Electricity energy-reserve market joint clearing method and computer-readable storage medium

Abstract

The present invention provides a method for joint clearing of electric energy and reserve market and a computer-readable storage medium, the method comprising: determining base state scenario information and non-base state scenario information according to the topological structure and transmission line parameters of the power grid system, historical fault data of generator sets and transmission lines, future meteorological information forecast data, historical meteorological data and historical actual data of renewable energy and load; establishing an electric energy and reserve joint optimization clearing model based on scenario simulation based on the information of base state and non-base state scenarios, which is composed of an objective function and constraints; establishing a pricing and settlement mechanism corresponding to the electric energy and reserve joint optimization clearing model. Based on the prediction of possible future system operation scenarios and the distribution information of reserve resources, the coordinated clearing of energy and reserve resources is achieved, overcoming the problem of setting reserve demand; the callability of reserve is guaranteed by network constraints in base state and non-base state scenarios, without the need to manually divide reserve partitions.

Description

Technical Field

[0001] The present invention relates to the field of electric energy technology, and in particular to an electric energy-reserve market joint clearing method and a computer-readable storage medium. Background Art

[0002] Reserves can fully improve the operational flexibility of the power grid, assist system operators in coping with the system operation risks brought about by the increasing proportion of renewable energy, and fully improve the power grid's ability to absorb renewable energy. However, the current electric energy-reserve deterministic optimization clearing model has important technical problems: Specifically, the existing model relies on manually selected reserve requirements, cannot guarantee the availability of reserves, and cannot establish a reasonable reserve pricing method and cost sharing method based on the model.

[0003] The selection of reserve demand is the most important technical issue. The current reserve clearing model relies heavily on the setting of the reserve demand parameter, and the selection of this parameter is often highly artificial and empirical, such as selecting the capacity of the largest online unit in the system or a fixed proportion of the total load as the reserve demand. These methods lack a solid theoretical basis and cannot correctly describe the operating risk level faced by the new power system with new energy as the main body.

[0004] In the existing model, when clearing the reserve, only the cleared reserve meets the reserve demand, without considering whether the reserve can be called when a fault occurs. Therefore, the callability of reserve resources is another important problem in the reserve optimization model. The reserve and electric energy have a transmission line transmission capacity coupling relationship: after the system issues a reserve call instruction to the unit, the unit will call part or all of its reserved reserve capacity according to the instruction, changing its own output level. This adjustment will affect the power flow distribution of the system and may cause the generation of new blocked lines. This affects the callability of reserve resources. At present, in order to solve the problem of reserve callability, the system operator will divide the entire system into several reserve sub-areas and clear the reserve in different areas. However, this reserve partitioning method lacks a solid theoretical basis, has an artificial experience color, and cannot fundamentally guarantee the callability of the reserve.

[0005] At the same time, since the existing model only considers the cleared reserve to meet the reserve demand, without considering the location of the reserve and the specific source of the reserve demand. Therefore, the pricing mechanism and reserve cost sharing mechanism corresponding to the current clearing model also have problems. In terms of electricity pricing, the current electricity pricing method lacks the characterization of different failure probabilities of generators and different power supply reliability requirements of users. For generators, if different units at the same node have different failure probabilities, their operating reliability will be different, and the electricity they provide will no longer be a homogeneous commodity, so the electricity prices they receive should also be different. At the same time, for users, different users at the same node may also have different power supply reliability requirements due to their different load properties. These different power supply reliability requirements will affect the system operation cost and scheduling method, so these user loads will no longer be homogeneous, and the corresponding electricity prices for these users should also be different. In summary, we need to improve the current node marginal pricing method of electricity and further improve the non-homogeneous pricing method of electricity.

[0006] At the same time, in terms of reserve pricing, the reserves provided by units at different locations have different callability and different values ​​for the system. Therefore, the reserves provided by units at different locations are non-homogeneous. For units at the same node, although the reserves they provide have the same callability, when the system calls their reserves, due to the different marginal costs of the units' electric energy, the adjustment costs of different units will also be different. Therefore, the reserves provided by units at the same node with different adjustment prices are also of different qualities and should also be different in terms of pricing. At the same time, for generators with different failure probabilities at the same node, the reliability of their reserve capacity will be different, and they are also non-homogeneous commodities. Therefore, the reserve prices received by these units should also be different. In summary, there are problems with the current regional unified pricing method for reserves, and further exploration of non-homogeneous pricing methods for reserves is needed. In addition, in terms of allocation of reserve costs, the fluctuation probability, fluctuation direction and fluctuation amplitude of different loads are different, and their positions in the system are also different. The uncertainty brought to the system is not proportional to their load share. Therefore, there are problems in allocating reserve costs according to load share, and a reasonable allocation method for reserve costs needs to be explored.

[0007] The disclosure of the above background technology content is only used to assist in understanding the concept and technical solution of the present invention. It does not necessarily belong to the prior art of this patent application. In the absence of clear evidence that the above content has been disclosed on the filing date of this patent application, the above background technology should not be used to evaluate the novelty and creativity of the present application. Summary of the invention

[0008] In order to solve the existing problems, the present invention provides an electric energy-reserve market joint clearing method and a computer-readable storage medium.

[0009] In order to solve the above problems, the technical solution adopted by the present invention is as follows:

[0010] A method for joint clearing of an electric energy-reserve market comprises the following steps: S1: determining base state scenario information and non-base state scenario information according to the topological structure and transmission line parameters of a power grid system, historical fault data of generator sets and transmission lines, future meteorological information forecast data, historical meteorological data and historical actual data of renewable energy and load; S2: establishing an electric energy-reserve joint optimization clearing model based on scenario simulation based on the information of the base state scenario and the non-base state scenario, wherein the electric energy-reserve joint optimization clearing model is composed of an objective function and constraint conditions; S3: establishing a pricing and settlement mechanism corresponding to the electric energy-reserve joint optimization clearing model.

[0011] Preferably, the base state scenario information includes: a predicted base state load power vector d; the output of renewable energy is modeled as a negative load and is included in the base state load power vector d; the base state line maximum long-term transmission capacity vector f; the base state transmission factor transfer matrix S; the non-base state scenario information includes: all possible non-base state scenarios k∈K, K is the set of all non-base state scenarios; the occurrence probability P of each scenario k ,k∈K;P k is a positive number, the probability P of all scenes in the K set k The sum is not greater than 1; the number set of generator sets that fail in each scenario Ω k ,k∈K; the maximum short-term transmission capacity vector f of the line in each scenario k ,k∈K; In the non-baseline scenario k, the line flow is allowed to temporarily exceed the maximum long-term transmission capacity of the line and will be k The difference between the maximum long-term transmission capacity vector f of the line in the base state is reflected in the transfer matrix S of the transmission factor in each scenario. k ,k∈K; load fluctuation vector π in each scenario k ,k∈K.

[0012] Preferably, establishing the electric energy-reserve joint optimization clearing model based on scenario simulation includes the following steps: S11: determining the constraints of the electric energy-reserve joint optimization clearing model based on scenario simulation, the constraints include the energy balance and transmission capacity constraints of the base state, the energy balance and transmission capacity constraints under all non-base state scenarios, the readjustment process constraints under all non-base state scenarios and the physical constraints of the unit itself; S12: determining the objective function of the electric energy-reserve joint optimization clearing model based on scenario simulation, the objective function is divided into three parts: the quoted cost of energy and reserve, the expected value of the readjustment cost generated by reserve call and load shedding in all non-base state scenarios, and the electric energy saved due to unit failure in all non-base state scenarios and the reserve cost; S13: solving the obtained electric energy-reserve joint optimization clearing model based on scenario simulation.

[0013] Preferably, the constraints for determining the electric energy-reserve joint optimization clearing model include the following: base state electric energy balance constraint: in the base state, the sum of the electric energy cleared from the unit by the power grid system should be equal to the sum of the predicted base state loads, that is: λ:∑g=∑d, where g is the electric energy of the unit and λ is the Lagrange multiplier corresponding to the energy balance constraint; base state transmission capacity constraint: in the base state, the flow of the branch should not exceed the maximum transmission capacity of the branch, that is: μ:S(gd)≤f, where μ is the Lagrange multiplier corresponding to the constraint; the physical constraints of the unit itself, including the capacity constraint and the ramp rate constraint of the unit: the sum of the electric energy cleared by the unit and the upward reserve cannot exceed the upper capacity limit of the unit, and the downward reserve cleared by the unit cannot exceed the difference between the electric energy cleared by the unit and the upper capacity limit of the unit, otherwise the reserve cleared by the unit may not be called, that is: υ : G ≤gr D , where r U The reserve for clearing the generator set is adjusted upward, r D To clear the generator set, is the maximum operating capacity of each unit, G is the minimum operating capacity of each unit, The Lagrange multiplier corresponding to the maximum operating capacity constraint of the unit is, υ is the Lagrange multiplier corresponding to the minimum operating capacity constraint of the unit; the upward and downward reserve of the unit clearing cannot exceed the ramping capacity of the unit within the pre-specified reserve response time, that is: in, The maximum upward climbing capability of the unit within the pre-specified standby response time. is the maximum downward climbing capability of the unit within the pre-specified standby response time. is the Lagrange multiplier corresponding to the upward climbing constraint of the unit; is the Lagrange multiplier corresponding to the downward climbing constraint of the unit; in addition, the reserve amount of the unit clearing must be greater than 0, that is: ρ :0≤r U , σ :0≤r D , ρ and σ are the Lagrange multipliers corresponding to these two lower bound constraints;

[0014] The energy balance constraint in any non-base state scenario k is expressed as the following equation:

[0015]

[0016] in, represents the actual power clearing of the unit considering the unit failure in the kth scenario, which is composed of the power clearing amount g and the set of units that fail in each scenario k Ω k Joint decision: If generator i fails in the kth scenario, i∈Ω k ,but If generator i works normally in the kth scenario, that is, but Represents the increased standby of the unit called in the kth scenario, that is, the output increase value of the unit in the kth scenario; represents the down-adjustment of the unit called in the kth scenario, that is, the output down-adjustment value of the unit in the kth scenario; therefore, represents the actual output of the unit in the kth scenario; from the user's perspective, δd k represents the load removal in the kth scenario, π k represents the load fluctuation in the kth scenario; therefore, (d+π k -δd k ) represents the actual load power of the user in the kth scenario;

[0017] The transmission capacity constraint in any non-base state scenario k is expressed as:

[0018]

[0019] Among them, S k is the transfer factor transfer matrix in the kth scenario, μ k is the Lagrange multiplier corresponding to the constraint;

[0020] Re-regulation process constraints: In the re-regulation process of non-baseline scenarios, the reserve call amount cannot exceed the reserve clearance amount of the unit, and the load removal amount cannot exceed the actual load power, which can be expressed as:

[0021]

[0022]

[0023]

[0024] in, represents the actual increased reserve capacity of the unit after considering the possible unit failure in the kth scenario, which is determined by the increased reserve clearance amount r U The set of units that fail in each scenario k is Ω k Joint decision: If generator i fails in the kth scenario, i∈Ω k ,but If generator i works normally in the kth scenario, that is, but represents the actual down-regulated reserve capacity of the unit in the kth scenario, which is determined by the down-regulated reserve clearing amount r D The set of units that fail in each scenario k is Ω k Joint decision: If generator i fails in the kth scenario, i∈Ω k ,but If generator i works normally in the kth scenario, that is, but is the Lagrange multiplier for the upward constraint on the unit output, is the Lagrange multiplier corresponding to the unit output reduction constraint, τ k is the Lagrange multiplier corresponding to the load removal constraint;

[0025] At the same time, the readjustment process must also meet the following constraints:

[0026]

[0027]

[0028] t k :0≤δd k

[0029] in, α k , β k , t k are the Lagrange multipliers corresponding to these lower bound constraints respectively.

[0030] Preferably, the quoted cost of the electric energy and the reserve is expressed as:

[0031]

[0032] Among them, C E Represents the unit's electric energy quotation vector, C URepresents the unit's reserve quotation vector, C D The downward reserve bid vector representing the unit;

[0033] The expected value of the sum of the unit's reserve call cost and the user's load shedding cost in all non-baseline scenarios is expressed as:

[0034]

[0035] in, The price of the unit's output is increased. C The output of the representative unit is reduced in price, C L represents the load shedding price;

[0036] In the non-baseline scenario, the energy saved due to unit failure and the clearing cost of the reserve are expressed as:

[0037]

[0038] in, Provides the relationship between the unit clearing power g and the unit actual power in the kth scenario due to unit failure. The difference between them, the energy cost corresponding to this difference will be saved: If generator i fails in the kth scenario, that is, i∈Ω k ,but If generator i works normally in the kth scenario, that is, but In the objective function, the expected value of this part of cost savings is considered; the same applies to the upward and downward reserve;

[0039] The objective function of the electric energy-reserve joint optimization clearing model is:

[0040]

[0041] Preferably, a distributed algorithm is used to solve the electric energy-reserve joint optimization clearing model, and the obtained g,r U ,r D As clearing power and spare capacity, δd k As the readjustment strategy for the kth scenario, the lowest expected system operating cost is obtained.

[0042] Preferably, establishing the pricing and settlement mechanism corresponding to the electric energy-reserve joint optimization clearing model includes the following steps: S31: establishing the pricing mechanism corresponding to the electric energy-reserve joint optimization clearing model, including the unit electric energy and reserve pricing mechanism and the load electric energy pricing mechanism; S32: establishing the settlement mechanism corresponding to the electric energy-reserve joint optimization clearing model, including the ex ante calculation stage and the ex post settlement stage; S33: establishing the market nature of the pricing and settlement mechanism corresponding to the electric energy-reserve joint optimization clearing model.

[0043] Preferably, the unit power and reserve pricing mechanism includes: after solving the power-reserve joint optimization clearing model, the clearing result of the i-th unit is recorded as g(i) * ,r U (i) * ,r D (i) * ; The electric energy and reserve clearing amount g(i),r of the i-th unit, which are originally decision variables in the electric energy-reserve joint optimization clearing model, are U (i),r D (i) Fixed at its optimal value g(i) * ,r U (i) * ,r D (i) * In the model obtained after the transformation, only the electric energy and reserve clearing amount of unit i are fixed, and the electric energy and reserve clearing amount of other units are still variables to be optimized. Therefore, the constraints only related to unit i in the constraints will be eliminated, specifically:

[0044]

[0045] G (i)≤g(i)-r D (i)

[0046]

[0047]

[0048] 0≤r U (i),0≤r D (i)

[0049] The quoted cost of the electric energy and reserve of unit i and the expected value of the reduced electric energy and reserve cost due to the possible failure of unit i will also be eliminated, specifically as follows:

[0050]

[0051] In the transformed model, a sensitivity analysis is performed on the fixed parameters such as the power and reserve of the i-th unit to obtain the power and reserve price of the unit:

[0052]

[0053]

[0054]

[0055] Among them, C * represents the optimal objective function of the model after transformation, which means the overall expected cost of other market participants except unit i, L * Represents the optimal Lagrangian function of the model after transformation. For electricity energy pricing, η g (i) represents the electricity price of the i-th unit, which is composed of the base state contribution (λ-S(:,n i ) T μ) is the contribution of all non-baseline scenarios with no failure of the i-th unit composition;

[0056] For standby pricing, η U (i) and η D (i) represents the upward reserve price and downward reserve price of the i-th unit, respectively. The Lagrange multiplier corresponding to the constraint that “the reserve call amount of all non-baseline scenarios cannot exceed the actual reserve capacity of the scenario” and accumulated;

[0057] The electric energy pricing mechanism of the load is:

[0058] According to the envelope theorem, the electric energy price of the load is expressed as follows:

[0059]

[0060] Where η d (j) represents the electricity price of user j, which is composed of the base state part (λ—S(:,n j ) T μ), the non-base state part contributed by all non-base state scenarios (∑ k＝1,…,K (λ k -S k (:,n j ) T μ k )) Load shedding constraints for all non-baseline scenarios (δd k ≤d) corresponds to the negative value of the multiplier cumulative composition;

[0061] in, It is related to the power supply reliability required by the user: For any user j, its power supply reliability requirement is determined by the load shedding price C submitted by it to the system. L (j) Parameter characterization: Load shedding price C of user j L The smaller (j) is, the lower the power supply reliability required by the user.

[0062] Preferably, the pre-settlement stage includes: in the energy-reserve joint optimization clearing model optimization stage, according to the electric energy and reserve clearing results obtained by the model and the electric energy and reserve clearing prices obtained by the transformed model, the cleared electric energy, reserve, basic load and possible load fluctuations in all scenarios are settled, specifically: The electric energy settlement formula for the i-th unit is: η g (i)*g(i); The reserve adjustment settlement formula for the i-th unit is: η U (i)*r U (i); Settlement formula for reserve adjustment of the i-th unit: η D (i)*r D (i); Basic load settlement formula for the jth user: η d (j)*d(j);

[0063] According to the envelope theorem, the load fluctuation price of the jth user in the kth scenario is expressed as follows:

[0064]

[0065] The load fluctuation settlement formula for the jth user is:

[0066] The post-settlement stage includes: in the post-settlement, the output increase is adopted With price cuts C Reserve call volume for units For settlement, the load shedding price C is used L The load δd for resection k To settle accounts, that is, the settlement formula for increasing the output of the i-th unit is: The settlement formula for reducing the output of the i-th unit is: The load shedding compensation settlement formula for the jth user is: C L (j)*δd k (j);

[0067] Preferably, the market properties of the pricing and settlement mechanism corresponding to the electric energy-reserve joint optimization clearing model include: node pricing consistency of electric energy: if the failure probability of all generators at a node is 0, and in all non-base state scenarios, the load of any user at the node will not be completely cut off, then all generators and users at the node will have the same electric energy price; node pricing consistency of reserve and unit output readjustment: for any non-base state scenario k, consider any two units i and j located at the same node n, the sum of the contributions of the scenario k to the expected values ​​of the reserve income and readjustment income of the two units is proportional to the output readjustment amount of the two units in the scenario, and the ratio is the contribution of the scenario to the electric energy price, that is:

[0068]

[0069] Individual rationality of the unit: For unit i, given the power and reserve price η g (i),η U (i),η D (i), the clearing result g(i),r of the electric energy-reserve joint optimization clearing model U (i),r D (i) The revenue of unit i can be maximized, and unit i will not deviate from the clearing result of the system; Cost recovery property of the unit in any scenario: Under the proposed market model and pricing settlement mechanism, no matter which non-base state scenario occurs, if it obeys market dispatch, the revenue obtained by any unit i from the market is not less than the operating cost of the unit; Profitable property: Under the proposed market model and pricing settlement mechanism, the expected net revenue will be equal to the blocking surplus Δ0=f of the base state T μ plus the blocking surplus in all non-base state scenarios And the expected net profit is non-negative.

[0070] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0071] The beneficial effects of the present invention are: providing an electric energy-reserve market joint clearing method and a computer-readable storage medium. The present invention proposes an electric energy-reserve market joint clearing method based on scenario simulation. In the present invention, the proposed model does not rely on the parameter of reserve demand set by artificial experience, but is based on the prediction of possible future system operation scenarios and reserve resource distribution information to achieve coordinated clearing of energy and reserve resources, thereby overcoming the problem of reserve demand setting; at the same time, the proposed model ensures the callability of the reserve through network constraints in base state and non-base state scenarios, so there is no need to manually divide the reserve partitions in the model.

[0072] Furthermore, the pricing and settlement mechanism proposed in the present invention realizes the pricing and backup cost sharing of electric energy and backup resources based on marginal cost, takes into account the power supply reliability requirements of unit failures and loads, considers the different callability of backup resources in different locations, and the different adjustment costs when backup resources in the same location are called, and fully considers the homogeneity of electric energy and backup.

[0073] Furthermore, under the premise of meeting certain assumptions, the clearing model and pricing settlement mechanism proposed in the present invention can establish many good market properties, while ensuring that the power generation units will not deviate from the market dispatch, ensuring the balance of income and expenditure and profit adequacy of the system operator, which illustrates the superiority of the proposed model and pricing settlement mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is a schematic diagram of an electric energy-reserve market joint clearing method in an embodiment of the present invention.

[0075] Figure 2 It is a schematic diagram of a method for establishing an electric energy-reserve joint optimization clearing model based on scenario simulation in an embodiment of the present invention.

[0076] Figure 3 It is a comparison between the average total cost of the system under the clearing mechanism of the present invention in the embodiment of the present invention and the average total cost of the system under the traditional clearing mechanism with different backup demand parameter settings. DETAILED DESCRIPTION

[0077] In order to make the technical problems, technical solutions and beneficial effects to be solved by the embodiments of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0078] It should be noted that when a component is referred to as being "fixed to" or "disposed on" another component, it can be directly on the other component or indirectly on the other component. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element. In addition, connection can be used for both fixing and circuit connection.

[0079] It should be understood that the orientation or position relationship indicated by terms such as "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0080] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0081] The present invention proposes a method for joint clearing of the electric energy-reserve market based on scenario simulation. It does not require manual setting of reserve demand or manual division of reserve partitions. Instead, it achieves coordinated clearing of energy and reserve resources based on predictions of possible future system operation scenarios and reserve resource distribution information, and realizes pricing and cost sharing of energy and reserve resources based on marginal costs.

[0082] like Figure 1 As shown, the present invention provides a method for joint clearing of electric energy-reserve market, comprising the following steps:

[0083] S1: Determine the base state scenario information and non-base state scenario information based on the topological structure and transmission line parameters of the power grid system, historical fault data of generator sets and transmission lines, future meteorological information forecast data, historical meteorological data, and historical actual data of renewable energy and load;

[0084] S2: establishing an electric energy-reserve joint optimization clearing model based on scenario simulation based on information of the base state scenario and the non-base state scenario, wherein the electric energy-reserve joint optimization clearing model is composed of an objective function and constraint conditions;

[0085] S3: Establish a pricing and settlement mechanism corresponding to the electric energy-reserve joint optimization clearing model.

[0086] The present invention proposes a method for joint clearing of the electric energy and reserve market based on scenario simulation. In the present invention, the proposed model does not rely on the parameter of reserve demand set by artificial experience, but is based on the prediction of possible future system operation scenarios and reserve resource distribution information to achieve coordinated clearing of energy and reserve resources, thereby overcoming the problem of setting reserve demand; at the same time, the proposed model ensures the callability of reserve through network constraints in base state and non-base state scenarios, so there is no need to manually divide reserve partitions in the model. In addition, the pricing and settlement mechanism proposed in the present invention realizes the pricing and reserve cost sharing of electric energy and reserve resources based on marginal cost, takes into account the power supply reliability requirements of unit failures and loads, takes into account the different callability of reserve resources at different locations, and the different adjustment costs when reserve resources at the same location are called, and fully considers the homogeneity of electric energy and reserve. Moreover, under the premise of meeting certain assumptions, the clearing model and pricing settlement mechanism proposed in the present invention can establish many good market properties, while ensuring that the power generation units will not deviate from the market dispatch, it ensures the balance of income and expenditure and profit sufficiency of the system operator, which illustrates the superiority of the proposed model and pricing settlement mechanism.

[0087] In one embodiment of the present invention, the base state scene information includes:

[0088] The predicted base load power vector d; the output of renewable energy is modeled as a negative load and included in the base load power vector d;

[0089] The maximum long-term transmission capacity vector f of the line in the base state;

[0090] The transfer factor transfer matrix S of the ground state;

[0091] Non-base state scene information includes:

[0092] All possible non-base state scenarios k∈K, where K is the set of all non-base state scenarios;

[0093] The probability of each scene occurring is P k ,k∈K;P k is a positive number, the probability P of all scenes in the K set k The sum is not greater than 1;

[0094] The number set of failed generators in each scenario Ω k ,k∈K;

[0095] The maximum short-term transmission capacity vector f of the line in each scenario k,k∈K; In the non-baseline scenario k, the line reserve call will cause the system power distribution to change. At this time, the system will allow the line power to temporarily exceed the maximum long-term transmission capacity of the line. The limit range is predetermined by the system operator and will be set at f k This is reflected in the difference between the maximum long-term transmission capacity vector f of the line in the base state;

[0096] The power transfer distribution factor matrix in each scenario is S k ,k∈K; In the non-base state scenario, line failure will cause the change of the system network topology, making the system transmission factor transfer matrix change from S in the base state to S in the non-base state scenario k k ;

[0097] The load fluctuation vector π in each scenario k ,k∈K.

[0098] In the electric energy-reserve joint optimization clearing model proposed in the present invention, renewable energy is modeled as negative load.

[0099] like Figure 2 As shown, establishing the electric energy-reserve joint optimization clearing model based on scenario simulation includes the following steps:

[0100] S11: determining the constraint conditions of the electric energy-reserve joint optimization clearing model based on scenario simulation, wherein the constraint conditions include energy balance and transmission capacity constraints in the base state, energy balance and transmission capacity constraints in all non-base state scenarios, re-regulation process constraints in all non-base state scenarios and physical constraints of the unit itself;

[0101] S12: Determine the objective function of the electric energy-reserve joint optimization clearing model based on scenario simulation, wherein the objective function is divided into three parts: the quoted cost of energy and reserve, the expected value of the readjustment cost generated by reserve call and load shedding in all non-baseline scenarios, and the electric energy saved due to unit failure in all non-baseline scenarios and the reserve cost;

[0102] S13: Solving the electric energy-reserve joint optimization clearing model obtained based on scenario simulation.

[0103] (2-1) The constraints of the model are described as follows:

[0104] (2-1-1) The constraints of the electric energy-reserve joint optimization clearing model are as follows:

[0105] Electric energy balance constraint of base state: In base state, the sum of electric energy cleared by the power grid system from the units should be equal to the sum of the predicted base state loads, that is:

[0106] λ:∑g=∑d

[0107] Where g is the electric energy of the unit, and λ is the Lagrange multiplier corresponding to the energy balance constraint;

[0108] (2-1-2) Base state transmission capacity constraint: In the base state, the branch flow should not exceed the branch's maximum transmission capacity, that is:

[0109] μ:S(gd)≤f

[0110] Among them, μ is the Lagrange multiplier corresponding to the constraint;

[0111] (2-1-3) The physical constraints of the unit itself, including the capacity constraints and ramp rate constraints of the unit:

[0112] The sum of the electric energy cleared by the unit and the upward reserve cannot exceed the upper capacity limit of the unit. At the same time, the downward reserve cleared by the unit cannot exceed the difference between the electric energy cleared by the unit and the upper capacity limit of the unit. Otherwise, the reserve cleared by the unit may not be called, that is:

[0113]

[0114] υ : G ≤gr D

[0115] Among them, r U The reserve for clearing the generator set is adjusted upward, r D To clear the generator set, is the maximum operating capacity of each unit, G is the minimum operating capacity of each unit, The Lagrange multiplier corresponding to the maximum operating capacity constraint of the unit is, υ is the Lagrange multiplier corresponding to the minimum operating capacity constraint of the unit;

[0116] The upward and downward reserve of the unit clearing cannot exceed the unit's climbing capacity within the pre-specified reserve response time, that is:

[0117]

[0118]

[0119] in, It is the maximum upward climbing capacity of the unit within the pre-specified standby response time. During actual operation, the standby response time is generally set to 10 minutes. is the maximum downward climbing capability of the unit within the pre-specified standby response time. is the Lagrange multiplier corresponding to the upward climbing constraint of the unit; is the Lagrange multiplier corresponding to the downward climbing constraint of the unit;

[0120] In addition, the reserve capacity of the unit must be greater than 0, that is:

[0121] ρ :0≤r U ,

[0122] σ :0≤r D ,

[0123] ρ and σ are the Lagrange multipliers corresponding to these two lower bound constraints;

[0124] (2-1-4) Energy balance constraint in any non-baseline scenario k: In a non-baseline scenario k, failures or loads or renewable energy may shift from the baseline power. Therefore, in any non-baseline scenario, the actual output of the units taking into account the failure of the units in the scenario and the units called for standby must be equal to the actual total load power of the scenario. If the energy balance cannot be met, some loads need to be removed. Therefore, the energy balance constraint in any non-baseline scenario k is expressed as the following equation:

[0125]

[0126] in, represents the actual power clearing of the unit considering the unit failure in the kth scenario, which is composed of the power clearing amount g and the set of units that fail in each scenario k Ω k Joint decision: If generator i fails in the kth scenario, i∈Ω k ,but If generator i works normally in the kth scenario, that is, but Represents the increased standby of the unit called in the kth scenario, that is, the output increase value of the unit in the kth scenario; represents the down-adjustment of the unit called in the kth scenario, that is, the output down-adjustment value of the unit in the kth scenario; therefore, represents the actual output of the unit in the kth scenario; from the user's perspective, δd k represents the load removal in the kth scenario, π k represents the load fluctuation in the kth scenario; therefore, (d+πk -δd k ) represents the actual load power of the user in the kth scenario;

[0127] (2-1-5) The transmission capacity constraint in any non-base state scenario k is expressed as:

[0128]

[0129] Among them, S k is the transfer factor transfer matrix in the kth scenario, μ k is the Lagrange multiplier corresponding to the constraint;

[0130] (2-1-6) Re-regulation process constraints: In the re-regulation process of non-baseline scenarios, the reserve call amount cannot exceed the reserve clearance amount of the unit, and the load removal amount cannot exceed the actual load power, expressed as:

[0131]

[0132]

[0133]

[0134] in, represents the actual increased reserve capacity of the unit after considering the possible unit failure in the kth scenario, which is determined by the increased reserve clearance amount r U The set of units that fail in each scenario k is Ω k Joint decision: If generator i fails in the kth scenario, i∈Ω k ,but If generator i works normally in the kth scenario, that is, but represents the actual down-regulated reserve capacity of the unit in the kth scenario, which is determined by the down-regulated reserve clearing amount r D The set of units that fail in each scenario k is Ω k Joint decision: If generator i fails in the kth scenario, i∈Ω k ,but If generator i works normally in the kth scenario, that is, but is the Lagrange multiplier for the upward constraint on the unit output, is the Lagrange multiplier corresponding to the unit output reduction constraint, τ k is the Lagrange multiplier corresponding to the load removal constraint;

[0135] At the same time, the readjustment process must also meet the following constraints:

[0136]

[0137]

[0138] t k :0≤δd k

[0139] in, α k , β k , t k are the Lagrange multipliers corresponding to these lower bound constraints respectively.

[0140] (2-2) When determining the objective function of the model, the objective function of the electric energy-reserve joint optimization clearing model proposed in the present invention is divided into three parts, namely, the quoted cost of energy and reserve, the expected value of the readjustment cost caused by reserve call and load shedding in all non-baseline scenarios, and the electric energy saved and reserve cost due to unit failure in all non-baseline scenarios.

[0141] (2-2-1) The quoted cost of electric energy and reserve is expressed as:

[0142]

[0143] Among them, C E Represents the unit's electric energy quotation vector, C U Represents the unit's reserve quotation vector, C D The downward reserve bid vector representing the unit;

[0144] (2-2-2) The expected value of the sum of the unit's reserve call cost and the user's load shedding cost in all non-baseline scenarios is expressed as:

[0145]

[0146] in, The output of the meter units has been increased. C The output of the representative unit is reduced in price, C L represents the load shedding price; in some actual market operations, and C The electricity energy quotation of the unit C E Equivalent, such as the ERCOT market in the United States.

[0147] (2-2-3) In the non-baseline scenario, the energy saved due to unit failure and the clearing cost of the backup are expressed as:

[0148]

[0149] in, Provides the relationship between the unit clearing power g and the unit actual power in the kth scenario due to unit failure. The difference between them, the energy cost corresponding to this difference will be saved: If generator i fails in the kth scenario, that is, i∈Ω k ,but If generator i works normally in the kth scenario, that is, but In the objective function, the expected value of this part of cost savings is considered; the same applies to the upward and downward reserve;

[0150] In summary, the objective function of the electric energy-reserve joint optimization clearing model is:

[0151]

[0152] (2-3) Solve the energy-reserve joint optimization clearing model obtained by steps (2-1) and (2-2). Distributed algorithms such as the Benders decomposition method and the critical region exploration method can be used in the solution process. The base state energy and reserve clearing problem is taken as the main problem, and the readjustment strategy optimization problem in different non-base state scenarios is taken as the sub-problem, so as to perform a distributed solution to the model. U ,r D As clearing power and spare capacity, δd k As the readjustment strategy for the kth scenario, the lowest expected system operating cost can be obtained.

[0153] (3) Establish a pricing and settlement mechanism corresponding to the electricity energy-reserve joint optimization clearing model. The specific steps are as follows:

[0154] S31: establishing a pricing mechanism corresponding to the electric energy-reserve joint optimization clearing model, including a unit electric energy and reserve pricing mechanism and a load electric energy pricing mechanism;

[0155] S32: establishing a settlement mechanism corresponding to the electric energy-reserve joint optimization clearing model, including a pre-calculation stage and a post-settlement stage;

[0156] S33: Establish the market nature of the pricing and settlement mechanism corresponding to the electric energy-reserve joint optimization clearing model.

[0157] (3-1-1) Unit electricity and reserve pricing mechanism corresponding to the model

[0158] After solving the electric energy-reserve joint optimization clearing model, the clearing result of the i-th unit is recorded as g(i) * ,rU (i) * ,r D (i) * ;

[0159] The energy and reserve clearing amount g(i),r of the i-th unit, which are originally decision variables in the energy-reserve joint optimization clearing model, are transformed into U (i),r D (i) Fixed at its optimal value g(i) * ,r U (i) * ,r D (i) * (i.e. the clearing result calculated by the model); through this operation, the model can be transformed.

[0160] In the transformed model, only the electric energy and reserve clearing capacity of unit i are fixed, and the electric energy and reserve clearing capacity of other units are still variables to be optimized. Therefore, the constraints related only to unit i in constraint condition (2-1) will be eliminated, specifically:

[0161]

[0162] G (i)≤g(i)-r D (i)

[0163]

[0164]

[0165] 0≤r U (i),0≤r D (i)

[0166] In addition to eliminating these constraints, in the transformed model, the quoted cost of the electric energy and reserve of unit i in (2-2-1) and the expected value of the reduced electric energy and reserve cost due to the possible failure of unit i in (2-2-3) will also be eliminated, specifically as follows:

[0167]

[0168] In the transformed model, the envelope theorem is applied to perform sensitivity analysis on the fixed parameters such as the power and reserve of the i-th unit to obtain the power and reserve price of the unit:

[0169]

[0170]

[0171]

[0172] Among them, C * represents the optimal objective function of the model after transformation, which means the overall expected cost of other market participants except unit i, L * Represents the optimal Lagrangian function of the model after transformation. For electricity energy pricing, η g (i) represents the electricity price of the i-th unit, which is composed of the base state contribution (λ-S(:,n i ) T μ) is the contribution of all non-baseline scenarios with no failure of the i-th unit composition;

[0173] For standby pricing, η U (i) and η D (i) represents the upward reserve price and downward reserve price of the i-th unit, respectively. The Lagrange multiplier corresponding to the constraint "the reserve call amount of all non-baseline scenarios cannot exceed the actual reserve capacity of the scenario" in (2-1-6) is and accumulated;

[0174] (3-1-2) Load electricity pricing mechanism:

[0175] According to the envelope theorem, the electric energy price of the load is expressed as follows:

[0176]

[0177] Where η d (j) represents the electricity price of user j, which is composed of the base state part (λ—S(:,n j ) T μ), the non-base state part contributed by all non-base state scenarios (∑ k＝1,…,K (λ k -S k (:,n j ) T μ k )) Load shedding constraints for all non-baseline scenarios (δd k ≤d) corresponds to the negative value of the multiplier cumulative composition;

[0178] in, It is related to the power supply reliability required by the user: For any user j, its power supply reliability requirement is determined by the load shedding price C submitted by it to the system. L (j) Parameter characterization: Load shedding price C of user j L The smaller (j) is, the lower the power supply reliability required by the user.

[0179] When the system needs to shed load, user j will be given priority over other users with higher load shedding prices at the same node. If user j's load is completely shelved in the kth scenario, the load shedding constraint corresponding to user j in the kth scenario will reach the upper bound, and the multiplier corresponding to the constraint will be greater than 0, so that the electricity price of user j is reduced accordingly This reduction in electricity price can be regarded as compensation for the system's lower power supply reliability requirements for users.

[0180] There are differences between the electric energy pricing theory proposed in the present invention and the traditional electric energy node marginal pricing theory: in the electric energy pricing theory proposed in the present invention, the electric energy prices of the generator set and the load at the same node may be different; for the generator set, the generator sets with different failure probabilities at the same node provide different amounts of electric energy, so their electric energy prices will be different, so as to achieve differential treatment of generator sets with different reliability; at the same time, for different users at the same node, if they have different power supply reliability requirements (different load shedding prices), the loads of these users will also be non-homogeneous, and they may receive different electric energy prices, so as to achieve differential treatment of their different power supply reliability requirements. In summary, the electric energy pricing method proposed in the present invention correctly reflects the commodity value of electric energy and reasonably considers the problem of the heterogeneity of electric energy.

[0181] At the same time, the reserve pricing theory proposed in the present invention is different from the traditional reserve regional marginal pricing theory: in the reserve pricing theory proposed in the present invention, since the reserve provided by the units at different nodes has different callability, the reserve provided by the units at different nodes is not a homogeneous commodity, so these units will have different reserve prices; in addition, since different units at the same node may have different output increase C and decrease prices C , different adjustment costs may be generated when calling the backup of these units, so the backup provided by these units is not a homogeneous commodity, so these units will have different backup prices; at the same time, because different units at the same node may have different failure probabilities, in this case, the backup capacity they provide has different reliability and is not a homogeneous commodity, so these units will have different backup prices. In summary, the backup pricing method proposed in the present invention correctly reflects the commodity value of the backup and reasonably considers the heterogeneity of the backup.

[0182] (3-2) Establish a settlement mechanism corresponding to the model

[0183] After solving the model and calculating the prices of various commodities according to the Lagrange multiplier, a settlement mechanism corresponding to the model can be established. According to the time sequence, the settlement mechanism proposed in the present invention can be divided into two stages, namely the ex-ante stage and the ex-post stage. In the model optimization stage, the system operator is not clear about which non-baseline scenario will occur in the future. Therefore, the system operator will clear the electric energy and reserve to deal with all possible faults and fluctuations, and the cleared electric energy, reserve, basic load and possible load fluctuations in all scenarios will be settled. Since these settlements occur before the non-baseline scenario occurs, the settlement at this stage is called ex-ante settlement. In the actual operation stage of the system, a non-baseline scenario will occur, and the expected faults and fluctuations in the scenario will occur. At this time, the system will execute the predetermined reserve call and load shedding strategy to maintain the balanced and stable operation of the system, and settle the reserve call and load shedding. Since these settlements occur after the non-baseline scenario occurs, the settlement at this stage is called ex-post settlement.

[0184] (3-2-1) Pre-settlement stage:

[0185] In the optimization stage of the energy-reserve joint optimization clearing model, the cleared electricity, reserve, basic load and possible load fluctuations in all scenarios are settled according to the clearing results of electricity and reserve obtained by the model and the clearing prices of electricity and reserve obtained by the transformed model. Specifically,

[0186] The electric energy settlement formula for the i-th unit is: η g (i)*g(i);

[0187] The settlement formula for the reserve adjustment of the i-th unit is: η U (i)*r U (i);

[0188] The downward reserve settlement formula for the i-th unit is: η D (i)*r D (i);

[0189] The basic load settlement formula for the jth user is: η d (j)*d(j);

[0190] According to the envelope theorem, the load fluctuation price of the jth user in the kth scenario is expressed as follows:

[0191]

[0192] In the ex ante settlement, users need to pay for all possible load fluctuations. Therefore, the load fluctuation settlement formula for the jth user is:

[0193] The load fluctuation settlement method proposed in the present invention is different from the traditional load fluctuation settlement method: in traditional research, users only need to pay for the actual fluctuations after the fact. However, when the market is cleared, the system has reserved sufficient reserves for all possible fluctuations of users, and these possible fluctuations in all scenarios together cause the system's reserve cost. Therefore, the traditional fluctuation settlement method of "users only pay for the actual fluctuations" will cause the system operator to be in a state of loss for a long time and unable to recover the system operation costs. On the contrary, the fluctuation settlement method of "users pay for all possible fluctuations" proposed in the present invention can effectively guarantee the cost recovery of the system operator and reduce the financial risk of the system operator.

[0194] (3-2-2) Post-settlement stage:

[0195] In the actual operation phase of the system, if no non-baseline scenario occurs, the system does not need to perform any reserve call or load shedding, and the system does not need to perform post-settlement. In contrast, if any non-baseline scenario k occurs, the system will follow The readjustment strategy is used to call the unit's reserve, and at the same time (δd k ) strategy to cut user load. For reserve call and load shedding, in the post-settlement, the output increase is adopted With price cuts C Reserve call volume for units For settlement, the load shedding price C is used L The load δd for resection k To settle, that is:

[0196] The settlement formula for increasing the output of the i-th unit is:

[0197] The settlement formula for reducing the output of the i-th unit is:

[0198] The load shedding compensation settlement formula for the jth user is: C L (j)*δd k (j);

[0199] (3-3) Market nature of establishing models and pricing and settlement mechanisms

[0200] Based on the model and the corresponding pricing and settlement mechanism proposed in the invention, under certain assumptions, some good market properties can be established to verify the innovativeness of the model and pricing and settlement mechanism proposed in the invention.

[0201] (3-3-1) Locational Uniform Pricing for Energy:

[0202] If the failure probability of all generators at a node is 0, and in all non-baseline scenarios, the load of any user at the node will not be completely cut off, then all generators and users at the node will have the same electricity price;

[0203] (3-3-2) Proportional Locational Uniform Pricing for Re-dispatch and Reserve:

[0204] For any non-baseline scenario k, consider any two units i and j located at the same node n. The sum of the contribution of scenario k to the expected value of reserve income and re-regulation income of the two units is proportional to the output re-regulation amount of the two units in this scenario, and the ratio is the contribution of this scenario to the electricity price, that is:

[0205]

[0206] (3-3-3) Individual Rationality of the Crew:

[0207] For unit i, given the power and reserve price η g (i),η U (i),η D (i), the clearing result g(i),r of the electric energy-reserve joint optimization clearing model U (i),r D (i) The revenue of unit i can be maximized, and unit i will not deviate from the system clearing result;

[0208] (3-3-4) Cost Recovery for Each Scenario:

[0209] Under the proposed market model and pricing settlement mechanism, no matter which non-baseline scenario occurs, if it obeys market dispatch, the income obtained by any unit i from the market is not less than the operating cost of the unit;

[0210] (3-3-5) Revenue Adequacy for the System Operator:

[0211] Under the proposed market model and pricing settlement mechanism, the expected net profit will be equal to the base state congestion rent Δ0 = f T μ plus the blocking surplus in all non-base state scenarios And the expected net profit is non-negative.

[0212] Therefore, the market settlement mechanism proposed in the present invention can achieve a balance between revenue and expenditure for market operators and fully ensure the adequacy of profits for market operators.

[0213] In order to demonstrate the beneficial effects of the clearing mechanism proposed in the present invention, the present invention compares the proposed clearing mechanism with the traditional clearing mechanism based on the IEEE 118-node standard power system. The specific steps are as follows:

[0214] 1. Select different backup demand parameters for the traditional clearing mechanism. Specifically, select different proportions of the total system load as different backup demands for the traditional clearing mechanism;

[0215] 2. Calculate the system's base state electricity and reserve clearing results and clearing costs under the proposed clearing mechanism and the traditional clearing mechanism with different reserve demand parameter settings;

[0216] 3. Based on the probability of occurrence of non-base state scenarios, 50,000 Monte Carlo samples are generated, corresponding to various possible operating states of the system;

[0217] 4. Calculate the average readjustment cost of the system in all Monte Carlo samples under the clearing mechanism of the present invention and the traditional clearing mechanism with different backup demand parameter settings;

[0218] 5. Superimpose the base state clearing cost obtained in 2 and the average readjustment cost obtained in 4 to obtain the average total operating cost of the system under the clearing mechanism of the present invention and the traditional clearing mechanism with different backup demand parameter settings. The results are as follows: Figure 3 shown. Figure 3 In the figure, the straight line is the average total operating cost of the system under the clearing mechanism of the present invention, and the broken line is the average total operating cost of the system under the traditional clearing mechanism that changes with different backup demand parameter settings.

[0219] from Figure 3 It can be learned that compared with the traditional clearing mechanism, the market clearing mechanism of the present invention can not only effectively reduce the system's reserve clearing volume, but also effectively reduce the system's total operating cost; according to the different settings of the reserve demand parameters, the total system cost reduction ratio of the clearing mechanism of the present invention compared with the traditional clearing mechanism is 10.99%-68.14%. This example fully demonstrates the superiority of the clearing mechanism of the present invention over the traditional clearing mechanism, and fully proves the beneficial effects of the present invention.

[0220] An embodiment of the present application also provides a control device, including a processor and a storage medium for storing a computer program; wherein the processor is used to execute at least the method described above when executing the computer program.

[0221] An embodiment of the present application also provides a storage medium for storing a computer program, which at least performs the method described above when executed.

[0222] An embodiment of the present application further provides a processor, which executes a computer program and at least performs the method described above.

[0223] The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAMEnhanced Synchronous Dynamic Random Access Memory), synchronous link dynamic random access memory (SLDRAM), direct memory bus random access memory (DRRAM). The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0224] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0225] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0226] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0227] A person skilled in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, etc. Various media that can store program codes.

[0228] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0229] The methods disclosed in several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0230] The features disclosed in several product embodiments provided in this application can be arbitrarily combined without conflict to obtain new product embodiments.

[0231] The features disclosed in several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0232] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art of the present invention, several equivalent substitutions or obvious variations can be made without departing from the concept of the present invention, and the performance or use is the same, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for joint clearing of electric energy and reserve market, characterized in that: The steps include: S1: Determine the base state scenario information and non-base state scenario information based on the topological structure and transmission line parameters of the power grid system, historical fault data of generator sets and transmission lines, future meteorological information forecast data, historical meteorological data, and historical actual data of renewable energy and load; The base state scene information includes: The predicted base load power vector d; the output of renewable energy is modeled as a negative load and included in the base load power vector d; The maximum long-term transmission capacity vector f of the line in the base state; The transfer factor transfer matrix S of the ground state; The non-base state scene information includes: All possible non-base state scenarios k∈K, where K is the set of all non-base state scenarios; The probability of each scene occurring is P k ,k∈K;P k is a positive number, the probability P of all scenes in the K set k The sum is not greater than 1; The number set of failed generators in each scenario Ω k ,k∈K; The maximum short-term transmission capacity vector f of the line in each scenario k ,k∈K; In the non-baseline scenario k, the line flow is allowed to temporarily exceed the maximum long-term transmission capacity of the line and will be k This is reflected in the difference between the maximum long-term transmission capacity vector f of the line in the base state; The transfer factor transfer matrix S in each scenario k ,k∈K; The load fluctuation vector π in each scenario k ,k∈K; S2: establishing an electric energy-reserve joint optimization clearing model based on scenario simulation based on information of the base state scenario and the non-base state scenario, wherein the electric energy-reserve joint optimization clearing model is composed of an objective function and constraint conditions; Establishing the electric energy-reserve joint optimization clearing model based on scenario simulation includes the following steps: S11: determining the constraint conditions of the electric energy-reserve joint optimization clearing model based on scenario simulation, wherein the constraint conditions include energy balance and transmission capacity constraints in the base state, energy balance and transmission capacity constraints in all non-base state scenarios, re-regulation process constraints in all non-base state scenarios and physical constraints of the unit itself; S12: Determine the objective function of the electric energy-reserve joint optimization clearing model based on scenario simulation, wherein the objective function is divided into three parts: the quoted cost of energy and reserve, the expected value of the readjustment cost caused by reserve call and load shedding in all non-baseline scenarios, and the expected value of the cost of the unit failure in all non-baseline scenarios. The amount of electricity saved and the standby cost due to faults; S13: solving the electric energy-reserve joint optimization clearing model obtained based on scenario simulation; S3: Establish a pricing and settlement mechanism corresponding to the electric energy-reserve joint optimization clearing model.

2. The electric energy-reserve market joint clearing method according to claim 1, characterized in that: The constraints for determining the electric energy-reserve joint optimization clearing model include the following: Electric energy balance constraint of base state: In base state, the sum of electric energy cleared by the power grid system from the units should be equal to the sum of the predicted base state loads, that is: λ:∑g=∑d Where g is the electric energy of the unit, and λ is the Lagrange multiplier corresponding to the energy balance constraint; Transmission capacity constraint of the base state: In the base state, the flow of the branch should not exceed the maximum transmission capacity of the branch, that is: μ:S(gd)≤f Among them, μ is the Lagrange multiplier corresponding to the constraint; The physical constraints of the unit itself, including the capacity constraints and ramp rate constraints of the unit: The sum of the electric energy cleared by the unit and the upward reserve cannot exceed the upper capacity limit of the unit. At the same time, the downward reserve cleared by the unit cannot exceed the difference between the electric energy cleared by the unit and the upper capacity limit of the unit. Otherwise, the reserve cleared by the unit may not be called, that is: v : G ≤g-r D Among them, r U The reserve for clearing the generator set is adjusted upward, r D To clear the generator set, is the maximum operating capacity of each unit, G is the minimum operating capacity of each unit, The Lagrange multiplier corresponding to the maximum operating capacity constraint of the unit is, v is the Lagrange multiplier corresponding to the minimum operating capacity constraint of the unit; The upward and downward reserve of the unit clearing cannot exceed the unit's climbing capacity within the pre-specified reserve response time, that is: in, It is the maximum upward climbing capability of the unit within the pre-specified standby response time. is the maximum downward climbing capability of the unit within the pre-specified standby response time. is the Lagrange multiplier corresponding to the upward climbing constraint of the unit; is the Lagrange multiplier corresponding to the downward climbing constraint of the unit; In addition, the reserve capacity of the unit must be greater than 0, that is: ρ :0≤r U σ :0≤r D ρ and σ are the Lagrange multipliers corresponding to these two lower bound constraints; The energy balance constraint in any non-base state scenario k is expressed as the following equation: in, represents the actual power clearing of the unit considering the unit failure in the kth scenario, which is composed of the power clearing amount g and the set of units that fail in each scenario k Ω k Joint decision: If generator i fails in the kth scenario, i∈Ω k ,but If generator i works normally in the kth scenario, that is, but Represents the increased standby value of the unit called in the kth scenario, that is, the increased output value of the unit in the kth scenario; represents the down-adjustment of the unit called in the kth scenario, that is, the output down-adjustment value of the unit in the kth scenario; therefore, represents the actual output of the unit in the kth scenario; from the user's perspective, δd k represents the load removal in the kth scenario, π k represents the load fluctuation in the kth scenario; therefore, (d+π k -δd k ) represents the actual load power of the user in the kth scenario; The transmission capacity constraint in any non-base state scenario k is expressed as: Among them, S k is the transfer factor transfer matrix in the kth scenario, μ k is the Lagrange multiplier corresponding to the constraint; Re-regulation process constraints: In the re-regulation process of non-baseline scenarios, the reserve call amount cannot exceed the reserve clearance amount of the unit, and the load removal amount cannot exceed the actual load power, which can be expressed as: in, represents the actual increased reserve capacity of the unit after considering the possible unit failure in the kth scenario, which is determined by the increased reserve clearance amount r U The set of units that fail in each scenario k is Ω k Joint decision: If generator i fails in the kth scenario, i∈Ω k ,but If generator i works normally in the kth scenario, that is, but represents the actual down-regulated reserve capacity of the unit in the kth scenario, which is determined by the down-regulated reserve clearing amount r D The set of units that fail in each scenario k is Ω k Joint decision: If generator i fails in the kth scenario, i∈Ω k ,but If generator i works normally in the kth scenario, that is, but is the Lagrange multiplier for the upward constraint on the unit output, is the Lagrange multiplier corresponding to the unit output reduction constraint, τ k is the Lagrange multiplier corresponding to the load removal constraint; At the same time, the readjustment process must also meet the following constraints: t k :0≤δd k Among them, α k ,β k ,τ k are the Lagrange multipliers corresponding to these lower bound constraints respectively.

3. The electric energy-reserve market joint clearing method according to claim 2, characterized in that: The quoted cost of the electric energy and reserve is expressed as: Among them, C E Represents the unit's electric energy quotation vector, C U Represents the unit's reserve quotation vector, C D The downward reserve bid vector representing the unit; The expected value of the sum of the unit's reserve call cost and the user's load shedding cost in all non-baseline scenarios is expressed as: in, The price of the unit's output is increased. C The output of the representative unit is reduced in price, C L represents the load shedding price; In the non-baseline scenario, the energy saved due to unit failure and the clearing cost of the reserve are expressed as: in, Provides the relationship between the unit clearing power g and the unit actual power in the kth scenario due to unit failure. The difference between them, the energy cost corresponding to this difference will be saved: If generator i fails in the kth scenario, that is, i∈Ω k ,but If generator i works normally in the kth scenario, that is, but In the objective function, the expected value of this part of cost savings is considered; the same applies to the upward and downward reserve; The objective function of the electric energy-reserve joint optimization clearing model is:

4. The electric energy-reserve market joint clearing method according to claim 3, characterized in that: The distributed algorithm is used to solve the electric energy-reserve joint optimization clearing model, and the obtained g,r U ,r D As clearing power and spare capacity, δd k As the readjustment strategy for the kth scenario, the lowest expected system operating cost is obtained.

5. The electric energy-reserve market joint clearing method according to claim 4, characterized in that: Establishing the pricing and settlement mechanism corresponding to the electric energy-reserve joint optimization clearing model includes the following steps: S31: establishing a pricing mechanism corresponding to the electric energy-reserve joint optimization clearing model, including a unit electric energy and reserve pricing mechanism and a load electric energy pricing mechanism; S32: establishing a settlement mechanism corresponding to the electric energy-reserve joint optimization clearing model, including a pre-calculation stage and a post-settlement stage; S33: Establish the market nature of the pricing and settlement mechanism corresponding to the electric energy-reserve joint optimization clearing model.

6. The electric energy-reserve market joint clearing method according to claim 5, characterized in that: The unit electricity and reserve pricing mechanism includes: After solving the electric energy-reserve joint optimization clearing model, the clearing result of the i-th unit is recorded as g(i) * ,r U (i) * ,r D (i) * ; The electric energy and reserve clearing amount g(i),r of the i-th unit, which are originally decision variables in the electric energy-reserve joint optimization clearing model, are U (i),r D (i) Fixed at its optimal value g(i) * ,r U (i) * ,r D (i) * In the model obtained after the transformation, only the electric energy and reserve clearing amount of unit i are fixed, and the electric energy and reserve clearing amount of other units are still variables to be optimized. Therefore, the constraints only related to unit i in the constraints will be eliminated, specifically: 0≤r U (i),0≤r D (I) The quoted cost of the electric energy and reserve of unit i and the expected value of the reduced electric energy and reserve cost due to the possible failure of unit i will also be eliminated, specifically as follows: In the transformed model, a sensitivity analysis is performed on the fixed parameters such as the power and reserve of the i-th unit to obtain the power and reserve price of the unit: Among them, C * represents the optimal objective function of the model after transformation, which means the overall expected cost of other market participants except unit i, L * Represents the optimal Lagrangian function of the model after transformation. For electricity energy pricing, η g (i) represents the electricity price of the i-th unit, which is composed of the base state contribution (λ-S(:,n i ) T μ) is the contribution of all non-baseline scenarios with no failure of the i-th unit composition; For standby pricing, η U (i) and η D (i) represents the upward reserve price and downward reserve price of the i-th unit, respectively. The Lagrange multiplier corresponding to the constraint that "the reserve call amount of all non-baseline scenarios cannot exceed the actual reserve capacity of the scenario" and accumulated; The electric energy pricing mechanism of the load is: According to the envelope theorem, the electric energy price of the load is expressed as follows: Where η d (j) represents the electricity price of user j, which is composed of the base state part (λ—S(:,n j ) T μ), the non-base state part contributed by all non-base state scenarios (∑ k＝1,…,K (λ k -S k (:,n j ) T μ k )) Load shedding constraints for all non-baseline scenarios (δd k ≤d) corresponds to the negative value of the multiplier cumulative composition; in, It is related to the power supply reliability required by the user: For any user j, its power supply reliability requirement is determined by the load shedding price C submitted by it to the system. L (j) Parameter characterization: Load shedding price C of user j L The smaller (j) is, the lower the power supply reliability required by the user.

7. The electric energy-reserve market joint clearing method according to claim 6, characterized in that: The pre-settlement stage includes: In the optimization stage of the energy-reserve joint optimization clearing model, the cleared electric energy, reserve, basic load and possible load fluctuations in all scenarios are settled according to the electric energy and reserve clearing results obtained by the model and the electric energy and reserve clearing prices obtained by the transformed model, specifically: The electric energy settlement formula for the i-th unit is: η g (i)*g(i); The settlement formula for the reserve adjustment of the i-th unit is: η U (i)*r U (i); The downward reserve settlement formula for the i-th unit is: η D (i)*r D (i); The basic load settlement formula for the jth user is: η d (j)*d(j); According to the envelope theorem, the load fluctuation price of the jth user in the kth scenario is expressed as follows: The load fluctuation settlement formula for the jth user is: The post-settlement stage includes: In the post-settlement, the output increase is adopted With price cuts C Reserve call amount for units For settlement, the load shedding price C is used L The load δd for resection k To settle, that is: The settlement formula for increasing the output of the i-th unit is: The settlement formula for reducing the output of the i-th unit is: The load shedding compensation settlement formula for the jth user is: C L (j)*δd k (j).

8. The electric energy-reserve market joint clearing method according to claim 7, characterized in that: The market properties of the pricing and settlement mechanism corresponding to the electric energy-reserve joint optimization clearing model include: Consistent node pricing of electric energy: If the failure probability of all generators at a node is 0, and in all non-baseline scenarios, the load of any user at the node will not be completely cut off, then all generators and users at the node will have the same electric energy price; Consistent property of node pricing for reserve and unit output readjustment: For any non-base state scenario k, consider any two units i and j located at the same node n. The sum of the contribution of the scenario k to the expected value of the reserve income and readjustment income of the two units is proportional to the output readjustment amount of the two units in the scenario, and the ratio is the contribution of the scenario to the electricity price, that is: Individual rationality of the unit: For unit i, given the power and reserve price η g (i),η U (i),η D (i) Under the above mentioned power energy-reserve joint optimization clearing model, the clearing result g(I),r U (i),r D (i) The revenue of unit i can be maximized, and unit i will not deviate from the system clearing result; Cost recovery properties of units in any scenario: Under the proposed market model and pricing settlement mechanism, no matter which non-baseline scenario occurs, if it obeys market dispatch, the income obtained by any unit i from the market is not less than the operating cost of the unit; Profitable property: Under the proposed market model and pricing settlement mechanism, the expected net profit will be equal to the base state blocking surplus Δ0 = f T μ plus the blocking surplus in all non-base state scenarios And the expected net profit is non-negative.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

Images