CN-121981460-A - Multi-park comprehensive energy system optimal scheduling method related to energy transaction

CN121981460ACN 121981460 ACN121981460 ACN 121981460ACN-121981460-A

Abstract

The invention relates to an optimized dispatching method of a multi-park comprehensive energy system related to energy transaction, belonging to the technical field of emission management and environmental protection. The method comprises the steps of constructing a double-layer game model containing carbon emission factors and optimizing the internal and external operation of the park comprehensive energy system. Firstly, constructing a carbon emission accounting model capable of coupling carbon so as to definitely determine carbon emission transfer caused by multi-body energy transaction and realize accurate assessment of system carbon emission. And secondly, establishing a Stackelberg game model for external optimization aiming at the relation between the upper energy network and the park system, and establishing a cooperative game model for internal optimization aiming at the park comprehensive energy system. In addition, a multi-agent cooperative game profit distribution mechanism is designed to quantify marginal contributions of various principals to alliance economy and environmental benefits. Finally, the carbon emission intensity is reduced, and the coordination and unification of the economy and the low carbon target are realized.

Inventors

LI HONGCHENG
YAN ZHEHUI
PENG CHENG
REN JIALE

Assignees

重庆邮电大学

Dates

Publication Date: 20260505
Application Date: 20260116

Claims (10)

1. An optimized dispatching method of a multi-park comprehensive energy system related to energy transaction is characterized by comprising the following steps: s1, collecting core data of each park comprehensive energy system in a multi-park comprehensive energy system MPIES, and establishing an equipment energy model; S2, establishing a carbon flow dynamic model, firstly establishing an equipment energy model to determine an energy conversion relation, and then calculating the energy carbon emission intensity; the carbon emission intensity of the clean energy source defaults to 0, natural gas is calculated according to the coefficient and heat value ratio, and the carbon emission intensity of the cold and hot power grid node is deduced; S3, establishing an objective function and a constraint, wherein the objective function is the minimum total cost, and the constraint comprises electric heating cold load supply and demand balance and peak-valley electric load response limit; the total cost comprises energy transaction, equipment degradation, operation and maintenance and carbon emission cost; s4, establishing a cooperative game optimization model, wherein the cooperative game optimization model is formed by all park comprehensive energy systems PIES to form a cooperative alliance, and aims at the minimum total running cost and carbon emission of the alliance, and on the basis of the internal constraint of a single park comprehensive energy system, the power balance of an alliance layer, the total output of equipment and the total energy transaction constraint among parks are newly added to realize the internal cooperative optimization; S5, establishing a Stackelberg game optimization model, wherein an upper ESP makes time-of-use purchase electricity/heat price, pursues the maximum profit, ensures balance of transaction amount and price constraint balance, and performs optimal scheduling on the comprehensive energy system of each lower park according to price, pursues the minimum cost and iterates to Nash balance; S6, establishing a multi-subject cooperative game gain distribution model, firstly accounting the alliance economic and environmental gains, then quantifying the cost/carbon emission reduction marginal contribution of each PIES to the subnet alliance, and dividing the gains according to the coefficients through a composite coefficient and a weight calculation distribution coefficient to stimulate cooperation.
2. The optimal scheduling method for a multi-campus integrated energy system involving energy transactions according to claim 1, wherein the core data of each of the multi-campus integrated energy systems comprises: the installed capacity, efficiency, operation and maintenance and degradation coefficients of the distributed energy equipment; electrical, thermal, cold load and demand response potential data for 24 hours; External energy network time-sharing price and internet-surfing price; energy carbon emission coefficient, carbon tax, initial carbon quota; scheduling period and energy conversion efficiency; The distributed energy equipment comprises photovoltaic, wind power, triple supply, a boiler, an electric refrigerator and energy storage equipment.
3. A multi-campus integrated energy system optimization scheduling method involving energy transactions according to claim 1, wherein the device energy model comprises: And (3) photovoltaic: wind power: Triple co-generation: Energy storage device: gas-fired boiler: Electric boiler: electric refrigerator: Wherein, the Power for the photovoltaic array; the power of the photovoltaic module under the standard test condition; The number of modules in series; G is the actual irradiation intensity; Is STC irradiation intensity; t is the temperature of the photovoltaic module; t is the time in hours; The power of the wind driven generator; Is air density; r is the radius of the blade of the wind driven generator; Is the wind speed; To start the cut-in wind speed; rated power of the wind driven generator; is the rated wind speed; wind speed is cut out safely; Is natural gas consumption; Is natural gas low-heat value; The power output is CHP; the heat energy output power of the CHP; Cold energy output power of CHP; The COP CHP is the coefficient of performance of the absorption refrigerating unit; 、 And The electricity and heat conversion efficiency and the loss rate of the cogeneration unit are respectively; omega is a binary variable, when the CHP running power is less than 25% of rated capacity, omega=0, otherwise, omega=1; 、 The charge and discharge power of the battery; 、 charging efficiency and discharging efficiency respectively; Initial energy state for the battery; is the final energy state of the battery; 、 Respectively the minimum and maximum energy states of the energy storage battery unit; 、 respectively charging and discharging power; 、 Maximum charge and discharge power respectively; And The output power and rated power of the gas boiler are respectively; Is natural gas consumption; The conversion efficiency of the gas boiler is achieved; The electric energy input power of the electric boiler; The conversion efficiency of the electric boiler is achieved; And The output power and the rated power of the electric refrigerating unit are respectively; Inputting power for electric energy of an electric refrigerating unit; Is the coefficient of performance of the electric refrigeration unit.
4. The multi-campus integrated energy system optimization scheduling method related to energy trading according to claim 1, wherein establishing a carbon flow state model and calculating energy carbon emission intensity comprises: Carbon flow state model: Wherein, the As a carbon flow state model, E gas is a natural gas carbon emission coefficient, and B gas is a natural gas calorific value; Park i electric wire netting, heat supply network, cold net node energy carbon emission intensity: Wherein, the The energy carbon emission intensity is the energy source of the power grid node, The energy carbon emission intensity for the nodes of the heat supply network, The energy carbon emission intensity of the cold net node is, In order to purchase the electric energy of the electric energy, For the intensity of carbon emissions of purchased electrical energy, In order to purchase the thermal energy of the product, In order to purchase the carbon emission intensity of the thermal energy, For the power of the photovoltaic array, Is the power of the wind driven generator and is used for controlling the power of the wind driven generator, Is the electric output power of the CHP, Is the heat energy output power of the CHP, Is the output power of the gas boiler, Is the heat energy output power of the gas boiler, Is the cold energy output power of the CHP, Is the output power of the electric refrigerating unit.
5. A multi-campus integrated energy system optimization scheduling method involving energy transactions according to claim 1, wherein the objective function comprises: Wherein, the In order to minimize the objective function of the object, For the cost of the energy trade, For the cost of equipment degradation, The cost of maintenance for the operation of the equipment, Is the carbon emission cost.
6. A multi-campus integrated energy system optimization scheduling method involving energy trading according to claim 1 or 5, wherein the energy trading, equipment degradation, operation and maintenance, carbon emission costs include: Energy transaction cost: Equipment degradation cost: equipment operation maintenance cost: Carbon emission cost: Wherein, the For the cost of the energy trade, For the cost of equipment degradation, The cost of maintenance for the operation of the equipment, In order to achieve the cost of carbon emissions, 、、 The price of electricity, heat and gas selling of the energy network is respectively; 、 The price of electric energy and heat energy sold to ESP by PIESi is shown, sigma is the degradation cost coefficient of energy storage equipment, omega CHP ,ω GB ,ω EB ,ω EC is the operation and maintenance cost coefficient of CHP, gas boiler, electric boiler and electric refrigerator, and zeta is the carbon tax price of PIESi in the current period, In order to purchase the electric energy of the electric energy, In order to sell the electric energy of the utility model, In order to purchase the thermal energy of the product, In order to sell the heat energy of the product, For the amount of natural gas to be purchased, For the purpose of energy storage and discharge power, For the purpose of storing the charge power, Is the electric output power of the CHP, The power is input to the gas boiler, The electric energy is input into the electric boiler, The power is input for the electric energy of the electric refrigerating unit, Is the total amount of carbon emission in the park.
7. The multi-campus integrated energy system optimization scheduling method related to energy trading according to claim 1, wherein the electrothermal cold load supply and demand balance and peak-to-average-valley electrical load response limit value comprise: balance of electric heating and cooling load supply and demand: peak-to-valley electrical load response limit: Wherein, the In order to purchase the electric energy of the electric energy, For the power of the photovoltaic array, Is the power of the wind driven generator and is used for controlling the power of the wind driven generator, Is the electric output power of the CHP, The electric energy is input into the electric boiler, The power is input for the electric energy of the electric refrigerating unit, Is the heat energy output power of the CHP, Is the heat energy output power of the gas boiler, Is the output power of the gas boiler, Is the cold energy output power of the CHP, For the output power of the electric refrigeration unit, As the amount of charge at peak time, Is the amount of electrical load at ordinary times, Is the amount of charge in the valley, For the total amount of electrical load maximum response, As the amount of charge on the campus, The maximum response amount of the electrical load per unit time period, 、 And The electric load, the thermal load and the cold load of the comprehensive energy system are respectively; , The electrical load transfer power and the thermal load curtailment power in the t period are respectively.
8. The multi-campus integrated energy system optimization scheduling method related to energy transactions according to claim 1, wherein the cooperative game optimization model comprises: Wherein, the For the purposes of the federation, In order to be a comprehensive cost, the device has the advantages of, For the cost of the energy trade, For the cost of equipment degradation, The cost of maintenance for the operation of the equipment, In order to achieve the cost of carbon emissions, In order to outsource the power of the electric power, For the power of the photovoltaic array, Is the power of the wind driven generator and is used for controlling the power of the wind driven generator, Is the electric output power of the CHP, For the purpose of energy storage and discharge power, For the purpose of storing the charge power, The electric energy is input into the electric boiler, The power is input to the electric refrigerator, For the electrical load of the integrated energy system, Power is transferred for the electrical load during the t period, In order to outsource the thermal energy power, Is the heat energy output power of the CHP, Is the heat energy output power of the gas boiler, Is the output power of the gas boiler, In order to integrate the thermal load of the energy system, Power is curtailed for the thermal load during the t period, Is the cold energy output power of the CHP, For the output power of the electric refrigeration unit, In order to integrate the cooling load of the energy system, As the amount of charge at peak time, Is the amount of electrical load at ordinary times, Is the amount of charge in the valley, For the total amount of load maximum response, As the amount of charge on the campus, Is the maximum charge capacity of the park.
9. The multi-campus integrated energy system optimization scheduling method related to energy transactions according to claim 1, wherein the jackberg game optimization model comprises: Wherein f t E, b, f t E, s, f t H, b, f t H, s are respectively electricity purchase price, electricity selling price, heat purchase price and heat selling price; Respectively the optimal electricity purchasing power, electricity selling power, heat purchasing power and heat selling power, In order to outsource the power of the electric power, For the power of the photovoltaic array, Is the power of the wind driven generator and is used for controlling the power of the wind driven generator, Is the electric output power of the CHP, For the purpose of energy storage and discharge power, For the purpose of storing the charge power, The electric energy is input into the electric boiler, The power is input to the electric refrigerator, For the electrical load of the integrated energy system, Power is transferred for the electrical load during the t period, In order to outsource the thermal energy power, Is the heat energy output power of the CHP, Is the heat energy output power of the gas boiler, Is the output power of the gas boiler, In order to integrate the thermal load of the energy system, Power is curtailed for the thermal load during the t period, Is the cold energy output power of the CHP, For the output power of the electric refrigeration unit, In order to integrate the cooling load of the energy system, As the amount of charge at peak time, Is the amount of electrical load at ordinary times, Is the amount of charge in the valley, For the total amount of load maximum response, As the amount of charge on the campus, For the maximum charge capacity of the campus, In order to ensure the electricity purchasing scheme, the electricity selling scheme, the heat purchasing scheme, the heat selling scheme and the time-sharing equipment scheduling scheme under the energy requirement of the system, ftE, b and FtE, s are time-sharing electricity prices and internet electricity prices in a t time period; the electric power is purchased for the park, The thermal power is purchased for the total of the campus, In order to purchase the electric power, In order to sell the electric power, the electric power is supplied to the electric power supply, In order to purchase the heat power, the heat source, Is the selling heat power.
10. The multi-campus integrated energy system optimization scheduling method related to energy transactions according to claim 9, wherein the multi-main cooperative game profit distribution model comprises: Wherein phi i (V)、φ i (E) is the running cost and the carbon emission benefit distribution value of the ith main body respectively, S i is a set formed by all subsets containing the member I in the alliance I, V (S) and E (S) are the total running cost and the total carbon emission of the alliance S respectively, V (S\ { I }) and E (S\ { I }) are the total running cost and the total carbon emission of the alliance S after removing the member I respectively, omega i is a composite coefficient, a is a weight factor, and pi i is a comprehensive benefit distribution coefficient; a park collection for participating in a federation; PIES numbers to participate in the game; is factorial.

Description

Multi-park comprehensive energy system optimal scheduling method related to energy transaction Technical Field The invention belongs to the technical field of energy and energy conservation, and particularly relates to an optimization scheduling method of a multi-park comprehensive energy system related to energy transaction. Background Under the background of global carbon emission reduction and energy transformation, the coupling of the internal main body of the park comprehensive energy system is increasingly complex, how to efficiently utilize the external multi-energy market and flexibly mobilize the participation of the internal multi-main body so as to improve the energy efficiency, make up the supply deficiency, realize the accurate control of carbon emission and have important research value. Disclosure of Invention In order to solve the technical problems, the invention provides a multi-park comprehensive energy system optimization scheduling method related to energy transaction, which is characterized by comprising the following steps: s1, collecting core data of each park comprehensive energy system in a multi-park comprehensive energy system MPIES, and establishing an equipment energy model; S2, establishing a carbon flow dynamic model, firstly establishing an equipment energy model to determine an energy conversion relation, and then calculating the energy carbon emission intensity; the carbon emission intensity of the clean energy source defaults to 0, natural gas is calculated according to the coefficient and heat value ratio, and the carbon emission intensity of the cold and hot power grid node is deduced; S3, establishing an objective function and a constraint, wherein the objective function is the minimum total cost, and the constraint comprises electric heating cold load supply and demand balance, peak-valley electric load response limit value, equipment output upper limit and energy storage energy state range; the total cost comprises energy transaction, equipment degradation, operation and maintenance and carbon emission cost; S4, establishing a cooperative game optimization model, wherein the cooperative game optimization model is formed by all park comprehensive energy systems PIES, aims at minimum total running cost and carbon emission of the alliance, and is newly added with the power balance of the alliance layer, the total output of equipment and the total energy transaction constraint among parks on the basis of individual constraint to realize internal cooperative optimization; S5, establishing a Stackelberg game optimization model, wherein an upper ESP makes time-of-use purchase electricity/heat price, pursues the maximum profit, ensures balance of transaction amount and price constraint balance, and performs optimal scheduling on the comprehensive energy system of each lower park according to price, pursues the minimum cost and iterates to Nash balance; S6, establishing a multi-subject cooperative game gain distribution model, firstly accounting the alliance economic and environmental gains, then quantifying the cost/carbon emission reduction marginal contribution of each PIES to the subnet alliance, and dividing the gains according to the coefficients through a composite coefficient and a weight calculation distribution coefficient to stimulate cooperation. The invention has the beneficial effects that: The invention establishes a multi-park comprehensive energy system optimal scheduling method related to energy transaction. By constructing a double-layer cooperation-leaded game optimization model with energy flow, carbon flow and value flow coupled, the problems of low energy efficiency, insufficient supply, fair and reasonable income distribution in the energy trading process among different parks and the like of the park comprehensive energy system are solved, the overall carbon emission of the park is reduced, and the low-carbon economic development of the industrial park comprehensive energy system is promoted. Drawings FIG. 1 is a schematic flow chart of the method of the present invention; FIG. 2 is a schematic diagram of a typical energy system for a campus; FIG. 3 is a schematic diagram of a multi-campus integrated energy system energy transaction; fig. 4 is a schematic diagram of an optimized scheduling method. Detailed Description The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. Fig. 1 and fig. 4 show an optimized dispatching method for a multi-park comprehensive energy system related to energy transaction, which specifically comprises