CN117236629A - Hierarchical cooperative scheduling method for electric-carbon coupled multi-energy system - Google Patents
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Abstract
The application discloses a hierarchical cooperative scheduling method of an electric-carbon coupled multi-energy system, which comprises the following steps: s1: establishing a multi-energy market collaborative demonstration model, and initializing the energy requirements of each period of PIES according to historical data; the upper energy operators perform joint clearing and calculate the node energy-carbon price information of each PIES; s2: performing autonomous optimization of the area; s3: constructing a PIES-CEA collaborative scheduling model, and constructing an electric carbon coupled Nash bargaining CEA optimization benefit model based on a collaborative game optimization theory transaction CEA; s4: introducing a double-layer iterative solution algorithm, taking an area autonomous model as a bottom layer, and taking a multi-energy market collaborative clear model as an upper layer to form a double-layer distributed optimal scheduling model, wherein decoupling and interaction are carried out between the upper layer and the lower layer through heterogeneous coordination operators; s5: and (3) taking the value of S4 as an initial value of S1, repeating the steps S1-S4, and continuously updating the variables mutually to achieve collaborative optimization. The application can more effectively realize the global energy optimization and carbon emission control of the system.
Description
Technical Field
The application relates to the technical field of electric energy scheduling, in particular to an electric-carbon coupled multi-energy system layered collaborative scheduling method.
Background
The multi-energy system formed by deep coupling of various energy systems plays roles of various links such as energy production, transmission, distribution and conversion, and aims to tightly combine various energy resources (including electric energy, natural gas, heat energy and the like) and facilities in areas such as cities, economic development areas, industrial parks and the like in a multi-energy coordination complementation and multi-end energy flow interaction mode so as to realize efficient coordination integration of the energy resources.
The multi-energy system fully considers the complementary characteristics of each energy system, and has important significance for improving the energy utilization efficiency and reducing the influence of energy on the environment. Along with the maturity of novel electric power system theory and research, the structural morphology of the electric power system in China is gradually changing from a high-carbon electric power system to a low-carbon even zero-carbon electric power system. Compared with the traditional power system, the high-proportion renewable energy source is connected to make the novel power system source network load more complex, and meanwhile, in order to realize low carbonization of each link of the power system, carbon emission mechanism analysis, low carbon benefit evaluation and carbon transaction markets are required to be fully integrated. The multi-energy system covers various energy sources such as electric power, gas, cold and hot, presents more complex energy combination and demand characteristics, and introduces a carbon transaction mechanism so that the optimization target of the energy system becomes more diversified. Therefore, how to realize deep coupling of electric energy and carbon emission in each link in a multi-element energy system with deep coupling of cold, heat, electricity and gas is still a problem to be solved.
At present, the research related to electric carbon scheduling is mainly focused on introducing a carbon emission related objective function in scheduling planning, the electric power system structure is single, and the method for realizing collaborative optimization scheduling among different main bodies and the multi-element energy system has not been studied in depth.
Disclosure of Invention
Aiming at the problems, the application aims to provide an electric carbon coupled multi-energy system layered collaborative scheduling method which can effectively realize the global energy optimization and carbon emission control of the system by fully considering the complementary characteristics of energy nodes and the energy-carbon coupling collaborative strategy of a multi-energy main body.
The technical scheme of the application is as follows:
an electric-carbon coupled multi-energy system layered cooperative scheduling method comprises the following steps:
s1: establishing a multi-energy market collaborative demonstration model, and initializing the energy demands of each period of PIES according to historical data
The upper layer energy operators according toPerforming joint clearing, and calculating node energy-carbon price information of each PIES based on marginal price theory and embedded carbon flow distribution>And->
S2: establishing an area autonomous optimization model, and each PIES is based on the corresponding node energy-carbon price informationAnd->Performing autonomous optimization of the area;
s3: constructing a PIES-CEA collaborative scheduling model, and constructing an electric carbon-coupled Nash bargaining CEA optimization benefit model based on a collaborative game optimization theory transaction CEA between energy nodes; obtaining optimal CEA transaction amount between PIES according to the electric carbon coupled Nash bargained CEA optimizing benefit modelAnd optimal trade price->Simultaneously updating corresponding energy demands
S4: introducing a double-layer iterative solution algorithm, taking an area autonomous model as a bottom layer, and taking a multi-energy market collaborative clear model as an upper layer to form a double-layer distributed optimal scheduling model, wherein decoupling and interaction are carried out between the upper layer and the lower layer through heterogeneous coordination operators;
when the iteration errors of the energy demands are smaller than the set threshold, the double-layer distributed optimal scheduling model reaches an equilibrium state, and the optimal CEA transaction amount is finally determined at the momentAnd trade price->At the same time the operator is +_ dependent on the corresponding energy requirements obtained>Determining the most reasonable node energy-carbon price +.>And->
S5: and (3) taking the value of the step S4 as the initial value of the step S1, repeating the steps S1-S4, and continuously updating the variables mutually to achieve collaborative optimization.
Preferably, in step S2, the area autonomous optimization model is:
wherein:respectively representing the upper-level energy purchasing cost, the energy production cost, the equipment operation and maintenance cost and the total carbon transaction cost of the nth PIES; t represents a settlement period taking a day as a period; />Respectively representing the node electricity price and the node gas price received by the nth PIES; />Respectively representing the electricity purchase quantity and the air purchase quantity at the time t; />Representing a set of corresponding devices in an nth PIES, wherein CU, S, WT, PV, CHP, EB, P2G, EES, HES, GES represent coal-fired units, air sources, wind power, photovoltaic, CHP units, electric boilers, P2G, electrochemical energy storage devices, thermal energy storage devices and gas energy storage devices in the corresponding devices, respectively; />Are all cost coefficients; />Respectively express the coal-fired unit and the gas at the time tThe output of the source; />Representing a unit operation cost of the corresponding device; /> Respectively represent the force of the corresponding device at time t, wherein +.>Indicating heat output, +.>Representing an electrical output; /> Respectively representing the charge and discharge power of the corresponding energy storage device at the moment t, wherein e, h and g respectively represent the electric, thermal and gas energy storage devices;respectively representing the CCER cost of the nth PIES, the upper CEA transaction cost and the CEA transaction cost among PIES;
the constraint set of the area autonomous optimization model is defined by the physical safety constraint set of the electric-gas-heat supply networkAnd carbon restriction set->The physical safety constraint set of the electric-gas-heat network is as follows:
wherein:representing a set of devices connected at an electrical node i, an air node j, a hot node h, wherein net represents an upper network; />Representing the power demand of the upper network at the moment t; p (P) ki,t 、P ij,t Then the flow of lines (k, i) and lines (i, j) at time t is represented; p (P) Di,t 、g Dj,t 、H Dh,t The electric, gas and heat loads of the corresponding nodes;respectively representing an electric node, a gas node and a thermal node set of the nth REI; />Representing the natural gas demand of an upper network at the time t; />Representing the power of natural gas production at the moment P2 Gt; f (f) mj,t 、f jn,t -the flow of air representing the duct (m, j) and the duct (j, n); />The natural gas consumption power at the moment of CHPt is represented; />Representing the total at hot node h at time tA heat source and a total heat load power; />The thermal load power at EB t;
the carbon constraint setConsists of a quota balance constraint and a CCER offset limit constraint, which are respectively:
wherein:respectively representing the carbon emission coefficient, the unit power supply quota and the unit heat supply quota of the corresponding equipment; omega CEA 、ω CCER Respectively representing the cancellation proportion of CEA and CCER; />The carbon quota obtained by the unit electric quantity generated by the CHP unit, the carbon quota obtained by the unit heat generated by the CHP unit and the carbon quota obtained by the unit electric quantity generated by the CU unit are respectively represented; />Respectively representing the quantity of CCER which can be obtained by the unit electric quantity of CU and GU production; kappa represents an electrothermal conversion coefficient;representing the amount of CEA purchased by the nth PIES towards the premium carbon trade market; />Represents the nth PIES with the restQuota transaction amount for PIES; />Represents the amount of CCER cancellation used by the nth PIES; n is n CU 、n GU The unit numbers of CU and GU are respectively represented;the power levels of CU and GU at time t are shown.
Preferably, the CCER cost of the nth PIES is calculated by:
wherein: lambda (lambda) CCER Representing the price of the unit CCER;respectively representing CCER values of wind power and photovoltaic;
the upper CEA transaction cost for the nth PIES is calculated by:
wherein:representing the CEA price purchased by the nth PIES towards the premium carbon trade market;
the inter-PIES CEA transaction cost for the nth PIES is calculated by:
wherein: omega shape DSO Representing a collection of PIES;representation ofQuota transaction prices of nth PIES with the remaining PIES.
Preferably, the CEA price purchased by the nth PIES towards the premium carbon trade market is calculated by:
wherein:represents->Or->And respectively representing the carbon prices of nodes connected with the nth PIES in the upper power grid and the gas grid.
Preferably, in step S3, the electrical carbon coupled nash bargained CEA optimization benefit model includes a total cost minimization sub-problem P1 and a revenue distribution sub-problem P2, where the total cost minimization sub-problem P1 is:
wherein: n represents the number of REI participating in the bargaining; s.t. represents a condition to be satisfied;representing a set of physical security constraints for an electro-pneumatic-thermal network; />Represents a carbon constraint set;
the revenue distribution sub-problem P2 is:
wherein:representing the total cost of the nth PIES when not engaged in the bargaining; />Indicating that the nth PIES is not consideredTotal cost of time optimization; />And->All represent the optimal solution to the total cost minimization sub-problem P1; />Represents the CEA price purchased by the mth PIES towards the premium carbon trade market.
Preferably, the total cost minimization sub-problem P1 and the benefit distribution sub-problem P2 are both solved using an ADMM algorithm.
Preferably, when solving the total cost minimization sub-problem P1 using the ADMM algorithm, auxiliary variables are introduced for the nth PIESAn augmented lagrangian function of a total cost minimization sub-problem P1 is constructed, the augmented lagrangian function of the total cost minimization sub-problem P1 being:
wherein:representing a total cost minimization sub-problem P1Augmenting the Lagrangian function; />Representing the optimized total cost of the nth PIES; mu (mu) 1,mn 、ρ 1,mn Lagrangian multipliers and penalty factors respectively representing CEA quantity coupling constraints; />Representing the amount of CEA purchased/sold by the mth PIES to the nth PIES; />Representing a two-norm operation;
updating variables between PIES by constant interactionsAnd->Until meeting the first convergence condition;
when solving the benefit allocation sub-problem P2 by adopting ADMM algorithm, introducing auxiliary variables for nth PIESAnd brings the optimal solution of the sub-problem P1 +.>And->Constructing an augmented Lagrangian function of the revenue distribution sub-problem P2, wherein the augmented Lagrangian function of the revenue distribution sub-problem P2 is as follows:
wherein:an augmented lagrangian function representing a revenue distribution sub-problem P2; mu (mu) 2,mn 、ρ 2,mn The Lagrangian multiplier and penalty factor of the price coupling constraint are respectively represented; />Representing the price of CEA for the mth PIES to the nth PIES;
updating variables between PIES by constant interactionsAnd->Until convergence condition two is satisfied.
Preferably, the first convergence condition is:
ΔE nm (k+1) ≤ε 1 (19)
wherein: ΔE nm (k+1) A dual residual representing the k+1st iteration; epsilon 1 Representing a transaction strategy iteration error set value;
the second convergence condition is:
wherein:representing the price of CEA traded by the nth REI to the mth REI when the (k+1) th iteration is performed; />Representing the price of CEA traded by the mth REI to the nth REI when the (k+1) th iteration is performed; epsilon 2 Representing the trade price iteration error set point.
Preferably, in step S4, the decoupling and interaction specifically includes the following sub-steps:
s41: for the kth iteration, k is equal to or greater than 2, the boundary is defined as follows:
wherein:respectively representing the maximum value and the minimum value of the electricity purchase quantity at the moment t in the k-th iteration; respectively representing the electricity purchasing quantity at t time in the k-2 and k-1 iterations; />Respectively representing the maximum value and the minimum value of the air purchase quantity at the moment t in the k-th iteration; />Respectively representing the air purchase quantity at t time in the k-2 and k-1 iterations;
order theJudging whether the convergence condition III is satisfied at the moment:
if yes, stopping iteration to obtain energy requirements, and entering step S5; if not, proceeding to step S42;
s42: the upper layer energy operators according toCombined clearing and updating->Andeach PIES is according to +.>Newly added boundary constraints Carbon bargaining between autonomous optimization of the area and PIES is performed, and energy requirements are updated>Judging whether the convergence condition III is satisfied at the moment:
if yes, stopping iteration to obtain energy requirements, and entering step S5; if not, proceeding to step S43;
s43: and repeating the steps S41-S43 until the iteration errors of the energy requirements are smaller than the set threshold value.
Preferably, the convergence condition three is:
wherein:respectively representing the electricity purchasing quantity of the k-th round and the k-1-th round of iteration; epsilon e Representing convergence tolerance of a given electricity purchasing requirement; />Respectively representing the iterative air purchasing amounts of the kth round and the kth-1 round; epsilon g Indicating convergence tolerance for a given gas purchase demand.
The beneficial effects of the application are as follows:
the application fully considers the complementary characteristics of the energy nodes and the energy-carbon coupling cooperative strategy of the multi-energy main body, and can effectively realize the global energy optimization and carbon emission control of the system. Wherein, by constructing a double-layer distributed optimal scheduling model, the upper layer is responsible for global coordination and the lower layer is responsible for regional autonomy. Introducing a two-dimensional electric-carbon coupling mechanism, and considering the complex conversion relation of the multi-energy equipment and the safety constraint of the regional electric-gas-thermal coupling network to realize the bidirectional CEA collaborative optimization scheduling among PIES and with the upper energy-carbon market; the ADMM algorithm is utilized, carbon coordination among the multiple energy nodes can be realized, and the privacy and flexibility of the collaborative optimization process are ensured; the dichotomy iterative optimization is introduced, so that the convergence of the system is improved, the oscillation phenomenon is effectively avoided, and the stability of the system is improved.
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In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a schematic diagram of a framework of a hierarchical collaborative scheduling method double-layer distributed optimal scheduling model of an electric-carbon coupled multi-energy system;
fig. 2 is a schematic flow chart of a hierarchical coordinated scheduling method of an electric-carbon coupled multi-energy system.
Detailed Description
The application will be further described with reference to the drawings and examples. It should be noted that, without conflict, the embodiments of the present application and the technical features of the embodiments may be combined with each other. It is noted that all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs unless otherwise indicated. The use of the terms "comprising" or "includes" and the like in this disclosure is intended to cover a member or article listed after that term and equivalents thereof without precluding other members or articles.
As shown in fig. 1-2, the application provides a hierarchical collaborative scheduling method of an electric-carbon coupled multi-energy system, which comprises the following steps:
s1: establishing a multi-energy market collaborative demonstration model, and initializing the energy demands of each period of PIES according to historical data
The upper layer energy operators according toPerforming joint clearing, and calculating node energy-carbon price information of each PIES based on marginal price theory and embedded carbon flow distribution>And->
It should be noted that, the calculation methods of the multi-energy market collaborative model and the node energy-carbon price information are all in the prior art, and the specific model and the calculation method are not described here again.
S2: establishing an area autonomous optimization model, and each PIES is based on the corresponding node energy-carbon price informationAnd->And (5) performing autonomous optimization of the area.
In a specific embodiment, the area autonomous optimization model is:
wherein:respectively representing the upper-level energy purchasing cost, the energy production cost, the equipment operation and maintenance cost and the total carbon transaction cost of the nth PIES; t represents a settlement period taking a day as a period; />Respectively representing the node electricity price and the node gas price received by the nth PIES; />Respectively representing the electricity purchase quantity and the air purchase quantity at the time t; />Representing a set of corresponding devices in an nth PIES, wherein CU, S, WT, PV, CHP, EB, P2G, EES, HES, GES represent coal-fired units, air sources, wind power, photovoltaic, CHP units, electric boilers, P2G, electrochemical energy storage devices, thermal energy storage devices and gas energy storage devices in the corresponding devices, respectively; />Are all cost coefficients; />Respectively representthe output of the coal-fired unit and the air source at the moment t; />Representing a unit operation cost of the corresponding device; /> Respectively represent the force of the corresponding device at time t, wherein +.>Indicating heat output, +.>Representing an electrical output; /> Respectively representing the charge and discharge power of the corresponding energy storage device at the moment t, wherein e, h and g respectively represent the electric, thermal and gas energy storage devices;respectively representing the CCER cost of the nth PIES, the upper CEA transaction cost and the CEA transaction cost among PIES;
the constraint set of the area autonomous optimization model is defined by the physical safety constraint set of the electric-gas-heat supply networkAnd carbon restriction set->The physical safety constraint set of the electric-gas-heat network is as follows:
wherein:representing a set of devices connected at an electrical node i, an air node j, a hot node h, wherein net represents an upper network; />Representing the power demand of the upper network at the moment t; p (P) ki,t 、P ij,t Then the flow of lines (k, i) and lines (i, j) at time t is represented; p (P) Di,t 、g Dj,t 、H Dh,t The electric, gas and heat loads of the corresponding nodes;respectively representing an electric node, a gas node and a thermal node set of the nth REI; />Representing the natural gas demand of an upper network at the time t; />Representing the power of natural gas production at the moment P2 Gt; f (f) mj,t 、f jn,t -the flow of air representing the duct (m, j) and the duct (j, n); />The natural gas consumption power at the moment of CHPt is represented; />At tTotal heat source and total heat load power at the heat-etching node h; />The thermal load power at EB t;
the carbon constraint setConsists of a quota balance constraint and a CCER offset limit constraint, which are respectively:
wherein:respectively representing the carbon emission coefficient, the unit power supply quota and the unit heat supply quota of the corresponding equipment; omega CEA 、ω CCER Respectively representing the cancellation proportion of CEA and CCER; />The carbon quota obtained by the unit electric quantity generated by the CHP unit, the carbon quota obtained by the unit heat generated by the CHP unit and the carbon quota obtained by the unit electric quantity generated by the CU unit are respectively represented; />Respectively representing the quantity of CCER which can be obtained by the unit electric quantity of CU and GU production; kappa represents an electrothermal conversion coefficient;representing the amount of CEA purchased by the nth PIES towards the premium carbon trade market; />Representing quota transactions amounts of the nth PIES and the remaining PIES; />Represents the amount of CCER cancellation used by the nth PIES; n is n CU 、n GU The unit numbers of CU and GU are respectively represented;the power levels of CU and GU at time t are shown.
In the above embodiment, the quota balance constraint includes a total carbon emission and a P2G carbon reduction amount of the first behavior REI, a CEA amount obtained for free by the second behavior, and a third behavior CEA transaction amount and a ccor offset amount.
In a specific embodiment, the CCER cost of the nth PIES is calculated by:
wherein: lambda (lambda) CCER Representing the price of the unit CCER;respectively representing CCER values of wind power and photovoltaic;
the upper CEA transaction cost for the nth PIES is calculated by:
wherein:representing the CEA price purchased by the nth PIES towards the premium carbon trade market;
the inter-PIES CEA transaction cost for the nth PIES is calculated by:
wherein: omega shape DSO Representing a collection of PIES;representing the quota trade price of the nth PIES with the remaining PIES.
In a specific embodiment, the CEA price purchased by the nth PIES towards the premium carbon trade market is calculated by:
wherein:represents->Or->And respectively representing the carbon prices of nodes connected with the nth PIES in the upper power grid and the gas grid.
In the above embodiment, the average node carbon price is employed as the transaction price for the PIES to market quotas, primarily because the daily clearing quota gap is considered. Other methods of obtaining the trade price of PIES to the upper market quotas in the prior art are also applicable to the present application.
S3: constructing a PIES-CEA collaborative scheduling model, and constructing an electric carbon-coupled Nash bargaining CEA optimization benefit model based on a collaborative game optimization theory transaction CEA between energy nodes; obtaining optimal CEA transaction amount between PIES according to the electric carbon coupled Nash bargained CEA optimizing benefit modelAnd optimal trade price->Simultaneously updating corresponding energy demands
In a specific embodiment, the electrical carbon coupled nash bargained CEA optimization benefit model includes a total cost minimization sub-problem P1 and a revenue distribution sub-problem P2, the total cost minimization sub-problem P1 being:
wherein: n represents the number of REI participating in the bargaining; s.t. represents a condition to be satisfied;representing a set of physical security constraints for an electro-pneumatic-thermal network; />Represents a carbon constraint set;
the revenue distribution sub-problem P2 is:
wherein:representing the total cost of the nth PIES when not engaged in the bargaining; />Indicating that the nth PIES is not consideredTotal cost of time optimization; />And->All represent the optimal solution to the total cost minimization sub-problem P1; />Represents the CEA price purchased by the mth PIES towards the premium carbon trade market.
In a specific embodiment, the total cost minimization sub-problem P1 and the revenue distribution sub-problem P2 are both solved using an ADMM algorithm.
In a specific embodiment, when solving the total cost minimization sub-problem P1 using ADMM algorithm, an auxiliary variable is introduced for the nth PIESAn augmented lagrangian function of a total cost minimization sub-problem P1 is constructed, the augmented lagrangian function of the total cost minimization sub-problem P1 being:
wherein:an augmented lagrangian function representing a total cost minimization sub-problem P1; />Representing the optimized total cost of the nth PIES; mu (mu) 1,mn 、ρ 1,mn Lagrangian multipliers and penalty factors respectively representing CEA quantity coupling constraints; />Representing the amount of CEA purchased/sold by the mth PIES to the nth PIES; />Representation ofPerforming two-norm operation;
updating variables between PIES by constant interactionsAnd->Until meeting the first convergence condition; optionally, the first convergence condition is: />
Wherein: ΔE nm (k+1) A dual residual representing the k+1st iteration; epsilon 1 Representing a transaction strategy iteration error set value;
key steps of the ADMM algorithm solution include:
a. for the k+1st iteration, each PIES sequentially carries out local calculation according to the decision information of the previous iteration or the strategy information of which part has been updated to obtain a new transaction strategyThe following formula is shown:
b. after all PIES completes the policy update, the Lagrangian multiplier μ is updated as follows nm (k+1) ：
c. The dual residuals at this time were calculated using the following:
in a specific embodiment, when solving the benefit allocation sub-problem P2 by adopting the ADMM algorithm, an auxiliary variable is introduced for the nth PIESAnd brings the optimal solution of the sub-problem P1 +.>And->Constructing an augmented Lagrangian function of the revenue distribution sub-problem P2, wherein the augmented Lagrangian function of the revenue distribution sub-problem P2 is as follows:
wherein:an augmented lagrangian function representing a revenue distribution sub-problem P2; mu (mu) 2,mn 、ρ 2,mn The Lagrangian multiplier and penalty factor of the price coupling constraint are respectively represented; />Representing the price of CEA for the mth PIES to the nth PIES;
updating variables between PIES by constant interactionsAnd->Until meeting the second convergence condition; optionally, the second convergence condition is:
wherein:representing the price of CEA traded by the nth REI to the mth REI when the (k+1) th iteration is performed; />Representing the price of CEA traded by the mth REI to the nth REI when the (k+1) th iteration is performed; epsilon 2 Representing the trade price iteration error set point.
And->The specific update procedure of (1) is similar to the ADMM distributed solving procedure of the sub-problem 1, and the specific procedure is not described here again. By solving the sub-problem P1 and the sub-problem P2 in sequence, the optimal CEA transaction amount between PIES can be determined respectivelyAnd optimal trade price->Simultaneously updating the corresponding energy requirement->
S4: introducing a double-layer iterative solution algorithm, taking an area autonomous model as a bottom layer, taking a multi-energy market collaborative clear model as an upper layer, forming a double-layer distributed optimal scheduling model, and decoupling and interaction between the upper layer and the lower layer through heterogeneous coordination operators (energy-carbon price information and energy demand information);
when the iteration errors of the energy requirements are smaller thanWhen a threshold value is set, the double-layer distributed optimal scheduling model reaches an equilibrium state, and the optimal CEA transaction amount is finally determined at the momentAnd trade price->At the same time the operator is +_ dependent on the corresponding energy requirements obtained>Determining the most reasonable node energy-carbon price +.>And->
In a specific embodiment, the decoupling and interaction comprises the following sub-steps:
s41: for the kth iteration, k is equal to or greater than 2, the boundary is defined as follows:
wherein:respectively representing the maximum value and the minimum value of the electricity purchase quantity at the moment t in the k-th iteration; respectively representing the electricity purchasing quantity at t time in the k-2 and k-1 iterations; />Respectively represent the time t in the k-th round of iterationMaximum and minimum value of air purchasing quantity; />Respectively representing the air purchase quantity at t time in the k-2 and k-1 iterations;
order theJudging whether the convergence condition III is satisfied at the moment:
if yes, stopping iteration to obtain energy requirements, and entering step S5; if not, proceeding to step S42;
s42: the upper layer energy operators according toCombined clearing and updating->Andeach PIES is according to +.>Newly added boundary constraints Carbon bargaining between autonomous optimization of the area and PIES is performed, and energy requirements are updated>Judging whether the convergence condition III is satisfied at the moment, and optionally, judging whether the convergence condition III is:
wherein:respectively representing the electricity purchasing quantity of the k-th round and the k-1-th round of iteration; epsilon e Representing convergence tolerance of a given electricity purchasing requirement; />Respectively representing the iterative air purchasing amounts of the kth round and the kth-1 round; epsilon g Representing convergence tolerance of a given gas purchasing demand;
if yes, stopping iteration to obtain energy requirements, and entering step S5; if not, proceeding to step S43;
s43: and repeating the steps S41-S43 until the iteration errors of the energy requirements are smaller than the set threshold value.
S5: and (3) taking the value of the step S4 as the initial value of the step S1, repeating the steps S1-S4, and continuously updating the variables mutually to achieve collaborative optimization.
In the above embodiment, the transmission and distribution collaborative scheduling further optimizes the global energy scheduling, is not limited to the maximization of energy utilization, and realizes the remarkable reduction of carbon emission at the system level, and meanwhile, when the PIES system mainly supplies energy in an electric-thermal energy complementary mode, the application can more accurately capture the restriction effect thereof, thereby providing a targeted optimization scheme for the energy distribution and carbon emission control of the system.
By introducing the electric carbon coupling mechanism, the PIES can more flexibly, economically and efficiently adjust the electric carbon scheduling strategy through the actual production condition, and the comprehensive operation cost and the carbon emission of the multi-energy system are effectively reduced.
According to the application, by introducing dichotomy iterative optimization (namely the double-layer iterative solving algorithm), the occurrence of oscillation phenomenon is effectively avoided, and the convergence and stability of the system in the double-layer model solving process are improved; the ADMM algorithm is adopted, so that the accuracy of a solving process is guaranteed, the privacy safety of each energy node is protected, and a reliable distributed solving process is realized.
In summary, the present application can more effectively realize the global energy optimization and carbon emission control of the system. Compared with the prior art, the application has obvious progress.
The present application is not limited to the above-mentioned embodiments, but is intended to be limited to the following embodiments, and any modifications, equivalents and modifications can be made to the above-mentioned embodiments without departing from the scope of the application.
Claims (10)
1. The hierarchical cooperative scheduling method of the electric-carbon coupled multi-energy system is characterized by comprising the following steps of:
s1: establishing a multi-energy market collaborative demonstration model, and initializing the energy demands of each period of PIES according to historical data
The upper layer energy operators according toPerforming joint clearing, and calculating node energy-carbon price information of each PIES based on marginal price theory and embedded carbon flow distribution>And->
S2: establishing an area autonomous optimization model, and each PIES is based on the corresponding node energy-carbon price informationAndperforming autonomous optimization of the area;
s3: constructing a PIES-CEA collaborative scheduling model, and constructing an electric carbon-coupled Nash bargaining CEA optimization benefit model based on a collaborative game optimization theory transaction CEA between energy nodes; obtaining optimal CEA transaction amount between PIES according to the electric carbon coupled Nash bargained CEA optimizing benefit modelAnd optimal trade price->Simultaneously updating corresponding energy demands
S4: introducing a double-layer iterative solution algorithm, taking an area autonomous model as a bottom layer, and taking a multi-energy market collaborative clear model as an upper layer to form a double-layer distributed optimal scheduling model, wherein decoupling and interaction are carried out between the upper layer and the lower layer through heterogeneous coordination operators;
when the iteration errors of the energy demands are smaller than the set threshold, the double-layer distributed optimal scheduling model reaches an equilibrium state, and the optimal CEA transaction amount is finally determined at the momentAnd trade price->At the same time the operator is +_ dependent on the corresponding energy requirements obtained>Determining the most reasonable node energy-carbon price +.>And->
S5: and (3) taking the value of the step S4 as the initial value of the step S1, repeating the steps S1-S4, and continuously updating the variables mutually to achieve collaborative optimization.
2. The hierarchical collaborative scheduling method for an electrical-carbon coupled multi-energy system according to claim 1, wherein in step S2, the autonomous optimization model of an area is:
wherein:respectively representing the upper-level energy purchasing cost, the energy production cost, the equipment operation and maintenance cost and the total carbon transaction cost of the nth PIES; t represents a settlement period taking a day as a period; />Respectively representing the node electricity price and the node gas price received by the nth PIES; />Respectively representing the electricity purchase quantity and the air purchase quantity at the time t; />Representing a set of corresponding devices in an nth PIES, wherein CU, S, WT, PV, CHP, EB, P2G, EES, HES, GES represent coal-fired units, air sources, wind power, photovoltaic, CHP units, electric boilers, P2G, electrochemical energy storage devices, thermal energy storage devices and gas energy storage devices in the corresponding devices, respectively; />Are all cost coefficients; />Respectively representing the output of the coal-fired unit and the air source at the moment t; />Representing a unit operation cost of the corresponding device; /> Respectively represent the force of the corresponding device at time t, wherein +.>Indicating heat output, +.>Representing electric power transmissionDischarging; /> Respectively representing the charge and discharge power of the corresponding energy storage device at the moment t, wherein e, h and g respectively represent the electric, thermal and gas energy storage devices;respectively representing the CCER cost of the nth PIES, the upper CEA transaction cost and the CEA transaction cost among PIES;
the constraint set of the area autonomous optimization model is defined by the physical safety constraint set of the electric-gas-heat supply networkAnd carbon constraint setThe physical safety constraint set of the electric-gas-heat network is as follows:
wherein:representing a set of devices connected at an electrical node i, an air node j, a hot node h, wherein net represents an upper network; />Representing the power demand of the upper network at the moment t; p (P) ki,t 、P ij,t Then the flow of lines (k, i) and lines (i, j) at time t is represented; p (P) Di,t 、g Dj,t 、H Dh,t The electric, gas and heat loads of the corresponding nodes; />Respectively representing an electric node, a gas node and a thermal node set of the nth REI; />Representing the natural gas demand of an upper network at the time t;representing the power of natural gas production at the moment P2 Gt; f (f) mj,t 、f jn,t -the flow of air representing the duct (m, j) and the duct (j, n); />The natural gas consumption power at the moment of CHPt is represented; />Representing the total heat source and total heat load power at the heat node h at time t;the thermal load power at EB t;
the carbon constraint setConsists of a quota balance constraint and a CCER offset limit constraint, which are respectively:
wherein:respectively representing the carbon emission coefficient, the unit power supply quota and the unit heat supply quota of the corresponding equipment; omega CEA 、ω CCER Respectively representing the cancellation proportion of CEA and CCER; />The carbon quota obtained by the unit electric quantity generated by the CHP unit, the carbon quota obtained by the unit heat generated by the CHP unit and the carbon quota obtained by the unit electric quantity generated by the CU unit are respectively represented;respectively representing the quantity of CCER which can be obtained by the unit electric quantity of CU and GU production; kappa represents an electrothermal conversion coefficient; />Representing the amount of CEA purchased by the nth PIES towards the premium carbon trade market; />Representing quota transactions amounts of the nth PIES and the remaining PIES; />Represents the amount of CCER cancellation used by the nth PIES; n is n CU 、n GU The unit numbers of CU and GU are respectively represented;the power levels of CU and GU at time t are shown.
3. The hierarchical collaborative scheduling method for an electrical-carbon coupled multi-energy system of claim 2, wherein the ccir cost for the nth PIES is calculated by:
wherein: lambda (lambda) CCER Representing the price of the unit CCER;respectively representing CCER values of wind power and photovoltaic;
the upper CEA transaction cost for the nth PIES is calculated by:
wherein:representing the CEA price purchased by the nth PIES towards the premium carbon trade market;
the inter-PIES CEA transaction cost for the nth PIES is calculated by:
wherein: omega shape DSO Representing a collection of PIES;represents the quota trade price and trade volume of the nth PIES and the rest PIES.
4. The hierarchical collaborative scheduling method for an electrical-carbon coupled multi-energy system according to claim 3, wherein the CEA price purchased by the nth PIES to the superior carbon trade market is calculated by:
wherein:represents->Or->And respectively representing the carbon prices of nodes connected with the nth PIES in the upper power grid and the gas grid.
5. The hierarchical collaborative scheduling method for an electrical carbon coupled multi-energy system according to claim 3, wherein in step S3 the electrical carbon coupled nash bargained CEA optimization benefit model includes a total cost minimization sub-problem P1 and a revenue distribution sub-problem P2, the total cost minimization sub-problem P1 being:
wherein: n represents the number of REI participating in the bargaining; s.t. represents a condition to be satisfied;representing a set of physical security constraints for an electro-pneumatic-thermal network; />Represents a carbon constraint set;
the revenue distribution sub-problem P2 is:
wherein:representing the total cost of the nth PIES when not engaged in the bargaining; />Indicating that the nth PIES is not consideredTotal cost of time optimization; />And->All represent the optimal solution to the total cost minimization sub-problem P1; />Represents the CEA price purchased by the mth PIES towards the premium carbon trade market.
6. The hierarchical collaborative scheduling method for an electrical-carbon coupled multi-energy system according to claim 5, wherein the total cost minimization sub-problem P1 and the revenue distribution sub-problem P2 are both solved using an ADMM algorithm.
7. The hierarchical collaborative scheduling method for an electrical-carbon coupled multi-energy system according to claim 6, wherein when solving the overall cost minimization sub-problem P1 using an ADMM algorithm, an auxiliary variable is introduced for an nth PIESBuilding a total cost minimization sub-queryAn augmented lagrangian function for the problem P1, the total cost minimization sub-problem P1 is the augmented lagrangian function:
wherein:an augmented lagrangian function representing a total cost minimization sub-problem P1; />Representing the optimized total cost of the nth PIES; mu (mu) 1,mn 、ρ 1,mn Lagrangian multipliers and penalty factors respectively representing CEA quantity coupling constraints; />Representing the amount of CEA purchased/sold by the mth PIES to the nth PIES; />Representing a two-norm operation;
updating variables between PIES by constant interactionsAnd->Until meeting the first convergence condition;
when solving the benefit allocation sub-problem P2 by adopting ADMM algorithm, introducing auxiliary variables for nth PIESAnd brings the optimal solution of the sub-problem P1 +.>And->Constructing an augmented Lagrangian function of the revenue distribution sub-problem P2, wherein the augmented Lagrangian function of the revenue distribution sub-problem P2 is as follows:
wherein:an augmented lagrangian function representing a revenue distribution sub-problem P2; mu (mu) 2,mn 、ρ 2,mn The Lagrangian multiplier and penalty factor of the price coupling constraint are respectively represented; />Representing the price of CEA for the mth PIES to the nth PIES;
updating variables between PIES by constant interactionsAnd->Until convergence condition two is satisfied.
8. The hierarchical collaborative scheduling method for an electrical-carbon coupled multi-energy system according to claim 7, wherein the convergence condition one is:
ΔE nm (k+1) ≤ε 1 (19)
wherein: ΔE nm (k+1) A dual residual representing the k+1st iteration; epsilon 1 Representing a transaction strategy iteration error set value;
the second convergence condition is:
wherein:representing the price of CEA traded by the nth REI to the mth REI when the (k+1) th iteration is performed; />Representing the price of CEA traded by the mth REI to the nth REI when the (k+1) th iteration is performed; epsilon 2 Representing the trade price iteration error set point.
9. The hierarchical collaborative scheduling method for an electrical-carbon coupled multi-energy system according to any one of claims 1-8, wherein in step S4, decoupling and interaction specifically comprises the sub-steps of:
s41: for the kth iteration, k is equal to or greater than 2, the boundary is defined as follows:
wherein:respectively representing the maximum value and the minimum value of the electricity purchase quantity at the moment t in the k-th iteration; /> Respectively representing the electricity purchasing quantity at t time in the k-2 and k-1 iterations; />Respectively representing the maximum value and the minimum value of the air purchase quantity at the moment t in the k-th iteration; />Respectively representing the air purchase quantity at t time in the k-2 and k-1 iterations;
order theJudging whether the convergence condition III is satisfied at the moment:
if yes, stopping iteration to obtain energy requirements, and entering step S5; if not, proceeding to step S42;
s42: the upper layer energy operators according toCombined clearing and updating->And->Each PIES is according to +.>Newly added boundary constraint-> Carbon bargaining between autonomous optimization and PIES is carried out, and energy demand is updatedJudging whether the convergence condition III is satisfied at the moment:
if yes, stopping iteration to obtain energy requirements, and entering step S5; if not, proceeding to step S43;
s43: and repeating the steps S41-S43 until the iteration errors of the energy requirements are smaller than the set threshold value.
10. The hierarchical collaborative scheduling method for an electrical-carbon coupled multi-energy system according to claim 9, wherein the convergence condition three is:
wherein:respectively representing the electricity purchasing quantity of the k-th round and the k-1-th round of iteration; epsilon e Representing convergence tolerance of a given electricity purchasing requirement; />Respectively representing the iterative air purchasing amounts of the kth round and the kth-1 round; epsilon g Indicating convergence tolerance for a given gas purchase demand.
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| CN117688520A (en) * | 2024-02-04 | 2024-03-12 | 国网安徽省电力有限公司经济技术研究院 | Electric-carbon price conduction data analysis method based on electric-carbon market association |
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| CN117689234A (en) * | 2024-02-04 | 2024-03-12 | 山东科技大学 | Multi-main-body double-layer game-based park comprehensive energy system scheduling method and system |
| CN117688520A (en) * | 2024-02-04 | 2024-03-12 | 国网安徽省电力有限公司经济技术研究院 | Electric-carbon price conduction data analysis method based on electric-carbon market association |
| CN117689234B (en) * | 2024-02-04 | 2024-05-03 | 山东科技大学 | Multi-main-body double-layer game-based park comprehensive energy system scheduling method and system |
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