Hydrogen production and hydrogenation integrated station electric energy-peak regulation combined market transaction method considering hydrogen load demand response

Abstract

The invention discloses a hydrogen production and hydrogenation integrated station electric energy-peak regulation combined market transaction method considering hydrogen load demand response. The method comprises the following steps: 1) constructing a hydrogen production and hydrogenation integrated station day-ahead electric energy-peak regulation combined market transaction mechanism and framework; 2) constructing a cost model of the hydrogen production and hydrogenation integrated station for regulating and controlling multi-type hydrogen load demand response; 3) considering uncertainty factors such as hydrogenation load of the hydrogen fuel cell vehicle, and proposing a hydrogen production and hydrogenation integrated station market transaction model with maximum operation income as a target based on distribution robustness; 4) constructing a day-ahead electric energy-peak regulation combined market clearing model containing the hydrogen production and hydrogenation integrated station by taking social welfare maximization as a target; and 5) proposing a market transaction strategy solving method based on a differential privacy-consistent alternating direction multiplier method (DP-C-ADMM). According to the invention, a cooperative transaction mechanism in which the hydrogen production and hydrogenation integrated station participates in multiple markets is constructed, and a transaction model based on distributed robustness and a transaction strategy solving method considering privacy protection are proposing.

Description

Technical Field

[0001] This invention relates to the field of optimized operation of electricity-hydrogen-transportation coupling systems, specifically a joint market trading method for electricity-peak shaving at integrated hydrogen production and refueling stations that takes into account hydrogen load demand response. Background Technology

[0002] With the maturation of technologies such as water electrolysis for hydrogen production and hydrogen fuel cell vehicles, the Hydrogen Production and Refueling Integrated Station (HPRS), as a key facility connecting the power system and hydrogen energy consumption, combines the capabilities of electricity conversion for hydrogen production, hydrogen storage and supply, and load regulation. By dynamically adjusting its hydrogen production capacity, it can meet the hydrogen load demand in the transportation sector and also has the potential to participate in peak shaving in the power system, which is of great significance for improving energy system efficiency and promoting low-carbon transformation. However, existing research mainly focuses on the trading models of HPRS in the electricity market, and has not yet explored its interactive trading mechanisms and models for participating in the peak shaving market. The peak shaving resource characteristics of HPRS differ significantly from those of traditional peak shaving power sources, and key elements such as its trading mechanisms and models for participating in the peak shaving market lack systematic design, resulting in the inability to effectively release the flexible adjustment potential of such facilities in the market environment. At the same time, existing market trading strategy solutions are insufficient to meet the privacy protection requirements of HPRS operators. The internal operational data of integrated hydrogen production and refueling stations is commercially sensitive information. Traditional distributed solution algorithms may lead to the leakage of core operational information during data interaction, reducing the enthusiasm of market participants. Therefore, there is an urgent need for a transaction strategy solution method that balances solution efficiency and privacy protection. Summary of the Invention

[0003] The purpose of this invention is to provide a joint market trading method for electricity and peak shaving at integrated hydrogen production and refueling stations that considers hydrogen load demand response, comprising the following steps:

[0004] 1) Establish a joint market trading mechanism and framework for day-ahead electricity and peak shaving at integrated hydrogen production and refueling stations;

[0005] 2) Construct a cost model for regulating the demand response of various types of hydrogen loads at an integrated hydrogen production and refueling station;

[0006] 3) Considering the uncertainties of hydrogen fuel cell vehicles and the cost model of regulating the demand response of various types of hydrogen loads at integrated hydrogen production and refueling stations, construct a market transaction model for integrated hydrogen production and refueling stations with the goal of maximizing operating revenue.

[0007] 4) Based on the market trading model of integrated hydrogen production and refueling stations, and with the goal of maximizing social welfare, construct a day-ahead electricity-peak shaving joint market clearing model that includes integrated hydrogen production and refueling stations;

[0008] 5) The day-ahead electricity-peak shaving joint market clearing model with hydrogen production and refueling stations was solved using the market trading strategy solution method based on differential privacy-consistency alternating direction multiplier method, and the electricity-peak shaving joint market clearing results were obtained.

[0009] Furthermore, the day-ahead electricity-peak shaving joint market trading mechanism for integrated hydrogen production and refueling stations refers to the following: the integrated hydrogen production and refueling stations (HPRS) provide "quotation-volume reporting" solutions in the electricity market in the form of a purchase and sale e-commerce platform, and report peak reduction and valley filling volumes in the peak shaving market in the form of peak shaving auxiliary service providers.

[0010] Furthermore, the day-ahead electricity-peak shaving joint market trading framework for integrated hydrogen production and refueling stations refers to:

[0011] HPRS, an integrated hydrogen production and refueling station, formulates a day-ahead electricity-peak shaving joint market trading strategy based on the obtained electricity market information.

[0012] In the electricity market, HPRS, based on the output of wind and solar turbines within its stations, declares the amount of electricity to be purchased and sold and the electricity price, and dispatches hydrogen storage, while meeting the hydrogen load demand of its own stations.

[0013] In the peak shaving ancillary service market, HPRS regulates hydrogen load demand response, combining the response amount with the remaining capacity of hydrogen production and storage equipment and hydrogen fuel cells in the electricity market, and submits applications for peak shaving and valley filling.

[0014] Furthermore, the types of hydrogen load demand response include lossless transferable hydrogen load demand response, lossy transferable hydrogen load demand response, and lossy reduceable hydrogen load demand response.

[0015] Furthermore, the cost model for regulating the demand response of various types of hydrogen loads at the integrated hydrogen production and refueling station includes a regulation cost model that impairs transferable hydrogen loads and a regulation cost model that impairs reduceable hydrogen loads.

[0016] Furthermore, the objective function of the market trading model for integrated hydrogen production and refueling stations. As shown below:

[0017] (16)

[0018] (17)

[0019] Furthermore, the constraints of the market trading model for integrated hydrogen production and refueling stations include equipment operation constraints, equipment capacity constraints, hydrogen energy balance constraints, hydrogen load demand response constraints, market trading constraints, and uncertainties in HFCV hydrogen refueling load and wind and solar power output.

[0020] Furthermore, the steps for constructing a day-ahead electricity-peak-shaving joint market clearing model with the goal of maximizing social welfare are as follows:

[0021] 4.1) Construct a day-ahead electricity-peak-shaving joint market trading model for conventional power generators with the objective of maximizing operational revenue, namely:

[0022] (36)

[0023] (37) (38)

[0024] (39) (40)

[0025] (41) (42)

[0026] 4.2) Based on the day-ahead electricity energy-peak shaving joint market trading model of conventional power generators, construct a day-ahead electricity energy-peak shaving joint market clearing model with HPRS;

[0027] The objective function of the day-ahead electricity-peak shaving joint market clearing model incorporating HPRS is shown below:

[0028] (43)

[0029] The constraints of the day-ahead electricity-peak shaving joint market clearing model incorporating HPRS are as follows:

[0030] (44)

[0031] (45)

[0032] (46)

[0033] (47)

[0034] (48)

[0035] (49)

[0036] (50) (51)

[0037] (52)

[0038] Furthermore, the steps for solving the day-ahead electricity-peak-shaving joint market clearing model for integrated hydrogen production and refueling stations include:

[0039] 5.1) Set up a power dummy variable and establish consistency constraints, namely:

[0040] (53) (54)

[0041] 5.2) Based on the day-ahead electricity-peak-shaving joint market clearing model including integrated hydrogen production and refueling stations, an augmented Lagrangian function for electricity market trading and clearing is constructed, namely:

[0042] (55)

[0043] 5.3) Based on the decomposition of the augmented Lagrangian function into optimization variables, the trading model for HPRSh participating in the day-ahead electricity-peak shaving joint market is obtained, namely:

[0044] (56)

[0045] (57)

[0046] 5.4) Parameters in the trading model After linearization, the transformed values ​​are shown in equations (59) to (63):

[0047] (58)

[0048] (59)

[0049] (60)

[0050] (61)

[0051] (62)

[0052] (63)

[0053] 5.5) Construct a trading model for conventional power generators (w) to participate in the day-ahead electricity-peak-shaving joint market and a market clearing model for the power trading center, namely:

[0054] (64)

[0055] (65)

[0056] 5.6) Solve the trading model for conventional power generator w participating in the day-ahead electricity-peak shaving joint market and the market clearing model for the power trading center to obtain the clearing result of the electricity-peak shaving joint market.

[0057] Furthermore, the steps for solving the trading model for conventional power generator w participating in the day-ahead electricity-peak shaving joint market and the market clearing model for the power trading center include:

[0058] 5.6.1) Set initial values ​​for global variables, dual variables, residuals, dual residuals, and iteration step size, and set the iteration number k to 1;

[0059] 5.6.2) Parallel solution of HPRS, conventional power generator trading model (56)-(57) and power trading center joint clearing model (64)-(65) to obtain the electricity market trading volume of each HPRS and conventional power generator at the k-th iteration. , Electricity volume for peak shaving market bidding , , , The winning bid volume of various HPRS and conventional power generators in the electricity market , Electricity volume won in the peak shaving market , , , ;

[0060] At this time, each HPRS and conventional power generator reports the electricity volume of the kth electricity market transaction to the power trading center. , And peak shaving market bidding volume , , , ,Right now:

[0061] (66)

[0062] The Laplace perturbation is shown below:

[0063] (67)

[0064] 5.6.3) Update global variables, dual variables, residuals, dual residuals, and iteration step size;

[0065] 5.6.4) Determine whether both the residual and the dual residual satisfy the convergence criteria. If so, output the power-peak-shaving joint market clearing result; otherwise, let k = k + 1 and return to step 5.6.2).

[0066] The technical effectiveness of this invention is undeniable. From the perspective of electricity-peak shaving joint market trading and hydrogen load demand response, this invention constructs a collaborative trading mechanism for integrated hydrogen production and refueling stations to participate in multiple markets, proposing a trading model based on a distributed bar and a method for solving trading strategies that considers privacy protection. Finally, numerical examples verify the effectiveness and adaptability of the proposed method. Attached Figure Description

[0067] Figure 1 This is a flowchart of a combined market trading method for electricity and peak shaving at integrated hydrogen production and refueling stations, taking into account hydrogen load demand response.

[0068] Figure 2 It is a day-ahead electricity-peak-shaving joint market trading mechanism that includes HPRS;

[0069] Figure 3 This includes the specific structure of the system and the locations of each HPRS and conventional power plant;

[0070] Figure 4 These are the wind and solar power output forecast curves and power distribution network load forecast curves, as well as the hydrogen refueling load forecast curves within HPRS1, HPRS2, and HPRS3.

[0071] Figure 5 It is the result of the power trading center's market clearing of electricity;

[0072] Figure 6 This is the result of the power trading center clearing out the peak-shaving ancillary services market. Detailed Implementation

[0073] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0074] Example 1:

[0075] See Figures 1 to 2 A method for joint market trading of electricity and peak shaving for integrated hydrogen production and refueling stations, taking into account hydrogen load demand response, includes the following steps:

[0076] 1) Establish a joint market trading mechanism and framework for day-ahead electricity and peak shaving at integrated hydrogen production and refueling stations;

[0077] 2) Construct a cost model for regulating the demand response of various types of hydrogen loads at an integrated hydrogen production and refueling station;

[0078] 3) Considering the uncertainties of hydrogen fuel cell vehicles and the cost model of regulating the demand response of various types of hydrogen loads at integrated hydrogen production and refueling stations, construct a market transaction model for integrated hydrogen production and refueling stations with the goal of maximizing operating revenue.

[0079] 4) Based on the market trading model of integrated hydrogen production and refueling stations, and with the goal of maximizing social welfare, construct a day-ahead electricity-peak shaving joint market clearing model that includes integrated hydrogen production and refueling stations;

[0080] 5) The day-ahead electricity-peak shaving joint market clearing model with hydrogen production and refueling stations was solved using the market trading strategy solution method based on differential privacy-consistency alternating direction multiplier method, and the electricity-peak shaving joint market clearing results were obtained.

[0081] Example 2:

[0082] A method for a combined electricity-peak shaving market transaction for an integrated hydrogen production and refueling station that considers hydrogen load demand response is described. The technical content is the same as in Example 1. Further, the day-ahead electricity-peak shaving market transaction mechanism for the integrated hydrogen production and refueling station refers to the following: the integrated hydrogen production and refueling station HPRS provides a "quotation-volume reporting" scheme in the electricity market in the form of a purchasing and selling e-commerce platform, and reports the peak reduction and valley filling quantities in the peak shaving market in the form of a peak shaving auxiliary service provider.

[0083] Example 3:

[0084] A method for joint market trading of electricity and peak shaving for integrated hydrogen production and refueling stations, considering hydrogen load demand response, with technical content identical to any one of Examples 1-2. Further, the day-ahead electricity and peak shaving joint market trading framework for integrated hydrogen production and refueling stations refers to:

[0085] HPRS, an integrated hydrogen production and refueling station, formulates a day-ahead electricity-peak shaving joint market trading strategy based on the obtained electricity market information.

[0086] In the electricity market, HPRS, based on the output of wind and solar turbines within its stations, declares the amount of electricity to be purchased and sold and the electricity price, and dispatches hydrogen storage, while meeting the hydrogen load demand of its own stations.

[0087] In the peak shaving ancillary service market, HPRS regulates hydrogen load demand response, combining the response amount with the remaining capacity of hydrogen production and storage equipment and hydrogen fuel cells in the electricity market, and submits applications for peak shaving and valley filling.

[0088] Example 4:

[0089] A method for joint market trading of electricity and peak shaving for integrated hydrogen production and refueling stations that takes into account hydrogen load demand response, with the same technical content as any one of Examples 1-3. Further, the hydrogen load demand response types include lossless transferable hydrogen load demand response, lossy transferable hydrogen load demand response, and lossy reduceable hydrogen load demand response.

[0090] The lossless, transferable hydrogen load demand response is shown below:

[0091] (1) (2)

[0092] (3) (4)

[0093] In the formula: The amount of hydrogen stored in the hydrogen storage tank after participating in demand response; This represents the initial hydrogen storage capacity of the hydrogen storage tank. and These refer to the amount of hydrogen transferred into and out of the hydrogen storage tank during the demand response process, respectively. To improve the power generation efficiency of hydrogen fuel cells; and These are the equivalent electrical power of the hydrogen input and output after being converted into electricity by the hydrogen fuel cell, respectively. and These are the upper and lower limits of the hydrogen storage tank capacity, respectively.

[0094] The demand response to the lossy transferable hydrogen load is shown below:

[0095] (5) (6)

[0096] (7) (8)

[0097] In the formula: and The HFCV hydrogen loadings before and after HPRS's participation in demand response are respectively; and The HFCV hydrogenation loads for transfer-in and transfer-out are respectively; The equivalent electrical power of HPRS switching to hydrogen load; The equivalent electrical power of the hydrogen load transferred out by the HPRS; To optimize the system's runtime; This is the proportionality coefficient for the transferable HFCV hydrogenation load.

[0098] The impaired demand response for reducing hydrogen load is shown below:

[0099] (9)

[0100] (10) (11)

[0101] In the formula: HPRS regulation can reduce the amount of hydrogen purchased during the hydrogen load reduction process; HPRS regulation can reduce hydrogen load and thus reduce the trading volume in the hydrogen market. A positive value indicates the purchase of hydrogen. This represents the initial trading volume of HPRS in the hydrogen energy market; a positive value indicates the purchase of hydrogen. The equivalent electrical power for reducing hydrogen load for HPRS; Increasing the cap on hydrogen purchases for HPRS would impair the cap on hydrogen load that can be reduced.

[0102] Example 5:

[0103] A method for joint market trading of electricity and peak shaving for integrated hydrogen production and refueling stations that considers hydrogen load demand response, with the same technical content as any one of Examples 1-4. Furthermore, the cost model for regulating the demand response of multiple types of hydrogen load in integrated hydrogen production and refueling stations includes a regulation cost model that impairs transferable hydrogen load and a regulation cost model that impairs reduceable hydrogen load.

[0104] The regulation cost model for impaired transferable hydrogen load is shown below:

[0105] (12)

[0106] (13)

[0107] (14)

[0108] In the formula: For unsatisfactory costs; and The unsatisfactory cost coefficient; For the hydrogenation load demand response of HFCV; The incentive compensation price set by HPRS for HFCV users participating in hydrogen load demand response; P HV,t The hydrogen loading at time t represents the hydrogen loading load after HFCV users participate in demand response; The cost of HPRS-mediated lossy transferable hydrogen load;

[0109] The following is a model of the control costs that could reduce hydrogen load:

[0110] (15)

[0111] In the formula: HPRS regulation can reduce the cost of hydrogen load reduction; This refers to the trading price in the hydrogen energy market.

[0112] Example 6:

[0113] A combined market trading method for electricity and peak shaving at integrated hydrogen production and refueling stations, considering hydrogen load demand response, is provided. The technical content is the same as any one of Examples 1-5. Further, the objective function of the market trading model for the integrated hydrogen production and refueling station is... As shown below:

[0114] (16)

[0115] (17)

[0116] In the formula: Let t be the electricity price traded in the energy market. The amount of electricity traded in the HPRSh electricity market at time t; and These are the peak-shaving price and valley-filling price at time t, respectively, published by the power trading center. and These represent the bidding capacities of HPRSh participating in the peak shaving and valley filling market at time t, respectively. This represents the initial trading volume of HPRSh in the hydrogen market; a positive value indicates the purchase of hydrogen. The unit price of hydrogen refueling for HFCV users at time t; The HFCV hydrogen loading rate after HPRSh adjusts the hydrogen load demand response at time t; HPRSh regulation can reduce the cost of hydrogen load reduction; Cost of HPRSh regulation of impaired transferable hydrogen load; Operating costs of equipment within HPRSh; The trading volume in the hydrogen energy market after HPRSh regulation at time t reduces hydrogen load due to loss; a positive value indicates hydrogen purchase. The HFCV hydrogenation load demand response at time t within HPRSh; and These are the actual outputs of the wind turbines and photovoltaic units within the HPRSh, respectively. The electrical load of the electrolytic cell in HPRSh at time t; The water consumption cost per unit of electrical power used in hydrogen electrolysis; and These represent the prediction errors of wind power and photovoltaic unit output within HPRSh at time t; c represents the actual HFCV hydrogen loading rate after HPRSh regulation of hydrogen load demand response at time t; WT_ope cPV_ope This represents the operation and maintenance cost coefficient for wind turbines and photovoltaic systems.

[0117] Example 7:

[0118] A method for joint market trading of electricity and peak shaving for integrated hydrogen production and refueling stations that considers hydrogen load demand response, with the same technical content as any one of Examples 1-6. Further, the constraints of the market trading model for integrated hydrogen production and refueling stations include equipment operation constraints, equipment capacity constraints, hydrogen energy balance constraints, hydrogen load demand response constraints, market trading constraints, and uncertainties in HFCV hydrogen refueling load and wind and solar power output.

[0119] The equipment operating constraints are as follows:

[0120] (18)

[0121] (19)

[0122] (20)

[0123] (twenty one)

[0124] In the formula: Let t be the amount of hydrogen consumed by the hydrogen fuel cell within HPRSh at time t. The discharge power of the hydrogen fuel cell within HPRSh at time t; The hydrogen-to-electricity conversion efficiency of hydrogen fuel cells; For the efficiency of hydrogen production by electrolysis; Energy consumption for hydrogen production via electrolysis; , The incline rate of the electrolytic cell at the bottom and top; Hydrogen has a high calorific value; The power of hydrogen production by electrolysis; , , , , , , The parameters include compressor power consumption, specific heat capacity at constant pressure, compressor inlet temperature, compressor energy efficiency, compression ratio, specific heat ratio of hydrogen, hydrogen flow rate of compressor, and hydrogen flow rate generated by the electrolytic hydrogen production equipment.

[0125] The equipment capacity constraints are as follows:

[0126] (twenty two) (twenty three)

[0127] (twenty four)

[0128] In the formula: and The HPRSh internal hydrogen fuel cell will contribute to the day-ahead electricity market and the peak shaving and peak regulation market, respectively. This refers to the rated capacity of the internal hydrogen fuel cell in HPRSh. and These represent the electricity load of the electrolyzer in the electricity market and the increased electricity load in the valley filling and peak shaving market at time t, respectively. and These represent the electricity load of the compressor in the electricity market and the increased electricity load in the valley filling and peak shaving market at time t, respectively.

[0129] The hydrogen energy balance constraints within the station are shown below:

[0130] (25)

[0131] The hydrogen load demand response constraints are as follows:

[0132] (26)

[0133] The output constraints of wind and solar turbine units are as follows:

[0134] (27) (28)

[0135] In the formula: To contribute to the day-ahead electricity market for HPRSh wind turbines; To contribute to the day-ahead electricity market for HPRSh's photovoltaic units; and The forecasts for the daytime power output of the HPRSh wind turbines and photovoltaic units are as follows.

[0136] The energy balance constraints are as follows:

[0137] (29)

[0138] (30) (31)

[0139] The constraints for HPRSh electricity market transactions are as follows:

[0140] (32)

[0141] In the formula: and Let be the minimum and maximum values ​​of the electricity traded by HPRSh in the day-ahead electricity market at time t, respectively.

[0142] The trading constraints of the HPRSh peak-shaving market are as follows:

[0143] (33) (34)

[0144] In the formula: and These represent the minimum and maximum values ​​of the peak shaving and peak-shaving bidding capacity of HPRSh participating in the peak shaving market at time t; and These represent the minimum and maximum values ​​of the peak-shaving bidding capacity of HPRSh participating in the peak-shaving market at time t.

[0145] The uncertainties regarding HFCV hydrogenation load and wind and solar power output are as follows:

[0146] (35)

[0147] In the formula: A∈{WT, PV, HV}, where WT, PV, and HV represent the wind turbine, photovoltaic, and HFCV hydrogen loading loads, respectively; and These are the probability density functions of downwind, solar output prediction error, and HFCV hydrogen loading, respectively, for empirical and actual distributions. and They are respectively consistent with probability distribution and The wind and solar power output prediction errors and the random variables of HFCV hydrogen loading; S N Let N be the sample set; N be the number of samples. For definition in Dirac function at point; Represents random variables The i-th sample value; It is an r-order norm; express and Joint probability distribution; fuzzy set Representing the empirical distribution The Wasserstein distance satisfies probability distribution The set; β represents the confidence level; C is the radius of the Wasserstein sphere; A It is a constant; is a scalar greater than 0; μ is the sample mean.

[0148] Example 8:

[0149] A method for joint market trading of electricity and peak shaving for integrated hydrogen production and refueling stations considering hydrogen load demand response, with technical content identical to any one of Examples 1-7, further comprising the following steps for constructing a day-ahead electricity and peak shaving joint market clearing model for integrated hydrogen production and refueling stations with the objective of maximizing social welfare:

[0150] 4.1) Construct a day-ahead electricity-peak-shaving joint market trading model for conventional power generators with the objective of maximizing operational revenue, namely:

[0151] (36)

[0152] (37) (38)

[0153] (39) (40)

[0154] (41) (42)

[0155] In the formula: This represents the power generation cost coefficient per unit of electricity generated by the gas turbine. The amount of electricity traded in the electricity market by the w-th conventional power generator at time t; and These represent the bidding capacity of the w-th conventional power generator at time t participating in the peak shaving and valley filling market, respectively. The wth conventional power generator regulates the upper limit of gas turbine output; The capacity factor for gas turbines participating in the peak-shaving market; The actual output of the gas turbine after the wth conventional power generator participates in peak shaving at time t; The ramp-up capability of the gas turbine during adjacent time periods for the wth conventional power generator regulation.

[0156] 4.2) Based on the day-ahead electricity energy-peak shaving joint market trading model of conventional power generators, construct a day-ahead electricity energy-peak shaving joint market clearing model with HPRS;

[0157] The objective function of the day-ahead electricity-peak shaving joint market clearing model incorporating HPRS is shown below:

[0158] (43)

[0159] In the formula: and These represent the number of HPRS and conventional power generators in the electricity market, respectively. and These represent the HPRSh at time t, determined by the power trading center, and the clearing amount of the wth conventional power generator in the electricity market. and These represent the winning bids for HPRSh's participation in the peak shaving and valley filling market at time t, respectively. and These are the bid prices of HPRSh in the day-ahead electricity market at time t and the bid prices of the wth conventional power generator in the day-ahead electricity market, respectively. and These represent the winning bids for the w-th conventional power generator at time t in the peak shaving and valley filling market.

[0160] The constraints of the day-ahead electricity-peak shaving joint market clearing model incorporating HPRS are as follows:

[0161] (44)

[0162] (45)

[0163] (46)

[0164] (47)

[0165] (48)

[0166] (49)

[0167] (50)

[0168] (51) (52)

[0169] In the formula: , and The electricity traded by node j at time t in the electricity market, peak shaving and valley filling, and peak regulation transactions of HPRSh. , and The nodes at time t are connected to the electricity trading volume of conventional power generators in the electricity market, peak shaving and valley filling. Let the load of node j be at time t; The maximum active power transmitted by line ij; The market demand for peak shaving auxiliary services at time t; and These represent the minimum and maximum values ​​of the day-ahead electricity price of HPRSh at time t; and These are the minimum and maximum bid prices in the day-ahead electricity market for the w-th conventional power generator at time t, respectively. and These are the minimum and maximum values ​​of the electricity market clearing price at time t, respectively.

[0170] Example 9:

[0171] A method for joint market trading of electricity and peak shaving for integrated hydrogen production and refueling stations considering hydrogen load demand response, with technical content identical to any one of Examples 1-8, further comprising the following steps for solving the day-ahead electricity and peak shaving joint market clearing model for integrated hydrogen production and refueling stations:

[0172] 5.1) Set up a power dummy variable and establish consistency constraints, namely:

[0173] (53) (54)

[0174] In the formula: and For the introduction of global consensus variables;

[0175] 5.2) Based on the day-ahead electricity-peak-shaving joint market clearing model including integrated hydrogen production and refueling stations, an augmented Lagrangian function for electricity market trading and clearing is constructed, namely:

[0176] (55)

[0177] In the formula: , , and These are the dual variables corresponding to the consistency constraints; This is the iteration step size.

[0178] 5.3) Based on the decomposition of the augmented Lagrangian function into optimization variables, the trading model for HPRSh participating in the day-ahead electricity-peak shaving joint market is obtained, namely:

[0179] (56)

[0180] (57)

[0181] 5.4) Parameters in the trading model After linearization, the transformed values ​​are shown in equations (59) to (63):

[0182] (58)

[0183] (59)

[0184] (60)

[0185] (61)

[0186] (62)

[0187] (63)

[0188] In the formula: , and As dual variables; , These are the Wasserstein radii of the wind and solar prediction errors, respectively. Wasserstein radius for hydrogen loading in HFCV; , and These are auxiliary variables introduced during the model transformation process; and These are the sample values ​​of the prediction error of wind power and photovoltaic unit output within HPRSh at time t; The HFCV hydrogen loading sample value after HPRSh regulates hydrogen load demand response at time t; , These represent the upper and lower limits of the HFCV hydrogenation load at time t; , These represent the upper and lower limits of the predicted wind turbine output error at time t; , These represent the upper and lower limits of the predicted output error of the photovoltaic unit at time t;

[0189] 5.5) Construct a trading model for conventional power generators (w) to participate in the day-ahead electricity-peak-shaving joint market and a market clearing model for the power trading center, namely:

[0190] (64)

[0191] (65)

[0192] 5.6) Solve the trading model for conventional power generator w participating in the day-ahead electricity-peak shaving joint market and the market clearing model for the power trading center to obtain the clearing result of the electricity-peak shaving joint market.

[0193] Example 10:

[0194] A method for a combined electricity-peak shaving market transaction for integrated hydrogen production and refueling stations considering hydrogen load demand response, with technical content identical to any one of Examples 1-9, further comprising the following steps for solving the transaction model for conventional power generator w participating in the day-ahead electricity-peak shaving market and the market clearing model for the power trading center:

[0195] 5.6.1) Set initial values ​​for global variables, dual variables, residuals, dual residuals, and iteration step size, and set the iteration number k to 1;

[0196] 5.6.2) Parallel solution of HPRS, conventional power generator trading model (56)-(57) and power trading center joint clearing model (64)-(65) to obtain the electricity market trading volume of each HPRS and conventional power generator at the k-th iteration. , Electricity volume for peak shaving market bidding , , , The winning bid volume of various HPRS and conventional power generators in the electricity market , Electricity volume won in the peak shaving market , , , ;

[0197] At this time, each HPRS and conventional power generator reports the electricity volume of the kth electricity market transaction to the power trading center. , And peak shaving market bidding volume , , , ,Right now:

[0198] (66)

[0199] In the formula: and These are HPRSh and the Laplace perturbation added by the wth conventional power generator to the traded electrical energy, respectively. and These are the DP sensitivity of HPRSh and the wth regular power distributor, respectively. and These represent the privacy budgets of HPRSh and the w-th regular distributor, respectively; the superscript k indicates the iteration number.

[0200] The Laplace perturbation is shown below:

[0201] (67)

[0202] In the formula, Parameters that control the size of r;

[0203] 5.6.3) Update global variables, dual variables, residuals, dual residuals, and iteration step size;

[0204] The global variables are updated as follows:

[0205] (68)

[0206] The dual variable is updated as follows:

[0207] (69)

[0208] The residual and dual residual are updated as follows:

[0209] (70) (71)

[0210] In the formula: , , , These are the residuals corresponding to the power-related variables at the k-th iteration; , These are the dual residuals corresponding to the power-related variables at the k-th iteration.

[0211] The iteration step size is updated as follows:

[0212] (72)

[0213] In the formula: , and These are the adaptive adjustment coefficients; To prevent extremely small positive numbers with a denominator of 0; and These are the residual set and the dual residual set, respectively.

[0214] 5.6.4) Determine whether both the residual and the dual residual satisfy the convergence criteria. If so, output the power-peak-shaving joint market clearing result; otherwise, let k = k + 1 and return to step 5.6.2).

[0215] The convergence criteria are as follows:

[0216] (73)

[0217] (74)

[0218] In the formula: and These are the residual set and the dual residual set at the k-th iteration, respectively; and These are the convergence margins of the residual and the dual residual, respectively.

[0219] Example 11:

[0220] A method for joint market trading of electricity and peak shaving for integrated hydrogen production and refueling stations, considering hydrogen load demand response, is technically the same as any one of Examples 1-10, and further includes the following steps:

[0221] 1) Establish a joint market trading mechanism and framework for day-ahead electricity and peak shaving at integrated hydrogen production and refueling stations.

[0222] 1.1) Establish a day-ahead electricity-peak-shaving joint market trading mechanism;

[0223] The proposed day-ahead electricity-peak shaving joint market trading mechanism relies on the power trading center, with hydrogen production and refueling integrated stations (HPRS) and conventional power generators as the main participants in the electricity market. HPRS market trading activities include: (1) providing "quote-volume" schemes in the electricity market in the form of a purchase and sale e-commerce platform; (2) reporting peak shaving and valley filling volumes in the peak shaving market in the form of a peak shaving ancillary service provider. HPRS fully considers the market trading rules and information released by the power trading center. If participating in the electricity market is more profitable, it will prioritize allocating more adjustable capacity to participate in the electricity market trading; if the peak shaving ancillary service market has a more significant profit advantage, it will increase the bidding capacity of the peak shaving ancillary service market.

[0224] 1.2) Establish HPRS Electricity-Peak Shaving Joint Market Trading Framework

[0225] HPRS formulates day-ahead energy-peak shaving joint market trading strategies based on the electricity market information it obtains.

[0226] ① In the electricity market, HPRS, based on the output of wind and solar turbines in the station, declares the amount of electricity to be purchased and sold and the electricity price on the basis of meeting the hydrogen load demand of its own station. At the same time, it can also dispatch hydrogen storage to arbitrage through "low charging and high discharging".

[0227] ② In the peak shaving ancillary service market, HPRS regulates hydrogen load demand response, combines its response with the remaining capacity of hydrogen production and storage equipment and hydrogen fuel cells in the electricity market, and declares peak shaving and valley filling amounts to obtain peak shaving revenue.

[0228] 2) Construct a cost model for regulating the demand response of various types of hydrogen loads at an integrated hydrogen production and refueling station.

[0229] 2.1) Classification of Hydrogen Load Demand Response

[0230] ① Non-destructive transferable hydrogen load: The inherent hydrogen load demand of a certain period can be completely transferred to other periods without causing a decline in the user's energy experience, such as hydrogen storage equipment. Hydrogen storage participating in demand response can be expressed as Equation (1)-Equation (2). Equation (1) represents the change in hydrogen storage before and after the hydrogen storage equipment participates in demand response; Equation (2) represents the equivalent electrical power of the change in hydrogen storage.

[0231] Due to the capacity limitations of hydrogen storage tanks, hydrogen storage must meet upper and lower capacity constraints before and after participating in demand response, as shown in equations (3)-(4):

[0232] ② Lossy Transferable Hydrogen Load: Transferring the inherent hydrogen load of a certain period to other periods will lead to a decline in the user's energy consumption experience, such as HFCV hydrogen refueling load. The peak and valley differences of HFCV hydrogen refueling load are obvious. Peak-shifting hydrogen refueling can be achieved by controlling the hydrogen refueling time and policy subsidies. Its participation in demand response can be expressed as Equations (5)-(8). Equation (5) represents the change of HFCV hydrogen refueling load after participating in demand response; Equation (6) represents the equivalent electrical power of HFCV hydrogen refueling load demand response; Equation (7) limits the total amount of HFCV hydrogen refueling load to remain consistent before and after hydrogen load demand response; Equation (8) constrains the upper and lower limits of HFCV hydrogen refueling load participation in hydrogen load demand response.

[0233] (5)

[0234] ③ Impaired Reducible Hydrogen Load: The inherent hydrogen load in a certain period is completely or partially reduced, and the reduced hydrogen load is not needed or cannot be re-consumed in other periods. For example, HPRS directly purchases hydrogen energy from the hydrogen energy market to meet part of the hydrogen load demand in a certain period. At the same time, it can also purchase more hydrogen and use hydrogen fuel cells to convert hydrogen into electricity to increase the power output, as shown in Equations (9)-(11). Equation (9) represents the change in the amount of hydrogen traded by HPRS in the hydrogen energy market before and after adjusting the impaired reducible hydrogen load; Equation (10) represents the equivalent power of HPRS increasing the amount of hydrogen purchased; Equation (11) limits the upper limit of HPRS increasing the amount of hydrogen purchased.

[0235] 2.2) Hydrogen load demand response regulation cost model

[0236] Based on the aforementioned differences in hydrogen load demand response, a cost model for HPRS regulation of various types of hydrogen load demand response is established. Since HPRS utilizes hydrogen storage equipment to regulate lossless transferable hydrogen load without incurring additional regulation costs, this paper primarily focuses on the cost of HPRS regulation of lossy transferable hydrogen load and lossy reducible hydrogen load.

[0237] I) Control cost model that impairs transferable hydrogen load

[0238] The dissatisfaction arising from the participation of HFCV hydrogenation load in demand response is modeled as dissatisfaction cost. In the model... For unsatisfactory costs; and The unsatisfactory cost coefficient; The response to the hydrogen loading demand of HFCV.

[0239] At this point, the cost of HPRS-mediated lossy transferable hydrogen load regulation can be expressed as: ,in, The incentive compensation price set by HPRS for HFCV users participating in hydrogen load demand response; P HV,t The hydrogen loading at time t represents the hydrogen load after HFCV users participate in the demand response.

[0240] II) Impaired regulation cost model for reducing hydrogen load

[0241] To mitigate the impact of reducible hydrogen load, HPRS can meet part of its hydrogen load demand by directly purchasing hydrogen from the hydrogen market. The cost of mitigating this impact can be expressed as:

[0242] 3) Considering uncertainties such as hydrogen refueling load for hydrogen fuel cell vehicles, a market trading model for integrated hydrogen production and refueling stations with the goal of maximizing operational revenue is proposed based on the analysis of the BLU rod.

[0243] A day-ahead electricity-peak shaving joint market trading model for HPRS is constructed with the goal of maximizing operating revenue. Bidding prices, bidding capacities, and bidding capacity strategies for participating in the electricity market and the peak shaving market are formulated. Taking HPRSh as an example, its operating revenue f from participating in the day-ahead electricity-peak shaving joint market is... HPRSh This includes revenue from the electricity market, peak shaving market, hydrogen sales revenue from the hydrogen market, hydrogen sales revenue to HFCV users, hydrogen load demand response control costs, and equipment operating costs.

[0244] HPRSh participation in the day-ahead electricity-peak shaving joint market requires compliance with safety operation constraints and energy balance constraints, specifically including equipment operation constraints, hydrogen energy balance constraints, hydrogen load demand response constraints, and market transaction constraints.

[0245] 3.1) Equipment operating constraints

[0246] HPRS produces hydrogen through electrolysis and purchases hydrogen from the hydrogen market. It uses a compressor to compress and store the hydrogen in a storage tank to meet the station's hydrogen refueling needs. It is also equipped with a hydrogen fuel cell, which can convert surplus hydrogen into electricity and feed it back to the power system. The operating constraints of the electrolyzer, compressor, storage tank, and hydrogen fuel cell are shown in equations (17) to (20), respectively.

[0247] Electrolyzers, compressors and hydrogen fuel cells must also meet capacity constraints when participating in the day-ahead electricity-peak shaving joint market, as shown in equations (21)-(23).

[0248] 3.2) Hydrogen energy balance constraints within the station

[0249] HPRSh must satisfy the hydrogen energy balance constraint, as shown in equation (24).

[0250] 3.3) Hydrogen load demand response constraints

[0251] HPRSh can regulate the demand response of various types of hydrogen loads at any given time, and when participating in the day-ahead electricity-peak shaving joint market, it should meet the rated capacity requirements of hydrogen fuel cells.

[0252] 3.4) Output constraints of wind and solar turbine units

[0253] The HPRSh is equipped with distributed power sources to meet its own hydrogen electrolysis production needs. When participating in the day-ahead electricity-peak shaving joint market, it must meet the unit output constraints, as shown in equations (26)-(27). To contribute to the day-ahead electricity market for HPRSh wind turbines; To contribute to the day-ahead electricity market for HPRSh's photovoltaic units; and The forecasts for the daytime power output of the HPRSh wind turbines and photovoltaic units are as follows.

[0254] 3.5) Power balance constraints

[0255] The internal energy balance constraint of HPRSh must be satisfied, as shown in equations (28)-(30).

[0256] 3.6) HPRSh Electricity Market Trading Constraints

[0257] When HPRS participates in the electricity market, its traded electricity volume must meet the constraints shown in equation (31).

[0258] 3.7) HPRSh Peak Shaving Market Trading Constraints

[0259] When HPRS participates in the peak shaving ancillary services market transaction, its peak shaving and valley filling peak shaving bid volume constraints are shown in Equation (32) and Equation (33), respectively.

[0260] 3.8) Uncertainty Constraints on HFCV Hydrogen Loading and Wind and Solar Power Output

[0261] The hydrogen refueling load of HFCV is highly uncertain due to the impact of user travel, and the wind and solar power output within the HPRSh station is also subject to prediction errors due to factors such as weather. Uncertainty fuzzy sets for HFCV hydrogen refueling load and wind and solar power output are established using Wasserstein distance, as shown in Equation (34).

[0262] Where A∈{WT, PV, HV}, where WT, PV, and HV represent the wind turbine, photovoltaic, and HFCV hydrogen loading loads, respectively; and These are the probability density functions of downwind, solar output prediction error, and HFCV hydrogen loading, respectively, for empirical and actual distributions. and They are respectively consistent with probability distribution and The wind and solar power output prediction errors and the random variables of HFCV hydrogen loading; S N Let N be the sample set; N be the number of samples. For definition in Dirac function at point; Represents random variables The i-th sample value; The L1 norm is of order r, depending on the order required by the model. Since the L1 norm can preserve the linearity of the model, this paper adopts the L1 norm. express and Joint probability distribution; fuzzy set Representing the empirical distribution The Wasserstein distance satisfies probability distribution The set; β represents the confidence level; C is the radius of the Wasserstein sphere; A It is a constant; is a scalar greater than 0; μ is the sample mean.

[0263] Considering the uncertainties in wind and solar power output and HFCV hydrogen loading, a trading model for HPRSh participating in the day-ahead electricity-peak shaving joint market is constructed based on the split-blown rod, as shown in equation (35). Among them, the inner layer... express For the worst-case distribution, the revenue from hydrogen sales by HPRSh to HFCV users, inner layer express and The operating costs of wind and solar power units within HPRSh under the worst-case distribution. and These represent the prediction errors of wind power and photovoltaic unit output within HPRSh at time t; The actual value of HFCV hydrogen loading after HPRSh adjusts the hydrogen load demand response at time t.

[0264] 4) Construct a day-ahead electricity-peak shaving joint market clearing model with the goal of maximizing social welfare, including an integrated hydrogen production and refueling station.

[0265] 4.1) Conventional power generation transaction model

[0266] Considering gas turbines as a representative of conventional power generator units, a day-ahead electricity-peak shaving joint market trading model for conventional power generators, aiming to maximize operating revenue, is constructed as shown in equation (37). Wherein, This represents the power generation cost coefficient per unit of electricity generated by the gas turbine. The amount of electricity traded in the electricity market by the w-th conventional power generator at time t; and These represent the bidding capacity of the w-th conventional power generator at time t in the peak shaving and valley filling market.

[0267] The output characteristics of a gas turbine are limited by its own capacity and ramping constraints. Its participation in the day-ahead electricity-peak shaving joint market requires meeting upper and lower output limits and ramping constraints, as shown in equations (38)-(43). Equations (38)-(39) limit the actual upper limit of the gas turbine's output; equations (40)-(41) constrain the upper capacity limit for conventional power generators participating in the peak shaving market; and equations (42)-(43) represent the gas turbine's ramping capability constraints. The wth conventional power generator regulates the upper limit of gas turbine output; The capacity factor for gas turbines participating in the peak-shaving market; The actual output of the gas turbine after the wth conventional power generator participates in peak shaving at time t; The ramp-up capability of the gas turbine during adjacent time periods for the wth conventional power generator regulation.

[0268] 4.2) Day-ahead Electricity-Peak Shaving Joint Market Clearing Model Including HPRS

[0269] With the goal of maximizing social welfare in each time period, a day-ahead electricity energy-peak shaving joint market clearing model for the power trading center is established, as shown in equation (44). and These represent the number of HPRS and conventional power generators in the electricity market, respectively. and These represent the HPRSh at time t, determined by the power trading center, and the clearing amount of the wth conventional power generator in the electricity market. and These represent the winning bids for HPRSh's participation in the peak shaving and valley filling market at time t, respectively. and These are the bid prices of HPRSh in the day-ahead electricity market at time t and the bid prices of the wth conventional power generator in the day-ahead electricity market, respectively. and These represent the winning bids for the w-th conventional power generator at time t in the peak shaving and valley filling market.

[0270] When conducting joint clearing of the power market and peak-shaving market, constraints such as safe operation, power balance, and market transaction must be met, as shown in equations (45) to (53). Equation (45) represents the power balance constraint of the power system nodes; equation (46) limits the upper and lower limits of line transmission power; equations (47) and (48) represent the power balance constraints of the power market and peak-shaving ancillary service market, respectively, where the dual multiplier of equation (47) represents the power price component of the marginal clearing price. Equations (49) and (50) respectively limit the upper limit of the clearing volume of HPRS and conventional power generators in the peak shaving ancillary service market; Equations (51)-(53) respectively limit the upper and lower limits of HPRS bids, conventional power generator bids and clearing prices in the electricity market.

[0271] in, , and The electricity traded by node j at time t in the electricity market, peak shaving and valley filling, and peak regulation transactions of HPRSh. , and The nodes at time t are connected to the electricity trading volume of conventional power generators in the electricity market, peak shaving and valley filling. Let the load of node j be at time t; The maximum active power transmitted by line ij; The market demand for peak shaving auxiliary services at time t; and These represent the minimum and maximum values ​​of the day-ahead electricity price of HPRSh at time t; and These are the minimum and maximum bid prices in the day-ahead electricity market for the w-th conventional power generator at time t, respectively. and These are the minimum and maximum values ​​of the electricity market clearing price at time t, respectively.

[0272] 5) A market trading strategy solution based on DP-C-ADMM is proposed.

[0273] 5.1) Day-ahead Electricity-Peak Shaving Joint Market Trading Model Based on DP-C-ADMM

[0274] Each entity in this paper aims to maximize its profits by simultaneously optimizing trading power and trading price. Ultimately, the trading volume of HPRS and conventional power generators in the electricity market is consistent with the clearing volume of HPRS and conventional power generators in the electricity market determined by the power trading center. Therefore, corresponding power virtual variables are set and consistency constraints are established.

[0275] in, and This is the introduced global consensus variable. The augmented Lagrangian function for electricity market trading and clearing is shown in equation (56). Wherein, , , and These are the dual variables corresponding to the consistency constraints; This is the iteration step size.

[0276] Based on the optimization variable decomposition (56), the trading model for HPRSh participating in the day-ahead electricity-peak shaving joint market is obtained, as shown in equation (57).

[0277] Equation (57) includes a min-max optimization model for the sub-Bruker bar. Based on the solution method for the sub-Bruker bar model, the solution method for equation (57) is as follows: After linearization, the transformed values ​​are shown in equations (60) to (64).

[0278] in, , and As dual variables; , These are the Wasserstein radii of the wind and solar prediction errors, respectively. Wasserstein radius for hydrogen loading in HFCV; , and These are auxiliary variables introduced during the model transformation process; and These are the sample values ​​of the prediction error of wind power and photovoltaic unit output within HPRSh at time t; The HFCV hydrogen loading sample value after HPRSh regulates hydrogen load demand response at time t; , These represent the upper and lower limits of the HFCV hydrogenation load at time t; , These represent the upper and lower limits of the predicted wind turbine output error at time t; , These represent the upper and lower limits of the predicted output error of the photovoltaic unit at time t.

[0279] Similarly, the trading model for conventional power generator w participating in the day-ahead power energy-peak shaving joint market and the market clearing model for the power trading center are decomposed as shown in equation (65) and equation (66), respectively.

[0280] To mitigate privacy concerns arising from the interaction of marginal information such as electricity trading volumes among market participants, each participant calculates their own electricity trading volume locally and then adds a Laplace perturbation to protect transaction privacy. This leads to the proposed solution method for the day-ahead electricity-peak shaving joint market trading strategy based on DP-C-ADMM. Each market participant adds a Laplace perturbation with a mean of 0. ,in Indicates sensitivity, This refers to a privacy budget.

[0281] 5.2) Solution Process for Day-ahead Electricity-Peak Shaving Joint Market Trading Strategy Based on DP-C-ADMM

[0282] Based on the above models, a solution process for the day-ahead electricity-peak shaving joint market trading strategy with HPRS based on DP-C-ADMM is established. The detailed steps are as follows:

[0283] 1) Set initial values ​​for global variables, dual variables, residuals, dual residuals, and iteration step size, and set the iteration count k to 1;

[0284] 2) Parallel solution of HPRS, conventional power generator trading model and power trading center joint clearing model yields: 1) the electricity trading volume of each HPRS and conventional power generator at the k-th iteration. , And peak-shaving market bidding electricity , , , ;2) The winning bid volume of each HPRS and conventional power generator in the electricity market , and the amount of electricity won in the peak-shaving market , , , After adding the Laplace perturbation to the electricity trading volume in the electricity market, each HPRS and conventional power generator shall report the electricity trading volume of the kth electricity market transaction to the power trading center as shown in equation (67). , And peak shaving market bidding volume , , , ;

[0285] In the formula: and These are HPRSh and the Laplace perturbation added by the wth conventional power generator to the traded electrical energy, respectively. and These are the DP sensitivity of HPRSh and the wth regular power distributor, respectively. and Here, HPRSh and the privacy budget of the w-th regular generator are respectively; the superscript k indicates the iteration number. To ensure algorithm convergence, the Laplace perturbation is set to decay with increasing iteration number, i.e.:

[0286] 3) Update global variables, dual variables, residuals, dual residuals, and iteration step size;

[0287] The global variables are updated according to equation (69).

[0288] The dual variable is updated according to equation (70).

[0289] The residuals and dual residuals are updated according to equations (71) and (72), respectively. , , , These are the residuals corresponding to the power-related variables at the k-th iteration; , These are the dual residuals corresponding to the power-related variables at the k-th iteration.

[0290] To accelerate the convergence speed of the DP-C-ADMM algorithm, this paper designs an adaptive step size adjustment mechanism. The update method is shown in equation (73). Wherein, , and These are the adaptive adjustment coefficients; To prevent extremely small positive numbers with a denominator of 0; and These are the residual set and the dual residual set, respectively.

[0291] 4) Determine whether both the residual and the dual residual satisfy the convergence criterion, as shown in equation (74). Wherein, and These are the residual set and the dual residual set at the k-th iteration, respectively; and These are the convergence margins of the residual and the dual residual, respectively.

[0292] If the residuals and dual residuals satisfy equation (74), and the peak-shaving market clearing quantity satisfies equation (75), then the algorithm is considered to have converged, and the game between each HPRS, conventional power generator and power trading center has reached equilibrium. At this point, the iteration stops, and the power energy-peak-shaving joint market clearing result is output; otherwise, let k=k+1 and return to step 2.

[0293] Example 12:

[0294] A method for verifying the joint market trading of electricity and peak shaving for integrated hydrogen production and refueling stations that takes into account hydrogen load demand response is described in the following main steps:

[0295] 1) The effectiveness of the proposed HPRS power-peak shaving joint market trading strategy was verified using an improved IEEE 141-node distribution network system. The specific system structure and the locations of each HPRS and conventional power plant are as follows: Figure 3 As shown in the figure, HPRS1, HPRS2, and HPRS3 are located at distribution network nodes 9, 22, and 58, respectively, while conventional power plants 1 and 2 are located at distribution network nodes 20 and 40, respectively. Node 1 in the distribution network is connected to the upstream power grid. The distributed power generation configuration within each HPRS is shown in Table 1, and the configuration schemes for the remaining hydrogen production and storage equipment are shown in Table 2. The predicted output of wind and solar power and the predicted load of the distribution network are shown in the figure. Figure 4 As shown in (a), the predicted hydrogen loading curves for HPRS1, HPRS2, and HPRS3 are as follows: Figure 4 As shown in (b), the power trading center releases relevant information about the peak-shaving market one day in advance, such as peak-shaving periods and prices. Specific information is shown in Table 3.

[0296] 2) Establish a joint market trading mechanism and framework for day-ahead electricity and peak shaving at integrated hydrogen production and refueling stations;

[0297] 2) Construct a cost model for regulating the demand response of various types of hydrogen loads at an integrated hydrogen production and refueling station;

[0298] 3) Considering uncertainties such as hydrogen refueling load for hydrogen fuel cell vehicles, a market trading model for integrated hydrogen production and refueling stations with the goal of maximizing operational revenue is proposed based on the analysis of the bibliometrics model.

[0299] 4) Construct a day-ahead electricity-peak shaving joint market clearing model with the goal of maximizing social welfare;

[0300] 5) A market trading strategy solution based on DP-C-ADMM is proposed.

[0301] 6) Based on the proposed solution method for the joint market trading strategy of electricity energy and peak shaving, the electricity market clearing result of the power trading center is as follows: Figure 5 As shown, this specifically includes the cleared electricity volume of each HPRS, the cleared electricity volume of conventional power generators, and the market cleared electricity price. The results of the power trading center's clearing of the peak-shaving ancillary services market are as follows: Figure 6 As shown in the figures, (b), (c), and (d) respectively illustrate the market clearing situation of each HPRS in peak shaving ancillary services, including peak shaving clearing volume and valley filling clearing volume.

[0302] Table 1 Distributed power supply configuration schemes within each HPRS

[0303]

[0304] Table 2 Equipment Configuration Scheme within Each HPRS

[0305]

[0306] Table 3 Peak-Shaving Periods and Prices

[0307]

[0308] Combination Figure 4 and Figure 5 It can be seen that each HPRS can sell more electricity to the power trading center between 11:00 and 14:00 to maximize profits. This is because the wind and solar turbines within each HPRS have higher output during this period, and the HFCV hydrogen refueling load is also at a relatively low level. However, between 16:00 and 21:00, due to a significant drop in wind and solar output and the arrival of the peak HFCV hydrogen refueling load, each HPRS has to purchase electricity from the power trading center to produce hydrogen to meet the hydrogen refueling needs of HFCV users. At this time, the role of each HPRS in the electricity market shifts from a retailer to a load factor, with conventional generators dominating the electricity sales market. The clearing price is lower than the adjacent time periods during the periods of 1:00-4:00, 13:00-15:00, and 22:00-24:00 because the total load demand in the electricity market is lower during these periods. In an environment that encourages competition among electricity retailers, each HPRS and conventional power generator will lower its electricity price during these periods to gain more market share, resulting in a slightly lower final clearing price. Conversely, during the periods of 9:00-12:00 and 17:00-21:00, the total load demand in the electricity market is higher than the adjacent time periods. Electricity retailers in the market will raise their electricity prices to obtain higher profits, resulting in a slightly higher final market clearing price than the adjacent time periods.

[0309] from Figure 6 It is evident that during peak shaving periods (8:00-12:00 and 17:00-21:00), each HPRS actively responds to the peak shaving demand in the electricity market. This is because the peak shaving compensation price is higher than the electricity sales price in the energy market during this period. To obtain more profit, each HPRS weighs its dispatchable resources and participates in both the energy market and the peak shaving ancillary service market. Similarly, during valley filling periods (1:00-7:00 and 13:00-14:00), since the electricity sales price in the energy market is lower than the valley filling peak shaving compensation price in the peak shaving market, each HPRS dispatches its internal resources and actively participates in valley filling peak shaving by increasing hydrogen production load to obtain higher operating revenue.

Claims

1. A method for electricity energy-peak shaving combined market transaction of hydrogen production and hydrogenation integrated station considering hydrogen load demand response, characterized in that, The method comprises the following steps: 1) constructing a day-ahead electricity energy-peak shaving joint market transaction mechanism and framework of the hydrogen production and hydrogenation integrated station; 2) constructing a cost model of the hydrogen production and hydrogenation integrated station regulating and controlling multiple types of hydrogen load demand response; 3) considering the uncertainty factors of hydrogen fuel cell vehicles and the cost model of the hydrogen production and hydrogenation integrated station regulating and controlling multiple types of hydrogen load demand response, constructing a market transaction model of the hydrogen production and hydrogenation integrated station with the maximum operation income as the target; 4) based on the market transaction model of the hydrogen production and hydrogenation integrated station, constructing a day-ahead electricity energy-peak shaving joint market clearing model containing the hydrogen production and hydrogenation integrated station with the maximum social welfare as the target; 5) using a market transaction strategy solving method based on differential privacy-consistency alternating direction multiplier method to solve the day-ahead electricity energy-peak shaving joint market clearing model containing the hydrogen production and hydrogenation integrated station, and obtaining the electricity energy-peak shaving joint market clearing result.

2. The method of claim 1, wherein the method further comprises: The day-ahead electricity energy-peak shaving joint market transaction mechanism of the hydrogen production and hydrogenation integrated station refers to: the hydrogen production and hydrogenation integrated station HPRS providing a "bid-quantity" scheme in the electricity energy market in the form of a power purchase and sale trader, and reporting the peak cutting amount and valley filling amount in the peak shaving market in the form of a peak shaving auxiliary service provider.

3. The method of claim 1, wherein the method further comprises: The day-ahead electricity energy-peak shaving joint market transaction framework of the hydrogen production and hydrogenation integrated station refers to: The hydrogen production and hydrogenation integrated station HPRS formulates a day-ahead electricity energy-peak shaving joint market transaction strategy according to the obtained electricity market information: In the electricity energy market, the HPRS reports the purchase and sale amount and price based on the wind and light unit output in the station, meets the hydrogen load demand of the station, and dispatches hydrogen storage; In the peak shaving auxiliary service market, the HPRS regulates and controls the hydrogen load demand response, combines the response amount with the hydrogen production and storage equipment and the remaining capacity of the hydrogen fuel cell in the electricity energy market, and reports the peak cutting amount and valley filling amount.

4. The method of claim 1, wherein the method further comprises: The hydrogen load demand response types include lossless transferable hydrogen load demand response, lossy transferable hydrogen load demand response and lossy reducible hydrogen load demand response; The lossless transferable hydrogen load demand response is as follows: （1） （2） （3） （4） In the formula: is the hydrogen storage amount of the hydrogen storage tank after participating in demand response; is the initial hydrogen storage amount of the hydrogen storage tank; and are the hydrogen conversion-in amount and the hydrogen conversion-out amount of the hydrogen storage tank during the demand response process, respectively; is the hydrogen fuel cell power generation efficiency; and are the equivalent electric power of the hydrogen conversion-in amount and the hydrogen conversion-out amount after hydrogen conversion by the hydrogen fuel cell, respectively; and are the upper and lower limits of the hydrogen storage tank capacity, respectively. The lossy transferable hydrogen load demand response is as follows: （5） （6） （7） （8） In the formula: and are the HFCV hydrogen charging loads before and after the HPRS participates in demand response, respectively; and are the HFCV hydrogen charging loads before and after the HPRS participates in demand response, respectively; is the equivalent electric power of the HPRS hydrogen charging load; is the equivalent electric power of the HPRS hydrogen charging load; is the number of system optimization operation time points; is the proportionality coefficient of the transferable HFCV hydrogen charging load; The lossy reducible hydrogen load demand response is as follows: （9） （10） （11） In the formula: is the increased hydrogen purchase amount in the process of reducing hydrogen load with loss under HPRS regulation; is the transaction amount in the hydrogen energy market after reducing hydrogen load with loss under HPRS regulation, and a positive value indicates hydrogen purchase; is the initial transaction amount in the hydrogen energy market under HPRS, and a positive value indicates hydrogen purchase; is the equivalent electric power of reducing hydrogen load under HPRS; is the upper limit of the increased hydrogen purchase amount under HPRS, i.e., the upper limit of reducing hydrogen load with loss.

5. The method of claim 1, wherein the method further comprises: The cost model of the hydrogen production and hydrogenation integrated station regulating and controlling multiple types of hydrogen load demand response includes a lossy transferable hydrogen load regulating and controlling cost model and a lossy reducible hydrogen load regulating and controlling cost model; The lossy transferable hydrogen load regulating and controlling cost model is as follows: （12） （13） （14） In the formula: is an unsatisfied cost; and is an unsatisfied cost coefficient; is an HFCV hydrogen refueling load demand response amount; is an incentive compensation price set by the HPRS to an HFCV user participating in hydrogen load demand response; P HV,t is a hydrogen refueling load of the HFCV user after participating in demand response at time t; is a cost of the HPRS regulating the lossy transferable hydrogen load; The lossy reducible hydrogen load regulating and controlling cost model is as follows: （15） wherein: is the cost of HPRS regulation of the loss-tolerant curable hydrogen load; is the hydrogen energy market transaction price.

6. The method of claim 1, wherein the method further comprises: Objective function of market transaction model of hydrogen production and hydrogenation integrated station As shown below: （16） （17） In the formula: is the electricity market transaction price at time t; is the HPRSh electricity market transaction capacity at time t; and are the peak shaving price and valley filling price published by the electricity trading center at time t, respectively; and are the bidding capacities of the HPRSh participating in the peak shaving market and the valley filling market at time t, respectively; is the initial transaction amount of the HPRSh in the hydrogen energy market, and a positive value indicates hydrogen purchase; is the HFCV hydrogen refueling unit price at time t; is the HFCV hydrogen refueling load after the HPRSh regulates the hydrogen load demand response at time t; is the cost of regulating the lossable and reducible hydrogen load of the HPRSh; is the cost of regulating the lossable and transferable hydrogen load of the HPRSh; is the operating cost of the equipment in the HPRSh; is the transaction amount of the HPRSh in the hydrogen energy market after regulating the lossable and reducible hydrogen load at time t, and a positive value indicates hydrogen purchase; is the HFCV hydrogen refueling load demand response amount in the HPRSh at time t; and are the actual outputs of the wind turbine generator and the photovoltaic generator in the HPRSh, respectively; is the electrolyzer electricity load in the HPRSh at time t; is the water consumption cost of consuming unit electric power for hydrogen production by electrolysis; and are the prediction errors of the wind turbine and the photovoltaic generator in the HPRSh at time t, respectively; is the actual value of the HFCV hydrogen refueling load after the HPRSh regulates the hydrogen load demand response at time t;c WT_ope , c PV_ope is the operation and maintenance cost coefficient of the wind turbine and the photovoltaic system.

7. The method of claim 1, wherein the method further comprises: The constraint conditions of the market transaction model of the hydrogen production and hydrogenation integrated station include equipment operation constraints, equipment capacity constraints, hydrogen energy balance constraints, hydrogen load demand response constraints, market transaction constraints, HFCV hydrogenation load and wind and light output uncertainty constraints; The equipment operation constraints are as follows: （18） （19） （20） （21） In the formula: is the hydrogen consumption of the hydrogen fuel cell in HPRSh at time t; is the discharging power of the hydrogen fuel cell in HPRSh at time t; is the hydrogen electricity conversion efficiency of the hydrogen fuel cell; is the electrolytic hydrogen production efficiency; is the electrolytic hydrogen production energy consumption; , is the electrolytic tank down and up ramp rate; is the high heat value of hydrogen; is the electrolytic hydrogen production power; , , , , , , is the compressor power consumption, constant pressure specific heat capacity, compressor inlet temperature, compressor energy efficiency, compression ratio, specific heat ratio of hydrogen, hydrogen flow rate of the compressor, hydrogen flow rate generated by the electrolytic hydrogen production equipment; The equipment capacity constraints are as follows: （22） （23） （24） wherein: and are the power output of the HPRSh internal hydrogen fuel cell in the day-ahead electricity energy market and the load following market, respectively; is the rated capacity of the HPRSh internal hydrogen fuel cell; and are the electricity load of the HPRSh internal electrolyzer in the electricity energy market and the increased electricity load in the valley filling market at time t, respectively; and are the electricity load of the HPRSh internal compressor in the electricity energy market and the increased electricity load in the valley filling market at time t, respectively. The hydrogen energy balance constraints in the station are as follows: （25） The hydrogen load demand response constraints are as follows: （26） The wind and light unit output constraints are as follows: （27） （28） wherein: is the day-ahead market power output of the wind turbine within HPRSh; is the day-ahead market power output of the PV within HPRSh; and are the day-ahead forecasted power outputs of the wind turbine and PV within HPRSh, respectively. The electricity energy balance constraints are as follows: （29） （30） （31） The HPRSh electricity energy market transaction constraints are as follows: （32） wherein: and are the minimum and maximum values of the HPRSh traded energy at time t in the day-ahead energy market, respectively. The HPRSh peak shaving market transaction constraints are as follows: （33） （34） In the formula: and are the minimum and maximum values of the HPRSh participating in the peak shaving market at time t; and are the minimum and maximum values of the HPRSh participating in the valley filling market at time t; The HFCV hydrogenation load and wind and light output uncertainty constraints are as follows: （35） where A ∈ {WT, PV, HV}, and WT, PV, HV represent wind turbine, photovoltaic, and HFCV hydrogen load, respectively; and are the probability density functions of the wind, photovoltaic power output prediction error and HFCV hydrogen load under the empirical distribution and the true distribution, respectively; and are the random variables of the wind, photovoltaic power output prediction error and HFCV hydrogen load, respectively, which conform to the probability distribution and S N is the sample set; N is the sample number; is the Dirac function defined at represents the ith sample value of the random variable is the r-order norm; represents the joint probability distribution of and ; the fuzzy set represents the Wasserstein distance from the empirical distribution satisfies is the set of probability distributions with Wasserstein distance less than ; β represents the confidence level; is the Wasserstein ball radius; C A is a constant; is a scalar greater than 0; μ is the sample mean.​​ 8. The method of claim 1, wherein the method further comprises: The steps of building the day-ahead electricity energy-peak shaving joint market clearing model with HPRS are as follows: 4.1) Build the day-ahead electricity energy-peak shaving joint market transaction model of conventional power generators with the maximum operating income as the target, that is: （36） （37） （38） （39） （40） （41） （42） In the formula: is the generation cost coefficient of the gas turbine unit per unit of electricity; is the electricity traded by the wth conventional power generator in the electricity market at time t; and are the bidding capacities of the wth conventional power generator participating in peak shaving and valley filling in the peak regulation market at time t, respectively; is the upper limit of the wth conventional power generator regulating the output of the gas turbine. Capacity coefficient of the gas turbine participating in the peak regulation market; Actual output of the gas turbine after participating in peak regulation by the wth conventional power supplier at time t; Climbing ability of the gas turbine of the wth conventional power supplier in the adjacent time period 4.2) Based on the day-ahead electricity energy-peak shaving joint market transaction model of conventional power generators, build the day-ahead electricity energy-peak shaving joint market clearing model with HPRS; The objective function of the day-ahead electricity energy-peak shaving joint market clearing model with HPRS is as follows: （43） In the formula: and are the number of HPRS and conventional power generators in the electricity market, respectively; and are the HPRSh and the wth conventional power generator's clearing quantity in the electricity energy market at time t decided by the electricity trading center, respectively; and are the HPRSh's winning bid quantity for peak shaving and valley filling in the peak shaving market at time t, respectively; and are the HPRSh's bidding price in the day-ahead electricity energy market at time t and the wth conventional power generator's bidding price in the day-ahead electricity energy market, respectively; and are the wth conventional power generator's winning bid quantity for peak shaving and valley filling in the peak shaving market at time t, respectively; The constraint conditions of the day-ahead electricity energy-peak shaving joint market clearing model with HPRS are as follows: （44） （45） （46） （47） （48） （49） （50） （51） （52） In the formula: , and are the electricity traded in the electricity market, the peak load regulation market and the valley load regulation market by the HPRSh connected to node j at time t, respectively; , and are the electricity traded in the electricity market, the peak load regulation market and the valley load regulation market by the conventional power generator connected to node j at time t, respectively; is the load of node j at time t; is the maximum active power transmission capacity of line ij; is the demand of the peak load regulation auxiliary service market at time t; and are the minimum and maximum of the electricity price bid by the HPRSh in the day-ahead electricity market at time t, respectively; and are the minimum and maximum of the electricity price bid by the wth conventional power generator in the day-ahead electricity market at time t, respectively; and are the minimum and maximum of the electricity market clearing price at time t, respectively.

9. The method of claim 1, wherein the method further comprises: The steps of solving the day-ahead electricity energy-peak shaving joint market clearing model with HPRS include: 5.1) Set the power virtual variable and establish the consistency constraint, that is: （53） （54） wherein: and are introduced global consensus variables; 5.2) Based on the day-ahead electricity energy-peak shaving joint market clearing model with HPRS, build the augmented Lagrangian function of electricity market transaction and clearing, that is: （55） wherein: , , and are the dual variables corresponding to the consistency constraints; is the iteration step size; 5.3) According to the optimization variable, decompose the augmented Lagrangian function to obtain the transaction model of HPRS participating in the day-ahead electricity energy-peak shaving joint market, that is: （56） （57） 5.4) Parameters in the transaction model The linearization process is performed, and the transformed equations are shown in Equations (59) - (63). （58） （59） （60） （61） （62） （63） In the formula: , and are dual variables; , are the Wasserstein radius of wind, light prediction error respectively; is the HFCV hydrogen load Wasserstein radius; , and are auxiliary variables introduced in the model transformation process; and are the predicted error sample values of the wind power and photovoltaic unit output in the HPRSh at time t respectively; is the HFCV hydrogen load sample value after the HPRSh regulates the hydrogen load demand response at time t; , are the upper and lower limits of the HFCV hydrogen load at time t respectively; , are the upper and lower limits of the wind power prediction error at time t respectively; , are the upper and lower limits of the photovoltaic unit output prediction error at time t respectively; 5.5) Build the transaction model of conventional power generator w participating in the day-ahead electricity energy-peak shaving joint market and the electricity trading center market clearing model, that is: （64） （65） 5.6) Solve the transaction model of conventional power generator w participating in the day-ahead electricity energy-peak shaving joint market and the electricity trading center market clearing model to obtain the electricity energy-peak shaving joint market clearing result.

10. The method of claim 9, wherein the method further comprises: The steps of solving the transaction model of conventional power generator w participating in the day-ahead electricity energy-peak shaving joint market and the electricity trading center market clearing model include: 5.6.1) Set the initial value of global variable, dual variable, residual, dual residual, and iteration step length, and set the iteration number k to 1; 5.6.2) solving the HPRS, regular power producer trading model (56)-(57) and the power trading center joint clearing model (64)-(65) in parallel, obtaining the electricity market trading power of each HPRS, regular power producer at the kth iteration , , the peak market bidding power , , , , the winning bid power of each HPRS, regular power producer in the electricity market , , the winning bid power in the peak market , , , ; At this time, each HPRS, conventional power generator reports the kth electricity market trading electricity to the electricity trading center , and peak market bidding amount , , , , that is: （66） wherein: and are the HPRSh, the Laplacian perturbation added by the wth conventional generator in the trading electricity energy, respectively; and are the HPRSh, the DP sensitivity of the wth conventional generator, respectively; and are the HPRSh, the privacy budget of the wth conventional generator, respectively; the superscript k denotes the iteration number. Wherein, the Laplace disturbance is as follows: （67） In the formula, is a parameter for controlling the size of r; 5.6.3) Update the global variable, dual variable, residual, dual residual, and iteration step length; The global variable is updated as follows: （68） The dual variable is updated as follows: （69） The residual and dual residual are updated as follows: （70） （71） wherein: , , , are the residual errors of the power-related variables at the kth iteration, respectively; , are the dual residual errors of the power-related variables at the kth iteration, respectively; The iteration step length is updated as follows: （72） wherein: , and are adaptive adjustment coefficients, respectively; is a small positive number to prevent denominator from being zero; and are residual set and dual residual set, respectively. 5.6.4) Judge whether the residual and dual residual meet the convergence judgment condition, if yes, output the electricity energy-peak shaving joint market clearing result, otherwise, let k=k+1, return to step 5.6.2); The convergence judgment condition is as follows: （73） （74） wherein: and are the residual set and the dual residual set at the kth iteration, respectively; and are the convergence margins for the residual and the dual residual, respectively.

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