Regional energy system planning method considering active power distribution network electricity price excitation

Abstract

The invention discloses a regional energy system planning method considering active power distribution network electricity price excitation. The method belongs to the technical field of integrated energy system planning, and comprises the following steps: firstly, modeling operation models of a wind turbine, a photovoltaic device, an energy storage device, a hydrogen storage system, a fuel cell and an electric hydrogen production device in a regional energy system; then, an active power distribution network operation model and a regional energy system planning model based on a stepped carbon transaction mechanism are established; a master-slave game model is constructed by taking an active power distribution network as a leader and a regional energy system as a follower, and a master-slave game model solving algorithm based on a talent competition algorithm is constructed to solve the master-slave game. And finally, verification is carried out in combination with four different models, and results prove that the model and the planning strategy provided by the invention effectively reduce the planning cost of the regional energy system and realize cross-seasonal storage and utilization of electric energy.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy system planning, and in particular relates to a regional energy system planning method taking into account active distribution network electricity price incentives. Background Art

[0002] The integrated energy system can effectively realize the cascade utilization of energy and effectively promote the consumption of renewable energy by giving play to the complementary characteristics of different energy sources. However, since the integrated energy system contains a large amount of renewable energy and often presents seasonal supply and demand imbalances. Seasonal energy storage technology can complement energy across seasons and realize long-term energy transfer. At present, seasonal energy storage technology mainly includes seasonal heat storage and seasonal hydrogen storage. As hydrogen energy is an important clean energy with the characteristics of high efficiency and purity, it has great application potential in the direction of sustainable energy transformation. For this reason, the present invention proposes a regional energy system planning method taking into account the incentive of active distribution network electricity prices. Summary of the invention

[0003] The present invention proposes a regional energy system planning method that takes into account the incentive of active distribution network electricity price. First, hydrogen storage system, electric hydrogen production device and fuel cell are considered in the regional energy system to realize the long-term storage of electric energy across seasons and energy forms. Subsequently, an active distribution network operation model and a regional energy system planning model based on a stepped carbon trading mechanism are established. A master-slave game model is constructed with the active distribution network as the leader and the regional energy system as the follower, and a master-slave game model solving algorithm based on the draft competition algorithm is constructed to solve the master-slave game, providing a solution for the planning and configuration of regional energy systems under the condition of seasonal imbalance of renewable energy.

[0004] The present invention can be achieved through the following technical solutions:

[0005] A method for regional energy system planning taking into account active distribution network electricity price incentives, characterized by comprising the following steps:

[0006] Step 1: Model the operation models of electric heat loads, wind turbines, photovoltaics, electric boilers, electric energy storage, hydrogen storage systems, fuel cells and electric hydrogen production devices in the regional energy system;

[0007] Step 2: Establish an active distribution network model and construct a regional energy system planning model based on the stepped carbon trading mechanism and the peak load regulation of the active distribution network;

[0008] Step 3: Taking the active distribution network as the leader and the regional energy system as the follower, a master-slave game model and its corresponding solution method are proposed.

[0009] In some embodiments, in step 1, the regional energy system contains wind turbines, photovoltaics, electric boilers, electric energy storage, hydrogen storage systems, fuel cells, electric hydrogen production devices and electric heat loads, of which wind turbines and photovoltaics are original equipment, and the remaining equipment needs to be configured. The hydrogen storage system contains short-term hydrogen storage and long-term hydrogen storage, of which short-term hydrogen storage is used for energy optimization of the regional energy system during the day, and long-term hydrogen storage is used for seasonal storage of hydrogen energy to achieve daytime energy optimization of the regional energy system; electric boilers are used to convert electrical energy into thermal energy to supply the thermal load of the regional energy system; electrical energy storage is used to store and release electrical energy; fuel cells are used to convert hydrogen energy in the hydrogen storage system into corresponding electrical energy and thermal energy for use in the regional energy system; the electric hydrogen production device is used to convert electrical energy into hydrogen energy and thermal energy, hydrogen energy is stored in the hydrogen storage system, and thermal energy is supplied to the regional energy system for use. Through the coordinated operation of the hydrogen storage system, fuel cells and hydrogen production devices in the regional energy system, it is possible to convert electrical energy into hydrogen energy and store it in the hydrogen storage system in seasons with abundant wind and solar resources, and then convert hydrogen energy into electrical energy for use in the system in seasons with relatively scarce wind and solar resources, thereby achieving cross-seasonal optimal utilization of electrical energy and improving the seasonal characteristics of electricity load.

[0010] In some embodiments, in step 2, the active distribution network is a distribution network system containing a large number of distributed power sources. The active distribution network can formulate reasonable electricity prices according to the self-generated wind and solar power output, so as to coordinate the electricity consumption behavior of the regional energy system, and guide the regional energy system to purchase and sell electricity to the active distribution network through electricity prices, thereby realizing peak shaving and valley filling of the active distribution network, promoting the wind and solar power consumption of the active distribution network, and improving its energy sales efficiency.

[0011] In some embodiments, in step 2, the step-by-step carbon trading mechanism is a carbon emission control method that allows producers to trade carbon emission rights in the market by establishing legal carbon emission rights. The regional energy system planning model under the influence of the step-by-step carbon trading mechanism and the active distribution network peak load regulation refers to a planning model for the remaining required equipment based on the existing equipment, taking into account the step-by-step carbon trading mechanism and the active distribution network time-sharing electricity purchase and sale price.

[0012] In some embodiments, in step 3, a master-slave game model is constructed with the active distribution network as the leader and the regional energy system as the follower. In the master-slave game model, the active distribution network generates a time-sharing electricity purchase price based on the power consumption of the regional energy system and transmits it to the regional energy system; then the regional energy system reasonably configures its own devices based on the electricity purchase price to the active distribution network.

[0013] In some embodiments, in step 3, a master-slave game model solution method based on a draft competition algorithm is proposed. The draft competition algorithm is a brute force search algorithm. Since the active distribution network and the regional energy system influence each other through the purchase and sale of electricity, and the purchase and sale of electricity will be affected by the price of electricity, the draft competition algorithm can be used to generate the purchase and sale price of the distribution network. The specific process of the master-slave game solution method based on the draft competition algorithm is as follows:

[0014] S1: Generate the purchase and sale prices of the distribution network using a competitive search algorithm, and transmit the purchase and sale prices to the regional energy system. In this process, the randomly generated purchase and sale prices must satisfy the constraints of formula (28);

[0015] S2: Based on the known price of electricity purchased and sold by the active distribution network, the regional energy system uses formula (24) as the objective function to optimize the self-generated equipment configuration, output and the purchase and sale of electricity to the active distribution network, and transmits the purchase and sale of electricity to the active distribution network to the active distribution network;

[0016] S3: The active distribution network calculates the objective function (18) according to the power purchase and sales situation of the regional energy system, and uses the draft competition algorithm to correct the power purchase and sales price, and returns to S1 to enter the next iteration.

[0017] Beneficial effects of the present invention:

[0018] When electric hydrogen production and fuel cells are configured, the configuration of electric boilers will be reduced; configuring long-term hydrogen storage and short-term hydrogen storage can reduce the system operating costs. This is because when short-term hydrogen storage and long-term hydrogen storage are configured, electric energy can be converted into hydrogen energy and stored in the hydrogen storage system, and then converted into electric energy for use in seasons when electricity is scarce, thereby alleviating the power consumption pressure of the regional energy system and reducing the cost of purchasing electricity from the main grid to the regional energy system; due to the decrease in the amount of electricity purchased from the main grid by the regional energy system, the revenue of the active distribution network will also be reduced. In addition, the regional energy system stores excess electric energy in the hydrogen storage system instead of selling it to the active distribution network, resulting in the active distribution network spending a higher price to build the upper-level grid, further reducing the revenue of the active distribution network.

[0019] In summary, the model and planning strategy proposed in the present invention effectively reduce the planning cost of the regional energy system and realize the cross-seasonal storage and utilization of electric energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are part of the present invention and are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but do not constitute an improper limitation of the present invention. Obviously, the accompanying drawings described below are only some embodiments. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0021] Figure 1 for the regional energy system structure;

[0022] Figure 2 It is the process of solving the master-slave game based on the draft competition algorithm;

[0023] Figure 3 The load active power and renewable energy output curves of the active distribution network on typical days in spring, summer, autumn and winter;

[0024] Figure 4 The electric and thermal load values ​​of the regional energy system on a typical day in spring, summer, autumn and winter;

[0025] Figure 5 Wind turbine and photovoltaic power generation capacity of the regional energy system on a typical day in spring, summer, autumn and winter.

[0026] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but are intended to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0028] 1. Construction of regional energy system

[0029] Figure 1 is the regional energy system structure, consisting of Figure 1It can be seen that the regional energy system contains wind turbines, photovoltaics, electric boilers, electric energy storage, hydrogen storage systems, fuel cells, electric hydrogen production devices and electric heat loads. Wind turbines and photovoltaics are distributed power sources that can generate electric power. Wind turbines and photovoltaics are original equipment, and the rest of the equipment needs to be configured; the hydrogen storage system contains short-term hydrogen storage and long-term hydrogen storage, of which short-term hydrogen storage is used for energy optimization of the regional energy system during the day, and long-term hydrogen storage is used for seasonal storage of hydrogen energy to achieve daytime energy optimization of the regional energy system; electric boilers are used to convert electrical energy into thermal energy to supply the thermal load of the regional energy system; electrical energy storage is used to store and release electrical energy; fuel cells are used to convert hydrogen energy in the hydrogen storage system into corresponding electrical energy and thermal energy for use in the regional energy system; electric hydrogen production devices are used to convert electrical energy into hydrogen energy and thermal energy, hydrogen energy is stored in the hydrogen storage system, and thermal energy is supplied to the regional energy system.

[0030] Through the coordinated operation of the hydrogen storage system, fuel cells and hydrogen production devices in the regional energy system, it is possible to convert electrical energy into hydrogen energy and store it in the hydrogen storage system in seasons with abundant wind and solar resources, and then convert hydrogen energy into electrical energy for use in the system in seasons with relatively scarce wind and solar resources, thereby achieving cross-seasonal optimal utilization of electrical energy and improving the seasonal characteristics of electricity load.

[0031] In the hydrogen storage system, the short-term hydrogen storage (STHS) model can be expressed as:

[0032]

[0033] Where: and are the hydrogen storage and release power and energy storage value of STHS at time t respectively; and are the hydrogen storage and release efficiency and the self-release rate of STHS respectively; C STHS and β STHS are the configuration capacity and energy multiple of STHS respectively; T is the total scheduling period in each typical day; x STHS is the investment status variable of STHS, which is a Boolean variable; Δt is the unit time step, which is 1h.

[0034] The mathematical model of long-term hydrogen storage (LTHS) can be expressed as:

[0035]

[0036] In formula (2) to formula (7), and They are the hydrogen storage and release power and energy storage value of LTHS at time t respectively; and are the hydrogen storage and release efficiency and self-release rate of LTHS respectively; CLTHS and β LTHS are the configuration capacity and energy multiplier of LTHS respectively; and are the hydrogen storage state variable and hydrogen release state variable of LTHS, respectively, which are Boolean variables; x LTHS is the investment state variable of LTHS, which is a Boolean variable; p(w) is the probability of typical day w occurring; W is the maximum number of typical days; and It is the hydrogen storage and release variable of LTHS, which is a Boolean variable.

[0037] The mathematical model of electrical energy storage (EES) can be expressed as:

[0038]

[0039] Where: and are the hydrogen storage and release power and energy storage value of EES at time t respectively; and are the hydrogen storage and release efficiency and the self-release rate of EES; C EES and β EES are the configuration capacity and energy multiplier of EES respectively; T is the total scheduling period in each typical day; x EES is the investment state variable of EES, which is a Boolean variable; Δt is the unit time step, which is 1h.

[0040] The models of fuel cell (FC) and electric hydrogen generator (EL) can be expressed as:

[0041]

[0042] In formula (9) to formula (15), and are the electric power consumed by EL, the electric power output by FC, the thermal power output by EL, the thermal power output by FC, the hydrogen production power of EL, the hydrogen power consumed by FC, the electric energy transmitted to EL and the electric energy output by FC at time t on the wth typical day; C EL / C FC , γ EL / γ FC and x FC / x EL They are the configured capacity, operating status variable (Boolean variable), minimum load factor and investment status variable (Boolean variable) of EL / FC respectively; and They are the waste heat utilization efficiency of EL, the waste heat utilization efficiency of FC, the electricity-to-hydrogen conversion efficiency of EL, and the hydrogen-to-electricity conversion efficiency of FC; and They are the minimum start / stop cycle and start / stop state variable of EL respectively.

[0043] The mathematical model of wind turbine (WT) and photovoltaic (PV) can be expressed as:

[0044]

[0045] In the formula, and are the original PV and WT processing upper limits in the regional energy system, respectively; and are the electric powers output by PV and WT at time t on the wth typical day.

[0046] The mathematical model of electric boiler (EB) can be expressed as:

[0047]

[0048] In the formula, C EB and x EB are the construction capacity and investment state variables of EB respectively; and are the electrical energy consumed and thermal energy output by EB at time t on the wth typical day; α EB is the electrothermal conversion coefficient of EB.

[0049] 2. Active distribution network model and regional energy system planning model based on step-by-step carbon trading mechanism

[0050] The active distribution network is a distribution network system that contains a large number of distributed power sources. The active distribution network can formulate reasonable electricity prices based on the self-generated wind and solar power output, thereby adjusting the electricity consumption behavior of the regional energy system, and guiding the regional energy system to purchase and sell electricity to the active distribution network through electricity prices, thereby achieving peak shaving and valley filling of the active distribution network, promoting the wind and solar power consumption of the active distribution network, and improving its energy sales efficiency.

[0051] The objective function of the active distribution network can be expressed as formula (18):

[0052] F ADN =max(I sell -I buy ) (18)

[0053] In the formula, F ADN is the objective function of the active distribution network; I sell The annual electricity sales revenue of the active distribution network; buy is the annual electricity purchase cost of the active distribution network. sell and I buy The expressions of are shown in equations (19)-(20) respectively.

[0054]

[0055] In the formula, and are the electric energy sold to the regional energy system and the electric energy purchased from the regional energy system by the active distribution network at time t on the wth typical day; t,sell and t,buy The electricity selling price and electricity purchasing price set by the active distribution network to the regional energy system at time t respectively; and λ t,buy They are respectively the electric energy and electricity selling price sold by the upper-level power grid to the active distribution network at time t on the wth typical day.

[0056] The tiered carbon trading mechanism is a carbon emission control method that allows producers to trade carbon emission rights in the market by establishing legal carbon emission rights. The tiered carbon trading mechanism mainly includes the carbon emission quota model of the regional energy system, the actual carbon emission model and the carbon emission trading model.

[0057] The carbon emission sources in the regional energy system mainly come from upstream electricity purchase and fuel cells, and its carbon emission quota model can be described as formula (21).

[0058]

[0059] In the formula, E w,IES is the carbon emission quota of the regional energy system under the wth typical day; e and χ g is the carbon emission quota coefficient; P w,t,buy , and They are respectively the electricity purchased by the regional energy system, the thermal power generated by FC and the electric power at time t on the wth typical day.

[0060] The actual carbon emission model of the regional energy system can be described as formula (22):

[0061]

[0062] In the formula, E w,IES,a is the actual carbon emission of the regional energy system on the wth typical day; a1, b1, a2, and b2 are all carbon emission calculation coefficients.

[0063] Through equations (21) and (22), the carbon emission rights trading amount E of the regional energy system on the wth typical day can be obtained: w,IES,Total Numerically equal to E w,IES,a -E w,IES , its step-by-step carbon trading model can be described as formula (23).

[0064]

[0065] In the formula, C w,co2 is the carbon trading cost of the regional energy system on the wth typical day; λ, α and l are the carbon trading base price, price growth rate and carbon emission interval length respectively.

[0066] The objective function of the regional energy system planning model under the influence of the stepped carbon trading mechanism and active distribution network peak regulation is the system annual total configuration operation cost F IES The lowest value can be described as shown in formula (24):

[0067] F IES =min(I buy -I sell +I ST +I ope +I CO2 ) (twenty four)

[0068] In the formula, I buy ,I sell ,I ST ,I ope and I CO2 They are the annual electricity purchase cost of the regional energy system from the active distribution network, the annual electricity sales revenue to the active distribution network, the annual configuration equipment cost, the annual operation and maintenance cost, and the annual carbon trading cost. buy and I sell The expressions of are shown in equations (19) and (20) respectively. ST ,I ope and I CO2 The expressions of are shown in equations (25)-(27) respectively.

[0069]

[0070] Where, θ = {EES, STHS, LTHS, EB, EL, FC}, is the set of equipment included in the regional energy system; r is the interest rate; v i , C i and Y i It is divided into the unit power investment cost, configuration capacity and service life of equipment i in the regional energy system; τ i is the unit power operation and maintenance cost of device i; P w,t,i is the power output of device i at time t on the wth typical day; C w,co2 is the carbon emission cost on the wth typical day, and its specific expression is shown in formula (23).

[0071] 3. Master-slave game model and its solution method

[0072] The master-slave game refers to a type of game in which one party acts first and the other party acts later. First, the active distribution network is used as the leader to announce the electricity purchase and sales price to the regional energy system. Then, the regional energy system is used as a follower to optimize its own equipment configuration, equipment output, etc. according to the electricity purchase and sales price set by the active distribution network, and feed back the electricity purchase and sales energy to the active distribution network to the active distribution network. The active distribution network then adjusts its own electricity price according to the electricity purchase and sales feedback from the regional energy system to achieve the purpose of maximizing the operating efficiency of the active distribution network. It can be seen from this that the two processes are interactive and the active distribution network and the regional energy system belong to different subjects with their own objective functions. A master-slave game is formed between the two subjects, and the master-slave game can be introduced to describe their behavior.

[0073] Since the active distribution network has the right to set its own prices, in order to avoid the active distribution network maliciously raising the electricity price during the game and ensure the interests of the regional energy system, the active distribution network should satisfy the constraint of formula (28) when setting the electricity sales price:

[0074]

[0075] In formula (28), It is the upper limit of the average electricity selling price of the active distribution network.

[0076] In addition, the regional energy system needs to meet the corresponding electricity and heat power balance constraints during the planning process, as shown in Equations (29)-(30), respectively.

[0077]

[0078] In formulas (29)-(30), and They are the load value in the regional energy system at time t on the wth typical day; wind turbine output value; photovoltaic output value; electric power consumed by hydrogen production; electric boiler power consumption; fuel cell power generation; electric energy storage power release; electric energy storage power absorption; power purchased from the active distribution network and power sold to the active distribution network; and They are the heat load value, electric boiler output thermal power, fuel cell output thermal power, electric hydrogen output thermal power and abandoned heat in the regional energy system at time t on the wth typical day.

[0079] The master-slave game model includes two main bodies: the active distribution network and the regional energy system. The objective function of the optimization model of the active distribution network is formula (18), which is composed of formula (18)-formula (20) and formula (28); the objective function of the regional energy system planning model is formula (24), which is composed of formula (1)-formula (17), formula (21)-formula (27) and formula (29)-formula (30).

[0080] The master-slave game solution model uses the draft competition algorithm for iterative solution. The draft competition algorithm is a brute force search algorithm. Since the active distribution network and the regional energy system affect each other through the purchase and sale of electricity, and the purchase and sale of electricity will be affected by the price of electricity, the draft competition algorithm can be used to generate the purchase and sale price of the distribution network. The specific solution process of the master-slave game solution method based on the draft competition algorithm is as follows Figure 2 The specific process is as follows:

[0081] S1: Generate the purchase and sale prices of the distribution network using a competitive search algorithm, and transmit the purchase and sale prices to the regional energy system. In this process, the randomly generated purchase and sale prices must satisfy the constraints of formula (28);

[0082] S2: Based on the known price of electricity purchased and sold by the active distribution network, the regional energy system uses formula (24) as the objective function to optimize the self-generated equipment configuration, output and the purchase and sale of electricity to the active distribution network, and transmits the purchase and sale of electricity to the active distribution network to the active distribution network;

[0083] S3: The active distribution network calculates the objective function (18) according to the power purchase and sales situation of the regional energy system, and uses the draft competition algorithm to correct the power purchase and sales price, and returns to S1 to enter the next iteration.

[0084] When using the draft competition algorithm to correct the purchase and sale prices of electricity, equations (31)-(32) are needed:

[0085]

[0086] Formulas (31) and (32) are the variable correction formulas in the draft competition algorithm. It represents the value of the jth dimension of the ith particle at the tth iteration, which represents the purchase and sale price of the active distribution network in the present invention, and the dimension is 48; t represents the current iteration number; iter Max represents the maximum number of iterations; A(i) represents the learning ability of the current particle; C is a random number in (0,0.1]; L1∈[0,1]; α is a random number in [-1,1]; Q is a random number in [0,2]; D and L3 are both 1×d matrices, but the elements in the D matrix are all 1, while the elements in L3 are randomly distributed between -1 and 1; P is a standard normal distribution with a mean of 0 and a variance of 1; when each contestant's position is updated, o is randomly drawn from the matrix [0.1, 0.2, 0.3, 0.4, 0.5]; L2∈[0,1].

[0087] 4. Comparison of the strategy of the present invention with the other four models

[0088] The configuration parameters of each device are shown in Table 1. The parameters related to the regional energy system are shown in Table 2. The simulation is based on the wind and solar load data under four typical days in spring, summer, autumn and winter, and the probability of each typical day is 0.25. The load active power and renewable energy output curves of the active distribution network under typical days in spring, summer, autumn and winter are shown in Figure 3 As shown in Figure 2, the electric and thermal load curves of the regional energy system are as follows: Figure 4 As shown in Figure 2, the wind turbine and photovoltaic power generation power of the regional energy system is as follows: Figure 5 The electricity price of the high-voltage main grid is 0.65 yuan / (kW·h), assuming that the time-of-use electricity purchase price set by the active distribution network is 80% of the time-of-use electricity sales price.

[0089] Table 1 Configuration parameters of each device

[0090]

[0091] Table 2 System related parameters

[0092]

[0093] The following five configuration models are set up for comparison.

[0094] Model 1: The regional energy system does not have a hydrogen storage system, electrolyzer, or fuel cell.

[0095] Model 2: The regional energy system does not have long-term hydrogen storage;

[0096] Model 3: The regional energy system does not have short-term hydrogen storage;

[0097] Model 4: District energy system configuration with all devices.

[0098] The master-slave game solving method based on the draft competition algorithm is used to solve the above four models. The equipment configuration results under each scheme are shown in Table 3. The operating cost of the regional energy system and the operating income of the active distribution network are shown in Table 4.

[0099] Table 3 Equipment configuration results of each solution

[0100]

[0101] Table 4 Active distribution network benefits and regional energy system costs for each scheme

[0102]

[0103] It can be seen from Tables 3 and 4 that when electric hydrogen production and fuel cells are configured, the configuration of electric boilers will be reduced. This is because electric hydrogen production and fuel cells can generate a certain amount of heat energy during operation, thereby reducing the dependence of the regional energy system on EB. In addition, the configuration of long-term hydrogen storage and short-term hydrogen storage can reduce the system operation cost. In the case of all equipment configured, the annual operation cost of the regional energy system in Scheme 4 is the lowest, which is 48.21 million yuan. This is because when short-term hydrogen storage and long-term hydrogen storage are configured, electric energy can be converted into hydrogen energy and stored in the hydrogen storage system. In the season of power shortage, hydrogen energy can be converted into electric energy for use, thereby alleviating the power consumption pressure of the regional energy system and reducing the cost of the regional energy system to purchase electricity from the main grid. Due to the decrease in the amount of electricity purchased by the regional energy system from the main grid, the income of the active distribution network will also decrease. In addition, the regional energy system stores excess electric energy in the hydrogen storage system instead of selling it to the active distribution network, resulting in the active distribution network spending a higher price to build the upper-level power grid, further reducing the income of the active distribution network.

[0104] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0105] In addition, those skilled in the art will understand that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments also means being within the scope of protection of the present invention and forming different embodiments. For example, in the above embodiments, those skilled in the art can use them in combination according to the known technical solutions and the technical problems to be solved by this application.

[0106] The above is only a preferred embodiment of the present invention, and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this patent can make some changes or modify the technical contents suggested above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the solution of the present invention.

Claims

1. A regional energy system planning method taking into account active distribution network electricity price incentives, characterized in that: The following steps are involved: Step 1: Model the operation models of electric heat loads, wind turbines, photovoltaics, electric boilers, electric energy storage, hydrogen storage systems, fuel cells and electric hydrogen production devices in the regional energy system; Step 2: Establish an active distribution network model and construct a regional energy system planning model based on the stepped carbon trading mechanism and the peak load regulation of the active distribution network; Step 3: Taking the active distribution network as the leader and the regional energy system as the follower, a master-slave game model and its corresponding solution method are proposed.

2. A regional energy system planning method taking into account active distribution network electricity price incentives according to claim 1, characterized in that: In step 1, the wind turbines and photovoltaics in the regional energy system are existing equipment, and the remaining equipment needs to be configured; The hydrogen storage system contains short-term hydrogen storage and long-term hydrogen storage. Short-term hydrogen storage is used for energy optimization of the regional energy system during the day, and long-term hydrogen storage is used for seasonal storage of hydrogen energy to achieve energy optimization of the regional energy system during the day. Electric boilers are used to convert electrical energy into thermal energy to supply the heat load of the regional energy system; Electrical energy storage is used to store and release electrical energy; Fuel cells are used to convert hydrogen energy in the hydrogen storage system into corresponding electrical energy and thermal energy for use in the regional energy system; The electric hydrogen production device is used to convert electrical energy into hydrogen energy and thermal energy. The hydrogen energy is stored in the hydrogen storage system, and the thermal energy is supplied to the regional energy system for use.

3. A regional energy system planning method taking into account active distribution network electricity price incentives according to claim 2, characterized in that: Through the coordinated operation of the hydrogen storage system, fuel cells and hydrogen production devices in the regional energy system, it is possible to convert electrical energy into hydrogen energy and store it in the hydrogen storage system in seasons with abundant wind and solar resources, and then convert hydrogen energy into electrical energy for use in the system in seasons with scarce wind and solar resources, thereby achieving cross-seasonal optimal utilization of electrical energy and improving the seasonal characteristics of electricity load.

4. A regional energy system planning method taking into account active distribution network electricity price incentives according to claim 2, characterized in that: In the hydrogen storage system, the short-term hydrogen storage (STHS) model is expressed as: Where: and are the hydrogen storage and release power and energy storage value of STHS at time t respectively; and are the hydrogen storage and release efficiency and the self-release rate of STHS respectively; C STHS and β STHS are the configuration capacity and energy multiple of STHS respectively; T is the total scheduling period in each typical day; x STHS is the investment status variable of STHS, which is a Boolean variable; Δt is the unit time step, which is 1h; The mathematical model of long-term hydrogen storage (LTHS) is expressed as: In formula (2) to formula (7), and are the hydrogen storage and release power and energy storage value of LTHS at time t respectively; and are the hydrogen storage and release efficiency and self-release rate of LTHS respectively; C LTHS and β LTHS are the configuration capacity and energy multiplier of LTHS respectively; and are the hydrogen storage state variable and hydrogen release state variable of LTHS, respectively, which are Boolean variables; x LTHS is the investment state variable of LTHS, which is a Boolean variable; p(w) is the probability of typical day w occurring; W is the maximum number of typical days; and is the hydrogen storage and release variable of LTHS, which is a Boolean variable; The mathematical model of electrical energy storage (EES) is expressed as: Where: and are the hydrogen storage and release power and energy storage value of EES at time t respectively; and are the hydrogen storage and release efficiency and the self-release rate of EES; C EES and β EES are the configuration capacity and energy multiplier of EES respectively; T is the total scheduling period in each typical day; x EES is the investment state variable of EES, which is a Boolean variable; Δt is the unit time step, which is 1h; The model of fuel cell (FC) and electric hydrogen generator (EL) is expressed as: In formula (9) to formula (15), and are the electric power consumed by EL, the electric power output by FC, the thermal power output by EL, the thermal power output by FC, the hydrogen production power of EL, the hydrogen power consumed by FC, the electric energy transmitted to EL and the electric energy output by FC at time t on the wth typical day; C EL / C FC , γ EL / γ FC and x FC / x EL They are the configured capacity, operating status variable (Boolean variable), minimum load factor and investment status variable (Boolean variable) of EL / FC respectively; and They are the waste heat utilization efficiency of EL, the waste heat utilization efficiency of FC, the electricity-to-hydrogen conversion efficiency of EL, and the hydrogen-to-electricity conversion efficiency of FC; and They are the minimum start / stop cycle and start / stop state variable of EL respectively; The mathematical model of wind turbine (WT) and photovoltaic (PV) is expressed as: In the formula, and They are the original PV and WT processing upper limits in the regional energy system, respectively; and are the electric powers output by PV and WT at time t on the wth typical day; The mathematical model of electric boiler (EB) is expressed as: In the formula, C EB and x EB are the construction capacity and investment state variables of EB respectively; and are the electrical energy consumed and thermal energy output by EB at time t on the wth typical day; α EB is the electrothermal conversion coefficient of EB.

5. A regional energy system planning method taking into account active distribution network electricity price incentives according to claim 1, characterized in that: In step 2, the active distribution network is a distribution network system containing a large number of distributed power sources. The active distribution network can formulate a reasonable electricity price according to the self-generated wind and solar power output, so as to adjust the electricity consumption behavior of the regional energy system, guide the regional energy system to purchase and sell electricity to the active distribution network through the electricity price, realize the peak shaving and valley filling of the active distribution network, promote the wind and solar power consumption of the active distribution network, and improve its energy sales efficiency; The tiered carbon trading mechanism is a carbon emission control method that allows producers to trade carbon emission rights in the market by establishing legal carbon emission rights; the regional energy system planning model based on the tiered carbon trading mechanism and the influence of active distribution network peak load regulation refers to a planning model for the remaining required equipment based on the existing equipment, taking into account the tiered carbon trading mechanism and the time-of-use electricity purchase and sale price of the active distribution network.

6. A regional energy system planning method taking into account active distribution network electricity price incentives according to claim 5, characterized in that: The objective function of the regional energy system planning model under the influence of the step-by-step carbon trading mechanism and active distribution network peak load regulation is the system annual total configuration operation cost F IES The lowest is described as shown in formula (24): F IES =min(I buy -IN sell +I ST +I ope +I CO2 ) (24) In the formula, I buy ,I sell ,I ST ,I ope and I CO2 They are the annual electricity purchase cost of the regional energy system from the active distribution network, the annual electricity sales revenue to the active distribution network, the annual configuration equipment cost, the annual operation and maintenance cost, and the annual carbon trading cost. buy and I sell The expressions of are shown in equations (19) and (20) respectively. ST ,I ope and I CO2 The expressions of are shown in equations (25)-(27) respectively. Where, θ = {EES, STHS, LTHS, EB, EL, FC}, is the set of equipment included in the regional energy system; r is the interest rate; v i , C i and Y i It is divided into the unit power investment cost, configuration capacity and service life of equipment i in the regional energy system; τ i is the unit power operation and maintenance cost of device i; P w,t,i is the power output of device i at time t on the wth typical day; C w,co2 is the carbon emission cost on the wth typical day.

7. A regional energy system planning method taking into account active distribution network electricity price incentives according to claim 1, characterized in that: In step 3, a master-slave game model is constructed with the active distribution network as the leader and the regional energy system as the follower; in the master-slave game model, the active distribution network generates a time-sharing electricity purchase price based on the power consumption of the regional energy system and transmits it to the regional energy system; then the regional energy system reasonably configures its own devices based on the electricity purchase price to the active distribution network; The objective function of the active distribution network is expressed as formula (18): F ADN =max(I sell -I buy ) (18) In the formula, F ADN is the objective function of the active distribution network; I sell The annual electricity sales revenue of the active distribution network; buy The annual electricity purchase cost for the active distribution network; The objective function of the regional energy system planning model under the influence of the step-by-step carbon trading mechanism and active distribution network peak load regulation is the system annual total configuration operation cost F IES The lowest is described as shown in formula (24): F IES =min(I buy -IN sell +I ST +I ope +I CO2 ) (24) In the formula, I buy ,I sell ,I ST ,I ope and I CO2 They are the annual electricity purchase cost of the regional energy system from the active distribution network, the annual electricity sales revenue to the active distribution network, the annual equipment configuration cost, the annual operation and maintenance cost, and the annual carbon trading cost.

8. A regional energy system planning method taking into account active distribution network electricity price incentives according to claim 1, characterized in that: In the step 3, a method for solving the master-slave game model based on a draft competition algorithm is proposed. The draft competition algorithm is a brute force search algorithm. Since the active distribution network and the regional energy system influence each other through the purchase and sale of electricity, and the purchase and sale of electricity will be affected by the price of electricity, the draft competition algorithm is used to generate the purchase and sale price of the distribution network.

9. A regional energy system planning method taking into account active distribution network electricity price incentives according to claim 8, characterized in that: The specific process of the master-slave game solution method based on the draft competition algorithm is as follows: S1: Generate the purchase and sale prices of the distribution network using a competitive search algorithm, and transmit the purchase and sale prices to the regional energy system. In this process, the randomly generated purchase and sale prices must satisfy the constraints of formula (28); In formula (28), The upper limit of the average electricity selling price of the active distribution network; S2: Based on the known price of electricity purchased and sold by the active distribution network, the regional energy system uses formula (24) as the objective function to optimize the self-generated equipment configuration, output and the purchase and sale of electricity to the active distribution network, and transmits the purchase and sale of electricity to the active distribution network to the active distribution network; F IES =min(I buy -IN sell +I ST +I ope +I CO2 ) (24) In the formula, I buy ,I sell ,I ST ,I ope and I CO2 They are the annual electricity purchase cost of the regional energy system from the active distribution network, the annual electricity sales revenue to the active distribution network, the annual configuration equipment cost, the annual operation and maintenance cost, and the annual carbon trading cost; S3: The active distribution network calculates the objective function (18) according to the power purchase and sales situation of the regional energy system, and uses the draft competition algorithm to correct the power purchase and sales price, and returns to S1 to enter the next iteration; F ADN =max(I sell -I buy ) (18) In the formula, F ADN is the objective function of the active distribution network; I sell The annual electricity sales revenue of the active distribution network; buy It is the annual electricity purchase cost of the active distribution network.

10. A regional energy system planning method taking into account active distribution network electricity price incentives according to claim 9, characterized in that: When using the draft competition algorithm to correct the purchase and sale prices of electricity, equations (31)-(32) are needed: Formulas (31)-(32) are the variable correction formulas in the draft competition algorithm; where: It represents the value of the jth dimension of the ith particle at the tth iteration, which represents the purchase and sale price of the active distribution network in the present invention, and the dimension is 48; t represents the current iteration number; iter Max represents the maximum number of iterations; A(i) represents the learning ability of the current particle; C is a random number in (0,0.1]; L1∈[0,1]; α is a random number in [-1,1]; Q is a random number in [0,2]; D and L3 are both 1×d matrices, but the elements in the D matrix are all 1, while the elements in L3 are randomly distributed between -1 and 1; P is a standard normal distribution with a mean of 0 and a variance of 1; when each contestant's position is updated, o is randomly drawn from the matrix [0.1, 0.2, 0.3, 0.4, 0.5]; L2∈[0,1].

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