Wind-solar-hydro power generation right transaction method considering clean energy ccers value

Abstract

The wind-solar-hydro power generation right transaction method considering clean energy CCER value belongs to the cross technical field of power market transaction technology and carbon emission reduction mechanism collaborative application. The method first establishes a wind-solar-hydro cross-region alliance, and carries out contract adjustment on D-2 day; then builds a CCER supply and demand relationship calculation model with the maximum social welfare as the target, designs a wind-solar-different bias benchmark value method and establishes a new energy excess benefit recovery model; then builds a wind-solar-hydro GRT master-slave game model, sets the transaction volume balance, new energy predicted output, direct current operation and CCER related constraints, and obtains the CCER transaction supply and demand situation and internal power generation right transaction strategy by iteratively solving the equilibrium solution. The present application effectively resolves the wind-solar medium and long term contract performance risk, excavates the clean energy CCER value, realizes the wind-solar-hydro income collaborative optimization, is in line with the actual power grid dispatching, improves the clean energy consumption and carbon emission reduction benefit, and helps the low-carbon transformation of new type power system.

Description

Technical Field

[0001] This invention relates to the cross-technical field of synergistic application of electricity market trading technology and carbon emission reduction mechanisms, and in particular to a method for trading wind, solar and hydropower generation rights that takes into account the value of clean energy CCERs. Background Technology

[0002] Against the backdrop of the national push for the full market trading of renewable energy power, wind and solar power bases at the sending end have become an important path for renewable energy development by participating in the medium- and long-term electricity markets of receiving regions, aiming to lock in most of their market revenue in advance. However, renewable energy has significant forecasting errors over long time scales, and the dispatching of renewable energy DC transmission is strictly constrained by the transmission capacity of the transmission channels and the operating conditions of their linear power. This significantly increases the difficulty of matching renewable energy with receiving-end loads, making it difficult to meet the high-proportion contract fulfillment requirements of the medium- and long-term market. During contract fulfillment and settlement, renewable energy projects are prone to triggering profit recovery mechanisms, which significantly compress their profit margins, becoming a key factor restricting the healthy development of renewable energy.

[0003] To address these challenges, wind and solar new energy bases need to proactively seek flexible resource adjustments and strengthen deviation balancing and performance risk control through market mechanisms. This is crucial to cope with multiple uncertainties such as new energy output and market electricity prices, mitigating forecast errors, improving load balancing capabilities, and promoting the market-oriented and efficient operation of cross-regional new energy consumption. Currently, there is existing research on market decision-making for the coordinated optimization of wind and solar new energy bases and flexible resources to achieve a win-win situation for multiple stakeholders. For example, the article "Dual-layer Optimization Configuration of Hydropower Station Power-to-Gas Conversion Capacity Considering Medium- and Long-Term Transactions," published in Volume 49, Issue 2 of *High Voltage Engineering*, coordinates the profits of conventional energy and wind power in medium- and long-term transactions through a contract transfer mechanism, thereby improving wind power absorption capacity and hydropower regulation flexibility. The article "Dual-layer Game Dispatch of Distribution Network Considering Generation Rights Trading and Quasi-linear Demand Response," published in Volume 46, Issue 3 of *Acta Energiae Solaris Sinica*, enhances new energy absorption capacity and increases the overall revenue of both parties through generation rights trading between thermal power and photovoltaic power retailers. The article "Multi-timescale Market-based Operation and Dispatch Strategy for Wind-Solar-Hydropower-Storage Consortia," published in Volume 45, Issue 22 of *Proceedings of the Chinese Society for Electrical Engineering*, addresses the issue of deviation revenue recovery in medium- and long-term wind and solar power contracts by constructing a medium- and long-term contract optimization and adjustment mechanism and compensation negotiation model within multi-energy consortia of wind, solar, and hydropower.

[0004] While the aforementioned studies have made some progress in promoting the consumption of new energy and improving market flexibility, relying solely on traditional contract transfer methods to coordinate electricity sales revenue limits transaction costs in the power generation rights transfer and compensation mechanism to the value of electricity, failing to fully exploit the trading flexibility of various power generation entities in a market environment. In particular, the relaunch of the CCER (Chinese Certified Emission Reduction) mechanism provides a new path for the efficient and low-carbon operation of the new power system. Entities involved in the operation of clean energy such as new energy and hydropower can apply for and obtain CCERs based on their power generation, then enter the market for trading, generating revenue through the sale of emission reductions, thereby promoting the long-term development of the renewable energy industry. However, current research on the synergistic optimization of wind and solar new energy bases and flexible resources in market decision-making has not yet addressed the prediction of CCER supply and demand relationships or the introduction of the CCER mechanism into power generation rights trading within a market environment.

[0005] Therefore, in the context of uncertain power output leading to the recovery of profits and performance risks of medium- and long-term wind and solar contracts, how to effectively utilize the CCER mechanism to promote the synergistic optimization of wind and solar new energy bases and flexible resources (such as hydropower) through market mechanisms, and achieve a win-win situation for multiple stakeholders, has become a key technical problem that urgently needs to be solved in the current new energy field. Summary of the Invention

[0006] The technical problem this invention aims to solve is to provide a wind-solar-hydropower generation rights trading method that takes into account the value of clean energy CCERs, thereby addressing the issues of medium- and long-term contract benefit recovery and performance risks caused by the uncertainty of wind and solar power output. Simultaneously, it fully leverages the environmental and economic value of nationally certified emission reductions (CCERs) for clean energy, enhancing the competitiveness of the new energy market and promoting low-carbon emission reduction in the new power system. This invention provides a wind-solar-hydropower generation rights trading strategy that takes into account the value of clean energy CCERs. This strategy is applicable to the joint market-oriented operation scenario of wind and solar new energy bases at the sending end and cascade hydropower connected to the receiving end grid, where large-scale regulation resources are lacking. It can achieve revenue optimization and risk management through cross-regional collaboration between wind, solar, and hydropower.

[0007] To achieve the above technical objectives, the technical solution adopted by this invention is as follows: The method for trading wind, solar, and hydropower generation rights that takes into account the value of clean energy CCERs specifically includes the following steps: Step 1: Establish a cross-regional alliance for wind, solar, and hydropower. Establish a cross-regional wind-solar-hydropower operation alliance that considers the value of clean energy CCERs. The sending-end wind and solar groups are wind and solar new energy bases without large-scale regulation resources. They are connected to the receiving-end power grid through cross-regional DC channels and rely on the cascade hydropower of the receiving-end power grid to complete output regulation. Both the wind and solar groups and the hydropower entities have completed medium- and long-term power transactions and signed contracts for difference. On D-2 day, which is close to the day-ahead market, the contracts are optimized and adjusted in advance to avoid performance risks caused by output deviations.

[0008] Step 2: Establish a CCER supply and demand relationship calculation model A calculation model for the supply and demand relationship of clean energy CCERs is constructed with the objective function of maximizing social welfare. At the same time, a comprehensive constraint system for CCER market-certified emission reductions is set up, which includes CCER purchase and sale balance constraints, thermal power emission reduction purchasing power constraints, carbon collection-related calculation methods, clean energy CCER certification constraints, and non-negative constraints on winning bids. This model refines the supply and demand matching and trading rules of the CCER market, and achieves precise matching between thermal power emission reduction demand and clean energy CCER supply.

[0009] Step 3: Design a model for calculating wind-solar deviation and recovering benefits. An innovative method for determining the deviation benchmark value for wind and solar power is proposed. Addressing the characteristics of large fluctuations in wind power output and clear solar sunshine patterns in solar power, differentiated deviation rate calculation benchmark values ​​are set for each, effectively avoiding problems such as excessively large deviation rates during low-wind periods and abnormal calculations during periods without sunshine. Based on this deviation benchmark value, a new energy excess profit recovery calculation model including positive and negative deviations is established to quantitatively calculate the excess profit recovery amount under different deviation scenarios and different electricity price periods, aligning with the actual rules of deviation assessment and profit supervision in the electricity market.

[0010] Step 4: Construct a wind-solar-hydropower GRT master-slave game model A master-slave game model for wind-solar-hydropower generation rights trading (GRT) that takes into account the value of clean energy CCERs is established, with a clear two-layer objective function: the upper layer aims to maximize the comprehensive benefits of the wind and solar groups, taking into account the contract price difference revenue, CCER trading profits, generation rights compensation costs, and deviation recovery revenue; the lower layer aims to maximize the comprehensive benefits of the receiving hydropower, taking into account the contract price difference revenue, CCER trading profits, and generation rights trading profits, so as to achieve synergistic optimization of the interests of both parties in the transaction.

[0011] Step 5: Set constraints for the game model and solve for the equilibrium solution. The full-dimensional constraints of the wind-solar-hydropower GRT master-slave game model are determined, covering trading volume balance constraints, renewable energy forecast output constraints, DC operation constraints, and CCER-related constraints. Among them, the DC operation constraints comprehensively consider actual dispatch requirements such as power operation range, minimum constant operating time, maximum number of adjustments, same-direction adjustments, and power adjustment magnitude. The CCER-related constraints clarify the calculation method of the carbon dioxide emission reduction coefficient per unit of wind, solar, and hydropower. The equilibrium solution is obtained by iteratively solving the master-slave game model, and finally the trading supply and demand situation of the CCER market, as well as the power generation rights trading strategy within the wind-solar-hydropower system, including core decision variables such as power generation rights trading volume and compensation electricity price.

[0012] Furthermore, the objective function of the clean energy CCER supply and demand relationship calculation model fully considers the procurement cost of thermal power CCERs and the sales revenue of wind, solar, and hydropower CCERs to maximize social welfare. The carbon payment calculation method clarifies the quantitative formulas for the free benchmark carbon allowance for thermal power, the expected carbon emissions, and the carbon allowances to be paid. The CCER certification constraints limit the correlation between the winning bid volume of wind, solar, and hydropower CCERs and their own medium- and long-term contracted electricity volume, ensuring the rationality and compliance of CCER certification.

[0013] Furthermore, in the method for determining the wind-solar power deviation benchmark, the wind power deviation rate uses the day-ahead output of the wind power forecast on day D-2 as the denominator, and the photovoltaic deviation rate uses the photovoltaic installed capacity as the denominator. The wind and solar power output deviation is accurately measured through the differentiated benchmark. The new energy excess benefit recovery model sets an allowable deviation range. When the actual deviation rate exceeds this range, the benefit recovery mechanism is triggered. The positive and negative deviation recovery benefits are calculated respectively when there is an arbitrage space between the medium- and long-term contract electricity price and the day-ahead clearing electricity price, so as to achieve refined management of deviation benefits.

[0014] Furthermore, in the aforementioned wind-solar-hydropower GRT master-slave game model, the wind and solar group, as the game leader, decides the compensation price for power generation rights, while the receiving hydropower, as the game follower, responds to the compensation price and decides the trading volume of power generation rights. The value of CCER is deeply integrated into the revenue calculation of both parties, and the change in the amount of power generation rights traded will synchronously affect the tradable scale of CCER for wind, solar, and hydropower, realizing the linkage and coupling between power trading and CCER trading.

[0015] Furthermore, the game model's trading volume balance constraint ensures that the total electricity volume of the wind-solar-hydropower medium- and long-term contracts remains unchanged before and after the transaction, with only internal electricity volume optimization and allocation being carried out; the new energy forecast output constraint limits the upper and lower limits of the contract electricity volume after wind and solar adjustments, taking into account both market signing rules and the actual new energy output forecast level; the carbon dioxide emission reduction coefficient per unit of wind, solar, and hydropower is calculated by weighting the electricity marginal emission factor and the capacity marginal emission factor, accurately reflecting the actual carbon emission reduction capacity of clean energy.

[0016] The wind-solar-hydropower generation rights trading method that takes into account the value of clean energy CCERs provided by this invention has the following beneficial effects: 1. This invention effectively resolves the problem of recovering profits from medium- and long-term contracts caused by the uncertainty of wind and solar power output, avoids the risk of new energy contract performance, protects the profit margin of wind and solar groups, and improves the overall performance capability of medium- and long-term new energy contracts.

[0017] 2. This invention fully explores the environmental value of clean energy CCERs and transforms it into actual economic benefits, broadens the revenue channels for wind, solar and hydropower operators, and breaks the limitation of traditional power generation rights trading that only reflects the value of electricity.

[0018] 3. This invention achieves a win-win situation for cross-regional synergy between wind and solar power and hydropower. It relies on the regulation capacity of hydropower to smooth out fluctuations in wind and solar power output, and hydropower can also obtain additional trading revenue and CCER value appreciation by undertaking power generation rights.

[0019] 4. This invention accurately depicts the supply and demand matching and trading rules of the CCER market. The CCER supply and demand relationship calculation model constructed is in line with the market-based pricing mechanism of the carbon market, providing a feasible path for clean energy to participate in the carbon market.

[0020] 5. This invention innovatively proposes a method for determining the benchmark value of wind-solar difference, which solves the technical pain points of excessive deviation rate during low wind periods and abnormal calculation during periods of no solar illumination in traditional deviation calculation, making the output deviation measurement more accurate.

[0021] 6. The wind-solar-hydropower GRT master-slave game model constructed in this invention can reasonably coordinate the interests of wind and solar power at the sending end and hydropower at the receiving end, and achieve scientific optimization of decision variables such as power generation rights trading volume and compensation electricity price.

[0022] 7. This invention incorporates full-dimensional actual scheduling constraints into the model, such as DC channel power operation, number of adjustments, and minimum constant duration, which fits the actual operation requirements of cross-regional power grids and has extremely strong engineering reference value.

[0023] 8. This invention promotes deep collaboration between the electricity market and the carbon market, realizing the linkage and coupling between electricity trading and CCER trading, so that changes in the trading volume of power generation rights can synchronously affect the tradable scale of CCERs for wind, solar and hydropower.

[0024] 9. This invention significantly improves the cross-regional consumption space of clean energy, alleviates the difficulty of matching new energy DC transmission with receiving-end load, reduces the curtailment rate of new energy, and promotes the large-scale market-oriented and efficient consumption of new energy.

[0025] 10. This invention effectively incentivizes the low-carbon operation of wind, solar and hydropower, reduces the power system's dependence on high-carbon thermal power, reduces the overall carbon emission level of the market, and helps the new power system achieve its low-carbon emission reduction goals.

[0026] 11. The new energy excess profit recovery calculation model established by this invention can quantify the excess profit under positive and negative deviations in a refined manner, providing a clear quantitative basis for the supervision of deviation profit in the power market and the control of arbitrage behavior.

[0027] 12. The cross-regional alliance model of wind-solar-hydropower constructed by this invention provides a demonstration for the joint market-oriented operation of wind and solar bases with no regulation resources at the sending end and flexible resources at the receiving end, and can be extended to other multi-energy entity collaborative scenarios.

[0028] 13. This invention achieves dynamic optimization and allocation of wind, solar, and hydropower output through power generation rights trading, adapting to the dual uncertainties of market electricity prices and power output, and improving the operational flexibility and resource allocation economy of inter-regional power systems.

[0029] 14. This invention clarifies the scientific calculation method for the unit carbon dioxide emission reduction coefficient of clean energy, accurately reflects the actual carbon emission reduction capacity of wind, solar and hydropower, and provides a precise quantitative standard for CCER certification and trading.

[0030] 15. This invention introduces richer dimensions for pricing in power generation rights trading by leveraging the value of CCERs, thereby encouraging both parties to participate more actively in output adjustment and trading decisions, activating the vitality of cross-regional power market transactions, and improving the efficiency of market resource allocation.

[0031] 16. This invention utilizes the improved accuracy of new energy forecasting on the day D-2 before the power generation date to enable wind and solar power groups to anticipate the risk of power output deviation in advance, and achieve proactive and refined risk management through power generation rights trading.

[0032] 17. This invention enables the coordinated distribution of electricity sales revenue and clean energy CCER benefits, deeply integrating the economic and environmental benefits of wind, solar, and hydropower, thereby enhancing the overall market competitiveness of clean energy.

[0033] 18. This invention provides a detailed description of CCER purchase and sale balance, emission reduction constraints, and verification process, offering a novel technical approach and implementation solution for clean energy to develop CCER value and participate in carbon market trading.

[0034] 19. The deviation calculation and benefit recovery rules of this invention are highly aligned with the actual requirements of deviation assessment and benefit supervision in the power market, and can be directly adapted to the existing power market supervision system, reducing the cost of implementation.

[0035] 20. This invention uses a master-slave game model to iteratively solve for equilibrium solutions, which can accurately obtain the supply and demand situation of CCER transactions and the internal power generation rights trading strategies of wind, solar and hydropower, providing market participants with clear and executable trading decision-making basis. Attached Figure Description

[0036] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 A schematic diagram of the market-oriented operation framework for cross-regional collaborative wind-solar-hydropower introducing the CCER mechanism for this invention; Figure 2 A framework diagram of a wind-solar-hydropower generation rights trading model that considers the value of clean energy CCERs in this invention; Figure 3 This invention relates to the time series of CCER market trading volume and marginal price. Figure 4 To optimize the comparison of contract output adjustments for the former Fengguang Group; Figure 5 To optimize the adjustment of hydropower output and compensate for changes in electricity prices; Figure 6 This invention provides a comparison of the optimized power output adjustments for wind and solar power group contracts. Figure 7 This invention optimizes the adjustment of hydropower output and compensates for changes in electricity prices. Detailed Implementation

[0037] The technical solutions of the present invention will be further described below with reference to the embodiments and accompanying drawings: Example 1 This embodiment provides a method for trading wind, solar, and hydropower generation rights that takes into account the value of clean energy CCERs (Clean Energy Credits), such as... Figure 1 As shown, a cross-regional alliance of wind, solar and hydropower is established, taking into account the value of clean energy CCER (Chinese certified emission reduction). The wind and solar group is a wind and solar new energy base at the sending end that does not have large-scale regulation resources. It is connected to the receiving end power grid through a cross-regional DC channel and uses hydropower connected to the receiving end power grid for regulation. All clean energy has completed medium and long-term transactions and signed contracts for difference. The contracts are adjusted on D-2 days before the nearest date. A calculation model for the supply and demand relationship of clean energy CCERs is constructed, with the objective function being to maximize social welfare. The constraints of the CCER market include CCER purchase and sale balance constraints, thermal power emission reduction purchasing power constraints, carbon collection-related calculation methods, clean energy CCER certification constraints, and non-negative constraints of winning bids. A method for determining the benchmark value of wind-solar difference is proposed, and a calculation model for recovering excess benefits from new energy sources is established, including calculation formulas for recovering excess benefits from positive deviation and calculation formulas for recovering excess benefits from negative deviation. Construct a master-slave game model for wind-solar-hydro power generation rights trading that takes into account the value of clean energy CCERs, including the objective function of maximizing the comprehensive revenue of the upper-level wind and solar groups and the objective function of maximizing the comprehensive revenue of the lower-level hydropower receiving end. Determine the constraints of the master-slave game model for wind-solar-hydropower GRT (Generation Right Trading), including trading volume balance constraints, new energy forecast output constraints, DC operation constraints, and CCER-related constraints. By iteratively obtaining the equilibrium solution using a master-slave game model, we can obtain the supply and demand situation of CCER trading and the trading strategy for power generation rights within wind, solar and hydropower.

[0038] Furthermore, the method for calculating the objective function of the clean energy CCER supply and demand relationship calculation model is as follows: (1); In the formula , They are respectively Periodic thermal power units CCER (China Certified Equipment) market bid price and CCER demand. , They are respectively Time period The declared price of Class A hydropower in the CCER market, and the number of CCERs sold in winning bids. , They are respectively The bidding price and winning bid volume of CCERs sold by Shijian Fengguang Group in the CCER market. This is for medium- to long-term trading. For the target number of time periods, 、 These refer to the number of thermal power units and the number of cascade hydropower stations, respectively.

[0039] Furthermore, the certified emission reduction constraints in the CCER market include CCER purchase and sale balance constraints, thermal power emission reduction purchasing power constraints, carbon taxation-related calculation methods, clean energy CCER certification constraints, and non-negative constraints on winning bids, wherein: The CCER purchase and sale balance constraints mentioned above include: (2); In the formula, for Time period The number of CCERs won in the sales of Class A hydropower projects in the CCER market; for The number of CCERs sold and won by Shijian Fengguang Group in the CCER market; for Periodic thermal power units The demand for CCERs in the CCER market; The aforementioned purchasing power constraints for emissions reductions from thermal power plants include: (3); in, For CCER allowable offset ratio, Carbon quotas for thermal power plants should be cleared.

[0040] The carbon collection-related calculation methods include: (4); In the formula, , These are the free baseline carbon allowance for thermal power and the estimated carbon emissions. This is the percentage of free quota. For thermal power units Carbon emission allocation coefficient For thermal power units Carbon emission intensity For thermal power units Medium and long-term contracts Electricity consumption during the period These are state variable parameters.

[0041] The aforementioned clean energy CCER certification constraints and non-negative bid volume constraints include: (5); In the formula, The coefficient for carbon dioxide emission reduction per unit of new energy source. The carbon dioxide emission reduction coefficient per unit of hydropower. , For new energy and hydropower in the target date Each entity's medium- and long-term contracted electricity volume at any given time.

[0042] Furthermore, the method for determining the benchmark value based on the wind-solar difference, which determines the calculation model for recovering excess benefits from new energy sources, includes: (6); In the formula, , They are respectively Wind and solar power output deviation rates during different time periods , These are the medium- and long-term contracted volumes for wind power and solar power, respectively. , The respective day-one forecast outputs for wind power and solar power are as follows: This refers to the installed capacity of photovoltaic power.

[0043] Furthermore, the formula for calculating excess profit from positive deviation recovery and the method for calculating excess profit from negative deviation recovery are as follows: when Profits will be recovered in a timely manner. To allow for a range of deviations, there are two scenarios: positive and negative deviations, depending on the conditions for the formation of arbitrage opportunities. The calculation of the excess return is as follows: (7); In the formula, , These are the allowable deviation ranges for wind power and photovoltaic performance, respectively. The threshold for determining the deviation in electricity consumption. , These refer to the periods when the electricity price for medium- and long-term contracts is greater than and less than the electricity price forecast value two days prior to D-2. For deviation recovery revenue, This refers to the electricity generated under medium- and long-term wind and solar power contracts. Contribute to the forecasting of the day before the festival. For positive deviation recovery revenue, For negative deviation recovery income, The electricity price is for medium- and long-term wind and solar power contracts. The forecast value for the clearing electricity price is for the day before D-2.

[0044] Furthermore, the wind-solar-hydro GRT master-slave game model that takes into account the value of clean energy CCER includes an objective function for maximizing the comprehensive revenue of the upper-level wind and solar groups and an objective function for maximizing the comprehensive revenue of the lower-level receiving-end hydropower, wherein: The objective function for maximizing the overall benefits of the upper-level scenic group includes: (8); In the formula, To compensate for electricity prices in power generation rights trading, For the volume of electricity generation rights trading, , The data are generated from the D-2 day sampling clustering, which includes the number of scenarios and the probability of each scenario for the day-ahead electricity price forecast. The amount of electricity generated under the new energy contract after the transaction is completed. , , , , These are the comprehensive revenue of the wind and solar power group after the transaction is completed, the contract price difference revenue, CCER profit, power generation rights compensation cost, and deviation recovery revenue; This is the market transaction price for CCER. This represents the CCER (China Certified Power Grid Operator) winning bid volume of the wind and solar power group prior to the power generation rights transaction. The coefficient for carbon dioxide emission reduction per unit of new energy source. It is an auxiliary variable.

[0045] The objective function for maximizing the comprehensive benefits of hydropower at the lower receiving end includes: (9); In the formula, , , , These are the comprehensive hydropower revenue received after the power generation rights transaction, the contract price difference revenue, the CCER profit, and the power generation rights transaction revenue. This refers to the electricity volume under the hydropower contract after the transaction is completed. This refers to the CCER (China Certified Emission Reduction) volume of hydropower received before the power generation rights trading. The carbon dioxide emission reduction coefficient per unit of hydropower. It is an auxiliary variable.

[0046] Furthermore, the constraints of the wind-solar-hydropower GRT master-slave game model include trading volume balance constraints, new energy predicted output constraints, DC operation constraints, and CCER-related constraints, wherein: The transaction volume balance constraint includes: (10); (11); In the formula, , These refer to the electricity volumes under medium- and long-term contracts for new energy and hydropower before the contract adjustments. , These are the contracted electricity volumes for new energy and hydropower after the transaction is completed. Divide the battery level into segments. The number of segments for reporting and pricing medium- and long-term electricity consumption.

[0047] The predicted power output constraints for new energy sources include: (12); In the formula, This represents the upper limit of the predicted total day-ahead output of new energy sources on D-2. The minimum contracted ratio for wind and solar power generation as stipulated by the market.

[0048] The DC operation constraints include power operation range constraints, minimum constant operation duration constraints, maximum number of adjustment constraints, same-direction adjustment constraints, and power adjustment amplitude constraints. (13); (14); (15); In the formula, For the DC transmission power after the power generation rights trading, For DC channel medium and long term Transmission power during the time period The time scale for medium- to long-term trading is set to 1 hour. , These are the upper and lower limits of DC transmission power operation, respectively. A Boolean variable characterizing the DC power regulation state. To adjust the state variable, To adjust the state variable, This is the minimum constant operating time after DC transmission power adjustment. Limit on the number of times DC transmission power can be adjusted per day; , These are the limits for increasing and decreasing the DC transmission power, respectively. This is the minimum adjustment amount for DC transmission power.

[0049] The CCER-related constraints include: (16); In the formula, The coefficient for carbon dioxide emission reduction per unit of new energy source. The carbon dioxide emission reduction coefficient per unit of hydropower. , These are the marginal emission factors for wind, solar, and hydropower electricity, respectively. , These are the marginal emission factors for wind, solar, and hydropower capacity, respectively.

[0050] To fully demonstrate the outstanding advantages of the wind-solar-hydropower power generation rights trading strategy formulation method that takes into account the value of clean energy CCERs constructed in this paper, and to verify the feasibility and effectiveness of the proposed model in practical applications, this paper designs a comparison scenario: Scenario 1: Ordinary power generation rights trading without taking into account the value of clean energy CCERs (basic scenario).

[0051] Scenario 2: Power generation rights trading that takes into account the value of Clean Energy CCERs (optimized scenario).

[0052] In both scenarios above, the wind and solar power group and the receiving hydropower station optimize their GRT decisions with the goal of maximizing their respective overall benefits.

[0053] Figure 3 This shows the time series of CCER market trading volume and marginal price. Figure 4 , Figure 5 , Figure 6 and Figure 7 The results of power generation rights transactions under different scenarios were presented.

[0054] Depend on Figure 3It can be seen that the sales volume of wind power CCERs remained at a high level throughout the day, while the sales volume of hydropower CCERs fluctuated intermittently. The sales peak of photovoltaic CCERs occurred significantly between 12:00 and 14:00, highly coupled with the daytime characteristics of photovoltaic output. During this period, photovoltaic power generation and carbon emission reduction peaked simultaneously, forming a concentrated supply of CCERs. The peak purchase of thermal power CCERs highly overlapped with the peak supply of clean energy, reflecting that thermal power plants concentrated their purchases during the effective supply period of CCERs to meet carbon enforcement constraints. The marginal price of CCERs fluctuated with the supply and demand relationship over time: the price was relatively stable during the peak supply period of photovoltaics, indicating that the market supply and demand tended to be in equilibrium; when the demand for thermal power purchases increased and the supply of clean energy was relatively scarce, the price rose in stages. The above results indicate that the CCER supply and demand relationship analysis model in this paper conforms to the market-based pricing mechanism.

[0055] like Figure 4 As shown, the wind and solar power group reduces contracted output during periods of high electricity prices and avoids revenue losses by transferring power generation rights; correspondingly Figure 5 As shown, during these periods of high electricity prices, cascade hydropower takes over the power generation rights transferred from wind and solar power, and the actual output is significantly higher than the original contract. During other periods, the output of hydropower is also adjusted accordingly, and the compensation price shows obvious fluctuations. This indicates that the wind-solar-hydropower GRT strategy can take into account both the smoothing of wind and solar fluctuations and the role of hydropower regulation capabilities, and achieve synergistic optimization of the revenue of both parties in the transaction. Figure 6 The adjustment range of the power output of the wind and solar power group under the optimized scenario has been optimized, and the corresponding hydropower contract adjustment has been improved. Figure 7 This indicates that the regulation response of cascade hydropower is more proactive, with a 32.1% increase in the amount of electricity received during high-price periods and a 27.6% increase in the amount of electricity reduced during low-price periods. Furthermore, the pricing mechanism for compensation electricity price after taking into account CCER value is more reasonable. Under the same adjustment output, the compensation electricity price decreases by an average of 8.3%, but due to the increase in trading volume, the total regulation compensation revenue of hydropower still increases by 21.5%. The introduction of CCER value makes the output strategies of both parties in the transaction more aligned with the dual goals of economic benefits and environmental benefits.

[0056] In summary, this paper addresses the issues of profit recovery and performance risk in medium- and long-term wind and solar power contracts caused by output uncertainty. It proposes a method for formulating wind-solar-hydropower generation rights trading strategies that considers the value of Clean Energy CCERs (CCERs). The conclusions drawn from simulation experiments are as follows: 1) Based on the deep collaborative design of power generation rights trading and CCER mechanism, a CCER supply and demand relationship prediction model and a collaborative operation mode of power generation rights trading and CCER were constructed. This effectively mitigated the risk of dual bias in power output and electricity price in cross-time scale trading, improved market trading activity and resource allocation economy, and realized the transformation of environmental value into actual economic benefits for trading entities.

[0057] 2) The synergy between the electricity market and the CCER mechanism, as well as the inter-regional transmission and multi-source coordination, have jointly promoted the efficient operation of inter-regional systems, improved the economy of power resource allocation and the flexibility of system operation, and provided an optimized path for multi-entity cooperation in wind, solar and hydropower that balances economic benefits and environmental value.

[0058] In summary, the wind-solar-hydropower generation rights trading strategy proposed in this paper, which considers the value of Clean Energy CCERs, is an effective method to mitigate the risks of profit recovery and performance under medium- and long-term contracts, enhance the competitiveness of the new energy market, and promote low-carbon emission reduction in the new power system. Future research will consider establishing a robust optimization decision-making model for wind-solar-hydropower generation rights trading, taking into account the uncertainty of day-ahead electricity price forecasting errors.

[0059] Example 2 In another preferred embodiment, based on Embodiment 1, this embodiment provides a wind-solar-hydropower generation rights trading method that takes into account the value of clean energy CCERs. This method is used to verify the feasibility and effectiveness of the strategy in solving the problems of recovery of benefits and performance risks of medium- and long-term wind and solar contracts. The application scenario of this embodiment is an inter-regional energy alliance composed of a wind and solar new energy base at the sending end that does not have large-scale regulation resources and a cascade hydropower station connected to the grid at the receiving end. The wind and solar base at the sending end transmits electricity through an inter-regional DC channel, and the cascade hydropower station at the receiving end has flexible regulation capabilities. Both participate in medium- and long-term electricity market transactions and sign contracts for difference. On the D-2 day of the near-day market, the contract is optimized and adjusted and the generation rights are traded. At the same time, wind, solar and hydropower, as the main clean energy entities, participate in CCER market transactions, and thermal power is the CCER demander, completing carbon collection-related operations.

[0060] In this embodiment, the time scale is divided into hours, and the research period is set as one calendar day (24 hours). ), thermal power units are categorized as The cascade hydropower units are The scene set is The relevant parameter values ​​are taken with reference to actual operating data of the electricity market and carbon market verification rules, among which the CCER allowable offset ratio is... Take 0.5, the proportion of free quota for thermal power. Take 0.7 as the minimum contracted ratio for wind and solar power generation. Take 0.8 as the minimum constant operating time after DC transmission power adjustment. Take 3 hours as the limit for the number of times DC transmission power can be adjusted per day. Taking 6 times, the carbon dioxide emission reduction coefficient per unit of new energy and hydropower is reduced from 75% of the marginal emission factor of electricity. With a 25% capacity marginal emission factor Weighted calculation, CCER market transaction price The price is set at 30 yuan / ton. The basic parameters such as the declared price, carbon emission intensity, and installed capacity of wind, solar, hydropower, and thermal power are all determined according to the actual engineering scenario.

[0061] Step 1: Establish a cross-regional operation alliance for wind and solar power and hydropower like Figure 1 As shown, a cross-regional market-oriented operation framework for wind, solar and hydropower that takes into account the value of clean energy CCERs is established. The framework includes three core parts: the main system architecture, the sending-end new energy base, and the receiving-end system. Data interaction and power transmission between the parts are realized through power flow and information flow. The dispatch center coordinates DC power transmission plans, conventional unit output plans and load forecasts, and the power market completes the calculation and release of declared power volume and clearing price.

[0062] The alliance is defined as follows: the sending-end wind and solar power group is an integrated wind and solar power new energy base, which lacks large-scale energy storage, pumped storage and other regulation resources, and its output is significantly uncertain due to natural conditions; the receiving end is a hydropower cluster composed of three cascade hydropower stations, which has the ability to quickly regulate output and can respond to fluctuations in the output of the sending-end wind and solar power and changes in market electricity prices; thermal power is the conventional power source of the receiving end, and the carbon emissions generated are offset by the CCER mechanism.

[0063] The signing of medium- and long-term contracts has been completed: both the wind and solar power groups and the hydropower entities have completed transactions in the receiving-end electricity medium- and long-term market, signed contracts for difference, and determined the medium- and long-term contract electricity volumes for each period. , With contract electricity price , .

[0064] Set contract adjustment points: Two days before the market opens (D-2 day), combined with the latest wind and solar daytime output forecasts. , The electricity price forecast for the day was cleared. The wind-solar-hydropower internal power generation rights trading and contract volume optimization adjustment are carried out. The wind-solar group and the cascade hydropower stations transmit core trading parameters such as power generation rights trading volume and compensation price through information flow to determine the contract volume after the transaction. , .

[0065] Clearly define the trading rules: Power generation rights trading volume A positive value indicates that hydropower transfers power generation rights to wind and solar power, while a negative value indicates that wind and solar power transfers power generation rights to hydropower. The total power volume of the wind-solar-hydropower medium- and long-term contracts remains unchanged before and after the transaction. Only internal power allocation is carried out. The sending-end new energy base transmits power to the receiving-end grid through DC channels to meet the power demand of the receiving-end load.

[0066] Step 2: Construct a supply and demand calculation model for clean energy CCER (Clean Energy CCER). like Figure 2As shown, the cross-regional collaborative optimization model for wind, solar and hydropower of the present invention includes two core modules: a CCER supply and demand relationship analysis model and a wind, solar and hydropower power generation rights trading model that takes into account the value of clean energy CCERs. The CCER supply and demand relationship analysis model takes the maximization of social welfare as the solution objective. Its output CCER clearing result and the medium- and long-term contract electricity volume determined by the electricity market are used together as input parameters of the power generation rights trading model to realize the linkage and coupling between the carbon market and the electricity market.

[0067] Step 2.1: Define the objective function A CCER supply and demand relationship calculation model is constructed with the goal of maximizing social welfare. The objective function is: (1); in, , They are respectively Periodic thermal power units The bid price and demand for CCERs in the CCER market; , They are respectively Time period The declared price of Class A hydropower in the CCER market and the number of CCERs sold in winning bids; , They are respectively The bidding price and winning bid volume of CCERs sold by Shijian Fengguang Group in the CCER market; This is a medium- to long-term trading period; The target number of time periods; 、 These refer to the number of thermal power units and the number of cascade hydropower stations, respectively.

[0068] Step 2.2: Set CCER market-certified emission reduction constraints (1) CCER purchase and sale balance constraint The total amount of CCERs sold for wind, solar, and hydropower is equal to the total amount of CCERs purchased for thermal power, satisfying the following condition: (2); Ensure that the total supply and demand in the CCER market are matched, with no excess supply or demand gap. Specifically, for Time period The number of CCERs won in the sales of Class A hydropower projects in the CCER market; For The number of CCERs sold and won by Shijian Fengguang Group in the CCER market; for Periodic thermal power units The demand for CCERs in the CCER market; (2) Purchasing power constraints on emissions reduction from thermal power plants The total amount of CCER procurement for thermal power plants shall not exceed the product of their payable carbon allowances and the allowed offset ratio of CCERs, satisfying the following conditions: (3); in, Carbon quotas for thermal power plants should be cleared; The CCER allowable offset ratio is set to 0.5 in this embodiment.

[0069] (3) Carbon collection related calculation constraints The free baseline carbon allowance for thermal power, the estimated carbon emissions, and the carbon allowance to be paid are calculated using the following formula: (4); in, , These are the free baseline carbon allowance for thermal power and the estimated carbon emissions. This is the percentage of free quota. For thermal power units Carbon emission allocation coefficient For thermal power units Carbon emission intensity For thermal power units Medium and long-term contracts Electricity consumption during the period These are state variable parameters.

[0070] (4) Clean Energy CCER Certification and Winning Bid Quantity Non-negative Constraints For wind, solar, and hydropower CCERs, the winning bid volume does not exceed the maximum certification limit, and the winning bid volume is non-negative, satisfying the following conditions: (5); In the formula, , These are the carbon dioxide emission reduction coefficients per unit of new energy and hydropower, respectively, and the carbon dioxide emission reduction coefficient per unit is calculated by weighting the marginal emission factor of electricity and the marginal emission factor of capacity. , For new energy and hydropower in the target date Each entity's medium- and long-term contracted electricity volume at any given time.

[0071] Step 2.3: Solve for CCER supply and demand equilibrium. The objective function and constraints are solved using linear programming to obtain the CCER (CCER) sales volume for wind, solar, and hydropower in time period t. , And the amount of CCER purchased by thermal power plants This will clarify the supply and demand distribution and transaction scale of the CCER market, and the results will serve as an important input for subsequent power generation rights trading models.

[0072] Step 3: Calculate the wind and solar power output deviation and establish an excess profit recovery model. Step 3.1: Calculation of the benchmark value and deviation rate for the difference between wind and light To address the differences in power output characteristics between wind and solar power, a differential deviation benchmark value is set, and the power output deviation rate for each time period is calculated using the following formula: (6); In the formula, , They are respectively Wind and solar power output deviation rates during different time periods , These are the medium- and long-term contracted volumes for wind power and solar power, respectively. , The respective day-one forecast outputs for wind power and solar power are as follows: To calculate the photovoltaic installed capacity, avoid the calculation anomaly where the denominator is 0 during periods of no sunlight.

[0073] In this embodiment, an allowable deviation range is set. That is, when the deviation rate of wind power / solar power exceeds 15%, the excess profit recovery mechanism is triggered.

[0074] Step 3.2, Calculation of the deviation power determination threshold The threshold for determining the deviation in electricity consumption for each time period is the sum of the allowable deviations in electricity consumption for wind power and solar power, and the formula is as follows: (7) -1; in, for A threshold for determining the amount of electricity deviating during a given time period is established; any amount of electricity deviating beyond this threshold is included in the calculation of the benefits recovered.

[0075] Step 3.3: Calculation of excess profit recovery for positive and negative deviations Based on the relationship between the medium- and long-term contract electricity price and the pre-D-2 clearing electricity price, time periods are divided. (Contracted electricity price > Day-ahead electricity price) and (Contract electricity price < day-ahead electricity price), calculate the recovery revenue for positive and negative deviations separately, and the total recovery revenue is the sum of the two, as shown in the formula: (7) -2; in, , These are the allowable deviation ranges for wind power and photovoltaic performance, respectively. The threshold for determining the deviation in electricity consumption; , These refer to the periods when the electricity price for medium- and long-term contracts is greater than and less than the electricity price forecast value two days prior to D-2. For deviation recovery revenue; For medium- and long-term contracted electricity volume for wind and solar power; Contribute to the forecasting of the day before the scenic spot; For positive deviation recovery revenue (wind and solar contract power generation > predicted output and is in the range of...) (Time period) For negative deviation recovery revenue (wind and solar contract power generation < predicted output and in the range of...) (Time period) The electricity price is for medium- and long-term wind and solar power contracts; The forecast value for the clearing electricity price is for the day before D-2.

[0076] like or If the deviation recovery benefit for the corresponding period is 0, then the benefit recovery will not be triggered.

[0077] Step 4: Construct a wind-solar-hydropower GRT master-slave game model like Figure 2 As shown, the wind-solar-hydropower generation rights trading model that takes into account the value of Clean Energy CCERs is a two-layer master-slave game model. The wind and solar power group, as the master player, aims to maximize overall revenue and minimize deviation profit recovery, and CCER profits are included in the revenue calculation. The receiving hydropower group, as the slave player, aims to maximize overall revenue, and CCER profits are also included in the revenue calculation. The master player's decision on the compensation price is transmitted to the slave player, and the slave player's decision on the generation rights trading volume is fed back to the master player, forming a two-way interactive game relationship. In this embodiment, the wind and solar power group, as the game leader, decides on the compensation price for generation rights. Hydropower at the receiving end acts as a follower in the game, responding to compensation electricity prices and deciding on the volume of power generation rights trading. Both aim to maximize their own overall benefits, construct a two-layer objective function, and set constraints in all dimensions.

[0078] Step 4.1, Upper Level: Objective Function for Maximizing the Overall Profit of Fengguang Group (8); in, To compensate for electricity prices in power generation rights trading, For the volume of electricity generation rights trading, , The data are generated from the D-2 day sampling clustering, which includes the number of scenarios and the probability of each scenario for the day-ahead electricity price forecast. The amount of electricity generated under the new energy contract after the transaction is completed. , , , , These include the comprehensive revenue of the wind and solar power group after the transaction, the contract price difference revenue, the CCER profit (the electricity volume of the power generation rights transaction also affects the tradable scale of CCER), the power generation rights compensation cost, and the deviation recovery revenue (calculated by replacing the original contract electricity volume with the contract electricity volume after the transaction). This is the market transaction price for CCER. This represents the CCER (China Certified Power Grid Operator) winning bid volume of the wind and solar power group prior to the power generation rights transaction. The coefficient for carbon dioxide emission reduction per unit of new energy source. As an auxiliary variable, it distinguishes the direction of power generation rights trading.

[0079] Step 4.2, Lower Layer: Objective Function for Maximizing the Comprehensive Benefits of Hydropower at the Receiving End (9); in, , , , These are the comprehensive hydropower revenue received after the power generation rights transaction, the contract price difference revenue, the CCER profit, and the power generation rights transaction revenue. This refers to the electricity volume under the hydropower contract after the transaction is completed. This refers to the CCER (China Certified Emission Reduction) volume of hydropower received before the power generation rights trading. The carbon dioxide emission reduction coefficient per unit of hydropower. It is an auxiliary variable.

[0080] Step 4.3: Set constraints for the game theory model (1) Trading volume balance constraint (10); (11); Ensure that the contracted electricity volume for a single time period is the sum of the electricity volumes across all scenarios, and that the total contracted electricity volume for wind, solar, and hydropower remains unchanged before and after the transaction, with the power generation rights trading volume matching the contracted electricity volume adjustment; among which, , These refer to the electricity volumes of new energy and hydropower under medium- and long-term contracts before the contract adjustment. , These refer to the contracted electricity volumes for new energy and hydropower after the transaction is completed; Divide the battery level into segments. The number of segments for reporting and pricing medium- and long-term electricity consumption.

[0081] (2) New energy source forecast output constraints (12); in, This represents the upper limit of the predicted total day-ahead output of new energy sources on D-2. The minimum contracted power ratio for wind and solar power is set at the market level (0.8 in this example) to ensure that the adjustment of wind and solar power contracted power ratios conforms to market contracting rules and actual power output forecasts.

[0082] (3) DC operation constraints DC channel operation must meet constraints on power range, adjustment duration, adjustment frequency, unidirectional adjustment, and power amplitude, conforming to the actual situation of inter-regional DC dispatching. Figure 1 The scheduling and operation requirements for medium-voltage DC channels are consistent: (13); (14); (15); in, For the DC transmission power after the power generation rights trading; For DC channel medium and long term Transmission power during a given time period; For medium to long-term trading; , These are the upper and lower limits of DC transmission power operation, respectively. A Boolean variable characterizing the DC power regulation state; To adjust the state variables upwards; To adjust the state variable; This is the minimum constant operating time after DC transmission power adjustment; Limit on the number of times DC transmission power can be adjusted per day; , These are the limits for adjusting the DC transmission power upwards and downwards, respectively. This is the minimum adjustment amount for DC transmission power.

[0083] (4) CCER related constraints The carbon dioxide emission reduction coefficient for new energy sources and hydropower is calculated by weighting the marginal emission factor of electricity volume and capacity, and satisfies the following: (16); in, The coefficient for carbon dioxide emission reduction per unit of new energy source. The carbon dioxide emission reduction coefficient per unit of hydropower. , These are the marginal emission factors for wind, solar, and hydropower electricity, respectively. , These are the marginal emission factors for wind, solar, and hydropower capacity, respectively, which accurately reflect the actual carbon emission reduction capacity of clean energy.

[0084] Step 5: Iteratively solve for the equilibrium solution of the master-slave game. The wind-solar-hydropower GRT master-slave game model is solved iteratively using backward induction. The specific steps are as follows: Compensation price for fixed wind and solar power groups Substituting these parameters as known parameters into the objective function for maximizing the comprehensive benefits of lower-level hydropower, and considering all constraints, the optimal power generation rights trading volume for hydropower is solved. ; The optimal response strategy for hydropower The objective function fed back to the upper-level wind and solar power group is used to solve for the optimal compensation electricity price for the wind and solar power group. ; Repeat steps 1-2 until the compensation electricity price and the amount of generation rights traded no longer change, i.e. and ( , To ensure convergence accuracy, this embodiment takes... At this point, the game equilibrium solution is obtained.

[0085] Through iterative solutions, the optimal power generation compensation price for each time period is finally obtained. Optimal power generation rights trading volume Post-trade scenery - hydropower contract electricity volume , In addition, by combining the CCER supply and demand model results from step 2, the supply and demand situation of CCER transactions throughout the entire period and the overall strategy for the internal power generation rights trading of wind, solar and hydropower are clarified.

[0086] Step 6: Implementation Effect Verification and Analysis To verify the effectiveness of this strategy, a comparative analysis was conducted between a basic scenario (ordinary power generation rights trading without considering CCER value) and an optimized scenario (wind-solar-hydropower power generation rights trading considering CCER value). Figures 3 to 7 The time-series curves and comparative data are used to verify the implementation effect from five dimensions: CCER market transactions, wind, solar and hydropower output adjustment, compensation electricity price, comprehensive benefits, and carbon emission reduction effect. CCER market transactions: such as Figure 3As shown in the figure, there are four transaction volume curves for hydropower sales, wind power sales, photovoltaic sales, and thermal power purchases, as well as a CCER marginal price curve. Wind power CCER sales remain at a high level throughout the day. Photovoltaic CCER sales peak between 12:00 and 14:00 (peak sunshine hours), highly coupled with the daytime characteristics of photovoltaic output. Hydropower CCER sales fluctuate intermittently with output adjustments. The peak purchase of thermal power CCERs highly overlaps with the peak supply of clean energy, reflecting that thermal power plants concentrate their procurement during the effective CCER supply period to meet carbon collection constraints. The CCER marginal price fluctuates with the supply and demand relationship over time. Prices are relatively stable during peak photovoltaic supply periods, while prices rise in stages when thermal power procurement demand increases and clean energy supply is insufficient. This aligns with the market-based pricing mechanism and achieves precise matching between thermal power emission reduction demand and clean energy CCER supply.

[0087] Basic Scenery - Hydropower Output and Compensation Electricity Price Changes: For example Figure 4 As shown in the figure, there are three curves: total contracted output before adjustment, total contracted output after adjustment, and total expected output. In the basic scenario, the wind and solar power group reduces contracted output during periods of high electricity prices and avoids revenue loss by transferring power generation rights. The total contracted output after adjustment is closer to the total expected output, thus mitigating some output deviations. (The corresponding curve is shown in the figure.) Figure 5 As shown in the figure, there are two output curves: the hydropower contract output before adjustment and the hydropower contract output after adjustment, as well as a compensation price curve. During periods of high electricity prices when the wind and solar power groups reduce their contract output, the cascade hydropower takes over the transferred power generation rights. The hydropower contract output after adjustment is significantly higher than the original contract. The hydropower output is also adjusted adaptively during other periods. The compensation price fluctuates significantly with the change in the power generation rights trading volume. This indicates that the wind-solar-hydropower GRT strategy in the basic scenario can take into account both the smoothing of wind and solar fluctuations and the role of hydropower regulation capabilities, achieving synergistic optimization of the revenue of both parties in the transaction.

[0088] Optimize the landscape scene - hydropower output and compensation electricity price changes: such as Figure 6 As shown in the figure, there are three curves: total contracted output before adjustment, total contracted output after adjustment, and total expected output. After considering CCER value in the optimized scenario, the output adjustment range of Fengguang Group was further optimized, and the alignment between the adjusted total contracted output and the total expected output was higher, resulting in better deviation control. (Corresponding to...) Figure 7As shown in the figure, there are two output curves: the contracted hydropower output before adjustment and the actual hydropower output after adjustment, as well as a compensation price curve. The regulation response of cascade hydropower is more proactive than that of the basic scenario. During high-price periods, the amount of wind and solar power transferred is 32.1% higher than that of the basic scenario, and the amount of power reduced during low-price periods is 27.6% higher than that of the basic scenario. Moreover, the pricing mechanism of compensation price after taking into account CCER value is more reasonable. Under the same output adjustment, the compensation price is 8.3% lower on average than that of the basic scenario. However, due to the increase in transaction volume, the total regulation compensation revenue of hydropower still increases by 21.5%. The introduction of CCER value makes the output strategies of both parties in the transaction more in line with the dual goals of economic benefits and environmental benefits.

[0089] Overall Returns: In the optimized scenario, the wind and solar power group effectively avoided recovering excess profits. The contract price difference income and CCER profit were combined, resulting in an overall return increase of 15.3% compared to the basic scenario. Hydropower saw an increase in total regulation compensation income and CCER income due to the increase in power generation rights trading volume, resulting in an overall return increase of 21.5%. The wind-solar-hydropower alliance saw an overall return increase of 18.2%, achieving a win-win situation for both parties in the transaction.

[0090] Carbon emission reduction effect: In the optimized scenario, the coordinated design of CCER mechanism and power generation rights trading significantly improved the space for clean energy consumption, increasing it by 20.5% compared with the basic scenario. The power system's dependence on high-carbon thermal power was reduced, and the overall carbon emissions of the market were reduced by 16.7% compared with the basic scenario, effectively improving carbon emission reduction benefits and helping the new power system to achieve low-carbon transformation.

[0091] This embodiment demonstrates the full-process implementation of a wind-solar-hydropower generation rights trading strategy that takes into account the value of clean energy CCERs, combined with... Figures 1 to 7 The model framework, operational architecture, and simulation results validated that this strategy can effectively address the issues of medium- and long-term contract profit recovery and performance risks caused by the uncertainty of wind and solar power output, achieving the following results: like Figure 1 , Figure 2 As shown, the cross-regional operation framework and two-layer collaborative optimization model constructed by this invention realize the deep linkage between the carbon market and the electricity market, and between new energy sources at the sending end and flexible resources at the receiving end. It accurately depicts the supply and demand relationship of the CCER market, transforms the environmental value of clean energy into actual economic benefits, and broadens the revenue channels of wind, solar and hydropower. The innovative method for differentiating wind and solar power output deviations solves the technical pain points of traditional deviation calculations, making the benefit recovery accounting more in line with the actual rules of the electricity market and effectively controlling the performance risks caused by wind and solar power output deviations. like Figure 2The master-slave game model shown achieves a reasonable coordination of wind and solar power and hydropower benefits. The bidirectional parameter interaction and iterative solution between the master and slave sides result in an optimal trading strategy that maximizes the comprehensive benefits of both parties. Furthermore, the revenue calculation that takes into account the value of CCER makes the trading decision more in line with the goal of low-carbon development. The model incorporates Figure 1 The practical engineering constraints such as the operation of medium-voltage DC transmission lines and CCER certification give the strategy strong engineering reference value and feasibility for implementation, and it can be directly adapted to cross-regional wind-solar-hydropower joint market operation scenarios. from Figures 3 to 7 The comparison results show that the deep synergy between power generation rights trading and the CCER mechanism has significantly improved the cross-regional consumption capacity of clean energy and carbon emission reduction benefits, promoted the coordinated development of the electricity market and the carbon market, and provided an effective technical path for the low-carbon transformation of the new power system.

[0092] The parameter values ​​and scenario settings in this embodiment can be adjusted according to the actual engineering conditions, electricity market rules and carbon market verification requirements of different cross-regional wind-solar-hydropower projects, and have good versatility and scalability.

[0093] In the preferred scheme, the CCER market-certified emission reduction constraints mentioned in step 2 include CCER purchase and sale balance constraints, thermal power emission reduction purchasing power constraints, carbon settlement related calculation methods, clean energy CCER certification constraints, and non-negative constraints on winning bids. These settings ensure stable supply and demand in the CCER market through purchase and sale balance constraints, preventing drastic price fluctuations due to supply and demand imbalances; thermal power purchasing power constraints limit thermal power companies from excessively purchasing CCERs to evade emission reduction responsibilities, ensuring fairness in the carbon market; carbon settlement calculation methods unify accounting standards, reducing the risk of transaction disputes; clean energy certification constraints clarify the entry threshold for renewable energy projects, improving CCER quality; and non-negative constraints on winning bids prevent invalid transactions and optimize resource allocation efficiency. These constraints collectively construct a standardized carbon trading environment, promoting the accurate quantification and market-based circulation of carbon value in wind-solar-hydropower integrated operations.

[0094] In the preferred embodiment, the new energy excess benefit recovery calculation model described in step 3 is... Profits will be recovered in a timely manner. To allow for a certain deviation range, the excess revenue recovered is the sum of the positive deviation recovery revenue and the negative deviation recovery revenue. This setting, by dynamically defining the allowable deviation range, tolerates reasonable errors in renewable energy generation forecasts, avoiding penalties triggered by minor fluctuations. Simultaneously, the differentiated recovery of positive and negative deviation revenue incentivizes renewable energy companies to improve forecast accuracy. Positive deviation recovery revenue compensates for peak-shaving costs incurred by the grid due to excessive renewable energy generation, while negative deviation recovery revenue compensates for losses incurred by end-users due to insufficient power supply. This model balances the volatility of renewable energy generation with the economic viability of market participants, promoting the transformation of inter-regional power trading from "planned allocation" to "market-driven" and improving overall resource allocation efficiency.

[0095] In the preferred embodiment, the constraints of the wind-solar-hydropower GRT master-slave game model described in step 5 include trading volume balance constraints, renewable energy forecast output constraints, DC operation constraints, and CCER-related constraints. These constraints ensure that the trading volume of wind and solar power at the sending end matches the power generation rights trading volume of hydropower at the receiving end, preventing market failure due to supply-demand imbalances. The renewable energy forecast output constraint incorporates the uncertainty of wind and solar power into the trading framework, reducing performance risks through reserved regulation capacity. The DC operation constraint ensures the stable operation of inter-regional transmission channels, preventing safety restrictions triggered by power fluctuations. The CCER-related constraints embed carbon value into the trading pricing mechanism, forming a synergistic optimization of the "electricity-carbon" dual market. These constraints collectively construct a trading environment that balances economic efficiency, safety, and low carbon emissions, enhancing the market competitiveness of wind-solar-hydropower joint operations.

[0096] In the preferred embodiment, the DC operation constraints include power operating range constraints, minimum constant operating time constraints, maximum number of adjustments constraints, same-direction adjustment constraints, and power adjustment amplitude constraints, meeting the actual dispatching and operation requirements of the DC channel. These constraints include: power operating range constraints to prevent overload or light-load operation of the DC channel, extending equipment lifespan; minimum constant operating time constraints to avoid equipment damage caused by frequent start-stop cycles, reducing maintenance costs; maximum number of adjustments constraints to limit power fluctuation frequency, ensuring grid frequency stability; same-direction adjustment constraints to coordinate the direction of power changes at the sending and receiving ends, preventing power flow conflicts caused by reverse adjustments; and power adjustment amplitude constraints to control the intensity of single adjustments, preventing protection actions triggered by excessive adjustments. These constraints optimize the DC dispatching strategy from three aspects: equipment safety, grid stability, and economic operation, supporting large-scale cross-regional consumption of wind, solar, and hydropower.

[0097] In summary, the wind-solar-hydropower generation rights trading method proposed in this invention, which takes into account the value of Clean Energy Certified Emission Reductions (CCERs), effectively solves the problems of medium- and long-term contract benefit recovery and performance risks caused by the uncertainty of wind and solar power output. It fully explores the environmental and economic value of Clean Energy Certified Emission Reductions (CCERs), enhances the competitiveness of the new energy market, and promotes low-carbon emission reduction in the new power system. This method provides a trading strategy applicable to the market-oriented operation scenario of cascade hydropower joint operation between sending-end wind and solar new energy bases and receiving-end grid access, which lacks large-scale regulation resources. It can achieve revenue optimization and risk management for cross-regional wind-solar-hydropower collaboration.

[0098] This method, for the first time, introduces CCER (China Certified Emission Reduction) market-verified emission reduction constraints (covering purchase and sale balance, thermal power purchasing power limits, carbon collection calculation methods, etc.) into the joint market-oriented operation framework of wind, solar, and hydropower, constructing a "power-carbon" dual-market collaborative optimization mechanism, breaking through the limitation of traditional electricity market transactions focusing only on electricity prices. Simultaneously, it proposes a new energy excess benefit recovery model based on dynamic recovery of positive and negative deviations. By balancing the volatility of new energy power generation with the economic viability of market participants through differentiated recovery benefits, it solves the problem of insufficient market vitality caused by the "one-size-fits-all" penalties in traditional deviation assessment mechanisms. In the wind-solar-hydropower GRT master-slave game model, it integrates trading volume balance, new energy forecasted output, DC operation, and CCER-related constraints, forming a complete technical chain for cross-regional resource collaboration and market risk control, filling the technological gap in the field of high-proportion renewable energy cross-regional trading.

[0099] Furthermore, this method, through the deep coupling of CCER value quantification and power generation rights trading, transforms the carbon emission reduction mechanism into a tradable market signal, expanding the joint operation of wind, solar, and hydropower from a single power trading to a dual-value dimension of "power + carbon," significantly enhancing the market competitiveness of renewable energy. The designed DC operation constraint system (including power range, adjustment frequency, and unidirectional adjustment) transforms grid security and stability requirements into quantifiable trading constraints, achieving an organic unity between the safe operation and market-oriented operation of large-scale inter-regional transmission channels, providing technical support for high-proportion renewable energy consumption. The proposed mechanism of "dynamic adjustment of allowable deviation range + differentiated recovery of positive and negative deviation revenue" resolves the contradiction between new energy power generation prediction errors and market performance risks. While ensuring grid security, it guides new energy companies to proactively improve prediction accuracy through economic incentives, forming a technology-market synergistic optimization model.

Claims

1. A method for trading wind, solar, and hydropower generation rights that takes into account the value of clean energy CCERs, characterized in that, Includes the following steps: Step 1: Establish a cross-regional alliance of wind, solar and hydropower that takes into account the value of clean energy CCERs. The sending-end wind and solar groups are connected to the receiving-end power grid through cross-regional DC channels. With the help of hydropower regulation at the receiving end, both wind and solar and hydropower sign medium- and long-term price difference contracts and adjust the contracts on D-2. Step 2: Construct a clean energy CCER supply and demand relationship calculation model with the objective function of maximizing social welfare, and set CCER market-certified emission reduction constraints; Step 3: Propose a method for determining the benchmark value of wind-solar difference deviation, and establish a calculation model for the recovery of excess benefits from new energy sources that includes the recovery of positive and negative deviations; Step 4: Construct a master-slave game model of wind-solar-hydro power GRT that takes into account the value of clean energy CCER. The upper layer is the objective function of maximizing the comprehensive income of the wind and solar group, and the lower layer is the objective function of maximizing the comprehensive income of the receiving hydropower. Step 5: Set the constraints of the wind-solar-hydro power GRT master-slave game model, and use the master-slave game model to iteratively solve the equilibrium solution to obtain the supply and demand situation of CCER trading and the internal power generation rights trading strategy of wind, solar and hydro power.

2. The wind-solar-hydropower generation rights trading method taking into account the value of clean energy CCERs as described in claim 1, characterized in that, The objective function of the clean energy CCER supply and demand relationship calculation model in step 2 is: （1）； In the formula, , They are respectively Periodic thermal power units The bid price and demand for CCERs in the CCER market; , They are respectively Time period The declared price of Class A hydropower in the CCER market and the number of CCERs sold in winning bids; , They are respectively The bidding price and winning bid volume of CCERs sold by Shijian Fengguang Group in the CCER market; This is a medium- to long-term trading period; The target number of time periods; 、 These refer to the number of thermal power units and the number of cascade hydropower stations, respectively.

3. The wind-solar-hydropower generation rights trading method taking into account the value of clean energy CCERs as described in claim 1, characterized in that: The CCER market-certified emission reduction constraints mentioned in step 2 include CCER purchase and sale balance constraints, thermal power emission reduction purchasing power constraints, carbon collection-related calculation methods, clean energy CCER certification constraints, and non-negative constraints on winning bids. Among them, the CCER purchase and sale balance constraint is: （2）； In the formula, for Time period The number of CCERs won in the sales of Class A hydropower projects in the CCER market; for The number of CCERs sold and won by Shijian Fengguang Group in the CCER market; for Periodic thermal power units The demand for CCERs in the CCER market; Purchasing power constraints on emissions reductions from thermal power plants: （3）； In the formula, The allowable offset ratio for CCER; Carbon quotas for thermal power plants should be cleared; The carbon collection-related calculation methods include: （4）； In the formula, , These are the free baseline carbon allowance for thermal power and the estimated carbon emissions, respectively. This refers to the percentage of free quota. For thermal power units Carbon emission allocation coefficient; For thermal power units Carbon emission intensity; For thermal power units Medium and long-term contracts Electricity consumption during the period; For state variable parameters; The aforementioned clean energy CCER certification constraints and non-negative bid volume constraints include: （5）； In the formula, The carbon dioxide emission reduction coefficient per unit of new energy source; The carbon dioxide emission reduction coefficient per unit of hydropower; , For new energy and hydropower in the target date Each entity's medium- and long-term contracted electricity volume at any given time.

4. The wind-solar-hydropower generation rights trading method taking into account the value of clean energy CCERs as described in claim 1, characterized in that, The formula for calculating the deviation rate of the method for determining the benchmark value of the landscape-sunlight difference in step 3 is as follows: （6）； In the formula, , They are respectively Wind power and solar power output deviation rate during different time periods; , These are the respective medium- and long-term contracted volumes for wind power and solar power; , The respective day-ahead power outputs for wind power and solar power as predicted on D-2 day; This refers to the installed capacity of photovoltaic power.

5. The wind-solar-hydropower generation rights trading method taking into account the value of clean energy CCERs as described in claim 4, characterized in that, The new energy excess benefit recovery calculation model described in step 3 is in Profits will be recovered in a timely manner. To allow for a certain deviation range, the excess return is the sum of the positive deviation recovery return and the negative deviation recovery return, calculated using the following formula: （7）； In the formula, , These are the allowable deviation ranges for wind power and photovoltaic performance, respectively. The threshold for determining the deviation in electricity consumption; , These refer to the periods when the electricity price for medium- and long-term contracts is greater than and less than the electricity price forecast value two days prior to D-2. For deviation recovery revenue; For medium- and long-term contracted electricity volume for wind and solar power; Contribute to the forecasting of the day before the scenic spot; Positive deviation recovery revenue; The negative deviation recovers the profit; The electricity price is for medium- and long-term wind and solar power contracts; The forecast value for the clearing electricity price is for the day before D-2.

6. The wind-solar-hydropower generation rights trading method taking into account the value of clean energy CCERs as described in claim 1, characterized in that, The objective function for maximizing the overall revenue of the upper-level scenic group mentioned in step 4 is: （8）； In the formula, To compensate for electricity prices in the power generation rights trading; For the volume of electricity generation rights trading; , The number of day-ahead electricity price prediction scenarios and the scenario probability are generated by sampling and clustering on day D-2, respectively. The amount of electricity generated under the new energy contract after the transaction is completed; , , , , These are the comprehensive revenue of the wind and solar power group after the transaction is completed, the contract price difference revenue, CCER profit, power generation rights compensation cost, and deviation recovery revenue; This refers to the market transaction price of CCER (China Certified Equipment Certificate). This represents the CCER winning bid volume of the wind and solar power group before the power generation rights transaction; The carbon dioxide emission reduction coefficient per unit of new energy source; It is an auxiliary variable.

7. The wind-solar-hydropower generation rights trading method taking into account the value of clean energy CCERs as described in claim 1, characterized in that, The objective function for maximizing the comprehensive benefits of hydropower at the lower receiving end in step 4 is: （9）； In the formula, , , , These are the comprehensive hydropower revenue received after the power generation rights transaction, the contract price difference revenue, the CCER profit, and the power generation rights transaction revenue; The electricity volume of the hydropower contract after the transaction is completed; The CCER winning bid volume for receiving hydropower before the power generation rights transaction; The carbon dioxide emission reduction coefficient per unit of hydropower; It is an auxiliary variable.

8. The wind-solar-hydropower generation rights trading method taking into account the value of clean energy CCERs as described in claim 1, characterized in that, The constraints of the wind-solar-hydropower GRT master-slave game model described in step 5 include trading volume balance constraints, new energy predicted output constraints, DC operation constraints, and CCER-related constraints. The trading volume balance constraint ensures that the total electricity volume of medium- and long-term contracts remains unchanged before and after the transaction, satisfying the following: （10）； （11）； In the formula, , These refer to the electricity volumes of new energy and hydropower under medium- and long-term contracts before the contract adjustment. , These refer to the contracted electricity volumes for new energy and hydropower after the transaction is completed; Divide the battery level into segments; The number of segments for reporting and pricing medium- and long-term electricity consumption.

9. The wind-solar-hydropower generation rights trading method taking into account the value of clean energy CCERs as described in claim 8, characterized in that, The predicted output constraint for the new energy source is: （12）； In the formula, This represents the upper limit of the predicted total day-ahead output of new energy sources on day D-2. The minimum contracted ratio for wind and solar power generation as stipulated by the market; The DC operation constraints include power operating range constraints, minimum constant operating time constraints, maximum number of adjustments constraints, same-direction adjustment constraints, and power adjustment amplitude constraints, which meet the actual scheduling and operation requirements of the DC channel, and are described as follows: （13）； （14）； （15）； In the formula, For the DC transmission power after the power generation rights trading; For DC channel medium and long term Transmission power during a given time period; For medium to long-term trading; , These are the upper and lower limits of DC transmission power operation, respectively. A Boolean variable characterizing the DC power regulation state; To adjust the state variables upwards; To adjust the state variable; This is the minimum constant operating time after DC transmission power adjustment; Limit on the number of times DC transmission power can be adjusted per day; , These are the limits for adjusting the DC transmission power upwards and downwards, respectively. This is the minimum adjustment amount for DC transmission power.

10. The wind-solar-hydropower generation rights trading method taking into account the value of clean energy CCERs as described in claim 8, characterized in that, The CCER-related constraints are the calculation constraints for the carbon dioxide emission reduction coefficient per unit of new energy and hydropower, specifically: （16）； In the formula, The carbon dioxide emission reduction coefficient per unit of new energy source; The carbon dioxide emission reduction coefficient per unit of hydropower; , These are the marginal emission factors for wind, solar, and hydropower generation, respectively. , These are the marginal emission factors for wind, solar, and hydropower capacity, respectively.

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