Net load curve peak-valley difference optimization method and equipment considering dynamic transmission fee

Abstract

The present invention relates to a method and device for optimizing the peak-to-valley difference of a net load curve, taking into account dynamic transmission fees. The method comprises the following steps: calculating a static transmission fee standard value; establishing a two-layer optimization model based on the static transmission fee standard value and a preset adjustment principle for adjusting the peak-to-valley difference of the net load curve using the dynamic transmission fee; the two-layer optimization model comprises an upper sub-model and a lower sub-model, wherein the upper sub-model aims to minimize the peak-to-valley difference of the net load curve and the dynamic adjustment amount of the transmission fee, and the lower sub-model aims to minimize the total social cost; and solving the two-layer optimization model to obtain the optimal optimization result for reducing the peak-to-valley difference of the net load curve, taking into account the dynamic transmission fee. Compared with existing technologies, the present invention has the advantages of significantly reducing the peak-to-valley difference rate of the net load curve.

Description

Technical Field

[0001] The present invention belongs to the field of distribution network operation optimization, and in particular relates to a method and device for optimizing the peak-to-valley difference of a net load curve taking into account dynamic transmission fees. Background Art

[0002] With the proposed goals of "carbon peak and carbon neutrality," distributed photovoltaic and wind power projects are being deployed on a large scale, and a new power system incorporating distributed renewable energy is gradually developing. In recent years, the installed capacity of distributed renewable energy has continued to increase, and with the massive influx of photovoltaic and wind power connected to the grid, the peak-to-valley variation in the power system's net load curve has steadily increased, placing significant pressure on the system's peak regulation. At the same time, the large-scale development of distributed renewable energy has also created absorption challenges. Market-based trading of distributed generation has been proposed to promote the absorption of distributed renewable energy, and while this has reduced the peak-to-valley variation in the net load curve to some extent, it remains suboptimal. In market-based trading of distributed generation, distributed generation projects trade electricity with nearby power users within the distribution network. Grid companies are responsible for transmitting the distributed generated electricity and collecting transmission fees. Reasonable calculation of the transmission fees payable by power users participating in these transactions plays a crucial role in both recovering grid companies' investments in transmission and distribution assets and maintaining safe system operation.

[0003] Currently, research on methods for calculating transmission fees for localized distributed generation (DG) trading falls into two main categories. One is static transmission fee calculation methods, also known as "fixed transmission fees," such as the contract path method, the stamp method, the MW-distance method, and the improved DC power flow investment cost pricing method. Existing distributed energy trading research often uses fixed transmission fees for calculation. The principle of static transmission fee calculation is to help grid companies recover transmission and distribution asset investments and operation and maintenance costs. Due to operational difficulties associated with static transmission fee methods, including pilot projects where power users bear little or no cross-subsidy in electricity prices, some scholars have proposed equitably allocating transmission fees based on marginal contribution to accurately differentiate between prosumers and consumers regarding their utilization of grid assets. However, these static transmission fee collection methods struggle to accurately recover transmission and distribution asset investments, nor do they encourage flexible power users to proactively adjust their load profiles. Consequently, they struggle to fully realize the potential of distributed generation market-based trading in absorbing distributed generation locally and smoothing the net load curve.

[0004] The other type is the dynamic transmission fee calculation method, which includes two collection methods: rolling update of transmission fees based on transaction results and pre-determining transmission fees by time period. In terms of rolling update of transmission fees based on transaction results, the document "LocalElectricity Market Design Utilizing Dynamic Network Usage Tariff" (Suto B, Divényi D.arXiv preprint arXiv: 2103.10175, 2021) proposed a dynamic transmission fee rate combined with the concept of local market. In terms of pre-determining transmission fees by time period, the document "Distributed Power Generation Multi-agent Collaborative Planning Method Based on Dynamic Update of Transmission Fees" (Yao Haotian, Xiang Yue, Liu Junyong. Automation of Electric Power Systems, 2021, 45(17): 70-78) proposed a distributed dynamic update method of transmission fees based on the alternating direction multiplier method, which can guide the distributed autonomous decision-making process of multiple investment entities. Dynamic transmission fees are often used to guide the decision-making process of trading entities, reduce line congestion, and rationally use distribution infrastructure. However, the existing dynamic transmission fee calculation method is also difficult to fully tap the potential of distributed power generation market-based transactions in absorbing distributed power generation nearby and smoothing the net load curve.

[0005] At present, when conducting market-based transactions of distributed power generation, the adjustment of transmission fees mostly only considers issues such as reducing network congestion, and does not consider the peak-to-valley difference of the net load curve to reduce the peak-to-valley problem of the power grid. Summary of the Invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a net load curve peak-valley difference optimization method and equipment taking into account dynamic network fees, which can significantly reduce the peak-valley difference rate of the net load curve.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] A method for optimizing the peak-to-valley difference of a net load curve considering dynamic transmission fees comprises the following steps:

[0009] Calculate the standard value of static network fee;

[0010] Based on the static transmission fee standard value and the preset dynamic transmission fee adjustment principle for adjusting the net load curve peak-to-valley difference, a two-layer optimization model is established. The two-layer optimization model includes an upper sub-model and a lower sub-model. The goal of the upper sub-model is to minimize the net load curve peak-to-valley difference and the dynamic adjustment amount of the transmission fee, and the goal of the lower sub-model is to minimize the total social cost.

[0011] The two-layer optimization model is solved to obtain the optimal optimization result considering the dynamic transmission fee to reduce the peak-to-valley difference of the net load curve.

[0012] Furthermore, the static network fee standard value is calculated based on the existing method.

[0013] Furthermore, the static transmission fee standard value is calculated based on the actual situation of the location where the distributed power generation market-based transactions are carried out, the transaction volume of the distributed power generation market-based transactions, and the transaction volume of traditional electricity transactions.

[0014] Furthermore, the objective function expression of the upper sub-model is:

[0015]

[0016] Among them, β is the weight coefficient of multi-objective optimization, P net,diff is the peak-to-valley difference of the net load curve for a day, is the dynamic transmission fee in the tth period under voltage level i, J i is the number of users participating in the local transaction of distributed generation at voltage level i, T represents the total number of time periods in a day, It is the standard value of static transmission fee at voltage level i.

[0017] Furthermore, the constraints of the upper sub-model include constraints on the dynamic adjustment range of the network fee and constraints on the average value of the dynamic network fee in each time period, wherein:

[0018] The dynamic adjustment range constraint of the network fee is expressed as:

[0019]

[0020] The mean constraint of the dynamic network fee in each period is expressed as:

[0021]

[0022] Among them, σ down and σ up They are respectively multiples of the lower and upper limits of the dynamic adjustment range relative to the static network fee standard value.

[0023] Furthermore, the objective function expression of the lower sub-model is:

[0024]

[0025] in, is the comprehensive energy cost of the jth user, is the electricity sales revenue of the kth distributed power producer, K is the total number of distributed power producers participating in the local transaction of distributed power generation, and J is the total number of users.

[0026] Furthermore, the constraints of the lower sub-model include the user's total load demand throughout the day, the generator's power sales balance constraint, the user's power purchase and consumption balance constraint, the user's maximum load power constraint, the upper and lower limit constraints of the user's flexible load transfer, the user's load downslope / climbing constraint, the variable positive value constraint, and the rationality constraint of each subject, where:

[0027] The total load demand constraint of the user throughout the day is expressed as:

[0028]

[0029] in, is the electricity load of user j in period t after participating in the local transaction of distributed generation, is the electricity load of user j when he does not participate in the local transaction in period t;

[0030] The power supply balance constraint of the power generator is expressed as:

[0031]

[0032] in, is the power generation power of generator k in the tth period, is the distributed electric power traded between generator k and user j in period t, is the power sold by generator k to the grid in period t;

[0033] The user's electricity purchase and consumption balance constraint is expressed as:

[0034]

[0035] in, is the power purchased by user j from the grid in period t;

[0036] The user maximum load power constraint is expressed as:

[0037]

[0038] in, is the maximum load power designed for user j during construction;

[0039] The upper and lower limit constraints of the user's flexible load transfer are expressed as:

[0040]

[0041] in, is the maximum load reduction rate of user j in each period, is the maximum increaseable load rate of user j in each period;

[0042] The user load downhill / uphill constraint is expressed as:

[0043]

[0044] in, are the maximum downhill power and maximum climbing power of user j’s load respectively;

[0045] The positive value constraint of the variable is expressed as:

[0046]

[0047] The rational constraints of each subject are expressed as:

[0048]

[0049] in, is the comprehensive energy cost when user j does not participate in the local transaction of distributed generation, It is the electricity sales revenue of distributed power producer k when it does not participate in the local distributed power generation transaction.

[0050] Furthermore, the KKT method is used to solve the double-layer optimization model, and the complementary relaxation conditions are linearized to transform the double-layer optimization model into a single-layer optimization model.

[0051] Furthermore, the complementary relaxation condition is linearized using the big-M method.

[0052] The present invention also provides an electronic device comprising one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the net load curve peak-valley difference optimization method considering dynamic transmission fees as described above.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] (1) The two-layer optimization model of the present invention, which considers the dynamic transmission fee to reduce the peak-to-valley difference of the net load curve, takes into account the situation of flexible power users participating in the market-based transaction of distributed power generation, and uses price signals to encourage nearby trading market entities to actively smooth the peak-to-valley difference of the net load curve, which can significantly reduce the peak-to-valley difference rate of the net load curve.

[0055] (2) The two-layer optimization model of the present invention is constructed based on the static transmission fee standard value, which can effectively ensure the reasonable recovery of the investment in transmission and distribution assets.

[0056] (3) The present invention adopts the KKT method and linear processing to transform the double-layer optimization model into a single-layer optimization model for solution, and the solution efficiency is high. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1Schematic diagram of the process of the optimization method of the present invention;

[0058] Figure 2 This is a framework diagram of a two-layer optimization problem for adjusting the peak-to-valley difference of the net load curve by considering dynamic transmission fees in the present invention;

[0059] Figure 3 It is the static and dynamic transmission fee information of different voltage levels in simulation calculation;

[0060] Figure 4 This is the net load curve under different static and dynamic transmission fee information in the simulation calculation. DETAILED DESCRIPTION

[0061] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0062] Example 1

[0063] This embodiment provides a method for optimizing the peak-valley difference of the net load curve considering the dynamic transmission fee. Figure 1 As shown, the following steps are included:

[0064] S1. Calculate the standard value of static network access fee.

[0065] In this embodiment, the static transmission fee standard value is calculated based on the existing method. Specifically, the static transmission fee standard value is calculated based on the actual situation of the location where the distributed power generation market-based transaction is carried out, the transaction volume of the distributed power generation market-based transaction, and the transaction volume of traditional electricity transactions.

[0066] In this embodiment, the existing method used is the stamp method static network fee calculation method, and the specific steps include:

[0067] 101) Select a postage stamp method for calculating static transmission fees that can accurately recover the investment costs of power transmission and distribution assets of power grid enterprises;

[0068] 102) Based on the actual conditions of the location where the market-based transactions of distributed power generation are carried out, the actual static transmission fee standard value shall be calculated based on the transaction volume of the market-based transactions of distributed power generation and the transaction volume of traditional electricity transactions.

[0069] S2. Based on the static transmission fee standard value and the preset dynamic transmission fee adjustment principle for adjusting the peak-valley difference of the net load curve, a two-layer optimization model is established, such as Figure 2As shown, the two-layer optimization model includes an upper sub-model and a lower sub-model. The goal of the upper sub-model is to minimize the peak-to-valley difference of the net load curve and the dynamic adjustment amount of the network fee, and the goal of the lower sub-model is to minimize the total social cost.

[0070] In order to fully utilize the adjustable load resources on the user side, guide flexible power users to actively adjust their load curves, and give full play to the potential of distributed power generation market transactions in absorbing distributed power generation nearby and smoothing the net load curve, the present invention proposes a dynamic transmission fee calculation method based on the static transmission fee. The adjustment principles of the dynamic transmission fee to adjust the peak-to-valley difference of the net load curve include:

[0071] ① The goal of dynamic adjustment of network charges is to reduce the peak-to-valley difference of the net load curve.

[0072] minP net,diff

[0073] Among them, P net,diff It is the peak-to-valley difference of the net load curve for a day.

[0074] ② Make dynamic adjustments based on the value of static transmission fees, minimize the amount of dynamic adjustments as much as possible, and ensure that power grid companies can reasonably recover their investment in power transmission and distribution assets.

[0075]

[0076] in, is the dynamic transmission fee in the tth period under voltage level i, J i is the number of users participating in the local transaction of distributed generation at voltage level i, and T represents the total number of time periods in a day.

[0077]

[0078] Among them, σ down and σ up They are respectively multiples of the lower and upper limits of the dynamic adjustment range relative to the static network fee.

[0079] ③ Based on principle ②, the average value of dynamic network access fees in each time period is equal to the static network access fee value.

[0080]

[0081] Based on the above-mentioned principle of adjusting the peak-to-valley difference of the net load curve by dynamically adjusting the network fee, the objective function and related constraints of the two-layer optimization model are established.

[0082] 201) Upper sub-model

[0083] The upper sub-model is a dynamic transmission fee optimization calculation model. In this embodiment, the upper objective of the two-layer optimization problem is to minimize the peak-to-valley difference of the net load curve and minimize the dynamic adjustment of the transmission fee. The objective function expression is:

[0084]

[0085] Among them, β is the weight coefficient of multi-objective optimization, P net,diff is the peak-to-valley difference of the net load curve for a day, is the dynamic transmission fee in the tth period under voltage level i, J i is the number of users participating in the local transaction of distributed generation at voltage level i, T represents the total number of time periods in a day, It is the standard value of static transmission fee at voltage level i.

[0086] The peak-to-valley difference of the net load curve is calculated as follows:

[0087] P net,diff =P net,max -P net,min

[0088] Among them, P net,max is the peak value of the net load curve in a day, P net,min It is the valley value of the net load curve within a day.

[0089]

[0090] in, is the net load in the tth period of the day.

[0091] The net load curve calculation formula is as follows:

[0092]

[0093] in, is the electricity load of user j in period t after participating in the local transaction of distributed generation, is the total load in the tth period that does not participate in the local transaction of distributed generation, and J is the total number of users who participate in the local transaction of distributed generation.

[0094] In order for power grid companies to reasonably recover their investment in transmission and distribution assets, according to the adjustment principle, the constraints of the upper sub-model include the dynamic adjustment range constraint of the transmission fee and the mean value constraint of the dynamic transmission fee in each period, among which,

[0095] The dynamic adjustment range constraint of the network fee is expressed as:

[0096]

[0097] The mean constraint of the dynamic network fee in each period is expressed as:

[0098]

[0099] Among them, σ down and σ up They are respectively multiples of the lower and upper limits of the dynamic adjustment range relative to the static network fee standard value.

[0100] 202) Lower sub-model

[0101] The lower sub-model is a distributed generation proximity transaction model, whose goal is to minimize the total social cost. The objective function expression is:

[0102]

[0103] in, is the comprehensive energy cost of the jth user, is the electricity sales revenue of the kth distributed power producer, K is the total number of distributed power producers participating in the local transaction of distributed power generation, and J is the total number of users.

[0104] Comprehensive energy cost

[0105]

[0106] in, is the cost of electricity purchased by user j from the grid, is the cost of electricity purchased by user j from distributed generators, is the cost of network fees for user j to participate in the nearest transaction, is the cost of transferring user j’s flexible load to other time periods.

[0107]

[0108] in, is the revenue of distributed generator k from selling electricity to the grid, is the revenue earned by distributed power generator k from selling electricity to nearby users.

[0109] The cost of electricity purchased from the grid:

[0110]

[0111] Among them, λ i,t is the terminal sales electricity price of the power grid enterprise at voltage level i in period t, is the power purchased by user j from the grid in period t, and Δt is the length of the period.

[0112] The cost of electricity purchased by users from power generators:

[0113]

[0114] Among them, π kj,t The clearing price of the transaction between generator k and user j in period t, is the distributed electric energy power traded between generator k and user j in period t.

[0115] The cost of network fees for users participating in nearby transactions:

[0116]

[0117] Flexible load transfer costs:

[0118]

[0119] Where μ is the cost factor for transferring load to other time periods, is the electricity load of user j when he does not participate in the local transaction in period t.

[0120] Profits from selling electricity from distributed generation to the grid:

[0121]

[0122] Among them, γ is the on-grid electricity price of distributed generation, is the electric power sold by generator k to the grid in period t.

[0123] Profits from selling electricity to users through distributed generation:

[0124]

[0125] because Therefore, the goal of the distributed generation proximity trading model can be simplified as follows:

[0126]

[0127] The constraints of the lower sub-model include the user's total load demand throughout the day, the power supply balance constraint of the power generator, the user's power purchase and use balance constraint, the user's maximum load power constraint, the upper and lower limit constraints of the user's flexible load transfer, the user's load downhill / climbing constraint, the variable positive value constraint and the rationality constraint of each subject.

[0128] The total load demand constraint of the user throughout the day is expressed as:

[0129]

[0130] in, is the electricity load of user j in period t after participating in the local transaction of distributed generation, is the electricity load of user j when he does not participate in the local transaction in period t.

[0131] The power supply balance constraint of the generator is expressed as:

[0132]

[0133] in, is the power generation power of generator k in the tth period, is the distributed electric power traded between generator k and user j in period t, is the electric power sold by generator k to the grid in period t.

[0134] The user's electricity purchase and consumption balance constraint is expressed as:

[0135]

[0136] in, is the power purchased by user j from the grid in period t.

[0137] The user's maximum load power constraint is expressed as:

[0138]

[0139] in, It is the maximum load power designed during construction of user j.

[0140] The upper and lower limit constraints of user flexible load transfer are expressed as:

[0141]

[0142] in, is the maximum load reduction rate of user j in each period, is the maximum increaseable load rate of user j in each time period.

[0143] The user load downhill / uphill constraint is expressed as:

[0144]

[0145] in, are the maximum downhill power and maximum climbing power of user j's load respectively.

[0146] The positive value constraint of a variable is expressed as:

[0147]

[0148] The rational constraints of each subject are expressed as:

[0149]

[0150] in, is the comprehensive energy cost when user j does not participate in the local transaction of distributed generation, It is the electricity sales revenue of distributed power producer k when it does not participate in the local distributed power generation transaction.

[0151] because So there must be π kj,t ≥γ makes distributed power generators When π kj,t =γ, we have Assume there is a user j Then there must be a solution where the user does not participate in the nearest transaction, so This makes the target smaller, which is contrary to the goal of the optimization problem. So there must be a suitable π kj,t Ensure that the rational constraints of each subject are satisfied.

[0152] S3. Solve the two-layer optimization model to obtain the optimal optimization result that takes into account the dynamic transmission fee to reduce the peak-to-valley difference of the net load curve.

[0153] In this embodiment, the KKT method is used to solve the two-level optimization model, and the Big M method is used to linearize the complementary relaxation conditions to transform the two-level optimization model into a single-level optimization model. The transformation process includes:

[0154] 301) The lower-level optimization model is converted into the constraints of the upper-level optimization model through the KKT condition. Specifically, the constraints of the lower-level optimization problem are divided into equality constraints and inequality constraints:

[0155] Equality constraints (l 1,j 、l 2,k,t 、l 3,j,t is the Lagrange multiplier of the equality constraint):

[0156]

[0157] Inequality constraints ( u 1,j,t ~u 5,k,j,t is the Lagrange multiplier for the inequality constraints):

[0158]

[0159] 302) Linearize the complementary slack conditions using the Big M method.

[0160] The large M method is used to linearize the lower constraint, and the For example, its dual variable is u 3,j,t , the linearization result is:

[0161]

[0162] Where M is a large custom constant, v 3,j,t It is a 0-1 variable.

[0163] The single-layer optimization model after simplification by 301) and 302) is as follows:

[0164]

[0165] The above two-stage optimization method considers dynamic transmission fees to reduce the peak-to-valley difference of the net load curve, and uses price signals to encourage nearby trading market players to actively smooth the peak-to-valley difference of the net load curve, thereby achieving more effective power system peak regulation.

[0166] If the above method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0167] In another embodiment, a power system peak regulation method may be provided based on the optimization result obtained by the above-mentioned net load curve peak-valley difference optimization method considering dynamic transmission fees.

[0168] Assume that there are five distributed generators (DGs) in the distribution network, A, B, C, D, and E, and five users (F, G, H, I, and J) participating in distributed generation local trading. DGs A, B, and C connect to the distribution network with users F, J, and H at a 10kV voltage level and can conduct local trading with each other. DGs D and E connect to the distribution network with users I and J at a 1kV voltage level and can also conduct local trading with each other.

[0169] According to the data of power generators and users, the lower bound of the dynamic adjustment range of transmission fee is set as down and upper bound σ up The multiples of the static transmission fee are 0.95 and 1.05. By solving the dynamic transmission fee double-layer optimization model, the dynamic transmission fee information at voltage levels of 10kV and 1kV can be obtained as follows: Figure 3 shown.

[0170] According to the distributed generation proximity transaction model, the transaction clearing results and net load curve under dynamic transmission fee can be calculated. Similarly, the net load curve under static transmission fee can also be calculated, such as Figure 4 shown.

[0171] Depend on Figure 4 It can be seen that the net load curve valley calculated under the static transmission fee is concentrated in the 69th to 72nd time period. In order to make full use of demand response resources, it can be seen that Figure 3 The dynamic transmission fee decreases during time periods 69-72, guiding users' flexible loads to the valleys of the net load curve during transactions, effectively reducing the peak-to-valley difference in the net load curve. This indicates that the dynamic transmission fee changes with the peak and valley periods of the overall net load curve, guiding users' flexible loads to shift to periods with lower transmission fees. This demonstrates the effectiveness of the proposed method of reducing the peak-to-valley difference in the net load curve by taking dynamic transmission fees into account.

[0172] Figure 4 When not participating in the distributed power generation market transaction, the peak-to-valley difference of the net load curve is 33384MW, and the peak-to-valley difference rate of the net load curve is 0.4641; when participating in the distributed power generation market transaction under the static grid transfer fee, the peak-to-valley difference of the net load curve is 21149MW, and the peak-to-valley difference rate of the net load curve is 0.3207; under the method proposed in the present invention to reduce the peak-to-valley difference of the net load curve by considering the dynamic grid transfer fee, the peak-to-valley difference of the net load curve is 16316MW, and the peak-to-valley difference rate of the net load curve is 0.2474.

[0173] It can be seen that the method proposed in the present invention has made a significant contribution to reducing the peak-to-valley difference of the net load curve, effectively reduced the peak-to-valley difference rate, and brought into play the potential of distributed power generation market-based transactions in absorbing distributed power generation nearby and smoothing the net load curve.

[0174] At the same time, since the net load curve peak-to-valley difference optimization method considering dynamic transmission fees of the present invention is based on static transmission fees, it can reasonably recover the investment in power transmission and distribution assets. On this trading day, a total of 921.46 million kWh of electricity was traded at the 10kV voltage level, and a total of 241.48 million kWh of electricity was traded at the 1kV voltage level. According to the static transmission fee, 56.4 million yuan should be recovered; according to the dynamic transmission fee, 56.5 million yuan was actually recovered, which is within an acceptable range. It can be seen that the method of the present invention can reasonably recover the investment in power transmission and distribution assets and protect the interests of users, reflecting the superiority and accuracy of the method.

[0175] Example 2

[0176] This embodiment provides an electronic device, including one or more processors, a memory, and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the net load curve peak-to-valley difference optimization method considering dynamic network access fees as described in Example 1.

[0177] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A method for optimizing the peak-to-valley difference of a net load curve considering dynamic transmission fees, characterized in that: The following steps are involved: Calculate the standard value of static network fee; Based on the static transmission fee standard value and the preset dynamic transmission fee adjustment principle for adjusting the net load curve peak-to-valley difference, a two-layer optimization model is established. The two-layer optimization model includes an upper sub-model and a lower sub-model. The goal of the upper sub-model is to minimize the net load curve peak-to-valley difference and the dynamic adjustment amount of the transmission fee, and the goal of the lower sub-model is to minimize the total social cost. Solving the two-layer optimization model to obtain the optimal optimization result considering the dynamic transmission fee to reduce the peak-to-valley difference of the net load curve; The adjustment principles for the dynamic transmission fee adjustment of the net load curve peak-to-valley difference include: ① The goal of dynamic adjustment of network charges is to reduce the peak-to-valley difference of the net load curve: in, P net,diff is the peak-to-valley difference of the net load curve for a day; ② Make dynamic adjustments based on the static transmission fee value, minimize the amount of dynamic adjustments as much as possible, and ensure that power grid companies can reasonably recover their investment in power transmission and distribution assets: in, At the voltage level i Next t Dynamic network fee for each time period, J i At the voltage level i The number of users participating in the local transaction of distributed generation, T represents the total number of time periods in a day, At the voltage level i The standard value of static network fee under ; ③ Based on principle ②, the average value of dynamic network fee in each period is equal to the static network fee value: ； The objective function expression of the upper sub-model is: in, is the weight coefficient of multi-objective optimization; The constraints of the upper sub-model include the dynamic adjustment range constraint of the network fee and the mean value constraint of the dynamic network fee in each period, where: The dynamic adjustment range constraint of the network fee is expressed as: The mean constraint of the dynamic network fee in each period is expressed as: in, and They are respectively the multiples of the lower and upper bounds of the dynamic adjustment range relative to the static network fee standard value; The objective function expression of the lower sub-model is: in, It is j The comprehensive energy cost of each user, It is k The electricity sales revenue of distributed power producers is K, which is the total number of distributed power producers participating in the local transaction of distributed power generation. is the total number of users; The constraints of the lower sub-model include the user's total load demand throughout the day, the power supply balance constraint of the generator, the user's power purchase and use balance constraint, the user's maximum load power constraint, the upper and lower limit constraints of the user's flexible load transfer, the user's load downhill / climbing constraint, the variable positive value constraint and the rationality constraint of each subject, among which, The total load demand constraint of the user throughout the day is expressed as: in, is a user j After participating in the nearby transaction of distributed generation t The electricity load during the period, is a user j In the t The electricity load when not participating in the nearest transaction during the period; The power supply balance constraint of the power generator is expressed as: in, For power generators k In the t The power generation during the period, A power generator k With users j In the t Distributed electric power for period trading, A power generator k In the t The amount of electric energy sold to the grid during the period; The user's electricity purchase and consumption balance constraint is expressed as: in, For users j In the t The power purchased from the grid during the period; The user maximum load power constraint is expressed as: in, is a user j The maximum load power designed during construction; The upper and lower limit constraints of the user's flexible load transfer are expressed as: in, is a user j The maximum load rate that can be reduced in each period, is a user j The maximum increase in load rate in each period; The user load downhill / uphill constraint is expressed as: in, 、 Users j Maximum downhill power and maximum climbing power of the load; The positive value constraint of the variable is expressed as: The rational constraints of each subject are expressed as: in, is a user j The comprehensive energy cost when not participating in the nearby transaction of distributed generation, Distributed power generators k The income from electricity sales when not participating in local transactions of distributed generation.

2. The net load curve peak-valley difference optimization method considering dynamic network fees according to claim 1 is characterized in that: The static network fee standard value is calculated based on the existing method.

3. The net load curve peak-valley difference optimization method considering dynamic network access fees according to claim 1 is characterized in that: The static transmission fee standard value is calculated based on the actual situation of the location where the distributed power generation market-based transactions are carried out, the transaction volume of the distributed power generation market-based transactions, and the transaction volume of traditional electricity transactions.

4. The net load curve peak-valley difference optimization method considering dynamic network access fees according to claim 1 is characterized in that: The KKT method is used to solve the double-layer optimization model, and the complementary relaxation conditions are linearized to transform the double-layer optimization model into a single-layer optimization model.

5. The net load curve peak-valley difference optimization method considering dynamic network fees according to claim 4 is characterized in that: The complementary relaxation conditions are linearized using the Big M method.

6. An electronic device, characterized in that: It includes one or more processors, a memory and one or more programs stored in the memory, wherein the one or more programs include instructions for executing the net load curve peak-valley difference optimization method considering dynamic network fees as described in any one of claims 1-5.

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