JP2002227721A - Cogeneration planning system and cogeneration optimization system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To efficiently support a program user to analyze sensitivity of an optimal solution for demand forecasting errors and equipment parameter uncertainty. SOLUTION: When finding an equipment optimal size 11 and an optimal operating pattern 12 by an optimization calculating means 10, a potential price 13 and an energy unit price 14 are found at the same time and displayed on a display device 16 by an image displaying means 15.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a cogeneration planning system or a cogeneration optimization system.

[0002]

2. Description of the Related Art In a conventional optimal plan, there is an apparatus which comprises two parts, an equipment scale optimization calculation and an equipment operation optimization calculation, and executes a calculation while reciprocating both calculations until convergence. For example, Koichi Ito and Ryohei Yokoyama, “Optimal Planning for Cogeneration”, Sangyo Tosho (Heisei 2) 111
-132 pages. The processing procedure by this method is as follows. First, the user gives the predicted value of the energy demand, the value of the equipment capacity, and the value of the maximum utility contract amount to the computer, and based on these values, causes the computer to execute the equipment operation optimization calculation, thereby obtaining the optimum operation pattern. . Next, the computer is caused to execute the device scale optimization calculation, and the value of the device capacity and the value of the utility maximum contract amount are corrected so that the total cost, that is, the sum of the facility cost and the usage-based cost, is reduced. Further, based on the result of the current device size optimization calculation, the computer is again caused to execute the device operation optimization calculation. The user causes the calculator to repeat this procedure until the calculation converges. Thereby, the user obtains the optimal scale and the optimal operation pattern of the energy supply device.

[0003]

Generally, in order to determine the optimal scale and the optimal operation pattern of the energy supply equipment,
Assume the predicted value of energy demand. However, since a predicted value generally includes a prediction error, the user should not immediately adopt an optimal solution based on a specific predicted value, but must determine whether to adopt the optimal solution while examining the effect of the prediction error. . However, in order to examine the effect of the prediction error using the conventional technology, the user changes the prediction value by a small amount, re-calculates the optimal solution based on the predicted value after the change, and calculates the optimal solution before and after the prediction value change. Must be compared.
Heretofore, the user has required a great deal of labor and time for this operation.

[0004] It is an object of the present invention to facilitate consideration of the effects of prediction errors.

[0005]

A feature of the present invention is to determine a value of a potential price of each constraint and present the value to a user. Here, the constraint conditions are, for example, a supply-demand balance constraint (equation constraint) for each energy type such as electric power, gas, steam, hot water and cold water, an input / output characteristic of each energy supply device (equation constraint), and each energy supply device. Refers to all or some of the upper and lower bound constraints (inequality constraints). The potential price means, for example, the sensitivity of how much the objective function value changes when the constant term of the constraint condition is changed by a unit amount in the optimal solution.

[0006]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of the present invention are applicable to a case where a plurality of energy supply devices such as a gas turbine generator and a heat storage device are combined to supply energy to an energy demanding area such as a building or a factory. The present invention relates to a computer program for calculating an optimum scale and an optimum operation pattern of the energy supply device according to a predicted value of energy demand of the energy supply device.

A first problem to be solved by the embodiment of the present invention is to obtain an optimum scale and an optimum operation pattern of an energy supply device, and at the same time, to facilitate an examination of the influence of a prediction error.

In the above, the necessity of analyzing the influence of the prediction error on the optimal solution has been described. However, in order to judge whether or not the optimal solution is adopted, in addition to changes in future electricity rates and gas rates, and technical advances, The user needs to consider the uncertain factors of various related parameters, such as improving the energy efficiency of equipment, and examine the effects thereof. Conventionally, the user has required a great deal of labor and time to study the effects of these uncertain factors.

The problem to be solved by the embodiment of the present invention is, in addition to the above-mentioned first problem, utility rates such as an electricity rate and a gas rate, and uncertainties of equipment parameters such as energy efficiency of the equipment. It may also make it easier to study the effects of

One feature of an embodiment of the present invention is to determine the value of the potential price for each constraint and present the value to the user. Here, the constraint conditions are, for example, electric power, gas,
All or some of the supply-demand balance constraints (equation constraints) for each energy type such as steam, hot water, and cold water, the input / output characteristics of each energy supply device (equation constraints), and the upper and lower limits of each energy supply device (inequality constraints) Point to. The potential price means, for example, the sensitivity of how much the objective function value changes when the constant term of the constraint condition is changed by a unit amount in the optimal solution. A general explanation of how to determine the potential price is described in, for example, Hiroshi Konno, "Linear Programming", Nikka Giren (1987), pp. 71-90.

In order to present the potential price to the user, it is necessary to first find the potential price. The means provided by the present invention for obtaining the potential price is a method of formulating the problem of finding the optimal scale and the optimal operation pattern as a single linear programming problem and solving it to find the optimal scale and the optimal operation pattern at once. It is. The linear programming problem formulated in this way is often a large-scale problem. For example, Hiroshi Konno, “Linear Programming”, Nikkagiren (1987) 14
By applying the method on page 7-162, a large-scale linear programming problem can be solved.

Conventionally, in order to avoid the problem from becoming large-scale, the problem of finding the optimal scale and the optimal operation pattern is divided into two subproblems, and the formulation includes discrete variables. Finding the potential price was difficult. Hereinafter, the reason why it is difficult to obtain a potential price in the related art and the solution in the embodiment of the present invention will be described. Since the potential price is the sensitivity of the objective function value to the parameter value, that is, the differential value of the objective function value by the parameter value, it is necessary to formulate the problem in a differentiable form.
To do so, we must avoid splitting the original problem into two parts and introducing discrete variables. However, in the prior art, differentiability has to be sacrificed in order to restrict the memory capacity of the computer and shorten the calculation time. That is, in the related art, it was difficult to find both the potential price and the optimal solution in a short time. On the other hand, in the embodiment of the present invention, non-differentiable elements such as discrete variables are excluded in the formulation, and, for example, Hiroshi Konno, "Linear Programming", Nisshin Giren (1987) 147-1
By applying the method on page 62, it has become possible to obtain both the potential price and the optimal solution in a short time. For example, Hiroshi Konno, “Linear Programming”, Nikka Giren (1987), pp. 147-162 describes a method for efficiently solving a large-scale linear programming problem.

FIG. 1 shows an example of a functional block diagram for realizing an embodiment of the present invention. In FIG. 1, an optimization problem formulation means 6 reads, as input data, equipment configuration data 1, demand data 2, energy utility data 3, equipment capacity unit price data 4, and equipment input / output characteristic data 5.
Based on these input data, the optimization problem formulation means 6 generates an objective function 7, equipment constraints 8, and supply / demand balance constraints 9
Generate Here, the objective function 7 is defined based on the energy utility data 3 and the equipment capacity unit price data 4 by the total of the facility cost and the operating cost, and is given in a linear form. The equipment constraint 8 is based on the device input / output characteristic data 5,
It is given by a linear equality constraint representing the input / output relationship of each device and a linear inequality constraint on the input value or output value of each device. Equipment restriction 8 includes a restriction that the input value or output value of the device at any time must not exceed the device scale, and
If the optimization problem is solved by including the device size in the decision variables, the minimum necessary device size, that is, the most economical device size can be obtained. The supply / demand balance constraint 9 is given by a linear equation constraint for each type of demand given by the demand data 2 based on the device configuration data 1. The optimization problem thus obtained is a linear programming problem. The above-mentioned method of formulating the optimization problem is described in, for example, Koichi Ito and Ryohei Yokoyama, “Optimal Planning of Cogeneration,” Sangyo Tosho Co., Ltd. (1990). However, this document introduces the 0-1 variable for distinguishing between running and stopped, but in the present invention, it is assumed that no distinction is made between running and stopped and the 0-1 variable is not used. . Thereby, since the discrete variables can be excluded from the optimization problem, the optimal solution and the potential price can be obtained by the ordinary linear programming. Next, the optimization calculation means 10 reads the objective function 7, the equipment constraint 8, and the supply-demand balance constraint 9, and keeps the constraint function while minimizing the objective function while optimizing the equipment size 11 and the optimal operation pattern 12.
Ask for. At the same time, a potential price 13 corresponding to the supply-demand balance constraint 9 is obtained. Further, the energy unit price 14 of each device is obtained. A method for finding the optimal solution is described in, for example, Hiroshi Konno, “Linear Programming”, Nisshin Giren (1987) 147
-162 pages. The potential price is calculated, for example, by Hiroshi Konno, “Linear Programming”, Nikka Giren (198)
7) It can be determined using the method on pages 71-90.
The energy unit price 14 of a certain device can be obtained from the energy input to the device and the energy efficiency of the device. For example, if the unit price of the input energy to a certain device is 10 yen and the energy efficiency of the device is 50%, the energy unit price of the device is 20 yen. Finally, the screen display means 15 reads the optimal equipment size 11, the optimal operation pattern 12, the potential price 13 and the energy unit price 14, and displays them on a suitable display device 16 such as a CRT.

FIG. 2 shows a flow of processing for realizing the present invention. In data reading 21, necessary input data, that is, device configuration data 1, demand data 2, energy utility data 3, device capacity unit price data 4, and device input / output characteristic data 5 are read. Next, the process proceeds to the optimization problem formulation 22, where the optimization problem is formulated based on the data read by the data reading unit 21. Optimization calculation execution 23
Then, the potential price is determined at the same time as finding the solution of the formulated optimization problem, and the optimal display and the potential price are displayed on the result display 24.

Hereinafter, embodiments of the present invention will be described based on specific examples. FIG. 3 is a diagram illustrating a device configuration, and corresponds to device configuration data 1. FIG. 4 shows demand at 24 points each day for electric power, steam, hot water and cold water, and corresponds to demand data 2. FIG. 5 shows the electricity and gas usage rates, the power contract capacity unit price, and the capacity unit price of each device, and corresponds to the energy utility data 3 and the device capacity unit price data 4. The device input / output characteristic data 5 is described, for example, by Koichi Ito and Ryohei Yokoyama, “Optimal Cogeneration Planning”, Sangyo Tosho Co., Ltd.
It should be set to an appropriate value following (1990). Using these data, we define an optimization problem and solve it to find the optimal solution and potential price. In the present invention, the objective function is formulated as follows.

Objective function:

[0017]

(Equation 1)

That is, the objective function J is the annual facility cost C
_c, defined by the sum of the annual base rate C _od and annual pay-as-you-go C _oe, the annual cost of equipment C _c and those obtained by dividing the sum of the initial equipment costs of each facility in the amortization period T _dep, annual base rate C _od is unit contract period the total contract base rate of electricity and gas T _con
The annual consumption rate _Coe is the sum of the electricity and gas usage and the rate per unit usage. In these expressions, the subscripts represent the following meanings. That is, GT is a gas turbine, RE is an electric turbo refrigerator, RW is a hot water absorption refrigerator, RS is a steam absorption refrigerator, BG is a gas boiler, BA is an auxiliary boiler, CT is a cooling tower, SH is a heat storage tank,
SC is ice heat storage, SE is a storage battery, HE is a heat exchanger, RD is a radiator, PC is a chilled water waste pump, EP is power from a power company, and FP is gas from a gas company. Also,
PE (t) and PF (t) represent the charge per unit usage of power and gas at time t, respectively, and E (t) and F (t) represent the usage of power and gas at time t, respectively.

In the embodiment of the present invention, the device characteristics are expressed by the following constraints.

Restrictions (equipment characteristics):

[0021]

(Equation 2)

Note that this example of the constraint condition is obtained by expressing the characteristics of the gas turbine shown in FIG. 6 by five equality constraints and one inequality constraint. The characteristics of the equipment other than the gas turbine can be expressed by the same equation constraints and inequality constraints. The energy balance condition is also expressed by the following constraint conditions.

Constraints (energy balance):

[0024]

(Equation 3)

Note that this example of the constraint condition represents a supply / demand balance of chilled water in the cogeneration system of FIG. Electric power, steam, and hot water supply / demand balance constraints are similarly expressed.

FIG. 7 shows, as an example, potential prices of electric power, steam, hot water and cold water, and the energy unit price of each device. FIG.
Numerical values 1 to 24 outside the left column of each table indicate time. FIG.
, There may be an up arrow or a down arrow to the right of the numerical value. The up arrow indicates that the energy unit price is higher than the potential price, and the down arrow indicates that the energy unit price is lower than the potential price. If there is no arrow, the energy unit price is equal to the potential price.

In FIG. 7, by comparing the energy unit price and the potential price, the magnitude relationship between the two can be understood, and as a result, it is possible to know what is the critical time at which the optimum scale of the constituent devices is determined. . For example, focusing on electricity, the potential price of 38.8 at 14:00 is higher than the energy unit price of 14.7 for the purchase of electricity from a power company, and
The energy unit price of gas turbine power is higher than 15.0. From this, it can be determined that the potential price of power at 14:00 includes equipment costs in addition to operating costs,
From this, it is understood that it is the power supply and demand situation at 14:00 that determines the optimum contracted capacity of the power company and the optimum size of the gas turbine. Another interpretation of the potential price of electricity at 14:00 is that the unit quantity of electricity demand at 14:00 has a value of 38.8, and if the electricity demand at this time is greater by the unit quantity, 38.8 units are needed to cover it. The interpretation that the incremental cost is required also holds.

With the same idea for steam, the potential price 15.8 at 22:00 is higher than the steam energy unit price of the gas turbine of 3.59 and higher than the steam energy unit price of the auxiliary boiler of 6.39. This indicates that the optimal scale of the gas turbine and the auxiliary boiler is determined from the steam supply and demand situation at 22:00.

As described above, by displaying the potential price and the energy unit price as shown in FIG. 7, it is possible to determine what demand at what point in time affects the optimal scale of which component device.
In addition, it is possible to know the specific value per unit of demand at that time.

For reference, respective supply and demand patterns of electric power, steam, hot water and cold water are shown in FIGS. 7 to 11, respectively. In the related art, only the supply and demand patterns as shown in FIGS. 7 to 11 are displayed, and the potential price is not displayed.
It was difficult to find out what was the critical point in determining the optimal size. However, if the potential price and the energy unit price in FIG. 7 are displayed as in the present invention, it is possible to relatively easily find a critical point by examining the magnitude relation between them.

FIG. 12 shows a graphical representation of the energy unit price and the potential price of the electric power shown in FIG. By displaying the graph in this way, it is sometimes easier to know whether the potential price of electricity at each point in time is determined by the energy price of the power company or that of the gas turbine, and when the critical point is is there. For example, in the case of FIG.
Since the potential price at the time exceeds the two energy unit price bar graphs, it is easy to see that 14:00 is a critical point.

According to the above, the potential price of each constraint and the unit energy cost of the equipment are displayed, so that the user can easily analyze the demand prediction error and the sensitivity of the optimal solution to the equipment parameters. Further, the user can directly and quantitatively know the sensitivity of the optimal solution to the uncertainty based on the magnitude relationship between the potential price and the energy unit price.

In the above description, an example of obtaining the optimum value has been described. However, the determination device for determining the scale and operation pattern of the components of the combined heat and power equipment, the energy supply and demand balance constraint and the device capacity constraint are satisfied. Like the combined heat and power planning system, which has a combined heat and power optimization device that reduces the sum of the equipment cost and the operating cost and displays the potential price of the constraint and the energy unit price of the component equipment. May be used to obtain a more preferable small value or large value.

[0034]

According to the present invention, the effect of the prediction error can be easily studied.

[Brief description of the drawings]

FIG. 1 is a functional block diagram of one embodiment of the present invention.

FIG. 2 is a processing flowchart in one embodiment of the present invention.

FIG. 3 is a device configuration diagram in one embodiment of the present invention.

FIG. 4 is a diagram showing a graph of demand data in one embodiment of the present invention.

FIG. 5 is a diagram showing a unit price and a capacity unit price in one embodiment of the present invention.

FIG. 6 is a model diagram of a gas turbine according to one embodiment of the present invention.

FIG. 7 is a diagram showing a display example of an energy unit price and a potential price in a table format according to an embodiment of the present invention.

FIG. 8 is a view showing a power supply and demand pattern in the embodiment.

FIG. 9 is a view showing a steam supply and demand pattern in the embodiment.

FIG. 10 is a view showing a hot water supply / demand pattern in the embodiment.

FIG. 11 is a view showing a cold water supply / demand pattern in the embodiment.

FIG. 12 is a diagram showing a display example in the form of a graph of an energy unit price and a potential price in the embodiment.

[Explanation of symbols]

1 ... device configuration data, 2 ... demand data, 3 ... energy utility data, 4 ... device capacity unit price data, 5 ...
Equipment input / output characteristic data, 6 ... Optimization problem formulation means, 7
... Objective function, 8: Equipment constraint, 9: Supply and demand balance constraint, 1
0: optimization calculation means, 11: equipment optimum scale, 12: optimum operation pattern, 13: potential price, 14: energy unit price, 15: screen display means, 16: display device.

 ──────────────────────────────────────────────────続 き Continuing on the front page (72) Inventor Toshiyuki Sawa 7-1-1, Omika-cho, Hitachi City, Ibaraki Prefecture Inside Hitachi, Ltd. Hitachi Research Laboratory, Ltd. (72) Inventor Hiroaki Suzuki 4-6-1 Kanda Surugadai, Chiyoda-ku, Tokyo Address: Hitachi, Ltd. Business Planning Division

Claims (2)

[Claims] An apparatus for determining the scale and operation pattern of components of a combined heat and power equipment, and a combined heat and power optimization apparatus that reduces the sum of equipment costs and operation costs while satisfying energy supply and demand balance constraints and equipment capacity constraints. And displaying the potential price of the constraint and the unit price of energy of the component equipment. 2. The combined heat and power supply that minimizes the sum of equipment costs and operation costs while satisfying energy supply and demand balance constraints and equipment capacity constraints by optimizing the scale and operation pattern of components of the combined heat and power equipment. An optimization system, wherein a potential price of the constraint and an energy unit price of the component device are displayed.