JP2005352614A - System, method, and program for energy cost evaluation - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To find analytic information on an ES merit frequency distribution etc., on which uncertainty such as future fuel consumption is reflected to decide on a proper fixed strategy (fixed unit price, fixed period, and fixed quantity) on the basis of a quantitative fact when an influence of a fuel unit price variation on an ES merit is stabilized by a fuel swap. <P>SOLUTION: In the present invention, an energy cost evaluation system which evaluates data regarding an energy cost is equipped with an energy cost calculating means of calculating data regarding an energy cost within an evaluation period as a frequency distribution on the basis of data regarding a fuel price and user's energy consumption. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an energy cost evaluation system, an energy system evaluation method, and an energy cost evaluation program for evaluating energy costs.

  A part of the energy cost reduction (hereinafter referred to as “ES merit”) obtained from energy saving measures by undertaking energy saving plans, equipment design, equipment introduction, operation and maintenance, etc. in energy saving measures at factories and buildings. The energy service business (hereinafter referred to as “ES business”) is being developed. As a form of this ES business, there is a form in which an ES company installs the installed equipment as its own asset and collects the equipment cost as a service fee from a part of the ES merit. The contract period is generally as long as 10 years. When using such an ES business, the end user pays a part of the ES merit as a service fee to the ES operator every year after the start of operation, instead of the initial investment, and the rest is the final merit Will enjoy as.

  While energy saving measures will reduce the amount of electricity purchased from electric power companies and reduce existing boiler fuel, etc., the fuel of the equipment introduced as a result of energy saving measures will newly increase, and the result of offsetting these will be ES benefits The ES merit varies depending on the unit price of the fuel for the refurbished equipment. In other words, in extreme cases such as past oil shocks, it is impossible to deny the possibility that annual ES merits will disappear.

  As a means for hedging the risk that such fluctuations in fuel unit price have on ES merit, there are fuel derivatives (particularly fuel swaps) which are financial means.

  For example, JCC swaps that trade fluctuations are traded using monthly customs clearance statistics (hereinafter referred to as JCC) as the crude oil import unit price. This is the clearing of the difference between the fixed unit price agreed in advance and the fluctuating JCC unit price between the financial institution and the swap user by the contracted amount (hereinafter referred to as “fixed quantity”) during the swap contract period. To do. If the JCC unit price is higher than the fixed unit price, the financial institution pays the swap user the difference multiplied by the fixed quantity. Conversely, if the JCC unit price is lower than the fixed unit price, the fixed quantity is added to the difference. The swap user pays the multiplied amount to the financial institution.

  From the perspective of swap users, when purchasing fuel separately, fuel costs are paid to the fuel supplier at the unit price of the market. If fluctuations in the unit price of the market are strongly correlated with changes in the unit price of the JCC (actually, A heavy oil In this case, the correlation is large.) By combining with liquidation with financial institutions, fuel is procured at a fixed unit price for the fixed quantity. The above is the mechanism of fuel cost fixation by fuel swap.

  The structure of fuel derivatives can be found in the following literature, for example.

The 6868th JPI Special Research Forum “Current Status and Prospects of Risk Management and Derivatives Trading Markets for Deregulation in the Energy Sector”

The problem to be solved by the present invention is that when stabilizing the influence of fuel unit price fluctuation on ES merit by fuel swap, the following appropriate immobilization strategy (fixed unit price, immobilization period, immobilization quantity) Therefore, it is necessary to obtain analytical information such as ES merit frequency distribution that reflects uncertainties such as future fuel consumption.
(1) Even if the market price of fuel becomes lower than the fixed unit price after fixation, it is necessary to purchase the fixed unit price that is high. The fixed unit price can also change depending on the financial market, but in view of the fixed unit price, I would like to know if it should be contracted now, or if it is better to wait a little longer until the fixed unit price falls.
(2) If the total fuel consumption used in the energy-saving equipment is fixed, the fuel cost is completely fixed, but the amount paid by the electric power company, which is another parameter that determines the ES merit by the energy-saving measures, also varies with the fuel cost. Therefore, this fluctuation remains when viewed as ES merit. I want to know the appropriate immobilization quantity where ES merit is most stable.
(3) Depending on the end user or ES provider, there may be a need to expect a merit when the fuel market unit price declines in the future, assuming that the unit price is not fixed completely, but a certain amount of risk is borne. During the period when the equipment utilization rate is high (ie, fuel consumption and power consumption are large), the impact of fluctuations in the unit price of the fuel market is large, so we want to set a higher fixation rate to minimize the risk. The period during which fuel consumption is expected to be low (that is, low fuel consumption and low power consumption) is that the risk of a decrease in ES merit is small. I want to know the appropriate immobilization strategy (fixed unit price, immobilization period, immobilization quantity) according to the risk return strategy of end users and ES providers.

  One feature of the present invention is an energy cost evaluation system for evaluating data relating to energy costs, a fuel price database storing data relating to fuel prices, an energy consumption database storing data relating to user energy consumption, and fuel prices. Energy cost calculating means for calculating data relating to energy costs within the evaluation period as a frequency distribution based on data relating to fuel prices stored in the database and energy consumption of users stored in the database, and energy cost calculating means It is assumed that an energy cost display means for displaying data on the energy cost calculated by the above is provided.

  The other features of the present invention will be described in detail in the section of the best mode for carrying out the invention.

  According to the present invention, since the ES merit can be grasped quantitatively, an appropriate energy cost reduction plan can be created.

  Examples of the present invention will be described below.

  In factories and buildings, a large amount of energy (electricity and heat) is consumed to operate production equipment, air conditioning equipment, lighting equipment, office automation equipment, and the like. In general, each business site procures the necessary energy through the following measures.

・ Procure power from the power company through the power grid.
・ Electricity, steam and cold / hot water will be generated by installing power generation equipment, boiler equipment and refrigerators on the premises.
・ Provide fuel from gas companies and oil companies to operate the facilities described in the previous section.

  Many businesses are actively engaged in energy conservation for the purpose of environmental measures and energy cost reduction. For example, the introduction of high-efficiency equipment, the introduction of fuel to generate electricity from the power generation equipment, and the exhaust heat at that time can be recovered as steam or hot water for use in absorption chillers and boiler surplus heat. Can be mentioned.

  In such energy saving measures, energy consumption is reduced, energy costs are reduced, and this is used for collecting equipment introduction costs for energy saving measures.

  In recent years, we have undertaken energy-saving plans, equipment design, equipment introduction, operation and maintenance, etc., and receive a part of the energy cost reduction amount (hereinafter referred to as “ES merit”) obtained by energy-saving measures as a success fee. The energy service business (hereinafter referred to as “ES business”) is also popular. As a form of the ES business, there is also a form in which the ES company installs the installed equipment as its own asset and collects the equipment cost as a service fee from a part of the ES merit. The contract period is generally as long as 10 years. When using such an ES business, the end user pays a part of the ES merit as a service fee to the ES operator every year after the start of operation, instead of the initial investment, and the rest is the final merit Will enjoy as.

  From the above, in implementing energy saving measures, whether or not ES benefits are achieved as planned is an important issue for both end users and ES providers. In particular, in the case of a business that is based on long-term economic efficiency such as 10 years in order to recover initial investment with energy saving measures that involve equipment renewal, ES benefits need to be achieved as planned over a long period of time. is there.

  For example, in the case of energy-saving measures that introduce cogeneration facilities (hereinafter referred to as “CGS”) that generate electricity and heat at the same time, fuel is newly consumed by CGS, but the amount of electricity purchased from electric power companies by CGS power generation is reduced. , Total energy consumption will be reduced by reducing fuel in existing equipment such as boilers by recovering CGS exhaust heat.

  The ES merit at this time can be roughly expressed by the following equation. However, in order to simplify the description, it is assumed that the fuel reduced by the introduction of CGS and the fuel of CGS are the same type. Even when multiple types of fuel are considered, the same can be considered in the following by adding the last term for each fuel type. In addition, since the influence of the unit price of fuel on the amount of energy cost reduction is discussed here, CGS maintenance costs that are not essential in this respect are not specified.

(Equation 1)
ESmerit = ΔKWC + ΔKWHC-ΔFC

(Equation 2)
ΔKWC = (KW_before−KW_after) × KW_rate × 12

(Equation 3)
ΔKWHC = (KWH_before−KWH_after) × ER_B

(Equation 4)
ΔFC = (FUEL_before−FUEL_after) × FUEL_rate
here,
ESmerit: Annual ES merits of energy costs due to energy saving measures ΔKWC: Annual reduction of electric power company basic charges due to energy saving measures ΔKWHC: Annual reductions of electric power company usage fees due to energy saving measures ΔFC: Annual increase in fuel costs due to energy saving measures
KW_before: Electric power company contract power before energy saving measures
KW_after: Electric power company contract power after energy saving measures
KW_rate: Unit price per unit power (basic unit price) charged according to the contracted power of the power company
KWH_before: Annual electricity purchased from the power company before energy conservation measures
KWH_after: Annual power purchased from power companies after energy conservation measures
KWH_rate: Unit price per unit amount of electricity charged according to the amount of electricity purchased from the power company (unit-rate unit price)
FUEL_before: Annual fuel consumption before energy saving measures
FUEL_after: Annual fuel consumption after energy saving measures
FUEL_rate: Unit price per unit amount of fuel

  According to actual energy-saving measures, the volume of the annual fuel cost increase ΔFC is large, and there are many cases where the annual ES merit ESmerit fluctuates greatly due to fluctuations in the fuel unit price FULE_rate. Actually, the unit price of fuel shows that the highest price is about 1.5 times the lowest price in the past 10 years of actual data, that is, the unit price fluctuates by about 20% from the central value. As a result, the annual ES merit ESmerit fluctuates greatly. In extreme cases, such as past oil shocks, the possibility that the annual ES merit ESmerit will disappear cannot be denied.

  As a means for hedging the risk that such fluctuations in fuel unit price have on ES merit, there are fuel derivatives (particularly fuel swaps) which are financial means.

  For example, JCC swaps that trade fluctuations are traded using monthly customs clearance statistics (hereinafter referred to as JCC) as the crude oil import unit price. This is the clearing of the difference between the fixed unit price agreed in advance and the fluctuating JCC unit price between the financial institution and the swap user by the contracted amount (hereinafter referred to as “fixed quantity”) during the swap contract period. To do. If the JCC unit price is higher than the fixed unit price, the financial institution pays the swap user the amount obtained by multiplying the difference by the fixed quantity. Conversely, if the JCC unit price is lower than the fixed unit price, the fixed quantity is added to the difference. The swap user pays the multiplied amount to the financial institution.

  From the perspective of swap users, when purchasing fuel separately, fuel costs are paid to the fuel supplier at the unit price of the market. If fluctuations in the unit price of the market are strongly correlated with changes in the unit price of the JCC (actually, A heavy oil In this case, the correlation is large.) By combining with liquidation with financial institutions, fuel is procured at a fixed unit price for the fixed quantity. The above is the mechanism of fuel cost fixation by fuel swap.

  The problem to be solved by the present invention is that when stabilizing the influence of fuel unit price fluctuation on ES merit by fuel swap, the following appropriate immobilization strategy (fixed unit price, immobilization period, immobilization quantity) Therefore, it is necessary to obtain analytical information such as ES merit frequency distribution that reflects uncertainties such as future fuel consumption.

(1) Even if the market price of the fuel becomes cheaper than the fixed unit price after fixation, it is necessary to purchase at a fixed unit price that is high. The fixed unit price can also change depending on the financial market, but in light of the fixed unit price, I would like to know if it should be contracted now, or if it is better to wait for a while until the fixed unit price falls.
(2) If the total fuel consumption used in the energy-saving equipment is fixed, the fuel cost is completely fixed, but the electric power company, which is another parameter that determines ES merits by energy-saving measures, also varies depending on the fuel cost. As the ES merit, this fluctuation remains. I want to know the appropriate immobilization quantity where ES merit is most stable.
(3) Depending on the end user or ES provider, there may be a need to expect a merit when the unit price of the fuel market declines in the future, assuming that the unit price is not fixed, but a certain level of risk is borne. The period when the equipment utilization rate is expected to be high (ie, fuel consumption and power consumption is large) is largely affected by fluctuations in the unit price of the fuel market, so we want to set a higher fixation rate in order to minimize risks. The period during which the rate is expected to be low (ie, fuel consumption and power consumption is low) is small because the risk of a decrease in ES merit is small. We want to know the appropriate immobilization strategy (fixed unit price, immobilization period, immobilization quantity) according to the risk return strategy of end users and ES providers.

  In order to solve these problems, the present invention provides means for simulating the total ES merit within a certain evaluation period, including the following means. Below, what uses this means is called a user.

(1) A lot of situation fluctuation patterns such as unit price of fuel market price during the evaluation period, consumption of end user fuel consumption and power consumption, unit price of electricity, etc. are generated according to user setting conditions, and ES merit at that time Means to find the frequency distribution of.
(2) Means for comparing the ES merit frequency distribution when the immobilization strategy is changed.
(3) Means for the user to set conditions for generating the situation variation pattern.
(4) Means for setting risk return constraints that specify conditions to be satisfied by the ES merit frequency distribution in advance and determining whether the ES merit frequency distribution obtained in (1) and (2) satisfies this.
(5) A means for displaying the ES merit frequency distribution satisfying the risk return constraint in an overlapping manner, or displaying the situation variation pattern generation status.

  According to the simulation means of the present invention, a fixed pattern (a fixed unit price, a fixed time, a fixed time) is assumed by assuming a variation pattern of various factors affecting the ES merit such as a fuel market price, energy consumption, and electricity cost. The characteristic of the change in ES merit when changing the (quantized quantity) can be quantitatively grasped as the change in the ES merit frequency distribution, so it is appropriate according to the user's risk return strategy without overestimating the risk. It is possible to formulate a simple immobilization strategy.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

  FIG. 1 is an overall functional block diagram of an ES merit analysis system according to an embodiment of the present invention.

The ES merit analysis system includes an ES merit analysis server 1 and an input / output device 8.
The ES merit analysis server 1 includes a performance data input unit 2, a performance database (hereinafter referred to as DB) management unit 3, an energy consumption DB 301, a fuel market unit price DB 302, a fuel fixed unit price DB 303, a power unit price DB 304, and an analysis condition setting unit 4. , ES merit variation analysis unit 5, calculation pattern DB management unit 6, situation variation pattern DB601, fuel unit price fixed pattern DB
602 and an analysis result presentation unit 7. Furthermore, the analysis condition setting unit 4 includes a situation variation pattern generation condition setting unit 401, a fuel unit price fixing pattern generation condition setting unit 402, and a risk return constraint setting unit 403. The ES merit variation analysis unit 5 is a situation variation pattern generation unit 501. The fuel unit price fixing pattern generation unit 502 and the ES merit distribution calculation unit 503, and the analysis result presentation unit 7 includes an ES merit fluctuation analysis result presentation unit 701 and a scenario generation status presentation unit 702. The input / output device 8 is means for inputting data to the ES merit analysis server 1 and displaying the analysis result.

  Although details of each block constituting the ES merit analysis server 1 will be described later, an outline of the overall processing will be described with reference to FIG.

  The actual data input unit 2 receives the past actual values of the energy consumption, fuel market unit price, fixed fuel unit price, and electric unit price of the end user input via the input / output device 8 via the actual DB management unit 3, respectively. The energy consumption DB 301, the fuel market unit price DB 302, the fuel fixed unit price DB 303, and the power unit price DB 304 are stored.

  The analysis condition setting unit 4 captures and sets a situation variation pattern generation condition, a fuel unit price fixing pattern generation condition, and a risk return constraint from the user via the input / output device 8 as calculation conditions for calculating the ES merit fluctuation distribution. The block corresponding to each of them is a situation variation pattern generation condition setting unit 401, a fuel unit price fixed pattern generation condition setting unit 402, and a risk return constraint setting unit 403.

First, the ES merit fluctuation analysis unit 5 generates a plurality of situation fluctuation patterns and fuel unit price fixing patterns according to the calculation conditions given from the analysis condition setting unit 4, and each of the situation fluctuation patterns via the calculation pattern DB management unit 6. It stores in DB601 and fuel unit price fixed rate pattern DB. Next, one fuel unit price fixing pattern is selected, the ES merit for all the situation variation patterns is calculated to obtain the frequency distribution, and this is repeated for all the fuel unit price fixing patterns. These ES merit distributions are passed to the analysis result presentation unit 7 as analysis results.

  The analysis result presentation unit 7 first acquires the ES merit distribution for each immobilization pattern from the ES merit variation analysis unit 5 and simultaneously fixes the generated situation variation pattern and fuel unit price via the calculation pattern DB management unit 6. The conversion patterns are acquired from the situation variation pattern DB 601 and the fuel unit price fixed pattern DB 602, respectively. Then, the difference in the ES merit distribution due to the fixed pattern, the situation variation pattern generation situation, and the like are processed into a graph or the like and displayed and output to the user via the input / output device 8.

  The performance DB management unit 3 stores and retrieves data of the energy consumption DB 301, the fuel market unit price DB 302, the fuel fixed unit price DB 303, and the power unit price DB 304 in response to requests from other functional blocks. The calculation pattern DB management unit 6 stores and retrieves data of the situation variation pattern DB 601 and the fuel unit price fixed pattern DB 602 in response to requests from other functional blocks. The calculation result DB management unit stores and retrieves data of the ES merit distribution calculation result DB in response to requests from other functional blocks.

  Next, details of the processing of each unit will be described. The actual data input unit 2 inputs the past actual values of the end user's energy consumption, fuel market unit price, fuel fixed unit price, and electric unit price input via the input / output device 8 via the actual DB management unit 3. The energy consumption DB 301, the fuel market unit price DB 302, the fuel fixed unit price DB 303, and the power unit price DB 304 are stored. FIG. 3 shows each data item. Fig. 3 (1) exemplifies fuel, in-house power generation, and power purchase from an electric power company. Fig. 3 (2) and (3) also show typical examples. . In Fig. 3 (4), the unit price of electricity relates to the purchase of electricity from the power company. The basic unit price is the unit price per kW of the basic charge of the power company, the metered charge is the unit price per kWh of the metered charge, and the total unit price is the basic charge. The unit price is simply the total of the metered charges divided by the amount of electricity purchased.

  Next, processing of each block of the analysis condition setting unit 4 will be described. The situation variation pattern generation condition setting unit 401 accepts basic trend data and volatility data input from the user via the input / output unit and passes them to the situation variation pattern generation unit 501. FIG. 4 (1) shows an example in the case of fuel market unit price. The basic trend data gives a reference value of the fuel unit price for each month within the evaluation period, and the volatility data shows the expected value of fluctuation of the fuel market unit price per month. Further, as a user setting support, a past actual value is extracted from the fuel market unit price DB 302 via the actual DB management unit 3, and past trend data, frequency distribution of fluctuation width per unit time, etc. are output to the input / output device 8. May be. The same applies to items other than fuel unit price.

  The fuel unit price fixing pattern generation condition setting unit 402 receives the upper and lower limit data of the initial value and the upper and lower limit data of the gradient input from the user via the input / output unit, and passes them to the fuel unit price fixing pattern generation unit 502. An example is shown in FIG. The upper and lower limit data of the initial value limits the range of the initial fixed quantity within the evaluation period for the generated fixed pattern, and the upper and lower limit data of the gradient is the range of the monthly increase and decrease amount for the generated fixed pattern Is limited.

  The risk / return constraint setting unit 403 receives reference point data, ES merit upper / lower limit data, and average value maximization flag data input from the user via the input / output unit, and passes them to the analysis result presentation unit 7. The reference point data and the ES merit upper / lower limit data give conditions to be satisfied by the ES merit distribution, and are used to exclude immobilization patterns that do not satisfy this condition as inappropriate by the analysis result presentation unit 7. Is for obtaining an immobilization pattern that gives an ES merit distribution with the maximum average value. In the example of FIG. 4 (3), a condition is given that the ES merit at the lower 5% in the ES merit distribution falls within the range of the upper and lower limits of the ES merit. If the ES merit upper limit data is set to none, it is assumed that the ES merit at the reference point is at least equal to or greater than the merit lower limit value. If the ES merit lower limit data is set to none at the same time, it is assumed that there is no restriction on the ES merit at the reference point. Furthermore, when the average value maximization flag data is set to 1, it is assumed that the ES merit distribution satisfying the ES merit constraint at the reference point is extracted with the maximum average value.

  For example, in the case of a fuel market unit price, the situation fluctuation pattern generation unit 501 uses the basic trend data and volatility data provided from the fuel market unit price fluctuation pattern generation condition setting unit to generate a probability pattern of the fuel market unit price within the evaluation period. Generate automatically. Specifically, the value generated stochastically according to the normal distribution with the volatility data as the standard deviation is added to the fuel market unit price reference value for the month set in the basic trend data, and this is calculated within the evaluation period. The fuel unit price for each month. Thus, one fuel market unit price fluctuation pattern is generated. Repeatedly, a large number of fuel market unit price fluctuation patterns are generated and stored in the situation fluctuation pattern DB 601 via the calculation pattern DB management unit 6. The same applies to items other than fuel unit price.

The fuel unit price fixing pattern generation unit 502 generates an initial value stochastically within the range of the initial value upper and lower limit data given from the fuel unit price fixing pattern generation condition setting unit 402, and within the range of the gradient upper and lower limit data. Probabilistically generate gradient values. Then, the fixed quantity for each month in the evaluation period is set as the quantity with the initial value for the first month, and as the quantity with the slope value added to the previous month after the next month. Is generated. The following is repeated to generate a large number of fuel unit price fixing patterns, and the calculation pattern DB management unit 6 is used to solve the fuel unit price fixing pattern DB.
Stored in 602.

  The ES merit distribution calculation unit 503 performs the following processing. First, one fixed pattern is extracted from the fuel unit price fixed pattern DB 602 via the calculation pattern DB management unit 6. Then, for all the situation variation patterns stored in the situation variation pattern DB 601, the ES merit when combined with the fixed pattern extracted earlier is calculated, and the frequency distribution is created by collecting these ES merits. The frequency distribution obtained here is the ES merit distribution when one selected immobilization pattern is applied. Similarly, ES merit distributions are created for all the immobilization patterns, and these are passed to the ES merit fluctuation analysis result presentation unit 701.

  The ES merit fluctuation analysis result presentation unit 701 receives the ES merit distribution for each immobilization pattern from the ES merit distribution calculation unit 503, graphs the difference of the ES merit distribution depending on the immobilization pattern, and outputs it to the input / output device 8. Output. For example, as shown in FIG. 5, when the ES merit distributions for each immobilization pattern are displayed in an overlapping manner, the immobilization pattern 3 has a higher average value than the immobilization pattern 2, but the ES merit at the reference point is low. Can be clearly understood. If the user wants to secure the ES merit above the ES merit lower limit value in FIG. 5, the immobilization pattern 2 that clears the ES merit lower limit is selected from the immobilization pattern 3 having a large expected value. In addition, by displaying only the ES merit distribution that satisfies the conditions of the reference point data and ES merit upper and lower limit data received from the risk return constraint setting unit 403, it may be possible to support the user's fixed pattern selection work. . Further, when the average value maximization flag data received from the risk / return constraint setting unit 403 is 1, the fixed pattern having the maximum ES merit average value is extracted and displayed separately, so that the risk return concept of the user can be improved. It is possible to extract and present an optimal immobilization pattern along the line.

  The scenario generation status presentation unit 702 graphs the generation status of the status variation pattern and the fuel unit price fixing pattern targeted in the ES merit variation analysis, and outputs the graph to the input / output device 8. For example, as shown in FIG. 6, it is conceivable that among the generated situation variation patterns, a pattern having the maximum value of the last month of the evaluation period, an average pattern, and a minimum pattern are superimposed and displayed. Thereby, it is possible to clearly confirm whether or not the generation state of the situation variation pattern designated by the user matches the user's intention.

  According to the simulation means shown in the embodiment of the present invention, a fixed pattern (fixed unit price) is assumed assuming a variation pattern of various factors affecting the ES merit such as fuel market price, energy consumption, and electricity bill. , Immobilization time, immobilization quantity) can change the ES merit change characteristics quantitatively as the ES merit frequency distribution change, so the user's risk without overestimating the risk It becomes possible to formulate an appropriate immobilization strategy according to the return strategy.

It is a whole functional block diagram of ES merit analysis system. It is explanatory drawing of the whole processing flow of ES merit analysis system. It is explanatory drawing of the data item managed as various performance data. It is explanatory drawing of the setting item of the situation fluctuation pattern generation condition setting part 401. FIG. It is explanatory drawing of the example of a display of the ES merit fluctuation analysis result presentation part 701. It is explanatory drawing of the example of a display of the scenario production | generation condition presentation part 702.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... ES merit analysis server, 2 ... Performance data input part, 3 ... Performance DB management part, 301 ... Energy consumption DB, 302 ... Fuel market unit price DB, 303 ... Fuel fixed unit price DB, 304 ... Electric power unit price DB, 4 ... Analysis condition setting unit 401 ... Situation variation pattern generation condition setting unit 402 ... Fuel unit price fixing pattern generation condition setting unit 403 ... Risk return constraint setting unit 5 ...
ES merit fluctuation analysis unit, 501 ... situation fluctuation pattern generation unit, 502 ... fuel unit price fixed pattern generation unit, 503 ... ES merit distribution calculation unit, 6 ... calculation pattern DB management unit, 601 ... situation fluctuation pattern DB, 602 ... Fuel unit price fixed pattern DB, 7 ... analysis result presentation unit,
701 ... ES merit fluctuation analysis result presentation unit, 702 ... Scenario generation status presentation unit, 8 ... I / O device.

Claims (12)

In an energy cost evaluation system that evaluates data related to energy costs,
A fuel price database that stores data on fuel prices;
An energy consumption database that stores data on user energy consumption;
Energy cost calculation means for calculating data relating to the energy cost within the evaluation period as a frequency distribution based on the data relating to the fuel price stored in the fuel price database and the energy consumption of the user stored in the energy consumption database;
An energy cost evaluation system comprising: energy cost display means for displaying data relating to the energy cost calculated by the energy cost calculation means. The fuel fixed unit price input means for inputting data relating to the fixed unit price of the fuel purchase price within the evaluation period according to claim 1,
A fuel fixed unit price database for storing data relating to a fixed unit price of the fuel purchase price within the evaluation period input by the fuel fixed unit price input means,
The energy cost calculated by the energy cost calculation means is calculated for each fixed unit price based on a fixed unit price of the fuel purchase price within the evaluation period read from the fuel fixed unit price database. Evaluation system. In Claim 1, the data regarding the fuel price stored in the fuel price database is data regarding the predicted value of the fuel price within the evaluation period,
The energy cost evaluation system characterized in that the data related to the user's energy consumption stored in the user's energy consumption database is data related to a predicted value of data related to the user's energy consumption within the evaluation period. In an energy cost evaluation system that evaluates data related to energy costs,
A fuel price performance database that stores data on past fuel prices;
An energy consumption performance database that stores data on energy consumption of past users;
Based on the data on the past fuel price stored in the fuel price record database and the energy consumption of the past user stored in the energy consumption record database, the fluctuation pattern of the fuel price or energy consumption within the evaluation period Fluctuation pattern generating means to generate,
Energy cost calculation means characterized in that data on energy costs within the evaluation period is calculated as a frequency distribution based on the fluctuation pattern of the fuel price or energy consumption within the evaluation period generated by the fluctuation pattern generation means;
An energy cost evaluation support system comprising energy cost display means for displaying data relating to the energy cost calculated by the energy cost calculation means. In an energy cost evaluation method for evaluating data on energy costs,
An energy cost calculation procedure for calculating, as a frequency distribution, data relating to energy costs within an evaluation period based on data relating to fuel prices stored in the fuel price database and data relating to user energy consumption stored in the energy consumption database;
The energy cost evaluation method characterized by including the energy cost display procedure which displays the data regarding the energy cost calculated by the said energy cost calculation means on a display means. A fuel price database that stores data on fuel prices on a computer; and
An energy consumption database that stores data on user energy consumption;
Energy cost calculation means for calculating data relating to the energy cost within the evaluation period as a frequency distribution based on the data relating to the fuel price stored in the fuel price database and the energy consumption of the user stored in the energy consumption database;
An energy cost evaluation program that functions as energy cost display means for displaying data related to energy costs calculated by the energy cost calculation means. In the energy saving merit analysis system that predicts and calculates the amount of energy cost reduction due to energy saving measures,
An evaluation period specifying means for specifying a period for evaluating the energy cost reduction amount;
Within the evaluation period, the unit price of fuel for energy-saving equipment to be improved, the unit price of electricity purchased when purchasing power from electric power companies, the consumption of utilities including fuel consumption and power consumption, and the unit price of fuel are fixed. A situation variation pattern generating means for generating a variation pattern of
An energy saving merit calculation means for calculating an energy cost reduction amount within an evaluation period for the situation fluctuation pattern generated by the situation fluctuation pattern generation means;
Multiple situation fluctuation patterns are generated by the situation fluctuation pattern generation means, the energy cost reduction amount within the evaluation period for each situation fluctuation pattern is calculated by the energy saving merit calculation means, and the energy cost reduction within the evaluation period for all the generated situation fluctuation patterns is calculated. An energy saving merit analysis system comprising energy saving merit distribution calculation means for calculating the amount as a frequency distribution. In the energy-saving merit analysis system of Claim 7,
A fuel unit price fixing pattern generating means for specifying a quantity to be replaced with a fixed unit price regardless of the fuel market unit price fluctuation pattern among the monthly fuel consumption when calculating the energy cost reduction amount,
An energy saving merit analysis system, wherein the energy saving merit calculation means replaces the unit price of the fixed quantity for each month specified by the fuel unit price fixing pattern generation means with a specified fixed unit price. In the energy-saving merit analysis system of Claim 7,
A basic trend specifying means for specifying a fluctuation pattern of a reference value when generating a situation fluctuation pattern, and a volatility specifying means for specifying an expected value of a fluctuation width per unit time,
The situation fluctuation pattern generation means adds the value generated stochastically according to the normal distribution with the standard value specified by the volatility designation means to the standard value specified by the basic trend designation means, and the situation fluctuation pattern Energy-saving merit analysis system characterized by generating In the energy-saving merit analysis system of Claim 8,
Energy saving merit fluctuation that generates multiple immobilization patterns by the fuel unit price immobilization pattern generation means, calculates the energy saving merit distribution for each immobilization pattern, and outputs a frequency distribution that superimposes the energy saving merit distribution of all the immobilization patterns An energy saving merit analysis system comprising an analysis result presentation means. In the energy-saving merit analysis system of Claim 8,
Fuel unit price fixing pattern generation condition specifying means for specifying the upper and lower limit values of the initial value of the fixed quantity and the upper and lower limits of the change gradient when generating the fuel unit price fixing pattern,
A fuel unit price fixing pattern generation unit that probabilistically generates a plurality of fuel unit price fixing patterns that satisfy the conditions specified by the fuel unit price fixing pattern generation condition specifying unit;
For the energy saving merit frequency distribution calculated for each fuel unit price fixed pattern, the conditions that the frequency distribution should satisfy are the ratio below the reference point, the upper and lower limit values that the energy cost reduction amount at the reference point should satisfy, and multiple A risk return constraint specifying means for specifying whether or not to extract the average value from the energy saving merit frequency distribution;
An energy saving merit analysis system comprising an energy saving merit fluctuation analysis result presentation means for selecting a fuel unit price fixing pattern that satisfies a condition specified by a risk return constraint specifying means and an energy saving merit frequency distribution thereof. In the energy-saving merit analysis system of Claim 7,
An energy saving merit analysis system comprising scenario generation status presentation means for outputting a plurality of status fluctuation patterns generated by status fluctuation pattern generation means in a superimposed manner.

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