Multi-element energy system capacity configuration method based on fractional programming theory

Abstract

The invention discloses a multi-element energy system capacity configuration method based on a fractional programming theory. The method comprises the following steps: firstly, establishing an energy system operation model comprising system power balance, new energy output, energy storage and an electric boiler; then, constructing a daily operation model under multiple scenes, and further establishing a capacity benefit evaluation function of the multi-element energy system; further, a multi-element energy configuration model of fractional planning is constructed; and finally, linearizing the fractional programming model through a variable replacement and decomposition algorithm, and finally solving through a solver to obtain a capacity configuration strategy of the multi-element energy system.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy capacity configuration, and more specifically, relates to a multi-energy system capacity configuration method based on fractional programming theory. Background Art

[0002] Faced with increasingly severe global environmental and energy problems, countries are actively developing new energy systems based on clean energy to achieve energy transformation. Multi-energy systems have gradually become a key method for optimizing energy structure. Due to the volatility, intermittency and uncertainty of new energy output, a single energy supply model cannot give full play to the complementarity and synergy between various energy sources, resulting in resource waste and imbalance between supply and demand. Multi-energy systems take into account the different characteristics of various energy sources, improve the overall utilization efficiency of energy by integrating diversified resources, and promote the development of energy systems in a green and efficient direction.

[0003] For investors, the economic benefits of the project are an important consideration. While ensuring the stability of the power system, how to maximize the cost-effectiveness of the coordinated configuration of multiple energy sources is a key issue in current research. There are usually three ways to deal with the economic aspects of the current energy configuration model: minimizing total cost, constraining investment costs, and game theory. The method of minimizing total cost takes total cost as the objective function, which can reduce the total cost of the project, but may lead to large initial investment costs and poor long-term economic benefits of the project; the method of constraining investment costs uses investment costs as constraints, which helps to control the initial investment costs, but the investment plan is not optimal, which may lead to excessive operating costs in the later stage and reduced long-term economic benefits; the game method model is complex, the data requirements are strict, the calculation difficulty is high, and the application is difficult. Therefore, a new configuration method is needed that can both improve the economic benefits of the project and be easy to apply. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a capacity configuration method for a multi-energy system based on fractional programming theory. By analyzing the impact of each energy source on the system operating cost and then establishing a fractional programming model, a configuration strategy with the highest return on investment is obtained.

[0005] To achieve the above-mentioned object of the invention, the present invention provides a method for configuring the capacity of a multi-energy system based on fractional programming theory, which is characterized by comprising the following steps:

[0006] (1) Construct an operation model of a multi-energy system;

[0007] (2) Constructing the capacity benefit evaluation function of the multi-energy system;

[0008] (3) Construct a capacity configuration model for a multi-energy system;

[0009] (4) The capacity configuration model of the multi-energy system is linearized, and then the capacity configuration strategy of the multi-energy system is obtained through the solver.

[0010] The object of the invention of the present invention is achieved in this way:

[0011] The invention discloses a method for capacity configuration of a multi-energy system based on fractional programming theory. First, an energy system operation model including system power balance, new energy output, energy storage and electric boiler is established. Then, a daily operation model under multiple scenarios is constructed, and then a capacity benefit evaluation function of the multi-energy system is established. Furthermore, a multi-energy configuration model of fractional programming is constructed. Finally, the fractional programming model is linearized through variable substitution and decomposition algorithm, and finally, the capacity configuration strategy of the multi-energy system is obtained by solving the problem through a solver.

[0012] At the same time, the multi-energy system capacity configuration method based on fractional programming theory of the present invention also has the following advantages:

[0013] Beneficial effects:

[0014] (1) Establish a capacity benefit evaluation function to intuitively evaluate the impact of multiple energy sources on the system's economic benefits and provide a reference for the optimization of multiple resource allocation plans;

[0015] (2) A capacity allocation model based on fractional programming theory is proposed, with the goal of maximizing the return on investment to obtain the allocation strategy with the best economic benefits. The model can express the investment economic benefits more accurately in the form of fractional objective function, making up for the shortcomings of common allocation methods in long-term economic benefits.

[0016] (3) The fractional programming model is linearized by variable substitution and approximated by cutting plane, which greatly speeds up the solution of the fractional model and makes it easier to solve. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the multi-energy system capacity configuration method based on fractional programming theory of the present invention;

[0018] Figure 2 It is a structural diagram of the power flow of a multi-energy system;

[0019] Figure 3 It is a display diagram of the capacity benefit evaluation function obtained by using the present invention;

[0020] Figure 4 It is a configuration capacity display diagram under different wind power investment parameters obtained by using the present invention; DETAILED DESCRIPTION

[0021] The specific implementation of the present invention is described below in conjunction with the accompanying drawings so that those skilled in the art can better understand the present invention. It should be noted that in the following description, when the detailed description of known functions and designs may dilute the main content of the present invention, these descriptions will be omitted here.

[0022] Example

[0023] In this embodiment, we establish a relevant model of multi-energy system capacity configuration in MATLAB simulation software, and then solve it through the solver to obtain the optimal capacity configuration strategy of the multi-energy system. Figure 1 As shown, the present invention describes in detail a method for capacity configuration of a multi-energy system based on fractional programming theory, which specifically includes the following steps:

[0024] S1. Construct an operation model of a multi-energy system;

[0025] In this embodiment, Figure 2 It describes the power flow in a multi-energy system. Photovoltaic power stations and wind farms supply power to electric loads and electric boilers. Electric boilers provide thermal energy through electricity-to-heat conversion to meet the thermal load demand. When there is surplus output power from photovoltaic power stations and wind farms, the excess electrical energy is stored in the energy storage device. If the photovoltaic and wind power is lower than the load, the energy storage device discharges to balance the load. If it is still lower than the load, the grid outputs power.

[0026] Based on the above principles, the operation model of the multi-energy system is constructed, including: the power balance model of the multi-energy system, the output model of wind power and photovoltaic power, the operation model of the energy storage device and the operation model of the electric boiler;

[0027] The power balance model of the multi-energy system is:

[0028]

[0029] Where, t∈{1,2,…,T}, T represents the total number of sampling moments, and are the output power of the photovoltaic power station and the wind farm in the sth scenario at time t, is the output power of the power grid in the sth scenario at time t, and is the discharge and charging power of the energy storage device in the sth scenario at time t, P l,t is the electric load power in the sth scenario at time t, is the output power of the electric boiler in the sth scenario at time t;

[0030] The output model of wind farm and photovoltaic power station is:

[0031]

[0032]

[0033] in, and They represent the output power of the wind farm and photovoltaic power station in the sth scenario at time t, B wt and B pv Respectively represent the capacity of the wind farm and photovoltaic power station, C w and C p They represent the required wind power capacity and photovoltaic capacity respectively;

[0034] The operation model of the energy storage device is:

[0035]

[0036] Among them, P e,max Represents the maximum power of the energy storage device, η ch and η dis Respectively represent the charging and discharging efficiency of the energy storage device. In this embodiment, η ch =η dis =0.95; is the amount of electricity stored in the energy storage device in the sth scenario at time t, E emax represents the maximum capacity of the energy storage device, Δt is the sampling interval;

[0037] In this embodiment, the first two formulas of the energy storage device operation model specify the upper and lower limits of the charge and discharge power; Formula 3 describes the variation law of the energy storage power in adjacent time periods; Formula 4 specifies the upper and lower limits of the energy storage power;

[0038] The operation model of the electric boiler is:

[0039]

[0040] Among them, P eb,max Indicates the maximum power of the electric boiler. represents the thermal power output of the electric boiler in the sth scenario at time t, η eb represents the electrical-thermal conversion efficiency, represents the heat load required in the sth scenario at time t; Formula 1 specifies the electric power of the electric boiler, Formula 2 describes the electric-thermal power conversion of the electric boiler, and Formula 3 represents the thermal power balance. In this embodiment, P eb,max =200kW,η eb =0.8;

[0041] In this embodiment, T=24, we collect known parameters in the operation model of the multi-energy system at sampling times t∈{1,2,…,T}, such as wait.

[0042] S2. Construct the capacity benefit evaluation function of the multi-energy system;

[0043] S2.1. Construct the daily operation cost objective function of the multi-energy system;

[0044]

[0045] In this embodiment, the off-peak period T L From 23:00 to 8:00 the next day, the electricity price is l = 0.43 yuan / kWh; during peak hours T H From 8:00 to 23:00, the electricity price is h =1.18 yuan / kWh.

[0046] The daily operating cost objective function of the multi-energy system is expressed in matrix form as:

[0047]

[0048] Among them, the capacity to be configured is defined as θ=[C w ; C p ; E emax ;P e,max ], including wind power, photovoltaic capacity, energy storage capacity and maximum energy storage power, A, b, B and c represent the constant coefficients of the operation model, and x represents the system operation variable;

[0049] In this embodiment, it is assumed that the multi-energy system operates under S=56 scenarios. Then, the daily operation cost objective function of the multi-energy system under the sth scenario when the configuration capacity is θ is expressed in matrix form as follows:

[0050]

[0051] Among them, the superscript represents transpose; x s is the operating variable of the multi-energy system in the sth scenario, which is specifically expressed as: c s represents the coefficient matrix in the sth scenario, c s 、x s The number of elements in is equal, among which The coefficient corresponding to each moment is λ l or h , when time t is the off-peak period, the corresponding electricity price λ l , when time t is the peak period, the corresponding electricity price λh , the coefficients corresponding to the other positions are all 0; A s 、b s , B s They represent the constant coefficient matrices corresponding to the operation model of the multi-energy system in the sth scenario, and satisfy: A s x s ≤ b s +B s θ;

[0052] S2.2. Combining different scenarios, the sum of the daily operating costs of the multi-energy system under different scenarios when the configuration capacity is θ is obtained, which is expressed as:

[0053]

[0054] Among them, ρ s represents the probability that the multi-energy system is in the sth scenario, taking ρ s =1 / 56;

[0055] S2.3. The capacity benefit evaluation function for constructing a multi-energy system is:

[0056] V(θ)=v av (0)-v av (θ)

[0057] Where V(θ) represents the capacity benefit evaluation function of the multi-energy system when the configuration capacity is θ, θ is the capacity to be configured, θ=[C w ; C p ; E emax ;P e,max ], C w is the wind power installed capacity, C p is the photovoltaic installed capacity, E emax is the energy storage capacity, P e,max Maximum power of energy storage; v av (0) represents the sum of daily operating costs under different scenarios when the configuration capacity is 0, v av (θ) represents the sum of daily operating costs under different scenarios when the configuration capacity is θ.

[0058] In this embodiment, Figure 3 It shows the value change of capacity benefit evaluation function under different θ, and E is fixed. emax :P e,max =3:1, C w and C p Take four points evenly from 0kW to 20kW, E emax Four points are evenly selected from 0kW to 30kW. Figure 3 It can be seen that wind power capacity has the greatest impact on the economic benefits of the system, followed by photovoltaics, and energy storage devices have the least impact.

[0059] S3. Construct a capacity configuration model for a multi-energy system;

[0060] Constructing investment cost function of multi-energy system;

[0061]

[0062] Among them, C in represents the total investment cost of the multi-energy system, κ represents the unit cost parameter, κ=[κ w κ p κ e κ ep ],κ w κ p κ e κ ep Respectively represent the unit capacity cost and unit cost of power devices of wind farm, photovoltaic power station and energy storage device; κ0 represents the fixed cost of deploying wind power, photovoltaic power station and energy storage device;

[0063] In this embodiment, take κ w =5500 yuan / kW, κ p =4200 yuan / kW, κ e =1200 yuan / kWh, κ ep =400 yuan / kW, κ0=120,000 yuan.

[0064] The objective function of the capacity allocation model is constructed by combining the investment cost function of the multi-energy system;

[0065]

[0066] S4. Linearize the capacity configuration model of the multi-energy system, and then obtain the capacity configuration strategy of the multi-energy system through the solver.

[0067] S5.1. The sum of the daily operating costs of the multi-energy system under different scenarios v av (θ) is converted into dual form and expressed as:

[0068]

[0069] Among them, the coefficient matrix satisfy: x=[x1,x2,…,x S ]; y is the optimal solution corresponding to θ;

[0070] S5.2. Dual form of v av(θ) is the cutting plane added to the capacity configuration model, and the added capacity configuration model and constraints are obtained:

[0071]

[0072] θ ≥ 0

[0073] Among them, ζ is a variable replacing v av (θ), y i For different θ i The corresponding optimal solution;

[0074] In this embodiment, a cutting plane is added to the planning model constraints to reduce the feasible region and facilitate the solution;

[0075] S5.3, linearizing the capacity configuration model with the added cutting plane;

[0076] Let the variable Then the capacity configuration model is linearized as:

[0077]

[0078] z>0

[0079]

[0080] S5.4. Sample θ to obtain multiple θ i , and then each θ i Substitute the even form of v av (θ), multiple optimal solutions y are obtained i Finally, each y i Substitute the linearized capacity configuration model and use the solver to obtain the variable

[0081] S5.5. According to variables Solve for the capacity configuration parameter θ;

[0082]

[0083] Finally, the capacity configuration strategy of the multi-energy system is obtained according to the optimal θ [C w ; C p ; E emax ;P e,max ].

[0084] In this embodiment, the GUROBI commercial solver is used to solve the model, and the final configuration result is C w =32.34kW, C w =32.34kW, E emax =26.09kWh, Pe,max =5.46kW.

[0085] In addition, we fix other parameters unchanged and adjust κ w Different configuration results, such as Figure 4 As shown, in κ w When increases, the wind power configuration capacity decreases, indicating that when configuring wind power, excessively high investment costs will reduce the economic benefits of the system. Therefore, the solution result will reduce the wind power configuration capacity.

[0086] Although the above describes the illustrative specific embodiments of the present invention to facilitate those skilled in the art to understand the present invention, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations using the concept of the present invention are protected.

Claims

1. A multi-energy system capacity configuration method based on fractional programming theory, characterized in that: The following steps are involved: (1) Construct an operation model of a multi-energy system; (2) Constructing the capacity benefit evaluation function of the multi-energy system; (3) Construct a capacity configuration model for a multi-energy system; (4) The capacity configuration model of the multi-energy system is linearized, and then the capacity configuration strategy of the multi-energy system is obtained through the solver.

2. The multi-energy system capacity configuration method based on fractional programming theory according to claim 1 is characterized in that: The operation model of the multi-energy system includes: a power balance model of the multi-energy system, output models of wind power and photovoltaic power, an operation model of an energy storage device, and an operation model of an electric boiler; The power balance model of the multi-energy system is: Where, t∈{1,2,…,T}, T represents the total number of sampling moments, and are the output power of the photovoltaic power station and the wind farm in the sth scenario at time t, is the output power of the power grid in the sth scenario at time t, and is the discharge and charging power of the energy storage device in the sth scenario at time t, P l,t is the electric load power in the sth scenario at time t, is the output power of the electric boiler in the sth scenario at time t; The output model of the wind farm and photovoltaic power station is: Among them, B wt and B pv Respectively represent the capacity of the wind farm and photovoltaic power station, C w and C p They represent the required wind power capacity and photovoltaic capacity respectively; The operation model of the energy storage device is: Among them, P e,max Represents the maximum power of the energy storage device, η ch and η dis represent the charging and discharging efficiency of the energy storage device, respectively. is the amount of electricity stored in the energy storage device in the sth scenario at time t, E emax represents the maximum capacity of the energy storage device, Δt is the sampling interval; The operation model of the electric boiler is: Among them, P eb,max Indicates the maximum power of the electric boiler. represents the thermal power output of the electric boiler in the sth scenario at time t, η eb represents the electrical-thermal conversion efficiency, Represents the heat load required in the sth scenario at time t.

3. The multi-energy system capacity configuration method based on fractional programming theory according to claim 1 is characterized in that: The capacity benefit evaluation function of the multi-energy system is: V(θ)=v av (0)-v av (θ) Where V(θ) represents the capacity benefit evaluation function of the multi-energy system when the configuration capacity is θ, θ is the capacity to be configured, θ=[C w ; C p ; E emax ;P e,max ], C w is the wind power installed capacity, C p is the photovoltaic installed capacity, E emax is the energy storage capacity, P e,max Maximum power of energy storage; v av (0) represents the sum of daily operating costs under different scenarios when the configuration capacity is 0, v av (θ) represents the sum of daily operating costs under different scenarios when the configuration capacity is θ, expressed as: Among them, ρ s represents the probability that the multi-energy system is in the sth scenario, s = 1, 2, ..., S, S represents the number of collected scenarios; v s (θ) represents the daily operating cost in the sth scenario when the configuration capacity is θ, expressed as: Among them, the superscript " " indicates transpose; x s is the operating variable of the multi-energy system in the sth scenario, which is specifically expressed as: c s represents the coefficient matrix in the sth scenario, c s 、x s The number of elements in is equal, among which The coefficient corresponding to each moment is λ l or h , when time t is the off-peak period, the corresponding electricity price λ l , when time t is the peak period, the corresponding electricity price λ h , the coefficients corresponding to the other positions are all 0; A s 、b s , B s They represent the constant coefficient matrices corresponding to the operation model of the multi-energy system in the sth scenario, and satisfy: A s x s ≤ b s +B s θ.

4. The multi-energy system capacity configuration method based on fractional programming theory according to claim 1 is characterized in that: The multi-energy system capacity configuration model is: Constructing investment cost function of multi-energy system; Among them, C in represents the total investment cost of the multi-energy system, κ represents the unit cost parameter, κ=[κ w κ p κ e κ ep ],κ w κ p κ e κ ep Respectively represent the unit capacity cost and unit cost of power devices of wind farm, photovoltaic power station and energy storage device; κ0 represents the fixed cost of deploying wind power, photovoltaic power station and energy storage device; The objective function of the capacity allocation model is constructed by combining the investment cost function of the multi-energy system; 5. The multi-energy system capacity configuration method based on fractional programming theory according to claim 1 is characterized in that: The method for linearizing and solving the multi-energy system capacity configuration model is as follows: (5.1) The sum of the daily operating costs of the multi-energy system under different scenarios v av (θ) is converted into dual form and expressed as: Among them, the coefficient matrix satisfy: x=[x1;x2;…;x S ]; y is the optimal solution corresponding to θ; (5.2), the dual form of v av (θ) is the cutting plane added to the capacity configuration model, and the added capacity configuration model and constraints are obtained: θ ≥ 0 Among them, ζ is a variable replacing v av (θ), y i For different θ i The corresponding optimal solution; (5.3), linearize the capacity configuration model with the added cutting plane; Let the variable Then the capacity configuration model is linearized as: z＞0 (5.4), sample θ and obtain multiple θ i , and then each θ i Substitute the dual form of v av (θ), multiple optimal solutions y are obtained i Finally, each y i Substitute the linearized capacity configuration model and use the solver to obtain the variable (5.5), according to the variable Solve for the capacity configuration parameter θ; Finally, the capacity configuration strategy of the multi-energy system is obtained according to the optimal θ [C w ; C p ; E emax ;P e,max ].

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