improving the energy utilization rate. Considering that the sub-energy systems are interconnected by a distribution network,
therefore, the distribution network is involved in the MA identity, and a main multi-slave energy trading relation is formed between the distribution network and each sub-energy system, and the whole trading framework is shown in figure 2.
The MA formulates a selling and purchasing price and sends the selling and purchasing price to each sub-energy system; each sub-energy system uploads the surplus/shortage electricity to be traded to the MA after performing optimized operation according to the price; the MA purchases electric energy from the sub-energy system with surplus electricity and then sells the electric energy to the sub-energy system without electricity, and when the surplus/shortage amount of electricity is unbalanced, the MA deals with a large power grid to meet balance. Therefore, the MA can intensively coordinate the supply and demand relationship formed by the surplus/shortage after the optimized operation of each sub-energy system by establishing the electricity price for sale and purchase, thereby improving the profit of the MA and reducing the operation cost of each sub-energy system.
In order to encourage each sub-energy system to trade with the MA and form the energy mutual aid of the internal system, the MA needs to ensure that the established selling and purchasing price of the MA is superior to the price of the power distribution network for the sub-energy system, namely the MA needs to meet the following requirements:
Figure BDA0002648935870000081
where T is the time of one scheduling cycle.
Figure BDA0002648935870000082
The prices of selling electricity and purchasing electricity of the MA at the time t are respectively;
Figure BDA0002648935870000083
Figure BDA0002648935870000084
the prices of selling electricity and purchasing electricity of the power grid at the moment t are respectively.
Master-slave gaming of 2MA and each sub-energy system
In the regional integrated energy system, the MA and the sub-energy systems are independent benefit subjects, the sub-energy systems can obtain the optimized operation mode only after obtaining the purchase and sale electricity price of the MA, and the benefits of the MA are related to the tradable electric quantity obtained after the sub-energy systems are optimized to operate, so that a layer of benefit game relationship exists between the MA and the sub-energy systems. As one party of operation and transaction, the MA has the authority of preferentially establishing the price, so that the MA establishes the electricity price for purchase and sale by using the identity of a leader, and the sub-energy system is used as a follower to respond to the electricity price, so that the MA and the sub-energy system can form a Stackelberg game.
The form of the game can be expressed as:
Figure BDA0002648935870000085
in the formula, (MA U.G. MG) is both sides of the game, and comprises a MA (leader) and a MG cluster (follower) of a sub energy system; p is a radical ofMA,s、pMA,bThe power selling price and the power purchasing price strategy set is MA; PRO is the yield of MA; u shapeiIs the benefit of sub energy system i; riThe set of operating strategies for sub-energy system i can be expressed as follows:
Figure BDA0002648935870000086
Figure BDA0002648935870000087
in the formula (I), the compound is shown in the specification,
Figure BDA0002648935870000088
for the set of operating strategies for the sub-energy system i at time t,
Figure BDA0002648935870000089
is the electrical power output by the gas turbine,
Figure BDA00026489358700000810
is the thermal power output by the waste heat boiler,
Figure BDA00026489358700000811
is the thermal power output by the gas-fired boiler,
Figure BDA00026489358700000812
in order to absorb the cold power output by the refrigerator,
Figure BDA00026489358700000813
is the cold power output by the electric refrigerator,
Figure BDA00026489358700000814
the storage capacity of the energy storage device.
3 MA electricity price optimization and sub-energy system operation optimization based on master-slave game
3.1MA benefit model
The income of the MA is related to the electricity price established by the MA and the transaction electric quantity between the sub energy systems, and the income is as follows:
Figure BDA00026489358700000815
Figure BDA0002648935870000091
Figure BDA0002648935870000092
Figure BDA0002648935870000093
Figure BDA0002648935870000094
in the formula, PROtThe profit of the MA at the time t is obtained;
Figure BDA0002648935870000095
respectively representing the purchase and sale of electric quantity of the sub-energy system i; n is a radical ofb/NsThe number of the electronic energy purchasing/selling systems is represented; delta PtThe total net inflow/outflow electric quantity of the MA at the time t is represented when the total net inflow/outflow electric quantity is greater than 0, and at the moment, after the MA finishes the electric quantity transaction with each sub energy system, the net inflow of the MA is finally delta PtWhen the electric quantity of (1) is less than 0, the electric quantity of (1) is used for selling to a large power grid, and finally, delta P flows outtThe electric quantity of (2) needs to buy delta P from a large power gridtThe amount of electricity of.
3.2 benefit model of sub-energy System
The daily operation cost of the sub-energy system is related to the self operation mode, and is influenced by the price of the MA purchased electricity, and the benefit of the sub-energy systemU iI.e. the negative of its daily operating cost:
Ui＝-Mi＝-(Mg,i+Me,i+Mc,i) (10)
in the formula, Mg,iRepresents a cost of gas; me,iRepresenting a fee for a transaction with the MA; mc,iRepresenting the cost of carbon emissions.
The gas cost is related to the natural gas price:
Figure BDA0002648935870000096
in the formula, CngThe price of each ton of natural gas; qLHVLow calorific value per ton of natural gas;
Figure BDA0002648935870000097
the equivalent power value of natural gas consumed by a gas turbine and a gas boiler of the sub-energy system i at the moment t;
transaction fee with MA:
Figure BDA0002648935870000098
carbon emission cost:
after the sub-energy system purchases electric energy and fuel gas, the sub-energy system can generate corresponding carbon emission to beta after the sub-energy system operates to meet the loade、βg(kg/(kW. h)) represents the equivalent CO of purchased electric power or gas2A discharge coefficient; (element/kg) denotes CO2Unit processing cost of (1), thenc,iCan be represented by the following formula:
Figure BDA0002648935870000101
3.3 operating constraints of the sub-energy System
3.3.1 energy conversion element restraint
The gas turbine inputs natural gas, converts it into electric energy and flue gas waste heat, and can be expressed as:
Figure BDA0002648935870000102
Figure BDA0002648935870000103
in the formula etaGTE,i、ηGTH,iThe power generation efficiency and the thermal efficiency coefficient of the gas turbine of the sub-energy system i are respectively.
The exhaust-heat boiler generates heat power by utilizing the waste of the gas turbine:
Figure BDA0002648935870000104
in the formula etaWH,iIs the thermal efficiency coefficient of the waste heat boiler of the sub energy system i.
The gas boiler converts natural gas into heat energy, and the coefficient eta of heat production and heat production efficiencyGB,iThe following steps are involved:
Figure BDA0002648935870000105
the absorption refrigerator absorbs heat to refrigerate:
Figure BDA0002648935870000106
in the formula, COPAC,iThe refrigeration coefficient of the absorption refrigerator of the sub energy system i;
Figure BDA0002648935870000107
the thermal power of the absorption refrigerator is input to the sub-energy system i at time t.
The electric refrigerator is a main device for converting electric energy into cold energy:
Figure BDA0002648935870000108
in the formula, COPEC,iThe refrigeration coefficient of the electric refrigerator of the sub energy system i;
Figure BDA0002648935870000109
the electric power of the electric refrigerator is input to the sub-energy system i at the time t.
3.3.2 energy storage device restraint
For the electric, hot and cold storage devices of the sub energy system, an energy accumulation and release process is provided, and energy constraint limitation and partial energy loss are provided during energy accumulation and release; there is also a maximum and minimum energy storage capacity limit for the entire device, and the rate of loss over time during storage. Therefore, a general mathematical model of the cooling, heating and power energy storage device can be established, and assuming that the charging/discharging power of the energy storage device in the Δ t period remains unchanged, the dynamic general model can be expressed as:
Figure BDA0002648935870000111
Figure BDA0002648935870000112
Figure BDA0002648935870000113
Figure BDA0002648935870000114
Figure BDA0002648935870000115
in the formula (20) - (24), subscript x represents an energy storage device, and the storage battery, the heat storage tank and the ice storage air conditioner are represented by BAT, HS and CS, respectively;x,ithe energy loss rate of the energy storage equipment;
Figure BDA0002648935870000116
respectively storing and discharging power; etaxc,i、ηxd,iRespectively the energy storage efficiency and the energy release efficiency of the energy storage equipment. Equations (21), (22) represent the energy storage device charging/discharging power constraints, where
Figure BDA0002648935870000117
A variable representing the charge/discharge state value of 0/1 represents charge when 0 is taken and represents charge when 1 is taken.
3.3.3 energy balance constraints
Figure BDA0002648935870000118
Figure BDA0002648935870000119
Figure BDA00026489358700001110
Wherein (25) represents the electric energy balance, ηT,iRepresenting the transmission efficiency of the transformer with the distribution network;
Figure BDA00026489358700001111
representing a wind-solar output prediction;
Figure BDA00026489358700001112
representing the electrical load. The formula (26) represents the heat energy balance,
Figure BDA00026489358700001113
indicating the thermal load. The formula (27) represents the cold energy balance,
Figure BDA00026489358700001114
indicating the cooling load.
3.4 Game Balanced solution
From the MA benefit model, the benefit function is related to the electricity price and the transaction electricity amount formulated by the benefit function, and the transaction electricity amount is influenced by the operation strategy of each sub energy system, so the MA benefit function can be expressed as:
PRO＝PRO(pMA,s,pMA,b,R1,...,Ri,...,RN) (28)
for the sub-energy system i, the benefit function is related to the self-operation strategy and the electricity price strategy formulated by the MA, and can be expressed as:
Ui＝Ui(pMA,s,pMA,b,R1,...,Ri,...,RN) (29)
the game strategy p can be known from the formulas (28) and (29)MA,s，pMA,b，R1,...,Ri,...,RNI.e. its solution set. When the power purchase and sale price formulation of the MA is an optimal strategy obtained based on the optimal operation strategy of the sub-energy system, and the operation mode of the sub-energy system is also the optimal strategy under the power price strategy, the MA is an optimal solution set (balanced solution), and the MA is calculated as follows:
Figure BDA0002648935870000121
Figure BDA0002648935870000122
it satisfies the following relation:
Figure BDA0002648935870000123
Figure BDA0002648935870000124
Figure BDA0002648935870000125
equation (30) indicates that, for any sub-energy system i, when other policy parties adopt the optimal policy, the sub-energy system cannot improve the operation benefit by independently changing the operation policy; equations (31), (32) indicate that the MA cannot increase revenue by changing the purchase price alone when the sub-energy system employs the optimal operation strategy.
4 solving equilibrium solution of master-slave game
And the MA and the sub-energy system cooperate to realize the solution of the Stackelberg equilibrium solution. For MA, the whole process needs to solve the following problem:
Figure BDA0002648935870000126
for sub-energy system i, the demand solves the following linear programming problem:
Figure BDA0002648935870000127
according to the formula (6), when the MA calculates the income PRO, the total purchase and sale electricity quantity of the regional integrated energy system is needed
Figure BDA0002648935870000128
Figure BDA0002648935870000129
Etc. that are provided by each sub-energy system; while the sub energy system is calculating UiWhen p is supplied from MAMA,sAnd pMA,bThe data, therefore, the solutions of equations (33) and (34) are mutually influenced, and the optimization objectives of the two are inconsistent, so that the two-layer iterative optimization method can be used for solving. And the bottom layer is used for optimizing and solving the operation of the sub energy system, and the CPLEX is adopted to solve the linear problem according to the operation model on the basis of the electricity price strategy given by the leader. And the upper layer is an MA electricity price strategy solution, the constraint condition of the formula (1) is considered, the PSO optimization algorithm is used for searching the upper layer optimal electricity price strategy, the electricity price strategy is used as a particle, and the income (6) under the electricity price is used as the fitness of the particle. The solving process of the whole master-slave game is shown in fig. 3.
Example 2
In this embodiment, the transaction optimization decision method provided in embodiment 1 is applied to a regional integrated energy system, and provides an auxiliary decision for the energy utilization rate of the regional integrated energy system.
The regional integrated energy system of the embodiment includes three sub-energy systems, and an intermediate agent MA is established based on the three sub-energy systems. Each energy supply and energy storage device is arranged in each energy sub-energy system, and each energy sub-energy system is connected with an external power grid and a natural gas network through a connecting line. The sub-energy system i is combined with the self-operation condition to trade with the MA to reduce the operation cost; and the MA acquires the electric quantity from the electronic energy selling system at a low price at the time t through the price difference of the electricity selling price, the electric quantity is sold to the electronic energy purchasing system at a high price, and finally the MA and the large power grid settle the unbalanced electric quantity to obtain benefits.
Table 1 shows the energy supply device and system parameters of the sub-energy systems 1,2, 3 in the model.
TABLE 1 energy supply device and System parameters for sub-energy systems
Figure BDA0002648935870000131
The energy storage devices and physical parameters of each sub-energy system are shown in table 2.
TABLE 2 energy storage devices and physical parameters for each sub-energy system
Figure BDA0002648935870000132
Figure BDA0002648935870000141
The 24-hour-day-ahead wind power, photovoltaic power generation unit output curve and the cold-thermal power load curve of the sub-energy systems 1,2 and 3 are respectively shown in fig. 4 and 5.
The electricity price of the power distribution network connected with the multi-micro energy system is formulated according to the time-of-use electricity price and can be divided into a valley time period, a normal time period and a peak time period. Wherein the valley time period is 1:00-6:00, 22:00-24:00, and the electricity purchasing and selling prices are 0.420 and 0.130 (yuan/kWh) respectively; the flat time period is 7:00-8:00, 11:00-12:00 and 16:00-17:00, and the electricity purchasing price is 0.708 and 0.380 (yuan/kWh) respectively; the peak time periods are 9:00-10:00, 13:00-15:00 and 18:00-21:00, and the electricity purchase price is 1.089 and 0.600 (yuan/kWh) respectively.
Analyzing the optimization result:
fig. 6 shows the benefit iterative convergence process of MA, and the optimization algorithm achieves convergence around iteration 25, and the final benefit is 1022.32 yuan.
When the MA benefit finally converges, theoretically, the MA power rate policy is a balanced solution at this time, and the power rate policy is shown in fig. 7.
From fig. 7 it can be derived: the MA electricity selling price and the purchase electricity price of each time interval are both between the electricity selling price and the purchase price of the power grid, and the constraint condition of the formula (1) is met. The electricity price changing time period is mainly 15:00-16:00 and 19:00-21:00, because in the regional integrated energy system of the paper, the electronic energy selling system and the electronic energy purchasing system exist at the moment, and the game behavior exists between the MA and the electronic energy purchasing systems. In the time period of 19:00-21:00, the electricity utilization peak period at night is achieved, and meanwhile, a sub-energy system with surplus electricity exists, and the MA enables the electronic energy selling system to intentionally sell more electricity by improving the electricity purchasing price; meanwhile, the MA reduces the price of electricity sold, so that the high-load sub-energy system tends to buy more electric energy at night. And the MA improves the transaction electric energy of each sub energy system and the MA by changing the purchase and sale price, thereby obtaining more benefits.
Table 3 shows the benefit comparison between MA and each sub-energy system in the power grid electricity price mode after optimization based on the master-slave game. It can be found from the table that the sub-energy system can reduce the operation cost under the operation strategy of the optimal solution of the game, but the reduced operation cost is not much compared with the cost, mainly because the sub-energy system participates in the identity of the follower in the game process, and the benefit is not as high as that of the leader MA.
TABLE 3 comparison of before and after optimization
Figure BDA0002648935870000151
Fig. 8 shows the amount of imbalance of MA trading with the grid, where positive values indicate that MA purchases power from the grid and negative values indicate that MA sells power to the grid. Therefore, the MA can enable the regional comprehensive energy system to absorb the electric energy of the power grid in the low load peak period and compensate the electric energy in the high load peak period of the power grid through the optimization of the electricity price strategy, so that the load curve of the power grid system is more stable. The method can be obtained from the time periods of 14:00-16:00 and 19:00-22:00, after the MA adopts the optimal electricity price strategy, the unbalanced electric quantity traded with the power grid is closer to 0, and the method shows that the method effectively promotes each sub-energy system to participate in trading and improves the energy mutual-help capacity of the regional comprehensive energy system.
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 (9)

1. A centralized transaction optimization decision method for a regional integrated energy system is characterized by comprising the following steps:
step S1, establishing a master-slave game mathematical model of each sub-energy system in the intermediate agent and the regional comprehensive energy system;
step S2, constructing a benefit model of an intermediate agent, a benefit model of a sub-energy system, operation constraints of the sub-energy system and a balanced solution form of a master-slave game based on a master-slave game mathematical model;
and step S3, solving the equilibrium solution of the master-slave game by adopting a two-layer iterative optimization method.
2. The centralized transaction optimization decision method for the regional integrated energy system according to claim 1, wherein the mathematical models of the master-slave game established in the step S1 are:
G＝{{(MA∪SRIES)},{pMA,s},{pMA,b},{R1,...,Ri,...,RN},{PRO},{U1,...,Ui,...,UN}}
wherein, (MA U.G MG) is both sides of the game, the intermediate agent is taken as the leader, the sub energy system is taken as the follower; p is a radical ofMA,s、pMA,bThe power selling price and the power purchasing price strategy set is MA; PRO is the yield of MA; u shapeiIs the benefit of sub energy system i; riThe set of operating strategies for sub-energy system i is represented by the following equation:
Figure FDA0002648935860000011
Figure FDA0002648935860000012
in the formula (I), the compound is shown in the specification,
Figure FDA0002648935860000013
for the set of operating strategies for the sub-energy system i at time t,
Figure FDA0002648935860000014
is the electrical power output by the gas turbine,
Figure FDA0002648935860000015
is the thermal power output by the waste heat boiler,
Figure FDA0002648935860000016
is the thermal power output by the gas-fired boiler,
Figure FDA0002648935860000017
in order to absorb the cold power output by the refrigerator,
Figure FDA0002648935860000018
is the cold power output by the electric refrigerator,
Figure FDA0002648935860000019
the storage capacity of the energy storage device.
3. The centralized transaction optimization decision method for the regional integrated energy system according to claim 2, wherein the step S2 specifically includes:
step S21, an intermediate agent benefit model is established, expressed as:
Figure FDA00026489358600000110
Figure FDA00026489358600000111
Figure FDA00026489358600000112
Figure FDA00026489358600000113
ΔPt＝Ps t-Pb t
in the formula, PROtThe income of the intermediate agent at the time t;
Figure FDA0002648935860000021
respectively representing the purchase and sale of electric quantity of the sub-energy system i; n is a radical ofb/NsThe number of the electronic energy purchasing/selling systems is represented; delta PtFor the intermediate agent's total net charge in/out at time t, when Δ PtWhen the electric quantity is more than 0, the intermediate agent and each sub-energy system electric quantity transaction is finished, and finally, the net flow of delta P is realizedtWhen delta P is used for selling to a large power gridtLess than 0 indicates a net flow of Δ PtThe electric quantity of (2) needs to buy delta P from a large power gridtThe amount of electricity of;
Figure FDA0002648935860000022
respectively selling electricity and purchasing electricity prices of the intermediate agent at the time t;
step S22, establishing a sub-energy system benefit model expressed as:
Ui＝-Mi＝-(Mg,i+Me,i+Mc,i)
in the formula, Mg,iRepresents a cost of gas; me,iRepresenting a transaction fee with the MA; mc,iRepresents a carbon emission cost;
step S23, establishing operation constraints of the sub-energy system, including energy conversion element constraints, energy storage equipment constraints and energy balance constraints;
step S24, according to the benefit model of the intermediate agent, the benefit function is related to the electricity price strategy and the transaction electric quantity, and the transaction electric quantity is influenced by the operation strategy of each sub-energy system, so the benefit function of the intermediate agent is simplified as follows:
PRO＝PRO(pMA,s,pMA,b,R1,...,Ri,...,RN)
for the sub-energy system i, the benefit function is related to the self-operation strategy and the electricity price strategy formulated by the intermediate agent, so that the benefit function of the sub-system is simplified as follows:
Ui＝Ui(pMA,s,pMA,b,R1,...,Ri,...,RN)
strategy p of gameMA,s，pMA,b，R1,...,Ri,...,RNThe game is a solution set of the master-slave game;
when the electricity purchasing/selling price strategy of the intermediate agent is an optimal strategy obtained based on the optimal operation strategy of the sub-energy system, and the operation strategy of the sub-energy system is also the optimal strategy under the electricity purchasing/selling price strategy, the optimal solution set is obtained and recorded as:
Figure FDA0002648935860000023
Figure FDA0002648935860000024
it satisfies the following relation:
Figure FDA0002648935860000025
Figure FDA0002648935860000026
Figure FDA0002648935860000027
4. the centralized transaction optimization decision method for the regional integrated energy system according to claim 3, wherein in the step S22, the gas cost is expressed as:
Figure FDA0002648935860000031
in the formula, CngThe price of each ton of natural gas; qLHVLow calorific value per ton of natural gas;
Figure FDA0002648935860000032
the equivalent power value of natural gas consumed by a gas turbine and a gas boiler of the sub-energy system i at the moment t;
the transaction fee with the intermediate agent is expressed as:
Figure FDA0002648935860000033
the carbon emission cost is expressed as:
Figure FDA0002648935860000034
in the formula, betae、βg(kg/(kW. h)) represents the equivalent CO of purchased electric power or gas2A discharge coefficient; (element/kg) denotes CO2Per unit processing cost.
5. The centralized transaction optimization decision method for the regional integrated energy system according to claim 3, wherein the energy conversion element constraint in the step S23 includes:
gas turbine converts natural gas into electrical energy
Figure FDA0002648935860000035
And the residual heat of the flue gas
Figure FDA0002648935860000036
Figure FDA0002648935860000037
Figure FDA0002648935860000038
In the formula etaGTE,i、ηGTH,iThe power generation efficiency and the thermal efficiency coefficient of the gas turbine of the sub-energy system i are respectively;
Figure FDA0002648935860000039
the equivalent power value of the natural gas consumed by the gas turbine at the moment t of the sub-energy system i;
exhaust-heat boiler generates heat power by using waste of gas turbine
Figure FDA00026489358600000310
Figure FDA00026489358600000311
In the formula etaWH,iThe thermal efficiency coefficient of the waste heat boiler of the sub energy system i;
heat production quantity for converting natural gas into heat energy by gas boiler
Figure FDA00026489358600000312
And coefficient of heat generation efficiency etaGB,iThe following steps are involved:
Figure FDA00026489358600000313
the absorption refrigerator absorbs heat to refrigerate:
Figure FDA00026489358600000314
in the formula (I), the compound is shown in the specification,
Figure FDA0002648935860000041
the absorption type of the sub energy system i is the cold power output by the refrigerator at the moment t; cOPAC,iThe refrigeration coefficient of the absorption refrigerator of the sub energy system i;
Figure FDA0002648935860000042
inputting the thermal power of the absorption refrigerator for the sub-energy system i at the moment t;
the electric refrigerator is a main device for converting electric energy into cold energy:
Figure FDA0002648935860000043
in the formula (I), the compound is shown in the specification,
Figure FDA0002648935860000044
the cold power output by the electric refrigerator of the sub energy system i at the moment t; cOPEC,iThe refrigeration coefficient of the electric refrigerator of the sub energy system i;
Figure FDA0002648935860000045
the electric power of the electric refrigerator is input to the sub-energy system i at the time t.
6. The centralized transaction optimization decision method for the regional integrated energy system according to claim 3, wherein the energy storage device constraints in the step S23 include:
establishing a general mathematical model of the cold, heat and electricity energy storage equipment:
Figure FDA0002648935860000046
Figure FDA0002648935860000047
Figure FDA0002648935860000048
Figure FDA00026489358600000419
Figure FDA0002648935860000049
in the formula, subscript x represents energy storage equipment, and the storage battery, the heat storage tank and the ice storage air conditioner are represented by BAT, HS and CS respectively;x,ithe energy loss rate of the energy storage equipment;
Figure FDA00026489358600000410
respectively storing and discharging power; etaxc,i、ηxd,iRespectively storing and releasing energy efficiency of the energy storage equipment;
Figure FDA00026489358600000411
a variable representing the charge/discharge state value of 0/1 represents charge when 0 is taken and represents charge when 1 is taken.
7. The centralized transaction optimization decision method for the regional integrated energy system according to claim 3, wherein the energy balance constraint in the step S23 is:
Figure FDA00026489358600000412
Figure FDA00026489358600000413
Figure FDA00026489358600000414
in the formula etaT,iRepresenting the transmission efficiency of the transformer with the distribution network;
Figure FDA00026489358600000415
representing a wind-solar output prediction;
Figure FDA00026489358600000416
representing an electrical load;
Figure FDA00026489358600000417
represents the thermal load;
Figure FDA00026489358600000418
indicating the cooling load.
8. The centralized transaction optimization decision method for the regional integrated energy system according to claim 3, wherein the solving process of the step S24 is specifically as follows:
for the intermediate agent, the need is to solve the following problem:
Figure FDA0002648935860000051
in the formula, T belongs to [1,2]T is the time of a scheduling cycle;
Figure FDA0002648935860000052
the prices of selling electricity and purchasing electricity of the MA at the time t are respectively;
Figure FDA0002648935860000053
respectively selling electricity and purchasing electricity prices of the power grid at the time t;
for sub-energy system i, the demand solves the following linear programming problem:
Figure FDA0002648935860000054
wherein f (x) is a target function of the sub energy system, and x is a decision variable; a represents a decision variable coefficient matrix, and B represents a constant matrix related to model equality constraint; x is the number ofmin/xmaxRepresents the minimum/maximum value that the decision variable may assume;
the solution of the intermediate agent and the solution of the sub-energy system are mutually influenced, and the optimization targets of the intermediate agent and the sub-energy system are inconsistent, so that a two-layer iterative optimization method is adopted for solving.
9. The centralized transaction optimization decision method for the regional integrated energy system according to claim 8, wherein the two-layer iterative optimization method specifically comprises:
the bottom layer is the operation optimization solution of the sub energy system, and on the basis of the electricity price strategy given by the leader, the CPLEX is adopted to solve the linear problem according to the operation model;
and the upper layer is used for solving the electricity price strategy of the intermediate agent, the electricity price constraint condition is considered, the electricity price strategy is used as a particle, the income under the electricity price is used as the fitness of the particle, and the PSO optimization algorithm is used for searching the upper layer optimal electricity price strategy, so that the optimal game solution set is obtained.
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CN113592648A (en) * 2021-07-21 2021-11-02 山东大学 Multi-agent transaction method and system of comprehensive energy system
CN114662381A (en) * 2022-02-24 2022-06-24 上海交通大学 Layered game-based fusion operation method and system for energy traffic system of port ship

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CN112686425A (en) * 2020-12-09 2021-04-20 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
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
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