A Planning Method, Device and Storage Medium for Thermal-Electric Decoupling of a Cogeneration Unit

Abstract

The present invention discloses a method, device and storage medium for thermoelectric decoupling planning of a combined heat and power unit. The method includes the following steps: First, a price-based demand response model is established to optimize the load curve, and an improved light robust optimization model is constructed on the power supply side to handle the uncertainty of wind power output; Then, the operation area of the CHP and the operation characteristics of the thermoelectrically decoupled thermal power unit are modeled; Next, a coordinated planning scheme model for thermoelectric decoupling of multiple CHP units is constructed. The planning model aims to maximize the sum of the thermoelectric decoupling transformation capacities of multiple CHP units and minimize the operation cost after decoupling, and includes constraint conditions; Finally, based on the thermoelectric decoupling planning model of multiple CHP units established in steps A and B, the cost functions of the CHP unit and the condensing-only unit are linearized to improve the calculation efficiency. Case studies show that the CHP dual-objective planning decoupling scheme considering the thermoelectric decoupling transformation cost can significantly enhance the system's peak shaving capacity and achieve the goal of full consumption of wind power.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system grid operation planning, and particularly relates to a heat-electric decoupling planning method, device and storage medium for combined heat and power (CHP) units. Background Art

[0002] With the proposal and development of the "dual carbon" goal, the problem of grid connection and consumption of large-scale clean energy such as wind power needs to be solved urgently. In order to meet the winter heating demand in Northeast China, North China and Northwest China (referred to as the "Three-North Regions") of China, the power supply is mainly based on combined heat and power (CHP) units. CHP units adopt the operation mode of "determining electricity by heat", and there is a strong coupling relationship between power generation output and heat supply output. The minimum power generation output is relatively high when meeting the system heat supply demand in winter, resulting in insufficient system peak shaving capacity and being unfavorable to the grid connection and consumption of clean energy such as wind power and photovoltaic power. Heat-electric decoupling is an effective way to solve the above problems. At present, the existing decoupling schemes only study the transformation scheme of the unit body for a single CHP unit, lacking the planning for decoupling multiple CHP units in the system. Therefore, it is urgent to develop a heat-electric decoupling planning scheme for the whole network CHP units considering their operation space. Summary of the Invention

[0003] In order to solve the technical problems existing in the background art, the present invention aims to provide a heat-electric decoupling planning method, device and storage medium for combined heat and power units. The method includes the following steps: First, build a price-based demand response model to optimize the load curve, and construct an improved light robust optimization model on the power supply side to handle the uncertainty of wind power output; then model the CHP operation area and the operation characteristics of the heat-electric decoupled CHP units; then construct a coordinated planning scheme model for heat-electric decoupling of multiple CHP units, and the planning model aims to maximize the sum of the heat-electric decoupling transformation capacities of multiple CHP units and minimize the operation cost after decoupling and includes constraint conditions; finally, linearize the cost functions of CHP units and pure condensing units based on the heat-electric decoupling planning model established in steps A and B to improve the calculation efficiency. Case analysis shows that the CHP dual-objective planning decoupling scheme considering the heat-electric decoupling transformation cost can greatly enhance the system peak shaving capacity and achieve the goal of full consumption of wind power.

[0004] In order to solve the technical problems, the technical solution of the present invention is:

[0005] A heat-electric decoupling planning method for combined heat and power units, comprising the following steps:

[0006] Build a price-based demand response model and an improved light robust optimization model;

[0007] Build the operating region model of the CHP unit, and transform the CHP unit to obtain the operating characteristic model of the CHP unit after thermoelectric decoupling;

[0008] Based on the above-mentioned price-based demand response model and the operating characteristic model of the CHP unit after thermoelectric decoupling, with the goal of maximizing the thermoelectric decoupling transformation capacity of multiple CHP units and minimizing the operating cost after decoupling, construct a coordinated planning model for the thermoelectric decoupling of multiple CHP units;

[0009] Based on the improved light robust optimization model and the above-mentioned thermoelectric decoupling planning model of multiple CHP units, linearize the cost functions of the CHP unit and the condensing-only unit to improve the calculation efficiency.

[0010] Furthermore, construct a price-based demand response model, which specifically includes:

[0011] The relationship between the electricity consumption of users in the current period and the electricity price in the current period and the electricity prices in other periods can be expressed as:

[0012]

[0013] In the formula: is the load at time t after DR; ε is the demand price elasticity coefficient; P load,t is the load at time t before DR; Δρ h is the change in the electricity price at time h after DR; is the electricity price at time h before DR;

[0014] According to the time-series load characteristics, divide the simulation period into 3 stages: peak period, normal period, and valley period, and introduce the demand price elasticity coefficient matrix E to represent the load response in different periods after implementing time-of-use electricity price; the time-of-use electricity price response model can be expressed as:

[0015]

[0016]

[0017] In the formula: are the electricity prices at peak, normal, and valley times after DR respectively; P load,f , P load,p , P load,g are the electricity prices at peak, normal, and valley times before DR respectively; Δρ f , Δρ p , Δρ g are the changes in electricity prices at peak, normal, and valley times after DR respectively; are the electricity prices at peak, normal, and valley times before DR respectively; E is the demand price elasticity matrix, where the diagonal elements are self-elasticity coefficients and the non-diagonal elements are cross-elasticity coefficients.

[0018] Furthermore, construct an improved light robust optimization model, which specifically includes:

[0019] Construct the box - type uncertainty set:

[0020]

[0021] where: b i,j is the j - th element of the i - th uncertain parameter; is the predicted value of the uncertain parameter b i,j ; is the fluctuation amplitude of the uncertain parameter b i,j ; ξ i,j is the fluctuation ratio; Γ i is the uncertainty;

[0022] Improve the light robust optimization model:

[0023]

[0024]

[0025]

[0026] where: w is the coefficient vector of the slack variable; γ is the slack variable vector; is the maximum value of the i - th slack variable.

[0027] Furthermore, model the operation region of the CHP unit, specifically including:

[0028] Construct the operation region model of the CHP unit:

[0029]

[0030] where: are the electrical output and thermal output of the CHP unit i at time t respectively; P i chp,max , P i chp,min are the maximum and minimum values of the electrical output when the CHP unit operates in pure condensing mode respectively; is the maximum thermal power that the CHP unit can output; are the boundary values of the CHP unit i at time t respectively; is the intersection point of the extension line of the BC segment of the CHP unit i and the vertical axis, as shown in Figure 2 ; are the slopes of the AB and CD sides of the CHP unit; is the slope of the BC side of the CHP unit;

[0031] Among them, the cost function of the operation region model of the CHP unit is:

[0032]

[0033] Where: a0, a1, a2, a3, a4, and a5 are the cost coefficients respectively.

[0034] Furthermore, a model of the operating characteristics of the CHP unit after thermoelectric decoupling is established, specifically including:

[0035]

[0036]

[0037] Where: CP i,t , CH i,t are the electrical output and heat output of the i-th CHP unit at time t after decoupling respectively; x i,1 , x i,2 are the decision variables of whether to add an EB and an HST to the i-th CHP unit respectively, x i,1 = 1, x i,2 = 1 indicate that the i-th CHP unit adds an EB and an HST respectively, and x i,1 = 0, x i,2 = 0 indicate that the i-th CHP unit does not add an EB and an HST respectively; E i , S i,t are the operating capacities of adding an electric boiler and a heat storage tank to the i-th CHP unit respectively; η EB is the efficiency of the electric boiler for converting electricity to heat.

[0038] Furthermore, a coordinated planning model for thermoelectric decoupling of multiple CHP units is constructed, specifically including:

[0039] Construct the upper-level model of the planning model:

[0040] The upper-level model aims to minimize the net load volatility obtained by tracking the wind power curve of the load:

[0041]

[0042]

[0043]

[0044] Where: T is the scheduling period, taking 24 hours; is the net load value obtained by superimposing the wind power on the typical daily load in month m after DR; is the average value of the net load at time t of the typical daily load in month m; is the total network load at time t of the typical daily load in month m after DR; is the predicted output of the wind farm w at time t of the typical daily load in month m;

[0045] Constraints of the upper-layer model:

[0046] The total DR front and back loads remain unchanged;

[0047]

[0048] In the formula: P load,t is the load at time t before DR;

[0049] DR upper and lower limit constraints during the t period;

[0050]

[0051] In the formula: P load,t is the load during the t period before DR; β is the upper limit of the DR proportion in each period;

[0052] The post-DR power purchase cost is lower than the pre-DR power purchase cost;

[0053]

[0054] In the formula: is the electricity price during the t period before DR; Δρ t is the change in the electricity price during the t period after DR;

[0055] Taking the maximum sum of the thermal-electric decoupling transformation capacities of multiple CHP units and the lowest operating cost of the system after decoupling as the objectives, the lower-layer model of the described planning model is constructed, specifically including:

[0056] The lower-layer model of the planning model takes the objective function 1 as the maximum sum of the thermal-electric decoupling transformation capacities of multiple CHP units:

[0057]

[0058] In the formula: N chp is the number of CHP units; C i,EB is the capacity of the electric boiler added to CHP unit i; C i,HSA is the capacity of the heat storage tank added to CHP unit i;

[0059] The lower-layer model of the planning model takes the objective function 2 as the lowest operating cost of multiple CHP units after decoupling during the heating season:

[0060]

[0061] In the formula: is the operating cost of the traditional thermal power unit; is the start-stop cost of the thermal power unit; is the operating cost of the CHP unit; is the annualized cost of the thermal-electric decoupling transformation of the CHP unit; is the penalty cost for abandoned wind;

[0062] The lower-level model of the planning model, whose constraint conditions include:

[0063] Reconstruction decision constraint;

[0064] x i,1 +x i,2 ≤1;

[0065] In the formula: x i,1 , x i,2 are the decision variables for whether to add an electric boiler and a heat storage tank to the i-th CHP unit respectively. x i,1 =1, x i,2 =1 respectively indicate that the i-th CHP unit adds an electric boiler and a heat storage tank. x i,1 =0, x i,2 =0 respectively indicate that the i-th CHP unit does not add an electric boiler and a heat storage tank;

[0066] Electric power balance constraint;

[0067]

[0068] In the formula: CP m,i,t is the electric power output of the i-th CHP unit after heat-electricity decoupling at the t-th time period of a typical day in month m; P m,j,t is the output of the j-th thermal power plant at the t-th time period of a typical day in month m; is the predicted output of the w-th wind farm at the t-th time period of a typical day in month m; is the wind rejection amount of the w-th wind farm at the t-th time period of a typical day in month m; is the load of node n at the t-th moment of a typical day in month m; N d is the total number of nodes;

[0069] Thermal power balance constraint;

[0070]

[0071] In the formula: CH m,i,t is the thermal power output of the i-th CHP unit after decoupling at the t-th time period of a typical day in month m; N h is the number of thermal load nodes; is the thermal load of the h-th thermal load node at the t-th time period of a typical day in month m;

[0072] Unit electric and thermal power output constraint;

[0073]

[0074]

[0075]

[0076] Where: P j max and P j min are the upper and lower limits of the output of the pure condensing thermal power unit j, respectively; v m,j,t is the operating status variable of the thermal power unit. When v m,j,t = 1, it means that the thermal power unit j is online; when v m,j,t = 0, it means that the thermal power unit j is offline; is the heat output of the CHP unit i at time t on a typical day in month m; is the upper limit of the heat output of the CHP unit i; is the electrical output of the CHP unit i at time t on a typical day in month m;

[0077] Wind curtailment constraint;

[0078]

[0079] Where: is the upper limit of the wind curtailment power;

[0080] Start-stop constraint;

[0081]

[0082] Where: are the continuous startup and shutdown durations of unit j at time t, respectively; and are the minimum startup and shutdown durations of unit j, respectively;

[0083] Ramp rate constraint;

[0084]

[0085]

[0086]

[0087] Where: are the upward and downward ramp rate limits of the pure condensing unit j, respectively; are the upward and downward ramp rate limits of the CHP unit i, respectively;

[0088] Heat storage tank operation constraint;

[0089] -C HSA,i ≤ S m,i,t - S m,i,t-1 ≤ C HSA,i ;

[0090]

[0091]

[0092]

[0093] S m,i,0 = S m,i,T ;

[0094] In the formula: C HSA,i is the capacity of the heat storage tank configured for CHP unit i; are the upper and lower limits of the configured capacity of heat storage tank i respectively; S m,i,t is the heat stored in heat storage tank i at time t on a typical day in month m; is the heat storage capacity of heat storage tank i; are the upper and lower limits of the heat storage capacity of heat storage tank i respectively; S m,i,0 is the heat stored in heat storage tank i at the initial moment; S m,i,T is the heat stored in heat storage tank i at the end of a cycle;

[0095] Operating constraints of the electric boiler;

[0096] 0 ≤ E m,i,t ≤ C EB,i ;

[0097]

[0098] In the formula: E m,i,t is the electricity consumption of the additional electric boiler of CHP unit i at time t on a typical day in month m; C EB,i is the capacity of the additional electric boiler of CHP unit i; are the upper and lower limits of the capacity of the additional electric boiler of CHP unit i respectively;

[0099] Normalize the upper layer model and the lower layer model of the said planning model by the improved ideal point method to obtain a single-objective model as follows:

[0100]

[0101] In the formula: f1 * , f2 * are the optimal values of the objective functions f1 and f2 respectively;

[0102] That is, the construction of the multi-CHP unit thermoelectric decoupling coordination planning model is realized.

[0103] Furthermore, linearize the cost functions of the CHP unit and the condensing-only unit, specifically including:

[0104] The operating cost function of the condensing-only unit is linearized in the following way:

[0105]

[0106]

[0107]

[0108] 0 ≤ P j (t) ≤ (P j+1 - P j )v(t);

[0109] In the formula: a, b, and c are cost function coefficients; g and are the cost function of the condensing unit and its linear approximation function respectively; N is the total number of segments into which the cost function is divided; α j is the slope of the j - th segment line; g(P1) is the cost function value at the minimum output value; v(t) is the operating state of the condensing unit at time t;

[0110] The following linear model is used to linearize the cost function of the CHP unit:

[0111] P Con = P chp + c v H chp

[0112] C = dP Con + e

[0113] C = d1P chp + d2H chp + e

[0114] In the formula: P Con is the electrical output of the CHP unit under the condensing condition; d and e are cost coefficients; c v is the slope of the operating boundary curve AB of the unit.

[0115] Furthermore, according to the linear correspondence conversion method in the improved light robust optimization model, the electric power balance constraint is transformed into the following formula:

[0116]

[0117] In the formula: obtained by sorting the elements from large to small;

[0118] That is, the optimization of the uncertainty of wind power output is realized.

[0119] A thermoelectric coupling planning device for a combined heat and power unit, the device includes:

[0120] The first construction module is used to construct an electricity price - based demand response model and an improved light robust optimization model;

[0121] The second construction module is used to construct the operating area model of the CHP unit, and perform transformation processing on the CHP unit to obtain the operating characteristic model of the CHP unit after thermoelectric decoupling;

[0122] The third construction module is used to construct a coordinated planning model for thermoelectric decoupling of multiple CHP units with the goal of maximizing the thermoelectric decoupling transformation capacity of multiple CHP units and minimizing the operating cost after decoupling based on the electricity price-based demand response model and the operating characteristic model of the CHP unit after thermoelectric decoupling;

[0123] The first calculation module is used to linearize the cost functions of the CHP unit and the condensing-only unit based on the improved light robust optimization model and the thermoelectric decoupling planning model of multiple CHP units to improve the calculation efficiency.

[0124] A computer storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the method as described above.

[0125] Compared with the prior art, the advantages of the present invention are as follows:

[0126] A thermoelectric decoupling planning method, device and storage medium for a combined heat and power unit takes into account the influence of the uncertainty of wind power on the source side and the demand response on the load side on the planning results. At the same time, the model aims to maximize the sum of the thermoelectric decoupling transformation capacities of multiple CHP units and minimize the operating cost of the system after decoupling, coordinates the contradiction between the transformation capacity and the cost, and the planning results are more reasonable. Description of the Drawings

[0127] Figure 1 It is the overall flowchart of a thermoelectric decoupling planning method for a combined heat and power unit of the present invention;

[0128] Figure 2 It is the operating condition diagram of the CHP unit before and after thermoelectric decoupling transformation;

[0129] Figure 3 It is the wind power output curve of a typical day in February. Detailed Embodiments

[0130] The following describes the specific embodiments of the present invention in conjunction with the embodiments:

[0131] It should be noted that the structures, ratios, sizes, etc. shown in this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limiting conditions under which the present invention can be implemented. Any modification of the structure, change of the proportional relationship or adjustment of the size should still fall within the scope covered by the technical content disclosed in the present invention without affecting the effects that the present invention can produce and the purposes that can be achieved.

[0132] Meanwhile, terms such as "upper", "lower", "left", "right", "middle", and "one" cited in this specification are only for the convenience of clear description and do not limit the scope of implementation of the present invention. Changes or adjustments in their relative relationships shall be regarded as the scope of implementation of the present invention when there is no substantial change in the technical content.

[0133] Embodiment 1:

[0134] A method for thermoelectric coupling planning of a combined heat and power unit includes the following steps:

[0135] Step A. Construct a price-based demand response and improved light robust optimization model;

[0136] Step A1: Construction of the price-based demand response model:

[0137]

[0138] In the formula: ΔDR t is the load response power; P load,t is the load power in the t-th period before DR; ε is the DR elasticity coefficient; Δρ h is the change in electricity price in the h-th period after DR; is the electricity price in the h-th period before DR.

[0139] Divide the load cycle into peak, flat, and valley periods, introduce a price elasticity matrix to describe the multi-period price response on the load side, and obtain the load power in each period after the time-of-use electricity price response:

[0140]

[0141]

[0142] In the formula: are the electricity prices at peak, flat, and valley times after DR respectively; P load,f , P load,p , P load,g are the electricity prices at peak, flat, and valley times before DR respectively; Δρ f , Δρ p , Δρ g are the changes in electricity prices at peak, flat, and valley times after DR respectively; are the electricity prices at peak, flat, and valley times before DR respectively; E is the demand price elasticity matrix, where the diagonal elements are self-elasticity coefficients and the non-diagonal elements are cross-elasticity coefficients.

[0143] Step A2: Construction of the improved light robust optimization model, including:

[0144] Step A21: Construction of the box uncertainty set:

[0145] The uncertain variable b on the right side of the traditional robust optimization constrainti Expressed in a boxed set as:

[0146]

[0147] Where: b i,j is the j-th element of the i-th uncertain parameter; is the predicted value of the uncertain parameter b i,j ; is the fluctuation amplitude of the uncertain parameter b i,j ; ξ i,j is the fluctuation ratio; Γ i is the uncertainty.

[0148] Step A22: Improve the light robust optimization model:

[0149]

[0150]

[0151]

[0152] Where: w is the coefficient vector of the slack variable; γ is the slack variable vector; is the maximum value of the i-th slack variable; Sorted from large to small.

[0153] Step B. Modeling the operating characteristics of the CHP unit before and after decoupling:

[0154] Step B1: Construct the operating model of the CHP unit:

[0155]

[0156] Where: are the electrical output and thermal output of CHP unit i at time t, respectively; P i chp，max 、P i chp，min are the maximum and minimum values of the electrical output of the CHP unit in pure condensing operation, respectively; is the maximum thermal power that the CHP unit can output; are the boundary values of CHP unit i at time t, respectively; is the intersection point of the extension line of the BC segment of CHP unit i and the vertical axis, as shown in Figure 1 ; are the slopes of the AB and CD sides of the CHP unit; is the slope of the BC side of the CHP unit.

[0157] The operating cost function of the CHP unit is:

[0158]

[0159] In the formula: a0, a1, a2, a3, a4, and a5 are cost coefficients respectively.

[0160] Step B2: The decoupled electrical and thermal output models of the cogeneration unit are as follows:

[0161]

[0162]

[0163] In the formula: CP i,t , CH i,t are the electrical output and thermal output of the i-th CHP unit at time t after decoupling respectively; x i,1 , x i,2 are the decision variables of whether to add EB and HST to the i-th CHP unit respectively. x i,1 = 1, x i,2 = 1 indicate that the i-th CHP unit adds EB and HST respectively. x i,1 = 0, x i,2 = 0 indicate that the i-th CHP unit does not add EB and HST respectively; E i , S i,t are the operating capacities of adding an electric boiler and a heat storage tank to the i-th CHP unit respectively; η EB is the efficiency of the electric boiler for converting electricity to heat.

[0164] Step C: Build a coordinated planning model for the thermal and electrical decoupling of multiple CHP units. The planning model aims to maximize the thermal and electrical decoupling transformation capacity of multiple CHP units and minimize the operating cost after decoupling, and includes constraint conditions;

[0165] Step C1: The upper-level model of the planning model:

[0166] Step C11: The upper-level model aims to minimize the net load volatility obtained by tracking the wind power curve of the load:

[0167]

[0168] In the formula: T is the scheduling period, taking 24 hours; is the net load value of the typical day in month m after DR, which is the sum of the load and wind power; is the average value of the net load at time t of the typical day in month m; is the total network load at time t of the typical day in month m after DR; is the predicted output of the wind farm w at time t of the typical day in month m.

[0169] Step C12: The constraint conditions of the upper-level model:

[0170] 1) The total DR front and back load remains unchanged

[0171]

[0172] Where: P load,t is the load at time t before DR.

[0173] 2) DR upper and lower limit constraints during period t

[0174]

[0175] Where: P load,t is the load during period t before DR; β is the upper limit of the DR proportion for each period.

[0176] 3) The post - DR power purchase cost is lower than the pre - DR power purchase cost

[0177]

[0178] Where: is the electricity price during period t before DR; Δρ t is the change in electricity price during period t after DR.

[0179] Step C2: The lower - layer model of the planning model aims to maximize the sum of the thermal - power decoupling transformation capacities of multiple CHP units and minimize the operating cost of the system after decoupling:

[0180] Step C21: The first objective function of the planning model is to maximize the sum of the thermal - power decoupling transformation capacities of multiple CHP units:

[0181]

[0182] Where: N chp is the number of CHP units; C i,EB is the capacity of the electric boiler added to CHP unit i; C i,HSA is the capacity of the heat storage tank added to CHP unit i.

[0183] Step C22: The second objective function of the planning model is to minimize the operating cost of multiple CHP units after decoupling during the heating season:

[0184]

[0185] Where: is the operating cost of traditional thermal power units; is the start - up and shut - down cost of thermal power units; is the operating cost of CHP units; is the annualized cost of the thermal - power decoupling transformation of CHP units; is the penalty cost for abandoned wind.

[0186] 1) The operating cost of traditional thermal power units.

[0187]

[0188] Where: M is the total heating duration, in months; N pcu is the number of traditional thermal power units; T is the duration of a scheduling period; g m,j,t is the operating cost of the j-th condensing-only thermal power unit at time t on a typical day in month m.

[0189] 2) Start-up and shut-down costs of traditional thermal power units.

[0190]

[0191] Where: su m,j,t , sd m,j,t are the start-up variable and shut-down variable of thermal power unit j at time t on a typical day in month m, respectively; su m,j,t = 1, sd m,j,t = 1 indicate that unit j starts up and shuts down, respectively; represent the start-up and shut-down costs of unit j, respectively.

[0192] 3) Operating costs of CHP units.

[0193]

[0194] Where: N chp is the number of CHP units; is the operating cost of CHP unit i at time t on a typical day in month m.

[0195] 4) Annualized cost of thermoelectric decoupling transformation of CHP units.

[0196]

[0197] Where: is the capacity of the electric boiler added to CHP unit i; is the capacity of the heat storage tank added to CHP unit i; γ EB , γ HSA are the unit construction cost of the electric boiler and the heat storage tank, respectively; λ is the capital recovery factor; r is the discount rate; y is the transformation service life.

[0198] 5) Penalty cost for abandoned wind.

[0199]

[0200] Where: N W is the number of wind farms; c is the penalty coefficient for abandoned wind; is the amount of abandoned wind at the w-th wind farm at time t on a typical day in month m.

[0201] Step C23: Based on the thermoelectric decoupling planning model of the cogeneration unit described in Step C2, its constraints include;

[0202] 1) Retrofit decision constraints.

[0203] x i,1 +x i,2 ≤1 (23)

[0204] Where: x i,1 、x i,2 are the decision variables for whether to add an electric boiler and a heat storage tank to the i-th CHP unit respectively. x i,1 =1, x i,2 =1 indicate that the i-th CHP unit adds an electric boiler and a heat storage tank respectively. x i,1 =0, x i,2 =0 indicate that the i-th CHP unit does not add an electric boiler and a heat storage tank respectively.

[0205] 2) Electric power balance constraint.

[0206]

[0207] Where: CP m,i,t is the electric power output of the i-th CHP unit after thermoelectric decoupling at time t of a typical day in month m; P m,j,t is the output of the j-th thermal power plant at time t of a typical day in month m; is the predicted output of the w-th wind farm at time t of a typical day in month m; is the wind curtailment of the w-th wind farm at time t of a typical day in month m; is the load at node n at time t of a typical day in month m; N d is the total number of nodes.

[0208] 3) Heat power balance constraint.

[0209]

[0210] Where: CH m,i,t is the heat output of the i-th CHP unit after decoupling at time t of a typical day in month m; N h is the number of heat load nodes; is the heat load of the h-th heat load node at time t of a typical day in month m.

[0211] 4) Unit electric and heat output constraints.

[0212]

[0213] Where: P j max 、P j minare the upper and lower limits of the output of the pure condensing thermal power unit j; v m,j,t is the operating state variable of the thermal power unit. When v m,j,t = 1 indicates that the thermal power unit j is in online operation, and v m,j,t = 0 indicates that the thermal power unit j is not online; is the thermal output of the CHP unit i at time t on a typical day in month m; is the upper limit of the thermal output of the CHP unit i; is the electrical output of the CHP unit i at time t on a typical day in month m.

[0214] 5) Wind curtailment constraint

[0215]

[0216] In the formula: is the upper limit of the wind curtailment power.

[0217] 6) Start-stop constraint.

[0218]

[0219] In the formula: are the continuous startup and shutdown durations of unit j at time t, respectively; and are the minimum startup and shutdown durations of unit j, respectively.

[0220] 7) Ramping constraint.

[0221]

[0222] In the formula: are the upward and downward ramping limits of the pure condensing unit j, respectively; R i U，chp 、R i U，chp are the upward and downward ramping limits of the CHP unit i, respectively.

[0223] 8) Thermal energy storage tank operation constraint.

[0224]

[0225] In the formula: C HSA,i is the capacity of the thermal energy storage tank configured for the CHP unit i; are the upper and lower limits of the configured capacity of the thermal energy storage tank i, respectively; S m,i,t is the heat stored in the thermal energy storage tank i at time t on a typical day in month m; is the heat storage capacity of the thermal energy storage tank i; are the upper and lower limits of the heat storage capacity of the thermal energy storage tank i, respectively; S m,i,0 is the initial heat storage of the thermal energy storage tank i; S m,i,Tis the heat storage amount in the heat storage tank at the end of one cycle of the heat storage tank i.

[0226] 9) Electric boiler operation constraints.

[0227]

[0228] In the formula: E m,i,t is the power consumption of the electric boiler added to CHP unit i at time t on a typical day in month m; C EB,i is the capacity of the electric boiler added to CHP unit i; are the upper and lower limits of the capacity of the electric boiler added to CHP unit i, respectively.

[0229] Step C3: Normalize the multi-objectives proposed in Step C2 using the improved ideal point method to obtain the single-objective model as follows:

[0230]

[0231] In the formula: f1 * , f2 * are the optimal values of the objective functions f1 and f2, respectively.

[0232] Step D. Based on the multi-CHP unit heat and power decoupling planning model established in Steps A, B, and C, linearize the cost functions of the CHP unit and the condensing-only unit to improve the calculation efficiency.

[0233] Step D1: The equivalent transformation of the wind power uncertainty parameters in Step C:

[0234] Equation (24) contains uncertainty parameters. By performing equivalent transformation using the light robust optimization model described in the steps, we can obtain:

[0235]

[0236] In the formula: is obtained by sorting the elements from largest to smallest.

[0237] Step D2: The operation cost function of the condensing-only unit is linearized in the following way:

[0238]

[0239] In the formula: a, b, and c are the cost function coefficients; g and are the cost function of the condensing-only unit and its linear approximation function, respectively; N is the total number of segments into which the cost function is divided; α j is the slope of the j-th segmented line; g(P1) is the cost function value at the minimum output value; v(t) is the operation state of the condensing-only unit at time t.

[0240] Step D3: The operating cost of the thermoelectric unit is related to both the electrical and thermal outputs of the unit. Considering that the average coal consumption of the unit only differs by about 10% between full load and half load, the following linear model is used to represent the cost function of the thermoelectric unit:

[0241]

[0242] In the formula: P Con is the electrical output of the CHP unit under the pure condensing condition; d and e are cost coefficients; c v is the slope of the operating boundary curve AB of the unit, as shown in Figure 1 .

[0243] Step D4: Input the relevant data required for the model, program based on the MATLAB R2020a platform and use the YALMIP toolbox, and call the commercial optimization software GUROBI to solve the model.

[0244] 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, the applicant further elaborates with specific embodiments.

[0245] The IEEE 24-node system is used to verify the effectiveness of the present invention. The system includes 2 conventional thermal power units, 6 CHP units, and 2 wind farms. The electric-to-thermal conversion efficiency of the configured electric boiler is taken as η EB = 0.98. The upper and lower limits of the configured capacity of the electric boiler are 100 MW and 50 MW respectively, and the unit capacity investment cost is 1 million yuan / MW; the upper and lower limits of the configured power of the heat storage tank are 100 MW and 50 MW respectively, the upper limit of the heat storage capacity of the heat storage tank is 1000 MWh, and the unit investment cost is 500,000 yuan / MW; the investment transformation service life is 20 years, the discount rate is taken as 8%, and the unit curtailment cost is 400 yuan / MWh. The self-elasticity coefficient and cross-elasticity coefficient in the demand price elasticity coefficient matrix are taken as -0.2 and 0.03 respectively; the peak, flat, and valley electricity prices are shown in Table 1.

[0246] Table 1 Peak, flat, and valley time-of-use electricity prices

[0247]

[0248] 1. Analysis of planning results

[0249] To highlight the impact of demand response and wind power uncertainty on the thermoelectric decoupling and coordination planning of multiple CHP units and the coordination between the retrofit capacity and retrofit operation cost of CHP units, three different scenarios are taken for analysis:

[0250] 1) Scenario 1, the thermoelectric decoupling and coordination planning of multiple CHP units without considering demand response and wind power volatility;

[0251] 2) Scenario 2, thermal and power decoupling coordinated planning of multiple CHP units without considering demand response;

[0252] 3) Scenario 3, thermal-electric decoupling coordinated planning of multiple CHP units taking into account demand response;

[0253] When the volatility of wind power is not considered in Scenario 1, the volatility ratio of the wind power uncertainty item described in Section 1.2.1 is ξ w,t =0, that is, the linear equation term in the weak robust planning model is 0, that is, Γ = 0; for scenario 2, there are only two wind farms connected to the grid in this paper, and Γ = 1.1 is taken when considering the volatility of wind power for weak robust planning.

[0254] The configuration results of the CHP unit electric boiler and heat storage tank and the system operation results under different scenarios are shown in Tables 2 and 3.

[0255] Table 2 Comparison of transformation decisions under different scenarios

[0256]

[0257] Table 3 System operation results

[0258]

[0259]

[0260] From the comparative analysis of the results in Table 2 and Table 3, it can be seen that when demand response and load fluctuation are not considered, in order to absorb wind power to a greater extent, electric boilers should be configured for CHP units for decoupling. Therefore, under scenario 1, there are 4 CHP units equipped with electric boilers, and the annualized transformation cost of thermal power decoupling is also the largest. Compared with scenario 1, scenario 2 considers the volatility of wind power without considering the load demand response, and processes the wind power fluctuation with the ILR method, that is, the economic efficiency of thermal power decoupling transformation is exchanged for the cost of partial wind abandonment. Therefore, compared with scenario 1, the wind abandonment cost in scenario 2 increases, and the proportion of heat storage tanks in the system transformation plan increases. As shown in Table 2, the CHP units in the system are equipped with 3 electric boilers and heat storage tanks. Finally, based on scenario 2, considering the load demand response, the electricity price is reduced when the load is low, and the electricity price is increased when the load is peak to dynamically adjust the load and fill the valley. It can be seen from Table 1 that the load-side DR has no effect on the total capacity of the CHP units equipped with electric boilers and heat storage tanks in the system, but it will change the unit number equipped with electric boilers and heat storage tanks. Moreover, under the same transformation decision, the system wind curtailment cost is significantly reduced, and the actual total operating cost of the system is lower than that of Scenario 2.

[0261] 2. Impact of thermal-electric decoupling of multiple CHP units on wind power consumption

[0262] To compare the impact of the thermoelectric decoupling transformation plan of CHP units on the accommodation of wind power, this paper takes the typical day before and after the transformation of Wind Farm 2 in February during the mid-heating period as an example for comparative analysis, as Figure 3 shown.

[0263] As Figure 3 can be seen, during the afternoon and late night periods, due to the low load, the system has a large amount of wind power abandoned, while the thermoelectric decoupling transformation of CHP units can effectively promote the accommodation of wind power in the system. After the thermoelectric decoupling transformation of CHP units, the total wind power accommodation of Wind Farm 2 on a typical day in February is 230,018.9 MWh. Compared with the total accommodation of 223,273 MWh before the thermoelectric decoupling transformation, the wind power accommodation has increased by 3.8%.

[0264] Through the above simulation analysis, it can be seen that the planning method of the present invention has obvious advantages in the accommodation of wind power, specifically manifested in: while coordinating the contradiction between the transformation capacity and the transformation cost, it can effectively promote the accommodation of wind power and reduce the system operation cost.

[0265] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in 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.

[0266] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0267] 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, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0268] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process or multiple processes and / or blocks Figure 1 one process or multiple processes and / or blocks Figure 1 or steps for implementing the functions specified in one block or multiple blocks.

[0269] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the scope of its protection. 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 after reading the present invention, various changes, modifications or equivalent replacements can still be made to the specific implementation manners of the invention. However, these changes, modifications or equivalent replacements are all within the scope of the protection of the pending claims of the invention.

Claims

1. A method for thermoelectric decoupling planning of a combined heat and power unit, characterized in that, It includes the following steps: Construct an electricity price-based demand response model and an improved robust optimization model; Construct an operating region model for CHP units, and perform transformation on the CHP units to obtain an operating characteristic model of the CHP units after thermoelectric decoupling; Based on the electricity price-based demand response model and the operating characteristic model of the CHP units after thermoelectric decoupling, construct a coordinated planning model for the thermoelectric decoupling of multiple CHP units with the goals of maximizing the total thermoelectric decoupling transformation capacity of multiple CHP units and minimizing the operating cost after decoupling; Based on the improved robust optimization model and the coordinated planning model for the thermoelectric decoupling of multiple CHP units, linearize the cost functions of the CHP units and condensing units only to improve the calculation efficiency; Construct a coordinated planning model for the thermoelectric decoupling of multiple CHP units, specifically including: Construct the upper-layer model of the planning model: The upper-layer model aims to minimize the net load volatility obtained by tracking the wind power curve with the load: Where: T is the scheduling period, taking 24 hours; is the net load value obtained by superimposing the typical daily load of the m-th month after DR and the wind power; is the average value of the net load in the t-th period of the typical day in the m-th month; is the total network load in the t-th period of the typical day in the m-th month after DR; is the predicted output of the wind farm w in the t-th period of the typical day in the m-th month; The constraint conditions of the upper-layer model: The total load remains unchanged before and after DR; The upper and lower limits of DR in period t are constrained; The electricity purchase cost after DR is lower than that before DR; Construct the lower-layer model of the planning model with the goals of maximizing the sum of the thermoelectric decoupling transformation capacities of multiple CHP units and minimizing the operating cost of the system after decoupling, specifically including: The lower-layer model of the planning model takes the objective function 1 as the maximum sum of the thermoelectric decoupling transformation capacities of multiple cogeneration units, and the objective function 1: Where: N chp is the number of CHP units; C i,EB is the capacity of the electric boiler added to CHP unit i; C i,HSA is the capacity of the heat storage tank added to CHP unit i; The lower-layer model of the planning model takes the objective function 2 as the minimum operating cost of multiple cogeneration units after decoupling during the heating season, and the objective function 2: In the formula: is the operating cost of traditional thermal power units; is the start-up and shut-down cost of thermal power units; is the operating cost of CHP units; is the annualized cost of the thermoelectric decoupling transformation of CHP units; is the penalty cost for abandoned wind; The constraint conditions of the lower-layer model of the planning model include; Transformation decision constraints; Electric power balance constraints; Thermal power balance constraints; Unit electrothermal output constraints; Wind curtailment constraints; Start-stop constraints; Ramp constraints; Heat storage tank operation constraints; Electric boiler operation constraints; Normalize the upper-layer model and the lower-layer model of the planning model by the improved ideal point method to obtain a single-objective model, as follows: In the formula: are the optimal values of the objective functions f1 and f2 respectively; That is, the construction of the coordinated planning model for the thermoelectric decoupling of multiple CHP units is realized.

2. The thermoelectric decoupling planning method for a combined heat and power unit according to claim 1, characterized in that Construct an electricity price-based demand response model, specifically including: The relationship between the electricity consumption of users in the current period and the electricity price in the current period and the electricity prices in other periods can be expressed as: Wherein: is the load at time t after DR; ε is the demand price elasticity coefficient; P load,t is the load at time t before DR; Δρ h is the change in electricity price at time h after DR; is the electricity price at time h before DR; According to the time-series load characteristics, divide the simulation period into 3 stages: peak period, normal period, and valley period, and introduce the demand price elasticity coefficient matrix E to represent the load response in different periods after the implementation of time-of-use electricity price; the time-of-use electricity price response model can be expressed as: In the formula: are the electricity prices at the peak, flat, and valley times after DR; P load,f , P load,p , P load,g are the electricity prices at the peak, flat, and valley times before DR; Δρ f , Δρ p , Δρ g are the change amounts of the electricity prices at the peak, flat, and valley times after DR, respectively; are the electricity prices at the peak, flat, and valley times before DR, respectively; E is the demand price elasticity matrix, where the diagonal elements are the self-elasticity coefficients and the non-diagonal elements are the cross-elasticity coefficients.

3. A method for thermoelectric decoupling planning of a combined heat and power unit according to claim 2, characterized in that, Construct an improved robust optimization model, specifically including: Construct a box-type uncertainty set: where: b i,j is the j-th element of the i-th uncertain parameter; is the predicted value of the uncertain parameter b i,j ; is the fluctuation amplitude of the uncertain parameter b i,j ; ξ i,j is the fluctuation ratio; Γ i is the uncertainty; Improved robust optimization model: where: w is the coefficient vector of the slack variables; γ is the slack variable vector; is the maximum value of the i-th slack variable.

4. A method for planning the thermal-electric decoupling of a combined heat and power unit according to claim 3, characterized in that, Model the operating region of the CHP unit, specifically including: Construct an operating region model for CHP units: In the formula: are the electric output and heat output of CHP unit i at time t, respectively; are the maximum and minimum values of the electric output when the CHP unit operates in pure condensing mode, respectively; is the maximum heat power that the CHP unit can output; are the boundary values of CHP unit i at time t, respectively; is the intersection point of the extension line of the BC segment of CHP unit i and the vertical coordinate, are the slopes of the AB and CD sides of the CHP unit; is the slope of the BC side of the CHP unit; Among them, the cost function of the operating region model of the CHP unit is: In the formula: a0, a1, a2, a3, a4, a5 are cost coefficients respectively.

5. A method for thermoelectric decoupling planning of a combined heat and power unit according to claim 4, characterized in that Model the operating characteristics of the CHP unit after thermoelectric decoupling, specifically including: Where: CP i,t and CH i,t are the electrical output and heat output of the i-th CHP unit at time t after decoupling, respectively; x i,1 and x i,2 are the decision variables for whether to add EB and HST to the i-th CHP unit, respectively. x i,1 = 1 and x i,2 = 1 indicate that the i-th CHP unit adds EB and HST, respectively. x i,1 = 0 and x i,2 = 0 indicate that the i-th CHP unit does not add EB and HST, respectively; E i and S i,t are the operating capacities of the electric boiler and heat storage tank added to the i-th CHP unit, respectively; η EB is the efficiency of the electric boiler for converting electricity to heat.

6. The method for thermoelectric decoupling planning of a cogeneration unit according to claim 1, wherein Linearize the cost functions of the CHP unit and the condensing unit, specifically including: The operating cost function of the condensing unit is linearized in the following way: 0 ≤ P j (t) ≤ (P j+1 -P j )v(t); Where: a, b, and c are cost function coefficients; g and are the cost functions of condensing units and their linear approximation functions respectively; N is the total number of segments into which the cost function is divided; α j is the slope of the j-th segment line; g(P1) is the cost function value at the minimum output value; v(t) is the operating state of the condensing unit at time t; Use the following linear model to linearize the cost function of the CHP unit: P Con = P chp + c v H chp C = dP Con + e C = d1P chp + d2H chp + e Where: P Con is the electrical output of the CHP unit under the pure condensing condition; d and e are cost coefficients; c v is the slope of the operating boundary curve AB of the unit.

7. A method for thermoelectric decoupling planning of a cogeneration unit according to claim 1, characterized in that According to the linear correspondence conversion method in the improved light robust optimization model, the electric power balance constraint is converted into the following formula: In the formula: obtained by sorting the elements in descending order; That is, the optimization processing of the uncertainty of wind power output is realized.

8. A thermoelectric cogeneration unit thermal electrolysis decoupling planning device, characterized in that The device includes: A first construction module, configured to construct a price-based demand response model and an improved light robust optimization model; A second construction module, configured to construct an operating region model of a CHP unit, and perform transformation processing on the CHP unit to obtain an operating characteristic model of the CHP unit after thermoelectric decoupling; A third construction module, configured to construct a coordinated planning model for thermoelectric decoupling of multiple CHP units with the goal of maximizing the thermoelectric decoupling transformation capacity of multiple CHP units and minimizing the operating cost after decoupling; A first calculation module, based on the improved light robust optimization model and the coordinated planning model for thermoelectric decoupling of multiple CHP units, linearizes the cost functions of the CHP unit and the condensing-only unit to improve the calculation efficiency; Constructing a coordinated planning model for thermoelectric decoupling of multiple CHP units specifically includes: Constructing the upper layer model of the planning model: The upper layer model aims to minimize the net load volatility obtained by tracking the wind power curve by the load: Where: T is the scheduling period, taking 24 hours; is the net load value obtained by superimposing the typical daily load of m months after DR and wind power; is the average value of the net load at time t of the typical day in month m; is the total network load at time t of the typical day in month m after DR; is the predicted output of wind farm w at time t of the typical day in month m; The constraint conditions of the upper layer model: The total load before and after DR remains unchanged; The upper and lower limit constraints of DR in period t; The power purchase cost after DR is lower than the power purchase cost before DR; Taking the goal of maximizing the sum of the thermoelectric decoupling transformation capacities of multiple CHP units and minimizing the operating cost of the system after decoupling, construct the lower layer model of the planning model, specifically including: The lower layer model of the planning model takes the objective function 1 as the maximum sum of the thermoelectric decoupling transformation capacities of multiple combined heat and power units; Where: N chp is the number of CHP units; C i,EB is the capacity of the electric boiler added to CHP unit i; C i,HSA is the capacity of the heat storage tank added to CHP unit i; The lower layer model of the planning model takes the objective function 2 as the minimum operating cost of multiple combined heat and power units after decoupling in the heating season; Wherein: is the operating cost of a traditional thermal power unit; is the start-up and shut-down cost of a thermal power unit; is the operating cost of a CHP unit; is the annualized cost of the thermoelectric decoupling transformation of a CHP unit; is the curtailment penalty cost; The lower layer model of the planning model, its constraint conditions include; Reconstruction decision constraint; Electric power balance constraint; Thermal power balance constraint; Unit electric and thermal output constraint; Wind curtailment constraint; Start-stop constraint; Ramp constraint; Heat storage tank operation constraint; Electric boiler operation constraint; Normalize the upper layer model and the lower layer model of the planning model by the improved ideal point method to obtain a single-objective model, as follows: Wherein: are the optimal values of the objective functions f1 and f2 respectively; That is, the construction of a coordinated planning model for thermoelectric decoupling of multiple CHP units is realized.

9. A computer storage medium, characterized in that, The computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the method according to any one of claims 1 to 7.

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