A risk dispatching method considering large and small hydropower generation rights transactions

Abstract

The present invention provides a risk scheduling method taking into account the trading of large and small hydropower generation rights. The method first comprehensively evaluates different large and small hydropower combinations based on four indicators: installed capacity, water complementarity, network loss cost and regulation capacity, and selects the optimal trading combination; then, taking the optimal trading combination as the research object, a large hydropower bidding model is established with reference to the market demand function in econometrics, and the logarithmic demand relationship is used to simulate the functional relationship between large hydropower quotations and trading electricity; finally, the optimal proportion of contract, spot and power generation rights trading electricity is determined according to the Sharpe ratio of large hydropower. The effectiveness of the results is proved by the example analysis of Nuozadu and various small hydropower groups, which effectively reduces the abandonment of small hydropower, and can increase the income of large hydropower within a certain risk range, so as to achieve the goal of mutual benefit and full utilization of water resources.

Description

Technical Field

[0001] The invention belongs to the technical field of hydropower station power generation optimization, and in particular relates to a risk scheduling method taking into account large and small hydropower generation rights transactions. Background Art

[0002] Power generation rights trading refers to the market behavior of paid transfer and purchase of contracted electricity with the goal of energy conservation, emission reduction and mutual benefit. Power generation rights trading of large and small hydropower stations can use large hydropower stations with good regulation performance and small hydropower stations with poor regulation performance to buy and sell part of the contracted electricity according to bilateral trading rules, which can effectively solve the problem of water abandonment and electricity storage of small hydropower stations, and bring excess profits to large hydropower stations. However, due to the uncertainty of water and electricity prices, large differences in installed capacity and regulation performance, power generation rights trading of large and small hydropower stations faces difficulties such as how to determine the transaction objects, how to establish a bidding model, and how to allocate the transaction electricity. Therefore, how to realize power generation rights trading of large and small hydropower stations in a market environment is a theoretical and practical problem that needs to be solved urgently for hydropower-rich power grids.

[0003] In recent years, scholars at home and abroad have paid extensive attention to the issue of power generation rights trading, mainly including the following aspects. ① Research on the trading mechanism and model of power generation rights, such as thermal power and hydropower, thermal power and wind power trading, etc. The above research is mainly based on trading of different types of power sources, and rarely considers the hydraulic connection in pure hydropower trading or the uncertainty of both parties to the transaction. ② Research on bidding issues in power generation rights trading, such as supply and demand balance bidding, high and low matching bidding, etc. The research content mainly focuses on predicting the quotation bottom line of both parties to the transaction or improving the negotiation efficiency to specify the quotation curve of the unit, and rarely considers the impact of the high and low quotation on the transaction volume and the success of the transaction. ③ Research on the problem of power allocation under power generation rights trading, such as reducing the price risk borne by power plants and optimizing risk measurement methods. Most of these studies aim to minimize risk to carry out power allocation or medium- and long-term contract transactions. However, reducing risks in the power market often means that benefits will also decrease. If only considering risks, more cost-effective power allocation combinations will often be missed. How to coordinate risks and benefits still needs in-depth consideration. Summary of the invention

[0004] Starting from the perspective of large hydropower, the present invention proposes a risk scheduling method that takes into account the trading of large and small hydropower generation rights based on the basic idea of ​​mutual benefit and win-win. The proposed method can reduce the water abandonment of small hydropower and increase the income of large hydropower within a certain risk range, thereby achieving the goal of mutual benefit and win-win and full utilization of water resources.

[0005] In order to realize the above-mentioned technical features, the purpose of the present invention is realized as follows: a risk scheduling method taking into account large and small hydropower generation rights transactions, the method first comprehensively evaluates different large and small hydropower combinations according to four indicators: installed capacity, water complementarity, network loss cost and regulation capacity, and selects the optimal trading combination; then, a large hydropower bidding model is established with reference to the market demand function in econometrics, and the logarithmic demand relationship is used to simulate the functional relationship between large hydropower quotations and trading electricity; finally, the optimal proportion of contract, spot and power generation rights trading electricity is determined according to the large hydropower Sharpe ratio.

[0006] The risk scheduling method taking into account the transaction of large and small hydropower generation rights has the following specific operating steps:

[0007] Step 1: Assess the power generation capacity of large and small hydropower in the current year;

[0008] Large and small hydropower stations have signed some medium- and long-term contracts years ago. However, due to the great uncertainty of medium- and long-term water inflow, large and small hydropower stations may not be able to fully fulfill their contracts or water resources may not be used efficiently during the performance of the contracts. For example, the actual water inflow of large hydropower stations is less, resulting in a rapid drop in water levels at the end of the year; small hydropower stations have more actual water inflows and face greater pressure to abandon water. At this time, large and small hydropower stations are willing to participate in the power generation rights trading market. Therefore, simulating the water inflow process of different frequencies is of great significance to the evaluation of large and small hydropower capacity and power generation rights trading. The Monte Carlo method is used to simulate the water inflow process of large and small hydropower stations, and the maximum power generation model is used to re-evaluate the maximum power generation capacity of large and small hydropower stations.

[0009] Step 2: Determine the trading partner of large hydropower generation rights:

[0010] At present, the power generation rights trading mode is mainly divided into centralized matchmaking and bilateral negotiation. The bilateral negotiation mode is widely used due to its strong autonomy, high fairness, strong operability and adaptability. However, one of the difficulties is how to find a suitable trading partner. Therefore, considering the four indicators of installed capacity, water complementarity, network loss cost and regulation capacity, the entropy weight method is used to select suitable trading partners among many cascade small hydropower stations.

[0011] Step 3: Solve the functional relationship between the electricity price and the electricity volume of power generation rights trading;

[0012] In the contract and spot markets, power plants usually trade directly with the power grid. However, transactions in the power generation rights trading market mainly occur between power generators. The market electricity price is not transparent enough, and it is difficult to obtain the historical electricity price of the market. With reference to the market demand function in econometrics, the functional relationship between the power generation rights trading price and the amount of electricity is fitted.

[0013] Step 4: Calculate the annual comprehensive income of large hydropower. The calculation formula is as follows:

[0014] S=S1+S2+S3+S4-C

[0015] Where: S represents the annual comprehensive income of large hydropower, in RMB; S1, S2, S3 represent the income of large hydropower in the contract market, spot market, and power generation rights trading market, in RMB; S4 represents the energy storage income of large hydropower, in RMB; C represents the transaction cost, in RMB;

[0016] Step 5: Calculate the risk of annual electricity trading of large hydropower;

[0017] Since the conditional value at risk CVaR is a modified method of the value at risk VaR, it represents the average value of the risk when the loss exceeds the VaR critical value, and can better reflect extreme situations and tail risks. Therefore, the conditional value at risk CVaR is used to quantify the risk of large hydropower annual electricity transactions. The calculation formula is as follows:

[0018]

[0019] Where: CVaR represents conditional risk value, in RMB; VaR represents risk value, in RMB; represents the mean annual revenue of large hydropower, in yuan; N represents the total number of Monte Carlo simulations; n represents the number of the current simulation scenario; β represents the confidence level; S n It represents the revenue of large hydropower in the current simulation situation, in yuan; Represents 0 and The larger value of

[0020] Step 6: Calculate the Sharpe ratio of annual electricity trading of large hydropower;

[0021] Generally, high-profit markets often have high risks. How to coordinate risks and benefits and reasonably allocate electricity in the contract, spot, and power generation rights trading markets is of great significance for large hydropower to avoid risks and increase returns. Modern investment theory shows that the Sharpe ratio can comprehensively consider risk and return factors and provide decision makers with the optimal investment portfolio. Therefore, the goal of optimizing the electricity allocation of large hydropower is to maximize the Sharpe ratio of large hydropower. The objective function is:

[0022]

[0023] Where: S p represents the Sharpe ratio of large hydropower; It represents the mean annual return of large hydropower, in Yuan; CVaR represents conditional value at risk, in Yuan.

[0024] The detailed steps of simulating the water inflow process of large and small hydropower stations in step 1 are as follows:

[0025] Step 1.1, calculate the multi-year average data of the ath power station, the calculation formula is as follows

[0026]

[0027]

[0028] Where: Y is the total number of years; y is the year number; is the annual average flow rate in year y, in m 3 / s; is the multi-year average flow, in m 3 / s; δ is the standard deviation of the multi-year average flow, in m 3 / s;

[0029] Step 1.2: Calculate the historical water inflow correlation coefficient ρ between the ath power station and the bth power station based on the historical runoff of the hydropower station. * , calculate the normal distribution ε3 and ε4 that conform to the historical water inflow correlation, the calculation formula is as follows:

[0030]

[0031] Where: θ is a certain angle value, in degrees; ε1 and ε2 are two independent normal distributions that conform to the standard; ε3 and ε4 are normal distributions that conform to the historical water inflow correlation of large and small hydropower stations;

[0032] Step 1.3, simulate the annual average flow of the ath power station and the bth power station this year After the processing in step 1.2, the power station flow retains the water inflow correlation of the basin where they are located. The calculation formula is as follows:

[0033]

[0034]

[0035] Where: are the simulated annual average flow rates of the ath power station and the bth power station, in m 3 / s; are the multi-year average flow rates of the ath power station and the bth power station, in m 3 / s; δ a , δ b is the standard deviation of the multi-year average flow of the a-th power station and the b-th power station, in m 3 / s;

[0036] Step 1.4: simulate the average annual flow of the ath power station and the bth power station in this year according to step 1.3 The annual average flow of the tth power station can be obtained by

[0037] Step 1.5: Based on the annual average flow of the tth power station obtained in step 1.4, According to the historical runoff table of the tth power station, select The historical year with the smallest difference is assumed to be the average flow in year l Based on the average monthly flow in the first year Average annual flow The ratio of the simulated monthly average flow of the tth power station in the i-th month is calculated. The calculation formula is as follows:

[0038]

[0039] Where: is the average annual flow of the tth power station in this year, in m 3 / s; For The historical year with the smallest difference is assumed to be the annual average flow in year l, in m 3 / s; is the average monthly flow rate of the ith month, in m 3 / s;

[0040] The annual water inflow process of each power station obtained through steps 1.1 to 1.5 is used to evaluate the maximum annual power generation of each power station using the maximum power generation model.

[0041] The detailed steps for determining the large hydropower generation rights transaction object in step 2 are as follows:

[0042] Comprehensively consider the four indicators of installed capacity, water complementarity, network loss cost and regulation capacity, and select appropriate trading objects from many cascade small hydropower station groups. Among them, installed capacity reflects the power generation capacity of the power station to a certain extent. When the installed capacity of large hydropower stations and small hydropower stations is quite different, it means that the transaction amount and possibility of power generation rights between them are also small. The decentralized transaction between large hydropower stations and multiple small hydropower stations is not considered for the time being. The following focuses on the other three indicators and the evaluation method of the optimal trading object;

[0043] (1) Water supply complementarity index;

[0044] The water inflow complementarity index can be reflected by water correlation. When the large hydropower is expected to be a flood year at the beginning of the year, the water inflow frequency is low, and more market share is obtained through market transactions. However, after the actual transaction, due to insufficient water inflow and high water inflow frequency, the reservoir may not operate economically at a low water level. At this time, if part of the market share can be traded out through power generation rights, it can not only obtain part of the transaction income, but also avoid the low water level operation of the reservoir. At this time, the most ideal trading object is a group of small hydropower stations with abundant water inflow but small market share. Therefore, the water inflow correlation is used to reflect the complementarity of large and small hydropower. This water inflow correlation is mainly reflected in the annual runoff of the total water inflow and the monthly runoff process of water inflow balance. The following describes how to calculate the annual runoff correlation of large and small hydropower and the monthly runoff process correlation of large and small hydropower respectively;

[0045] Calculate the correlation between annual runoff of large and small hydropower stations, the calculation formula is as follows:

[0046]

[0047] Where: a P represents the correlation coefficient of the total annual runoff of large and small hydropower stations; B , P S is the water inflow frequency of large and small hydropower stations this year, in %; μ B , μ S is the average water frequency of large and small hydropower over the years, in %; δ B ,δ S is the standard deviation of water frequency of large and small hydropower stations over the years, in %;

[0048] Calculate the correlation of monthly runoff process between large and small hydropower stations. The calculation formula is as follows:

[0049]

[0050] Where: b is the correlation coefficient of monthly runoff process between large and small hydropower stations; i represents the month; Respectively represent the average flow of the i-th month of large and small hydropower simulations, in m 3 / s; Represents the 12-month average runoff of large and small hydropower simulations, in m 3 / s;

[0051] According to the correlation coefficient ρ of the total annual runoff of large and small hydropower a Correlation coefficient between monthly runoff process of large and small hydropower stations ρ b , calculate the comprehensive correlation coefficient of large and small hydropower water, the calculation formula is as follows:

[0052]

[0053] Whenc >0, indicating that the water inflow from large and small hydropower stations is positively correlated, ρ c The larger the value, the worse the complementarity of water supply between large and small hydropower stations. c <0, indicating that the water inflow from large and small hydropower stations is negatively correlated, ρ c The smaller the value, the better the complementarity of water supply between large and small hydropower stations. c =0, it means that there is no linear relationship between the water supply of large and small hydropower stations;

[0054] (2) Network loss cost indicator;

[0055] Since the power generation rights transaction changes the original power flow distribution of the power grid, it increases the power transmission network loss, which leads to an increase in transaction costs. This part of the network loss is mainly composed of two parts: transformer loss and line loss. Assuming that the transformer level and number of transformer layers experienced by various hydropower plants are the same during the entire transformer process, the transformer loss between various hydropower plants is the same under the same transmission capacity. The difference lies in the line loss. Since the line loss is proportional to the power transportation distance, the power transportation distance is used to indirectly reflect the transaction network loss cost. The network loss cost calculation formula is as follows:

[0056]

[0057] Where: C1 is the transmission line loss, unit yuan; Q3 is the power generation right trading quantity, unit kWh; is the average distance from the small hydropower group to the main grid, in km; k is the number of the small hydropower station; K is the total number of small hydropower stations; L k is the distance from the current small hydropower station to the main grid, in km; λ is the transmission distance P3 is the power price of power generation right transaction, in RMB / kWh;

[0058] (3) Reservoir capacity regulation capability index;

[0059] The reservoir regulation capacity is reflected by the reservoir capacity regulation coefficient. The larger the difference in the reservoir capacity regulation coefficient between large and small hydropower stations, the greater the space for reservoir capacity compensation between the two, and the higher the feasibility of the power generation rights transaction between the two. The calculation formula of the reservoir capacity regulation capacity index is as follows:

[0060]

[0061]

[0062] Where: is the comprehensive reservoir capacity regulation coefficient of the small hydropower group; k is the number of the small hydropower station; K is the total number of small hydropower stations; V k is the regulating storage capacity of the kth power station, in m 3; V is the total regulating storage capacity of all power stations in the small hydropower group, in m 3 ; φ k is the regulation capacity coefficient of the kth power station; is the average inflow flow of the kth power station over many years, in m 3 ;

[0063] Using the four indicators of installed capacity, water complementarity, network loss cost and regulation capacity, the entropy weight method is then used to calculate the comprehensive evaluation of large hydropower and various candidate trading objects.

[0064] The detailed steps for solving the functional relationship between the electricity price and the electricity volume of power generation rights trading in step 3 are as follows:

[0065] Generally speaking, the power generation rights transaction for this month or this trading cycle is usually declared and carried out in the previous month. Through the power generation rights transaction, the large hydropower transferred part of the monthly contracted electricity to the small hydropower station group in the previous month, so that they can generate more electricity in the next month. After the transaction is completed, the contracted electricity of the transaction needs to be decomposed into subsequent periods. Assuming that the small hydropower group decomposes the monthly contracted electricity of the transaction into each day according to the amount of water inflow per day, and puts it into the day-ahead spot market for profit, and compensates part of the profit to the large hydropower, the profit of the large hydropower in this power generation rights transaction is as follows:

[0066] S3=αP2Q3

[0067] In the formula: S3 is the income of large hydropower in the power generation rights trading market, in yuan; P2 is the spot market electricity price, in yuan / kWh; Q3 is the power volume traded in the power generation rights market, in kWh; α is the compensation ratio of small hydropower groups to large hydropower, assuming the "50-50" principle, α is 50%;

[0068] The expression of the revenue S3 of large hydropower in the power generation rights trading market is S3=P3Q3. Combining the two expressions of the revenue S3 of large hydropower in the power generation rights trading market, the expression of the power generation rights trading price P3 can be obtained as P3=αP2;

[0069] Due to the uncertainty of spot electricity prices, it is assumed that the spot electricity price P2 follows a normal distribution. It can be obtained that the fluctuation range of the spot electricity price P2 is (P2-δ2)~(P2+δ2), where is the average price of spot market electricity, in yuan / kWh, δ2 is the standard deviation of spot price, in yuan / kWh, and the lower limit P of the power generation right transaction price P3 can be obtained. 3,min =α(P2-δ2) and upper limit P 3,max =α(P2+δ2);

[0070] The range of power generation rights trading volume Q3 is as follows:

[0071] 0<Q3≤(Qe -Q c )

[0072] Where: Q3 is the electricity volume of power generation rights trading, in kWh; Q e , Q c They represent the maximum power generation of the small hydropower group based on the predicted water inflow simulation and the contracted power volume signed by the small hydropower group this year, in kWh. e Greater than Q c , indicating that after the small hydropower stations have completed the annual contracted electricity, there is still surplus electricity. If this part of the surplus electricity is not traded in the market, it will face the risk of being abandoned. This part of the electricity can be traded with large hydropower stations for power generation rights to avoid the risk of water abandonment;

[0073] When the price of large hydropower increases, the trading willingness of small hydropower groups will decrease, resulting in a decrease in trading volume. This trend of decreasing trading volume as the trading price increases is similar to the market demand elasticity function in economics, which indicates the degree of response of price changes to trading volume. Therefore, the most widely used logarithmic demand function is used to describe the relationship between trading price and trading volume in the power generation rights market. The formula is as follows:

[0074] lnP3=g+hlnQ3

[0075] In the formula: P3 represents the electricity price of power generation rights trading, in units of RMB / kWh; Q3 represents the power volume of power generation rights trading, in units of kWh; g and h are parameters to be determined, where h represents price elasticity, indicating how much the trading volume will decrease for every 1% increase in the bid price. The calculation steps of parameters g and h are as follows:

[0076] Step 3.1, draw a curve diagram of the logarithmic demand function according to the formula lnP3 = g + hlnQ3, and assume that the starting and ending points of the curve are A and B respectively;

[0077] Step 3.2: Combine the range of power generation rights transaction quantity Q3 and the upper and lower limits of power generation rights transaction price P3 to obtain the coordinates of points A and B, where Q 3,min You can take a smaller value such as 0.01, and substitute the coordinates of points A and B into the formula lnP3=g+hlnQ3 to obtain the parameters g and h.

[0078] The detailed steps for solving the annual comprehensive income of large hydropower in step 4 are:

[0079] The annual revenue of large hydropower consists of five parts, namely, contract market revenue S1, spot market revenue S2, power generation rights trading market revenue S3, energy storage revenue S4 and transaction cost C. Since the functional relationship between power generation rights trading electricity price and electricity volume has been obtained, the power generation rights trading market revenue S3 can be solved. The following focuses on the solution process of the other four parts;

[0080] Step 4.1, solve the contract market return S1 and the spot market return S2;

[0081] In the contract and spot markets, power plants usually trade directly with the grid. Therefore, the electricity prices P1 and P2 in the contract and spot markets are relatively open and transparent. The average contract market electricity price can be obtained by searching online. and standard deviation δ1, the mean of spot market electricity prices and standard deviation δ2, assuming that the electricity prices in the two markets follow a normal distribution with their mean and standard deviation as parameters The fluctuations of the contract and spot market electricity prices P1 and P2 can be obtained;

[0082] Since the range of power generation right transaction quantity Q3 is 0<Q3≤(Q e -Q c ), a value is selected as Q3 within this range, and the contract quantity Q1 of large hydropower is assumed to be the decision variable of the present invention, then the spot market quantity Q2 is:

[0083] Q2=Q a -Q1-Q3

[0084] Where: Q a is the annual power generation of large hydropower, in kWh; Q1, Q2, and Q3 represent the contract market power, spot power, and power generation rights trading power, in kWh;

[0085] According to the electricity price in the contract and spot market, Q2 = Q a -Q1-Q3 can get the expression of contract market return S1 as S1=P1Q1, and the expression of spot market return S2 as S2=P2Q2;

[0086] Based on the average of the contract market electricity price Average spot market electricity price Get the average return of the contract market The expression is The expression for the average return in the spot market is for

[0087] Step 4.2, solve the large hydropower storage benefit S4;

[0088] Under the power generation rights trading model, large hydropower transfers part of the power generation contract to small hydropower for a fee during the flood season, and uses its own regulation reservoir advantage to store this part of the power generation water as energy storage. This part of the stored electricity is the power generation rights trading electricity Q3. Since large hydropower originally intended to invest this part of the power generation rights trading electricity into the contract market for profit, the large hydropower energy storage income S4 ​​is:

[0089]

[0090] Where: S4 is the large hydropower storage income, in yuan; is the average of the electricity price in the contract market, in RMB / kWh; Q3 represents the electricity volume of power generation rights trading, in kWh;

[0091] Step 4.3, solve the transaction cost C;

[0092] The transaction cost C is mainly the network loss cost of the transaction, which is usually shared by both parties to the transaction. The calculation formula is as follows:

[0093]

[0094] In the formula: C1 represents the transmission line loss, in yuan; C2 represents the transformer loss, in yuan; is the average distance from the small hydropower group to the main grid, in km; λ is the transmission distance The line loss rate of the lower high-voltage transmission is in %; P3 is the electricity price for power generation rights trading, in yuan / kWh; X is the total number of transformers from the power station to the converter station; x is the transformer layer number; λ x is the transformer loss rate of the transformer, in %; Q3 is the power generation right trading quantity, in kWh;

[0095] Step 4.4, solve for the annual comprehensive income S of large hydropower;

[0096] According to the expressions of large hydropower contract market income S1, spot market income S2, power generation rights trading market income S3, energy storage income S4 ​​and transaction cost C, the annual comprehensive income S of large hydropower can be obtained. To simplify the calculation, it can be assumed that the proportion of contract, spot and power generation rights trading market electricity to the annual total hydropower electricity Q a The proportions are r1, r2, and r3 respectively; the annual comprehensive income S of large hydropower consists of five parts, among which the power generation right transaction price P3 can be determined, and the power generation right transaction market can be regarded as a risk-free market, while the transaction cost C and energy storage income S4 ​​are not affected by the fluctuations of contract and spot market electricity prices, and can also be regarded as a risk-free market. Therefore, the market risk mainly comes from the fluctuations of contract and spot market electricity prices. According to the average of the contract market electricity price Average spot market electricity price Finally, we can get the annual comprehensive income S of large hydropower, the average annual income of large hydropower The Sharpe ratio S of annual electricity trading of large hydropower p As shown below:

[0097]

[0098] Where: is the mean annual comprehensive income of large hydropower, in yuan; They represent the average of the contract and spot markets, in units of RMB / kWh; r1, r2, and r3 represent the proportion of the annual electricity volume of large hydropower in the contract, spot, and power generation rights trading markets, respectively. a The ratio of, in %; is the average distance from the small hydropower group to the main grid, in km; λ is the transmission distance The line loss rate of the lower high-voltage transmission is in %; P3 is the electricity price for power generation rights trading, in yuan / kWh; X is the total number of transformers from the power station to the converter station; x is the transformer layer number; λ x is the transformer loss rate of the transformer, in %; Q3 is the power generation right trading quantity, in kWh; Q a is the annual power generation of large hydropower, in kWh; S p is the Sharpe ratio of large hydropower; CVaR is the conditional value at risk, in yuan; S is the annual comprehensive return of large hydropower, in yuan;

[0099] The detailed steps for calculating the Sharpe ratio of large hydropower annual electricity trading in step 6 are:

[0100] The Sharpe ratio S of annual electricity volume traded by large hydropower p The decision variable is the proportion of spot market electricity to the total annual electricity of large hydropower Q a The ratio r2, the correlation coefficient between the contract and the spot market ρ, change the above decision variables to make the large hydropower Sharpe ratio S p To achieve the maximum, the specific steps are as follows:

[0101] Step 6.1, set the correlation coefficient between the contract and the spot market ρ = 0.1, the range is ρ < 1;

[0102] Step 6.2: Based on the forecast water inflow and contracted electricity of the small hydropower group, the abandoned water power of the small hydropower group is obtained, and combined with the annual electricity of large hydropower to obtain the range and maximum value r of r3 3,max , set the initial value of r3 to 0%;

[0103] Step 6.3: Increase the proportion of electricity generated by power generation rights trading to the annual electricity generated by large hydropower stations Q a The ratio r3, the step size is 0.1%;

[0104] Step 6.4, increase r2 from 0% with a step size of 0.1%, then r1 = 1-r2-r3, calculate the Sharpe ratio and market risk of large hydropower in all cases;

[0105] Step 6.5, return to step 3, change r3 and continue calculating until all r3 are calculated;

[0106] Step 6.6, record the maximum Sharpe ratio, minimum risk and the proportion of electricity traded in large hydropower contracts, spot and power generation rights under each scenario;

[0107] Step 6.7, change the market correlation coefficient ρ. The step size of the market correlation coefficient is 0.1. Increase the step size and return to step 3 until all market correlation coefficients ρ are calculated.

[0108] The present invention has the following beneficial effects:

[0109] 1. From the perspective of large hydropower, the present invention provides a solution for large hydropower to select appropriate small hydropower groups in power generation rights trading.

[0110] 2. The large and small hydropower generation rights trading model proposed in the present invention can reduce the water abandonment of small hydropower and increase the income of large hydropower within a certain risk range, thereby achieving the goal of mutual benefit and full utilization of water resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0112] Figure 1 The Sharpe ratio of large hydropower is S p Solving flowchart.

[0113] Figure 2 It is other parameters and geographical distribution map of each power station.

[0114] Figure 3 It is the selection and analysis of large hydropower generation rights trading objects.

[0115] Figure 4 It is the trading result of Nuozadu under different market correlations.

[0116] Figure 5 is CVaR and S p Change curve.

[0117] Figure 6 It is the curve showing how risk changes with the proportion of spot electricity under different market correlations.

[0118] Figure 7 It is the trend of electricity prices under weak market correlation.

[0119] Figure 8 It is the trend of electricity prices under strong market correlation.

[0120] Fig. 9 It is the optimal spot electricity ratio under different market correlations.

[0121] Fig.10 It is the transaction result between Nuozadu and all trading partners.

[0122] Table 1 shows the trading results of Nuozadu at different confidence levels. DETAILED DESCRIPTION

[0123] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0124] Embodiment 1:

[0125] The trading of large and small hydropower generation rights is one of the effective ways to increase the grid-connected rate of hydropower and solve the problem of abandoned water and power. However, how to match the trading objects and reasonably allocate the trading power has always been a problem in the hydropower power market. Therefore, a risk scheduling model and solution method considering the trading of large and small hydropower generation rights are proposed. The solution idea of ​​this model is to first determine the indicators for large hydropower to select trading objects based on factors such as income and cost in the trading of power rights, and use the entropy weight method to obtain the final evaluation of each trading object and select the optimal trading object; then, according to the impact of large hydropower quotations on trading shares, a large hydropower bidding model is established with reference to the market demand function in econometrics; finally, the proportion of optimal contract, spot and power generation rights trading is determined based on CVaR and Sharpe ratio. The cascade hydropower stations of Lancang River, Yili River, Xi'er River and Daying River in Yunnan are taken as the research objects. The results show that the proposed model can reduce the abandonment of small hydropower and keep the income of large hydropower at a high level under the premise of reducing risks, which is of great significance for large hydropower to participate in the trading of power rights and allocate power.

[0126] The technical problem to be solved by the present invention is to provide a risk scheduling method taking into account large and small hydropower generation rights transactions. The method firstly comprehensively evaluates different large and small hydropower combinations according to four indicators: installed capacity, water complementarity, network loss cost and regulation capacity, and selects the optimal transaction combination; then, taking the optimal combination as the research object, a large hydropower bidding model is established with reference to the market demand function in econometrics, and the logarithmic demand relationship is used to simulate the functional relationship between large hydropower quotations and transaction electricity; finally, the optimal proportion of contract, spot and generation rights transaction electricity is determined according to the large hydropower Sharpe ratio.

[0127] Objective function: Generally speaking, markets with high profits are often also risky. How to coordinate risk and return is of great significance to large hydropower in avoiding risks and increasing returns. Modern investment theory shows that the Sharpe ratio can comprehensively consider risk and return factors and provide the optimal investment portfolio. Therefore, the objective function is to optimize the distribution of large hydropower in the contract, spot and power generation rights trading markets with the maximum Sharpe ratio of large hydropower as the goal.

[0128]

[0129] Where: S p represents the Sharpe ratio of large hydropower; It represents the mean annual income of large hydropower, in yuan; CVaR represents conditional value at risk, in yuan; S represents the annual comprehensive income of large hydropower, in yuan; S1, S2, S3 represent the income of large hydropower in the contract market, spot market and power generation rights trading market, in yuan; S4 represents the energy storage income of large hydropower, in yuan; C represents the transaction cost, in yuan.

[0130] The focus of the present invention is to solve the income S3 of large hydropower in the power generation rights trading market. The constraints in this aspect are mainly listed below. The constraint condition expression is as follows:

[0131] (1) Electricity price constraints for power generation rights trading:

[0132] P 3,min ≤P3≤P 3,max

[0133] Where: P3 is the electricity price for power generation rights, in yuan / kWh; 3,min , P 3,max are the lower and upper limits of P3, and this constraint is intended to limit the fluctuation range of electricity prices in power generation rights transactions.

[0134] (2) Constraints on electricity volume in power generation rights trading

[0135] 0<Q3≤(Q e -Q c )

[0136] Where: Q3 is the electricity volume of power generation rights trading, in kWh; Q e , Q c They represent the maximum power generation of the small hydropower group based on the predicted water inflow simulation and the contracted power of the small hydropower group this year. e Greater than Q c , indicating that after the small hydropower stations have completed their annual contracted electricity, there is still surplus electricity. If this surplus electricity is not traded in the market, it will face the risk of being abandoned. This part of electricity can be traded with large hydropower stations for power generation rights to avoid the risk of water abandonment.

[0137] (3) Constraints on the total proportion of electricity in the large hydropower market

[0138] r1+r2+r3=1

[0139] Where: r1, r2, r3 are the proportions of electricity generated in the contract, spot, and power generation rights trading markets to the total annual electricity generated by the power station, in %.

[0140] (4) Constraints on total electricity volume in the large hydropower market

[0141] Q1+Q2+Q3=Q a

[0142] Where: Q a is the annual power generation of large hydropower, in kWh; Q1, Q2, and Q3 are the electricity invested by large hydropower in the contract, spot, and power generation rights trading markets, in kWh.

[0143] (5) Revenue constraints of large hydropower generation rights trading market

[0144] S3=P3Q3

[0145] In the formula: S3 represents the income of large hydropower in the power generation rights trading market, in yuan; P3 represents the power generation rights trading electricity price, in yuan / kWh; Q3 represents the power generation rights trading electricity, in kWh.

[0146] A risk dispatching method considering large and small hydropower generation rights trading. This method first comprehensively evaluates different large and small hydropower combinations based on four indicators: installed capacity, water complementarity, network loss cost and regulation capacity, and selects the optimal trading combination; then, a large hydropower bidding model is established with reference to the market demand function in econometrics, and the logarithmic demand relationship is used to simulate the functional relationship between large hydropower quotations and trading electricity; finally, the optimal proportion of contract, spot and power generation rights trading electricity is determined based on the large hydropower Sharpe ratio.

[0147] The risk scheduling method taking into account the transaction of large and small hydropower generation rights has the following specific operating steps:

[0148] Step 1: Assess the power generation capacity of large and small hydropower in the current year;

[0149] Large and small hydropower stations have signed some medium- and long-term contracts years ago. However, due to the great uncertainty of medium- and long-term water inflow, large and small hydropower stations may not be able to fully fulfill their contracts or water resources may not be used efficiently during the performance of the contracts. For example, the actual water inflow of large hydropower stations is less, resulting in a rapid drop in water levels at the end of the year; small hydropower stations have more actual water inflows and face greater pressure to abandon water. At this time, large and small hydropower stations are willing to participate in the power generation rights trading market. Therefore, simulating the water inflow process of different frequencies is of great significance to the evaluation of large and small hydropower capacity and power generation rights trading. The Monte Carlo method is used to simulate the water inflow process of large and small hydropower stations, and the maximum power generation model is used to re-evaluate the maximum power generation capacity of large and small hydropower stations.

[0150] Step 2: Determine the trading partner of large hydropower generation rights:

[0151] At present, the power generation rights trading mode is mainly divided into centralized matchmaking and bilateral negotiation. The bilateral negotiation mode is widely used due to its strong autonomy, high fairness, strong operability and adaptability. However, one of the difficulties is how to find a suitable trading partner. Therefore, considering the four indicators of installed capacity, water complementarity, network loss cost and regulation capacity, the entropy weight method is used to select suitable trading partners among many cascade small hydropower stations.

[0152] Step 3: Solve the functional relationship between the electricity price and the electricity volume of power generation rights trading;

[0153] In the contract and spot markets, power plants usually trade directly with the power grid. However, transactions in the power generation rights trading market mainly occur between power generators. The market electricity price is not transparent enough, and it is difficult to obtain the historical electricity price of the market. With reference to the market demand function in econometrics, the functional relationship between the power generation rights trading price and the amount of electricity is fitted.

[0154] Step 4: Calculate the annual comprehensive income of large hydropower. The calculation formula is as follows:

[0155] S=S1+S2+S3+S4-C

[0156] Where: S represents the annual comprehensive income of large hydropower, in RMB; S1, S2, S3 represent the income of large hydropower in the contract market, spot market, and power generation rights trading market, in RMB; S4 represents the energy storage income of large hydropower, in RMB; C represents the transaction cost, in RMB;

[0157] Step 5: Calculate the risk of annual electricity trading of large hydropower;

[0158] Since the conditional value at risk CVaR is a modified method of the value at risk VaR, it represents the average value of the risk when the loss exceeds the VaR critical value, and can better reflect extreme situations and tail risks. Therefore, the conditional value at risk CVaR is used to quantify the risk of large hydropower annual electricity transactions. The calculation formula is as follows:

[0159]

[0160] Where: CVaR represents conditional risk value, in RMB; VaR represents risk value, in RMB; represents the mean annual revenue of large hydropower, in yuan; N represents the total number of Monte Carlo simulations; n represents the number of the current simulation scenario; β represents the confidence level; S n It represents the revenue of large hydropower in the current simulation situation, in yuan; Represents 0 and The larger value of

[0161] Step 6: Calculate the Sharpe ratio of annual electricity trading of large hydropower;

[0162] Generally, high-profit markets often have high risks. How to coordinate risks and benefits and reasonably allocate electricity in the contract, spot, and power generation rights trading markets is of great significance for large hydropower to avoid risks and increase returns. Modern investment theory shows that the Sharpe ratio can comprehensively consider risk and return factors and provide decision makers with the optimal investment portfolio. Therefore, the goal of optimizing the electricity allocation of large hydropower is to maximize the Sharpe ratio of large hydropower. The objective function is:

[0163]

[0164] Where: S p represents the Sharpe ratio of large hydropower; It represents the mean annual return of large hydropower, in Yuan; CVaR represents conditional value at risk, in Yuan.

[0165] The detailed steps of simulating the water inflow process of large and small hydropower stations in step 1 are as follows:

[0166] Step 1.1, calculate the multi-year average data of the ath power station, the calculation formula is as follows:

[0167]

[0168]

[0169] Where: Y is the total number of years; y is the year number; is the annual average flow rate in year y, in m 3 / s; is the multi-year average flow, in m 3 / s; δ is the standard deviation of the multi-year average flow, in m 3 / s;

[0170] Step 1.2: Calculate the historical water inflow correlation coefficient ρ between the ath power station and the bth power station based on the historical runoff of the hydropower station. * , calculate the normal distribution ε3 and ε4 that conform to the historical water inflow correlation, and the calculation formula is as follows:

[0171]

[0172] Where: θ is a certain angle value, in degrees; ε1 and ε2 are two independent normal distributions that conform to the standard; ε3 and ε4 are normal distributions that conform to the historical water inflow correlation of large and small hydropower stations;

[0173] Step 1.3, simulate the annual average flow of the ath power station and the bth power station this year After the processing in step 1.2, the power station flow retains the water inflow correlation of the basin where they are located. The calculation formula is as follows:

[0174]

[0175]

[0176] Where: are the simulated annual average flow rates of the ath power station and the bth power station, in m 3 / s; are the multi-year average flow rates of the ath power station and the bth power station, in m 3 / s; δ a ,δ b is the standard deviation of the multi-year average flow of the a-th power station and the b-th power station, in m 3 / s;

[0177] Step 1.4: simulate the average annual flow of the ath power station and the bth power station in this year according to step 1.3 The annual average flow of the tth power station can be obtained by

[0178] Step 1.5: Based on the annual average flow of the tth power station obtained in step 1.4, According to the historical runoff table of the tth power station, select The historical year with the smallest difference is assumed to be the average flow in year l Based on the average monthly flow in the first year Average annual flow The ratio of the simulated monthly average flow of the tth power station in the i-th month is calculated. The calculation formula is as follows:

[0179]

[0180] Where: is the average annual flow of the tth power station in this year, in m 3 / s; For The historical year with the smallest difference is assumed to be the annual average flow in year l, in m 3 / s; is the average monthly flow rate of the ith month, in m 3 / s;

[0181] The annual water inflow process of each power station obtained through steps 1.1 to 1.5 is used to evaluate the maximum annual power generation of each power station using the maximum power generation model.

[0182] The detailed steps for determining the large hydropower generation rights transaction object in step 2 are as follows:

[0183] Comprehensively consider the four indicators of installed capacity, water complementarity, network loss cost and regulation capacity, and select appropriate trading objects from many cascade small hydropower station groups. Among them, installed capacity reflects the power generation capacity of the power station to a certain extent. When the installed capacity of large hydropower stations and small hydropower stations is quite different, it means that the transaction amount and possibility of power generation rights between them are also small. The decentralized transaction between large hydropower stations and multiple small hydropower stations is not considered for the time being. The following focuses on the other three indicators and the evaluation method of the optimal trading object;

[0184] (1) Water supply complementarity index;

[0185] The water inflow complementarity index can be reflected by water correlation. When the large hydropower is expected to be a flood year at the beginning of the year, the water inflow frequency is small, and more market share is obtained through market transactions. However, after the actual transaction, due to insufficient water inflow and high water inflow frequency, the reservoir may not operate economically at a low water level. At this time, if part of the market share can be traded out through power generation rights, it can not only obtain part of the transaction income, but also avoid the low water level operation of the reservoir. At this time, the most ideal trading object is a group of small hydropower stations with abundant water inflow but small market share. Therefore, the water inflow correlation is used to reflect the complementarity of large and small hydropower. This water inflow correlation is mainly reflected in the annual runoff of the total water inflow and the monthly runoff process of water inflow balance. The following describes how to calculate the annual runoff correlation of large and small hydropower and the monthly runoff process correlation of large and small hydropower respectively;

[0186] Calculate the correlation between annual runoff of large and small hydropower stations, the calculation formula is as follows:

[0187]

[0188] Where: a P represents the correlation coefficient of the total annual runoff of large and small hydropower stations; B , P S is the water inflow frequency of large and small hydropower stations this year, in %; μ B , μ S is the average water frequency of large and small hydropower over the years, in %; δ B , δ S is the standard deviation of water frequency of large and small hydropower stations over the years, in %;

[0189] Calculate the correlation of monthly runoff process between large and small hydropower stations. The calculation formula is as follows:

[0190]

[0191] Where: b is the correlation coefficient of monthly runoff process between large and small hydropower stations; i represents the month; Respectively represent the average flow of the i-th month of large and small hydropower simulations, in m 3 / s; Represents the 12-month average runoff of large and small hydropower simulations, in m 3 / s;

[0192] According to the correlation coefficient ρ of the total annual runoff of large and small hydropower a Correlation coefficient between monthly runoff process of large and small hydropower stations ρ b , calculate the comprehensive correlation coefficient of large and small hydropower water, the calculation formula is as follows:

[0193]

[0194] When c When >0, it means that the water inflow from large and small hydropower stations is positively correlated, ρ c The larger the value, the worse the complementarity of water supply between large and small hydropower stations. c <0, indicating that the water inflow from large and small hydropower stations is negatively correlated, ρ c The smaller the value, the better the complementarity of water supply between large and small hydropower stations. c =0, it means that there is no linear relationship between the water supply of large and small hydropower stations;

[0195] (2) Network loss cost indicator;

[0196] Since the power generation rights transaction changes the original power flow distribution of the power grid and increases the power transmission network loss, the transaction cost increases. This part of the network loss is mainly composed of two parts: transformer loss and line loss. Assuming that the transformer level and number of transformer layers experienced by various hydropower plants are the same during the entire transformer process, the transformer loss is the same under the same transmission capacity. The difference lies in the line loss. Since the line loss is proportional to the power transportation distance, the power transportation distance is used to indirectly reflect the transaction network loss cost. The network loss cost calculation formula is as follows:

[0197]

[0198] Where: C1 is the transmission line loss, unit yuan; Q3 is the power generation right trading quantity, unit kWh; is the average distance from the small hydropower group to the main grid, in km; k is the number of the small hydropower station; K is the total number of small hydropower stations; L k is the distance from the current small hydropower station to the main grid, in km; λ is the transmission distance P3 is the power price of power generation right transaction, in RMB / kWh;

[0199] (3) Reservoir capacity regulation capability index;

[0200] The reservoir regulation capacity is reflected by the reservoir capacity regulation coefficient. The larger the difference in the reservoir capacity regulation coefficient between large and small hydropower stations, the greater the space for reservoir capacity compensation between the two, and the higher the feasibility of the power generation rights transaction between the two. The calculation formula of the reservoir capacity regulation capacity index is as follows:

[0201]

[0202]

[0203] Where: is the comprehensive reservoir capacity regulation coefficient of the small hydropower group; k is the number of the small hydropower station; K is the total number of small hydropower stations; V k is the regulating storage capacity of the kth power station, in m 3 ; V is the total regulating storage capacity of all power stations in the small hydropower group, in m 3 ; φ k is the regulation capacity coefficient of the kth power station; is the average inflow flow of the kth power station over many years, in m 3 ;

[0204] Using the four indicators of installed capacity, water complementarity, network loss cost and regulation capacity, the entropy weight method is then used to calculate the comprehensive evaluation of large hydropower and various candidate trading objects.

[0205] The detailed steps for solving the functional relationship between the electricity price and the electricity volume of power generation rights trading in step 3 are as follows:

[0206] Generally speaking, the power generation rights transaction for this month or this trading cycle is usually declared and carried out in the previous month. Through the power generation rights transaction, the large hydropower transferred part of the monthly contracted electricity to the small hydropower station group in the previous month, so that they can generate more electricity in the next month. After the transaction is completed, the contracted electricity of the transaction needs to be decomposed into subsequent periods. Assuming that the small hydropower group decomposes the monthly contracted electricity of the transaction into each day according to the amount of water inflow per day, and puts it into the day-ahead spot market for profit, and compensates part of the profit to the large hydropower, the profit of the large hydropower in this power generation rights transaction is as follows:

[0207] S3=αP2Q3

[0208] In the formula: S3 is the income of large hydropower in the power generation rights trading market, in yuan; P2 is the spot market electricity price, in yuan / kWh; Q3 is the power volume traded in the power generation rights market, in kWh; α is the compensation ratio of small hydropower groups to large hydropower, assuming the "50-50" principle, α is 50%;

[0209] The expression of the revenue S3 of large hydropower in the power generation rights trading market is S3=P3Q3. Combining the two expressions of the revenue S3 of large hydropower in the power generation rights trading market, the expression of the power generation rights trading price P3 can be obtained as P3=αP2;

[0210] Due to the uncertainty of spot electricity prices, it is assumed that the spot electricity price P2 follows a normal distribution. It can be obtained that the fluctuation range of the spot electricity price P2 is (P2-δ2)~(P2+δ2), where is the average price of spot market electricity, in yuan / kWh, δ2 is the standard deviation of spot price, in yuan / kWh, and the lower limit P of the power generation right transaction price P3 can be obtained. 3,min =α(P2-δ2) and upper limit P 3,max =α(P2+δ2);

[0211] The range of power generation rights trading volume Q3 is as follows:

[0212] 0<Q3≤(Q e -Q c )

[0213] Where: Q3 is the electricity volume of power generation rights trading, in kWh; Q e , Q c They represent the maximum power generation of the small hydropower group based on the predicted water inflow simulation and the contracted power volume signed by the small hydropower group this year, in kWh. e Greater than Q c , indicating that after the small hydropower stations have completed the annual contracted electricity, there is still surplus electricity. If this part of the surplus electricity is not traded in the market, it will face the risk of being abandoned. This part of the electricity can be traded with large hydropower stations for power generation rights to avoid the risk of water abandonment;

[0214] When the price of large hydropower increases, the trading willingness of small hydropower groups will decrease, resulting in a decrease in trading volume. This trend of decreasing trading volume as the trading price increases is similar to the market demand elasticity function in economics, which indicates the degree of response of price changes to trading volume. Therefore, the most widely used logarithmic demand function is used to describe the relationship between trading price and trading volume in the power generation rights market. The formula is as follows:

[0215] lnP3=g+hlnQ3

[0216] In the formula: P3 represents the electricity price of power generation rights trading, in units of RMB / kWh; Q3 represents the power volume of power generation rights trading, in units of kWh; g and h are parameters to be determined, where h represents price elasticity, indicating how much the trading volume will decrease for every 1% increase in the bid price. The calculation steps of parameters g and h are as follows:

[0217] Step 3.1, draw a curve diagram of the logarithmic demand function according to the formula lnP3 = g + hlnQ3, and assume that the starting and ending points of the curve are A and B respectively;

[0218] Step 3.2: Combine the range of power generation rights transaction quantity Q3 and the upper and lower limits of power generation rights transaction price P3 to obtain the coordinates of points A and B, where Q 3,minYou can take a smaller value such as 0.01, and substitute the coordinates of points A and B into the formula lnP3=g+hlnQ3 to obtain the parameters g and h.

[0219] The detailed steps for solving the annual comprehensive income of large hydropower in step 4 are:

[0220] The annual revenue of large hydropower consists of five parts, namely, contract market revenue S1, spot market revenue S2, power generation rights trading market revenue S3, energy storage revenue S4 and transaction cost C. Since the functional relationship between power generation rights trading electricity price and electricity volume has been obtained, the power generation rights trading market revenue S3 can be solved. The following focuses on the solution process of the other four parts;

[0221] Step 4.1, solve the contract market return S1 and the spot market return S2;

[0222] In the contract and spot markets, power plants usually trade directly with the grid. Therefore, the electricity prices P1 and P2 in the contract and spot markets are relatively open and transparent. The average contract market electricity price can be obtained by searching online. and standard deviation δ1, the mean of spot market electricity prices and standard deviation δ2, assuming that the electricity prices in the two markets follow a normal distribution with their mean and standard deviation as parameters The fluctuations of the contract and spot market electricity prices P1 and P2 can be obtained;

[0223] Since the range of power generation right transaction quantity Q3 is 0<Q3≤(Q e -Q c ), a value is selected as Q3 within this range, and the contract quantity Q1 of large hydropower is assumed to be the decision variable of the present invention, then the spot market quantity Q2 is:

[0224] Q2=Q a -Q1-Q3

[0225] Where: Q a is the annual power generation of large hydropower, in kWh; Q1, Q2, and Q3 represent the contract market power, spot power, and power generation rights trading power, in kWh;

[0226] According to the electricity price in the contract and spot market, Q2 = Q a -Q1-Q3 can get the expression of contract market return S1 as S1=P1Q1, and the expression of spot market return S2 as S2=P2Q2;

[0227] Based on the average of the contract market electricity price Average spot market electricity price Get the average return of the contract market The expression is The expression for the average return in the spot market is for

[0228] Step 4.2, solve the large hydropower storage benefit S4;

[0229] Under the power generation rights trading model, large hydropower transfers part of the power generation contract to small hydropower for a fee during the flood season, and uses its own regulation reservoir advantage to store this part of the power generation water as energy storage. This part of the stored electricity is the power generation rights trading electricity Q3. Since large hydropower originally intended to invest this part of the power generation rights trading electricity into the contract market for profit, the large hydropower energy storage income S4 ​​is:

[0230]

[0231] Where: S4 is the large hydropower storage income, in yuan; is the average of the electricity price in the contract market, in RMB / kWh; Q3 represents the electricity volume of power generation rights trading, in kWh;

[0232] Step 4.3, solve the transaction cost C;

[0233] The transaction cost C is mainly the network loss cost of the transaction, which is usually shared by both parties to the transaction. The calculation formula is as follows:

[0234]

[0235] In the formula: C1 represents the transmission line loss, in yuan; C2 represents the transformer loss, in yuan; is the average distance from the small hydropower group to the main grid, in km; λ is the transmission distance The line loss rate of the lower high-voltage transmission is in %; P3 is the electricity price for power generation rights trading, in yuan / kWh; X is the total number of transformers from the power station to the converter station; x is the transformer layer number; λ x is the transformer loss rate of the transformer, in %; Q3 is the power generation right trading quantity, in kWh;

[0236] Step 4.4, solve for the annual comprehensive income S of large hydropower;

[0237] According to the expressions of large hydropower contract market income S1, spot market income S2, power generation rights trading market income S3, energy storage income S4 ​​and transaction cost C, the annual comprehensive income S of large hydropower can be obtained. To simplify the calculation, it can be assumed that the proportion of contract, spot and power generation rights trading market electricity to the annual total hydropower electricity Q aThe proportions are r1, r2, and r3 respectively; the annual comprehensive income S of large hydropower consists of five parts, among which the power generation right transaction price P3 can be determined, and the power generation right transaction market can be regarded as a risk-free market, while the transaction cost C and energy storage income S4 ​​are not affected by the fluctuations of contract and spot market electricity prices, and can also be regarded as a risk-free market. Therefore, the market risk mainly comes from the fluctuations of contract and spot market electricity prices. According to the average of the contract market electricity price Average spot market electricity price Finally, we can get the annual comprehensive income S of large hydropower, the average annual income of large hydropower The Sharpe ratio S of annual electricity trading of large hydropower p As shown below:

[0238]

[0239] Where: is the mean annual comprehensive income of large hydropower, in yuan; They represent the average of the contract and spot markets, in units of RMB / kWh; r1, r2, and r3 represent the proportion of the annual electricity volume of large hydropower in the contract, spot, and power generation rights trading markets, respectively. a The ratio of, in %; is the average distance from the small hydropower group to the main grid, in km; λ is the transmission distance The line loss rate of the lower high-voltage transmission is in %; P3 is the electricity price for power generation rights trading, in yuan / kWh; X is the total number of transformers from the power station to the converter station; x is the transformer layer number; λ x is the transformer loss rate of the transformer, in %; Q3 is the power generation right trading quantity, in kWh; Q a is the annual power generation of large hydropower, in kWh; S p is the Sharpe ratio of large hydropower; CVaR is the conditional value at risk, in yuan; S is the annual comprehensive return of large hydropower, in yuan;

[0240] The detailed steps for calculating the Sharpe ratio of large hydropower annual electricity trading in step 6 are:

[0241] The Sharpe ratio S of annual electricity trading of large hydropower p The decision variable is the proportion of spot market electricity to the total annual electricity of large hydropower Q a The ratio r2, the correlation coefficient between the contract and the spot market ρ, change the above decision variables to make the large hydropower Sharpe ratio S p To achieve the maximum, the specific steps are as follows:

[0242] Step 6.1, set the correlation coefficient between the contract and the spot market ρ = 0.1, the range is ρ < 1;

[0243] Step 6.2: Based on the forecast water inflow and contracted electricity of the small hydropower group, the abandoned water power of the small hydropower group is obtained, and combined with the annual electricity of large hydropower to obtain the range and maximum value r of r3 3,max , set the initial value of r3 to 0%;

[0244] Step 6.3: Increase the proportion of electricity generated by power generation rights trading to the annual electricity generated by large hydropower stations Q a The ratio r3, the step size is 0.1%;

[0245] Step 6.4, increase r2 from 0% with a step size of 0.1%, then r1 = 1-r2-r3, calculate the Sharpe ratio and market risk of large hydropower in all cases;

[0246] Step 6.5, return to step 3, change r3 and continue calculating until all r3 are calculated;

[0247] Step 6.6, record the maximum Sharpe ratio, minimum risk and the proportion of electricity traded in large hydropower contracts, spot and power generation rights under each scenario;

[0248] Step 6.7, change the market correlation coefficient ρ. The step size of the market correlation coefficient is 0.1. Increase the step size and return to step 3 until all market correlation coefficients ρ are calculated.

[0249] Embodiment 2:

[0250] Taking Yunnan power grid as the research background, in order to verify the effectiveness of the method of the present invention, several large and small hydropower groups in Yunnan Province were selected for analysis. Nuozadu and Xiaowan were selected as the large hydropower stations for analysis. Both large power stations have an annual regulation capacity of more than 100%, and have strong regulation and storage capabilities. The Xi'er River cascade, Yili River cascade, Dayingjiang River cascade, and Gongguoqiao single power stations were selected as small hydropower groups. They are characterized by small regulation reservoir capacity and high risk of water abandonment. Figure 2 Other parameters and geographical distribution maps of each power station, Figure 3 It is a selection and analysis chart of large hydropower generation rights trading objects. Figure 4 This is the Nuozadu trading result chart under different market correlations. Figure 5 is CVaR and S p Change curve chart, Figure 6 This is a curve chart showing the change in risk with the spot electricity ratio under different market correlations. Figure 7 , Figure 8 These are the electricity price trend charts under weak market correlation and strong market correlation. Fig. 9 is the optimal spot electricity ratio under different market correlations, Fig.10Figure 1 is the trading result diagram of Nuozadu with all trading objects. Table 1 is the trading results of Nuozadu under different confidence levels. The following analysis is carried out from two aspects. First, the two large power stations of Nuozadu and Xiaowan are taken as the trading subjects, and the best small hydropower trading objects are selected from the above four small hydropower stations (groups); secondly, Nuozadu is taken as the research object, and its annual electricity allocation strategy is analyzed and compared, and the following conclusions are drawn:

[0251] The first step is to select and analyze the trading targets of large hydropower generation rights. Figure 3 This is a diagram for selecting and analyzing large hydropower generation rights trading objects. Figure 3 As shown in (a), the optimal trading partner of Nuozadu is Dayingjiang cascade power station group, because Dayingjiang has the largest installed capacity and the weakest regulation ability, which greatly improves the comprehensive evaluation of Dayingjiang power station group; although Gongguoqiao power station is almost the same as Dayingjiang cascade power station in regulation performance and installed capacity, Gongguoqiao and Nuozadu belong to the same basin, which greatly reduces the water complementarity between them. The water complementarity index has a large weight in Nuozadu's trading partner, which is 27.2%, thus reducing Gongguoqiao's score in Nuozadu's trading partner. Figure 3 (b) is the evaluation result with Xiaowan Power Station as the trading subject. It can be seen that the weights of various indicators in Xiaowan are not the same when selecting the optimal trading object. The reason is that the reservoir capacity, installed capacity and other conditions of Nuozadu and Xiaowan are different. When selecting suitable trading objects, the importance of various indicators is also different. The optimal trading objects of Nuozadu and Xiaowan are Dayingjiang cascade hydropower station group and Gongguoqiao power station respectively, which shows that when large hydropower plants select trading objects, the parameter differences between each power station will affect the optimal trading object, and different large hydropower plants may choose different trading objects.

[0252] The second is the trading results of Nuozadu under different market correlations. Figure 4 This is a graph of the trading results of Nuozadu under different market correlations. In order to prove that the introduction of the power generation rights trading market can effectively improve the Sharpe ratio of Nuozadu or reduce market risks, the present invention compares the trading results of Nuozadu under different contract and spot market correlation coefficients ρ when Nuozadu and Dayingjiang are traded. Figure 4 It can be seen that in each market situation, from the Sharpe ratio S p From the perspective of CVaR, the CVaR of Nuozadu participating in power generation rights trading is lower than that of not participating. The reason is that the risk-free power generation rights trading market is introduced to hedge the risk of electricity price risks in the contract market and spot market, thereby reducing the overall market risk. p and CVaR, indicating that the method of the present invention can increase the profit and reduce the risk for the power generator.

[0253] Then, the trading results of Nuozadu under different confidence levels are shown in Table 1. In order to prove that the introduction of power generation rights trading market can effectively improve the Sharpe ratio of Nuozadu or reduce market risks, the present invention compares the trading results of Nuozadu under different confidence levels when Nuozadu and Dayingjiang are traded. As shown in Table 1, under the three confidence levels, the Sharpe ratio S of Nuozadu is p After the introduction of the power generation rights trading market, the S-values ​​increased by 0.18, 0.27 and 0.33 respectively, and all the excess power generation contracts of Dayingjiang participated in the power generation rights market, indicating that the introduction of the power generation rights trading market can increase the unit risk return of power generators. p =5.61, which means that every 1% risk can get 5.61% return. p is 5.43, which is higher than the S p It was reduced by 0.18, indicating that compared with not participating in the power generation rights transaction, Nuozadu can obtain 0.18% more profit for every 1% risk, and compared with not participating in the power generation rights transaction, Nuozadu's expected profit increased by 167 million yuan and the risk decreased by 11 million yuan, indicating that the introduction of the power generation rights trading market can increase the expected profit of power generators and reduce market risks.

[0254] This is followed by an analysis of the power allocation strategy for Nuozadu. Figure 5 is CVaR and S p In order to analyze the difference between the two power allocation strategies of maximum Sharpe ratio and minimum risk, the present invention assumes that the correlation between the contract and the spot market is ρ = 0.1, the confidence level is β = 90%, and observes the CVaR and S when all the excess power generation contracted by Dayingjiang is involved in the power generation rights transaction. p As the spot electricity ratio changes, such as Figure 5 As shown. Figure 5 It can be seen that the Nuozadu electricity allocation strategy at the lowest risk point and the highest Sharpe ratio point is not the same. The lowest risk does not mean the highest Sharpe ratio, which means that the electricity allocation strategy with the lowest risk is not the optimal one. When allocating annual electricity, power producers must not only consider risks but also comprehensively consider returns in order to obtain the optimal investment portfolio.

[0255] Then there is the risk analysis of Nuozadu under different market correlations. Figure 6 This is a curve chart showing the change in risk with the spot electricity ratio under different market correlations. Figure 7 , Figure 8 These are the electricity price trend charts under weak market correlation and strong market correlation. Fig. 9In order to study the market risk of Nuoza under different situations, this paper sets nine different contracts and spot market correlations ρ, assuming that all the abandoned hydropower in Dayingjiang participates in the power generation rights trading market, and analyzes the changes in the market risk of Nuoza under different market correlations, the impact of different electricity price situations on the risk of Nuoza, and the optimal spot electricity ratio of Nuoza under different market correlations. Figure 7 and Figure 8 The average market loss price P * It represents the average of the decrease in electricity prices in the contract market and the spot market. The calculation formula is as follows:

[0256]

[0257] Where: Represents the average electricity price of the contract market and the spot market, in RMB / kWh; N represents the total number of Monte Carlo simulations; n represents the number of the current simulation scenario; P 1,n , P 2,n They represent the electricity prices in the contract market and spot market under the current situation, respectively, in Yuan / kWh; Represents 0 and The larger value of

[0258] Depend on Figure 6 It can be seen that when the correlation coefficient between the contract and spot markets is low, CVaR decreases first and then increases, indicating that the risks of the contract market and the spot market are offset. Reasonable allocation of the electricity ratio between the two markets can hedge risks and reduce the market risk of power generators. As the market correlation increases, CVaR will increase directly, indicating that under strong market correlation, this risk offset disappears. The reason for the above phenomenon is mainly due to the influence of contract and spot market electricity prices. When the correlation between the two markets is weak, such as Figure 7 As shown in Figure 1, since the market electricity price is volatile, when the electricity price in one market decreases, the electricity price in another market may increase, such as points C and D, resulting in an average loss of electricity price P. * will decrease, and the overall risk will also decrease. As the correlation coefficient ρ between the contract market and the spot market increases, Figure 8 As shown in Figure 1, the stronger the correlation between the contract market and the spot market, the greater the probability of a drop in the spot market price when the contract market price drops. The trend of the spot market price can be predicted by the trend of the contract market price, such as points E and F. In this market situation, the average loss price P *There is no risk hedging between the spot market and the contract market. Because of the strong market correlation, the trend of the contract market electricity price can affect the trend of the spot electricity price, and the spot market electricity price fluctuates greatly and has higher risks. In order to avoid the spot market electricity price risk, the spot electricity ratio will be reduced accordingly. Fig. 9 This shows that when the contract and spot markets are closely linked, power generators should reduce their electricity input into the spot market and invest more electricity into the contract market with lower risks to reduce overall market risks.

[0259] Finally, the Sharpe ratio analysis of each power generation rights trading object is presented. Fig.10 In order to verify whether the method for evaluating the optimal trading partner for large hydropower proposed in the present invention is effective, the trading results between Nuozadu and all trading partners are calculated, as shown in the figure below: Fig.10 As shown. Fig.10 It can be seen that under the correlation of various markets, the Sharpe ratio S p From high to low, Nuozadu trades with Daying River, Gongguo Bridge, Xi'er River, and Yili River. Figure 3 The evaluation results in (a) are consistent. This shows that when large hydropower selects small hydropower groups for power generation rights trading, it can first screen the small hydropower groups according to indicators such as installed capacity and regulation capacity to initially obtain the selection range of trading objects, and then use the Sharpe ratio as the target, comprehensively consider risk and return factors, and make the final selection of the preliminarily screened small hydropower groups to obtain the optimal trading object.

[0260] Table 1: Nuozadu transaction results at different confidence levels:

[0261]

Claims

1. A risk scheduling method taking into account large and small hydropower generation rights transactions, characterized by: Firstly, a comprehensive evaluation of different hydropower combinations is conducted based on four indicators: installed capacity, water complementarity, network loss cost and regulation capacity, and the optimal trading combination is selected. Then, a large hydropower bidding model is established with reference to the market demand function in econometrics, and the functional relationship between large hydropower bids and trading power is simulated using the logarithmic demand relationship. Finally, the optimal proportion of contract, spot and power generation rights trading power is determined based on the large hydropower Sharpe ratio. The specific steps are as follows: Step 1: Assess the power generation capacity of large and small hydropower in the current year; The Monte Carlo method is used to simulate the water inflow process of large and small hydropower stations, and the maximum power generation model is used to re-evaluate the maximum power generation capacity of large and small hydropower stations; Step 2: Determine the trading partner of large hydropower generation rights: Taking into account the four indicators of installed capacity, water complementarity, network loss cost and regulation capacity, the entropy weight method is used to select suitable trading partners among many cascade small hydropower stations. Step 3: Solve the functional relationship between the electricity price and the electricity volume of power generation rights trading; The logarithmic demand function is used to fit the functional relationship between the electricity price and the electricity volume of power generation rights trading; Step 4: Calculate the annual comprehensive income of large hydropower. The calculation formula is as follows: Where: S It represents the annual comprehensive income of large hydropower, in yuan; S 1. S 2. S 3 represents the revenue of large hydropower in the contract market, spot market and power generation rights trading market, in RMB; S 4 represents the energy storage benefits of large hydropower, in yuan; C is the transaction cost, in yuan; Step 5: Calculate the risk of annual electricity trading of large hydropower; Since the conditional value at risk QUR Value at Risk VAR The correction method represents a loss of more than VaR The average value of risk at the critical value can better reflect extreme situations and tail risks, so the conditional risk value is used. QUR To quantify the risk of large hydropower annual electricity trading, the calculation formula is as follows: Where: represents the conditional value at risk, in yuan; represents the risk value, in yuan; It represents the mean annual revenue of large hydropower, in yuan; N Represents the total number of Monte Carlo simulations; n Indicates the number of the current simulation scenario; Indicates confidence; S n It represents the revenue of large hydropower in the current simulation situation, in yuan; Represents 0 and The larger value of ; Step 6: Calculate the Sharpe ratio of annual electricity trading of large hydropower; The objective function for optimizing the distribution of large hydropower is to maximize the Sharpe ratio of large hydropower: Where: represents the Sharpe ratio of large hydropower; It represents the mean annual revenue of large hydropower, in yuan; Represents conditional value at risk, in yuan.

2. A risk scheduling method taking into account large and small hydropower generation rights transactions according to claim 1, characterized in that: The detailed steps of simulating the water inflow process of large and small hydropower stations in step 1 are as follows: Step 1.1, calculate the a The multi-year average data of each power station is calculated as follows: Where: Y is the total number of years; y Number the year; For the y The annual average flow rate is in m 3 / s; is the multi-year average flow, in m 3 / s; δ is the standard deviation of the multi-year average flow, in m 3 / s; Step 1.2: Calculate the runoff of the hydropower station based on its historical runoff. a Power station, b Correlation coefficient of historical water inflow of each power station , calculate the normal distribution that conforms to the historical water inflow correlation ε 3. ε 4. The calculation formula is as follows: Where: θ is a certain angle value, in degrees; ε 1. ε 2 are two independent standard normal distributions; ε 3. ε 4 is a normal distribution that complies with the historical water inflow correlation of large and small hydropower stations; Step 1.3, simulate a Power station, b The average annual flow of each power station this year , , the power station flow after step 1.2 retains the water inflow correlation of the basin where they are located, and the calculation formula is as follows: Where: , Respectively a Power station, b The annual average flow rate simulated by each power station, in m 3 / s; , Respectively a Power station, b The average flow rate of each power station over many years, in m 3 / s; , For the a Power station, b The standard deviation of the multi-year average flow of each power station, in m 3 / s; Step 1.4: simulate the a Power station, b The average annual flow of each power station this year , By using the method t The average annual flow of each power station this year ; Step 1.5, according to the first t The average annual flow of each power station this year , compare t The historical runoff table of each power station is selected The historical year with the smallest difference is l Average annual flow , according to l Average monthly traffic volume Average annual flow , calculate the ratio t Power station i The average monthly flow rate of the simulation , the calculation formula is as follows: Where: For the t The average annual flow of each power station this year, in m 3 / s; For The historical year with the smallest difference is l The annual average flow rate is in m 3 / s; For the i Monthly average flow rate, in m 3 / s; The annual water inflow process of each power station obtained through steps 1.1 to 1.5 is used to evaluate the maximum annual power generation of each power station using the maximum power generation model.

3. A risk scheduling method taking into account large and small hydropower generation rights transactions according to claim 1, characterized in that: The detailed steps for determining the large hydropower generation rights transaction object in step 2 are as follows: Comprehensively consider the four indicators of installed capacity, water complementarity, network loss cost and regulation capacity, select the appropriate trading object among many cascade small hydropower stations, three indicators and the evaluation method of the optimal trading object; (1) Water supply complementarity index; The complementary index of water inflow can be used to reflect water correlation, which is mainly reflected in the annual runoff of the total water inflow and the monthly runoff process of water inflow balance. Calculate the correlation between annual runoff of large and small hydropower stations, the calculation formula is as follows: Where: It represents the correlation coefficient of the total annual runoff of large and small hydropower stations; P B , P S The water inflow frequency of large and small hydropower stations this year, in %; μ B , μ S It is the average water frequency of large and small hydropower over the years, unit is %; δ B , δ S The standard deviation of water frequency of large and small hydropower stations over the years, unit is %; Calculate the correlation of monthly runoff process between large and small hydropower stations. The calculation formula is as follows: Where: is the correlation coefficient of monthly runoff process between large and small hydropower stations; i Indicates the month; , Represents the first i Average monthly flow, in m 3 / s; , Represents the 12-month average runoff of large and small hydropower simulations, in m 3 / s; According to the correlation coefficient of the total annual runoff of large and small hydropower Correlation coefficient between monthly runoff process of large and small hydropower stations , calculate the comprehensive correlation coefficient of large and small hydropower water, the calculation formula is as follows: when ρ c When >0, it means that the water inflow from large and small hydropower stations is positively correlated. The larger the value, the worse the complementarity of water supply between large and small hydropower stations. When <0, it means that the water inflow from large and small hydropower stations is negatively correlated. The smaller it is, the better the complementarity of water supply between large and small hydropower stations is; =0, indicating that there is no linear relationship between the water inflow of large and small hydropower stations; (2) Network loss cost indicators; Network loss mainly consists of two parts: transformer loss and line loss. During the entire transformer process, the transformer level and number of transformer layers experienced by various hydropower stations are the same. Under the same transmission capacity, the transformer loss is the same. The difference lies in the line loss. The line loss is proportional to the power transportation distance. The power transportation distance is used to indirectly reflect the transaction network loss cost. The network loss cost calculation formula is as follows: Where: is the transmission line loss, in yuan; The power volume of power generation rights trading, in kWh; is the average distance from the small hydropower group to the main grid, in km; k Number the small hydropower plant; K is the total number of small hydropower stations; is the distance from the current small hydropower station to the main grid, in km; Transmission distance Line loss rate of high voltage transmission, unit is %; The electricity price for power generation rights trading, in yuan / kWh; (3) Reservoir capacity regulation capability index; The reservoir regulation capacity is reflected by the reservoir capacity regulation coefficient. The larger the difference in the reservoir capacity regulation coefficient between large and small hydropower stations, the greater the space for reservoir capacity compensation between the two, and the higher the feasibility of the power generation rights transaction between the two. The calculation formula of the reservoir capacity regulation capacity index is as follows: Where: is the comprehensive storage capacity regulation coefficient of small hydropower groups; k Number the small hydropower plant; K is the total number of small hydropower stations; V k For the k The regulating storage capacity of each power station, in m 3 ; V is the total regulating storage capacity of all power stations in the small hydropower group, in m 3 ; For the k The regulation capacity coefficient of each power station; For the k The average inflow flow of each power station over many years, in m 3 ; Using the four indicators of installed capacity, water complementarity, network loss cost and regulation capacity, the entropy weight method is then used to calculate the comprehensive evaluation of large hydropower and various candidate trading objects.

4. A risk scheduling method taking into account large and small hydropower generation rights transactions according to claim 1, characterized in that: The detailed steps for solving the functional relationship between the electricity price and the electricity volume of power generation rights trading in step 3 are as follows: The small hydropower group breaks down the monthly contracted electricity into each day according to the amount of water inflow per day, and puts it into the day-ahead spot market to make a profit, and compensates part of the profit to the large hydropower. The profit of the large hydropower in this power generation right transaction is as follows: Where: S 3 is the revenue of large hydropower in the power generation rights trading market, in yuan; is the spot market electricity price, in yuan / kWh; The amount of electricity traded in the power generation rights market, in kWh; The compensation ratio of small hydropower groups to large hydropower, Take 50%; Large hydropower has a high profit margin in the power generation rights trading market. S The expression of 3 is , the two major hydropower stations benefit from the power generation rights trading market S 3, we can get the power generation right transaction price P 3The expression is ; Spot electricity price Normal distribution ~ N ( , δ 2 2 ), you can get the spot electricity price The fluctuation range is ,in is the average price of electricity in the spot market, in yuan / kWh. δ 2 is the standard deviation of spot electricity price, in yuan / kWh, which can be used to get the electricity price of power generation rights trading The lower limit and upper limit ; Electricity generation rights trading volume The range is as follows: Where: The amount of electricity traded for power generation rights, in kWh; Q e , Q c They represent the maximum power generation of the small hydropower group based on the predicted water inflow simulation and the contracted power volume signed by the small hydropower group this year, in kWh; Q e Greater than Q c , indicating that after the small hydropower stations have completed their annual contracted electricity, there is still surplus electricity; The logarithmic demand function is used to describe the relationship between transaction price and transaction volume in the power generation rights market. The formula is as follows: Where: It indicates the electricity price of power generation rights trading, in yuan / kWh; Indicates the amount of electricity traded for power generation rights, in kWh; g , h is the parameter to be sought, where h Indicates price elasticity, which means how much the transaction volume will decrease when the quotation increases by 1%. g and h The calculation steps are as follows: Step 3.1, according to the formula Draw a curve diagram of the logarithmic demand function, assuming that the starting and ending points of the curve are A , B ; Step 3.2: Joint power generation rights trading volume Q 3. Scope and electricity price of power generation rights trading P The upper and lower limits of 3 give A , B The coordinates of two points, where Q 3,min A smaller value such as 0.01 can be used. A , B Substituting the coordinates of two points into the formula The parameters to be requested can be obtained g , h .

5. A risk scheduling method taking into account large and small hydropower generation rights transactions according to claim 1, characterized in that: The detailed steps for solving the annual comprehensive income of large hydropower in step 4 are: The annual revenue of large hydropower consists of five parts: contract market revenue S 1. Spot market income S 2. Revenue from the power generation rights trading market S 3. Energy storage benefits S 4. Transaction costs C Since the functional relationship between the power generation rights trading price and the power volume has been obtained, the power generation rights trading market revenue can be solved S 3. The solution process is as follows; Step 4.1, solve the contract market profit S 1 and spot market returns S 2; The query can obtain the average contract market electricity price and standard deviation δ 1. Average spot market electricity price and standard deviation δ 2. The electricity prices in the two markets follow a normal distribution with their mean and standard deviation as parameters. P 1~ N ( , δ 1 2 )、 P 2~ N ( , δ 2 2 ), you can get the contract and spot market electricity prices P 1. P 2. Fluctuation of Due to the power generation rights trading Q The range of 3 is , select a value within this range as Q 3. Set the contracted electricity volume of large hydropower Q 1 is the decision variable of the present invention, then the spot market electricity Q 2 is: Where: Q a is the annual power generation of large hydropower, in kWh; , , They represent the contract market electricity, spot electricity, and power generation rights trading electricity, in kWh; Based on the electricity prices in the contract and spot market, Lianli Contract market income can be obtained S 1The expression is , Spot market income S 2 The expression is ; Based on the average of the contract market electricity price , the average spot market electricity price , and get the average return of the contract market The expression is , the expression of average return in spot market for ; Step 4.2, solve for the large hydropower storage benefits S 4; In the power generation rights trading model, large hydropower transfers part of the power generation contract to small hydropower for a fee during the flood season, and uses its own regulation reservoir capacity advantage to store this part of the power generation water as energy storage. This part of the stored electricity is the power generation rights trading electricity Q 3. Since the large hydropower originally intended to put this part of the power generation rights trading electricity into the contract market for profit, the large hydropower energy storage income S 4 is: Where: S 4 is the revenue from large hydropower storage, in yuan; is the average of the electricity price in the contract market, in yuan / kWh; Q 3 represents the amount of electricity generated by power generation rights trading, in kWh; Step 4.3, solve for transaction costs C ; Transaction costs C It is mainly the network loss cost of the transaction, which is usually shared by both parties of the transaction. The calculation formula is as follows: Where: It represents the transmission line loss, in Yuan; It represents the transformer loss, in yuan; is the average distance from the small hydropower group to the main grid, in km; Transmission distance Line loss rate of high voltage transmission, unit is %; The electricity price for power generation rights trading, in yuan / kWh; X is the total number of voltage transformations from the power station to the converter station; x Number the transformer layer; is the transformer loss rate of the transformer, unit is %; Q 3 is the electricity volume of power generation rights trading, in kWh; Step 4.4, solve for the annual comprehensive income of large hydropower S ; According to the market revenue of large hydropower S 1. Spot market income S 2. Revenue from the power generation rights trading market S 3. Energy storage benefits S 4. Transaction costs C The expression of annual comprehensive income of large hydropower can be obtained S To simplify the calculation, we can assume that the electricity generated by the contract, spot, and power generation rights trading markets accounts for the total annual electricity generated by large hydropower. Q a The proportions are , , ; Annual comprehensive income of large hydropower S It consists of five parts, including the electricity price of power generation rights trading P 3 It can be determined that the power generation rights trading market can be regarded as a risk-free market, and the transaction cost C and energy storage benefits S 4. It is not affected by the fluctuation of electricity prices in the contract and spot market, and can also be regarded as a risk-free market. Therefore, the market risk mainly comes from the fluctuation of electricity prices in the contract and spot market. According to the average of the electricity prices in the contract market, , the average spot market electricity price , and finally the annual comprehensive income of large hydropower can be obtained S , the average annual revenue of large hydropower , Sharpe ratio of annual electricity trading of large hydropower As shown below: Where: is the mean annual comprehensive income of large hydropower, in yuan; , Represent the average of contract and spot markets respectively, in RMB / kWh; , , The proportion of electricity generated by the contract, spot and power generation rights trading markets in the total annual electricity generated by large hydropower Q a The ratio of %, unit is % is the average distance from the small hydropower group to the main grid, in km; Transmission distance Line loss rate of high voltage transmission, unit is %; The electricity price for power generation rights trading, in yuan / kWh; X is the total number of voltage transformations from the power station to the converter station; x Number the transformer layer; is the transformer loss rate of the transformer, in %; Q 3 is the electricity volume of power generation rights trading, in kWh; Q a is the annual power generation of large hydropower, in kWh; The Sharpe ratio for large hydropower; is the conditional value at risk, in yuan; S It is the annual comprehensive income of large hydropower, in Yuan.

6. A risk scheduling method taking into account large and small hydropower generation rights transactions according to claim 1, characterized in that: The detailed steps for calculating the Sharpe ratio of large hydropower annual electricity trading in step 6 are: Sharpe ratio of annual electricity trading of large hydropower The decision variable is the proportion of spot market electricity to the total annual electricity of large hydropower Q a The proportion , Correlation coefficient between contract and spot market ρ , changing the above decision variables to make the Sharpe ratio of large hydropower To achieve the maximum, the specific steps are as follows: Step 6.1, set the correlation coefficient between the contract and the spot market , the range is ; Step 6.2: Based on the forecast water inflow and the contracted electricity of the small hydropower group, the abandoned water power of the small hydropower group is obtained, and combined with the annual electricity of large hydropower to obtain Range and maximum value ,set up r 3The initial value is 0%; Step 6.3: Increase the proportion of electricity generated by power generation rights trading to the annual electricity generated by large hydropower Q a The proportion , the step size is 0.1%; Step 6.4, increase from 0% , the step size is 0.1%, then , calculate the Sharpe ratio and market risk of large hydropower under all scenarios; Step 6.5, return to step 3 and change Continue calculating until all ; Step 6.6, record the maximum Sharpe ratio, minimum risk and the proportion of electricity traded in large hydropower contracts, spot and power generation rights under each scenario; Step 6.7, Change the market correlation coefficient ρ , the step size of the market correlation coefficient is 0.1, increase the step size, and return to step 3 until all market correlation coefficients are calculated ρ .

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