A method and system for capacity configuration of a combined heat and power plant

Abstract

The application provides a cogeneration equipment capacity configuration method and system, comprising the following steps: determining a first optimal capacity of the cogeneration equipment based on the power load by using the maximum rectangle method; performing optimization calculation on the daily energy consumption cost of the cogeneration equipment under the equal gradient capacity by using a genetic algorithm to obtain a second optimal capacity; and comparing the equivalent cost optimization indicators of the first optimal capacity and the second optimal capacity in combination with the energy efficiency and the equipment utilization rate, and taking the capacity corresponding to the maximum equivalent cost optimization indicator as the optimal cost-based capacity of the cogeneration equipment. The method and system solve the household cogeneration equipment capacity configuration problem by using the maximum rectangle method and the genetic algorithm, can better ensure the optimal capacity of the cogeneration equipment compared with a single calculation method, solve the problems of high energy cost and low energy efficiency of the existing household cogeneration equipment caused by the complexity of the capacity optimization configuration of the cogeneration micro-grid, and improve the economic benefits.

Description

Technical Field

[0001] This invention belongs to the field of energy internet technology, specifically relating to a method and system for configuring the capacity of combined heat and power equipment. Background Technology

[0002] As people's demands for quality of life increase, global energy consumption is growing rapidly, leading to energy crises and climate problems. Simultaneously, with the gradual increase in urbanization, residents' demands for electricity and heating are constantly rising. Therefore, how to utilize existing energy sources efficiently and cleanly is an urgent problem to be solved. The application of residential combined heat and power (CHP) equipment is one of the important ways to improve energy efficiency and reduce carbon dioxide emissions.

[0003] With the deepening development of industrialization and informatization, residential cogeneration has attracted widespread attention from scholars both domestically and internationally. However, research on capacity configuration technology for residential cogeneration, especially considering energy costs, remains a weak point in the field of energy internet research. On the user side, the time-varying nature of factors such as load and energy prices, as well as the randomness and volatility of renewable energy sources, significantly interfere with the capacity configuration of residential cogeneration equipment. Different capacity configuration results lead to variations in installation costs, usage frequency, and energy efficiency of residential cogeneration equipment. Inappropriate installed capacity may increase energy costs and reduce energy efficiency. Currently, the calculation method for optimizing the capacity configuration of residential cogeneration equipment considering energy costs is simplistic and cannot guarantee the optimal capacity. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, this invention proposes a method and system for configuring the capacity of combined heat and power (CHP) equipment. This method and system provide a technical approach to determine the capacity configuration of residential CHP equipment by considering energy cost optimization, thereby optimizing daily energy loss.

[0005] The solution adopted to achieve the above objectives is as follows:

[0006] An improved method for configuring the capacity of a combined heat and power (CHP) unit includes:

[0007] Based on the power load, the first optimal capacity of the maximum rectangular legal capacity cogeneration equipment;

[0008] Under the condition of equal gradient capacity, the genetic algorithm is used to optimize the daily energy consumption cost of cogeneration equipment and obtain the second optimal capacity.

[0009] Combining energy efficiency and equipment utilization, the equivalent cost optimization index of the first optimal capacity and the second optimal capacity is compared, and the capacity corresponding to the maximum equivalent cost optimization index is taken as the cost-optimal cogeneration equipment capacity.

[0010] The first preferred technical solution provided by the present invention is improved in that the first optimal capacity of the maximum rectangular capacity cogeneration equipment based on the power load includes:

[0011] Based on historical power load data, plot the load distribution curve;

[0012] Draw a rectangle with the origin of the coordinate axes and the line connecting the points on the curve as its diagonal;

[0013] The width of the rectangle with the largest area is taken as the first optimal capacity of the cogeneration equipment.

[0014] The second preferred technical solution provided by the present invention is improved in that the optimization calculation of the daily energy consumption cost of the cogeneration equipment using a genetic algorithm under the same gradient capacity to obtain the second optimal capacity includes:

[0015] Using the first gradient, a genetic algorithm is used to optimize the daily energy cost once, and the capacity range of the cogeneration equipment with the best cost is obtained.

[0016] Within the capacity range, a second gradient is used, and a genetic algorithm is employed to perform secondary optimization of the daily energy consumption cost of the cogeneration equipment to obtain the second optimal capacity.

[0017] The second isogradient is smaller than the first isogradient.

[0018] The third preferred technical solution provided by the present invention is improved in that, by setting a first equal gradient and using a genetic algorithm to optimize the daily energy cost once, the capacity range of the cogeneration equipment with the optimal cost is obtained, including:

[0019] Using the minimum daily energy cost as the objective function and user demand, equipment efficiency, and energy transmission as constraints, a first-order gradient is adopted, and a genetic algorithm is used for optimization to obtain the capacity range of the cost-optimal cogeneration equipment.

[0020] The fourth preferred technical solution provided by the present invention is improved in that, within the capacity range, a second gradient is adopted, and a genetic algorithm is used to perform secondary optimization of the daily energy consumption cost of the cogeneration equipment to obtain a second optimal capacity, including:

[0021] Within the stated capacity range, with the objective function of minimizing daily energy cost and constraints of user demand, equipment efficiency, and energy transmission, a second gradient is used, and a genetic algorithm is employed for secondary optimization to obtain the second optimal capacity based on the cost-optimal cogeneration equipment.

[0022] The fifth preferred technical solution provided by the present invention is improved in that, by combining energy efficiency and equipment utilization rate, comparing the equivalent cost optimization index of the first optimal capacity and the second optimal capacity, and taking the capacity corresponding to the maximum equivalent cost optimization index as the cost-optimal cogeneration equipment capacity, the following steps are included:

[0023] Calculate the energy efficiency and equipment utilization rate corresponding to the first and second optimal capacities, respectively.

[0024] Based on the energy efficiency, equipment utilization rate and energy consumption cost, the equivalent cost optimization index of the first optimal capacity and the second optimal capacity is calculated respectively.

[0025] The equivalent cost optimization index of the first optimal capacity and the second optimal capacity is compared, and the capacity corresponding to the maximum equivalent cost optimization index is taken as the cost-optimal cogeneration equipment capacity.

[0026] The sixth preferred technical solution provided by the present invention is improved in that the energy efficiency is calculated as follows:

[0027]

[0028] in, η represents the energy efficiency corresponding to the first or second optimal capacity. CHPE (t) indicates the use of The corresponding capacity configuration is the power output efficiency of the cogeneration equipment at time t, η CHPH (t) indicates the use of The corresponding capacity configuration refers to the thermal energy output efficiency of the cogeneration equipment at time t, where L represents the number of time periods.

[0029] The seventh preferred technical solution provided by the present invention is improved in that the equipment utilization rate is calculated as follows:

[0030]

[0031] Among them, h CHP P represents the equipment utilization rate corresponding to the first or second optimal capacity. CHPin (t) indicates the use of h CHP The corresponding capacity configuration refers to the power output of the gas supplied to the cogeneration equipment at time t, P. R The rated power of the combined heat and power (CHP) equipment is indicated by L, and the number of time periods is indicated by L.

[0032] The eighth preferred technical solution provided by the present invention is improved in that the preferred equivalent cost index is calculated as follows:

[0033]

[0034]

[0035] Where W1 represents the optimal equivalent cost index corresponding to the first optimal capacity. This indicates the energy efficiency corresponding to the first optimal capacity. C1 represents the equipment utilization rate corresponding to the first optimal capacity, C2 represents the energy consumption cost corresponding to the first optimal capacity, and W2 represents the equivalent cost optimization index corresponding to the second optimal capacity. This indicates the energy efficiency corresponding to the second optimal capacity. C1 represents the equipment utilization rate corresponding to the second optimal capacity, C2 represents the energy consumption cost corresponding to the second optimal capacity, a1 represents the efficiency coefficient, a2 represents the utilization rate coefficient, a3 represents the cost coefficient, and a1+a2+a3=1.

[0036] An improved cogeneration equipment capacity configuration system includes: a first calculation module, a second calculation module, and a comparison module;

[0037] The first calculation module is used to determine the first optimal capacity of the maximum rectangular capacity cogeneration equipment based on the power load.

[0038] The second calculation module is used to optimize the daily energy consumption cost of cogeneration equipment using a genetic algorithm under the same gradient capacity, so as to obtain the second optimal capacity.

[0039] The comparison module is used to combine energy efficiency and equipment utilization rate to compare the equivalent cost optimization index of the first optimal capacity and the second optimal capacity, and take the capacity corresponding to the maximum equivalent cost optimization index as the cost-optimal cogeneration equipment capacity.

[0040] The ninth preferred technical solution provided by the present invention is improved in that the first calculation module includes: a curve drawing unit, a rectangle drawing unit and a first optimal capacity unit;

[0041] The curve plotting unit is used to plot load distribution curves based on historical power load data;

[0042] The rectangle drawing unit is used to draw a rectangle with the origin of the coordinate axis and the line connecting the points on the curve as the diagonal;

[0043] The first optimal capacity unit is used to determine the first optimal capacity of the cogeneration equipment based on the width of the rectangle with the largest area.

[0044] The tenth preferred technical solution provided by the present invention is improved in that the second calculation module includes: a primary optimization unit and a secondary optimization unit;

[0045] The first optimization unit is used to perform a first optimization of daily energy costs using a genetic algorithm with a first gradient, to obtain the capacity range of the cogeneration equipment with the best cost.

[0046] The secondary optimization unit is used to perform secondary optimization of the daily energy consumption cost of the cogeneration equipment using a genetic algorithm within the capacity range, employing a second gradient, to obtain the second optimal capacity.

[0047] The second isogradient is smaller than the first isogradient.

[0048] Compared with the closest existing technology, the present invention has the following beneficial effects:

[0049] This invention, based on electricity load, determines the first optimal capacity of a combined heat and power (CHP) unit using the maximum rectangle method. Under equal capacity gradients, a genetic algorithm is used to optimize the daily energy cost of the CHP unit, yielding a second optimal capacity. Combining energy efficiency and equipment utilization, the equivalent cost optimization index of the first and second optimal capacities is compared, and the capacity corresponding to the maximum equivalent cost optimization index is taken as the cost-optimal CHP unit capacity. In addition to the genetic algorithm, this invention also utilizes the maximum rectangle method, considering the constraints of electrical and thermal energy based on optimal energy cost, to solve the capacity configuration problem of residential CHP units. The maximum rectangle method significantly reduces calculation time, while the genetic algorithm reduces optimization losses. Furthermore, employing two different calculation methods, compared to a single method, better ensures the obtaining of the optimal CHP unit capacity, solving the problem of complex capacity optimization configuration in CHP microgrids, which leads to high energy costs and low energy efficiency in existing residential CHP units, thus improving economic benefits.

[0050] Meanwhile, in this invention, the output efficiency of the cogeneration equipment is considered as a function of the input power only. Compared with constant output efficiency as the optimization criterion, more accurate optimization results can be achieved. Different types of cogeneration equipment and loads are used as the calibrated standards, and the optimization results will provide engineers with suggestions for selecting and calibrating cogeneration equipment. Attached Figure Description

[0051] Figure 1 A schematic diagram of a method for configuring the capacity of a combined heat and power (CHP) device provided by the present invention;

[0052] Figure 2 This is a schematic diagram of the maximum rectangle method involved in the present invention;

[0053] Figure 3 A schematic diagram illustrating the method for finding the theoretically optimal capacity of a domestic combined heat and power (CHP) unit using a genetic algorithm, as per the present invention.

[0054] Figure 4 A schematic diagram of the basic structure of a combined heat and power (CHP) equipment capacity configuration system provided by the present invention;

[0055] Figure 5 This invention provides a detailed structural diagram of a combined heat and power (CHP) equipment capacity configuration system. Detailed Implementation

[0056] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0057] Example 1:

[0058] A schematic diagram of a method for configuring the capacity of a combined heat and power (CHP) device provided by this invention is shown below. Figure 1 As shown, it includes:

[0059] Step 1: Based on the power load, determine the first optimal capacity of the maximum rectangular legal capacity cogeneration equipment;

[0060] Step 2: Under the condition of equal gradient capacity, use the genetic algorithm to optimize the daily energy consumption cost of the cogeneration equipment and obtain the second optimal capacity;

[0061] Step 3: Combining energy efficiency and equipment utilization, compare the equivalent cost optimization index of the first optimal capacity and the second optimal capacity, and take the capacity corresponding to the maximum equivalent cost optimization index as the cost-optimal cogeneration equipment capacity.

[0062] Specifically, a method for configuring the capacity of a combined heat and power (CHP) unit includes:

[0063] Step 101: Use the maximum rectangular capacity cogeneration equipment based on the power load.

[0064] Record the electricity demand for each minute of the year as sample points. Find the maximum and minimum electricity demand for each year, then divide the difference between the maximum and minimum into 10 to 20 equal intervals. All sample points need to be placed within their respective intervals, and the number of sample points within each interval must also be calculated. Plot as follows: Figure 2 The load distribution curve is then plotted. Finally, based on the load distribution curve, a diagram is drawn with the origin of the coordinate axis and the line connecting points on the curve as the diagonal, as shown below. Figure 2 Find a sufficient number of rectangles and identify the one with the largest area. The width of this rectangle represents the rated electrical output capacity of the combined heat and power (CHP) unit, i.e., the first optimal capacity.

[0065] Step 102: Optimize daily energy consumption cost using a genetic algorithm.

[0066] The daily energy cost expression is:

[0067]

[0068] In equation (1): F(t) is the daily energy cost, C E (t) and C g (t) is the daily price of electricity and heat, then P Ein (t),P Gin (t) and P CHPin F(t) represents the average daily electrical energy output from the grid, the gas supplied to the boiler, and the gas supplied to the combined heat and power (CHP) unit, respectively. The objective function is to optimize the daily energy cost, i.e., minF(t). Here, the subscript E represents electrical energy, the subscript G represents thermal energy, the subscript CHP represents combined heat and power, and the subscript E... in Indicates power grid supply, subscript G in Indicates boiler supply, subscript CHP in This indicates the supply of combined heat and power (CHP) equipment.

[0069] To meet the electricity and heat requirements of the house, the constraints of the equation can be written as follows:

[0070] P E (t)=P Ein (t)+η CHPE ×P CHPin (t) (2)

[0071] P H (t)=P Gin (t)×η B +η CHPH ×P CHPin (t) (3)

[0072] In equations (2) and (3): P E (t) and P H (t) represents the electricity and heat demand per minute for a residential home, respectively. η CHPE and η CHPH These are the output electrical energy and thermal efficiency of a combined heat and power (CHP) unit, respectively. η B This refers to the boiler's thermal conversion efficiency. Additionally, the extra constraints imposed by the system can be expressed as the following constraint expressions:

[0073] η CHPEmin ≤η CHPE ≤η CHPEmax (4)

[0074] η CHPHmin ≤η CHPH ≤η CHPHmax (5)

[0075]

[0076] P Ein (t)≥0 (7)

[0077] P Gin (t)≥0 (8)

[0078] In the formula: η represents the minimum power output efficiency of a residential combined heat and power (CHP) unit. CHPE η represents the actual power output efficiency of a household combined heat and power (CHP) unit. CHPEmax η represents the maximum power output efficiency of a residential cogeneration unit. CHPHmin η represents the minimum thermal output efficiency of a residential cogeneration unit. CHPH η is the actual value of the thermal energy output efficiency of a household combined heat and power (CHP) unit. CHPHmax This represents the maximum heat output efficiency of a residential cogeneration unit. P is the proportionality factor. R The rated daily power of residential cogeneration equipment is indicated by the subscript R, where R represents rated power and P represents rated power. Ein (t), P Gin (t) and P CHPin (t) represents the average daily electrical energy output from the power grid, the gas supplied to the boiler, and the gas supplied to the cogeneration equipment, respectively. The subscript CHPH represents the thermal energy of the cogeneration equipment, and the subscript CHPE represents the electrical energy of the cogeneration equipment.

[0079] In step 102, when optimizing the daily energy cost using a genetic algorithm, the first gradient is used for calculation.

[0080] Step 103: Within the interval obtained in step 102, further optimize the daily energy consumption cost of the household cogeneration equipment under the same gradient capacity using a genetic algorithm to seek the optimal solution.

[0081] The results obtained in step 102 are plotted as curves using MATLAB software. The capacity corresponding to the optimal cost is found on the curve, and the range of the capacity of the household cogeneration equipment based on the optimal cost is obtained. Then, the daily energy consumption cost is further optimized using a genetic algorithm: the objective function expression and constraint expression are the same as in step 102, but compared with step 102, the capacity is further separated in the range using a smaller isogradient, i.e., the second isogradient.

[0082] Based on this method, a genetic algorithm will be used to calculate the daily energy cost for a specific cogeneration unit capacity. By plotting the rated capacity and annual average energy cost, the feasible range of the cogeneration unit capacity can be obtained. Then, the region between the maximum and minimum values ​​is divided into five equal parts, and further calculations are performed within the interval where the cost is likely lowest. The method is described below. Figure 3 .

[0083] In steps 102 and 103, the first and second gradients are set according to actual needs, and the second gradient is smaller than the first gradient. For example, the first gradient is set to 500 watts and the second gradient is set to 100 watts.

[0084] Step 104: Comparing the results obtained in Steps 101 and 103, the effective energy efficiency of the cogeneration equipment and the ratio of average input power to rated power of the cogeneration equipment are used to test the energy consumption performance of the cogeneration equipment. The capacity of different categories of cogeneration equipment based on cost optimization, considering energy efficiency and equipment utilization, is obtained.

[0085] Two parameters are defined to verify the performance of the cogeneration equipment at constant capacity. First, the effective energy efficiency of the cogeneration equipment is defined to calculate the annual average energy efficiency of the cogeneration equipment under different capacities. Second, the ratio of the average output power to the rated power of the cogeneration equipment, i.e., the equipment utilization rate, is defined to indicate whether the cogeneration equipment is being fully utilized. Equations (9) and (10) are functions related to the above variables.

[0086]

[0087]

[0088] In equation (9), It is the effective energy efficiency of cogeneration equipment, η CHPE (t) and η CHPH (t) represents the output efficiency of the combined heat and power (CHP) equipment in terms of electrical and thermal energy at time t, respectively. η CHPE (t) and η CHPH (t) should all be greater than zero because the off state of the cogeneration equipment should be excluded in order to calculate the effective energy efficiency of the cogeneration equipment. In equation (10), h CHP It is the equipment utilization rate, which is the ratio of the average output power of a combined heat and power (CHP) unit to its rated power. L is the sample size.

[0089] When comparing two options, to address the possibility that the lower-cost option may also be less efficient, an equivalent cost optimization index W is defined. The option with the larger equivalent cost optimization index W value is selected as the optimal solution, and the corresponding capacity value is the capacity of the cost-optimal residential cogeneration equipment. C represents the corresponding energy consumption cost.

[0090]

[0091]

[0092] Where W1 represents the optimal equivalent cost index corresponding to the first optimal capacity. This represents the energy efficiency corresponding to the first optimal capacity. C1 represents the equipment utilization rate corresponding to the first optimal capacity, C2 represents the energy consumption cost corresponding to the first optimal capacity, and W2 represents the equivalent cost optimization index corresponding to the second optimal capacity. This indicates the energy efficiency corresponding to the second optimal capacity. C1 represents the equipment utilization rate corresponding to the second optimal capacity, C2 represents the energy consumption cost corresponding to the second optimal capacity, a1 represents the efficiency coefficient, a2 represents the utilization coefficient, and a3 represents the cost coefficient, and a1+a2+a3=1. In this embodiment, the efficiency coefficient a1 can be taken as 25%, the utilization coefficient a2 can be taken as 25%, and the cost coefficient a3 can be taken as 50%.

[0093] Example 2:

[0094] The following is a specific embodiment of the capacity configuration of a combined heat and power (CHP) unit.

[0095] Step 201: The following 5 steps are involved in using the maximum rectangular capacity cogeneration equipment based on the power load.

[0096] The first step is to record the electricity demand every minute of the year as sample points; this is essential. The second step is to find the maximum and minimum electricity demand for each year, and then divide the difference between the maximum and minimum into 10 to 20 equal intervals. Too many intervals (more than 20) will cause fluctuations in load distribution, which will complicate the plotting of the load distribution curve. Conversely, too few intervals (less than 10) will reduce the accuracy of the curve results. Therefore, dividing the maximum and minimum values ​​into 10 to 20 equal intervals is necessary. The third step is to place all sample points within their respective intervals, and the number of sample points within each interval must also be calculated. The fourth step is to plot the curve as follows: Figure 2 The load distribution curve. Finally, based on the load distribution curve, a plot should be drawn as follows. Figure 2 Draw a sufficient number of rectangles and find the largest one. The width of this rectangle represents the rated electrical output capacity of the combined heat and power (CHP) unit. The more rectangles drawn in this step, the higher the accuracy.

[0097] Step 202: Optimize daily energy consumption cost using a genetic algorithm:

[0098] The daily energy cost expression is:

[0099]

[0100] In equation (1): F(t) is the daily energy cost, C e (t) and C g (t) is the daily price of electricity and heat, then P Ein (t), P Gin(t) and P CHPin F(t) represents the average daily electrical energy output from the power grid, the fuel gas supplied to the boiler, and the fuel gas supplied to the combined heat and power (CHP) unit, respectively. The objective function is to optimize the daily energy cost, i.e., minF(t).

[0101] To meet the electricity and heat requirements of the house, the constraints of the equation can be written as follows:

[0102] P E (t)=P Ein (t)+η CHPE ×P CHPin (t) (2)

[0103] P H (t)=P Gin (t)×η B +η CHPH ×P CHPin (t) (3)

[0104] In equations (2) and (3): P E (t) and P H (t) represents the electricity and heat demand per minute for a residential home, respectively. η CHPE and η CHPH These are the output electrical energy and thermal efficiency of the combined heat and power (CHP) equipment, respectively. η B This refers to the boiler's thermal conversion efficiency. Additionally, the extra constraints imposed by the system can be expressed as the following constraint expressions:

[0105]

[0106]

[0107]

[0108] P Ein (t)≥0 (7)

[0109] P Gin (t)≥0 (8)

[0110] In the formula: η represents the minimum power output efficiency of a residential combined heat and power (CHP) unit. CHPE η represents the actual power output efficiency of a household combined heat and power (CHP) unit. CHPEmax This represents the maximum power output efficiency of a residential combined heat and power (CHP) unit. η represents the minimum thermal output efficiency of a residential cogeneration unit. CHPH η represents the actual power output efficiency of a household combined heat and power (CHP) unit. CHPHmax The maximum thermal output efficiency of a residential cogeneration unit is given by ζ, where ζ is the proportionality factor and P is the power factor. RFor the rated daily power of residential cogeneration equipment, P Ein (t), P Gin (t) and P CHPin (t) represents the average daily electrical energy output from the power grid, the gas supplied to the boiler, and the gas supplied to the combined heat and power (CHP) equipment, respectively.

[0111] Equations (4) and (5) describe the output efficiency limitations of electrical and thermal energy in a combined heat and power (CHP) system, and equation (6) shows that a CHP system is either shut down or operates under available operating conditions. In equation (6), ζ is a reference value, and the input power of the CHP system will fluctuate between 10% and 100% of its rated power. In this invention, ζ is set to 10%, and the optimal capacity obtained by the maximum rectangle method will be used to find the optimal energy cost under different daily conditions. Equations (7) and (8) demonstrate that electrical and thermal energy can only be output, not input; in other words, they describe the flow of energy.

[0112] In this paper, the output efficiency of a combined heat and power (CHP) plant is considered to be a function of the CHP input power only. Table 1 shows the efficiency of the CHP at different input powers, sourced from: W. Zhimin, G. Chenghong, L. Furong, P. Bale, and S. Hongbin, "Active Demand Response Using Shared Energy Storage for Household Energy Management," Smart Grid, IEEE Transactions on, vol. 4, pp. 1888-1897, 2013.

[0113] Table 1: Output efficiency of cogeneration plants with different input power

[0114]

[0115] According to Table 1, the power and heat output efficiencies of a typical CHP device can be fitted using the MATLAB curve fitting toolbox as follows:

[0116]

[0117]

[0118] In the formula ζ E and ζ H These are the output efficiency coefficients of electrical and thermal energy from combined heat and power (CHP) equipment. Their values ​​are only related to the type of CHP equipment; in other words, they relate to the design of the CHP equipment and the electrical output efficiency ζ of the gas turbine and fuel cell. EThe electrical output efficiencies ζ for the gas engine and fuel cell are 0.783 and 1.298, respectively. H The values ​​are 1.610 and 1.187.

[0119] Step 203: Within the interval obtained in step 202, further optimize the daily energy consumption cost of the residential cogeneration equipment using a genetic algorithm under the same gradient capacity, and seek the optimal solution:

[0120] If a combined heat and power (CHP) unit operates at low input power, both its thermal and electrical output efficiencies will decrease. In this invention, the output efficiency of the CHP unit is considered a function only of its input power. The results are plotted as curves using MATLAB software, and the capacity corresponding to the optimal cost is identified on the curves, thus obtaining the range of the cost-optimal residential CHP unit capacity. Then, a genetic algorithm is further used to optimize daily energy consumption costs: the objective function expression and constraint expression are the same as in step 202, but compared to step 202, the capacity within the range is further separated using a smaller uniform gradient.

[0121] Considering that genetic algorithms typically require a long computation time to obtain optimization results and that the output of commercial CHP units is usually quantized to 100W, the following calculations will seek the optimal capacity for each type of CHP in discrete increments of hundreds of watts (increments that are n times 100W).

[0122] The objective function, constraints, and equations in this step are similar to those in step 202. The goal is to find the available range of combined heat and power (CHP) equipment capacity, and then determine the optimal capacity for different types of CHP equipment. The output power of the CHP equipment will initially be set at 1000 watts, then increased by 500 watts each time until a fixed value is reached. After this fixed value, the daily energy cost will increase with the increase in CHP equipment capacity. Based on this method, a genetic algorithm will be used to calculate the daily energy cost for a specific CHP equipment capacity. By plotting the rated capacity and annual average energy cost table, the feasible range of CHP equipment capacity can be obtained. Then, the region between the maximum and minimum values ​​is divided into five equal parts, meaning that four additional samples need to be tested. By optimizing the daily energy cost at these four points using a genetic algorithm, the theoretically optimal CHP equipment capacity can be obtained. The method can be found in [link to method]. Figure 3 .

[0123] Step 204: The effective energy efficiency of cogeneration equipment and the ratio of average input power to rated power of cogeneration equipment were used to test the energy consumption performance of cogeneration equipment. The capacity of different categories of cogeneration equipment based on cost optimization was obtained. The method for comparing the results is as follows:

[0124] As mentioned earlier, a combined heat and power (CHP) unit is a high-efficiency power generation device. However, if an inappropriate capacity CHP unit is installed, the system's energy efficiency will be significantly reduced. Here, two parameters are defined to verify the performance of the CHP unit's capacity determination result. First, the effective CHP unit energy efficiency is defined to calculate the annual average energy efficiency of CHP units under different capacities. Energy efficiency refers to the ratio of the amount of energy utilized to the amount of energy actually consumed. Therefore, 0 ≤ this value ≤ 1, and the closer the value is to 1, the more energy is utilized. Second, the ratio of the average output power to the rated power of the CHP unit is defined as the equipment utilization rate to indicate whether the CHP unit is being fully utilized. 0 ≤ this value ≤ 1, and the larger the value, the higher the utilization rate of the CHP unit. Equations (9) and (10) are functions related to the above variables.

[0125]

[0126]

[0127] In equation (9), It is the effective energy efficiency of cogeneration equipment, η CHPE (t) and η CHPH (t) represents the output efficiency of the combined heat and power (CHP) equipment at time t, and L′ is the sample size. In equation (9), η CHPE (t) and η CHPH (t) should all be greater than zero because the off state of the cogeneration equipment should be excluded in order to calculate the effective energy efficiency of the cogeneration equipment. In equation (10), h CHP It is the ratio of the average output power of a combined heat and power (CHP) unit to its rated power. L is the sample size.

[0128] When comparing two options, to address the possibility that the lower-cost option may also be less efficient, an equivalent cost optimization index W is defined. The option with the larger equivalent cost optimization index W value is selected as the optimal solution, and the corresponding capacity value is the capacity of the cost-optimal residential cogeneration equipment. C represents the corresponding cost.

[0129]

[0130]

[0131] Where W1 represents the equivalent cost of the first option, i.e., the optimal equivalent cost index W; W2 represents the equivalent cost of the second option, i.e., the optimal equivalent cost index W; C1 represents the cost of the first option; and C2 represents the cost of the second option.

[0132] Compare all results and find the capacity value corresponding to the optimal cost. This value is the capacity of the residential cogeneration equipment based on the optimal cost. That is, calculate the equivalent cost optimization index W from the results of steps 201 and 203, and take the scheme with the largest equivalent cost optimization index as the optimal cost scheme, and configure the cogeneration equipment capacity accordingly.

[0133] Example 3:

[0134] Based on the same inventive concept, this invention also provides a combined heat and power (CHP) equipment capacity configuration system. Since the principles by which these devices solve technical problems are similar to those of the CHP equipment capacity configuration method, the repetitive parts will not be described again.

[0135] The basic structure diagram of the system is as follows: Figure 4 As shown, it includes:

[0136] The system comprises a first calculation module, a second calculation module, and a comparison module.

[0137] The first calculation module is used to determine the first optimal capacity of the maximum rectangular legal capacity cogeneration equipment based on the power load.

[0138] The second calculation module is used to optimize the daily energy consumption cost of cogeneration equipment using a genetic algorithm under the same gradient capacity, so as to obtain the second optimal capacity.

[0139] The comparison module is used to combine energy efficiency and equipment utilization rate to compare the equivalent cost optimization index of the first optimal capacity and the second optimal capacity, and the capacity corresponding to the maximum equivalent cost optimization index is the cost-optimal cogeneration equipment capacity.

[0140] Detailed structural diagram of the combined heat and power (CHP) equipment capacity configuration system is shown below. Figure 5 As shown.

[0141] The first calculation module includes: a curve drawing unit, a rectangle drawing unit, and a first optimal capacity unit;

[0142] The curve plotting unit is used to plot load distribution curves based on historical power load data.

[0143] The rectangle drawing unit is used to draw a rectangle with the origin of the coordinate axis and the line connecting points on the curve as the diagonal.

[0144] The first optimal capacity unit is used to determine the first optimal capacity of the cogeneration equipment based on the width of the rectangle with the largest area.

[0145] The second calculation module includes: a primary optimization unit and a secondary optimization unit;

[0146] The first optimization unit is used to perform the first optimization of daily energy costs using a genetic algorithm with the first gradient, and to obtain the capacity range of the cogeneration equipment with the best cost.

[0147] The secondary optimization unit is used to perform secondary optimization of the daily energy consumption cost of the cogeneration equipment within the capacity range using a second gradient and a genetic algorithm to obtain the second optimal capacity.

[0148] The second gradient is smaller than the first gradient.

[0149] The comparison module includes: an efficiency and utilization unit, an optimal index calculation unit, and a comparison unit;

[0150] The efficiency and utilization unit is used to calculate the energy efficiency and equipment utilization corresponding to the first optimal capacity and the second optimal capacity, respectively.

[0151] The optimal index calculation unit is used to calculate the equivalent cost optimal index of the first optimal capacity and the second optimal capacity based on energy efficiency, equipment utilization rate and energy consumption cost, respectively.

[0152] The comparison unit is used to compare the equivalent cost optimization index of the first optimal capacity and the second optimal capacity, and the capacity corresponding to the maximum equivalent cost optimization index is the cost-optimal cogeneration equipment capacity.

[0153] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0154] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0155] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0156] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit its protection scope. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this application, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims pending approval.

Claims

1. A method of configuring the capacity of a combined heat and power plant, characterized by The method comprises the following steps: determining a first optimal capacity of a cogeneration device based on power load by using a maximum rectangle method; performing an optimization calculation on daily energy consumption cost of the cogeneration device under an equal-gradient capacity by using a genetic algorithm to obtain a second optimal capacity; combining energy efficiency and device utilization rate, comparing equivalent cost optimization indicators of the first optimal capacity and the second optimal capacity, and taking a capacity corresponding to a maximum equivalent cost optimization indicator as a cost-optimized cogeneration device capacity; the method for determining the first optimal capacity of the cogeneration device based on the power load comprises the following steps: drawing a load distribution curve based on historical data of power load; drawing a rectangle with a coordinate axis origin and a connecting line between a point on the curve as a diagonal line; taking a width of a maximum area rectangle as the first optimal capacity of the cogeneration device; the method for combining the energy efficiency and the device utilization rate, comparing the equivalent cost optimization indicators of the first optimal capacity and the second optimal capacity, and taking the capacity corresponding to the maximum equivalent cost optimization indicator as the cost-optimized cogeneration device capacity comprises the following steps: respectively calculating energy efficiency and device utilization rate corresponding to the first optimal capacity and the second optimal capacity; respectively calculating equivalent cost optimization indicators of the first optimal capacity and the second optimal capacity based on the energy efficiency, the device utilization rate and the energy consumption cost; comparing the equivalent cost optimization indicators of the first optimal capacity and the second optimal capacity, and taking the capacity corresponding to the maximum equivalent cost optimization indicator as the cost-optimized cogeneration device capacity; the energy efficiency is calculated according to the following formula: wherein, η (t) represents the energy efficiency corresponding to the first optimal capacity or the second optimal capacity, CHPE η (t) represents the electrical energy output efficiency of the cogeneration plant at time t when the corresponding capacity configuration is adopted, η (t) represents the electrical energy output efficiency of the cogeneration plant at time t when the corresponding capacity configuration is adopted, CHPH η (t) represents the thermal energy output efficiency of the cogeneration plant at time t when the corresponding capacity configuration is adopted, η (t) represents the thermal energy output efficiency of the cogeneration plant at time t when the corresponding capacity configuration is adopted, the device utilization rate is calculated according to the following formula: wherein h CHP represents the device utilization rate corresponding to the first optimal capacity or the second optimal capacity, P CHPin (t) represents the corresponding power of the fuel gas supplied to the cogeneration device at time t when the capacity configuration of h CHP represents the corresponding power of the fuel gas supplied to the cogeneration device at time t when the capacity configuration of h R represents the rated power of the cogeneration device, and L represents the number of time periods. the equivalent cost optimization indicator is calculated according to the following formula: W1 represents an equivalent cost optimization indicator corresponding to the first optimal capacity, represents the energy efficiency corresponding to the first optimal capacity, represents the equipment utilization rate corresponding to the first optimal capacity, C1 represents the energy consumption cost corresponding to the first optimal capacity, W2 represents an equivalent cost optimization indicator corresponding to the second optimal capacity, represents the energy efficiency corresponding to the second optimal capacity, represents the equipment utilization rate corresponding to the second optimal capacity, C2 represents the energy consumption cost corresponding to the second optimal capacity, a1 represents an efficiency coefficient, a2 represents a utilization rate coefficient, a3 represents a cost coefficient, and a1+a2+a3=1.

2. The method of claim 1, wherein, the method for performing the optimization calculation on the daily energy consumption cost of the cogeneration device under the equal-gradient capacity by using the genetic algorithm to obtain the second optimal capacity comprises the following steps: adopting a first equal gradient, performing a first optimization on daily energy cost by using the genetic algorithm to obtain a capacity interval in which the cost-optimized cogeneration device capacity is located; adopting a second equal gradient, performing a second optimization on the daily energy consumption cost of the cogeneration device by using the genetic algorithm to obtain the second optimal capacity in the capacity interval; wherein the second equal gradient is smaller than the first equal gradient.

3. The method of claim 2, wherein, the method for adopting the first equal gradient, performing the first optimization on the daily energy cost by using the genetic algorithm to obtain the capacity interval in which the cost-optimized cogeneration device capacity is located comprises the following steps: adopting the first equal gradient, performing the first optimization by using the genetic algorithm to obtain the capacity interval in which the cost-optimized cogeneration device capacity is located, with a minimized daily energy cost as a target function and user demand, device efficiency and energy transmission as constraint conditions.

4. The method of claim 2, wherein, the method for adopting the second equal gradient, performing the second optimization on the daily energy consumption cost of the cogeneration device by using the genetic algorithm to obtain the second optimal capacity in the capacity interval comprises the following steps: adopting the second equal gradient, performing the second optimization by using the genetic algorithm to obtain the second optimal capacity of the cost-optimized cogeneration device, with a minimized daily energy cost as a target function and user demand, device efficiency and energy transmission as constraint conditions.

5. A cogeneration plant capacity configuration system characterized by comprising: The method comprises the following steps: a first calculation module, a second calculation module and a comparison module. The first calculation module is configured to determine a first optimal capacity of the cogeneration device based on the power load by using a maximum rectangle method; The second calculation module is configured to determine a second optimal capacity of the cogeneration device by using a genetic algorithm to optimize a daily energy consumption cost of the cogeneration device under an equal gradient capacity; The comparison module is configured to compare equivalent cost optimization indicators of the first optimal capacity and the second optimal capacity in combination with energy efficiency and device utilization, and to select a capacity corresponding to a maximum equivalent cost optimization indicator as the optimal capacity of the cogeneration device based on cost optimization; The first calculation module includes a curve drawing unit, a rectangle drawing unit, and a first optimal capacity unit; The curve drawing unit is configured to draw a load distribution curve based on historical data of the power load; The rectangle drawing unit is configured to draw a rectangle with a connecting line between an origin of a coordinate axis and a point on the curve as a diagonal line; The first optimal capacity unit is configured to determine a width of the rectangle with a maximum area as the first optimal capacity of the cogeneration device; The comparison of the equivalent cost optimization indicators of the first optimal capacity and the second optimal capacity in combination with the energy efficiency and the device utilization, and the selection of the capacity corresponding to the maximum equivalent cost optimization indicator as the optimal capacity of the cogeneration device based on cost optimization, include: The energy efficiency and the device utilization corresponding to the first optimal capacity and the second optimal capacity are calculated respectively; The equivalent cost optimization indicators of the first optimal capacity and the second optimal capacity are calculated based on the energy efficiency, the device utilization, and the energy consumption cost; The equivalent cost optimization indicators of the first optimal capacity and the second optimal capacity are compared, and the capacity corresponding to the maximum equivalent cost optimization indicator is selected as the optimal capacity of the cogeneration device based on cost optimization; The energy efficiency is calculated according to the following formula: wherein, η (t) represents the energy efficiency corresponding to the first optimal capacity or the second optimal capacity, CHPE η (t) represents the electrical energy output efficiency of the cogeneration device at time t when the corresponding capacity configuration is adopted, η (t) represents the electrical energy output efficiency of the cogeneration device at time t when the corresponding capacity configuration is adopted, CHPH η (t) represents the electrical energy output efficiency of the cogeneration device at time t when the corresponding capacity configuration is adopted, η (t) represents the electrical energy output efficiency of the cogeneration device at time t when the corresponding capacity configuration is adopted, The device utilization is calculated according to the following formula: wherein h CHP represents the device utilization rate corresponding to the first optimal capacity or the second optimal capacity, P CHPin (t) represents the corresponding power of the fuel gas supplied to the cogeneration device at time t when the capacity configuration of h CHP is adopted, P R represents the rated power of the cogeneration device, and L represents the number of time periods. The equivalent cost optimization indicator is calculated according to the following formula: W1 represents an equivalent cost optimization indicator corresponding to the first optimal capacity, represents the energy efficiency corresponding to the first optimal capacity, represents the equipment utilization rate corresponding to the first optimal capacity, C1 represents the energy consumption cost corresponding to the first optimal capacity, W2 represents an equivalent cost optimization indicator corresponding to the second optimal capacity, represents the energy efficiency corresponding to the second optimal capacity, represents the equipment utilization rate corresponding to the second optimal capacity, C2 represents the energy consumption cost corresponding to the second optimal capacity, a1 represents an efficiency coefficient, a2 represents a utilization rate coefficient, a3 represents a cost coefficient, and a1+a2+a3=1.

6. The system of claim 5, wherein, The second calculation module includes a primary optimization unit and a secondary optimization unit; The primary optimization unit is configured to use a first equal gradient to perform primary optimization of a daily energy cost by using a genetic algorithm, and to obtain a capacity interval of the optimal capacity of the cogeneration device based on cost optimization; The secondary optimization unit is configured to use a second equal gradient to perform secondary optimization of a daily energy consumption cost of the cogeneration device by using a genetic algorithm in the capacity interval, and to obtain the second optimal capacity; The second equal gradient is smaller than the first equal gradient.

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