CN111753431B - Computing method and computing equipment for optimal configuration in comprehensive energy system - Google Patents
Computing method and computing equipment for optimal configuration in comprehensive energy system Download PDFInfo
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Abstract
The invention discloses a calculation method of optimal configuration in a comprehensive energy system, which is suitable for being executed in calculation equipment, and comprises the following steps: acquiring electric power parameters of a comprehensive energy system, wherein the comprehensive energy system comprises a fan, a storage battery, an electric boiler and a heat storage tank, and the basic parameters comprise preset parameters and operation parameters transmitted from the comprehensive energy system; constructing a single machine model of the comprehensive energy system, wherein the single machine model comprises a fan model, a storage battery model, a heat storage tank model and an electric boiler model; constructing a capacity planning model based on a single machine model, wherein the capacity planning model comprises an objective function and constraint conditions, and the objective function comprises minimization of system peak-valley fluctuation, minimization of system annual construction cost and minimization of operation cost; substituting the electric power parameters into a single machine model and a capacity planning model, and solving the capacity planning model to obtain the optimal capacity configuration of each device in the comprehensive energy system. A computing device for performing the method is also disclosed.
Description
Technical Field
The present invention relates to the field of power systems, and in particular, to a computing method and computing device for optimal configuration in a comprehensive energy system.
Background
In recent years, with the aggravation of pollution problems, clean energy sources such as wind energy, solar energy and the like are rapidly developed, and the proportion of the clean energy sources in a power grid is increased. The large-scale new energy power generation such as wind power is beneficial to relieving the situation of shortage of power supply in China, reducing the power generation cost, reducing the emission of greenhouse gases and improving the social benefit of power generation, and is a necessary choice for helping the rapid development of economy and society in China. However, the random fluctuation and the anti-peak shaving characteristic of wind power generation also have certain influence on the safety and the stability of the power grid, so that the peak-valley difference of the load of the power grid is gradually increased, the problems of power dispatching and power grid peak shaving are more remarkable, and the system has to take the wind discarding measures in individual time intervals, so that the economy of the system is reduced. Therefore, when the wind power integration scale is enlarged, corresponding measures are needed to relieve the impact of wind power integration on the system.
In the face of the gradually serious peak shaving problem, the electric storage equipment with the rapid charge and discharge characteristics enters the research category of expert scholars. The energy storage is used as an effective technical means, can directly store electric energy, and suppresses the fluctuation of wind power by adjusting the output of the wind storage combined system through charging and discharging, so that peak clipping and valley filling are realized. The wind power and energy storage combined system can also improve the prediction precision of wind power output and the capability of low voltage ride through, and provides technical support for large-scale wind power access. One of the key problems is how to estimate the energy storage capacity to meet the effective and reasonable operation of the energy storage system when considering the constraint relation between the peak shaving effect and the input cost.
The existing research is mainly aimed at energy storage capacity optimization configuration which is carried out by taking economy as a target in the process of energy storage participation peak shaving in the wind storage combined system. But the energy storage capacity configuration obtained by the final calculation still fails to achieve the best peak shaving effect and economic benefit. Therefore, the energy storage system and the calculation method need to be improved, and the configuration capacity capable of achieving the optimal peak shaving effect is efficiently and accurately obtained.
Disclosure of Invention
Accordingly, the present invention provides a computing method and computing device for optimal configuration in an integrated energy system in an attempt to solve or at least alleviate the above-identified problems.
According to one aspect of the present invention, there is provided a computing method of optimal configuration in an integrated energy system, adapted to be executed in a computing device, the method comprising the steps of: acquiring electric power parameters of a comprehensive energy system, wherein the comprehensive energy system comprises a fan, a storage battery, an electric boiler and a heat storage tank, and the basic parameters comprise preset parameters and operation parameters transmitted from the comprehensive energy system; constructing a single machine model of the comprehensive energy system, wherein the single machine model comprises a fan model, a storage battery model, a heat storage tank model and an electric boiler model; constructing a capacity planning model of the comprehensive energy system based on a single machine model, wherein the capacity planning model comprises an objective function and constraint conditions, and the objective function comprises minimization of system peak-valley fluctuation, minimization of system annual construction cost and minimization of operation cost; substituting the electric power parameters into a single machine model and a capacity planning model, and solving the capacity planning model to obtain the optimal capacity configuration of each device in the comprehensive energy system.
Optionally, in the calculation method according to the present invention, the fan model includes a wind power output constraint, and the expression is:
wherein:to describe the interval variable of the change of the fan load rate at random wind speed,/->For the actual power of the fan, M WG Is the rated capacity of the fan.
Optionally, in the calculation method according to the present invention, the battery model includes a battery capacity constraint expressed as:
wherein: t is the operation period of the comprehensive energy system, t epsilon [1: t (T)]T is T; Δt is the duration of a single period;the energy storage state of the storage battery at the moment t; lambda (lambda) ES Is the self-discharge rate of the storage battery; />Charging/discharging the storage battery at the time t; η (eta) ES-ch /η Es-dch The charge/discharge efficiency of the storage battery at the time t; m is M ES The installation capacity of the storage battery; mu (mu) ES-min /μ ES-max Is a storage batteryThe minimum/maximum energy storage coefficient of (c).
Optionally, in the calculation method according to the present invention, the storage battery model further includes a charging power constraint, a discharging power constraint, a charging and discharging state constraint, and a start-end power storage constraint, and expressions thereof are respectively:
wherein:a variable of 0-1 of the charge/discharge state of the storage battery at the moment t; p (P) ES-max The maximum power of the storage battery is set; />And->Respectively t 0 And the energy storage state of the storage battery at the moment T.
Optionally, in the calculation method according to the present invention, the heat storage tank model includes a heat storage tank capacity constraint expressed as:
Wherein:the heat storage state of the heat storage tank at the time t; lambda (lambda) TS Is the self-heat release rate of the heat storage tank; />Filling/discharging/heating power for the heat storage tank at the moment t; η (eta) TS-ch /η TS-dch The heat storage tank is charged and discharged efficiently at the moment t; />The variable is 0-1 of the charge/discharge state of the heat storage tank at the moment t; m is M TS The installation capacity of the heat storage tank; mu (mu) TS-min /μ TS-max Is the minimum/maximum energy storage coefficient of the heat storage tank.
Optionally, in the calculation method according to the present invention, the heat storage tank model further includes a heat filling power constraint, a heat releasing power constraint, a heat filling and releasing state constraint, and a start-end heat storage amount constraint, and expressions thereof are respectively:
wherein:the variable is 0-1 of the charge/discharge state of the heat storage tank at the moment t; h TS-max Maximum power of the heat storage tank; />And->Respectively t 0 And the energy storage state of the heat storage tank at the moment T.
Optionally, in the calculation method according to the invention, the electric boiler model comprises a heat generation power constraint, expressed as:
wherein: η is the heat production efficiency of the electric boiler,for the heat production capacity of an electric boiler, +.>Is the good point power of the electric boiler, M EB Is the installation capacity of the boiler with the point.
Optionally, in the calculation method according to the present invention, the objective functions of minimizing the system peak-to-valley fluctuation, minimizing the system annual construction cost and the operation cost are respectively:
minf 2 ＝C inv +C opt
Wherein: p (P) ξ Peak-valley difference of electric load before peak clipping and valley filling; p (P) ES-dch-max Is negative in peak valueThe energy generation amount reduced by the energy storage system in the loading stage; p (P) ES-ch-max Energy absorbed by the energy storage system for the off-peak load phase; c (C) inv The construction cost is the system year; c (C) opt The cost is maintained for the annual operation of the system.
Optionally, in the calculation method according to the invention,
wherein: i is the type of device and,installation cost per unit capacity of equipment, M i For the installation capacity of the apparatus, y i For planning years, r is interest rate; />Maintenance costs per unit capacity of equipment.
Optionally, in the calculation method according to the present invention, the constraint condition of the capacity planning model includes at least one of a balance constraint, a daily air volume constraint, and a system peak shaver shortage probability constraint.
Optionally, in the calculation method according to the present invention, the expression of the equilibrium constraint is:
wherein:the output of other units is realized; />For the system electrical load; />0-1 variable which is insufficient peak regulation for the system; />Is a peak regulation shortage; />Is the system thermal load.
Optionally, in the calculation method according to the present invention, the expressions of the daily air volume constraint and the system peak shaving shortage probability constraint are respectively:
wherein:maximum power of wind power generation at t moment; a. b, K lack Are all preset parameters.
Alternatively, in the method according to the invention, the capacity planning model is solved using a multi-objective genetic algorithm.
Optionally, in the computing method according to the invention, the integrated energy system is provided with a data acquisition station for acquiring electrical parameters of the devices in the integrated energy system and transmitting the electrical parameters to the computing device.
According to yet another aspect of the present invention, there is provided a computing device comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing the computing method of optimal configuration in the integrated energy system as described above.
According to yet another aspect of the present invention, there is provided a readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform the method of computing an optimal configuration in an integrated energy system as described above.
According to the technical scheme, the limitation of single power storage equipment is considered, the wind power plant is provided with the electric boiler and the heat storage tank in addition to the storage battery, so that the comprehensive energy system is formed, and the peak clipping and valley filling effects can be better achieved. In order to reasonably configure the equipment capacity in the comprehensive energy system, the invention establishes a multi-objective robust optimization model, calculates the minimum capacity of each equipment required to be installed in the wind power plant, so as to carry out load transfer and reduce peak-to-valley fluctuation. The equipment capacity configuration calculated by the invention can achieve the optimal peak shaving effect and the optimal economic benefit at the same time, and improves the accuracy of the calculation result. The invention utilizes NSGA-II to solve by combining with an approximate ideal point method (TOPSIS), determines the capacity optimization configuration of the system equipment, and improves the calculation efficiency.
Drawings
To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings, which set forth the various ways in which the principles disclosed herein may be practiced, and all aspects and equivalents thereof are intended to fall within the scope of the claimed subject matter. The above, as well as additional objects, features, and advantages of the present disclosure will become more apparent from the following detailed description when read in conjunction with the accompanying drawings. Like reference numerals generally refer to like parts or elements throughout the present disclosure.
FIG. 1 illustrates a block diagram of a computing device 100, according to one embodiment of the invention;
FIG. 2 illustrates a schematic diagram of an integrated energy system according to one embodiment of the invention;
FIG. 3 illustrates a flow chart of a method 300 of computing an optimal configuration in an integrated energy system according to one embodiment of the invention;
FIG. 4 illustrates a solution flow diagram for a capacity planning model in accordance with one embodiment of the present invention;
FIG. 5 illustrates a schematic diagram of a wind power output prediction curve according to one embodiment of the invention;
fig. 6 shows a schematic diagram of a pareto front set according to one embodiment of the invention; and
FIG. 7 shows a schematic diagram of system peak-to-valley fluctuations in accordance with an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Fig. 1 is a block diagram of an example computing device 100. In a basic configuration 102, computing device 100 typically includes a system memory 106 and one or more processors 104. The memory bus 108 may be used for communication between the processor 104 and the system memory 106.
Depending on the desired configuration, the processor 104 may be any type of processing including, but not limited to: a microprocessor (μp), a microcontroller (μc), a digital information processor (DSP), or any combination thereof. The processor 104 may include one or more levels of caches, such as a first level cache 110 and a second level cache 112, a processor core 114, and registers 116. The example processor core 114 may include an Arithmetic Logic Unit (ALU), a Floating Point Unit (FPU), a digital signal processing core (DSP core), or any combination thereof. The example memory controller 118 may be used with the processor 104, or in some implementations, the memory controller 118 may be an internal part of the processor 104.
Depending on the desired configuration, system memory 106 may be any type of memory including, but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. The system memory 106 may include an operating system 120, one or more applications 122, and program data 124. In some implementations, the application 122 may be arranged to operate on an operating system with program data 124. The program data 124 includes instructions, and in the computing device 100 according to the present invention, the program data 124 contains instructions for executing the computing method 300 for optimal configuration in the integrated energy system.
Computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (e.g., output devices 142, peripheral interfaces 144, and communication devices 146) to basic configuration 102 via bus/interface controller 130. The example output device 142 includes a graphics processing unit 148 and an audio processing unit 150. They may be configured to facilitate communication with various external devices such as a display or speakers via one or more a/V ports 152. Example peripheral interfaces 144 may include a serial interface controller 154 and a parallel interface controller 156, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 158. An example communication device 146 may include a network controller 160, which may be arranged to facilitate communication with one or more other computing devices 162 via one or more communication ports 164 over a network communication link.
The network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media in a modulated data signal, such as a carrier wave or other transport mechanism. A "modulated data signal" may be a signal that has one or more of its data set or changed in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or special purpose network, and wireless media such as acoustic, radio Frequency (RF), microwave, infrared (IR) or other wireless media. The term computer readable media as used herein may include both storage media and communication media.
Computing device 100 may be implemented as a server, such as a file server, a database server, an application server, a WEB server, etc., as part of a small-sized portable (or mobile) electronic device, such as a cellular telephone, a Personal Digital Assistant (PDA), a personal media player device, a wireless WEB-watch device, a personal headset device, an application-specific device, or a hybrid device that may include any of the above functions. Computing device 100 may also be implemented as a personal computer including desktop and notebook computer configurations. In some embodiments, the computing device 100 is configured to perform the computing method 300 of optimal configuration in the integrated energy system according to the invention.
As described above, the grid-connected fluctuation of the wind farm is large, and a series of interference is brought to the power system. The energy storage device plays an important role in smoothing wind power fluctuation. Considering the limitation of single electricity storage equipment, the invention constructs the comprehensive energy system shown in figure 2, and the comprehensive energy system comprises a fan, a storage battery, an electric boiler and a heat storage tank, so that the peak clipping and valley filling effects can be better achieved. The input end of the miniature comprehensive energy system is connected with an external power grid, and the output end of the miniature comprehensive energy system is connected with a user. In normal operation, electrical energy generated by the wind farm is delivered to the grid. Since the fluctuation of wind power is large, and its power characteristics are generally opposite to the load characteristics. The wind power is connected, so that the uncertainty of load fluctuation is greatly increased, and the peak shaving capability requirement of the system is also improved after the grid-connected wind power scale is increased to a certain extent.
The energy storage device is switched on and off in a time-sharing control manner. When the load level is low, the excess electrical energy generated by the fan is stored in the battery. When the output of the fan can not meet the requirements of the power grid and the load, different control strategies are adopted to control the discharge of the battery. In addition, the system is also provided with an electric boiler, when the wind farm generates excessive electricity and the ideal peak regulation effect cannot be achieved only by means of the storage battery, the excessive electric energy can be converted into heat energy by the electric boiler for a user to use or be stored in the heat storage tank.
According to one embodiment, the integrated energy system may also be provided with a data acquisition station communicatively coupled to the computing device 100 for acquiring operational parameters of the devices in the integrated energy system and transmitting the operational parameters to the computing device 100. Further, each device of the integrated energy system may be provided with a data acquisition station for acquiring the operating parameters of the corresponding device. Each data acquisition station is communicatively coupled to the computing device 100 for transmitting acquired data to the computing device. Wherein the operating parameters include real-time parameters and periodic parameters.
Fig. 3 illustrates a flow chart of a computing method 300 of optimal configuration in an integrated energy system, suitable for execution in a computing device (e.g., computing device 100), in accordance with one embodiment of the present invention. As depicted in fig. 3, the method begins at step S310.
In step S310, electric power parameters of the integrated energy system are acquired, and the basic parameters include preset parameters and operation parameters transmitted from the integrated energy system.
The basic parameters here, that is, the parameters needed to be substituted into the subsequent model solution, may specifically include parameters to the right of the equal sign in the following formulas, and other parameters used to find the parameters to the right of the equal sign, and particularly include the parameters in table 1 below. The preset parameters include planning years, population number, maximum iteration times, crossover probability, mutation probability, etc., and parameters transmitted from the device include various power parameters, energy parameters, etc.
Subsequently, in step S320, a stand-alone model of the integrated energy system is constructed, the stand-alone model including a fan model, a battery model, a heat storage tank model, and an electric boiler model.
1) Fan model
In nature, due to wind speedRandomness and intermittence, so fan output is an uncertainty variable. To this end, the invention defines a load factorRepresenting the ratio of the actual power generated by the fan to the rated capacity of the fan at the wind speed of the period t, and->Representing the lower and upper bounds, respectively, of the fluctuation interval.
According to one embodiment, the fan model includes a wind power output constraint expressed as:
wherein:to describe the interval variable of the change of the fan load rate at random wind speed,/->For the actual power of the fan, M WG Is the rated capacity of the fan. Here, the output power of the wind turbine is mainly affected by the wind speed at the location.
2) Storage battery model
The accumulator is switched on and off in a time-sharing control mode. When the load level is low, the excess electrical energy generated by the fan is stored in the battery. When the output of the fan can not meet the requirements of the power grid and the load, different control strategies are adopted to control the discharge of the battery. In the charge and discharge process, when the state of charge is not out of limit, the remaining energy of the battery is taken into account.
According to one embodiment, the battery model includes a battery capacity constraint expressed as:
wherein: t is the operation period of the comprehensive energy system, t epsilon [1: t (T)]T is T; Δt is the duration of a single period;the energy storage state of the storage battery at the moment t; lambda (lambda) ES Is the self-discharge rate of the storage battery; />Charging/discharging the storage battery at the time t; η (eta) ES-ch /η ES-dch The charge/discharge efficiency of the storage battery at the time t; m is M ES The installation capacity of the storage battery; mu (mu) ES-min /μ ES-max Is the minimum/maximum energy storage coefficient of the storage battery.
According to another embodiment, the battery model further comprises a charge power constraint, a discharge power constraint, a charge-discharge state constraint, and a start-end charge capacity constraint. The charge-discharge power constraint represents the upper and lower line threshold values of charge and discharge, the charge-discharge state constraint represents that the charge and discharge cannot be carried out at the same time, and the start-end charge quantity constraint represents that the start-end charge quantity of the storage battery in a scheduling period is kept unchanged. The expressions for these four constraints are:
wherein:a variable of 0-1 of the charge/discharge state of the storage battery at the moment t; p (P) ES-max The maximum power of the storage battery is set; />And->Respectively t 0 And the energy storage state of the storage battery at the moment T.
3) Heat storage model
According to one embodiment, the heat storage tank model includes a heat storage tank capacity constraint expressed as:
Wherein:the heat storage state of the heat storage tank at the time t; lambda (lambda) TS Is the self-heat release rate of the heat storage tank; />Filling/discharging/heating power for the heat storage tank at the moment t; η (eta) TS-ch /η TS-dch The heat storage tank is charged and discharged efficiently at the moment t; />The variable is 0-1 of the charge/discharge state of the heat storage tank at the moment t; m is M TS The installation capacity of the heat storage tank; mu (mu) TS-min /μ TS-max Is the minimum/maximum energy storage coefficient of the heat storage tank.
According to a further embodiment, the heat storage tank model further comprises a charge power constraint, a heat release power constraint, a charge-discharge state constraint, and a start-end heat storage capacity constraint. The heat storage tank is characterized in that the heat storage tank is provided with a heat storage power constraint, a heat storage power constraint and a heat storage power constraint, wherein the heat storage power constraint represents an upper line threshold and a lower line threshold of heat storage and heat storage, the heat storage power constraint represents that heat storage and heat storage cannot be carried out simultaneously at the same time, and the heat storage power constraint represents that the heat storage capacity of the heat storage tank is kept unchanged in the beginning and the end of a scheduling period. The expressions for these four constraints are:
wherein:the variable is 0-1 of the charge/discharge state of the heat storage tank at the moment t; h TS-max Maximum power of the heat storage tank; />And->Respectively t 0 And T time heat storageAnd (3) the energy storage state of the tank.
4) Electric boiler model
The electric boiler uses electric power as energy, utilizes resistance heating or electromagnetic induction heating, and heats water to a certain temperature through a heat exchange part of the boiler to supply to an end user.
According to yet another embodiment, the electric boiler model includes a heat generation power constraint expressed as:
Wherein: η is the heat production efficiency of the electric boiler,for the heat production capacity of an electric boiler, +.>Is the good point power of the electric boiler, M EB Is the installation capacity of the boiler with the point.
Subsequently, in step S330, a capacity planning model of the integrated energy system is constructed based on the stand-alone model, the capacity planning model including an objective function including minimization of system peak-to-valley fluctuation, minimization of system annual construction cost and minimization of operation cost, and constraints.
According to the basic framework of the miniature comprehensive energy system, an optimization model can be established to realize the optimization design of energy conversion and energy storage device capacity. Considering that the superiority of the integrated energy system in the power grid peak shaving compared with the traditional wind storage combined system is generally represented by lower economic cost, lower wind rejection rate, better peak shaving effect and the like, a multi-objective optimization model for capacity planning can be constructed according to the method. Wherein, the peak regulation effect and the economic cost are the targets which are easier to quantitatively analyze. Therefore, the optimal system planning scheme is realized by adopting a multi-objective optimization model. The multi-objective optimization problem includes: 1) Peak regulation target: system peak-to-valley fluctuation is minimized; 2) Economic objectives, namely annual construction and operation costs, are minimized.
Specifically, objective functions of minimizing system peak-valley fluctuation, and minimizing system annual construction cost and running cost are respectively:
minf 2 ＝C inv +C opt
wherein: p (P) ξ Peak-valley difference of electric load before peak clipping and valley filling; p (P) ES-dch-max Generating power reduced by the energy storage system for peak load periods; p (P) ES-ch-max Energy absorbed by the energy storage system for the off-peak load phase; c (C) inv The construction cost is the system year; c (C) opt The cost is maintained for the annual operation of the system.
According to one embodiment, the annual construction cost C of the system Inv All investments in the initial stages of construction are meant to be equivalent to annual costs for purchasing energy production and storage facilities. Annual running cost C opt Refers to the cost per year required to maintain the proper operation of the system. The expressions for these two costs are:
wherein: i is the type of device and,installation cost per unit capacity of equipment, M i For the installation capacity of the apparatus, y i For planning years, r is interest rate; />Maintenance costs per unit capacity of equipment.
According to another embodiment, the constraints of the capacity planning model include at least one of a balance constraint, a daily air rejection constraint, and a system peak shaver deficiency probability constraint. The daily air discarding quantity constraint is used for ensuring the efficient consumption of wind power and ensuring that the air discarding quantity of the system in one day is within a range. The constraint of the system undershoot probability refers to that the system undershoot probability is smaller than a set parameter in a typical day.
Wherein the expression of the balance constraint is:
wherein:the output of other units is realized; />For the system electrical load; />0-1 variable which is insufficient peak regulation for the system; />Is a peak regulation shortage; />Is the system thermal load.
The expression of the daily air volume constraint and the system peak shaving shortage probability constraint are respectively as follows:
wherein:maximum power of wind power generation at t moment; a. b, K lack Are all preset parameters.
Then, in step S340, the electric power parameters are substituted into the stand-alone model and the capacity planning model, and the capacity planning model is solved, so as to obtain the optimal capacity configuration of each device in the integrated energy system.
According to one embodiment, the capacity planning model is solved using a multi-objective genetic algorithm. The mathematical model of the miniature comprehensive energy system provided by the invention is a typical multi-objective robust optimization problem, the uncertainty problem of wind power generation is considered in the model, and under the condition of meeting system constraint, the aim of optimizing peak shaving effect and economy is achieved by reasonably configuring the capacity of equipment in the system. For such problems, since the uncertainty is described in a form of a robust interval, the problem cannot be solved directly, and therefore the uncertainty problem needs to be converted into a deterministic problem to be solved.
Therefore, the invention converts the uncertain parameters in the model into deterministic multi-objective problems by utilizing the dual cone method and the worst optimal principle, thereby eliminating interval variables in the model. And solving the transformed deterministic multi-objective problem by using an improved multi-objective genetic algorithm, so as to obtain an optimal planning scheme of the system. A general model of robust optimization can be described as:
wherein: f (f) i Is the ith objective function; x is a decision variable; xi is uncertaintyCoefficients; g i Is the ith constraint; u is the set of uncertainty coefficients. The general processing thought of the problem is: firstly, adopting peer-to-peer transformation to convert the uncertainty problem into a deterministic problem. Dividing decision variables into two parts, the parts being determined separatelyAnd uncertainty component->Namely:
the robust optimization model can meet all uncertainty conditions in the uncertainty set, so that the maximum uncertainty can be met, and the uncertainty constraint condition is transformed into:
the conversion method is utilized to convert the original optimization model into:
in the method, in the process of the invention,is a system peak-valley fluctuation; />Is an economic cost; />A determining section for containing an uncertain decision variable;is the boundary value of the uncertainty component ζ.
As can be seen from the converted formula, the converted model is a two-stage robust optimization problem and contains an uncertain variable and coupling between the maximum value and the minimum value of the uncertain variable, so that the method cannot be directly solved. Therefore, the model is converted again by adopting a dual cone method, and the following formula is obtained:
M i ＝[I L×L ；0 1×L ]m i ＝[0 L×1 ；Γ]
K 1 ＝{[τ L×1 ；t]∈R L+1 ||τ||≤t}
wherein U is a set of uncertain variables xi; Γ is the total uncertainty; mu is an adjustable robust coefficient, and the robustness of the system can be changed by adjusting mu; l is the boundary value of the robust interval; m is M 1 And m 1 Two matrices for definition; i L×L Is a unit matrix; k (K) 1 Is a convex cone.
Will beExpressed as ++norm cone, get ++>The dual of the ++norm cone is taken as a 1-norm cone, and the following formula is obtained:
according to the robust conversion method, the constraint conditions are subjected to robust conversion aiming at uncertainty of the fan output, so that the constraint conditions are converted into deterministic multi-objective optimization problems, and the problem is solved further. The mixed integer nonlinear model containing uncertain parameters in the invention can be converted into a deterministic mixed integer nonlinear model through the conversion of uncertain constraints. The NSGA-II algorithm is adopted to solve the deterministic optimization problem after conversion, and the solving thinking is as follows: firstly initializing a population, randomly generating a population with the size of N, then carrying out non-dominant sorting on each individual in the population, and carrying out crowding degree calculation. Finding the non-dominant solution set with the highest rank according to the non-dominant ranking, and then classifying the rest individuals into respective ranks. The crowding level of each individual in the same class is calculated in order to provide support for the following selection. Two individual comparisons were then randomly selected from the population. If the two individuals are different in rank, the highest rank individual is selected. If the two grades are the same, an individual with high crowding degree is selected. And then the genetic operations of selection, hybridization and mutation are utilized to obtain the first-generation excellent individuals. And starting from the second generation, merging the parent population and the offspring population, re-carrying out non-dominant sorting and congestion degree calculation, and selecting according to the calculation result. The proper individual combination forms a new father population, then a new son population is generated by genetic algorithm, etc., until the iteration times are reached, the program is ended, and the pareto frontier diagram is obtained. Finally, an optimal solution is obtained by adopting a decision method of approaching an ideal point method (TOPSIS), and the algorithm flow is shown in figure 4.
The following describes a specific case of the method for calculating the optimal configuration of the integrated energy system according to the present invention. A predicted output curve of the wind power output is first generated according to the historical data, as shown in fig. 5, wherein the wind power output deviation is considered to be 20%. The original wind farm installation was set to 200MW, and in this system, the technical and economic parameters of various devices are shown in Table 1. For NSGA-II algorithm, the population number is set to be 200, the maximum iteration number is 500, the crossover probability is 0.9, and the variation probability is 0.1. Further, it is assumed that the decision maker expects and fluctuates weight coefficients for each optimization objective (0.6,0.4). It should be noted that the weight coefficient can be set according to different requirements, so as to realize capacity optimization and economic benefit analysis of the wind power plant energy storage system.
Table 1 device parameters
The invention sets three scenes for comparing and analyzing the functions of wind power and energy storage in the peak regulation process of the power system. The method comprises the following steps of setting a scene 1 into a wind power plant, setting a scene 2 into a wind power plant to be equipped with electric energy storage, and setting a scene 3 into an integrated energy system by arranging an electric boiler and thermal energy storage on the basis of the scene 2. And respectively carrying out optimization solution on the three scenes. The Pareto front set obtained by the multi-objective robust optimization model is shown in fig. 6. The graph shows that the Pareto solution set formed by the method is uniform in distribution, and can provide information for a decision maker in the optimal configuration of the comprehensive energy system equipment. The results show that the economic cost of the system is closely related to the peak shaving effect. The better the peak shaving effect is, the higher the economic cost is; and when the economic cost is low, the peak regulation effect is poor. Therefore, in practical application, the two targets are comprehensively weighed according to the expected requirement of a decision maker, and the optimal scheme is scientifically determined.
The system peak-to-valley fluctuation conditions in the three scenarios are shown in fig. 7, and the system equipment configuration capacity is shown in table 2. It can be seen from fig. 7 that after installation of the electric boiler and the electric/thermal energy storage in the system, the system is changed from a single electric power system to a comprehensive energy system with electric-thermal conversion. On one hand, the electric energy storage equipment releases energy in the load peak time, so that the regulating pressure of the peak machine set is relieved, and the starting and stopping time and the regulating time of the peak machine set are reduced; on the other hand, in the load valley period, the system can consume wind power in two modes of charging the electric energy storage equipment or converting electric energy into heat energy through the electric boiler and storing the heat energy in the heat energy storage equipment, so that the wind abandoning rate is reduced.
Table 2 system optimization results
As can be seen from table 2, the peak-to-valley fluctuation of the system after installation of the energy storage device in the wind farm is significantly reduced compared to scenario 1 and scenario 2, because the energy storage system absorbs additional wind power during the load valley period and releases energy during the peak load period. However, due to the high installation cost of the electric energy storage, the economic cost of the system is obviously increased after a large amount of electric energy storage is configured compared with that of the system without the electric energy storage, and the scheme has poor economical efficiency. Compared with the scene 1 and the scene 3, the combined power system and the thermodynamic system are a small comprehensive energy system, and the randomness and the fluctuation of wind power output can be balanced through the energy storage of stored energy and the conversion of different energy forms, so that the peak-to-valley fluctuation of the system is obviously reduced. Compared with the scene 2 and the scene 3, after the electric boiler and the thermal energy storage are configured in the system, although the peak-valley fluctuation of the system slightly rises, the energy storage benefit is obviously increased. This means that when a thermal energy storage device is introduced into the system, the system can indirectly participate in peak regulation by converting electric energy into heat energy, and a better peak regulation effect of the system can be ensured. Secondly, the investment of the heat energy storage is far smaller than that of the electric energy storage, so that a certain amount of heat energy storage can be configured to realize the optimal system economy under the condition of not losing the peak shaving effect.
In summary, the invention discusses the problem of capacity optimization configuration of the comprehensive energy system considering uncertainty of wind power generation, and according to wind power prediction data, when the power of the wind power generator and the energy storage device meets the load requirement in the energy charging and discharging process of the energy storage device, an optimization method of the optimal equipment capacity is provided, and the equipment capacity is reasonably distributed. The invention takes investment cost and maintenance cost of various devices and peak-valley difference change of the system before and after peak clipping and valley filling as targets, adopts a multi-target robust optimization method to determine the optimal configuration capacity of each device, improves the accuracy and the calculation efficiency of calculation results, and can reduce the peak-valley fluctuation ratio before and after peak clipping and valley filling and the economic cost of the system to the greatest extent.
The method of A9, A8, wherein,
wherein: i is the type of device and,installation cost per unit capacity of equipment, M i For the installation capacity of the apparatus, y i For planning years, r is interest rate; />Maintenance costs per unit capacity of equipment.
A10, the method of any of A1-A9, wherein the constraint condition of the capacity planning model comprises at least one of a balance constraint, a daily air volume constraint, and a system peak shaver shortage probability constraint.
A11, the method of a10, wherein the expression of the equilibrium constraint is:
wherein:the output of other units is realized; />For the system electrical load; />0-1 variable which is insufficient peak regulation for the system; />Is a peak regulation shortage; />Is the system thermal load.
A11, the method as set forth in A10, wherein the expressions of the daily air-abandon constraint and the system peak shaving shortage probability constraint are respectively:
wherein:maximum power of wind power generation at t moment; a. b, K lack Are all preset parameters.
A12, the method of any of A1-a11, wherein the capacity planning model is solved using a multi-objective genetic algorithm.
A13. the method of any of A1-a11, wherein the integrated energy system is provided with a data acquisition station for acquiring power parameters of each device in the integrated energy system and transmitting the power parameters to the computing device.
The various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of the present invention, or certain aspects or portions of the methods and apparatus of the present invention, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention.
In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Wherein the memory is configured to store program code; the processor is configured to perform the method of the invention in accordance with instructions in said program code stored in the memory.
By way of example, and not limitation, computer readable media comprise computer storage media and communication media. Computer-readable media include computer storage media and communication media. Computer storage media stores information such as computer readable instructions, data structures, program modules, or other data. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. Combinations of any of the above are also included within the scope of computer readable media.
In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with examples of the invention. The required structure for a construction of such a system is apparent from the description above. In addition, the present invention is not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in a device as described in this embodiment, or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into a plurality of sub-modules.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Furthermore, some of the embodiments are described herein as methods or combinations of method elements that may be implemented by a processor of a computer system or by other means of performing the functions. Thus, a processor with the necessary instructions for implementing the described method or method element forms a means for implementing the method or method element. Furthermore, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is for carrying out the functions performed by the elements for carrying out the objects of the invention.
As used herein, unless otherwise specified the use of the ordinal terms "first," "second," "third," etc., to describe a general object merely denote different instances of like objects, and are not intended to imply that the objects so described must have a given order, either temporally, spatially, in ranking, or in any other manner.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of the above description, will appreciate that other embodiments are contemplated within the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is defined by the appended claims.
Claims (14)
1. A method of computing an optimal configuration in an integrated energy system, adapted to be executed in a computing device, the method comprising the steps of:
acquiring electric power parameters of the comprehensive energy system, wherein the comprehensive energy system comprises a fan, a storage battery, an electric boiler and a heat storage tank, and the electric power parameters comprise preset parameters and operation parameters transmitted from the comprehensive energy system;
constructing a single machine model of the comprehensive energy system, wherein the single machine model comprises a fan model, a storage battery model, a heat storage tank model and an electric boiler model;
constructing a capacity planning model of the comprehensive energy system based on the single machine model, wherein the capacity planning model comprises an objective function and constraint conditions, and the objective function comprises minimization of system peak-valley fluctuation, minimization of system annual construction cost and minimization of operation cost; and
substituting the electric power parameters into the single machine model and the capacity planning model, carrying out robust conversion on constraint conditions with uncertainty in the capacity planning model, and solving by adopting a combination of a non-dominant ranking genetic algorithm NSGA-II and an approach ideal point method to obtain the optimal capacity configuration of each device in the comprehensive energy system;
The objective functions of minimizing the peak-valley fluctuation of the system and minimizing the annual construction cost and the running cost of the system are respectively as follows:
minf 2 ＝C inv +C opt
wherein: minf (min f) 1 Minimizing an objective function for system peak-to-valley fluctuations; minf (min f) 2 Minimizing objective functions for system annual construction costs and operating costs; p (P) ξ To cut peak and fillPeak-valley difference of electric load before valley; p (P) ES-dch-max Generating power reduced by the energy storage system for peak load periods; p (P) ES-ch-max Energy absorbed by the energy storage system for the off-peak load phase; c (C) inv The construction cost is the system year; c (C) opt The cost is maintained for the annual operation of the system.
2. The method of claim 1, wherein the fan model includes a wind power output constraint expressed as:
wherein:to describe the interval variable of the change of the fan load rate at random wind speed,/->For the actual power of the fan, M WG Is the rated capacity of the fan.
3. The method of claim 1, wherein the battery model includes a battery capacity constraint expressed as:
wherein: t is the operation period of the comprehensive energy system, t is [1:T ]]T is T; Δt is the duration of a single period;storage for a storage battery at time tAn energy state; lambda (lambda) ES Is the self-discharge rate of the storage battery; />The charge/discharge power of the storage battery at the time t; η (eta) ES-ch /η ES-dch The charge/discharge efficiency of the storage battery at the time t; m is M ES The installation capacity of the storage battery; mu (mu) ES-min /μ ES-max Is the minimum/maximum energy storage coefficient of the storage battery.
4. The method of claim 3, wherein the battery model further comprises a charge power constraint, a discharge power constraint, a charge-discharge state constraint, and a start-end charge capacity constraint, which are expressed by:
wherein:a variable of 0-1 of the charge/discharge state of the storage battery at the moment t; p (P) ES-max The maximum power of the storage battery is set; />And->Respectively t 0 And the energy storage state of the storage battery at the moment T.
5. The method of any of claims 1-4, wherein the heat storage tank model comprises a heat storage tank capacity constraint expressed as:
wherein:the heat storage state of the heat storage tank at the time t; lambda (lambda) TS Is the self-heat release rate of the heat storage tank; />Charging/discharging power of the heat storage tank at the moment t; η (eta) TS-ch /η TS-dch The heat storage tank is charged/discharged at the time t; />The variable is 0-1 of the charge/discharge state of the heat storage tank at the moment t; m is M TS The installation capacity of the heat storage tank; mu (mu) TS-min /μ TS-max Is the minimum/maximum energy storage coefficient of the heat storage tank.
6. The method of claim 5, wherein the heat storage tank model further comprises a charge power constraint, a heat release power constraint, a charge-discharge state constraint, and a start-end heat storage capacity constraint, which are expressed by:
Wherein:a 0-1 variable of the charge/discharge state of the heat storage tank at the time t; h TS-max Maximum power of the heat storage tank;and->Respectively t 0 And the energy storage state of the heat storage tank at the moment T.
7. The method of any of claims 1-4, wherein the electric boiler model includes a heat generation power constraint expressed as:
wherein: η (eta) EB For the heat production efficiency of the electric boiler,for the heat-generating power of the electric boiler, P t EB For the power consumption of the electric boiler, M EB Is the installation capacity of the electric boiler.
8. The method of claim 1, wherein,
wherein: i is the type of device and,installation cost per unit capacity of equipment, M i For the installation capacity of the apparatus, y i For planning years, r is interest rate; />Maintenance costs per unit capacity of equipment.
9. The method of any of claims 1-4, wherein the constraints of the capacity planning model include at least one of a balance constraint, a daily air rejection constraint, a system undershoot probability constraint.
10. The method of claim 9, wherein the expression of the equilibrium constraint is:
wherein: p (P) t grid The output of other units is realized; p (P) t D For the system electrical load;0-1 variable which is insufficient peak regulation for the system;is a peak regulation shortage; / >Is the system thermal load.
11. The method of claim 9, wherein the expressions of the daily air volume constraint and the system peak shaver shortage probability constraint are respectively:
wherein: p (P) t WG-max Maximum power of wind power generation at t moment; a. b, K lack Are all preset parameters.
12. The method of any one of claims 1-4, wherein the integrated energy system is provided with a data acquisition station for acquiring power parameters of each device in the integrated energy system and transmitting the power parameters to the computing device.
13. A computing device, comprising:
at least one processor; and
at least one memory including computer program instructions;
the at least one memory and the computer program instructions are configured to, with the at least one processor, cause the computing device to perform the method of any of claims 1-12.
14. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a server, cause the server to perform any of the methods of claims 1-12.
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