Optimization method of unit operation strategy under the coordination of electricity energy market and capacity market

Abstract

The present invention belongs to the technical field of power system operation control, and relates to a method for optimizing unit operation strategies under the coordination of an electric energy market and a capacity market, comprising: 1. constructing a decision optimization model for upper-level power generation enterprises to participate in an electric energy spot market and an electric capacity market, 2. constructing a lower-level electric energy spot market clearing model, 3. constructing a lower-level electric capacity market clearing model, 4. converting the lower-level market clearing problem into a KKT optimality condition, 5. linearizing the model, and 6. calling a Gurobi solver to solve the linearized mixed integer linear programming problem; the present invention fully considers the coordinated operation relationship between the electric energy spot market and the electric capacity market, realizes the short-term optimization of the electric energy market and the long-term planning of the electric capacity market complement each other, enhances the linkage between markets, further improves the overall market efficiency, and effectively guarantees the reliability margin of the system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation control, and relates to a method for optimizing unit operation strategies under the coordination of electric energy market and capacity market. Background Art

[0002] At present, with the continuous increase in the penetration rate of new energy, the uncertainty and volatility of the power system have increased significantly, and the problem of ensuring system reliability has become more prominent. In the new power system, the coordinated operation of the power capacity market and the electric energy market is of great significance to ensuring the reliability and economy of the system. As a commonly used and effective mechanism for ensuring the surplus of power generation capacity, the power capacity market mechanism ensures sufficient power generation capacity during the peak demand period of the system through market-based means, thereby improving the reliability of the system; while the electric energy market focuses on the short-term balance of power supply and demand to ensure the economic transmission of electric energy.

[0003] When formulating the actual operation strategy of the unit, power generation companies need to fully consider market conditions, such as market production arrangements, price signals, cost-benefit analysis, etc. Even if a unit has a strong power generation capacity, if the market conditions are not ideal, the power generation company may still be unwilling to fully devote the capacity of its unit to the electric energy market, which will lead to insufficient system reserve rate in the long run. As a forward market, the clearing results of the power capacity market will provide signals for the decision-making behavior of power generation companies in the electric energy spot market: the winning unit needs to promise to maintain the availability of a certain capacity of its unit in the electric energy spot market. This availability commitment requires power generation units to give priority to fulfilling capacity contracts when arranging production plans in the electric energy market to avoid high capacity default penalties, rather than simply pursuing the maximization of profits in the electric energy spot market.

[0004] However, the existing research on optimization of power generation unit operation strategies has not yet combined long-term power capacity planning with short-term market demand. The power capacity market and the electric energy market usually operate independently. The optimization strategy is often based on the best of a single market, lacks consideration of the mutual influence between markets, and price signal transmission is insufficient. Insufficient market coordination will make it difficult for power generation units to obtain effective feedback on market interactions from market prices, resulting in a certain degree of blindness in power capacity declaration, electric energy declaration, and power production planning, and unable to find the best balance between capacity benefits and real-time energy benefits, and make comprehensive optimal decisions.

[0005] Therefore, an optimization method is needed to solve the above technical problems, which fully considers the coordinated operation relationship between the electricity spot market and the electricity capacity market, can improve the overall market efficiency, and effectively ensure the reliability margin of the system. Summary of the invention

[0006] The present invention takes into account the strategic behavior of power generation entities in the collaborative operation environment of the power capacity market and the electric energy spot market, and proposes a method for optimizing the unit operation strategy under the collaboration of the electric energy market and the capacity market. The present invention constructs a two-layer master-slave game model for generator sets participating in the power capacity market and the electric energy spot market. In the upper model, the generator sets aim to maximize the overall profit of participating in the two markets and make decisions on their market declaration behaviors in each market; in the lower model, each market clears according to its own mechanism, and the clearing results interact with the upper decision-making behavior, and finally the power output curve of the generator set is obtained.

[0007] The technical solution adopted by the present invention to solve the technical problem is: a method for optimizing the unit operation strategy under the coordination of the electric energy market and the capacity market, comprising the following steps:

[0008] Step 1: Construct a decision-making optimization model for upper-level power generation enterprises to participate in the electric energy spot market and the electric energy capacity market; the goal of the decision-making optimization model is to maximize their comprehensive profits in the electric energy spot market and the electric energy capacity market;

[0009] Step 2: construct a lower-level electric energy spot market clearing model; the lower-level electric energy spot market clearing model optimizes the allocation of the declared output of the generator sets with the goal of minimizing the market operation cost;

[0010] Step 3: Construct a lower-level power capacity market clearing model; the lower-level power capacity market clearing model aims to maximize the total social welfare and ensures that the overall benefits of the power system when purchasing power capacity are maximized;

[0011] Step 4: Convert the optimization problem of the lower-level electric energy spot market clearing model constructed in step 2 and the lower-level electric capacity market clearing model constructed in step 3 into equivalent KKT optimality conditions, so that the two-layer model of each generator unit decision is converted into a single-layer mathematical programming MPEC model with equilibrium constraints;

[0012] Step 5: Linearize the mathematical programming MPEC model with equilibrium constraints constructed in step 4, and simplify the complex original model into a mixed integer linear programming MILP problem;

[0013] Step 6: Call the Gurobi solver to solve the mixed integer linear programming (MILP) problem in step 5, and then derive the optimal operation strategy of the generator set in the power capacity market and the electric energy spot market.

[0014] Preferably, in step 1, the decision optimization model takes the power generation enterprise as the decision-making subject, and decides its declaration behavior in the electric energy spot market and the electric power capacity market according to the technical constraints of the unit, market rules and price signals;

[0015] The objective function of the decision optimization model is:

[0016] (1)

[0017] (2)

[0018] (3)

[0019] (4)

[0020] In formulas (1) to (4), represents the revenue of power generation enterprise g in the electric energy spot market, represents the revenue of power generation enterprise g in the power capacity market, Representation scene The default capacity penalty for power generation company g is Representation scene The clearing power generation of thermal power unit i during period t is: Representation scene The energy clearing price of node n in period t is: represents the power generation cost of unit i, represents the power capacity clearing price in region s, represents the bid value of the power capacity of unit i, represents the unit default capacity penalty coefficient, Representation scene The maximum output declared by unit i in the electricity spot market, 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; Formula (2) represents the profit of unit i in the electric energy spot market, Formula (3) represents the profit of unit i in the electric capacity market, and Formula (4) represents the penalty imposed on unit i for failing to fulfill the bid for electric capacity.

[0021] Preferably, in step 2, the lower-layer electric energy spot market clearing model takes into account the output limitation of the unit, the flexible adjustment constraint of the unit, the node power balance, and the line flow constraint to ensure the most economical resource allocation under the condition of satisfying the safe operation of the system;

[0022] The objective function of the lower-level electricity spot market clearing model is:

[0023] (8)

[0024] In formula (8), Representation scene The electricity energy quotation of unit i; Representation scene The clearing power generation of thermal power unit i during period t.

[0025] Preferably, in step 3, the power capacity demand curve is represented by a segmented step demand curve to reflect the comprehensive consideration of system reliability and economy; the lower power capacity market clearing model also considers the capacity transmission constraints between regions, so that each region of the system can achieve capacity balance and ensure that the system has sufficient power capacity;

[0026] The objective function of the lower-level power capacity market clearing model is:

[0027] (17)

[0028] In formula (17), represents the price of the dth segment of the power capacity demand curve in region s, represents the clearing capacity of the dth segment of the electricity capacity demand curve of region s, represents the power capacity quotation of unit i, Represents the bid value of the power capacity of unit i.

[0029] Preferably, in step 5, for the multiplication terms of continuous variables and continuous variables in the objective function, they are converted into equivalent linear forms based on the strong duality theorem; for the complementary slack conditions in the KKT conditions, they are converted into linear inequality constraints using the big M method; for the MAX function, inequality constraints are used for replacement.

[0030] The beneficial effects of the present invention are:

[0031] 1. The present invention fully considers the cooperative operation relationship between the electric energy spot market and the electric power capacity market, that is, the capacity of the unit that wins the bid in the electric power capacity market must fulfill its capacity commitment in the electric energy spot market. This relationship realizes the short-term optimization of the electric energy market and the long-term planning of the electric power capacity market, enhances the linkage between markets, further improves the overall market efficiency, and effectively guarantees the reliability margin of the system.

[0032] 2. The present invention combines the price signals of the electric energy spot market and the electric energy capacity market to construct a comprehensive decision-making optimization model for power generation enterprises, guides power generation enterprises to comprehensively consider the benefits of the two types of markets, formulate market strategies, and find the balance point with the greatest comprehensive benefits between long-term capacity benefits and short-term electric energy benefits.

[0033] 3. The present invention simplifies the two-layer model into a single-layer model by converting the lower-layer electric energy spot market and power capacity market clearing problem into KKT optimality conditions. This conversion enables the generator set to make market declarations and production plan arrangements based on more accurate market feedback information, thereby maximizing revenue. At the same time, this conversion reduces the complexity of problem solving, ensures the efficiency and practicality of the model, and enables it to be applied to larger-scale examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a model framework and solution process diagram of the unit operation strategy optimization method under the coordination of the electric energy market and the capacity market of the present invention;

[0035] Figure 2 is a modified IEEE 5-node system topology diagram of the present invention;

[0036] Figure 3 is a capacity demand curve diagram of each area of ​​the present invention;

[0037] Figure 4 This is a comparison chart of the maximum output of electric energy reported by the unit #1 of the present invention in the electric energy market;

[0038] Figure 5 It is a comparison chart of the operating curves of unit #1 of the present invention on a typical day (summer). DETAILED DESCRIPTION

[0039] 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.

[0040] refer to Figures 1 to 5 The present invention provides a method for optimizing the unit operation strategy under the coordination of the electric energy market and the capacity market. It includes the following steps:

[0041] Step 1: Construct a decision-making optimization model for upper-level power generation enterprises to participate in the electric energy spot market and the electric power capacity market. The model takes power generation enterprises as the decision-making subject, and aims to maximize their comprehensive profits in the electric energy spot market and the electric power capacity market. According to the technical constraints of the units, market rules and price signals, the model decides on their declaration behaviors in the electric energy spot market and the electric power capacity market.

[0042] Step 2: Construct the lower-level electric energy spot market clearing model. The electric energy spot market clearing model aims to minimize the market operation cost and optimizes the allocation of the declared output of the generator sets. The market clearing process takes into account the output limit of the generator set, the flexible adjustment constraint of the generator set, the node power balance, the line flow constraint, etc., to ensure the most economical resource allocation under the condition of meeting the safe operation of the system.

[0043] Step 3: Construct a lower-level power capacity market clearing model. This model aims to maximize total social welfare and ensure that the overall benefits of the power system when purchasing power capacity are maximized. The power capacity demand curve is represented by a segmented step-type demand curve to reflect the comprehensive consideration of system reliability and economy. The model also considers the capacity transmission constraints between regions, so that each region of the system can achieve capacity balance and ensure that the system has sufficient power capacity.

[0044] Step 4, convert the lower market clearing problem into KKT optimality conditions (Karush–Kuhn–Tucker optimality conditions). For the electric energy spot market and power capacity market clearing models constructed in steps 2 and 3, the present invention converts the lower optimization problem into an equivalent KKT optimality condition. This condition is used as a constraint condition for the upper model, so that the two-layer model of each generator unit decision is converted into a single-layer mathematical program with equilibrium constraints (MPEC) problem.

[0045] Step 5, linearization of the model. In order to improve the computational efficiency of the model and ensure that it has good convergence in the solution process, the present invention linearizes the above-constructed mathematical programming with equilibrium constraints (MPEC) model. By converting the nonlinear conditions into linear form, the complex original model is simplified to a mixed-integer linear programming (MILP) problem, so that it can be effectively solved by a mature solver. Among them, for the multiplication terms of continuous variables and continuous variables in the objective function, they are converted into equivalent linear forms based on the strong duality theorem; for the complementary relaxation conditions in the KKT conditions, the big M method is used to convert them into linear inequality constraints; for the MAX function, inequality constraints are used for replacement.

[0046] Step 6: Call the Gurobi solver to solve the linearized mixed integer linear programming (MILP) problem. Under the premise of ensuring the solution accuracy, the Gurobi solver gradually approaches the optimal solution through an efficient system branching and delimiting strategy, and then obtains the optimal operation strategy of the generator set in the power capacity market and the electric energy spot market.

[0047] The present invention mainly includes the following key technical steps: first, a two-layer master-slave game model of power generation enterprises participating in the power capacity market and the electric energy spot market is established; second, the two-layer decision-making model of the power generation enterprise is converted into a single-layer model through the KKT optimality condition to obtain the MPEC model; then, the model is linearized through the big M method, strong duality theorem, etc., and it is converted into a MILP problem; finally, the Gurobi solver is called to efficiently solve the model to obtain the optimal operation strategy of the power generation unit.

[0048] The following are specific steps of the method for optimizing the generator set operation strategy under the coordination of the power capacity market and the electric energy spot market in this specific implementation method.

[0049] 1. Construct a decision-making optimization model for upper-level power generation enterprises to participate in the electricity spot market and the electricity capacity market.

[0050] The goal of the upper-level power generation enterprise decision optimization model is to maximize the overall profit of the power generation enterprise in the electric energy spot market and the electric energy capacity market. The decision variables of the power generation unit include the maximum electric energy output and electric energy price declared in the electric energy spot market, and the capacity price declared in the electric energy capacity market.

[0051] 1) Objective function

[0052] The power generation company g is the decision-making entity, and its goal is to maximize the total profit in the electricity spot market and the power capacity market:

[0053] (1)

[0054] (2)

[0055] (3)

[0056] (4)

[0057] in, is the revenue of power generation enterprise g in the electric energy spot market, is the revenue of power generation 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 thermal power unit i during period t is: For the scene The energy clearing price of node n in period t is: is the power generation cost of unit i, is the electricity capacity clearing price in region s, is the bid value of the power capacity of unit i, is the unit default capacity penalty coefficient, For the scene The maximum output declared by unit i in the electricity spot market, 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. Formula (2) represents the profit of unit i in the electric energy spot market, formula (3) represents the profit of unit i in the power capacity market, and formula (4) represents the penalty imposed on unit i for failing to fulfill the bid power capacity. It should be noted that the electric energy spot market selects four typical days in spring, summer, autumn and winter as representative scenarios for clearing, and the power capacity market clearing price is a daily price, so it needs to be multiplied by the corresponding coefficient to convert to annual profit.

[0058] 2) Constraints

[0059] The application of power generation enterprises to participate in the electricity spot market and the electricity capacity market is subject to a series of constraints:

[0060] (5)

[0061] (6)

[0062] (7)

[0063] in, For the scene The electricity price of unit i is the upper and lower limits of the unit price in the electricity market. is the minimum technical output of unit i, is the equivalent availability factor of unit i, is the installed capacity of unit i, is the power capacity quotation of unit i, Formula (5) represents the upper and lower limits of the power capacity quotation of unit i, Formula (6) represents the upper and lower limits of the maximum power output declared by unit i, and Formula (7) represents the upper and lower limits of the power capacity quotation of unit i.

[0064] 2. Construct a clearing model for the lower-level electricity spot market.

[0065] The lower-level electricity spot market clearing model aims to minimize the system operating costs. It optimizes the output allocation of units in each period of the system to ensure the economy of the electricity market and the safety of system operation.

[0066] 1) Objective function

[0067] The goal of clearing the lower-level electricity spot market is to minimize system operating costs:

[0068] (8)

[0069] 2) Constraints

[0070] The clearing of the electric energy spot market must meet the technical output constraints of each unit and the system operation safety constraints:

[0071] (9)

[0072] (10)

[0073] (11)

[0074] (12)

[0075] (13)

[0076] (14)

[0077] (15)

[0078] (16)

[0079] in For the scene The load of node n in period t, , For the scene The actual output of wind turbine w and photovoltaic generator v during period t, is the admittance of line nm, For the scene The phase angle of node n during period t, is the maximum ramp-up power of thermal power unit i, is the maximum down-slope power of thermal power unit i, is the maximum transmission power of line nm, , Respectively for scenes The predicted output of wind turbine w and photovoltaic generator v during 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. Equation (9) is the node power balance constraint, Equation (10) limits the winning bid output of the generator unit in the electric energy market, Equations (11)-(12) are the up and down ramp constraints of the unit, Equation (13) is the line flow constraint, Equation (14) is the voltage phase angle constraint, and Equations (15)-(16) are the output constraints of wind farms and photovoltaic power stations. , , , , , , , , , , , , are the dual variables of the corresponding constraints respectively.

[0080] 3. Construct a lower-level electricity capacity market clearing model.

[0081] The electricity capacity market ensures that the power system will have sufficient reliable power generation capacity in the future through capacity auctions.

[0082] 1) Objective function

[0083] The goal of the electricity capacity market is to maximize total social welfare, which is:

[0084] (17)

[0085] in, Quote for segment d of the electricity capacity demand curve for region s, is the clearing capacity of the dth segment of the electricity capacity demand curve in region s.

[0086] 2) Constraints

[0087] (18)

[0088] (19)

[0089] (20)

[0090] (twenty one)

[0091] in, is the power capacity transmitted from region r to region s, is the maximum demand capacity of the dth segment of the power 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 region r is connected to region s, , , , , , , are the dual variables of the corresponding constraints. Formula (18) is the regional power capacity clearing balance constraint, Formula (19) is the power capacity clearing constraint of thermal power unit i, Formula (20) is the demand side segment clearing power capacity restriction constraint, and Formula (21) is the line transmission power capacity restriction constraint.

[0092] Fourth, transform the two-layer model into a single-layer MPEC problem.

[0093] The underlying electricity spot market and power capacity market clearing problems are essentially linear convex optimization problems. The KKT optimality conditions for this type of problem can be transformed by the following steps:

[0094] The original problem of linear convex optimization problem has the general form:

[0095] (twenty two)

[0096] in, is the objective function, is an inequality constraint, is an equality constraint.

[0097] Introducing Lagrange multipliers (inequality constraints) and (equality constraint), the corresponding Lagrangian function for:

[0098] (twenty three)

[0099] The KKT optimality condition for this optimization problem includes the following parts:

[0100] ① Feasibility conditions

[0101] Solution to the original problem Satisfy all constraints:

[0102] (twenty four)

[0103] ② Gradient conditions

[0104] At the optimal solution, the gradient of the objective function can be expressed as a linear combination of the gradients of all inequality and equality constraint functions:

[0105] (25)

[0106] ③Complementary relaxation conditions

[0107] For inequality constraints, if , then the corresponding Lagrange multiplier , otherwise if ,but .

[0108] (26)

[0109] ④ Non-negativity conditions of Lagrange multipliers

[0110] The Lagrange multipliers for inequality constraints must be non-negative:

[0111] (27)

[0112] By constructing the KKT condition of the lower-level market clearing problem and introducing it as a constraint condition of the upper-level power generation enterprise decision-making model, the two-level market decision-making process is transformed into a single-level MPEC problem. At this time, the objective function of the upper-level model remains unchanged, but the KKT condition from the lower-level market clearing problem is added to its constraint condition, thereby ensuring that the decision of the upper-level power generation unit is optimized under the premise of meeting the optimal clearing of the lower-level market.

[0113] The above method transforms the lower market clearing problem into the KKT optimality condition, which is:

[0114] 1) KKT conditions of the lower-level electricity spot market clearing model

[0115] ① Feasibility conditions

[0116] That is, the original model constraints (9) to (16).

[0117] ② Gradient conditions

[0118] (28)

[0119] (29)

[0120] (30)

[0121] (31)

[0122] (32)

[0123] ③Complementary relaxation conditions

[0124] (33)

[0125] (34)

[0126] (35)

[0127] (36)

[0128] (37)

[0129] (38)

[0130] (39)

[0131] (40)

[0132] (41)

[0133] (42)

[0134] (43)

[0135] (44)

[0136] (45)

[0137] (46)

[0138] ④ Non-negativity conditions of Lagrange multipliers

[0139] (47)

[0140] (48)

[0141] (49)

[0142] (50)

[0143] (51)

[0144] (52)

[0145] 2) KKT conditions of the lower-tier power capacity market clearing model

[0146] ① Feasibility conditions

[0147] Original model constraint (18) to constraint (21).

[0148] ② Gradient conditions

[0149] (53)

[0150] (54)

[0151] (55)

[0152] ③Complementary relaxation conditions

[0153] (56)

[0154] (57)

[0155] (58)

[0156] (59)

[0157] (60)

[0158] (61)

[0159] ④ Non-negativity conditions of Lagrange multipliers

[0160] (62)

[0161] (63)

[0162] (64)

[0163] Finally, equations (1)-(4) constitute the objective function, and equations (5)-(7), (9)-(16), (18)-(21), and (28)-(64) constitute the MPEC model for the power generation enterprise g decision-making under constraints.

[0164] 5. Model Linearization

[0165] (1) Linearization of the multiplication of continuous variables

[0166] In the objective function, , Nonlinear terms involving the multiplication of continuous variables can be linearized by using the strong duality theorem, gradient conditions, and complementary relaxation conditions. Take it as an example, the linearization process is as follows:

[0167] The strong dual theorem of the lower-level power capacity market clearing model is:

[0168] (65)

[0169] From equation (65), gradient condition (53), and complementary relaxation conditions (56)-(57), we can deduce:

[0170] (66)

[0171] (2) Complementary Relaxation Constraint Linearization

[0172] The complementary relaxation condition constraints (33)-(46), (56)-(61) can be linearized using the big M method to constrain For example, it can be replaced by the following formula:

[0173] (67)

[0174] (68)

[0175] (69)

[0176] in, , is a sufficiently large positive real number.

[0177] (3) MAX function linearization

[0178] For the following optimization problems:

[0179] (70)

[0180] can be restated as:

[0181] (71)

[0182] Using this method, we introduce auxiliary variables , equation (4) can be replaced by equations (72)-(74) to eliminate the nonlinear term:

[0183] (72)

[0184] (73)

[0185] (74)

[0186] 6. Call the Gurobi solver for solving.

[0187] Example

[0188] This embodiment uses the modified IEEE 5-node system as the implementation environment to solve the problem and illustrate the implementation effect of the present invention. The system topology is as follows: Figure 2 As shown, the system is divided into three areas, and the capacity demand curve of each area is as follows Figure 3 shown.

[0189] It is assumed that the decision-making enterprise owns thermal power unit #1. Under two circumstances, considering only the electric energy spot market and considering the coordination of the power capacity market and the electric energy spot market, the unit operation strategy of the power generation enterprise is solved, and the MPEC program is run to obtain the operation strategy of unit #1 in each typical scenario day under different circumstances.

[0190] The price reported by unit #1 in the electric energy market, the maximum output of reported electric energy and the comparison are shown in Table 1 and Figure 4 . When only the electric energy spot market is considered, the unit will choose to appropriately reduce the declared maximum output in the market, so as to affect the node marginal price during the net load peak period to a certain extent and increase the electric energy clearing price. Therefore, compared with the declared maximum capacity, although the scalar volume is reduced, its income in the electric energy market increases due to the increase in the node marginal price. After considering the coordination between the power capacity market and the electric energy spot market, since the unit promises to provide a certain amount of power capacity in the power capacity market to ensure the reliability of the system, if these promises are not fully fulfilled in the actual electric energy market, corresponding penalties will be imposed. Therefore, the unit will fulfill its capacity commitments in the power capacity market as much as possible, and adjust its participation strategies in different markets, sacrificing part of its income in the electric energy spot market and obtaining more income in the power capacity market, so as to achieve a higher profit level overall.

[0191]

[0192] The operating curve comparison of unit #1 on a typical day (summer) is as follows: Figure 5 As shown in the figure. When only the electric energy spot market is considered, due to the low bid price (280 yuan / MWh), unit #1 is able to win the bid with its declared capacity in each period and operate at the declared maximum electric energy output. When the power capacity market and the electric energy spot market are coordinated, the unit bid is relatively high (300 yuan / MWh), and the bid electric energy is significantly reduced during the net load valley period.

[0193] Table 2 shows the profit of unit #1 under different circumstances. When only the spot market for electric energy is considered, the total profit of unit 1 is 202,679,900 yuan / year. After considering the power capacity market, the total profit of unit 1 increases to 20,433.49 yuan. It can be seen that participating in the power capacity market can increase the total profit of the unit, help it recover fixed costs, and guide it to make reasonable investments.

[0194]

[0195] From the above, we can see that when the power capacity market is not taken into account, the units may pay more attention to influencing the clearing results of the power energy market by adjusting prices and the maximum power output declaration strategy to maximize their profits in the power energy spot market. After considering the power capacity market, the income obtained by the units through the power capacity market provides income security for the enterprises. Even if the profits in the power energy market are reduced, the power generation enterprises can still maintain or increase the total profits. Therefore, while ensuring the reliability of the system, the power capacity market compensates the fixed costs of the power generation units to provide capacity guarantee capabilities to a certain extent, which helps to promote the optimization of the profit structure of power generation enterprises, balance the power capacity income and the power energy income, and improve their enthusiasm for providing greater balance support capabilities for the system.

[0196] In summary, the present invention fully considers the synergistic operation relationship between the electric energy spot market and the electric power capacity market, that is, the capacity of the unit that wins the bid in the electric power capacity market must fulfill its capacity commitment in the electric energy spot market. This relationship realizes the short-term optimization of the electric energy market and the long-term planning of the electric power capacity market, which complement each other, enhances the linkage between markets, further improves the overall market efficiency, and effectively guarantees the reliability margin of the system. Therefore, while fully considering the synergistic operation relationship, the present invention can effectively improve the overall market efficiency and ensure the reliability of the system.

[0197] 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 optimizing unit operation strategy under the coordination of electric energy market and capacity market, characterized in that: The following steps are involved: Step 1: Construct a decision optimization model for upper-level power generation enterprises to participate in the electric energy spot market and the electric energy capacity market; the goal of the decision optimization model is to maximize their comprehensive profits in the electric energy spot market and the electric energy capacity market; Step 2: construct a lower-level electric energy spot market clearing model; the lower-level electric energy spot market clearing model optimizes the allocation of the declared output of the generator sets with the goal of minimizing the market operation cost; Step 3: construct a lower-level power capacity market clearing model; the lower-level power capacity market clearing model aims to maximize total social welfare and ensures that the overall benefits of the power system when purchasing power capacity are maximized; Step 4: convert the optimization problem of the lower-level electric energy spot market clearing model constructed in step 2 and the lower-level electric capacity market clearing model constructed in step 3 into an equivalent KKT optimality condition, so that the two-layer model of each generator unit decision is converted into a single-layer mathematical programming MPEC model with equilibrium constraints; Step 5: Linearize the mathematical programming MPEC model with equilibrium constraints constructed in step 4 to simplify the complex original model into a mixed integer linear programming MILP problem; Step 6, calling the Gurobi solver to solve the mixed integer linear programming MILP problem described in step 5, and then deriving the optimal operation strategy of the generator set in the power capacity market and the electric energy spot market; In step 1, the decision optimization model takes the power generation enterprise as the decision-making subject, and decides its declaration behavior in the electric energy spot market and the electric power capacity market according to the technical constraints of the unit, market rules and price signals; The objective function of the decision optimization model is: (1) (2) (3) (4) In formulas (1) to (4), represents the revenue of power generation enterprise g in the electric energy spot market, represents the revenue of power generation enterprise g in the power capacity market, Representation scene The default capacity penalty for power generation company g is Representation scene The clearing power generation of thermal power unit i during period t is: Representation scene The energy clearing price of node n in period t is: represents the power generation cost of unit i, represents the power capacity clearing price in region s, represents the bid value of the power capacity of unit i, represents the unit default capacity penalty coefficient, Representation scene The maximum output declared by unit i in the electricity spot market, 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; Formula (2) represents the profit of unit i in the electric energy spot market, Formula (3) represents the profit of unit i in the electric capacity market, and Formula (4) represents the penalty imposed on unit i for failing to fulfill the bid for electric capacity; In step 2, the lower-layer electric energy spot market clearing model takes into account the output limit of the unit, the flexible adjustment constraint of the unit, the node power balance, and the line flow constraint to ensure the most economical resource allocation under the condition of satisfying the safe operation of the system; The objective function of the underlying electricity spot market clearing model is: (8) In formula (8), Representation scene The electricity energy quotation of unit i; Representation scene The clearing power generation of thermal power unit i during period t.

2. The method for optimizing unit operation strategy under the coordination of electric energy market and capacity market according to claim 1 is characterized in that: In step 3, the power capacity demand curve is represented by a segmented step-type demand curve to reflect the comprehensive consideration of system reliability and economy; the lower-layer power capacity market clearing model also considers the capacity transmission constraints between regions, so that each region of the system can achieve capacity balance and ensure that the system has sufficient power capacity; The objective function of the lower-level power capacity market clearing model is: (17) In formula (17), represents the price of the dth segment of the power capacity demand curve in region s, represents the clearing capacity of the dth segment of the power capacity demand curve of region s, represents the power capacity quotation of unit i, Represents the bid value of the power capacity of unit i.

3. The method for optimizing unit operation strategy under the coordination of electric energy market and capacity market according to claim 1 is characterized in that: In step 5, the continuous variables and the multiplication terms of the continuous variables in the objective function are converted into equivalent linear forms based on the strong duality theorem; the complementary relaxation conditions in the KKT conditions are converted into linear inequality constraints using the big M method; and the MAX function is replaced by an inequality constraint.

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