Mixed-pumping photovoltaic multi-scale joint scheduling method and system for medium-and-long-term electric quantity decomposition

Abstract

The invention discloses a mixed pumping photovoltaic multi-scale joint scheduling method and system for medium-and-long-term electric quantity decomposition, and the method comprises the steps: building a mixed pumping-photovoltaic medium-and-long-term contract electric quantity decomposition model, building a corresponding mixed pumping-photovoltaic medium-and-long-term contract electric quantity decomposition model based on a mixed pumping power station and a photovoltaic power station, and solving the models, and obtaining a daily decomposition electric quantity boundary condition; based on the obtained daily decomposition electric quantity boundary conditions, a mixed pumping-photovoltaic combined system short-term scheduling model considering daily decomposition electric quantity constraints is established; and an improved NSGA-III optimization solving method is adopted to solve the constructed mixed pumping-photovoltaic combined system short-term scheduling model of the day-filtering decomposition electric quantity constraint, and mixed pumping photovoltaic multi-scale combined scheduling of medium and long-term electric quantity decomposition is realized based on an obtained result. According to the method, prediction-electric quantity decomposition-scheduling under different scales can be effectively joined, and key problems under different scales and solving ideas are provided.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid planning, and specifically relates to a hybrid photovoltaic multi-scale joint dispatching method and system for medium- and long-term electricity decomposition. Background Art

[0002] For hybrid pumped storage power stations (hereinafter referred to as "hybrid pumped power stations"), the implementation of hybrid pumped-photovoltaic integration can effectively improve the level of new energy consumption, greatly improve the utilization rate of transmission channels and the competitiveness of electricity prices. In the power market environment, hybrid pumped power stations and photovoltaic power stations jointly sign a monthly contract for the next month with the trading center and determine the transaction price. After the monthly contract for electricity is signed, the power generation side needs to formulate a decomposition plan to fulfill the contracted electricity. How to formulate a decomposition plan so that the power plant can complete the electricity contract and make the decomposition results effective and enforceable has become the difficulty of the contract electricity decomposition problem.

[0003] In the context of the deepening of the market-oriented reform of electricity, because the medium- and long-term contractual electricity of mixed pumping-photovoltaic must participate in the deviation electricity assessment, considering the monthly assessment to provide daily decomposed contractual electricity for the short-term day-ahead dispatch of mixed pumping-photovoltaic, how to combine the decomposed daily contractual electricity with the short-term optimized dispatch of reservoir photovoltaic has become a new difficulty. Specifically, there are two aspects of the problem: on the one hand, how to meet the daily decomposed contractual electricity constraints through mixed pumping-photovoltaic complementary dispatch under multiple scenarios of reservoir water inflow and photovoltaic output; on the other hand, how to effectively formulate the dispatch plan of the day-ahead mixed pumping-photovoltaic combined system and the units in the plant based on the daily decomposed electricity. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a hybrid photovoltaic multi-scale joint scheduling method and system with medium- and long-term electricity decomposition in response to the deficiencies in the above-mentioned prior art, which is used to solve the technical problem that hybrid photovoltaic joint scheduling cannot meet the needs in the new environment.

[0005] The present invention adopts the following technical solutions: The hybrid photovoltaic multi-scale joint dispatching method with medium- and long-term electricity decomposition includes the following steps: Establish a hybrid pumping-photovoltaic medium- and long-term contract electricity decomposition model. Based on the hybrid pumping power station and the photovoltaic power station, establish and solve the corresponding hybrid pumping-photovoltaic medium- and long-term contract electricity decomposition model to obtain the daily decomposition electricity boundary conditions; Based on the obtained daily power decomposition boundary conditions, a short-term scheduling model of the hybrid-photovoltaic combined system considering the daily power decomposition constraints is established; The optimized solution method of improved NSGA-III is used to solve the short-term scheduling model of hybrid-photovoltaic combined system with daily power consumption constraints. Based on the obtained results, multi-scale joint scheduling of hybrid-photovoltaic with medium- and long-term power consumption decomposition is realized.

[0006] Preferably, corresponding hybrid pumping-photovoltaic medium- and long-term contract electricity decomposition models are established based on the hybrid pumping power station and the photovoltaic power station, and the specific solutions are as follows: The hybrid power station and photovoltaic power station obtain trading electricity through the output forecast value of the next month, and carry out bundled transactions with the trading center to determine the monthly contract electricity; The hybrid power station and the photovoltaic power station respectively determine the planned power generation for each day of the month through the established monthly contract power decomposition model; After the day ends, the actual power generation is obtained, and by comparing it with the planned power generation, the daily deviation power is obtained; The deviation electricity is brought into the contract electricity correction model, and combined with the latest mixed-pumping-photovoltaic forecast information, the remaining daily planned electricity is corrected; The corrected value is brought into the scheduling model of the next day for scheduling, and the deviation electricity is calculated after the end of the day. The above steps are repeated until the end of the month to obtain the daily contract electricity decomposition results, daily scheduling results, and deviation electricity results.

[0007] Preferably, the monthly contract electricity decomposition model of the photovoltaic power station is as follows:

[0008] in, The photovoltaic power station is Daily power breakdown value; The photovoltaic power station is The daily electricity consumption forecast.

[0009] Preferably, the monthly contract electricity decomposition model of the mixed pumping power station is based on the goal of minimizing the water consumption of the mixed pumping power station within the month and minimizing the deviation between the actual final water level of the reservoir at the end of the month and the planned final water level. The objective function is as follows: The water consumption of the hydropower station is minimal during the decomposition period:

[0010] The deviation between the actual final water level of the hydropower station and the planned final water level is the smallest:

[0011] Among them, T is the decomposition period; The hydropower station is Daily water consumption, is the actual final water level of the hydropower station at the end of the month; It is the planned water level at the end of the month for the hydropower station.

[0012] Preferably, the constraints of the monthly contract electricity decomposition model of the mixed pumping power station are as follows: Power balance constraints:

[0013] in, It is a mixed pumping station. Daily power breakdown value; It is the monthly contractual electricity value of the mixed pumping power station; Water balance constraints:

[0014] in, The hydropower station is The storage capacity at the end of the day; The hydropower station is Daily inbound flow; The hydropower station is Daily power generation flow; is the time step; Power generation flow constraints:

[0015] in, The hydropower station is The lower limit of the daily power generation flow; The hydropower station is The upper limit of the daily power generation flow; Outbound traffic constraints:

[0016] in, The hydropower station is The lower limit of the daily outbound flow, m 3 / s; The hydropower station is The daily outbound flow limit; Power generation head constraints:

[0017] in, The hydropower station is Daily power generation head; The power station is Tailwater level of the day; The power station is Daily head loss; Output constraints:

[0018]

[0019] in, The power station is Average daily output; is the output coefficient of the hydropower station; The hydropower station is The water head of the day; The hydropower station is The lower limit of daily output; The hydropower station is The daily output limit; Water level constraints:

[0020]

[0021] in, The hydropower station is The lower limit of upstream water level at the end of the day; The hydropower station is The upper limit of the upstream water level at the end of the day; is the initial water level during the decomposition period; Water level and storage capacity constraints:

[0022] in, It is the water level and storage capacity relationship function of the hydropower station; Tailwater level discharge flow constraint:

[0023] in, is the tailwater level discharge function of the hydropower station.

[0024] Preferably, the deviation electricity is brought into the contract electricity correction model, and combined with the latest mixed-pumping-photovoltaic forecast information, the remaining daily planned electricity is corrected as follows: Set the deviation power assessment threshold; compare the daily completed power with the planned power to obtain the daily deviation power; calculate the Gini coefficient , and compare it with the set assessment value to determine whether it needs to be corrected; if no correction is needed, the result is output directly; if correction is needed, the planned electricity for the remaining days of the month is corrected based on the prediction error probability distribution and the deviation electricity.

[0025] Preferably, the short-term dispatch model of the hybrid extraction-photovoltaic joint system considering the daily decomposition power constraint is solved in two levels. The first level is the hybrid extraction-photovoltaic complementary multi-objective day-ahead dispatch model under the daily decomposition power constraint, and the second level is the economic dispatch model of the in-plant units. The objective function of the multi-objective day-ahead scheduling model of hybrid extraction and photovoltaic complementarity under the daily decomposed power constraint is as follows: Minimum daily water consumption:

[0026] in, yes The outflow of the hydropower station during the period; is the time step; is the total number of periods calculated; The deviation between the planned power and the contracted power of the water-solar hybrid system is minimal:

[0027] in, It is the daily contracted electricity of the hydropower-photovoltaic system; The hydropower station is The amount of electricity during the time period; The photovoltaic power station is The amount of electricity during the time period; Minimum photovoltaic abandonment:

[0028] in, is the abandoned light power of the photovoltaic power station at time t; The objective function of the economic dispatch model of the plant units is as follows: The water consumption of hydropower station is minimal:

[0029] in, yes Time unit At the water head The next output is The power generation flow at that time, the unit is m 3 / s; yes Time unit The net water head is in m; yes Time unit The output, in kW; It's the crew The startup water consumption; It's the crew Downtime water consumption; yes Time unit Start and stop state variables; is the total number of optimized hydropower station units.

[0030] Preferably, the constraints of the hybrid extraction-photovoltaic complementary multi-objective day-ahead scheduling model under the daily decomposed power constraint are as follows: Photovoltaic output constraints

[0031] in, It is The maximum value of photovoltaic output during the period; It is Photovoltaic output during the time period; is the installed capacity of photovoltaic power generation; Hydropower output constraints

[0032] in, It is The lower limit of hydropower output during the period; It is The lower limit of hydropower output during the period; Constraints on daily power generation contracts of combined power plants

[0033] in, It is the daily execution of the combined hydropower station obtained by solving; It is the daily power output deviation tolerance of the combined solar power station; Daily contract power constraints for hydropower stations

[0034] in, is the daily execution of the hydropower station obtained by decomposition; is the daily power output deviation tolerance of the hydropower station; Water balance constraints

[0035] in, is the storage capacity of the power station in period t; is the flow rate of the power station during period t; is the outflow flow of the power station in period t; Power generation flow constraints

[0036] in, The hydroelectric station is The lower limit of power generation flow in the time period; The hydroelectric station is The upper limit of power generation flow during the time period; Outbound flow constraints

[0037] in, The hydroelectric station is The lower limit of outbound flow in the time period; The hydroelectric station is The upper limit of outbound traffic during the time period; Power generation head constraint

[0038] in, The hydroelectric station is The generating head during the period; The hydroelectric station is Tailwater level of the time period; Output Constraint

[0039]

[0040] in, The hydroelectric station is Average output during the period; is the output coefficient of the hydropower station; The power station is The lower limit of output during the time period; The power station is Output limit for the time period; Water level constraint

[0041]

[0042] in, The hydroelectric station is The lower limit of the upstream water level at the end of the time period; The power station is The upper limit of the upstream water level at the end of the period; is the initial water level of the period; Water level storage capacity constraints

[0043] in, It is the water level and storage capacity relationship function of the power station; Tailwater level discharge flow constraint

[0044] in, is the tailwater level discharge function of power station i; The constraints of the economic dispatch model of the plant units are as follows: Output balance constraints:

[0045] in, It is in The hydropower station in the time period has planned to produce power a few days ago; Unit output constraints:

[0046] in, It is the minimum output of the hydropower station; It is the maximum output of the hydropower station; For hydropower stations The effort of the moment; Minimum start and stop constraints for units:

[0047]

[0048] in, For the crew exist The power-on signal at the moment; For the crew exist Shutdown signal at any time; The minimum startup time of the unit; The minimum downtime of the unit.

[0049] Preferably, the optimized solution method of improved NSGA-III is used to solve the short-term dispatch model of the hybrid-photovoltaic combined system with daily decomposed power constraints as follows: First, set the parameters and randomly initialize the population; Crossover, mutation, and fitness allocation; Fast non-dominated sorting merge population; Update the parent population with elite strategy; Based on non-uniform variation of reference points; When the environment does not change, if the termination condition is met, the result is output; if the termination condition is not met, the result is returned and crossover, mutation, and fitness allocation are repeated; When the environment changes, the Pareto frontier is predicted, the initial population is generated, and it is judged again whether the termination conditions are met and the results are output.

[0050] In a second aspect, an embodiment of the present invention provides a hybrid photovoltaic multi-scale joint dispatching system with medium- and long-term power decomposition, including: The solution module establishes a hybrid pumping-photovoltaic medium- and long-term contract electricity decomposition model. Based on the hybrid pumping power station and the photovoltaic power station, the corresponding hybrid pumping-photovoltaic medium- and long-term contract electricity decomposition model is established and solved to obtain the daily decomposition electricity boundary conditions; The construction module is used to establish a short-term dispatch model of the hybrid-PV combined system considering the daily decomposition power constraints based on the obtained daily decomposition power boundary conditions; The scheduling module uses the improved NSGA-III optimization solution method to solve the short-term scheduling model of the hybrid-photovoltaic combined system with daily power consumption constraints, and realizes the hybrid-photovoltaic multi-scale joint scheduling with medium- and long-term power consumption decomposition based on the obtained results.

[0051] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the hybrid photovoltaic multi-scale joint scheduling method with medium- and long-term electricity decomposition are implemented.

[0052] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned hybrid photovoltaic multi-scale joint scheduling method with medium- and long-term electricity decomposition.

[0053] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the hybrid photovoltaic multi-scale joint scheduling method with medium- and long-term electricity decomposition are implemented.

[0054] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned hybrid photovoltaic multi-scale joint scheduling method with medium- and long-term electricity decomposition.

[0055] Compared with the prior art, the present invention has at least the following beneficial effects: A hybrid photovoltaic multi-scale joint dispatch method with medium- and long-term electricity decomposition is proposed. Aiming at the connection problem between the medium- and long-term and short-term of hydro-photovoltaic joint dispatch, a medium- and long-term contract electricity decomposition model of hydropower and photovoltaic is established based on reservoir runoff and photovoltaic output forecast. Due to the randomness and uncertainty of photovoltaic output and the fact that photovoltaic output cannot be stored, a method for decomposing photovoltaic monthly contract electricity based on predicted values ​​is proposed; and since hydropower must take into account the economy and safety of hydropower operation, a monthly electricity decomposition model is established with the goal of minimizing the monthly water consumption of hydropower stations and minimizing the deviation between the actual end water level of the reservoir and the planned end water level at the end of the month. The proposed method improves the drawbacks of the traditional average allocation method, realizes the effective decomposition of transaction electricity from month to day, and proposes a correction strategy based on the Gini coefficient theory to solve the problem of deviation between actual power generation and planned power generation, so that the monthly decomposition electricity is more executable. Aiming at the limitation of monthly contract electricity on short-term dispatch of hydro-photovoltaic joint dispatch and considering the problem of photovoltaic absorption, this paper proposes a hydro-photovoltaic short-term dispatch model based on daily decomposition electricity. Firstly, a day-ahead water-solar joint scheduling model was established with the goal of minimizing reservoir water consumption, minimizing daily contract electricity deviation and maximizing photovoltaic consumption. Secondly, an economic scheduling model of the in-plant units was established based on the optimized output curve, and solved by the NSGA-III algorithm.

[0056] Furthermore, after the hydro-solar bundled transaction, the monthly contract electricity of hydro-solar needs to be decomposed into days, so as to carry out short-term optimization scheduling within the day. The decomposition process of monthly contract electricity is the hub connecting multiple time scales. It can not only provide key information for day-ahead optimization scheduling, but also realize the rolling correction of deviation within the month according to the feedback results of daily deviation electricity. The short-time scale electricity in the hydro-solar joint operation mode is to decouple the monthly electricity by relying on the medium-term scale electricity decomposition. The actual operation results of the short-term time scale will feedback and correct the medium-term scale contract electricity decomposition process.

[0057] Furthermore, for hydropower stations, the purpose of formulating a monthly contract electricity decomposition model is to arrange the daily power generation plan of the hydropower station under the condition of satisfying various constraints of the reservoir so that it meets the economic optimal goal. However, when decomposing the monthly contract electricity of the hydropower station, it cannot be directly decomposed according to the daily runoff forecast curve. This is because the reservoir can store incoming water. On the other hand, the reservoir has its own reservoir scheduling plan. If it is decomposed according to the daily runoff forecast curve, the result may not be the optimal value for the economic operation of hydropower. The economic operation of hydropower is usually the lowest cost of hydropower generation, which is mainly reflected in the minimum unit water consumption. During the monthly decomposition period, the monthly total power generation and the reservoir water level at the beginning and end of the month are generally given. However, during the decomposition process, the actual water level at the end of the month has a probability of deviating from the planned water level, which may affect the subsequent water storage and power generation plans of the reservoir. In summary, this paper decomposes the monthly contract electricity with the goal of minimizing the monthly water consumption of the hydropower station and minimizing the deviation between the actual water level at the end of the month and the planned water level at the end of the month.

[0058] Furthermore, the setting of constraint conditions ensures that in addition to the optimization goal: the objective function, the boundary conditions that various hydropower or reservoirs need to meet are also met during the entire optimization scheduling process.

[0059] Furthermore, since there is a deviation between the daily actual values ​​and the predicted values ​​of hydropower and photovoltaics, and there is also a deviation between the actual daily completed electricity volume of hydropower and photovoltaics and the planned completed contractual electricity volume, it is necessary to make rolling revisions to the contractual electricity volume completion plan for the remaining days of the month after the end of each day, and rearrange the power generation plan of hydropower and photovoltaic power stations based on the latest forecast results and the deviation from the completed electricity volume of the previous day.

[0060] Furthermore, the first level is a multi-objective day-ahead scheduling model of hydro-photovoltaic complementarity under the daily decomposed electricity constraint. The reservoir is scheduled according to the reservoir inflow to cooperate with the photovoltaic output to generate electricity to meet the daily decomposed contract electricity constraint. The different power generation characteristics of different power stations and the executability of the decomposition results are taken into account, and the electricity decomposed to the day is further decomposed to the hour; the second level is the economic scheduling model of the in-plant units, which further decomposes the scheduling results decomposed to the hour at the first-level reservoir level to the in-plant units. While optimizing the load distribution between the units of the hydropower station, the economic operation of the hydropower station must also be taken into account. The connection and coupling of the two models maximizes the executability of the daily decomposed electricity results.

[0061] Furthermore, the setting of constraint conditions ensures that in addition to the optimization goal: the objective function, the boundary conditions that various hydropower or reservoirs need to meet are also met during the entire optimization scheduling process.

[0062] Furthermore, NSGA-III was improved from two aspects: structured reference point set and population adaptive normalization, forming a dynamic multi-objective optimization algorithm. As the environment changes, the corresponding initial population will also be updated, and the process is affected by the Pareto frontier prediction strategy. As the population evolves, some individuals in the population undergo non-uniform mutation according to the adaptive non-uniform mutation strategy, making the population more diverse and converging faster.

[0063] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0064] In summary, the present invention can effectively connect "forecasting-power decomposition-dispatching" at different scales, and puts forward key issues and solutions at different scales.

[0065] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a flowchart of the long-term contract electricity decomposition framework proposed by the present invention; Figure 2 Schematic diagram for model solution; Figure 3 This is a schematic diagram of the mid- and long-term contract electricity completion curve based on the Gini coefficient; Figure 4 It is the reference point distribution map of NSGA-III algorithm; Figure 5 It is the flow chart of the improved NSGA-III multi-objective optimization algorithm; Figure 6 This is the hybrid extraction-photovoltaic multi-scale combination proposed by the present invention that takes into account the decomposition of medium- and long-term contract electricity; Figure 7 A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 8 The block diagram of a chip provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0069] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0070] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0071] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0072] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0073] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0074] The present invention provides a hybrid photovoltaic multi-scale joint dispatching method with medium- and long-term electricity decomposition. First, a hybrid-photovoltaic medium- and long-term contract electricity decomposition model is established, and different decomposition models are established for hybrid power stations and photovoltaic power stations for solving. The photovoltaic electricity decomposition model is mainly decomposed based on the predicted value, and the hybrid power station also needs to take into account the economy and safety of operation. Therefore, the decomposition is carried out with the minimum water consumption within the month and the minimum deviation of the reservoir water level at the end of the month as the goal. However, in the actual operation process, due to the inaccuracy of photovoltaic and hydropower predictions and certain uncontrollable factors in the operation process, it is inevitable that there will be deviations between the planned electricity and the actual electricity of photovoltaic power stations and hydropower stations. In order to reduce this part of the deviation electricity, a deviation correction strategy based on the Gini coefficient theory is proposed to correct the planned electricity, so that the medium- and long-term contract electricity decomposition is more effective and executable. Secondly, a short-term dispatching model of a hybrid-photovoltaic joint system considering the daily decomposition electricity constraint is established. The mixed pumping-photovoltaic medium- and long-term contract electricity decomposition model is divided into two levels. The first level is the mixed pumping-photovoltaic complementary multi-objective day-ahead scheduling model under the daily decomposition electricity constraint. According to the reservoir water volume scheduling, the mixed pumping power station cooperates with the photovoltaic output to generate electricity to meet the daily decomposition contract electricity constraint. Considering the power generation characteristics and decomposition results of different power stations, the electricity that is gradually decomposed to the day will be further decomposed to the hour. The second level is the in-plant unit economic scheduling model, which further decomposes the scheduling results of the first-level power station level to the in-plant units, while optimizing the load distribution between the units of the mixed pumping power station, it also takes into account its economic operation. The connection and coupling of the two models maximizes the executability of the daily decomposition electricity results.

[0075] See also Figure 6At the long-term time scale level, there are two main issues to consider: one is the determination of the electricity volume of the water, wind and solar power trading contracts, and the other is the long-term dispatch plan of hydropower. In order to determine the annual electricity volume of hydropower and the long-term dispatch plan of hydropower, a long-term dispatch plan of hydropower is formulated through the long-term runoff forecast of hydropower, and the water level boundary is determined for the medium-term scale electricity decomposition and the short-term scale optimization dispatch, and the monthly power generation of hydropower is predicted; the electricity forecast information of wind power and photovoltaic power can be obtained through historical data, so as to carry out annual electricity trading. This electricity volume will be the assessment electricity volume, and the actual power generation will be compared with it during settlement.

[0076] At the medium-term scale level: In order to effectively connect the results of medium- and long-term market transactions with short-term optimization scheduling, it is necessary to decompose the annual contract electricity. Electricity decomposition is a key step in providing the daily shared electricity of water, wind and solar power for short-term day-ahead optimization scheduling, and is a way to decouple electricity from medium- and long-term to short-term. In terms of hydropower, the daily runoff forecast data obtained from the medium-term forecast of hydropower is used to obtain the daily shared contract electricity of hydropower through the monthly contract electricity decomposition model of the hydropower station, and the hydropower station formulates a day-ahead optimization scheduling plan based on this electricity; for wind power and photovoltaic power generation, the daily shared contract electricity is obtained through the contract electricity decomposition model based on the daily wind and solar output forecast data of the medium-term forecast. At the medium-term level, there is another important link, which is the correction and re-decomposition of the contract electricity. After receiving the short-term scale deviation electricity feedback, it is necessary to correct the remaining daily contract electricity of the month. After re-forecasting the daily runoff of hydropower and the daily power generation of wind and solar power, the remaining daily contract electricity is re-decomposed, and the new daily electricity constraint is output to the day-ahead scheduling optimization link of the new day.

[0077] At the short-term level: the day-ahead optimization scheduling is carried out based on the daily decomposition contract electricity of hydropower and new energy and the short-term forecast results. The short-term forecast includes the hourly forecast of water runoff and the hourly power forecast of new energy. Due to the uncertainty factors of new energy and the peak-shaving demand of the system, the pumped storage unit will change the operation mode and unit output according to the actual situation. At this time, the actual power generation will deviate from the planned power generation on the day before. After calculating the deviation, the daily deviation electricity of the day is obtained. In order to meet the deviation assessment of the monthly settlement, it is necessary to process the daily deviation electricity and feed it back to the medium-term scale electricity decomposition link, re-correct the contract electricity of the remaining days and decompose the electricity, form the deviation rolling within the month, and correct the decomposition results of the monthly contract electricity of the medium-term scale. According to the deviation rolling correction, the actual monthly power generation is finally obtained, and the deviation assessment is carried out accordingly.

[0078] The present invention provides a hybrid photovoltaic multi-scale joint dispatching method for medium- and long-term power decomposition, comprising the following steps: S1. Establish a hybrid pumping-photovoltaic medium- and long-term contract electricity decomposition model. Based on the hybrid pumping power station and the photovoltaic power station, establish and solve the corresponding hybrid pumping-photovoltaic medium- and long-term contract electricity decomposition model respectively; See also Figure 1 , including the following steps: S101, hybrid power stations and photovoltaic power stations obtain trading electricity through the output forecast value of the next month, and carry out bundled transactions with the trading center to determine the monthly contract electricity; S102, the hybrid power station and the photovoltaic power station respectively determine the planned power generation for each day of the month through the established monthly contract power decomposition model; 1) Monthly contract electricity decomposition model for photovoltaic power stations Generally speaking, the monthly contracted electricity quantity signed by a photovoltaic power station is decomposed based on the predicted daily electricity quantity curve, and the impact of weather changes and changes in the actual operating conditions of the system are considered, and then corrections are made; the specific formula is as follows: (1) in, The photovoltaic power station is Daily power breakdown value; The photovoltaic power station is The daily electricity consumption forecast.

[0079] 2) Monthly contract electricity decomposition model for hybrid power stations For mixed pumping power stations, the purpose of formulating a monthly contract electricity decomposition model is to arrange the daily power generation plan of the mixed pumping power station under the condition of satisfying various constraints of the reservoir so that it meets the economic optimal goal. However, when decomposing the monthly contract electricity of the mixed pumping power station, it is impossible to refer to the decomposition method of the monthly contract electricity of the photovoltaic power station, that is, it is impossible to decompose directly according to the daily runoff prediction curve. This is because photovoltaics cannot store, while reservoirs can store incoming water. On the other hand, reservoirs have their own reservoir scheduling plans. If decomposition is performed according to the daily runoff prediction curve, the result may not be the optimal value for mixed pumping economic operation. Mixed pumping economic operation usually has the lowest mixed pumping power generation cost, which is mainly reflected in the minimum unit water consumption. During the monthly decomposition period, the total monthly power generation and the reservoir water level at the beginning and end of the month are generally given. However, during the decomposition process, the actual water level at the end of the month has a probability of deviating from the planned water level at the end, which may affect the subsequent water storage, power generation and other plans of the reservoir.

[0080] In summary, the monthly contract electricity decomposition will be carried out with the goal of minimizing the water consumption of the mixed pumping power station within the month and minimizing the deviation between the actual and planned final water levels of the reservoir at the end of the month.

[0081] Objective Function Objective function 1: Minimize water consumption during the hydropower station decomposition period (2) Among them, T is the decomposition period; The hydropower station is Daily water consumption.

[0082] Objective function 2: The deviation between the actual final water level of the hydropower station and the planned final water level is minimized (3) in, is the actual final water level of the hydropower station at the end of the month; It is the planned water level at the end of the month for the hydropower station.

[0083] Constraints (1) Power balance constraints (4) in, It is a mixed pumping station in Daily power breakdown value; It is the monthly contract electricity value of the mixed pumping power station.

[0084] (2) Water balance constraints (5) in, The hydropower station is Storage capacity at the end of the day, m 3 ; The hydropower station is Daily inflow, m 3 / s; The hydropower station is Daily power generation flow, m 3 / s; is the time step.

[0085] (3) Power generation flow constraints (6) in, The hydropower station is The lower limit of daily power generation flow, m 3 / s; The hydropower station is The upper limit of the daily power generation flow, m 3 / s.

[0086] (4) Outbound flow constraints (7) in, The hydropower station is The lower limit of the daily outbound flow, m 3 / s; The hydropower station is The daily outbound flow limit, m 3 / s.

[0087] (5) Power generation head constraints (8) in, The hydropower station is Daily power generation head, m; The power station is Tailwater level of the day, m; The power station is Daily head loss, m.

[0088] (6) Output constraints (9) (10) in, The power station is Average daily output; is the output coefficient of the hydropower station; The hydropower station is The water head of the day; The hydropower station is The lower limit of daily output; The hydropower station is The maximum output per day.

[0089] (7) Water level constraints (11) (12) in, The hydropower station is The lower limit of upstream water level at the end of the day; The hydropower station is The upper limit of the upstream water level at the end of the day; is the initial water level during the decomposition period.

[0090] (8) Water level and storage capacity constraints (13) in, It is the water level and storage capacity relationship function of the hydropower station.

[0091] (9) Tailwater level discharge flow constraint (14) in, is the tailwater level discharge function of the hydropower station.

[0092] Solution steps The decision variables of this model are the water level at the end of the period of the mixed pumping power station and the power consumption of the period contract. The solution process is as follows: Figure 2 As shown: Input relevant parameters of the hydropower station and set the algorithm operation parameters; Generate an initial population; Individual repair operations; Individual evaluation, calculation of affinity; Distinguish between dominant individuals and ordinary individuals; Dominant individual cloning operation; Mutation operations of hybrid EDA; Ordinary individuals are degraded and new individuals are added; Unique processing, calculation of individual affinity; If the iteration termination condition is not met, return to re-divide the dominant individuals and ordinary individuals; if the iteration termination condition is met, end.

[0093] S103, after the end of the day (i), the actual power generation of the day is obtained, and the daily deviation power is obtained by comparing it with the planned power generation; The specific correction strategies based on the Gini coefficient theory are: The Gini coefficient was originally proposed by American economists as an indicator to measure the fairness of income distribution, and was later widely used in many industries. In the electricity market, power plants need to generate electricity based on the electricity contracts signed with the trading center. However, in the actual scheduling process, there will inevitably be deviations between the actual daily electricity completed and the planned electricity. This will result in under-generation or over-generation of power plants during the monthly settlement at the end of the month. At this time, the power plant will face monthly assessments, and when the under-generation or over-generation exceeds the threshold, there will be penalties. Therefore, in the process of monthly electricity decomposition, it is necessary to consider the deviation electricity and formulate a deviation electricity correction strategy. The medium- and long-term contract electricity completion curve based on the Gini coefficient is as follows: Figure 3 shown.

[0094] Figure 3 In the figure, the blue curve is the mid- to long-term contract power decomposition curve, and the orange curve is the actual power completion curve. According to the definition of the Gini coefficient, the deviation power is the difference between the contract power decomposition curve and the actual power completion curve. As shown, the Gini coefficient The calculation is as follows: (15) From formula (15), we can see that the Gini coefficient Used to characterize the completion of the contracted electricity volume, the smaller the Gini coefficient is, the smaller the deviation in electricity volume is, and vice versa.

[0095] S104, bringing the deviation electricity into the contract electricity correction model, and combining the latest mixed-pumping-photovoltaic forecast information to correct the remaining daily planned electricity; Formulate a contract power correction strategy based on the Gini coefficient. The specific ideas are as follows: First, set the deviation power assessment threshold; Then compare the daily completed power with the planned power to get the daily deviation power; Calculating the Gini coefficient , and compare it with the set assessment value to determine whether correction is needed; If no correction is required, the result is output directly; If correction is needed, the planned electricity consumption for the remaining days of the month will be corrected based on the forecast error probability distribution and the deviation electricity consumption.

[0096] The calculation formula is as follows: (16) (17) in, is the prediction error; is the deviation charge; It is the time period The actual dispatch plan contract completed electricity; It is the time period Decomposed contract electricity; Is the period under positive forecast error Correction value of actual contract completed electricity quantity; is the period with negative forecast error The revised value of the actual contract completed electricity.

[0097] S105, bringing the corrected value into the scheduling model of the next day for scheduling, and calculating the deviation power after the end of the day, and repeating the above steps until the end of the month (T).

[0098] S2. Establish a short-term dispatch model for the hybrid-photovoltaic combined system considering the daily power consumption constraints; The short-term dispatch model of the hybrid-photovoltaic combined system considering the daily decomposed electricity constraint is solved in two levels. The first level is the hybrid-photovoltaic complementary multi-objective day-ahead dispatch model under the daily decomposed electricity constraint, and the second level is the economic dispatch model of the in-plant units.

[0099] 1) Multi-objective day-ahead scheduling model of hybrid extraction and photovoltaic complementarity under daily power consumption constraints Objective Function Objective function 1: Minimum daily water consumption (18) in, yes The outflow of the hydropower station during this period, in m 3 / s; is the time step; is the total number of periods calculated.

[0100] Objective function 2: Minimize the deviation between the planned power and the contracted power of the water-solar hybrid system (19) in, is the daily contracted electricity of the hydropower-photovoltaic system, in kWh; The hydropower station is The power consumption during the time period, in kWh; The photovoltaic power station is The electricity consumption during the time period, in kWh.

[0101] Objective function 3: Minimize the amount of photovoltaic power abandonment (20) in, It is the abandoned light power of the PV power station at the tth moment, in kW.

[0102] The constraints are as follows: (1) Photovoltaic output constraints (twenty one) in, It is The maximum value of photovoltaic output during the period, in kW; It is Photovoltaic output during the period, in kW; is the installed capacity of photovoltaic power generation, in kW.

[0103] (2) Hydropower output constraints (twenty two) in, It is The lower limit of hydropower output during the period, in kW; It is The lower limit of hydropower output during a period, in kW.

[0104] (3) Constraints on daily contractual electricity volume of combined power plants (twenty three) in, is the daily performance of the combined hydropower and solar power plant obtained by solving the problem, and the unit of electricity is kWh; It is the daily electricity output deviation tolerance of the combined solar power plant, which is determined based on the completion of the power plant contract and is 3.0%.

[0105] (4) Daily contractual power consumption constraints of hydropower stations (twenty four) in, It is the daily execution of the hydropower station obtained by decomposition, and the unit of electricity is kWh; It is the daily electricity output deviation tolerance of the hydropower station, which is determined based on the completion of the hydropower station contract and is 3.0%.

[0106] (5) Water balance constraints (25) in, is the storage capacity of the power station in period t, in m 3 ; is the flow rate of the power station during period t, in m 3 / s; is the outflow flow of the power station in period t, in m 3 / s.

[0107] (6) Power generation flow constraints (26) in, The hydroelectric station is The lower limit of the power generation flow in the time period, in m 3 / s; The hydroelectric station is The upper limit of power generation flow in the time period, in m 3 / s.

[0108] (7) Outbound flow constraints (27) in, The hydroelectric station is The lower limit of the outbound flow rate in the time period, in m 3 / s; The hydroelectric station is The upper limit of the outbound flow rate in the time period, in m 3 / s.

[0109] (8) Power generation head constraints (28) in, The hydroelectric station is The power generation head of the time period, in m; The hydroelectric station is Tail water level of the time period, in meters.

[0110] (9) Output constraints (29) (30) in, The hydroelectric station is The average output of the time period, in kW; is the output coefficient of the hydropower station; The power station is The lower limit of output during the time period, in kW; The power station is The output limit of the time period, in kW.

[0111] (10) Water level constraints (31) (32) in, The hydroelectric station is The lower limit of the upstream water level at the end of the time period, in meters; The power station is The upper limit of the upstream water level at the end of the time period, in meters; is the initial water level of the period.

[0112] (11) Water level and storage capacity constraints (33) in, It is the water level and storage capacity relationship function of the power station.

[0113] (12) Tailwater level discharge flow constraint (34) in, is the tailwater level discharge function of power station i.

[0114] 2) Economic dispatch model of in-plant units The day-ahead planned output curve obtained by the hydropower-solar complementary multi-objective day-ahead scheduling model under the daily decomposed electricity constraint is allocated to each unit through the in-plant unit economic scheduling model. Taking into account various factors such as the unit start-up and shutdown sequence, the unit start-up and shutdown time, and ensuring the safe and stable operation of the power station and the units, the optimal start-up and shutdown state combination of the units and the optimal load distribution strategy between the started units are formulated to minimize the water consumption of the hydropower station during the scheduling period.

[0115] The specific objective function and constraints are as follows: Objective Function From the perspective of water conservation and power generation, taking into account the contracted power of each unit of the hydropower station, the specific objective function is as follows: Objective function 1: Minimize water consumption of hydropower station (35) in, yes Time unit At the water head The next output is The power generation flow at that time, the unit is m 3 / s; yes Time unit The net water head is in m; yes Time unit The output, in kW; It's the crew The startup water consumption; It's the crew Downtime water consumption; yes Time unit Start and stop state variables; is the total number of optimized hydropower station units.

[0116] The constraints are as follows: In addition to the basic reservoir constraints in the hydropower-solar complementary multi-objective day-ahead dispatch model under the daily contract power constraints, the following constraints are also included in the economic dispatch model of the plant units: (1) Output balance constraints: (36) in, It is in The hydropower station in this period is scheduled to start output a few days ago.

[0117] (2) Unit output constraints: (37) in, It is the minimum output of the hydropower station; It is the maximum output of the hydropower station; For hydropower stations The effort of the moment.

[0118] (3) Minimum start and stop constraints of the unit: (38) (39) in, For the crew exist The power-on signal (action) at the moment, 1-power-on, 0-power-off; For the crew exist Shutdown signal (action) at the moment, 1-shutdown, 0-no shutdown; The minimum startup time of the unit; The minimum downtime of the unit.

[0119] S3. Use the improved NSGA-III optimization solution method to solve the hybrid-photovoltaic combined system short-term scheduling model with daily power consumption constraints constructed in step S2, and realize hybrid-photovoltaic multi-scale joint scheduling with medium- and long-term power consumption decomposition based on the obtained results.

[0120] 1) Basic principles of NSGA-III algorithm NSGA-III implements the elite retention mechanism and fitness classification mechanism based on NSGA-II, but in order to enrich the diversity of solutions and ensure their uniformity, the reference points are set in advance, and the reference points can be generated by themselves through structured or determined by the decision maker according to his will. The distribution of reference points, for the optimization problem with three objectives, has four objective axes and a total of fifteen reference points, such as Figure 4 shown.

[0121] Depend on Figure 4 It can be seen that for the selection of Pareto solution sets, the general approach is to give priority to solutions near the reference point to ensure that the solution set is evenly distributed, but for more complex targets, it is difficult to ensure the diversity of the solution set. This can be solved by introducing the Pareto frontier prediction strategy, which is based on structured reference points and combines current group information to establish a time series, which can improve the convergence speed and make the population richer.

[0122] The strategy proceeds as follows: Confirm the reference point in the current population and find the individual with the shortest distance as the population Pm, so that the population mutates non-uniformly based on the non-uniform mutation strategy. The individuals in the population Pm mutate in the following way: (39) in, is a random quantity, the variation range is related to the number of generations, and depends on λ, which is 0.35 in this paper. r is set to 0.5, and the maximum number of generations is 100.

[0123] 2) Improved NSGA-III method NSGA-III is improved from two aspects: structured reference point set and population adaptive normalization, forming a dynamic multi-objective optimization algorithm. As the environment changes, the corresponding initial population will also be updated, and the process is affected by the Pareto frontier prediction strategy. As the population evolves, some individuals in the population perform non-uniform mutation according to the adaptive non-uniform mutation strategy, making the population more diverse and converging faster. The specific steps are as follows: Figure 5 As shown: First, set the parameters and randomly initialize the population; Crossover, mutation, and fitness allocation; Fast non-dominated sorting merge population; Update the parent population with elite strategy; Based on non-uniform variation of reference points; When the environment does not change, if the termination condition is met, the result is output; if the termination condition is not met, the result is returned and crossover, mutation, and fitness allocation are repeated; When the environment changes, the Pareto frontier is predicted, the initial population is generated, and it is judged again whether the termination conditions are met and the results are output.

[0124] In another embodiment of the present invention, a hybrid photovoltaic multi-scale joint dispatching system with medium- and long-term electricity decomposition is provided. The system can be used to implement the above-mentioned hybrid photovoltaic multi-scale joint dispatching method with medium- and long-term electricity decomposition. Specifically, the hybrid photovoltaic multi-scale joint dispatching system with medium- and long-term electricity decomposition includes a solution module, a construction module and a dispatching module.

[0125] Among them, the solution module establishes a hybrid pumping-photovoltaic medium- and long-term contract electricity decomposition model, and establishes and solves the corresponding hybrid pumping-photovoltaic medium- and long-term contract electricity decomposition models based on the hybrid pumping power station and the photovoltaic power station, and obtains the daily decomposition electricity boundary conditions; The construction module is used to establish a short-term dispatch model of the hybrid-PV combined system considering the daily decomposition power constraints based on the obtained daily decomposition power boundary conditions; The scheduling module uses the improved NSGA-III optimization solution method to solve the short-term scheduling model of the hybrid-photovoltaic combined system with daily power consumption constraints, and realizes the hybrid-photovoltaic multi-scale joint scheduling with medium- and long-term power consumption decomposition based on the obtained results.

[0126] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the hybrid photovoltaic multi-scale joint scheduling method with medium- and long-term power decomposition, including: A hybrid-pumping-photovoltaic medium- and long-term contract electricity decomposition model is established. Based on the hybrid-pumping power station and the photovoltaic power station, the corresponding hybrid-pumping-photovoltaic medium- and long-term contract electricity decomposition models are established and solved to obtain the daily decomposition electricity boundary conditions; based on the obtained daily decomposition electricity boundary conditions, a hybrid-pumping-photovoltaic combined system short-term scheduling model considering the daily decomposition electricity constraints is established; the constructed hybrid-pumping-photovoltaic combined system short-term scheduling model considering the daily decomposition electricity constraints is solved by the improved NSGA-III optimization solution method, and the hybrid-pumping-photovoltaic multi-scale joint scheduling with medium- and long-term electricity decomposition is realized based on the obtained results.

[0127] See also Figure 7 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for calculating the fluid composition in the reservoir transformation wellbore in the embodiment is realized. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the hybrid photovoltaic multi-scale joint dispatching system with long-term electricity decomposition in the embodiment are realized. To avoid repetition, it is not described one by one here.

[0128] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 7This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0129] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0130] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0131] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0132] See also Figure 8 , the terminal device is a chip, and the chip 600 of this embodiment includes a processor 622, which may be one or more, and a memory 632 for storing a computer program executable by the processor 622. The computer program stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. In addition, the processor 622 may be configured to execute the computer program to execute the above-mentioned hybrid photovoltaic multi-scale joint scheduling method for medium- and long-term electricity decomposition.

[0133] In addition, the chip 600 may further include a power supply component 626 and a communication component 650, wherein the power supply component 626 may be configured to perform power management of the chip 600, and the communication component 650 may be configured to implement communication, for example, wired or wireless communication, of the chip 600. In addition, the chip 600 may further include an input / output interface 658. The chip 600 may operate based on an operating system stored in the memory 632.

[0134] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs. It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-VolatileMemory), such as at least one disk memory.

[0135] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the hybrid photovoltaic multi-scale joint scheduling method for medium- and long-term electricity decomposition in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows: A hybrid-pumping-photovoltaic medium- and long-term contract electricity decomposition model is established. Based on the hybrid-pumping power station and the photovoltaic power station, the corresponding hybrid-pumping-photovoltaic medium- and long-term contract electricity decomposition models are established and solved to obtain the daily decomposition electricity boundary conditions; based on the obtained daily decomposition electricity boundary conditions, a hybrid-pumping-photovoltaic combined system short-term scheduling model considering the daily decomposition electricity constraints is established; the constructed hybrid-pumping-photovoltaic combined system short-term scheduling model considering the daily decomposition electricity constraints is solved by the improved NSGA-III optimization solution method, and the hybrid-pumping-photovoltaic multi-scale joint scheduling with medium- and long-term electricity decomposition is realized based on the obtained results.

[0136] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0137] In summary, the present invention provides a hybrid-pumping photovoltaic multi-scale joint scheduling method and system for medium- and long-term electricity decomposition, establishes a hybrid-pumping photovoltaic medium- and long-term contract electricity decomposition model, and makes the medium- and long-term contract electricity decomposition more effective and executable. A hybrid-pumping photovoltaic joint system short-term scheduling model considering daily decomposition electricity constraints is established. The first level is a hybrid-pumping photovoltaic complementary multi-objective day-ahead scheduling model under daily decomposition electricity constraints, and the second level is an in-plant unit economic scheduling model. The connection and coupling of the two models maximizes the executability of the daily decomposition electricity results.

[0138] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0139] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0140] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0141] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0142] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0144] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0145] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0146] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0148] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A hybrid photovoltaic multi-scale joint dispatching method with medium- and long-term electricity decomposition, characterized by: The following steps are involved: Establish a hybrid pumping-photovoltaic medium- and long-term contract electricity decomposition model. Based on the hybrid pumping power station and the photovoltaic power station, establish and solve the corresponding hybrid pumping-photovoltaic medium- and long-term contract electricity decomposition model to obtain the daily decomposition electricity boundary conditions; Based on the obtained daily power decomposition boundary conditions, a short-term scheduling model of the hybrid-photovoltaic combined system considering the daily power decomposition constraints is established; The optimized solution method of improved NSGA-III is used to solve the short-term scheduling model of hybrid-photovoltaic combined system with daily power consumption constraints. Based on the obtained results, multi-scale joint scheduling of hybrid-photovoltaic with medium- and long-term power consumption decomposition is realized.

2. The hybrid photovoltaic multi-scale joint dispatching method with medium- and long-term electricity decomposition according to claim 1 is characterized in that: Based on the hybrid pumping power station and the photovoltaic power station, the corresponding hybrid pumping-photovoltaic medium- and long-term contract electricity decomposition model is established and the specific solution is as follows: The hybrid power station and photovoltaic power station obtain trading electricity through the output forecast value of the next month, and carry out bundled transactions with the trading center to determine the monthly contract electricity; The hybrid power station and the photovoltaic power station respectively determine the planned power generation for each day of the month through the established monthly contract power decomposition model; After the day ends, the actual power generation is obtained, and by comparing it with the planned power generation, the daily deviation power is obtained; The deviation electricity is brought into the contract electricity correction model, and combined with the latest mixed-pumping-photovoltaic forecast information, the remaining daily planned electricity is corrected; The corrected value is brought into the scheduling model of the next day for scheduling, and the deviation electricity is calculated after the end of the day. The above steps are repeated until the end of the month to obtain the daily contract electricity decomposition results, daily scheduling results, and deviation electricity results.

3. The hybrid photovoltaic multi-scale joint dispatching method with medium- and long-term electricity decomposition according to claim 2 is characterized in that: The monthly contract electricity decomposition model of photovoltaic power stations is as follows: in, The photovoltaic power station is Daily power breakdown value; The photovoltaic power station is The daily electricity consumption forecast.

4. The hybrid photovoltaic multi-scale joint dispatching method with medium- and long-term electricity decomposition according to claim 2 is characterized in that: The monthly contract electricity decomposition model of the mixed pumping power station is based on the goal of minimizing the water consumption of the mixed pumping power station within the month and minimizing the deviation between the actual final water level of the reservoir at the end of the month and the planned final water level. The objective function is as follows: The water consumption of the hydropower station is minimal during the decomposition period: The deviation between the actual final water level of the hydropower station and the planned final water level is the smallest: Among them, T is the decomposition period; The hydropower station is Daily water consumption, is the actual final water level of the hydropower station at the end of the month; It is the planned water level at the end of the month for the hydropower station.

5. The hybrid photovoltaic multi-scale joint dispatching method with medium- and long-term electricity decomposition according to claim 4 is characterized in that: The constraints of the monthly contract electricity decomposition model of the hybrid power station are as follows: Power balance constraints: in, It is a mixed pumping station. Daily power breakdown value; It is the monthly contractual electricity value of the mixed pumping power station; Water balance constraints: in, The hydropower station is The storage capacity at the end of the day; The hydropower station is Daily inbound flow; The hydropower station is Daily power generation flow; is the time step; Power generation flow constraints: in, The hydropower station is The lower limit of the daily power generation flow; The hydropower station is The upper limit of the daily power generation flow; Outbound traffic constraints: in, The hydropower station is The lower limit of the daily outbound flow, m 3 / s; The hydropower station is The daily outbound flow limit; Power generation head constraints: in, The hydropower station is Daily power generation head; The power station is Tailwater level of the day; The power station is Daily head loss; Output constraints: in, The power station is Average daily output; is the output coefficient of the hydropower station; The hydropower station is The water head of the day; The hydropower station is The lower limit of daily output; The hydropower station is The daily output limit; Water level constraints: in, The hydropower station is The lower limit of upstream water level at the end of the day; The hydropower station is The upper limit of the upstream water level at the end of the day; is the initial water level during the decomposition period; Water level and storage capacity constraints: in, It is the water level and storage capacity relationship function of the hydropower station; Tailwater level discharge flow constraint: in, is the tailwater level discharge function of the hydropower station.

6. The hybrid photovoltaic multi-scale joint dispatching method with medium- and long-term electricity decomposition according to claim 2 is characterized in that: The deviation electricity is brought into the contract electricity correction model, and combined with the latest mixed-pumping-photovoltaic forecast information, the remaining daily planned electricity is corrected as follows: Set the deviation power assessment threshold; compare the daily completed power with the planned power to obtain the daily deviation power; calculate the Gini coefficient , and compare it with the set assessment value to determine whether correction is needed; If no correction is required, the result is output directly; If correction is needed, the planned electricity consumption for the remaining days of the month will be corrected based on the forecast error probability distribution and the deviation electricity consumption.

7. The hybrid photovoltaic multi-scale joint dispatching method with medium- and long-term electricity decomposition according to claim 1 is characterized in that: The short-term dispatch model of the hybrid-photovoltaic combined system considering the daily decomposition power constraint is solved in two levels. The first level is the hybrid-photovoltaic complementary multi-objective day-ahead dispatch model under the daily decomposition power constraint, and the second level is the economic dispatch model of the in-plant units. The objective function of the multi-objective day-ahead scheduling model of hybrid extraction and photovoltaic complementarity under the daily decomposed power constraint is as follows: Minimum daily water consumption: in, yes The outflow of the hydropower station during the period; is the time step; is the total number of periods calculated; The deviation between the planned power and the contracted power of the water-solar hybrid system is minimal: in, It is the daily contracted electricity of the hydropower-photovoltaic system; The hydropower station is The amount of electricity during the time period; The photovoltaic power station is The amount of electricity during the time period; Minimum photovoltaic abandonment: in, is the abandoned light power of the photovoltaic power station at time t; The objective function of the economic dispatch model of the plant units is as follows: The water consumption of hydropower station is minimal: in, yes Time unit At the water head The next output is The power generation flow at that time; yes Time unit of net water head; yes Time unit contribution; It's the crew The startup water consumption; It's the crew Downtime water consumption; yes Time unit Start and stop state variables; is the total number of optimized hydropower station units.

8. The hybrid photovoltaic multi-scale joint dispatching method with medium- and long-term electricity decomposition according to claim 7 is characterized in that: The constraints of the hybrid extraction-PV complementary multi-objective day-ahead scheduling model under the daily decomposed power constraints are as follows: Photovoltaic output constraints in, It is The maximum value of photovoltaic output during the period; It is Photovoltaic output during the time period; is the installed capacity of photovoltaic power generation; Hydropower output constraints in, It is The lower limit of hydropower output during the period; It is The lower limit of hydropower output during the period; Constraints on daily power generation contracts of combined power plants in, It is the daily execution of the combined hydropower station obtained by solving; It is the daily power output deviation tolerance of the combined solar power station; Daily contract power constraints for hydropower stations in, is the daily execution of the hydropower station obtained by decomposition; is the daily power output deviation tolerance of the hydropower station; Water balance constraints in, is the storage capacity of the power station in period t; is the flow rate of the power station during period t; is the outflow flow of the power station in period t; Power generation flow constraints in, The hydroelectric station is The lower limit of power generation flow in the time period; The hydroelectric station is The upper limit of power generation flow during the time period; Outbound flow constraints in, The hydroelectric station is The lower limit of outbound flow in the time period; The hydroelectric station is The upper limit of outbound traffic during the time period; Power generation head constraint in, The hydroelectric station is The generating head during the period; The hydroelectric station is Tailwater level of the time period; Output Constraint in, The hydroelectric station is Average output during the period; is the output coefficient of the hydropower station; The power station is The lower limit of output during the time period; The power station is Output limit for the time period; Water level constraint in, The hydroelectric station is The lower limit of the upstream water level at the end of the time period; The power station is The upper limit of the upstream water level at the end of the time period; is the initial water level of the period; Water level storage capacity constraints in, It is the water level and storage capacity relationship function of the power station; Tailwater level discharge flow constraint in, is the tailwater level discharge function of power station i; The constraints of the economic dispatch model of the plant units are as follows: Output balance constraints: in, It is in The hydropower station in the time period has planned to produce power a few days ago; Unit output constraints: in, It is the minimum output of the hydropower station; It is the maximum output of the hydropower station; For hydropower stations The effort of the moment; Minimum start and stop constraints for units: in, For the crew exist The power-on signal at the moment; For the crew exist Shutdown signal at any time; The minimum startup time of the unit; The minimum downtime of the unit.

9. The hybrid photovoltaic multi-scale joint dispatching method with medium- and long-term electricity decomposition according to claim 1 is characterized in that: The optimized solution method of improved NSGA-III is used to solve the short-term dispatch model of hybrid-photovoltaic combined system with daily decomposed power constraints. Specifically: First, set the parameters and randomly initialize the population; Crossover, mutation, and fitness allocation; Fast non-dominated sorting merge population; Update the parent population with elite strategy; Based on non-uniform variation of reference points; When the environment does not change, if the termination condition is met, the result is output; if the termination condition is not met, the result is returned and crossover, mutation, and fitness allocation are repeated; When the environment changes, the Pareto frontier is predicted, the initial population is generated, and it is judged again whether the termination conditions are met and the results are output.

10. A hybrid photovoltaic multi-scale joint dispatching system with medium- and long-term power decomposition, characterized in that: include: The solution module establishes a hybrid pumping-photovoltaic medium- and long-term contract electricity decomposition model. Based on the hybrid pumping power station and the photovoltaic power station, the corresponding hybrid pumping-photovoltaic medium- and long-term contract electricity decomposition model is established and solved to obtain the daily decomposition electricity boundary conditions; The construction module is used to establish a short-term dispatch model of the hybrid-PV combined system considering the daily decomposition power constraints based on the obtained daily decomposition power boundary conditions; The scheduling module uses the improved NSGA-III optimization solution method to solve the short-term scheduling model of the hybrid-photovoltaic combined system with daily power consumption constraints, and realizes the hybrid-photovoltaic multi-scale joint scheduling with medium- and long-term power consumption decomposition based on the obtained results.

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