Annual hydropower and electric quantity distribution method and system considering monthly coordination

Abstract

The invention discloses an annual hydropower and electric quantity distribution method and system considering monthly coordination, and relates to the field of medium and long term power and electric quantity balance analysis, and the method comprises the steps: obtaining wind power historical output curve information, considering the probability distribution characteristics of wind power output, solving upper and lower limit output values in a confidence interval, establishing an annual hydropower and electric quantity distribution model considering monthly coordination, and outputting a monthly decomposition result of hydropower and electric quantity; and carrying out statistics on the output condition at each moment, and calculating the renewable energy consumption condition. According to the method, the monthly coordination constraint of the reservoir is considered, and the monthly coordination effect of water resources all the year round is achieved; the mode that monthly loads in an existing annual electric power and electric quantity balance model are represented by single typical daily loads is adopted, and large-scale redundancy calculation in the whole time period is avoided while it is guaranteed that the result has a good application effect. And the result of the wind power output probabilistic model is fused into the hydroelectric power decomposition model, so that the adaptability of the hydroelectric power decomposition scheme to the new energy power system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medium- and long-term electric power balance analysis, and in particular to an annual hydropower distribution method and system taking into account monthly coordination. Background Art

[0002] From the perspective of hydropower technology, hydropower units have outstanding advantages such as rapid start and stop, strong climbing ability, large output adjustment range and almost zero carbon emissions. They have long been an important peak-shaving and frequency-regulating power source for the power grid. For basin-controlled hydropower stations with annual to multi-year regulation capabilities, large-scale seasonal power transfer can be achieved by optimizing and controlling the water level of the reservoir at key time nodes (the beginning of the flood season, the end of the flood season and the end of the year). With the help of the natural replenishment of water, wind and light during the year, seasonal fluctuations in new energy can be compensated and the system's supply capacity can be improved. This study coordinates reservoir scheduling and power balance issues in the annual power balance.

[0003] The existing annual power and electricity balance usually adopts a monthly balance method, with a single typical daily load representing the load level of the month. The model constraints usually only include the upper and lower limits of the unit's output, the up and down ramp constraints, and the system power balance constraints. Hydropower, as an important regulating power source in the system, especially large reservoir hydropower units, has more than quarterly regulation capabilities, but the monthly balance uses a certain monthly hydropower generation constraint, which cannot achieve monthly optimization of hydropower. Therefore, based on the existing research, this study considers the monthly coordination constraints of the reservoir and constructs a full-year optimization model, so that the optimization model of the annual power and electricity balance can meet the actual application of the project while being more conducive to the precise optimization of resource allocation. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: how to overcome the drawbacks of the given monthly hydropower generation in the traditional balance, taking into account the monthly coordination constraints of the reservoirs, so as to achieve the coordination of water resources among the months throughout the year.

[0006] In order to solve the above technical problems, the present invention provides the following technical solution: an annual hydropower electricity allocation method considering monthly coordination, which comprises the following steps:

[0007] Obtain basic technical data and operation boundary data from the dispatching and operation department;

[0008] Based on the historical wind power output curve information obtained, the probability distribution characteristics of wind power output are considered, and the upper and lower output limits are solved under the confidence interval as the output boundary values ​​of wind turbines in the annual hydropower electricity allocation model;

[0009] Taking the basic technical data and operation boundary data obtained from the dispatching and operation department as input data, an annual hydropower electricity allocation model considering monthly coordination is established to solve the monthly decomposition results of the output hydropower electricity;

[0010] Statistics are collected on the output of each type of generating unit at each moment, and the consumption of renewable energy is calculated.

[0011] As a preferred solution of the annual hydropower electricity allocation method considering monthly coordination described in the present invention, wherein: the basic technical data are technical parameters of various types of power sources in the power system, including the capacity, minimum technical output, and ramp rate of the unit;

[0012] The operation simulation boundary data includes historical information on load demand and renewable energy power generation, moment-by-moment forecast of renewable energy output, forecast of water inflow to hydropower plants, inter-provincial power transmission and reception plan data, and planned production data of conventional power sources and renewable energy.

[0013] As a preferred solution of the annual hydropower electricity allocation method considering monthly coordination described in the present invention, wherein: the probability distribution characteristics of wind power output are considered, and the upper and lower limit output values ​​are solved under the confidence interval. The output boundary value of the wind turbine in the annual hydropower electricity allocation model is obtained by subtracting the historical measured output data of the wind farm and the moment-by-moment forecast data of the day before, and the wind power output forecast deviation value is obtained, and the expression is:

[0014]

[0015] Among them, ε w (t) represents the wind power forecast deviation value, represents the actual historical power that can be generated by wind power in period t, is the predicted value of wind power at the historical point in time period t;

[0016] The actual wind power that can be generated is obtained by adding the existing power prediction value and the wind power output uncertainty error value, and the expression is:

[0017]

[0018] in, represents the predicted power point of wind power in the future period t, P w (t) is the actual wind power that can be generated.

[0019] As a preferred solution of the annual hydropower electricity allocation method considering monthly coordination described in the present invention, wherein: the solving of the upper and lower limit output values ​​under the confidence interval includes obtaining the upper and lower limits of the wind power that can be generated according to different confidence intervals, and the expression is:

[0020] P w,max(t) = min[a:Pro{P w (t)≤a}≥β]

[0021] P w,min (t) = max[b:Pro{P w (t)≥b}≥β]

[0022] Among them, P w,max (t) is the upper limit of wind power generation in period t; P w,min (t) is the lower limit of wind power output in time period t; β is the confidence level, a is the upper limit of wind power output, b is the lower limit of wind power output, and β is the probability;

[0023] The wind power output probability distribution obtained by the kernel density function obtains the upper and lower limits of wind power output under the probability β;

[0024] The method of solving the upper and lower limit output values ​​under the confidence interval also includes using a kernel density function to obtain the probability distribution of wind power output at each moment, and the expression is:

[0025]

[0026] in, is the probability density function, h is the window width of the kernel density estimation, x is the independent variable of the kernel function, K h is the scaling kernel function, n is the number of samples, x i is the value of sample i;

[0027] K(·) is the kernel function, and the Gaussian kernel function is selected for estimation. The expression is:

[0028]

[0029] As a preferred solution of the annual hydropower electricity allocation method considering monthly coordination described in the present invention, wherein: the establishment of the annual hydropower electricity allocation model considering monthly coordination is to construct the objective function of the minimum total operating cost, and the expression is:

[0030]

[0031] in, represents the system operating cost at time t, is the operating cost and positive and negative standby cost of the thermal power unit at time t, is the operating cost and positive and negative reserve cost of the hydropower unit at time t, is the wind power operation cost and wind curtailment penalty cost at time t, is the load shedding cost at time t;

[0032]

[0033] Among them, N g is the total number of thermal power units; N h is the total number of hydropower units; are the unit power generation cost, output, unit start-stop cost, and start-stop capacity of thermal power unit i at time t, are respectively the positive reserve and negative reserve cost coefficients, positive reserve and negative reserve capacities of thermal power i at time t, h i,t are the unit power generation cost and output of hydropower i at time t, are respectively the positive reserve and negative reserve cost coefficients, positive reserve and negative reserve capacities of hydropower i at time t, c w 、c l are the cost coefficients of abandoned renewable energy, They are abandoned renewable energy power and load shedding power respectively.

[0034] As a preferred solution of the annual hydropower electricity allocation method considering monthly coordination described in the present invention, wherein: the objective function includes a hydropower unit model, a thermal power unit model, a wind power unit model and an energy storage unit model;

[0035] The hydropower unit model includes the upper and lower limit constraints of the hydropower unit output, the hydropower unit climbing constraint, the hydropower unit power generation and power constraint, the hydropower power transfer constraint, the hydropower power upper and lower limit constraints, and the hydropower unit reserve capacity constraint, and the expression is:

[0036]

[0037]

[0038] in, They represent the minimum and maximum output of hydropower unit i at time t, They represent the up and down climbing rates of hydropower unit i, h imax is the maximum power value of hydropower, h i,t is the actual power value of hydropower, N m is the total number of time periods in the mth month; is the power generation of hydropower i in the mth month, is the available power generation of hydropower i in the mth month, η is the power coefficient, are the positive reserve and negative reserve capacities provided by hydropower unit i at time t, respectively; Respectively represent the upper and lower limits of the output of hydropower unit i; are the maximum positive reserve capacity and negative reserve capacity that hydropower unit i can provide, respectively;

[0039] The thermal power unit model includes the upper and lower limit constraints of the thermal power unit output, the unit climbing constraint, and the thermal power unit spare capacity constraint, and the expression is:

[0040]

[0041] in, g i,t They represent the minimum, maximum and actual output of thermal power unit i at time t, μ gup , μ gdown Respectively represent the up and down climbing rates of thermal power unit i, g i,t-1 is the output of thermal power unit i at time t-1; are the positive reserve and negative reserve capacities provided by thermal power unit i at time t, respectively; are the upper and lower limits of the output of thermal power unit i respectively; are the maximum positive reserve capacity and negative reserve capacity provided by thermal power unit i respectively;

[0042] The wind turbine model includes upper and lower limits of wind power output, which are expressed as:

[0043]

[0044] in, They represent the lower and upper limits of the power that can be generated by the wind turbine at time t under the confidence level;

[0045] The energy storage unit model includes upper and lower limit constraints of charge and discharge power, the relationship between energy and charge and discharge power, and upper and lower limit constraints of electric energy. The expression is:

[0046]

[0047] in, and is the charging and discharging power of energy storage system i at time t, S i is the capacity of energy storage system i, η s,i is the charging and discharging efficiency of energy storage system i; E s,i,t is the state of charge of energy storage system i at time t; T s,i is the continuous charging or discharging time of energy storage system i;

[0048] The power balance constraint is expressed as:

[0049]

[0050] in, For wind power curtailment, is the load loss, P d,t For system load.

[0051] As a preferred solution of the annual hydropower electricity allocation method considering monthly coordination described in the present invention, wherein: the calculation of renewable energy consumption is to use a solver to solve the annual hydropower electricity allocation model, output the monthly decomposition results of hydropower electricity and the output of each type of unit at each moment, and statistically analyze the hydropower and new energy power abandonment of the system, and the power abandonment at time t, the expression is:

[0052]

[0053] in, is the amount of renewable energy abandoned at time t, and Δt is the time interval.

[0054] Another object of the present invention is to provide an annual hydropower electricity distribution system that takes monthly coordination into consideration. By comprehensively considering the output characteristics and operating constraints of wind power, thermal power and hydropower, the system can solve the problems in the existing system such as unreasonable hydropower electricity distribution leading to insufficient release of the regulation capacity of hydropower units and the need to improve the level of renewable energy consumption.

[0055] In order to solve the above technical problems, the present invention provides the following technical solutions: an annual hydropower electricity distribution system considering monthly coordination, comprising: a data acquisition module, a probability analysis module, an optimization model module, a scheduling simulation module and a statistical analysis module;

[0056] The data acquisition module is to obtain basic technical data and operation boundary data from the dispatching and operation department as input conditions for the probability analysis module and the dispatching simulation module;

[0057] The probability analysis module analyzes the probability distribution of wind power output, calculates the upper and lower output values ​​under the confidence interval, and evaluates the uncertainty of wind power output as the wind power output boundary of the scheduling simulation module;

[0058] The optimization model module is to construct an annual hydropower electricity distribution model, minimize operating costs, and consider the output limits and constraints of different types of units;

[0059] The dispatch simulation module is used to solve the execution model and generate the monthly hydropower electricity distribution and moment-by-moment output;

[0060] The statistical analysis module is used to count the consumption and abandoned power of renewable energy and evaluate the overall system performance and optimization effect.

[0061] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the annual hydropower electricity allocation method considering monthly coordination as described above are implemented.

[0062] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the annual hydropower electricity allocation method considering monthly coordination as described above.

[0063] Beneficial effects of the present invention: The annual hydropower electricity allocation method considering monthly coordination proposed in the present invention overcomes the drawbacks of the given monthly hydropower generation in the traditional balance, and considers the monthly coordination constraints of the reservoir, thereby realizing the coordination of water resources throughout the year; the monthly load in the existing annual power balance model is represented by a single typical daily load, which ensures that the result has a good application effect while avoiding large-scale redundant calculations throughout the period. Based on the deviation between the predicted results of the historical wind power output curve and the actual value, the results of the wind power output probabilistic model are integrated into the hydropower electricity decomposition model to improve the adaptability of the hydropower electricity decomposition scheme to the new energy power system. Compared with the prior art, the hydropower electricity decomposition method proposed in the present invention is suitable for new energy power systems, and can significantly improve the system's renewable energy consumption level. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0065] Figure 1 An overall flow chart of the annual hydropower electricity allocation method considering monthly coordination provided by the first embodiment of the present invention;

[0066] Figure 2 A full-year monthly maximum load curve diagram of the test system in the annual hydropower electricity allocation method considering monthly coordination provided by the third embodiment of the present invention;

[0067] Figure 3 A diagram showing the annual distribution of installed power capacity of a test system for an annual hydropower electricity allocation method taking into account monthly coordination provided by a third embodiment of the present invention;

[0068] Figure 4 A comparison diagram of hydropower generation before and after monthly coordination in the annual hydropower electricity allocation method considering monthly coordination provided by the third embodiment of the present invention;

[0069] Figure 5 A comparison of the amount of abandoned renewable energy power before and after monthly coordination in an annual hydropower power allocation method considering monthly coordination provided in a third embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0071] Example 1, reference Figure 1 In one embodiment of the present invention, a method for annual hydropower allocation considering monthly coordination is provided, comprising:

[0072] The present invention overcomes the drawback of the given monthly hydropower generation in the traditional balance, takes into account the monthly coordination constraints of the reservoir, and thus realizes the coordination of water resources throughout the year.

[0073] Secondly, the existing annual power balance model uses a single typical day load to represent the monthly load, which ensures that the results have good application effects while avoiding large-scale redundant calculations throughout the entire period;

[0074] Finally, in order to improve the adaptability of the hydropower decomposition scheme to the new energy power system, based on the deviation between the historical wind power output curve prediction results and the actual value, the uncertainty of wind power output is analyzed and a probabilistic model of wind power output is constructed.

[0075] An annual hydropower electricity allocation method considering monthly coordination. The model takes system economic efficiency as the goal, including the operating cost of each unit and the penalty cost of wind power abandonment, taking into account the system power balance and reserve constraints, hydropower station reservoir operation constraints, unit up and down ramp constraints, thermal power unit minimum start and stop time constraints, reservoir monthly coordination constraints, etc. The model is essentially an optimization problem. It includes the following steps:

[0076] 1) Obtain basic technical data and operation boundary data of the system from the dispatching and operation department.

[0077] 2) Considering the probability distribution characteristics of wind power output, the upper and lower output values ​​are solved within a certain confidence interval as the output boundary values ​​of wind turbines in the annual hydropower electricity allocation model;

[0078] 3) Establish an annual hydropower electricity allocation model that takes into account monthly coordination;

[0079] 4) Solve the annual hydropower electricity allocation model, obtain the monthly decomposition results of hydropower electricity from the model output data, conduct statistics on the output of each type of unit at each moment, and calculate the renewable energy consumption of the system.

[0080] Step 1: Obtain basic system technical data and operation boundary data from the dispatching and operation department. The calculation model input data obtained from relevant departments include:

[0081] The boundary data for the operation simulation include: technical parameters of various types of power sources in the power system, historical information on load demand and renewable energy power generation, moment-by-moment forecast of renewable energy output, water inflow forecast for hydropower plants, inter-provincial power transmission and reception plan data, and planned commissioning data for conventional power sources and renewable energy.

[0082] Step 2: Construct a probabilistic model of wind power output. Considering the probability distribution characteristics of its output, solve its upper and lower output values ​​within a certain confidence interval as the output boundary values ​​of wind turbines in the annual hydropower electricity allocation model. Specifically:

[0083] Step 2.1: Based on the historical measured output data of the wind farm and the moment-by-moment forecast data of the day before, the wind power output forecast deviation value is obtained by difference. The calculation formula is:

[0084]

[0085] Among them, ε w (t) represents the wind power forecast deviation value, represents the actual historical power that can be generated by wind power in period t, is the predicted value of wind power at the historical point in time period t.

[0086] Step 2.2: The actual wind power that can be generated is obtained by adding the existing power forecast value and the wind power output uncertainty error value. The wind power that can be generated is a probability distribution, and the probability distribution of the power that can be generated will change in different time periods with the changes in the future forecast value and the uncertainty probability distribution. The calculation formula is:

[0087]

[0088] in, represents the predicted power point of wind power in the future period t, P w (t) is the actual wind power that can be generated.

[0089] Step 2.3: Obtain the wind power limit based on different confidence intervals, including the upper and lower limits of wind power. The limit value will change with the time-varying probability distribution of wind power. Define the upper and lower limits of wind power in period t as follows:

[0090] P w,max (t) = min[a:Pro{P w (t)≤a}≥β] (3)

[0091] P w,min (t)=max[b:Pro{Pw (t)≥b}≥β] (4)

[0092] Where P w,max (t) is the upper limit of wind power generation in period t; P w,min (t) is the lower limit of wind power generation in period t; β is the confidence level. Formula (3) is confident that the wind power generation in period t is less than a with a probability greater than β, where a is the upper limit of wind power output. Formula (4) means that the wind power generation in period t is greater than b with a probability greater than β, where b is the lower limit of wind power output.

[0093] It should be further explained that the upper and lower limits of wind power output are obtained by probability statistics of multiple historical output curves. The kernel density function is proposed to obtain the distribution of wind power output under probability β.

[0094] Step 2.4: Use the kernel density function to obtain the probability distribution of wind power output at each moment. The kernel density estimation function is:

[0095]

[0096] In the formula, is the probability density function, h>0 is a smooth function, called the window width of the kernel density estimation, x is the independent variable of the kernel function, K h is the scaling kernel function, n is the number of samples, x i is the value of sample i.

[0097] K(·) is the kernel function, and the Gaussian kernel function is selected for estimation:

[0098]

[0099] Step 3: Establish an annual hydropower electricity allocation model considering monthly coordination. Specifically:

[0100] Step 3.1: Establish the objective function. The objective function considers the lowest total operating cost, including the operating cost of each power source, the backup cost, and the penalty for wind curtailment and load shedding.

[0101]

[0102] in, represents the system operating cost at time t, is the operating cost and positive and negative standby cost of the thermal power unit at time t, is the operating cost and positive and negative reserve cost of the hydropower unit at time t, is the wind power operation cost and wind curtailment penalty cost at time t, is the load shedding cost at time t.

[0103] The cost components of each part are as follows:

[0104]

[0105] Among them, N g is the total number of thermal power units; N h is the total number of hydropower units; are the unit power generation cost, output, unit start-stop cost, and start-stop capacity of thermal power unit i at time t, are respectively the positive reserve and negative reserve cost coefficients, positive reserve and negative reserve capacities of thermal power i at time t, h i,t are the unit power generation cost and output of hydropower i at time t, are respectively the positive reserve and negative reserve cost coefficients, positive reserve and negative reserve capacities of hydropower i at time t, c w 、c l are the cost coefficients of abandoned renewable energy, They are abandoned renewable energy power and load shedding power respectively.

[0106] Step 3.2: Add the hydropower unit model. The hydropower unit model is a three-stage hydropower output model that takes into account the daily power distribution. It includes the upper and lower output constraints of the hydropower unit (9), the ramp constraint of the hydropower unit (10), the power generation and power constraint of the hydropower unit (11), the hydropower power transfer constraint (12), the upper and lower limits of the hydropower power (13), and the reserve capacity constraint of the hydropower unit (14).

[0107]

[0108]

[0109] in, They represent the minimum and maximum output of hydropower unit i at time t, They represent the up and down climbing rates of hydropower unit i, h imax is the maximum power value of hydropower, h i,t is the actual power value of hydropower, N m is the total number of time periods in the mth month; is the power generation of hydropower i in the mth month, is the available power generation of hydropower i in the mth month, η is the power coefficient, are the positive reserve and negative reserve capacities provided by hydropower unit i at time t, respectively; Respectively represent the upper and lower limits of the output of hydropower unit i; are the maximum positive reserve capacity and negative reserve capacity that hydropower unit i can provide respectively.

[0110] Step 3.3: Add the thermal power unit model. It mainly includes the upper and lower limit constraints of the thermal power unit output (15), the unit ramp constraint (16), and the thermal power unit reserve capacity constraint (17).

[0111]

[0112] in, g i,t They represent the minimum, maximum and actual output of thermal power unit i at time t, μ gup , μ gdown Respectively represent the up and down climbing rates of thermal power unit i, g i,t-1 is the output of thermal power unit i at time t-1; are the positive reserve and negative reserve capacities provided by thermal power unit i at time t, respectively; are the upper and lower limits of the output of thermal power unit i; r i max , r i min are the maximum positive reserve capacity and negative reserve capacity provided by thermal power unit i respectively.

[0113] Step 3.4: Add the wind turbine model. This is mainly for the upper and lower limits of wind power output, where the upper and lower limits of wind power output come from the wind power generation limits under different confidence intervals obtained in step 2.

[0114]

[0115] in, They respectively represent the lower limit and upper limit of the power that can be generated by the wind turbine at time t under the confidence level.

[0116] Step 3.5: Add the energy storage unit model, including the upper and lower limits of charge / discharge power, the relationship between energy and charge / discharge power, and the upper and lower limits of electric energy.

[0117]

[0118] in, and is the charging and discharging power of energy storage system i at time t, S i is the capacity of energy storage system i, η s,i is the charging and discharging efficiency of energy storage system i; E s,i,t is the state of charge of energy storage system i at time t; T s,i is the continuous charging or discharging time of the energy storage system i.

[0119] Step 3.6: Add system power balance constraints.

[0120]

[0121] in, For wind power curtailment, is the load loss, P d,t For system load.

[0122] Step 4: Solve the annual hydropower electricity allocation model of step 3. The model proposed in this invention is an optimization model and can be solved directly by the solver. Obtain the monthly decomposition results of hydropower electricity and the output of each type of unit at each moment, and statistically analyze the hydropower and new energy power abandonment of the system (i.e., the renewable energy power that is not absorbed by the system). The power abandonment calculation formula at time t is as follows:

[0123]

[0124] in, is the amount of renewable energy abandoned at time t, and Δt is the time interval.

[0125] Embodiment 2 is an embodiment of the present invention, which provides a system for an annual hydropower electricity allocation method considering monthly coordination, including: a data acquisition module, a probability analysis module, an optimization model module, a scheduling simulation module and a statistical analysis module;

[0126] The data acquisition module obtains basic technical data and operation boundary data from the dispatching and operation department as input conditions for the probability analysis module and the dispatching simulation module;

[0127] The probability analysis module analyzes the probability distribution of wind power output, calculates the upper and lower output values ​​under the confidence interval, and evaluates the uncertainty of wind power output, which serves as the wind power output boundary of the scheduling simulation module;

[0128] The optimization model module is to build an annual hydropower electricity distribution model to minimize operating costs and consider the output limits and constraints of different types of units;

[0129] The dispatch simulation module is used to solve the execution model and generate the monthly hydropower electricity distribution and moment-by-moment output;

[0130] The statistical analysis module is used to count the consumption and abandoned power of renewable energy and evaluate the overall system performance and optimization effect.

[0131] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0132] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0133] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0134] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0135] Example 3, reference Figure 2 to Figure 5 This is another embodiment of the present invention. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments. This embodiment experiments the existing traditional method and the method of this embodiment respectively.

[0136] The annual hydropower electricity allocation method considering monthly coordination proposed in the present invention is applied in a wind, solar, water and fire test system, and compared with the results of renewable energy power abandonment without monthly coordination of hydropower generation, verifying the effectiveness of monthly coordination of hydropower electricity.

[0137] Reference Figure 2 This is the annual monthly maximum load curve of the test system in the calculation example. The annual maximum load is about 49.9 million kilowatts. Figure 3 It is the distribution of the installed power capacity of the test system throughout the year.

[0138] It should be noted that the treatment of each type of unit is as follows:

[0139] Wind power: Aggregated into a virtual wind turbine with an installed capacity of about 15 million kilowatts, using the power generation capacity curve to consider the upper and lower limits of power generation within a certain confidence interval;

[0140] Photovoltaic: Aggregated into a virtual photovoltaic unit with an installed capacity of about 50 million kilowatts, using a power generation capacity curve;

[0141] Hydropower: Aggregated into a virtual hydropower unit with an installed capacity of 88 million kilowatts, taking into account the monthly output limit requirements of hydropower;

[0142] Thermal power: The installed capacity is about 13 million kilowatts, taking into account the unit maintenance arrangements, ramping, and start-up and shutdown requirements.

[0143] Reference Figure 4 and Figure 5They are the comparison of hydropower generation before and after monthly coordination, and the comparison of renewable energy abandonment before and after monthly coordination. Before monthly coordination of hydropower, renewable energy abandonment was mainly distributed in January-April, July, and November-December, with renewable energy abandonment of about 7.2 billion kWh throughout the year. Among them, the abandonment in July during the flood season was relatively large, about 1.8 billion kWh, accounting for 25% of the annual abandonment. After monthly coordination of hydropower, hydropower generation in July during the flood season was transferred to the dry seasons of January-March and December, freeing up power generation space for renewable energy generation during the flood season. The abandonment of renewable energy throughout the year was about 5.3 billion kWh, a decrease of 1.9 billion kWh compared with before monthly coordination of hydropower. The abandonment of power in July during the flood season was greatly reduced, by about 17 kWh. The effectiveness of monthly coordination of hydropower generation was verified.

[0144] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. Annual hydropower allocation method considering monthly coordination, characterized in that: include: Obtain basic technical data and operation boundary data from the dispatching and operation department; Based on the historical wind power output curve information obtained, the probability distribution characteristics of wind power output are considered, and the upper and lower output limits are solved under the confidence interval as the output boundary values ​​of wind turbines in the annual hydropower electricity allocation model; Taking the basic technical data and operation boundary data obtained from the dispatching and operation department as input data, an annual hydropower electricity allocation model considering monthly coordination is established to solve the monthly decomposition results of the output hydropower electricity; Statistics are collected on the output of each type of generating unit at each moment, and the consumption of renewable energy is calculated.

2. The annual hydropower allocation method considering monthly coordination as claimed in claim 1, characterized in that: The basic technical data are technical parameters of various types of power sources in the power system, including the capacity, minimum technical output, and ramp rate of the unit; The operation simulation boundary data includes historical information on load demand and renewable energy power generation, moment-by-moment forecast of renewable energy output, forecast of water inflow to hydropower plants, inter-provincial power transmission and reception plan data, and planned production data of conventional power sources and renewable energy.

3. The annual hydropower allocation method considering monthly coordination as claimed in claim 2, characterized in that: The above-mentioned probability distribution characteristics of wind power output are considered, and the upper and lower limit output values ​​are solved under the confidence interval. The output boundary value of the wind turbine in the annual hydropower electricity allocation model is obtained by subtracting the historical measured output data of the wind farm and the moment-by-moment forecast data of the day before, and the wind power output forecast deviation value is obtained. The expression is: Among them, ε w (t) represents the wind power forecast deviation value, represents the actual historical power that can be generated by wind power in period t, is the predicted value of wind power at the historical point in time period t; The actual wind power that can be generated is obtained by adding the existing power prediction value and the wind power output uncertainty error value, and the expression is: in, represents the predicted power point of wind power in the future period t, P w (t) is the actual wind power that can be generated.

4. The annual hydropower allocation method considering monthly coordination as claimed in claim 3, characterized in that: Solving the upper and lower limit output values ​​under the confidence interval includes obtaining the upper and lower limits of the wind power that can be generated according to different confidence intervals, and the expression is: P w,max (t)=min[a:Pro{P w (t)≤a}≥β] P w,min (t)=max[b:Pro{P w (t)≥b}≥β] Among them, P w,max (t) is the upper limit of wind power generation in period t; P w,min (t) is the lower limit of wind power output in time period t; β is the confidence level, a is the upper limit of wind power output, b is the lower limit of wind power output, and β is the probability; The wind power output probability distribution obtained by the kernel density function obtains the upper and lower limits of wind power output under the probability β; The method of solving the upper and lower limit output values ​​under the confidence interval also includes using a kernel density function to obtain the probability distribution of wind power output at each moment, and the expression is: in, is the probability density function, h is the window width of the kernel density estimation, x is the independent variable of the kernel function, K h is the scaling kernel function, n is the number of samples, x i is the value of sample i; K(·) is the kernel function, and the Gaussian kernel function is selected for estimation. The expression is:

5. The annual hydropower allocation method considering monthly coordination as claimed in claim 4, characterized in that: The establishment of the annual hydropower electricity allocation model considering monthly coordination is to construct the objective function of the minimum total operating cost, which is expressed as: in, represents the system operating cost at time t, is the operating cost and positive and negative standby cost of the thermal power unit at time t, is the operating cost and positive and negative reserve cost of the hydropower unit at time t, is the wind power operation cost and wind curtailment penalty cost at time t, is the load shedding cost at time t; Among them, N g is the total number of thermal power units; N h is the total number of hydropower units; gi,t, are the unit power generation cost, output, unit start-stop cost, and start-stop capacity of thermal power unit i at time t, are respectively the positive reserve and negative reserve cost coefficients, positive reserve and negative reserve capacities of thermal power i at time t, hi,t are the unit power generation cost and output of hydropower i at time t, are respectively the positive reserve and negative reserve cost coefficients, positive reserve and negative reserve capacities of hydropower i at time t, c w 、c l are the cost coefficients of abandoned renewable energy, They are abandoned renewable energy power and load shedding power respectively.

6. The annual hydropower allocation method considering monthly coordination as claimed in claim 5, characterized in that: The objective function includes a hydropower unit model, a thermal power unit model, a wind power unit model and an energy storage unit model; The hydropower unit model includes the upper and lower limit constraints of the hydropower unit output, the hydropower unit climbing constraint, the hydropower unit power generation and power constraint, the hydropower power transfer constraint, the hydropower power upper and lower limit constraints, and the hydropower unit reserve capacity constraint, and the expression is: in, They represent the minimum and maximum output of hydropower unit i at time t, They represent the up and down climbing rates of hydropower unit i, h imax is the maximum power value of hydropower, h i,t is the actual power value of hydropower, N m is the total number of time periods in the mth month; is the power generation of hydropower i in the mth month, is the available power generation of hydropower i in the mth month, η is the power coefficient, are the positive reserve and negative reserve capacities provided by hydropower unit i at time t, respectively; Respectively represent the upper and lower limits of the output of hydropower unit i; are the maximum positive reserve capacity and negative reserve capacity that hydropower unit i can provide, respectively; The thermal power unit model includes the upper and lower limit constraints of the thermal power unit output, the unit climbing constraint, and the thermal power unit spare capacity constraint, and the expression is: in, g i,t They represent the minimum, maximum and actual output of thermal power unit i at time t, μ gup , μ gdown Respectively represent the up and down climbing rates of thermal power unit i, g i,t-1 is the output of thermal power unit i at time t-1; are the positive reserve and negative reserve capacities provided by thermal power unit i at time t, respectively; are the upper and lower limits of the output of thermal power unit i respectively; are the maximum positive reserve capacity and negative reserve capacity provided by thermal power unit i respectively; The wind turbine model includes upper and lower limits of wind power output, which are expressed as: in, They represent the lower and upper limits of the power that can be generated by the wind turbine at time t under the confidence level; The energy storage unit model includes upper and lower limit constraints of charge and discharge power, the relationship between energy and charge and discharge power, and upper and lower limit constraints of electric energy. The expression is: in, and is the charging and discharging power of energy storage system i at time t, Si is the capacity of energy storage system i, ηs,i is the charging and discharging efficiency of energy storage system i; s,i,t is the state of charge of energy storage system i at time t; Ts,i is the continuous charging or discharging time of energy storage system i; The power balance constraint is expressed as: in, For wind power curtailment, is the load loss, P d,t For system load.

7. The annual hydropower allocation method considering monthly coordination as claimed in claim 6, characterized in that: The calculation of renewable energy consumption is to use a solver to solve the annual hydropower electricity allocation model, output the monthly decomposition results of hydropower electricity and the output of each type of unit at each moment, and count the hydropower and new energy power abandonment of the system. The power abandonment at time t is expressed as: in, is the amount of renewable energy abandoned at time t, and Δt is the time interval.

8. A system using the annual hydropower electricity allocation method considering monthly coordination as claimed in any one of claims 1 to 7, characterized in that: It includes data acquisition module, probability analysis module, optimization model module, scheduling simulation module and statistical analysis module; The data acquisition module is to obtain basic technical data and operation boundary data from the dispatching and operation department as input conditions for the probability analysis module and the dispatching simulation module; The probability analysis module analyzes the probability distribution of wind power output, calculates the upper and lower output values ​​under the confidence interval, and evaluates the uncertainty of wind power output as the wind power output boundary of the scheduling simulation module; The optimization model module is to construct an annual hydropower electricity distribution model, minimize operating costs, and consider the output limits and constraints of different types of units; The dispatch simulation module is used to solve the execution model and generate the monthly hydropower electricity distribution and moment-by-moment output; The statistical analysis module is used to count the consumption and abandoned power of renewable energy and evaluate the overall system performance and optimization effect.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the annual hydropower electricity allocation method considering monthly coordination according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the annual hydropower electricity allocation method considering monthly coordination as described in any one of claims 1 to 7 are implemented.

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