Based on Distributed Optimal Power Flow Method

Based on the distributed optimal power flow method, it refers to decoupling the interconnection lines between regions to achieve zoning control. The decoupling methods include an indirect method and a direct method. The indirect method refers to adding a new virtual busbar on the interconnection line, and the power balance equation of the new virtual busbar serves as a decoupling constraint condition. Then, the coupling constraints are relaxed into the objective function by Lagrange relaxation method or augmented Lagrange method. The direct method means that the boundary conditions of the region are directly relaxed into the objective function using the Lagrange relaxation method or the augmented Lagrange method.

ATC Based Transaction Reduction Method

The ATC based reduction method refers to the system administrator reducing transactions on the transmission line according to certain rules when the transmission line is overloaded. The commonly used reduction methods include the proportional reduction method, the reduction method based on the contribution of transactions to blocked lines, and the first-in, first-in service method. The proportional reduction method refers to reducing the transmission volume on each line based on the ratio of theoretical transmission capacity to required transmission capacity.

Rescheduling Method

The rescheduling method refers to market participants trading according to their own plans without knowing the transmission capacity of the system, and then system operators making security corrections based on the submitted transactions. If these transactions can meet the network constraints, all transactions are accepted. If they cannot meet the network constraints, a rescheduling method is adopted to determine feasible transactions. For cross regional re scheduling, it is necessary to coordinate with system operators in adjacent regions. In the downstream area of congestion, system operators dispatch more expensive generator units, while in the upstream area of congestion, those relatively cheaper units are eliminated.

Market Splitting Method

In the absence of system congestion, the price of the entire system will be unified. When blockages occur, the system is divided into several regions according to a predetermined partitioning plan, forming partitioned transaction volumes and prices, with no power exchange between regions. Then the system operators buy electricity from the low-priced area and sell it in the high-priced area until the power exchange between regions meets the transmission constraints. The price difference of regional clearance without power exchange between regions is greater than the price difference formed by system operators through limited power exchange. The congestion income generated by the price difference between regions belongs to the system operation center.

Comparison of Dynamic Management Mechanisms

Based on the implementation principles of various dynamic management mechanisms, the advantages and disadvantages of these mechanisms are summarized in Table 1. Due to the advantages and disadvantages of various dynamic management mechanisms, it is possible to consider combining multiple dynamic management mechanisms for blocking management. This paper utilizes the advantages of the rescheduling method that can provide effective economic signals, and the ATC reduction method that can effectively prevent congestion, combining the two methods to optimize congestion costs.

Price Allocation Mechanism

The price allocation mechanism includes system marginal electricity price and node marginal electricity price. The system marginal electricity price refers to the minimum cost of purchasing electricity for each additional unit of electricity usage demand in the system, provided that the operation of the power grid system has been determined. Node marginal electricity price refers to the minimum purchase cost that the system increases to meet the unit active power demand of a certain node at the same stable operating level of the power grid, without affecting the safe and stable operation of the power grid.

Transmission Rights Trading Mechanism

The transmission rights trading mechanism refers to the division of transmission energy rights from one node to another in the power grid system, allowing the owners of transmission rights to obtain the right to use the corresponding transmission capacity and corresponding economic benefits. By separating transmission rights from the electricity trading market, the electricity market and capacity market can be independent of each other, thereby enabling both to work together in the congestion management of the power system. The transmission rights trading mechanism is divided into physical transmission rights and financial transmission rights. The physical transmission rights refer to the transmission rights obtained by power generators through auction based on their own capacity usage needs. When the system experiences congestion, power generation companies with physical transmission rights can use the transmission capacity, while other power generation companies that have not purchased physical transmission rights are prohibited from using the transmission capacity. The financial transmission right refers to the pricing of congestion based on the theory of real-time electricity price through market mechanism, that is, the node marginal price is used to participate in the bidding in the spot market.

Comparison of Market Steady State Mechanisms

Based on the implementation principles of various market steady-state mechanisms, the advantages and disadvantages of these mechanisms are summarized in Table 2. The transmission rights trading mechanism plays an important role in risk hedging for power generation companies, so this paper uses the transmission rights trading mechanism to optimize congestion risk.

Objective Function

On the basis of qualitative analysis of the existing market, this section quantifies congestion costs and, in order to guide the system in selecting appropriate congestion management methods, optimizes congestion costs with the objective function of minimizing congestion costs. The specific objective function is shown in Equation (1):

where C_t^ce is the blocking cost at time t. J is the set of units that increase power generation output in the receiving area. I is the set of units that reduce power generation output in the sending end area. S is load set for increasing load demand at the sending end. Ҫ is the load set for reducing load demand at the receiving end. ∆P_j,t^up is the increase in power output of the j-th unit in the receiving end region at time t. ū_j,ge^up is the unit output cost adjusted for the j-th unit in the receiving area. ∆P_i,t^down is the reduction in power output of the i-th unit in the supply area at time t. ū_i,ge^down is the unit output cost adjusted for the i-th unit in the supply area. ∆L^up_s,t represents the increase in the s-class load in the transmitting area at time t. ū_s,load^up is the compensation cost of Unit demand response of s-class load in the receiving end area. ∆L^down_h,t is the reduction of the h-class load in the receiving area at time t. ū_h,load^down is the compensation cost for unit demand response of the h-class load in the sending end area. ∆P_total,t^ab is the total amount of wind and photovoltaic abandoned at time t. ū_total,t^ab is the unit cost of wind and photovoltaic abandonment at time t. ∆L^loss_total,t is the total amount of load shedding at time t. ū_total,t^loss is the unit load shedding cost at time t. P_t^block is the remaining blocking amount at time t. ū_t^block is the average penalty cost per unit of blocking volume at time t.

Constraint Condition

When adjusting the output and load demand of the unit, it is not allowed to exceed the upper and lower limits of the unit and the adjustable load, as shown in Equation (2):

where P_j^min and P_j^max is the minimum and maximum output of unit j. P_j,t⁰ is the output of unit j without transmission capacity constraints. P_j^min and P_j^max is the minimum and maximum output of unit i. P_i,t⁰ is the output of unit i without transmission capacity constraints. ΔL_s^max is the maximum regulating capacity of the s-class load. ΔL_h^max is the maximum regulation amount of the h-class load.

Initial Clearing Price on the Power Generation Side

The clearance price is formed through unconstrained scheduling, which quotes the sending end generator units from low to high, and the units with low quotations are prioritized for clearance. The load demand of the receiving end is quoted from high to low, and users with high quoted load demand prioritize meeting it. Therefore, the low quoted unit and the high quoted load demand users reach a transaction, and the clearing price is the average value of the unit quotation and load quotation. This mechanism is in line with the principle of incentive compatibility. On the one hand, it encourages generator sets to quote low prices, which can reach transactions with high priced load demand users and improve profits. On the other hand, encouraging load demand users to quote high prices and be able to conclude transactions with low-priced power generation units. Through this clearing mechanism, the quotation of generator sets will reflect their own Cost of electricity by source as much as possible, while the load demand users will reflect their own power consumption cost as much as possible. Based on this, the generation side clearing price is formed as shown in Equation (3):

where ū_0,v^clear is the clearing price for the transaction reached in the v-th pair. ū_v^ge is the generation side quotation for the v-th pair that reached the transaction. ū_v^load is the load side quotation for the v-th pair to achieve the transaction.

Allocation Cycle and Allocation Objects

Clarify that the allocation period for congestion costs is 1 hour, with balanced settlement conducted every hour and current allocation. Unbalanced settlement is conducted directly during each settlement cycle, so there is no need to establish a balance account for fund custody. The advantage of this method is that there is no need to establish a balance account, while the disadvantage is that each settlement cycle requires unbalanced cost calculation, which is relatively cumbersome in processing.

Fig. 1.

Blocking surplus diversion strategy chart.

The allocation targets are clearly defined as market-oriented users and market-oriented units, where market-oriented users (receiving area) refer to all market-oriented users participating in market transactions, and market-oriented units (sending area) refer to all units participating in market transactions.

Allocation Indicator System

When there is a deviation between the clearance demand of the receiving area and the actual demand, as well as the clearance supply of the sending area and the actual supply, it will lead to an increase in congestion costs. Therefore, deviation is introduced as one of the allocation indicators. The larger the deviation of the allocation object, the more congestion costs should be borne. The formula for calculating the deviation is shown in Equation (4):

where ∆P_j,t^error , P_j,t^ac and P_j,t^clear represents the deviation, actual supply, and clearance of unit j in the receiving area. ∆P_i,t^error , P_i,t^ac and P_i,t^clear represents the deviation, actual supply, and clearance of unit i in the delivery area. ∆L^error_s,t , L^ac_s,t and L^clear_s,t represents the deviation amount, actual demand amount, and clearance amount of users in the sending end area, respectively. ∆L^error_h,t , L^ac_h,t and L^clear_h,t represents the deviation, actual demand, and clearance of user h in the receiving area.

The closer the distance between the sending unit and the receiving unit, the smaller the capacity of the transmission channel it occupies, and the smaller the congestion cost it needs to bear. Therefore, spatial distance is introduced as the second indicator for allocation, as shown in equation (5):

where D_i is the total spatial distance of the power transmission unit i at the sending end. D_io is the initial spatial position of the sending end unit i. D_ih is the spatial location where the sending unit i is sent to the receiving user h. σ_ih is a boolean variable. If the sending unit i transmits electricity to the receiving user h, then σ_ih = 1, otherwise σ_ih = 0. D_h is the total spatial distance of the receiving user h receiving electricity. D_ho is the initial spatial position of the receiving user h. D_hi is the spatial location of receiver group i for the receiving user h. σ_hi is a Boolean variable. If the receiving user h receives the electricity from the sending unit i, i, σ_hi = 1, otherwise σ_hi = 0. D_j is the total spatial distance of the power transmitted by the receiving unit j. D_jo is the initial spatial position of the receiving unit j. D_js is the spatial location where the receiving unit j is sent to the receiving user s. σ_js is a boolean variable. If the receiving unit j transmits electricity to the sending user s, then σ_js = 1, otherwise σ_js = 0. D_s is the total spatial distance received by the sending end user s for receiving electricity. D_so is the total spatial distance received by the sending end user s for receiving electricity. D_sj is the spatial position of the sender user s receiver group j. σ_sj is a boolean variable. If the sending user s receives the electricity from the receiving unit j, then σ_sj = 1, otherwise σ_sj = 0.

When the unit provides more electricity and the user’s load demand is higher, the impact on line congestion is greater, and the congestion cost it bears should also be greater. Therefore, contribution degree is introduced as the second indicator for allocation, as shown in Equation (6):

where η_j,t, η_i,t, η_s,t and η_h,t represents the contribution of receiving unit j, sending unit i, sending user s, and receiving user h at time t.

Congestion Cost Allocation

Due to the fact that deviation, spatial distance, and contribution are all cost based indicators, equation (10) is used to standardize the three congestion allocation indicators:

where θ̃_o is the standardized treatment value of indicator o. θ_max and θ_min represent the maximum and minimum values of the indicator.

Based on equation (10), the standardized values of three types of indicators are obtained, and the comprehensive indicator values of the unit and user are shown in Equation (8):

where β_j,t, β_i,t, β_s,t and β_h,t represents the comprehensive indicator values of receiving unit j, sending unit i, sending user s, and receiving user h at time t. ·~ represents the standardized indicator value. ω₁, ω₂ and ω₃ represents the weights of the indicators of deviation, spatial distance, and contribution, as shown in Equation (9):

Based on the comprehensive indicator values of various units and users, the congestion cost allocation result is shown in Equation (10):

where C^ce_j,t , C_i,t^ce , C_s,t^ce and C_h,t^ce represents the congestion allocation costs of the receiving unit j, the sending unit i, the sending user s, and the receiving user h at time t.

Final Clearing Price on the Power Generation Side

Based on the clearing results of congestion costs, the clearing results of various units and users are obtained as shown in Equation (11):

where ū_zz,t,j^clear, ū_zz,t,i^clear, ū_zz,t,s^clear and ū_zz,t,h^clear represents the final clearance results of receiving unit j, sending unit i, sending user s, and receiving user h at time t.

Primary Market Optimization Strategy

1) Optimization strategy of market participants in primary market

In the primary market, market participants declare their own needs and prices to maximize benefits, as shown in Equation (12):

where R_1v represents the returns of market participant v in the primary transmission rights market. T_v is the holding period of transmission rights for market participant v. Q_1v,t^tr is the declared volume of market participant v in the Primary market at time t. p̄_ex,v,t^LMP is the expected benefit of unit transmission rights for market participant v at time t. p̄_de,v,t^tr is the declared unit transmission rights price of market participant v at time t.

The declaration capacity of market participants in the primary market cannot exceed their maximum declaration capacity, as shown in Equation (13):

where Q_1v,max^tr is the maximum declared capacity of market participants.

2) Primary market system operator optimization strategy

Rank the transmission rights prices declared by market participants from high to low, and obtain the order of declared prices as [p̄_de,1,t^tr', p̄_de,2,t^tr',⋯, p̄_de,v,t^tr',⋯, p̄_de,V,t^tr']. Where, p̄_de,v,t^tr' is the unit transmission right price ranked in the v position at time t, and the declared price of the market participant who purchased the last unit transmission right is taken as the clearing price of the primary market. The system operator takes the maximization of revenue as the objective function to form the clearing volume of the primary market, as shown in Equation (14):

where R_1,so^total represents the revenue of the system operator in the primary transmission rights market. Q_1v,t^clear and p̄_clear,t^1,tr represents the clearing volume and clearing price of market participant v at time t in the primary transmission rights market.

The overall clearing capacity of all participants cannot exceed the maximum existing transmission capacity, as shown in Equation (15):

where Q_total^so is the maximum transmission capacity.

Secondary Market Optimization Strategy

1) Optimization strategy of market participants in secondary market

In the Secondary market, if the participants are buyers, the optimization strategy is consistent with Equation (12) in the Primary market. If the participants are sellers of transmission rights, the optimization strategy is to maximize the revenue from selling rights in the secondary market and the revenue from purchasing rights in the primary market, as shown in Equation (16):

where R_2v is the income of participant v from selling transmission rights in the Secondary market. Q_2v,t^sale and p̄_sale,t^2,tr is the sales volume and selling price of the transmission right of participant v at time t in the Secondary market.

The sales volume of market participants in the Secondary market shall not exceed their bid winning volume in the Primary market, as shown in Equation (17):

2) Secondary market operator optimization strategy

In the secondary market, the system operator takes the maximum system benefit as the objective function to match the buyers and sellers of transmission rights to reach a transaction, as shown in Equation (18):

where R_2,so^total is the social benefits of system operators in the secondary market. p̄_sale,s,t^2,tr is the declared price of seller s at time t in the secondary market. p̄_buy,b,t^2,tr is the declared price of buyer b at time t in the Secondary market. p_clear,t^2,tr is the clearing price of the secondary market. Q_2v,t^clear is the clearing volume of the Secondary market. S is a collection of sellers and sellers. B is a collection of buyers.

The clearing price of the Secondary market is the average of the declared price of the seller and the declared price of the buyer, as shown in Equation (19):

The total clearing amount in the secondary market cannot exceed the total clearing amount in the primary market, as shown in Equation (20):

Optimization Results of Congestion Cost

Based on the required transmission capacity of the sender and receiver, as well as the existing transmission capacity of the system, the blocking capacity for each time period is obtained as shown in Fig. 6.

From Fig. 5, it can be seen that the blocking capacity of the system occurs during periods of 7:00-12:00, 14:00-16:00, and 21:00. Due to high load demand and high wind power output during these periods, the transmission demand exceeds the existing transmission capacity limit of 6000kW. Based on the blocking capacity and the blocking cost optimization strategy proposed in Section 3.1 of this article, the blocking management optimization strategy is obtained as shown in Fig. 7:

From Fig. 6, it can be seen that the blocking problem during time periods such as 7:00, 11:00, 12:00, 14:00, 16:00, and 22:00 is resolved by calling thermal power generation in other regions, as the blocking capacity during these time periods does not exceed the upper limit of thermal power generation regulation. The blocking problem at 15:00 is jointly met by calling the demand response and other thermal power units in other regions, because the blocking capacity during this time period has exceeded the upper limit of the thermal power unit regulation, and the demand response needs to be called to solve the remaining blocking capacity. The blocking problem during time periods such as 8:00 to 10:00 is solved by calling for demand response, thermal power generation units, and reducing the demand of the receiving end. This is mainly because during this period of severe blocking, both thermal power generation units and demand response have exceeded the upper limit of regulation, and ATC needs to apply the reduction method to compensate for the remaining blocking capacity. From this, it can be found that when optimizing congestion management, the priority order of various strategies is to call other regions’ thermal power generation>call demand response>reduce the demand of the receiving end. This is mainly because the cost of calling other regions’ thermal power generation is lower than other methods. From an economic perspective, other regions’ thermal power generation will be prioritized to solve the congestion problem.

Fig. 4.

User load demand in the receiving area.

Fig. 5.

Supply of wind turbines in the power supply area.

Fig. 6.

Blocking capacity at each time period.

Fig. 7.

Optimization strategy for blockage management.

Blocking Surplus Diversion Results

According to the congestion cost allocation index system constructed in section 3.2, calculate the values of wind turbine 1, wind turbine 2, user 1, and user 2 indicators. Set the weights of contribution, deviation, and spatial distance to one-third, and calculate the congestion cost allocation ratio for market participants. As shown in Fig. 8.

From Fig. 7, it can be seen that in terms of deviation performance, the minimum deviation for User 1 is 6%, and the maximum deviation for Wind Turbine 2 is 14.63%. In terms of spatial distance performance, the maximum spatial distance for user 1 is 0.7917, while the minimum spatial distance for wind turbine 2 is 0.6921. In terms of contribution, User 1 has a maximum contribution of 0.2722, while User 2 has a minimum contribution of 0.2278. The size of the allocation ratio is 0.2518 for User 2>0.2509 for Wind Turbine 1>0.2502 for Wind Turbine 2>0.2471 for User 1, indicating that User 1 has the smallest allocation ratio for congestion costs. This may be because User 1’s deviation is much smaller than other market participants, and under the same weight, User 1’s superior deviation performance allows User 1 to bear lower congestion costs. User 2 bears the highest proportion of congestion costs, mainly because it performs mediocrely in various indicators.

Set the initial clearing result on the power generation side to 0.6084, and based on the allocation ratio, obtain the congestion costs that each market participant needs to bear at each time. This is used to correct the initial clearing result and obtain the final clearing price of the unit and user, as shown in Fig. 9.

As shown in Fig. 8, there are differences in clearing prices among market participants during the time periods of 7:00, 11:00, 12:00, 14:00, 16:00, and 22:00 when congestion capacity occurs. On the one hand, compared to other time periods, the clearance prices of wind turbine 1 and wind turbine 2 during the period of congestion capacity have decreased, indicating that during the period of congestion capacity, the efficiency of the generator unit has decreased. Compared to other time periods, the clearing prices of users 1 and 2 during the period of congestion capacity increase, indicating that during the period of congestion capacity, the electricity cost of users increases. By reducing the efficiency of generator sets and increasing the cost of electricity consumption for users, it is possible to determine whether congestion occurs during the time period. On the other hand, during the period of severe congestion from 8:00 to 10:00, the adjustment of clearing prices by market participants is higher than that of other time periods.

Fig. 8.

Index values and allocation ratios of each market participant.

Fig. 9.

Final clearing price for each market participant.

Optimization Results of Blocking Risk

According to the congestion surplus dredging results of Wind 1, Wind 2, User 1 and User 2, in order to hedge the congestion risk, market participants are allowed to apply for bidding transmission rights in the Primary market of transmission rights. The declared volume and declared price of each participant in the primary market are shown in Table 3.

From Table 3, it can be seen that User 2 has the highest declared price, mainly because User 2 shares more congestion costs in the congestion surplus. Compared to User 1 and other units, User 2 is more motivated to participate in the primary and secondary markets. Therefore, the system operator will give priority to User 2 winning the bid of 3723 kW when matching and clearing. The overall transmission rights of the system operator are 6000 kW, and based on the upper limit of transmission rights trading, the remaining 2277 kW will be included in the bid for wind turbines with higher declared prices. Therefore, the clearing result in the Primary market is shown in Table 4, and it is calculated that the total revenue of the system operator in the Primary market is 42.89 yuan.

In the secondary market, bidders in the primary market may sell the transmission rights they have won, while users 1 and wind turbines 1 who have not won the bid in the primary market may buy the transmission rights they have sold. The declared volume and declared price of each participant in the Secondary market are shown in Table 5 in combination with their own benefit function. A negative declared quantity represents the quantity of transmission rights sold, while a positive declared quantity represents the quantity of transmission rights purchased.

As can be seen from Table 5, User 1 and Wind 1 predict that the probability of future congestion is relatively high, so they will increase the declared price in the Secondary market to purchase the transmission right. User 2 and Wind 2 predict that the probability of future congestion is small, and declare in the secondary market at a price higher than the clearing price in the Primary market to obtain the market price difference. Arrange user 1 and unit 1 of the buyer in descending order of quotation, and user 2 and unit 2 of the seller in descending order of quotation. The seller with lower price shall have priority in entering into transactions with the buyer with higher price, and the average declared price between the buyer and seller shall be used as the clearing price. The clearance results are shown in Table 6.

From Table 6, it can be seen that User 1 purchased 1719 kW of transmission rights from Wind 2, and the clearance price for both was 0.0079 yuan/kW. Wind 1 purchases transmission rights of 723 kW and 2162 kW from Wind 2 and User 2, respectively, with a clearance price of 0.0078 yuan/kWh for wind turbine 2 nd 0.0081 yuan/kWh for User 2. In the secondary market, the overall situation is that supply exceeds demand, so the transmission purchase demand of user 1 and Wind 1 is met, but the supply capacity of Wind 1 with high declared price is still surplus.

Validity Analysis

1) Effectiveness analysis of congestion cost optimization strategy

In response to the optimization of congestion costs by considering the rescheduling method and ATC reduction method proposed in this article, in order to verify the effectiveness of this method, three scenarios are set as follows:

Scenario 1: Using only the rescheduling method for congestion cost optimization;

Scenario 2: Using only ATC reduction method for congestion cost optimization;

Scenario 3: Balancing the rescheduling method and ATC reduction method for congestion cost optimization, which is the method proposed in this article.

Based on the above three scenarios, using congestion cost as an evaluation indicator, the congestion costs under the three scenarios are shown in Fig. 10.

From Fig. 10, it can be seen that the blocking costs under the three scenarios are 1968.15 yuan, 2161.02 yuan, and 1818.62 yuan, respectively. Scenario 3 has the lowest blocking cost. Compared to Scenario 3, Scenario 1 and Scenario 2 have increased their congestion costs by 8.22% and 18.83%, respectively. This is mainly because scenario 1 using only the rescheduling method may result in high unit adjustment costs and demand response call costs. However, using only the ATC reduction method will result in high costs of wind and photovoltaic abandonment and load shedding. Scenario 3, which combines ATC reduction method and rescheduling method, can fully allocate scheduling resources in the system and achieve optimal configuration efficiency, indicating that Scenario 3 is more effective in reducing congestion costs compared to other scenarios.

2) Analysis of the effectiveness of blocking surplus diversion strategies

This paper establishes a congestion cost pool for direct allocation and constructs a multidimensional allocation indicator system to channel congestion surplus. To verify the effectiveness of this method, three scenarios are set up as follows:

Scenario 1: Using the traditional unblocking method to unblock congestion surplus, that is, first allocating congestion costs, and returning them to the user when the allocation results deviate from the actual costs;

Scenario 2: Establish a congestion cost pool for direct allocation, using contribution as a single allocation indicator;

Scenario 3: Establish a congestion cost pool for direct allocation, and construct multidimensional allocation indicators for contribution, spatial distance, and deviation, which is the method proposed in this article.

Fig. 11.

Final clearance electricity prices for three scenarios.

Fig. 10.

Blocking costs in three scenarios.

Based on the above three scenarios, the final clearance price of wind turbine unit 2 is taken as the research object, and the market price guidance of the above three scenarios is compared, as shown in Fig. 11.

From Fig. 11, it can be seen that in scenario 1, regardless of whether it is a blocking period or a non blocking period, the final clearance electricity price is 0.6084 yuan/kWh, indicating that the difference between blocking and non blocking periods cannot be reflected through allocation and return. The market price during the blocking period in scenario 2 is lower than the market price during the non blocking period. Although it can serve as a blocking signal guide, compared to Scenario 3, the price during the blocking period is still higher than scenario 3. This is mainly because Scenario 2 only considers contribution and does not take into account the significant output deviation in Scenario 2, resulting in a smaller actual congestion cost that should be borne by Wind Turbine Unit 2 in Scenario 2. Based on this, it indicates the effectiveness of establishing a congestion cost pool and multi-dimensional allocation indicators.

3) Effectiveness analysis of congestion risk optimization strategies

This paper constructs a transmission rights trading hedging congestion risk based on a two-level market. To verify the effectiveness of this method, three scenarios are set up as follows:

Scenario 1: In congestion management, the construction of a transmission rights market is not considered to hedge congestion risks;

Scenario 2: Consider hedging congestion risk in the transmission rights market, but only constructing a primary bidding market;

Scenario 3: Consider hedging congestion risk in the transmission rights market and construct a two-level market, with the first level being a bidding market and the second level being a two-way market.

Based on the above three scenarios, the effectiveness of the above three scenarios is compared using system benefits and social benefits as evaluation indicators, as shown in Table 7.

From Table 7, it can be seen that scenario 1 without constructing a transmission rights market has a total benefit of 0, while scenario 2 with considering a primary bidding market has a total benefit of 17.5024 yuan, which is much lower than scenario 3 with constructing a secondary market. This is mainly because there is no secondary two-way trading market, and market participants cannot transfer or purchase after bidding, which will lead to more conservative bidding strategies of market participants in the primary market, reduce the declared price of market participants in the primary market, and thus reduce the system efficiency. This indicates that constructing a two-level market for transmission rights trading can enhance the enthusiasm of participants in the primary bidding market and the overall efficiency of system operators.