Wind, light and water cross-region joint bidding and direct current power transmission optimization method

Abstract

The wind, light and water cross-region joint bidding and DC power transmission optimization method belongs to the field of new energy power market operation and cross-region DC power transmission optimization, and aims to solve the problems of sending end wind and light base cross-region participating in receiving end market bidding and DC power transmission plan optimization. The invention constructs a sending end wind and light and receiving end cascade hydropower joint bidding body, and participates in medium and long term and day-ahead market through cross-region DC channel; the medium and long term establishes an upper layer bidding model with the maximum difference contract income as the target, and a lower layer social welfare maximization dispatching model containing DC operation characteristic constraint; the day-ahead establishes an upper layer CFD power decomposition and day-ahead bidding model, and a lower layer day-ahead dispatching model containing cascade hydropower output; the master-slave game iteration is adopted to obtain the optimal bidding strategy, market dispatching result and DC power transmission scheme. The invention couples CFD and DOCC constraints, relies on water power to suppress wind and light fluctuation, stabilizes new energy income, improves market economy and cross-region cooperation ability, and improves new energy consumption level.

Description

Technical Field

[0001] This invention relates to the field of power market operation and energy optimization allocation technology, and in particular to a method for cross-regional joint bidding of wind, solar and hydropower and optimization of DC power transmission. Background Technology

[0002] With the transformation of the global energy structure and the advancement of sustainable development goals, the construction of new power systems is accelerating. The large-scale development of new energy sources and the diversified allocation of flexible resources are key forces driving the energy revolution. New energy sources, especially wind and solar power, are playing an increasingly important role in the global energy system due to their significant advantages of being clean and renewable. However, the output characteristics of these new energy sources pose significant challenges to the stable operation of the power system. Because wind and solar power are significantly affected by natural conditions, their output is highly volatile and uncertain, making it difficult to synchronize with real-time changes in electricity load. This leads to frequent supply-demand imbalances, affecting the security and economic efficiency of the power system.

[0003] Meanwhile, as a crucial platform for energy resource allocation, the electricity market's price formation mechanism is influenced by a complex interplay of factors, including supply and demand, fuel price fluctuations, policy adjustments, and the behavior of market participants. For renewable energy power generation entities, this market price uncertainty constitutes significant operational risk, directly impacting the stability of their revenue and return on investment. Particularly in inter-regional power transmission scenarios, renewable energy power generation entities face even more complex market environments and physical operational constraints, further exacerbating their operational difficulties.

[0004] In my country, the rapid development of ultra-high voltage direct current (UHVDC) transmission lines has provided strong technical support for the inter-regional consumption of renewable energy. DC transmission technology, with its significant advantages such as large transmission capacity, long transmission distance, and low line loss, has become an important bridge connecting renewable energy-rich areas with load centers. However, the operating characteristics of DC transmission lines are exceptionally complex, involving multiple DC operation characteristic constraints (DOCC), including power operating range, minimum constant operating time, maximum number of adjustments, and power adjustment amplitude. These constraints not only affect the safe and stable operation of DC transmission lines but also directly impact the formulation and implementation of inter-regional power transmission plans.

[0005] Currently, in the practice of electricity market operation, renewable energy power generation entities face the severe challenge of electricity market price fluctuations across time scales. To effectively hedge against this market price volatility risk, contracts for difference (CFDs) in the medium- and long-term market have become an important risk management tool. Through the CFD mechanism, renewable energy power generation entities can lock in their core revenues and reduce the uncertainty caused by market price fluctuations. For example, the article "A Day-ahead Market Bidding Model for Cascade Hydropower Stations Considering Electricity Price Risk and Contracts for Difference," published in Volume 46, Issue 5 of *Automation of Electric Power Systems*, constructs a bidding model that combines the CFD settlement mechanism with short-term optimized scheduling of cascade hydropower to participate in the day-ahead market. The article "A Method for Decomposing Medium- and Long-Term Contract Electricity Volumes of Cascade Hydropower Stations Considering Different Trading Methods," published in Volume 45, Issue 7 of *Proceedings of the Chinese Society for Electrical Engineering*, constructs a multi-objective optimization model that considers both CFD revenues and performance deviations of cascade hydropower.

[0006] However, while the CFD mechanism has played a significant role in stabilizing renewable energy revenue, existing research has significant shortcomings in designing operational strategies and constructing relevant models for the inter-regional market-based consumption of renewable energy. In particular, there is a lack of effective mechanisms for synergistic optimization between renewable energy and flexible resources (such as hydropower), making it difficult to achieve effective coupling between renewable energy output and electricity market price signals, thus restricting the flexibility and efficiency of renewable energy participation in market transactions.

[0007] Specifically, existing research largely focuses on optimizing DC operation modes, improving the efficiency and stability of renewable energy transmission by adjusting DC transmission parameters and optimizing operation and control logic. For example, the article "Research on Optimizing Inter-regional UHVDC Transmission to Enhance Renewable Energy Absorption Capacity" in Volume 52, Issue 4 of *China Electric Power* proposes a UHVDC power optimization method based on regional complementarity characteristics; and the article "Wind-Solar-Thermal UHVDC Transmission Scheduling Method Considering Peak Shaving Trends of Receiving-End Grids" in Volume 42, Issue 8 of *Acta Energiae Solaris Sinica* proposes a multi-segment piecewise linear UHVDC transmission scheduling method by optimizing the DC power curve. However, these studies have not yet addressed the design of operational strategies and the construction of related models for the market-based absorption of renewable energy across regions, making it difficult to effectively solve key issues such as coordinated scheduling and benefit distribution in market-based scenarios for the cross-regional absorption of renewable energy.

[0008] To address the aforementioned issues, this invention proposes an innovative method for cross-regional joint bidding and DC power transmission optimization involving wind, solar, and hydropower. This method aims to systematically solve numerous challenges faced by wind and solar power groups participating in cross-regional bidding for medium- and long-term and day-ahead electricity markets, while fully leveraging the unique role of the medium- and long-term CFD market mechanism in stabilizing renewable energy revenue. By comprehensively considering DOCC constraints, this method can significantly improve the power supply stability for cross-regional renewable energy consumption and ensure the safe and efficient operation of DC transmission channels. Furthermore, this method provides a scientific and efficient bidding strategy and market operation plan for sending-end wind and solar renewable energy bases lacking large-scale regulation resources, helping to improve the overall economic efficiency of market operation, strengthen the collaborative capabilities among cross-regional entities, and promote the continuous improvement of the level of cross-regional renewable energy consumption. For the construction of new power systems, this invention undoubtedly has significant reference value and promising prospects for widespread application. Summary of the Invention

[0009] The technical problem to be solved by this invention is to provide a method for cross-regional joint bidding for wind, solar and hydropower and optimization of DC power transmission, which solves the technical problems of cross-regional participation of wind and solar new energy bases in the bidding decision-making of medium- and long-term and day-ahead power markets of receiving power grids, and the difficulty in coordinating and optimizing cross-regional DC power transmission plans.

[0010] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: This invention provides a method for cross-regional joint bidding for wind, solar and hydropower and optimization of DC power transmission. By constructing a two-layer master-slave game framework that integrates Contracts for Difference (CFD) and DC Operating Characteristic Constraints (DOCC), a full-process market-based bidding and clearing system covering medium- and long-term as well as day-ahead is formed, providing a complete technical solution for the cross-regional market-based consumption of new energy.

[0011] This invention first constructs a cross-regional wind-solar-hydro consortium that coordinates sending-end wind and solar power with receiving-end cascade hydropower. Wind and solar renewable energy bases lacking large-scale regulation capacity serve as the sending end, connecting to the receiving-end power grid via a cross-regional DC channel. This consortium forms a unified cooperative alliance with the receiving-end cascade hydropower, jointly participating in the medium- and long-term electricity market and the day-ahead electricity market bidding. Based on wind and solar power output and supported by cascade hydropower for flexible regulation, this consortium mitigates power surges caused by random fluctuations in wind and solar power, making cross-regional power transmission more stable and controllable. Simultaneously, by participating in market competition as a whole, it enhances bargaining power and market responsiveness, achieving complementary advantages and synergistic optimization between cross-regional renewable energy and regulatory resources.

[0012] At the medium- to long-term market level, this invention establishes a two-layer master-slave game model. The upper layer is a medium- to long-term bidding decision model for wind-solar-hydro consortia, with the objective function of maximizing expected CFD returns. The power generation side uses a 24-scale segmented increasing curve for bidding and quantity allocation. Constraints cover the medium- to long-term predicted output boundaries of wind and solar power, the minimum market contract signing ratio, the upper limit of wind / solar installed capacity, and the diurnal output characteristics of photovoltaics, ensuring that the bidding strategy conforms to the physical laws of new energy power generation. The lower layer is a medium- to long-term electricity market clearing model considering DOCC constraints, with the optimization objective of maximizing social welfare. Constraints include power balance constraints, bilateral bidding and quantity allocation constraints for power purchase and sale, constraints on the winning bid volume of conventional power sources, and complete DC operation characteristic constraints, specifically including constraints on inter-regional transaction volume, power operation range constraints, minimum constant operating time constraints, maximum number of adjustments constraints, same-direction adjustment constraints, and power adjustment magnitude constraints. This ensures that the market clearing results strictly match the actual operating boundaries of DC transmission projects, improving the executability of medium- to long-term power transmission plans.

[0013] At the day-ahead market level, this invention constructs a two-layer decision-making model that connects with medium- and long-term markets. The upper layer is a CFD power allocation and day-ahead bidding decision-making model, aiming to maximize the sum of CFD contract revenue and day-ahead market revenue. It adopts a 96-scale single-segment bidding and quantity quotation method, rationally allocating medium- and long-term contract power to various day-ahead time periods. Constraints include new energy power generation constraints, contract power allocation constraints, DC channel physical capacity constraints, and wind and solar power ratio constraints, ensuring contract fulfillment while enhancing the flexible arbitrage space in the day-ahead market. The lower layer is a day-ahead power market clearing model considering DOCC constraints and the characteristics of cascade hydropower, aiming to maximize social welfare. It introduces a cascade hydropower output model and water balance equations to characterize the coupling relationship between upstream and downstream hydropower station reservoir capacity, flow, water level, and power generation, while satisfying constraints such as power balance, cascade hydropower water balance, conventional unit ramp-up, and upper and lower output limits. This achieves precise matching of receiving-end regulation resources and inter-regional wind and solar power transmission, supporting refined scheduling operation across 96 time periods.

[0014] This invention employs a master-slave game iterative mechanism to collaboratively solve the entire model. The wind-solar-hydro consortium is designated as the leader, responsible for formulating the optimal bid and power allocation strategy. The medium- and long-term and day-ahead power markets are designated as followers, completing market clearing according to the upper-level strategy and providing feedback on the clearing price and winning bid volume. Through multiple rounds of iterative updates until a stable equilibrium is reached, the final output includes the medium- and long-term and day-ahead market clearing prices, the winning bid volumes for each entity, the consortium's optimal bidding strategy, and an optimized scheme for inter-regional DC power transmission. This method locks in the medium- and long-term returns of new energy through a CFD mechanism, ensures the safe and stable operation of DC transmission through DOCC constraints, and mitigates wind and solar fluctuations using cascade hydropower. It significantly improves the overall economic efficiency of market operation, strengthens the collaborative capabilities of cross-regional power generation entities, and greatly enhances the level of cross-regional absorption of new energy. It provides a systematic and scalable engineering implementation path for large-scale wind and solar bases without regulation resources to participate in inter-regional power market transactions.

[0015] The present invention provides a method for cross-regional joint bidding for wind, solar and hydropower and optimization of DC power transmission, which has the following beneficial effects: 1. This invention constructs a cross-regional wind-solar-hydro joint system that coordinates the operation of the sending-end hydropower-wind-solar-hydro power generation system and the receiving-end cascade hydropower system. It fully leverages the complementary advantages of wind and solar power generation and hydropower regulation capabilities, effectively mitigates fluctuations in new energy output, improves the stability and reliability of cross-regional DC power transmission plans, and ensures continuous and stable cross-regional power transmission.

[0016] 2. This invention proposes a "medium-to-long-term-day" cross-timescale market-based operation mechanism for wind, solar and hydropower inter-regional consortia. While ensuring the stability of medium-to-long-term contract revenue, it enhances the flexibility of the day-ahead market and achieves coordinated optimization and efficient connection of the electricity market across multiple time scales.

[0017] 3. This invention enables cross-regional wind, solar and hydropower consortia to participate in the medium- and long-term electricity market and day-ahead market bidding transactions in a unified manner, thereby strengthening the collaborative capabilities among cross-regional power generation entities, improving the efficiency of cross-regional power resource allocation and the overall economic efficiency of market operation, and reducing market transaction costs.

[0018] 4. This invention establishes a medium- to long-term market bidding strategy model with the goal of maximizing the expected return of Contracts for Difference (CFDs), enabling new energy entities to formulate optimal bidding strategies based on future market price expectations, thereby improving the level and certainty of medium- to long-term market trading returns.

[0019] 5. This invention constructs a medium- and long-term power market clearing model that considers DC operating characteristic constraints (DOCC). During the market clearing process, it simultaneously meets the actual operating constraints of inter-regional DC channels, making the resulting power transmission plan more in line with the actual grid dispatch rules and improving the feasibility of the scheme.

[0020] 6. This invention establishes a CFD power decomposition and day-ahead bidding decision model to achieve a reasonable allocation between medium- and long-term contract power and day-ahead market trading power, effectively reducing the risk of new energy performance deviation and improving the overall profitability of the consortium, while ensuring the rigid execution of the contract.

[0021] 7. This invention constructs an energy market clearing model that considers the output characteristics of cascade hydropower in the day-ahead market stage. It describes the hydraulic connection between hydropower stations through water balance constraints, enabling the receiving-end regulation resources to coordinate and optimize operation with cross-regional new energy power transmission, thereby improving the regulation matching accuracy.

[0022] 8. This invention introduces multi-dimensional operational constraints into the market clearing model, such as the DC channel power operating range, minimum constant operating time, maximum number of adjustments, same-direction adjustment, and power adjustment amplitude, making the inter-regional power transmission scheme more in line with the actual operating requirements of DC transmission projects and ensuring the safety and stability of the DC system.

[0023] 9. This invention combines the formulation of consortium bidding strategy with the process of clearing the electricity market through master-slave game modeling. It can simultaneously obtain the market clearing result and the consortium's optimal bidding strategy, improve the model's solution efficiency and decision-making scientificity, and support rapid decision-making.

[0024] 10. This invention utilizes cascade hydropower to absorb fluctuating power output from new energy sources, thereby achieving coordinated allocation of wind and solar power generation and hydropower regulation resources. This enhances the cross-regional absorption capacity of new energy sources while simultaneously improving the operational flexibility and anti-disturbance capabilities of the power system.

[0025] 11. This invention enables the sending-end renewable energy base and the receiving-end regulating power source to form a stable cooperative relationship through the cross-regional consortium model, providing a feasible operational path for large-scale wind and solar bases without regulating resources to participate in the electricity market and broadening the channels for renewable energy to enter the market.

[0026] 12. This invention enables large-scale long-distance transmission of new energy power through inter-regional DC transmission channels, which significantly improves the inter-regional consumption level of new energy while meeting the load demand at the receiving end, and promotes the large-scale optimized allocation of clean energy.

[0027] 13. This invention considers both the revenue from electricity trading and the goal of maximizing social welfare in the market model, so that the optimization of the revenue of the power generation entity and the improvement of the overall economic efficiency of the system can be achieved in a coordinated manner, taking into account both market efficiency and public interest.

[0028] 14. This invention optimizes the cross-regional power transmission strategy for new energy sources, alleviates the difficulty of matching the fluctuations in new energy output at the sending end with the load demand at the receiving end, reduces the risk of curtailment of new energy, and increases the utilization hours of new energy power generation.

[0029] 15. By introducing the Contracts for Difference (CFD) mechanism, this invention effectively hedges against the risk of price fluctuations in the electricity market across time scales, stabilizes the expected returns of new energy power generation entities, and enhances the investment attractiveness and operational stability of new energy projects.

[0030] 16. This invention enables the refined dynamic decomposition of medium- and long-term contract power volume into day-ahead periods, reducing the deviation between contract power volume and actual output, lowering the cost of deviation assessment, and improving the operational economic efficiency of market entities.

[0031] 17. This invention improves the utilization rate and transmission efficiency of inter-regional DC channels, maximizes the transmission capacity of channels while meeting operational constraints, reduces transmission energy loss, and improves the overall economic benefits of inter-regional power transmission.

[0032] 18. This invention enhances the power system's ability to accommodate high proportions of volatile new energy sources, reduces the pressure on grid dispatching and operation control, improves the level of safe and stable system operation, and supports the construction of new power systems.

[0033] 19. This invention promotes the efficient and coordinated utilization of wind, solar and hydropower, reducing dependence on conventional fossil energy units and lowering the carbon emission intensity of the power system.

[0034] 20. This invention forms a standardized and scalable cross-regional market-based bidding technology system, providing technical demonstration and practical reference for the construction of a unified national electricity market, and promoting the standardized development of market-based trading of new energy. Attached Figure Description

[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of the cross-regional, cross-timescale market framework of the water, wind, and light cross-regional consortium of the present invention; Figure 2 This is a long-term DC power transmission plan diagram for various scenarios in this invention; Figure 3 This is the power allocation diagram for the long-term electricity market clearing in this invention; Figure 4 This is the result of the long-term contract decomposition and bidding decision in this invention; Figure 5 This is the power distribution diagram for the 96-period market clearing period of this invention; Figure 6 This invention relates to a DC power transmission plan for the current market clearing period of 96 hours. Detailed Implementation

[0036] 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 cross-regional joint bidding for wind, solar, and hydropower and optimization of DC power transmission, as detailed below: like Figure 1 As shown, a cross-regional consortium of wind, solar and hydropower is established, which is a joint bidding process between the sending-end hydropower, wind and solar power consortium and the receiving-end cascade hydropower. The consortium is a sending-end wind and solar new energy base 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 the hydropower connected to the receiving-end power grid for regulation. The consortium, as a cooperative alliance, conducts medium- and long-term and day-ahead electricity market bidding.

[0037] The medium- and long-term market bidding strategy of the cross-regional wind, solar and hydropower consortium takes maximizing the expected return of CFD (Contract for Difference) as the objective function. Power generators use a 24-scale segmented incremental curve to bid and submit their quantities. The constraints include medium- and long-term forecasted output of wind and solar power and market contract signing ratio constraints, wind and solar installed capacity constraints, and photovoltaic output characteristics constraints.

[0038] A medium- to long-term electricity market clearing model is established that considers DOCC (Direct Current Operation Characteristic Constraints) at the lower level. This model includes a social welfare maximization objective function, and constraints such as electricity balance constraints, electricity purchase and sale quotation constraints, conventional power source winning bid constraints, and DOCC constraints. The DOCC constraints include medium- to long-term inter-regional transaction electricity constraints, power operating range constraints, minimum constant operating time constraints, maximum number of adjustment constraints, same-direction adjustment constraints, and power adjustment magnitude constraints.

[0039] The current daytime market bidding strategy of the cross-regional wind, solar and hydropower consortium aims to maximize the sum of CFD contract revenue and daytime revenue. A CFD power decomposition and daytime bidding decision model is constructed, and power generation companies adopt a 96-scale single-segment bidding and quantity quotation. The constraints of the CFD power decomposition and daytime bidding decision model include new energy power generation constraints, decomposed power constraints, DC channel physical constraints, and power ratio constraints.

[0040] A day-ahead electricity market clearing model considering DOCC at the lower level is established, with the objective function of maximizing social welfare; it includes a cascade hydropower output model, and the day-ahead electricity market clearing constraints include power balance constraints, cascade hydropower water balance constraints, and conventional unit operation constraints. The solution is obtained by using a master-slave game iterative mechanism to obtain the medium- and long-term and day-ahead market clearing results, the optimal bidding strategy of the consortium, and the optimized scheme for cross-regional DC power transmission.

[0041] Furthermore, the calculation formula using the maximization of expected CFD returns as the objective function, and the 24-scale segmented increasing curve pricing and quantity reporting method are as follows: (1); In the formula, This refers to the expected returns in the medium to long term, i.e., the returns from medium to long-term CFDs. This is for medium- to long-term trading. The target number of time periods; Divide the battery level into segments. The number of segments for reporting electricity consumption and pricing; for time Medium- to long-term clearing electricity prices for the segment. Forecast electricity prices for medium- to long-term trading days; This refers to the electricity volume won by the wind and solar power group in the medium and long term market. This refers to the long-term market winning bids for electricity generated by the hydro-wind-solar consortium, including wind power winning bids. Photovoltaic winning bid electricity Hydropower winning bid volume .

[0042] The aforementioned medium- and long-term forecasted output and market contract ratio constraints for wind and solar power, wind and solar installed capacity constraints, and photovoltaic output characteristic constraints include, further, the following: (2); In the formula For the medium and long-term market power volume won by the wind and solar power group This represents the upper limit of total power generation from wind and solar power in the medium to long term. The minimum contracted ratio for wind and solar power generation as stipulated by the market; , These represent the capacities of wind power and photovoltaic units, respectively. , These represent the photovoltaic output and the downtime periods, respectively.

[0043] The social welfare maximization objective function of the lower-level DOCC-considered medium- to long-term electricity market clearing model is, further, where: (3); In the formula, , These are, respectively, medium- and long-term market electricity sales revenue and market electricity purchase cost; , For users In the price range The declared electricity price and electricity consumption; The number of users in the receiving-end power grid; , , These are the electricity purchase costs for thermal power, hydropower, and wind and solar power in the medium and long term markets, respectively. , , These are the medium- and long-term electricity prices declared by each power generator for wind, solar, thermal, and hydropower. , , These are the medium- and long-term electricity volumes declared by the power generation companies for wind, solar, thermal, and hydropower respectively. , These refer to the number of thermal power units and the number of cascade hydropower stations, respectively.

[0044] The aforementioned power balance constraints, power purchase and sale quotation constraints, conventional power source winning bid constraints, DOCC constraints, and further include: The power balance constraint is: (8); In the formula This refers to the transmission loss of the DC tie line.

[0045] The constraints on the quantity of electricity purchased and sold, and the constraints on the winning bid quantity for conventional power sources, include: (9); In the formula, , These are the minimum and maximum bids for wind and solar power generation systems, respectively. , These are the minimum and maximum bids for thermal power generation entities, respectively. , They are respectively , Duan Xin Energy's application for electricity price; , These are the upper and lower limits for the electricity volume that thermal power generation entities can declare; , These are the upper and lower limits for the electricity volume that hydropower generating entities can declare; , These are the upper and lower limits for the electricity volume that wind and solar power generators can declare; , These are the upper and lower limits of the total electricity volume that thermal power generating entities can declare; , These are the upper and lower limits of the total electricity volume that hydropower generating entities can declare; , These are the upper and lower limits for the total electricity volume declared by wind and solar power generators, respectively. , users respectively Upper and lower limits of declared electricity volume , users respectively Upper and lower limits of the total declared electricity volume The number of power consumption segments to be declared for users; for Maximum power generation of a hydropower station; , thermal power units The minimum and maximum electricity consumption that can be declared.

[0046] Furthermore, DOCC constraints include: medium- and long-term inter-regional trading volume constraints, power operating range constraints, minimum constant operating time constraints, maximum number of adjustments constraints, same-direction adjustment constraints, and power adjustment magnitude constraints. (10); (11); (12); 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 This serves as a medium- to long-term trading timeframe. , 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.

[0047] The CFD power decomposition and day-ahead bidding decision model includes a calculation formula with the objective function of maximizing the sum of CFD contract revenue and day-ahead revenue. The power generator uses a 96-scale single-segment bidding method. Further, it includes: (4); In the formula, Generate sampling clusters A scenic scene within a scene The corresponding probability of occurrence; For the target date The contract price for medium- and long-term electricity during the specified period; For the target date Market forecasts for the current period indicate a clearing of electricity prices; for Time period corresponding to the scene The electricity generated under the wind and solar power contracts; Water, wind and light complex Time period corresponding to the scene Electricity volume to be tendered next day; The target time period is 96 hours on the day of the event.

[0048] The constraints of the CFD power decomposition and day-ahead bidding decision model include new energy power generation constraints, decomposed power constraints, DC channel physical constraints, and power ratio constraints. Further, they include: The constraints on new energy power generation include: (13); In the formula , They are respectively Time period corresponding to the scene The daily bidding volume of photovoltaic and wind power is expected to be [number] units. , The targets are wind power and photovoltaic power respectively. Hourly D-2 day forecast output. , These represent the maximum and minimum power generation of the wind turbine, respectively. , These represent the maximum and minimum power generation of the photovoltaic unit, respectively.

[0049] The decomposed energy constraints include: (5); In the formula, Breaking down medium- and long-term contracts for new energy to Total electricity consumption during the period; For the corresponding Total electricity volume of medium- and long-term wind and solar power contracts; breakdown of medium- and long-term renewable energy contracts to The total electricity consumption during a given time period is conserved, meaning it equals the total contracted electricity consumption for the corresponding time period t. At the same time, to prevent hydro-wind-solar consortia from maximizing profits by concentrating contracted electricity volume into a single favorable time period, and to allow for some dispatch flexibility on the generation side, the market requires... The electricity volume allocated to each time period is controlled within a certain percentage of the total electricity volume of the corresponding hourly contract. Load factors for each time period; It is the dispersion coefficient; ; This is the maximum concentration coefficient.

[0050] The physical constraints of the DC channel, including the power ratio constraints, include: (14); In the formula, , The maximum and minimum transmittable capacity limits for DC channels. This indicates the amount of electricity newly bid in the market compared to medium- and long-term contracts; the amount of medium- and long-term contract electricity allocated to the 96 time period. Considering DC transmission loss The maximum and minimum transmittable capacity of the channel must be met. , ; This indicates that the amount of electricity bid for the market today is higher than that for medium- and long-term contracts, and the proportion of electricity allocated from medium- and long-term contracts to the total amount of electricity bid today meets the requirements for the proportion of medium- and long-term contracts.

[0051] The aforementioned lower-level day-ahead electricity market clearing model, which considers DOCC and clears with the objective function of maximizing social welfare, further includes: (15); In the formula, , users respectively The date of the declaration of the target Electricity price during specific time periods and electricity consumption; , , These represent the costs incurred in acquiring thermal power, hydropower, and wind and solar power from the market at the present time. , , The bids submitted for thermal power, hydropower, and wind and solar power respectively are for the following categories: Time-of-use electricity pricing; , , The bids submitted for thermal power, hydropower, and wind and solar power respectively are for the following categories: Battery consumption during a given time period.

[0052] The aforementioned cascade hydropower output model, with its day-ahead electricity market clearing constraints, further includes: The power output model of the cascade hydropower is as follows: (6); In the formula, This refers to gravitational acceleration under standard conditions. For the efficiency of cascade hydropower generation; , , They are respectively The target date of the hydropower station Power generation flow during the period, water level upstream of the dam, and tailwater level; , , They are respectively The target date of the hydropower station Reservoir capacity, outflow, and discharge volume for different time periods; water level-reservoir capacity function relationship. Tailwater level - discharge flow rate function relationship Fitting is performed using a polynomial.

[0053] The aforementioned day-ahead electricity market clearing constraints include power balance constraints, cascade hydropower water volume balance constraints, and conventional unit operation constraints, further including: The power balance constraint includes: (16); The water balance constraints for the cascade hydropower projects include: (7); In the formula, for The target date of the hydropower station Storage capacity for a given period of time; for The target date of the hydropower station Storage capacity for a given period of time; for The target date of the hydropower station Inbound flow during a specific time period; , for The target date of the hydropower station Power generation flow and water discharge flow during specific time periods; for The target date of the hydropower station The outflow rate during a given period; The duration of the trading session on that day.

[0054] The conventional unit operating constraints include: (17); (18); In the formula, , These represent the upper and lower limits of the output of thermal power generating units. , These represent the upper and lower limits of the output of the hydroelectric generator set. , thermal power units The upper and lower limits of the uphill climb, , They are respectively The upper and lower limits of the ramp gradient for a hydropower station; , They are respectively Upper and lower limits of reservoir water level at a hydropower station. , They are respectively Upper and lower limits of outflow from the reservoir of a hydropower station. , They are respectively Upper and lower limits of reservoir capacity for hydropower stations.

[0055] The DOCC constraints include the aforementioned DC operation-related constraints, specifically the constraints on medium- and long-term inter-regional trading volume, power operation range, minimum constant operating time, maximum number of adjustments, same-direction adjustment, and power adjustment amplitude.

[0056] Example 2 In another preferred embodiment, based on embodiment 1, this embodiment provides a method for cross-regional joint bidding for wind, solar and hydropower and optimization of DC power transmission, verifies the actual effectiveness of the proposed model, and sets up two scenarios, the medium- and long-term market and the day-ahead market, to conduct a comparative analysis before and after.

[0057] DC transmission power optimization is a key technical condition, by Figure 2 It is evident that the DC power curves differ significantly under different wind and solar power output scenarios, indicating that transmission plans can be dynamically adjusted based on the characteristics of new energy output to meet transmission channel constraints. In aggregation scenarios, the power curve is smoother and more robust, capable of supporting medium- and long-term planning decisions under DC constraints. The medium- and long-term clearing results are as follows: Figure 3 As shown, the system can prioritize the consumption of clean energy, and new energy sources account for a higher proportion due to their strong competitiveness. Cascade hydropower undertakes the base load and mid-load.

[0058] With day-ahead trading broken down into 96 time periods, day-ahead clearing results Figure 5 Bidding strategy as of now Figure 4 This indicates that the bidding strategy of the hydro-wind-solar consortium is consistent with the price difference signal, achieving elastic decomposition of contracted electricity volume: priority is given to fulfilling contracts when the price difference is positive, and day-ahead bidding is increased when the price difference is negative. The newly added bidding volume is significantly negatively correlated with the price difference, verifying the effectiveness of the price difference-driven mechanism; the 96-period day-ahead dispatch achieved precise matching of power output and short-term load, increasing the proportion of renewable energy and improving the daytime consumption rate by 5%, indicating that high-time-resolution clearing helps promote renewable energy consumption. Day-ahead DC transmission power exhibits significant price-driven characteristics: DC transmission power ( Figure 6 During peak electricity price periods, power output increased by 37.5% compared to before the adjustment, and the adjustment response time was synchronized with the day-ahead dispatch cycle. This collaborative mechanism efficiently expanded the absorption range of wind and solar power to other regions, increasing the absorption rate by 15% compared to the scenario without inter-regional transmission.

[0059] In summary, this invention addresses the issues of cross-regional participation of hydro-wind-solar consortia in the bidding for medium- and long-term and day-ahead electricity markets at the sending-end and the optimization of cross-regional DC power transmission plans. It proposes a bidding strategy for cross-regional consortia in the medium- and long-term and day-ahead electricity markets that considers DOCC and CFD mechanisms. Simulation analysis yields the following conclusions: 1. The established peak-valley characteristics of market clearing results across time scales accurately reflect market supply and demand relationships and resource scarcity, validating the effectiveness of medium- and long-term bidding strategies that prioritize the consumption of clean energy. Through scenario-based analysis and dynamic adjustment mechanisms, the medium- and long-term and day-ahead DC power transmission plans are optimized and adjusted, reducing system operating costs while meeting channel physical constraints and ensuring power supply reliability under multiple uncertainties across time scales.

[0060] 2. The proposed dynamic power allocation day-ahead bidding strategy, 96-period refined day-ahead clearing, and cross-regional DC collaborative optimization mechanism can significantly improve the renewable energy absorption capacity and market operation efficiency. Among them, the price difference-driven contract allocation increases the market revenue of the hydro-wind-solar consortium by 22%, the 96-period day-ahead clearing model increases the renewable energy absorption rate by 5%, and the cross-regional DC model further increases the renewable energy absorption rate by 15%, achieving efficient connection between medium- and long-term contracts and day-ahead settlement.

[0061] 3. The proposed competitive clearing strategy for inter-regional consortia, incorporating DOCC constraints and CFD mechanisms, in the medium- and long-term and day-ahead electricity markets is an effective way to improve the overall economic efficiency of market operation, strengthen the collaborative capabilities of inter-regional entities, and enhance the inter-regional absorption of renewable energy. Future plans include further optimizing market operation strategies through power generation rights trading during the transition between medium- and long-term and day-ahead periods; and considering the uncertainties in renewable energy output and predicted electricity prices when constructing bidding models for various time scales to improve the robustness of power generation entity decisions.

[0062] In the preferred embodiment, the DOCC constraints in step 2 include: medium- and long-term inter-regional transaction volume, power operating range, minimum constant operating time, maximum number of adjustments, same-direction adjustment, and power adjustment amplitude constraints. These settings, by clearly defining the medium- and long-term inter-regional transaction volume constraints, allow for precise planning of the scale of inter-regional renewable energy transmission, avoiding resource waste and grid overload. Power operating range constraints ensure the safe and stable operation of the DC transmission channel and prevent equipment damage. Minimum constant operating time and maximum number of adjustments constraints balance the flexibility of the power transmission plan with system stability. Same-direction adjustment and power adjustment amplitude constraints reduce power fluctuations caused by frequent and large adjustments, improving the feasibility and reliability of the DC power transmission plan and effectively reducing operating costs and risks.

[0063] In the preferred scheme, the constraints of the CFD power allocation and day-ahead upper-level decision-making model in step 3 include new energy power generation constraints, allocated power constraints, and DC channel physical constraints. The new energy power generation constraints are based on actual power generation capacity, ensuring reasonable power allocation and avoiding performance difficulties due to overestimation. The allocated power constraints rationally distribute medium- and long-term contract power to the day-ahead market, balancing trading volume across different time periods and reducing deviation risk. The DC channel physical constraints consider factors such as channel transmission capacity and losses, ensuring that power allocation conforms to actual physical conditions. These settings work synergistically to improve the accuracy and scientific nature of power allocation, ensure the rationality of the consortium's bidding decisions in the day-ahead market, and enhance overall revenue stability.

[0064] In the preferred scheme, the master-slave game described in step 4 uses a consortium as the leader to formulate a bidding strategy, and a market clearing model as the follower to respond with bids. After iterating to equilibrium, the optimal DC power transmission plan and the winning bid volumes for each entity are output. This setup, with the consortium as the leader formulating the bidding strategy, fully leverages its resource integration advantages and formulates more competitive bids based on overall interests. The market clearing model, acting as the follower to respond with bids, simulates real market reactions, making the game process more realistic. Through iterative optimization to equilibrium, the bidding strategy and power transmission plan are continuously improved, and the final optimal solution achieves a deep coupling between market mechanisms and physical constraints. This improves the economic efficiency of market operation, ensures the level of new energy consumption, and provides scientific and efficient decision support for cross-regional joint participation of wind, solar, and hydropower in the power market.

[0065] In summary, this invention proposes an optimized method for cross-regional joint bidding for wind, solar, and hydropower transmission, effectively solving specific technical problems in the field of new energy power generation, particularly in the process of wind and solar new energy bases participating in cross-regional bidding for receiving-end electricity markets and optimizing cross-regional DC power transmission plans. Addressing the challenges of large fluctuations in new energy output, high uncertainty in electricity market prices, and complex DC operating characteristic constraints (DOCC) in existing technologies, this invention demonstrates significant advantages in several aspects.

[0066] In terms of collaborative operation mode, the wind-solar-hydro cross-regional consortium is adopted for the first time, which combines the sending-end wind and solar new energy bases that do not have large-scale regulation resources with the receiving-end cascade hydropower stations. The power is connected to the receiving-end power grid through cross-regional DC channels and regulated by hydropower, achieving cross-regional consumption of new energy and coordinated allocation of regulation resources, breaking through the limitations of traditional single energy participation in the market.

[0067] In terms of model construction, a two-layer market clearing model considering DC operating characteristic constraints (DOCC) was constructed. The upper layer is a bidding model for inter-regional wind-solar-hydro consortia, and the lower layer is a power market clearing model considering DOCC constraints. A master-slave game iterative mechanism is used to achieve coordinated optimization of inter-regional power transmission plans and market bidding. At the same time, a medium- and long-term power allocation and day-ahead bidding decision model based on contracts for difference (CFD) was proposed to coordinate the allocation of medium- and long-term contract power and day-ahead market trading power, reduce the risk of new energy contract performance deviation, and improve the overall profitability of the consortium.

[0068] In terms of constraint integration and optimization framework design, DC operating characteristic constraints (DOCC) are fully integrated into the market clearing model, covering constraints on inter-regional power trading volume, power operating range, and minimum constant operating time, ensuring that inter-regional power transmission plans comply with the actual operating limitations of DC channels and enhancing the feasibility and reliability of the plans. A two-layer optimization framework based on master-slave game theory is also designed, jointly solving the bidding strategies and power transmission plan optimizations of the medium- and long-term market and the day-ahead market. Through iterative interaction between the upper-layer bidding model and the lower-layer clearing model, deep coupling between market mechanisms and physical constraints is achieved, significantly improving the economic efficiency of market operation and the level of new energy consumption.

Claims

1. A method for cross-regional joint bidding for wind, solar, and hydropower and optimization of DC power transmission, characterized in that, Includes the following steps: Step 1: Construct an inter-regional consortium that combines wind and solar power at the sending end with cascade hydropower at the receiving end, connects to the receiving end power grid via a DC channel, and participates in market bidding in a unified manner; Step 2: Establish a medium- to long-term upper-level bidding model with 24-scale segmented pricing that aims to maximize CFD profits, and a medium- to long-term clearing model that takes into account DOCC constraints and aims to maximize social welfare. Step 3: Establish a day-ahead upper-level decision-making model with CFD and day-ahead revenue maximization as the objective and a 96-scale single-segment pricing model, and a day-ahead clearing model that incorporates cascade hydropower and DOCC constraints; Step 4: Use master-slave game iteration to solve the problem and output the market clearing result, the optimal bidding strategy and the cross-regional DC power transmission optimization scheme.

2. The method for cross-regional joint bidding and DC power transmission optimization based on claim 1, characterized in that, The objective function of the medium-to-long-term upper-level bidding model described in step 2 is: （1）； In the formula, For medium- to long-term CFD returns; This is a medium- to long-term trading period; The target number of time periods; Divide the battery level into segments; The number of segments for reporting electricity consumption and pricing; for time Medium- to long-term clearing electricity prices for the segment; Forecast electricity prices for medium- to long-term trading days; This refers to the electricity volume won by the wind and solar power group in the medium and long term market. The electricity volume won in the medium- and long-term market for the hydropower-wind-solar consortium; The amount of wind power won in the bid; The amount of electricity won in photovoltaic projects; This refers to the amount of hydropower that was won in the bid.

3. The method for cross-regional joint bidding and DC power transmission optimization based on claim 1, characterized in that, The constraints of the medium-to-long-term upper-level bidding model described in step 2 are: （2）； In the formula, This refers to the electricity volume won by the wind and solar power group in the medium and long term market. This represents the upper limit of the total power generation from wind and solar power in the medium to long term. The minimum contracted ratio for wind and solar power generation as stipulated by the market; , These represent the capacities of wind power and photovoltaic units, respectively. , These represent the photovoltaic output and the downtime periods, respectively.

4. The method for cross-regional joint bidding and DC power transmission optimization based on claim 1, characterized in that, The social welfare objective function of the medium- to long-term clearing model described in step 2 is: （3）； In the formula, , These are, respectively, medium- and long-term market electricity sales revenue and market electricity purchase cost; , For users In the price range The declared electricity price and electricity consumption; This refers to the number of users in the receiving-end power grid. , , These are the electricity purchase costs for thermal power, hydropower, and wind and solar power in the medium and long term markets, respectively. , , These are the medium- and long-term electricity prices declared by each power generator for wind, solar, thermal, and hydropower. , , These are the medium- and long-term electricity volumes declared by the power generation companies for wind, solar, thermal, and hydropower respectively. , These refer to the number of thermal power units and the number of cascade hydropower stations, respectively.

5. The method for cross-regional joint bidding and DC power transmission optimization based on claim 1, characterized in that, The DOCC constraints mentioned in step 2 include: medium- and long-term inter-regional trading volume, power operating range, minimum constant operating time, maximum number of adjustments, same-direction adjustment, and power adjustment magnitude constraints.

6. The method for cross-regional joint bidding and DC power transmission optimization based on claim 1, characterized in that, The objective function of the current-day upper-level decision-making model described in step 3 is: （4）； In the formula, Generate sampling clusters A scenic scene within a scene The corresponding probability of occurrence; For the target date The contracted electricity price for medium- and long-term electricity periods; For the target date Market forecasts for the current period will clear electricity prices; for Time period corresponding to the scene The electricity generated under the wind and solar power contracts; Water, wind and light complex Time period corresponding to the scene Electricity volume to be tendered next day; The target time period is 96 hours on the day of the event.

7. The method for cross-regional joint bidding and DC power transmission optimization based on claim 1, characterized in that, The constraints of the CFD power decomposition and day-ahead upper-level decision model mentioned in step 3 include new energy power generation constraints, decomposed power constraints, and DC channel physical constraints, wherein the decomposed power constraints are: （5）； In the formula, Breaking down medium- and long-term contracts for new energy to Total electricity consumption during the period; For the corresponding Total electricity volume under medium- and long-term contracts for wind and solar power during specific periods; Load factors for each time period; It is the dispersion coefficient; This is the maximum concentration coefficient.

8. The method for cross-regional joint bidding and DC power transmission optimization based on claim 1, characterized in that, The cascade hydropower output model described in step 3 is as follows: （6）； In the formula, This refers to gravitational acceleration under standard conditions. For the efficiency of cascade hydropower generation; , , They are respectively The target date of the hydropower station Power generation flow rate during the period, water level upstream of the dam, and tailwater level; , , They are respectively The target date of the hydropower station Reservoir capacity, outflow, and discharge volume for different time periods; water level-reservoir capacity function relationship. Tailwater level - discharge flow rate function relationship Fitting is performed using a polynomial.

9. The method for cross-regional joint bidding and DC power transmission optimization based on claim 1, characterized in that, The water balance constraint for the cascade hydropower project mentioned in step 3 is: （7）； In the formula, for The target date of the hydropower station Storage capacity for a given period of time; for The target date of the hydropower station Storage capacity for a given period of time; for The target date of the hydropower station Inbound flow during a specific time period; , for The target date of the hydropower station Power generation flow and water discharge flow during specific time periods; for The target date of the hydropower station The outflow rate during a given period; The duration of the trading session on that day.

10. The method for cross-regional joint bidding and DC power transmission optimization based on claim 1, characterized in that: The master-slave game described in step 4 uses the consortium as the leader to formulate bidding strategies and the market clearing model as the follower to respond to bids. After iterating to equilibrium, the optimal DC power transmission plan and the winning bid amount of each entity are output.

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