CN117272664A - Multi-energy planning method based on large-scale mixed integer decomposition coordination algorithm - Google Patents
Multi-energy planning method based on large-scale mixed integer decomposition coordination algorithm Download PDFInfo
- Publication number
- CN117272664A CN117272664A CN202311291927.0A CN202311291927A CN117272664A CN 117272664 A CN117272664 A CN 117272664A CN 202311291927 A CN202311291927 A CN 202311291927A CN 117272664 A CN117272664 A CN 117272664A
- Authority
- CN
- China
- Prior art keywords
- iteration
- optimal solution
- energy
- energy planning
- solution
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000004422 calculation algorithm Methods 0.000 title claims abstract description 25
- 238000000354 decomposition reaction Methods 0.000 title claims abstract description 18
- 238000006467 substitution reaction Methods 0.000 claims abstract description 6
- 238000004134 energy conservation Methods 0.000 claims description 4
- 230000009977 dual effect Effects 0.000 claims description 3
- 230000003247 decreasing effect Effects 0.000 claims description 2
- 230000006870 function Effects 0.000 description 5
- 238000005457 optimization Methods 0.000 description 5
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 4
- 230000001052 transient effect Effects 0.000 description 3
- 239000003345 natural gas Substances 0.000 description 2
- 230000004888 barrier function Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002040 relaxant effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Evolutionary Computation (AREA)
- Development Economics (AREA)
- General Engineering & Computer Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Geometry (AREA)
- Game Theory and Decision Science (AREA)
- Computer Hardware Design (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a multi-energy planning method based on a large-scale mixed integer decomposition coordination algorithm, which comprises the following steps: constructing a multi-energy planning problem and dividing the multi-energy planning problem into a plurality of sub-problems optimized for each device; solving the linear multi-energy planning relaxation problem of each sub-problem, and integrating the solving results of all the sub-problems to form an approximate solution; forming a feasible solution set S according to the approximate solution; searching a temporary optimal solution meeting the condition of substitution optimality from a feasible solution set S, if not, solving each sub-problem by adopting a branch cutting method B & C and searching, and updating a pair multiplier, an iteration step length and a penalty coefficient; judging whether the temporary optimal solution meets the preset optimal solution standard, if so, searching for the optimal solution according to the temporary optimal solution, and outputting the current optimal solution as a planning scheme of multi-energy planning when the current situation meets the iteration stop condition. The invention has the advantages of quick operation and high quality.
Description
Technical Field
The invention relates to the power technology, in particular to a multi-energy planning method based on a large-scale mixed integer decomposition coordination algorithm.
Background
The traditional planning mode of a single energy system can not further improve the energy utilization efficiency, and has a barrier in the aspect of energy complementation. The multi-energy system can accommodate various energy systems such as electricity, heat, natural gas and the like including clean energy, improves the utilization efficiency of various energy, promotes the coordination and optimization among the energy systems, and realizes the complementary and mutual utilization of various energy. The planning problem of the multi-energy system particularly relates to physical site selection and equipment capacity selection of equipment such as a pure condensing generator set, a cogeneration unit, a heat pump, a boiler, energy storage and the like, and the equipment comprises a continuous decision variable and a discrete integer variable. How to obtain a fast and high-quality energy planning scheme is a problem to be solved in the prior art.
Disclosure of Invention
The invention aims to: aiming at the problems existing in the prior art, the invention provides a rapid and high-quality multi-energy planning method based on a large-scale mixed integer decomposition coordination algorithm.
The technical scheme is as follows: the multi-energy planning method based on the large-scale mixed integer decomposition coordination algorithm comprises the following steps:
(1) The following multi-energy planning problem is constructed:
wherein x is the operation state vector of each device, and n is x X 1-dimensional continuous variable, y is a state indicating vector of whether each device is selected as an energy device, n y X 1-dimensional integer variable d x And d y Is a coefficient vector, d x Is composed of the running cost coefficients of all the devices, d y Is composed of initial investment cost coefficients of all equipment,represents n x Real number field of dimensions, < >>Represents n y An integer domain of dimensions, Ω representing a multi-energy system operational feasibility domain;
(2) Dividing the multi-energy planning problem into a plurality of sub-problems optimized for each device, wherein each sub-problem is provided with a punishment item with punishment coefficients and an energy conservation constraint vector with a pair multiplier;
(3) Solving a linear multi-energy programming relaxation problem of each sub-problem according to the punishment coefficient and the pair multiplier in the last iteration, and integrating the solving results of all the sub-problems to form an approximate solution (x ', y'); when the sub-problem is solved for the first time in an iterative mode, calculating according to the punishment coefficient and a preset initial value of the pair multiplier;
(4) Rounding up or down each non-integer element in y ', and correspondingly adjusting x' according to constraint, so as to obtain a plurality of feasible solutions, and forming a feasible solution set S;
(5) Searching a temporary optimal solution meeting the condition of substitution optimality from the feasible solution set S, and executing the step (6) if the temporary optimal solution is not found; otherwise, executing the step (8);
(6) Solving each sub-problem by adopting a branch cutting method B & C, if no temporary optimal solution is found from the solving result, executing the step (7), otherwise, executing the step (8);
(7) Decreasing the punishment coefficient according to a preset method, adding 1 to the iteration number, and returning to the execution step (3);
(8) Updating a pair multiplier, an iteration step length and a penalty coefficient;
(9) Judging whether the temporary optimal solution meets the preset optimal solution standard, if not, adding 1 to the iteration times, and returning to the execution step (3); if yes, searching an optimal solution according to the temporary optimal solution, and executing the step (10);
(10) Checking whether the iteration stop condition is met, if not, adding 1 to the iteration times, and returning to the execution step (3); and if yes, stopping iteration, and outputting the current optimal solution as a planning scheme of the multi-energy planning.
Further, the sub-problem in the step (2) is specifically:
wherein L is c (x j ,x -j ,y j ,y -j ,λ)＝L c (x,y,λ)＝(d x ) T x+(d y ) T y+(λ) T g(x,y)+c||g(x,y)|| 1 ，g(x,y)＝A x,0 x+A y,0 y-b 0 ，x＝(x j ,x -j )，y＝(y j ,y -j )，x -j And y -j Expressed from x and yRemoval of x j And y j The resulting vector, J, represents the number of device types,representing the power of the j-th device, +.>Status indication, y, indicating whether the j-th device is selected as an energy device j =0 indicates that the j-th device is not selected, y j =1 indicates that the j-th device is selected, +.> And b j Are coefficient matrices related only to the safe operation of the device j, omega j Represents the j-th equipment operation feasible domain, lambda is a dual multiplier, c is a penalty coefficient, A x,0 、A y,0 、b 0 Are coefficient matrices that are globally constrained.
Further, in the step (5), searching the temporary optimal solution from the feasible solution set S to satisfy the condition of replacing optimality specifically includes:
for each feasible solution in the feasible solution set S, calculating an objective function value of the multi-energy planning problem, and selecting a feasible solution corresponding to the minimum value of the objective function value;
judging whether the selected feasible solution meets the following alternative optimality conditions:
in the method, in the process of the invention,λ k respectively represents x at the kth iteration j ,y j Values of lambda>Respectively represents x at the k-1 th iteration j ,y j ,x -j ,y -j Is a value of (2);
if yes, the selected feasible solution is used as a temporary optimal solution.
Further, in the step (7), the reducing the penalty coefficient according to the preset method specifically includes:
the penalty factor is reduced as follows:
c k+1 ＝c k /β,β＞1
wherein, c k+1 、c k And respectively representing penalty coefficients after and before updating in the kth iteration, wherein beta is an updating coefficient.
Further, the specific formula for updating the pair multiplier in the step (8) is as follows:
λ k+1 ＝λ k +s k ·g(x k ,y k )
g(x k ,y k )＝A x,0 x k +A y,0 y k -b 0
wherein lambda is k+1 、λ k Respectively represent the post-update and pre-update pairs, s, of the kth iteration k Represents the iteration step length, x in the kth iteration k ,y k Representing the values of x, y at the kth iteration,represents x at the kth iteration j 、y j Is used as a reference to the value of (a),represents the k-1 th iterationTime x 1 ,x J ,y 1 ,y J Is a value of (2).
The specific formula for updating the iteration step length is as follows:
wherein s is k 、s k-1 Represents the iteration step length and x in the k-1 iteration k-1 ,y k-1 ,x k ,y k Representing the values of x and y, alpha, at the k-1 and k iterations, respectively k M, ρ, r are all intermediate variables;
the specific formula for updating the penalty coefficient is:
c k+1 ＝β·c k ,β＞1
wherein, c k+1 、c k And respectively representing penalty coefficients after and before updating in the kth iteration, wherein beta is an updating coefficient.
Further, the initial value of the iteration step is:
wherein s is 0 Representing the initial value of the iteration step, x 0 ,y 0 ,λ 0 And respectively representing the values of x, y and lambda at the kth iteration, and q represents the optimal estimated value of the multi-energy planning problem.
Further, the preset optimal solution standard specifically includes:
||g(x k ,y k )|| 2 ≤δ
x k ,y k the values of x and y at the kth iteration are respectively represented, and delta represents a preset threshold value.
Further, the searching for the optimal solution according to the provisional optimal solution in the step (9) specifically includes:
searching an optimal solution around the temporary optimal solution by using a heuristic algorithm; or (b)
And fixing y variables of which the temporary optimal solution is more than half, only selecting the rest y to bring into the multi-energy planning problem, and solving the optimal solution meeting the global constraint by using a B & C algorithm.
Further, the iteration stop condition specifically includes:
||g(x k ,y k )|| 2 epsilon or s is less than or equal to k ≤γ
Wherein x is k ,y k Respectively representing the values of x and y at the kth iteration, s k The value of the iteration step length at the kth iteration is represented, epsilon and gamma represent preset thresholds.
Compared with the prior art, the invention has the beneficial effects that: the invention can quickly obtain a high-quality planning scheme, and has high operation speed.
Drawings
Fig. 1 is a schematic flow chart of a multi-energy planning method based on a large-scale mixed integer decomposition coordination algorithm.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment provides a multi-energy planning method based on a large-scale mixed integer decomposition coordination algorithm, as shown in fig. 1, comprising the following steps:
(1) And constructing a multi-energy planning problem.
The multi-energy planning problem is as follows
Wherein x is eachA spare running state vector of n x X 1-dimensional continuous variable, y is a state indicating vector of whether each device is selected as an energy device, n y X 1-dimensional integer variable d x And d y Is a coefficient vector, d x Is composed of the running cost coefficients of all the devices, d y Is composed of initial investment cost coefficients of all equipment,represents n x Real number field of dimensions, < >>Represents n y An integer field of dimensions, Ω represents a multi-energy system operational feasible field.
(2) The multi-energy planning problem is divided into a plurality of sub-problems that are optimized for each device.
Wherein, each sub-problem is provided with a punishment term with punishment coefficient and an energy conservation constraint vector with a pair multiplier. The method comprises the following steps:
wherein L is c (x j ,x -j ,y j ,y -j ,λ)＝L c (x,y,λ)＝(d x ) T x+(d y ) T y+(λ) T g(x,y)+c||g(x,y)|| 1 ，g(x,y)＝A x,0 x+A y,0 y-b 0 ，x＝(x j ,x -j )，y＝(y j ,y -j )，x -j And y -j Representing the removal of x from x and y j And y j The resulting vector, J, represents the number of device types,representing the power of the j-th device, +.>Indicating whether the j-th device is selected as the energy sourceStatus indication of device, y j =0 indicates that the j-th device is not selected, y j =1 indicates that the j-th device is selected, +.> And b j Are coefficient matrices related only to the safe operation of the device j, omega j Represents the j-th equipment operation feasible domain, lambda is a dual multiplier, c is a penalty coefficient, A x,0 、A y,0 、b 0 Are coefficient matrices that are globally constrained. Global constraint of A x,0 x+A y,0 y＝b 0 Refers to energy conservation constraints such as power supply and demand balance, thermal supply and demand balance, natural gas supply and demand balance, and the like.
(3) And solving the linear multi-energy programming relaxation problem of each sub-problem according to the punishment coefficient and the pair multiplier in the last iteration, and integrating the solving results of all the sub-problems to form an approximate solution (x ', y').
When the sub-problem is solved for the first time in an iterative mode, the sub-problem is calculated according to the punishment coefficient and a preset initial value of the pair multiplier.
In each iteration, it is not necessary to solve a sub-problem completely, and only a "good enough" transient solution satisfying the condition of substitution optimality needs to be found. Finding a "good enough" transient solution Jie Bi to find the optimal solution is much easier. Thus, rather than requiring a precise optimization method (e.g., branch cut B & C), the present invention builds a multi-energy planning coarse model to calculate a "good enough" provisional solution faster.
Inspiring the concept of order optimization, the invention provides an efficient method for obtaining a 'good enough' transient solution based on the concept of an 'approximate solution'. The linear multi-energy programming relaxation problem is generated by relaxing the integer requirements in the multi-energy programming sub-problem, which is then used as a "coarse model" to approximate the sub-problem, so that an "approximate solution" (x ', y') can be obtained by linear programming.
(4) And rounding up or down each non-integer element in y ', and correspondingly adjusting x' according to the constraint, so as to obtain a plurality of feasible solutions and form a feasible solution set S.
Specifically, if all elements in y ' are integers, (x ', y ') is the optimal solution to the atomic problem. If not, the non-integer element is rounded up or down to produce a feasible set S that satisfies the sub-problem. For convenience of description, it is assumed that all elements in y' are non-integer elements:
by rounding up or down, it is possible to obtain
And demodulating the whole continuous variable x' according to the rounding so as to meet constraint conditions, thereby forming a feasible set S. Since the feasible solution in S is close to the optimal solution, S has a higher probability of containing a sufficiently good sub-problem solution.
(5) Searching a temporary optimal solution meeting the condition of substitution optimality from the feasible solution set S, and executing the step (6) if the temporary optimal solution is not found; otherwise, executing the step (8).
Specifically, firstly, calculating an objective function value of a multi-energy planning problem, selecting a feasible solution corresponding to the minimum value of the objective function value, and judging whether the selected feasible solution meets the following alternative optimality conditions:
in the method, in the process of the invention,λ k respectively represents x at the kth iteration j ,y j Values of lambda>Respectively represents x at the k-1 th iteration j ,y j ,x -j ,y -j Is a value of (2). If yes, the selected feasible solution is used as a temporary optimal solution.
Because the linear pluripotent programming relaxation problem is usually easy to solve, much less computational effort is required than B & C. The proposed method will be very effective if the optimal solution obtained by B & C is obtained after a large number of iterations. In addition, since each iteration satisfies the convergence condition, the solution of the sub-problem is still within the range of effective coordination, thereby forming a high-quality global solution.
(6) And (3) solving each sub-problem by adopting a branch cutting method B & C, if no temporary optimal solution is found from the solving result, executing a step (7), otherwise, executing a step (8).
(7) And (3) reducing the penalty coefficient according to a preset method, adding 1 to the iteration number k, and returning to the execution step (3).
Specifically, the penalty factor is reduced according to the following formula:
c k+1 ＝c k /β,β＞1
wherein, c k+1 、c k And respectively representing penalty coefficients after and before updating in the kth iteration, wherein beta is an updating coefficient.
(8) The pair multiplier, iteration step size and penalty coefficient are updated.
The specific formula for updating the pair multiplier is as follows:
λ k+1 ＝λ k +s k ·g(x k ,y k )
g(x k ,y k )＝A x,0 x k +A y,0 y k -b 0
wherein lambda is k+1 、λ k Respectively represent the post-update and pre-update pairs, s, of the kth iteration k Represents the iteration step length, x in the kth iteration k ,y k Representing the values of x, y at the kth iteration,represents x at the kth iteration j 、y j Is used as a reference to the value of (a),represents x at the k-1 th iteration 1 ,x J ,y 1 ,y J Is a value of (2).
The specific formula for updating the iteration step length is as follows:
wherein s is k 、s k-1 Represents the k-1 th iterationTime iteration step length, x k-1 ,y k-1 ,x k ,y k Representing the values of x and y, alpha, at the k-1 and k iterations, respectively k M, ρ, r are all intermediate variables;
the specific formula for updating the penalty coefficient is:
c k+1 ＝β·c k ,β＞1
wherein, c k+1 、c k And respectively representing penalty coefficients after and before updating in the kth iteration, wherein beta is an updating coefficient.
A good multiplier initial value may reduce the number of iterations. Heuristic algorithms are typically used to initialize the multipliers. For optimization problems that need to be solved multiple times per day, the multiplier may also be initialized with the values obtained from the previous optimization run. The step initialization calculation formula is as follows:
wherein s is 0 Representing the initial value of the iteration step, x 0 ,y 0 ,λ 0 And respectively representing the values of x, y and lambda at the kth iteration, and q represents the optimal estimated value of the multi-energy planning problem. This essentially relaxes the global constraint since it is put into the Lagrangian function.
(9) Judging whether the temporary optimal solution meets the preset optimal solution standard, if not, adding 1 to the iteration number k, and returning to the execution step (3); if yes, searching an optimal solution according to the temporary optimal solution, and executing the step (10).
The preset optimal solution standard specifically comprises the following steps:
||g(x k ,y k )|| 2 ≤δ
x k ,y k the values of x and y at the kth iteration are respectively represented, and delta represents a preset threshold value.
The searching of the optimal solution according to the temporary optimal solution specifically comprises the following steps: searching an optimal solution around the temporary optimal solution by using a heuristic algorithm; or fixing y variables of which the temporary optimal solution is more than half, only selecting the rest y to bring into the multi-energy planning problem, and solving the optimal solution meeting the global constraint by using a B & C algorithm.
(10) Checking whether the iteration stop condition is met, if not, adding 1 to the iteration times, and returning to the execution step (3); and if yes, stopping iteration, and outputting the current optimal solution as a planning scheme of the multi-energy planning.
The iteration stop condition is specifically as follows:
||g(x k ,y k )|| 2 epsilon or s is less than or equal to k ≤γ
Wherein x is k ,y k Respectively representing the values of x and y at the kth iteration, s k The value of the iteration step length at the kth iteration is represented, epsilon and gamma represent preset thresholds. I.e. when the degree of violation of the global constraint is small or the step size is small, the iteration can be stopped.
The present invention may be implemented in computer program code, which may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages, to perform the operations of the present invention.
Claims (10)
1. A multi-energy planning method based on a large-scale mixed integer decomposition coordination algorithm is characterized by comprising the following steps:
(1) The following multi-energy planning problem is constructed:
wherein x is the operation state vector of each device, and n is x X 1-dimensional continuous variable, y is a state indicating vector of whether each device is selected as an energy device, n y X 1-dimensional integer variables are used, x and d y Is a coefficient vector, d x Is composed of the running cost coefficients of all the devices, d y Is composed of initial investment cost coefficients of all equipment,represents n x Real number field of dimensions, < >>Represents n y An integer domain of dimensions, Ω representing a multi-energy system operational feasibility domain;
(2) Dividing the multi-energy planning problem into a plurality of sub-problems optimized for each device, wherein each sub-problem is provided with a punishment item with punishment coefficients and an energy conservation constraint vector with a pair multiplier;
(3) Solving a linear multi-energy programming relaxation problem of each sub-problem according to a penalty coefficient and a pair multiplier in the last iteration, and integrating solving results of all the sub-problems to form an approximate solution (x ','); when the sub-problem is solved for the first time in an iterative mode, calculating according to the punishment coefficient and a preset initial value of the pair multiplier;
(4) Rounding up or down each non-integer element in y ', and correspondingly adjusting x' according to constraint, so as to obtain a plurality of feasible solutions, and forming a feasible solution set S;
(5) Searching a temporary optimal solution meeting the condition of substitution optimality from the feasible solution set S, and executing the step (6) if the temporary optimal solution is not found; otherwise, executing the step (8);
(6) Solving each sub-problem by adopting a branch cutting method B & C, if no temporary optimal solution is found from the solving result, executing the step (7), otherwise, executing the step (8);
(7) Decreasing the punishment coefficient according to a preset method, adding 1 to the iteration number, and returning to the execution step (3);
(8) Updating a pair multiplier, an iteration step length and a penalty coefficient;
(9) Judging whether the temporary optimal solution meets the preset optimal solution standard, if not, adding 1 to the iteration times, and returning to the execution step (3); if yes, searching an optimal solution according to the temporary optimal solution, and executing the step (10);
(10) Checking whether the iteration stop condition is met, if not, adding 1 to the iteration times, and returning to the execution step (3); and if yes, stopping iteration, and outputting the current optimal solution as a planning scheme of the multi-energy planning.
2. The multi-energy planning method based on the large-scale mixed integer decomposition coordination algorithm according to claim 1, wherein the method comprises the following steps: the sub-problems in step (2) are specifically:
wherein L is c (x j ,x -j ,y j ,y -j ,λ)＝L c (x,y,λ)＝(d x ) T x+(d y ) T y+(λ) T g(x,y)+c||g(x,y)|| 1 ，g(x,y)＝A x,0 x+A y,0 y-b 0 ，x＝(x j ,x -j )，y＝(y j ,y -j )，x -j And y -j Representing the removal of x from x and y j And y j The resulting vector, J, represents the number of device types,representing the power of the j-th device, +.>Status indication, y, indicating whether the j-th device is selected as an energy device j =0 indicates that the j-th device is not selected, y j =1 indicates that the j-th device is selected, +.> And b j Are coefficient matrices related only to the safe operation of the device j, omega j Represents the j-th equipment operation feasible domain, lambda is a dual multiplier, c is a penalty coefficient, A x,0 、A y,0 、b 0 Are coefficient matrices that are globally constrained.
3. The multi-energy planning method based on the large-scale mixed integer decomposition coordination algorithm according to claim 2, wherein the method comprises the following steps: the searching for the temporary optimal solution satisfying the condition of substitution optimality from the feasible solution set S in the step (5) specifically includes:
for each feasible solution in the feasible solution set S, calculating an objective function value of the multi-energy planning problem, and selecting a feasible solution corresponding to the minimum value of the objective function value;
judging whether the selected feasible solution meets the following alternative optimality conditions:
in the method, in the process of the invention,λ k respectively represents x at the kth iteration j ，y j Values of lambda>Respectively represents x at the k-1 th iteration j ,y j ,x -j ,y -j Is a value of (2);
if yes, the selected feasible solution is used as a temporary optimal solution.
4. The multi-energy planning method based on the large-scale mixed integer decomposition coordination algorithm according to claim 1, wherein the method comprises the following steps: the step (7) of reducing the penalty coefficient according to the preset method specifically includes:
the penalty factor is reduced as follows:
c k+1 ＝c k /β,β＞1
wherein, c k+1 、c k And respectively representing penalty coefficients after and before updating in the kth iteration, wherein beta is an updating coefficient.
5. The multi-energy planning method based on the large-scale mixed integer decomposition coordination algorithm according to claim 2, wherein the method comprises the following steps: the specific formula for updating the pair multiplier in the step (8) is as follows:
λ k+1 ＝λ k +s k ·g(x k ,y k )
g(x k ,y k )＝A x,0 x k +A y,0 y k -b 0
wherein lambda is k+1 、λ k Respectively represent the post-update and pre-update pairs, s, of the kth iteration k Represents the iteration step length, x in the kth iteration k ,y k Representing the values of x, y at the kth iteration,represents x at the kth iteration j 、y j Is used as a reference to the value of (a),represents x at the k-1 th iteration 1 ,x J ,y 1 ,y J Is a value of (2).
The specific formula for updating the iteration step length is as follows:
wherein s is k 、s k-1 Represents the iteration step length and x in the k-1 iteration k-1 ,y k-1 ,x k ,y k Representing the values of x and y, alpha, at the k-1 and k iterations, respectively k M, ρ, r are all intermediate variables.
6. The multi-energy planning method based on the large-scale mixed integer decomposition coordination algorithm according to claim 2, wherein the method comprises the following steps: the specific formula for updating the penalty coefficient is:
c k+1 ＝β·c k ,β＞1
wherein, c k+1 、c k And respectively representing penalty coefficients after and before updating in the kth iteration, wherein beta is an updating coefficient.
7. The multi-energy planning method based on the large-scale mixed integer decomposition coordination algorithm according to claim 2, wherein the method comprises the following steps: the initial value of the iteration step is as follows:
wherein s is 0 Representing the initial value of the iteration step, x 0 ,y 0 ,λ 0 And respectively representing the values of x, y and lambda at the kth iteration, and q represents the optimal estimated value of the multi-energy planning problem.
8. The multi-energy planning method based on the large-scale mixed integer decomposition coordination algorithm according to claim 2, wherein the method comprises the following steps: the preset optimal solution standard specifically comprises the following steps:
||g(x k ，y k )|| 2 ≤δ
x k ,y k the values of x and y at the kth iteration are respectively represented, and delta represents a preset threshold value.
9. The multi-energy planning method based on the large-scale mixed integer decomposition coordination algorithm according to claim 2, wherein the method comprises the following steps: the searching for the optimal solution according to the temporary optimal solution in the step (9) specifically comprises the following steps:
searching an optimal solution around the temporary optimal solution by using a heuristic algorithm; or (b)
And fixing y variables of which the temporary optimal solution is more than half, only selecting the rest y to bring into the multi-energy planning problem, and solving the optimal solution meeting the global constraint by using a B & C algorithm.
10. The multi-energy planning method based on the large-scale mixed integer decomposition coordination algorithm according to claim 2, wherein the method comprises the following steps: the iteration stop condition is specifically as follows:
||g(x k ,y k )|| 2 epsilon or s is less than or equal to k ≤γ
Wherein x is k ,y k Respectively representing the values of x and y at the kth iteration, s k The value of the iteration step length at the kth iteration is represented, epsilon and gamma represent preset thresholds.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311291927.0A CN117272664A (en) | 2023-10-08 | 2023-10-08 | Multi-energy planning method based on large-scale mixed integer decomposition coordination algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311291927.0A CN117272664A (en) | 2023-10-08 | 2023-10-08 | Multi-energy planning method based on large-scale mixed integer decomposition coordination algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117272664A true CN117272664A (en) | 2023-12-22 |
Family
ID=89214059
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311291927.0A Pending CN117272664A (en) | 2023-10-08 | 2023-10-08 | Multi-energy planning method based on large-scale mixed integer decomposition coordination algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117272664A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117818052A (en) * | 2024-01-03 | 2024-04-05 | 南京玄创智能装备有限公司 | Iterative search-based planning method for mixed machining sequence of increasing and decreasing materials |
-
2023
- 2023-10-08 CN CN202311291927.0A patent/CN117272664A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117818052A (en) * | 2024-01-03 | 2024-04-05 | 南京玄创智能装备有限公司 | Iterative search-based planning method for mixed machining sequence of increasing and decreasing materials |
| CN117818052B (en) * | 2024-01-03 | 2024-06-11 | 南京玄创智能装备有限公司 | Iterative search-based planning method for mixed machining sequence of increasing and decreasing materials |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Optimal generating unit maintenance scheduling for multi-area system with network constraints | |
| Zhang et al. | Optimal operation of large-scale cascaded hydropower systems in the upper reaches of the Yangtze River, China | |
| CN107330568A (en) | Energy storage, distributed power source and the power distribution network coordinated planning method decoupled based on Benders | |
| CN117272664A (en) | Multi-energy planning method based on large-scale mixed integer decomposition coordination algorithm | |
| CN106505633B (en) | Method and device for determining wind and light access capacity | |
| Darivianakis et al. | A data-driven stochastic optimization approach to the seasonal storage energy management | |
| WO2019196427A1 (en) | Method and device for optimizing backup on the basis of supporting fault event constrained unit commitment | |
| CN104868465A (en) | Power system grid structure reconfiguration and optimization method based on fuzzy chance constraint | |
| Fallahi et al. | The value of energy storage in optimal non-firm wind capacity connection to power systems | |
| CN113112141A (en) | Energy optimization scheduling method and device for comprehensive energy system | |
| CN107171365A (en) | Multiple target stochastic and dynamic economic load dispatching method with asynchronous iteration is decoupled based on scene | |
| CN110020506B (en) | Differential format selection method based on operation optimization of electric heating type comprehensive energy system | |
| CN113435659B (en) | Scene analysis-based two-stage optimized operation method and system for comprehensive energy system | |
| CN110429591A (en) | A kind of power transmission network utilization rate appraisal procedure based on electric system timing coupling | |
| CN117154727B (en) | Reliability control method and system for electric power system | |
| CN114091767A (en) | Method and system for robust random planning of data-driven comprehensive energy system | |
| CN111614125B (en) | Method for controlling micro-grid, power management system and energy management system | |
| CN112418732A (en) | Multi-energy hub-containing comprehensive energy system planning method and system based on maximum flow and minimum cut theorem | |
| CN110061510B (en) | Rapid solving method and system for time period decoupling safety constraint economic dispatch | |
| CN117035335A (en) | Multi-stage energy storage and power transmission network collaborative planning method and system | |
| CN115514016A (en) | Method, device and equipment for determining combination of power system unit | |
| CN115712976A (en) | Heat energy and electric load optimization method based on multi-element heat supply unit and related equipment | |
| Tian et al. | Iterative linearization approach for optimal scheduling of multi-regional integrated energy system | |
| Hayes et al. | Viable computation of the largest Lyapunov characteristic exponent for power systems | |
| dos Santos et al. | Accelerating dual dynamic programming for stochastic hydrothermal coordination problems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |