A method for balancing the deviation of bidding electricity quantity of a cascade hydropower station

Abstract

The present application relates to a kind of step cascade hydropower station bidding power deviation equalization method, comprising the following steps: obtaining the relevant data of step cascade hydropower station, including the number of upstream and downstream power station, the net head of hydropower station and the average power flow of hydropower station;Based on the relevant data, by the way of generation contract transfer transaction and imbalance penalty combination, deviation power balance mechanism is constructed to make up deviation power, wherein the generation contract transfer transaction includes contract market transaction and downstream power station and upstream power station negotiation transaction, and the imbalance penalty is as virtual transaction;Based on the deviation power balance mechanism, downstream power station deviation power risk decision model is constructed based on CVaR framework;Solving the downstream power station deviation power risk decision model, obtain minimum CVaR risk, to optimize hydropower station bidding power flow matching.Compared with prior art, the present application has the advantages of optimizing hydropower station bidding power flow matching and the like.

Description

Technical Field

[0001] This invention relates to the field of cascade hydropower, and in particular to a method for balancing the bidding deviation of electricity volume in cascade hydropower stations. Background Technology

[0002] Hydropower resources are concentrated in certain areas, and the rolling development of river basins has resulted in numerous cascade hydropower stations. With the gradual construction and improvement of the electricity market, it has become a trend for cascade hydropower to participate in the electricity market and compete with other hydropower and thermal power plants. In the development of cascade hydropower, it is common practice to build hydropower stations with large reservoir capacities and good regulation capabilities upstream (referred to as upstream power stations), and downstream power stations without daily / weekly regulation (referred to as downstream power stations). Upstream power stations benefit from the large reservoir capacity and can flexibly bid based on runoff forecasts, reservoir capacity, and market conditions. Downstream power stations, with smaller reservoir capacities, are highly dependent on the compensation and regulation of upstream power stations and find it difficult to participate in market bidding independently. Coordinated power generation across all cascade levels is necessary to optimize the allocation of hydropower resources. However, currently, there is a widespread phenomenon of inconsistent investment entities for cascade hydropower stations. Upstream power stations lack the driving force to comprehensively consider the power generation benefits of downstream power stations, making it difficult to achieve joint optimization of cascade hydropower resources across the entire river basin.

[0003] In reality, the construction and management units of various cascade hydropower stations differ, and development even spans multiple provinces and regions. This severely hinders information sharing and results in non-standardized reservoir hydrological information, contradicting the requirements for unified dispatch and implementation. Furthermore, when collaborative operation and joint bidding fail to meet expected benefits, cascade hydropower stations tend to operate independently. The market strategies of each power station's independent bidding are considered private information. Downstream power stations can only formulate bidding strategies based on forecasts of upstream power stations, leading to an imbalance in the matching of bidding power and flow between upstream and downstream power stations, potentially resulting in downstream power stations having no water to generate or wasting water. Summary of the Invention

[0004] The purpose of this invention is to provide a method for balancing the deviation of bidding power volume in cascade hydropower stations by optimizing the matching of bidding power volume and flow rate.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A method for balancing the bidding deviation of electricity volume in cascade hydropower stations includes the following steps:

[0007] Obtain relevant data on cascade hydropower stations, including the number of upstream and downstream stations, the net head of the hydropower stations, and the average power generation flow of the hydropower stations;

[0008] Based on the aforementioned data, a deviation power balance mechanism is constructed by combining power generation contract transfer transactions and imbalance penalties to compensate for the deviation power. The power generation contract transfer transactions include contract market transactions and negotiated transactions between downstream power plants and upstream power plants, and the imbalance penalties are virtual transactions.

[0009] Based on the aforementioned deviation power balance mechanism, a downstream power plant deviation power risk decision-making model is constructed using the CVaR framework.

[0010] Solve the downstream power plant deviation power risk decision model to obtain the minimum CVaR risk.

[0011] Furthermore, the specific steps for constructing the deviation power balance mechanism include:

[0012] The power output characteristics of the hydropower station are calculated based on the aforementioned parameters.

[0013] Calculate the deviation of the bidding power volume of the downstream power station based on the power output characteristics of the hydropower station;

[0014] A mechanism for balancing the biased electricity volume is constructed by using power generation contract transfer transactions and imbalance penalties to compensate for the deviation in the bid electricity volume.

[0015] Furthermore, the calculation expression for the power output characteristics of the hydropower station is as follows:

[0016] E H,i =η i ·Q H,i ·H i ·T

[0017] In the formula: E H,i Let η be the power generation of hydropower station i; i is the hydropower station number, where u is the upstream power station number and d is the downstream power station number; i Q is the comprehensive average output coefficient of hydropower station i; H,i H represents the average power generation flow rate. i H is the net head of hydropower station i. If it is a run-of-river hydropower station, H i The average head is taken, and T is the time scale, which is 1 day.

[0018] Furthermore, the calculation expression for the deviation of the bidding power volume of the downstream power station is as follows:

[0019]

[0020] E H,d =η d ·Q H,d ·H d ·T

[0021]

[0022] In the formula: △E H,d The deviation in the bidding volume of downstream power plants; The downstream winning bid volume represents the total electricity volume; i represents the hydropower station number, where u represents the upstream power station number and d represents the downstream power station number; T represents the time scale, taken as 1 day; E H,d For the power generation of downstream power plants; QH,u Q H,d These represent the average power generation flow rates of the upstream and downstream power plants, respectively; η u η d These are the combined average power output coefficients of the upstream and downstream power plants, respectively; H u H d These are the net head of the upstream power station and the average head of the downstream power station, respectively.

[0023] Furthermore, the specific steps for constructing the downstream power plant deviation power risk decision model include:

[0024] A transaction revenue model is constructed based on the aforementioned balancing mechanism, wherein the transaction revenue model includes a centralized transaction revenue model for power generation contracts, a negotiated transaction revenue model between downstream power plants and upstream power plants, and a transaction revenue model with virtual transferees.

[0025] Based on the CVaR framework and trading revenue model, a decision-making model for the deviation power volume of downstream power plants is constructed.

[0026] Furthermore, the expression for the centralized trading revenue model of the power generation contract is as follows:

[0027] y1=p MCP -p1

[0028] The probability density of p1 can be described by a skewed distribution:

[0029]

[0030] In the formula: y1 represents the revenue per unit of electricity; p MCP p1 is the day-ahead clearing price in the spot market; p1 is the transaction price of downstream power plants in the centralized contract trading market; z is a random variable, u is a location parameter, and σ is a random variable. 2 ∈(0,∞) is the scale parameter, λ is the skewness parameter, λ>0 the distribution is right-skewed, λ<0 the distribution is left-skewed, and λ=0 the skewed distribution degenerates into a normal distribution; ψ(·) and ψ(·) are the density function and distribution function of the standard normal distribution, respectively.

[0031] Furthermore, the process of constructing the revenue negotiation model between the downstream power plant and the upstream power plant includes:

[0032] The expression for calculating the increase in power generation flow from the upstream power station is:

[0033]

[0034] The increased power generation of the downstream power station is calculated based on the increased power generation flow of the upstream power station, and the expression is as follows:

[0035]

[0036] The total offset amount of the deviation power generated based on the increased power generation of the downstream power station is calculated, and the expression is as follows:

[0037]

[0038] Based on the total offsetting amount of the deviation power, the offsetting coefficients for the deviation power of upstream and downstream power plants are set, and the expression is as follows:

[0039] k u,d =1+(η) d ·H d ) / (η u ·H u )

[0040] Set the negotiated transfer price for the power generation contracts of the upstream and downstream power plants, and construct a negotiation transaction revenue model between the downstream and upstream power plants based on the hedging coefficient. The expression is as follows:

[0041] y2=k u,d ·p MCP -p2

[0042] In the formula: ΔQ H,u This represents the average power generation flow of the upstream power station; For; T is the time scale, taken as 1 day; η u η d These are the combined average power output coefficients of the upstream and downstream power plants, respectively; H u H d These are the net head of the upstream power station and the average head of the downstream power station, respectively; ΔE' H,d Increase power generation for downstream power plants; ΔE H,d,1 y1 represents the total offsetting amount of the deviation electricity; y2 represents the unit revenue of the downstream power station offsetting the deviation electricity; p MCP p1 represents the day-to-day clearing price in the spot market; p2 represents the negotiated price for the transfer of power generation contracts between upstream and downstream power plants.

[0043] Furthermore, the expression for the transaction revenue model with the virtual transferee is:

[0044]

[0045] In the formula: y3 is the unit revenue of the downstream power station's deviation power; u is the system supply and demand relationship indicator, where u=1 means the system supply exceeds demand and the power generation plan is adjusted downward, and u=0 means the system supply falls short of demand and the power generation plan is adjusted upward. p MCP Price penalties and rewards, p MCP This is the clearing price in the spot market.

[0046] Furthermore, the expression for the downstream power plant deviation power risk decision-making model is as follows:

[0047]

[0048]

[0049] In the formula: Z n x1 is a dummy variable; x2 is the transaction volume in the centralized contract market; x3 is the transaction volume negotiated with upstream power plants; ΔE is a dummy transaction volume. H,d The deviation in the bidding volume of downstream power plants; The estimated value of the contract transfer loss for the downstream power plant's deviation in electricity volume; N represents N sample data; k u,d This is the offsetting coefficient for the deviation in power generation between upstream and downstream power plants; These are the upper and lower limits of the electricity volume for participating in type i transactions, respectively; δ i The number of transaction types and the weight of transaction volume.

[0050] Furthermore, the downstream power plant deviation power risk decision model is solved by combining Monte Carlo simulation and binary encoded genetic algorithm.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] (1) This invention analyzes the imbalance in the matching of the winning bid electricity and power generation flow of upstream and downstream power plants in the day-ahead spot market. It proposes that downstream power plants make up for the deviation electricity through intraday power generation contract transfer transactions and system imbalance penalties, and convert the deviation electricity into the problem of optimal allocation in the combined transaction. In view of the price uncertainty in various transactions, a risk decision model for the deviation electricity of downstream power plants is established based on the CVaR framework. Under the constraint of the pre-set expected loss upper limit, the combined transaction optimizes the minimum CVaR risk, thereby optimizing the matching of the winning bid electricity and power flow of upstream and downstream power plants according to the minimum risk.

[0053] (2) This invention uses a combination of Monte Carlo simulation and binary coding genetic algorithm to find the minimum risk and obtain the optimal combination of transactions, thus achieving a reasonable trade-off between reducing losses and avoiding risks. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0055] Figure 2 This is a diagram showing the relationship between downstream power plant deviation power loss and risk in this invention;

[0056] Figure 3 This is a comparison diagram of CVaR and VaR of the present invention;

[0057] Figure 4 This is a diagram illustrating the impact of the downstream power plant's net head on average loss and trading risk.

[0058] Figure 5 This is a diagram illustrating the impact of downstream power plant headwater on combined trading in this invention.

[0059] Figure 6 This is a diagram illustrating the impact of the average market price of centralized contracts on average loss and trading risk according to the present invention.

[0060] Figure 7 This is a diagram illustrating the impact of the average market price of centralized contracts on portfolio trading according to the present invention.

[0061] Figure 8 This is a diagram illustrating the impact of the comprehensive coefficient of this invention on average loss and transaction risk;

[0062] Figure 9 This is a diagram illustrating the impact of the comprehensive coefficient of this invention on portfolio trading. Detailed Implementation

[0063] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0064] S1. Obtain relevant data on cascade hydropower stations.

[0065] Cascade hydropower stations are divided into upstream and downstream power stations. Relevant data include the number of upstream and downstream power stations, the net head of the hydropower station, and the average power generation flow of the hydropower station.

[0066] S2. Based on the relevant data, a deviation power balance mechanism is constructed by combining power generation contract transfer transactions and imbalance penalties to compensate for the deviation power. The power generation contract transfer transactions include contract market transactions and negotiated transactions between downstream power plants and upstream power plants. The imbalance penalty is a virtual transaction.

[0067] The specific steps combining intraday power generation contract transfer transactions and system penalties are as follows:

[0068] 01) Analysis of hydropower station output characteristics and power deviation

[0069] The specific characteristics of hydroelectric conversion are as follows:

[0070] E H,i =η i ·Q H,i ·H i ·T

[0071] In the formula: E H,i η represents the power generation of hydropower station i; i is the hydropower station number; η i Q is the comprehensive average output coefficient of hydropower station i; H,i H represents the average power generation flow rate.i H is the net head of hydropower station i. If it is a run-of-river hydropower station, H i The average head is taken, and T is the time scale, which is 1 day.

[0072] 02) Bidding Electricity Deviation Analysis

[0073] Hydropower stations typically offer lower bids during the high-water season, operating close to their physical limits. The probability of flow imbalance between upstream and downstream stations is low, and the room for adjustment is limited. During the dry season, water resources are scarce, and upstream stations, relying on their large reservoir capacity, may tend to hoard water to obtain higher profits. Therefore, we only consider the scenario of flow imbalance between upstream and downstream power generation during the dry season. Assuming that after the spot market clearing, the upstream station is designated u, the downstream station is designated d, and the winning bid volume for the upstream station is... The downstream winning bid volume is The average power generation flow of the upstream power station is:

[0074]

[0075] The amount of water generated by the upstream power station can achieve the following power generation capacity for the downstream power station:

[0076] E H,d =η d ·Q H,d ·H d ·T

[0077] The deviation between the contracted power generation capacity and the actual power generation capacity of downstream power plants is as follows:

[0078]

[0079] In the formula: η u η d These are the combined average power output coefficients of the upstream and downstream power plants, respectively; H u H d These are the net head of the upstream power station and the average head of the downstream power station, respectively. If ΔE H,d A value greater than 0 indicates that the downstream power plants have won bids for high electricity volumes, resulting in a tight supply of hydropower for power generation; △E H,d =0, then the upstream and downstream winning bids for electricity and water consumption are completely matched, △E H,d A value less than 0 indicates that the downstream power station won a bid for a relatively small amount of electricity, while the water volume is abundant.

[0080] 03) Analysis of the structure of the power generation contract trading market

[0081] For deviations in the winning bids for downstream power plants in the day-ahead spot market, adjustments can be made through intraday contract power trading ranging from one hour to several hours. This allows for the transfer of contract power to another party for execution, effectively mitigating market risks arising from fluctuations in power supply and demand. Currently, the contract transfer transactions primarily involve power generation rights trading, characterized by: ① Power plant generation quotas are allocated by the government and are transferred and purchased for compensation without compromising system security; ② The trading parties are power generators, with the grid company only providing transaction matching and security verification services; ③ Power generation quotas are generally transferred from high-energy-consuming units to low-energy-consuming units. However, with the opening up of a fully competitive electricity market, grid dispatching agencies and local government departments will no longer interfere with power generation planning, and the market's role in resource allocation and energy conservation and emission reduction will be formalized into a standardized system.

[0082] The intraday power generation contract trading market serves as a supplementary and fine-tuning secondary market to the day-ahead spot market, with a relatively small trading volume. This embodiment refers to the power generation rights trading mechanism and model, assuming that its trading method is centralized matching and bilateral negotiation. Bilaterally negotiated transactions are marked in the centralized trading and included in unified security verification and market clearing.

[0083] Considering the hydroelectric coupling relationship between upstream and downstream power plants, increasing the power generation of upstream power plants is beneficial to correcting the deviation in power generation of downstream power plants. Therefore, bilateral negotiation transactions with upstream power plants and matching transactions with other power plants in the centralized contract market are considered. In addition, the transaction risks brought about by price fluctuations may result in the deviation in power generation not being fully absorbed by the transferee. Some of the deviation in power generation will be subject to system penalties, which can be regarded as virtual transferees. This forms a transaction structure of a single seller and multiple buyers: the transferor is the downstream power plant, the transaction object is the deviation in power generation of the downstream power plant, and the transactions involved are centralized power generation contract transactions (M1), negotiated transactions with upstream power plants (M2), and virtual transactions with system penalties (M3).

[0084] S3. Based on the aforementioned deviation power balance mechanism, a decision-making model for deviation power risk of downstream hydropower stations is constructed using the CVaR framework.

[0085] The principle of Conditional Value at Risk (CvaR) is as follows:

[0086] The high volatility of intraday power generation contract prices brings trading risks, which can be approximated by financial market investment theory as a financial investment decision: the downstream power plant is the investment decision-maker, the power generation contract transferee is the investment target, minimizing investment risk is the decision objective, and quantifying trading risk is the foundation of the decision-making process. VaR and CVaR risk measurement indicators are used, with the following principles:

[0087] Let f(x,y) be the decision variable. The loss function is given below, where x is the investment decision vector and y∈R. mLet be a random vector representing random factors in the market. Assuming the probability density function of y is p(y), for a given x, the probability that f(x,y) caused by y does not exceed a certain specific loss level α is:

[0088]

[0089] ψ(x,α) is the cumulative loss distribution function under x, which is non-decreasing and right-continuous with respect to α. When the loss f(x,y) caused by the random vector y does not exceed the confidence level β∈[0,1] of the critical value α, α... β (x) and Let VaR and CVaR be the loss function f(x,y) for decision variable x, respectively.

[0090] α β (x)=min{α∈R:ψ(x,α)≥β}

[0091]

[0092] Is the loss greater than α? β The CVaR value at time (x) is usually difficult to solve analytically, so a transformation function F can be introduced. β (x,α) substitution Approximate calculation:

[0093]

[0094] In the formula: (f(x,y)-α) + Let max{f(x,y)-α,0} be the expression. When it is difficult to obtain the analytical expression for p(y), the cumulative probability distribution, i.e., Fi, can be calculated based on historical data of y or the probability density function of Monte Carlo simulation samples. β The integral term in equation (x, α). Let y 1 ,y 2 ,…,y N Given N sample data for y, then the function F β The estimated value of (x,α) is:

[0095]

[0096] In practical applications, the optimal portfolio vector x and the corresponding CVaR and VaR values ​​are usually solved based on the above formula.

[0097] Uncertainties in the marginal cost and expected revenue of the acquired generating units will lead to different probability distributions of revenue from various transactions involving upstream power plants. Based on various revenue probability models, a CVaR combined trading model for balancing the deviation power generation of downstream power plants is established, specifically as follows:

[0098] 01) Analysis of the revenue model for centralized trading of power generation contracts

[0099] Drawing on the design concept of the hourly power generation rights trading market, downstream power plants are organized to participate in centralized intraday power generation contract trading. The trading time scale is from 0:00 to 24:00 on the declaration date. The trading entities are hydropower, thermal power, and new energy power generation that bid for grid connection. Transactions are matched according to the "high-low matching" principle, and the transaction price is the average of the bids from both parties. Let the transaction price of the downstream power plant in the centralized contract trading market be p1, and the day-ahead spot market clearing price be p. MCP The revenue per unit of electricity is:

[0100] y1=p MCP -p1

[0101] As the delivery date approaches, the profit-seeking nature of both parties and the scarcity of power generation resources will drive up the transaction price. The probability density of p1 can be described by a skewed distribution.

[0102]

[0103] In the formula: z is a random variable, μ is a position parameter, and σ 2 ∈(0,∞) is the scale parameter, λ is the skewness parameter, λ>0 the distribution is right-skewed, λ<0 the distribution is left-skewed, and λ=0 the skewed distribution degenerates into a normal distribution; Let f and ψ(·) be the density function and distribution function of the standard normal distribution, respectively, and their expressions are given in the following equation. SN (z;u,σ 2 ,λ) can be simplified as z~SN(μ,σ) 2 ,λ), when μ=0, σ 2 When λ = 1, it is a standard partially normal random variable, denoted as z ~ SN(λ).

[0104]

[0105] 02) Analysis of the revenue model for negotiating transactions with upstream power plants

[0106] Downstream power plants negotiate with upstream power plants to transfer a portion of their deviation electricity volume under a contract, assuming the transferred volume is . Then the increase in power generation flow of the upstream power station ΔQ H,u for:

[0107]

[0108] The increased power generation of downstream power plants is as follows:

[0109]

[0110] The total offset amount for the deviation power is:

[0111]

[0112] Let the offsetting coefficient for the deviation in power generation between upstream and downstream power plants be:

[0113] k u,d =1+(η) d ·H d ) / (η u ·H u )

[0114] Let p2 be the negotiated price for the transfer of power generation contracts between the upstream and downstream power plants. Then the unit revenue of the downstream power plant for offsetting deviation power generation is:

[0115] y2=k u,d ·p MCP -p2

[0116] The principal-agent mechanism is an incentive mechanism to address the problem of information asymmetry: the principal provides certain incentives to the agent, enabling the agent to achieve their own interests while acting in accordance with the principal's interests. This can be represented as:

[0117] Pr{π(χ(c,ε),k)<R0}≤θ

[0118] In the formula: π represents the transferee's revenue from completing the substitution task; χ(c,ε) represents the transferee's effort level, which can also represent the maximum electricity volume they intend to transact; c is the power generation cost (yuan / MWh); ε is the comprehensive coefficient of water resource scarcity and opportunity cost, which can be quantified in monetary form (yuan / MWh); k is the incentive the transferee receives from the transferor; R0 is the transferee's retention utility, i.e., the transferee's minimum expected revenue; θ is the transferee's risk tolerance. It can be seen that, under the transferor's incentive, as long as the probability that the transferee's revenue after the transaction is less than the retention utility satisfies the risk tolerance θ, the transferee will be willing to transact. The power generation cost c of a hydropower station generally does not change much, and the comprehensive coefficient ε is the main factor affecting the transaction, assumed to follow a normal distribution: ε~N(μ,σ) 2 The transaction prices of upstream and downstream power plants can be expressed as: p2 = p(c, ε, k).

[0119] 03) Establish a transaction revenue model with the virtual transferee.

[0120] Drawing on the approach of using penalties by electricity market regulators to compensate for wind power bidding discrepancies, and referencing Norway's real-time balancing market bidding discrepancy reward and penalty rules, when actual power generation is lower than the bid volume, the imbalanced volume is penalized and rewarded in the real-time market settlement: if the system is in a period of supply shortage (the system's power generation plan needs to be adjusted upwards), the less power generated will be subject to a penalty higher than the day-ahead market clearing price p. MCP The price (denoted as) Penalty; if the system supply exceeds demand, the power generation plan needs to be adjusted downwards, and the reduced power generation will be penalized with a penalty lower than p.MCP price Rewards; the price for deviation penalties is set by the market manager. Let u be the system supply and demand indicator, where u = 1 indicates that the system supply exceeds demand and the power generation plan is adjusted downwards, and u = 0 indicates that the system supply falls short of demand and the power generation plan is adjusted upwards. The unit revenue per unit of deviation power generation for downstream power plants is:

[0121]

[0122] The deviation between the actual demand of the system and the power generation plan follows a normal distribution: △E~N(0,σ) 2 If the system's power generation plan deficit is greater than ΔE, then... H,d The probability is:

[0123]

[0124] 04) Establish a decision-making model for downstream hydropower stations

[0125] Let x T Let {x1, x2, x3} be a set of downstream power plant power generation contract trading combinations, where: x1 is the trading volume in the centralized contract market; x2 is the trading volume negotiated with upstream power plants; and x3 is the trading volume in virtual transactions. Let y = {y1, y2, y3} be the revenue vector of downstream power plants transferring unit contract electricity, then the total revenue from the transfer of deviation electricity contracts is:

[0126] g(x,y)=x T ·y=x1·y1+x2·y2+x3·y3

[0127] stx1+k u,d ·x2+x3=ΔE H,d

[0128] Furthermore, when formulating contract trading combinations, to ensure that downstream power plants make decisions based on actual scenarios and to control the dispersion of various trading volumes, a binary variable is introduced to control the number of participating trading types and the weight of the trading volume. Let:

[0129]

[0130] set up and Let these be the upper and lower limits for the electricity volume participating in type i transactions. Then, the constraints on the number of contract trading markets and the weight of trading volume are as follows:

[0131]

[0132] The expected return from contract electricity trading is:

[0133] E[g(x,y)]=x1·E(y1)+x2·E(y2)+x3·E(y3)

[0134] The loss function of the downstream hydropower station is f(x,y)=-g(x,y), which can be expressed as:

[0135] f(x,y)=-x T y

[0136] Substituting the loss function formula for the downstream hydropower station mentioned above into the transformation function F β (x,α) yields a quantitative model for the transfer loss of downstream power plant deviation electricity contracts:

[0137]

[0138] Taking sample values ​​y = {y1, y2, y3} for the revenue y from each type of electricity unit in a transaction, the estimated value of the above formula is:

[0139]

[0140] Let the dummy variable Z n =(-x T y n -a) + Therefore, the downstream power plant trading portfolio optimization model that minimizes CVaR can be expressed as:

[0141]

[0142]

[0143] S4. Solve the downstream hydropower station risk decision model to obtain the minimum CVaR risk, so as to optimize the matching of hydropower station bidding power flow.

[0144] Genetic algorithms are stochastic parallel search algorithms based on the principles of natural selection and genetics. They can seek the global optimum without any initialization information, are highly efficient and have no convergence problem, and are widely used in power system planning, electricity markets, and other fields. This embodiment combines Monte Carlo simulation and binary encoded genetic algorithms to solve for the minimum CVaR of downstream power plant deviation power combination transactions.

[0145] To verify the effectiveness of the above method, this embodiment takes an upstream and downstream hydropower station in a cascade as an example. The two hydropower stations belong to different power generation entities and bid independently in the day-ahead spot market. The upstream power station has a large reservoir capacity and annual regulation capability, while the downstream power station only has daily regulation capability. The unified clearing price (MCP) in the day-ahead spot market is 550 yuan / MW·h, and the total deviation power is 100MW·h. The operating parameters of the cascade hydropower stations are shown in Table 1 below, and the trading parameters are shown in Table 2 below.

[0146] Table 1 Operating parameters of cascade hydropower stations

[0147]

[0148] Table 2 Various Transaction Parameters

[0149]

[0150] The analysis steps in this embodiment include: a) power generation contract transaction analysis; b) efficient frontier analysis; c) impact of operating net head on transactions; d) transaction price volatility sensitivity analysis.

[0151] a) Analysis of power generation contract transactions

[0152] a01) Randomly generate 1000 sets of simulated quantities based on the probability distributions of p1, ε, and ΔE: △E n}(n=1,2,…,1000), and calculate 1000 sets of samples:

[0153] (a02) Set the information level β to 0.9 and 0.95 respectively. If the downstream power station conducts three types of transactions: centralized contract transaction (M1), negotiated transaction with upstream power station (M2), and system penalty virtual transaction (M3), the deviation power balance results are shown in Table 3.

[0154] (a03) Analysis of Table 3: Table 3 shows that if downstream power plants do not take compensatory measures for the deviation in electricity output and directly accept system penalties, they will face losses as high as 80.245 yuan / MW·h. Furthermore, the CVaR and VaR risk values ​​are also relatively high, both at 220 yuan / MW·h. The reason for the identical CVaR and VaR values ​​is that the system imbalance penalty is a fixed value set by the system administrator (ISO). The calculated loss random number is a binary variable, and the tail values ​​are consistent within a certain confidence level. The CVaR and VaR of balancing the deviation in electricity output through centralized contract trading are lower than those of negotiating with upstream power plants, but the average loss is higher. The latter has higher CVaR and VaR risk values, but the average loss is -8.7899 yuan / MW·h, meaning that downstream power plants still have a greater chance of profiting. Therefore, a reasonable balance needs to be struck between risk and return. Table 3 shows the optimization results of three types of transaction combinations when the confidence levels are 0.9 and 0.95, and the maximum loss is 500 yuan and 750 yuan, respectively. x1, x2, and x3 are the combined transaction optimization results of participating in centralized contract transactions (M1), negotiating transactions with upstream power plants (M2), and system penalty virtual transactions (M3).

[0155] Table 3 Results of Independent Completion of Three Types of Transactions

[0156]

[0157] Table 4 Results of Power Generation Contract Portfolio Transactions

[0158]

[0159] (a04) Analysis of Table 4: As shown in Table 4, in the combined transaction of offset power volume balancing, with a fixed upper limit of loss, the risk aversion of downstream power plants increases at a confidence level of β = 0.95 compared to β = 0.9. They prefer to avoid high risks with more stable losses, resulting in increased CVaR and VaR. At this time, downstream power plants increase the volume of transactions negotiated with upstream power plants and reduce the volume of transactions under centralized contracts to ensure that they are within their loss tolerance range and improve their risk avoidance capabilities. For the same confidence level β = 0.9, the lower limit of expected loss is 500 compared to 750. Downstream power plants arrange a larger transaction volume with upstream power plants. This is because negotiating with upstream power plants to offset the power volume has a greater chance of profit, which is an effective decision to meet the upper limit of expected loss constraints. Since the expected loss of the system penalty scheme for balancing offset power volume is large, the virtual transaction volume is extremely small and can be ignored. The above optimization results also represent the balance between overall loss control and risk avoidance in the decision-making process of offset power volume compensation for downstream power plants.

[0160] b) Efficient Frontier Analysis

[0161] (b01) Concept: The efficient frontier can intuitively describe the relationship between expected return and risk. It can be understood as the portfolio with the highest expected return for the same level of risk or the lowest risk for the same expected return. Establishing the efficient frontier of power generation contract trading portfolios intuitively describes the relationship between return and risk, and selects the optimal trading portfolio.

[0162] b02) Analysis of the relationship between downstream power plant deviation power loss and risk: Figure 2 The lower region of the efficient frontier curve represents the feasible region of trading combinations, with each point corresponding to a trading combination. A1, A2, B1, and B2 correspond to the four trading combinations in Table 2. Comparatively, at any point within the feasible region, such as point C, under the same expected loss (also represented as the average loss of the trading combination), the risk level of the trading combination on the efficient frontier curve is significantly lower than that of other points within the feasible region. Furthermore, at the same risk level, the expected loss of the trading combination on the efficient frontier is less than that of other points within the feasible region, meaning that the efficient frontier combination trading set represents the optimal trading combination under fixed expected loss or fixed risk. In addition, both efficient frontier curves show an increasing trend, implying that reducing expected loss will inevitably lead to an increase in CVaR. The right-hand lower frontier curve, with a higher confidence level, shifts to the right, indicating that the higher the confidence level, the more risk-averse the power generation contract seller, and the greater the risk. Both of these situations align with actual trading scenarios. Downstream power plants can use the efficient frontier curve as a reference to select appropriate trading combinations based on their own risk aversion.

[0163] b03) Figure 3A comparison of CVaR and VaR curves at the same confidence level is provided. It can be seen that at a confidence level of 0.95, the efficient frontier curve of CVaR is to the left of VaR. This is because CVaR measures the average loss exceeding that of VaR, making it more robust.

[0164] c) The impact of operating water purification head on trading

[0165] c01) During daily operation, the upstream power station benefits from the large reservoir capacity and the head change is not significant, while the downstream power station has a small reservoir capacity and weak regulation capacity, and the water level changes frequently within a day. If the changes in the forebay and tailwater levels of the downstream power station are effectively controlled and the head efficiency is improved, the power generation can be increased under the same water flow to make up for more of the deviation in power generation. Figure 4 The optimal results of combined trading under different net head operation modes of downstream power plants are given, with a maximum loss of 750 yuan and a confidence level of 0.9.

[0166] c02) by Figure 5 It is evident that as the net head of downstream power plants increases, the head benefit increases significantly, leading to a larger offsetting power volume per unit of traded electricity with upstream power plants. This reduces the magnitude of power loss due to deviations in downstream power plants. When the net head reaches 65m, the combined trading with minimal trading risk has already yielded profits, and the benefits from negotiated trading with upstream power plants hold an absolute advantage. Furthermore, the corresponding CVaR and VaR also decrease accordingly. Therefore, downstream power plants should control the forebay and tailrace water levels as much as possible, increase the operating net head, and arrange reasonable and refined load distribution within the plant to achieve better economic benefits with limited water resources.

[0167] d) Analysis of the sensitivity of trading price fluctuations

[0168] d01) Figure 6 , Figure 7 This study examines the impact of centralized contract transaction prices on the average loss and risk level of downstream power plants, assuming a maximum loss of 750 yuan and a confidence level of 0.9, and the resulting optimal trading portfolio. It reveals that as the average price increases, the average loss of downstream power plants decreases, while CVaR and VaR increase. This is because the higher centralized contract transaction price increases the CVaR and VaR values ​​per unit of transaction volume, leading to a greater allocation of contracts with upstream power plants in the trading portfolio. This enhances the downstream power plants' competitiveness in transaction allocation, resulting in higher CVaR and VaR and lower average loss from contracts with upstream power plants. This change fully reflects a trade-off between risk and return. Further increases in the average price could lead to a situation where trading with upstream power plants, while possessing both absolute loss and risk advantages, is potentially completely replaced by M2.

[0169] d02) Figure 8 , Figure 9This study examines the impact of the comprehensive coefficient for transactions with upstream power plants on average loss and risk levels, with a maximum loss of 750 yuan and a confidence level of 0.9, as well as the optimization results of combined transactions. As the comprehensive coefficient increases, the transaction costs with upstream hydropower increase, leading to greater losses. Consequently, the transaction volume in combined transactions is squeezed, and the allocation of transaction volume in centralized contracts increases, resulting in increased risks (CVaR and VaR) and losses. When the comprehensive coefficient ε > 845 yuan / MWh, the rate of increase in risk and loss slows significantly. This is because at this point, the competitiveness of negotiating transactions with upstream power plants decreases, the room for change in transaction volume allocation is minimal, and the impact on transaction risk and loss decreases.

[0170] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0171] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0172] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0173] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0174] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0175] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0176] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for balancing the bidding deviation of electricity volume in cascade hydropower stations, characterized in that, Includes the following steps: Obtain relevant data on cascade hydropower stations, including the number of upstream and downstream stations, the net head of the hydropower stations, and the average power generation flow of the hydropower stations; Based on the aforementioned data, a deviation power balancing mechanism is constructed by combining power generation contract transfer transactions and imbalance penalties to compensate for deviations in power generation. The power generation contract transfer transactions include contract market transactions and negotiated transactions between downstream and upstream power plants. The imbalance penalty is implemented as a virtual transaction. The specific steps for constructing the deviation power balancing mechanism include: The power output characteristics of the hydropower station were calculated based on the aforementioned data. Calculate the deviation of the bidding power volume of the downstream power station based on the power output characteristics of the hydropower station; A mechanism for balancing the biased electricity volume is constructed by using power generation contract transfer transactions and imbalance penalties to compensate for the deviation in the bid electricity volume. Based on the aforementioned deviation power balance mechanism, a downstream power plant deviation power risk decision-making model is constructed using the CVaR framework. The specific steps for constructing the downstream power plant deviation power risk decision-making model include: A transaction revenue model is constructed based on the aforementioned balancing mechanism, wherein the transaction revenue model includes a centralized transaction revenue model for power generation contracts, a negotiated transaction revenue model between downstream power plants and upstream power plants, and a transaction revenue model with virtual transferees. Based on the CVaR framework and trading revenue model, a decision-making model for downstream power plant deviation power volume risk is constructed. The expression of the decision-making model for downstream power plant deviation power volume risk is as follows: In the formula: It is a dummy variable; x 1 represents the trading volume in the centralized contract market; x 2. To negotiate transaction volume with upstream power plants; x 3 represents the volume of virtual transactions; The deviation in the bidding volume of downstream power plants; This is an estimated value for the transfer loss of the downstream power plant's deviation electricity volume contract; N for N One sample data; This is the offsetting coefficient for the deviation in power generation between upstream and downstream power plants; , Participation i Upper and lower limits for transaction volume; The number of transaction types and the weight of transaction volume; Solve the downstream power station deviation power risk decision model to obtain the minimum CVaR risk, so as to optimize the matching of hydropower station bidding power flow. The downstream power station deviation power risk decision model is solved by combining Monte Carlo simulation and binary encoded genetic algorithm.

2. The method for balancing the bidding deviation of cascade hydropower stations according to claim 1, characterized in that, The calculation expression for the power output characteristics of the hydropower station is as follows: In the formula: E H,i For hydroelectric power station i The amount of electricity generated; i Number the hydropower stations, among which u Number the upstream power station. d Number the downstream power plants; η i For hydroelectric power station i Overall average output coefficient; Q H,i Average power generation flow; H i For hydroelectric power station i The head of clean water, if it is a run-of-river hydroelectric power station, H i Take the average head. T For the time scale, take 1. d .

3. The method for balancing the bidding deviation of cascade hydropower stations according to claim 1, characterized in that, The calculation formula for the deviation of the bidding power volume of the downstream power station is as follows: In the formula: △ E H,d The deviation in the bidding volume of downstream power plants; For downstream winning bid electricity volume; i Number the hydropower stations, among which u Number the upstream power station. d Number the downstream power plants; T For the time scale, take 1. d ; For the power generation of downstream power plants; , These represent the average power generation flow rates of the upstream and downstream power plants, respectively. η u , η d These are the combined average power output coefficients of the upstream and downstream power plants, respectively. H u , H d These are the net head of the upstream power station and the average head of the downstream power station, respectively.

4. The method for balancing the bidding deviation of cascade hydropower stations according to claim 1, characterized in that, The expression for the revenue model of the centralized trading of power generation contracts is as follows: in, p The probability density of 1 can be described by a skewed distribution: In the formula: y 1 represents revenue per unit of electricity; p MCP This is the clearing price for the current spot market; p 1 represents the transaction price of downstream power plants in the centralized contract trading market; z For random variables, u For position parameters, σ 2 ∈(0,∞) is the scale parameter. λ This is the skewness parameter. λ >0 indicates a right-skewed distribution. λ <0 indicates a left-skewed distribution. λ If the value is 0, the skewed distribution degenerates into a normal distribution; φ (·)and ψ (·) represents the density function and distribution function of the standard normal distribution, respectively.

5. The method for balancing the bidding deviation of cascade hydropower stations according to claim 1, characterized in that, The process of constructing the revenue negotiation model between downstream power plants and upstream power plants includes: The expression for calculating the increase in power generation flow from the upstream power station is: The increased power generation of the downstream power station is calculated based on the increased power generation flow of the upstream power station, and the expression is as follows: The total offset amount of the deviation power generated based on the increased power generation of the downstream power station is calculated, and the expression is as follows: Based on the total offsetting amount of the deviation power, the offsetting coefficients for the deviation power of upstream and downstream power plants are set, and the expression is as follows: Set the negotiated transfer price for the power generation contracts of the upstream and downstream power plants, and construct a negotiation transaction revenue model between the downstream and upstream power plants based on the hedging coefficient. The expression is as follows: In the formula: This represents the average power generation flow of the upstream power station; for; T For the time scale, take 1. d ; η u , η d These are the combined average power output coefficients of the upstream and downstream power plants, respectively. H u , H d These are the net head of the upstream power station and the average head of the downstream power station, respectively. Increase power generation for downstream power plants; This refers to the total offset amount of the deviation power. y 2 represents the unit revenue per unit of downstream power plant offsetting deviation electricity generation; p MCP This is the clearing price for the current spot market; p 2 represents the negotiated price for the transfer of power generation contracts between upstream and downstream power plants.

6. The method for balancing the bidding deviation of electricity volume in cascade hydropower stations according to claim 1, characterized in that, The expression for the profit model of the transaction with the virtual transferee is as follows: In the formula: y 3 represents the unit revenue per unit of downstream power plant's deviation in electricity volume; u To mark the supply and demand relationship in the system, record... u =1 indicates that the system supply exceeds demand, and the power generation plan will be adjusted downwards. u =0 indicates that the system is experiencing a supply shortage and the power generation plan will be adjusted upwards. , p+ 3 They are respectively p MCP Price penalties and rewards, p MCP This is the clearing price in the spot market.

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