CN113283051B - Energy hub based on advanced adiabatic compressed air energy storage and pricing decision method - Google Patents

Energy hub based on advanced adiabatic compressed air energy storage and pricing decision method Download PDF

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CN113283051B
CN113283051B CN202110400315.5A CN202110400315A CN113283051B CN 113283051 B CN113283051 B CN 113283051B CN 202110400315 A CN202110400315 A CN 202110400315A CN 113283051 B CN113283051 B CN 113283051B
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王满商
梅生伟
耿军平
陈来军
李若冰
魏韡
马嵩阳
郑天文
白珈于
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State Grid Jiangsu Electric Power Co ltd Zhenjiang Power Supply Branch
Tsinghua University
Sichuan Energy Internet Research Institute EIRI Tsinghua University
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Tsinghua University
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Abstract

The invention discloses an energy junction based on advanced adiabatic compressed air energy storage and a pricing decision method. Aiming at maximizing the profit of the energy hub, establishing an energy hub self-regulating digital model in consideration of the internal operation constraint of the system; establishing an economic dispatch mathematical model of the distribution company by taking the minimum running cost of the distribution company as a target; and (5) establishing an economic dispatch mathematical model of the thermodynamic company aiming at minimizing the operation cost of the thermodynamic company. And then, establishing a double-layer game model according to the transaction mechanism of the energy hub participating in the electric-thermal comprehensive energy system. And finally, solving a double-layer game problem by adopting an iterative algorithm based on Nash equilibrium definition and mode search to obtain pricing equilibrium points of electricity price and heat price. Thereby providing pricing basis for the energy hub to participate in the transaction of the electric-thermal comprehensive energy system.

Description

Energy hub based on advanced adiabatic compressed air energy storage and pricing decision method
Technical Field
The invention relates to an energy hub based on advanced heat insulation compressed air energy storage and a pricing decision method, and belongs to the technical field of new energy.
Background
With the industrial development and the economic growth, the peak-valley difference of the load of the power system is gradually increased, and the peak regulation pressure of the power grid is aggravated by the anti-peak regulation characteristic of the new energy. Energy storage is one of the most flexible means to solve the peak shaving problem. The advanced adiabatic compressed air energy storage (AdvancedAdiabatic CompressedAir Energy Storage, AA-CAES) is a large-scale physical energy storage technology with great development potential and application prospect, has the advantages of large energy storage capacity, high efficiency, no environmental pollution and the like, and naturally has the combined heat and power energy storage capability, and can be used as an energy conversion unit and comprehensive energy storage equipment to be connected into an electric-thermal comprehensive energy system. The power grid, the heat supply network and the like are generally operated independently, and the power grid and the heat supply network are physically coupled through mutual conversion, transmission and storage of different energies by an Energy Hub (EH), so that decisions of power grid and heat supply network operators (such as a power distribution company and a thermal company) such as price making and the like are also coupled through comprehensive load response of the Energy Hub. However, the interaction between different systems in the integrated energy source is not involved in the prior art, the influence of the running state of the energy hub on economy is rarely considered, and the industry lacks an energy hub pricing decision method for considering the integrated load response of the energy hub and the intrinsic coupling and game of different operators.
Disclosure of Invention
The invention aims to provide an energy hub based on advanced heat-insulating compressed air energy storage and a pricing decision method, wherein the energy hub based on advanced heat-insulating compressed air energy storage is firstly established; and then, a double-layer game theory model is established, the coupling and mutual influence between the power grid and the heat supply network are captured through the comprehensive demand response of the energy hub, and the equilibrium state that stakeholders are not willing to unilaterally change the strategy is deduced, so that a pricing basis is provided for the participation of the energy hub in the transaction of the comprehensive energy system.
The aim of the invention is realized by the following technical scheme:
an energy hub based on advanced adiabatic compressed air energy storage comprises a compressor, an air storage chamber, an expander, a heat recovery system, a solar heat collector, a high-temperature heat storage device, a heat exchanger, a low-temperature heat storage device and a heat pump; the compressor is respectively connected with the air storage chamber and the heat recovery system, the air storage chamber is connected with the expansion machine, the heat recovery system and the solar heat collector are connected with the high-temperature heat storage device, the high-temperature heat storage device is respectively connected with the expansion machine and the heat exchanger, the heat exchanger and the heat pump are respectively connected with the low-temperature heat storage device, the high-temperature heat storage device is used for storing high-temperature heat storage media heated by the outlet air of the compression side and the solar heat collector, the low-temperature heat storage device is used for storing low-temperature heat storage media heated by the heat pump and low-temperature heat storage media cooled and converted by the heat exchanger, and the air storage chamber is used for storing pressure potential energy; the compressor inputs low-valley electricity, wind-discarding electricity and photoelectric energy to compress air, high-pressure air generated by the compressor is stored in the air storage chamber, high-temperature compression heat generated by the compressor is recovered by the heat recovery system and stored in the high-temperature heat storage equipment, and the high-pressure air released by the air storage chamber is heated by high-temperature heat energy released by the high-temperature heat storage device and then enters the expansion machine to expand and do work, so that electric energy is output to the outside; the solar heat collector converts light energy into high-temperature heat energy and stores the high-temperature heat energy in the high-temperature heat storage device, the heat pump is driven by electric energy to generate low-temperature heat energy, the high-temperature heat energy in the high-temperature heat storage device can be cooled by the heat exchanger and converted into low-temperature heat energy, and the low-temperature heat energy output by the heat exchanger and the low-temperature heat energy output by the heat pump can be stored in the low-temperature heat storage device or externally supplied to the heat load of the system.
The object of the invention can be further achieved by the following technical measures:
the pricing decision method of the energy hub based on advanced adiabatic compressed air energy storage comprises the following steps:
1) Establishing a mathematical model of an energy hub based on advanced adiabatic compressed air energy storage:
(1-1) the steady-state thermal equilibrium equation inside the energy hub is:
wherein the method comprises the steps ofThe thermal power of the solar heat collector is t time period; />And->Respectively for t-period advanced adiabatic compressed air energy storage charging power and generating power, < >>Heat power of the heat exchanger is t time period; />And->Respectively storing heat and releasing heat of the high-temperature heat storage device in a t period; />And->The heat storage and release power of the high-temperature heat storage device at the t period are respectively; />And->Respectively storing heat and releasing heat of the low-temperature heat storage device in a t period; />And->The heat storage and release power of the low-temperature heat storage device at the t period are respectively; />Outputting thermal power for the energy hub in the t period;
(1-2) the input and output electric power of the energy hub are respectively:
wherein the method comprises the steps ofAnd->The energy hub inputs and outputs electric power respectively for the period t; />And->The state of energy storage, charging and power generation of the advanced adiabatic compressed air in the period t respectively; />And->The method comprises the steps of respectively carrying out advanced adiabatic compressed air energy storage charging and generating power in a t period;
(1-3) the heat pump internal energy conversion process can be expressed as:
wherein the method comprises the steps ofInputting electric power for the heat pump in the t period; mu (mu) HP The electric heating conversion coefficient of the heat pump;
(1-4) the internal energy conversion process of the solar collector is expressed as:
wherein eta SC The light-heat conversion coefficient of the solar heat collector; a is that SC Is the solar collector area;the illumination intensity is t time period;
(1-5) advanced adiabatic compressed air energy storage internal energy conversion relationship is expressed as:
wherein the coefficient c 1 The ratio of the heat absorption power to the charging power of the heat exchanger in the compression process; coefficient c 2 The heat release power of the heat exchanger is the ratio of the heat release power to the generated power in the expansion process; coefficient alpha 1 Is the ratio of compressed process air mass flow to charge power; coefficient alpha 2 The ratio of the expansion process air mass flow to the generated power;and->Compression and expansion process air mass flow for the period t, respectively;
(1-6) energy storage States (SOC) of the high-temperature heat storage device, the low-temperature heat storage device and the air storage chamber in the period t can be expressed as:
wherein the method comprises the steps ofAnd->The heat storage amount of the high-temperature heat storage device and the low-temperature heat storage device in the t period is respectively; mu (mu) lossH Sum mu lossL The heat loss coefficients, eta, of the high-temperature heat storage device and the low-temperature heat storage device are respectively chH And eta disH The heat storage efficiency and the heat release efficiency of the high-temperature heat storage device are respectively; η (eta) chL And eta disL The heat storage efficiency and the heat release efficiency of the low-temperature heat storage device are respectively; η (eta) chG And eta disG The air storage chamber is inflated and deflated respectively; r is R g Is an ideal gas constant; t (T) GSU And V GSU The temperature and the volume of the air storage chamber are respectively; Δt is a unit scheduling period duration; (1-7) the heat storage and release processes of the high temperature heat storage device and the low temperature heat storage device cannot be performed simultaneously, and the inflation and deflation of the air storage chamber cannot be performed simultaneously, and the constraint can be expressed as:
(1-8) in order to ensure that the energy storage states of the heat storage device and the air storage chamber are unchanged at the beginning and the end of a scheduling period, the following constraint needs to be considered:
(1-9) upper and lower limit constraints of other variables are uniformly classified
Upper and lower bounds ofvariables (1r)
(2) The transaction mode of the energy hub participating in the operation of the electric-thermal comprehensive energy system is as follows:
firstly, a distribution company and a heating power company respectively give a peak-valley electricity price and a peak-valley heat price which are traded with an energy hub; the energy hub determines the running state quantity of each internal component according to price information by self-income maximization, and reports the buying and selling electric quantity and the heating capacity of each period to a distribution company and a heating power company respectively; finally, the distribution company and the thermal company carry out economic dispatch before the day according to the transaction amount reported by the energy hub and with the aim of minimizing the operation cost, and determine the output of other units in the system and the electricity or heat purchasing quantity to the external network;
(3) Establishing an energy hub self-regulating digital model:
(3-1) the energy hub carries out self-scheduling according to the electricity price given by the distribution company and the heat price given by the thermal company, determines the transaction electric quantity and the heat quantity respectively with two large companies, and optimizes the energy flow in the energy hub; energy hub self-scheduling can be modeled as a mixed integer linear programming problem:
max F EH (2a)
s.t.EH operation constraints(1) (2b)
wherein F is EH For energy hub profit, i.e. the profit of selling electricity and heating minus the electricity purchase cost, in particularCan be expressed as:
wherein T is Hh And T Lh Respectively collecting heat load peak and valley time periods; t (T) Hp And T Lp Respectively collecting peak and valley periods of the electric load; a is that Hb And A Lb The peak heat value and the valley heat value of the energy hub are respectively; c (C) Hb And C Lb The peak electricity price and the valley electricity price of the energy hub are respectively; the internal operation constraint (2 b) of the energy hub is composed of formulas (1 a) - (1 r), and the upper limit and lower limit constraints of the transaction amount of the energy hub and the distribution company and the thermal company are given by formulas (2 c) - (2 e), whereinMaximum transaction electric quantity for energy hub and distribution company, < ->The heat is maximally exchanged for the energy hub and the thermal company;
(3-2) assume that problem (2) is at a given price (C Hb ,C Lb ,A Hb ,A Lb ) The optimal solution at isBecause the distribution company needs to preferentially meet the requirement of reporting the electric quantity of the energy hub when carrying out economic dispatch, the thermal company needs to preferentially meet the requirement of reporting the heat supply quantity of the energy hub when carrying out economic dispatch, and the coupling constraint of the energy hub, the distribution company and the thermal company defined by the interface variable is respectively as follows:
wherein the method comprises the steps ofTrade electric quantity for t time period energy hub and distribution company; />The heat is exchanged between the energy hub and the thermal company in the period t;
(4) Establishing a mathematical model of economic dispatch of a distribution company in the future:
(4-1) the distribution network is generally a radiation network, and active and reactive losses and voltage changes need to be considered, and a branch power flow model of the distribution network is as follows:
wherein p is j And q j Active power and reactive power injected at node j respectively; p (P) ij And Q ij Active and reactive power flowing on branch ij, respectively;is the square of the current flowing on branch ij; pi (j) is a line end node number set with node j as an initial node; r is (r) ij And x ij The resistance and reactance of branch ij; />Square the voltage at node j; />Is the maximum value of the current of the branch ij; v (V) j And->The upper limit and the lower limit of the voltage of the node j are respectively;
(4-2) subjecting the second-order form of the net loss constraint (3 d) to convex relaxation to obtain
Thereby obtaining the alternating current power flow constraint of the distribution network after convex relaxation
Cons_CVX_PDN＝{(3a)-(3c),(3e)-(3g)} (3h)
(4-3) taking the minimized running cost of the distribution company as an objective function, taking the alternating current power flow constraint of the power grid and the transaction electric quantity constraint of the energy hub and the distribution company into consideration, and establishing a daily economic dispatch model of the distribution company as follows:
min F PDC (4a)
s.t.Cons_CVX_PDN (4b)
Cons_Couple_PDC (4c)
Upper and lower bounds of variables (4d)
wherein F is PDC The running cost for the distribution company is as follows:
the first term of the formula is the power generation cost of the gas turbine in the system, the second term is the power purchase cost of the distribution company to the energy hub, and the third term is the power purchase cost of the distribution company to the external power grid. Wherein GT is a gas turbine numbering set; alpha 2,i And alpha 1,i The primary term and the secondary term coefficients of the cost function of the ith gas turbine are respectively;active power emitted by the ith gas turbine in a t period; c (C) Hg And C Lg The power distribution company purchases electricity to an external power grid at peak electricity price and low valley electricity price respectively; />The electricity quantity is purchased outwards in a network for a period t;
(5) Establishing a mathematical model of economic dispatch of a thermal company in the future:
taking the minimized operation cost of a thermal company as an objective function, taking into consideration the heat supply and demand balance constraint in the system and the heat constraint of the transaction of an energy hub and the thermal company, and establishing a mathematical model of economic dispatch of the thermal company in the future as follows:
min F HC (5a)
Cons_Couple_HC (5c)
Upper and lower bounds of variables(5d)
objective function F HC Operating cost for thermal power company:
the first term of the formula is a gas pot in the systemFurnace heat supply cost, the second is the heat purchasing cost of a thermal company to an energy hub, and the third is the heat purchasing cost to an external heat supply network; GB is a gas boiler numbering set; beta 2,i And beta 1,i The primary term and the secondary term coefficients of the cost function of the ith gas boiler are respectively;heating power for the ith gas boiler in the period t; a is that g The heat price for buying heat to the external power grid for the thermal company; />Purchasing heat to the external heat supply network for a period t; />Thermal load power for the t period;
(6) According to the transaction mechanism of the energy hub participating in the electric heating comprehensive energy system, a double-layer game model is established:
(6-1) optimal pricing models for distribution companies are:
wherein the method comprises the steps ofTo be fixed as (C) Hb ,C Lb ) The optimal value of the above problem (4);C Hb and->Respectively peakLower and upper limits of electricity prices;C Lb and->The lower limit and the upper limit of the off-peak electricity price are respectively;
(6-2) optimal pricing model for heating companies is:
wherein the method comprises the steps ofTo fix the electricity price as (A) Hb ,A Lb ) The optimum value of the above problem (5);A Hb and->The lower limit and the upper limit of the peak heat price are respectively;A Lb and->A lower limit and an upper limit of the off-peak heat value, respectively;
(1) Based on Nash equilibrium definition, solving the double-layer game model by adopting an iterative algorithm, thereby obtaining pricing equilibrium points of electricity price and heat price.
The pricing decision method of the energy hub based on advanced adiabatic compressed air energy storage, wherein the iterative algorithm of the step (7) comprises the following steps:
the first step: let n=1, initialize electricity and heat prices, letSetting a maximum iteration number N and a convergence judgment standard epsilon;
and a second step of: the fixed peak heat price and the low valley heat price are respectivelyAnd->Solving the problem (6) by adopting mode search to obtain the optimal response of the heat price to the electricity price and respectively assigning values to +.>And->
And a third step of: the fixed peak electricity price and the low valley electricity price are respectivelyAnd->Solving the problem (7) by adopting mode search to obtain the optimal response of the electricity price to the heat price and respectively assigning values to +.>And->
Fourth step: if the following convergence conditions are satisfied, i.e. Ending the iteration, and outputting the price of the balance point as +.>If n+1=n, terminating the iterative process and prompting non-convergence; otherwise, let n=n+1, return to the second step.
Compared with the prior art, the invention has the beneficial effects that: the energy hub based on advanced adiabatic compressed air energy storage can improve energy supply flexibility and realize energy cascade utilization. The energy hub pricing decision method based on advanced adiabatic compressed air energy storage captures the interaction and decision coupling between the power grid and the heat supply network operators through comprehensive demand response of the energy hub, and obtains the equilibrium state that stakeholders are not willing to unilaterally change the strategy, thereby providing pricing basis for the energy hub to participate in the transaction of the electric-thermal comprehensive energy system.
Drawings
FIG. 1 is a diagram showing the structure composition and internal energy flow relationship of a novel energy junction based on advanced adiabatic compressed air energy storage;
FIG. 2 is a flow chart of an energy hub pricing scheme decision.
Detailed Description
The invention will be further described with reference to the drawings and the specific examples.
As shown in fig. 1, 1 is a compressor, 2 is an air storage chamber, 3 is an expander, 4 is a heat recovery system, 5 is a solar collector, 6 is a high-temperature heat storage device, 7 is a heat exchanger, 8 is a low-temperature heat storage device, and 9 is a heat pump.
The energy hub based on advanced adiabatic compressed air energy storage comprises a compressor, an air storage chamber, an expander, a heat recovery system, a solar heat collector, a high-temperature heat storage device, a heat exchanger, a low-temperature heat storage device and a heat pump; the compressor is respectively connected with the air storage chamber and the heat recovery system, the air storage chamber is connected with the expansion machine, the heat recovery system and the solar heat collector are connected with the high-temperature heat storage device, the high-temperature heat storage device is respectively connected with the expansion machine and the heat exchanger, the heat exchanger and the heat pump are respectively connected with the low-temperature heat storage device, the high-temperature heat storage device is used for storing high-temperature heat storage media heated by the outlet air of the compression side and the solar heat collector, the low-temperature heat storage device is used for storing low-temperature heat storage media heated by the heat pump and low-temperature heat storage media cooled and converted by the heat exchanger, and the air storage chamber is used for storing pressure potential energy; the compressor inputs low-valley electricity, wind-discarding electricity and photoelectric energy to compress air, high-pressure air generated by the compressor is stored in the air storage chamber, high-temperature compression heat generated by the compressor is recovered by the heat recovery system and stored in the high-temperature heat storage equipment, and the high-pressure air released by the air storage chamber is heated by high-temperature heat energy released by the high-temperature heat storage device and then enters the expansion machine to expand and do work, so that electric energy is output to the outside; the solar heat collector converts light energy into high-temperature heat energy and stores the high-temperature heat energy in the high-temperature heat storage device, the heat pump is driven by electric energy to generate low-temperature heat energy, the high-temperature heat energy in the high-temperature heat storage device can be cooled by the heat exchanger and converted into low-temperature heat energy, and the low-temperature heat energy output by the heat exchanger and the low-temperature heat energy output by the heat pump can be stored in the low-temperature heat storage device or externally supplied to the heat load of the system.
As shown in fig. 2, the pricing decision method of the energy hub based on advanced adiabatic compressed air energy storage comprises the following steps:
(1) Mathematical model of energy hub based on advanced adiabatic compressed air energy storage is established:
(1-1) the steady-state thermal equilibrium equation inside the energy hub is:
wherein the method comprises the steps ofThe thermal power of the solar heat collector is t time period; />And->Respectively for t-period advanced adiabatic compressed air energy storage charging power and generating power, < >>Heat power of the heat exchanger is t time period; />And->Respectively storing heat and releasing heat of the high-temperature heat storage device in a t period; />And->The heat storage and release power of the high-temperature heat storage device at the t period are respectively; />And->Respectively storing heat and releasing heat of the low-temperature heat storage device in a t period; />And->The heat storage and release power of the low-temperature heat storage device at the t period are respectively;outputting thermal power for the energy hub in the t period;
(1-2) the input and output electric power of the energy hub are respectively:
wherein the method comprises the steps ofAnd->The energy hub inputs and outputs electric power respectively for the period t; />And->The state of energy storage, charging and power generation of the advanced adiabatic compressed air in the period t respectively; />And->The method comprises the steps of respectively carrying out advanced adiabatic compressed air energy storage charging and generating power in a t period;
(1-3) the heat pump internal energy conversion process can be expressed as:
wherein the method comprises the steps ofInputting electric power for the heat pump in the t period; mu (mu) HP The electric heating conversion coefficient of the heat pump;
(1-4) the internal energy conversion process of the solar collector is expressed as:
wherein eta SC Is solar energy heat collectionThe photothermal conversion coefficient of the device; a is that SC Is the solar collector area;the illumination intensity is t time period;
(1-5) advanced adiabatic compressed air energy storage internal energy conversion relationship is expressed as:
wherein the coefficient c 1 The ratio of the heat absorption power to the charging power of the heat exchanger in the compression process; coefficient c 2 The heat release power of the heat exchanger is the ratio of the heat release power to the generated power in the expansion process; coefficient alpha 1 Is the ratio of compressed process air mass flow to charge power; coefficient alpha 2 The ratio of the expansion process air mass flow to the generated power;and->Compression and expansion process air mass flow for the period t, respectively;
(1-6) energy storage States (SOC) of the high-temperature heat storage device, the low-temperature heat storage device and the air storage chamber in the period t can be expressed as:
wherein the method comprises the steps ofAnd->The heat storage amount of the high-temperature heat storage device and the low-temperature heat storage device in the t period is respectively; mu (mu) lossH Sum mu lossL The heat loss coefficients, eta, of the high-temperature heat storage device and the low-temperature heat storage device are respectively chH And eta disH The heat storage efficiency and the heat release efficiency of the high-temperature heat storage device are respectively; η (eta) chL And eta disL The heat storage efficiency and the heat release efficiency of the low-temperature heat storage device are respectively; η (eta) chG And eta disG The air storage chamber is inflated and deflated respectively; r is R g Is an ideal gas constant; t (T) GSU And V GSU The temperature and the volume of the air storage chamber are respectively; Δt is a unit scheduling period duration;
(1-7) the heat storage and release processes of the high temperature heat storage device and the low temperature heat storage device cannot be performed simultaneously, and the inflation and deflation of the air storage chamber cannot be performed simultaneously, and the constraint can be expressed as:
(1-8) in order to ensure that the energy storage states of the heat storage device and the air storage chamber are unchanged at the beginning and the end of a scheduling period, the following constraint needs to be considered:
(1-9) upper and lower limit constraints of other variables are uniformly classified
Upper and lower bounds ofvariables (1r)
(2) The transaction mode of the energy hub participating in the operation of the electric-thermal comprehensive energy system is as follows:
firstly, a distribution company and a heating power company respectively give a peak-valley electricity price and a peak-valley heat price which are traded with an energy hub; the energy hub determines the running state quantity of each internal component according to price information by self-income maximization, and reports the buying and selling electric quantity and the heating capacity of each period to a distribution company and a heating power company respectively; finally, the distribution company and the thermal company carry out economic dispatch before the day according to the transaction amount reported by the energy hub and with the aim of minimizing the operation cost, and determine the output of other units in the system and the electricity or heat purchasing quantity to the external network;
(3) Establishing an energy hub self-regulating digital model:
(3-1) the energy hub carries out self-scheduling according to the electricity price given by the distribution company and the heat price given by the thermal company, determines the transaction electric quantity and the heat quantity respectively with two large companies, and optimizes the energy flow in the energy hub; energy hub self-scheduling can be modeled as a mixed integer linear programming problem:
max F EH (2a)
s.t.EH operation constraints(1) (2b)
wherein F is EH The profit of the energy hub, namely the profit of selling electricity and supplying heat, minus the electricity purchasing cost can be expressed as:
wherein T is Hh And T Lh Respectively collecting heat load peak and valley time periods; t (T) Hp And T Lp Respectively collecting peak and valley periods of the electric load; a is that Hb And A Lb The peak heat value and the valley heat value of the energy hub are respectively; c (C) Hb And C Lb The peak electricity price and the valley electricity price of the energy hub are respectively; the internal operation constraint (2 b) of the energy hub is composed of formulas (1 a) - (1 r), and the upper limit and lower limit constraints of the transaction amount of the energy hub and the distribution company and the thermal company are given by formulas (2 c) - (2 e), whereinMaximum transaction electric quantity for energy hub and distribution company, < ->The heat is maximally exchanged for the energy hub and the thermal company;
(3-2) assume that problem (2) is at a given price (C Hb ,C Lb ,A Hb ,A Lb ) The optimal solution at isBecause the distribution company needs to preferentially meet the requirement of reporting the electric quantity of the energy hub when performing economic dispatch, the thermal company needs to preferentially meet the requirement of reporting the heat supply quantity of the energy hub when performing economic dispatch, and the coupling of the energy hub, the distribution company and the thermal company defined by interface variablesThe contract bundles are respectively:
wherein the method comprises the steps ofTrade electric quantity for t time period energy hub and distribution company; />The heat is exchanged between the energy hub and the thermal company in the period t;
(4) Establishing a mathematical model of economic dispatch of a distribution company in the future:
(4-1) the distribution network is generally a radiation network, and active and reactive losses and voltage changes need to be considered, and a branch power flow model of the distribution network is as follows:
wherein p is j And q j Active power and reactive power injected at node j respectively; p (P) ij And Q ij Active and reactive power flowing on branch ij, respectively;is the square of the current flowing on branch ij; pi (j) is a line end node number set with node j as an initial node; r is (r) ij And x ij The resistance and reactance of branch ij; />Square the voltage at node j; />Is the maximum value of the current of the branch ij; v (V) j And->The upper limit and the lower limit of the voltage of the node j are respectively;
(4-2) subjecting the second-order form of the net loss constraint (3 d) to convex relaxation to obtain
Thereby obtaining the alternating current power flow constraint of the distribution network after convex relaxation
Cons_CVX_PDN＝{(3a)-(3c),(3e)-(3g)} (3h)
(4-3) taking the minimized running cost of the distribution company as an objective function, taking the alternating current power flow constraint of the power grid and the transaction electric quantity constraint of the energy hub and the distribution company into consideration, and establishing a daily economic dispatch model of the distribution company as follows:
min F PDC (4a)
s.t.Cons_CVX_PDN (4b)
Cons_Couple_PDC (4c)
Upper and lower bounds of variables (4d)
wherein F is PDC The running cost for the distribution company is as follows:
the first term of the formula is the power generation cost of the gas turbine in the system, the second term is the power purchase cost of the distribution company to the energy hub, and the third term is the power purchase cost of the distribution company to the external power grid. Wherein GT is a gas turbine numbering set; alpha 2,i And alpha 1,i The primary term and the secondary term coefficients of the cost function of the ith gas turbine are respectively;active power emitted by the ith gas turbine in a t period; c (C) Hg And C Lg The power distribution company purchases electricity to an external power grid at peak electricity price and low valley electricity price respectively; />The electricity quantity is purchased outwards in a network for a period t;
(5) Establishing a mathematical model of economic dispatch of a thermal company in the future:
taking the minimized operation cost of a thermal company as an objective function, taking into consideration the heat supply and demand balance constraint in the system and the heat constraint of the transaction of an energy hub and the thermal company, and establishing a mathematical model of economic dispatch of the thermal company in the future as follows:
min F HC (5a)
Cons_Couple_HC (5c)
Upper and lower bounds of variables (5d)
objective function F HC Operating cost for thermal power company:
the first term of the formula is the heat supply cost of a gas boiler in the system, the second term is the heat purchasing cost of a thermal company to an energy hub, and the third term is the heat purchasing cost to an external heat supply network; GB is a gas boiler numbering set; beta 2,i And beta 1,i The primary term and the secondary term coefficients of the cost function of the ith gas boiler are respectively;heating power for the ith gas boiler in the period t; a is that g The heat price for buying heat to the external power grid for the thermal company; />Purchasing heat to the external heat supply network for a period t; />Thermal load power for the t period;
(6) According to the transaction mechanism of the energy hub participating in the electric heating comprehensive energy system, a double-layer game model is established:
(6-1) optimal pricing models for distribution companies are:
wherein the method comprises the steps ofTo be fixed as (C) Hb ,C Lb ) The optimal value of the above problem (4);C Hb and->The lower limit and the upper limit of the peak electricity price are respectively;C Lb and->The lower limit and the upper limit of the off-peak electricity price are respectively;
(6-2) optimal pricing model for heating companies is:
wherein the method comprises the steps ofTo fix the electricity price as (A) Hb ,A Lb ) The optimum value of the above problem (5);A Hb and->The lower limit and the upper limit of the peak heat price are respectively;A Lb and->A lower limit and an upper limit of the off-peak heat value, respectively;
(7) And in Nash equilibrium definition, solving the double-layer game model by adopting an iterative algorithm, so as to obtain pricing equilibrium points of electricity price and heat price:
the iterative algorithm of step (7) has the steps as follows:
the first step: let n=1, initialize electricity and heat prices, letSetting a maximum iteration number N and a convergence judgment standard epsilon;
and a second step of: the fixed peak heat price and the low valley heat price are respectivelyAnd->Solving the problem (6) by adopting mode search to obtain the optimal response of the heat price to the electricity price and respectively assigning values to +.>And->
And a third step of: the fixed peak electricity price and the low valley electricity price are respectivelyAnd->Solving the problem (7) by adopting mode search to obtain the optimal response of the electricity price to the heat price and respectively assigning values to +.>And->
Fourth step: if the following convergence conditions are satisfied, i.e. Ending the iteration, and outputting the price of the balance point as +.>If n+1=n, terminating the iterative process and prompting non-convergence; otherwise, let n=n+1, return to the second step.
In addition to the above embodiments, other embodiments of the present invention are possible, and all technical solutions formed by equivalent substitution or equivalent transformation are within the scope of the present invention.

Claims (2)

1. The pricing decision method of the energy junction based on advanced adiabatic compressed air energy storage comprises a compressor, an air storage chamber, an expander, a heat recovery system, a solar heat collector, a high-temperature heat storage device, a heat exchanger, a low-temperature heat storage device and a heat pump; the compressor is respectively connected with the air storage chamber and the heat recovery system, the air storage chamber is connected with the expansion machine, the heat recovery system and the solar heat collector are connected with the high-temperature heat storage device, the high-temperature heat storage device is respectively connected with the expansion machine and the heat exchanger, the heat exchanger and the heat pump are respectively connected with the low-temperature heat storage device, the high-temperature heat storage device is used for storing high-temperature heat storage media heated by the outlet air of the compression side and the solar heat collector, the low-temperature heat storage device is used for storing low-temperature heat storage media heated by the heat pump and low-temperature heat storage media cooled and converted by the heat exchanger, and the air storage chamber is used for storing pressure potential energy; the compressor inputs low-valley electricity, wind-discarding electricity and photoelectric energy to compress air, high-pressure air generated by the compressor is stored in the air storage chamber, high-temperature compression heat generated by the compressor is recovered by the heat recovery system and stored in the high-temperature heat storage equipment, and the high-pressure air released by the air storage chamber is heated by high-temperature heat energy released by the high-temperature heat storage device and then enters the expansion machine to expand and do work, so that electric energy is output to the outside; the solar heat collector converts light energy into high-temperature heat energy and stores the high-temperature heat energy in the high-temperature heat storage equipment, the heat pump is driven by electric energy to generate low-temperature heat energy, the high-temperature heat energy in the high-temperature heat storage equipment can be cooled by the heat exchanger and converted into low-temperature heat energy, and the low-temperature heat energy output by the heat exchanger and the low-temperature heat energy output by the heat pump can be stored in the low-temperature heat storage device or externally supplied to the heat load of the system;
the pricing decision method is characterized by comprising the following steps:
1) Establishing a mathematical model of an energy hub based on advanced adiabatic compressed air energy storage:
(1-1) the steady-state thermal equilibrium equation inside the energy hub is:
wherein the method comprises the steps ofThe thermal power of the solar heat collector is t time period; />And->Respectively for t-period advanced adiabatic compressed air energy storage charging power and generating power, < >>Heat power of the heat exchanger is t time period; />And->Respectively storing heat and releasing heat of the high-temperature heat storage device in a t period; />And->The heat storage and release power of the high-temperature heat storage device at the t period are respectively; />And->Respectively storing heat and releasing heat of the low-temperature heat storage device in a t period; />And->The heat storage and release power of the low-temperature heat storage device at the t period are respectively;outputting thermal power for the energy hub in the t period;
(1-2) the input and output electric power of the energy hub are respectively:
wherein the method comprises the steps ofAnd->The energy hub inputs and outputs electric power respectively for the period t; />And->The state of energy storage, charging and power generation of the advanced adiabatic compressed air in the period t respectively; />And->The method comprises the steps of respectively carrying out advanced adiabatic compressed air energy storage charging and generating power in a t period;
(1-3) the heat pump internal energy conversion process can be expressed as:
wherein the method comprises the steps ofInputting electric power for the heat pump in the t period; mu (mu) HP The electric heating conversion coefficient of the heat pump;
(1-4) the internal energy conversion process of the solar collector is expressed as:
wherein eta SC The light-heat conversion coefficient of the solar heat collector; a is that SC Is the solar collector area;the illumination intensity is t time period;
(1-5) advanced adiabatic compressed air energy storage internal energy conversion relationship is expressed as:
wherein the coefficient c 1 The ratio of the heat absorption power to the charging power of the heat exchanger in the compression process; coefficient c 2 The heat release power of the heat exchanger is the ratio of the heat release power to the generated power in the expansion process; coefficient alpha 1 Is the ratio of compressed process air mass flow to charge power; coefficient alpha 2 The ratio of the expansion process air mass flow to the generated power;and->Compression and expansion process air mass flow for the period t, respectively;
(1-6) energy storage States (SOC) of the high-temperature heat storage device, the low-temperature heat storage device and the air storage chamber at the period t can be expressed as:
wherein the method comprises the steps ofAnd->The heat storage amount of the high-temperature heat storage device and the low-temperature heat storage device in the t period is respectively; mu (mu) lossH Sum mu lossL The heat loss coefficients, eta, of the high-temperature heat storage device and the low-temperature heat storage device are respectively chH And eta disH The heat storage efficiency and the heat release efficiency of the high-temperature heat storage device are respectively; η (eta) chL And eta disL The heat storage efficiency and the heat release efficiency of the low-temperature heat storage device are respectively; η (eta) chG And eta disG The air storage chamber is inflated and deflated respectively; r is R g Is an ideal gas constant; t (T) GSU And V GSU The temperature and the volume of the air storage chamber are respectively; Δt is a unit scheduling period duration;
(1-7) the heat storage and release processes of the high temperature heat storage device and the low temperature heat storage device cannot be performed simultaneously, and the inflation and deflation of the air storage chamber cannot be performed simultaneously, and the constraint can be expressed as:
(1-8) in order to ensure that the energy storage states of the heat storage device and the air storage chamber are unchanged at the beginning and the end of a scheduling period, the following constraint needs to be considered:
(1-9) upper and lower limit constraints of other variables are uniformly classified
Upper and lower bounds of variables(1r)
(2) The transaction mode of the energy hub participating in the operation of the electric-thermal comprehensive energy system is as follows:
firstly, a distribution company and a heating power company respectively give a peak-valley electricity price and a peak-valley heat price which are traded with an energy hub; the energy hub determines the running state quantity of each internal component according to price information by self-income maximization, and reports the buying and selling electric quantity and the heating capacity of each period to a distribution company and a heating power company respectively; finally, the distribution company and the thermal company carry out economic dispatch before the day according to the transaction amount reported by the energy hub and with the aim of minimizing the operation cost, and determine the output of other units in the system and the electricity or heat purchasing quantity to the external network;
(3) Establishing an energy hub self-regulating digital model:
(3-1) the energy hub carries out self-scheduling according to the electricity price given by the distribution company and the heat price given by the thermal company, determines the transaction electric quantity and the heat quantity respectively with two large companies, and optimizes the energy flow in the energy hub; energy hub self-scheduling can be modeled as a mixed integer linear programming problem:
max F EH (2a)s.t.EH operation constraints(1)(2b)
wherein F is EH The profit of the energy hub, namely the profit of selling electricity and supplying heat, minus the electricity purchasing cost can be expressed as:
wherein T is Hh And T Lh Respectively collecting heat load peak and valley time periods; t (T) Hp And T Lp Respectively collecting peak and valley periods of the electric load; a is that Hb And A Lb The peak heat value and the valley heat value of the energy hub are respectively; c (C) Hb And C Lb The peak electricity price and the valley electricity price of the energy hub are respectively; the internal operation constraint (2 b) of the energy hub is composed of formulas (1 a) - (1 r), and the upper limit and lower limit constraints of the transaction amount of the energy hub and the distribution company and the thermal company are given by formulas (2 c) - (2 e), whereinMaximum transaction electric quantity for energy hub and distribution company, < ->The heat is maximally exchanged for the energy hub and the thermal company;
(3-2) assume that problem (2) is at a given price (C Hb ,C Lb ,A Hb ,A Lb ) The optimal solution at isBecause the distribution company needs to preferentially meet the requirement of reporting the electric quantity of the energy hub when carrying out economic dispatch, the thermal company needs to preferentially meet the requirement of reporting the heat supply quantity of the energy hub when carrying out economic dispatch, and the coupling constraint of the energy hub, the distribution company and the thermal company defined by the interface variable is respectively as follows:
wherein the method comprises the steps ofTrade electric quantity for t time period energy hub and distribution company; />The heat is exchanged between the energy hub and the thermal company in the period t;
(4) Establishing a mathematical model of economic dispatch of a distribution company in the future:
(4-1) the distribution network is a radiation network, active and reactive losses and voltage changes need to be considered, and a branch power flow model of the distribution network is as follows:
wherein p is j And q j Active power and reactive power injected at node j respectively; p (P) ij And Q ij Active and reactive power flowing on branch ij, respectively;is the square of the current flowing on branch ij; pi (j) is a line end node number set with node j as an initial node; r is (r) ij And x ij The resistance and reactance of branch ij; />Square the voltage at node j; />Is the maximum value of the current of the branch ij; v (V) j And->The upper limit and the lower limit of the voltage of the node j are respectively;
(4-2) subjecting the second-order form of the net loss constraint (3 d) to convex relaxation to obtain
Thereby obtaining the alternating current power flow constraint of the distribution network after convex relaxation
Cons_CVX_PDN= { (3 a) - (3 c), (3 e) - (3 g) } (3 h) (4-3) taking the minimum running cost of the distribution company as an objective function, taking into account the alternating current power flow constraint of the power grid and the transaction electric quantity constraint of the energy hub and the distribution company, and establishing a daily economic dispatch model of the distribution company as follows:
min F PDC (4a) s.t. Cons_CVX_PDN (4 b) Cons_coupler_PDC (4 c) Upper and lower bounds of variables (4 d) wherein F PDC The running cost for the distribution company is as follows:
the first term of the formula is the power generation cost of a gas turbine in the system, the second term is the power purchase cost of a power distribution company to an energy hub, the third term is the power purchase cost of the power distribution company to an external power grid, and GT is a gas turbine number set; alpha 2,i And alpha 1,i The primary term and the secondary term coefficients of the cost function of the ith gas turbine are respectively;active power emitted by the ith gas turbine in a t period; c (C) Hg And C Lg The power distribution company purchases electricity to an external power grid at peak electricity price and low valley electricity price respectively; />The electricity quantity is purchased outwards in a network for a period t;
(5) Establishing a mathematical model of economic dispatch of a thermal company in the future:
taking the minimized operation cost of a thermal company as an objective function, taking into consideration the heat supply and demand balance constraint in the system and the heat constraint of the transaction of an energy hub and the thermal company, and establishing a mathematical model of economic dispatch of the thermal company in the future as follows:
min F HC (5a)
Cons_Couple_HC (5c)
Upper and lower bounds of variables (5d)
objective function F HC Operating cost for thermal power company:
the first term of the formula is the heat supply cost of a gas boiler in the system, the second term is the heat purchasing cost of a thermal company to an energy hub, and the third term is the heat purchasing cost to an external heat supply network; GB is a gas boiler numbering set; beta 2,i And beta 1,i The primary term and the secondary term coefficients of the cost function of the ith gas boiler are respectively;heating power for the ith gas boiler in the period t; a is that g The heat price for buying heat to the external power grid for the thermal company; />Purchasing heat to the external heat supply network for a period t; />Thermal load power for the t period;
(6) According to the transaction mechanism of the energy hub participating in the electric heating comprehensive energy system, a double-layer game model is established:
(6-1) optimal pricing models for distribution companies are:
wherein the method comprises the steps ofTo be fixed as (C) Hb ,C Lb ) The optimal value of the above problem (4);C Hb and->The lower limit and the upper limit of the peak electricity price are respectively;C Lb and->The lower limit and the upper limit of the off-peak electricity price are respectively;
(6-2) optimal pricing model for heating companies is:
wherein the method comprises the steps ofTo fix the electricity price as (A) Hb ,A Lb ) The optimum value of the above problem (5);A Hb and->The lower limit and the upper limit of the peak heat price are respectively; a is that Lb And->A lower limit and an upper limit of the off-peak heat value, respectively;
(7) Based on Nash equilibrium definition, solving the double-layer game model by adopting an iterative algorithm, thereby obtaining pricing equilibrium points of electricity price and heat price.
2. A method of pricing decision for an advanced adiabatic compressed air energy storage based energy hub as claimed in claim 1, wherein the iterative algorithm of step (7) is followed by the steps of:
the first step: let n=1, initialize electricity and heat prices, letSetting a maximum iteration number N and a convergence judgment standard epsilon;
and a second step of: the fixed peak heat price and the low valley heat price are respectivelyAnd->Solving the problem (6) by adopting mode search to obtain the optimal response of the heat price to the electricity price and respectively assigning values to +.>And->
And a third step of: the fixed peak electricity price and the low valley electricity price are respectivelyAnd->Solving the problem (7) by adopting mode search to obtain the optimal response of the electricity price to the heat price and respectively assigning values to +.>And->
Fourth step: if the following convergence conditions are satisfied, i.e. Ending the iteration, and outputting the price of the balance point as +.>If n+1=n, terminating the iterative process and prompting non-convergence; otherwise, let n=n+1, return to the second step.
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