Market value evaluation method for power transmission section planning of power grid company

Abstract

The invention relates to a power grid company power transmission section planning market value evaluation method, which comprises the following steps that: firstly, a power grid company obtains a power transmission section capacity expansion amount and a capacity expansion value distribution scheme, and obtains a power transmission line capacity expansion income distribution coefficient; then, establishing a model including a game process of an in-province power purchase agent and an out-province power purchase agent in combination with a power transmission section capacity expansion amount and a power transmission line capacity expansion income distribution coefficient, determining an inter-province market marginal price, and further obtaining an inter-province power transmission section capacity expansion market value; a market clearing model of an in-province centralized matching mode is established in combination with the capacity expansion amount of the power transmission section, a model for describing node marginal electricity prices of different areas in the province is established, and then the market value of the capacity expansion of the power transmission section in the province is obtained; the section planning scheme of a power grid company is optimized through the market value of inter-province and intra-province power transmission section capacity expansion.

Description

Technical Field

[0001] The invention relates to a market value evaluation method for power transmission section planning of a power grid company, and belongs to the technical field of power systems. Background Art

[0002] Transmission section planning is usually based on load forecasting and power source planning, and short-term, medium-term, and long-term grid development planning is carried out to meet the demand for load growth and ensure the reliability of grid operation by building or expanding transmission lines. However, with the accelerated development of new power systems dominated by new energy, the traditional transmission section planning model faces severe challenges. The current section planning concept focuses on predicting load demand first, and then selecting the planning scheme that meets the load demand and has the lowest cost. However, this concept ignores the complex processes in the current electricity spot market and the game behavior between market players, and it is difficult to fully respond to the diversified demands and dynamic changes in the electricity market environment.

[0003] With the advancement of my country's power market reform, the contradiction between traditional grid planning concepts and emerging power market mechanisms has become increasingly prominent. The introduction of the power spot market requires that transmission section planning not only meet the load growth, but also take into account complex constraints such as stabilizing market price fluctuations and meeting real-time matching of supply and demand. At present, there is no research on evaluating the market value of transmission sections in the context of power spot trading, and then evaluating section construction plans through value signals. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention provides an evaluation method for determining the market value of a transmission section in the context of power spot trading.

[0005] In order to achieve the above-mentioned purpose, the technical solution proposed by the present invention is: a market value evaluation method for power transmission section planning of a power grid company, specifically comprising:

[0006] The power grid company obtains the transmission section expansion capacity and expansion value allocation plan, and thereby obtains the transmission line expansion benefit allocation coefficient;

[0007] Combined with the transmission section expansion capacity and the transmission line expansion revenue distribution coefficient, a model including the game process of the provincial power purchasing agent and the out-of-province power purchasing agent is established to determine the inter-provincial market marginal price, and then obtain the market value of the inter-provincial transmission section expansion;

[0008] Combined with the expansion of transmission section capacity, a market clearing model of centralized matching within the province is established, and a model describing the node marginal electricity prices in different regions within the province is established to obtain the market value of the expansion of transmission section capacity within the province.

[0009] The further design of the above technical solution is: the model of the game process involving the power purchasing agents within the province and the power purchasing agents outside the province is:

[0010]

[0011] f 2 =max{0,ζ j ·(D j -D j )}

[0012]

[0013] Among them, f 1 represents the revenue that the power purchasing agent can obtain from the congestion surplus of the inter-provincial section, mc b represents the marginal electricity purchase price in the inter-provincial market, mc s represents the marginal electricity price in the inter-provincial market, τ represents the transmission line expansion revenue allocation coefficient set by the power grid company, and D j represents the amount of electricity purchased by the electricity purchasing agent j in the province; f 2 represents the load loss penalty of the power purchasing agent, D j represents the lower limit of the power purchase demand of agent j in the inter-provincial market, ζ j represents the load loss penalty coefficient of agent j; f 3 represents the energy income of the electricity purchasing agent, u j represents the energy efficiency coefficient of agent j; b j represents the power purchase price of agent j in the inter-provincial market, b j and Indicates the upper and lower limits of the electricity purchase quotation.

[0014] The load loss penalty model of the power purchasing agent is processed by the Big-M method to obtain the processed load loss penalty model:

[0015] z=max{0,ζ j ·( D j -D j )}

[0016] z≥0

[0017] z ≥ ζ j ·( D j -D j )

[0018] z≤M·u

[0019] z≤ζ j ·( D j -D j )+M·(1-u)

[0020] u∈{0,1}

[0021] Among them, z, M, and u represent the auxiliary decision variables, large integer constants, and 0-1 variables introduced by the Big-M method, respectively.

[0022] The constraints of the load loss penalty model are:

[0023]

[0024]

[0025] Among them, γ represents the Lagrange multiplier of the inter-provincial market supply and demand balance constraint; π l represents the shadow price of the cross-section blocking constraint, and represents the shadow price of the power purchase constraint within the province, and GSF represents the shadow price of the out-of-province power purchase constraint; 1 represents the power generation transfer coefficient matrix of the inter-provincial transmission section, l represents the index of the section, j represents the index of the agent within the province, 1-j represents the index of the agent outside the province, and F l and ΔF l represents the capacity of section l, G represents the power generation of the power source outside the province; ω is a 0-1 variable introduced by the Big-M method.

[0026] The decision model of the provincial power purchasing agent obtained by combining the load loss penalty model is:

[0027]

[0028] The decision model of out-of-province power purchasing agents obtained by combining the load loss penalty model is:

[0029]

[0030] The market value of the inter-provincial transmission section expansion is:

[0031] v 1 =(mc b -mc s )

[0032] Among them, v 1 It represents the market value of expanding the capacity of inter-provincial transmission sections.

[0033] The market clearing model of the provincial centralized matching method is:

[0034]

[0035] Among them, g i represents the winning bid electricity of region i in the province, c i represents the power generation cost of region i within the province; GSF 2represents the power generation transfer coefficient matrix of the transmission section within the province, y represents the index of the section, i represents the index of the power generation area, x represents the index of the load area, and F y and ΔF y represents the current capacity and incremental capacity of channel y, d x represents the demand within the province, L 2 is the number of sections, NG is the number of power generation areas, and ND is the number of load areas.

[0036] The model describing the node marginal electricity price in different regions of the province is:

[0037]

[0038] Where λ represents the Lagrange multiplier of the supply-demand balance constraint; They represent the negative / positive shadow prices of the blocking constraint of the section y within the province; L represents the augmented Lagrangian function, Represents the node marginal electricity price in each area of ​​the province.

[0039] The market value of the expansion of the transmission section within the province is:

[0040]

[0041] Among them, v 2,y It represents the market value of the expansion of the transmission section within the province, (y,from) represents the index of the first node of the transmission section y, and (y,to) represents the index of the last node of the transmission section y.

[0042] Compared with the prior art, the present invention has the following advantages:

[0043] The present invention provides a value assessment method for section planning of power grid companies under the background of power spot trading. The priority of section construction plans can be assessed through value signals, so as to prompt power grid companies to make more reasonable investment decisions and improve market efficiency.

[0044] The present invention comprehensively considers the game process and clearing efficiency of inter-provincial and intra-provincial market players, and evaluates the market value of the transmission section expansion plan through market price signals, which can promote market players to conduct reasonable bidding behavior. At the same time, the market evaluation process of intra-provincial channels highlights the importance of market value indicators. By utilizing market value indicators, the efficiency of optimal allocation of resources within the province can be significantly improved. According to the market value evaluation method of the present invention, power grid companies can measure the construction priority of transmission sections under different energy structures and the formulation of value distribution ratios, thereby optimizing investment decision-making plans and enhancing the economy and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1A diagram of the evaluation model solution process in an embodiment of the present invention;

[0046] Figure 2 It is a 500KV transmission section system diagram of a certain province in an embodiment of the present invention;

[0047] Figure 3 It is a diagram of the inter-provincial market game process in an embodiment of the present invention;

[0048] Figure 4 This is a diagram of the provincial section value assessment result in an embodiment of the present invention;

[0049] Figure 5 This is a diagram of the results of the cross-section value assessment within the province in an embodiment of the present invention;

[0050] Figure 6 This is a comparison chart of the value expansion of the section within the province in the embodiment of the present invention;

[0051] Figure 7 : is an analysis diagram of the influence of the distribution coefficient τ in the embodiment of the present invention;

[0052] Figure 8 This is a comparison chart of the value of inter-provincial and intra-provincial section expansion in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] Example

[0055] like Figure 1 As shown, the method of this embodiment includes the following steps:

[0056] Step 1: The power grid company obtains the transmission section capacity expansion and expansion value allocation plan, and thereby obtains the transmission line capacity expansion benefit allocation coefficient;

[0057] Step 2: Based on the inter-provincial market clearing rules, Jiangsu Province's power purchasing agents need to engage in Nash game with power purchasing agents outside the province to compete for power generation resources outside the province in limited transmission channels.

[0058] Therefore, this embodiment combines the transmission section expansion capacity and the transmission line expansion revenue distribution coefficient to establish a model involving the game process of the provincial power purchasing agent and the out-of-province power purchasing agent, determines the inter-provincial market marginal price, and obtains the market value of the inter-provincial transmission section expansion according to the bidding results of the power purchasing agents of each province;

[0059] Combined with the expansion of transmission section capacity, a market clearing model of centralized matching is established within the province, and a model describing the node marginal electricity prices in different regions within the province is established. Then, combined with the remaining load demand and power generation capacity in different regions within the province and the blocking surplus of the transmission channels within the province, the market value of the expansion of transmission section capacity between regions within the province is obtained.

[0060] The construction of the inter-provincial transmission section value assessment model includes the following steps:

[0061] ① The EPEC model including the game process of power purchasing agents within the province and outside the province is established as follows:

[0062]

[0063] f 2 =max{0,ζ j ·( D j -D j )} (3)

[0064]

[0065] In the above model, objective (1) represents the maximization of the revenue of the power purchasing agent; f 1 represents the revenue that the power purchasing agent can obtain from the congestion surplus of the inter-provincial section, where mc b represents the marginal electricity purchase price in the inter-provincial market, mc s represents the marginal electricity price in the inter-provincial market, τ represents the transmission line expansion revenue allocation coefficient set by the power grid company, and D j represents the amount of electricity purchased by the electricity purchasing agent j in the province; f 2 represents the load loss penalty of the power purchasing agent, where D j represents the lower limit of the power purchase demand of agent j in the inter-provincial market, ζ j represents the load loss penalty coefficient of agent j; f 3 represents the energy income of the electricity purchasing agent, where u j represents the energy efficiency coefficient of agent j; b j represents the power purchase quotation of agent j in the inter-provincial market. Different agents need to submit b j , compete for power sources from other provinces within the limited cross-sectional capacity; b j and Indicates the upper and lower limits of the electricity purchase quotation.

[0066] ② The model of competing for power sources outside the province through power purchase quotations in the inter-provincial market to avoid possible load loss penalties is:

[0067] {D j ,D 1-j}=argmin{-b j ·D j -b 1-j ·D 1-j} (6)

[0068] G-(D j +D 1-j )=0 (7)

[0069]

[0070]

[0071] v 1 =(mc b -mc s ) (11)

[0072] In the above model, objective (6) represents the social welfare maximization objective of the inter-provincial electricity spot market, where D 1-j represents the winning bid quantity of the out-of-province power purchasing agent; constraint (7) represents the supply and demand balance constraint of the inter-provincial market, where G represents the power generation of the out-of-province power source; constraint (8) represents the blocking constraint of the inter-provincial channel, where GSF 1 represents the power generation transfer coefficient matrix of the inter-provincial transmission section, l represents the index of the section, j represents the index of the agent within the province, 1-j represents the index of the agent outside the province, and F l and ΔF l represents the capacity of section l; constraints (9) and (10) represent the power purchasing constraints of the power purchasing agent within the province and the power purchasing agent outside the province respectively; model (11) represents the power purchasing constraints of the inter-provincial section when the inter-provincial section capacity is F l +ΔF l The difference between the marginal buyer's price and the marginal seller's price in the inter-provincial market. When the section capacity is sufficient, both the power purchasing agent within the province and the power purchasing agent outside the province can meet the power purchasing demand through the inter-provincial section. In the equilibrium solution of the EPEC model, the quotations of both parties are close to the quotations of the power supply outside the province, and the value of section expansion is low; when the section capacity is insufficient, the power purchasing agent within the province and the power purchasing agent outside the province cannot meet the power purchasing demand of both parties through the inter-provincial section. In the equilibrium solution of the EPEC model, the quotations of both parties are close to the market price ceiling, and the value of section expansion is high.

[0073] Considering that models (1) to (11) are a typical EPEC model, and model (3) contains nonlinear terms, it is difficult to solve directly. Therefore, this embodiment first converts models (6) to (10) into a series of equality / inequality constraints through KKT conditions, thereby converting the EPEC model into a single-layer Nash game model to obtain a market equilibrium solution. The model is as follows:

[0074]

[0075]

[0076] In the above model, constraints (21) to (22) represent the stability conditions of the inter-provincial market equilibrium solution, where γ represents the Lagrange multiplier of the inter-provincial market supply and demand balance constraint; constraint (23) represents the original constraint condition of the inter-provincial market; constraint (24) represents the dual variable constraint of the inter-provincial market, where π l represents the shadow price of the cross-section blocking constraint, and represents the shadow price of the power purchase constraint within the province, and represents the shadow price of the out-of-province power purchase constraint, L 1 is the number of sections; constraint (25) represents the transformation process of the complementary relaxation constraints of model (8); constraints (26) and (27) respectively represent the transformation process of the complementary relaxation constraints of model (9); constraints (28) and (29) respectively represent the transformation process of the complementary relaxation constraints of model (10).

[0077] On this basis, model (3) is processed by the Big-M method, and the models are shown in (30) to (35), where z, M, and u represent the auxiliary decision variables, large integer constants, and 0-1 variables introduced by the Big-M method, respectively.

[0078] z=max{0,ζ j ·( D j -D j )} (twenty one)

[0079] z ≥ 0 (22)

[0080] z ≥ ζ j ·( D j -D j ) (twenty three)

[0081] z≤M·u (24)

[0082] z≤ζ j ·(D j -D j )+M·(1-u) (25)

[0083] u∈{0,1}(26)

[0084] Through the above process, model (3) in the market agent objective function is replaced by the decision variable z, and models (1) to (10) can be converted into linear programming models by adding constraints (21) to (29) and (31) to (35). Therefore, the decision model of the agent within the province is shown in (36) to (39), and the decision model of the agent outside the province is shown in (40) to (43):

[0085]

[0086] Constraints (31) to (35) (29)

[0087] Constraints (21) to (29) (30)

[0088]

[0089] Constraints (31) to (35) (33)

[0090] Constraints (21) to (29) (34) In the above model, the decision variables of the intra-provincial agent in the inter-provincial market are:

[0091] Need to continue to receive quotations from agents outside the province 1-j , in order to optimize their own decisions.

[0092] The decision variables of out-of-province agents in the inter-provincial market are:

[0093] Need to continue to receive quotes from agents within the province j , in order to optimize its own decision, ω represents the 0-1 variable introduced by the Big-M method. Therefore, by constructing the Nash game process, we can solve models (36) to (43), and finally determine the market value of inter-provincial transmission section expansion through model (11).

[0094] The construction of the provincial channel value assessment model includes the following steps:

[0095] ① The market clearing model for centralized matching within the province is established as follows:

[0096]

[0097]

[0098] In the above model, objective (12) represents the social welfare maximization objective of the provincial electricity spot market, where g i represents the winning bid electricity of region i in the province, c i represents the power generation cost of region i within the province; constraint (13) represents the supply and demand balance constraint of the provincial power spot market; constraints (14)-(15) represent the congestion constraints of the sections within the province, where GSF 2 represents the power generation transfer coefficient matrix of the transmission section within the province, y represents the index of the section, i represents the index of the power generation area, x represents the index of the load area, and F y and ΔF y represents the current capacity and incremental capacity of channel y, d x represents the demand within the province, L 2is the number of sections, NG is the number of power generation areas, and ND is the number of load areas; constraint (16) represents the upper and lower limits of power generation in power generation area i; constraint (17) represents the coupling constraint of power purchase load in inter-provincial and intra-provincial spot markets, that is, load users in Jiangsu Province need to purchase power with a value of of electricity to meet its load requirements.

[0099] ② The model to describe the node marginal electricity price in different regions of the province is established as follows:

[0100]

[0101] In the above model, equation (18) represents the augmented Lagrangian function of models (12) to (16), where λ represents the Lagrangian multiplier of the supply and demand balance constraint; They represent the negative / positive shadow price of the congestion constraint of section y within the province; Formula (19) represents the node marginal electricity price of each region within the province, which is composed of the electricity balance price and the shadow price of each transmission section; Model (20) represents the expansion value of section y within the province, that is, the congestion surplus under the node marginal price system; (y,from) represents the head node index of section y, and (y,to) represents the tail node index of section y. After determining the market value of inter-provincial transmission capacity expansion, combined with the residual load demand and power generation capacity of different regions within the province, the market value of inter-regional transmission capacity expansion within the province can be determined through models (12) to (20).

[0102] Step 3: Based on the market value of inter-provincial transmission section expansion and the market value of intra-provincial inter-regional transmission section expansion, the market value of key transmission sections is evaluated, and the effects of different expansion plans and market value allocation plans are simulated to optimize the section planning plan of the power grid company and determine the most economical line construction plan considering spot market factors.

[0103] Application Examples

[0104] like Figure 2 As shown, the intra-provincial power transmission system adopted in this embodiment is simplified into a 5-node system, namely Xuzhou-Suzhou-Huaihe node (photovoltaic power transmission area), Lianyungang node (nuclear power transmission area), Yannan node (coastal wind power transmission area), Huaitai node (Jiangbei load center) and Southern Jiangsu region (Jiangnan load center), and each region transmits electric energy through a high-voltage AC transmission section. The inter-provincial power transmission system is simplified into a 3-node system, namely, the out-of-province power supply node (such as Yanmenguan, Ximeng), J province load node (Jiangsu) and Z province load node (such as Zhejiang, Shanghai), and J province and Z province share the inter-provincial transmission section L1.

[0105] like Figure 3As shown in Figure 1, the game process of the power purchasing agents of Province J and Province Z in terms of power purchase quotations is shown when the capacities of the inter-provincial transmission sections L1 and L2 are 200MW and 100MW respectively. Since the total minimum power purchase demand of Province J and Province Z is 250MW, which is greater than the transmission capacity of Section AJ of 200MW, it is impossible to meet the minimum power purchase demand of each province through Section AJ. Considering that the electric energy commodity in the inter-provincial market is bound to its corresponding transmission rights, in order to avoid load loss penalties, the power purchasing agents of Province J and Province Z continuously raise the power purchase price in the inter-provincial market through models (35) to (42) to compete for the limited transmission channel capacity, as shown in Figure 1. Figure 3 This competitive behavior leads to abnormal fluctuations in inter-provincial electricity purchase prices, and the quotations of power purchase agents in each province have reached their upper limit, causing the inter-provincial market resource allocation process to fail.

[0106] In this context, the expansion of inter-provincial channels can effectively alleviate unreasonable market competition and make the quotations of provincial agents return to a reasonable level. Therefore, according to model (11), Figure 3 The expansion value of section AJ in the scenario is 980￥-200￥=780￥, which means the increase in social welfare in the inter-provincial market when section AJ expands its unit capacity. Figure 4 Describes the change of the expansion value of section AJ with the expansion capacity ΔF of section AJ. Figure 4 Explains the reasons for the change in expansion value.

[0107] Depend on Figure 4 It can be found that as the capacity of AJ section increases, the market value of its expansion shows a downward trend. This is because after the section capacity is sufficient, Province J and Province Z gradually return to reasonable bidding behavior. For example, when the capacity of AJ is lower than 240MW, the limited capacity cannot meet the minimum power purchase demand (250MW) of the two provinces. In order to avoid high load loss penalties, Province J and Province Z both compete for the limited section capacity with the highest bid, such as Figure 4 As shown in the previous paragraph. At this time, the market value of AJ expansion reaches its peak, and AJ expansion construction can alleviate the unreasonable bidding behavior of the two provinces. When the AJ capacity is higher than 250MW, the section capacity can meet the minimum power purchase demand of the two provinces. Provinces J and Z make market decisions based on their own energy efficiency coefficient u and expansion benefit coefficient τ, and the market price returns to a reasonable level, as shown in Figure 4 At this time, the market value of AJ's expansion has declined, and its expansion value depends on the reasonable difference between the purchase and sale of electricity.

[0108] according to Figure 3 Transaction results (100MW of electricity was purchased from the inter-provincial market, 50MW of surplus load was in the Huaitai area, and 150MW of surplus load was in the Jiangnan area). Figure 5The market value of capacity expansion of different transmission sections in the provincial market is shown, where the blue data represents the node marginal price and the results of local power generation resource clearing, and the red font represents the market value of capacity expansion of transmission sections and the results of power flow distribution of the sections. Figure 5 It can be found that there are two transmission sections blocked in J Province, namely 1-3 and 3-5. Among them, due to the insufficient transmission capacity of section 1-3 within the province, the cheap electricity in the Jiangbei area is difficult to be transmitted to the Jiangnan area, which forces the Jiangnan area to use local thermal power, and its energy cost is as high as 280￥ / MW. Therefore, according to the calculation of model (19), the expansion value of section 1-3 within the province is the highest (reaching 192.7￥), which means that when section 1-3 is expanded per unit capacity, the reduction value of the energy supply cost in the provincial market is reduced.

[0109] at the same time, Figure 6 It shows the changes in the cost of energy supply in the province when the capacity of sections 1-3 and 3-5 is expanded and the capacity of all sections is expanded according to the market value ratio. The traditional section planning concept relies on a fixed investment return model. Grid companies usually make large-scale advance investments to ensure long-term benefits, such as the continuous expansion of blocked sections 1-3 and 3-5. The initial investment can indeed alleviate the congestion of the section and reduce the cost of energy supply in the province. However, with the fixed increase of subsequent investment, the marginal benefits of section expansion gradually decrease. For example, in Figure 6 In the second half of the period, the cost of energy supply in the province will no longer decrease. In contrast, the proportional investment model based on market value can adjust the expansion investment method in real time and dynamically according to market demand, such as Figure 6 As shown, the cost of energy supply in the province can be continuously reduced. Figure 6 It is verified that in a dynamic market environment, the capacity expansion strategy based on market value can better adapt to the changes in provincial market construction and power supply structure and improve energy utilization efficiency.

[0110] Figure 7The influence of different expansion revenue allocation coefficients τ on the inter-provincial section value assessment is shown. It can be seen that when the expansion capacity of section AJ is less than 25MW, the change of coefficient τ has no significant effect on the expansion value. This is because the severely insufficient section capacity makes Province J and Province Z unable to meet the basic load demand, and the revenue compensation brought by coefficient τ cannot make up for the high load loss penalty. Therefore, the power grid company should give priority to increasing the section capacity when formulating the expansion strategy. When the expansion capacity is between 25MW and 50MW, the different values ​​of coefficient τ have a significant impact on the expansion value of AJ, because its congestion has been alleviated to a certain extent, and the higher revenue compensation can offset part of the load loss penalty, thereby stabilizing the inter-provincial market. At this stage, the power grid company should pay attention to the strategy of optimizing the allocation coefficient τ. When the expansion capacity of AJ exceeds 50MW, the change of coefficient τ has less impact on the expansion value of AJ. At this time, the section capacity is sufficient, and the expansion value of AJ mainly depends on the purchase and sale of electricity in the inter-provincial market. Therefore, the power grid company needs to avoid over-investment to ensure the effective use of resources.

[0111] Taking Province J as an example, Figure 8 The influence of different energy structures and budget ratios on the evaluation results of inter-provincial sections and intra-provincial sections is described. Among them, the inter-provincial section AJ is shared by Province J and Province Z, so Province J only bears half of the expansion cost. At the same time, it is assumed that there are three stages of inter-provincial and intra-provincial energy supply ratios: conservative stage (1:9), development stage (3:7) and stable stage (6:4). In the conservative stage, the load within the province is mainly met by the power generation resources of the province, and the dependence on resources outside the province is relatively low; in the development stage, as the bottleneck of power generation resources within the province appears, power generation resources outside the province begin to participate in load balancing through the inter-provincial market; after entering the stable stage, the load balance of Province J depends on the joint support of resources within and outside the province, and the degree of dependence on power generation resources outside the province is relatively high.

[0112] exist Figure 8 In the current period, the budget for cross-section expansion is set at RMB 200 million, the revenue allocation coefficient τ is set at 0.5, and the provincial budget is based on Figure 8 The market value ratio of the section is allocated to all transmission sections within the province, and the inter-provincial budget is allocated to section AJ. Figure 8The results show the impact of different budget allocations on the market value of transmission sections when the energy structure of J Province is in the conservative stage, development stage, and stable stage. In the conservative stage, the load within the province mainly depends on the spot market within the province, resulting in a higher market value for the expansion of the transmission section within the province. In the development stage, as the proportion of energy supply from the inter-provincial market increases, different budget allocation schemes have a significant impact on the market value. When the inter-provincial budget ratio is lower than 40%, the expansion value of the inter-provincial transmission section is higher; when the ratio is higher than 60%, the expansion value of the inter-provincial section is lower than the provincial section reinforcement plan. Therefore, the power grid company should take into account both the intra-provincial and inter-provincial energy supply in the development stage and coordinate and optimize the expansion plan. Finally, in the stable stage, the inter-provincial market has provided 60% of the load for J Province, and the market value of the inter-provincial line remains at a high level, indicating that the capacity of the inter-provincial section needs to be continuously expanded to meet the interactive needs of inter-provincial energy.

[0113] The technical solutions of the present invention are not limited to the above-mentioned embodiments, and any technical solutions obtained by equivalent replacement methods fall within the scope of protection required by the present invention.

Claims

1. A method for evaluating the market value of power transmission section planning of a power grid company, characterized by: The power grid company obtains the transmission section expansion capacity and expansion value allocation plan, and thereby obtains the transmission line expansion benefit allocation coefficient; Combined with the transmission section expansion capacity and the transmission line expansion revenue distribution coefficient, a model including the game process of the provincial power purchasing agent and the out-of-province power purchasing agent is established to determine the inter-provincial market marginal price, and then obtain the market value of the inter-provincial transmission section expansion; Combined with the expansion of transmission section capacity, a market clearing model of centralized matching within the province is established, and a model describing the node marginal electricity prices in different regions of the province is established to obtain the market value of the expansion of transmission section capacity within the province.

2. The method for evaluating the market value of power transmission section planning of a power grid company according to claim 1 is characterized by: The game model involving the power purchasing agents within the province and the power purchasing agents outside the province is: f2=max{0,ζ j ·( D j -D j )} Among them, f1 represents the income that the power purchasing agent can obtain from the blocking surplus of the inter-provincial section, mc b represents the marginal electricity purchase price in the inter-provincial market, mc s represents the marginal electricity price in the inter-provincial market, τ represents the transmission line expansion revenue allocation coefficient set by the power grid company, and D j represents the amount of electricity purchased by the power purchasing agent j in the province; f2 represents the load loss penalty of the power purchasing agent, D j represents the lower limit of the power purchase demand of agent j in the inter-provincial market, ζ j represents the load loss penalty coefficient of agent j; f3 represents the energy income of the power purchasing agent, u j represents the energy efficiency coefficient of agent j; b j represents the power purchase price of agent j in the inter-provincial market, b j and Indicates the upper and lower limits of the electricity purchase quotation.

3. The method for evaluating the market value of power transmission section planning of a power grid company according to claim 2 is characterized by: The load loss penalty model of the power purchasing agent is processed by the Big-M method to obtain the processed load loss penalty model: z=max{0,ζ j ·( D j -D j )} z≥0 z≥ζ j ·( D j -D j ) z≤M·u z≤ζ j ·( D j -D j )+M·(1-u) u∈{0,1} Among them, z, M, and u represent the auxiliary decision variables, large integer constants, and 0-1 variables introduced by the Big-M method, respectively.

4. The method for evaluating the market value of power transmission section planning of a power grid company according to claim 3 is characterized by: The constraints of the load loss penalty model are: Among them, γ represents the Lagrange multiplier of the inter-provincial market supply and demand balance constraint; π l represents the shadow price of the cross-section blocking constraint, and represents the shadow price of the power purchase constraint within the province, and represents the shadow price of the power purchase constraint of the out-of-province agent; GSF1 represents the power generation transfer coefficient matrix of the inter-provincial transmission section, l represents the index of the section, j represents the index of the agent within the province, 1-j represents the index of the out-of-province agent, F l and ΔF l represents the capacity of section l, G represents the power generation of the power source outside the province; ω is a 0-1 variable introduced by the Big-M method.

5. The method for evaluating the market value of power transmission section planning of a power grid company according to claim 4 is characterized by: The decision model of the provincial power purchasing agent obtained by combining the load loss penalty model is:

6. The method for evaluating the market value of power transmission section planning of a power grid company according to claim 5 is characterized by: The decision model of out-of-province power purchasing agents obtained by combining the load loss penalty model is:

7. The method for evaluating the market value of power transmission section planning of a power grid company according to claim 6 is characterized by: The market value of the inter-provincial transmission section expansion is: v1=(mc b -mc s ) Among them, v1 represents the market value of the expansion of inter-provincial transmission sections.

8. The method for evaluating the market value of power transmission section planning of a power grid company according to claim 1 is characterized by: The market clearing model of the provincial centralized matching method is: Among them, g i represents the winning bid electricity of region i in the province, c i represents the power generation cost of region i within the province; GSF2 represents the power generation transfer coefficient matrix of the transmission section within the province, y represents the index of the section, i represents the index of the power generation area, x represents the index of the load area, and F y and ΔF y represents the current capacity and incremental capacity of channel y, d x It represents the demand in the province, L2 is the number of sections, NG is the number of power generation areas, and ND is the number of load areas.

9. The method for evaluating the market value of power transmission section planning of a power grid company according to claim 8, characterized in that: The model describing the node marginal electricity price in different regions of the province is: Where λ represents the Lagrange multiplier of the supply-demand balance constraint; They represent the negative / positive shadow prices of the blocking constraint of the section y within the province; L represents the augmented Lagrangian function, Represents the node marginal electricity price in each area of ​​the province.

10. The method for evaluating the market value of power transmission section planning of a power grid company according to claim 9, characterized in that: The market value of the expansion of the transmission section within the province is: Among them, v 2,y It represents the market value of the expansion of the transmission section within the province, (y,from) represents the index of the first node of the transmission section y, and (y,to) represents the index of the last node of the transmission section y.

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