CN112989536A - Scene decomposition-based optimal scheduling method for electric multi-energy flow system - Google Patents
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Abstract
The invention discloses an optimal scheduling method of an electric multi-energy flow system based on scene decomposition, which comprises the following steps: constructing an operation cost optimization target and constraint conditions of the electric multi-energy flow system, wherein the constraint conditions comprise electric power system constraint and natural gas system constraint; establishing a day-ahead scheduling model of the electrical multi-energy flow system according to the optimization target and the constraint condition; simulating the output of a fan to obtain a plurality of operation scenes of the electric multi-energy flow system, decomposing a day-ahead scheduling model into a plurality of corresponding single scene models according to the operation scenes, and solving each single scene model until the unit combinations in all the operation scenes are the same; and determining a target scheduling scheme of the electrical multi-energy flow system according to the unit combination. According to the optimized scheduling method provided by the invention, the operation cost of the target obtained by solving is higher in accuracy, and meanwhile, parallel operation is supported, so that the solving time is obviously reduced, and the solving efficiency is greatly improved.
Description
Technical Field
The invention relates to the technical field of comprehensive energy systems, in particular to an optimal scheduling method of an electric multi-energy flow system based on scene decomposition.
Background
In various comprehensive energy systems, due to the characteristics of cleanness, high efficiency and the like of natural gas, the electric multi-energy flow system is widely concerned at home and abroad. The electric multi-energy flow system is also outstanding in new energy consumption. The gradually-rising electric gas conversion technology promotes the bidirectional flow of two kinds of energy, and converts redundant wind and light output into natural gas; the energy storage and the pipeline in the natural gas system can respectively realize the long-term and short-term storage of the converted energy, and finally realize the energy consumption.
However, uncertainty is introduced in the grid connection process of renewable energy, and challenges are brought to model establishment and optimal scheduling of the multi-energy flow system. In the prior art, robust planning and stochastic planning are mainly adopted as two means for processing uncertainty problems. The two-stage robust optimization compresses the data set to be processed by considering the optimal solution under the worst condition, can accelerate the solution of the scheduling problem, but the result is questioned due to excessive conservation. The stochastic programming jointly solves the possible uncertain scenes, the reality of the result can be basically restored, and the complexity of the model and the solving difficulty are exponentially increased due to multiple scenes.
Disclosure of Invention
The invention aims to provide an optimal scheduling method of an electric multi-energy flow system based on scene decomposition, so as to solve the technical problems of low accuracy of target cost and low solving efficiency in the related technology.
The purpose of the invention can be realized by the following technical scheme:
the optimal scheduling method of the electric multi-energy flow system based on scene decomposition comprises the following steps:
constructing an operation cost optimization target and constraint conditions of the electric multi-energy flow system, wherein the constraint conditions comprise electric power system constraint and natural gas system constraint;
establishing a day-ahead scheduling model of the electrical multi-energy flow system according to the optimization target and the constraint condition;
simulating the output of a fan to obtain a plurality of operation scenes of the electric multi-energy flow system, decomposing a day-ahead scheduling model into a plurality of corresponding single scene models according to the operation scenes, and solving each single scene model until the unit combinations in all the operation scenes are the same;
and determining a target scheduling scheme of the electrical multi-energy flow system according to the unit combination.
Optionally, solving each single scene model until the unit combinations in all the operating scenes are the same includes:
and solving each single scene model according to a stochastic programming acceleration algorithm until the unit combinations in all the operating scenes are the same.
Optionally, solving each single-scene model according to a stochastic programming acceleration algorithm until the unit combinations in all the operating scenes are the same includes:
and solving the unit combination of each single scene model, if the unit combinations among the single scene models are different, adding a punishment factor to each single scene model, and repeating the solving process until the unit combinations in all the operating scenes are the same.
Optionally, the operation cost optimization goal of the electrical multi-energy flow system is:
in which the three items indicated by parentheses represent the same itemThree sources of operating costs are found: the unit output cost, the gas well and energy storage output cost, the load shedding cost and the wind abandoning cost; t is a time period, T is a set of all time periods, i is a unit serial number, G is a set of all gas units, and C is a set of all coal-fired units; l is the serial number of the nodes of the power system, and B is the set of all the nodes of the power system; n is the natural gas node serial number; n is the set of all natural gas nodes; w is the serial number of the fan, and W is the set of all fans; s is an energy storage serial number, and S is a set of all energy storages; SC is an operation scene, and SC is a set of all operation scenes; pscRepresenting the occurrence probability of the scene sc;
respectively the output of the unit i, the gas well g and the energy storage s at the moment t and the scene sc,
respectively corresponding unit output cost;
respectively at the moment t and the scene sc, the power load shedding of the power system node l, the load shedding of the natural gas node n and the air discharge amount of the fan w,
respectively the corresponding unit cost.
Optionally, the power system constraints comprise: node balance constraint, unit output constraint, unit climbing constraint and direct current flow constraint.
Optionally, the natural gas system constraints comprise: node balance constraint, pipeline quality constraint, equipment operation constraint, unit energy conversion constraint and pipeline flow constraint.
Optionally, the conduit flow constraint is:
wherein the content of the first and second substances,(c, d) represents a natural gas pipeline, wherein c and d are the first node and the last node of the pipeline respectively; PL is the set of all pipes;
and
respectively the gas pressures of the natural gas node c and the natural gas node d at the moment t and the scene sc; dcd，LcdRespectively representing the diameter and length of the ducts (c, d); r, T, Z, F and rho are respectively a universal gas constant, temperature, a compression coefficient, a friction coefficient and natural gas density under 1 atmospheric pressure;
is the average flow rate of the pipe (c, d) at time t and scene sc.
Optionally, solving the pipeline flow constraint according to an improved second-order cone constraint algorithm specifically includes: determining the pipeline flow direction in the future 24 hours according to the pipeline topological situation, splitting the pipeline flow constraint into a second-order cone constraint and a convex constraint which can be solved by a solver, and adding a penalty function to solve the convex constraint.
Optionally, determining a target scheduling scheme of the electrical multi-energy flow system according to the unit combination includes:
and substituting the unit combination into each single scene model to obtain values of other variables in the single scene model, and finally obtaining a target scheduling scheme of the electrical multi-energy flow system.
Optionally, simulating the fan output to obtain a plurality of operation scenarios of the electrical multi-energy flow system includes: 3000 possible wind speed curves in the future 24 hours are generated according to an ARMA method to obtain 3000 random scenes, and scene reduction is performed according to a K-average method to obtain 15 typical operation scenes.
The invention provides an optimal scheduling method of an electric multi-energy flow system based on scene decomposition, which comprises the following steps: constructing an operation cost optimization target and constraint conditions of the electric multi-energy flow system, wherein the constraint conditions comprise electric power system constraint and natural gas system constraint; establishing a day-ahead scheduling model of the electrical multi-energy flow system according to the optimization target and the constraint condition; simulating the output of a fan to obtain a plurality of operation scenes of the electric multi-energy flow system, decomposing a day-ahead scheduling model into a plurality of corresponding single scene models according to the operation scenes, and solving each single scene model until the unit combinations in all the operation scenes are the same; and determining a target scheduling scheme of the electrical multi-energy flow system according to the unit combination.
Based on the technical scheme, the invention has the beneficial effects that: the method comprises the steps that random planning, pipeline dynamic characteristics and unit combination problems are simultaneously considered in an electrical multi-energy flow system, wherein the random planning truly reflects the supply and demand relationship of the next day through a plurality of operation scenes, the pipeline dynamic characteristics describe the compressibility of natural gas, and the unit combination provides important reference for starting and stopping of the unit of the next day; simulating random wind energy output, generating a plurality of typical operation scenes, uniformly decomposing a day-ahead scheduling model of the electric multi-energy flow system according to the operation scenes, meeting convergence conditions in fewer cycles, and stopping iteration; meanwhile, parallel computation among all operation scenes is supported, and the method has higher solving efficiency. According to the optimized scheduling method provided by the invention, the operation cost of the target obtained by solving is higher in accuracy, and meanwhile, parallel calculation is executed, so that the solving time is obviously reduced, and the solving efficiency is greatly improved.
Drawings
FIG. 1 is a flowchart of a method for optimizing a scheduling method of an electrical multi-energy flow system based on scene decomposition according to the present invention;
FIG. 2 is a flowchart of a scheduling model according to a first prior art of the present invention;
FIG. 3 is a flowchart of a scheduling model of the second prior art of the present invention;
FIG. 4 is a typical scene of a wind speed error of the optimal scheduling method of the electrical multi-energy flow system based on scene decomposition;
FIG. 5 is a typical scene of wind energy output of the optimal scheduling method of the electrical multi-energy flow system based on scene decomposition.
Detailed Description
Interpretation of terms:
the unit combination: and according to the expected operation cost, the unit start-stop condition on the day predetermined one day before the operation day.
The embodiment of the invention provides an optimal scheduling method of an electric multi-energy flow system based on scene decomposition, which aims to solve the technical problems of low accuracy of target cost and low solving efficiency in the related technology.
Unless defined otherwise, 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 invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
A traditional power system and a natural gas system in China adopt an independent management mode, and energy interconnection and information interaction between the two systems are relatively lacked. In recent years, due to the needs of integrating regional energy, improving economic benefits and realizing collaborative optimization, a concept of a comprehensive energy system is provided, and the purpose is to promote unified planning and scheduling of energy systems such as electricity, gas, heat and cold.
In various comprehensive energy systems, due to the characteristics of cleanness, high efficiency and the like of natural gas, the electric multi-energy flow system is widely concerned at home and abroad. The natural gas does not discharge sulfur oxide and nitrogen oxide in the combustion process, and meanwhile, compared with coal, the natural gas has higher heat value and is more fully combusted. By 2030, the worldwide gas unit output will increase by 230%, at which time natural gas will also account for 28% of the total energy consumption.
Meanwhile, the electric multi-energy flow system is more prominent in new energy consumption. The gradually-rising electric gas conversion technology promotes the bidirectional flow of two kinds of energy, and converts redundant wind and light output into natural gas; the energy storage and the pipeline in the natural gas system can respectively realize the long-term and short-term storage of the converted energy, and finally realize the energy consumption. However, uncertainty is introduced in the grid connection process of renewable energy, and challenges are brought to model establishment and optimal scheduling of the multi-energy flow system. In view of this, scholars at home and abroad mainly adopt robust planning and stochastic planning as two means for handling uncertainty problems. The two-stage robust optimization compresses the data set to be processed by considering the optimal solution under the worst condition, can accelerate the solution of the scheduling problem, but the result is questioned due to excessive conservation. The stochastic programming jointly solves the possible uncertain scenes, the reality of the result can be basically restored, and the complexity of the model and the solving difficulty are exponentially increased due to multiple scenes. The mature algorithms of the Benders decomposition method, the column and constraint generation method, and the like are all beginning to be applied to the acceleration of stochastic programming.
In the same scene, the pipeline flow in the natural gas system is usually described by nonlinear non-convex formulas such as a Panhandle equation, a Weymouth equation and the like, and an additional auxiliary calculation means is needed. The traditional piecewise linear method can approach a nonlinear equation infinitely in theory, but a large amount of artificial integer variables are introduced due to the requirement on the calculation precision, so that the calculation time of a single scene is several hours. In recent years, the gradually improved second-order cone programming can realize effective convex relaxation on the basis of not increasing artificial integer variables. The currently most advanced second-order cone reconstruction techniques, such as linearization based on taylor expansion, continuous cone planning, penalty function methods, etc., have been able to reduce the relaxation error to 0.01% by simple iterative algorithms.
The technical scheme of the prior art I is as follows:
in 9.2019, CSEE Journal of Power and Energy Systems published a paper written by the Zhang Yongjun subject group at south China university, which proposed a Stochastic Dynamic Economic Dispatch model of Wind Energy penetration for electrical multi-Energy flow Systems (title: Stochastic Dynamic Economic Dispatch of Wind-integrated electric and Natural Gas Systems considerating Security Risk Congestions).
The electric multi-energy flow system model aims to reduce target cost of day-ahead scheduling, including fuel cost of coal and natural gas in a scheduling period, as much as possible. The model is subject to the following constraints:
(1) and (4) electrical multi-energy flow system equipment constraints including energy conversion equations of the gas turbine set and the electric gas conversion device.
(2) Constraints in the power system. The static constraints comprise node balance constraints, output constraints of all units, output adjustment constraints of the balance units and the like. The dynamic constraints include a hill climbing constraint under each sub-scene of the unit.
(3) Constraints in natural gas systems. The static constraints comprise node balance constraints, pipeline flow equations, pipeline quality constraints, and operation constraints of gas wells, electric gas conversion, compressors, and the like. Wherein, the nonlinear and non-convex pipeline flow equation is approximated by a piecewise linear method containing artificial integer variables. In addition, because the dynamic model is adopted to depict the transmission process of the natural gas, the inflow and the outflow of the pipeline have different values, and the pipeline also has short-term gas storage capacity and is closer to the real situation. Thus, dynamic constraints include time variations in pipe quality in addition to gas well hill climbing constraints.
Aiming at the problem of insufficient modeling of abandoned wind in the current wind power model, the model provides a fan scale factor concept, and the output PW of the fan is considered to be in direct proportion to the maximum possible output PKW at the moment. The proportionality coefficient kappa is a decision variable between 0 and 1 to realize the optimal wind energy scheduling. Meanwhile, considering that the problem that models are too conservative when each scene strictly meets the constraint requirement is considered, the article improves the traditional safety constraint in electric power transmission and node air pressure into safety risk constraint, and an improved risk analysis tool CVaR is adopted for evaluation.
The prior art applies the model to a multi-energy flow system consisting of an IEEE-39 node power system and a Belgian 20 node natural gas system. The error data of the wind power is randomly generated by Latin hypercube sampling, and scene reduction is carried out by a fast back substitution method. In terms of software, the mixed integer model is modeled by GAMS and introduced into Gurobi for solution. The flow of modeling and solving is shown in fig. 2. The effect of scaling factors and introduction of CVaR on future scheduling decisions and expected costs was analyzed at the end of the text.
The first prior art has the following disadvantages:
(1) no acceleration algorithm is used and the program runs very slowly.
In a single scenario, the model approximates a non-linear, non-convex pipe flow equation using piecewise linear methods. The method introduces 3 (N1) artificial auxiliary integer variables (N is the number of divided linear sections) for each pipeline, each moment and each scene, so that the calculation time is exponentially increased.
In a multi-scenario, the model adopts simple stochastic programming and does not adopt any decomposition acceleration algorithm. This further fuses integer variables within all single scenes into the same program, making the computation more complex. And preliminarily estimating, wherein the solving time of the stochastic programming model is more than 3 hours.
(2) The unit combinations are ignored.
The unit combination is used as an important decision variable in the day-ahead planning, and has profound significance for the unit operation of the next day. The introduction of the concepts such as the start-stop cost, the start-stop delay and the like of the related unit enables the output model of the unit to better accord with the actual situation. In this context, however, the authors are guessed not to put the crew composition variables into the stochastic programming model for the purpose of reducing integer variables.
The second technical scheme in the prior art is as follows:
in 2015, in 4 months, an EGTran model proposed by the university of Enlionie, M.Shahidehpour project group is used for rapidly solving the wind energy penetration problem in the electrical multi-energy flow system by a Benders decomposition method and a Newton-Raphson iteration method. This article is published in IEEE Transactions on Stateable Energy. (article title: coding of Interactive Natural Gas and electric Induction of healing the Wind Energy in storage Day-Ahead Scheduling.)
The target cost of the EGTran model is the sum of the unit output cost and the load shedding cost under multiple scenes. In the constraint of the multi-energy flow system, besides the basic constraint sets (1), (2) and (3), the constraint also comprises a rotation standby constraint, a unit combination constraint, a load shedding constraint, an environmental emission constraint and the like.
The wind energy data is obtained by corresponding wind speed data and the output curve. Wherein, the wind speed reference value is randomly generated by Weber distribution; the random scene of the wind speed error is obtained by performing scene generation by autoregressive moving average model (ARMA) and scene reduction by K-average.
The whole electric multi-energy flow system model is disassembled into a main problem (unit combination problem) and a sub problem (power system transmission problem and natural gas system operation problem) through a Benders decomposition method. And once the sub-problem is violated, applying the Benders cut constraint to the main problem and re-solving the main problem. The problem of the pipeline flow in the subproblem is solved iteratively through a Newton-Raphson method. The EGTran solution flow is shown in FIG. 3.
Compared with the prior art I, the combined algorithm of 'Benders decomposition-Newton Raphson iteration' used by the EGTran model enables the solving efficiency of the model to be high: for the random planning problem of a 5-scene 118-node system, the optimal result can be obtained in only 235 seconds.
The second prior art has the following defects:
(1) the pipeline flow is not characterized using a dynamic model.
As described above, the dynamic model describes the short-term gas storage capacity of the pipeline by assuming the difference between the inflow and outflow of the two sides of the pipeline, and can more accurately reflect the flowing and storage conditions of the natural gas between the pipelines. In the scheduling model at this moment, only the static model is used for depicting, and the regulating effect of the management and storage is ignored; under the action of uncertain factors such as wind power and the like, tidal current results in the natural gas system are not accurate and reliable enough.
(2) The related algorithm has complex flow and more iteration times.
Although the joint algorithm used by the EGTran joint algorithm has higher computational efficiency, the EGTran joint algorithm still has larger space capable of being improved in the aspects of the difficulty in using the algorithm, the solving time and the like.
First, the Benders decomposition method is relatively subjective in distinguishing between major problems and sub-problems. Except that the relevant constraints of the unit combination must be included in the main problem, other constraints have no obvious classification standard and need to be judged according to subjective experience. This presents certain difficulties in the classification of new constraints that are subsequently added.
In addition, in terms of time cost, the Benders algorithm needs to heuristically search for an optimal solution through a large number of iterations of 'feasibility inspection, cut set constraint addition and re-solving'; the number of iterations is expected to be more than 15. In each scene, Newton-Raphson iteration is carried out on the pipeline flow equation, which greatly slows down the solving process in a single scene.
(3) It is not friendly to parallel operation.
The Benders decomposition method can obtain an effective decomposition stochastic programming model, but is not simply divided according to scenes. In contrast, the individual solution between the scenes only occupies a small part of the whole algorithm, and therefore, the whole program basically does not support the parallel operation. Therefore, the CPU utilization rate cannot be improved, which is more disadvantageous to the analysis and solution of a larger system and more scene stochastic models.
The technical problem to be solved by the embodiment of the invention is as follows:
(1) the electric multi-energy flow system random optimization scheduling method based on scene decomposition is provided, so that a random planning model has higher solving efficiency.
The improved algorithm aims to uniformly decompose the original model according to scenes, meet the convergence condition in fewer cycles and stop iteration. Meanwhile, parallel computation among scenes is supported in the whole process, the CPU utilization efficiency is improved, and the operation time is reduced.
In a single scene, the method aims to avoid complex algorithms such as iteration, piecewise linearity and the like; by improving the second-order cone programming which is developed rapidly at the present stage, the nonlinear and non-convex pipeline flow equation is solved rapidly. The improvement of the second-order cone relaxation algorithm ensures that the constraint is more independent of iteration and can be better integrated into the scene decomposition algorithm.
(2) And simultaneously introducing random planning, pipeline dynamic characteristics and unit combination problems into the electric multi-energy flow system model.
The three concepts of random planning, pipeline dynamic characteristics and unit combination bring about rapid increase of constraint and variable, so that the three concepts can be introduced into the existing stage model and efficient solution can be realized. These concepts are essential for accurate delineation of day-ahead scheduling models: randomly planning to truly reflect the supply and demand relationship of the next day through multiple scenes; the pipeline dynamics describe the compressibility characteristics of natural gas; the unit combination provides important reference for starting and stopping the unit the next day. The improvement of the decomposition algorithm aims to provide an accurate and efficient solution thought for the complex problem of introducing three concepts and create conditions for adding more complex constraints.
The optimal scheduling method of the electric multi-energy flow system based on scene decomposition, provided by the embodiment of the invention, comprises the following steps:
constructing an operation cost optimization target and constraint conditions of the electric multi-energy flow system, wherein the constraint conditions comprise electric power system constraint and natural gas system constraint;
establishing a day-ahead scheduling model of the electrical multi-energy flow system according to the optimization target and the constraint condition;
simulating the output of a fan to obtain a plurality of operation scenes of the electric multi-energy flow system, decomposing a day-ahead scheduling model into a plurality of corresponding single scene models according to the operation scenes, and solving each single scene model until the unit combinations in all the operation scenes are the same;
and determining a target scheduling scheme of the electrical multi-energy flow system according to the unit combination.
In this embodiment, the day-ahead scheduling model adopted by the electrical multi-energy flow system assumes a random wind energy output, and generates a plurality of typical operation scenarios accordingly. The operation cost of the day-ahead scheduling model provided by the embodiment is aimed at minimizing the operation cost of the electric multi-energy flow system under all time periods T ∈ T and all operation scenes SC ∈ SC, as shown in formula (1).
In the formula, three parenthesized items represent three sources of operating costs: the unit output cost, the gas well and energy storage output cost, the load shedding cost and the wind abandoning cost; t is a time period, T is a set of all time periods, i is a unit serial number, G is a set of all gas units, and C is a set of all coal-fired units; l is the serial number of the nodes of the power system, and B is the set of all the nodes of the power system; n is the natural gas node serial number; n is the set of all natural gas nodes; w is the serial number of the fan, and W is the set of all fans; s is an energy storage serial number, and S is a set of all energy storages; SC is an operation scene, and SC is a set of all operation scenes; pscRepresenting the occurrence probability of the scene sc;
respectively the output of the unit i, the gas well g and the energy storage s at the moment t and the scene sc,
respectively corresponding unit output cost;
respectively at the moment t and the scene sc, the power load shedding of the power system node l, the load shedding of the natural gas node n and the air discharge amount of the fan w,
respectively the corresponding unit cost.
In this embodiment, the constraint conditions of the day-ahead scheduling model adopted by the electrical multi-energy flow system include power system constraint and natural gas system constraint; the power system constraints comprise node balance constraints, unit output constraints, unit climbing constraints and direct current power flow constraints; the natural gas system constraints comprise node balance constraints, pipeline quality constraints, equipment operation constraints, unit energy conversion constraints and pipeline flow constraints.
In the power system, the constraints related to the unit output include unit combination constraints and up/down hill climbing constraints.
Wherein, ci,tThe unit combination variable of the ith unit in the t period (the unit combination is the only physical quantity which does not change with the scene in the model and is also the only integer variable);
respectively the maximum value and the minimum value of the output force of the ith unit; RU (RU)i、RDiThe maximum upper and lower climbing values of the ith unit are respectively;
the output of the unit i at the time t-1 and the scene sc is shown.
In order to solve the problem, the internal impedance of the transmission line is ignored, and the direct current power flow is adopted to carry out power system depiction.
The specific constraints include power flow equations, power transfer constraints, and node balancing constraints.
Wherein a, b and a ', b' each represent the first and last nodes of the power transmission lines (a, b) and (a ', b').
The voltage phase angles of node a and node b at time t and scene sc, respectively, are represented. Br is the set of all power transmission lines.
The actual power flow of the power transmission lines (a, b) and (a ', b') at time t, respectively scene sc. XabIs the nominal inductive reactance, PF, of the transmission line (a, b)abIs the upper limit of the tidal power of the transmission line (a, b).
Is the load value of node l at time t, scene sc.
Is the contribution of the crew at node l (if present) at time t, scene sc.
The natural gas system consists of a gas well, an energy storage, a compressor and a pipeline; gas well related constraints are output constraints; the energy storage related constraints are reserve constraints, gas inlet/outlet constraints and evolution of reserve with time; the compressor related constraints include a compression factor constraint and an equality constraint for the two-sided airflow.
Wherein the content of the first and second substances,
the output of the gas well g at the moment t and the scene sc is shown;
respectively the minimum and maximum allowable values of gas well g capacity.
Respectively representing the reserve capacity of the stored energy s at the moment t and the moment t +1 and under the scene sc;
respectively the minimum and maximum allowable values of the reserves;
the electric quantity flowing into the stored energy s at the moment t and the scene sc respectively; WR (pulse Width modulation)s、IRsThe upper limit of the electric quantity flowing out of the energy storage s and the upper limit of the electric quantity flowing into the energy storage s are respectively; (q, j) represents a natural gas compressor, wherein q, j represent the head and tail nodes of the compressor, respectively; PC is the set of all compressors;
the pressure of the nodes q and j under the scene sc are respectively; CM (compact message processor)qjTo compressMaximum compression factor of the machine (q, j);
and
the natural gas flows out of and into the compressor (q, j) at time t and scene sc, respectively.
The constraints associated with the pipeline include: a pipeline mass equation, a mass change equation with time, and a pipeline flow equation. Where the flow of the formula is taken as the average of the incoming and outgoing gas flows.
Wherein, (c, d) represents a natural gas pipeline, c, d are the first two nodes and the last two nodes of the pipeline respectively, PL is the set of all pipelines;
is the average air pressure of the duct (c, d) at time t and scene sc,
the average pipeline flow of the pipelines (c, d) at the time t and the scene sc;
and
respectively the gas pressures of the natural gas node c and the natural gas node d at the moment t and the scene sc; dcd，LcdRespectively representing the diameter and length of the ducts (c, d);
is the inventory of the pipeline (c, d) at time t and scene sc; r, T, Z, F, rho are respectively the universal gas constant, temperature, compression coefficient, friction coefficient, and natural gas density at 1 atmosphere.
Other constraints involved in natural gas systems are: the system comprises node pressure upper and lower limit constraints, node balance constraints and initial and final state constraints of pipeline total flow.
Wherein
The node air pressure of the natural gas node n at the time t and the scene sc is shown.
Natural gas outflow for the pipeline (c ', d') at time t and scene sc;
respectively the upper limit and the lower limit of the gas pressure of the natural gas node n, GTPiIn order to improve the energy conversion efficiency of the gas turbine unit i,
for the load of the natural gas node n at time t, PI is a proportionality coefficient related to the total inventory, and is generally selected to be about 1 to maintain the continuous operation of the natural gas system for multiple days.
Referring to fig. 4 and 5, the wind energy data is formed by superimposing the predicted data and the random error. The prediction data is obtained from a public database provided by the national buoy data center. The random error is generated by a common ARMA (1,1) sequence to obtain 3000 random scenes; then, the K-average method is adopted to reduce the operation time, so as to obtain a plurality of typical operation scenes, for example, 15 typical scenes are generated.
It is worth noting that the stochastic scenario is 3000 wind speed curves (representing 3000 possible variations of wind speed in the next 24 hours) generated according to the ARMA method. Typical scenes are the "most representative" ten more scenes extracted from these random scenes (i.e. the bulk wind speed curves), which are achieved by the K-means. Fig. 4 is the most representative scenes, for example, there are some scenes in which the wind speed gradually increases within 24 hours, and some scenes in which the wind speed first increases and then decreases.
(3) Algorithm solution
And for multi-scene random planning, a random optimization acceleration algorithm based on scene decomposition is adopted. Firstly, the day-ahead scheduling model of the electrical multi-energy flow system is abbreviated as:
wherein c is a unit combination variable, xscVectors consisting of the remaining variables. QscIs the feasible domain of the model under the scene sc.
The acceleration algorithm based on scene decomposition is shown as algorithm 1. Decomposing the day-ahead scheduling model of the formula (21) according to the operation scene to obtain respective results; and applying punishment to the unit combination variables inconsistent between the operation scenes by adding a punishment function, and searching heuristically until all the unit combinations are consistent.
It is to be noted that the day-ahead scheduling model of equation (21) is a short hand for merging the objective function (1) and the constraint conditions (2) - (20). Wherein (c, x)sc)∈QscIt is indicated that the constraints (2) - (20) should be satisfied in each of the different representative scenarios sc. In the context of the objective function, the function,
the price (mainly including a part of the output cost of the unit) related to the unit combination variable in the objective function (1) is obtained, because the unit combination is consistent in each operation scene, and each operation scene does not need to be considered independently; while other prices require each scenario to be considered separately,
i.e. extra price per scene in addition to the unit assembly cost.
The day-ahead scheduling model provided by this embodiment can be obviously decomposed according to the scene (for example, the formula (21) can be rewritten to
Can be further disassembled into a plurality of single scene models "
Because the disassembly breaks the coupling relation formed by the combination of the units among the operation scenes, the disassembled single scene model cannot be directly and simply solved. Therefore, it is necessary to keep the unit combination c consistent in each scene, which is the reason for adding the penalty function in the algorithm 1 of this embodiment.
As shown in Table 1, in Algorithm 1, in the objective function
Namely a penalty item, which ensures that the unit combination variable is as much as possible in each operation sceneAnd are consistent, as much as possible (in order for the algorithm to converge as quickly) during the preceding and following iterations.
TABLE 1
In this embodiment, for the penalty factor κ, a multiple of the coefficient of the unit combination variable in the objective function is usually selected, because the factor has a faster convergence level. Considering that the objective function of the model does not have items corresponding to the unit combination, the unit output is carried out
The rewrite is:
wherein the content of the first and second substances,
is defined as follows: if the unit i does not output power at the moment t and the scene sc, changing the value to 0; otherwise, the difference value is the difference value obtained by subtracting the lower limit of the output force from the real output force. This step of rewriting makes the coefficients of the set combination in the objective function be
The penalty term k is adjusted by the coefficient on the basis of the penalty term k.
In the same scene, an improved second-order cone constraint method is adopted for the nonlinear equation of the pipeline flow. The specific algorithm is detailed in algorithm 2. Firstly, according to the topological situation of the pipeline, determining the pipeline flow direction in the future 24 hours, thereby eliminating an absolute value (corresponding to the 1 st row); the original equation is then split into a solver-solvable second-order cone constraint (corresponding to lines 5 and 9) and a convex constraint, which is solved by means of a penalty function (corresponding to lines 7 and 11). The algorithm further relaxes the convex constraint to obtain a weakened constraint
Add to the model to further tighten the constraints (corresponding to lines 6, 10). This also reduces the need for error reduction by iteration.
TABLE 2
Referring to fig. 1, the optimized scheduling method for an electrical multi-energy flow system provided in this embodiment is verified in an electrical multi-energy flow system of an IEEE-24 node power system and a belgium 20 node natural gas system. To illustrate the acceleration of the algorithm of this embodiment to the stochastic programming problem, an electrical multi-energy flow algorithm is designed, as shown in fig. 5. The result shows that the algorithm reduces the original solution time 4035 seconds to 55 seconds, and the acceleration effect is remarkable. Meanwhile, the target function obtained by the algorithm is highly consistent with the original random programming method.
According to the scene decomposition-based optimal scheduling method for the electric multi-energy flow system, the problems of random planning, pipeline dynamic characteristics and unit combination are considered in the electric multi-energy flow system, and the operation cost of a day-ahead scheduling model can be accurately depicted; the method comprises the following steps that a supply and demand relation of a next day is truly reflected through a plurality of operation scenes in random planning, the compressibility of natural gas is described through the dynamic characteristics of pipelines, and the unit combination provides important reference for starting and stopping of a unit of the next day; a random programming acceleration algorithm (algorithm 1) with fast convergence and high solving efficiency is designed, the random programming acceleration algorithm can uniformly decompose a day-ahead scheduling model according to scenes, meets the convergence condition in fewer cycles, and stops iteration; meanwhile, parallel computation among scenes is supported in the whole process, the CPU utilization efficiency is improved, and the operation time is reduced. In a single scene, the embodiment of the invention improves the second-order cone programming which is developed rapidly at the present stage, rapidly solves the nonlinear and non-convex pipeline flow equation, can avoid complex algorithms such as iteration and piecewise linearity, enables the constraint to be independent of the iteration, and can be better integrated into the scene decomposition algorithm of the embodiment.
The optimal scheduling method of the electrical multi-energy flow system provided by the embodiment of the invention has the following beneficial effects:
(1) the solution time is short.
As mentioned above, the original solution time 4035 seconds for use is reduced to 55 seconds. The solution time and the resulting objective function value are shown in figure 5. The method 1 is a traditional random planning algorithm, the method 2 is a traditional two-stage algorithm, and the method 3 is a scene decomposition-based random optimal scheduling method for the electric multi-energy flow system. It can be seen that while the calculation time is significantly reduced, method 3 can ensure that the target cost obtained has higher accuracy. In contrast, although the conventional two-stage method has a faster operation time, it introduces an inevitable load shedding of the power system, so that the target result is distorted. Table 3 shows the output costs of the conventional stochastic programming, the conventional two-phase method and the method.
TABLE 3
In the embodiment of the invention, the modeling of the power system and the natural gas system is the constraint conditions (2) - (20) (abstracted and abbreviated as (21)), the constraint conditions established by the power system and the natural gas system are applicable to all operation scenes, uncertain wind energy is the source of multiple scenes, the wind energy has multiple possible values, and each value/curve corresponds to a single operation scene.
The unit combination obtained in this embodiment is the variable c in the formula (21), which is an 0/1 variable determining the unit switch; this quantity is the most easily extracted result, since it must be consistent among the decision results for each typical run scenario. And (4) replacing the result of the unit combination obtained by solving back to each single scene model, namely, independently solving the other single scenes. Since the scenes are decoupled at the moment, each operation scene can be solved independently, and other variable values in each operation scene can be obtained.
(2) Parallel operations are supported.
Compared with the similar Benders algorithm, the cross multiplier method and the like, the method provided by the embodiment has the characteristic that the whole process supports parallel operation, and is beneficial to full utilization of a CPU and improvement of solving efficiency. For 2-core, 4-core, 8-core computer processors, the solution time is reduced to a lesser extent.
It should be noted that other means may be adopted to solve the pipeline power flow in the electrical multi-energy flow system, such as a piecewise linear method, a newton-raphson iteration method, various other forms of second-order cone relaxation method, and the like.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. The optimal scheduling method of the electric multi-energy flow system based on scene decomposition is characterized by comprising the following steps:
constructing an operation cost optimization target and constraint conditions of the electric multi-energy flow system, wherein the constraint conditions comprise electric power system constraint and natural gas system constraint;
establishing a day-ahead scheduling model of the electrical multi-energy flow system according to the optimization target and the constraint condition;
simulating the output of a fan to obtain a plurality of operation scenes of the electric multi-energy flow system, decomposing a day-ahead scheduling model into a plurality of corresponding single scene models according to the operation scenes, and solving each single scene model until the unit combinations in all the operation scenes are the same;
and determining a target scheduling scheme of the electrical multi-energy flow system according to the unit combination.
2. The optimal scheduling method for the scene decomposition-based electrical multi-energy flow system according to claim 1, wherein solving each single scene model until the unit combinations in all the operation scenes are the same comprises:
and solving each single scene model according to a stochastic programming acceleration algorithm until the unit combinations in all the operating scenes are the same.
3. The optimal scheduling method for the scene decomposition-based electrical multi-energy flow system according to claim 2, wherein solving each single scene model according to a stochastic programming acceleration algorithm until the unit combinations in all the operating scenes are the same comprises:
and solving the unit combination of each single scene model, if the unit combinations among the single scene models are different, adding a punishment factor to each single scene model, and repeating the solving process until the unit combinations in all the operating scenes are the same.
4. The optimal scheduling method for the electrical multi-energy flow system based on the scene decomposition as claimed in claim 3, wherein the operation cost optimization goal of the electrical multi-energy flow system is:
in the formula, three parenthesized items represent three sources of operating costs: the unit output cost, the gas well and energy storage output cost, the load shedding cost and the wind abandoning cost; t is a time period, T is a set of all time periods, i is a unit serial number, G is a set of all gas units, and C is a set of all coal-fired units; l is the serial number of the nodes of the power system, and B is the set of all the nodes of the power system; n is the natural gas node serial number; n is the set of all natural gas nodes; w is the serial number of the fan, and W is the set of all fans; s is an energy storage serial number, and S is a set of all energy storages; SC is an operation scene, and SC is a set of all operation scenes; pscRepresenting the occurrence probability of the scene sc;
respectively the output of the unit i, the gas well g and the energy storage s at the moment t and the scene sc,
respectively corresponding unit output cost;
respectively at the moment t and the scene sc, the power load shedding of the power system node l, the load shedding of the natural gas node n and the air discharge amount of the fan w,
respectively the corresponding unit cost.
5. The optimal scheduling method for the scene decomposition based electric multi-energy flow system according to claim 4, wherein the electric system constraints comprise: node balance constraint, unit output constraint, unit climbing constraint and direct current flow constraint.
6. The optimal scheduling method for the scene decomposition based electrical multi-energy flow system according to claim 5, wherein the natural gas system constraints comprise: node balance constraint, pipeline quality constraint, equipment operation constraint, unit energy conversion constraint and pipeline flow constraint.
7. The optimal scheduling method for the scene decomposition based electrical multi-energy flow system according to claim 6, wherein the pipeline flow constraint is as follows:
wherein, (c, d) represents a natural gas pipeline, wherein c, d are the first and last two nodes of the pipeline respectively; PL is the set of all pipes;
and
respectively the gas pressures of the natural gas node c and the natural gas node d at the moment t and the scene sc; dcd，LcdRespectively representing the diameter and length of the ducts (c, d); r, T, Z, F and rho are respectively a universal gas constant, temperature, a compression coefficient, a friction coefficient and natural gas density under 1 atmospheric pressure;
is the average flow rate of the pipe (c, d) at time t and scene sc.
8. The optimal scheduling method for the scene decomposition-based electrical multi-energy flow system according to claim 7, wherein solving the pipeline flow constraint according to an improved second-order cone constraint algorithm specifically comprises: determining the pipeline flow direction in the future 24 hours according to the pipeline topological situation, splitting the pipeline flow constraint into a second-order cone constraint and a convex constraint which can be solved by a solver, and adding a penalty function to solve the convex constraint.
9. The optimal scheduling method for the electrical multi-energy flow system based on the scene decomposition as claimed in claim 8, wherein the determining the target scheduling scheme of the electrical multi-energy flow system according to the unit combination comprises:
and substituting the unit combination into each single scene model to obtain values of other variables in the single scene model, and finally obtaining a target scheduling scheme of the electrical multi-energy flow system.
10. The optimal scheduling method for the electrical multi-energy flow system based on the scene decomposition as claimed in claim 9, wherein simulating the fan output to obtain a plurality of operation scenes of the electrical multi-energy flow system comprises: 3000 possible wind speed curves in the future 24 hours are generated according to an ARMA method to obtain 3000 random scenes, and scene reduction is performed according to a K-average method to obtain 15 typical operation scenes.
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