Auxiliary service transaction method based on multiple subjects and EWA algorithm

Abstract

The invention discloses an auxiliary service transaction method based on multiple subjects and an EWA algorithm, and the method comprises the steps: constructing a strategy selection model which is used for updating a corresponding quotation according to a strategy value; clearing by adopting a sorting method according to a price priority principle; building a strategy selection probability model of the main body by utilizing an EWA algorithm: building an attraction model of the enterprise main body by utilizing empirical factors, selecting weight parameters, discount rates and profit, building a strategy selection probability model of the main body by utilizing the attraction model and sensitivity parameters, and calculating a strategy value by utilizing the strategy selection probability model of the main body. And according to the strategy value, the multi-subject quotation of the auxiliary service market is updated. The technical scheme gives full play to the function of marketization resource allocation, can discuss the formation mechanism of the auxiliary service price based on the function, further provides an extension function, can be used for exploring the carbon cost conduction effect and influence factors thereof on the auxiliary service market, and improves the service quality. Therefore, a policy basis is provided for reasonably sharing the carbon cost of the auxiliary service, strengthening the connection between the auxiliary service and the carbon market, and promoting the stable transformation of the green power system under a carbon transaction mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon market, and in particular to an auxiliary service trading method based on multi-agent and EWA algorithm. Background Art

[0002] Large-scale access to new energy sources has increased the volatility of the power system, and the demand for backup ancillary services has also increased. Coal-fired units are the main suppliers of backup ancillary services due to their stable and predictable output, but their carbon emissions cannot be ignored. With the increase in my country's carbon price and carbon quota auction ratio, carbon costs will become an important part of the ancillary services market.

[0003] Existing research focuses more on the electricity energy market and ignores the ancillary service market. In addition, existing literature mostly adopts a cost minimization approach and ignores the role of market-based resource allocation. Summary of the invention

[0004] In the existing technology, the research on the ancillary service market and the role of market-based resource allocation have been neglected, resulting in its inability to serve the market's energy needs.

[0005] In view of the above problems, a method for ancillary service trading based on multi-agents and EWA algorithm is proposed. By constructing a strategy selection model, and using the EWA algorithm and exploring the impact of sensitive parameters such as carbon price, carbon quota auction ratio, and sufficiency on standby ancillary service trading, the strategy selection probability model of the subject is constructed to calculate the strategy value, so as to update the quotations of multiple subjects in the ancillary service market according to the strategy value. This technical solution gives full play to the function of market-oriented resource allocation, and the formation mechanism of ancillary service prices can be explored based on this. This technical solution further provides extended functions, which can be used to explore the carbon cost transmission effect and its influencing factors in the ancillary service market, so as to provide a policy basis for reasonably allocating the carbon cost of ancillary services, strengthening the connection between ancillary services and carbon markets, and promoting the smooth transformation of green power systems under the carbon trading mechanism.

[0006] An auxiliary service transaction method based on multi-agent and EWA algorithm is characterized by comprising:

[0007] Step 100: construct a subject quotation model;

[0008] Step 200: clearing out by using a sorting method and according to the principle of price priority;

[0009] Step 300: Use the EWA algorithm to build a strategy selection probability model for the subject:

[0010] The attractiveness model of the enterprise entity is constructed using the experience factor, selection weight parameter, discount rate and profit, the strategy selection probability model of the entity is constructed using the attractiveness model and sensitivity parameter, and the strategy value is calculated using the strategy selection probability model of the entity.

[0011] In conjunction with the auxiliary service transaction method based on multi-agent and EWA algorithm described in the present invention, in a first possible implementation, step 100 includes:

[0012] Step 110, constructing a first-phase quotation model of the subject using the unit attribute parameters;

[0013] Step 120: construct a first quotation model, a second quotation model, and a third quotation model so that the first quotation model, the second quotation model, and the third quotation model correspond to the first strategy value, the second strategy value, and the third strategy value, respectively, for updating the quotation of the unit in the subsequent period;

[0014] In combination with the first possible implementation manner of the present invention, in a second possible implementation manner, step 110 includes:

[0015] 111. Calculate the expected cost of coal-fired units based on fuel costs, carbon emissions, carbon quota benchmark values, exogenous carbon prices, carbon quota auction ratios, start-up and shutdown costs, capacity compensation costs, and spinning reserve costs;

[0016] 112. Obtain the marginal power generation cost of green power units and energy storage units as the expected cost of providing ancillary services;

[0017] 113. Calculate the first-phase quotation model based on the expected cost and expected profit margin of the unit.

[0018] In combination with the first possible implementation manner of the present invention, in a third possible implementation manner, step 120 includes:

[0019] Step 121: construct the first quotation model according to the clearing price of the auxiliary service market, the first weight parameter, the second weight parameter and the quotation of the current period;

[0020] Step 122: Match the first quotation model with the first strategy value.

[0021] In combination with the first possible implementation manner of the present invention, in a fourth possible implementation manner, step 120 further includes:

[0022] Step 123: construct the second quotation model according to the clearing price of the auxiliary service market, the third weight parameter and the quotation of the current period;

[0023] Step 124: Match the second quotation model with the second strategy value.

[0024] In combination with the fourth possible implementation manner of the present invention, in a fifth possible implementation manner, step 120 further includes:

[0025] Step 125: construct the third quotation model according to the clearing price of the auxiliary service market, the second weight parameter, the first weight parameter and the quotation of the current period;

[0026] Step 126: Match the third quotation model with the third strategy value.

[0027] In combination with the first possible implementation manner of the present invention, in a sixth possible implementation manner, step 200 includes:

[0028] Step 210, sorting the bids of the standby units from low to high;

[0029] Step 220: Obtain the clearing price when the declared reserve power reaches the total demand.

[0030] In combination with the sixth possible implementation manner of the present invention, in a seventh possible implementation manner, step 120 further includes:

[0031] Step 230: If the bid price is greater than the clearing price, the bidding fails and the transaction volume is 0;

[0032] Step 240: If the declared price is less than the clearing price, the bidding is successful and the transaction volume is the declared volume;

[0033] Step 250: If the declared price is equal to the clearing price, the bidding is successful and the traded electricity is allocated according to the allocation model.

[0034] In combination with the first possible implementation manner of the present invention, in an eighth possible implementation manner, step 300 includes:

[0035] Step 310: construct a profit model based on the clearing price, the declared quantity and transaction volume of the standby units in the auxiliary service market, and the cost of the standby units;

[0036] Step 320: Calculate the profit of the tth period using the profit model.

[0037] In combination with the eighth possible implementation manner of the present invention, in a ninth possible implementation manner, step 300 further includes:

[0038] Step 330: construct an attractiveness model;

[0039] Step 340: Calculate the subject's strategy selection probability using the attractiveness model and determine the bidding strategy.

[0040] The ancillary service trading method based on multiple subjects and EWA algorithm described in the present invention is implemented by constructing a strategy selection model, and using the EWA algorithm and exploring the impact of sensitive parameters such as carbon price, carbon quota auction ratio, and sufficiency on standby ancillary service transactions to construct a strategy selection probability model of the subject to calculate the strategy value, thereby updating the quotations of multiple subjects in the ancillary service market according to the strategy value. This technical solution gives full play to the function of market-oriented resource allocation, and the formation mechanism of ancillary service prices can be explored based on this. The technical solution further provides an extended function, which can be used to explore the carbon cost transmission effect and its influencing factors in the ancillary service market, thereby providing a policy basis for reasonably allocating the carbon cost of ancillary services, strengthening the connection between ancillary services and the carbon market, and promoting the smooth transformation of the green power system under the carbon trading mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0042] Figure 1 It is a schematic diagram for explaining the present invention;

[0043] Figure 2 It is a schematic diagram of the first process of the auxiliary service transaction method based on multi-agent and EWA algorithm in the present invention;

[0044] Figure 3 It is a schematic diagram of the second process of the auxiliary service transaction method based on multi-agent and EWA algorithm in the present invention;

[0045] Figure 4 It is a schematic diagram of the third process of the auxiliary service transaction method based on multi-agent and EWA algorithm in the present invention;

[0046] Figure 5 It is a schematic diagram of the fourth process of the auxiliary service transaction method based on multi-agent and EWA algorithm in the present invention;

[0047] Figure 6 It is a fifth flow chart of the auxiliary service transaction method based on multi-agent and EWA algorithm in the present invention;

[0048] Figure 7 It is a sixth flow chart of the auxiliary service transaction method based on multi-agent and EWA algorithm in the present invention;

[0049] Figure 8 It is a seventh flow chart of the auxiliary service transaction method based on multi-agents and EWA algorithm in the present invention;

[0050] Fig. 9 It is a schematic diagram of the eighth process of the auxiliary service transaction method based on multi-agent and EWA algorithm in the present invention;

[0051] Fig.10 It is a ninth flow chart of the auxiliary service transaction method based on multi-agent and EWA algorithm in the present invention;

[0052] Fig.11 This is the tenth flow chart of the auxiliary service transaction method based on multi-agent and EWA algorithm in the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the accompanying drawings in the invention to clearly and completely describe the technical solutions in the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0055] It should be noted that when an element is referred to as being "fixed to" or "disposed on" another element, it can be directly on the other element or indirectly on the other element. 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.

[0056] 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 drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0057] 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 the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0058] Glossary:

[0059] EWA algorithm: Experience-WeightedAttraction learning algorithm

[0060] In the existing technology, the research on the ancillary service market and the role of market-based resource allocation have been neglected, resulting in its inability to serve the market's energy needs.

[0061] Aiming at the above problems, an auxiliary service transaction method based on multi-agent and EWA algorithm is proposed.

[0062] A transaction method for auxiliary services based on multi-agent and EWA algorithm, such as Figure 2 , Figure 2 The first flow chart of the auxiliary service transaction method based on multi-agent and EWA algorithm in the present invention includes:

[0063] Step 100. Step 100. Build a subject quotation model.

[0064] Preferably, if Figure 3 , Figure 3 Schematic diagram of the second process of the auxiliary service trading method based on multi-agent and EWA algorithm in the present invention; step 100 includes: step 110: constructing a quotation model for the first period according to the attribute parameters of the unit; step 120, constructing a first quotation model, a second quotation model, and a third quotation model so that the first quotation model, the second quotation model, and the third quotation model correspond to the first strategy value, the second strategy value, and the third strategy value, respectively, for updating the quotation of the unit in the subsequent period.

[0065] Preferably, if Figure 4 , Figure 4 The third flow chart of the auxiliary service transaction method based on multi-agent and EWA algorithm in the present invention; step 110, including:

[0066] Step 111, calculating the expected cost of the coal-fired unit according to the fuel cost, carbon emissions, carbon quota benchmark value, exogenous carbon price, carbon quota auction ratio, start-up and shutdown costs, capacity compensation costs, and spinning reserve costs;

[0067] Coal-fired unit cost model for:

[0068]

[0069] Among them, m y，t is the unit power generation cost of the backup service provider enterprise y in period t, n y，t is the CO2 emission per unit of power generation of the backup service provider enterprise y in period t, B is the carbon quota benchmark value (according to the officially announced carbon quota allocation benchmark value for conventional coal-fired units of 300 MW and below), pc is the exogenous carbon price, μ represents the carbon quota auction ratio, spin y，t Indicates the rotating standby unit of the standby unit, onoff y，t represents the start-up and shutdown costs of the standby units allocated according to their power generation, capacom y，t It represents the capacity compensation cost of the standby units allocated according to their power generation.

[0070] Step 112: Obtain the marginal power generation cost of the green power unit and the energy storage unit as the expected cost of providing auxiliary services;

[0071] The cost model of green power units is: The cost model of the energy storage unit is: m y，t is the marginal power generation cost of the green power (energy storage) unit.

[0072] Step 113, calculating the first-phase quotation model of the unit according to the expected cost and expected profit rate;

[0073] For the first phase, the quotation of the backup service provider can be calculated as the product of its cost and profit margin. The quotation model for the first phase is:

[0074]

[0075] Among them, M y,t =1+margin y,t , is the quote multiplier, is the bid price of standby unit y in the standby auxiliary service market, max gin y，t is the expected profit margin of the standby unit.

[0076] Preferably, if Figure 5 , Figure 5 It is a schematic diagram of the fourth process of the auxiliary service transaction method based on multi-agents and EWA algorithm in the present invention; step 120, including: step 121, constructing a first quotation model according to the clearing price of the auxiliary service market, the first weight parameter, the second weight parameter and the quotation of the current period; step 122, matching the first quotation model with the first strategy value.

[0077] Preferably, if Figure 6 , Figure 6This is a fifth process diagram of the auxiliary service transaction method based on multi-agents and EWA algorithm in the present invention; step 120 also includes: step 123, constructing a second quotation model according to the clearing price of the auxiliary service market, the third weight parameter and the quotation of the current period; step 124, matching the second quotation model with the second strategy value.

[0078] Preferably, if Figure 7 , Figure 7 It is a sixth process diagram of the auxiliary service transaction method based on multi-agent and EWA algorithm in the present invention; step 120 also includes: step 125, constructing a third quotation model according to the clearing price of the auxiliary service market, the second weight parameter, the first weight parameter and the quotation of the current period; step 126, matching the third quotation model with the third strategy value.

[0079] In this embodiment, the standby unit y is defined to adjust the bid price in the ancillary service market in the next period according to the strategy value θ∈{1, 2, 3}

[0080] If θ=1:

[0081]

[0082] If θ=2:

[0083]

[0084] If θ=3:

[0085]

[0086] Among them, y is the subject of parameter auxiliary service market, t is the period, is the next period's quotation of market entity y.

[0087] In this embodiment, when the strategy value is 1, corresponding to the first quotation model, the first weight parameter and the second weight parameter can take values ​​of 1 / 4 and 3 / 4 respectively.

[0088] In this embodiment, when the strategy value is 2, corresponding to the second quotation model, the first weight parameter and the third weight parameter can take a value of 1 / 2.

[0089] In this embodiment, when the policy value is 1, corresponding to the first quotation model, the second weight parameter and the first weight parameter can take values ​​of 3 / 4 and 1 / 4 respectively.

[0090] Step 200: Use the sorting method and clear out according to the principle of price priority.

[0091] Preferably, if Figure 8 , Figure 8It is a seventh flow chart of the auxiliary service trading method based on multi-agents and EWA algorithm in the present invention; step 200 includes: step 210, sorting the quotations of the standby units from low to high; step 220, obtaining the clearing price when the declared standby power reaches the total demand.

[0092] The sorting method is adopted to clear the market according to the principle of price priority. The market clearing price is determined based on the total demand and the price-sorted quotation sequence. For standby units whose bid price is higher than the market clearing price, the transaction volume is 0. Special treatment is taken for special situations where the total demand is higher than the total bid amount.

[0093] Preferably, if Fig. 9 , Fig. 9 It is a schematic diagram of the eighth process of the auxiliary service trading method based on multi-agent and EWA algorithm in the present invention; step 200 also includes: step 230, if the declared price is greater than the clearing price, the bidding fails and the transaction volume is 0; step 240, if the declared price is less than the clearing price, the bidding is successful, and the transaction volume is the declared volume; step 250, if the declared price is equal to the clearing price, the bidding is successful, and the transaction volume is allocated according to the allocation model.

[0094] The market clearing adopts the queuing method, arranging the bids of the standby units from low to high, y = 1, 2... When the cumulative declared standby power reaches the total demand, the market clearing electricity price is obtained like Figure 1 , m = 5, Figure 1 is a schematic diagram for explaining the clearing in the present invention; that is, the price at the intersection of the supply curve and the demand curve. For the standby unit y, if its The bidding fails and the transaction capacity (power) is 0; if The bid is successful, and the transaction volume is the declared capacity (volume), that is, like Then the transaction capacity (electricity) needs to be apportioned:

[0095]

[0096] Where w is the first power generation enterprise in the queue whose bid price is equal to the market clearing price (e.g. Figure 1 , w=4); D t is the total demand. In particular, if The transaction capacity (electricity) of the standby unit is the declared capacity (electricity), that is, And the market clearing price is the last bid price, that is

[0097] Step 300, using the EWA algorithm to construct the subject's strategy selection probability model: using the experience factor, selection weight parameter, discount rate and profit to construct the enterprise subject's attractiveness model, using the attractiveness model and sensitivity parameters to construct the subject's strategy selection probability model, and using the subject's strategy selection probability model to calculate the strategy value.

[0098] Preferably, if Fig.10 , Fig.10 It is a ninth flow chart of the auxiliary service transaction method based on multi-agent and EWA algorithm in the present invention; step 300 includes: step 310, building a profit model according to the clearing price, the declared quantity and transaction volume of the standby unit in the auxiliary service market and the cost of the standby unit; step 320, calculating the profit of the tth period using the profit model.

[0099] The profit model is:

[0100]

[0101] Using peb t represents the alternative market clearing price, represents the declared quantity of standby unit y in the standby capacity (electricity) market, represents the actual bid amount of standby unit y in the standby capacity (power) market, C y，t represents the cost of standby unit y.

[0102] Preferably, if Fig.11 , Fig.11 It is a tenth flow chart of the auxiliary service transaction method based on multi-agent and EWA algorithm in the present invention; Step 300 also includes: Step 330, constructing an attractiveness model;

[0103] Attractiveness Model for:

[0104]

[0105] Among them, σ is the selection weight parameter, which is the weight of the unselected strategy; is the discount rate of past attractiveness; It represents the strategy set of all agents except agent y at time t. I is a symbolic function that indicates whether strategy j (j is a strategy parameter) is adopted at time t: if adopted, the value is 1; if not adopted, the value is 0.

[0106] Where N(t) is the empirical factor:

[0107] N(t)=ρ×N(t-1)+1 (9),

[0108] ρ is the discount factor and N(t-1) is the empirical parameter.

[0109] Step 340: Calculate the subject's strategy selection probability using the attractiveness model and determine the bidding strategy.

[0110] Probabilistic model of strategy selection of agents Can be:

[0111]

[0112] π y，t is the profit of subject y in the bidding process in period t. Each subject has an attractiveness vector A y (t), the value of each dimension represents the attractiveness of its corresponding strategy. is the probability that subject y chooses strategy j at time t, parameter λ is used to measure the subject’s sensitivity to attraction, m y represents the cost of entity y.

[0113] The bidding strategy of the subject is the strategy with the highest probability. In order to explore the influencing factors of the carbon cost transmission effect, in the implementation mode of this application, sensitivity analysis is conducted on 23 parameters such as the number of units, unit parameters, exogenous variables, and EWA hyperparameters, the influencing factors of the number of standby price convergence periods are analyzed, and the influencing factors of the carbon cost transmission effect are analyzed.

[0114] Specifically: During the simulation, first, set the number of coal-fired units, green power units, and energy storage in the backup market, set the number of simulation periods, set the number of simulations (to eliminate the randomness of the simulation), set the unit parameters (cost, expected profit margin, power generation capacity, carbon emissions), set the default values ​​of exogenous variables such as carbon price, carbon quota auction ratio, carbon quota allocation benchmark value, load gap, load gap prediction ratio, margin, green backup capacity ratio, energy storage capacity ratio, quotation cap, EWA hyperparameters, and other parameters.

[0115] In order to reflect the differences in parameters at the unit level and time series level, for unit parameters, interval planning is used at different units and time points to generate random fluctuations in the benchmark values; for load gaps, interval planning is used at different time points to generate random fluctuations based on the benchmark values. The setting of these parameter benchmark values ​​is based on existing authoritative literature and policy documents and has been calibrated.

[0116] A sensitivity analysis was conducted on 23 parameters, including the number of units, unit parameters, exogenous variables, and EWA hyperparameters. The impact of a single parameter change can be explored by fluctuating 5% and 10% around the parameter benchmark value, and the sensitivity coefficient of the reserve power price relative to the parameter change can be obtained. Based on the results of the sensitivity analysis, several parameters with a greater impact on the reserve power price were obtained. These parameters were regarded as the main factors affecting the reserve market, and the remaining parameters were used as evidence of the robustness of the simulation model.

[0117] Explore the factors affecting the number of reserve price convergence periods. Potential influencing factors include: carbon quota allocation benchmark value, coal-fired unit fuel cost, coal-fired unit start-up and shutdown cost, coal-fired unit marginal carbon emissions, green power unit kilowatt-hour cost, energy storage unit kilowatt-hour cost, load gap, expected profit margin, load gap forecast ratio, adequacy, green reserve capacity ratio, energy storage capacity ratio, carbon price, carbon quota auction ratio, and quotation ceiling.

[0118] Explore the factors affecting the carbon cost transmission effect. The carbon cost transmission effect refers to the ratio of the change rate of the equilibrium reserve electricity price to the change rate of the carbon cost. Potential influencing factors include: carbon quota allocation benchmark value, coal-fired unit fuel cost, coal-fired unit start-up and shutdown cost, green power unit kilowatt-hour cost, energy storage unit kilowatt-hour cost, load gap, expected profit margin, load gap forecast ratio, redundancy, green reserve capacity ratio, energy storage capacity ratio, and quotation ceiling.

[0119] The ancillary service trading method based on multiple subjects and EWA algorithm of the present invention is implemented by constructing a strategy selection model, and using the EWA algorithm and exploring the influence of sensitive parameters such as carbon price, carbon quota auction ratio, and sufficiency on standby ancillary service transactions to construct a strategy selection probability model of the subject to calculate the strategy value, thereby updating the quotations of multiple subjects in the ancillary service market according to the strategy value. This technical solution gives full play to the function of market-oriented resource allocation, and the formation mechanism of ancillary service prices can be explored based on this. This technical solution further provides an extended function, which can be used to explore the carbon cost transmission effect and its influencing factors in the ancillary service market, thereby providing a policy basis for reasonably allocating the carbon cost of ancillary services, strengthening the connection between ancillary services and the carbon market, and promoting the smooth transformation of the green power system under the carbon trading mechanism.

[0120] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An auxiliary service transaction method based on multi-agent and EWA algorithm, characterized by comprising: Step 100: construct a subject quotation model; Step 200: clearing out by using a sorting method and according to the principle of price priority; Step 300: Use the EWA algorithm to build a strategy selection probability model for the subject: The attractiveness model of the enterprise entity is constructed using the experience factor, selection weight parameter, discount rate and profit, the strategy selection probability model of the entity is constructed using the attractiveness model and sensitivity parameter, and the strategy value is calculated using the strategy selection probability model of the entity.

2. The auxiliary service transaction method based on multi-agent and EWA algorithm according to claim 1 is characterized in that: The step 100 comprises: Step 110, constructing a first-phase quotation model of the subject using the unit attribute parameters; Step 120, construct a first quotation model, a second quotation model, and a third quotation model so that the first quotation model, the second quotation model, and the third quotation model correspond to the first strategy value, the second strategy value, and the third strategy value, respectively, for updating the quotation of the unit in the subsequent period.

3. The auxiliary service transaction method based on multi-agent and EWA algorithm according to claim 2 is characterized in that: The step 110 includes:

111. Calculate the expected cost of coal-fired units based on fuel costs, carbon emissions, carbon quota benchmark values, exogenous carbon prices, carbon quota auction ratios, start-up and shutdown costs, capacity compensation costs, and spinning reserve costs; 112. Obtain the marginal power generation cost of green power units and energy storage units as the expected cost of providing ancillary services; 113. Calculate the first-phase quotation model based on the expected cost and expected profit margin of the unit.

4. The auxiliary service transaction method based on multi-agent and EWA algorithm according to claim 2 is characterized in that: The step 120 includes: Step 121: construct the first quotation model according to the clearing price of the auxiliary service market, the first weight parameter, the second weight parameter and the quotation of the current period; Step 122: Match the first quotation model with the first strategy value.

5. The auxiliary service transaction method based on multi-agent and EWA algorithm according to claim 2 is characterized in that: The step 120 further includes: Step 123: construct the second quotation model according to the clearing price of the auxiliary service market, the third weight parameter and the quotation of the current period; Step 124: Match the second quotation model with the second strategy value.

6. The auxiliary service transaction method based on multi-agent and EWA algorithm according to claim 2 is characterized in that: The step 120 further includes: Step 125: construct the third quotation model according to the clearing price of the auxiliary service market, the second weight parameter, the first weight parameter and the quotation of the current period; Step 126: Match the third quotation model with the third strategy value.

7. The auxiliary service transaction method based on multi-agent and EWA algorithm according to claim 1 is characterized in that: The step 200 comprises: Step 210, sorting the bids of the standby units from low to high; Step 220: Obtain the clearing price when the declared reserve power reaches the total demand.

8. The auxiliary service transaction method based on multi-agent and EWA algorithm according to claim 1 is characterized in that: The step 200 further includes: Step 230: If the bid price is greater than the clearing price, the bidding fails and the transaction volume is 0; Step 240: If the declared price is less than the clearing price, the bidding is successful and the transaction volume is the declared volume; Step 250: If the declared price is equal to the clearing price, the bidding is successful and the traded electricity is allocated according to the allocation model.

9. The auxiliary service transaction method based on multi-agent and EWA algorithm according to claim 1 is characterized in that: The step 300 includes: Step 310: construct a profit model based on the clearing price, the declared quantity and transaction volume of the standby units in the auxiliary service market, and the cost of the standby units; Step 320: Calculate the profit of the tth period using the profit model.

10. The auxiliary service transaction method based on multi-agent and EWA algorithm according to claim 1 is characterized in that: The step 300 further includes: Step 330: construct an attractiveness model; Step 340: Calculate the subject's strategy selection probability using the attractiveness model and determine the bidding strategy.

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