Power transaction clearing processing method and device, storage medium and computer equipment

Abstract

The invention discloses a power transaction clearing processing method and device, a storage medium and computer equipment, and mainly aims to solve the problems that an existing optimal scheduling result of a water-wind-light complementary system may cause blockage of a direct-current power transmission channel and reduction of new energy consumption. Comprising the following steps: aggregating new energy power stations sharing a direct-current transmission channel, respectively predicting output power, and carrying out error analysis to obtain prediction error distribution; combining all the hydroelectric generating sets sharing the direct current power transmission channel in the same area, and then carrying out modeling processing based on the obtained virtual hydroelectric generating sets to obtain an active power model of a new energy power sending-out point; based on the new energy power sending point active power model and the prediction error distribution, introducing a direct current transmission channel opportunity constraint into the power transaction clearing model to obtain a target power transaction clearing model; and carrying out optimization solution on the target power transaction clearing model to obtain an energy configuration result under the AC-DC hybrid power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a method and device for processing power transaction clearing, a storage medium, and a computer device. Background Art

[0002] With the vigorous development of wind power generation and solar photovoltaic power generation, the anti-peak characteristics, uncontrollability and uncertainty of large-scale aggregated new energy power generation in the output mode are becoming more and more prominent. As a flexible balancing power source, hydropower stations with reservoirs have the characteristics of low minimum load, fast start-up time, and strong load tracking ability, which can provide significant buffering for the influx of renewable energy power generation. In order to make full use of the DC transmission channel and absorb as many renewable power generation resources as possible, water-wind-solar complementary systems dominated by hydropower have sprung up like mushrooms after rain.

[0003] The dispatching and operation of hydropower and renewable energy generating units involve multi-channel power network connections, complex hydraulic and electrical relationships, and traditional hydropower constraints. Existing dispatching research on hydro-wind-solar complementary systems mainly focuses on considering the typical regional agglomeration effect of renewable energy power plants, while ignoring the dispatching optimization considering multi-channel DC transmission. However, the hydro-wind-solar complementary integrated energy system dominated by hydropower may lead to the blockage of DC transmission channels. Therefore, the power dispatching results obtained by using the existing optimal dispatching scheme may cause the blockage of DC transmission channels, thereby reducing the absorption of renewable power generation resources. Summary of the invention

[0004] In view of this, the present invention provides a method and device for processing power transaction clearing, a storage medium, and a computer device, the main purpose of which is to solve the problem that the existing optimization scheduling results of the hydro-wind-solar complementary system may lead to blockage of the DC transmission channel and reduced new energy consumption.

[0005] According to one aspect of the present invention, a method for processing power transaction clearing is provided, comprising:

[0006] The wind power station and the photovoltaic power station that share a DC transmission channel are regarded as an aggregated wind power station and an aggregated photovoltaic power station, and error analysis is performed on the predicted output power of the aggregated wind power station and the aggregated photovoltaic power station to obtain a predicted error distribution;

[0007] All the hydropower units that share the DC transmission channel in the same area are merged to obtain multiple virtual hydropower units, and modeling is performed based on the virtual hydropower units to obtain an active power model of the new energy power delivery point;

[0008] Establishing a power transaction clearing model with the goal of minimizing power generation costs, and introducing DC transmission channel opportunity constraints into the power transaction clearing model based on the active power model of the new energy power delivery point and the prediction error distribution, to obtain a target power transaction clearing model;

[0009] The target power transaction clearing model is optimized and solved to obtain the energy configuration result under the AC / DC hybrid power grid.

[0010] Furthermore, the predicted output powers of the aggregated wind power station and the aggregated photovoltaic power station are respectively analyzed for errors to obtain predicted error distribution, including:

[0011] Determine the wind power forecast output error corresponding to each time period based on the forecast output power and the actual output power of the aggregated wind power station; and determine the photovoltaic forecast output error corresponding to each time period based on the forecast output power and the actual output power of the aggregated photovoltaic power station;

[0012] The probability density distribution of the wind power prediction output error is analyzed using a kernel density function to obtain a wind power prediction error distribution; and the probability density distribution of the photovoltaic prediction output error is analyzed using the kernel density function to obtain a photovoltaic prediction error distribution.

[0013] Furthermore, the modeling process is performed based on the virtual hydropower unit to obtain the active power model of the new energy power delivery point, including:

[0014] Determining corresponding associated wind power stations and associated photovoltaic power stations for DC power injection using each of the shared DC transmission channels;

[0015] Determining the power output of each of the virtual hydropower units based on the shared DC transmission channel, and determining the wind-related output power of the associated wind power station and the photovoltaic-related output power of the associated photovoltaic power station;

[0016] An active power model of the new energy power delivery point is established based on the power output of the virtual hydropower unit, the wind power-related output power and the photovoltaic power-related output power.

[0017] Furthermore, after obtaining the active power model of the new energy power delivery point, the method also includes adding constraints to the active power model of the new energy power delivery point; the constraints include: water balance constraints, upper and lower limit constraints on total discharge flow, upper and lower limit constraints on overflow discharge, upper and lower limit constraints on power discharge, tailwater water level constraints, forepool water level constraints, upper and lower limit constraints on forepool water level, head constraints and upper and lower limit constraints on power.

[0018] Furthermore, the power transaction clearing model established with the goal of minimizing power generation costs includes:

[0019] Determine other nodes in the AC / DC hybrid power grid except the DC power injection node as regular nodes;

[0020] Obtaining the power of the generator set and the power generation cost of the generator set corresponding to the conventional node;

[0021] The electricity trading clearing model is established based on the power of the generator set at each of the conventional nodes and the power generation cost of the generator set.

[0022] Furthermore, the introduction of DC transmission channel opportunity constraints into the power transaction clearing model based on the active power model of the new energy power delivery point and the prediction error distribution includes:

[0023] Based on the active power model of the new energy power delivery point, the probability distribution function of random variables and the confidence level are introduced to obtain the initial DC transmission channel opportunity constraint;

[0024] The initial DC transmission channel opportunity constraint is equivalently transformed by using the prediction error distribution to obtain the DC transmission channel opportunity constraint.

[0025] Furthermore, before optimizing and solving the target power transaction clearing model, the method further includes:

[0026] Determine the corresponding wind prediction error distribution inverse function and photovoltaic prediction error distribution inverse function based on the probability density distribution of wind prediction output error and the probability density distribution of photovoltaic prediction output error in the prediction error distribution;

[0027] The inverse function of the wind power forecast error distribution and the inverse function of the photovoltaic power forecast error distribution are used to perform a linear transformation on the DC transmission channel opportunity constraint, so that the DC transmission channel opportunity constraint is transformed from a probabilistic constraint to a deterministic constraint.

[0028] According to another aspect of the present invention, there is provided a processing device for clearing electricity transactions, comprising:

[0029] A prediction analysis module, used to treat the wind power station and the photovoltaic power station that share the DC transmission channel as an aggregated wind power station and an aggregated photovoltaic power station, and respectively perform error analysis on the predicted output power of the aggregated wind power station and the aggregated photovoltaic power station to obtain a prediction error distribution;

[0030] The reorganization and modeling module is used to merge all the hydropower units that share the DC transmission channel in the same area to obtain multiple virtual hydropower units, and perform modeling based on the virtual hydropower units to obtain the active power model of the new energy power delivery point;

[0031] A model improvement module is used to establish a power transaction clearing model with the goal of minimizing power generation costs, and introduce a DC transmission channel opportunity constraint into the power transaction clearing model based on the active power model of the new energy power delivery point and the prediction error distribution to obtain a target power transaction clearing model;

[0032] The optimization solution module is used to optimize and solve the target power transaction clearing model to obtain the energy configuration result under the AC / DC hybrid power grid.

[0033] Furthermore, the prediction analysis module includes:

[0034] An error analysis unit, configured to determine a wind power forecast output error corresponding to each time period based on the forecast output power and the actual output power of the aggregated wind power station; and to determine a photovoltaic forecast output error corresponding to each time period based on the forecast output power and the actual output power of the aggregated photovoltaic power station;

[0035] The probability density analysis unit is used to analyze the probability density distribution of the wind power prediction output error using a kernel density function to obtain a wind power prediction error distribution; and to analyze the probability density distribution of the photovoltaic prediction output error using the kernel density function to obtain a photovoltaic prediction error distribution.

[0036] Furthermore, the reorganization and modeling module includes:

[0037] An associated determination unit, used to determine corresponding associated wind power stations and associated photovoltaic power stations for DC power injection using each of the shared DC transmission channels;

[0038] A power determination unit, configured to determine the power output of each of the virtual hydropower units based on the shared DC transmission channel, and determine the wind-related output power of the associated wind power station and the photovoltaic-related output power of the associated photovoltaic power station;

[0039] A model building unit is used to build the active power model of the new energy power delivery point based on the power output of the virtual hydropower unit, the wind power-related output power and the photovoltaic power-related output power.

[0040] Furthermore, the device also includes a hydropower constraint setting module, which is used to:

[0041] Add constraints to the active power model of the new energy power delivery point, and the constraints include: water balance constraints, upper and lower limit constraints of total discharge flow, upper and lower limit constraints of overflow discharge, upper and lower limit constraints of power discharge, tailwater water level constraints, forepool water level constraints, upper and lower limit constraints of forepool water level, head constraints and upper and lower limit constraints of power.

[0042] Furthermore, the model improvement module includes a modeling unit, which is used to:

[0043] Determine other nodes in the AC / DC hybrid power grid except the DC power injection node as regular nodes;

[0044] Obtaining the power of the generator set and the power generation cost of the generator set corresponding to the conventional node;

[0045] The electricity trading clearing model is established based on the power of the generator set at each of the conventional nodes and the power generation cost of the generator set.

[0046] Furthermore, the model improvement module further includes an improvement unit, which is used to:

[0047] Based on the active power model of the new energy power delivery point, the probability distribution function of random variables and the confidence level are introduced to obtain the initial DC transmission channel opportunity constraint;

[0048] The initial DC transmission channel opportunity constraint is equivalently transformed by using the prediction error distribution to obtain the DC transmission channel opportunity constraint.

[0049] Furthermore, the device also includes a conversion module, which is used to:

[0050] Determine the corresponding wind prediction error distribution inverse function and photovoltaic prediction error distribution inverse function based on the probability density distribution of wind prediction output error and the probability density distribution of photovoltaic prediction output error in the prediction error distribution;

[0051] The inverse function of the wind power forecast error distribution and the inverse function of the photovoltaic power forecast error distribution are used to perform a linear transformation on the DC transmission channel opportunity constraint, so that the DC transmission channel opportunity constraint is transformed from a probabilistic constraint to a deterministic constraint.

[0052] According to another aspect of the present invention, a storage medium is provided, wherein at least one executable instruction is stored in the storage medium, and the executable instruction enables a processor to execute operations corresponding to the above-mentioned method for processing electricity transaction clearing.

[0053] According to another aspect of the present invention, there is provided a computer device, comprising a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0054] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the above-mentioned power transaction clearing processing method.

[0055] By means of the above technical solution, the technical solution provided by the embodiment of the present invention has at least the following advantages:

[0056] The present invention provides a method and device for processing power transaction clearing, a storage medium, and a computer device. Compared with the prior art, the present invention uses a wind power station and a photovoltaic power station that share a DC transmission channel as an aggregated wind power station and an aggregated photovoltaic power station, and respectively performs error analysis on the predicted output power of the aggregated wind power station and the aggregated photovoltaic power station to obtain a predicted error distribution; merges all hydropower units that share a DC transmission channel in the same area to obtain multiple virtual hydropower units, and performs modeling based on the virtual hydropower units to obtain an active power model of a new energy power delivery point; establishes a power transaction clearing model with the goal of minimizing power generation costs, and introduces a DC transmission channel opportunity constraint into the power transaction clearing model based on the active power model of the new energy power delivery point and the predicted error distribution to obtain a target power transaction clearing model; optimizes and solves the target power transaction clearing model to obtain an energy configuration result under an AC / DC hybrid power grid. The present invention makes full use of the flexibility of hydropower to buffer the problem of multi-DC channel transmission and the problem of renewable energy consumption. The present invention proposes a hydropower unit reorganization strategy based on set theory, which merges all units in a hydropower plant that use the same DC transmission channel into virtual hydropower units, and establishes an AC / DC hybrid power grid clearing dispatching model based on the strategy. The hydraulic and electrical connections between the virtual hydropower units are recoupled, successfully and efficiently realizing the dispatching operation of the water-wind-solar integrated energy system integrated into the power grid under the AC / DC hybrid power grid.

[0057] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0059] Figure 1 A schematic flow chart of a method for processing power transaction clearing provided by an embodiment of the present invention is shown;

[0060] Figure 2 A schematic flow chart of another method for processing power transaction clearing provided by an embodiment of the present invention is shown;

[0061] Figure 3 A schematic diagram showing a flow chart of another method for processing power transaction clearing provided by an embodiment of the present invention;

[0062] Figure 4 A schematic flow chart of another method for processing power transaction clearing provided by an embodiment of the present invention is shown;

[0063] Figure 5 A schematic diagram showing the structure of a power transaction clearing processing device provided by an embodiment of the present invention;

[0064] Figure 6 A schematic structural diagram of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0065] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0066] The embodiment of the present invention provides a method for processing power transaction clearing, such as Figure 1 As shown, the method includes:

[0067] 101. The wind power station and the photovoltaic power station that share the DC transmission channel are regarded as an aggregated wind power station and an aggregated photovoltaic power station, and error analysis is performed on the predicted output power of the aggregated wind power station and the aggregated photovoltaic power station to obtain a predicted error distribution;

[0068] In an embodiment of the present invention, the current execution end aggregates the renewable energy power stations that share the same DC transmission channel based on the regional aggregation effect, that is, the wind power stations that share the DC transmission channel are regarded as aggregated wind power stations, and the photovoltaic power stations that share the DC transmission channel are regarded as aggregated photovoltaic power stations. Due to the frequent changes in meteorological factors, the output of renewable energy is random and uncontrollable, which is not conducive to the safe operation of the power grid. In an embodiment of the present invention, the current execution end performs error analysis on the predicted output power of the above-mentioned aggregated wind power station and aggregated photovoltaic power station, respectively, to obtain a prediction error distribution. Among them, the purpose of the error analysis is to appropriately estimate the prediction error of the renewable energy output. It has been verified that with the aggregation of renewable energy power generation units, the prediction error of its new energy output gradually decreases.

[0069] 102. All the hydropower units that share a DC transmission channel in the same area are merged to obtain a plurality of virtual hydropower units, and modeling is performed based on the virtual hydropower units to obtain an active power model of a new energy power delivery point;

[0070] In the embodiment of the present invention, due to the hydraulic coupling relationship between the upstream and downstream hydropower plants in the same cascade hydropower system, and the complex electrical coupling relationship between multiple groups of upstream and downstream power plants, as well as between different units in a single hydropower plant, due to the use of different DC transmission channels to access the AC power grid, a complex and huge modeling problem is formed between different hydropower units. In order to effectively deal with these problems, the current execution end in the embodiment of the present invention proposes a hydropower unit reorganization strategy based on set theory to reduce the scale of the problem, merge all hydropower units sharing the DC transmission channel in the same area, obtain multiple virtual hydropower units, and further reorganize and model the hydraulic and electrical coupling relationship between the virtual hydropower units to obtain the active power model of the new energy power delivery point.

[0071] 103. Establishing a power transaction clearing model with the goal of minimizing power generation cost, and introducing DC transmission channel opportunity constraints into the power transaction clearing model based on the active power model of the new energy power delivery point and the prediction error distribution, to obtain a target power transaction clearing model;

[0072] In the embodiment of the present invention, the current execution end establishes a power transaction clearing model with the goal of minimizing the power generation cost, and introduces the DC transmission channel opportunity constraint into the power transaction clearing model established in this step based on the active power model of the new energy power delivery point and the prediction error distribution obtained in step 102 to obtain the target power transaction clearing model.

[0073] 104. Optimize and solve the target power transaction clearing model to obtain the energy configuration result under the AC / DC hybrid power grid.

[0074] In an embodiment of the present invention, the current execution end optimizes and solves the target power transaction clearing model to obtain an energy configuration result under the AC / DC hybrid power grid. The optimization and solution can use an existing optimization algorithm to solve the model.

[0075] Furthermore, as a refinement and extension of the specific implementation of the above embodiment, in order to more accurately estimate the distribution of the prediction error, another method for processing the clearing of power transactions is provided, such as Figure 2 As shown, the steps respectively perform error analysis on the predicted output power of the aggregated wind power station and the aggregated photovoltaic power station to obtain the predicted error distribution, including:

[0076] 201. Determine a wind power forecast output error corresponding to each time period based on the forecast output power and the actual output power of the aggregated wind power station; and determine a photovoltaic forecast output error corresponding to each time period based on the forecast output power and the actual output power of the aggregated photovoltaic power station;

[0077] In the embodiment of the present invention, the current execution end determines the wind power forecast output error corresponding to each time period based on the forecast output power and the actual output power of the aggregated wind power station. The specific formula is as follows:

[0078]

[0079] Where NW and NS represent wind turbines and photovoltaic units, respectively; l represents the DC transmission line where the power generation unit is located; t represents the time period; w and s represent the wind turbine and photovoltaic units to which the power belongs, respectively; W l and S l They represent the number of wind turbines and photovoltaic units using the DC transmission line l respectively; and denote the total power generation of the aggregated wind power plant and the aggregated photovoltaic power plant using the DC transmission line l during period t, respectively; and They respectively represent the output of the w-th wind turbine and the s-th photovoltaic unit using the DC transmission line l in time period t. and They represent the predicted output errors of the aggregated wind power station and the aggregated photovoltaic power station in period t respectively; and They represent the actual output power of the aggregated wind power station and the aggregated photovoltaic power station in time period t respectively; and They represent the predicted output power of the aggregated wind power station and the aggregated photovoltaic power station in period t respectively.

[0080] 202. Analyze the probability density distribution of the wind power prediction output error using a kernel density function to obtain a wind power prediction error distribution; and analyze the probability density distribution of the photovoltaic prediction output error using the kernel density function to obtain a photovoltaic prediction error distribution.

[0081] In an embodiment of the present invention, the current execution end uses a kernel density function to analyze the probability density distribution of the wind power forecast output error to obtain a wind power forecast error distribution; and uses the kernel density function to analyze the probability density distribution of the photovoltaic forecast output error to obtain a photovoltaic forecast error distribution. For simplicity, it is assumed that the power forecast errors of the aggregated units in each period are independent of each other. The relative forecast error of the aggregated units and the probability density function of the forecast error are expressed as follows:

[0082]

[0083] in, and They represent the relative prediction errors of the aggregated wind power plant and aggregated PV power plant using the DC transmission line l in period t, respectively; and They represent the probability density functions of the prediction errors of the aggregated wind power plant and the aggregated photovoltaic power plant using the DC transmission line l in time period t, respectively; M and N represent the number of samples used in the kernel density estimation method, respectively; and They respectively represent the mth statistical sample of the aggregated wind power station and the nth statistical sample of the aggregated photovoltaic power station using the DC transmission line l; h represents the bandwidth in the kernel density estimation method; ke() represents the kernel density function.

[0084] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to more accurately determine the power output based on the DC transmission line, another method for processing power transaction clearing is provided, such as Figure 3 As shown, the steps are based on the modeling of the virtual hydropower unit to obtain the active power model of the new energy power delivery point, including:

[0085] 301. Determine the corresponding associated wind power station and the associated photovoltaic power station for DC power injection using each of the shared DC transmission channels;

[0086] 302. Determine the power output of each of the virtual hydropower units based on the shared DC transmission channel, and determine the wind-related output power of the associated wind power station and the photovoltaic-related output power of the associated photovoltaic power station;

[0087] 303. Establish an active power model of the new energy power delivery point based on the power output of the virtual hydropower unit, the wind power-related output power, and the photovoltaic power-related output power.

[0088] In the embodiment of the present invention, the current execution end adopts a hydropower unit reorganization strategy based on set theory and then performs modeling processing. First, the corresponding associated wind power station and associated photovoltaic power station for DC power injection using each shared DC transmission channel are determined. Then, the power output of each virtual hydropower unit is determined based on the shared DC transmission channel, which is recorded as And determine the wind power output power of the associated wind power station, recorded as Determine the photovoltaic associated output power of the associated photovoltaic power station, denoted as Next, based on the power output of the virtual hydropower unit Wind power output Output power associated with photovoltaic Establish the active power model of the new energy power delivery point. The specific formula is as follows:

[0089]

[0090] Among them, P l,tIt represents the active power of the new energy power delivery point transported by the DC transmission line l; represents the power output of the vth virtual hydropower unit in the ith hydropower plant using the DC transmission line l during the tth period; and They represent the total power generation in period t of the aggregated wind power plant and the aggregated photovoltaic power plant using the DC transmission line l.

[0091] It should be noted that after the step of obtaining the active power model of the new energy power delivery point, the method also includes adding constraints to the active power model of the new energy power delivery point, including: water balance constraints, upper and lower limit constraints of total discharge flow, upper and lower limit constraints of overflow discharge, upper and lower limit constraints of power discharge, tailwater water level constraints, forepool water level constraints, upper and lower limit constraints of forepool water level, head constraints and upper and lower limit constraints of power.

[0092] Among them, the water balance constraint indicates that the hydropower plant storage capacity at the t+1th moment is related to the hydropower plant storage capacity at the tth moment, the local natural runoff, the total discharge of the upstream hydropower plant, and the total discharge of the hydropower plant. In addition, the total discharge of the hydropower plant is equal to the sum of the power discharge and the overflow discharge. The specific formula is as follows:

[0093]

[0094] Among them, the upper and lower limit constraints of the total discharge flow indicate that the total discharge flow of the hydropower plant has corresponding maximum and minimum limits. The specific formula is as follows:

[0095]

[0096] Among them, the upper and lower limit constraints of overflow discharge indicate that there are corresponding maximum and minimum limits on the overflow discharge of the hydropower plant. The specific formula is as follows:

[0097]

[0098] Among them, the upper and lower limits of power emission indicate that there are corresponding maximum and minimum limits on the power emission of the hydropower plant. The specific formula is as follows:

[0099]

[0100] Among them, the tailwater level constraint indicates that there is a corresponding functional relationship between the tailwater level of the hydropower plant and the total discharge flow. The specific formula is as follows:

[0101]

[0102] Among them, the forebay water level constraint indicates that there is a corresponding functional relationship between the forebay water level and the reservoir capacity of the hydropower plant. The specific formula is as follows:

[0103]

[0104] Among them, the upper and lower limit constraints of the forebay water level indicate that the forebay water level of the hydropower plant has corresponding maximum and minimum limits. The specific formula is as follows:

[0105]

[0106] Among them, the head constraint indicates that there is a functional relationship between the head of the hydropower plant and the forebay water level and tailwater level at this moment and the next moment. The specific formula is as follows:

[0107]

[0108] Among them, the power upper and lower limit constraints indicate that there is a corresponding functional relationship between the power of the hydropower plant and the water head and power discharge. Based on the above functional relationship, the power of the hydropower plant has corresponding maximum and minimum limits. The specific formula is as follows:

[0109]

[0110] In the above constraints, i, w and s represent the indexes of hydropower units, wind power units and photovoltaic units respectively; I l , W l and S l They represent the number of hydropower units, wind turbine units and photovoltaic units using the DC transmission line l; g represents the upstream hydropower plant; and V represents the minimum and maximum output of the vth unit in the ith hydropower plant using the DC transmission line l during period t; i,t represents the storage capacity of the ith hydropower plant in the tth period; R i,t represents the local natural runoff of the tth hydropower plant in the tth period; and It represents the total discharge flow, power discharge and overflow drainage of the vth virtual hydropower unit in the i-th hydropower plant in the t-th period; and denote the maximum and minimum total flow of the ith hydropower plant respectively; and They represent the maximum and minimum water abandonment of the ith hydropower plant respectively; f i ZQ represents the relationship function between the total flow and tailwater level of the ith hydropower plant; represents the tailwater level of the ith hydropower station in the tth period; Z i,t represents the water level of the forebay of the ith hydropower plant in the tth period; f i ZQ () describes the functional relationship between tailwater level and power discharge; f iZV () describes the functional relationship between hydropower reservoir capacity and forebay water level; 和 represents the maximum and minimum water level in the forebay of the i-th hydropower plant; A two-dimensional function describing the relationship between the power output, power flow and water head of the vth virtual hydropower unit in the i-th hydropower plant; H i,t It represents the water head level of the i-th hydropower plant in the t-th period.

[0111] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to reduce the cost of clearing power transactions, another method for processing clearing power transactions is provided, wherein a power transaction clearing model is established with the goal of minimizing the power generation cost, including:

[0112] Determine other nodes in the AC / DC hybrid power grid except the DC power injection node as regular nodes;

[0113] Obtaining the power of the generator set and the power generation cost of the generator set corresponding to the conventional node;

[0114] The electricity trading clearing model is established based on the power of the generator set at each of the conventional nodes and the power generation cost of the generator set.

[0115] In the embodiment of the present invention, the current execution end determines other nodes in the AC / DC hybrid power grid except the DC power injection node as regular nodes, which are recorded as n; and obtains the generator power P corresponding to the regular node. n,t and the unit power generation cost C n ; Based on the generator power P of each conventional node n,t and the unit power generation cost C n The electricity trading clearing model is established, and the specific formula is as follows:

[0116]

[0117] Where n represents the number of other nodes (hereinafter referred to as “conventional nodes”) in the AC / DC hybrid power grid except the DC power injection node, N represents the total number of conventional nodes, and P n,t represents the power of the generator set at the normal node during period t; C n represents the power generation cost of the unit at the conventional node, and T represents the scheduling period.

[0118] It should be noted that the power transaction clearing model is subject to the following AC and DC power grid system operation constraints:

[0119] C a,t =L a,t -P a,t

[0120]

[0121] Where a represents the DC power injection node number in the AC / DC hybrid power grid, A represents the total number of such nodes; P a,t represents the power generation of DC power node a; C a,t and L a,t Denote the net load and load of DC power node a in time period t respectively; D n,t Indicates the load of a regular node; and They represent the aggregated wind power output and aggregated photovoltaic power output at the DC power injection node a and utilizing the DC transmission line l in the tth period, respectively; and They represent the output power of the w-th wind farm and the s-th photovoltaic farm in the t-th period located at the DC power injection node a and utilizing the DC transmission line l; and They represent the maximum transmission power and the minimum transmission power of the DC transmission line l respectively; and They represent the upper and lower limits of the unit output at node n; and they represent the upper and lower limits of the transmittable power of the AC transmission line m; SF m,n represents the power transfer matrix between AC transmission line m and conventional node n; SF m,a Represents the power transfer matrix between the AC transmission line m and the DC power injection node a.

[0122] Furthermore, as a refinement and extension of the specific implementation of the above embodiment, in order to introduce differentiated DC transmission channel opportunity constraints to the aggregated wind power station and the aggregated photovoltaic power station respectively to compensate for their fluctuations within a certain confidence interval and further avoid insufficient or wasteful hydropower compensation margin, another method for processing power transaction clearing is provided, such as Figure 4 As shown, the steps of introducing the DC transmission channel opportunity constraint into the power transaction clearing model based on the active power model of the new energy power delivery point and the prediction error distribution include:

[0123] 401. Based on the active power model of the new energy power delivery point, a random variable probability distribution function and a confidence level are introduced to obtain an initial DC transmission channel opportunity constraint;

[0124] In the embodiment of the present invention, the current execution end introduces the probability distribution function of random variables and the confidence level based on the active power model of the new energy power delivery point to obtain the initial DC transmission channel opportunity constraint, which is specifically expressed as follows:

[0125]

[0126] Among them, Pr{} represents the probability that the formula containing random variables is true; β and γ represent the confidence level.

[0127] 402. Perform equivalent transformation on the initial DC transmission channel opportunity constraint using prediction error distribution to obtain the DC transmission channel opportunity constraint.

[0128] In the embodiment of the present invention, the current execution end uses the prediction error distribution of step 101 to perform equivalent transformation on the initial DC transmission channel opportunity constraint introduced in step 401 to obtain the DC transmission channel opportunity constraint. From the analysis of step 201 and step 202, it can be seen that by introducing the cumulative distribution function of the prediction error of the new energy unit, the opportunity constraint equation can be transformed into an equivalent linear form: and There are the following formulas:

[0129]

[0130] Combined with the above formula, the above two formulas can be transformed into:

[0131]

[0132] After the above transformation, the DC transmission channel opportunity constraints are obtained.

[0133] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to facilitate optimization and solution, the probability constraint is converted into a deterministic constraint, and another method for processing power transaction clearing is provided. Before optimizing and solving the target power transaction clearing model, the method further includes:

[0134] Determine the corresponding wind prediction error distribution inverse function and photovoltaic prediction error distribution inverse function based on the probability density distribution of wind prediction output error and the probability density distribution of photovoltaic prediction output error in the prediction error distribution;

[0135] The inverse function of the wind power forecast error distribution and the inverse function of the photovoltaic power forecast error distribution are used to perform a linear transformation on the DC transmission channel opportunity constraint, so that the DC transmission channel opportunity constraint is transformed from a probabilistic constraint to a deterministic constraint.

[0136] In the embodiment of the present invention, the current execution end determines the corresponding inverse function of the wind power prediction error distribution based on the probability density distribution of the wind power prediction output error in the prediction error distribution, which is recorded as In addition, the inverse function of the photovoltaic prediction error distribution is determined based on the probability density distribution of the photovoltaic prediction output error in the prediction error distribution, which is recorded as

[0137] The current execution end uses the above wind prediction error distribution inverse function and the inverse function of the photovoltaic prediction error distribution The DC transmission channel opportunity constraint obtained in step 402 is subjected to linear transformation processing, so that the DC transmission channel opportunity constraint is transformed from a probabilistic constraint to a deterministic constraint. Specifically, due to and as well as and The DC transmission channel opportunity constraint in the above step 402 can be finally transformed into a deterministic constraint, and the formula is as follows:

[0138]

[0139] After the above transformation, the power trading clearing model in the embodiment of the present invention is finally transformed into a mixed integer linear programming model. The embodiment of the present invention reconstructs the differentiated DC transmission channel opportunity constraints in a deterministic form, and linearizes the original power trading clearing model into a mixed integer linear programming model, further reducing the computational burden and achieving efficient dispatching operation.

[0140] The embodiment of the present invention provides a method for processing power transaction clearing. Compared with the prior art, the present invention uses the wind power station and photovoltaic power station sharing the DC transmission channel as the aggregated wind power station and the aggregated photovoltaic power station, and respectively performs error analysis on the predicted output power of the aggregated wind power station and the aggregated photovoltaic power station to obtain the predicted error distribution; merges all the hydropower units sharing the DC transmission channel in the same area to obtain multiple virtual hydropower units, and performs modeling based on the virtual hydropower units to obtain the active power model of the new energy power delivery point; establishes a power transaction clearing model with the goal of minimizing the power generation cost, and introduces the DC transmission channel opportunity constraint into the power transaction clearing model based on the active power model of the new energy power delivery point and the predicted error distribution to obtain the target power transaction clearing model; optimizes and solves the target power transaction clearing model to obtain the energy configuration result under the AC / DC hybrid power grid. The present invention makes full use of the flexibility of hydropower to buffer the multi-DC channel transmission problem and the renewable energy consumption problem. The present invention proposes a hydropower unit reorganization strategy based on set theory, which merges all units in a hydropower plant that use the same DC transmission channel into virtual hydropower units, and establishes an AC / DC hybrid power grid clearing dispatching model based on the strategy. The hydraulic and electrical connections between the virtual hydropower units are recoupled, successfully and efficiently realizing the dispatching operation of the water-wind-solar integrated energy system integrated into the power grid under the AC / DC hybrid power grid.

[0141] As the above Figure 1 The embodiment of the present invention provides a processing device for clearing power transactions, such as Figure 5As shown, the device comprises:

[0142] The prediction analysis module 51 is used to treat the wind power station and the photovoltaic power station that share the DC transmission channel as an aggregated wind power station and an aggregated photovoltaic power station, and respectively perform error analysis on the predicted output power of the aggregated wind power station and the aggregated photovoltaic power station to obtain a prediction error distribution;

[0143] The reorganization and modeling module 52 is used to merge all the hydropower units that share the DC transmission channel in the same area to obtain multiple virtual hydropower units, and perform modeling based on the virtual hydropower units to obtain an active power model of the new energy power delivery point;

[0144] The model improvement module 53 is used to establish a power transaction clearing model with the goal of minimizing the power generation cost, and introduce the DC transmission channel opportunity constraint into the power transaction clearing model based on the active power model of the new energy power delivery point and the prediction error distribution, so as to obtain the target power transaction clearing model;

[0145] The optimization solution module 54 is used to optimize and solve the target power transaction clearing model to obtain the energy configuration result under the AC / DC hybrid power grid.

[0146] Furthermore, the prediction analysis module 51 includes:

[0147] An error analysis unit, configured to determine a wind power forecast output error corresponding to each time period based on the forecast output power and the actual output power of the aggregated wind power station; and to determine a photovoltaic forecast output error corresponding to each time period based on the forecast output power and the actual output power of the aggregated photovoltaic power station;

[0148] The probability density analysis unit is used to analyze the probability density distribution of the wind power prediction output error using a kernel density function to obtain a wind power prediction error distribution; and to analyze the probability density distribution of the photovoltaic prediction output error using the kernel density function to obtain a photovoltaic prediction error distribution.

[0149] Furthermore, the reorganization and modeling module 52 includes:

[0150] An associated determination unit, used to determine corresponding associated wind power stations and associated photovoltaic power stations for DC power injection using each of the shared DC transmission channels;

[0151] A power determination unit, configured to determine the power output of each of the virtual hydropower units based on the shared DC transmission channel, and determine the wind-related output power of the associated wind power station and the photovoltaic-related output power of the associated photovoltaic power station;

[0152] A model building unit is used to build the active power model of the new energy power delivery point based on the power output of the virtual hydropower unit, the wind power-related output power and the photovoltaic power-related output power.

[0153] Furthermore, the device also includes a hydropower constraint setting module, which is used to:

[0154] Add constraints to the active power model of the new energy power delivery point, and the constraints include: water balance constraints, upper and lower limit constraints of total discharge flow, upper and lower limit constraints of overflow discharge, upper and lower limit constraints of power discharge, tailwater water level constraints, forepool water level constraints, upper and lower limit constraints of forepool water level, head constraints and upper and lower limit constraints of power.

[0155] Furthermore, the model improvement module 53 includes a modeling unit, which is used to:

[0156] Determine other nodes in the AC / DC hybrid power grid except the DC power injection node as regular nodes;

[0157] Obtaining the power of the generator set and the power generation cost of the generator set corresponding to the conventional node;

[0158] The electricity trading clearing model is established based on the power of the generator set at each of the conventional nodes and the power generation cost of the generator set.

[0159] Furthermore, the model improvement module 53 also includes an improvement unit, which is used to:

[0160] Based on the active power model of the new energy power delivery point, the probability distribution function of random variables and the confidence level are introduced to obtain the initial DC transmission channel opportunity constraint;

[0161] The initial DC transmission channel opportunity constraint is equivalently transformed by using the prediction error distribution to obtain the DC transmission channel opportunity constraint.

[0162] Furthermore, the device also includes a conversion module, which is used to:

[0163] Determine the corresponding wind prediction error distribution inverse function and photovoltaic prediction error distribution inverse function based on the probability density distribution of wind prediction output error and the probability density distribution of photovoltaic prediction output error in the prediction error distribution;

[0164] The inverse function of the wind power forecast error distribution and the inverse function of the photovoltaic power forecast error distribution are used to perform a linear transformation on the DC transmission channel opportunity constraint, so that the DC transmission channel opportunity constraint is transformed from a probabilistic constraint to a deterministic constraint.

[0165] The embodiment of the present invention provides a processing device for clearing power transactions. Compared with the prior art, the present invention uses wind power stations and photovoltaic power stations that share a DC transmission channel as aggregated wind power stations and aggregated photovoltaic power stations, and respectively performs error analysis on the predicted output power of the aggregated wind power station and the aggregated photovoltaic power station to obtain a predicted error distribution; merges all hydropower units that share a DC transmission channel in the same area to obtain multiple virtual hydropower units, and performs modeling based on the virtual hydropower units to obtain an active power model of a new energy power delivery point; establishes a power transaction clearing model with the goal of minimizing power generation costs, and introduces DC transmission channel opportunity constraints into the power transaction clearing model based on the active power model of the new energy power delivery point and the predicted error distribution to obtain a target power transaction clearing model; optimizes and solves the target power transaction clearing model to obtain an energy configuration result under an AC / DC hybrid power grid. The present invention makes full use of the flexibility of hydropower to buffer the problem of multi-DC channel transmission and the problem of renewable energy consumption. The present invention proposes a hydropower unit reorganization strategy based on set theory, which merges all units in a hydropower plant that use the same DC transmission channel into virtual hydropower units, and establishes an AC / DC hybrid power grid clearing dispatching model based on the strategy. The hydraulic and electrical connections between the virtual hydropower units are recoupled, successfully and efficiently realizing the dispatching operation of the water-wind-solar integrated energy system integrated into the power grid under the AC / DC hybrid power grid.

[0166] According to one embodiment of the present invention, a storage medium is provided, wherein the storage medium stores at least one executable instruction, and the computer executable instruction can execute the processing method for clearing electricity transactions in any of the above method embodiments.

[0167] Figure 6 A schematic diagram of the structure of a computer device provided according to an embodiment of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the computer device.

[0168] like Figure 6 As shown, the computer device may include: a processor (processor) 602 , a communication interface (Communications Interface) 604 , a memory (memory) 606 , and a communication bus 608 .

[0169] The processor 602 , the communication interface 604 , and the memory 606 communicate with each other via a communication bus 608 .

[0170] The communication interface 604 is used to communicate with other devices such as clients or other servers.

[0171] The processor 602 is used to execute the program 610, and specifically can execute the relevant steps of the above-mentioned power transaction clearing processing method.

[0172] Specifically, the program 610 may include program codes, which include computer operation instructions.

[0173] The processor 602 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the computer device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0174] The memory 606 is used to store the program 610. The memory 606 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0175] The program 610 may be specifically configured to enable the processor 602 to perform the following operations:

[0176] The wind power station and the photovoltaic power station that share a DC transmission channel are regarded as an aggregated wind power station and an aggregated photovoltaic power station, and error analysis is performed on the predicted output power of the aggregated wind power station and the aggregated photovoltaic power station to obtain a predicted error distribution;

[0177] All the hydropower units that share the DC transmission channel in the same area are merged to obtain multiple virtual hydropower units, and modeling is performed based on the virtual hydropower units to obtain an active power model of the new energy power delivery point;

[0178] Establishing a power transaction clearing model with the goal of minimizing power generation costs, and introducing DC transmission channel opportunity constraints into the power transaction clearing model based on the active power model of the new energy power delivery point and the prediction error distribution, to obtain a target power transaction clearing model;

[0179] The target power transaction clearing model is optimized and solved to obtain the energy configuration result under the AC / DC hybrid power grid.

[0180] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0181] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for clearing power transactions, characterized in that: include: The wind power station and the photovoltaic power station that share a DC transmission channel are regarded as an aggregated wind power station and an aggregated photovoltaic power station, and error analysis is performed on the predicted output power of the aggregated wind power station and the aggregated photovoltaic power station to obtain a predicted error distribution; All the hydropower units that share the DC transmission channel in the same area are merged to obtain multiple virtual hydropower units, and modeling is performed based on the virtual hydropower units to obtain an active power model of the new energy power delivery point; Establishing a power transaction clearing model with the goal of minimizing power generation costs, and introducing DC transmission channel opportunity constraints into the power transaction clearing model based on the active power model of the new energy power delivery point and the prediction error distribution, to obtain a target power transaction clearing model; The target power transaction clearing model is optimized and solved to obtain the energy configuration result under the AC / DC hybrid power grid.

2. The method according to claim 1, characterized in that The error analysis of the predicted output power of the aggregated wind power station and the aggregated photovoltaic power station is performed respectively to obtain the predicted error distribution, including: Determine the wind power forecast output error corresponding to each time period based on the forecast output power and the actual output power of the aggregated wind power station; and determine the photovoltaic forecast output error corresponding to each time period based on the forecast output power and the actual output power of the aggregated photovoltaic power station; The probability density distribution of the wind power prediction output error is analyzed using a kernel density function to obtain a wind power prediction error distribution; and the probability density distribution of the photovoltaic prediction output error is analyzed using the kernel density function to obtain a photovoltaic prediction error distribution.

3. The method according to claim 1, characterized in that The modeling process based on the virtual hydropower unit is performed to obtain the active power model of the new energy power delivery point, including: Determining corresponding associated wind power stations and associated photovoltaic power stations for DC power injection using each of the shared DC transmission channels; Determining the power output of each of the virtual hydropower units based on the shared DC transmission channel, and determining the wind-related output power of the associated wind power station and the photovoltaic-related output power of the associated photovoltaic power station; An active power model of the new energy power delivery point is established based on the power output of the virtual hydropower unit, the wind power-related output power and the photovoltaic power-related output power.

4. The method according to claim 1, characterized in that After obtaining the active power model of the new energy power delivery point, the method further includes: Adding constraint conditions to the active power model of the new energy power delivery point; The constraints include: water balance constraints, upper and lower limit constraints on total discharge flow, upper and lower limit constraints on overflow discharge, upper and lower limit constraints on power discharge, tailwater water level constraints, forebay water level constraints, upper and lower limit constraints on forebay water level, head constraints and upper and lower limit constraints on power.

5. The method according to claim 1, characterized in that The power transaction clearing model established with the goal of minimizing power generation costs includes: Determine other nodes in the AC / DC hybrid power grid except the DC power injection node as regular nodes; Obtaining the power of the generator set and the power generation cost of the generator set corresponding to the conventional node; The electricity trading clearing model is established based on the power of the generator set at each of the conventional nodes and the power generation cost of the generator set.

6. The method according to claim 1, characterized in that The introducing of DC transmission channel opportunity constraints into the power transaction clearing model based on the active power model of the new energy power delivery point and the prediction error distribution includes: Based on the active power model of the new energy power delivery point, the probability distribution function of random variables and the confidence level are introduced to obtain the initial DC transmission channel opportunity constraint; The initial DC transmission channel opportunity constraint is equivalently transformed by using the prediction error distribution to obtain the DC transmission channel opportunity constraint.

7. The method according to any one of claims 1 to 6, characterized in that: Before optimizing and solving the target power transaction clearing model, the method further includes: Determine the corresponding wind prediction error distribution inverse function and photovoltaic prediction error distribution inverse function based on the probability density distribution of wind prediction output error and the probability density distribution of photovoltaic prediction output error in the prediction error distribution; The inverse function of the wind power forecast error distribution and the inverse function of the photovoltaic power forecast error distribution are used to perform a linear transformation on the DC transmission channel opportunity constraint, so that the DC transmission channel opportunity constraint is transformed from a probabilistic constraint to a deterministic constraint.

8. A processing device for clearing power transactions, characterized in that: include: A prediction analysis module, used to treat the wind power station and the photovoltaic power station that share the DC transmission channel as an aggregated wind power station and an aggregated photovoltaic power station, and respectively perform error analysis on the predicted output power of the aggregated wind power station and the aggregated photovoltaic power station to obtain a prediction error distribution; The reorganization and modeling module is used to merge all the hydropower units that share the DC transmission channel in the same area to obtain multiple virtual hydropower units, and perform modeling based on the virtual hydropower units to obtain the active power model of the new energy power delivery point; A model improvement module is used to establish a power transaction clearing model with the goal of minimizing power generation costs, and introduce a DC transmission channel opportunity constraint into the power transaction clearing model based on the active power model of the new energy power delivery point and the prediction error distribution to obtain a target power transaction clearing model; The optimization solution module is used to optimize and solve the target power transaction clearing model to obtain the energy configuration result under the AC / DC hybrid power grid.

9. A storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction executes an operation corresponding to the method for processing power transaction clearing according to any one of claims 1 to 7.

10. A computer device, comprising a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the method for processing power transaction clearing as described in any one of claims 1-7.

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