A market approach for grid-connected energy storage ancillary services that combines electricity energy and capacity markets

Abstract

The present invention belongs to the technical field of power system planning and power market mechanism, and relates to a market method for grid-type energy storage auxiliary services that combines electric energy and capacity markets, including: establishing a transfer factor matrix PTDF, establishing an upper-layer investment model for grid-type energy storage, establishing a lower-layer joint clearing model, establishing a lower-layer capacity market clearing model, converting a double-layer model into a single-layer model based on duality theory, converting nonlinear terms into mixed integer linear programming problems, solving based on a gurobi commercial solver, and conducting economic evaluation of grid-type energy storage investment planning; the present invention further explores the multiple values ​​of grid-type energy storage as a flexibility and grid support resource by considering that grid-type energy storage simultaneously participates in electric energy, frequency regulation auxiliary services and capacity markets, and effectively solves the problem of difficult investment cost recovery of grid-type energy storage.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system planning and power market mechanism, and relates to a grid-building energy storage auxiliary service market method combining electric energy and capacity markets. Background Art

[0002] The proportion of installed capacity and electricity of renewable energy power generation has increased rapidly year by year. However, due to the strong randomness and uncertainty of the output of renewable energy, this has brought great challenges to the safe and stable operation of the power system. Grid-connected energy storage uses the control technology of the converter to enable the energy storage system to actively support the stability of the grid voltage, frequency and power angle. It is regarded as a key technology and feasible solution under the new power system. At present, the construction of grid-connected energy storage still has a high investment cost. In order to recover the investment cost, grid-connected energy storage can make profits by participating in the power market and fully tap the various values ​​of grid-connected energy storage resources, including maintaining the power balance of the system and actively supporting the power grid. In the environment where the power market mechanism is constantly improving, grid-connected energy storage can participate in the spot electricity market for arbitrage and participate in the capacity market to recover fixed costs. At the same time, considering that grid-connected energy storage has the ability to support frequency, it can provide frequency regulation auxiliary services. The price signals generated by the participation of grid-connected energy storage in the operation of the electricity market will affect the benefits obtained from grid-connected energy storage planning, and the results of grid-connected energy storage planning will affect the clearing prices of the participating markets. The two are related. Therefore, there is currently a lack of grid-connected energy storage auxiliary service market methods that consider the combined electricity and capacity markets associated with grid-connected energy storage planning and market operation.

[0003] At present, the coordination mechanisms of spot electricity and frequency regulation ancillary service markets in different power markets are different, and can be mainly divided into two categories: joint clearing and sequential clearing. Considering that grid-type energy storage has good frequency support capabilities, but at the same time its operation has strong time coupling, the difference in market clearing mechanisms will have a greater impact on the operating characteristics and benefits of grid-type energy storage. In the sequential clearing mode, the electricity energy market and the ancillary service market operate independently and are optimized separately, while joint clearing takes both into consideration to maximize overall benefits. Sequential clearing is simple to operate, but it will lead to market segmentation and efficiency loss; although joint clearing is more difficult to operate, it can improve market competitiveness and stability and promote the optimal allocation of resources. Therefore, joint clearing is more conducive to building a fair, transparent and efficient electricity market system.

[0004] The capacity market is an important mechanism to ensure the capacity adequacy of the power system. At present, it is difficult to meet the system capacity adequacy requirements by relying solely on traditional thermal power units. It is necessary to consider the participation of new energy and grid-connected energy storage resources in the capacity market. The current clearing price of the spot electricity market is the marginal cost of the unit, which makes it difficult to recover the fixed cost of the unit. In order to recover the investment cost of grid-connected energy storage, it has become an important means for grid-connected energy storage to participate in the capacity market to obtain certain capacity benefits.

[0005] Therefore, how to consider grid-connected energy storage's simultaneous participation in the electric energy, frequency regulation auxiliary services, and capacity markets, further explore the multiple values ​​of grid-connected energy storage as a flexible and grid-supporting resource, and solve the problem of difficulty in recovering investment costs for grid-connected energy storage has become an urgent need for power system planning and power market mechanisms. Summary of the invention

[0006] The technical solution adopted by the present invention to solve the technical problem is: a method for building a grid-type energy storage auxiliary service market by combining the electric energy and capacity markets, comprising the following steps:

[0007] Step 1: Determine the period for market clearing and pricing, obtain typical wind, solar and load data, thermal power unit output data, and economic and technical parameters related to grid-connected energy storage as input data;

[0008] Step 2: According to the physical model of the electrical network, establish the transfer factor matrix PTDF;

[0009] Step 3: Establish an upper-level grid-type energy storage investment model with the goal of minimizing the cost of grid-type energy storage investors;

[0010] Step 4: Establish a lower-level joint clearing model for the electricity energy and ancillary service markets with the goal of maximizing social welfare;

[0011] Step 5: Establish a capacity market bottom-level clearing model with the goal of maximizing social welfare;

[0012] Step 6: Based on the duality theory, the KKT condition is used to constrain the lower market clearing model and transform the two-layer model into a single-layer model;

[0013] Step 7: Linearize the nonlinear terms based on KKT conditions and the big M method, and finally transform it into a mixed integer linear programming problem;

[0014] Step 8: Solve the mixed integer linear programming problem based on the gurobi commercial solver to obtain the investment power and investment capacity of the grid-connected energy storage;

[0015] Step 9: Conduct an economic evaluation of the grid-connected energy storage investment plan through cost and benefit analysis, and use the ratio of benefit to cost as the return on investment.

[0016] Preferably, in step 1, the input data also includes: quotation data of power generation resources including grid-connected energy storage in the electric energy market, frequency regulation auxiliary service market and capacity market.

[0017] Preferably, in step 3, the upper-layer grid-type energy storage investment model is:

[0018] (1)

[0019] In formula (1), Represents the total cost of grid-connected energy storage investors, Represents the daily investment cost of grid-type energy storage, Represents the operation and maintenance cost of grid-connected energy storage, Indicates the benefits of grid-connected energy storage in the electric energy market, Indicates the benefits of grid-connected energy storage in the frequency regulation auxiliary service market. Represents the revenue of grid-connected energy storage in the capacity market.

[0020] Preferably, in step 4, the lower-layer joint clearing model is:

[0021] (14)

[0022] In formula (14), Represents the total cost of the system in the electric energy market and frequency regulation ancillary service market, Indicates the purchase cost of electricity provided by thermal power, represents the purchase cost of frequency regulation capacity provided by thermal power, Indicates the purchase cost of electricity provided by grid-connected energy storage, Indicates the purchase cost of frequency regulation capacity provided by grid-connected energy storage. It represents the penalty cost for abandoning wind and solar power of new energy units.

[0023] Preferably, in step 5, the capacity market lower-level clearing model is:

[0024] (36)

[0025] In formula (36), represents the total cost of the system in the capacity market, represents the winning bid capacity cost of purchasing thermal power units, Indicates the cost of purchasing the winning capacity unit of grid-connected energy storage. represents the winning bid capacity cost of purchasing new energy units, Indicates the system's winning capacity demand value.

[0026] The beneficial effects of the present invention are:

[0027] 1. The present invention can further explore the multiple values ​​of grid-type energy storage as a flexible and grid-supporting resource by considering that grid-type energy storage can participate in the electric energy, frequency regulation auxiliary services and capacity markets at the same time. Therefore, the present invention effectively solves the problem of difficulty in recovering investment costs of grid-type energy storage.

[0028] 2. The method of the present invention takes into account the mutual influence between the grid-type energy storage investment plan and the operation of the power market. The grid-type energy storage investment plan is from the perspective of the grid-type energy storage investor for the purpose of profitability, which is different from the centralized and unified planning of the system. Therefore, the present invention stimulates the vitality of the grid-type energy storage investors, encourages more market players to participate in the investment and construction of grid-type energy storage, and further promotes the active support of grid-type energy storage to the power grid.

[0029] 3. The ancillary service market trading mechanism proposed in the present invention combines the actual grid-type energy storage investment cost and the current electricity market environment, and comprehensively considers the expected return on investment. Therefore, the present invention can be used as a basis for grid-type energy storage investors in actual operation to make reasonable and economical grid-type energy storage investment decisions. Therefore, the present invention has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of the grid-type energy storage auxiliary service market method of the present invention combining the electric energy and capacity markets;

[0031] Figure 2 It is a double-layer model diagram established by the present invention;

[0032] Figure 3 This is a diagram of the investment situation of the grid-type energy storage of the present invention at different investment costs;

[0033] Figure 4 It is a diagram of the grid-type energy storage investment situation under different market participation conditions of the present invention. DETAILED DESCRIPTION

[0034] 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.

[0035] refer to Figures 1 to 4 In this embodiment, a grid-type energy storage auxiliary service market method combining electric energy and capacity markets proposed by the present invention includes the following steps:

[0036] 1) Determine the cycle that requires market clearing and pricing, obtain typical wind, light and load data within the cycle, thermal power unit output data, economic parameters and technical parameters related to grid-connected energy storage, and quotation data of power generation resources including grid-connected energy storage in the electric energy market, frequency regulation auxiliary service market and capacity market, etc., as input to the program.

[0037] 2) According to the physical model of the power network, the transfer factor matrix PTDF is established to prepare for the subsequent calculation of the power grid flow. The PTDF matrix of the power network contains all the information of the network topology. Each row represents a transmission line and each column represents a node. The matpower toolbox in Matlab can call the makePTDF function to obtain the PTDF matrix of the power network based on the basic data of the power network topology.

[0038] 3) Establish an upper-level grid-type energy storage investment model with the goal of minimizing the cost of grid-type energy storage investors, in which the total investment cost of grid-type energy storage is converted into daily investment cost, the time period of grid-type energy storage investment and operation is unified, and the investment quantity limit constraints of grid-type energy storage, the association constraints of grid-type energy storage investment power and investment capacity, the upper and lower limits of grid-type energy storage investment power and investment capacity, and the total investment cost limit constraints are considered to achieve the optimal location and capacity of grid-type energy storage.

[0039] The upper-layer grid-type energy storage investment optimization model is:

[0040] Objective function:

[0041] (1)

[0042] The constraints are:

[0043] (2)

[0044] (3)

[0045] (4)

[0046] (5)

[0047] (6)

[0048] (7)

[0049] (8)

[0050] (9)

[0051] (10)

[0052] (11)

[0053] (12)

[0054] (13)

[0055] Among them, formula (2) is the daily investment cost of grid-type energy storage, formula (3) is the operation and maintenance cost of grid-type energy storage, formula (4) is the income of grid-type energy storage in the electric energy market, formula (5) is the income of grid-type energy storage in the frequency regulation auxiliary service market, formula (6) is the income of grid-type energy storage in the capacity market, formula (7) is the daily power investment cost coefficient, formula (8) is the daily capacity investment cost coefficient, formula (9) is the grid-type energy storage investment quantity restriction constraint, formula (10) is the grid-type energy storage investment capacity and investment power association constraint, formula (11) is the grid-type energy storage investment capacity restriction constraint, formula (12) is the grid-type energy storage investment power restriction constraint, and formula (13) is the grid-type energy storage total investment cost restriction constraint.

[0056] K is the number of scenarios; S is the set of grid-type energy storage; T is the total number of time periods contained in a clearing cycle; r is the discount rate; L is the life cycle of the grid-type energy storage; Represents the total cost of grid-connected energy storage investors; and They are respectively expressed as the investment cost per unit power and per unit capacity of grid-connected energy storage; is the number of days in a year; It is expressed as the probability of scenario k occurring; and They represent the investment power and investment capacity of the s-th grid-type energy storage respectively; It is expressed as the operation and maintenance cost coefficient of grid-connected energy storage; and are the discharge and charging powers of the s-th grid-type energy storage cleared in the electric energy market at time t under scenario k; It represents the winning bid capacity of the s-th grid-connected energy storage in the frequency regulation ancillary service market at time t under scenario k; represents the winning bid capacity of the s-th grid-type energy storage in the capacity market under scenario k; , and They represent the clearing electricity prices of the s-th grid-connected energy storage in the electric energy market, frequency regulation auxiliary service market and capacity market under scenario k respectively; Indicates whether the s-th grid-type energy storage is built, which is a 01 variable; The maximum investment amount for grid-connected energy storage; The correlation factor between the investment power and investment capacity of grid-connected energy storage; , They represent the minimum and maximum investment capacity of the grid-type energy storage of the sth station respectively; and They represent the minimum and maximum investment power of the s-th grid-type energy storage; The maximum investment budget for grid-type energy storage.

[0057] 4) Establish a lower-level joint clearing model for the electricity energy and ancillary service market with the goal of maximizing social welfare, taking into account the system active power balance constraints, thermal power unit output limit constraints, thermal power unit climbing limit constraints, new energy unit output limit constraints, line flow constraints, grid-type energy storage charging and discharging power constraints, grid-type energy storage capacity constraints, grid-type energy storage maximum frequency regulation capacity constraints, thermal power unit maximum frequency regulation capacity constraints, frequency regulation demand balance constraints, etc., to ultimately meet the electricity energy demand and frequency regulation demand in the system.

[0058] The joint clearing model of the lower-level electricity energy and ancillary service market is:

[0059] Objective function:

[0060] (14)

[0061] The constraints are:

[0062] (15)

[0063] (16)

[0064] (17)

[0065] (18)

[0066] (19)

[0067] (20)

[0068] (twenty one)

[0069] (twenty two)

[0070] (twenty three)

[0071] (twenty four)

[0072] (25)

[0073] (26)

[0074] (27)

[0075] (28)

[0076] (29)

[0077] (30)

[0078] (31)

[0079] (32)

[0080] (33)

[0081] (34)

[0082] (35)

[0083] Among them, formula (15) is the purchase cost of providing electricity for thermal power; formula (16) is the purchase cost of providing frequency regulation capacity for thermal power; formula (17) is the purchase cost of providing electricity for grid-connected energy storage; formula (18) is the purchase cost of providing frequency regulation capacity for grid-connected energy storage; formula (19) is the penalty cost of abandoning wind and solar power for new energy units; formula (20) represents the system active power balance constraint; formula (21) represents the output limit constraint of thermal power units; formula (22) represents the ramp limit constraint of thermal power units; formulas (23) and (24) represent the output limit constraints of wind power and photovoltaic new energy units respectively; formula (25 ) represents the active power flow of the line, formula (26) represents the line power flow restriction constraint; formula (27) and formula (28) represent the discharge and charging power restriction constraints of the grid-connected energy storage, respectively; formula (29) represents the capacity period coupling constraint of the grid-connected energy storage, formula (30) and formula (31) represent the capacity restriction constraints of the grid-connected energy storage, formula (32) represents that the capacity of the grid-connected energy storage at the initial moment and the end moment of the cycle is equal; formula (33) is the maximum frequency regulation capacity restriction constraint that the grid-connected energy storage can provide; formula (34) is the maximum frequency regulation capacity restriction constraint that the thermal power unit can provide; formula (35) is the system frequency regulation demand balance constraint.

[0084] G, W, PV, and D are the collections of thermal power units, wind power units, photovoltaic units, and loads, respectively; Represents the total cost of the system in the electric energy market and frequency regulation ancillary service market; and are the quotations of thermal power unit g in the electric energy market and frequency regulation auxiliary service market under scenario k respectively; and are the winning bids of thermal power unit g in the electric energy market and frequency regulation ancillary service market at time t under scenario k, respectively; , and They are the discharge and charging quotations of grid-connected energy storage s in the electric energy market and the quotations of frequency regulation auxiliary service market under scenario k respectively; Penalty costs for units that abandon wind and solar power; and are the predicted outputs of wind turbine w and photovoltaic generator pv at time t under scenario k respectively; and is the actual output of wind turbine w and photovoltaic unit pv at time t in scenario k; and are the minimum and maximum outputs of thermal power unit g respectively; and are the downward and upward limiting climbing rates of thermal power unit g respectively; is the active power flow of line l at time t in scenario k; is the transfer factor matrix; , , , , They are the association matrices between nodes and thermal power units, grid-connected energy storage, wind turbines, photovoltaic units, and loads; is the actual load of load d at time t under scenario k; and are the minimum and maximum values ​​of active power flow of line l respectively; is the actual capacity of the grid-type energy storage s at time t under scenario k; and are the charging and discharging efficiency coefficients of the grid-type energy storage respectively; , and They are the minimum, maximum and initial charge states of the grid-type energy storage; and are the maximum frequency regulation capacity ratios of grid-connected energy storage s and thermal power generation units g; is the frequency modulation demand at time t in scenario k; , , , , , , , , , , , , , , , , , , , , , , , , , They are the dual multipliers of the corresponding constraints in equations (20) to (35), separated by colons and listed after the corresponding constraints.

[0085] 5) Establish a lower-level clearing model for the capacity market with the goal of maximizing social welfare, taking into account the system capacity supply and demand balance constraints, the winning capacity constraints of thermal power units, the winning capacity constraints of grid-connected energy storage, the winning capacity constraints of new energy units, and the winning capacity constraints of capacity demand, and ultimately meet the system capacity demand at the peak load moment.

[0086] The lower capacity market clearing optimization model is:

[0087] Objective function:

[0088] (36)

[0089] The constraints are:

[0090] (37)

[0091] (38)

[0092] (39)

[0093] (40)

[0094] (41)

[0095] (42)

[0096] (43)

[0097] (44)

[0098] (45)

[0099] (46)

[0100] Among them, formula (37) is the winning capacity cost of purchasing thermal power units; formula (38) is the winning capacity cost of purchasing grid-connected energy storage units; formula (39) is the winning capacity cost of purchasing new energy units; formula (40) is the winning capacity demand value of the system; formula (41) is the system capacity supply and demand balance constraint; formula (42) is the winning capacity constraint of thermal power units; formula (43) is the winning capacity constraint of grid-connected energy storage; formula (44) is the winning capacity constraint of wind turbines; formula (45) is the winning capacity constraint of photovoltaic units; formula (46) is the winning capacity constraint of system capacity demand.

[0101] SG is the set of segment numbers of system capacity requirements; represents the total cost of the system in the capacity market; , , , , They are the quotations of thermal power unit g, grid-connected energy storage s, wind turbine unit w, photovoltaic unit pv, and capacity demand segment sg in the capacity market under scenario k; , , , , They are the winning capacity of thermal power unit g, grid-connected energy storage s, wind turbine unit w, photovoltaic unit pv, and capacity demand segment sg in the capacity market under scenario k; , , are the credible capacity factors of grid-connected energy storage s, wind turbine w and photovoltaic unit pv respectively; and are the actual installed capacities of wind turbine w and photovoltaic unit pv respectively; , , , , , , , , , , They are the dual multipliers of the corresponding constraints of Equations (41) to (46), separated by colons and listed after the corresponding constraints.

[0102] 6) The two-layer problem is non-convex and NP-hard, and is often difficult to solve. Since the proposed lower-layer model is a linear programming problem, based on the duality theory, the KKT condition can be used to constrain the lower-layer market clearing model and transform the two-layer problem into a single-layer problem, making the problem solvable.

[0103] The KKT condition of the joint clearing model of the lower-level electricity energy and ancillary service markets is:

[0104] The original constraints are equations (20) to (35).

[0105] The first-order optimality condition is:

[0106] (47)

[0107] (48)

[0108] (49)

[0109] (50)

[0110] (51)

[0111] (52)

[0112] (53)

[0113] (54)

[0114] (55)

[0115] (56)

[0116] (57)

[0117] (58)

[0118] The complementary slack constraints are:

[0119] (59)

[0120] (60)

[0121] (61)

[0122] (62)

[0123] (63)

[0124] (64)

[0125] (65)

[0126] (66)

[0127] (67)

[0128] (68)

[0129] (69)

[0130] (70)

[0131] (71)

[0132] (72)

[0133] (73)

[0134] (74)

[0135] (75)

[0136] (76)

[0137] (77)

[0138] (78)

[0139] The strong duality condition is:

[0140] (79)

[0141] The KKT condition of the lower capacity market clearing model is:

[0142] The original constraints are equations (41) to (46).

[0143] The first-order optimality condition is:

[0144] (80)

[0145] (81)

[0146] (82)

[0147] (83)

[0148] (84)

[0149] The complementary slack constraints are:

[0150] (85)

[0151] (86)

[0152] (87)

[0153] (88)

[0154] (89)

[0155] (90)

[0156] (91)

[0157] (92)

[0158] (93)

[0159] (94)

[0160] The strong duality condition is:

[0161] (95)

[0162] The above respective complementary relaxation conditions and strong duality conditions are equivalent.

[0163] The original two-layer problem is transformed into a single-layer problem, which consists of equations (1)-(13), (20)-(35), (41)-(78), and (80)-(94).

[0164] 7) There are nonlinear terms in Equations (4)-(6), (59)-(78), and (85)-(94). The nonlinear terms can be linearized based on the KKT condition and the big M method and finally transformed into a mixed integer linear programming problem.

[0165] The nonlinear term in equation (4)-(5) is and , the linearization process is:

[0166] Based on the first-order optimal conditions (51), (52) and (58) and the complementary relaxation constraints (69)-(72) and (75)-(76), it is transformed into:

[0167] (96)

[0168] (97)

[0169] (98)

[0170] (99)

[0171] (100)

[0172] There are still nonlinear terms in formula (100), and finally based on the strong duality condition formula (79), it is transformed into:

[0173] (101)

[0174] The nonlinear term in formula (6) is , the linearization process is:

[0175] Based on the first-order optimal condition (81) and the complementary relaxation constraint conditions (87)-(88), it is transformed into:

[0176] (102)

[0177] There are still nonlinear terms in formula (102), and finally based on the strong duality condition formula (95), it is transformed into:

[0178] (103)

[0179] Formula (59)-Formula (78) and Formula (85)-Formula (94) are nonlinear terms in the complementary relaxation constraints, which can be linearized using the big M method:

[0180] The principle of the Big M method is:

[0181] (104)

[0182] (105)

[0183] (106)

[0184] Among them, x and y are continuous variables, z is a 0.1 variable, and M is a large positive value, generally 10. 5 , ⊥ means the product of the two is 0.

[0185] Finally, by processing the above nonlinear terms, the entire two-layer model is transformed into a single-layer mixed integer linear programming problem.

[0186] 8) Solve the mixed integer linear programming problem based on the gurobi commercial solver to obtain the investment power and investment capacity of the grid-connected energy storage. Perform an economic evaluation of the grid-connected energy storage investment plan through cost-benefit analysis, and use the ratio of benefit to cost as the rate of return on investment.

[0187] Example

[0188] Based on the actual data of a power system planning and power market mechanism project, the proposed grid-connected energy storage auxiliary service joint market mechanism is used to analyze and evaluate the investment planning results of grid-connected energy storage under different scenarios.

[0189] Considering that grid-type energy storage can participate in spot electricity, frequency regulation auxiliary services and capacity market to make profits at the same time, the unit investment cost of grid-type energy storage is set at low, medium and high levels. The actual unit investment cost of grid-type energy storage is medium. The evaluation results are as follows: Figure 3 As shown, it can be found that when the unit investment cost of grid-type energy storage is low, the investment capacity of grid-type energy storage far exceeds the other two cases, and when the unit investment cost of grid-type energy storage is high, the investment capacity of grid-type energy storage is far lower than the other two cases. This shows that different degrees of unit investment costs of grid-type energy storage will affect the final investment capacity of grid-type energy storage; in terms of investment return rate, in the case of high unit investment cost, grid-type energy storage investors obtain a higher rate of return by reducing energy storage investment capacity, and in the case of low unit investment cost, grid-type energy storage investors obtain a higher rate of return by increasing grid-type energy storage investment capacity. The investment returns in these two cases exceed the situation under the actual investment cost. In actual energy storage planning, grid-type energy storage investors can consider the changes in the unit investment cost of grid-type energy storage based on the model proposed by the present invention and reasonably adjust their own grid-type energy storage investment decisions.

[0190] When the unit investment cost of fixed grid-type energy storage is the actual investment cost, considering that grid-type energy storage will make different investment decisions when participating in different electricity markets, four market participation plans are given, among which Plan 1 is that grid-type energy storage only participates in the electric energy market, Plan 2 is that grid-type energy storage participates in the electric energy and frequency regulation auxiliary service market, Plan 3 is that grid-type energy storage participates in the electric energy and capacity market, and Plan 4 is that grid-type energy storage participates in the electric energy, frequency regulation auxiliary service and capacity market at the same time. The evaluation and calculation results are as follows Figure 4As shown, it can be found that in the case of only participating in the electric energy market, the grid-type energy storage investors will not invest in grid-type energy storage because the single income channel cannot recover their own investment costs; in the case of grid-type energy storage participating in the electric energy and frequency regulation auxiliary service market, the flexible adjustment and active support capabilities of grid-type energy storage are utilized to increase its income in the frequency regulation auxiliary service market, and a small amount of grid-type energy storage is invested and built, achieving a higher rate of return on investment; in the case of grid-type energy storage participating in the electric energy and capacity market, the capacity market will compensate the investment capacity cost of grid-type energy storage to a certain extent, so a large amount of energy storage is built. On this basis, Scheme 4 considers the participation of grid-type energy storage in the frequency regulation auxiliary service market. Although the energy storage investment capacity decreases, the rate of return on energy storage investment increases. Therefore, in the actual grid-type energy storage planning, energy storage investors can reasonably adjust their own grid-type energy storage investment decisions based on the model proposed by the present invention and considering the current electricity market mechanism.

[0191] In summary, the present invention can further explore the multiple values ​​of grid-type energy storage as a flexible and grid-supporting resource by considering that grid-type energy storage participates in electric energy, frequency regulation auxiliary services, and capacity markets at the same time, and effectively solves the problem of difficult investment cost recovery of grid-type energy storage. Therefore, the present invention has broad application prospects in the fields of power system planning and power market mechanisms.

[0192] It should be emphasized that the above are only preferred embodiments of the present invention and do not impose any form of limitation on the present invention. Any simple modification of the above embodiments based on the technical essence of the present invention also falls within the protection scope of the present invention. Other equivalent changes and modifications still fall within the scope of the technical solution of the present invention.

Claims

1. A grid-based energy storage auxiliary service market method combining electric energy and capacity markets, characterized in that: The following steps are involved: Step 1: Determine the period for market clearing and pricing, obtain typical wind, solar and load data, thermal power unit output data, and economic and technical parameters related to grid-connected energy storage as input data; Step 2: According to the physical model of the electrical network, establish the transfer factor matrix PTDF; Step 3: Establish an upper-level grid-type energy storage investment model with the goal of minimizing the cost of grid-type energy storage investors; Step 4: Establish a lower-level joint clearing model for the electricity energy and ancillary service markets with the goal of maximizing social welfare; Step 5: Establish a capacity market bottom-level clearing model with the goal of maximizing social welfare; Step 6: Based on the duality theory, the KKT condition is used to constrain the lower market clearing model and transform the two-layer model into a single-layer model; Step 7: Linearize the nonlinear terms based on KKT conditions and the big M method, and finally transform it into a mixed integer linear programming problem; Step 8: Solve the mixed integer linear programming problem based on the gurobi commercial solver to obtain the investment power and investment capacity of the grid-connected energy storage; Step 9: Conduct an economic evaluation of the grid-connected energy storage investment plan through cost and benefit analysis, and use the ratio of benefit to cost as the return on investment.

2. The grid-type energy storage auxiliary service market method for combining electric energy and capacity markets according to claim 1 is characterized in that: In step 1, the input data also includes: quotation data of power generation resources including grid-connected energy storage in the electric energy market, frequency regulation auxiliary service market and capacity market.

3. The grid-type energy storage auxiliary service market method for combining electric energy and capacity markets according to claim 1 is characterized in that: In step 3, the upper-layer grid-type energy storage investment model is: (1) In formula (1), Represents the total cost of grid-connected energy storage investors, Represents the daily investment cost of grid-type energy storage, Represents the operation and maintenance cost of grid-connected energy storage, Indicates the benefits of grid-connected energy storage in the electric energy market, Indicates the benefits of grid-connected energy storage in the frequency regulation auxiliary service market. Represents the revenue of grid-connected energy storage in the capacity market.

4. The grid-type energy storage auxiliary service market method for combining electric energy and capacity markets according to claim 1 is characterized in that: In step 4, the lower-level joint clearing model is: (14) In formula (14), Represents the total cost of the system in the electric energy market and frequency regulation ancillary service market, Indicates the purchase cost of electricity provided by thermal power, represents the purchase cost of frequency regulation capacity provided by thermal power, Indicates the purchase cost of electricity provided by grid-connected energy storage, Indicates the purchase cost of frequency regulation capacity provided by grid-connected energy storage. It represents the penalty cost for abandoning wind and solar power of new energy units.

5. The grid-type energy storage auxiliary service market method for combining electric energy and capacity markets according to claim 1 is characterized in that: In step 5, the capacity market lower-level clearing model is: (36) In formula (36), represents the total cost of the system in the capacity market, represents the winning bid capacity cost of purchasing thermal power units, Indicates the cost of purchasing the winning capacity unit of grid-connected energy storage. represents the winning bid capacity cost of purchasing new energy units, Indicates the system's winning capacity demand value.

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