An ultra-high voltage electric energy optimal allocation method based on competitive price mechanism

Abstract

The application provides a method for optimal allocation of extra-high voltage electric energy based on a competitive price mechanism, and constructs a direct current sending end bidding sending model: setting a selling price constraint condition and an output power constraint condition of the direct current sending end, setting a selling revenue maximization of the direct current sending end as a target, and setting a selling revenue target function; constructs a direct current receiving end bidding purchasing model: setting a purchasing price constraint condition and an input power constraint condition of the direct current receiving end, setting a direct current receiving end purchasing expenditure minimization as a target, and setting a purchasing expenditure target function; adopts two kinds of clearing modes of centralized bidding and double hanging and double picking to construct a power market clearing model, the power market clearing model is: the transaction parties first carry out centralized bidding, and the non-traded part is determined by double hanging and double picking; based on the selling revenue target function and the purchasing expenditure target function, a genetic algorithm is used to solve the non-traded part to obtain a Nash equilibrium solution for optimal allocation.

Description

Technical Field

[0001] This invention belongs to the field of electrical engineering technology, specifically relating to a method for optimizing the allocation of ultra-high voltage power based on a competitive electricity pricing mechanism. Background Technology

[0002] Based on my country's energy endowment, the country's power supply strategy revolves around "west-to-east transmission, north-south interconnection, and nationwide grid interconnection," resulting in a cross-provincial and cross-regional power transmission and reception pattern characterized by "ultra-high voltage (UHV) transmission, large power grids, and large generating units." For a long time, cross-provincial and cross-regional power transmission and reception has been characterized by "more planning than market," and "more transmission than trading," failing to effectively incorporate price factors. This has led to the inaccurate representation of actual power supply and demand in the transmitted and received volumes, and the inaccurate reflection of the value of electricity at different times in the transaction prices. In the actual operation of UHV projects, this manifests as problems such as the maximum transmission capacity not reaching expectations, low channel utilization hours, and the failure to fully realize the benefits of power transmission.

[0003] From the perspective of the sending end of UHV power trading, the types of UHVDC power resources fed in are diverse, with significant differences in power generation characteristics, and the construction of supporting power sources for some channels is lagging behind. To fully mobilize the enthusiasm of power sources at the sending end for trading and rationally guide the bundling of power resources, it is necessary to study and establish a market-based bidding mechanism for power transmission, while ensuring stable transmission and high channel utilization. From the perspective of the receiving end of UHV power trading, due to the uneven absorption capacity and distinct seasonal characteristics among provinces, the "targeted pricing" UHV power absorption model is not conducive to maximizing the transmission benefits of UHVDC. To balance grid supply and demand through market mechanisms rather than planned control, and to further explore and utilize the margin of DC channels, it is necessary to propose competitive power purchase schemes at the receiving end, matching the competitive bidding for power transmission, on a channel-by-channel and phase-by-phase basis, in order to improve the utilization rate of power volume and transmission channels and achieve optimal power allocation. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes an ultra-high voltage power energy optimization allocation method based on a competitive electricity price mechanism. Based on electricity price characteristics, the method optimizes the allocation of inter-provincial power transmission to improve the utilization rate of power volume and transmission channels.

[0005] To achieve the above objectives, this invention provides a method for optimal allocation of ultra-high voltage power based on a competitive electricity pricing mechanism, characterized by the following steps:

[0006] Step S1: Construct a DC power transmission model with competitive bidding: Set constraints on the selling price and output power of the DC power transmission end, and set the objective function for power sales revenue with the goal of maximizing the revenue from power sales at the DC power transmission end.

[0007] Step S2: Construct a DC receiving-end competitive bidding power purchase model: Set the purchase price constraints and input power constraints for the DC receiving end, and set the power purchase expenditure objective function with the goal of minimizing the DC receiving end power purchase expenditure;

[0008] Step S3: Construct a power market clearing model using a dual-bid model that combines centralized bidding and double-listing / double-acquisition. The power market clearing model is as follows: the two parties first conduct centralized bidding, and the unsuccessful portion of the centralized bidding is determined by double-listing / double-acquisition.

[0009] Step S4: Based on the objective function of electricity sales revenue and the objective function of electricity purchase expenditure, a genetic algorithm is used to solve the unsold portion in step S3 to obtain the Nash equilibrium solution for optimized allocation.

[0010] Preferably, the selling price constraint in step S1 includes:

[0011] 1) Coal-fired power, wind power, and photovoltaic power will be subject to floating price quotations based on the benchmark coal-fired power price at the sending end;

[0012] 2) Hydropower prices are based on the on-grid electricity price, with floating prices determined by combining seasonal electricity pricing.

[0013] 3) The price of electricity collected from other provinces at the sending end is based on the average settlement price between provinces and is subject to floating pricing.

[0014] Preferably, the output power constraint condition in step S1 includes:

[0015] 1) All renewable energy supplied with DC power transmission should be guaranteed to be purchased in full;

[0016] 2) The DC power transmission capacity is matched with the coal-fired power plant to meet the minimum output rate requirements;

[0017] 3) Power collected from outside the province should meet the restrictions of inter-provincial transmission channels and the power balance of the province where the power is collected.

[0018] Preferably, the expression for the objective function of electricity sales revenue in step S1 is:

[0019]

[0020] In the formula, M m,t,i P represents the electricity price sold by DC line i in the t-th hour of the m-th month. m,t,i This represents the clearing power of the i-th DC line in the t-th hour of the m-th month.

[0021] Preferably, the purchase price constraint in step S2 is as follows: the price is based on the benchmark coal-fired power price at the receiving end, and tends to increase the price to obtain external power support when the supply and demand situation is tight, and tends to decrease the price to obtain cheaper electricity when the supply and demand situation is relatively loose. The floating ratio is a maximum increase of 10% and a maximum decrease of 15%.

[0022] Preferably, the input power constraint conditions in step S2 include:

[0023] 1) The upper and lower limits of the external power supply that needs to be purchased are obtained by conducting phased production operation simulations;

[0024] 2) The purchase of external electricity must not affect the consumption of its own renewable energy or worsen the peak-shaving situation;

[0025] 3) Power exchange is within the capacity of the transmission channel.

[0026] Preferably, the expression for the objective function of electricity purchase expenditure in step S2 is:

[0027]

[0028] In the formula, N m,t,j Q represents the electricity purchase price in the j-th province for the t-th hour of the m-th month. m,t,j This represents the electricity purchased in the j-th province during the t-th hour of the m-th month.

[0029] Preferably, the genetic algorithm in step S4 includes the following steps:

[0030] Step S41: Encoding, treating the DC sending end and the monthly typical day's reporting and pricing strategy of the sending end as a gene, each gene has 24 chromosomes to correspond to 24 hours, and each chromosome has two pieces of information: value and quantity, to construct the initial population P0;

[0031] Step S42: Selection. Based on the objective function of electricity sales revenue and the objective function of electricity purchase expenditure, dominant individuals are selected from the initial population to form the first-generation elite population P0. E ;

[0032] Step S43: Crossover, based on the first-generation elite population P0 E They exchange and recombine to form new offspring genes;

[0033] Step S44: Mutation, mutating the offspring gene to form a new offspring;

[0034] Step S45; Repeat steps S42 to S44 until a Nash equilibrium solution is found.

[0035] Preferably, in step S41, the encoding is generated using random numbers based on the selling price constraint, output power constraint, purchase price constraint, and input power constraint.

[0036] Preferably, the range of mutations in step S44 includes:

[0037] 1) The sending-end coal-fired power, wind power, and photovoltaic power will be quoted based on the benchmark price of coal-fired power.

[0038] 2) Hydropower from the sending end is quoted based on the on-grid price of hydropower, combined with the seasonal price for peak and off-peak periods; power collected from outside the province is quoted based on the average settlement price between provinces.

[0039] 3) The receiving provinces will use the provincial benchmark price for coal-fired electricity as the base price for floating price quotations.

[0040] The beneficial effects of the present invention include at least the following:

[0041] This invention considers competitive approaches such as bidding to provide decision-making suggestions for signing long-term agreements in ultra-high voltage (UHV) power trading based on reasonable prediction and advance simulation. It conforms to the UHV power trading mechanism that optimizes the interests of both the sending and receiving ends, and also conforms to the UHV power market clearing mechanism that optimizes the interests of both the sending and receiving ends. By optimizing the inter-provincial power trading methods based on price and negotiating the transactions, the loss of power is reduced, thereby improving the utilization rate of power and transmission channels. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0043] Figure 2 This is a schematic diagram of a one-way listing and trading process;

[0044] Figure 3 This is a diagram illustrating the rotating listing and trading process.

[0045] Figure 4 This is a schematic diagram illustrating four types of supply and demand relationships under the centralized bidding model;

[0046] Figure 5 This is a diagram illustrating the price clearing process under centralized bidding.

[0047] Figure 6 This is a schematic diagram illustrating the encoding and selection process of the genetic algorithm provided in an embodiment of the present invention;

[0048] Figure 7 This is a schematic diagram illustrating the crossover and mutation process of the genetic algorithm provided in an embodiment of the present invention;

[0049] Figure 8This is a flowchart illustrating the compound bidding and genetic algorithm in an embodiment of the present invention;

[0050] Figure 9 This is a schematic diagram of DC clearing for #1. Detailed Implementation

[0051] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] like Figure 1 The diagram shown is a flowchart of the method of the present invention. The present invention provides a method for optimizing the allocation of ultra-high voltage power based on a competitive electricity pricing mechanism, comprising the following steps:

[0053] Step S1: Construct a DC power transmission model with competitive bidding: Set constraints on the selling price and output power of the DC power transmission end, and set the objective function for power sales revenue with the goal of maximizing the revenue from power sales at the DC power transmission end.

[0054] Specifically, in this embodiment of the invention, the selling price constraint includes:

[0055] 1) Coal-fired power, wind power, and photovoltaic power will be subject to floating price quotations based on the benchmark coal-fired power price at the sending end;

[0056] 2) Hydropower prices are based on the on-grid electricity price, with floating prices determined by combining seasonal electricity pricing.

[0057] 3) The price of electricity collected from other provinces at the sending end is based on the average settlement price between provinces and is subject to floating pricing.

[0058] Output power constraints include:

[0059] 1) All renewable energy supplied with DC power transmission should be guaranteed to be purchased in full;

[0060] 2) The DC power transmission capacity is matched with the coal-fired power plant to meet the minimum output rate requirements;

[0061] 3) Power collected from outside the province should meet the restrictions of inter-provincial transmission channels and the power balance of the province where the power is collected.

[0062] The objective function for electricity sales revenue is expressed as follows:

[0063]

[0064] In the formula, M m,t,i P represents the electricity price sold by DC line i in the t-th hour of the m-th month. m,t,i This represents the clearing power of the i-th DC line in the t-th hour of the m-th month.

[0065] Based on the power source type and generation characteristics at the sending end, with the goal of maximizing the revenue from electricity sales at the sending end, a monthly and hourly "electricity + price" bidding process is formed on the power transmission side according to the principle of "benchmark price + upward and downward fluctuation".

[0066] Step S2: Construct a DC receiving-end competitive bidding power purchase model: Set the purchase price constraints and input power constraints for the DC receiving end, and set the power purchase expenditure objective function with the goal of minimizing the DC receiving end power purchase expenditure;

[0067] Specifically, in this embodiment of the invention, the purchase price constraint is as follows: the price fluctuates based on the benchmark coal-fired power price at the receiving end. When the supply and demand situation is tight, the price tends to be raised to obtain external power support, and when the supply and demand situation is relatively loose, the price tends to be lowered to obtain cheaper electricity. The fluctuation ratio is a maximum upward fluctuation of 10% and a maximum downward fluctuation of 15%.

[0068] Input power constraints include:

[0069] 1) The upper and lower limits of the external power required for purchase are determined through phased production operation simulation. A lower limit is set to fully guarantee the absorption capacity of the DC transmission line by the receiving province, while an upper limit is set to fully guarantee the survival space of the province's thermal power plants. This floating external power demand within the upper and lower limits effectively hands over the decision-making power for the absorption of UHV power to the market.

[0070] 2) The purchase of external electricity must not affect the consumption of its own renewable energy or worsen the peak-shaving situation;

[0071] 3) Power exchange is within the capacity of the transmission channel.

[0072] The objective function for electricity purchase expenditure is expressed as follows:

[0073]

[0074] In the formula, N m,t,j Q represents the electricity purchase price in the j-th province for the t-th hour of the m-th month. m,t,j This represents the electricity purchased in the j-th province during the t-th hour of the m-th month.

[0075] With the goal of minimizing the cost of purchasing ultra-high-voltage electricity, a monthly and hourly bidding process for "electricity + price" is formed at the receiving end based on the principle of "benchmark price + upward and downward fluctuation".

[0076] Step S3: Construct a power market clearing model using a dual-bid model that combines centralized bidding and double-listing / double-acquisition. The power market clearing model is as follows: the two parties first conduct centralized bidding, and the unsuccessful portion of the centralized bidding is determined by double-listing / double-acquisition.

[0077] The following explains the electricity market clearing models, including one-way listing, alternating listing, two-way listing, and dual listing and dual removal. Listing transactions can involve one market participant listing and the other removing, or both parties listing and removing independently, either sequentially or simultaneously, or the trading system automatically matching and clearing listings with matched prices. Listing transactions can be conducted between market participants in two provinces with signed agreements; they can also be conducted between market participants in multiple electricity purchasing and selling provinces, subject to mutual agreement.

[0078] One-way listing refers to a single entity issuing an offer for the electricity supply or demand and its price, with another party submitting an application to accept the offer. In practice, the formation of a transaction is related to the timing of the bidding application. Since this invention simulates a year-round, all-time market scenario in a medium- to long-term context, it assumes that both parties submit their applications simultaneously. In one-way listing, the transaction price is the listing price. The one-way listing process is as follows: Figure 2 As shown.

[0079] Rotating listing involves the electricity purchaser and seller taking turns listing and delisting. For example, first the seller lists, then the purchaser delists, then the purchaser lists again, and so on, alternating between the two. The transaction price in this rotating listing process is the listing price, and the procedure is as follows: Figure 3 As shown.

[0080] The "double listing and double picking" method involves both the buyer and seller of electricity listing and picking up their bids simultaneously. The trading platform automatically matches the electricity volume with a purchase price greater than or equal to the sales price according to the price priority principle. The double listing and double picking can also be carried out in multiple rounds. The transaction price is equal to the average of the listing prices of both parties or the bid corresponding to the party with the smaller listed volume.

[0081] Centralized bidding refers to a method in which both buyers and sellers of electricity submit their bids for electricity volume and price, and a supply and demand curve is formed based on the monotonic rise / fall of the price, with the price then cleared uniformly by a trading institution. Centralized bidding consists of three parts: curve formation, supply and demand analysis, and price clearing.

[0082] The DC sending-end prices are sorted from low to high to form the supply curve; the DC receiving-end prices are sorted from high to low to form the demand curve. It is important to note that since the supply and demand curves need to be overlaid for analysis, the electricity prices on both sides must be aligned to the same level, adopting the DC landing price level.

[0083] By superimposing the supply and demand curves, four scenarios may emerge depending on the different quantities of supply and demand, such as... Figure 4 As shown, Figure 4 Situations 1 and 2 indicate a relatively balanced supply and demand, with transactions occurring in the portion where the purchase price is greater than or equal to the sales price (shaded area). Figure 4Situations 3 and 4 represent situations where supply exceeds demand and supply is less than demand, respectively, and there are also unsuccessful transactions. In these four situations, if a transaction is not completed at the DC sending end, the only recourse is to reduce the DC power transmission or to trade power generation rights with the receiving power plant; if a transaction is not completed at the DC receiving end, the unsuccessful demand can only participate in the transaction of the next DC line.

[0084] The price clearing methods in centralized bidding are divided into unified price clearing and pair-by-pair matching clearing. Under the unified price clearing mechanism, all market participants will settle accounts based on the price of the last pair of supply and demand transactions. Figure 5 As shown in (a) above. Pair-by-pair matching clearing involves matching the supply and demand bids for each pair of transactions and determining the transaction price for each matched pair, as shown in (a). Figure 5 As shown in (b) of the diagram.

[0085] This invention analyzes the aforementioned electricity prices and proposes a dual-bid clearing mechanism for the ultra-high voltage (UHV) power market. Based on centralized competitive pricing, dual-bid transactions, and balanced allocation between the sending and receiving ends, it integrates centralized bidding with dual-bid settlement. Specifically, for the portion of centralized bidding that fails to reach a transaction, dual-bid settlement is implemented, with the unsuccessful buyers and sellers submitting continuous bids, automatically matched, and the final price being the average of their bids. Dual-bid pricing is a beneficial mechanism for both buyers and sellers. The DC sending end will not reduce its transmission power due to unsuccessful transactions, thus avoiding energy loss, while the DC receiving end can still purchase the electricity it participated in bidding for, meeting its electricity demand.

[0086] Step S4: Based on the objective function of electricity sales revenue and the objective function of electricity purchase expenditure, a genetic algorithm is used to solve the unsold portion in step S3 to obtain the Nash equilibrium solution for optimized allocation.

[0087] Since the proposed dual-bid electricity market clearing model does not have a globally optimal solution, this embodiment employs a genetic algorithm to obtain a Nash equilibrium solution. This means that, for both the sending and receiving ends, given the competitive strategies of all other market participants, the bidding strategy of either party is optimal. This satisfies the needs of both ends with minimal resource consumption, improves electricity utilization, and maximizes the utilization of power generation resources. The method for solving the dual-bid model using a genetic algorithm in this embodiment is as follows: Figure 8 As shown.

[0088] Specifically, genetic algorithms are a class of algorithms designed by drawing inspiration from the evolutionary laws of the biological world. They transform the problem-solving process into a process similar to chromosome crossover and mutation in biological evolution. Genetic algorithms generally include four processes: encoding, selection, crossover, and mutation. In this embodiment of the invention, the genetic algorithm includes the following steps:

[0089] Step S41: Encoding. The pricing strategy for the DC sending end and the typical monthly days of the sending end are treated as a gene. Each gene has 24 chromosomes corresponding to 24 hours. Each chromosome contains two pieces of information: value and quantity, to construct the initial population P0; For example... Figure 6 As shown in the embodiment of the present invention, 100 genes are generated by random number generation within the upper and lower limits of "valence" and "quantity" during encoding, forming the initial population P0.

[0090] Step S42: Select, such as Figure 7 As shown, based on the objective functions of electricity sales revenue and electricity purchase expenditure, dominant individuals are selected from the initial population to form the first-generation elite population P0. E In this embodiment of the invention, superior genes are selected from the aforementioned 100 genes and passed on to the next generation, continuously improving the quality of the solution and moving closer to the final Nash equilibrium solution.

[0091] Step S43: Crossover, based on the first-generation elite population P0 E The process of exchanging and recombinating chromosomes from two parent genes to form new offspring genes is called gene generation. Figure 7 As shown, two genes, P0, were selected from the first-generation elite population. E Father and P0 E The mother gene swaps the chromosomes corresponding to "time 2", "time 4", and "time 9" on these two genes, thus forming a pair of offspring genes P1-child 1 and P1-child 2. The crossover operation is a key step in the genetic algorithm to continuously expand the search range of solutions.

[0092] Step S44: Mutation, mutating the offspring gene to form new offspring; such as Figure 8 As shown, the chromosome corresponding to the offspring gene at time "9" is mutated from "price quoted at 320 yuan / MWh, quantity reported at 1 million MWh" to "price quoted at 335 yuan / MWh, quantity reported at 900,000 MWh". This mutation operation is to enhance the algorithm's ability to search the local solution space and avoid missing high-quality solutions.

[0093] The range of mutations includes:

[0094] 1) The sending-end coal-fired power, wind power, and photovoltaic power will be quoted based on the benchmark price of coal-fired power.

[0095] 2) Hydropower from the sending end is quoted based on the on-grid price of hydropower, combined with the seasonal price for peak and off-peak periods; power collected from outside the province is quoted based on the average settlement price between provinces.

[0096] 3) The receiving provinces will use the provincial benchmark price for coal-fired electricity as the base price for floating price quotations.

[0097] Step S45; Repeat steps S42 to S44 until a Nash equilibrium solution is found.

[0098] The following is an illustration through a specific example: the most complex case involving five DC lines and all four provinces is selected for bidding display, which is divided into three parts: determining the buyer, market clearing, and benefit assessment.

[0099] Buyer identified:

[0100] The purpose of this step is to determine the clearing order of DC lines #1, #2, #3, #4, and #5. The primary principle is to ensure that the minimum marketable electricity volume for each DC line is traded, i.e., half of the total electricity volume of each DC line; otherwise, power curtailment will occur. To ensure the trading of this minimum marketable electricity volume, the floor price for each DC line under the minimum marketable electricity volume composition needs to be calculated, as shown in Table 1. Specifically, for DC lines #1, #2, #3, and #4, the floor price corresponds to a 50% maximum downward discount for wind and solar power, and a 15% maximum downward discount for thermal power based on the coal benchmark price. For DC line #5, due to the high-water season, hydropower is quoted at a 25% maximum downward discount based on the benchmark price corresponding to August. The resulting floor prices for each DC line are shown in Table 1.

[0101] Table 1

[0102]

[0103]

[0104] By comparing the minimum transaction floor price of each DC line, five DC lines are determined in ascending order and cleared out in the order of 24153.

[0105] Market clearing:

[0106] The bidding strategies and clearing results for the DC sending and receiving ends are shown in Table 2. A market clearing diagram is shown below. Figure 9 As shown.

[0107] Table 2

[0108]

[0109] Judging from the clearing results, 760,000 kWh of electricity from DC 1 and grid D were traded, and all the electricity from DC 1 was traded in the market. The transaction price was the average of the two bid prices, which was 355 yuan / MWh.

[0110] Benefit analysis:

[0111] 1) DC receiving-end benefits

[0112] By using the method provided by this invention to conduct ultra-high voltage power bidding transactions, the average purchase price of electricity at the receiving end has been reduced in a coordinated manner. As shown in Table 3, compared with the competitive consumption mode (market electricity) and the DC independent consumption mode (planned electricity) at the landing point, the average purchase price of electricity in the three provinces of grid A, grid B, and grid C has decreased at this time, with reductions of RMB 8, 22, and 27 per kilowatt-hour, respectively. At this time, the average purchase price of electricity in grid D has not changed.

[0113] Through the competitive bidding of ultra-high voltage (UHV) power, the optimal allocation of UHV power has been promoted on a larger scale. DC line #1 opened up the grid B consumption market, DC line #4 opened up the grid D consumption market, and DC line #3 opened up the grid C consumption market. At this time, the inter-provincial trading volume reached 5.24 million kilowatt-hours, significantly improving the utilization efficiency of inter-provincial AC power transmission channels.

[0114] Table 3

[0115]

[0116] 2) DC sending end benefits

[0117] The UHV power bidding transaction has two benefits for DC sending end: price increase and volume increase. First, the sending end can appropriately increase the selling price per kilowatt-hour to obtain higher profits while ensuring the transaction. For example, when DC 1 is consumed by grid A alone, the landed electricity price is 344 yuan / MWh. By transferring it to grid B province with a higher electricity price, the average transaction price of DC 1 at that time, converted to grid A province, is 349 yuan / MWh, which is 5 yuan / MWh higher.

[0118] Secondly, by possessing flexible peak-shaving power sources such as thermal power, the sending end can open up consumption markets in other provinces through bidding, effectively increasing the amount of electricity transmitted and boosting electricity sales revenue. When DC 3 is consumed independently by grid B, due to the limited consumption space of grid B, it can only consume a maximum of 6.4 million kWh, with a landing price of 416 yuan / kWh. However, when DC 3 opens up the grid C market through bidding, it effectively increases the amount of electricity transmitted to 8 million kWh, with an average landing price of 388 yuan / kWh. Overall, the electricity sales revenue of DC 3 increases by 420,000 yuan at this point.

[0119] From both the sending and receiving ends, the "multi-province multi-energy bundling, multi-province shared channels, and grid central coordination" proposed in this invention is an ultra-high voltage power trading mechanism that aligns with the overall optimization interests of both the sending and receiving ends. The "centralized competition to discover prices, dual-listing and dual-acceptance to facilitate transactions, and mutual benefit and win-win for both the sending and receiving ends" is an ultra-high voltage power market clearing mechanism that aligns with the overall optimization interests of both the sending and receiving ends. At the same time, it improves the efficiency of power transmission and the utilization rate of power transmission channels. Due to the increase in transaction volume, it reduces power waste and improves resource utilization.

[0120] Those skilled in the art will readily understand that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimal allocation of ultra-high voltage power based on a competitive electricity pricing mechanism, characterized in that: Includes the following steps: Step S1: Construct a DC power transmission model with competitive bidding: Set constraints on the selling price and output power of the DC power transmission end, and set the objective function for power sales revenue with the goal of maximizing the revenue from power sales at the DC power transmission end. Step S2: Construct a DC receiving-end competitive bidding power purchase model: Set the purchase price constraints and input power constraints for the DC receiving end, and set the power purchase expenditure objective function with the goal of minimizing the DC receiving end power purchase expenditure; Step S3: Construct a power market clearing model using a dual-bid model that combines centralized bidding and double-listing / double-acquisition. The power market clearing model is as follows: the two parties first conduct centralized bidding, and the unsuccessful portion of the centralized bidding is determined by double-listing / double-acquisition. Step S4: Based on the objective function of electricity sales revenue and the objective function of electricity purchase expenditure, a genetic algorithm is used to solve the unsold portion in step S3 to obtain the Nash equilibrium solution for optimized allocation; The selling price constraints mentioned in step S1 include: 1) Coal-fired power, wind power, and photovoltaic power will be quoted based on the benchmark coal-fired power price at the sending end; 2) Hydropower prices are based on the on-grid electricity price, with floating prices determined by combining seasonal electricity pricing. 3) The price of electricity collected from other provinces at the sending end will be based on the average settlement price between provinces and will be subject to a floating price. The output power constraint conditions mentioned in step S1 include: 1) All renewable energy supplied with DC power transmission should be guaranteed to be purchased in full; 2) The DC power transmission capacity is matched with the coal-fired power plant to meet the minimum output rate requirements; 3) Power collected from outside the province should meet the restrictions of inter-provincial transmission channels and the power balance of the province where the power is collected.

2. The method for optimal allocation of ultra-high voltage power based on a competitive electricity pricing mechanism according to claim 1, characterized in that: The expression for the objective function of electricity sales revenue mentioned in step S1 is: ； In the formula, M m,t,i P represents the electricity price sold by DC line i in the t-th hour of the m-th month. m,t,i This represents the clearing power of the i-th DC line in the t-th hour of the m-th month.

3. The method for optimal allocation of ultra-high voltage power based on a competitive electricity pricing mechanism according to claim 1, characterized in that: The purchase price constraint in step S2 is as follows: the price fluctuates based on the benchmark coal-fired power price at the receiving end. When the supply and demand situation is tight, the price tends to be raised to obtain external power support, and when the supply and demand situation is relatively loose, the price tends to be lowered to obtain cheaper electricity. The fluctuation ratio is a maximum upward fluctuation of 10% and a maximum downward fluctuation of 15%.

4. The method for optimal allocation of ultra-high voltage power based on a competitive electricity pricing mechanism according to claim 3, characterized in that: The input power constraint conditions mentioned in step S2 include: 1) Obtain the upper and lower limits of the external power supply that needs to be purchased by conducting phased production operation simulations; 2) The purchase of external electricity must not affect the consumption of its own renewable energy or worsen the peak-shaving situation; 3) Power exchange is within the capacity of the transmission channel.

5. The method for optimal allocation of ultra-high voltage power based on a competitive electricity pricing mechanism according to claim 4, characterized in that: The expression for the objective function of electricity purchase expenditure in step S2 is: ； In the formula, N m,t,j Q represents the electricity purchase price in the j-th province for the t-th hour of the m-th month. m,t,j This represents the electricity purchased in the j-th province during the t-th hour of the m-th month.

6. The method for optimal allocation of ultra-high voltage power based on a competitive electricity pricing mechanism according to claim 1, characterized in that: The genetic algorithm described in step S4 includes the following steps: Step S41: Encoding, treating the DC sending end and the monthly typical day's reporting and pricing strategy of the sending end as a gene, each gene has 24 chromosomes to correspond to 24 hours, and each chromosome has two pieces of information: value and quantity, to construct the initial population P0; Step S42: Selection. Based on the objective function of electricity sales revenue and the objective function of electricity purchase expenditure, dominant individuals are selected from the initial population to form the first-generation elite population. ; Step S43: Crossover, based on the first-generation elite population They exchange and recombine to form new offspring genes; Step S44: Mutation, mutating the offspring gene to form a new offspring; Step S45; Repeat steps S42 to S44 until a Nash equilibrium solution is found.

7. The method for optimal allocation of ultra-high voltage power based on a competitive electricity pricing mechanism according to claim 6, characterized in that: In step S41, the encoding is generated using random numbers based on the selling price constraint, output power constraint, purchase price constraint, and input power constraint.

8. The method for optimal allocation of ultra-high voltage power based on a competitive electricity pricing mechanism according to claim 6, characterized in that: The range of mutations described in step S44 includes: 1) The sending-end coal-fired power, wind power, and photovoltaic power will be quoted based on the benchmark price of coal-fired power. 2) Hydropower from the sending end is quoted based on the on-grid price of hydropower, combined with the seasonal price for peak and off-peak periods; power collected from outside the province is quoted based on the average settlement price between provinces. 3) The receiving provinces will use the provincial benchmark price for coal-fired electricity as the base price for floating price quotations.

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