Optimal operation of rural photovoltaic and hydrogen energy storage considering source load uncertainty

Abstract

The present application relates to a kind of rural photovoltaic and hydrogen energy storage collaborative operation optimization method considering source load uncertainty, belong to energy system optimization technical field.The present application will be reasonably physically connected by rural unit and hydrogen energy storage system through electricity-hydrogen coupling facility (electrolytic cell and hydrogen fuel cell), form rural photovoltaic and hydrogen storage combined system, to give full play to complementary advantage and synergistic effect.However, forming the above system means, in addition to source side photovoltaic random output, at least the interference of load side uncertainty, such as electric load, to operation.In view of this, it is necessary to explore the scientific optimization method of rural photovoltaic and hydrogen storage combined system operation scheduling to support its economic, low carbon, stable operation and renewable energy consumption under the interference of source and load bilateral uncertainty, guarantee the reasonable realization of the above potential benefits.

Description

Technical Field

[0001] This invention belongs to the field of energy system optimization technology, and in particular relates to an optimization method for the coordinated operation of rural photovoltaic and hydrogen energy storage considering the uncertainty of source load. Background Technology

[0002] Currently, greenhouse gas emissions from rural areas in my country account for approximately 15% of the national total, with rural energy activities accounting for about 60% of these emissions. Therefore, accelerating the construction of low-carbon villages is urgently needed to ensure the achievement of dual-carbon goals. Rural areas possess abundant exploitable clean energy resources, such as rural photovoltaic systems, and have environmental advantages for building clean energy systems tailored to local conditions. Furthermore, rural photovoltaic units are characterized by being clean, low-carbon, long-lasting, low-maintenance, and widely applicable. Given these circumstances, it is imperative to utilize the abundant local solar energy and other clean and renewable energy sources in rural areas to establish multi-energy rural photovoltaic systems that integrate various clean energy sources. This will meet the clean energy needs of rural industries, agriculture, and daily life, thereby promoting the large-scale development of renewable energy and driving the low-carbon transformation of rural energy consumption.

[0003] Due to the intermittent and random nature of photovoltaic power generation, its direct grid connection inevitably disrupts the stable operation of the power system, leading to significant curtailment of solar power. Therefore, how to fully utilize rural photovoltaic units for stable operation and improve their absorption capacity is a key research focus. Hydrogen energy, an abundant, green, low-carbon, and widely applicable secondary energy source, will play a vital role in the safe and low-carbon construction of new multi-energy systems. In recent years, a comprehensive energy utilization model integrating hydrogen production from electricity, hydrogen storage, and hydrogen cogeneration—the hydrogen energy storage system—has been proposed. During periods of low electricity load or high renewable energy output, excess electricity can be converted into hydrogen for storage or sold to the hydrogen market. The hydrogen energy storage system acts as a load for the power system and a gas source for the hydrogen system, exhibiting rapid response and flexible dispatch capabilities. Therefore, the effective utilization of hydrogen energy storage systems can provide a reasonable solution to resource waste such as solar power curtailment and the inability to store electricity on a large scale for extended periods.

[0004] Based on the above problems, this invention proposes an optimization method for the coordinated operation of rural photovoltaic and hydrogen energy storage considering source-load uncertainty. Summary of the Invention

[0005] The purpose of this invention is to propose an optimization method for the coordinated operation of rural photovoltaic and hydrogen energy storage systems, considering source-load uncertainties, to address the challenges of economic operation, low-carbon emissions, stable operation, and efficient renewable energy consumption under the interference of uncertainties on both the source and load sides. This method can effectively integrate clean energy resources in rural areas, improve energy utilization efficiency, reduce greenhouse gas emissions, promote the low-carbon transformation of rural energy consumption structures, and simultaneously ensure the stable operation of the power system, providing strong support for the sustainable development of rural areas.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] An optimization method for the coordinated operation of rural photovoltaic and hydrogen energy storage considering source-load uncertainty is proposed. The method is based on a rural photovoltaic and hydrogen energy storage joint system, which includes a variety of energy devices, which are divided into energy input devices, energy storage devices and energy conversion devices according to their specific functions.

[0008] The method includes the following:

[0009] S1. Taking into account the uncertainties of photovoltaic output and electrical load, fully characterize the uncertainties on both the source and load sides, construct the uncertainty set, and design an optimization model for the coordinated operation of rural photovoltaic and hydrogen energy storage.

[0010] S2. Design a multi-objective function to minimize the daily operating cost of a rural photovoltaic and hydrogen energy storage combined system;

[0011] S3. Construct the constraints of the rural photovoltaic and hydrogen energy storage collaborative operation optimization model. The constraints are designed as a two-stage structure to overcome the interference of multiple uncertainties on the rural photovoltaic and hydrogen energy storage joint system. The first stage is based on the prediction information to construct the constraints of equipment operation and multi-energy balance. The second stage is based on the constraint of flexible resource scheduling according to the prediction error to ensure the stable operation of the system and the consumption of renewable energy under multiple uncertainties.

[0012] S4. Combining the operations described in S1 to S3, optimize the coordinated operation of rural photovoltaic and hydrogen energy storage.

[0013] Preferably, S1 specifically includes the following:

[0014] Divide a scheduling day into T time periods, and let t As a time index, the prediction errors of photovoltaic power output and electrical load are respectively represented as random vectors. = , = Error value , The calculation formula is as follows:

[0015] (1)

[0016] (2)

[0017] in, , They are respectively t Forecast values ​​of photovoltaic power output and electrical load for the specified time period; , They are respectively t Actual values ​​of photovoltaic output and electrical load during the time period;

[0018] make Then depict Uncertainty is equivalent to characterization , The joint uncertainty; based on this, the possible range of values ​​for the above uncertainty prediction error is constructed as a support set g, which is expressed by the following formula:

[0019] (3)

[0020] in, and They represent ( The minimum and maximum values ​​in the sample data;

[0021] Furthermore, based on sample moment information, the joint probability distribution of multivariate prediction errors is... f The possible range is constructed as a fuzzy set. G Its formula is expressed as:

[0022] (4)

[0023] Where P0(g) contains all functions defined on the support set g; E is the expectation operator; , express , The expected value is equal to the corresponding sample mean. , ; , express , The expected value of the absolute value of the deviation from the corresponding expected value shall not exceed the sample empirical value. , ( );

[0024] because , Due to its nonlinear characteristics, auxiliary variables are introduced respectively. , replace , ( ), and thus fuzzy sets G Expanded to the following linear characteristics H Its formula is expressed as:

[0025] (5)

[0026] in, for The joint probability distribution, , , ; It contains all functions defined on h, where h is The support set, in its specific form, is as follows:

[0027] (6)

[0028] Based on the above H A collaborative operation optimization model for a rural photovoltaic and hydrogen energy storage system was constructed.

[0029] Preferably, S2 specifically includes the following:

[0030] The multi-objective function is specifically:

[0031] (7)

[0032] (8)

[0033] Equation (7) is the system's economic objective function, which has a two-layer structure min{sup}, including an outer layer problem and an inner layer problem;

[0034] The outer layer problem combines the predicted values ​​of photovoltaic output and electrical load to minimize the daily operating cost of the system. / They represent t Electricity / hydrogen prices in the electricity / hydrogen energy market during the period; express t During the period, the rural photovoltaic and hydrogen energy storage combined system was t Hydrogen sales volume during the specified time period; and These respectively represent the combined rural photovoltaic and hydrogen energy storage systems in t Electricity purchase and sales volume during specific time periods; , , , , These represent the unit maintenance costs for each piece of equipment; express t Actual dispatched electrical energy of rural photovoltaic units during the specified time period; / These represent the penalty coefficients for wasted light / electric cutting loads, respectively. / They represent t Wasted light volume / Electrical cutting load during the time period;

[0035] therefore, t Period operating costs include: negative revenue from system transactions with the electricity market. The negative revenue from selling hydrogen to the hydrogen market Operating and maintenance costs of photovoltaic equipment The cost of abandoning light Power load shedding penalty cost Operating and maintenance costs of electrolytic cells Operating and maintenance costs of hydrogen compressors and the operation and maintenance costs of hydrogen fuel cells ;

[0036] The inner problem is in fuzzy sets Find the expected value of the operating adjustment cost. The largest "worst" distribution;

[0037] Equation (7) represents the nesting of inner and outer problems, indicating that in The goal is to minimize the expected daily operating cost of the system under the worst-case distribution.

[0038] Equation (8) is the objective function for the system's carbon emissions, which is a single-layer objective structure min; where, This refers to the mass of carbon dioxide emitted by a rural photovoltaic and hydrogen energy storage combined system for every 1 kWh of electricity sold by the upper-level power grid.

[0039] Preferably, S3 specifically includes the following:

[0040] (3.1) Constraints related to the first phase of operation:

[0041] Based on photovoltaic output and electricity load forecasts , ( The equipment and multi-energy balance operation constraints of the rural photovoltaic and hydrogen energy storage combined system are as follows:

[0042] 3.1.1) Constraints on rural photovoltaic units

[0043] (9)

[0044] (10)

[0045] Equation (9) represents the power dispatch output of rural photovoltaic systems. and Reduction amount The relationship; Equation (10) represents the reduction of rural photovoltaic power. The upper and lower limits;

[0046] 3.1.2) Electrolytic cell constraint

[0047] (11)

[0048] Equation (11) represents the electrical power utilized by the electrolytic cell. The upper and lower limits;

[0049] 3.1.3) Hydrogen compressor constraints

[0050] (12)

[0051] Equation (12) represents the upper and lower limits of electrical energy utilization by the hydrogen compressor;

[0052] 3.1.4) Constraints on hydrogen storage tank facilities

[0053] (13)

[0054] (14)

[0055] (15)

[0056] (16)

[0057] (17)

[0058] in, , A set of 0-1 variables, representing t The hydrogen charging and discharging status of the hydrogen storage tank facility during a given time period; / and / These represent the maximum and minimum hydrogen mass values ​​for filling / discharging hydrogen into the hydrogen storage tank facility, respectively. yes The amount of hydrogen stored in the hydrogen storage tank facility at any given time; and These represent the maximum and minimum hydrogen storage capacity in the hydrogen storage tank facility;

[0059] Equation (13) is used to ensure that the charging and discharging of hydrogen do not occur simultaneously; Equations (14) and (15) represent the upper and lower limits of charging and discharging hydrogen in the hydrogen storage tank facility, respectively; Equation (16) represents the upper and lower limits of storing hydrogen in the hydrogen storage tank facility; Equation (17) is the periodic requirement for the utilization of the hydrogen storage tank facility, and the amount of hydrogen stored in the hydrogen storage tank facility is equal at the beginning and end of the scheduling cycle.

[0060] 3.1.5) Constraints of Hydrogen Fuel Cells

[0061] (18)

[0062] (19)

[0063] in, This indicates the maximum ramp power of the hydrogen fuel cell; and These are the minimum and maximum discharge power of the hydrogen fuel cell; Equations (18) and (19) represent the ramp rate and capacity limit, respectively;

[0064] 3.1.6) Constraints on electricity market transactions

[0065] (20)

[0066] (twenty one)

[0067] (twenty two)

[0068] in, , 0-1 variables represent The status of the system selling and purchasing electricity to the electricity market during specific time periods; This is the upper limit for the system's electricity trading volume;

[0069] Equations (20) and (21) represent restrictions on the system's purchase and sale of electricity from the electricity market; Equation (22) indicates that the system's purchase and sale of electricity cannot be carried out simultaneously.

[0070] 3.1.7) Constraints on Hydrogen Energy Market Transactions

[0071] (twenty three)

[0072] in, It represents the upper limit of hydrogen sales in the hydrogen energy market within a certain period.

[0073] 3.1.8) Electrical load constraint

[0074] (twenty four)

[0075] Equation (24) represents the upper and lower limits of the electrical cutting load;

[0076] 3.1.9) Power Balance Constraints

[0077]

[0078] (25)

[0079] (26)

[0080] Equations (25) and (26) represent the system's electrical and hydrogen energy balance constraints, respectively.

[0081] (3.2) Constraints related to the second phase of operation:

[0082] To address the multivariate uncertainty interference caused by the discrepancy between predicted and actual values ​​of photovoltaic output and electrical load in rural photovoltaic-hydrogen energy storage combined systems, and to ensure stable system operation and renewable energy consumption, the following flexible resource adjustment strategy based on multivariate linear affine regression is proposed:

[0083] (27)

[0084] (28)

[0085] (29)

[0086] (30)

[0087] (31)

[0088] (32)

[0089] in, , , , , , ( ) are respectively , , , , , ( The rescheduling result; , , , They respectively represent the corresponding , , , , , The adjustment rate, ;

[0090] The rescheduling mechanism described by equations (27)-(32) is as follows: Taking equation (27) as an example, when an error occurs, according to the increment... Further, the first-stage scheduling results of the electrolyzers will be... Adjusted to ; Construct subsequent two-stage operational constraints to provide H The feasible space for flexible resource rescheduling under arbitrary error scenarios includes:

[0091] 3.2.1) Constraints on Rural Photovoltaic Equipment

[0092] (33)

[0093] (34)

[0094] Equation (33) represents the actual photovoltaic dispatch output. Prediction error and the actual amount of photovoltaic reduction The transformation relationship; Equation (34) represents The actual scope;

[0095] 3.2.2) Electrolytic cell constraints

[0096] (35)

[0097] (36)

[0098] Equation (35) indicates that after rescheduling the electrolyzer, It remains between the corresponding upper and lower boundaries; Equation (36) represents the actual hydrogen production capacity of the electrolyzer. and The transformation relationship;

[0099] 3.2.3) Hydrogen compressor constraints

[0100] (37)

[0101] (38)

[0102] (39)

[0103] After rescheduling the electrolyzer, equation (37) represents the actual mass of hydrogen produced by the electrolyzer. The actual mass of hydrogen compressed by the hydrogen compressor Equal; Equation (38) represents the actual electrical energy utilization of the hydrogen compressor. Compared with actual hydrogen compression The correspondence; Equation (39) represents It still satisfies the corresponding upper and lower bound constraints;

[0104] 3.2.4) Constraints on hydrogen storage tank facilities

[0105] (40)

[0106] (41)

[0107] (42)

[0108] (43)

[0109] (44)

[0110] After rescheduling the hydrogen storage facilities, equation (40) represents the actual amount of hydrogen stored. and , The correspondence; equations (41)-(43) represent , and The corresponding upper and lower bound constraints are satisfied; Equation (44) indicates that the utilization of the hydrogen storage tank facility meets the periodic requirements;

[0111] 3.2.5) Constraints of Hydrogen Fuel Cells

[0112] (45)

[0113] (46)

[0114] (47)

[0115] (48)

[0116] After rescheduling the hydrogen fuel cell, equations (45) and (46) represent the actual amount of hydrogen utilized. and the heat and electricity in actual production and The correspondence; Equations (47) and (48) represent Meet the corresponding ramp rate and capacity limits;

[0117] 3.2.6) Electrically disconnecting load

[0118] (49)

[0119] After rescheduling, equation (49) represents the load shedding. The actual scope;

[0120] 3.2.7) Power Balance Constraints

[0121]

[0122] (50)

[0123] (51)

[0124] After the rescheduling of each flexible resource, equations (50)-(51) are used to ensure the actual power and airflow balance of the electric and hydrogen nodes in the system, respectively;

[0125] According to equations (27)-(51), the objective function in equation (7) The item is further expanded as follows:

[0126] (52)

[0127] In summary, the proposed multi-objective distributed robust model for the operation and scheduling of the rural photovoltaic and hydrogen energy storage joint system is given by equations (7) to (52).

[0128] The present invention further protects a computer device, characterized in that the computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the instruction, program, code set or instruction set is loaded and executed by the processor to realize the above-mentioned optimization method for the coordinated operation of rural photovoltaic and hydrogen energy storage considering source-load uncertainty.

[0129] The present invention further protects a computer-readable storage medium, characterized in that the computer-readable storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein the instruction, program, code set, or instruction set is loaded and executed by a processor to realize the above-mentioned optimization method for the coordinated operation of rural photovoltaic and hydrogen energy storage considering source-load uncertainty.

[0130] Compared with the prior art, the present invention has the following beneficial effects:

[0131] (1) This invention proposes an optimization method for the coordinated operation of rural photovoltaic and hydrogen energy storage considering the uncertainty of source load. This method can ensure that the system can coordinate the scheduling of various internal facilities on the basis of participating in the electricity and hydrogen markets, thereby improving economic and environmental benefits and promoting the consumption of renewable energy.

[0132] (2) This invention combines rural photovoltaic systems with hydrogen energy storage systems to form a rural photovoltaic and hydrogen energy storage joint system and further conducts multi-objective operation optimization of the system, thereby expanding the existing energy system dispatching theory system that introduces hydrogen energy. The designed rural photovoltaic and hydrogen energy storage collaborative operation optimization method ensures the coordinated operation of each device in the system to participate in electricity and hydrogen market transactions, realizes the satisfaction of electricity load supply and high proportion of renewable energy consumption, and contributes to the sustainable energy development of rural areas. Attached Figure Description

[0133] Figure 1 This is a schematic diagram of the rural photovoltaic and hydrogen energy storage combined system proposed in this invention. Detailed Implementation

[0134] The following will provide a detailed description of an optimization method for the coordinated operation of rural photovoltaic and hydrogen energy storage, considering source-load uncertainty, and its optimized scheduling method, which relates to the present invention. It should be emphasized that the following description is merely exemplary and not intended to limit the scope or application of the invention.

[0135] This invention proposes an optimization method for the coordinated operation of rural photovoltaic (PV) and hydrogen storage considering source-load uncertainties. It involves physically connecting rural power units and hydrogen storage systems through electro-hydrogen coupling facilities (electrolyzers and hydrogen fuel cells) to form a combined rural PV and hydrogen storage system, fully leveraging complementary advantages and synergistic effects. However, forming such a system means that in addition to the random output of PV on the source side, at least some uncertainty on the load side, such as electrical load, will interfere with operation. Therefore, it is necessary to explore scientific optimization methods for the operation and scheduling of the combined rural PV and hydrogen storage system to support its economical, low-carbon, and stable operation and renewable energy consumption under the interference of uncertainties on both the source and load sides, ensuring the reasonable realization of the aforementioned potential benefits.

[0136] The following description, in conjunction with relevant accompanying drawings and specific examples, illustrates the proposed optimization method for the coordinated operation of rural photovoltaic and hydrogen energy storage considering source-load uncertainty, specifically including the following content.

[0137] Example 1:

[0138] This invention proposes an optimization method for the coordinated operation of rural photovoltaic and hydrogen energy storage considering source-load uncertainty. It is based on a combined rural photovoltaic and hydrogen energy storage system, a multi-energy system coupling electricity and hydrogen. This system can participate in the electricity and hydrogen markets as an energy producer and seller, buying and selling electricity and hydrogen. The combined rural photovoltaic and hydrogen energy storage system includes various energy devices, categorized by function as: energy input devices (rural photovoltaic units), energy storage devices (hydrogen storage tanks), and energy conversion devices (electrolyzers, hydrogen fuel cells). The specific structure is as follows... Figure 1 As shown.

[0139] according to Figure 1 As shown, in this integrated system, electrical energy comes from rural photovoltaic units, hydrogen fuel cells, and the electricity market, and is utilized by facilities such as electrolyzers and hydrogen compressors. Hydrogen energy comes from hydrogen compressors and hydrogen storage tanks (produced by the electrolyzers), and is distributed for use by the hydrogen compressors and stored in the hydrogen storage tanks. It should be noted that, due to the technical limitations of the energy storage facilities, charging and discharging of hydrogen storage tanks cannot be carried out simultaneously. Furthermore, the aforementioned energy devices do not operate independently, but rather work together based on energy conversion facilities such as electrolyzers and hydrogen fuel cells to achieve multi-energy coupling and comprehensive utilization of electricity and hydrogen.

[0140] The optimization model for the coordinated operation of rural photovoltaic and hydrogen energy storage in this invention is as follows:

[0141] 1. Characterization of uncertainty and construction of uncertainty sets

[0142] In traditional system optimization research, the uncertainties inherent in energy sources and loads are not considered in the constructed scheduling models. However, in practical applications, systems are often affected by multivariate uncertainties and random fluctuations, leading to variable operating conditions, which further increases the difficulty of energy scheduling. Currently, methods used to address uncertainties in system optimization problems mainly include stochastic optimization and robust optimization. However, due to the introduction of numerous scenarios, the optimization models constructed by stochastic optimization methods inevitably contain a large number of decision variables and constraints, resulting in high computational complexity. Furthermore, since robust optimization methods achieve optimization under the "worst-case" scenario within the uncertainty set, which has a low probability of occurrence, robust optimization models are too conservative and difficult to achieve ideal optimization results for multi-energy system operation. In recent years, distributed robust methods have been introduced into research related to energy system operation optimization. Distributed robust methods can use fuzzy sets to characterize the possible range of changes in the probability distribution of disturbance factors and construct corresponding optimization models based on the "worst-case" distribution in the fuzzy sets. Distributed robust methods overcome the shortcomings of stochastic optimization and robust optimization. On the one hand, they reduce the conservatism of decision-making by taking into account probabilistic information; on the other hand, they save computation time by eliminating random scenarios.

[0143] This method takes into account the uncertainties of photovoltaic power output and electrical load. The poor prediction accuracy and high randomness of these factors inevitably interfere with the coordinated operation optimization of the joint system. Therefore, this method first fully characterizes the uncertainties on both the source and load sides, providing support for further constructing a joint system operation optimization model.

[0144] Given the predicted photovoltaic output and electrical load values, the corresponding prediction error is a random parameter, and its possible range (uncertainty) and probability distribution will be characterized in detail in this section. If a scheduling day is divided into T time periods, and let... As a time index, the prediction errors of photovoltaic power output and electrical load can be expressed as random vectors, respectively. = , = Error value , The following can be calculated:

[0145] (1)

[0146] (2)

[0147] in, , They are respectively Forecast values ​​of photovoltaic power output and electrical load for each time period. , They are respectively Actual values ​​of photovoltaic output and electrical load during the specified time period.

[0148] make Then depict Uncertainty is equivalent to characterization , The combined uncertainty.

[0149] Based on this, the possible range of values ​​for the aforementioned uncertainty prediction error is constructed as a support set g:

[0150] (3)

[0151] in, and They represent ( The minimum and maximum values ​​in the sample data. Furthermore, based on sample moment information, the joint probability distribution of multivariate prediction errors. The possible range is constructed as a fuzzy set. :

[0152] (4)

[0153] In equation (4), P0(g) contains all functions defined on the support set g. For expectation value operators. Fuzzy sets. The first two lines indicate , The expected value is equal to the corresponding sample mean. , The third and fourth lines represent , The expected value of the absolute value of the deviation from the corresponding expected value shall not exceed the sample empirical value. , ( ).because , Due to its nonlinear characteristics, this method introduces auxiliary variables respectively. , replace , ( Therefore, fuzzy sets It is expanded to have the following linear characteristics. :

[0154] (5)

[0155] In equation (5), f for joint probability distribution ( , , ), Include All functions defined above, yes The support set, in the following specific form:

[0156] (6)

[0157] The subsequent optimization model for the coordinated operation of rural photovoltaic and hydrogen energy storage systems based on this method is essentially... and The one that was constructed.

[0158] 2. Multi-objective function

[0159] (7)

[0160] (8)

[0161] Equation (7) is the system's economic objective function, with a two-layer structure: min{sup}. Besides the inner layer, the outer layer problem combines the predicted values ​​of photovoltaic output and electrical load to minimize the system's daily operating cost. Among these, / They represent Electricity / hydrogen prices in the electricity / hydrogen energy market during the period; express During the period, the rural photovoltaic and hydrogen energy storage combined system was Hydrogen sales volume during the specified time period; and These respectively represent the combined rural photovoltaic and hydrogen energy storage systems in Electricity purchase and sales volume during specific time periods; , , , , These represent the unit maintenance costs for the corresponding equipment; express Actual dispatched electrical energy of rural photovoltaic units during the specified time period; / These represent the penalty coefficients for wasted light / electric cutting loads, respectively. / They are Waste light volume / electric cutting load during the time period. Therefore, ( Operating costs for a given period include: the negative value of revenue generated from transactions between the system and the electricity market, i.e. The negative value of the revenue obtained from selling hydrogen to the hydrogen market, i.e. The operating and maintenance costs of photovoltaic equipment, i.e. The cost of abandoning light, i.e. The cost of power load shedding, i.e. The operating and maintenance costs of the electrolyzer, hydrogen compressor, and hydrogen fuel cell, i.e. , , .

[0162] Furthermore, to mitigate the impact of uncertainties in photovoltaic output and electrical load on system operation, the operation of some flexible resources (such as electrolyzers, hydrogen compressors, and hydrogen storage facilities) needs to be rescheduled based on multivariate forecast errors, thereby incurring operational adjustment costs. (The specific expression is given in detail later). Considering that the value and distribution of the prediction error are uncertain, the inner problem of this invention is in fuzzy sets. Find the expected value of the operating adjustment cost. The largest "worst" distribution. Therefore, the nesting of inner and outer problems, i.e., formula (1), indicates that in The goal is to minimize the expected daily operating cost of the system under the worst-case distribution.

[0163] Equation (8) is the method to minimize the carbon emissions of a rural photovoltaic and hydrogen energy storage combined system, i.e., the environmental target. Carbon emissions are indirectly caused by purchasing electricity from the upstream power grid. For every 1 sold to the upper-level power grid The mass of carbon dioxide emitted by the rural photovoltaic and hydrogen storage combined system. Since this invention studies the day-ahead scheduling optimization of the rural photovoltaic and hydrogen storage combined system, the adjustment of flexible resource operation due to uncertainty will not cause system and market transactions, and thus will not cause changes in the system's carbon emissions. Therefore, equation (8) is a Single-layer target structure.

[0164] 3. Constraints

[0165] Considering the interference of multiple uncertainties on the rural photovoltaic and hydrogen energy storage combined system, the model's constraints are constructed into a two-stage structure. In the first stage, this invention constructs constraints on equipment operation and multi-energy balance based on predicted information. In the second stage, this invention considers a support set that includes the random fluctuation range of prediction errors for both source and load sides, such as photovoltaic output and electrical load. Furthermore, constraints on rescheduling flexible resources (based on prediction errors) are added to ensure stable system operation and renewable energy consumption under various uncertainties. Given the different characteristics of each device, the present invention specifically establishes the following operation-related constraints.

[0166] (1) Constraints related to the first phase of operation

[0167] Based on photovoltaic output and electricity load forecasts , ( The equipment and multi-energy balance operation constraints of the rural photovoltaic and hydrogen energy storage combined system are as follows.

[0168] 1) Rural photovoltaic units

[0169] (9)

[0170] (10)

[0171] Equation (9) represents the power dispatch output of rural photovoltaic systems. and Reduction amount The relationship. Equation (10) represents the reduction of rural photovoltaic power. The upper and lower limits.

[0172] 2) Electrolytic cell

[0173] (11)

[0174] Equation (11) represents the electrical power utilized by the electrolytic cell. The upper and lower limits.

[0175] 3) Hydrogen compressor

[0176] (12)

[0177] Equation (12) represents the upper and lower limits of the electrical energy utilized by the hydrogen compressor.

[0178] 4) Hydrogen storage tank facilities

[0179] (13)

[0180] (14)

[0181] (15)

[0182] (16)

[0183] (17)

[0184] in, , It is a set of 0-1 variables, representing The hydrogen charging and discharging status of the hydrogen storage tank facility during a given time period; / and / These represent the maximum and minimum hydrogen mass values ​​for filling / discharging hydrogen into the hydrogen storage tank facility, respectively. yes The amount of hydrogen stored in the hydrogen storage tank facility at any given time; and Equation (13) represents the maximum and minimum hydrogen storage capacity in the hydrogen storage tank facility. Equation (14) ensures that the hydrogen charging and discharging processes do not occur simultaneously. Equations (15) represent the upper and lower limits for charging and discharging hydrogen in the hydrogen storage tank facility, respectively. Equation (16) represents the upper and lower limits for storing hydrogen in the hydrogen storage tank facility. To ensure the sustainable use of the energy storage facility, Equation (17) specifies the periodic requirements for the use of the hydrogen storage tank facility, namely, the amount of hydrogen stored in the hydrogen storage tank facility should be equal at the beginning and end of the scheduling cycle.

[0185] 5) Hydrogen fuel cells

[0186] (18)

[0187] (19)

[0188] in, This indicates the maximum ramp power of the hydrogen fuel cell; and These are the minimum and maximum discharge power of the hydrogen fuel cell. Equations (18) and (19) represent the ramp rate and capacity limit, respectively.

[0189] 6) Electricity market transaction constraints

[0190] (20)

[0191] (twenty one)

[0192] (twenty two)

[0193] in, , 0-1 variables represent The status of the system selling and purchasing electricity to the electricity market during specific time periods; This is the upper limit of the system's electricity purchase and sale. Equations (20) and (21) represent the restrictions on the system's purchase and sale of electricity in the electricity market. Equation (22) indicates that the system's purchase and sale of electricity cannot be carried out simultaneously.

[0194] 7) Trading constraints in the hydrogen energy market

[0195] (twenty three)

[0196] in, It represents the upper limit of hydrogen sales in the hydrogen energy market within a certain period.

[0197] 9) Electrically cut off load

[0198] (twenty four)

[0199] Equation (24) represents the upper and lower limits of the electric cutting load, respectively.

[0200] 10) Power balance constraints

[0201]

[0202] (25)

[0203] (26)

[0204] Equations (25) and (26) represent the system's electrical and hydrogen energy balance constraints, respectively.

[0205] (2) Constraints related to the operation of the second phase

[0206] Obviously, there are often errors between the predicted and actual values ​​of photovoltaic power output and electrical load. To combat the resulting multivariate uncertainties, the flexible resources (including electrolyzers, hydrogen storage facilities, hydrogen fuel power plants, curtailed photovoltaic power, and load shedding) within the rural photovoltaic and hydrogen storage combined system should be rescheduled to ensure stable system operation and renewable energy consumption. The following flexible resource adjustment (rescheduling) strategy based on multivariate linear affine regression is proposed:

[0207] (27)

[0208] (28)

[0209] (29)

[0210] (30)

[0211] (31)

[0212] (32)

[0213] in, , , , , , ( ) are respectively , , , , , ( The rescheduling results of ).

[0214] , , , ( ) respectively represent the corresponding , , , , , The adjustment rate. The rescheduling mechanism described by equations (27)-(32) is as follows: Taking equation (27) as an example, when an error occurs, the adjustment should be made according to the increment. Further, the first-stage scheduling results of the electrolyzers will be... Adjusted to Therefore, it is necessary to construct subsequent two-stage operational constraints to provide... The feasible space for flexible resource rescheduling under arbitrary error scenarios.

[0215] 1) Rural photovoltaic equipment

[0216] (33)

[0217] (34)

[0218] Equation (33) represents the actual photovoltaic dispatch output. Prediction error and the actual amount of photovoltaic reduction The transformation relationship is expressed in equation (34). The actual range.

[0219] 2) Electrolytic cell

[0220] (35)

[0221] (36)

[0222] Equation (32) indicates that after rescheduling the electrolytic cell according to Equation (35), It still needs to be within the corresponding upper and lower limits. Equation (36) gives the actual hydrogen production capacity of the electrolyzer. and The transformation relationship.

[0223] 3) Hydrogen compressor

[0224] (37)

[0225] (38)

[0226] (39)

[0227] After rescheduling the electrolyzer, equation (37) represents the actual mass of hydrogen produced by the electrolyzer. The actual mass of hydrogen compressed by the hydrogen compressor Equal. Equation (38) represents the actual electrical energy utilization of the hydrogen compressor. Compared with actual hydrogen compression The correspondence. Furthermore, The corresponding upper and lower bound constraints still need to be satisfied, as shown in equation (39).

[0228] 4) Hydrogen storage tank facilities

[0229] (40)

[0230] (41)

[0231] (42)

[0232] (43)

[0233] (44)

[0234] After rescheduling the hydrogen storage facilities, equation (40) represents the actual amount of hydrogen stored. How to base , Calculated and obtained; , and The corresponding upper and lower bound constraints still need to be met, as shown in equations (41)-(43); in addition, the utilization of hydrogen storage tank facilities still needs to meet the periodic requirements, as shown in equation (44).

[0235] 5) Hydrogen fuel cells

[0236] (45)

[0237] (46)

[0238] (47)

[0239] (48)

[0240] After rescheduling the hydrogen fuel cell, equations (45) and (46) represent the actual amount of hydrogen utilized. and the heat and electricity in actual production and The correspondence is shown in equations (47) and (48). The corresponding ramp rate and capacity limits still need to be met.

[0241] 6) Electrically cut off load

[0242] (49)

[0243] After rescheduling, the electrical load shedding expressed by equation (49) The actual range.

[0244] 7) Power balance constraints

[0245]

[0246] (50)

[0247] (51)

[0248] After the various flexible resources are rescheduled, equations (50)-(51) are used to ensure the actual power and airflow balance of the electric and hydrogen nodes in the system, respectively.

[0249] According to equations (27)-(51), the objective function in equation (7) The item can be further expanded as follows:

[0250] (52)

[0251] In summary, the proposed multi-objective distributed robust model for the operation and scheduling of the rural photovoltaic and hydrogen energy storage joint system is given by {Equation (7)-Equation (52)}.

[0252] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An optimization method for the coordinated operation of rural photovoltaic and hydrogen energy storage considering source-load uncertainty, characterized in that, The method is based on a rural photovoltaic and hydrogen energy storage combined system, which includes a variety of energy devices, which are divided into energy input devices, energy storage devices and energy conversion devices according to their specific functions. The method includes the following: S1. Taking into account the uncertainties of photovoltaic power output and electrical load, fully characterizing the uncertainties on both the source and load sides, constructing an uncertainty set, and designing an optimization model for the coordinated operation of rural photovoltaic and hydrogen energy storage; specifically including the following: Dividing a scheduling day into T time periods, and letting t be the time exponent, the prediction errors of photovoltaic output and electricity load are respectively represented as random vectors. = , = Error value , The calculation formula is as follows: (1) (2) in, , These are the predicted values ​​for photovoltaic power output and electrical load during time period t, respectively. , These represent the actual values ​​of photovoltaic power output and electrical load during time period t, respectively. make Then depict Uncertainty is equivalent to characterization , The joint uncertainty; based on this, the possible range of values ​​for the above uncertainty prediction error is constructed as a support set g, which is expressed by the following formula: (3) in, and They represent ( The minimum and maximum values ​​in the sample data; Furthermore, based on sample moment information, the possible range of the joint probability distribution f of the multivariate prediction error is constructed as a fuzzy set G, whose formula is expressed as: (4) Where P0(g) contains all functions defined on the support set g; E is the expectation operator; , express , The expected value is equal to the corresponding sample mean. , ; , express , The expected value of the absolute value of the deviation from the corresponding expected value shall not exceed the sample empirical value. , ( ); because , Due to its nonlinear characteristics, auxiliary variables are introduced respectively. , replace , ( Furthermore, the fuzzy set G is extended to H with linear characteristics, and its formula is expressed as follows: (5) Where f is The joint probability distribution, , , ; It contains all functions defined on h, where h is The support set, in its specific form, is as follows: (6) Based on the above H and h, an optimization model for the coordinated operation of a rural photovoltaic and hydrogen energy storage combined system is constructed. S2. Design a multi-objective function to minimize the daily operating cost of a rural photovoltaic and hydrogen energy storage combined system; S3. Construct the constraints of the rural photovoltaic and hydrogen energy storage collaborative operation optimization model. The constraints are designed as a two-stage structure to overcome the interference of multiple uncertainties on the rural photovoltaic and hydrogen energy storage joint system. The first stage is based on the prediction information to construct the constraints of equipment operation and multi-energy balance. The second stage is based on the constraint of flexible resource scheduling according to the prediction error to ensure the stable operation of the system and the consumption of renewable energy under multiple uncertainties. S4. Combining steps S1~S3, optimize the coordinated operation of rural photovoltaic and hydrogen energy storage.

2. The optimization method for the coordinated operation of rural photovoltaic and hydrogen energy storage considering source-load uncertainty as described in claim 1, characterized in that, S2 specifically includes the following: The multi-objective function is specifically: (7) (8) Equation (7) is the system's economic objective function, which has a two-layer structure min{sup}, including an outer layer problem and an inner layer problem; The outer layer problem combines the predicted values ​​of photovoltaic output and electrical load to minimize the daily operating cost of the system. / These represent the electricity and hydrogen prices in the electricity and hydrogen energy markets during time period t, respectively. This represents the amount of hydrogen sold by the rural photovoltaic and hydrogen storage combined system during time period t. and These represent the electricity purchased and sold by the rural photovoltaic and hydrogen energy storage combined system during time period t, respectively. , , , , These represent the unit maintenance costs for each piece of equipment; This represents the actual dispatched electrical energy of rural photovoltaic units during time period t; / These represent the penalty coefficients for wasted light / electric cutting loads, respectively. / These represent the amount of abandoned light and the electrical switching load during time period t, respectively. Therefore, the operating costs for period t include the negative value of the revenue obtained from transactions between the system and the electricity market. The negative revenue from selling hydrogen to the hydrogen market Operating and maintenance costs of photovoltaic equipment The cost of abandoning light Power load shedding penalty cost Operating and maintenance costs of electrolytic cells Operating and maintenance costs of hydrogen compressors and the operation and maintenance costs of hydrogen fuel cells ; The inner problem is in fuzzy sets Find the expected value of the operating adjustment cost in the middle. The largest "worst" distribution; Equation (7) represents the nesting of inner and outer problems, indicating that in The goal is to minimize the expected daily operating cost of the system under the worst-case distribution. Equation (8) is the objective function for the system's carbon emissions, which is a single-layer objective structure min; where, This refers to the mass of carbon dioxide emitted by a rural photovoltaic and hydrogen energy storage combined system for every 1 kWh of electricity sold by the upper-level power grid.

3. The optimization method for the coordinated operation of rural photovoltaic and hydrogen energy storage considering source-load uncertainty as described in claim 2, characterized in that, S3 specifically includes the following: (3.1) Constraints related to the first phase of operation: Based on photovoltaic output and electricity load forecasts , ( The equipment and multi-energy balance operation constraints of the rural photovoltaic and hydrogen energy storage combined system are as follows: 3.1.1) Constraints on rural photovoltaic units (9) (10) Equation (9) represents the power dispatch output of rural photovoltaic systems. and Reduction amount The relationship; Equation (10) represents the reduction of rural photovoltaic power. The upper and lower limits; 3.1.2) Electrolytic cell constraint (11) Equation (11) represents the electrical power utilized by the electrolytic cell. The upper and lower limits; 3.1.3) Hydrogen compressor constraints (12) Equation (12) represents the upper and lower limits of electrical energy utilization by the hydrogen compressor; 3.1.4) Constraints on hydrogen storage tank facilities (13) (14) (15) (16) (17) in, , is a set of 0-1 variables, representing the hydrogen charging and discharging status of the hydrogen storage tank facility at time t; / and / These represent the maximum and minimum hydrogen mass values ​​for filling / discharging hydrogen into the hydrogen storage tank facility, respectively. yes The amount of hydrogen stored in the hydrogen storage tank facility at any given time; and These represent the maximum and minimum hydrogen storage capacity in the hydrogen storage tank facility; Equation (13) is used to ensure that the charging and discharging of hydrogen do not occur simultaneously; Equations (14) and (15) represent the upper and lower limits of charging and discharging hydrogen in the hydrogen storage tank facility, respectively; Equation (16) represents the upper and lower limits of storing hydrogen in the hydrogen storage tank facility; Equation (17) is the periodic requirement for the utilization of the hydrogen storage tank facility, and the amount of hydrogen stored in the hydrogen storage tank facility is equal at the beginning and end of the scheduling cycle. 3.1.5) Constraints of Hydrogen Fuel Cells (18) (19) in, This indicates the maximum ramp power of the hydrogen fuel cell; and These are the minimum and maximum discharge power of the hydrogen fuel cell; Equations (18) and (19) represent the ramp rate and capacity limit, respectively; 3.1.6) Constraints on electricity market transactions (20) (21) (22) in, , 0-1 variables represent The status of the system selling and purchasing electricity to the electricity market during specific time periods; This is the upper limit for the system's electricity trading volume; Equations (20) and (21) represent restrictions on the system's purchase and sale of electricity from the electricity market; Equation (22) indicates that the system's purchase and sale of electricity cannot be carried out simultaneously. 3.1.7) Constraints on Hydrogen Energy Market Transactions (23) in, It represents the upper limit of hydrogen sales in the hydrogen energy market within a certain period. 3.1.8) Electrical load constraint (24) Equation (24) represents the upper and lower limits of the electrical cutting load; 3.1.9) Power Balance Constraints (25) (26) Equations (25) and (26) represent the system's electrical and hydrogen energy balance constraints, respectively. (3.2) Constraints related to the second phase of operation: To address the multivariate uncertainty interference caused by the discrepancy between predicted and actual values ​​of photovoltaic output and electrical load in rural photovoltaic-hydrogen energy storage combined systems, and to ensure stable system operation and renewable energy consumption, the following flexible resource adjustment strategy based on multivariate linear affine regression is proposed: (27) (28) (29) (30) (31) (32) in, , , , , , ( ) are respectively , , , , , ( The rescheduling result; , , , They respectively represent the corresponding , , , , , The adjustment rate, ; The rescheduling mechanism described by equations (27)-(32) is as follows: Taking equation (27) as an example, when an error occurs, according to the increment... Further, the first-stage scheduling results of the electrolyzers will be... Adjusted to The subsequent two-stage operational constraints are constructed to provide the feasible space for flexible resource rescheduling under any error scenario in H, specifically including: 3.2.1) Constraints on Rural Photovoltaic Equipment (33) (34) Equation (33) represents the actual photovoltaic dispatch output. Prediction error and the actual amount of photovoltaic reduction The transformation relationship; Equation (34) represents The actual scope; 3.2.2) Electrolytic cell constraints (35) (36) Equation (35) indicates that after rescheduling the electrolyzer, It remains between the corresponding upper and lower boundaries; Equation (36) represents the actual hydrogen production capacity of the electrolyzer. and The transformation relationship; 3.2.3) Hydrogen compressor constraints (37) (38) (39) After rescheduling the electrolyzer, equation (37) represents the actual mass of hydrogen produced by the electrolyzer. The actual mass of hydrogen compressed by the hydrogen compressor Equal; Equation (38) represents the actual electrical energy utilization of the hydrogen compressor. Compared with actual hydrogen compression The correspondence; Equation (39) represents It still satisfies the corresponding upper and lower bound constraints; 3.2.4) Constraints on hydrogen storage tank facilities (40) (41) (42) (43) (44) After rescheduling the hydrogen storage facilities, equation (40) represents the actual amount of hydrogen stored. and , The correspondence; equations (41)-(43) represent , and The corresponding upper and lower bound constraints are satisfied; Equation (44) indicates that the utilization of the hydrogen storage tank facility meets the periodic requirements; 3.2.5) Constraints of Hydrogen Fuel Cells (45) (46) (47) (48) After rescheduling the hydrogen fuel cell, equations (45) and (46) represent the actual amount of hydrogen utilized. and the heat and electricity in actual production and The correspondence; Equations (47) and (48) represent Meet the corresponding ramp rate and capacity limits; 3.2.6) Electrically disconnecting load (49) After rescheduling, equation (49) represents the load shedding. The actual scope; 3.2.7) Power Balance Constraints (50) (51) After the rescheduling of each flexible resource, equations (50)-(51) are used to ensure the actual power and airflow balance of the electric and hydrogen nodes in the system, respectively; According to equations (27)-(51), the objective function in equation (7) The item is further expanded as follows: (52) In summary, the proposed multi-objective distributed robust model for the operation and scheduling of the rural photovoltaic and hydrogen energy storage joint system is given by equations (7) to (52).

4. A computer device, characterized in that, The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, code set, or instruction set, and the instruction, program, code set, or instruction set is loaded and executed by the processor to implement the rural photovoltaic and hydrogen energy storage coordinated operation optimization method considering source-load uncertainty as described in any one of claims 1-3.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, at least one program, code set, or instruction set, which is loaded and executed by a processor to implement the rural photovoltaic and hydrogen energy storage collaborative operation optimization method considering source-load uncertainty as described in any one of claims 1-3.

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