Method and device for optimizing peak-valley difference of net load curve considering dynamic network passing cost

Abstract

The invention relates to a net load curve peak-valley difference optimization method and equipment considering dynamic network cost. The method comprises the following steps: calculating a static network cost standard value; establishing a double-layer optimization model which comprises an upper layer sub-model and a lower layer sub-model on the basis of the static network crossing cost standard value and a preset adjustment principle for adjusting the peak-valley difference of a net load curve according to the dynamic network crossing cost, the target of the upper-layer sub-model is to minimize the peak-valley difference of the net load curve and the dynamic adjustment amount of the network cost, and the target of the lower-layer sub-model is to minimize the total social cost; and solving the double-layer optimization model, and obtaining an optimal optimization result which considers the dynamic network passing cost to reduce the peak-valley difference of the net load curve. Compared with the prior art, the method has the advantages that the peak-valley difference rate of the net load curve can be remarkably reduced, and the like.

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 a dynamic network access fee. Background Art

[0002] With the proposal of the "carbon peak and carbon neutrality" goals, distributed photovoltaic and wind power are being built on a large scale, and new power systems including distributed renewable energy are gradually developing. In recent years, the installed capacity of distributed renewable energy has been continuously improved, and a large number of photovoltaic and wind power have been connected to the power grid, which has increased the peak-to-valley difference of the net load curve of the power system, bringing great pressure to the peak regulation of the power system. At the same time, the large-scale development of distributed renewable energy has also caused the problem of consumption. The market-based transaction of distributed power generation has been proposed to promote the consumption of distributed renewable energy, which has reduced the peak-to-valley difference of the net load curve to a certain extent, but it is still not ideal. In the market-based transaction of distributed power generation, the distributed power generation project unit conducts power transactions with the nearest power users in the distribution network, and the power grid company undertakes the power transmission of distributed power generation and collects the network fee. Reasonable calculation of the network fee that power users should pay for participating in the transaction plays an important role in recovering the power transmission and distribution asset investment of the power grid company and maintaining the safe operation of the system.

[0003] At present, the research on the calculation method of the transmission fee for the nearby transaction of distributed power generation is mainly divided into two categories. One is the static transmission fee calculation method, that is, the "fixed transmission fee", such as: contract path method, stamp method, MW-distance method, improved DC power flow investment cost pricing method, etc. In the existing distributed energy trading research, fixed transmission fees are mostly used for calculation. The principle of static transmission fee calculation is to help power grid companies recover the investment and operation and maintenance costs of transmission and distribution assets. Due to the certain difficulties in the operation of the static transmission fee method, there is a problem that pilot power users bear little or no cross-subsidy of electricity prices. Some scholars have proposed to fairly allocate the transmission fee according to marginal contribution and accurately distinguish the degree of utilization of power grid assets by producers and consumers. However, the above static transmission fee collection method is difficult to accurately recover the investment in power grid transmission and distribution assets, and it is also impossible to guide flexible power users to actively adjust the load curve, and it is difficult to fully exert the potential of distributed power generation market-based transactions in absorbing distributed power generation nearby and smoothing the net load curve.

[0004] The other is a dynamic transmission fee calculation method, which includes two collection methods: rolling update of transmission fees based on transaction results and pre-determining transmission fees for different time periods. In terms of rolling update of transmission fees based on transaction results, the document "Local Electricity Market Design Utilizing Dynamic Network Usage Tariff" (Suto B, Divényi D.arXiv preprint arXiv: 2103.10175, 2021) proposes a dynamic transmission fee rate combined with the concept of local market. In terms of pre-determining transmission fees for different time periods, 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) proposes 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 charges 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 methods are 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 network access fees mostly only takes into account issues such as reducing network congestion, without considering the peak-to-valley difference of the net load curve to reduce the peak load 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 to provide a net load curve peak-to-valley difference optimization method and equipment taking into account dynamic network access fees, which can significantly reduce the peak-to-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, wherein the two-layer optimization model includes an upper sub-model and a lower sub-model, wherein 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 that takes into account the dynamic transmission fee to reduce the peak-to-valley difference of the net load curve.

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

[0013] Furthermore, the static grid access 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 network access 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 grid access fee under 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 dynamic network fee mean value constraint for each time 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 standard value of the static network fee.

[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 total load demand of users throughout the day, the balance constraint of power sales by power generators, the balance constraint of power purchase and use by users, the maximum load power constraint of users, the upper and lower limit constraints of flexible load transfer of users, the downhill / climbing constraint of users' load, the positive value constraint of variables and the rationality constraint of each subject, among which,

[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 the tth period 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 variable positive value constraint 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 nearby transaction of distributed generation, It is the electricity sales revenue of distributed power generator k when it does not participate in the nearby transaction of distributed power generation.

[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-to-valley difference optimization method considering dynamic network access 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 that considers dynamic transmission fees to reduce the peak-to-valley difference of the net load curve takes into account the situation where flexible power users participate in the market-based transactions 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 solving, and the solving efficiency is high. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1It is a schematic diagram of the process of the optimization method of the present invention;

[0058] Figure 2 It is a framework diagram of a double-layer optimization problem for adjusting the peak-to-valley difference of a 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 It is the net load curve under different static and dynamic transmission fee information in simulation calculation. DETAILED DESCRIPTION

[0061] The present invention is described in detail below in conjunction with 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-to-valley difference of a net load curve taking into account a dynamic network access 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 grid access fee standard value is calculated based on the existing method. Specifically, the static grid access 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 a stamp method for calculating static network fees, and the specific steps include:

[0067] 101) Select a postage stamp static transmission fee calculation method that can accurately recover the investment cost of power transmission and distribution assets of power grid enterprises;

[0068] 102) Based on the actual conditions of the location where the distributed power generation market-based transactions are carried out, the actual static grid access fee standard value shall be calculated based on the transaction volume of distributed power generation market-based transactions 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-to-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 access fee, and the goal of the lower sub-model is to minimize the total social cost.

[0070] In order to make full use of the adjustable load resources on the user side, guide the flexible power users to actively adjust the load curve, 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 grid transmission and distribution assets.

[0075]

[0076] in, is the dynamic network access 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 charges in each time period is equal to the static network charges.

[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 double-layer optimization model are established.

[0082] 201) Upper sub-model

[0083] The upper sub-model is a dynamic network 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 reduce the dynamic adjustment amount of the network fee as much as possible. 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 network access 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 grid access fee under voltage level i.

[0086] The calculation of the peak-to-valley difference of the net load curve is 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 a day.

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

[0092]

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

[0094] In order for power grid enterprises 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 dynamic network fee mean value constraint for each time 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 standard value of the static network fee.

[0100] 202) Lower sub-model

[0101] The lower sub-model is a distributed generation nearby 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 nearby transactions, 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, It is the revenue of distributed power generator k from selling electricity to nearby users.

[0109] The cost of purchasing electricity from the power grid:

[0110]

[0111] Among them, λ i,t is the terminal sales electricity price of the power grid company at voltage level i in period t, is the power purchased by user j from the grid in the tth period, 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, It is the distributed electric energy power traded between generator k and user j in period t.

[0115] The network fee cost for users to participate in nearby transactions:

[0116]

[0117] Flexible load transfer costs:

[0118]

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

[0120] The benefits of distributed generation selling electricity to the grid:

[0121]

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

[0123] The benefits of distributed generation selling electricity to users:

[0124]

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

[0126]

[0127] The constraints of the lower sub-model include the total load demand of users throughout the day, the balance constraint of power sales by power generators, the balance constraint of power purchase and use by users, the maximum load power constraint of users, the upper and lower limit constraints of flexible load transfer of users, the downhill / climbing constraint of users' load, the positive value constraint of variables and the rationality constraints 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 the tth period after participating in the local transaction of distributed generation, It 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, It 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 / climbing constraint is expressed as:

[0144]

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

[0146] The variable positive value constraint 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 nearby transaction of distributed generation, It is the electricity sales revenue of distributed power generator k when it does not participate in the nearby transaction of distributed power generation.

[0151] because So there must be a 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 that 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-layer optimization model, and the big M method is used to linearize the complementary relaxation conditions to transform the two-layer optimization model into a single-layer optimization model. The transformation process includes:

[0154] 301) The lower-level optimization model is converted into the constraint conditions of the upper-level optimization model through the KKT condition. Specifically, the constraint conditions 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 for 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 relaxation conditions using the Big M method.

[0160] The large M method is used to linearize the lower constraints. 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 simplified 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, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform 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 codes.

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

[0168] Assume that there are 5 distributed power generators A, B, C, D, E in the distribution network, and 5 users F, G, H, I, J participate in the distributed power generation nearby transaction. Distributed power generators A, B, C and users F, J, H are connected to the distribution network at a voltage level of 10kV, and can conduct nearby transactions with each other; distributed power generators D, E and users I, J are connected to the distribution network at a voltage level of 1kV, and can conduct nearby transactions 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 the 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 under voltage levels of 10kV and 1kV can be obtained as follows: Figure 3 shown.

[0170] According to the distributed generation nearby 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 in the 69th to 72th period has decreased, guiding the user's flexible load to be transferred to the valley of the net load curve in the transaction, effectively reducing the peak-to-valley difference of the net load curve. It can be seen that the dynamic transmission fee changes with the peak-to-valley period of the overall net load curve, and can guide the user's flexible load to be transferred to the period with low transmission fee, reflecting the role of the method of reducing the peak-to-valley difference of the net load curve by considering the dynamic transmission fee proposed by the present invention in helping to reduce the peak-to-valley difference of the net load curve.

[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 access 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 that considers the dynamic grid access fee to reduce the peak-to-valley difference of the net load curve, 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 preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined 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, wherein the two-layer optimization model includes an upper sub-model and a lower sub-model, wherein 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; The two-layer optimization model is solved 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.

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

3. The method for optimizing the peak-to-valley difference of a net load curve considering dynamic network access fees according to claim 1, characterized in that: The static grid access 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 distributed power generation market-based transactions, and the transaction volume of traditional electricity transactions.

4. The method for optimizing the peak-to-valley difference of a net load curve considering dynamic network access fees according to claim 1, characterized in that: The objective function expression of the upper sub-model is: 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 network access 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 grid access fee under voltage level i.

5. The method for optimizing the peak-to-valley difference of the net load curve considering the dynamic transmission fee according to claim 4 is characterized in that: 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 time period, where: The dynamic adjustment range constraint of the network fee is expressed as: The dynamic network fee mean value constraint for each time period is expressed as: 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 standard value of the static network fee.

6. The method for optimizing the peak-to-valley difference of a net load curve considering dynamic network access fees according to claim 1, characterized in that: The objective function expression of the lower sub-model is: 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.

7. The method for optimizing the peak-to-valley difference of the net load curve considering the dynamic transmission fee according to claim 6 is characterized in that: The constraints of the lower sub-model include the total load demand of users throughout the day, the balance constraint of power supply by power generators, the balance constraint of power purchase and use by users, the maximum load power constraint of users, the upper and lower limit constraints of flexible load transfer of users, the downhill / climbing constraint of users' load, the positive value constraint of variables 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 the electricity load of user j in the tth period 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; The power supply balance constraint of the power generator is expressed as: 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; The user's electricity purchase and consumption balance constraint is expressed as: in, is the power purchased by user j from the grid in period t; The user maximum load power constraint is expressed as: in, is the maximum load power designed for user j during construction; The upper and lower limit constraints of the user's flexible load transfer are expressed as: 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; The user load downhill / uphill constraint is expressed as: in, are the maximum downhill power and maximum climbing power of user j’s load respectively; The variable positive value constraint is expressed as: The rational constraints of each subject are expressed as: in, is the comprehensive energy cost when user j does not participate in the nearby transaction of distributed generation, It is the electricity sales revenue of distributed power generator k when it does not participate in the nearby transaction of distributed power generation.

8. The method for optimizing the peak-to-valley difference of a net load curve considering dynamic network access fees according to claim 1, 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.

9. The method for optimizing the peak-to-valley difference of the net load curve considering the dynamic transmission fee according to claim 8, characterized in that: The complementary relaxation condition was linearized using the Big-M method.

10. An electronic device, characterized in that: It includes one or more processors, a memory and one or more programs stored in the memory, and 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 any one of claims 1-9.

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