Method for Evaluating Unit's Effective Flexibility Capacity Considering Strategic Behaviors of Power Generation Enterprises

Abstract

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 units considering the strategic behaviors of power generation enterprises, including: 1. Constructing a decision-making model for power generation enterprises to participate in the spot electricity energy market and the power capacity market at the upper layer; 2. Constructing a clearing model for the spot electricity energy market at the lower layer; 3. Constructing a clearing model for the power capacity market at the lower layer; 4. Transforming the clearing problem of the lower-layer market into KKT optimality conditions; 5. Transforming the model into a linear form; 6. Using the Gurobi solver to solve the MILP model. The present invention not only considers the technical characteristics of the units, but also fully considers the strategic behaviors of power generation enterprises in the market, and more truly reflects the decision-making preferences of power generation enterprises under different market conditions. Therefore, the present invention can ensure that the decision-making results of each power generation enterprise in the spot electricity energy market and the power capacity market conform to the equilibrium state of the market, realizing the efficient operation of the overall market and the optimal allocation of resources.

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 units considering the strategic behaviors of power generation enterprises. Background Art

[0002] In the new power system, the proportion of new energy is increasing continuously, and the variation range of the net load characteristics has increased significantly. Especially in the case of extreme climate events and the growth of electricity demand brought about by economic development, the flexible regulation demand of the system has increased significantly. It has become crucial to ensure sufficient power capacity to cope with the peak-valley fluctuations of the net load. As an effective power generation capacity adequacy guarantee mechanism commonly used internationally today, the power capacity market mechanism ensures through market-based means that the power system will have sufficient power generation capacity in the future to meet the adequacy demand of the system, 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 units, bringing them additional income in addition to the spot electricity energy market and the ancillary service market, and helping them recover the fixed costs invested.

[0003] Theoretically, the effective capacity of a generator unit participating in the power capacity market is the available capacity determined by its own technical characteristics (such as installed capacity, operation historical data, etc.). However, in the actual market, the market behaviors of power generation enterprises are not only determined by technical capabilities, but also affected by their initiative and strategic choices, specifically manifested as their willingness to provide capacity in the market. This willingness is affected by various factors, including market price signals, cost-benefit analysis, etc. Even if a unit has strong capabilities, if the market conditions are not ideal, the power generation enterprise may still be reluctant to fully put the power capacity it has into the market. Therefore, when evaluating the effective capacity of units, it is necessary to consider whether power generation enterprises have sufficient motivation to transform their technical capabilities into market behaviors at the same time. It is of great significance to study the interaction mechanism between power generation enterprises and various markets.

[0004] However, the existing methods for evaluating the effective flexibility capacity of generator units lack the consideration of the market strategic behaviors of power generation enterprises and the coupled operation of the power capacity market and the spot electricity energy market. Most evaluation methods simply evaluate based on the technical characteristics of generator units such as installed capacity, ramp rate, failure rate, maintenance rate, etc., or rely on historical operation data such as the average ramp amplitude during the peak-valley period of the net load to evaluate the expected regulation ability of generator units. They have not combined long-term capacity planning with short-term market demand, nor considered the strategic behaviors of market players, and comprehensively evaluated the interaction of multiple interests, which results in the evaluation results of the effective flexibility capacity of units not matching the actual market supply. In addition, most of the power capacity market mechanisms designed in the existing research only consider ensuring the adequacy of system reliability capacity and have not considered the demand of the power system for flexibility capacity.

[0005] Therefore, an evaluation method for the effective flexibility capacity of generating units considering both technical capabilities and market behavior is needed to solve the above technical problems. Summary of the Invention

[0006] Based on the coupled operation relationship between the electricity capacity market and the electricity energy market, this invention simultaneously considers reliability capacity and flexibility capacity products in the electricity capacity market, incorporates the market strategic behavior of power generation enterprises into the evaluation of the effective capacity of generating units, and proposes an evaluation method for the effective flexibility capacity of generating units considering 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 electricity energy market and the electricity capacity market. In the upper-layer model, the power generation enterprise makes decisions on its bid price and bid volume in each market with the goal of maximizing the overall profit from participating in the two markets; in the lower-layer model, each market clears according to its own mechanism, and the clearing result interacts dynamically with the upper-layer decision-making behavior. Finally, through model solution, the effective flexibility capacity of generating units considering both technical capabilities and market behavior is obtained.

[0007] The technical solution adopted by this invention to solve the technical problem is: an evaluation method for the effective flexibility capacity of generating units considering the strategic behavior of power generation enterprises, including the following steps:

[0008] Step 1, construct a decision-making model for power generation enterprises to participate in the spot electricity energy market and the electricity capacity market in the upper layer. This decision-making model takes the power generation enterprise as the decision-making subject, and the goal is to maximize its overall revenue in the electricity energy market and the electricity capacity market. The model includes the bid price and output constraints of generating units, and the decision-making content includes the maximum output declaration, flexible regulation ability, electricity energy price in the electricity energy market, as well as the bid price and declared volume of reliability capacity and flexibility capacity in the capacity market.

[0009] Step 2, construct a clearing model for the spot electricity energy market in the lower layer. The goal of this clearing model is to minimize the market operation cost and optimize the allocation of the output declared by power generation enterprises. During the clearing process, constraints such as the output limit of generating units, flexible regulation ability limit, system node power balance, and line power flow limit are considered to ensure the optimal allocation of resources under the physical constraints of the system.

[0010] Step 3, construct a clearing model for the electricity capacity market in the lower layer. The goal of this lower-layer electricity capacity market clearing model is to maximize the total social welfare. Among them, the capacity demand curve is represented by a piecewise stepped demand curve, and clearing is carried out separately for reliability capacity and flexibility capacity. This model also considers the capacity transmission constraints between regions to ensure the effective allocation and sharing of capacity resources between different regions, achieve capacity balance in each region of the system, and optimize the resource allocation of the overall network.

[0011] Step 4: Transform the lower-layer market clearing problem into KKT optimality conditions (Karush–Kuhn–Tucker optimality conditions). For the clearing models of the spot electricity energy market and the electricity capacity market constructed in Steps 2 and 3, use the KKT optimality conditions to represent the originally complex optimization problem in the form of constraints, so as to better integrate into the overall model. In this way, the two-layer optimization model is transformed into a single-layer mathematical program with equilibrium constraints (MPEC) problem.

[0012] Step 5: Transform the model into a linear form. To improve the computational efficiency of the model and ensure the stability of the solution, a linearization transformation is performed on the MPEC model constructed in Step 4. The complex non-linear model is linearized into a mixed-integer linear programming (MILP) problem that is easier to solve, so that it can be efficiently processed by existing solvers. Specifically, for the product terms involving continuous variables in the objective function, use strong duality to transform them into a linear form; for the complementary slack constraints in the KKT conditions, introduce the big-M method to achieve linearization; for the MAX function, use equivalent inequality constraints for replacement.

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

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

[0015] Preferably, in the said Step 1, the decision-making model includes: the bid and output constraints of the units, and the decision-making content of the decision-making model includes: the maximum output declaration, flexible regulation ability, electricity energy price in the electricity energy market, and the bids and declared quantities of the reliability capacity and flexibility capacity in the capacity market;

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

[0017] (1)

[0018] (2)

[0019] (3)

[0020] (4)

[0021] Among them, represents the profit of enterprise g in the spot electricity energy market, represents the profit of enterprise g in the electricity capacity market, represents the scenario the default capacity penalty of power generation enterprise g in represents the scenario the cleared generated electricity of generator set i at time t in represents the scenario the electricity energy clearing price of node n at time t in represents the generation cost of unit i, represents the reliability capacity clearing price of region s, represents the awarded scalar of the reliability capacity of unit i, represents the flexibility capacity clearing price of region s, represents the awarded scalar of the flexibility capacity of unit i, represents the scenario the maximum output of the electricity energy declared by unit i in represents the scenario the flexible ramping ability declared by unit i in 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 2, during the clearing process of the lower-layer spot electricity energy market clearing model, the output limit of the unit, the flexible regulation ability limit, the system node power balance, and the line power flow limit constraints are considered to ensure the optimal allocation of resources under the system physical constraints;

[0023] The objective function of the lower-layer spot electricity energy market clearing model is:

[0024] (11)

[0025] In formula (11), represents the electricity energy bid of unit i in the scenario; represents the scenario the cleared generated electricity of generator set i at time t in

[0026] Preferably, in step 3, the lower-layer power capacity market clearing model simultaneously considers the capacity transmission constraints between regions to ensure the effective allocation and sharing of capacity resources between different regions, achieve capacity balance in each region of the system, and optimize the resource allocation of the overall network;

[0027] The objective function of the lower-layer power capacity market clearing model is:

[0028] (20)

[0029] In formula (20), represents the bid for the d-th segment of the reliability capacity demand curve in region s, represents the cleared capacity of the d-th segment of the reliability capacity demand curve in region s, represents the bid for the d-th segment of the flexibility capacity demand curve in region s, represents the cleared capacity of the d-th segment of the flexibility capacity demand curve in region s.

[0030] Preferably, in step 5, the specific steps include: for the product terms involving continuous variables in the objective function, use strong duality to transform them into linear forms; for the complementary slack constraints in the KKT conditions, introduce the big M method to achieve linearization; for the MAX function, use equivalent inequality constraints for substitution.

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

[0032] 1. In the evaluation of the effective flexibility capacity of generating units, the present invention not only considers the technical characteristics of the units but also fully considers the strategic behaviors of power generation enterprises in the market, making the evaluation results more in line with the actual market situation and more truly reflecting the decision-making preferences of power generation enterprises under different market conditions.

[0033] 2. The present invention designs a power capacity market mechanism that simultaneously considers the double adequacy of power system reliability and flexibility. 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's net load through reasonable flexibility capacity allocation, better meeting the needs of the new power system.

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

[0035] 4. The present invention transforms the lower - level problem in the two - layer problem into an equivalent optimality condition. This transformation directly reflects the market - clearing result of the lower - level in the upper - level decision - making process, ensuring that the decision - making results of each power generation enterprise in the spot electricity energy market and the power capacity market conform to the market equilibrium state, and realizing the efficient operation of the overall market and the optimal allocation of resources. Brief Description of the Drawings

[0036] Figure 1 It is a model framework and solution process diagram of the method for evaluating the effective flexibility capacity of units considering the strategic behavior of power generation enterprises according to the present invention;

[0037] Figure 2 It is a topological diagram of the improved IEEE 5 - node system according to the present invention. Detailed Embodiments

[0038] Next, the relevant technologies in the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0039] Reference Figures 1 to 2 , the following gives the implementation manner of the method for evaluating the effective flexibility capacity of units considering the strategic behavior of power generation enterprises according to the present invention.

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

[0041] The goal of this model is to maximize the comprehensive profit of power generation enterprises through their decisions in the spot electricity energy market and the power capacity market. The decision - making variables of power generation enterprises include the maximum output declared in the spot electricity energy market, the flexible ramping ability, the electricity energy price, as well as the quotes and quantities of reliability capacity and flexibility capacity in the power capacity market.

[0042] 1) Objective function

[0043] Taking power generation enterprise g as an example, its specific goal is:

[0044] (1)

[0045] (2)

[0046] (3)

[0047] (4)

[0048] Wherein, is the profit of enterprise g in the spot electricity energy market, is the profit of enterprise g in the electricity capacity market, is the scenario the default capacity penalty of power generation enterprise g in is the scenario the cleared power generation of generator set i at time t in is the scenario the electricity energy clearing price of node n at time t in is the power generation cost of unit i, is the reliability capacity clearing price of region s, is the awarded scalar of the reliability capacity of unit i, is the flexibility capacity clearing price of region s, is the awarded scalar of the flexibility capacity of unit i, is the scenario the maximum power output of electricity energy declared by unit i in is the scenario the flexible ramping ability declared by unit i in 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] Among them, is the electricity energy bid of unit i in the scenario is the upper and lower limits of the unit's electricity energy bid, is the minimum technical output of unit i, is the equivalent available factor of the unit, is the installed capacity of unit i, is the reliability capacity bid of unit i, is the flexibility capacity bid of unit i, are the upper and lower limits of the reliability capacity bid for the unit, is the flexibility capacity bid for unit i, 、 are the upper and lower limits of the flexibility capacity bid for the unit, is the maximum reliability capacity declared by unit i, is the minimum ratio of the declared reliability capacity to the installed capacity, is the maximum flexibility capacity declared by unit i, is the maximum ramping rate of the unit. Constraint (5) limits the electricity energy bid of the unit, constraint (6) limits the declared electricity energy output of the unit, constraints (7) and (8) limit the reliability and flexibility capacity bids, and equations (9) - (10) limit the maximum reliability and flexibility capacities declared by the unit.

[0057] II. Construct the clearing model of the lower - layer spot electricity energy market.

[0058] The clearing model of the lower - layer spot electricity energy market aims to determine the generation output and clearing electricity price of each generating unit in each time period at the lowest system cost.

[0059] 1) Objective function

[0060] (11)

[0061] 2) Constraint conditions

[0062] (12)

[0063] (13)

[0064] (14)

[0065] (15)

[0066] (16)

[0067] (17)

[0068] (18)

[0069] (19)

[0070] where is the load at node n in time period t in scenario , 、 are the actual outputs of wind turbine w and photovoltaic generator v in time period t in scenario respectively. is the admittance of line nm, is the scenario the voltage phase angle of node n at time t in the scenario, is the maximum transmission power of line nm, 、 are respectively the scenario the predicted output powers of wind power w and photovoltaic v at time t in the scenario, indicates that node m and node n are connected by a transmission line, indicates that units i, w, and v are located at node n. Constraint (12) is the system node power balance constraint, constraint (13) is the upper and lower limit constraint of the winning bid power of the unit, constraints (14) - (15) are the upper and lower limit constraints of the flexible ramp regulation of the unit in adjacent time periods, constraint (16) is the upper and lower limit constraint of the line transmission power, constraint (17) is the upper and lower limit constraint of the system node voltage phase angle, and formulas (18) - (19) indicate that the actual output powers of the wind farm and the photovoltaic power station are less than their predicted output powers.

[0071] III. Construct the lower - layer power capacity market clearing model.

[0072] The power capacity market ensures that the power system can have sufficient reliability capacity and flexibility capacity in the future through the market mechanism, and the goal is to maximize the total market surplus.

[0073] 1) Objective function

[0074] (20)

[0075] Among them, is the bid price of the d - th segment of the reliability capacity demand curve in region s, is the cleared capacity of the d - th segment of the reliability capacity demand curve in region s, is the bid price of the d - th segment of the flexibility capacity demand curve in region s, is the cleared capacity of the d - th segment of the flexibility capacity demand curve in region s.

[0076] 2) Constraint conditions

[0077] (21)

[0078] (22)

[0079] (23)

[0080] (24)

[0081] (25)

[0082] (26)

[0083] (27)

[0084] (28)

[0085] Among them, is the reliability capacity transmitted from region r to region s, is the flexibility capacity transmitted from region r to region s, is the maximum demand capacity of the d-th segment of the reliability capacity demand curve of region s, is the maximum demand capacity of the d-th segment of the flexibility capacity demand curve of region s, is the transmission capacity limit between region r and region 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 of reliability and flexibility capacity in region clearing, constraints (23)-(24) represent the upper and lower limits of reliability and flexibility capacity for successful bid of units, constraints (25)-(26) represent the clearing quantity limit of reliability and flexibility capacity for each segment on the demand side, and constraints (27)-(28) represent the regional transmission capacity limit.

[0086] IV. Transform the lower-layer market clearing problem into KKT optimality conditions.

[0087] By transforming the lower-layer problem into KKT conditions and embedding them into the upper-layer model, the entire two-layer optimization problem can be transformed into a single-layer MPEC problem, which is convenient for solution. The KKT conditions of each lower-layer market clearing model are specifically as follows:

[0088] 1) KKT conditions of the spot electric energy 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 slackness conditions, and dual feasibility conditions).

[0123] 2) KKT Conditions of 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 the gradient condition, complementary slackness condition, and dual feasibility condition).

[0157] Finally, the objective functions are given by formulas (1)-(4), and the constraints for the decision-making of power generation enterprise g are the MPEC model with formulas (5)-(10) and (62)-(93).

[0158] V. Transforming the Model into a Linear Form

[0159] (1) Linearizing the Product Terms of Continuous Variables

[0160] For the objective function 、 For this type of product term of continuous variables, the strong duality theorem is used to perform linearization transformation on it.

[0161] Taking as an example, its linearization process is as follows:

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

[0163] (94)

[0164] It can be derived from the strong duality theorem formula, the gradient conditions (70)-(71), and the complementary slackness conditions (76)-(79):

[0165] (95)

[0166] (2)Linearization of complementary slackness constraints

[0167] By introducing binary variables and large M values, the large M method is used to perform linearization transformation on the complementary slackness conditions (42)-(55), (76)-(87).

[0168] Taking the constraint as an example, it can be replaced by the following formula:

[0169] (96)

[0170] (97)

[0171] (98)

[0172] Among them, 、 are real numbers large enough to ensure the feasibility of the constraints.

[0173] (3)Linearization of the MAX function

[0174] For the following form of optimization problem:

[0175] (99)

[0176] It can be rewritten as:

[0177] (100)

[0178] Using this method, by introducing the auxiliary variable , 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] VI. Converting the model into a linear form

[0185] Through the above method, the double - layer problem is converted into a single - layer MILP problem, and the Gurobi solver can be directly called for solution.

[0186] Embodiment

[0187] To illustrate the effectiveness and feasibility of the invention, an embodiment of the method for evaluating the effective flexibility capacity of units considering the strategic behavior of power generation enterprises according to the present invention is given below.

[0188] The embodiment uses an improved IEEE 5 - node system for verification, and its system topology diagram is as Figure 2 shown. By simulating the decision - making situations of power generation enterprises in the spot electric energy market and the power capacity market in this small - scale system, it is illustrated how the proposed evaluation method is applied in a specific power system.

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

[0190]

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

[0192] In spring and autumn scenarios, whether only considering the spot electric energy market or the coordination mechanism between the power capacity market and the spot electric energy market, thermal power unit #1 declared its available ultimate flexibility capacity, i.e., 45.00 / 15 min. This is because the system net load is relatively stable in these two seasons, and the price signals provided by the electric energy market itself are sufficient to motivate the unit to declare its maximum flexibility capacity. The unit tends to offer a low price and a high declared volume to obtain benefits. Therefore, the coordination mechanism of the power capacity market has no additional impact on the unit's declaration behavior in this scenario. This indicates that the additional incentive effect of the capacity market is relatively limited when the net load is stable.

[0193] In summer and winter scenarios, the system net load fluctuation increases significantly. If only considering the spot electric energy market, the flexible ramping ability declared by thermal power unit #1 in the market under this condition is only 42.00 / 40.00 (MW / 15 min), failing to fully utilize its technical potential. This is because by reducing the declared volume of flexible ramping ability, the unit can remove itself from the role of marginal unit at the peak of net load fluctuation, enabling other units with higher bids to become marginal units and driving up the nodal electricity price. This strategic behavior can further improve its overall revenue when the spot electric energy market is price-sensitive. After considering the power capacity market, since this mechanism provides additional compensation for the unit's flexibility capacity, it successfully prompts the unit to increase its flexible ramping ability provided in the electric energy market to 45.00 / 44.00 (MW / 15 min). This shows that in scenarios with strong net load fluctuations, the power capacity market can effectively make up for the insufficient incentive for flexibility capacity supply in the spot electric energy market, thus better meeting the system's demand for flexibility adequacy under various extreme conditions.

[0194] This result fully demonstrates that the power capacity market mechanism considering flexibility requirements can make up for the insufficient incentive for flexibility capacity in the spot electric energy market, prompting units to adjust their declaration strategies according to system requirements, utilize 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 electric energy market on unit decisions, the evaluation of the effective flexibility capacity of units not only reflects the maximum flexible ramping ability determined by technical characteristics but also shows the significant influence of the market environment on their willingness to provide, making the evaluation results more in line with the actual market operation conditions and providing a scientific and effective reference for market design and system planning.

[0195] In summary, in the evaluation of the effective flexibility capacity of the generating unit, the present invention not only considers the technical characteristics of the unit, but also fully considers the strategic behaviors of power generation enterprises in the market, making the evaluation results more in line with the actual market situation and more truly reflecting the decision-making preferences of power generation enterprises under different market conditions. Therefore, the present invention can ensure that the decision-making results of each power generation enterprise in the spot electric energy market and the power capacity market conform to the market equilibrium state, realizing the efficient operation of the overall market and the optimal allocation of resources.

[0196] It should be emphasized that the above are only the preferred embodiments of the present invention, and there is no limitation to the present invention in any form. Any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall 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 then obtain the effective flexibility capacity evaluation result of the unit participating in the market; 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; 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.

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 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 the dth segment of the flexible capacity demand curve of region s, represents the reliability capacity quotation of unit i, represents the flexibility capacity quotation of unit i.

3. 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.

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