Method for participating in optimized clearing of electric power spot market through electric heat storage

Abstract

The invention relates to an electric power spot market clearing method, in particular to an electric power spot market optimized clearing method with participation of electric heat storage. According to the operation characteristics of electric heat storage and renewable energy sources, a renewable energy source power limiting and sharing model and an electric heat storage and renewable energy source power recovery calculation model are established, and on the basis, the flow of optimizing the clearing method is provided; a high-proportion renewable energy power system market clearing model compatible with electric heat storage operation economy, system cost and section constraint is designed, a clearing model and algorithm which are reasonable in design, capable of optimizing operation and suitable for electric power spot market operation are provided, electric heat storage can participate in electric power spot market transaction and optimizing operation control can be achieved, and the electric power spot market transaction efficiency is improved. And the application requirements of market operation and renewable energy consumption are met. The electric heat storage is applied to the electric power spot market, and optimal scheduling is carried out by establishing a mathematical model, so that the renewable energy consumption capability is improved.

Description

Technical Field

[0001] The present invention relates to a clearing method for the electricity spot market, and in particular to an optimized clearing method for electric thermal storage participating in the electricity spot market. Background Art

[0002] The installed capacity structure of the thermal power industry is continuously optimized, and the proportion of renewable energy installed capacity is increasing rapidly. Solar power generation and wind power will gradually assume the position of the main power source, but there are problems such as uneven seasonal distribution, large output volatility, and high randomness, which require solving the technical problems of reliable and stable regulation. At the same time, the heating transformation of thermal power units is accelerating, and the power grid peak shaving situation is severe. The heating transformation of thermal power units can improve energy utilization efficiency, but it also increases the load management difficulty of the power system and brings certain challenges to the safe and stable operation of the power system.

[0003] As a flexible energy storage method, electric thermal storage can be adjusted bidirectionally on the power generation side and the power consumption side, and is an important means to effectively respond to the output fluctuations of renewable energy and promote the consumption of renewable energy. However, there is a coupling between the current electric thermal storage technology and the operation mode of thermal power units, lacking a clear method and profit model for participating in the market, and unable to accurately adjust the coordination between the operation mode and renewable energy. Therefore, how to improve the operation control technology of electric thermal storage to achieve its effective coordination with renewable energy and how to participate in spot market transactions are urgent problems to be solved. Summary of the Invention

[0004] Aiming at the problems existing in the above-mentioned prior art, the present invention proposes a clearing model and algorithm for electric thermal storage participating in the electricity spot market, and its purpose is to provide a clearing model and algorithm that are reasonably designed, can be optimized for operation, and are applicable to the operation of the electricity spot market, which can realize the participation of electric thermal storage in the electricity spot market transaction, optimize the operation control, and meet the application requirements of market operation and renewable energy consumption. The technical solutions are as follows:

[0005] An optimized clearing method for electric thermal storage participating in the electricity spot market, comprising:

[0006] Respectively perform unconstrained UC calculation, constrained UC calculation, and constrained ED calculation to obtain the constrained ED calculation result;

[0007] Based on the constrained ED calculation result and the renewable energy curtailment equalization model, perform renewable energy curtailment equalization calculation;

[0008] Based on the calculation results of renewable energy curtailment equalization, determine the curtailment value caused by non-section reasons; record the curtailment value caused by non-section reasons, and calculate the nodal price based on the said nodal price calculation model; based on the recorded curtailment value caused by non-section reasons and the electric thermal energy storage and renewable energy reconnection calculation model, conduct the operation of electric thermal energy storage. The total operation amount of electric thermal energy storage shall not be greater than this value. After clearing the electric thermal energy storage, the renewable energy is also reconnected accordingly, and the nodal price is calculated based on the nodal price calculation model.

[0009] Furthermore, the renewable energy curtailment equalization model is as follows:

[0010] The objective function is

[0011]

[0012] where t is the time period, is the curtailment amount of the i-th renewable energy unit in the t-th time period, is the predicted output of the i-th renewable energy in the time period t.

[0013] Furthermore, the constraint conditions of the renewable energy curtailment equalization model include the renewable energy curtailment equality constraint and the renewable energy output constraint.

[0014] Furthermore, the renewable energy curtailment equality constraint is as follows:

[0015] The sum of the curtailment amounts of all renewable energies in the t-th time period is equal to the total curtailment amount in this time period. The total curtailment amount is statistically obtained according to the results of the Security Constrained Economic Dispatch (SCED) model;

[0016]

[0017] where, is the total curtailment amount of renewable energy in the t-th time period.

[0018] Furthermore, the renewable energy output constraint is

[0019]

[0020] where, is the optimized output of the i-th renewable energy unit in the t-th time period. The sum of the optimized output of renewable energy and the curtailment amount is equal to the predicted output of renewable energy at this moment;

[0021] Considering the power flow constraint of the key section, this constraint can be described as

[0022]

[0023] where, respectively represent the minimum and maximum limits of the power flow transmission of section s; Gs-i Denote the generator output power transfer distribution factor of the node where the renewable energy unit \(i\) is located with respect to section \(s\); \(G\) s-n Denote the generator output power transfer distribution factor of the node where the thermal power unit \(n\) is located with respect to section \(s\); Denote the optimized output of the thermal power unit during the security-constrained unit commitment (SCUC) stage; \(G\) s-j Denote the generator output power transfer distribution factor of the node where the tie line \(j\) is located with respect to section \(s\); \(T\) j,t Denote the scheduled power of the tie line \(j\) at time period \(t\), positive for power input and negative for power output; \(G\) s-k Denote the generator output power transfer distribution factor of node \(k\) with respect to section \(s\); \(D\) k,t Is the bus load value of node \(k\) at time period \(t\); Respectively denote the positive and reverse power flow relaxation setting values of section \(s\) during the security-constrained unit commitment (SCUC) stage; This constraint ensures that the equal sharing of renewable energy will not cause new section over-limit or exacerbate the existing congested sections.

[0024] Furthermore, the objective function of the nodal price calculation model is

[0025]

[0026] Where \(N\) is the total number of units; \(T\) is the total number of time periods considered, with one time period every 15 minutes in day \(D\), considering 96 time periods, and considering 2 time periods of peak and trough loads in day \(D + 1\), so \(T\) is 98; \(P\) i,t Is the output of unit \(i\) at time period \(t\); \(C\) i,t (\(P\) i,t ) is the operating cost of unit \(i\) at time period \(t\), which is a multi-segment linear function related to the output intervals declared by the unit and the corresponding energy prices; \(M'\) is the network power flow constraint relaxation penalty factor used for nodal price calculation; Respectively are the positive and reverse power flow relaxation variables of line \(l\); \(NL\) is the total number of lines; Respectively are the positive and reverse power flow relaxation variables of section \(s\); \(NS\) is the total number of sections.

[0027] Furthermore, the constraint conditions of the nodal price calculation model include:

[0028] System load balance constraint

[0029] For each time period \(t\), the load balance constraint can be described as:

[0030]

[0031] Where \(P\) i,t Is the output of unit \(i\) at time period \(t\); \(T\) j,t$P_{j,t}$ is the scheduled power of tie line $j$ in period $t$, with power input being positive and power output being negative; $N_T$ is the total number of tie lines, and $D$ t is the system load in period $t$;

[0032] Generator output upper and lower limit constraints

[0033] The output of the generator should be within its maximum / minimum output range, and its constraint condition can be described as

[0034]

[0035] For the generators that are shut down in the SCUC optimization results, in the formula both are taken as zero; for the non - price - determined generators, in the above formula both are taken as the winning bid output of generator $i$ in period $t$ in the security - constrained economic dispatch (SCED) optimization results For the price - determined generators, in the above formula take the following values:

[0036]

[0037] where $\delta$ is the proportion allowing the generator to deviate from the day - ahead SCED optimization results in the LMP model, $P_{i,\max}$ and $P_{i,\min}$ are the maximum and minimum outputs of the generator in the day - ahead SCED model respectively;

[0038] Generator group output upper and lower limit constraints

[0039] The output of the generator group should be within its maximum / minimum output range, and its constraint condition can be described as:

[0040]

[0041] where $P_{j,\max,t}$ and $P_{j,\min,t}$ are the maximum and minimum outputs of generator group $j$ in period $t$;

[0042] Generator ramping constraint

[0043] When the generator is ramping up or down, it should meet the ramping rate requirements; the ramping constraint can be described as:

[0044]

[0045] where $\Delta P$ i U is the maximum up - ramping rate of generator $i$, and $\Delta P$ i D is the maximum down - ramping rate of generator $i$;

[0046] Line power flow constraint

[0047] The line power flow constraint can be described as:

[0048]

[0049] Among them, P l max is the power flow transfer limit of line l; G l-i is the generator output power transfer distribution factor of the node where unit i is located to line l; G l-j is the generator output power transfer distribution factor of the node where tie line j is located to line l; K is the number of nodes in the system; G l-k is the generator output power transfer distribution factor of node k to line l; D k,t is the bus load value of node k at time t; are the positive and reverse power flow relaxation variables of line l respectively;

[0050] Section power flow constraint

[0051] Considering the power flow constraint of the key section, this constraint can be described as:

[0052]

[0053] Among them, P s min , P s max are the power flow transfer limits of section s respectively; G s-i is the generator output power transfer distribution factor of the node where unit i is located to section s; G s-j is the generator output power transfer distribution factor of the node where tie line j is located to section s; T j,t represents the planned power of tie line j at time t (in is positive, out is negative); G s-k is the generator output power transfer distribution factor of node k to section s; D k,t is the bus load value of node k at time t; are the positive and reverse power flow relaxation variables of section s respectively.

[0054] Furthermore, the nodal price calculation model

[0055] Unit output expression:

[0056]

[0057] Among them, NM is the total number of unit bid segments, P i,t,m is the winning bid power of unit i in the m-th output interval at time t, are the upper and lower bounds of the m-th output interval declared by unit i respectively;

[0058] Unit operating cost expression:

[0059]

[0060] Among them, C i,t,m is the energy price corresponding to the m-th output interval declared by unit i;

[0061] Solve the above nodal price calculation model to obtain the Lagrange multipliers of the system load balance constraint, line and section power flow constraints at each time period. Then, the nodal price of node i at time period t is:

[0062]

[0063] Among them, λ t is the Lagrange multiplier of the system load balance constraint at time period t; is the Lagrange multiplier of the maximum forward power flow constraint of line l. When the line power flow exceeds the limit, this Lagrange multiplier is the network power flow constraint relaxation penalty factor; is the Lagrange multiplier of the maximum reverse power flow constraint of line l. When the line power flow exceeds the limit, this Lagrange multiplier is the network power flow constraint relaxation penalty factor; is the Lagrange multiplier of the maximum forward power flow constraint of section s. When the section power flow exceeds the limit, this Lagrange multiplier is the network power flow constraint relaxation penalty factor; is the Lagrange multiplier of the maximum reverse power flow constraint of section s. When the section power flow exceeds the limit, this Lagrange multiplier is the network power flow constraint relaxation penalty factor; G l-k is the generator output power transfer distribution factor of node k to line l; G s-k is the generator output power transfer distribution factor of node k to section s.

[0064] Furthermore, the electro-thermal energy storage and renewable energy power restoration calculation model is as follows:

[0065] Objective function

[0066] In the electro-thermal energy storage adjustment calculation, the objective function is adjusted to:

[0067]

[0068] Among them, NE is the total number of renewable energy units, ESH is the total number of electro-thermal energy storage units, and the output of the remaining types of units is fixed; T is the total number of time periods considered, with one time period every 15 minutes on day D, considering 96 time periods; is the output of renewable energy unit i at time period t, P k,t is the total output of electro-thermal energy storage plant k; is the operating cost of renewable energy unit i at time period t. The quotation of renewable energy is negative, C k,t (P k,t) is the objective function value of the electric energy storage and heat supply power plant k at time period t;

[0069] The bid price of renewable energy is negative, so renewable energy generation is cleared first. The output of the electric energy storage and heat supply is negative and the bid price is 0. Therefore, the electricity consumed by increasing the input of the electric energy storage and heat supply is the electricity consumed by increasing the consumption of renewable energy. By jointly optimizing the output of the electric energy storage and heat supply and renewable energy, the consumption capacity of renewable energy is improved;

[0070] Load balance constraint

[0071] Except for renewable energy and electric energy storage and heat supply units, the output of other units is fixed;

[0072]

[0073] Among them, is the sum of the output of renewable energy units, is the sum of the output of electric energy storage and heat supply units, is the sum of the output of the remaining units, D t is the load at time period t;

[0074] The total absolute value of the output of all electric energy storage and heat supply units cannot exceed the unconstrained limited power:

[0075]

[0076] Output range constraint of the electric energy storage and heat supply of power plant k:

[0077]

[0078] Among them, is the total sum of the output of all thermal power units in power plant k, is the electric energy storage and heat supply capacity of power plant k at time period t. The total amount of electric energy storage and heat supply called in the power plant is restricted by its own upper limit and also by the total output of the thermal power units in the plant;

[0079] Relationship constraint between the electric energy storage and heat supply power plant and the electric energy storage and heat supply unit

[0080] The output of the electric energy storage and heat supply of power plant k is obtained by adding the output of all the electric energy storage and heat supply units in the plant:

[0081]

[0082] Among them, ∑P e,t represents the sum of the output of adjustable electric energy storage and heat supply units, represents the sum of the output of non-adjustable electric energy storage and heat supply units;

[0083] The upper and lower limits of the output of adjustable electric energy storage and heat supply units are:

[0084]

[0085] Among them, is the upper limit of the output of the electric heat storage e;

[0086] The upper and lower limits of the output of the non-adjustable electric heat storage unit are:

[0087]

[0088] Among them, α e,t is the 0-1 variable of the operation of the electric heat storage unit e at time t. It is 1 when operating and 0 when shut down. is the capacity of the electric heat storage unit e at time t. If such an electric heat storage unit is turned on, the output can only be its capacity;

[0089] Section power flow constraint

[0090] Set the upper limit of the relaxation amount of all over-limit sections to the over-limit relaxation amount in the original constraint calculation:

[0091]

[0092] Among them, P s min , P s min are the power flow transmission limits of section s respectively; G s-i is the sensitivity factor of the node where the renewable energy unit i is located to section s; G s-j is the sensitivity factor of the node where the tie line j is located to section s, T j,t is the active power of the tie line j at time t; G s-k is the sensitivity factor of node k to section s, D k,t is the bus load value of node k at time t, G s-e is the sensitivity factor of the electric heat storage unit e to section s; G s-o is the sensitivity factor of the node where the remaining unit o other than the renewable energy and electric heat storage units is located to section s, P o,t is the output of the remaining unit o other than the renewable energy and electric heat storage units, and O is the total number of the remaining units other than the renewable energy and electric heat storage units; are the positive and reverse power flow relaxation amount fixed values of section s in the original constraint calculation respectively, in this way to prevent the situation that the section over-limit is aggravated after adding the electric heat storage;

[0093] Electric heat storage maximum output adjustment

[0094] The electric heat storage capacity of each power plant is the smaller value of the capacity curve declared by the power plant itself and the actual available electric heat storage capacity in the plant:

[0095]

[0096] Among them, is the electric heat storage capacity of power plant k at time period t, is the sum of the in-plant electric heat storage capacities, is the declared capacity curve of the power plant;

[0097] The adjustment calculation of electric heat storage uses a certain logic to allocate the curtailed energy. The weighted average allocation logic of electric heat storage is

[0098]

[0099] Among them is the allocated output of the electric heat storage of power plant k at time period t, is the sum of the maximum available capacities of all power plants at time period t, P t abd is the curtailed energy of renewable energy due to non-section reasons;

[0100]

[0101] The output of the electric heat storage of the power plant after allocation and the original upper limit of electric heat storage The smaller value of will be used as the upper limit of the output of the first-stage quotation of the electric heat storage of the power plant. The upper limit of the output of the second-stage quotation is The first-stage quotation should be much larger than the second-stage quotation. Through the two-stage quotation method, the capacity of all electric heat storage is preferentially and evenly distributed After all the electric heat storage is filled in the first stage, then the output of the electric heat storage in the second stage is called;

[0102] Calculation of the objective function value of the electric heat storage power plant

[0103]

[0104] Among them, C k,1 、C k,2 respectively represent the first-stage and second-stage quotations of the electric heat storage power plant k. Currently, the corresponding quotations of all electric heat storage power plants are the same; P k,t,1 and P k,t,2 respectively represent the first-stage and second-stage outputs of the electric heat storage power plant. C k,1 is much larger than C k,2 ;

[0105] P k,t =P k,t,1 +P k,t,2 (28)

[0106] The sum of the two-stage outputs of the electric heat storage power plant needs to be equal to the total output.

[0107] An optimization clearing device for electric heat storage participating in the power spot market, including

[0108] A calculation module, configured to perform unconstrained UC calculation, constrained UC calculation, and constrained ED calculation respectively, and obtain a constrained ED calculation result;

[0109] A renewable energy curtailment equalization calculation module, configured to perform renewable energy curtailment equalization calculation based on the constrained ED calculation result and the renewable energy curtailment equalization model;

[0110] A determination module, configured to determine a curtailment value caused by non-section reasons based on the renewable energy curtailment equalization calculation result;

[0111] A nodal price calculation module, configured to record the curtailment value caused by non-section reasons and perform nodal price calculation based on the nodal price calculation model;

[0112] An operation of electric thermal energy storage and nodal price calculation module, configured to perform the operation of electric thermal energy storage based on the recorded curtailment value caused by non-section reasons and the electric thermal energy storage and renewable energy re-power calculation model. The total operation amount of electric thermal energy storage cannot be greater than this value. After clearing the electric thermal energy storage, the renewable energy is also re-powered accordingly, and nodal price calculation is performed based on the nodal price calculation model.

[0113] Furthermore, the device is used to implement the steps of any one of the methods for optimizing the clearing of the electric thermal energy storage participating in the electricity spot market.

[0114] A computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, it implements the steps of any one of the methods for optimizing the clearing of the electric thermal energy storage participating in the electricity spot market.

[0115] A storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of any one of the methods for optimizing the clearing of the electric thermal energy storage participating in the electricity spot market.

[0116] Compared with the existing electric thermal energy storage operation control technology, the present invention has the following advantages and effects: According to the operation characteristics of electric thermal energy storage and renewable energy, the present invention establishes a renewable energy curtailment equalization model, a nodal price calculation model, and an electric thermal energy storage and renewable energy re-power calculation model. On this basis, the process of the optimization clearing algorithm is proposed, and a market clearing model for a high-proportion renewable energy power system that is compatible with the operation economy of electric thermal energy storage, system cost, and section constraints is designed. The electric thermal energy storage is applied to the electricity spot market, and optimization scheduling is performed through establishing a mathematical model to improve the renewable energy consumption capacity. Description of the Drawings

[0117] Figure 1It is a flowchart of an optimized clearing method for electric heat storage participating in the electricity spot market of the present invention. Specific embodiments

[0118] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0119] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0120] The present invention will be further described in detail below in conjunction with the drawings.

[0121] Embodiment 1

[0122] As Figure 1 shown, an optimized clearing method for electric heat storage participating in the electricity spot market includes a renewable energy curtailment equalization model, a nodal price calculation model, an electric heat storage and renewable energy re-power calculation model, and an algorithm. The algorithm includes the following steps:

[0123] Perform unconstrained UC calculation;

[0124] After performing unconstrained UC calculation, perform constrained UC calculation;

[0125] After performing constrained UC calculation, perform constrained ED calculation;

[0126] Based on the constrained ED calculation results and the renewable energy curtailment equalization model, perform renewable energy curtailment equalization calculation;

[0127] Based on the renewable energy curtailment equalization calculation results, determine whether there is a curtailment value caused by non-section reasons; if so, record the curtailment value caused by non-section reasons; if not, based on the nodal price calculation model, perform nodal price calculation;

[0128] Record the curtailment value caused by non-section reasons;

[0129] Based on the nodal price calculation model, perform nodal price calculation;

[0130] Based on the recorded curtailment value caused by non-section reasons and the electric heat storage and renewable energy re-power calculation model, perform the operation of electric heat storage. The total operation amount of electric heat storage cannot be greater than this value. After clearing the electric heat storage, the renewable energy is also re-powered accordingly;

[0131] Based on the node price calculation model, the node price is calculated.

[0132] Embodiment 2

[0133] The content of this embodiment different from Embodiment 1 is as follows:

[0134] Renewable energy curtailment equalization model

[0135] The objective function is

[0136]

[0137] where t is the time period, is the curtailment amount of the i-th renewable energy unit in the t-th time period, is the predicted output of the i-th renewable energy in the t-th time period;

[0138] The constraint conditions of the renewable energy curtailment equalization model are as follows:

[0139] Renewable energy curtailment equality constraint:

[0140] The sum of the curtailment amounts of all renewable energies in the t-th time period is equal to the total curtailment amount in that time period, and the total curtailment amount is statistically obtained according to the results of the security-constrained economic dispatch (SCED) model;

[0141]

[0142] where, is the total curtailment amount of renewable energy in the t-th time period;

[0143] The renewable energy output constraint is

[0144]

[0145] where, is the optimized output of the i-th renewable energy unit in the t-th time period, and the sum of the optimized output and curtailment amount of the renewable energy is equal to the predicted output of the renewable energy at that moment;

[0146] Considering the power flow constraint of the key section, this constraint can be described as

[0147]

[0148] where, P s min 、P s max respectively represent the minimum and maximum limits of the power flow transmission of section s; G s-i represents the generator output power transfer distribution factor of the node where the renewable energy unit i is located to section s; G s-nDenote the generator output power transfer distribution factor of the node where the thermal power unit n is located with respect to section s; Denote the optimized output of the thermal power unit during the security-constrained unit commitment (SCUC) stage; G s-j Denote the generator output power transfer distribution factor of the node where the tie line j is located with respect to section s; G s-k Denote the generator output power transfer distribution factor of node k with respect to section s; Denote the positive and reverse power flow relaxation setting values of section s during the security-constrained unit commitment (SCUC) stage respectively; This constraint ensures that the equal distribution of renewable energy will not lead to new section violations or exacerbate the existing congested sections.

[0149] Embodiment 3

[0150] The content that this embodiment differs from Embodiment 2 is as follows:

[0151] Node price calculation model

[0152] The objective function is

[0153]

[0154] Where: Among them, N is the total number of units; T is the total number of time periods considered, where each time period is 15 minutes in a day D, considering 96 time periods, and considering 2 time periods of peak and valley loads in day D + 1, so T is 98; P i,t Is the output of unit i at time period t; C i,t (P i,t ) is the operating cost of unit i at time period t, which is a multi-segment linear function related to the output intervals declared by the unit and the corresponding energy prices; M′ is the network power flow constraint relaxation penalty factor used for node price calculation; Are the positive and reverse power flow relaxation variables of line l respectively; NL is the total number of lines; Are the positive and reverse power flow relaxation variables of section s respectively; NS is the total number of sections.

[0155] The constraint conditions of the node price calculation model include:

[0156] System load balance constraint

[0157] For each time period t, the load balance constraint can be described as:

[0158]

[0159] Where, P i,t Is the output of unit i at time period t, T j,t Is the planned power of tie line j at time period t (in is positive, out is negative), NT is the total number of tie lines, D tis the system load at time period t;

[0160] Generator output upper and lower limit constraints

[0161] The output of the generator should be within its maximum / minimum output range, and its constraint conditions can be described as:

[0162]

[0163] For the generators that are shut down in the SCUC optimization results, in the formula all are taken as zero; for the non-priceable generators, in the above formula all are taken as the winning bid output of generator i at time period t in the security-constrained economic dispatch (SCED) optimization results For the priceable generators, in the above formula take the following values:

[0164]

[0165] where δ is the proportion that allows the generator to deviate from the day-ahead SCED optimization results in the LMP model, are the maximum and minimum outputs of the generator in the day-ahead SCED model respectively;

[0166] Generator group output upper and lower limit constraints

[0167] The output of the generator group should be within its maximum / minimum output range, and its constraint conditions can be described as:

[0168]

[0169] where, are the maximum and minimum outputs of generator group j at time period t;

[0170] Generator ramping constraint

[0171] When the generator is ramping up or down, it should meet the ramping rate requirements. The ramping constraint can be described as:

[0172]

[0173] where ΔP i U is the maximum up-ramping rate of generator i, and ΔP i D is the maximum down-ramping rate of generator i;

[0174] Line flow constraint

[0175] The line flow constraint can be described as:

[0176]

[0177] Among them, P l max is the power flow transfer limit of line l; G l-i is the generator output power transfer distribution factor of the node where unit i is located to line l; G l-j is the generator output power transfer distribution factor of the node where tie line j is located to line l; K is the number of nodes in the system; G l-k is the generator output power transfer distribution factor of node k to line l; D k,t is the bus load value of node k at time t. are the positive and reverse power flow relaxation variables of line l respectively;

[0178] Section power flow constraint

[0179] Considering the power flow constraint of the key section, this constraint can be described as:

[0180]

[0181] Among them, P s min 、P s max are the power flow transfer limits of section s respectively; G s-i is the generator output power transfer distribution factor of the node where unit i is located to section s; G s-j is the generator output power transfer distribution factor of the node where tie line j is located to section s; G s-k is the generator output power transfer distribution factor of node k to section s. are the positive and reverse power flow relaxation variables of section s respectively;

[0182] Unit output expression:

[0183]

[0184]

[0185] Among them, NM is the total number of sections of the unit bid, P i,t,m is the winning power of unit i in the m-th output interval at time t, are the upper and lower bounds of the m-th output interval declared by unit i respectively;

[0186] Unit operating cost expression:

[0187]

[0188] Among them, C i,t,m is the energy price corresponding to the m-th output interval declared by unit i;

[0189] Solve the above nodal price calculation model to obtain the Lagrange multipliers of the system load balance constraints, line and section power flow constraints in each time period. Then, the nodal price of node i in time period t is as follows:

[0190]

[0191] where: λ t is the Lagrange multiplier of the system load balance constraint in time period t; is the Lagrange multiplier of the maximum forward power flow constraint of line l. When the line power flow exceeds the limit, this Lagrange multiplier is the relaxation penalty factor of the network power flow constraint; is the Lagrange multiplier of the maximum reverse power flow constraint of line l. When the line power flow exceeds the limit, this Lagrange multiplier is the relaxation penalty factor of the network power flow constraint; is the Lagrange multiplier of the maximum forward power flow constraint of section s. When the section power flow exceeds the limit, this Lagrange multiplier is the relaxation penalty factor of the network power flow constraint; is the Lagrange multiplier of the maximum reverse power flow constraint of section s. When the section power flow exceeds the limit, this Lagrange multiplier is the relaxation penalty factor of the network power flow constraint; G l-k is the generator output power transfer distribution factor of node k to line l; G s-k is the generator output power transfer distribution factor of node k to section s.

[0192] Example 4

[0193] The differences between this example and Example 3 are as follows:

[0194] Electrical energy storage and renewable energy re-powering calculation model

[0195] Objective function

[0196] In the electrical energy storage adjustment calculation, the objective function is adjusted to:

[0197]

[0198] where NE is the total number of renewable energy units, ESH is the total number of electrical energy storage units, and the output of other types of units is fixed; T is the total number of time periods considered, with one time period every 15 minutes in day D, considering 96 time periods; is the output of renewable energy unit i in time period t, P k,t is the total output of electrical energy storage power plant k; is the operating cost of renewable energy unit i in time period t. The bid price of renewable energy is negative, C k,t (P k,t ) is the objective function value of electrical energy storage power plant k in time period t;

[0199] The offer price of renewable energy is negative, so renewable energy power generation is cleared first. The output of electric thermal energy storage is negative and the offer price is 0. Therefore, the electricity consumed by increasing the input of electric thermal energy storage is the same as the electricity consumed by increasing the consumption of renewable energy. By jointly optimizing the output of electric thermal energy storage and renewable energy, the consumption capacity of renewable energy can be improved;

[0200] Load balance constraint

[0201] Except for renewable energy and electric thermal energy storage units, the output of other units is fixed;

[0202]

[0203] Among them, is the sum of the output of renewable energy units, is the sum of the output of electric thermal energy storage units, is the sum of the output of the remaining units, D t is the load at time period t;

[0204] The total absolute value of the output of all electric thermal energy storage units cannot exceed the unconstrained limited power:

[0205]

[0206] Output range constraint of electric thermal energy storage in power plant k:

[0207]

[0208] Among them, is the total sum of the output of all thermal power units in power plant k. The total amount of electric thermal energy storage called in the power plant is restricted by its own upper limit and also by the total output of the thermal power units in the plant;

[0209] Relationship constraint between electric thermal energy storage power plant and electric thermal energy storage unit

[0210] The output of electric thermal energy storage in power plant k is obtained by adding the output of all electric thermal energy storage units in the plant:

[0211]

[0212] Among them, ∑P e,t represents the sum of the output of adjustable electric thermal energy storage units, represents the sum of the output of non-adjustable electric thermal energy storage units;

[0213] The upper and lower limits of the output of adjustable electric thermal energy storage units are:

[0214]

[0215] Among them, is the upper limit of the output of electric thermal energy storage e;

[0216] The upper and lower limits of the output of the non-adjustable electric heat storage unit are as follows:

[0217]

[0218] Among them, α e,t is the 0-1 variable of the operation of the electric heat storage unit e at time t, which is 1 during operation and 0 during shutdown. is the capacity of the electric heat storage unit e at time t. If such an electric heat storage unit is turned on, the output can only be its capacity.

[0219] Section power flow constraint

[0220] Set the upper limit of the relaxation amount of all over-limit sections to the over-limit relaxation amount in the original constraint calculation:

[0221]

[0222] Among them, P s min and P s min are the power flow transmission limits of section s respectively; G s-i is the sensitivity factor of the node where unit i is located to section s; G s-j is the sensitivity factor of the node where tie line j is located to section s, T j,t is the active power of tie line j at time t; G s-k is the sensitivity factor of node k to section s, G s-e is the sensitivity factor of the electric heat storage unit e to section s; are the positive and reverse power flow relaxation amount fixed values of section s in the original constraint calculation respectively, in this way to prevent the situation that the over-limit of the section is aggravated after adding the electric heat storage.

[0223] Adjustment of the maximum output of electric heat storage

[0224] The electric heat storage capacity of each power plant is the smaller value of the capacity curve declared by the power plant itself and the actual available electric heat storage capacity in the plant:

[0225]

[0226] Among them, is the electric heat storage capacity of power plant k at time t, is the sum of the electric heat storage capacities in the plant, is the capacity curve declared by the power plant;

[0227] The adjustment calculation of the electric heat storage adopts a certain logic to distribute the discarded energy, and the weighted average distribution logic of the electric heat storage is:

[0228]

[0229] Among them The electric heat storage distribution output of power plant k at time period t is the sum of the maximum available capacities of all power plants at time period t, P t abd is the amount of renewable energy curtailed due to non-section reasons;

[0230]

[0231] The electric heat storage output of the power plant after distribution and the original upper limit of electric heat storage The smaller value of will be used as the output upper limit of the first-stage quotation of the power plant's electric heat storage. The output upper limit of the second-stage quotation is The first-stage quotation should be much larger than the second-stage quotation. Through the two-stage quotation method, the capacity evenly distributed when all electric heat storage is preferentially filled to the middle After all electric heat storage is filled to the middle in the first stage, then call the electric heat storage output in the second stage;

[0232] Calculation of the objective function value of the electric heat storage power plant

[0233]

[0234] Among them, C k,1 and C k,2 respectively represent the first-stage and second-stage quotations of power plant k of electric heat storage. Currently, the corresponding quotations of all electric heat storage power plants are the same. P k,t,1 and P k,t,2 respectively represent the first-stage and second-stage outputs of the electric heat storage power plant. C k,1 is much larger than C k,2 ;

[0235] P k,t =P k,t,1 +P k,t,2 (28)

[0236] The sum of the two-stage outputs of the electric heat storage power plant needs to be equal to the total output.

[0237] Embodiment 5

[0238] The present invention further provides an embodiment, which is an optimization clearing device for electric heat storage participating in the electricity spot market, including:

[0239] A calculation module for respectively performing unconstrained UC calculation, constrained UC calculation, and constrained ED calculation to obtain the constrained ED calculation result;

[0240] A renewable energy curtailment equalization calculation module for performing renewable energy curtailment equalization calculation based on the constrained ED calculation result and the renewable energy curtailment equalization model;

[0241] A determination module, configured to determine a curtailment value caused by non-section reasons based on the calculation result of equalizing curtailment of renewable energy;

[0242] A nodal price calculation module, configured to record the curtailment value caused by non-section reasons and perform nodal price calculation based on the nodal price calculation model;

[0243] An operation of electric heat storage and nodal price calculation module, configured to perform the operation of electric heat storage based on the recorded curtailment value caused by non-section reasons and the calculation model of electric heat storage and renewable energy re-powering. The total operation amount of electric heat storage shall not be greater than this value. After the electric heat storage is cleared, the renewable energy is also re-powered accordingly, and nodal price calculation is performed based on the nodal price calculation model.

[0244] Further, the device is used to implement the steps of any one of the methods for optimizing the clearing of electric heat storage participating in the electricity spot market.

[0245] Embodiment 6

[0246] Based on the same inventive concept, an embodiment of the present invention further provides a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, the steps of any one of the methods for optimizing the clearing of electric heat storage participating in the electricity spot market described in the above Embodiments 1-4 are implemented.

[0247] Embodiment 7

[0248] Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium. A computer program is stored on the computer storage medium. When the computer program is executed by a processor, the steps of any one of the methods for optimizing the clearing of electric heat storage participating in the electricity spot market described in the above Embodiments 1-4 are implemented.

[0249] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0250] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a means for implementing the functions specified in the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0251] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction means that implements the functions specified in the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0252] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0253] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: it is still possible to modify the specific embodiments of the present invention or make equivalent substitutions, and any modification or equivalent substitution that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A method for optimizing the clearing of electric thermal energy in the electricity spot market, characterized in that: include: Perform unconstrained UC calculation, constrained UC calculation and constrained ED calculation respectively to obtain constrained ED calculation results; Based on the constrained ED calculation results and the renewable energy curtailment equalization model, performing renewable energy curtailment equalization calculation; Based on the calculation results of renewable energy power curtailment, determine the power curtailment value caused by non-section reasons; The power restriction value caused by non-section reasons is recorded, and the node electricity price is calculated based on the node electricity price calculation model; the electric thermal storage is put into operation based on the power restriction value caused by non-section reasons and the electric thermal storage and renewable energy power restoration calculation model. The total amount of electric thermal storage put into operation cannot be greater than this value. After the electric thermal storage is cleared, the renewable energy is also restored accordingly, and the node electricity price is calculated based on the node electricity price calculation model.

2. The method for optimizing the clearing of electric thermal energy in the electricity spot market according to claim 1, characterized in that: The renewable energy power curtailment equalization model is as follows: The objective function is: Where t is the time period, is the power limit of the i-th renewable energy unit in period t, is the predicted output of the i-th renewable energy source in time period t.

3. The method for optimizing clearing of electric thermal energy in the electricity spot market according to claim 2, characterized in that: The constraint condition of the renewable energy power curtailment equalization model includes a renewable energy power curtailment equation constraint, and the renewable energy power curtailment equation constraint is as follows: The sum of the power restrictions of all renewable energy sources in period t is equal to the total power restriction in that period. The total power restriction is calculated based on the results of the security-constrained economic dispatch model. in, is the total power curtailment of renewable energy in period t; The constraints of the renewable energy power-limiting equalization model include renewable energy output constraints, which are: in, The optimized output of the i-th renewable energy unit in period t, the sum of the optimized output of renewable energy and the power limit is equal to the predicted output of renewable energy at that moment; Considering the flow constraint of the key section, the constraint can be described as in, Respectively represent the minimum and maximum limits of power flow transmission in section s; G s-i G represents the generator output power transfer distribution factor of the node where the renewable energy unit i is located to the section s; s-n It represents the generator output power transfer distribution factor of the node where the thermal power unit n is located to the section s; represents the optimized output of the thermal power unit in the safety constraint unit combination stage; G s-j T represents the power transfer distribution factor of the generator output at the node of the tie line j to the section s; j,t G represents the planned power of tie line j in time period t, with positive input and negative output; s-k D represents the generator output power transfer distribution factor of node k to section s; k,t is the bus load value of node k in period t; They respectively represent the positive and reverse power flow relaxation constants of section s in the safety constraint unit combination stage; this constraint ensures that the equal distribution of renewable energy will not cause new sections to exceed the limit or aggravate the existing blocked sections.

4. The method for optimizing clearing of electric thermal energy in the electricity spot market according to claim 1, characterized in that: The objective function of the node electricity price calculation model is: Where N is the total number of units; T is the total number of time periods considered, where one time period is 15 minutes on D day, 96 time periods are considered, and two time periods of peak and valley load are considered on D+1 day, so T is 98; P i,t is the output of unit i in period t; C i,t (P i,t ) is the operating cost of unit i in time period t, which is a multi-segment linear function related to the output intervals reported by the unit and the corresponding energy prices; M′ is the network power flow constraint relaxation penalty factor used for node power price calculation; are the forward and reverse power flow relaxation variables of line l respectively; NL is the total number of lines; are the positive and negative flow relaxation variables of section s respectively; NS is the total number of sections; The node electricity price calculation model constraints include: System load balancing constraints For each time period t, the load balancing constraint can be described as: Among them, P i,t is the output of unit i in period t; T j,t is the planned power of tie line j in time period t, input is positive and output is negative; NT is the total number of tie lines, D t is the system load during time period t; Unit output upper and lower limit constraints: The output of the unit should be within its maximum / minimum output range, and its constraints can be described as: For the units that are shut down in the SCUC optimization results, are all taken as zero; for non-priceable units, The winning bid output of unit i in time period t in the security constrained economic dispatch (SCED) optimization result is taken as For priced units, Take the following values: Where δ is the ratio of the unit allowed to deviate from the day-ahead SCED optimization result in the LMP model, are the maximum and minimum outputs of the units in the day-ahead SCED model respectively; Upper and lower limits of the unit group output: The output of the group should be within its maximum / minimum output range, and its constraints can be described as: in, is the maximum and minimum output of unit group j in time period t; Unit climbing constraints: When the unit is climbing up or down a slope, it should meet the climbing rate requirements; the climbing constraint can be described as: P i,t -P i,t-1 ≤ΔP i U (10) P i,t-1 -P i,t ≤ΔP i D Among them, ΔP i U is the maximum ramp rate of unit i, ΔP i D is the maximum down-slope rate of unit i; Line flow constraints: The line power flow constraint can be described as: Among them, P l max is the power transmission limit of line l; G l-i is the generator output power transfer distribution factor of the node where unit i is located to line l; G l-j is the power transfer distribution factor of the generator output from the node of tie line j to line l; K is the number of nodes in the system; G l-k is the generator output power transfer distribution factor of node k to line l; D k,t is the bus load value of node k in period t; are the forward and reverse power flow relaxation variables of line l respectively; Section flow constraints: Considering the flow constraint of the key section, the constraint can be described as: Among them, P s min , P s max are the power flow transmission limits of section s; G s-i G is the generator output power transfer distribution factor of the node where unit i is located to section s; s-j is the generator output power transfer distribution factor of the node of tie line j to section s; T j,t G represents the planned power of tie line j in period t (input is positive, output is negative); s-k D is the generator output power transfer distribution factor of node k to section s; k,t is the bus load value of node k in period t; are the positive and reverse flow relaxation variables of section s respectively.

5. The method for optimizing clearing of electric thermal energy in the electricity spot market according to claim 1, characterized in that: The node electricity price calculation model Unit output expression: Among them, NM is the total number of sections of the unit quotation, P i,t,m is the winning bid power of unit i in the mth output interval in time period t, They are the upper and lower bounds of the mth output interval reported by unit i; Unit operating cost expression: Among them, C i,t,m The energy price corresponding to the mth output interval declared by unit i; Solving the above node electricity price calculation model, we can obtain the Lagrange multipliers of the system load balance constraints, line and section power flow constraints in each period, and the node electricity price of node i in period t is: Among them, λ t is the Lagrange multiplier of the system load balance constraint in period t; is the Lagrange multiplier of the maximum forward power flow constraint of line l. When the line power flow exceeds the limit, the Lagrange multiplier is the penalty factor for the relaxation of the network power flow constraint. is the Lagrange multiplier of the maximum reverse power flow constraint of line l. When the line power flow exceeds the limit, the Lagrange multiplier is the penalty factor for the relaxation of the network power flow constraint. is the Lagrange multiplier of the maximum positive flow constraint of section s. When the section flow exceeds the limit, the Lagrange multiplier is the penalty factor for the relaxation of the network flow constraint. is the Lagrange multiplier of the maximum reverse flow constraint of section s. When the section flow exceeds the limit, the Lagrange multiplier is the penalty factor for the relaxation of the network flow constraint. G l-k is the generator output power transfer distribution factor of node k to line l; G s-k is the generator output power transfer distribution factor for node k to section s.

6. The method for optimizing clearing of electric thermal energy in the electricity spot market according to claim 1, characterized in that: The calculation model of electric thermal storage and renewable energy recovery is as follows: Objective function: In the electric heat storage adjustment calculation, the objective function is adjusted to: Among them, NE is the total number of renewable energy units, ESH is the total number of electric thermal storage units, and the output of other types of units is fixed; T is the total number of time periods considered, where one time period is every 15 minutes on D day, and 96 time periods are considered; is the output of renewable energy unit i in period t, P k,t is the total output of the electric thermal storage power plant k; is the operating cost of renewable energy unit i in period t. The bid of renewable energy is a negative value. C k,t (P k,t ) is the objective function value of the electric thermal storage power plant k in time period t; The bid for renewable energy is negative, so renewable energy power generation is cleared first. The output of electric thermal storage is negative, and the bid is 0. Therefore, the extra electricity used for electric thermal storage is the extra electricity consumed by renewable energy. By jointly optimizing the output of electric thermal storage and renewable energy, the renewable energy consumption capacity can be improved. Load balancing constraints: Except for renewable energy and electric thermal storage units, the output of other units is fixed; in, is the sum of the output of renewable energy units, is the sum of the outputs of the electric thermal storage units, is the sum of the outputs of the remaining units, D t is the load in period t; The total output absolute value of all electric thermal storage units cannot exceed the unlimited power limit: The output range constraints of electric thermal storage of power plant k are: in, is the total output of all thermal power units in power plant k, is the electric heat storage capacity of power plant k in time period t. The total amount of electric heat storage in the power plant will be constrained by its own upper limit and the total output of the thermal power units in the power plant; Relationship constraints between electric thermal storage power plants and electric thermal storage units: The electric thermal storage output of power plant k is obtained by adding the outputs of all the electric thermal storage units in the plant: Among them, ∑P e,t represents the sum of the outputs of the adjustable electric thermal storage units, Represents the sum of the outputs of the non-adjustable electric thermal storage units; The upper and lower limits of the output of the adjustable electric thermal storage unit are: in, is the output upper limit of the electric thermal storage e; The upper and lower limits of the output of non-adjustable electric thermal storage units are: Among them, α e,t is the 0-1 variable of the operation of the electric thermal storage unit e in period t, which is 1 when it is running and 0 when it is stopped. is the capacity of the electric thermal storage unit e in time period t. If this type of electric thermal storage is turned on, the output can only be its capacity; Section flow constraints: Set the upper limit of the relaxation amount of all over-limit sections to the over-limit relaxation amount in the original constraint calculation: Among them, P s min , P s min are the power flow transmission limits of section s; G s-i G is the sensitivity factor of the node where the renewable energy unit i is located to the section s; s-j is the sensitivity factor of the node where the tie line j is located to the section s, T j,t G is the active power of the tie line in period t; s-k is the sensitivity factor of node k to section s, D k,t is the bus load value of node k in period t, G s-e G is the sensitivity factor of the electric thermal storage unit e to the cross section s; s-o is the sensitivity factor of the node o of the remaining units except renewable energy and electric thermal storage units to the section s, P o,t is the output of the remaining units o except renewable energy and electric thermal storage units, O is the total number of units except renewable energy and electric thermal storage units; They are respectively the positive and negative flow relaxation values ​​of section s in the original constraint calculation, in this way, it is prevented that the cross-section is aggravated after adding electric thermal storage; Maximum output adjustment of electric thermal storage: The electric heat storage capacity of each power plant is the smaller value of the capacity curve reported by the power plant and the actual available electric heat storage capacity in the plant: in, is the electric heat storage capacity of power plant k in period t, is the sum of the electric heat storage capacity in the plant, Declare capacity curves for power plants; The adjustment calculation of electric heat storage uses a certain logic to distribute the abandoned energy. The weighted average distribution logic of electric heat storage is: in is the electric thermal energy distribution output of power plant k in time period t, is the sum of the maximum available capacities of all power plants in time period t, P t abd Renewable energy abandonment for non-cross-sectional reasons; The power plant's thermal energy output after allocation The upper limit of the original electric heat storage The smaller value of The output limit of the first quotation of the electric thermal storage of the power plant will be The first quotation is much larger than the second quotation. Through the two-stage quotation method, all electric thermal storages are given priority to fully allocate the evenly distributed capacity. After all the electric thermal storage in the first stage is full, the electric thermal storage in the second stage is called upon to output power; Calculation of the objective function value of the electric thermal storage power plant: Among them, C k,1 , C k,2 They represent the first and second quotations of the electric thermal storage power plant k, respectively. Currently, the corresponding quotations of all electric thermal storage power plants are the same; P k,t,1 and P k,t,2 They represent the output of the first and second sections of the thermal power plant, respectively. k,1 Much larger than C k,2 ; P k,t =P k,t,1 +P k,t,2 (28) The sum of the two outputs of the electric thermal storage power plant must be equal to the total output.

7. An optimized clearing device for electric thermal storage participating in the electricity spot market, characterized in that: include A calculation module, used to respectively perform unconstrained UC calculation, constrained UC calculation and constrained ED calculation to obtain constrained ED calculation results; A renewable energy curtailment equalization calculation module, used for performing renewable energy curtailment equalization calculation based on the constrained ED calculation result and the renewable energy curtailment equalization model; A determination module, for determining the power restriction value caused by non-section reasons based on the calculation result of renewable energy power restriction equalization; A node electricity price calculation module, used to record the power restriction value caused by non-section reasons, and perform node electricity price calculation based on the node electricity price calculation model; The electric heat storage commissioning and node electricity price calculation module is used to commission the electric heat storage based on the power limit value caused by non-section reasons and the electric heat storage and renewable energy power restoration calculation model. The total amount of electric heat storage commissioning cannot be greater than this value. After the electric heat storage is cleared, the renewable energy is also restored accordingly, and the node electricity price is calculated based on the node electricity price calculation model.

8. The device for optimizing and clearing electric thermal energy in the electricity spot market according to claim 7, characterized in that: The device is used to implement the steps of a method for optimizing clearing of electric thermal storage participating in the electricity spot market as described in any one of claims 2-6.

9. A computer device, characterized in that: The invention comprises a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the computer program, the steps of a method for optimizing clearing of electric thermal energy participating in the electricity spot market as described in any one of claims 1 to 6 are implemented.

10. A storage medium, characterized in that: The storage medium stores a computer program, and the computer program is executed by a processor to perform the steps of a method for optimizing clearing of electric thermal energy participating in the electricity spot market as described in any one of claims 1 to 6.

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