CN112598480B - Computer implementation method for inter-provincial medium-long term clean energy electric power transaction recommendation rate - Google Patents
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Abstract
The invention relates to a computer implementation method of a recommendation rate of clean energy electric power trading in middle and long term in a province, which is characterized in that the prediction data of clean energy power generation, the consumption data of clean energy, the average trading power price in middle and long term and the space of a power transmission channel of the middle and long term are used as the basis for establishing the recommendation rate of the clean energy electric power trading in middle and long term in the province, and a database which can be stored/read by a computer is generated; calculating provincial medium and long term wind power transaction recommendation rate, photovoltaic transaction recommendation rate and hydropower transaction recommendation rate, and calculating provincial transaction price recommendation rate and transmission capacity recommendation rate of a transmission channel; and weighting the medium-long term wind power transaction recommendation rate, the photovoltaic transaction recommendation rate, the hydropower transaction recommendation rate, the provincial-level transaction price recommendation rate and the transmission capacity recommendation rate of the transmission channel to generate the inter-provincial medium-long term clean energy power transaction recommendation rate. The invention calculates the transaction recommendation rate of each province through index modeling and normalized analysis algorithm based on various influence factors, and can improve the scientificity and rationality of transaction organization.
Description
Technical Field
The invention belongs to the technical field of inter-provincial clean energy electric power trading, and particularly relates to a computer implementation method for inter-provincial medium and long-term clean energy electric power trading recommendation rate.
Background
The reverse distribution of the power resources and loads in China determines the important value of cross-province and cross-district power trading in optimizing the power resource configuration. In recent years, on one hand, the construction of western energy bases is increased in China, and on the other hand, the construction of trans-provincial and trans-regional power channels with the construction of extra-high voltage power grids as a core is increased. The cross-provincial and cross-regional electric power trading volume is rapidly increased, the increase proportion is far larger than the increase proportion of the electric quantity in the country at the same period, and a direct promotion effect is generated on optimizing the electric power production and consumption structure and solving the problem of abandoning wind power by abandoning photoelectricity.
However, due to the lack of accurate knowledge and conscious application of the mechanism for promoting resource optimal configuration in the trans-provincial and trans-regional power trading, the resource optimal configuration in the trans-provincial and trans-regional power trading in China is not sufficiently played, so that the utilization efficiency of power resources is insufficient. On one hand, the problem of more serious wind power and photoelectric abandonment still exists in the northwest five provinces (districts); on the other hand, the annual average utilization rate of trans-provincial power transmission lines such as shogao direct current and debao direct current is low. According to the statistical data of the national energy bureau, the phenomena of wind power abandonment, light power abandonment and insufficient channel utilization exist while the scale of the power trading is greatly increased across provinces and regions, and the phenomenon is ubiquitous in the whole country.
At present, the middle-term and long-term power trading in the cross-province is mainly organized by means of a unified power market trading platform, including annual trading and monthly trading, wherein the trading varieties are divided into types such as protocol plans, direct trading, power generation right trading and the like, the direct trading and the power generation right trading belong to marketization trading, and the protocol plans refer to annual cross-province priority power generation plans issued by the country according to national instruction plans, inter-province government framework agreements and countries. The power generation and receiving plan of the clean energy mainly depends on a protocol plan, a bottom-of-guarantee consumption plan is difficult to implement, a marketized transaction organization needs to be effectively increased, and the consumption level of the clean energy is improved through marketized adjustment.
Disclosure of Invention
In order to solve the technical problems, the invention mainly aims to solve the problems that the power trading in provinces and in long and medium periods lacks auxiliary decision analysis and recommendation, and secondly solves the problems that the abandoned wind power is serious, and the consumption of clean energy is difficult to implement. The medium and long-term recommendation results of wind power, photovoltaic, hydropower and new energy of each province provided by the invention can provide trading indexes and trading recommendation rate ranking conditions of clean energy of different provinces in different months, and provide a more accurate and effective decision means for making annual trading plans and monthly trading plans. The technical scheme adopted by the invention is as follows:
a computer implementation method for the recommendation rate of the clean energy electric power trading in the middle and long term in the province is characterized in that the clean energy power generation prediction data, the clean energy consumption data, the average trading electricity price in the middle and long term and the power transmission channel space of the middle and long term are used as the basis for establishing the recommendation rate of the clean energy electric power trading in the middle and long term in the province, and a database which can be stored/read by a computer is generated; calculating provincial medium and long term wind power transaction recommendation rate, photovoltaic transaction recommendation rate and hydropower transaction recommendation rate, and calculating provincial transaction price recommendation rate and transmission capacity recommendation rate of a transmission channel; and weighting the medium-long term wind power transaction recommendation rate, the photovoltaic transaction recommendation rate, the hydropower transaction recommendation rate, the provincial-level transaction price recommendation rate and the transmission capacity recommendation rate of the transmission channel to generate the inter-provincial medium-long term clean energy power transaction recommendation rate.
Preferably, the index related to the method is calculated and recommended in units of provinces and in cycles of months, and the calculation steps are as follows:
step 1, calculating provincial medium and long term wind power trading recommendation rate;
respectively calculating four subentry indexes of the monthly generated energy change rate of the wind power, the predicted abandoned wind rate, the medium and long term distance average value of the wind resources and the proportion of the installed capacity of the wind power, and finally weighting;
step 2, calculating the provincial medium and long-term photovoltaic transaction recommendation rate;
respectively calculating four subentry indexes of photovoltaic monthly power generation capacity change rate, predicted light abandon rate, light resource medium and long term distance average value and photovoltaic installed capacity ratio, and finally weighting;
step 3, calculating the provincial mid-term and long-term hydropower transaction recommendation rate;
respectively calculating three subentry indexes of the predicted water abandon rate, the medium and long term average water resource distance value and the water and electricity installed capacity ratio, and finally weighting;
step 4, calculating the provincial transaction price recommendation rate;
respectively calculating the provincial average trading power price, the general average trading power price of the external province and the proportion; the rule of the provincial transaction price recommendation rate is as follows: the lower the provincial-level average trading power price is, namely the lower the proportion of the provincial-level average trading power price to the total average trading power price of the other provinces is, the higher the recommendation rate is;
step 5, calculating the recommendation rate of the transmission capacity of the transmission channel;
respectively calculating all power transmission spaces of the power transmission channels, used power transmission spaces of the power transmission channels and residual available power transmission spaces of the power transmission channels; the power transmission channel power transmission capacity recommendation rate is regulated as follows: the larger the residual available power transmission space of the power transmission channel is, the higher the recommendation rate of the power transmission capacity of the power transmission channel is;
step 6, calculating the inter-provincial medium and long-term clean energy electric power transaction recommendation rate;
and carrying out weighting setting on the provincial mid-and-long-term wind power transaction recommendation rate, the provincial mid-and-long-term photovoltaic transaction recommendation rate, the provincial mid-and-long-term hydropower transaction recommendation rate, the provincial trade price recommendation rate and the channel transmission capacity recommendation rate, and calculating to obtain the mid-and-long-term clean energy power transaction recommendation rate between each month and each province.
The invention has the beneficial effects that:
the invention provides a method and a system for recommending inter-provincial medium and long term electric power transactions, which solve the problem that the inter-provincial electric power medium and long term transactions lack an auxiliary decision analysis means, and provide a medium and long term electric power transaction recommendation method based on inter-provincial clean energy power generation prediction data and comprehensively considering various influence factors such as clean energy consumption, transaction electricity price, power transmission channel space and the like.
1) Analyzing the available power transmission space of the power transmission channel, and providing a recommendation basis for improving the space utilization rate of the power transmission channel as a judgment basis for judging whether power can be transmitted or not;
2) Analyzing inter-provincial transaction settlement information as a judgment basis for transaction price and transaction category optimization, and providing a recommendation basis for improving economic benefits;
3) The medium and long-term transaction assistant decision recommendation model and the related subentry recommendation model are established, and various key information is effectively associated and combined through indexed modeling, so that a user can clearly perceive the distribution condition of each subentry index and the total index;
4) Valuable recommendation indexes can be better selected by carrying out normalization processing on various recommendation indexes;
5) And according to the flexible recommendation configuration mode, weighting configuration solving is carried out on each coefficient in the model according to the requirements and the emphasis points of the market subject, so as to obtain effective recommendation information for guiding the transaction.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are illustrative of some embodiments of the invention and that other drawings falling within the scope of the present application may be derived by those skilled in the art without inventive step.
FIG. 1 is a logical block diagram of the steps of an embodiment of the present invention;
fig. 2 is a schematic diagram of a provincial medium-long term power transaction recommendation model according to an embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention.
FIG. 1 is a logic block diagram of steps of an embodiment of the present invention; fig. 2 is a schematic diagram of a provincial medium-long term power transaction recommendation model according to an embodiment of the present invention. A computer implementation method for the inter-provincial medium and long-term clean energy electric power transaction recommendation rate is characterized in that various factors influencing the medium and long-term transaction of clean energy are subjected to index modeling, normalization calculation processing is carried out according to recommendation percentages, and the inter-provincial medium and long-term clean energy electric power transaction recommendation rate of each province is calculated through weighting calculation of various indexes subjected to normalization processing. The method of the invention establishes 1 item of first-level index, 5 items of second-level index and 15 items of second-level index in total; the evaluation adopts a percentage evaluation mode, the value range is 0 to 100 percent, and the recommendation rate of each level of index is the sum of the recommendation rates of the lower level index; the indexes of all levels are provided with corresponding weights, the first-level index weight is the sum of the weights of all second-level indexes, and the sum of the itemized weights of all second-level indexes under the second-level indexes is 100%.
The index model established by the method of the invention is as follows:
1. establishing a power trading recommendation rate (L1) of the inter-provincial middle and long-term clean energy as a first-level index;
2. establishing a secondary index influencing the medium and long term electricity generation trading recommendation rate: the system comprises a wind power transaction recommendation rate (L1P 1), a photovoltaic transaction recommendation rate (L1P 2), a hydropower transaction recommendation rate (L1P 3), a transaction price recommendation rate (L1P 4) and a channel power transmission capacity recommendation rate (L1P 5).
3. Establishing each index subentry influencing the secondary index:
1) Establishing a wind power trading recommendation rate subentry index, comprising the following steps: the method comprises the following steps of wind power monthly power generation capacity change rate (P1A 1), predicted wind abandon rate (P1A 2), wind resource medium and long term distance average value (P1A 3) and wind power installed capacity ratio (P1A 4).
2) Establishing photovoltaic trade recommendation rate subentry indexes, comprising the following steps: the photovoltaic power generation capacity change rate (P2A 1), the predicted light abandon rate (P2A 2), the light resource medium and long term distance average value (P2A 3) and the photoelectric installed capacity ratio (P2A 4).
3) Establishing a hydropower transaction recommendation rate subentry index, which comprises the following steps: forecasting water abandon rate (P3A 1), medium and long term distance average value (P3A 2) of water resource and water installed capacity ratio (P3A 3).
4) Establishing a hydropower transaction recommendation rate subentry index, which comprises the following steps: provincial average trading electricity price (P4A 1) and general provincial average trading electricity price (P4A 2).
5) Establishing a recommendation rate index of space availability of a power transmission channel, comprising the following steps: the power transmission channel has a remaining available power transmission space (P5A 1) and a power transmission channel total power transmission space (P5A 2).
The indexes related to the method are calculated and recommended by taking provinces as units and months as periods, and the specific calculation steps are as follows:
step 1, calculating provincial medium and long term wind power trading recommendation rate;
and respectively calculating four subentry indexes of the monthly generated energy change rate of the wind power, the predicted wind abandoning rate, the medium and long term distance average value of the wind resources and the proportion of the installed capacity of the wind power, and finally weighting.
The change rate of the monthly generated energy of wind power is as follows: and reflecting the change condition of the monthly prediction power generation amount and the synchronous power generation amount. The negative number represents that the predicted generated electricity quantity is less; the positive number indicates that the predicted generated power amount is too much, and the space for inter-provincial exchange is larger.
Predicting the air abandon rate: under the condition of monthly wind resource consumption level, the lower the predicted value is, the better the consumption is; the higher the predicted value is, the worse the consumption is, and the higher the inter-provincial power trading recommendation rate is.
Wind resource medium-long term average value: the change range of the wind resource prediction value and the historical average value is embodied, and the historical average value integrates data of 30 years.
The ratio of installed capacity of wind power: the proportion of the provincial wind power installed capacity to the provincial total installed capacity is higher, and the recommendation rate is higher.
The method comprises the steps that wind power trading recommendation rate (L1P 1) is comprehensively recommended and evaluated on the basis of inter-provincial medium-and-long-term wind power generation amount prediction and prediction data of wind resources, the inter-provincial power generation electricity consumption condition and the new energy consumption condition are considered, and a wind power medium-and-long-term trading recommendation decision analysis model is constructed; the calculation formula is as follows:
L1P1＝(P1A1*k11+P1A2*k12+P1A3*k13)*P1A4*K14。
in the formula, K11: the wind power electric quantity change percentage coefficient; k12: a wind resource absorption level prediction value coefficient; k13: a wind resource medium-long term pitch-average coefficient; k14: and the proportion coefficient of the installed capacity of the wind power.
Wind power monthly power generation amount change rate (P1A 1) = (prediction of medium-long-term power generation amount of each province wind power-historical synchronous wind power generation amount)/historical synchronous wind power generation amount.
Predicted wind curtailment rate (P1 A2) = historical electricity usage/predicted electricity usage = historical contemporaneous wind curtailment rate.
Long-term pitch-average value (P1 A3) = (predicted value of wind resource-historical average)/historical average in wind resource.
The ratio of installed wind power capacity to installed wind power capacity (P1 A4) = installed wind power capacity/total installed wind, light and water capacity.
Step 2, calculating the provincial medium and long-term photovoltaic transaction recommendation rate;
and respectively calculating four subentry indexes of photovoltaic monthly generated energy change rate, predicted light abandoning rate, light resource medium and long term distance average value and photovoltaic installed capacity ratio, and finally weighting.
And (3) paraphrasing each photovoltaic index in the step (2) is similar to the provincial medium-long term wind power trading recommendation rate.
The method comprises the following steps that (1) the photovoltaic transaction recommendation rate (L1P 2) is comprehensively recommended and evaluated by considering the power consumption situation of inter-provincial power generation and the new energy consumption situation on the basis of inter-provincial medium and long term photovoltaic power generation prediction and prediction data of light resources, and a photovoltaic medium and long term transaction recommendation decision analysis model is constructed; the calculation formula is as follows:
L1P2＝(P2A1*k21+P2A2*k22+P2A3*K23)*P2A4*K24。
wherein, K21: the photovoltaic power generation capacity change percentage coefficient; k22: predicting a wind curtailment rate coefficient; k23: a long-term pitch-average coefficient of the optical resource; k24: and the photovoltaic installed capacity ratio coefficient.
Photovoltaic power generation amount change rate (P2 A1) = (prediction of photovoltaic medium-long term power generation amount in each province-historical contemporaneous photovoltaic power generation amount)/historical contemporaneous photovoltaic power generation amount.
Predicted light rejection rate (P2 A2) = historical power usage/predicted power usage × historical contemporaneous light rejection rate.
Long-term distance flat value (P2 A3) = (predicted value of optical resource-historical average)/historical average in optical resource.
The installed photovoltaic capacity ratio (P2 A4) = installed photovoltaic capacity/total installed wind, light, solar and water capacity.
Step 3, calculating the provincial medium and long term hydropower transaction recommendation rate;
and respectively calculating three subentry indexes of the predicted water abandon rate, the medium and long term average water resource distance value and the water and electricity installed capacity ratio, and finally weighting.
And 3, paraphrasing various indexes of the hydropower in the step 3 is similar to the provincial medium-long term wind power trading recommendation rate.
The hydropower transaction recommendation rate (L1P 3) is based on the prediction data of water resources, comprehensive recommendation evaluation is carried out by considering the particularity of the water resources, the inter-provincial power generation electricity consumption condition and the hydropower consumption condition, and a hydropower medium and long term transaction recommendation decision analysis model is constructed; the calculation formula is as follows:
L1P3＝(P3A1*k31+P3A2*k32)*P3A3*K33。
in the formula, K31: a medium-long term average coefficient of rainfall; k32: predicting a water abandoning rate coefficient; k33: the water installed capacity ratio coefficient.
Long-term distance flat value (P3A 1) = (water resource predicted value-historical average)/historical average in water resource.
Predicted water abandonment rate (P3 A2) = historical power consumption/predicted power consumption = historical contemporaneous water abandonment rate.
The ratio of the installed capacity of water to the installed capacity of water (P3A 3) = the installed capacity of water/the total installed capacity of wind, light and water.
Step 4, calculating the provincial transaction price recommendation rate;
respectively calculating the provincial average trading power price, the general average trading power price of the external province and the proportion; the rules of the provincial transaction price recommendation rate are as follows: the lower the provincial-level average trading power price is, i.e., the lower the proportion of the provincial-level average trading power price is, the higher the recommendation rate is.
The calculation formula of the trading price recommendation rate (L1P 4) is as follows:
if K < =1, L1P4=100%; or if 1-K-woven fabric is 2, L1P4= (2-k) × 100%; or if K > =2,l1p4=0.
Provincial average transaction electricity rates (P4 A1) = historical electricity charges/historical electricity amounts of each province.
Total average transaction electricity prices of the outsources (P4 A2) = historical electricity charges of all outsources/historical electricity amounts of all outsources.
K = provincial average electricity trading price (P4 A1)/general average electricity trading price (P4 A2) of the foreigners, and the lower the K value, the lower the electricity trading price.
Step 5, calculating the recommendation rate of the transmission capacity of the transmission channel;
respectively calculating all power transmission spaces of the power transmission channels, used power transmission spaces of the power transmission channels and residual available power transmission spaces of the power transmission channels; the power transmission channel power transmission capacity recommendation rate is regulated as follows: the larger the residual available power transmission space of the power transmission channel is, the higher the recommendation rate of the power transmission capacity of the power transmission channel is. The calculation formula of the power transmission channel power transmission capacity recommendation rate (L1P 5) is as follows:
a power transmission channel power transmission capacity recommendation rate (L1P 5) = power transmission channel remaining available power transmission space (P5 A1)/power transmission channel entire power transmission space (P5 A2).
Power transmission channel total power transmission space (P5 A2): and obtaining the maximum transmission power of the transmission channel by integrating with time.
Power transmission channel used power transmission space: the transmitted power for which the transaction has been completed is integrated with time.
Power transmission channel remaining available power transmission space (P5 A1) = power transmission channel full power transmission space (P5 A2) — power transmission channel used power transmission space.
Step 6, calculating the inter-provincial medium and long-term clean energy electric power transaction recommendation rate;
and carrying out weighting setting on the provincial mid-and-long term wind power transaction recommendation rate, the provincial mid-and-long term photovoltaic transaction recommendation rate, the provincial mid-and-long term hydropower transaction recommendation rate, the provincial trade price recommendation rate and the channel transmission capacity recommendation rate, and calculating to obtain the mid-and-long term clean energy power transaction recommendation rate of each province in each month.
The method is characterized by integrating wind power, photovoltaic and hydropower medium and long term transaction recommendation decision models, transaction electricity price recommendation models and power transmission channel recommendation models, taking high quality and low price as final targets, and constructing provincial medium and long term clean energy power transaction recommendation rate (L1) for medium and long term transaction auxiliary decision recommendation, wherein the calculation formula is as follows:
L1＝(K1*(K10*L1P1+K20*L1P2+K30*L1P3)+K2*L1P4+K3*L1P5)*K4。
wherein, K1: a provincial clean energy power generation transaction recommendation coefficient; k10: provincial wind power trading recommendation coefficients; k20: provincial photoelectric transaction recommendation coefficients; k30: a provincial hydropower transaction recommendation coefficient; k2: a provincial power transmission price recommendation coefficient; k3: channel residual space availability factor; k4: and whether an available power transmission channel exists or not, if the power transmission channel is overhauled, the K4 parameter value is 0, and if not, the K4 parameter value is 1.
Finally, it is to be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the technical solutions of the present invention, and the scope of the present invention is not limited thereto. Those skilled in the art will understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.
Claims (2)
1. The computer implementation method of the inter-provincial medium and long-term clean energy power trading recommendation rate is characterized in that medium and long-term clean energy power generation prediction data, clean energy consumption data, medium and long-term average trading power price and power transmission channel space are used as a basis for establishing the inter-provincial medium and long-term clean energy power trading recommendation rate, and a database which can be stored/read by a computer is generated; calculating provincial medium and long term wind power transaction recommendation rate, photovoltaic transaction recommendation rate and hydropower transaction recommendation rate, and calculating provincial transaction price recommendation rate and transmission capacity recommendation rate of a transmission channel; weighting the medium-long term wind power transaction recommendation rate, the photovoltaic transaction recommendation rate, the hydropower transaction recommendation rate, the provincial-level transaction price recommendation rate and the transmission capacity recommendation rate of a transmission channel to generate the inter-provincial medium-long term clean energy power transaction recommendation rate;
indexes related to the method are calculated and recommended by taking provinces as units and months as periods, and the calculation steps are as follows:
step 1, calculating the provincial medium-long term wind power trading recommendation rate; respectively calculating four subentry indexes of the monthly generated energy change rate of the wind power, the predicted abandoned wind rate, the medium and long term distance average value of the wind resources and the proportion of the installed capacity of the wind power, and finally weighting;
step 2, calculating the provincial medium and long-term photovoltaic transaction recommendation rate; respectively calculating four subentry indexes of photovoltaic monthly power generation capacity change rate, predicted light abandon rate, light resource medium and long term distance average value and photovoltaic installed capacity ratio, and finally weighting;
step 3, calculating the provincial medium and long term hydropower transaction recommendation rate; respectively calculating three subentry indexes of the predicted water abandon rate, the medium and long term average water resource distance value and the water and electricity installed capacity ratio, and finally weighting;
step 4, calculating the provincial transaction price recommendation rate; respectively calculating the provincial average trading power price, the general average trading power price of the external province and the proportion; the rules of the provincial transaction price recommendation rate are as follows: the lower the provincial-level average trading power price is, namely the lower the proportion of the provincial-level average trading power price to the total average trading power price of the other provinces is, the higher the recommendation rate is;
step 5, calculating the power transmission capacity recommendation rate of the power transmission channel; respectively calculating all power transmission spaces of the power transmission channels, used power transmission spaces of the power transmission channels and residual available power transmission spaces of the power transmission channels; the power transmission channel power transmission capacity recommendation rate is regulated as follows: the larger the residual available power transmission space of the power transmission channel is, the higher the recommendation rate of the power transmission capacity of the power transmission channel is;
step 6, calculating the inter-provincial medium and long-term clean energy electric power transaction recommendation rate; weighting and setting the provincial mid-and-long-term wind power transaction recommendation rate, the provincial mid-and-long-term photovoltaic transaction recommendation rate, the provincial mid-and-long-term hydropower transaction recommendation rate, the provincial trade price recommendation rate and the channel transmission capacity recommendation rate, and calculating to obtain the mid-and-long-term clean energy power transaction recommendation rate between each province and each month;
carrying out index modeling on various factors influencing the medium and long term transaction of the clean energy, carrying out normalization calculation processing according to the recommendation percentage, and calculating various indexes subjected to the normalization processing in a weighting calculation mode to obtain the medium and long term clean energy electric power transaction recommendation rate of each province;
establishing 1 item of first-level index, 5 items of second-level index and 15 items of second-level index; the evaluation adopts a percentage evaluation mode, the value range is 0 to 100 percent, and the recommendation rate of each level of index is the sum of the recommendation rates of the lower level index; setting corresponding weights for each level of indexes, wherein the first-level index weight is the sum of the weights of all second-level indexes, and the sum of the itemized weights of all second-level indexes under the second-level indexes is 100%;
the first-level index is the inter-provincial medium-long term clean energy power trading recommendation rate L1;
the secondary indexes are as follows: the system comprises a wind power transaction recommendation rate L1P1, a photovoltaic transaction recommendation rate L1P2, a hydropower transaction recommendation rate L1P3, a transaction price recommendation rate L1P4 and a channel power transmission capacity recommendation rate L1P5;
the secondary indexes are divided into items: the method comprises the following steps of wind power monthly electricity generation change rate P1A1, predicted wind abandoning rate P1A2, wind resource medium and long term distance flat value P1A3, wind installed capacity proportion P1A4, photovoltaic electricity generation change rate P2A1, predicted light abandoning rate P2A2, light resource medium and long term distance flat value P2A3, photoelectric installed capacity proportion P2A4, predicted water abandoning rate P3A1, water resource medium and long term distance flat value P3A2, water installed capacity proportion P3A3, provincial average transaction electricity price P4A1, provincial average transaction electricity price P4A2, remaining available power transmission space P5A1 of a power transmission channel and all power transmission space P5A2 of the power transmission channel;
the calculation formula of the inter-provincial medium and long-term clean energy power transaction recommendation rate L1 is as follows:
L1＝(K1*(K10*L1P1+K20*L1P2+K30*L1P3)+K2*L1P4+K3*L1P5)*K4；
wherein, K1: a provincial clean energy power generation transaction recommendation coefficient; k10: provincial wind power trading recommendation coefficients; k20: provincial photoelectric transaction recommendation coefficients; k30: a provincial level hydropower transaction recommendation coefficient; k2: a provincial power transmission price recommendation coefficient; k3: channel residual space availability factor; k4: whether an available power transmission channel exists or not is judged, if the power transmission channel is overhauled, the K4 parameter value is 0, and if not, the K4 parameter value is 1;
the calculation formula of the wind power trading recommendation rate L1P1 is as follows:
L1P1＝(P1A1*K11+P1A2*K12+P1A3*K13)*P1A4*K14；
in the formula, K11: the wind power electric quantity change percentage coefficient; k12: a wind resource absorption level prediction value coefficient; k13: a wind resource medium-long term pitch-mean coefficient; k14: the proportion coefficient of the installed wind power capacity;
the wind power monthly power generation amount change rate P1A1= (prediction of medium-long power generation amount of each province wind power-historical synchronous wind power generation amount)/historical synchronous wind power generation amount;
predicted wind abandon rate P1A2= historical power consumption/predicted power consumption and historical contemporaneous wind abandon rate;
the wind resource medium-long term pitch-average value P1A3= (wind resource predicted value-historical average value)/historical average value;
the ratio of installed wind power capacity P1A4= installed wind power capacity/total installed wind, light and water capacity;
the calculation formula of the photovoltaic trade recommendation rate L1P2 is as follows:
L1P2＝(P2A1*K21+P2A2*K22+P2A3*K23)*P2A4*K24；
wherein, K21: the photovoltaic power generation capacity change percentage coefficient; k22: predicting a wind curtailment rate coefficient; k23: a long-term pitch-average coefficient of the optical resource; k24: the photovoltaic installed capacity ratio coefficient;
the photovoltaic power generation capacity change rate P2A1= (prediction of photovoltaic medium-long term power generation capacity-historical contemporaneous photovoltaic power generation capacity)/historical contemporaneous photovoltaic power generation capacity in each province;
predicted light abandonment rate P2A2= historical power consumption/predicted power consumption and historical contemporaneous light abandonment rate;
the light resource medium-long term pitch flat value P2A3= (light resource predicted value-historical average)/historical average;
the ratio of the installed photovoltaic capacity P2A4= installed photovoltaic capacity/total installed photovoltaic/wind, light and water capacity;
the calculation formula of the hydropower transaction recommendation rate L1P3 is as follows:
L1P3＝(P3A1*K31+P3A2*K32)*P3A3*K33；
wherein, K31: a medium-long term average coefficient of rainfall; k32: predicting a water abandoning rate coefficient; k33: the water and electricity installed capacity ratio coefficient;
the long-term pitch value P3A1 in the water resource is = (water resource predicted value-historical average value)/historical average value;
predicted water abandon rate P3A2= historical power consumption/predicted power consumption and historical contemporaneous water abandon rate;
the ratio of the installed capacity of water to the installed capacity of water is P3A3= installed capacity of water/total installed capacity of wind, light and water;
the calculation formula of the power transmission channel power transmission capacity recommendation rate L1P5 is as follows:
a power transmission channel power transmission capacity recommendation rate L1P5= a power transmission channel remaining available power transmission space P5 A1/a power transmission channel total power transmission space P5A2;
power transmission channel total power transmission space P5A2: obtaining the maximum transmission power of the transmission channel and time integral;
power transmission channel used power transmission space: acquiring the transmission power and the time integral of the completed transaction;
transmission channel remaining available transmission space P5A1= transmission channel total transmission space P5A2 — transmission channel used transmission space.
2. The computer-implemented method of inter-provincial, medium-and long-term clean energy electricity trading recommendation rate according to claim 1, wherein the trade price recommendation rate L1P4 is calculated by the following formula:
if K < =1, L1P4=100%; or if 1-K-woven fabric is 2, L1P4= (2-K) × 100%; or if K > =2, l1p4=0;
the provincial level average transaction electricity price P4A1= the historical electricity charge of each province/the historical electric quantity of each province;
the total average transaction electricity price P4A2= the historical electricity charge/the historical electricity quantity of all the outstations;
k = provincial average trading electricity price P4 A1/general provincial average trading electricity price P4A2.
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