A method and system for the retirement planning of a coal-fired power plant

Abstract

The present invention relates to a method and system for the retirement planning of a coal-fired power plant. The method includes the following steps: establishing a first mathematical model for the retirement process of the coal-fired power plant and a second mathematical model for the Carnot battery retrofit process; based on the first mathematical model and the second mathematical model, establishing an objective function and constraint conditions for the coordinated planning of the power plant retirement and the Carnot battery retrofit; based on the constraint conditions, using the nested column constraint generation algorithm to solve the objective function and obtain an optimal planning scheme. The present invention realizes the planning of the retirement scheme of the coal-fired power plant by jointly planning the retirement and retrofit of the CFPP and using the nested column constraint generation algorithm to solve the objective function of the joint planning of the retirement and retrofit of the CFPP. Moreover, by jointly planning the retirement and retrofit of the CFPP, converting the CFPP into a Carnot battery can not only reduce its stranded cost, but also keep it in the system to provide a certain regulation capacity.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and particularly to a method and system for retiring a coal-fired power plant. Background Art

[0002] Under the guidance of the "carbon neutrality" goal, accelerating the low-carbon transformation of the power system is the key to achieving low-carbon development. Traditional coal-fired generators account for the largest share in power supply and have the highest carbon emissions. The retirement of aging coal-fired power plants (CFPPs) plays an important role in the transformation of a low-carbon power grid. However, the retirement of CFPPs and the large-scale grid connection of variable renewable energy (VRE) pose great challenges to the safe and stable operation of the power system. VRE is connected to the grid through a power electronic interface, resulting in a reduction in the synchronous inertia of the system, which may threaten the frequency security of the system. Therefore, the uncoordinated retirement of a large number of aging CFPPs in a short period will threaten the safe operation of the power grid and bring certain stranded costs. How to find the best planning scheme and determine when and which CFPPs should be retired while considering the growing demand and large-scale VRE integration is an important issue. Summary of the Invention

[0003] In view of this, the present invention provides a method and system for retiring a coal-fired power plant to realize the planning of the retirement plan of the coal-fired power plant and promote the process of retiring the coal-fired power plant.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for retiring a coal-fired power plant, the method comprising the following steps:

[0006] Establish a first mathematical model for the retirement process of the coal-fired power plant and a second mathematical model for the transformation process of the Carnot battery; wherein, the transformation process of the Carnot battery is a process of transforming the coal-fired power plant into a Carnot battery by adding an electric-to-heat device and a heat storage device;

[0007] According to the first mathematical model and the second mathematical model, establish an objective function and constraint conditions for the collaborative planning of power plant retirement and Carnot battery transformation;

[0008] Based on the constraint conditions, use the nested column constraint generation algorithm to solve the objective function and obtain the optimal planning scheme.

[0009] Optionally, the first mathematical model includes the retirement cost and repair cost of the coal-fired power plant, and the second mathematical model includes the planning cost of the Carnot battery;

[0010] The decommissioning cost of a coal-fired power plant is:

[0011] The rehabilitation cost of a coal-fired power plant is:

[0012] The planning cost of a Carnot battery is:

[0013] Among them, is the decommissioning cost of coal-fired power plant i in stage s; and respectively represent the decommissioning decision variables of coal-fired power plant i in stages s - 1 and s, c u represents the unit capacity disposal cost of a coal-fired power plant, c s represents the unit capacity recovery cost of a coal-fired power plant, r rt represents the decreasing rate of the recovery cost; L i represents the lifespan of coal-fired power plant i, y is the year of the base year, is the capacity of coal-fired power plant i, ε represents the discount rate;

[0014] is the rehabilitation cost of coal-fired power plant i in stage s; and respectively represent the rehabilitation decision variables of coal-fired power plant i in stages s - 1 and s, c rh is the unit capacity renovation cost of a coal-fired power plant;

[0015] is the planning cost of Carnot battery j in stage s; and respectively represent the retrofit decision variables of the Carnot battery in stages s - 1 and s; c eh is the cost of the electric-to-thermal conversion equipment per megawatt; c Q is the cost of molten salt thermal energy storage per megawatt-hour; c rm is the maintenance cost of the Carnot battery per megawatt; is the capacity of Carnot battery j; is the rated power of the electric-to-thermal conversion equipment; is the molten salt thermal energy storage capacity.

[0016] Optionally, the objective function is:

[0017]

[0018] Among them, C total represents the total cost, and respectively represent the decommissioning cost and rehabilitation cost of coal-fired power plant i in stage s, Denote the planned cost of the Carnot battery \(j\) in stage \(s\). and respectively denote the power generation cost, reserve cost, and carbon emission cost of the typical day \(r\) in stage \(s\). \(S\) represents the set of stages, \(\Omega\) rt represents the set of coal-fired power plants, \(\Omega\) cb is the set of coal-fired power plants retrofitted into Carnot batteries, \(T\) s is the number of operating days in stage \(s\); \(\pi\) r is the proportion of the typical day \(r\), \(P\) i,t,r,s , and \(E\) i,t,r,s are the power output, primary frequency regulation reserve, upward regulation reserve, downward regulation reserve, and carbon emissions of the coal-fired power plant \(i\) under the typical day \(r\); \(c\) i is the power generation cost of the conventional unit, denotes the upward regulation reserve cost of the conventional unit, denotes the downward regulation reserve cost of the conventional unit, denotes the frequency regulation reserve cost of the conventional unit, \(c\) e denotes the carbon emission cost coefficient, and \(t\) represents the time period under the typical day.

[0019] Optionally, the constraint conditions include the retirement constraint of the coal-fired power plant, the repair constraint of the coal-fired power plant, the retrofit constraint of the Carnot battery, the budget constraint, and the system operation constraint;

[0020] The system operation constraint includes the basic scenario constraint and the uncertain scenario constraint;

[0021] The basic scenario constraint includes the first node power balance constraint, the line transmission power constraint, the new energy output constraint, the coal-fired power plant output constraint and reserve constraint, the Carnot battery operation constraint, the frequency change rate constraint, the frequency lowest point constraint, and the constraint that the Carnot battery provides frequency support;

[0022] The uncertain scenario constraint includes the second node power balance constraint, the coal-fired power plant regulation constraint, and the power regulation constraint of the Carnot battery.

[0023] Optionally, based on the constraint conditions, the nested column constraint generation algorithm is used to solve the objective function to obtain the optimal planning scheme, specifically including:

[0024] The process of using the nested column constraint generation algorithm to solve the objective function based on the constraint conditions is divided into an outer iteration and an inner iteration; the outer iteration includes a master problem iteration and a subproblem iteration.

[0025] A coal-fired power plant retirement planning system, the system includes:

[0026] A mathematical model establishment module for establishing a first mathematical model for the decommissioning process of a coal-fired power plant and a second mathematical model for the transformation process of a Carnot battery; wherein, the transformation process of the Carnot battery is a process of transforming a coal-fired power plant into a Carnot battery by adding an electric-to-thermal device and a heat storage device;

[0027] An objective function and constraint condition determination module for determining an objective function and constraint conditions for the collaborative planning of power plant decommissioning and Carnot battery transformation according to the first mathematical model and the second mathematical model;

[0028] A solution module for solving the objective function based on the constraint conditions by using a nested column constraint generation algorithm to obtain an optimal planning scheme.

[0029] Optionally, the first mathematical model includes the decommissioning cost and repair cost of a coal-fired power plant, and the second mathematical model includes the planning cost of a Carnot battery;

[0030] The decommissioning cost of a coal-fired power plant is:

[0031] The repair cost of a coal-fired power plant is:

[0032] The planning cost of a Carnot battery is:

[0033] Wherein, is the decommissioning cost of coal-fired power plant i in stage s; and respectively represent the decommissioning decision variables of coal-fired power plant i in stages s - 1 and s, c u represents the unit capacity disposal cost of a coal-fired power plant, c s represents the unit capacity recovery cost of a coal-fired power plant, r rt represents the decreasing rate of the recovery cost; L i represents the life of coal-fired power plant i, y is the year of the base year, is the capacity of coal-fired power plant i, and ε represents the discount rate;

[0034] is the repair cost of coal-fired power plant i in stage s; and respectively represent the repair decision variables of coal-fired power plant i in stages s - 1 and s, c rh is the unit capacity renovation cost of a coal-fired power plant;

[0035] is the planning cost of Carnot battery j in stage s; and respectively represent the transformation decision variables of the Carnot battery in stages s - 1 and s; ceh is the cost of the electric - to - heat conversion equipment per megawatt; c Q is the cost of molten - salt thermal energy storage per megawatt - hour; c rm is the maintenance cost of the Carnot battery per megawatt; is the capacity of the Carnot battery j; is the rated power of the electric - to - heat conversion equipment; is the molten - salt thermal energy storage capacity.

[0036] Optionally, the objective function is:

[0037]

[0038] where C total represents the total cost, and respectively represent the decommissioning cost and the repair cost of the coal - fired power plant i in the s stage, represents the planning cost of the Carnot battery j in the s stage, and respectively represent the power generation cost, the reserve cost, and the carbon emission cost of the typical day r in the s stage. S represents the set of stages, Ω rt represents the set of coal - fired power plants, Ω cb is the set of coal - fired power plants retrofitted into Carnot batteries, T s is the number of operating days in the s stage; π r is the proportion of the typical day r, P i,t,r,s , and E i,t,r,s are the power output, primary frequency regulation reserve, upward regulation reserve, downward regulation reserve, and carbon emissions of the coal - fired power plant i under the typical day r; c i is the power generation cost of the conventional unit, represents the upward regulation reserve cost of the conventional unit, represents the downward regulation reserve cost of the conventional unit, represents the frequency regulation reserve cost of the conventional unit, c e represents the carbon emission cost coefficient, and t represents the time period under the typical day.

[0039] Optionally, the constraint conditions include the decommissioning constraint of the coal - fired power plant, the repair constraint of the coal - fired power plant, the retrofit constraint of the Carnot battery, the budget constraint, and the system operation constraint;

[0040] The system operation constraint includes the basic scenario constraint and the uncertain scenario constraint;

[0041] The basic scenario constraint includes the first - node power balance constraint, the line transmission power constraint, the new - energy output constraint, the coal - fired power plant output constraint and reserve constraint, the Carnot battery operation constraint, the frequency change rate constraint, the frequency minimum point constraint, and the constraint that the Carnot battery provides frequency support;

[0042] The uncertain scenario constraints include the second node power balance constraint, the regulation constraint of the coal-fired power plant, and the power regulation constraint of the Carnot battery.

[0043] Optionally, the solving module specifically includes:

[0044] The process of solving the objective function based on the constraint conditions by using the nested column constraint generation algorithm is divided into an outer iteration and an inner iteration; the outer iteration includes a master problem iteration and a sub-problem iteration.

[0045] According to the specific embodiments provided by the present invention, the following technical effects are disclosed by the present invention:

[0046] The present invention discloses a method and system for retiring a coal-fired power plant, and the method includes the following steps:

[0047] Establish a first mathematical model for the retirement process of the coal-fired power plant and a second mathematical model for the transformation process of the Carnot battery; according to the first mathematical model and the second mathematical model, establish an objective function and constraint conditions for the collaborative planning of the power plant retirement and the Carnot battery transformation; based on the constraint conditions, use the nested column constraint generation algorithm to solve the objective function to obtain an optimal planning scheme. By jointly planning the retirement and transformation of the CFPP and using the nested column constraint generation algorithm to solve the objective function of the joint planning of the CFPP retirement and transformation, the present invention realizes the planning of the coal-fired power plant retirement plan and promotes the process of the coal-fired power plant retirement. Moreover, by jointly planning the retirement and transformation of the CFPP, converting the CFPP into a Carnot battery can not only reduce its stranded cost, but also retain it in the system to provide a certain regulation capacity.

[0048] The present invention plans on a daily (short time scale) basis, taking into account that the changes in the new energy power output and load on a short time scale may lead to serious frequency deviations or new energy output curtailment, thus improving the accuracy of the planning. Description of the Drawings

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0050] Figure 1 It is a flowchart of a method for retiring a coal-fired power plant provided in Embodiment 1 of the present invention;

[0051] Figure 2Schematic diagram of the total cost indicators of Case 1 and Case 2 provided in Embodiment 3 of the present invention;

[0052] Figure 3 Schematic diagram of the frequency change rate indicators of Case 1 and Case 2 provided in Embodiment 3 of the present invention;

[0053] Figure 4 Schematic diagram of the lowest frequency point indicators of Case 1 and Case 2 provided in Embodiment 3 of the present invention;

[0054] Figure 5 Schematic diagram of the capacity change of different types of energy of Case 2 provided in Embodiment 3 of the present invention during the planning period;

[0055] Figure 6 Schematic diagram of the capacity change of different types of energy of Case 3 provided in Embodiment 3 of the present invention during the planning period. Detailed implementation manners

[0056] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0057] The purpose of the present invention is to provide a method and system for decommissioning planning of coal-fired power plants to realize the planning of decommissioning schemes of coal-fired power plants and promote the process of decommissioning of coal-fired power plants.

[0058] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0059] Embodiment 1

[0060] The decommissioning of CFPP will lead to a reduction in the regulation ability of the system and generate certain stranded costs. Converting CFPP into a Carnot battery can not only reduce its stranded costs but also keep it in the system to provide a certain amount of regulation ability. However, there is currently no research on the joint planning of CFPP decommissioning and transformation. In addition, existing methods mostly plan the decommissioning of CFPP from the perspective of medium- and long-term power and electricity balance. However, with the increase in the penetration rate of new energy power generation, the changes in new energy power generation output and load on a short time scale may lead to serious frequency deviations or new energy output curtailment. Therefore, it is necessary to consider the inertia and flexibility requirements on a short time scale during the CFPP decommissioning process.

[0061] Based on the above analysis, Embodiment 1 of the present invention provides a method for decommissioning planning of coal-fired power plants, as Figure 1As shown, the method includes the following steps:

[0062] Step 101, establish a first mathematical model for the decommissioning process of a coal-fired power plant and a second mathematical model for the transformation process of a Carnot battery; wherein, the transformation process of the Carnot battery is the process of transforming a coal-fired power plant into a Carnot battery by adding an electric-to-thermal device and a heat storage device.

[0063] The first mathematical model includes the decommissioning cost and the repair cost of the coal-fired power plant, and the second mathematical model includes the planning cost of the Carnot battery.

[0064] The disposal cost and the recycling cost are two main components of the decommissioning cost, and the decommissioning cost of the CFPP can be expressed as:

[0065]

[0066] If then the CFPP is decommissioned; then the CFPP remains in the system. Constraint Ensure that the CFPP is in a decommissioned state in the next stage after making the decommissioning decision. Constraint Restrict CFPPs that have not reached the service life from being decommissioned. For units that need to be repaired and retained in the system, Indicates that a renovation and retention decision is made when the coal power plant i reaches the service life in the s-th stage; Indicates that the CFPP is not renovated and decommissioned. The renovation cost of the CFPP can be expressed as:

[0067]

[0068] The constraints for CFPP renovation include:

[0069]

[0070]

[0071] Step A2: To transform the CFPP into a Carnot battery, it is necessary to add molten salt heat storage and electric-to-thermal equipment to the CFPP. The planning cost of the Carnot battery can be expressed as:

[0072]

[0073] Constraint Ensure that the transformed Carnot battery remains in the system in the next stage. Constraint j ∈ i, i ∈ Ω cb Restrict the CFPP from being renovated or transformed into a Carnot battery simultaneously.

[0074] In the formula: is the decommissioning cost of CFPP i in the s-th stage; is the repair cost of CFPP i in the s stage; r rt is the decreasing rate of the recovery cost; c u is the disposal cost per unit capacity of CFPP; c s is the recovery cost per unit capacity of CFPP; c rh is the renovation cost per unit capacity of CFPP; is the capacity of CFPP; ε represents the discount rate; L i is the life of CFPP; y is the year of the base year; indicates whether CFPP i has reached the retirement age; is the planning cost of Carnot battery j in the s stage; represents the decision variable for the transformation of the Carnot battery; c eh is the cost of the electric - to - heat conversion equipment per MW; c Q is the cost of molten salt thermal energy storage per MWh; c rm is the maintenance cost per MW of the Carnot battery; is the capacity of the Carnot battery; is the rated power of the electric - to - heat conversion equipment; is the molten salt thermal energy storage capacity; Ω cb is the set of CFPPs transformed into Carnot batteries.

[0075] Step 102: According to the first mathematical model and the second mathematical model, establish the objective function and constraint conditions for the collaborative planning of the retirement of the power plant and the transformation of the Carnot battery.

[0076] The implementation principle of step 102 of the present invention is as follows:

[0077] This step gives a compact model for the collaborative planning of the retirement of a multi - stage coal - fired power plant and the transformation of the Carnot battery, as shown in equations (7) - (10). The objective function is to minimize the planning cost and the typical daily operation cost:

[0078]

[0079] The constraint conditions are:

[0080] Ax + Bp ≤ g (8)

[0081]

[0082] In the formula, Ax + Bp ≤ g is the planning constraint for the retirement of the coal - fired power plant and the transformation of the Carnot battery, as well as the typical daily operation constraint under the basic scenario, is the typical daily operation constraint under the uncertain scenario. x and p represent the vectors of 0 - 1 variables and continuous variables under the basic scenario; v and represent the vectors of 0 - 1 variables and continuous variables under the uncertain scenario; represents the uncertain vector of new energy output. A, B, E, H, F, G, Q, R, a, b, g, l are respectively constant coefficient matrices or vectors.

[0083] Specifically, the objective function in step 102 is:

[0084] The objective function is to minimize the total cost C in formula (11) total , including: the decommissioning cost of CFPP and the repair cost the transformation cost of the Carnot battery and the system operation cost at each stage. The system operation cost includes the power generation cost the reserve cost and the carbon emission cost as shown in formula (12).

[0085]

[0086] In the formula, c i , c e are respectively the power generation cost, upward reserve, downward reserve, frequency regulation reserve, and carbon emission cost coefficient of CFPP; P i,t,r,s , E i,t,r,s is the output, primary frequency regulation reserve, upward reserve, downward reserve, and carbon emission of CFPP under a typical day; π r is the proportion of typical day r; T s is the number of operating days in stage s.

[0087] The constraint conditions in step 102 are:

[0088] The constraint conditions include, in addition to the decommissioning constraint and repair constraint of CFPP, and the transformation constraint of the Carnot battery, the budget constraint of the system investment cost, as shown in formula (13).

[0089]

[0090] In the formula, Π max is the upper limit of the investment cost.

[0091] The system operation constraints under a typical day include the constraints under the basic scenario and the uncertain scenario.

[0092] Constraints in the basic scenario: The first-node power balance constraint, as shown in Equation (14); the line transmission power constraint, as shown in Equations (15)-(16); the new energy output constraint, as described in Equation (17). The CFPP output constraint and the reserve constraint, as shown in Equation (18), the CFPP start-stop constraint and the ramp constraint. The Carnot battery operation constraint, as shown in Equations (19)-(24); in addition, it also includes the frequency security constraint of the system, where the frequency change rate constraint, as shown in Equations (25)-(26), and the lowest frequency point constraint, as shown in Equation (27). The constraint for the Carnot battery to provide frequency support, as shown in Equations (28)-(29).

[0093]

[0094] P l,t,r,s =(θ m,t.r.s -θ o,t,r,s ) / x l ,θ ref =0 (15)

[0095] -P l max ≤P l,t,r,s ≤P l max (16)

[0096]

[0097] |Δf nadir |=|Δf(t=t * )|≤Δf max (27)

[0098]

[0099] In the formula: P n,t,r,s is the new energy generation output; is the new energy predicted output; P l,t,r,s is the transmission power of the transmission line; θ m,t,r,s is the node phase angle; x l is the reactance of line l; is the upper limit of the line transmission power; ΔQ j,t,r,s and Δt PFR are the heat storage change and the duration when the Carnot battery provides primary frequency response respectively; f 0 is the frequency reference value; f RoCoF H is the total inertia of the system; H i ,H j are the inertia constants of the CFPP and the Carnot battery respectively; u i and are the operating states of the CFPP and the Carnot battery respectively; is the maximum frequency change rate limit of the system; Δf nadir is the maximum frequency deviation, t * is the time when the frequency reaches the lowest point; Δf max is the maximum frequency deviation allowed by the system.

[0100] The model under the basic scenario considers a set of determined representative days to prepare for the load and renewable energy availability conditions in each planning stage. However, new energy generation is uncertain, and this paper uses the robust optimization method to reduce the system operation risk brought by new energy generation. The idea is that the CFPP and the Carnot battery adjust their output to cope with any uncertain realization in the uncertain set. The modeling of the uncertain set of new energy generation output is shown in Equation (31).

[0101]

[0102] In the formula, is the uncertain output of new energy. Γ T and Γ S are the time and space uncertainty budgets respectively; β is the prediction error of new energy output.

[0103] The constraint conditions under the uncertain scenario include: the power balance constraint of the second node (32); the flexible regulation constraint of the CFPP (33). Constraints (34)-(35) are the power regulation constraints of the Carnot battery.

[0104]

[0105] In the formula, and are the CFPP and new energy output under the uncertain scenario respectively; and are the charging and discharging powers of the Carnot battery under the uncertain scenario respectively; is the line transmission power under the uncertain scenario.

[0106] Step 103: Based on the above constraint conditions, use the nested column constraint generation algorithm to solve the objective function to obtain the optimal planning scheme.

[0107] The principle of Step 103 is:

[0108] Step B1: The proposed collaborative planning model for the retirement of coal-fired power plants and the transformation of Carnot batteries divides its original model into an outer-layer C&CG iteration and an inner-layer C&CG iteration through the nested C&CG decomposition method.

[0109] Step B2: For the outer C&CG iteration, set the number of outer iterations iter = 1. Decompose the original problem into a first-stage master problem and a second-stage sub-problem. The master problem includes planning constraints, operation constraints under the basic scenario, and the C&CG cutting planes returned in the sub-problem. The objective function of the master problem is:

[0110]

[0111] And all the obtained C&CG optimal cutting plane sets. The master problem is a mixed-integer linear programming problem, which is solved by the commercial software Gurobi. Substitute the obtained x and p in the master problem into the sub-problem to solve the sub-problem of the maximum power imbalance. The objective function of the sub-problem is:

[0112]

[0113] Where s is the introduced slack variable to ensure that the sub-problem has a feasible solution. The constraint conditions include:

[0114]

[0115] Solve the sub-problem through the inner C&CG iteration in Step B3 to obtain the objective value ψ and the worst-case scenario of new energy output If ψ > 0, add a new variable s iter+1 , v iter+1 , generate the C&CG cutting plane:

[0116]

[0117] Set iter = iter + 1, go to Step 2; otherwise, output the optimal solution and end.

[0118] Step B3: Set the number of inner iterations inner = 1; set LB = -∞, UB = +∞; initialize the 0-1 variable vector v. Step 2: Replace the 0-1 variables in the sub-problem with v (inner) to obtain the equivalent dual model of the sub-problem. The objective of the dual model is:

[0119]

[0120] The constraint conditions include:

[0121]

[0122] Fη (μ) ≤0, Mη (μ) ≤0, η (μ) ≤0 or unlimited (43)

[0123]

[0124] where η is the vector of dual variables. Solve the sub-problem to obtain the optimal solution к of the sub-problem and the worst-case scenario Update UB = min{UB, к}. Then, based on the obtained Solve the problem:

[0125]

[0126] Obtain the optimal value of v. Update LB = max{LB, f T s}. If UB - LB < ε (ε = 10 -2 ), derive ψ and Return to the outer C&CG iteration in step B2; otherwise, set inner = inner + 1 and iterate step B3.

[0127] The specific implementation of step 103 is as follows:

[0128] The process of using the nested column constraint generation algorithm to solve the objective function is divided into an outer iteration (i.e., the outer C&CG iteration) and an inner iteration (i.e., the inner C&CG iteration).

[0129] For the outer C&CG iteration, assume that the original problem is decomposed into a first-stage master problem and a second-stage sub-problem. The master problem includes planning constraints, operating constraints under the basic scenario, and the C&CG cutting planes returned in the sub-problem. The objective function of the master problem is: The constraint conditions include: node power balance constraint, line transmission power constraint, new energy output constraint, coal-fired power plant output constraint and reserve constraint, Carnot battery operation constraint, frequency change rate constraint, frequency minimum point constraint, and the constraint that the Carnot battery provides frequency support, as well as all obtained C&CG optimal cutting plane cut sets. The master problem is a mixed-integer linear programming problem and is solved by the commercial software Gurobi. Substitute the unit retirement plan result and unit combination result obtained from the master problem into the sub-problem to solve the sub-problem of the maximum power imbalance. The objective function of the sub-problem is to minimize the security violation under uncertain scenarios. The constraint conditions include: the second node power balance constraint, the regulation constraint of the coal-fired power plant, and the power regulation constraint of the Carnot battery. Solve the sub-problem through the inner C&CG iteration in step B3 to obtain the sub-problem objective value ψ and the worst-case scenario of the new energy output. If ψ > 0, generate a C&CG cutting plane and return it to the master problem.

[0130] For the inner-layer C&CG iteration: Set the number of inner-layer iterations inner = 1; Set the lower bound LB = -∞ and the upper bound UB = +∞; Initialize the 0-1 variable vector, perform an equivalent dual model for the sub-problem, solve the dual model, obtain the optimal solution к and the worst-case scenario of the sub-problem, and update UB = min{UB, к}. Then, based on the original problem of solving the sub-problem obtained, obtain the optimal value of the 0-1 variable and the lower bound f T s. Update LB = max{LB, f T s}. If UB - LB < ε (ε = 10 -2 ), export ψ and return to the outer-layer C&CG iteration; otherwise, continue the inner-layer C&CG iteration.

[0131] Example 2

[0132] Embodiment 2 of the present invention provides a decommissioning planning system for a coal-fired power plant. The system includes:

[0133] A mathematical model establishment module for establishing a first mathematical model for the decommissioning process of a coal-fired power plant and a second mathematical model for the Carnot battery transformation process; wherein, the Carnot battery transformation process is a process of transforming a coal-fired power plant into a Carnot battery by adding an electric-to-heat device and a heat storage device.

[0134] A target function and constraint condition determination module for determining a target function and constraint conditions for the collaborative planning of power plant decommissioning and Carnot battery transformation according to the first mathematical model and the second mathematical model.

[0135] A solution module for solving the target function by using a nested column constraint generation algorithm based on the constraint conditions to obtain an optimal planning scheme.

[0136] Among them, the first mathematical model includes the decommissioning cost and repair cost of the coal-fired power plant, and the second mathematical model includes the planning cost of the Carnot battery;

[0137] The decommissioning cost of the coal-fired power plant is:

[0138] The repair cost of the coal-fired power plant is:

[0139] The planning cost of the Carnot battery is:

[0140] Among them, is the decommissioning cost of coal-fired power plant i at stage s; and respectively represent the decommissioning decision variables of coal-fired power plant i at stage s - 1 and stage s, and c u represents the unit capacity disposal cost of the coal-fired power plant, and cs represents the recovery cost per unit capacity of a coal-fired power plant, r rt represents the decreasing rate of the recovery cost; L i represents the lifespan of coal-fired power plant i, y is the year of the base year, is the capacity of coal-fired power plant i, ε represents the discount rate;

[0141] is the repair cost of coal-fired power plant i in stage s; and respectively represent the repair decision variables of coal-fired power plant i in stages s - 1 and s, c rh is the refurbishment cost per unit capacity of a coal-fired power plant;

[0142] is the planning cost of Carnot battery j in stage s; and respectively represent the retrofit decision variables of the Carnot battery in stages s - 1 and s; c eh is the cost of the electricity-to-heat conversion equipment per megawatt; c Q is the cost of molten salt thermal energy storage per megawatt-hour; c rm is the maintenance cost of the Carnot battery per megawatt; is the capacity of Carnot battery j; is the rated power of the electricity-to-heat conversion equipment; is the molten salt thermal energy storage capacity.

[0143] The objective function is:

[0144]

[0145] where C total represents the total cost, and respectively represent the retirement cost and repair cost of coal-fired power plant i in stage s, represents the planning cost of Carnot battery j in stage s, and respectively represent the power generation cost, reserve cost and carbon emission cost of the typical day r in stage s, S represents the set of stages, Ω rt represents the set of coal-fired power plants, Ω cb is the set of coal-fired power plants retrofitted into Carnot batteries, T s is the number of operating days in stage s; π r is the proportion of the typical day r, P i,t,r,s , and E i,t,r,s are the output, primary frequency regulation reserve, upward regulation reserve, downward regulation reserve and carbon emissions of coal-fired power plant i under the typical day r; c i is the power generation cost of the conventional unit, represents the upward reserve cost of conventional units, represents the downward reserve cost of conventional units, represents the frequency regulation reserve cost of conventional units, c e represents the carbon emission cost coefficient, and t represents the time period under a typical day.

[0146] The constraint conditions include the retirement constraint of coal-fired power plants, the repair constraint of coal-fired power plants, the transformation constraint of Carnot batteries, the budget constraint, and the system operation constraint; the system operation constraint includes the basic scenario constraint and the uncertain scenario constraint; the basic scenario constraint includes the first node power balance constraint, the line transmission power constraint, the new energy output constraint, the coal-fired power plant output constraint and the reserve constraint, the Carnot battery operation constraint, the frequency change rate constraint, the frequency minimum point constraint, and the constraint for the Carnot battery to provide frequency support; the uncertain scenario constraint includes the second node power balance constraint, the coal-fired power plant regulation constraint, and the power regulation constraint of the Carnot battery.

[0147] The solution module specifically includes:

[0148] The process of solving the objective function based on the constraint conditions by using the nested column constraint generation algorithm is divided into an outer iteration and an inner iteration; the outer iteration includes a master problem iteration and a sub-problem iteration.

[0149] Embodiment 3

[0150] To enable those skilled in the art to better understand the present invention and understand the advantages of the present invention over the prior art, Embodiment 3 of the present invention further elaborates on the method in Embodiment 1 and the system in Embodiment 2.

[0151] Embodiment 3 of the present invention applies the proposed low-carbon power grid transformation method to an improved IEEE RTS-79 (IEEE Reliability Test System-79) system. The improved IEEE RTS-79 system includes 32 coal-fired power units, 38 transmission lines, and 17 load nodes. Five years are combined into a transition stage, and there are 4-stage plans in the transition period, and the decision is completed at the beginning of each stage. The peak load with a growth rate of 5% is shown in Table 1. Embodiment 3 of the present invention assumes that there are sufficient wind energy and sunlight conditions on Bus 16 and Bus 12 respectively, and all requirements for establishing VRE power plants on these buses are met. The planned installed capacities of wind power and photovoltaic power in each stage are shown in Table 1. The total installed capacity of CFPP in the system at the beginning of the planning is 3405 MW. The CFPPs of the candidate transformed Carnot batteries include 9-11, 12, 31-32. The method in Embodiment 1 of the present invention is implemented on MATLAB 2018b using YALMIP and Gurobi-9.0.1.

[0152] Table 1 Peak Load and New Energy Generation Installed Capacity in Each Stage (MW)

[0153]

[0154] To analyze the effectiveness of the proposed model, the following three cases are compared. Case 1 and Case 2 only plan for the retirement of CFPPs, and Case 1 does not consider frequency security constraints. Case 3 considers frequency security constraints and jointly plans for the retirement of CFPPs and the retrofit of Carnot batteries.

[0155] Table 2 compares the CFPP retirement planning results before and after considering frequency security constraints. Comparing Case 1 and Case 2, it can be seen that after considering the frequency security constraints of the system, the retirement capacity of CFPPs in each stage decreases, especially in the third stage. This is because as the installed capacity of new energy generation gradually increases and coal-fired power units are retired, considering the frequency security constraints of the system increases the inertia demand of the system, so that the planned retired CFPPs are retained in the system after renovation. As Figure 2 can be seen, after considering frequency security constraints, the total cost increases by 6.17%. On the one hand, more CFPPs need to be retained in the system after renovation to provide inertia support, which increases the system's planning cost. On the other hand, more CFPPs need to remain in operation, which increases the system's operating cost.

[0156] Table 2 CFPP Retirement Capacity Planning Results before and after Considering Frequency Security Constraints (MW)

[0157] Planning Phase Stage1 Stage2 Stage3 Stage4 Total Plan 654 700 507 197 2058 Case 1 654 700 155 0 1509 Case 2 630 630 0 0 1260

[0158] To illustrate the impact of CFPP retirement on the frequency security of the system,[[]] Figure 3 and Figure 4 compare the frequency security indicators on a typical day in Stage 3 and Stage 4, where Figure 3 the left and right figures in Figure 4 respectively represent the frequency change rate indicators in Stage 3 and Stage 4, and the left and right figures in Figure 4 respectively represent the lowest frequency indicators in Stage 3 and Stage 4. Case 1 does not consider the frequency security constraints of the system, and the frequency change rate and the lowest frequency in some periods of the typical day exceed the frequency security limits. Although considering frequency security constraints will reduce the retirement capacity of CFPPs, it ensures the security of system operation in the low-carbon transformation of the power system. Therefore, system frequency security needs to be reasonably considered in the CFPP retirement planning model.

[0159] To illustrate the advantages of the proposed method, Case 3 was compared with Case 2, and the results are shown in Table 3. In Case 3, the CFPP was retrofitted into a Carnot battery. During the entire planning period, 1811 MW of CFPP was retired in Case 3, and 552 MW of Carnot battery entered the system. In Case 2, the retirement percentage of CFPP was 37%. Case 3 will ultimately reach a CFPP retirement percentage of over 53%.

[0160] Table 3 Planning Results of Case 2 and Case 3 (MW)

[0161]

[0162]

[0163] Figure 5 shows the capacity changes of different types of energy in Case 2 during the planning period, Figure 6 shows the capacity changes of different types of energy in Case 3 during the planning period. In Case 2, there was no CFPP retirement in Stages 3 and 4, and the proportion of CFPP installed capacity at the end of the planning period was 44.3%. This is because the installed capacity of VRE gradually increased, and the system needed to have sufficient CFPP to provide inertia and flexibility regulation capabilities. From Figure 5 and 6 it can be seen that retrofitting the CFPP into a Carnot battery can effectively promote the retirement of the CFPP.

[0164] The test results of the numerical example show that the proposed method can promote the process of retiring coal-fired power plants and is conducive to realizing the low-carbon transformation of the power system.

[0165] Based on the above embodiments, the advantages of the present invention are as follows:

[0166] First of all, the present invention establishes a mathematical model for the retirement of coal-fired power plants and the retrofitting of Carnot batteries. By adding electric-to-heat equipment and heat storage devices, an aging coal-fired power plant can be retrofitted into a Carnot battery. Then, a multi-stage grid low-carbon transformation planning method is established to carry out collaborative planning for the retirement of coal-fired power plants and the retrofitting of Carnot batteries. An operation model considering frequency security constraints and new energy power generation uncertainty is embedded in the planning model, and simulation is carried out through a typical day. The proposed model is solved using the nested column constraint generation (C&CG) algorithm. Finally, tests are carried out on the improved IEEE-RTS 79 system. The results show that the proposed method can promote the process of retiring coal-fired power plants and is conducive to realizing the low-carbon transformation of the power system.

[0167] In the present specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other. For the apparatuses disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description in the method section.

[0168] In this article, specific examples are used to elaborate on the principles and implementation manners of the present invention. The descriptions of the above embodiments are only used to help understand the method of the present invention and its core idea. At the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for decommissioning planning of a coal-fired power plant, characterized in that, the method comprises the following steps: Establish a first mathematical model for the decommissioning process of a coal-fired power plant and a second mathematical model for the transformation process of a Carnot battery; wherein, the transformation process of the Carnot battery is the process of transforming a coal-fired power plant into a Carnot battery by adding an electric-to-thermal device and a heat storage device; According to the first mathematical model and the second mathematical model, establish an objective function and constraint conditions for the coordinated planning of power plant decommissioning and Carnot battery transformation; Based on the constraint conditions, use the nested column constraint generation algorithm to solve the objective function and obtain the optimal planning scheme; The first mathematical model includes the decommissioning cost and repair cost of a coal-fired power plant, and the second mathematical model includes the planning cost of a Carnot battery; The decommissioning cost of a coal-fired power plant is as follows: The repair cost of a coal-fired power plant is: The planned cost of the Carnot battery is: Among them, is the retirement cost of coal-fired power plant i in stage s; and respectively represent the retirement decision variables of coal-fired power plant i in stage s - 1 and stage s, c u represents the unit capacity disposal cost of the coal-fired power plant, c s represents the unit capacity recovery cost of the coal-fired power plant, r rt represents the decreasing rate of the recovery cost; L i represents the lifespan of coal-fired power plant i, y is the year of the base year, is the capacity of coal-fired power plant i, and ε represents the discount rate; is the repair cost of coal-fired power plant i in stage s; and represent the repair decision variables of coal-fired power plant i in stages s - 1 and s respectively, c rh is the unit capacity renovation cost of the coal-fired power plant; is the planned cost of Carnot battery j in stage s; and respectively represent the retrofit decision variables of the Carnot battery in stage s - 1 and stage s; c eh is the cost of the electric - to - heat conversion equipment per megawatt; c Q is the cost of molten - salt thermal energy storage per megawatt - hour; c rm is the maintenance cost of the Carnot battery per megawatt; is the capacity of Carnot battery j; is the rated power of the electric - to - heat conversion equipment; is the molten - salt thermal energy storage capacity.

2. The method for decommissioning planning of a coal-fired power plant according to claim 1, characterized in that, the objective function is: Among them, C total represents the total cost, and represent the decommissioning cost and the restoration cost of coal-fired power plant i in stage s, respectively, represents the planning cost of Carnot battery j in stage s, and represent the power generation cost, the reserve cost and the carbon emission cost of the typical day r in stage s, respectively. S represents the set of stages, Ω rt represents the set of coal-fired power plants, Ω cb is the set of coal-fired power plants retrofitted into Carnot batteries, T s is the number of operating days in stage s; π r is the proportion of the typical day r, P i,t,r,s , and E i,t,r,s are the output, primary frequency regulation reserve, upward regulation reserve, downward regulation reserve and carbon emission of coal-fired power plant i under the typical day r; c i is the power generation cost of the conventional unit, represents the upward regulation reserve cost of the conventional unit, represents the downward regulation reserve cost of the conventional unit, represents the frequency regulation reserve cost of the conventional unit, c e represents the carbon emission cost coefficient, and t represents the time period under the typical day.

3. The method for decommissioning planning of a coal-fired power plant according to claim 1, characterized in that, the constraint conditions include the decommissioning constraint of a coal-fired power plant, the repair constraint of a coal-fired power plant, the transformation constraint of a Carnot battery, the budget constraint and the system operation constraint; The system operation constraint includes a basic scenario constraint and an uncertain scenario constraint; The basic scenario constraint includes the first node power balance constraint, the line transmission power constraint, the new energy output constraint, the coal-fired power plant output constraint and the reserve constraint, the Carnot battery operation constraint, the frequency change rate constraint, the frequency lowest point constraint, and the constraint that the Carnot battery provides frequency support; The uncertain scenario constraint includes the second node power balance constraint, the coal-fired power plant regulation constraint, and the power regulation constraint of the Carnot battery.

4. The method for decommissioning planning of a coal-fired power plant according to claim 1, characterized in that, based on the constraint conditions, using the nested column constraint generation algorithm to solve the objective function and obtain the optimal planning scheme specifically includes: Dividing the process of using the nested column constraint generation algorithm to solve the objective function based on the constraint conditions into an outer iteration and an inner iteration; the outer iteration includes a master problem iteration and a sub-problem iteration.

5. A decommissioning planning system for a coal-fired power plant, characterized in that, the system includes: A mathematical model establishment module for establishing a first mathematical model for the decommissioning process of a coal-fired power plant and a second mathematical model for the transformation process of a Carnot battery; wherein, the transformation process of the Carnot battery is the process of transforming a coal-fired power plant into a Carnot battery by adding an electric-to-thermal device and a heat storage device; An objective function and constraint condition determination module for determining an objective function and constraint conditions for the coordinated planning of power plant decommissioning and Carnot battery transformation according to the first mathematical model and the second mathematical model; A solution module for using the nested column constraint generation algorithm to solve the objective function based on the constraint conditions and obtain the optimal planning scheme; The first mathematical model includes the decommissioning cost and repair cost of a coal-fired power plant, and the second mathematical model includes the planning cost of a Carnot battery; The decommissioning cost of a coal-fired power plant is: The repair cost of a coal-fired power plant is: The planned cost of the Carnot battery is: Among them, is the retirement cost of coal-fired power plant i in stage s; and represent the retirement decision variables of coal-fired power plant i in stages s - 1 and s respectively, c u represents the disposal cost per unit capacity of the coal-fired power plant, c s represents the recovery cost per unit capacity of the coal-fired power plant, r rt represents the decreasing rate of the recovery cost; L i represents the lifespan of coal-fired power plant i, y is the year of the base year, is the capacity of coal-fired power plant i, and ε represents the discount rate; is the repair cost of coal-fired power plant i in stage s; and respectively represent the repair decision variables of coal-fired power plant i in stage s - 1 and stage s, c rh is the renovation cost per unit capacity of the coal-fired power plant; is the planned cost of the Carnot battery j in stage s; and represent the retrofit decision variables of the Carnot battery in stages s - 1 and s, respectively; c eh is the cost of the power - to - heat equipment per megawatt; c Q is the cost of molten - salt thermal energy storage per megawatt - hour; c rm is the maintenance cost of the Carnot battery per megawatt; is the capacity of the Carnot battery j; is the rated power of the power - to - heat equipment; is the molten - salt thermal energy storage capacity.

6. The decommissioning planning system for a coal-fired power plant according to claim 5, characterized in that, the objective function is: Among them, C total represents the total cost, and respectively represent the decommissioning cost and the restoration cost of coal-fired power plant i in stage s, represents the planning cost of Carnot battery j in stage s, and respectively represent the power generation cost, the reserve cost and the carbon emission cost of the typical day r in stage s. S represents the set of stages, and Ω rt represents the set of coal-fired power plants, and Ω cb is the set of coal-fired power plants transformed into Carnot batteries. T s is the number of operating days in stage s; π r is the proportion of the typical day r, and P i,t,r,s , and E i,t,r,s are the output, primary frequency regulation reserve, upward regulation reserve, downward regulation reserve and carbon emissions of coal-fired power plant i under the typical day r; c i is the power generation cost of the conventional unit, represents the upward regulation reserve cost of the conventional unit, represents the downward regulation reserve cost of the conventional unit, represents the frequency regulation reserve cost of the conventional unit, and c e represents the carbon emission cost coefficient, and t represents the time period under the typical day.

7. The retired planning system of a coal-fired power plant according to claim 5, characterized in that, the constraint conditions include the retirement constraint of the coal-fired power plant, the repair constraint of the coal-fired power plant, the transformation constraint of the Carnot battery, the budget constraint and the system operation constraint; the system operation constraint includes a basic scenario constraint and an uncertain scenario constraint; the basic scenario constraint includes the first node power balance constraint, the line transmission power constraint, the new energy output constraint, the coal-fired power plant output constraint and the reserve constraint, the Carnot battery operation constraint, the frequency change rate constraint, the lowest frequency point constraint, and the constraint for the Carnot battery to provide frequency support; the uncertain scenario constraint includes the second node power balance constraint, the coal-fired power plant regulation constraint, and the power regulation constraint of the Carnot battery.

8. The retired planning system of a coal-fired power plant according to claim 5, characterized in that, the solution module specifically includes: dividing the process of solving the objective function by using the nested column constraint generation algorithm based on the constraint conditions into an outer iteration and an inner iteration; the outer iteration includes a master problem iteration and a subproblem iteration.

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