CN114861404A - Electric heating comprehensive energy system real-time scheduling method for guaranteeing feasibility - Google Patents
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Abstract
The invention discloses a real-time scheduling method for an electric heating comprehensive energy system for guaranteeing feasibility, which comprises the following steps of: 1) acquiring system parameters of an electric heating comprehensive energy system; 2) establishing a real-time electric heating combined scheduling model; 3) a modified Benders decomposition algorithm strictly guaranteeing feasibility is designed and applied to solving of a distributed real-time scheduling problem of the electric heating comprehensive energy system; 4) and outputting a distributed real-time scheduling result of the electric heating comprehensive energy system with feasibility guarantee. The invention strictly ensures the safety of the electric heating comprehensive energy system through novel feasible cutting before the circular iteration, greatly reduces the iteration times required by the algorithm convergence, and can safely apply the temporary scheduling scheme in the iteration process to the actual system in real time, so the real-time scheduling method can meet the requirements of real-time scheduling on efficiency and safety.
Description
Technical Field
The invention relates to the technical field of scheduling and optimizing of an electric heating integrated energy system, in particular to a real-time scheduling method for the electric heating integrated energy system, which guarantees feasibility.
Background
The traditional distributed scheduling method cannot effectively ensure the safety of real-time operation of the actual electric heating integrated energy system. Due to a relaxation mechanism of the dual decomposition algorithm, relaxed regional coupling constraint cannot be strictly met even after the algorithm is converged, and an error exists; while the original score resolving rule needs to continuously superimpose feasible segmentations on the power grid dispatching subproblems, and a feasible solution can be obtained through a large number of iterations. Whether the scheduling scheme can strictly meet all the constraints of the scheduling model in real time is a problem which cannot be effectively solved by a distributed optimization theory for a long time. Therefore, aiming at the daily scheduling problem of the electric heating integrated energy system, in order to solve the problems that the traditional distributed scheduling algorithm is low in efficiency and difficult to ensure the safety of the system, a novel feasible segmentation generation method is urgently needed to be provided, a modified Bends decomposition algorithm which strictly ensures the feasibility is designed, and the method is applied to solving the distributed real-time scheduling problem of the electric heating integrated energy system.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provide a real-time scheduling method for an electric heating integrated energy system, which guarantees feasibility, strictly ensures the safety of the electric heating integrated energy system through a novel feasible cut before cyclic iteration, greatly reduces the iteration times required by algorithm convergence, and can meet the requirements of real-time scheduling on efficiency and safety because a temporary scheduling scheme in an iteration process can be safely applied to an actual electric heating integrated energy system in real time.
The purpose of the invention can be achieved by adopting the following technical scheme:
a real-time scheduling method for an electric heating comprehensive energy system guaranteeing feasibility comprises the following steps:
s1, inputting system parameters of the electric heating comprehensive energy system;
s2, establishing a real-time electric-heat combined scheduling model;
s3, applying the modified Bender decomposition algorithm to the electric heating comprehensive energy system distributed real-time scheduling problem to solve;
and S4, outputting a distributed real-time scheduling result of the electric heating comprehensive energy system with feasibility guarantee.
Furthermore, the system parameters of the electric heating integrated energy system comprise the compound power of an injection node, the line power flow, the node voltage and the wind abandon penalty cost, the specific heat capacity of the side water of the heat supply network, the pipeline water supply flow rate, the node temperature of the water supply network and the water return network, the inlet and outlet temperature of the water supply network and the water return network, the power of the heat load and the pipeline length. The input of system parameters provides a data base for modeling, and other data can be calculated through the existing parameters.
Further, a mathematical model is established for the real-time electric-heating combined dispatching model through the obtained system parameters of the electric-heating integrated energy system, and the formula of the real-time electric-heating combined dispatching model established in the step S2 is expressed as follows:
s.t.D E x E +B E h E ≤b E (A)
D H x H +F E h E +F H h H ＝f H (B)
wherein x is E Representing internal variables of the grid, including vectors, x, consisting of the power generation, the up/down rotation reserve capacity of the generator unit, the phase angle of the bus and the power generation of the wind farm H Representing internal variables of the heat supply network, including vector composed of node temperature of supply/return side of pipeline node, temperature of supply/return side of heat source, inlet temperature of supply/return pipeline, outlet temperature of supply/return pipeline, and temperature of heat load supply/return side, B E And D E Coefficient matrices of boundary variables and state variables, respectively, b E Right hand term vector, D, being a constraint of the grid inequality H Is a heat supply network internal variable front coefficient matrix, F E Is a coefficient matrix before the electric output of the heat source, F H Is a coefficient matrix before heat source heat output, f H For equality constraint of the right-hand term, D H 、F E 、F H 、f H Both in relation to parameters of the heat supply network and the electricity network,x H and
lower and upper bounds, h, of state variables inside the heat supply network E For the electric power of the heat source, h H The heat source is used for generating heat,h H and
c in the formula (1) of an objective function is the lower bound and the upper bound of the heat output of the heat source H (h H ) The cost for the heat source inside the heat supply network is expressed as:
C H (h H )＝c H T h H (2)
wherein, c H T C in the formula (1) of an objective function as a heat source heat cost coefficient vector E (x E ,h E ) The wind curtailment cost, the coupling heat source (such as a cogeneration device) and the generating cost of the heat unit are combined to form a quadratic function, and the quadratic function is expressed as:
C E (x E ,h E )＝h E T Gh E +c E T h E (3)
wherein G is a coefficient matrix, c E T Is a heat source electricity cost coefficient vector.
Furthermore, in order to protect the privacy problem of the power grid and the heat supply network, the distributed method is adopted to solve the optimal scheduling problem of the electric heating integrated energy system, so that the power grid control center and the heat supply network control center can achieve almost the same result as centralized solution only by exchanging a small amount of boundary information, and different subject privacy can be protected while the solution precision is not lost. The process of step S3 is as follows:
s31, solving the optimal scheduling problem of the electric heating integrated energy system by adopting a distributed method, and solving the problem by decomposing. Decomposing the original electric heating comprehensive energy system optimization scheduling problem into a power grid main problem and a heat supply network sub-problem by a feasible cutting generation method; the economic dispatching problem of the power grid is used as the main problem of the power grid, and the modeling is as follows:
s.t.D H x H +F E h E +F H h H ＝f H (B)
wherein the content of the first and second substances,
representing the thermal output of the stationary cogeneration unit, is a boundary variable, h ', determined by the grid control centre' E Is heat source electric power h E Virtual copy of (2), heat source power output h E Serving as a decision variable in the operation of the heat supply network, inf represents the lower bound of the solving function;
fixing
The heat supply network operation problem is optimized as a heat supply network sub-problem:
D H x H +F E h' E +F H h H ＝f H (F)
therefore, the feasible cuts to be generated by the heat supply network control center will be added to the main problem of the power network:
wherein h' H Is heat source heat output h H Virtual copy of (2), heat source heat output h H As a decision-making variable in the operation of the power grid,
is a divisible first and second coefficient matrix, g FC For a sectionable coefficient vector, the expression is as follows:
wherein I is an identity matrix, e H Is a unit vector;
the modified main grid problems are as follows:
wherein the content of the first and second substances,
is the optimal solution of the internal variables of the power grid,
the argmin represents the minimum value of the solving function and provides a target function for a constant determined by a heat supply network control center;
s32, solving the optimization problem of the electric heating comprehensive energy system through Benders decomposition;
the Benders decomposition has excellent performance in solving a mathematical programming problem structure with complex variables, so the Benders decomposition is adopted for iterative solution. In each iteration of the Benders decomposition, the power grid control center solves the main problem of the power grid and sends the boundary state to the heat supply network control center; then, the heat supply network control center judges whether the heat supply network subproblems determined by the constraint condition formulas (E), (F), (C) and (D) are feasible or not, and if feasible, an optimal cut is generated; otherwise, a feasible cut will be generated; applying additional constraint to the boundary variable and adding to the main problem of the power grid;
correspond to different
The optimal cutting is a cutting plane of a boundary variable, the optimal cutting provides a lower boundary of an optimal value of the operation cost of the heat network, and the constraint condition formula (E) uses a Lagrange multiplier vector lambda * Expressed, the heat net subproblem is expressed in the form:
D H x H +F E h' E +F H h H ＝f H (F)
therefore, for any heat source that satisfies the constraint formula (F) E To obtain:
η H +G OC h E ≥g OC (10)
equation (17) is the optimal cut obtained from equation (16), where G OC Coefficient matrix for optimum cutting, g OC Coefficient vectors, G, for optimum segmentation OC ＝-(λ * ) T ，
Is the optimal solution, η, to the heat net subproblem H The estimated value of the optimal cost for the operation of the heat supply network on the power grid side is obtained;
s33, implementing a modified Benders decomposition strategy for guaranteeing feasibility of solving problems of the electric heating comprehensive energy system:
determining feasible cutting by a heat supply network control center, and sending constraint conditions to a power grid control center; secondly, the power grid control center sets an iteration index k as 1 and a target value f (0) Solving the modified main problem by using the current objective function and constraint, and obtaining the optimal solution
And
and will be
Sending to the heat supply network, and solving the following heat supply network sub-problems by the heat supply network control center:
D H x H +F E h' E +F H h H ＝f H (F)
to obtain
Optimal Lagrange multiplier vector λ corresponding to constraint (M) k And sending the corresponding parameters of the optimal cutting to the power grid, then carrying out convergence judgment, and calculating the kth iteration objective function by the power grid control center
If the absolute value of the difference between two adjacent objective function values in the iteration process is smaller than a preset judgment threshold constant epsilon, the iteration process is ended, otherwise, the power grid control center solves the following enhanced main problem of correcting the power grid:
s.t.D E x E +B E h E ≤b E (A)
wherein, the first and the second end of the pipe are connected with each other,
respectively, after iteration for the (k + 1) th time, the power grid internal variable, the power grid boundary variable, the heat supply network boundary variable and the estimated value of the optimal cost of the power grid side for the heat supply network operation are obtained, then, the iteration index is updated, k is enabled to be equal to k +1, and the new value is obtained
Sending the data to a heat supply network, solving the formula (12) again by a heat supply network control center, circularly processing until a convergence condition is met, and ending an iteration process;
s34, applying the corrected Benders decomposition for guaranteeing feasibility to the electric heating comprehensive energy system:
the provided modified Bender algorithm with feasibility guarantee is applied to the electric heating comprehensive energy system scheduling problem with robustness, uncertainty caused by renewable energy (such as a wind power plant) is well considered, and the output power of the wind power plant should meet the following relation:
wherein, the first and the second end of the pipe are connected with each other,
uploading the predicted available wind power interval to a power grid control center for each wind farm,
representing the lower bound of the predicted available wind power of the g-th wind farm at the t-th moment,
representing the upper bound of the predicted available wind power of the g wind farm at the t moment. The power grid control center determines and informs each wind power plant of the interval of wind power allowed to be output
Represents the lower bound of the wind power output allowed by the g wind power plant at the t moment by the power grid,
representing the upper bound of the wind power that the grid allows the g-th wind farm to output wind power at the t-th moment,
wind power is output for the g wind power plant at the t moment reference,
for the actual output wind power of the g wind power plant at the t moment, formula (13) indicates that the output power interval allowed by the wind power plant is a subset of the predicted power interval;
p as a deterministic variable since the deviation of the output wind power from the reference point is proportionally balanced by the thermal unit g,t And
using both uncertainty variables in constraints
And
alternatively, equation (22) is obtained:
where Kg is the coefficient for the sum of all thermal units to be 1, p g,t Outputting thermal power for the g wind power plant at the t moment based on the reference,
outputting actual thermal power for the ith thermal power generating unit at the tth moment,
the g' th wind power plant outputs the actual wind power at the t-th moment,
is a thermal power generator assembly,
Is a set of wind farms,
is a set of times;
further obtaining the cost of the waste wind:
thus, the uncertainty introduced by the wind farm can be characterized with a robust model:
wherein the content of the first and second substances,
in order to be an indeterminate set,
in order to define the lower bound of the uncertainty set,
upper bound of uncertainty set, definition
For the cost function of the wind curtailment, σ g A wind abandon penalty factor, wherein W is a matrix determined by considering the parameters of an electric heating comprehensive energy system of renewable energy;
the robust model is equivalently expressed as follows:
D H x H +F E h' E +F H h H ＝f H (F)
in the above formula
And
is specifically expressed as follows:
wherein the matrix W + And W - Consisting of positive and negative elements of W, i.e. W + ＝max(W,0)，W - Min (W,0), max (W,0) representing elements greater than 0 in the fetch matrix W, min (W,0) representing elements less than 0 in the fetch matrix W;
this problem can be solved directly in a distributed way by using the proposed modified pentes decomposition of the guaranteed feasibility. Therefore, the modified Benders decomposition that guarantees feasibility is fully compatible with the robust model.
Further, the step S4 process is as follows:
and solving a distributed scheduling result of the electric heating integrated energy system, and outputting output of each device in the power grid and the heat supply network, operation cost, electricity purchasing cost and air abandoning amount of the electric heating integrated energy system. And providing a scheduling reference scheme for the power grid control center and the heat supply network control center through the solved result.
Compared with the prior art, the invention has the following advantages and effects:
(1) reliability: all feasible constraints are generated before iteration and are sent to the power grid control center by the heat grid control center, so that the feasibility of the heat grid control center and the power grid control center is ensured from the beginning.
(2) High efficiency: an improved distributed solution scheme is provided, namely the correction of feasibility is guaranteed, and the solution scheme is applied to distributed scheduling of electric heating comprehensive energy sources, so that the solving speed and the iteration times are obviously reduced.
(3) Expansibility: in the method, problem modeling adopts a matrix form, and the scheduling problem of the electric heating comprehensive energy system with different scales can be solved.
(4) Real-time performance: the safety of the electric heating comprehensive energy system is strictly ensured through the novel feasible cutting before the cyclic iteration, so all scheduling schemes in the iteration process can be applied.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a real-time scheduling method of an electric heating comprehensive energy system for guaranteeing feasibility, which is disclosed by the invention;
FIG. 2 is a diagram of an electric heat integrated energy system according to an embodiment of the present invention;
FIG. 3 is a diagram of a simulation system in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
The embodiment discloses a real-time scheduling method for an electric heating comprehensive energy system, which guarantees feasibility, and comprises the following steps:
s1, inputting system parameters of the electric heating comprehensive energy system;
in this embodiment, the system parameters of the electric heat comprehensive energy system include the compound power of the injection node, the line tide, the node voltage, the wind abandon punishment cost, the specific heat capacity of the heat supply network side water, the pipeline water supply flow rate, the node temperature of the water supply network and the return water network, the inlet and outlet temperature of the water supply network and the return water network, the power of the heat load, and the pipeline length. The input of system parameters provides a data base for modeling, and other data can be calculated through the existing parameters.
S2, establishing a real-time electric-heat combined scheduling model;
in this embodiment, the formula of the real-time electric-thermal combination scheduling model established in step S2 is expressed as follows:
s.t.D E x E +B E h E ≤b E (A)
D H x H +F E h E +F H h H ＝f H (B)
wherein x is E Representing internal variables of the grid, including vectors, x, consisting of the power generation, the up/down rotation reserve capacity of the generator unit, the phase angle of the bus and the power generation of the wind farm H Representing internal variables of the heat supply network, including vector composed of node temperature of supply/return side of pipeline node, temperature of supply/return side of heat source, inlet temperature of supply/return pipeline, outlet temperature of supply/return pipeline, and temperature of heat load supply/return side, B E And D E Coefficient matrices for boundary variables and state variables, respectively, b E Right hand term vector, D, being a constraint of the grid inequality H Is a heat supply network internal variable front coefficient matrix, F E Is a coefficient matrix before the electric output of the heat source, F H Is a coefficient matrix before heat source thermal output, f H For equality constraint of the right-hand term, D H 、F E 、F H 、f H Heat supply network and electricityThe parameters of the network are related to each other,x H and
lower and upper bounds, h, of state variables inside the heat supply network E For the electric power of the heat source, h H The heat source is used for generating heat,h H and
c in the formula (1) of an objective function is the lower bound and the upper bound of the heat output of the heat source H (h H ) The cost for the heat source inside the heat supply network is expressed as:
C H (h H )＝c H T h H (2)
wherein, c H T C in the formula (1) of an objective function as a heat source heat cost coefficient vector E (x E ,h E ) The wind curtailment cost, the coupling heat source (such as a cogeneration device) and the generating cost of the heat unit are combined to form a quadratic function, and the quadratic function is expressed as:
C E (x E ,h E )＝h E T Gh E +c E T h E (3)
wherein G is a coefficient matrix heat source electricity cost coefficient matrix, c E T Is a heat source electricity cost coefficient vector.
S3, applying the modified Bender decomposition algorithm to the electric heating comprehensive energy system distributed real-time scheduling problem to solve;
in this embodiment, the process of step S3 is as follows:
s31, decomposing the original electric heating comprehensive energy system optimization scheduling problem into a power grid main problem and a heat supply network sub-problem by a feasible cutting generation method; the economic dispatching problem of the power grid is used as the main problem of the power grid, and the modeling is as follows:
s.t.D H x H +F E h E +F H h H ＝f H (B)
wherein the content of the first and second substances,
representing the thermal output of the stationary cogeneration unit, is a boundary variable, h ', determined by the grid control centre' E Is heat source electric output h E Virtual copy of (2), heat source power h E Serving as a decision variable in the operation of the heat supply network, inf represents the lower bound of the solving function;
fixing
The heat supply network operation problem is optimized as a heat supply network sub-problem:
D H x H +F E h' E +F H h H ＝f H (F)
therefore, the feasible cuts to be generated by the heat supply network control center will be added to the main problem of the power network:
wherein, h' H Heat source heat output h H Virtual copy of (2), heat source heat output h H As a decision-making variable in the operation of the power grid,
is a divisible first and second coefficient matrix, g FC For a sectionable coefficient vector, the expression is as follows:
wherein I is an identity matrix, e H Is a unit vector;
the modified main grid problems are as follows:
wherein the content of the first and second substances,
is the optimal solution of the internal variables of the power grid,
the argmin represents the minimum value of the solving function and provides a target function for a constant determined by a heat supply network control center;
s32, solving the optimization problem of the electric heating comprehensive energy system through Benders decomposition;
in each iteration of the Benders decomposition, the power grid control center solves the main problem of the power grid and sends the boundary state to the heat supply network control center; then, the heat supply network control center judges whether the heat supply network subproblems determined by the constraint condition formulas (E), (F), (C) and (D) are feasible or not, and if feasible, an optimal cut is generated; otherwise, a feasible cut will be generated; applying additional constraint to the boundary variable and adding to the main problem of the power grid;
correspond to different
The optimal cut is a cut plane of a boundary variable, the optimal cut provides a lower boundary of an optimal value of the operation cost of the heat network, and the Lagrange multiplier vector lambda is used for a constraint condition formula (E) * Expressed, the heat net subproblem is expressed in the form:
D H x H +F E h' E +F H h H ＝f H (F)
therefore, for any heat source that satisfies the constraint formula (F) E To obtain:
η H +G OC h E ≥g OC (10)
equation (17) is the optimal cut obtained from equation (16), where G OC Coefficient matrix for optimum cutting, g OC Coefficient vectors, G, for optimum segmentation OC ＝-(λ * ) T ，
Is the optimal solution, η, to the heat net subproblem H The estimated value of the optimal cost for the operation of the heat supply network on the power grid side is obtained;
s33, implementing a modified Benders decomposition strategy for guaranteeing feasibility of solving problems of the electric heating comprehensive energy system:
firstly, determining feasible cutting by a heat supply network control center, and sending constraint conditions to a power grid control center; secondly, the power grid control center sets an iteration index k as 1 and a target value f (0) Solving the modified main problem by using the current objective function and constraint, and obtaining the optimal solution
And
and will be
Sending to the heat supply network, and solving the following heat supply network sub-problems by the heat supply network control center:
D H x H +F E h' E +F H h H ＝f H (F)
to obtain
Optimal Lagrange multiplier vector λ corresponding to constraint (M) k And sending the corresponding parameters of the optimal cutting to the power grid, then carrying out convergence judgment, and calculating the kth iteration objective function by the power grid control center
If the absolute value of the difference between two adjacent objective function values in the iteration process is smaller than a preset judgment threshold constant epsilon, the iteration process is ended, otherwise, the power grid control center solves the following enhanced main problem of correcting the power grid:
s.t.D E x E +B E h E ≤b E (A)
wherein the content of the first and second substances,
respectively, after iteration for the (k + 1) th time, the power grid internal variable, the power grid boundary variable, the heat supply network boundary variable and the estimated value of the optimal cost of the power grid side for the heat supply network operation are obtained, then, the iteration index is updated, k is enabled to be equal to k +1, and the new value is obtained
Sending the data to a heat supply network, solving the formula (12) again by a heat supply network control center, circularly processing until a convergence condition is met, and ending an iteration process;
s34, applying the corrected Benders decomposition for guaranteeing feasibility to the electric heating comprehensive energy system;
the provided modified Pends algorithm with feasibility guarantee is applied to the electric heating comprehensive energy system scheduling with robustness, wherein uncertainty caused by renewable energy (such as a wind power plant) is well considered, and the output power of the wind power plant satisfies the following relation:
wherein the content of the first and second substances,
uploading the predicted available wind power interval to a power grid control center for each wind farm,
representing the lower bound of the predicted available wind power of the g-th wind farm at the t-th moment,
and the upper bound of the predicted available wind power of the g wind power plant at the t moment is represented. The power grid control center determines and informs each wind power plant of the interval of wind power allowed to be output
Represents the lower bound of the wind power output allowed by the g wind power plant at the t moment by the power grid,
representing the upper bound of the wind power that the grid allows the g-th wind farm to output wind power at the t-th moment,
wind power is output for the g wind power plant at the t moment reference,
for the actual output wind power of the g wind power plant at the t moment, formula (13) indicates that the output power interval allowed by the wind power plant is a subset of the predicted power interval;
p as a deterministic variable since the deviation of the output wind power from the reference point is proportionally balanced by the thermal unit g,t And
using both uncertainty variables in constraints
And
alternatively, equation (22) is obtained:
where Kg is the coefficient for the sum of all thermal units to be 1, p g,t Outputting thermal power for the g wind power plant at the t moment based on the reference,
outputting actual thermal power for the ith thermal power generating unit at the tth moment,
the g' th wind power plant outputs the actual wind power at the t-th moment,
is a thermal power generator assembly,
Is a set of wind farms,
is a set of times;
further obtaining the cost of the waste wind:
the robust model is:
wherein the content of the first and second substances,
in order to be an indeterminate set,
in order to define the lower bound of the uncertainty set,
upper bound of uncertainty set, definition
For the cost function of the wind curtailment, σ g A wind curtailment penalty factor, wherein W is a matrix determined by system parameters;
the robust model is equivalently expressed as follows:
D H x H +F E h' E +F H h H ＝f H (F)
in the above formula
And
the specific expression of (A) is as follows:
wherein the matrix W + And W - Consisting of positive and negative elements of W, i.e. W + ＝max(W,0)，W - Min (W,0), max (W,0) representing elements greater than 0 in the fetch matrix W, min (W,0) representing elements less than 0 in the fetch matrix W;
this problem can be solved directly in a distributed way by using the proposed modified pentes decomposition of the guaranteed feasibility. Therefore, the modified Benders decomposition that guarantees feasibility is fully compatible with the robust model.
And S4, outputting a distributed real-time scheduling result of the electric heating comprehensive energy system with feasibility guarantee.
And solving a distributed scheduling result of the electric heating integrated energy system, and outputting output of each device in the power grid and the heat supply network, operation cost, electricity purchasing cost and air abandoning amount of the electric heating integrated energy system.
The simulation system is a small-sized electric heating comprehensive energy system consisting of a 6-node power grid and a 6-node heat supply network, wherein a thermoelectric coupling device electric pump and a CHP are arranged at the No. 6 node of the power grid, and a wind power plant is also arranged at the No. 6 node of the power grid, as shown in figure 3.
Table 1 is the simulation result. With table 1, centralized scheduling reduces the air volume by 74.5% compared to individual scheduling, thereby reducing the overall cost by 15.7%. The comprehensive dispatching efficiency of the wind power plant reaches 90.45 percent.
TABLE 1 economic performance of different scheduling strategies
| Isolated scheduling | Centralized scheduling | The method mentioned | |
| Wind abandon penalty cost (10) 4 $) | 1.5870 | 0.3875 | 0.3875 |
| Total scheduling cost (10) 4 $) | 1.4225 | 6.1875 | 6.1875 |
The centralized scheduling and the real-time scheduling method for guaranteeing the feasibility of the electric heating integrated energy system disclosed in the embodiment 1 finally obtain the accurate optimal solution of the integrated scheduling model, but the distributed solution of the method can protect the privacy of the power grid and the heat supply network to a great extent.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (5)
1. A real-time scheduling method for an electric heating integrated energy system guaranteeing feasibility is characterized by comprising the following steps:
s1, inputting system parameters of the electric heating comprehensive energy system;
s2, establishing a real-time electric heating combined scheduling model;
s3, applying the modified Bender decomposition algorithm to the electric heating comprehensive energy system distributed real-time scheduling problem to solve;
and S4, outputting a distributed real-time scheduling result of the electric heating comprehensive energy system with feasibility guarantee.
2. The method for real-time scheduling of an electric heating comprehensive energy system guaranteeing feasibility according to claim 1, wherein the system parameters of the electric heating comprehensive energy system comprise a compound power of an injection node, a line power flow, a node voltage, a wind curtailment penalty cost, a specific heat capacity of side water of a heat supply network, a pipeline water supply flow rate, a node temperature of a water supply network and a water return network, inlet and outlet temperatures of the water supply network and the water return network, a power of a heat load, and a pipeline length.
3. The feasibility-guaranteeing real-time scheduling method for the electric-thermal integrated energy system according to claim 1, wherein the formula of the real-time electric-thermal combined scheduling model established in the step S2 is expressed as follows:
s.t.D E x E +B E h E ≤b E (A)
D H x H +F E h E +F H h H ＝f H (B)
wherein x is E Representing internal variables of the grid, including vectors, x, consisting of the power generation, the up/down rotation reserve capacity of the generator unit, the phase angle of the bus and the power generation of the wind farm H Representing internal variables of the heat supply network, including vector composed of node temperature of supply/return side of pipeline node, temperature of supply/return side of heat source, inlet temperature of supply/return pipeline, outlet temperature of supply/return pipeline, and temperature of heat load supply/return side, B E And D E Coefficient matrices for boundary variables and state variables, respectively, b E Right hand term vector, D, being a constraint of the grid inequality H Is a heat supply network internal variable front coefficient matrix, F E Is a coefficient matrix before the electric output of the heat source, F H Is a coefficient matrix before heat source heat output, f H For equality constraint of the right-hand term, D H 、F E 、F H 、f H Heat supply network and power networkIs related to the parameters of (a) a,x H and
lower and upper bounds, h, of state variables inside the heat supply network E For the electric power of the heat source, h H The heat source is used for generating heat,h H and
c in the formula (1) of an objective function is the lower bound and the upper bound of the heat output of the heat source H (h H ) The cost for the heat source inside the heat supply network is expressed as:
C H (h H )＝c H T h H (2)
wherein, c H T C in the formula (1) of an objective function as a heat source heat cost coefficient vector E (x E ,h E ) The wind curtailment cost is combined with a quadratic function of the power generation cost of the coupling heat source and the heat unit, and the wind curtailment cost is expressed as follows:
C E (x E ,h E )＝h E T Gh E +c E T h E (3)
wherein G is a coefficient matrix heat source electricity cost coefficient matrix, c E T Is a heat source electricity cost coefficient vector.
4. The method for guaranteeing the feasibility of real-time scheduling of the electric heating comprehensive energy system according to claim 3, wherein the process of the step S3 is as follows:
s31, decomposing the original electric heating comprehensive energy system optimization scheduling problem into a power grid main problem and a heat supply network sub-problem by a feasible cutting generation method; taking the economic dispatching problem of the power grid as the main problem of the power grid, and modeling as follows:
s.t.D H x H +F E h E +F H h H ＝f H (B)
wherein the content of the first and second substances,
representing the thermal output of the stationary cogeneration unit, is a boundary variable, h ', determined by the grid control centre' E Is heat source electric power h E Virtual copy of (2), heat source power h E Serving as a decision variable in the operation of the heat supply network, inf represents the lower bound of the solving function;
fixing
The heat supply network operation problem is optimized as a heat supply network sub-problem:
D H x H +F E h' E +F H h H ＝f H (F)
therefore, the feasible cuts to be generated by the heat supply network control center will be added to the main problem of the power network:
wherein, h' H Is heat source heat output h H Virtual copy of (2), heat source heat output h H As a decision-making variable in the operation of the power grid,
is a divisible first and second coefficient matrix, g FC For a sectionable coefficient vector, the expression is as follows:
wherein I is an identity matrix, e H Is a unit vector;
the modified main grid problems are as follows:
wherein the content of the first and second substances,
is the optimal solution of the internal variables of the power grid,
the argmin represents the minimum value of the solving function and provides a target function for a constant determined by a heat supply network control center;
s32, solving the optimization problem of the electric heating comprehensive energy system through Benders decomposition;
in each iteration of the Benders decomposition, the power grid control center solves the main problem of the power grid and sends the boundary state to the heat supply network control center; then, the heat supply network control center judges whether the heat supply network subproblems determined by the constraint condition formulas (E), (F), (C) and (D) are feasible or not, and if feasible, an optimal cut is generated; otherwise, a feasible cut will be generated; applying additional constraint to the boundary variable and adding to the main problem of the power grid;
correspond to different
The optimal cutting is a cutting plane of a boundary variable, the optimal cutting provides a lower boundary of an optimal value of the operation cost of the heat network, and the constraint condition formula (E) uses a Lagrange multiplier vector lambda * By way of illustration, the heat net subproblem is represented in the form:
D H x H +F E h' E +F H h H ＝f H (F)
therefore, for any heat source that satisfies the constraint formula (F) E To obtain:
η H +G OC h E ≥g OC (10)
the formula (10) is obtained from the formula (9)Wherein G is OC Coefficient matrix for optimum cutting, g OC Coefficient vectors, G, for optimum segmentation OC ＝-(λ * ) T ，
Is the optimal solution, η, to the heat net subproblem H The estimated value of the optimal cost for the operation of the heat supply network on the power grid side is obtained;
s33, implementing a modified Benders decomposition strategy for guaranteeing feasibility of solving problems of the electric heating comprehensive energy system:
firstly, determining feasible cutting by a heat supply network control center, and sending constraint conditions to a power grid control center; secondly, the power grid control center sets an iteration index k as 1 and a target value f (0) Solving the modified main problem by using the current objective function and constraint, and obtaining the optimal solution
And
and will be
Sending to the heat supply network, and solving the following heat supply network sub-problems by the heat supply network control center:
D H x H +F E h' E +F H h H ＝f H (F)
to obtain
Optimal Lagrange multiplier vector λ corresponding to constraint (M) k And sending the corresponding parameters of the optimal cutting to the power grid, then carrying out convergence judgment, and calculating the kth iteration objective function by the power grid control center
If the absolute value of the difference between two adjacent objective function values in the iteration process is smaller than a preset judgment threshold constant epsilon, the iteration process is ended, otherwise, the power grid control center solves the following enhanced main problem of correcting the power grid:
s.t.D E x E +B E h E ≤b E (A)
wherein the content of the first and second substances,
respectively, after iteration for the (k + 1) th time, the power grid internal variable, the power grid boundary variable, the heat supply network boundary variable and the estimated value of the optimal cost of the power grid side for the heat supply network operation are obtained, then, the iteration index is updated, k is enabled to be equal to k +1, and the new value is obtained
Sending the data to a heat supply network, solving the formula (12) again by a heat supply network control center, circularly processing until a convergence condition is met, and ending an iteration process;
s34, applying the corrected Benders decomposition for guaranteeing feasibility to the electric heating comprehensive energy system;
the provided modified Benders algorithm with feasibility guarantee is applied to the electric heating comprehensive energy system scheduling with robustness, wherein the output power of the wind power plant should meet the following relation:
wherein the content of the first and second substances,
uploading the predicted available wind power interval to a power grid control center for each wind farm,
representing the lower bound of the predicted available wind power of the g-th wind farm at the t-th moment,
representing the upper bound of the predicted available wind power of the g wind farm at the t moment. The power grid control center determines and informs each wind power plant of the interval of wind power allowed to be output
Represents the lower bound of the wind power output allowed by the g wind power plant at the t moment by the power grid,
representing the upper bound of the wind power that the grid allows the g-th wind farm to output wind power at the t-th moment,
wind power is output for the g wind power plant at the t moment reference,
for the actual output wind power of the g wind power plant at the t moment, formula (13) indicates that the output power interval allowed by the wind power plant is a subset of the predicted power interval;
p as a deterministic variable since the deviation of the output wind power from the reference point is proportionally balanced by the thermal unit g,t And
using both uncertainty variables in constraints
And
alternatively, equation (22) is obtained:
where Kg is the coefficient for the sum of all thermal units to be 1, p g,t For the g wind farmThe thermal power is output at the reference time t,
outputting actual thermal power for the ith thermal power generating unit at the tth moment,
the g' th wind power plant outputs the actual wind power at the t-th moment,
is a thermal power generator assembly,
Is a set of wind farms,
is a set of times;
further obtaining the cost of the waste wind:
the robust model is:
wherein the content of the first and second substances,
in order to be an indeterminate set,
in order to define the lower bound of the uncertainty set,
upper bound of uncertainty set, definition
For the cost function of the wind curtailment, σ g A wind abandon penalty factor, wherein W is a matrix determined by considering the parameters of an electric heating comprehensive energy system of renewable energy;
the robust model is equivalently expressed as follows:
D H x H +F E h' E +F H h H ＝f H (F)
in the above formula
And
the specific expression of (A) is as follows:
wherein the matrix W + And W - Consisting of positive and negative elements of W, i.e. W + ＝max(W,0)，W - Min (W,0), max (W,0) denotes an element greater than 0 in the fetch matrix W, and min (W,0) denotes an element smaller than 0 in the fetch matrix W.
5. The feasibility-guaranteeing real-time scheduling method for the electric-thermal integrated energy system according to claim 1, wherein the step S4 is as follows:
and solving a distributed scheduling result of the electric heating integrated energy system, and outputting output of each device in the power grid and the heat supply network, operation cost, electricity purchasing cost and air abandoning amount of the electric heating integrated energy system.
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| CN117151426B (en) * | 2023-10-25 | 2024-02-09 | 广东电网有限责任公司中山供电局 | Electrothermal energy system scheduling method and related device based on asymmetry of electrothermal information |
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