Medium-and-long-term marketization operation bidding strategy for wind-light-water storage combination

Abstract

The invention relates to the technical field of power system operation and control, in particular to a medium-and-long-term marketization operation bidding strategy for a wind-light-water-storage combination. Aiming at the problems of volatility, large prediction error, limited profit space and the like of wind and light power generation in the medium-and-long-term power market, the invention proposes that wind and light power generation and cascade hydropower are combined to participate in medium-and-long-term market bidding transaction together; wind and light power generation is encouraged to fully participate in the market by introducing a carbon transaction and green certificate transaction mechanism, and a double-layer optimization model is constructed by considering the influence of current market electricity price fluctuation; the upper layer is a combination quantity report and quotation decision-making model, and the lower layer is a market clearing model so as to realize combination market expected total revenue maximization and social welfare maximization; the strategy significantly improves the medium and long term contract competitiveness and profit space of the wind and light new energy, promotes the consumption of the new energy, and has good environmental benefits and economic benefits.

Description

Technical Field

[0001] The present invention relates to the field of power system operation and control technology, and in particular to a mid- and long-term market-oriented operation bidding strategy for a wind, solar, and water storage consortium. Background Art

[0002] In the context of the construction of a new power system, wind and solar power generation, as an important component of clean energy, has become an important way to promote the large-scale consumption of new energy by participating in the medium- and long-term power market and strengthening its connection with the spot market. However, the volatility of wind and solar power generation and the uncertainty of its long-term prediction errors have brought many challenges to the decomposition of medium- and long-term market contracts with high contract ratio requirements. Specifically, wind and solar power generation is often at a disadvantage when decomposing medium- and long-term market contracts, and faces the risk of biased profit recovery during contract settlement, which has led to a significant compression of the profit space for new energy power generation.

[0003] In order to address the above issues, domestic and foreign scholars and industry experts have conducted a lot of research and achieved certain results. For example, the 41st issue of the Proceedings of the CSEE compared the spot markets of various countries and the spot markets of various provinces in China to promote the operation mechanism of new energy consumption, and emphasized the guiding role of medium- and long-term market prices on the spot market. The 36th issue of Electric Power Automation Equipment took into account the decomposition of medium- and long-term contract electricity in the day-ahead dispatch decision of the integrated energy system, and realized the nested optimization of contract electricity decomposition and dispatch plan formulation. However, these studies are mainly based on the spot trading period, and the day-ahead dispatch plan is optimized based on the signed medium- and long-term contracts, without fully considering the operation strategy of the power generation entity in the medium- and long-term trading stage.

[0004] In addition, the 45th issue of Automation of Electric Power Systems takes into account medium- and long-term physical contracts and establishes an optimal dispatch model with a more balanced bidding space in the spot market after the decomposition of medium- and long-term contracts, while the 46th issue of Automation of Electric Power Systems takes into account the uncertainty of electricity prices under the background of difference contracts and establishes a cascade hydropower day-ahead optimal dispatch model. Although these studies have improved the dispatch efficiency of renewable energy power generation to a certain extent, they have failed to fully solve the disadvantages of wind and solar power generation in the medium- and long-term market.

[0005] It is worth noting that most existing studies explore medium- and long-term trading decisions from a single time scale (such as year, season, and month). For example, the 11th issue of Volume 35 of the Journal of Electric Power Systems and Automation constructed a medium- and long-term day-ahead two-stage decision-making model for the wind-fire alliance under carbon quotas, but failed to further explore the connection between medium- and long-term electricity and spot market electricity at different dispatching time granularities. The 10th issue of Volume 47 of the Journal of Electric Power Systems and Automation takes power grid companies as the main body and establishes a medium- and long-term transaction rolling optimization proxy power purchase decision-making model on three scales: year, month, and day. However, the decomposition of medium- and long-term electricity is in the form of equal distribution. For wind and solar power generation, this decomposition method violates its output law and is difficult to effectively balance benefits and risks.

[0006] The 11th issue of Volume 44 of Power System Technology attempts to include the deterministic part of the medium- and long-term wind power forecast in medium- and long-term transactions, and the uncertain part in day-ahead transactions, but the existing forecasting methods have low forecasting accuracy in the medium- and long-term scale and are difficult to meet actual needs. The 8th issue of Volume 40 of the Proceedings of the CSEE, based on the current electricity quantity identification rules of the Yunnan power market, takes into account both medium- and long-term transactions and day-ahead market transactions to formulate a monthly cascade hydropower generation plan, but its effect is limited for wind and solar power generation entities with fluctuating and uncontrollable output.

[0007] In summary, existing research has made some progress in promoting the participation of renewable energy power generation in the medium- and long-term market, but there are still many shortcomings. In particular, there is a lack of systematic bidding strategies and optimization models for the joint participation of wind and solar power generation and flexible regulating power sources in the market. Therefore, the present invention proposes a medium- and long-term market-oriented operation bidding strategy for a wind, solar, and water storage consortium, which aims to enhance the medium- and long-term contract competitiveness and profit margins of wind and solar new energy by forming a consortium of wind, solar, and cascade hydropower to participate in medium- and long-term market bidding transactions, while introducing carbon trading and green certificate trading to encourage wind and solar to fully participate in the market and achieve win-win results for multiple entities. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a medium- and long-term market-oriented operation bidding strategy for a wind, solar, and water storage consortium, so as to solve the problem in the prior art that when wind and solar power generation participate in the medium- and long-term electricity market, it is difficult to predict the long-term power generation, resulting in contract decomposition disadvantages and deviation profit recovery risks, and then the problem of falling volume and price when participating in transactions.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is a medium- and long-term market-oriented operation bidding strategy for wind, solar, water and storage consortiums. The strategy forms a wind, solar and water consortium through wind, solar and cascade hydropower, and jointly participates in the time-sharing bidding transactions in the medium- and long-term market to enhance the profit space of the consortium, and includes the following steps: Step 1: Introduce carbon trading and green certificate trading mechanisms to reflect the carbon emission attributes of thermal power and the green attributes of wind and solar power, so as to encourage wind and solar power to fully participate in mid- and long-term market bidding and thus obtain more profits; Step 2: Consider the impact of the expected market price fluctuations on the consortium's bidding results to lock in the consortium's medium- and long-term interests; Step 3: On the mid- to long-term market trading day before the month, the day-ahead market electricity price is expected, and the mid- to long-term trading two-layer decision-making model of the wind, solar and water consortium is constructed with the goal of maximizing the expected benefits of the mid- to long-term contracts. The upper layer is the consortium's quotation decision model, which takes into account the future day-ahead market clearing price and formulates the consortium's time-sharing and segmented output quotation strategy to maximize the consortium's expected total market benefits; the lower layer is the mid- to long-term market clearing model, which aims to maximize social welfare and meet market demand clearing; Step 4: Determine the bidding constraints of the upper-level consortium's bidding decision-making model, including the winning power constraints of cascade hydropower stations, the winning power and output constraints of wind and solar power in the medium- and long-term market, the market quotation constraints, and the green certificate holding constraints; determine the constraints of the lower-level medium- and long-term market clearing model, including the market clearing power balance constraints, the power constraints reported by the power generation side, and the power constraints reported by the power consumption side; Step 5: Use KKT (Karush-Kuhn-Tucker, also known as optimization conditions) conditions to solve the two-layer optimization model in Step 3, obtain the medium- and long-term time-sharing contract curve of the consortium, and maximize the interests of the wind, solar and water consortium under joint bidding.

[0010] In the preferred solution, the expected medium- and long-term market benefits of the consortium in step 2 include the CFD income and green certificate trading income, with maximizing the interests of the consortium as the objective function. The specific calculation formula of the objective function is as follows: (1) In the formula, , is the trading session and its duration; T is the trading cycle; The number of power segment quotation is the number of segments. It is the power segmentation sub-segment; To clear electricity prices in the medium and long term market, Day-ahead expected electricity price; decision variables ( , , , ) are the mid- and long-term market winning bids for consortiums, wind power, photovoltaic power and cascade hydropower; and The price of green certificates and the quantity of green certificates sold; and All are dual variables; is an auxiliary variable; is a robustness parameter, which can be regarded as the number of time periods that take into account the uncertainty of electricity prices. The larger the parameter, the more conservative the result, and the smaller the parameter, the more radical the result; Represents the total number of time periods; and Forecast D-1 day for the month ahead The upper and lower bounds of electricity prices at each moment.

[0011] In the preferred solution, the power constraint condition for the winning bid of the cascade hydropower station in step 4 is specifically expressed as: (2) In the formula, , For the The winning bid output of each hydropower station and the upper limit of the hydropower station output, is the number of cascade power stations.

[0012] In the preferred solution, the power and output constraints of the wind and solar power medium- and long-term market bids in step 4 are specifically expressed as follows: (3) (4) (5) (6) In the formula, , The upper limit of the total electricity volume of wind power and photovoltaic power predicted in the month before the target date, The minimum power contract ratio stipulated in the medium and long-term market. and is the capacity of wind power and photovoltaic units, and It refers to the period when photovoltaic power is generating power during the day and the period when it is unable to generate power.

[0013] In the preferred solution, the market quotation and quotation constraint conditions in step 4 are specifically expressed as follows: (7) (8) In the formula, and are the minimum and maximum quote values, respectively. and for time Duan He Segment quotation.

[0014] In the preferred solution, the green certificate holding constraint in step 4 is specifically expressed as: (9) In the formula, The number of green certificates sold for the consortium.

[0015] In the preferred solution, the lower-level medium- and long-term market clearing model in step 4 is specifically expressed as: (10) In the formula, For consumer surplus, for producer surplus; and For users Quote segment Price and power, and For thermal power units Price and power, Make an offer for the consortium.

[0016] In the preferred solution, the market clearing power balance constraint in step 4 is specifically expressed as: (11).

[0017] In the preferred solution, the power constraint reported by the power generation side in step 4 is specifically expressed as: (12) (13) (14) (15) In the formula, , , , They are Time period consortium and conventional unit quotation period The upper and lower limits of the reported power; , , , for The upper and lower limits of the total reported power of the period consortium and conventional units.

[0018] In the preferred solution, the power constraint reported by the electricity user in step 4 is specifically expressed as: (16) (17) In the formula, and For users Report the upper and lower limits of power, and It is the total reported power on the load side.

[0019] The mid- and long-term market-oriented operation bidding strategy for a wind, solar, and water storage consortium provided by the present invention has the following beneficial effects: 1. The present invention solves the problem in the prior art that when wind and solar power generation participate in the medium and long-term electricity market, it is difficult to predict the long-term power generation, resulting in the disadvantage of contract decomposition and the risk of deviation profit recovery, and then the volume and price fall when participating in the transaction; 2. The present invention solves the problem of profit margin compression, specifically the problem that the profit margin of renewable energy power generation is greatly compressed due to market fluctuations and forecast errors; 3. The present invention solves the problem of linking the medium- and long-term market with the spot market. Specifically, existing research mainly focuses on the spot trading period and does not fully consider the operating strategies of power generation entities in the medium- and long-term trading stage. 4. The present invention solves the problem of volatility of renewable energy power generation, specifically the volatility of wind and solar power generation and the uncertainty of its long-term prediction error, which puts it at a disadvantage when decomposing mid- and long-term market contracts and faces the risk of deviation profit recovery when settling contracts; 5. The present invention significantly improves the competitiveness of wind, solar and water storage consortiums and scientific and reasonable bidding strategies in the medium and long-term electricity market, and increases their contracted electricity and profitability; 6. The present invention effectively reduces the risk and cost of wind and solar power generation in the operation process and improves its economic benefits by introducing cascade hydropower and energy storage facilities as flexible power sources and constructing a double-layer optimization model; 7. The present invention encourages the active participation of wind and solar power generation by integrating green certificate trading and carbon trading mechanisms, promotes the sustainable development of new energy, and provides strong support for the construction of a new power system; 8. The present invention forms a consortium with wind and solar energy and cascade hydropower to jointly participate in the market, thereby enhancing the mid- and long-term contract competitiveness and profit margins of wind and solar energy; 9. The present invention considers the impact of carbon trading and green certificate trading in the bidding model, encouraging wind and solar energy to fully participate in the market and obtain more profits; 10. The present invention takes into account the fluctuation of the expected electricity price in the market in the bidding strategy, which is conducive to locking in the medium and long-term interests of the consortium; 11. The present invention constructs a two-layer optimization model, comprehensively considers the maximization of the consortium's benefits and the balance of market supply and demand, and improves the scientificity and effectiveness of decision-making; 12. The present invention improves the competitiveness of wind and solar energy in the medium and long-term market through the construction of a wind, solar, water and storage consortium and the optimization of bidding strategies; 13. The present invention introduces carbon trading and green certificate trading, combined with scientific bidding strategies, effectively expanding the profit space of the consortium; 14. The present invention is not only beneficial to new energy power generation enterprises, but also promotes the win-win situation of market entities such as thermal power companies and users through the balance of market supply and demand; 15. The present invention promotes the sustainable and healthy development of the new energy power generation industry by optimizing the bidding strategy and market participation model of new energy power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further described below in conjunction with the accompanying drawings and implementation examples: Figure 1 The wind, solar, water and storage consortium of the present invention participates in the medium and long-term market transaction framework; Figure 2 The price difference and the bid output force of the consortium when the robustness parameter of the present invention takes different values; Figure 3 This is the market equilibrium result of Example 2 of the present invention when the robustness parameters are 0, 12 and 24 during the period t=2. DETAILED DESCRIPTION

[0021] The technical solution of the present invention is further described below in conjunction with the embodiments and drawings: Example 1 like Figure 1 As shown, a mid- to long-term market-oriented operation bidding strategy for a wind-solar-water-storage consortium is provided. The strategy forms a wind-solar-water consortium through wind-solar and cascade hydropower, and jointly participates in the mid- to long-term market time-sharing bidding transactions to increase the consortium's profit margin, and includes the following steps: Step 1: Introduce carbon trading and green certificate trading mechanisms to reflect the carbon emission attributes of thermal power and the green attributes of wind and solar power, so as to encourage wind and solar power to fully participate in mid- and long-term market bidding and thus obtain more profits; Step 2: Consider the impact of the expected market price fluctuations on the consortium's bidding results to lock in the consortium's medium- and long-term interests; Step 3: On the mid- to long-term market trading day before the month, the day-ahead market electricity price is expected, and with the goal of maximizing the expected benefits of the mid- to long-term contract, a two-layer decision-making model for the mid- to long-term transactions of the wind, solar and water consortium is constructed; the upper layer is the consortium's quotation decision model, which is used to estimate the future day-ahead market clearing price and formulate the consortium's time-sharing and segmented output quotation strategy to maximize the consortium's expected total market benefits; the lower layer is the mid- to long-term market clearing model, which aims to maximize social welfare and meet market demand clearing; Step 4: Determine the bidding constraints of the upper-level consortium's bidding decision-making model, including the winning power constraints of cascade hydropower stations, the winning power and output constraints of wind and solar power in the medium- and long-term market, the market quotation constraints, and the green certificate holding constraints; determine the constraints of the lower-level medium- and long-term market clearing model, including the market clearing power balance constraints, the power constraints reported by the power generation side, and the power constraints reported by the power consumption side; Step 5: Use KKT conditions to solve the two-layer optimization model in Step 3, obtain the medium- and long-term time-segment contract curve of the consortium, and maximize the interests of the wind, solar and water consortium under joint bidding.

[0022] In this embodiment, the expected medium- and long-term market benefits of the consortium in step 2 include the CFD income and green certificate trading income, with maximizing the benefits of the consortium as the objective function. The specific calculation formula of the objective function is as follows: (1) In the formula, , is the trading session and its duration, Set to 1 hour; T is the trading cycle, set to 24 hours on the target day; The number of power segment quotation is the number of segments. It is the power segmentation sub-segment; To clear electricity prices in the medium and long term market, Day-ahead expected electricity price; decision variables ( , , , ) are the mid- and long-term market winning bids for consortiums, wind power, photovoltaic power and cascade hydropower; and The price of green certificates and the quantity of green certificates sold; and All are dual variables; is an auxiliary variable; is a robustness parameter, which can be regarded as the number of time periods that take into account the uncertainty of electricity prices. The larger the parameter, the more conservative the result, and the smaller the parameter, the more radical the result; Represents the total number of time periods; and Forecast D-1 day for the month ahead The upper and lower bounds of electricity prices at each moment.

[0023] Furthermore, the power constraint condition for the winning bid of the cascade hydropower station in step 4 is specifically expressed as: (2) In the formula, , For the The winning bid output of each hydropower station and the upper limit of the hydropower station output, is the number of cascade power stations.

[0024] Furthermore, the power and output constraints of the wind and solar power medium- and long-term market bids in step 4 are specifically expressed as follows: (3) (4) (5) (6) In the formula, , The upper limit of the total electricity volume of wind power and photovoltaic power predicted in the month before the target date, The minimum power contract ratio stipulated in the medium and long-term market. and is the capacity of wind power and photovoltaic units, and It refers to the period when photovoltaic power is generating power during the day and the period when it is unable to generate power.

[0025] Furthermore, the market quotation and quotation quantity constraint conditions in step 4 are specifically expressed as follows: (7) (8) In the formula, and are the minimum and maximum quote values, respectively. and for time Duan He Segment quotation.

[0026] Furthermore, the green certificate holding constraint in step 4 is specifically expressed as: (9) In the formula, The number of green certificates sold for the consortium.

[0027] Furthermore, the lower-level medium- and long-term market clearing model in step 4 is specifically expressed as follows: (10) In the formula, For consumer surplus, for producer surplus; and For users Quote segment Price and power, and For thermal power units Price and power, Make an offer for the consortium.

[0028] Furthermore, the market clearing power balance constraint in step 4 is specifically expressed as: (11).

[0029] Furthermore, the power constraint reported by the power generation side in step 4 is specifically expressed as: (12) (13) (14) (15) In the formula, , , , They are Time period consortium and conventional unit quotation period The upper and lower limits of the reported power; , , , for The upper and lower limits of the total reported power of the period consortium and conventional units.

[0030] Furthermore, the power constraint reported by the electricity user in step 4 is specifically expressed as: (16) (17) In the formula, and For users Report the upper and lower limits of power, and It is the total reported power on the load side.

[0031] Example 2 In another preferred embodiment, based on the above-mentioned embodiment 1, a specific implementation process of a mid- and long-term market-oriented operation bidding strategy for a wind, solar, and water storage consortium is provided below, including key links such as strategy formulation, model construction, and solution.

[0032] 1. Strategy formulation and model building This embodiment aims to improve the bidding competitiveness and profit margin of the consortium in the medium and long-term market by constructing a two-layer optimization model for bidding decision-making in the medium and long-term market of the wind, solar, and water storage consortium. The specific strategies are as follows: 1. Construct a two-layer optimization model: 1) Upper model: consortium quotation decision model; this model aims to maximize the expected total revenue of the consortium market, considers the impact of green certificate trading and carbon trading, estimates the future day-ahead market clearing price, and formulates the consortium's time-sharing and segmented output quotation strategy. The objective function and constraints are as follows: (1) The objective function combines the CFD income and green certificate trading income, takes into account the uncertainty of electricity prices, and introduces robustness parameters for adjustment. The specific calculation formula is as follows: (1).

[0033] (2) Constraints: A. Power constraints for cascade hydropower stations to ensure that the output of the hydropower station does not exceed its output limit: (2).

[0034] B. Wind and solar power mid- and long-term market bidding quantity and output constraints, ensuring that the wind and solar power bidding quantity meets the forecasted quantity upper limit and market contract ratio requirements, while meeting the unit output constraints: (3) (4) (5) (6).

[0035] C. Market quotation and quotation constraints: the power generation side adopts a five-segment non-decreasing curve to quote and quotate, avoiding the abuse of market power: (7) (8).

[0036] D. Green Certificate Holding Restrictions: The amount of green certificate electricity sold shall not exceed the amount of electricity won in the bid: (9).

[0037] 2) Lower model: medium- and long-term market clearing model; with the goal of maximizing social welfare, it receives declarations from power generation and electricity consumption entities and conducts market centralized optimization clearing; the objective function and constraints are as follows: (1) Objective function: the market electricity sales revenue minus the electricity purchase cost to maximize social welfare: (10).

[0038] (2) Constraints: A. Market clearing power balance constraints to ensure market supply and demand balance: (11) B. Power reporting constraints on the power generation side, which limit the upper and lower limits of the power reported by the power generation side entities and the upper and lower limits of the total reported power: (12) (13) (14) (15).

[0039] C. Power reporting constraints on the power consumption side, limiting the upper and lower limits of the power reported by users and the upper and lower limits of the total power reported on the load side: (16) (17).

[0040] 2. Model solution: The KKT conditions are used to solve the two-layer optimization model and obtain the medium- and long-term time-phased contract curve of the consortium. The effectiveness and practicality of the model are verified through simulation.

[0041] 2. Implementation steps 1. Data collection and preprocessing: Collect historical power generation data, forecast data, electricity price data, green certificate price and carbon quota data of wind power, photovoltaic power and cascade hydropower; clean and organize the data to ensure that the data is accurate and complete.

[0042] 2. Model parameter settings: According to historical data and actual conditions, various parameters in the model are set, including the upper and lower limits of electricity prices, green certificate prices, robustness parameters, etc.

[0043] 3. Model solution and result analysis: Use mathematical programming software or self-programming solving tools to solve the two-layer optimization model; analyze the solution results, including key indicators such as the consortium's winning bid output, market price difference, and social welfare.

[0044] 4. Strategy adjustment and optimization: According to the solution results and market feedback, the bidding strategy is adjusted and optimized; for example, the quotation strategy, green certificate and carbon trading strategy are adjusted to improve the market competitiveness and profit margin of the consortium.

[0045] 5. Implementation and monitoring: Apply the optimized bidding strategy to the actual market bidding, monitor the market dynamics and the operation of the consortium in real time, and adjust the strategy in time according to market changes to ensure the long-term and stable development of the consortium.

[0046] III. Implementation Effect: Analysis and Explanation of Implementation Results In order to further verify and illustrate the implementation effect, the robustness parameters in this embodiment are The price difference and the winning bid output of the consortium when taking different values ​​and the t=2 period The market equilibrium results for 0, 12 and 24 are shown below Figure 2~3 ,like Figure 2 As shown, The winning output of the consortium and the market price difference when the values ​​are 0, 12 and 24 curve; The value reflects the consortium's , which in turn affects their bidding strategies; Figure 3 In the period when the spread is positive, compared with =0, =24 when the number of winning bids is small; this is because The higher the value, the more conservative the consortium is. The higher the expectations, the =24, in order to obtain more profits, the consortium is more inclined to exchange "quantity" for "price", shrinking the supply as much as possible in exchange for higher electricity prices; and in the period when the price difference is negative, The winning bid volume is higher than 24 hours When it is 0; this is due to When the price difference is positive, the wind and solar power companies exchange "volume" for "price", and the number of winning bids is compressed; in order to stabilize its green certificate income and meet the market bidding volume ratio requirements, the consortium has to maintain a certain bidding volume during the negative price difference period; =12 is between the above two cases; Figure 3 As shown in the figure, taking the period t=2 as an example, the detailed iterative process of the consortium bidding model is shown. The five quotations when it is 0 are all the reserve prices. The 24-hour consortium chooses to bid a higher electricity price and follows the marginal price, exchanging a smaller number of winning bids for a higher electricity price; The bidding curve under 12 is The reason is that when the clearing electricity prices of the two models are the same, there is only one decision solution for maximizing social welfare in the lower model.

[0047] Four typical examples of medium- and long-term transactions are designed, and the results are compared and analyzed to verify the effectiveness and advantages of the method in this paper. The four examples are: the example without considering carbon trading and green certificate trading, called REF (Reference); CT (Carbon-Trade); GT (Green certificate-Trade), CGT (CarbonGreen certificate-Trade). The quantity and price of thermal power companies' applications and winning bids under each typical example are shown in Tables 1 and 2. Table 1 shows the carbon quota and green certificate surplus and shortage and quotation changes of thermal power companies. Table 2 shows the number of winning bids of each market player under the four calculation examples. Combining Table 1 and Table 2, under CT and CGT, the number of winning bids of thermal power companies with insufficient carbon quotas is significantly lower than that under REF, while the number of winning bids of thermal power companies with surplus carbon quotas increases. The reason is that thermal power companies convert carbon quota transaction costs or income into electricity market quotations, resulting in an increase or decrease in their quotations. The number of winning bids on the demand side has decreased, but the market equilibrium result reflects the carbon emission properties of thermal power generation, which is conducive to restricting units with insufficient carbon quotas from obtaining more profits and improving the benefits of units with surplus carbon quotas.

[0048] Table 1 Carbon certificates and quotations of thermal power companies

[0049] Table 2 The number of winning bids of each market player under four examples

[0050] The impact of green certificate trading on thermal power companies is similar to that of carbon trading; for consortiums, after the introduction of green certificate trading, the consortium's profits under GT and CGT are significantly higher than those under REF and CT, and the former's winning volume of wind and solar power projects has increased significantly compared with the latter.

[0051] By implementing the mid- and long-term market-oriented bidding strategy for wind, solar, and water storage consortiums of the present invention, the bidding competitiveness and profit margin of the consortium in the mid- and long-term market are significantly improved, as shown in the following aspects: 1. The consortium's winning bid volume and electricity price are more reasonable, and the market price difference income increases; 2. Green certificate trading and carbon trading mechanisms effectively promote full participation and profitability of wind and solar energy; 3. The consortium will be more competitive in the market and better able to cope with electricity price fluctuations and market risks.

[0052] In summary, this embodiment successfully achieves efficient operation and profit maximization of the wind, solar, water and storage consortium in the medium and long-term market by constructing a two-layer optimization model, formulating strategies and implementation steps in detail, and adjusting and optimizing strategies in real time. It solves the problem of wind and solar power generation in the existing technology that it is difficult to predict the long-term power generation when participating in the medium and long-term electricity market, resulting in contract decomposition disadvantages and deviation profit recovery risks, and then the decline in both quantity and price when participating in transactions; by introducing cascade hydropower and energy storage facilities as flexible power sources, a consortium is formed with wind and solar power generation to jointly participate in medium and long-term electricity market bidding transactions, so as to enhance the medium and long-term contract competitiveness of wind and solar new energy and its profit space; the present invention also fully considers the impact of market mechanisms such as green certificate trading and carbon trading on bidding results, aiming to achieve win-win results for multiple subjects and sustainable development of new energy; a two-layer optimization model is constructed, with the upper layer maximizing the interests of the consortium and the lower layer maximizing social welfare; this model design fully considers the complexity and diversity of the market, and achieves the dual improvement of the consortium and social welfare through two-layer optimization; The invention integrates a variety of constraints, including the constraints on the winning electricity volume and output of wind and solar power in the medium and long-term market, the winning power constraints of cascade hydropower stations, and the constraints on the market quotation volume; the comprehensive application of these constraints makes the bidding strategy of the consortium more in line with the actual market situation and enhances the feasibility and effectiveness of the strategy; by adjusting the robustness parameters, the consortium can formulate a flexible bidding strategy based on the expected attitude towards the fluctuation of the day-ahead electricity price; in the period when the price difference is positive, the consortium can choose to exchange "quantity" for "price" and reduce the supply in exchange for a higher electricity price; in the period when the price difference is negative, in order to stabilize the green certificate income and meet the market bidding volume ratio requirements, the consortium can maintain a certain bidding volume; this flexible bidding strategy helps the consortium to obtain a larger profit space in the market; by introducing the wind, solar, and water storage consortium and the time-divided centralized bidding strategy, the scheme significantly improves the medium- and long-term contract competitiveness and profit space of wind and solar new energy; at the same time, considering the impact of green certificates and carbon trading, it further encourages the active participation and profit growth of wind and solar new energy. This creative technical solution is of great significance for promoting the consumption of renewable energy and the sustainable development of the electricity market.

Claims

1. A mid- to long-term market-oriented operation bidding strategy for a wind, solar, and water storage consortium, characterized in that: The strategy forms a wind-solar-water consortium through wind-solar and cascade hydropower, and jointly participates in the time-sharing bidding transactions in the medium- and long-term market to increase the profit margin of the consortium, and includes the following steps: Step 1: Introduce carbon trading and green certificate trading mechanisms to reflect the carbon emission attributes of thermal power and the green attributes of wind and solar power, so as to encourage wind and solar power to fully participate in mid- and long-term market bidding and thus obtain more profits; Step 2: Consider the impact of the expected market price fluctuations on the consortium's bidding results to lock in the consortium's medium- and long-term interests; Step 3: On the mid- to long-term market trading day before the month, the day-ahead market electricity price is expected, and the mid- to long-term trading two-layer decision-making model of the wind, solar and water consortium is constructed with the goal of maximizing the expected benefits of the mid- to long-term contracts. The upper layer is the consortium's quotation decision model, which formulates the consortium's time-sharing and segmented output quotation strategy based on the future day-ahead market clearing price to maximize the consortium's expected total market benefits; the lower layer is the mid- to long-term market clearing model, which aims to maximize social welfare and meet market demand clearing; Step 4: Determine the bidding constraints of the upper-level consortium's bidding decision-making model, including the winning power constraints of cascade hydropower stations, the winning power and output constraints of wind and solar power in the medium- and long-term market, the market quotation constraints, and the green certificate holding constraints; determine the constraints of the lower-level medium- and long-term market clearing model, including the market clearing power balance constraints, the power constraints reported by the power generation side, and the power constraints reported by the power consumption side; Step 5: Use KKT conditions to solve the two-layer optimization model in Step 3, obtain the medium- and long-term time-segment contract curve of the consortium, and maximize the interests of the wind, solar and water consortium under joint bidding.

2. The mid- and long-term market-oriented operation bidding strategy for a wind, solar, water and storage consortium according to claim 1 is characterized in that: In step 2, the expected medium- and long-term market benefits of the consortium include CFD income and green certificate trading income, with maximizing the interests of the consortium as the objective function. The specific calculation formula of the objective function is as follows: （1） In the formula, , is the trading session and its duration; T is the trading cycle; The number of power segment quotation is the number of segments. It is the power segment sub-segment; To clear electricity prices in the medium and long term market, The expected electricity price on the day before; Decision variables ( , , , ) are the mid- and long-term market winning bids for consortiums, wind power, photovoltaic power and cascade hydropower; and The price of green certificates and the quantity of green certificates sold; and All are dual variables; is an auxiliary variable; is a robustness parameter, which can be regarded as the number of time periods that take into account the uncertainty of electricity prices. The larger the parameter, the more conservative the result, and the smaller the parameter, the more radical the result; Represents the total number of time periods; and Forecast D-1 day for the month ahead The upper and lower bounds of electricity prices at each moment.

3. The mid- and long-term market-oriented operation bidding strategy for a wind, solar, water and storage consortium according to claim 1 is characterized in that: The power constraint conditions for the winning bid of the cascade hydropower station in step 4 are specifically expressed as follows: （2） In the formula, , For the The winning bid output of each hydropower station and the upper limit of the hydropower station output, is the number of cascade power stations.

4. The mid- to long-term market-oriented operation bidding strategy for a wind, solar, water and storage consortium according to claim 1 is characterized in that: The specific conditions for the winning bid of wind and solar power in the medium- and long-term market and the output constraints in step 4 are as follows: （3） （4） （5） （6） In the formula, , The upper limit of the total electricity volume of wind power and photovoltaic power predicted in the month before the target date, The minimum power contract ratio stipulated in the medium and long-term market. and is the capacity of wind power and photovoltaic units, and It refers to the period when photovoltaic power is generating power during the day and the period when it is unable to generate power.

5. The mid- and long-term market-oriented operation bidding strategy for a wind, solar, water and storage consortium according to claim 1 is characterized in that: The market quotation and quotation constraint conditions in step 4 are specifically expressed as follows: （7） （8） In the formula, and are the minimum and maximum quote values, respectively. and for time Duan He Segment quotation.

6. The mid- and long-term market-oriented operation bidding strategy for a wind, solar, water and storage consortium according to claim 1 is characterized in that: The green certificate holding constraint conditions in step 4 are specifically expressed as follows: （9） In the formula, The number of green certificates sold for the consortium.

7. The mid- to long-term market-oriented operation bidding strategy for a wind, solar, water and storage consortium according to claim 1 is characterized in that: The lower-middle and long-term market clearing model in step 4 is specifically expressed as: （10） In the formula, For consumer surplus, for producer surplus; and For users Quote segment The price and power of 1 and For thermal power units Price and power, Make an offer for the consortium.

8. The mid- to long-term market-oriented operation bidding strategy for a wind, solar, water and storage consortium according to claim 1 is characterized in that: The market clearing power balance constraint in step 4 is specifically expressed as: （11）。 9. The mid- to long-term market-oriented operation bidding strategy for a wind, solar, water and storage consortium according to claim 1 is characterized in that: The power constraint reported by the power generation side in step 4 is specifically expressed as: （12） （13） （14） （15） In the formula, , , , They are Time period consortium and conventional unit quotation period The upper and lower limits of the reported power; , , , for The upper and lower limits of the total reported power of the period consortium and conventional units.

10. The mid- to long-term market-oriented operation bidding strategy for a wind, solar, water and storage consortium according to claim 5 is characterized in that: The power constraint reported by the electricity user in step 4 is specifically expressed as: （16） （17） In the formula, and For users Report the upper and lower limits of power, and It is the total reported power on the load side.

Images