Generator set effective flexibility capacity evaluation method considering power generation enterprise strategy behaviors

Abstract

The invention belongs to the technical field of power system operation control, and relates to a power generation enterprise strategy behavior-considered unit effective flexibility capacity evaluation method, which comprises the steps of 1, constructing a decision model of an upper-layer power generation enterprise participating in a spot electric energy market and a power capacity market, 2, constructing a clearing model of a lower-layer spot electric energy market, the method comprises the following steps: 1, constructing a lower-layer power capacity market clearing model, 4, converting a lower-layer market clearing problem into a KKT optimality condition, 5, converting the model into a linear form, 6, solving an MILP model by using a Gurobi solver, and 7, constructing a lower-layer power capacity market clearing model. The method not only considers the technical characteristics of the unit, but also fully considers the strategic behaviors of the power generation enterprises in the market, and more truly reflects the decision preferences of the power generation enterprises under different market conditions. Therefore, the decision-making results of each power generation enterprise in the spot electric energy market and the electric power capacity market can be ensured to accord with the equilibrium state of the market, and efficient operation of the whole market and optimal allocation of resources are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation control, and relates to a method for evaluating the effective flexibility capacity of a unit taking into account the strategic behavior of a power generation enterprise. Background Art

[0002] The proportion of new energy in new power systems continues to increase, and the change range of net load characteristics has increased significantly. Especially in the case of extreme climate events and economic development leading to an increase in electricity demand, the system's flexible adjustment needs have increased significantly, and it has become crucial to ensure sufficient power capacity to cope with the fluctuations in net load peaks and valleys. As an effective mechanism for ensuring the adequacy of power generation capacity commonly used internationally today, the power capacity market mechanism uses market-based means to ensure that the power system will have sufficient power generation capacity in the future to meet the system's adequacy requirements, avoid power shortages, and ensure the safe and stable operation of the system. At the same time, the power capacity market reflects the support and regulation value of the unit, bringing it additional income in addition to the spot electricity market and ancillary service market, helping it recover its fixed investment costs.

[0003] In theory, the effective capacity of a generator unit participating in the power capacity market is the capacity that can be provided determined by its own technical characteristics (such as installed capacity, historical operating data, etc.). In the actual market, the market behavior of power generation companies is not only determined by technical capabilities, but also affected by their initiative and strategic choices, which is specifically manifested in their willingness to provide capacity in the market. This willingness is affected by many factors, including market price signals, cost-benefit analysis, etc. Even if a unit has strong capabilities, if market conditions are not ideal, the power generation company may still be reluctant to fully put its power capacity into the market. Therefore, when evaluating the effective capacity of the unit, it is necessary to consider whether the power generation company has sufficient motivation to transform its technical capabilities into market behavior. It is of great significance to study the interaction mechanism between power generation companies and various markets.

[0004] However, the existing methods for evaluating the effective flexibility capacity of power generation units lack consideration of the market strategic behavior of power generation companies and the coupled operation of the power capacity market and the spot power market. Most evaluation methods simply evaluate based on the technical characteristics of the power generation units, such as installed capacity, ramp rate, failure rate, and maintenance rate, or rely on historical operating data such as the average ramp amplitude during net load peaks and valleys to evaluate the expected regulation capacity of the power generation units. They have not yet combined long-term capacity planning with short-term market demand, nor have they considered the strategic behavior of market players and comprehensively evaluated the interaction of multiple interests, which has led to the inconsistency between the evaluation results of the effective flexibility capacity of the units and the actual market supply. In addition, most of the power capacity market mechanisms designed by existing research only consider the capacity margin to ensure system reliability, and have not yet considered the demand for flexibility capacity in the power system.

[0005] Therefore, an evaluation method for the effective flexibility capacity of units that comprehensively considers technical capabilities and market behaviors is needed to solve the above technical problems. Summary of the invention

[0006] Based on the coupled operational relationship between the power capacity market and the electric energy market, the present invention considers both reliability capacity and flexibility capacity products in the power capacity market, and incorporates the market strategy behavior of power generation enterprises into the effective capacity evaluation of the unit, and proposes a method for evaluating the effective flexibility capacity of the unit that considers the strategic behavior of power generation enterprises. This method constructs a two-layer master-slave game model for power generation enterprises to participate in the spot electric energy market and the power capacity market. In the upper model, power generation enterprises decide their quotations and quantities in each market with the goal of maximizing the overall profit of participating in the two markets; in the lower model, each market is cleared according to its own mechanism, and the clearing results interact dynamically with the upper decision-making behavior. Finally, through the model solution, the effective flexibility capacity of the unit under comprehensive consideration of technical capabilities and market behavior is obtained.

[0007] The technical solution adopted by the present invention to solve the technical problem is: a method for evaluating the effective flexibility capacity of a unit considering the strategic behavior of a power generation enterprise, comprising the following steps:

[0008] Step 1: Construct a decision model for upper-level power generation enterprises to participate in the spot electricity market and the electricity capacity market. This decision model takes power generation enterprises as the decision-making subject, and its goal is to maximize its overall benefits in the electricity market and the electricity capacity market. The model includes the unit's quotation and output constraints. The decision content includes the maximum output declaration, flexible adjustment capability, electricity price in the electricity market, and the quotation and declaration of reliability capacity and flexibility capacity in the capacity market.

[0009] Step 2: Construct a clearing model for the lower-level spot electricity market. The goal of this clearing model is to minimize the market operating costs and optimize the allocation of the output reported by power generation companies. In the clearing process, constraints such as unit output limitations, flexible adjustment capacity limitations, system node power balance, and line flow limitations are considered to ensure the optimal allocation of resources under the physical constraints of the system.

[0010] Step 3: Construct a lower-level power capacity market clearing model. The goal of this lower-level power capacity market clearing model is to maximize total social welfare. The capacity demand curve is represented by a segmented step-type demand curve, which clears reliability capacity and flexibility capacity separately. The model also takes into account the capacity transmission constraints between regions, ensuring that capacity resources can be effectively allocated and shared between different regions, so that each region of the system achieves capacity balance and optimizes the resource allocation of the overall network.

[0011] Step 4: Convert the lower-level market clearing problem into Karush–Kuhn–Tucker optimality conditions. For the clearing models of the spot electricity market and the power capacity market constructed in steps 2 and 3, the KKT optimality conditions are used to express the originally complex optimization problem in the form of constraints, so as to better integrate it into the overall model. In this way, the two-layer optimization model can be converted into a single-layer mathematical program with equilibrium constraints (MPEC) problem.

[0012] Step 5: Convert the model into a linear form. In order to improve the computational efficiency of the model and ensure the stability of the solution, the MPEC model constructed in step 4 is linearized, and the complex nonlinear model is linearized into a more easily solvable mixed integer linear programming (MILP) problem, so that it can be efficiently processed by existing solvers. Specifically, for the product terms between continuous variables in the objective function, strong duality is used to convert them into a linear form; for the complementary relaxation constraints in the KKT conditions, linearization is achieved by introducing the big M method; for the MAX function, equivalent inequality constraints are used to replace it.

[0013] Step 6: Use the Gurobi solver to solve the MILP model. By setting relevant solution parameters, the calculation efficiency and solution accuracy are ensured to meet the constraints of the model and the requirements of the objective function, obtain the optimal solution, and then obtain the effective flexibility capacity evaluation results of the unit participating in the market.

[0014] The present invention comprehensively considers the market strategy behavior, market competition environment and market clearing results of power generation enterprises, ensuring that the evaluation results of the effective flexibility capacity of the unit not only reflect the technical performance of the power generation unit, but also fully reflect its strategic behavior in the process of participating in the market. This evaluation method can more accurately reflect the available flexibility capacity of the power generation unit under actual market conditions, thereby providing a more practical reference for market operation and system planning.

[0015] Preferably, in step 1, the decision model includes: the quotation and output constraint of the unit, and the decision content of the decision model includes: the maximum output declaration, flexible adjustment capability, and electricity price in the electric energy market, and the quotation and declaration amount of reliability capacity and flexibility capacity in the capacity market;

[0016] The objective function of the decision model is:

[0017] (1)

[0018] (2)

[0019] (3)

[0020] (4)

[0021] in, represents the profit of enterprise g in the spot electricity market, represents the profit of enterprise g in the electricity capacity market, Representation scene The default capacity penalty for power generation company g is Representation scene The clearing power generation of generator set i in period t is Representation scene The electricity clearing price of the intermediate node n in period t, represents the power generation cost of unit i, represents the reliability capacity clearing price of region s, represents the reliability capacity of unit i, represents the flexible capacity clearing price of region s, represents the bid amount of the flexibility capacity of unit i, Representation scene The maximum output of electric energy declared by unit i, Representation scene Flexible climbing capability declared by unit i, Indicates that thermal power unit i belongs to power generation enterprise g, Indicates that node n is the node where unit i is located, Indicates that region s is the region where unit i is located.

[0022] Preferably, in step 3, the lower-layer power capacity market clearing model simultaneously considers the capacity transmission constraints between regions, ensuring that capacity resources can be effectively allocated and shared between different regions, so that each region of the system achieves capacity balance and optimizes resource allocation of the overall network;

[0023] The objective function of the lower-level power capacity market clearing model is:

[0024] (20)

[0025] In formula (20), represents the price of the reliability capacity demand curve of region s for segment d, represents the clearing capacity of the dth segment of the reliability capacity demand curve of region s, represents the price of segment d of the flexibility capacity demand curve of region s, represents the clearing capacity of segment d of the flexibility capacity demand curve of region s; Represents the reliability capacity quotation of unit i.

[0026] Preferably, in step 2, the clearing model of the lower-layer spot electricity energy market takes into account the output limitation of the unit, the flexible adjustment capacity limitation, the system node power balance and the line flow limitation constraints during the clearing process to ensure the optimal allocation of resources under the physical constraints of the system;

[0027] The objective function of the clearing model of the lower-level spot electricity market is:

[0028] (11)

[0029] In formula (11), Representation scene The electricity energy quotation of unit i; Representation scene The clearing power generation of generator set i in period t.

[0030] Preferably, in step 5, the specific steps include: for the product terms between continuous variables in the objective function, converting them into linear form by using strong duality; for the complementary relaxation constraints in the KKT conditions, linearization is achieved by introducing the big M method; for the MAX function, using equivalent inequality constraints for replacement.

[0031] The beneficial effects of the present invention are:

[0032] 1. In the evaluation of the effective flexibility capacity of the generator set, the present invention not only takes into account the technical characteristics of the generator set, but also fully considers the strategic behavior of the power generation enterprise in the market, so that the evaluation result is more in line with the actual market situation and more truly reflects the decision-making preferences of the power generation enterprise under different market conditions.

[0033] 2. The present invention designs an electricity capacity market mechanism that takes into account both the reliability and flexibility of the power system. This market mechanism can not only ensure the reliable operation of the power system under high load conditions, but also cope with the high volatility of the system net load through reasonable flexibility capacity configuration, and better adapt to the needs of the new power system.

[0034] 3. The present invention takes into account the coupled operation of the power capacity market and the spot electricity energy market. The established model focuses on the combination of long-term capacity guarantee and short-term market demand and pays attention to the system operation details. While further improving the overall market efficiency, it makes the effective flexibility capacity calculation results more in line with the actual operation of the power system.

[0035] 4. The present invention transforms the lower-level problem in the double-level problem into an equivalent optimality condition. This transformation directly reflects the market clearing results of the lower level in the decision-making process of the upper level, ensuring that the decision results of each power generation enterprise in the spot electricity energy market and the power capacity market are in line with the equilibrium state of the market, thereby achieving efficient operation of the overall market and optimal allocation of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a model framework and solution process diagram of the effective flexibility capacity evaluation method of the unit considering the strategic behavior of the power generation enterprise of the present invention;

[0037] Figure 2 It is the improved IEEE 5-node system topology diagram of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the relevant technologies in the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] refer to Figures 1-2 , the implementation method of the method for evaluating the effective flexibility capacity of a unit taking into account the strategic behavior of power generation enterprises of the present invention is given below.

[0040] 1. Construct a decision-making model for upper-level power generation enterprises to participate in the spot electricity energy market and the electricity capacity market.

[0041] The goal of the model is to maximize the comprehensive profit of power generation enterprises through their decisions in the spot power market and the power capacity market. The decision variables of power generation enterprises include their maximum output, flexible ramping capability, and power price declared in the spot power market, as well as the reliability capacity and flexibility capacity quotation and quantity in the power capacity market.

[0042] 1) Objective function

[0043] Taking power generation enterprise g as an example, its specific goals are:

[0044] (1)

[0045] (2)

[0046] (3)

[0047] (4)

[0048] in, is the profit of enterprise g in the spot electricity market, is the profit of enterprise g in the power capacity market, For the scene The default capacity penalty for power generation company g is For the scene The clearing power generation of generator set i in period t is For the scene The electricity clearing price of the intermediate node n in period t, is the power generation cost of unit i, is the reliability capacity clearing price of region s, is the reliability capacity bid value of unit i, is the flexible capacity clearing price for region s, is the bid amount of the flexibility capacity of unit i, For the scene The maximum output of electric energy declared by unit i, For the scene Flexible climbing capability declared by the middle unit i, Indicates that thermal power unit i belongs to power generation enterprise g, Indicates that node n is the node where unit i is located, Indicates that region s is the region where unit i is located.

[0049] 2) Constraints

[0050] (5)

[0051] (6)

[0052] (7)

[0053] (8)

[0054] (9)

[0055] (10)

[0056] in, For the scene The electricity energy quotation of unit i, The upper and lower limits of the unit's electricity quotation, is the minimum technical output of unit i, is the equivalent availability factor of the unit, is the installed capacity of unit i, is the reliability capacity quotation of unit i, The upper and lower limits of the unit reliability capacity quotation, Quote the flexibility capacity of unit i, , The upper and lower limits of the unit flexibility capacity quotation, is the maximum reliability capacity declared for unit i, To declare the minimum proportion of reliability capacity to installed capacity, The maximum flexibility capacity declared for unit i, is the maximum ramp rate of the unit. Constraint (5) limits the unit's electricity energy bid, constraint (6) limits the unit's electricity energy declared output, constraints (7) and (8) limit the reliability and flexibility capacity bids, and formulas (9) and (10) limit the maximum reliability and flexibility capacity declared by the unit.

[0057] 2. Construct a lower-level spot electricity market clearing model.

[0058] The lower-level spot electricity market clearing model aims to determine the power output and clearing price of each generator set in each time period with the lowest system cost as the goal.

[0059] 1) Objective function

[0060] (11)

[0061] 2) Constraints

[0062] (12)

[0063] (13)

[0064] (14)

[0065] (15)

[0066] (16)

[0067] (17)

[0068] (18)

[0069] (19)

[0070] in For the scene The load of node n in period t, , For the scene The actual output of wind turbine group w and photovoltaic generator group v during period t, is the admittance of line nm, For the scene The voltage phase angle of the middle node n during the period t is, is the maximum transmission power of line nm, , Respectively for scenes The predicted output of wind power w and photovoltaic power v in period t, It means that node m is connected to node n through a transmission line. Indicates that units i, w, v are located at node n. Constraint (12) is the system node power balance constraint, constraint (13) is the upper and lower limit constraints of the unit's winning power, constraints (14)-(15) are the upper and lower limit constraints of the flexible ramp adjustment of the unit in adjacent time periods, constraint (16) is the upper and lower limit constraints of the line transmission power, constraint (17) is the upper and lower limit constraints of the system node voltage phase angle, and formulas (18)-(19) indicate that the actual output of the wind farm and photovoltaic power station is less than their predicted output power.

[0071] 3. Construct a lower-level electricity capacity market clearing model.

[0072] The electricity capacity market uses market mechanisms to ensure that the power system will have sufficient reliability capacity and flexibility capacity in the future, with the goal of maximizing total market surplus.

[0073] 1) Objective function

[0074] (20)

[0075] in, Quote for segment d of the reliability capacity demand curve for region s, is the clearing capacity of the dth segment of the reliability capacity demand curve of region s, Quote for segment d of the flexibility capacity demand curve for region s, is the clearing capacity of segment d of the flexibility capacity demand curve of region s.

[0076] 2) Constraints

[0077] (twenty one)

[0078] (twenty two)

[0079] (twenty three)

[0080] (twenty four)

[0081] (25)

[0082] (26)

[0083] (27)

[0084] (28)

[0085] in, is the reliability capacity of transmission from region r to region s, is the flexibility capacity of transmission from region r to region s, is the maximum required capacity of the dth segment of the reliability capacity demand curve of region s, is the maximum demand capacity of the dth segment of the flexibility capacity demand curve of region s, is the transmission capacity limit between area r and area s, Indicates that unit i is located in region s, Indicates that there is a transmission channel between region r and region s. Constraints (21)-(22) represent the balance between regional clearing reliability and flexibility capacity, constraints (23)-(24) represent the upper and lower limits of the unit's winning reliability and flexibility capacity, constraints (25)-(26) represent the clearing quantity limit of each section of demand-side reliability and flexibility capacity, and constraints (27)-(28) represent regional transmission capacity limits.

[0086] 4. Convert the lower-level market clearing problem into the KKT optimality condition.

[0087] By converting the lower-level problem into KKT conditions and embedding them into the upper-level model, the entire two-level optimization problem can be converted into a single-level MPEC problem, which is easier to solve. The KKT conditions of the lower-level market clearing model are as follows:

[0088] 1) KKT conditions for the spot electricity market clearing model

[0089] (29)

[0090] (30)

[0091] (31)

[0092] (32)

[0093] (33)

[0094] (34)

[0095] (35)

[0096] (36)

[0097] (37)

[0098] (38)

[0099] (39)

[0100] (40)

[0101] (41)

[0102] (42)

[0103] (43)

[0104] (44)

[0105] (45)

[0106] (46)

[0107] (47)

[0108] (48)

[0109] (49)

[0110] (50)

[0111] (51)

[0112] (52)

[0113] (53)

[0114] (54)

[0115] (55)

[0116] (56)

[0117] (57)

[0118] (58)

[0119] (59)

[0120] (60)

[0121] (61)

[0122] Among them, constraints (29)-(36) are the original feasibility constraints, and constraints (37)-(61) are the KKT optimality conditions (including gradient conditions, complementary relaxation conditions, and dual feasibility conditions).

[0123] 2) KKT conditions for the power capacity market clearing model

[0124] (62)

[0125] (63)

[0126] (64)

[0127] (65)

[0128] (66)

[0129] (67)

[0130] (68)

[0131] (69)

[0132] (70)

[0133] (71)

[0134] (72)

[0135] (73)

[0136] (74)

[0137] (75)

[0138] (76)

[0139] (77)

[0140] (78)

[0141] (79)

[0142] (80)

[0143] (81)

[0144] (82)

[0145] (83)

[0146] (84)

[0147] (85)

[0148] (86)

[0149] (87)

[0150] (88)

[0151] (89)

[0152] (90)

[0153] (91)

[0154] (92)

[0155] (93)

[0156] Among them, constraints (62)-(69) are the original feasibility constraints, and constraints (70)-(93) are the KKT optimality conditions (including gradient conditions, complementary relaxation conditions, and dual feasibility conditions).

[0157] Finally, we get the MPEC model of power generation enterprise g decision-making with formula (1)-(4) as the objective function and formula (5)-(10) and (62)-(93) as the constraint conditions.

[0158] 5. Convert the model into linear form

[0159] (1) Linearization of product terms of continuous variables

[0160] For the objective function , This type of multiplication term between continuous variables and continuous variables is linearized using the strong duality theorem.

[0161] by Take it as an example, the linearization process is as follows:

[0162] The strong duality theorem formula of the lower-level power capacity market clearing model is:

[0163] (94)

[0164] From the strong duality theorem, gradient conditions (70)-(71), and complementary relaxation conditions (76)-(79), we can deduce:

[0165] (95)

[0166] (2) Complementary Relaxation Constraint Linearization

[0167] By introducing binary variables and large M values, the large M method is used to linearize the complementary slack condition constraints (42)-(55), (76)-(87).

[0168] To constrain For example, it can be replaced by the following formula:

[0169] (96)

[0170] (97)

[0171] (98)

[0172] in, , is a real number large enough to ensure the feasibility of the constraint.

[0173] (3) MAX function linearization

[0174] For the following optimization problems:

[0175] (99)

[0176] It can be rewritten as:

[0177] (100)

[0178] Using this method, we introduce auxiliary variables , formula (4) can be replaced by the following equivalent inequality constraints and converted into a linear form:

[0179] (101)

[0180] (102)

[0181] (103)

[0182] (104)

[0183] (105)

[0184] 6. Convert the model into linear form

[0185] Through the above method, the two-layer problem is converted into a single-layer MILP problem, which can be solved by directly calling the Gurobi solver.

[0186] Example

[0187] In order to illustrate the effectiveness and feasibility of the invention, an embodiment of the method for evaluating the effective flexibility capacity of a unit taking into account the strategic behavior of power generation enterprises described in the present invention is given below.

[0188] The embodiment adopts the improved IEEE 5-node system for verification, and its system topology diagram is as follows: Figure 2 By simulating the decision-making of power generation enterprises in the small system in the spot power energy market and the power capacity market, it is shown how the proposed evaluation method can be applied in a specific power system.

[0189] Taking the thermal power unit #1 with an installed capacity of 200MW as the decision-making subject, if the flexibility capacity of the thermal power unit #1 is evaluated only by technical characteristics, it is its flexible climbing capacity, that is, 45 MW / 15min. Now, using the method proposed in the present invention, considering the strategic behavior of power generation enterprises in the market, the effective flexibility capacity of its units is evaluated. The evaluation results of the effective flexibility capacity of unit #1 (the flexible climbing capacity declared by the unit in the electric energy market) are shown in the following table:

[0190]

[0191] As can be seen from Table 1, the effective flexibility capacity of thermal power unit #1 varies under different market conditions. Under different seasonal scenarios, there are seasonal differences in the unit's flexibility capacity declaration, and the market mechanism has different impacts on its declaration behavior.

[0192] In the spring and autumn scenarios, whether considering only the spot electricity market or the coordination mechanism of the electricity capacity market and the spot electricity market, thermal power unit #1 declared the maximum flexibility capacity it could provide, which is 45.00 / 15min. This is because the net load of the system is relatively stable in these two seasons, and the price signal provided by the electricity market itself is sufficient to motivate the unit to declare the maximum flexibility capacity. The unit tends to bid low and report high volume to obtain profits. Therefore, the coordination mechanism of the electricity capacity market did not have an additional impact on the unit's declaration behavior in this scenario. This shows that when the net load is stable, the additional incentive effect of the capacity market is relatively limited.

[0193] In the summer and winter scenarios, the net load fluctuation of the system is significantly enhanced. If only the spot electricity market is considered, the flexible ramping capacity reported by thermal power unit #1 in the market under this condition is only 42.00 / 40.00 (MW / 15min), which fails to fully realize its technical potential. This is because by reducing the amount of flexible ramping capacity reported, the unit can remove itself from the role of marginal unit at the peak of net load fluctuation, thereby making other units with higher bids become marginal units and raising the node electricity price. This strategic behavior can further improve its overall benefits under the price sensitivity of the spot electricity market. After considering the power capacity market, since the mechanism provides additional compensation for the flexible capacity of the unit, it successfully prompts the unit to increase its flexible ramping capacity provided in the electricity market to 45.00 / 44.00 (MW / 15min). This shows that in the scenario of strong net load fluctuation, the power capacity market can effectively make up for the problem of insufficient incentives for the supply of flexible capacity in the spot electricity market, so as to better meet the system's demand for flexibility under various extreme conditions.

[0194] This result fully proves that the power capacity market mechanism that takes into account flexibility demand can make up for the lack of incentives for flexibility capacity in the spot electricity market, prompting units to adjust their application strategies according to system demand, give play to their flexibility potential, and improve the overall flexibility resource supply capacity of the system. By considering the impact of the market strategy behavior of units participating in the power capacity market and the spot electricity market on unit decision-making, the effective flexibility capacity assessment of the unit not only reflects the maximum flexible ramping capability determined by technical characteristics, but also shows the significant effect of the market environment on its willingness to provide, making the assessment results more in line with the actual market operation and providing a scientific and effective reference for market design and system planning.

[0195] To sum up, in the evaluation of the effective flexibility capacity of the power generating set, the present invention not only takes into account the technical characteristics of the unit, but also fully considers the strategic behavior of the power generation enterprise in the market, so that the evaluation result is more in line with the actual market situation and more truly reflects the decision-making preferences of the power generation enterprise under different market conditions. Therefore, the present invention can ensure that the decision results of each power generation enterprise in the spot electricity energy market and the power capacity market are in line with the equilibrium state of the market, thereby realizing the efficient operation of the overall market and the optimal allocation of resources.

[0196] It should be emphasized that the above are only preferred embodiments of the present invention and do not limit the present invention in any form. Any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for evaluating the effective flexibility capacity of a unit considering the strategic behavior of power generation enterprises, characterized in that: The following steps are involved: Step 1: Construct a decision model for upper-level power generation enterprises to participate in the spot electric energy market and the electric capacity market. The decision model takes the power generation enterprises as the decision-making subjects, and the goal of the decision model is to maximize their overall benefits in the electric energy market and the electric capacity market; Step 2: construct a clearing model for the lower-level spot electricity energy market, wherein the goal of the clearing model for the lower-level spot electricity energy market is to minimize the market operation cost and optimize the allocation of the output reported by the power generation enterprises; Step 3: construct a lower-level power capacity market clearing model, wherein the goal of the lower-level power capacity market clearing model is to maximize the total social welfare. In the lower-level power capacity market clearing model, the capacity demand curve is represented by a segmented step-type demand curve, and reliability capacity and flexibility capacity are cleared respectively. Step 4: Convert the lower-level market clearing problem into the KKT optimality condition; for the clearing models of the spot electricity energy market and the power capacity market constructed in steps 2 and 3, use the KKT optimality condition to express the originally complex optimization problem in the form of constraints, so as to better integrate it into the overall model; Step 5: Convert the model into a linear form. The MPEC model constructed in step 4 is linearized, and the complex nonlinear model is linearized into a mixed integer linear programming problem that is easier to solve, so that it can be efficiently processed by the existing solver. Step 6: Use the Gurobi solver to solve the MILP model and obtain the effective flexibility capacity evaluation results of the unit participating in the market.

2. The method for evaluating the effective flexibility capacity of a unit considering the strategic behavior of a power generation enterprise according to claim 1 is characterized in that: In step 1, the decision model includes: the unit's quotation and output constraint, and the decision content of the decision model includes: the maximum output declaration, flexible adjustment capability, and electricity price in the electric energy market, and the quotation and declaration amount of reliability capacity and flexibility capacity in the capacity market; The objective function of the decision model is: (1) (2) (3) (4) in, represents the profit of enterprise g in the spot electricity market, represents the profit of enterprise g in the electricity capacity market, Representation scene The default capacity penalty for power generation company g is Representation scene The clearing power generation of generator set i in period t is Representation scene The electricity clearing price of the intermediate node n in period t, represents the power generation cost of unit i, represents the reliability capacity clearing price of region s, represents the reliability capacity of unit i, represents the flexible capacity clearing price of region s, represents the bid amount of the flexibility capacity of unit i, Representation scene The maximum output of electric energy declared by unit i, Representation scene Flexible climbing capability declared by the middle unit i, Indicates that thermal power unit i belongs to power generation enterprise g, Indicates that node n is the node where unit i is located, Indicates that region s is the region where unit i is located.

3. The method for evaluating the effective flexibility capacity of a unit considering the strategic behavior of a power generation enterprise according to claim 2 is characterized in that: In step 3, the lower-layer power capacity market clearing model simultaneously considers the capacity transmission constraints between regions, ensures that capacity resources can be effectively allocated and shared between different regions, achieves capacity balance in various regions of the system, and optimizes resource allocation of the overall network; The objective function of the lower-level power capacity market clearing model is: (20) In formula (20), represents the price of the reliability capacity demand curve of region s for segment d, represents the clearing capacity of the dth segment of the reliability capacity demand curve of region s, represents the price of segment d of the flexibility capacity demand curve of region s, represents the clearing capacity of segment d of the flexibility capacity demand curve of region s; Represents the reliability capacity quotation of unit i.

4. The method for evaluating the effective flexibility capacity of a unit considering the strategic behavior of a power generation enterprise according to claim 1 is characterized in that: In step 2, the clearing model of the lower-level spot electricity energy market takes into account the output limit of the unit, the flexible adjustment capacity limit, the system node power balance and the line flow limit constraints during the clearing process to ensure the optimal allocation of resources under the physical constraints of the system; The objective function of the clearing model of the lower-level spot electricity energy market is: (11) In formula (11), Representation scene The electricity energy quotation of unit i; Representation scene The clearing power generation of generator set i in period t.

5. The method for evaluating the effective flexibility capacity of a unit considering the strategic behavior of a power generation enterprise according to claim 1 is characterized in that: In step 5, the specific steps include: for the product terms between continuous variables in the objective function, using strong duality to convert them into linear form; for the complementary relaxation constraints in the KKT conditions, linearization is achieved by introducing the big M method; for the MAX function, using equivalent inequality constraints to replace it.

Images