JP2003113739A - Operation planning system for energy supply equipment - Google Patents

Abstract

PROBLEM TO BE SOLVED: To plan an appreciable operation of energy supply equipment with a heat storage tank in shorter time. SOLUTION: An operation planning system for energy supply equipment comprises a demand inputting means 22 for inputting energy demand, a heat storage tank heat input/radiation stage storing means 23 for storing a heat input/radiation stage corresponding to a heat storage level of the heat storage tank, and an operation optimizing means 24 for determining an operation plane of the energy supply equipment. A heat storage volume of the heat storage tank is divided into a plurality of heat storage levels, and a heat storage tank heat input/radiation volume is divided into one or a plurality of heat radiation stages. An operating point is computed so as to minimize any one or more of an energy consumption cost, a CO2 -converted generation volume and an energy-consumption-converted volume corresponding to each heat storage level at each time.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an energy supply facility operation planning system for optimizing the operation of an energy supply facility having a heat storage tank.

[0002]

2. Description of the Related Art In recent years, cogeneration systems capable of simultaneously generating steam heat and electric power and energy supply facilities introducing a heat storage tank have been developed on the side of energy consumers due to demands for environmental preservation, energy saving and cost saving. .

FIG. 10 is a system diagram showing an example of a device configuration of such an energy supply facility. In the energy supply facility A shown in FIG. 10, the substation facility 1 supplies the electric power 101 received from the power system 2 to the load facility 3 and the electric refrigerator (MR) 4.
The cogeneration facility 5 includes the fuel facility 6 through the fuel 102.
Is supplied to generate electric power 103 and steam heat 104. The boiler 7 receives the fuel 102 supplied from the fuel equipment 6 and generates steam heat 104. The electric power 103 generated by the cogeneration facility 5 is supplied to the load facility 3. The steam heat 104 generated by the cogeneration facility 5 and the boiler 7 is supplied to the load facility 3 and the absorption chiller (ABR) 8.

The absorption refrigerator (ABR) 8 produces cold heat 105 by the steam heat 104 supplied from the cogeneration facility 5. Cold heat 105 generated by the absorption chiller (ABR) 8 is supplied to the load equipment 3. The electric refrigerator (MR) 4 generates cold heat 106 using the electric power 101 supplied from the substation facility 1 as a power source. The cold heat 106 generated by the electric refrigerator (MR) 4 is stored in the heat storage tank 9
Is stored in. The heat storage tank 9 supplies the stored cold heat 105 to the load equipment 3.

The load equipment 3 is composed of an electric power load equipment 10 and a heat load equipment 11, and the heat load equipment 11 is further composed of a steam heat load equipment 12 and a cold heat load equipment 13. The power load facility 10 is, for example, an electric motor, office lighting, or the like. The steam heat load equipment 12 is, for example, cleaning of semiconductor substrates, heating, etc., and the cold heat load equipment 13 is, for example, cooling.

The structure of a conventional operation planning apparatus for the energy supply facility A shown in FIG. 10 will be described with reference to FIG. As shown in FIG. 11, the conventional operation planning apparatus B is composed of a demand input means 14 and an operation optimizing means 15. The operation optimization means 15 uses the following formula (1
By solving the optimization problem given by a) to (1zb), the total value of the weighted cost, the CO ₂ conversion generation amount, and the energy consumption conversion amount is minimized.
An operation plan for the energy supply facility A shown in FIG. 10 is determined.

[0007]

[Equation 1]

The symbols of the above formulas (1a) to (1zb) will be described (with reference to FIG. 10) as follows.

The subscript {z} represents the z-th time point, for example, F
_{Z}Is the fuel supplied from the fuel equipment 6 at time z
It represents the flow rate (102). E is from the present time (z = 1) to the nth
Weighted cost up to time (z = n), CO_TwoExchange
It represents the total value of the total discharge flow rate and the energy consumption conversion amount.
M_{Z}Is the cost at time z, C_{Z}Is the CO at time z _Two
Converted discharge flow rate, O_{Z}Is the energy consumption conversion amount at time z
Respectively.

P is electric power flowing through the substation equipment 1, P _rl is electric power supplied from the electric power system 2 to the electric power load 10, and P _cl is electric power generated by the cogeneration equipment 5. H _cl is heat generated from the cogeneration facility 5, H _bl is the boiler 7
Generated heat, F _c represents a cogeneration fuel flow rate, and F _b represents a boiler fuel flow rate.

Α, β, γ are weighting parameters, θ _MF
Is the fuel F cost conversion parameter, σ _MP is the substation facility 1
It represents the cost conversion variable of the system power flowing through. θ _CF is the CO ₂ conversion parameter of the fuel F, σ _CP is the CO ₂ conversion variable of the power supplied from the power system 2, θ _OF is the oil conversion parameter of the fuel F, σ _OP is the oil conversion of the power supplied from the power system 2. Represents a variable.

[0012] a _C , ..., F _C represent fuel F _c conversion parameters of the generated power P _cl and the generated heat H _cl of the cogeneration facility 5. a _b, ..., _{c b} is generated heat H of the boiler 7
_bl fuel F _b conversion parameter, P ₁ is power load facility 1
The electric power load of 0 and H _s represent the steam heat load of the steam heat load facility.

H _ca is heat of steam supplied to the absorption refrigerator (ABR) 8, H _ac is cold heat generated by the absorption refrigerator (ABR) 8, and a _ca , b _ca , and c _ca are absorption refrigerators (ABR). )
8 represents the supply steam heat H _ca conversion parameter of the generated cold heat H _ac of No. 8.

P _m is electric power supplied to the electric refrigerator (MR) 4, and H _si is generated cold heat of the electric refrigerator (MR) 4, that is, heat input to the heat storage tank 9. a _{m, b m, c m} is the electric refrigerator (MR) 4 supply power _{P m} terms parameters generated cold _{H si of, a am, b am, c} a m electric refrigerator (MR)
4 represents the supply cooling heat H _am conversion parameter of the generated cold heat H _si of No. 4.

H _sc is cold heat accumulated in the heat storage tank 9, H _sc
so represents cold heat supplied from the heat storage tank 9 to the cold load equipment 13, H _sl represents heat radiation loss of the heat storage tank 9, a _sl represents heat radiation loss approximate parameter of the heat storage tank 9, and H _c represents cold heat load.

P ^min and P ^max represent the lower limit value and the upper limit value of the electric power flowing through the substation equipment 1, respectively. P
_cl ^min and P _cl ^max represent the lower limit value and the upper limit value of the generated power of the cogeneration facility 5, respectively.
H _cl ^min and _H ^{cl ma x} each represent a cogeneration heat generated lower and upper limits of 5,
H _bl ^min and H _bl ^max are respectively measured by the boiler 7
Represents the lower limit value and the upper limit value of the generated heat of.

H _ac ^min and H _ac ^max respectively represent the lower limit value and the upper limit value of the cold heat generated in the absorption refrigerator (ABR) 8, and H _si ^min and H _si ^max respectively represent the electric refrigerator (MR) 4. It represents the lower limit and the upper limit of the generated cold heat. H _so ^min and H _so ^max represent the lower limit value and the upper limit value of the cold heat supplied from the heat storage tank 9 to the cold heat load equipment 13, respectively, and H _sc ^min and H _sc
^max represents the lower limit value and the upper limit value of the cold heat accumulated in the heat storage tank 9, respectively.

The above formula (1a) is a formula relating to the objective function, and formulas (1b) to (1zb) are formulas relating to the constraint condition. Of these, expressions (1b), (1c), (1d)
Is for converting the fuel flow rate F (102) supplied from the fuel equipment 6 and the electric power P (101) flowing through the substation equipment 1 into a cost M, a CO ₂ conversion discharge flow C, and an oil conversion fuel flow O, respectively. .

Equation (1e) approximates the relationship between the electric power P _cl and heat H _cl generated by the cogeneration facility 5 and the fuel F _c of the cogeneration facility 5. Formula (1f) is obtained by approximating the relationship between the generated heat _{H bl} and fuel _{F b} of the boiler 7.

The formula (1h) is an absorption refrigerator (ABR) 8
In which the approximating a relationship between the generation cold H _ac supply steam heat H _{c a.} Formula (1i) represents the power _{P m} to be required for the electric refrigerator (MR) 4 generates a cold _{H si.}

The expressions (1j) and (1k) represent the heat balance and the heat storage loss of the heat storage tank 9, respectively. Formula (1l)
~ Expression (1o) is a constraint condition expression related to the load supply and demand balance, and Expression (1p) represents the maximum contracted power. Formula (1r) ~
(1zb) represents the operating range of the cogeneration facility 5, the boiler 7, the absorption refrigerator (ABR) 8, the electric refrigerator (MR) 4, the heat storage tank 9, and the heat storage tank pump.

In the above description, the time point z is z = 1, ..., N
Although (n = 24) is set and the load prediction from the present time to 24 hours ahead is targeted, this is based on the fact that the operation cycle of the heat storage tank is generally a 24-hour cycle. here,
Assuming that d is the time width from the time point z to the time point z + 1, d = 1 hour and n × d = 24 hours in the above example. It is assumed that the prediction load is constant in the time section [z, z + 1]. Here, the smaller the time width d is, the more predictable the load can be with a finer time width, and the larger the time width d, the longer the load can be predicted. However, from time point z = 1 to z = n The number of repeated calculations of is increased. On the other hand, the larger the time width d, the more roughly the load can be predicted, and the smaller the n, the shorter the load can be predicted. In that case, the number of calculations is reduced. .

Power load P _{l {z}} , steam heat load H
_{The s {z}} and the cooling load H _{c {z}} are data given as the demand forecast input 201 from the demand input means 14 shown in FIG. 11. The operation optimizing means 15 solves the optimization problem represented by the equations (1a) to (1zb) by these demand forecast inputs 201. As a result, the optimal operation plan, so that the given objective function (Equation (1a)) is minimized,
That is, the operation output _{Pcl {z}} , _{Hcl {z}} , _{Hbl {z}} , H of the cogeneration facility 5, the boiler 7, the absorption refrigerator 8, the electric refrigerator (MR) 4, and the heat storage tank 9 is set.
_{ac {z}} , H _{si { z}} (1a) to (1zb) H
_{so {z}} , z = 1, ..., 24 are determined.

As a means for solving the non-linear problem shown in the equations (1a) to (1zb), a known computer language, for example, a NUOPT-modeling language applied when solving a linear programming problem can be used. This NUOPT-
When solving the non-linear problem given to the equations (1a) to (1zb) using a modeling language, the differential (slope) information of the equation is used for the calculation, so that the equations (1a) to (1zb) are given as follows. , Discontinuous variables (1 or 0) δ _{c {z}} , δ _{b {z}} , which represent the stoppage of equipment,
It cannot handle δ _{a c {z}} and δ _{m {z}} .
When such a discontinuous variable is included, the conventional operation optimizing means 15 needs to process according to the procedure shown in FIG.

In FIG. 12, a conventional operation optimizing means 1
The process procedure of No. 5 will be described. After the start, i = 1 is set (S101), and the driving optimization process (S102) corresponding to the driving pattern i is set, and i = i + 1 is set (S103).
And an operation plan corresponding to the operation pattern of the Max set is calculated by iterative processing of Max times, and the calculated M
The objective function value (equation (1
a)) the smallest operation plan P _{cl {z}} , H
_{cl {z}} , H _{bl {z}} , H _{ac {z}} , H _{si {z
}} , H _{so {z}} , z = 1, ..., 24 are determined as the optimal plan (S105). The operation pattern is a discontinuous variable (1 or 0) δ _{c {z}} , δ _{b {z}} ,
This corresponds to a combination of δ _{ac {z}} and δ _{m {z}} , and the number of operation patterns in this case is a power of 2 of the number of discontinuous variables, that is, a power of the number of time points z, and Ma
There are x = (2 ⁴ ) ²⁴ = 7.9 ²⁸ operation patterns.

[0026]

As described above [0007], before Treatment with prior art, repeated number of calculations becomes huge (e.g. 7.9 ²⁸ combinations), the optimal solution is obtained,
There is a problem that it takes a long time.

The present invention has been made in view of such circumstances, and it is an object of the present invention to provide an operation plan system for energy supply equipment, which can perform a suitable operation plan in a shorter time.

[0028]

In order to achieve the above object, the invention according to claim 1 is an operation planning system for energy supply equipment having a heat storage tank, and demand input means for inputting energy demand, A heat storage tank heat radiation stage storage means for storing the heat radiation stage corresponding to the heat storage level of the heat storage tank;
And an operation optimization means for determining an operation plan of the energy supply equipment, the heat storage tank heat radiation stage storage means divides the heat storage amount of the heat storage tank into a plurality of heat storage levels, and the heat storage tank heat radiation amount. The operation optimizing means is divided into one or a plurality of heat radiation stages and stored, and one or more of the energy consumption cost, the CO ₂ conversion generation amount, and the energy consumption conversion amount corresponding to each heat storage level at each time point is the minimum. By satisfying the input energy demand, one or more of the energy consumption cost of the energy supply facility, the CO ₂ conversion generation amount, and the energy consumption conversion amount are minimized by determining the operating point And an operation plan system for energy supply equipment, characterized by determining an operation plan for the energy supply equipment.

In the invention according to claim 2, the invention according to claim 1
At each point in the operation planning system for energy supply equipment
Energy consumption cost corresponding to each heat storage level in
CO _TwoOne or more of the conversion generation amount and energy consumption conversion amount
The operating point corresponding to input / output heat
In the process of obtaining the operating point, the
Energy consumption corresponding to heat input to the heat tank or each heat dissipation step
Cost, CO_TwoOf the conversion generation amount and energy consumption conversion amount
Predetermine by determining the operating point where one or more is the minimum
Energy consumption cost of energy supply equipment during the period,
CO_TwoOne or more of the conversion generation amount and energy consumption conversion amount
Operation plan of the energy supply equipment so that
Energy Supply Facility Operation Planning System for High Speed Determination
I will provide a.

In the invention according to claim 3, claims 1 to 2
In the operation planning system of the energy supply equipment according to any one of the above, a heat storage level upper and lower limit setting means for presetting the upper and lower limit heat storage levels of the operable heat storage tank is provided, and within the set upper and lower heat storage levels at each time point. Energy consumption cost corresponding to each heat storage level, the CO ₂ conversion generation amount, and the operating point that minimizes one or more of the energy consumption conversion amount, the energy consumption cost of the energy supply facility in the predetermined period, the CO ₂ conversion There is provided an operation plan system for an energy supply facility that determines an operation plan of the energy supply facility at a higher speed so that one or more of the generated amount and the energy consumption conversion amount are minimized.

In the invention according to claim 4, claims 1 to 3
In the operation planning system of the energy supply equipment according to any one of the above, only the heat loss of the heat storage tank at each point in the heat radiation process, so as to reduce the amount of heat stored and stored in the heat storage tank at each stage, A heat loss correction unit that corrects the heat storage and heat radiation amount of the heat storage tank is provided, and by correcting the heat storage and heat radiation amount, one of the energy consumption cost of the energy supply facility, the CO ₂ conversion generation amount, and the energy consumption conversion amount in a predetermined period is There is provided an operation planning system for energy supply equipment, which determines an operation plan value of the energy supply equipment including heat loss of the heat storage tank so that one or more of the energy supply equipment is minimized.

According to the invention of claim 5, claims 1 to 3
In the operation planning system for energy supply equipment described in any one of 1 to 3, heat for converting the heat loss of the heat storage tank at each point in the heat radiation process into one or more of energy consumption cost, CO ₂ conversion generation amount, and energy consumption conversion amount. The operation plan value of the energy supply equipment is provided with the heat storage so that at least one of the energy consumption cost of the energy supply equipment, the CO ₂ conversion generation amount, and the energy consumption conversion amount in a predetermined period becomes a quasi-minimum. (EN) An operation planning system for energy supply equipment that determines heat loss of a tank.

In the invention according to claim 6, claims 1 to 5
In the operation planning system of the energy supply equipment described in any one of 1 to 3, a constraint means for limiting one or more of energy consumption cost, CO ₂ conversion generation amount or energy consumption conversion amount is provided, and while satisfying the constraint conditions, Energy consumption cost of energy supply equipment in a predetermined period, CO
₍₂₎ An operation planning system for energy supply equipment is provided that determines the operation plan value of the energy supply equipment including the heat loss of the heat storage tank so that the rest of the converted amount and the energy consumption converted amount are minimized.

According to the invention of claim 7, claims 1 to 6
In the operation planning system of the energy supply equipment according to any one of the above, a state setting means for setting an operation state of the energy generator set arbitrarily is provided, and the operation state is included in a constraint condition, so that a predetermined period In order to minimize the energy consumption cost of the energy supply equipment, the operation plan of the energy supply equipment is determined, and an operation plan system of the energy supply equipment is provided which enables evaluation of the operation plan or intervention of manual operation.

In the invention according to claim 8, claims 1 to 7
In the operation planning system for energy supply equipment according to any one of 1 to 3, the heat storage level that displays the heat storage level curve that can be operated and the heat storage level curve that is determined to minimize the energy consumption cost of the energy supply equipment in a predetermined period Provided is an operation planning system of an energy supply facility, which is provided with a display means and can confirm a storage and heat dissipation process of a heat storage tank.

In the invention according to claim 9, claims 1 to 8
In the operation planning system of the energy supply equipment according to any one of the above, by including a demand prediction means for predicting energy demand and inputting the predicted demand, while satisfying the predicted energy demand, in a predetermined period There is provided an operation planning system for an energy supply facility that determines an operation plan for the energy supply facility such that one or more of the energy consumption cost of the energy supply facility, the CO ₂ conversion generation amount, and the energy consumption conversion amount are minimized.

According to a tenth aspect of the present invention, there is provided a storage medium storing a program for driving energy supply equipment by the system according to any one of the first to ninth aspects.

[0038]

BEST MODE FOR CARRYING OUT THE INVENTION Embodiments of an operation planning system for energy supply equipment according to the present invention will be described below with reference to FIGS. The energy supply equipment to be operated is the same as the equipment configuration shown in FIG. 10, and will be described with reference to FIG. 10 as it is.

First Embodiment (FIGS. 1 to 8 and 10) FIG. 1 is a system diagram showing the configuration of an operation planning system for energy supply equipment according to the first embodiment of the present invention.

As shown in FIG. 1, the system of the present embodiment has a demand forecasting means 2 for forecasting energy demand.
1, a demand input means 22 for inputting a demand schedule based on the predicted demand, a heat storage tank heat radiation stage storage means 23 for storing the heat radiation stage of the heat storage tank 9, and information fetched from these means. And an operation optimizing means 24 for determining an operation plan. Further, means for inputting various information for operation optimization to the operation optimization means 24, that is, operation optimization means 25 for each time heat input / output heat, heat storage level upper / lower limit setting means 26, heat loss correction means 27, restriction means. 28 and an operating state setting means 29. Furthermore, the heat storage level display means 30 for output display determined by the operation optimization means 24 is provided, and this heat storage level display means 30 shows the heat storage level curve which can be operated and the energy consumption cost of the energy supply equipment A in a predetermined period. The heat storage level curve determined to be the minimum is displayed.

The operation optimizing means 24 uses the following equation (2a)
By solving the optimization problem given by ˜ (2zg), the energy supply facility A having the heat storage tank 9 is set so that the total value of the weighted cost, the CO ₂ conversion generation amount, and the energy consumption conversion amount is minimized. Determine the operation plan.

[0042]

[Equation 2]

Among the above equations, equations (2a) to (2zb)
Are the formulas (1a) to
Although it corresponds to (1zb), in the present embodiment,
As shown in equation (2z), the lower limit value H _sc ^{min of} heat storage
_{Z} and the upper limit value _H ^{sc ma} _{x {z}} is set to correspond to the time point z. Further, the formula (2zc) and the formula (2zd) are formulas relating to the heat radiation and heat radiation stages. In these formulas, δ _L is the stage index, and L _{z} is the maximum stage. Formula (2ze), Formula (2zf) and Formula (2z
g) is a constraint condition expression corresponding to the CO ₂ conversion generation amount, the energy consumption conversion amount, and the energy consumption cost, respectively.

In the present embodiment, as shown in the equations (2zc) and (2zd), the heat radiation amount of the heat storage tank 9 is set to 1
Or divided into multiple heat radiation stages. Then, the operation optimizing means 24 determines an operation plan by using a dynamic programming method. That is, the demand forecast result 21 to the demand forecast result 120 is the demand input unit 22.
And the demand forecast input from the demand input means 22 | z |
, Electric power load P _{l {z}} , steam heat load H _{s {z}} ,
The cooling load H _{c {z}} is input to the operation optimizing means 24. Further, the operation optimization means 24, as a heat storage level on the lower limit value 124 from the heat storage level upper and lower limit setting means 26 at each time point ^{_{z, H sc min {z}}} , H s c max
_{Z} is input, H _sl is input as the heat radiation reduction heat amount 127 due to heat loss from the heat loss correction means 27, and the constraint condition 128 corresponding to the equations (2ze) to (2zg) is input from the constraint means 28. It Further, the operation optimizing means 2
4 shows the operation state setting result 129 from the operation setting means 29.
As δ _{c {z}} , δ _{b {z}} , δ _{ac {z}} , δ
_{m {z}} is input, and L _{z} is input as the maximum heat storage tank heat radiation step 130 from the heat storage tank heat radiation step storage unit 23.

Further, between the operation optimizing means 24 and each time input / output heat correspondence operation optimizing means 25, D (z-1, δ _L {z-1} as the input / output heat correspondence optimization result 122 is obtained. ，
j {z-1}) and the optimization result 123 corresponding to each heat storage level
And V (z, δ _A , j) are input and output.

FIG. 2 is a flow chart showing a processing procedure for executing the operation plan in the operation optimizing means 24.

As shown in FIG. 2, start (S301)
After that, each of the above inputs is performed as an initial process (S320), and z = 1, δ _L = in S303 to S305.
1, j = 1 is set.

Next, determination of the target device pattern (S305
If a) is performed and the answer is yes, the objective function value maximum processing (S305b) is performed, and if the answer is no, the driving optimization processing (S306) is performed. Then, at S306 and S307 to S312, the time point z and the heat radiation / dissipation stage δ.
_The operation optimization process corresponding to _L and the device pattern j is performed, and the optimization problem given in Formula (3) is solved by using NUOPT-modeling language. As a result, the cogeneration facility 5, the boiler 7, the absorption refrigerator (AB) is minimized so that the objective function (equation (3a)) is minimized.
R) 8, electric refrigerator (MR) 4 and heat storage tank 9 operation outputs P _{cl {z}} , H _{cl {z}} , H _{bl {z }} , H
_{ac {z}} , H _{si {z}} , H _{so {z}} , z = 1,
..., 24 are determined.

The device pattern j in this case is a discontinuous variable (1 or 0) δ _{c {z}} , δ _{b { z}} ,
Corresponding to a combination of δ _{ac {z}} and δ _{m {z}} , the number K of device patterns (S308) in this case is a power of 2 of the number of discontinuous variables. That is, there are K = 2 ⁴ = 16 operation patterns.

[0050]

[Equation 3]

In S305a, the equipment operation pattern set by the operation state setting means 29 is determined. The operating state setting means 29 manually sets the operating state as shown in FIG. 3 as "0, 1 or blank" in the operating state setting unit 29a on the CRT screen, for example. As shown in FIG. 3, z = 1, ..., N corresponds to time points 1 ,. The following settings are made in the operating state setting unit 29a.

1: Corresponding equipment (δ _{c {z}} , δ
_{b {z}} , δ _{ac {z}} or δ _{m {z}} ) is designated during operation at time point z.

0: Corresponding equipment (δ _{c {z}} , δ
_{b {z}} , δ _{ac {z}} or δ _{m {z}} ) is designated to be stopped at time z.

Blank part: Corresponding equipment (δ _{c {z}} , δ _{b {z}} , δ _{ac {z}} or δ
_{m {z}} ) has no operation stop designation at time z.

The correspondence between the device pattern j (j = 1, ..., 16) and the device states δ _c , δ _b , δ _ac , δ _m is shown in FIG. 4 as a correspondence table. The correspondence table shown in FIG. 4 is stored in the memory unit of the operation optimizing unit 24. For example, when j = 2, in S306, δ _c =
0, δ _b = 0, δ _ac = 0, δ _m = 1 and equation (3)
Solves the problem given by.

Details of the processing in the non-target device pattern (S305a) shown in FIG. 2 will be described with reference to FIG.

As shown in FIG. 5, the non-target device pattern S305a includes steps S601 to S605. In S601, the manually set device pattern as shown in FIG. 3 given by the operation state setting means 29 and the device pattern shown in FIG. 4 stored in the operation optimizing means 24 are input.

Next, in step S602, i = 1 is set.
After that, in S603, the input pattern is collated.
It Where δ_{i {z}}(I = 1, ..., 4) are respectively
δ_{c {Z}}, Δ_{b {z}}, Δ_{ac {z}}, Δ_{m {z}}To
Assuming that they correspond, as a result of collation, "δ
_{i {z}}(Fig. 3) = 1 and δ_{i {z}}(Fig. 4) = 0 "
Or “δ _{i {z}}(Fig. 3) = 0 and δ_{i {z}}(Figure 4)
= 1 ”, the corresponding“ time point z, heat radiation stage δ ”_L, Machine
Pattern j ”is given by the operating state setting means 29.
Since it is different from the manual setting,
The objective function value (equation (3a))
Then, the processing of S305b is executed.

As a result of such matching, all i (i =
1, ..., 4), “δ _{i {z }} (FIG. 3) = 1 and δ _{i {z}} (FIG. 4) = 0” or “δ _{i {z}} (FIG. 3)
= 0 and δ _{i {z}} (FIG. 4) = 1 ”, the corresponding“ time point z, heat radiation stage δ _L , equipment pattern j ”is different from the manual setting given by the operating state setting means 29. Therefore, the processing of S306 is executed.

That is, the objective function (Equation (3a)) is minimized by solving the optimization problem given in Equation (3) using the NUOPT-modeling language.
Operation output _{Pcl {z}} , _{Hcl {z}} , _{Hbl {z} of} the cogeneration facility 5, the boiler 7, the absorption refrigerator (ABR) 8, the electric refrigerator (MR) 4 and the heat storage tank 9.
H _{ac {z}} , H _{si {z}} , H _{so {z}} , z = 1,
..., 24 are determined.

Next, referring to FIG. 6, the relationship between the heat storage tank heat radiation stage and the heat storage tank level will be described. In the present embodiment, the cost conversion variable σ of system power leaving the substation 1
_It is assumed that _{MP {z 1 }} changes depending on the time point z. Here, assuming that the electricity charge in the night time zone is cheaper than that in the daytime, generally, the heat storage tank 9 stores heat at night, but often uses the stored heat in the daytime.

In FIG. 6, the horizontal axis represents the time point z. z = 1, ..., 4 are nighttime low charge time zones, z = 5
, N (= 24) is the daytime high-price charge period, σ
The _{MP {z 1} <σ MP} {z2}. However, z1 =
1, ..., 4, z2 = 5 ,.

Further, the vertical axis of FIG. 6 represents the step index of the heat storage level. As shown in FIG. 6, the stage index of the heat storage level is δ _A = 0, ..., 4, and the heat storage stage index in the case of heat storage (z = 1, ..., 4) is δ _L.
= 0,1 and the heat radiation stage index for heat radiation (z = 5, ..., 24) is δ _L = 0,1,2, and the heat radiation end time point is z = 12, each time point (z = 1, , 24) the possible transitions of the heat storage tank level are represented hierarchically, as indicated by the bold line in FIG. Therefore, assuming that the upper and lower limit values of the stage index at each time point with respect to the stage index δ _A of the heat storage level are A ^max _{{ z}} and A ^mix _{z} , the heat storage level upper and lower limit setting means 26 makes the following settings. .

[0064]

[Equation 4]

FIG. 7 shows in detail the processing of the operation optimization means 25 corresponding to heat input / output at each time point. As shown in FIG. 7, each time heat input / output heat-corresponding operation optimizing means 25 is configured to perform the two-step processing of S801 and S802.

At step S801, the time point z-
1. Heat radiation stage δ _{L {z-1}} and equipment pattern j
Objective function value D (z-1, δ) corresponding to _{{z-1}
L {z-1}} , j _{{ z-1}} ) (equation (3a)) is obtained by the following equations (5a) to (5d), and the objective function value corresponding to the time point z, the heat storage level δ _A and the device pattern j. G (z, δ
_A process of converting into _{A {z}} , j) is performed.

[0067]

[Equation 5]

Further, in step S802, the value function value V (z, δ _{A {z}} , j _{z} ) corresponding to each time point z, the heat storage level δ _A and the device pattern j is calculated by the dynamic programming method. The following formula (6a),
The process of calculating in (6b) is performed.

[0069]

[Equation 6]

Next, in step S313 shown in FIG. 2, V (z, δ _{A {z}} , j _{z} ) given by the above equations (6a) and (6b) is used to obtain the following equation. In (7a) to (7d), the operation plan for z = n, ... 1 is determined.

[0071]

[Equation 7]

[0072] However, the meaning of the formula (7a) and formula (7b) (V (z, δ A {z}, j {z})) δ A {z} such that a _{minimum, j {z}} And δ _{A {z}} ^* and j _{{ z}} ^{* respectively,} and the meanings of equation (7c) and equation (7d) are (G
(Z, δA _{z} ^* , j _{{z }} ^* ) + V (z-1, δ
Δ _A such that _{A {z-1}} , j _{z-1} )) is minimized
_This means that _{z-1} and j _{z-1} are δ _{A {z-1}} ^* and j _{z-1} ^* , respectively.

Here, j _{z} ^* and δ _{A {z}} ^* (z =
1, ..., 24) corresponding to the optimum solutions of the equations (3a) and (3b), that is, the cogeneration facility 5, the boiler 7, the absorption refrigerator (ABR) 8, the electric refrigerator (MR) 4, and the heat storage tank 9 Operation output P _{cl { z}} ^* , H _{cl {z}} ^* ,
H _{bl {z}} ^* , H _{ac {z}} ^* , H _{si {z}} ^* , H
_{so {z}} ^* , z = 1, ..., 24 is the optimum operation plan.

In the step S314 shown in FIG. 2, the optimum solution j _{z} ^* , δ _{A {z}} ^* , P obtained above is obtained.
_{cl {z}} ^* , H _{cl {z}} ^* , H _{bl {z}} ^* , H
_{ac { z}} ^* , H _{si {z}} ^* , H _{so {z}} ^* , z =
, ..., 24 are displayed to the operator and output to the control device. In this embodiment, the output to the heat storage level display means 30 is performed as an output for display to the operator.

In FIG. 8, the heat storage level display means 30 is a CRT.
The example of a heat storage level display in the case of implement | achieving with a display is shown. As shown in this FIG. 8, the thin line displays the possible driving levels (equation (4)) corresponding to FIG.
The thick line represents an example of the heat storage level of the optimum plan obtained by the equation (7).

Then, the demand forecasting means 21 forecasts the heat demand and the power demand. When the demand target is an office, the demand is generally correlated with the atmospheric temperature. Therefore, in the present embodiment, based on the atmospheric temperature predicted by a weather forecast or the like, the power load P _{l { z}} , the steam Heat load H
_{s {z}} and cold heat load H _{c {z}} (z = 1, ..., N)
Demand forecast is made. When the relationship between atmospheric temperature and demand is expressed by a regression model, these relationships are expressed by the following equation (8
a), (8b), (8c).

[0077]

[Equation 8]

Here, _{Ta {z}} is the atmospheric temperature at the time of prediction, A _{Pl0 to} A _Ply are power heat demand prediction parameters, A _{Hs0 to} A _Hsy are steam heat demand prediction parameters, and
A _{H c0 to} A _Hcy represent cooling heat demand prediction parameters, and y represents the order of the regression model.

These prediction parameters A _{Pl0 -A
Ply} , A _{Hs0 to} A _Hsy and A _{Hc0 to} A _Hcy are
For example, past atmospheric temperature and each demand P _{l {z}} ,
It is possible to use the actual data of H _{s {z }} and H _{c {z}} to perform pre-calculation by the method of least squares. For example, the parameters A _{P10 to} A _Ply , A _{Hs0 to} A are used by using the past few weeks to several years of actual data.
_Hsy , A _{Hc0 to} A _Hcy are calculated in _advance , and the calculated parameters A _{Pl0 to} A _Ply , A _Hs0 to
Using A _Hsy , A _{Hc0 to} A _Hcy and the atmospheric temperature data T _{a {z}} predicted by the weather forecast of the next day, the demand forecast of the next day (P _{l {z}} , H _{s {z}} , H _{c {Z}} ). Although y is the order of the regression model, it is generally set to the third order.

The demand predicting means 21 predicts the demand from the atmospheric temperature as described above, and outputs the predicted demand forecasting result 120 to the demand inputting means 22. The demand inputting means 22 inputs the demand forecasting result 120 from the demand forecasting means 21, and inputs the demand forecasting result 120 to the demand forecasting input 121 (P _{1 {z}} , H.
_It is output to the operation optimizing means 24 as _{s {z}} , H _{c {z}} ). By the above processing of the operation optimizing means 24 based on this demand prediction, the operation plan is output, and the process ends (S315 in FIG. 2).

According to the present embodiment described above, by dividing the heat storage level of the heat storage tank 9, it is possible to determine the optimum operation plan by using the dynamic programming method. Further, in the optimum operation plan calculation, the process of S306 shown in FIG. 2 takes the longest calculation time. Here, by performing the process of S306 in response to heat radiation and heat radiation, the number of times of processing of this S306 is the maximum, and the number of device patterns × the number of heat radiation / radiation levels × the number of time points = 2 ⁴ × 3 × 24 = 1152.
Therefore, the number of calculations can be significantly reduced as compared with the number of calculations by the conventional technique.

Further, by setting the heat storage tank level which can be operated by the heat storage level upper and lower limit setting means 26, the number of calculations can be further reduced.

The heat storage loss of the heat storage tank 9 at each stage is corrected so that the heat storage and heat storage amount of the heat storage tank 9 at each stage is reduced by the heat loss of the heat storage tank at each point of the heat radiation process. It is possible to determine the optimum plan including the above.

Furthermore, by introducing one or more of the energy consumption cost, the CO ₂ conversion generation amount or the energy consumption conversion amount as a constraint condition, the energy consumption cost, CO
₂ While satisfying one or more of the converted amount of energy or the converted amount of energy consumption to a predetermined constraint condition, the energy supply facility energy consumption cost, the amount of generated CO ₂ and the remaining amount of converted energy consumption are minimized in a predetermined period. In addition, the operation plan can be decided.

Further, by setting the arbitrarily set operation state of the energy generating device in the constraint condition, it is possible to realize the operation plan in which the operation plan can be evaluated or the manual operation can be intervened. Further, by displaying the heat storage level curve that can be operated and the optimum heat storage level curve, it is possible to easily confirm the heat storage and release process of the heat storage tank determined by the optimum operation plan.

Furthermore, by combining with the demand forecasting means 21 for forecasting the energy demand, it is possible to determine the operation plan of the energy supply equipment while satisfying the forecasted energy demand.

Second Embodiment (FIG. 9) FIG. 9 is a system diagram showing the configuration of the second embodiment of the present invention. In this embodiment, a heat loss conversion means 31 is provided instead of the heat loss correction means 27 shown in the first embodiment. Other means are the same as those in the first embodiment.

In the present embodiment having such a configuration,
The luck optimizing means 24 determines the operation plan by solving the following equations (9a) to (9c) instead of the equations (3a) and (3b).

[0089]

[Equation 9]

Here, p _{Hsl {z}} is a penalty parameter for converting the heat loss into one or more of the energy consumption cost, the CO ₂ conversion generation amount, and the energy consumption conversion amount, and p _{Hsl {z} of the equation (9a). _} × H _{sl {z}} is calculated by the heat loss conversion means 31, and the calculation result is the heat loss conversion result 131, which is the operation optimization means 24.
Is output to.

According to the present embodiment, by converting the heat loss into one or more of the energy consumption cost, the CO ₂ conversion generation amount, and the energy consumption conversion amount, only the heat loss of the heat storage tank 9 at each point of the heat radiation process. , An operation plan such that one or more of the energy consumption cost of the energy supply facility A, the CO ₂ conversion generation amount, and the energy consumption conversion amount become a quasi-minimum without reducing the heat storage / radiation amount of the heat storage tank 9 at each stage. The value can be determined.

Other Embodiments The system of the present invention is not limited to the configuration directly incorporated in each means as shown in each of the above-mentioned embodiments, and the drive program in each step is a floppy (registered trademark) disk or optical disk. It can be stored in various other storage media and can be attached to and detached from the device.

[0093]

As described above in detail, according to the present invention, by dividing the heat storage level, a dynamic programming method is used to quickly determine an operation plan of an energy supply facility having a non-linear characteristic. can do.

[Brief description of drawings]

FIG. 1 is a system diagram showing a system configuration according to a first embodiment of the present invention.

FIG. 2 is a flowchart showing a procedure for executing the system according to the first embodiment of the present invention.

FIG. 3 is an explanatory diagram of an operating state setting unit shown in FIG.

FIG. 4 is an explanatory diagram showing a correspondence between device patterns and operating states in the embodiment.

FIG. 5 is a detailed diagram showing a procedure of a non-target device pattern processing means in the embodiment.

FIG. 6 is an operation explanatory view showing a relationship between a heat storage tank heat radiation stage and a heat storage level in the embodiment.

FIG. 7 is a detailed diagram showing a procedure of the operation optimization means corresponding to heat input / output at each time point in the embodiment.

FIG. 8 is an explanatory view showing a heat storage level display means in the embodiment.

FIG. 9 is a system diagram showing a system configuration according to a second embodiment of the present invention.

FIG. 10 is a system diagram showing a configuration example of equipment of an energy system.

FIG. 11 is a system diagram showing a conventional system.

FIG. 12 is a flowchart showing the operation of the conventional system.

[Explanation of symbols]

1 substation equipment 2 power system 3 load equipment 4 Electric refrigerator (MR) 5 Cogeneration equipment 6 fuel equipment 7 Boiler 8 Absorption refrigerator (ABR) 9 heat storage tank 10 Power load equipment 11 Heat load equipment 12 Steam heat load equipment 13 Cooling load equipment 14 Demand input means 15 Operation optimization means 21 Demand forecasting means 22 Demand input means 23 Heat storage tank heat radiation stage storage means 24 Operation optimization means Operation optimization means for heat input / output at 25th time 26 Heat storage level upper / lower limit setting means 27 Heat loss correction means 28 Means of restriction 29 Operating state setting means 30 Heat storage level display means 31 Heat loss conversion means

   ─────────────────────────────────────────────────── ─── Continued front page    (72) Inventor Tohru Kaiji             1-1 Shibaura, Minato-ku, Tokyo Co., Ltd.             Toshiba headquarters office (72) Inventor Toshihiro Yamada             No. 1 Toshiba-cho, Fuchu-shi, Tokyo Toshiba Corporation             Fuchu Office (72) Inventor K. Takagi             No. 1 Toshiba-cho, Fuchu-shi, Tokyo Toshiba Corporation             Fuchu Office (72) Inventor Reichi Ueto             No. 1 Toshiba-cho, Fuchu-shi, Tokyo Toshiba Corporation             Fuchu Office F term (reference) 3L060 AA03 AA08 CC19 DD08 EE41                       EE45

Claims (10)

[Claims] 1. An operation planning system for an energy supply facility having a heat storage tank, comprising: a demand input means for inputting energy demand; and a heat storage tank heat radiation stage storage for storing a heat radiation stage corresponding to a heat storage level of the heat storage tank. Means and operation optimizing means for determining an operation plan of the energy supply facility,
The heat storage tank heat radiation stage storage means divides the heat storage amount of the heat storage tank into a plurality of heat storage levels and sets the heat storage tank heat radiation amount to 1
Or, the operation optimizing means minimizes one or more of the energy consumption cost, the CO ₂ conversion generation amount, and the energy consumption conversion amount corresponding to each heat storage level at each time point. By determining the operating point, one or more of the energy consumption cost, the CO ₂ conversion generation amount, and the energy consumption conversion amount of the energy supply facility in a predetermined period is minimized while satisfying the input energy demand. An operation planning system for energy supply equipment, characterized by determining an operation plan for the energy supply equipment. 2. The operation planning system for energy supply equipment according to claim 1, wherein at least one of the energy consumption cost, the CO ₂ conversion generation amount, and the energy consumption conversion amount corresponding to each heat storage level at each time point is minimized. An operation optimizing means corresponding to each time input / output heat for obtaining an operating point is provided, and in the process of obtaining the operating point, energy consumption cost corresponding to heat input to the heat storage tank or each heat radiation stage, CO ₂ conversion generation amount and energy consumption By determining an operating point where one or more of the converted amounts is the minimum, one or more of the energy consumption cost of the energy supply facility, the CO ₂ converted generation amount, and the energy consumed converted amount are minimized in a predetermined period. An operation planning system for energy supply equipment that determines an operation plan for energy supply equipment at high speed. 3. The operation planning system for energy supply equipment according to claim 1, further comprising heat storage level upper / lower limit setting means for presetting upper / lower heat storage levels of operable heat storage tanks. By determining the operating point that minimizes one or more of the energy consumption cost, the CO ₂ conversion generation amount, and the energy consumption conversion amount corresponding to each heat storage level within the set upper and lower limit heat storage levels at the time point, the energy in the predetermined period is calculated. An operation plan system for an energy supply facility that determines an operation plan of the energy supply facility at a higher speed so that one or more of the energy consumption cost of the supply facility, the CO ₂ conversion generation amount, and the energy consumption conversion amount are minimized. 4. The operation planning system for energy supply equipment according to any one of claims 1 to 3, wherein only the heat loss of the heat storage tank at each point of the heat radiation process, the amount of heat stored and discharged by the heat storage tank at each stage. So as to reduce the heat loss correction means for correcting the amount of heat stored and stored in the heat storage tank at each stage,
By correcting the heat storage / radiation amount, the operation plan value of the energy supply facility is minimized so that one or more of the energy consumption cost of the energy supply facility, the CO ₂ conversion generation amount, and the energy consumption conversion amount in the predetermined period are minimized. Is an operation planning system for energy supply equipment, which determines the heat loss in the heat storage tank. 5. The operation planning system for energy supply equipment according to any one of claims 1 to 3, wherein the heat loss of the heat storage tank at each point of the heat radiation process is calculated as energy consumption cost, CO ₂ conversion generation amount and energy consumption. A heat loss conversion means for converting into one or more of the converted amounts is provided so that one or more of the energy consumption cost of the energy supply facility, the generated amount of CO ₂ conversion and the converted amount of energy consumption in a predetermined period becomes a quasi-minimum. An operation planning system for energy supply equipment, which determines an operation plan value of energy supply equipment including heat loss of the heat storage tank. 6. The operation planning system for energy supply equipment according to any one of claims 1 to 5, further comprising restriction means for restricting one or more of energy consumption cost, CO ₂ conversion generation amount, or energy consumption conversion amount. The operation plan value of the energy supply facility is stored so that the energy consumption cost of the energy supply facility, the CO ₂ conversion generation amount, and the rest of the energy consumption conversion amount in the predetermined period are minimized while satisfying the constraint conditions. An operation planning system for energy supply equipment that determines heat loss in the tank. 7. The operation planning system for energy supply equipment according to any one of claims 1 to 6, further comprising an operation state setting means for setting an operation state of the energy generator set arbitrarily, the operation state By setting the above condition as a constraint condition, the operation plan of the energy supply facility is determined so that the energy consumption cost of the energy supply facility in the predetermined period is minimized, and the energy supply that enables the evaluation of the operation plan or the intervention of the manual operation. Equipment operation planning system. 8. The operation planning system for energy supply equipment according to any one of claims 1 to 7, wherein the heat storage level curve in which operation is possible and the energy consumption cost of the energy supply equipment in a predetermined period are determined to be minimum. A heat storage level display means for displaying the stored heat storage level curve,
An operation planning system for energy supply equipment that enables you to check the heat storage and heat dissipation process of a heat storage tank. 9. The operation planning system for energy supply equipment according to any one of claims 1 to 8, further comprising a demand predicting means for predicting energy demand, and the predicted demand is input to predict the demand. Energy that determines the operation plan of the energy supply equipment so that at least one of the energy consumption cost of the energy supply equipment, the CO ₂ conversion generation amount, and the energy consumption conversion amount is minimized while satisfying the energy demand. Operation planning system for supply equipment. 10. A storage medium storing a program for driving energy supply equipment by the system according to claim 1.