Power market clearing optimization method and system, and computer equipment

Abstract

The invention provides an electricity market clearing optimization method and system and computer equipment, and the method comprises the steps: formulating a double-market clearing rule for conventional energy to participate in a subsidy market and a unified market at the same time, and carrying out the first market clearing; when the renewable energy is abandoned after the first time of market clearing, triggering a readjustment clearing rule for the renewable energy, and carrying out second time of market clearing; the method comprises the following steps: constructing a double-layer clearing optimization model comprising an upper-layer optimization sub-model and a lower-layer optimization sub-model for a double-stage market clearing process consisting of first-time market clearing and second-time market clearing; and solving the double-layer clearing optimization model by using an improved particle swarm optimization algorithm to obtain a target energy bidding strategy. According to the invention, the technical problem that the traditional electricity market mechanism cannot adapt to the development of renewable energy sources is solved, the competitiveness of the renewable energy sources in the market is improved, and the stable and sustainable development of the electricity market is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power market, and in particular to a power market clearing optimization method and system, and computer equipment. Background Art

[0002] With the global emphasis on and utilization of renewable energy, my country started a new round of power system reform in 2015, aiming to promote the competition of renewable energy in the power market. However, due to the low cost of renewable energy generation and the near-zero marginal cost, coupled with the randomness and incomplete rationality of the bidding strategies of market players, the traditional "unified purchase" power market mechanism can no longer adapt to the current situation, resulting in sharp fluctuations in market clearing prices, zero electricity prices and negative electricity prices.

[0003] In order to deal with the problem of renewable energy consumption, the main methods currently include quota system, green certificate trading, fixed electricity price and premium subsidy. The quota system ensures market demand by forcing power companies to purchase a certain proportion of renewable energy electricity, but it lacks flexibility and may increase the operating costs of power companies. Green certificate trading uses market-based means to trade renewable energy certificates, encouraging power companies to voluntarily increase the use of renewable energy electricity, although its complex trading mechanism may affect the promotion effect. The fixed electricity price is set by the government to purchase renewable energy electricity to ensure the profits of power generation companies, but it may lead to government fiscal pressure and market distortion. Premium subsidies give renewable energy power generation companies additional subsidies on the basis of market electricity prices to make up for the cost difference between them and conventional energy. Although this method can improve the market competitiveness of renewable energy, the source and management of subsidy funds remain a challenge. Although the above methods have promoted the development of renewable energy to a certain extent, most of them ignore the bidding strategies and game behaviors of renewable energy in the market. Summary of the invention

[0004] To this end, the present invention provides an electricity market clearing optimization method and system, and computer equipment, aiming to solve the technical problem in the prior art that the traditional electricity market mechanism cannot adapt to the bottleneck of renewable energy development, which is not conducive to the stability and sustainable development of the electricity market.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions: According to a first aspect of the present invention, the present invention provides a method for optimizing electricity market clearing, the method comprising: Formulate dual market clearing rules for conventional energy participating in both the subsidy market and the unified market, and use the dual market clearing rules to conduct the first market clearing; When renewable energy is abandoned after the first market clearing, a readjustment clearing rule for renewable energy is triggered, and the second market clearing is performed using the readjustment clearing rule; For the two-stage market clearing process consisting of the first market clearing and the second market clearing, a two-layer clearing optimization model including an upper optimization sub-model and a lower optimization sub-model is constructed; the objective function of the upper optimization sub-model is to maximize the profit of the power generation enterprise; the objective function of the lower optimization sub-model is to maximize the social welfare; The double-layer clearing optimization model is solved using an improved particle swarm optimization algorithm to obtain the target energy bidding strategy.

[0006] Optionally, the double market clearing rule includes: The conventional energy units corresponding to the conventional energy sources respectively declare a first price-volume curve for bidding in the subsidy market and a second price-volume curve for bidding in the unified market, and the final settlement price is the sum of the subsidy electricity price in the subsidy market and the clearing electricity price in the unified market; Among them, the upper and lower limits of the subsidized electricity price are determined based on the long-term average marginal cost price difference between conventional energy units and renewable energy units.

[0007] Optionally, the readjustment of the clearing rules includes: For the unconsumed renewable energy, modify the corresponding bidding strategy and re-bid so that the renewable energy can win the bid under the unchanged bidding strategy of the conventional energy unit corresponding to the conventional energy, so as to ensure the maximum utilization of the renewable energy; The difference compensation is made for the transferred electricity volume of the conventional energy; the transferred electricity volume is the difference between the market-cleared electricity volume of the conventional energy in the first winning bid and the market-cleared electricity volume in the second winning bid.

[0008] Optionally, for the first market clearing, the objective function of the upper optimization sub-model is expressed as:

[0009] in, The objective function of the upper optimization sub-model at the first market clearing; Index for the unit; Index for the moment; is the quotation segment index; is the number of time periods; is the number of quotation segments; For Node At the moment The node marginal price of For the crew At the moment marginal cost of generators; The final subsidized electricity price for the subsidy market; For the crew At the moment No. The winning bid quantity of the segment; and / or, For the first market clearing, the lower optimization sub-model calculates the cleared electricity quantity and cleared electricity price according to the enterprise bidding strategy output by the upper optimization sub-model. The formula of the objective function is expressed as:

[0010] in, The objective function of the lower optimization sub-model for the first market clearing; Bid price for the unit; Electricity efficiency; The power demand on the user side; For the crew bid prices in subsidized markets; and / or, For the second market clearing, the upper optimization sub-model calculates the optimal clearing strategy of the renewable energy unit according to the clearing electricity quantity and clearing electricity price output by the lower optimization sub-model. The formula of the objective function is expressed as:

[0011] in, The objective function of the upper optimization sub-model during the second market clearing; Second clearing node At the moment The node marginal price of Secondary clearing time group At the moment No. The winning bid quantity of the segment; and / or, For the second market clearing, the upper optimization sub-model calculates the final target energy bidding strategy according to the optimal clearing strategy of the renewable energy units output by the upper optimization sub-model. The formula of the objective function is expressed as:

[0012] in, It is the objective function of the lower optimization sub-model during the second market clearing.

[0013] Optionally, the constraint condition of the upper optimization sub-model is at least one of a power quantity constraint and a power price constraint; The formula of the power constraint is expressed as:

[0014] in, is the amount of renewable energy not absorbed in the first market clearing; Secondary clearing time group No. The declared capacity of the segment; For the crew No. The winning bid quantity of the segment; and / or, The formula of the electricity price constraint is expressed as:

[0015] in, is the bid price of the renewable energy unit during the secondary clearing; is the bid price of the renewable energy unit at the first clearing; and / or, The constraint condition of the lower optimization sub-model is at least one of a system power flow balance constraint, a line power capacity constraint, an energy unit output constraint, a voltage phase angle constraint, a unit ramp constraint, a spinning reserve constraint, and a start / stop time constraint; The formula of the system power flow balance constraint is expressed as:

[0016]

[0017] in, , , They represent system nodes respectively; the system power flow balance constraint represents the steady-state distribution of voltage and power at each system node; and / or, The formula of the line power capacity constraint is expressed as:

[0018] in, For Node n arrive The upper limit of line transmission capacity; and / or, The voltage phase angle constraint is expressed as:

[0019] Among them, the node with network node number 1 is set as the reference node, that is, ; and / or, The formula for the energy unit output constraint is expressed as:

[0020]

[0021] in, For the crew No. The winning bid quantity of the segment; For the crew No. The declared capacity of the segment; The winning bid power of each section of the unit; is the maximum power generation capacity; Contribute to the minimum technology; For the crew At the moment The start and stop variables; 0 represents shutdown; 1 represents startup; and / or, The formula of the unit climbing constraint is expressed as:

[0022]

[0023] in, For the crew Downward climbing rate; For the crew The upward climbing rate; and / or, The formula for the spinning reserve constraint is expressed as:

[0024]

[0025] in, For the moment The spinning reserve requirement is to ensure that the power can meet the load demand when the output of uncertain renewable energy units fluctuates or the system fails; and / or, The formula of the start / stop time constraint is expressed as:

[0026] in, For the crew At the moment Continuous power-on time; For the crew At the moment Continuous downtime; is the minimum continuous power-on time; Minimum continuous downtime.

[0027] Optionally, the using of an improved particle swarm algorithm to solve the double-layer clearing optimization model to obtain a target energy bidding strategy includes: generating an initial particle population, wherein each particle in the initial particle population represents a potential energy bidding strategy; Setting algorithm parameters for the initial population of particles and evaluating the quality of the particles using a fitness function; Iteratively update particle speed and position, and perform local search and group search on each particle; The fitness function is used to evaluate the fitness of particles in the new position and update the individual and global optimal solutions. When the fitness change is less than the threshold or the maximum number of iterations is reached, the iteration is terminated and the global optimal solution is output as the target energy bidding strategy.

[0028] Optionally, the iterative updating of particle speed and position includes: During the iteration process, the inertia weight is dynamically adjusted using the following formula to balance the global search and local search capabilities:

[0029] in, is the maximum number of iterations; is the inertia weight; is the maximum value of inertia weight; is the minimum value of inertia weight; According to the particle's current speed, position, individual optimal position and global optimal position, the particle's speed is updated. The formula is as follows:

[0030] in, and is the learning factor; and is a random number; For particles The best historical position; is the global optimal position.

[0031] Optionally, the performing local search and group search on each particle includes: After each particle position update, a local search is performed on each particle to enhance the accuracy of the local optimal solution; The particle swarm is divided into multiple subgroups, which search different areas independently. After each subgroup searches independently for a period of time, the optimal solutions of each subgroup are exchanged regularly to update the global optimal solution. A dynamic expansion and contraction boundary mechanism is introduced to ensure uniform distribution of particles. In each iteration, the search boundary is dynamically adjusted according to the fitness distribution of particles to ensure that particles can fully explore the search space.

[0032] According to a second aspect of the present invention, the present invention provides an electricity market clearing optimization system, the system comprising: A one-time clearing module is used to formulate dual-market clearing rules for conventional energy that participates in both the subsidy market and the unified market, and use the dual-market clearing rules to perform the first market clearing; A secondary clearing module, for triggering a readjustment clearing rule for renewable energy when renewable energy abandonment occurs after the first market clearing, and performing a second market clearing using the readjustment clearing rule; A model building module, for building a two-stage market clearing process consisting of the first market clearing and the second market clearing, a two-stage market clearing optimization model including an upper optimization sub-model and a lower optimization sub-model; the objective function of the upper optimization sub-model is to maximize the profit of the power generation enterprise; the objective function of the lower optimization sub-model is to maximize social welfare; The model solving module is used to solve the double-layer clearing optimization model using an improved particle swarm optimization algorithm to obtain a target energy bidding strategy.

[0033] According to the third aspect of the present invention, the present invention provides a computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the electricity market clearing optimization method as described in any one of the first aspects of the present invention when executing the computer program.

[0034] The present invention adopts the above technical solution and has at least the following beneficial effects: Through the scheme of the present invention, a dual market clearing rule is formulated for conventional energy to participate in both the subsidy market and the unified market at the same time, and the dual market clearing rule is used for the first market clearing; when renewable energy abandonment occurs after the first market clearing, the readjustment clearing rule for renewable energy is triggered, and the second market clearing is performed using the readjustment clearing rule; for the two-stage market clearing process consisting of the first market clearing and the second market clearing, a dual-layer clearing optimization model including an upper optimization sub-model and a lower optimization sub-model is constructed; the dual-layer clearing optimization model is solved using an improved particle swarm optimization algorithm to obtain a target energy bidding strategy. As a result, the renewable energy absorption rate is improved, and the abandonment problem is reduced through the readjustment clearing mechanism; market fairness and social welfare are optimized, and the cost difference between traditional energy and renewable energy is balanced through the dual market clearing mechanism; the improved PSO algorithm improves the search capability and accuracy; the bottleneck that the traditional electricity market mechanism cannot adapt to the development of renewable energy is solved, the competitiveness of renewable energy in the market is improved, and the stability and sustainable development of the electricity market are promoted.

[0035] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A schematic diagram of a flow chart of a method for optimizing power market clearing provided by an embodiment of the present invention is shown; Figure 2 A schematic diagram of a process of two-stage market clearing provided by an embodiment of the present invention is shown; Figure 3 A schematic diagram of the structure of a double-layer clearing optimization model provided by an embodiment of the present invention is shown; Figure 4 A schematic diagram of the process of an improved particle swarm optimization algorithm provided by an embodiment of the present invention is shown; Figure 5 A schematic diagram showing the structure of a power market clearing optimization system provided by an embodiment of the present invention is shown; Figure 6 A schematic diagram of the physical structure of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0038] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0039] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0040] The embodiment of the present invention provides a method for optimizing electricity market clearing. Figure 1 As shown, at least the following steps S101 to S104 may be included: Step S101, formulate dual market clearing rules for conventional energy participating in both the subsidy market and the unified market, and use the dual market clearing rules to perform the first market clearing.

[0041] In the current electricity market clearing mechanism, all renewable energy units and conventional energy units are required to submit their own price and volume curves. Market organizers sort these curves from low to high and intersect with the demand curve. The intersection is the market clearing point, which determines the winning bid volume and market clearing price. As the penetration rate of renewable energy increases, the market clearing price will decline. However, for conventional energy units with high power generation costs, too low clearing prices cannot cover their costs, resulting in their inability to make a profit; while for renewable energy units with low power generation costs, the clearing price is much higher than their power generation costs, so the market competition environment is unfavorable to conventional energy units.

[0042] In order to ensure that conventional energy and renewable energy can compete fairly in a unified market, the present invention proposes a two-stage market clearing process, such as Figure 2 As shown, it includes the entire process of the first market clearing and the second market clearing.

[0043] In an embodiment of the present invention, the dual market clearing rules formulated for conventional energy are as follows: conventional energy units separately declare a first price-volume curve for bidding in the subsidy market and a second price-volume curve for bidding in the unified market, and the final settlement price is the sum of the subsidized electricity price in the subsidy market and the clearing electricity price in the unified market; the upper and lower limits of the subsidized electricity price are determined based on the long-term average marginal cost price difference between conventional energy units and renewable energy units.

[0044] That is to say, conventional energy units need to declare price-volume curves twice: once for participating in the subsidy market and obtaining subsidized electricity prices; the other time for participating in the unified market with renewable energy units for bidding, and participating in the dual market clearing mechanism of the unified market and the subsidy market at the same time, and the final settlement price is the sum of the subsidized electricity price in the subsidy market and the clearing electricity price in the unified market. By subsidizing the electricity of conventional energy units, the winning electricity and market clearing price are finally formed in the unified market, avoiding the clearing of renewable energy electricity at the price of conventional energy electricity, and reducing the clearing electricity price of renewable energy electricity. In this way, conventional energy units can lower their bids in the unified market, thereby competing with renewable energy units at the same price level, ensuring the market share of conventional energy units. At the same time, in order to truly reflect the impact of fuel cost fluctuations and differences in energy consumption management levels on the cost of conventional energy electricity generation, the embodiment of the present invention introduces a market-based competition mechanism to achieve reasonable declaration of subsidy prices among conventional energy units. Therefore, the upper and lower limits of the subsidy price are determined based on the long-term average marginal cost price difference between traditional energy units and new energy units. In order to avoid malicious high prices from conventional energy units in the subsidy market, those who bid more than the upper and lower limits of the subsidy quotation will be punished. Through this mechanism, conventional energy units can compete fairly with renewable energy units in the unified market, guaranteeing the market power and profits of conventional energy units and prompting all market players to quote according to the real power generation cost. When conventional energy electricity is subsidized, its competitiveness in the unified market is greatly enhanced, which may lead to the consumption rate of renewable energy electricity failing to meet the predetermined indicators, that is, renewable energy may be abandoned after the first market clearing.

[0045] Step S102: When renewable energy is abandoned after the first market clearing, the readjustment and clearing rules for renewable energy are triggered, and the second market clearing is performed using the readjustment and clearing rules.

[0046] In the embodiment of the present invention, the readjustment and clearing rules formulated for renewable energy are as follows: for renewable energy that has not been absorbed, the corresponding bidding strategy is modified to re-participate in the bidding, so that the renewable energy can win the bid under the unchanged bidding strategy of the conventional energy units corresponding to the conventional energy, so as to ensure the maximum use of renewable energy; the difference compensation is made for the transferred electricity of conventional energy; the transferred electricity is the difference between the market-clearing electricity of conventional energy in the first winning bid and the market-clearing electricity of conventional energy in the second winning bid.

[0047] That is to say, in order to appropriately increase the consumption rate of renewable energy electricity, the embodiment of the present invention keeps the bidding strategy of conventional energy units unchanged on the basis of the first clearing. The unabsorbed capacity of renewable energy will modify the bidding strategy, re-participate in the bidding, and conduct a second bid. It can be understood that the bid of the renewable energy unit in the second bid must be lower than the first bid to ensure that it can win the bid in the market where the strategy of the conventional energy unit remains unchanged.

[0048] Furthermore, if after the second clearing, there are still renewable energy sources that have not been absorbed, the energy will be directly abandoned. This mechanism gives full play to the resource allocation role of the market, helps maintain market fairness, and encourages renewable energy units and conventional energy units to bid rationally. After the unabsorbed renewable energy electricity is re-bid and cleared, a new clearing price and winning bid electricity are formed. In the second market clearing, the unabsorbed renewable energy electricity needs to be re-bid, which is equivalent to part of the power generation of conventional energy units transferred to renewable energy. In order to compensate for the losses of conventional energy units, the transferred electricity is compensated according to the difference between the two market clearing volumes, and the compensation electricity price is the difference between the two market clearing prices. Therefore, for conventional energy units, although the power generation is reduced, they can obtain additional income through the compensation mechanism, which encourages them to transfer power generation rights. For renewable energy units, although the market clearing price of the winning bid in the second clearing is lower than the market clearing price in the first clearing, they can still make a profit, which is in line with the principles of individual rationality and incentive compatibility.

[0049] It should also be noted that the winning bids for renewable energy in the first market clearing are settled at the first market clearing electricity price, and the winning bids for renewable energy in the second market clearing are settled at the second market clearing electricity price. The winning bids for conventional energy in the second market clearing are still settled at the first market clearing electricity price, and each kilowatt-hour of electricity generated will receive a corresponding subsidy. The difference between the winning bids for conventional energy in the first and second market clearings will be compensated by the difference between the two clearing prices.

[0050] Through the two-stage market clearing process proposed in the present invention, fair competition between conventional energy and renewable energy in the market is achieved, the absorption rate of renewable energy is improved, and the stability and sustainable development of the power market are promoted.

[0051] Step S103, for the two-stage market clearing process consisting of the first market clearing and the second market clearing, a two-layer clearing optimization model including an upper optimization sub-model and a lower optimization sub-model is constructed.

[0052] like Figure 3As shown, it is a structural diagram of the two-layer clearing optimization model proposed in the embodiment of the present invention. The clearing optimization model needs to consider the objective function and constraints. In order to simultaneously consider the market clearing process and the strategic behavior of market players, the embodiment of the present invention adopts a random two-layer optimization model, which includes an upper optimization sub-model and a lower optimization sub-model. The upper and lower optimization sub-models are interconnected and constrained. The optimal decision-making process of market players in the upper optimization sub-model depends on the node electricity price and the winning capacity in the lower optimization sub-model, while the market clearing process of the lower optimization sub-model depends on the optimal bidding strategy of the upper model. Furthermore, in order to ensure the maximization of the profits of power generation enterprises and the maximization of social welfare, the embodiment of the present invention takes the maximization of the profits of power generation enterprises as the objective function of the upper optimization sub-model, and takes the maximization of social welfare as the objective function of the lower optimization sub-model.

[0053] For the first stage of market clearing, the upper optimization sub-model takes the profit maximization of each power generation enterprise as the objective function, and the formula is expressed as:

[0054] in, The objective function of the upper optimization sub-model at the first market clearing; Index for the unit; Index for the moment; is the quotation segment index; is the number of time periods; is the number of quotation segments; For Node At the moment The node marginal price of For the crew At the moment marginal cost of generators; The final subsidized electricity price for the subsidy market; For the crew At the moment No. The winning bid quantity of the segment.

[0055] Furthermore, the node marginal electricity price is positioned as the average value of the marginal electricity benefit increment on the electricity consumption side and the marginal power generation cost increment on the power generation side under the unit load increment. The formula is as follows:

[0056] in, is the marginal generation cost on the generation side; The marginal benefit of electricity consumption on the electricity consumption side; Bid price for the unit; For electricity efficiency; It is the power demand on the user side.

[0057] Furthermore, the node marginal price The formula is as follows:

[0058] in, is the marginal generation cost on the generation side; The marginal benefit of electricity consumption on the electricity consumption side; For Node At the moment The user-side power demand.

[0059] Furthermore, the constraints of the upper optimization sub-model in the first market clearing are as follows ①~③: ① The sum of all declared capacities of the units is equal to the upper limit of the biddable capacity, which can be expressed as:

[0060] For the crew No. The declared capacity of the segment.

[0061] ② The unit quotation curve must increase monotonically, and the formula is:

[0062] The upper limit of the offer.

[0063] ③ The subsidy quotation is determined based on the difference in the long-term average marginal cost between conventional energy units and renewable energy units. The formula is:

[0064] in, For the crew bid prices in subsidized markets; This is the upper limit of the subsidy quotation.

[0065] The objective function of the lower optimization submodel corresponding to the first clearing process is to maximize the social welfare of the entire market, including user benefits and electricity purchase costs, and describes the market clearing process. The lower optimization submodel calls the safety constraint unit combination program according to the strategy of the power generation enterprise obtained by the upper optimization submodel, obtains the unit start and stop plan within 24 hours, and then calls the safety constraint economic dispatch program to calculate the node electricity price and electricity energy for 24 hours. The formula is expressed as:

[0066] in, The objective function of the lower optimization sub-model for the first market clearing; Bid price for the unit; Electricity efficiency; The power demand on the user side; For the crew Bid prices in subsidized markets.

[0067] The constraints for the lower optimization sub-model include system power flow balance constraints, line power capacity constraints, energy unit output constraints, voltage phase angle constraints, unit ramp constraints, spinning reserve constraints, and start / stop time constraints, etc. The formulas are as follows④~⑨: ④The formula for system power flow balance constraint is expressed as:

[0068]

[0069] in, , , They represent system nodes respectively; the system power flow balance constraint represents the steady-state distribution of voltage and power at each system node.

[0070] ⑤The formula for line power capacity constraint is:

[0071] in, For Node n arrive The upper limit of line transmission capacity.

[0072] ⑥The formula for voltage phase angle constraint is expressed as:

[0073] Among them, the node with network node number 1 is set as the reference node, that is, .

[0074] ⑦ The formula for energy unit output constraint is expressed as:

[0075]

[0076] in, For the crew No. The winning bid quantity of the segment; For the crew No. The declared capacity of the segment; The winning bid power of each section of the unit; is the maximum power generation capacity; Contribute to the minimum technology; For the crew At the moment The start and stop variables; 0 represents shutdown; 1 represents startup.

[0077] ⑧The formula for the unit climbing constraint is expressed as:

[0078]

[0079] in, For the crew Downward climbing rate; For the crew The upward climbing rate.

[0080] ⑨ The formula for the spinning reserve constraint is:

[0081]

[0082] in, For the moment The spinning reserve demand is to ensure that the electricity can meet the load demand when the output of uncertain renewable energy units fluctuates or the system fails.

[0083] ⑩The formula for the start / stop time constraint is:

[0084] in, For the crew At the moment Continuous power-on time; For the crew At the moment Continuous downtime; is the minimum continuous power-on time; Minimum continuous downtime.

[0085] In the embodiment of the present invention, the second stage of market clearing is the re-bidding of the unabsorbed renewable energy to participate in the clearing. For the second stage of market clearing, the upper optimization sub-model selects the optimal strategy for the renewable energy unit according to the winning bid amount and the market clearing electricity price transmitted from the lower optimization sub-model. The objective function formula is expressed as:

[0086] in, The objective function of the upper optimization sub-model during the second market clearing; Second clearing node At the moment The node marginal price of Secondary clearing time group At the moment No. The winning bid quantity of the segment; Compensation for the amount of electricity transferred from conventional energy units to renewable energy units.

[0087] In the second stage of market clearing, the constraints of the upper optimization sub-model are based on ① and ②, and the power consumption constraints and electricity price constraints are added. The formulas are as follows ⑪~⑫: ⑪The formula for power constraint is expressed as:

[0088] in, is the amount of renewable energy not absorbed in the first market clearing; Secondary clearing time group No. The declared capacity of the segment; For the crew No. That is to say, for renewable energy units, the upper limit of declared capacity is the unabsorbed capacity in the first market clearing.

[0089] ⑫ The formula for electricity price constraint is:

[0090] in, is the bid price of the renewable energy unit during the secondary clearing; It is the bid price of the renewable energy unit at the first clearing, which means the second bid price of the renewable energy unit must be lower than the first bid price.

[0091] Furthermore, in the second stage of market clearing, the lower optimization sub-model can optimize social welfare according to user benefits, electricity purchase costs, compensation costs and the optimal strategy passed down from the upper layer to obtain the market clearing result. The formula is as follows:

[0092] in, Secondary clearing time group At the moment No. The winning bid quantity of the segment; is the bid price of the renewable energy unit at the first clearing; For the crew bid prices in subsidized markets; Electricity efficiency; It is the power demand on the user side.

[0093] Step S104, using an improved particle swarm optimization algorithm to solve the double-layer clearing optimization model to obtain a target energy bidding strategy.

[0094] An improved particle swarm optimization (PSO) algorithm introduced in the embodiment of the present invention improves the global search capability and local search accuracy and prevents premature convergence by introducing strategies such as adaptive inertia weight adjustment, local search, cluster search and dynamic boundary expansion. Figure 4 As shown in the figure, solving the double-layer clearing optimization model based on the algorithm can include the following steps: generating an initial population of particles, each particle in the initial population of particles represents a potential energy bidding strategy. Setting algorithm parameters for the initial population of particles, and using the fitness function to evaluate the quality of particles; iteratively updating particle speed and position, and performing local search and group search on each particle; using the fitness function to evaluate the fitness of particles in new positions, and updating individual and global optimal solutions; when the fitness change is less than the threshold or reaches the maximum number of iterations, terminating the iteration and outputting the global optimal solution as the target energy bidding strategy.

[0095] In actual operation, a random number containing n The initial population of particles, each particle represents a potential solution. The initial position of each particle and speed The random setting is within the defined range. Then set the algorithm parameters, including the maximum number of iterations , inertia weight , acceleration constant and , Set the maximum value of inertia weight and minimum value , used for adaptive adjustment. The fitness value of each particle is calculated, and the fitness function is used to evaluate the pros and cons of the particle in the search space. It should be noted that the fitness function can be set according to the specific needs of the power market, such as maximizing the profit of power generation enterprises, maximizing social welfare, etc. In the process of iteratively updating the particle speed and position, the inertia weight is dynamically adjusted using the following formula to balance the global search and local search capabilities:

[0096] in, is the maximum number of iterations; is the inertia weight; is the maximum value of inertia weight; is the minimum value of the inertia weight.

[0097] According to the particle's current speed, position, individual optimal position and global optimal position, the particle's speed is updated. The formula is as follows:

[0098] in, and is the learning factor; and is a random number; For particles The best historical position; is the global optimal position.

[0099] Furthermore, a local search is performed on each particle to enhance the accuracy of the local optimal solution. After each particle position is updated, a local search is performed to refine the current solution of the particle. The particle group is divided into multiple subgroups, and different areas are searched independently. After each subgroup searches independently for a period of time, the optimal solutions of each subgroup are regularly exchanged, and the global optimal solution is updated to prevent falling into the local optimum. In addition, the embodiment of the present invention also introduces a dynamic expansion and contraction boundary mechanism to ensure uniform distribution of particles and avoid premature convergence. In each iteration process, the search boundary is dynamically adjusted according to the fitness distribution of the particles to ensure that the particles can fully explore the search space. Evaluate the fitness value of the new position, and update the individual and global optimal solutions. When the fitness change is less than the threshold or reaches the maximum number of iterations, the optimization process is terminated, that is, the iteration is terminated, and the global optimal solution is output, which is the target energy bidding strategy.

[0100] The embodiment of the present invention uses an improved particle swarm optimization algorithm to solve the double-layer clearing optimization model, which has the following advantages: (1) Improved global search capability: The global search capability of the algorithm is enhanced through adaptive inertia weight adjustment and speed constraint mechanism, which prevents particles from falling into local optimality too early; (2) Enhanced local search accuracy: Local search and cluster search strategies improve search accuracy and can find better solutions; (3) Improving convergence speed: The dynamic expansion and contraction boundary mechanism maintains the diversity of the particle swarm, prevents premature convergence, and accelerates the convergence speed of the algorithm.

[0101] The embodiment of the present invention provides an optimization method for clearing the electricity market. In view of the current spot market mechanism in which renewable energy units and conventional energy units cannot compete fairly, a two-stage two-layer optimization market clearing model considering the bidding strategy of market entities is designed, which increases the market power of high-cost conventional energy units and improves their profitability. At the same time, the bidding strategy of renewable energy units is more reasonable, creating a fair market competition environment; improving the renewable energy consumption rate, reducing the problem of energy abandonment through readjustment of the clearing mechanism; optimizing market fairness and social welfare, and balancing the cost difference between traditional energy and renewable energy through the dual market clearing mechanism. In addition, the readjustment of the market clearing mechanism also increases the consumption of renewable energy electricity as much as possible, and compensates for the transferred power generation of conventional energy units, ensuring their profitability. The improved PSO algorithm improves the search capability and accuracy, and accelerates convergence to the global optimal solution. The present invention solves the bottleneck that the traditional electricity market mechanism cannot adapt to the development of renewable energy, improves the competitiveness of renewable energy in the market, and promotes the stability and sustainable development of the electricity market.

[0102] Further, as Figure 1 The specific implementation of the present invention provides a power market clearing optimization system, such as Figure 5 As shown, the system may include: a primary clearing module 510 , a secondary clearing module 520 , a model building module 530 and a model solving module 540 .

[0103] The one-time clearing module 510 can be used to formulate dual market clearing rules for conventional energy participating in both the subsidy market and the unified market, and use the dual market clearing rules to perform the first market clearing; The secondary clearing module 520 can be used to trigger the readjustment clearing rules for renewable energy when renewable energy abandonment occurs after the first market clearing, and use the readjustment clearing rules to perform the second market clearing; The model building module 530 can be used to build a two-stage market clearing process consisting of a first market clearing and a second market clearing, a two-stage market clearing optimization model including an upper optimization sub-model and a lower optimization sub-model; the objective function of the upper optimization sub-model is to maximize the profit of the power generation enterprise; the objective function of the lower optimization sub-model is to maximize the social welfare; The model solving module 540 can be used to solve the double-layer clearing optimization model using an improved particle swarm optimization algorithm to obtain a target energy bidding strategy.

[0104] It should be noted that for other corresponding descriptions of the functional modules involved in the power market clearing optimization system provided in the embodiment of the present invention, reference can be made to Figure 1 The corresponding description of the method shown will not be repeated here.

[0105] Based on the above Figure 1 The method shown and Figure 5 The embodiment of the system shown in the figure, the embodiment of the present invention also provides a physical structure diagram of a computer device, such as Figure 6 As shown, the computer device may include a communication bus, a processor, a memory and a communication interface, and may also include an input and output interface and a display device, wherein each functional unit may communicate with each other through the bus. The memory stores a computer program, and the processor is used to execute the program stored in the memory and execute the steps of the method for constructing a page navigation bar described in the above embodiment.

[0106] Those skilled in the art can clearly understand that the specific working processes of the systems, devices, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and for the sake of brevity, they are not further described here.

[0107] In addition, the functional units in various embodiments of the present invention may be physically independent of each other, or two or more functional units may be integrated together, or all functional units may be integrated into one processing unit. The above integrated functional units may be implemented in the form of hardware, or in the form of software or firmware.

[0108] A person skilled in the art can understand that if the integrated functional unit is implemented in the form of software 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 can be essentially or all or 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, which includes several instructions to enable a computing device (such as a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention when running the instructions. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0109] Alternatively, all or part of the steps of implementing the aforementioned method embodiments may be accomplished by hardware associated with program instructions (such as a computing device such as a personal computer, a server, or a network device), and the program instructions may be stored in a computer-readable storage medium. When the program instructions are executed by a processor of the computing device, the computing device executes all or part of the steps of the method described in the various embodiments of the present invention.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that within the spirit and principles of the present invention, the technical solutions described in the aforementioned embodiments can still be modified, or some or all of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate from the protection scope of the present invention.

Claims

1. A method for optimizing electricity market clearing, characterized in that: The method comprises: Formulate dual market clearing rules for conventional energy participating in both the subsidy market and the unified market, and use the dual market clearing rules to conduct the first market clearing; When renewable energy is abandoned after the first market clearing, a readjustment clearing rule for renewable energy is triggered, and the second market clearing is performed using the readjustment clearing rule; For the two-stage market clearing process consisting of the first market clearing and the second market clearing, a two-layer clearing optimization model including an upper optimization sub-model and a lower optimization sub-model is constructed; the objective function of the upper optimization sub-model is to maximize the profit of the power generation enterprise; the objective function of the lower optimization sub-model is to maximize the social welfare; The double-layer clearing optimization model is solved using an improved particle swarm optimization algorithm to obtain the target energy bidding strategy.

2. The method according to claim 1, characterized in that The double market clearing rules include: The conventional energy units corresponding to the conventional energy sources respectively declare a first price-volume curve for bidding in the subsidy market and a second price-volume curve for bidding in the unified market, and the final settlement price is the sum of the subsidy electricity price in the subsidy market and the clearing electricity price in the unified market; Among them, the upper and lower limits of the subsidized electricity price are determined based on the long-term average marginal cost price difference between conventional energy units and renewable energy units.

3. The method according to claim 1, characterized in that The readjustment and clearing rules include: For the unconsumed renewable energy, modify the corresponding bidding strategy and re-bid so that the renewable energy can win the bid under the unchanged bidding strategy of the conventional energy unit corresponding to the conventional energy, so as to ensure the maximum utilization of the renewable energy; The difference compensation is made for the transferred electricity volume of the conventional energy; the transferred electricity volume is the difference between the market-cleared electricity volume of the conventional energy in the first winning bid and the market-cleared electricity volume in the second winning bid.

4. The method according to claim 1, characterized in that: For the first market clearing, the objective function of the upper optimization sub-model is expressed as follows: in, The objective function of the upper optimization sub-model at the first market clearing; Index for the unit; Index for the moment; is the quotation segment index; is the number of time periods; is the number of quotation segments; For Node At the moment The node marginal price of For the crew At the moment marginal cost of generators; The final subsidized electricity price for the subsidy market; For the crew At the moment No. The winning bid quantity of the segment; and / or, For the first market clearing, the lower optimization sub-model calculates the cleared electricity quantity and cleared electricity price according to the enterprise bidding strategy output by the upper optimization sub-model. The formula of the objective function is expressed as: in, The objective function of the lower optimization sub-model for the first market clearing; Bid price for the unit; Electricity efficiency; The power demand on the user side; For the crew bid prices in subsidized markets; and / or, For the second market clearing, the upper optimization sub-model calculates the optimal clearing strategy of the renewable energy unit according to the clearing electricity quantity and clearing electricity price output by the lower optimization sub-model. The formula of the objective function is expressed as: in, The objective function of the upper optimization sub-model during the second market clearing; Second clearing node At the moment The node marginal price of Secondary clearing time group At the moment No. The winning bid quantity of the segment; and / or, For the second market clearing, the upper optimization sub-model calculates the final target energy bidding strategy according to the optimal clearing strategy of the renewable energy units output by the upper optimization sub-model. The formula of the objective function is expressed as: in, It is the objective function of the lower optimization sub-model during the second market clearing.

5. The method according to claim 1, characterized in that The constraint condition of the upper optimization sub-model is at least one of a power constraint and an electricity price constraint; The formula of the power constraint is expressed as: in, is the amount of renewable energy not absorbed in the first market clearing; Secondary clearing time group No. The declared capacity of the segment; For the crew No. The winning bid quantity of the segment; and / or, The formula of the electricity price constraint is expressed as: in, is the bid price of the renewable energy unit during the secondary clearing; is the bid price of the renewable energy unit at the first clearing; and / or, The constraint condition of the lower optimization sub-model is at least one of a system power flow balance constraint, a line power capacity constraint, an energy unit output constraint, a voltage phase angle constraint, a unit ramp constraint, a spinning reserve constraint, and a start / stop time constraint; The formula of the system power flow balance constraint is expressed as: in, , , They represent system nodes respectively; the system power flow balance constraint represents the steady-state distribution of voltage and power at each system node; and / or, The formula of the line power capacity constraint is expressed as: in, For Node n arrive The upper limit of line transmission capacity; and / or, The voltage phase angle constraint is expressed as: Among them, the node with network node number 1 is set as the reference node, that is, ; and / or, The formula for the energy unit output constraint is expressed as: in, For the crew No. The winning bid quantity of the segment; For the crew No. The declared capacity of the segment; The winning bid power of each section of the unit; is the maximum power generation capacity; Contribute to the minimum technology; For the crew At the moment The start and stop variables; 0 represents shutdown; 1 represents startup; and / or, The formula of the unit climbing constraint is expressed as: in, For the crew Downward climbing rate; For the crew The upward climbing rate; and / or, The formula for the spinning reserve constraint is expressed as: in, For the moment The spinning reserve requirement is to ensure that the power can meet the load demand when the output of uncertain renewable energy units fluctuates or the system fails; and / or, The formula of the start / stop time constraint is expressed as: in, For the crew At the moment Continuous power-on time; For the crew At the moment Continuous downtime; is the minimum continuous power-on time; Minimum continuous downtime.

6. The method according to claim 1, characterized in that The improved particle swarm algorithm is used to solve the double-layer clearing optimization model to obtain the target energy bidding strategy, including: generating an initial particle population, wherein each particle in the initial particle population represents a potential energy bidding strategy; Setting algorithm parameters for the initial population of particles and evaluating the quality of the particles using a fitness function; Iteratively update particle speed and position, and perform local search and group search on each particle; The fitness function is used to evaluate the fitness of particles in the new position and update the individual and global optimal solutions. When the fitness change is less than the threshold or reaches the maximum number of iterations, the iteration is terminated and the global optimal solution is output as the target energy bidding strategy.

7. The method according to claim 6, characterized in that The iterative update of particle velocity and position includes: During the iteration process, the inertia weight is dynamically adjusted using the following formula to balance the global search and local search capabilities: in, is the maximum number of iterations; is the inertia weight; is the maximum value of the inertia weight; is the minimum value of inertia weight; According to the particle's current speed, position, individual optimal position and global optimal position, the particle's speed is updated. The formula is as follows: in, and is the learning factor; and is a random number; For particles The best historical position; is the global optimal position.

8. The method according to claim 6, characterized in that The local search and group search for each particle include: After each particle position update, a local search is performed on each particle to enhance the accuracy of the local optimal solution; The particle swarm is divided into multiple subgroups, which search different areas independently. After each subgroup searches independently for a period of time, the optimal solutions of each subgroup are exchanged regularly to update the global optimal solution. A dynamic expansion and contraction boundary mechanism is introduced to ensure uniform distribution of particles; in each iteration, the search boundary is dynamically adjusted according to the fitness distribution of particles to ensure that particles can fully explore the search space.

9. An electricity market clearing optimization system, characterized in that: The system comprises: A one-time clearing module is used to formulate dual-market clearing rules for conventional energy that participates in both the subsidy market and the unified market, and use the dual-market clearing rules to perform the first market clearing; A secondary clearing module, for triggering a readjustment clearing rule for renewable energy when renewable energy abandonment occurs after the first market clearing, and performing a second market clearing using the readjustment clearing rule; A model building module, for building a two-stage market clearing process consisting of the first market clearing and the second market clearing, a two-stage market clearing optimization model including an upper optimization sub-model and a lower optimization sub-model; the objective function of the upper optimization sub-model is to maximize the profit of the power generation enterprise; the objective function of the lower optimization sub-model is to maximize social welfare; The model solving module is used to solve the double-layer clearing optimization model using an improved particle swarm optimization algorithm to obtain a target energy bidding strategy.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the power market clearing optimization method according to any one of claims 1 to 8 is implemented.

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