KR100759756B1 - System for demand side management evaluation in the electric power industry through internet - Google Patents

Abstract

Electric power demand management evaluation system through the Internet for facilitating the establishment of energy supply and demand policy by establishing a database and rational analysis system for demand management evaluation and enabling users to easily evaluate demand management through the Internet. This is provided.

Demand management evaluation system through the Internet of the present invention, DB server for classifying, storing and managing the existing statistical data on the power demand for each period, load device type and customer type; Based on the performance data managed by the DB server, the user is provided with access through the network to the authenticated user, and based on the performance data managed by the DB server, the amount of power and load for each predetermined power consumption unit during the reference period are calculated. A baseline demand forecasting module that provides an estimate of the demand for demand, a potential calculation module that provides the potential for each of the energy savings and load savings that can be achieved when a given load is replaced by a DSM device, and the incremental cost of the alternative power. Or an evasion cost estimating module for estimating the cost for the additional supply of electric power by the power supply company, and a cost for providing an evaluation result of the economics of the predetermined demand management program in consideration of the reference demand, the potential amount and the evading cost. It includes a web server including an effect analysis module.

 Demand forecasting, power, energy, resource assessment, potential, DSM, demand management assessment, cost effectiveness, avoidance costs

Description

Power demand management evaluation system through the Internet {SYSTEM FOR DEMAND SIDE MANAGEMENT EVALUATION IN THE ELECTRIC POWER INDUSTRY THROUGH INTERNET}

1 illustrates a configuration of a demand management evaluation system 100 according to a preferred embodiment of the present invention.

2 shows the configuration of the web server 20 of the present invention.

3 illustrates a user interface screen for reference demand prediction.

Figure 4 shows the flow of the method of calculating the total number of households in the market unit of the incandescent lamp among residential lighting of the reference year.

5 illustrates a flow of a method for calculating a reference device diffusion rate.

6 illustrates a flow of a method for calculating unit power consumption (UEC) per household of a reference device.

7-14 illustrate user interface screens provided by the reference demand forecasting module.

15 illustrates a user interface screen provided by the latent amount calculation module to calculate the latent amount based on the above-described standard demand forecasting result.

16 to 19 are diagrams illustrating the flow of the latent amount calculation method. This section describes the procedure for estimating potential based on residential end-use.

20 to 24 illustrate a user interface screen provided by the latent amount calculation module 14 of the web server 20 of FIG. 2.

25 to 27 illustrate the flow of the method for calculating the avoidance cost.

28 to 31 illustrate screens provided by the avoidance cost estimation module 16.

32 illustrates a cost effectiveness analysis procedure performed by the cost effectiveness analysis module 18 in the web server 20 of FIG.

33 through 35 illustrate screens of a user interface provided by the cost effectiveness analysis module 18 in the web server 20 of FIG. 2.

The present invention relates to a power demand management evaluation system, and more particularly, to accurately predict the power demand to enable the establishment of an uninterrupted energy supply and demand plan, and to improve the energy efficiency of the power system through rational resource evaluation. To provide a power demand management evaluation system and method.

The power system consists of consumers in the industrial, commercial and home sectors, which have different patterns of power consumption, depending on the type of industry and type of residence. In addition, electric devices used in each consumer are gradually being replaced by high efficiency devices according to policy and autonomous choice of consumers.

Therefore, in order to establish a power supply and demand plan that considers the load of the entire power system for a certain period of time, and to ensure a stable supply and demand, it is necessary to secure a database for each customer required for demand forecasting, and to scatter relevant variables. There is a need for a system that can be managed collectively.

In addition, in order to efficiently establish a replacement policy with high-efficiency devices for improving energy efficiency, the socio-economic replacement cost and the cost reduction effect obtained through this should be compared by an accurate model. Metering and evaluation in accordance with alternative policies should be based on a close analysis of the involvement of institutions and consumers.

However, until now, individual investigations by relevant government agencies and policy-making departments have been carried out, so that not only systematic and efficient database management has been carried out, but also that the survey results have not been shared in a timely manner. The situation is not working well. In addition, even in the evaluation process based on the survey results, reliable measurement and evaluation results cannot be achieved due to differences in application models and inaccurate predictions by the host institutions. Therefore, there are many difficulties in establishing a policy for improving energy efficiency. .

The present invention is to overcome the above problems, by establishing a database and a reasonable analysis system for the demand management evaluation, by making it easy for users connected through the Internet to evaluate the demand management, the establishment of energy supply and demand policy It is to provide a power demand management evaluation system through the Internet to facilitate.

In another aspect, the present invention is to provide a potential calculation system that can predict the demand for the reference devices and calculate the potential of the high-efficiency devices that can replace them, and evaluate the effects of the replacement.

It is also an object of the present invention to provide a system for calculating the benefit indicators for individual demand management programs and analyzing their cost effectiveness.

Furthermore, the present invention is intended to improve investment efficiency by providing reliable market information in consideration of investment efficiency to governments, dedicated agencies, power companies, consumers, and related equipment suppliers.

According to a first aspect of the present invention for achieving the above object, a demand management evaluation system through the Internet is a DB for classifying, storing, and managing existing statistical data on power demand for each period, load device type, and customer type. server; Based on the performance data managed by the DB server, the user is provided with access through the network to the authenticated user, and based on the performance data managed by the DB server, the amount of power and load for each predetermined power consumption unit during the reference period are calculated. A baseline demand forecasting module that provides an estimate of the demand for demand, a potential calculation module that provides the potential for each of the energy savings and load savings that can be achieved when a given load is replaced by a DSM device, and the incremental cost of the alternative power. Alternatively, the electricity supplier may provide an evasion cost calculation module for estimating the cost of the additional supply of electric power, and provide an evaluation result of the economics of the predetermined demand management program in consideration of the reference demand, the potential amount, and the avoidance cost. And a web server including a cost effectiveness analysis module.

Here, it is preferable that the statistical data stored in the DB server can be updated at any time by a predetermined authorized statistical institution connected through the Internet.

In addition, the user interface screen provided by the web server, it is preferable to provide a selection menu that allows the user to select one of the standard demand forecast, potential calculation, avoidance cost calculation and cost-effectiveness analysis items.

When the user selects the reference demand forecast item, the user interface screen may provide an initial screen for selecting a user device classification and device technology, a predicted sector of power or load, a market unit for each type, and a forecast period. By the user's selection, the web server requests statistical data related to the item selected by the user from the DB server and provides it through the user interface, so that the user can modify and correct the data as necessary. It is preferable to.

The statistical data provided to the user may include a quantity or a ratio of each item of a market unit for each type during the prediction period selected by the user, power consumption, usage time, usage rate, penetration rate, and the like according to the user device classification and device technology selected by the user. Preferably, the user includes a distribution number, and the user can correct and supplement each item of the data on the screen when the statistical data is inappropriate.

In addition, the web server, the user device classification and device technology selected by the user, the amount of electricity or load forecasting, the market unit by type and the forecast period of each period of the customer unit or each unit of power consumption and the total of each period It may be to output the power usage.

In addition, the web server, in order to predict the penetration rate, it is preferable that the user can select and apply any one of the statistical model of the simple growth rate, regression analysis, Gompertz model, Bass model analysis or Lawrence-Lawton diffusion equation. Do.

In addition, the user interface screen, when the user selects the potential calculation item, it is possible to select the device classification and device technology of the replacement target device to a high-efficiency device, the list of the replacement device and the number of supply per period and It is preferable to present the spread rate by applying one of simple growth rate, regression growth rate, Gompertz model, Bass model analysis, or Lawrence-Lawton diffusion equation.

Further, the user interface screen, it is preferable that the user can use or input power consumption of the high-efficiency replacement device provided by the system as it is,

According to the user's input, the web server may have a weight factor that is a weight factor for considering the ratio of the actual applicable part to the penetration rate of the corresponding device, and the power consumption that is reduced when the corresponding device technology is converted into an efficiency improving device technology. It is preferable to calculate and output the reduction factor, which is the ratio of, and the unfinished coefficient, which is the ratio of the entire application target of the part to which the efficiency-enhancing device technology has not yet been applied.

In addition, the web server is phased in consideration of the technical replacement possibility, such as the maximum technical potential, the number of lifetime of the device and the purchase of new equipment, which is the potential power saving potential when temporarily switching to the DSM technology temporarily by the most efficient technology in the current state. It is preferable to calculate and output the technical potential including the step-by-step technical potential, which is an expected power saving potential when replacing with.

In addition, the web server preferably calculates and outputs an economic potential, which is a power saving potential considering only the DSM technology that is effectively determined when considering the cost among the technical potentials.

Here, when the user selects the evaluating cost calculation item, the user interface screen outputs the avoiding cost for the power generation facility and the transmission and distribution facility by reflecting the socioeconomic index, the facility characteristic index, and the facility and fuel cost index. desirable.

In addition, the user interface screen, if the user selects the cost-effectiveness analysis item, according to the schedule and the contents according to the alternative program to the DSM device based on a predetermined policy or planning plan and the economic benefit of the program and the required It is preferable to calculate and output the cost through the standard demand prediction, the potential amount calculation and the avoidance cost calculation process.

Hereinafter, with reference to the accompanying drawings will be described in detail a preferred embodiment of the present invention.

1 illustrates a configuration of a demand management evaluation system 100 according to a preferred embodiment of the present invention. The illustrated demand management evaluation system provides relevant information through the Internet (1) for all relevant institutions, research, and academia involved in demand management. As shown, the system 100 includes a DB server 10, a web server 20, a firewall, and the like.

DB server 10 is a server system that manages all the analysis data in the demand management evaluation system 100 is a place where a database for demand management evaluation, data collection and management, data analysis and the like is made. Certified organizations and laboratories in charge of statistical processing can update the socio-economic and technical statistics to the database of the system from time to time.

The web server 20 interworks with the DB server 10 and is an important system for connecting to external users and administrators. Every authenticated user who uses the demand management evaluation system 100 provides a user interface for acquiring information and performing analysis. The software of the demand management evaluation system is implemented on a web basis.

2 shows the configuration of the web server 20 of the present invention. Web server 20 of the present invention, the network connection means 17 to perform data transmission and reception via a network, a user interface module 11 for providing an interface screen for input and output of a user, and the user interface module 11 Program modules such as reference demand prediction module 12, potential estimation module 14, avoidance cost estimation module 16, and cost effectiveness analysis module 18, which perform various predictions and calculations according to the user's input. And a DB server interworking module 15 for requesting necessary data in association with a DB server by driving the program modules.

3 illustrates a user interface screen for reference demand prediction. Baseline demand forecasting refers to predicting the total power consumption and peak load of the baseline device technology by end-use by application. The reference demand forecast is the same reference device technology, but approaches such as usage time and concurrent load rates differ from application to application, and the reference units (eg, furniture or area) of the demand forecast are different.

First, in order to forecast baseline demand for residential and business use, it is necessary to first identify relevant data or indicators and establish a database for research, analysis and development. After the establishment of the database, baseline demand forecasting requires the selection of use, end-use and setting of baseline technology. It then identifies the predicted sector (whether it is power or load), and sets the market and supply units. Second, predictions are made from the base year to the end of the period based on the power consumption of the reference technology, time of use (substituting the peak acceptance rate for load prediction), utilization rate and penetration rate. Third, the annual power consumption of the unit is calculated based on the reference technology characteristic index, and finally, the total power consumption (or peak load) of the reference technology is predicted.

Hereinafter, a detailed procedure of the reference demand prediction of the residential incandescent lamp illustrated with reference to FIGS. 4 to 6 will be described. Figure 3 shows the flow of the method of calculating the total number of households in the market unit of the incandescent lamp among residential lighting of the base year. First, in order to calculate the total number of households in the market unit, the residential market units (single-family houses, apartments, townhouses and others) are identified, and the total number of households in the market unit is calculated based on the data on the composition ratio by housing type and the distribution of furniture by housing type.

4 illustrates a flow of a method for calculating a reference device diffusion rate. In the case of the incandescent light bulb, the incandescent light bulbs in the unit market and the lighting equipment composition ratio can be estimated.

5 illustrates a flow of a method of calculating a unit power consumption (UEC) per household of a reference device. The UEC per household of the reference device can be estimated by estimating the UEC per unit of the incandescent light bulb based on the power consumption and the usage time, and by estimating the actual number of households per household in consideration of the number of households and the usage rate.

The above-described calculation is performed through the reference demand prediction module 12 shown in FIG. 2, and the user can select basic preconditions for the prediction in the initial screen as shown in FIG. 3. Users can select end-use device technology, select power and load forecasting segments, and enter market units and forecast periods.

The screen then exemplifies basic statistical data for performing predictions and provides an interface for the user to make corrections to these data as needed. 7-14 illustrate user interface screens provided by the reference demand forecasting module. First, in the screen illustrated in FIG. 7, a market unit composition ratio may be selected, and in the screen illustrated in FIG. 8, it may be determined whether the distribution criterion is based on the availability lake or the number of general households. After the basic data for demand forecasting are established through the above process, the system performs the prediction based on the basic data.

The baseline demand forecasting module provides an interface for the user to check the baseline forecast and then intervene the user's desired estimation process after the system reflection of these basic data.

Through the screens illustrated in FIGS. 9 to 12, the user may more accurately estimate power consumption, usage time, usage rate, and diffusion rate for the device technology. The estimation process is described in more detail based on the diffusion rate illustrated in FIG. 12 as follows. For dissemination rates, the forecasting system provides an interface for applying and modifying dissemination diffusion models, such as simple growth rate, regression analysis, Gompertz model analysis, Bass model analysis, and Lawrence-Lawton diffusion.

If the user wants to perform a Gompertz model analysis, the system pops up the previous year's estimate of the penetration rate. After setting the performance coverage, select OK to perform Gompertz model analysis on the performance, calculate the coefficients for the mimic, innovation, and saturation rates, and provide an interface for user changes if necessary. . If you are satisfied with the coefficients, select OK and the Gompertz model tracking of penetration rate is applied to the actual penetration rate prediction.

After selecting the graph icon on the main screen for the penetration rate estimation, the user interface displays a graph of the estimation results for each model. If you select OK on the diffusion rate estimation screen, all processes are automatically calculated based on the changed estimation data and the result is reflected in the system. The estimation interface for abnormal diffusion rate is similarly applied to power consumption, usage time, and utilization rate menu.

FIG. 13 and FIG. 14 show power consumption per unit and total power consumption of a device or a furniture according to an energy prediction result. When the prediction category is selected as the load, the load indicator can be viewed.

15 illustrates a user interface screen provided by the latent amount calculation module to calculate the latent amount based on the above-described standard demand forecasting result. Potential amount is an index that indicates the amount of energy savings that can be obtained by using a device in place of an existing device. The types of potentials can be divided into technical potentials, economic potentials, maximum reachable potentials, program potentials and naturally occurring potentials. When selecting the corresponding reference demand prediction project that is desired to be calculated in the latent amount calculation initial screen illustrated in FIG. 15, the selected end-use and device technology classification are shown, and the latent amount calculation can be started as a basic input.

Technical potential means the sum of all potentials that can be achieved if all technologies are implemented in an applicable and physically feasible state, for example, can be estimated as follows.

<Technical potential estimation formula>

Technical potential (energy) = (total area or household (m2 / furniture) × reference equipment UEC or EUI (kWh / m2 or household) × applicable factor × incomplete factor × execution factor × saving factor × adjustment factor)

Technical potential (load) = (peak load (KW) × peak reduction rate) × (total area or household (m2 / furniture)

Here, the applicable factor is the floor area of the part where the efficiency-enhancing technology can be applied within a given market environment, or the proportion of households (e.g. for compact fluorescent lamps, the area using incandescent lighting for the entire area in a business office). Ratio (%)). In addition, the runnable coefficient refers to the floor area or the number of households in the part where the efficiency improvement technique can be applied from an engineering point of view. The reduction factor is the ratio of the power consumption that is saved when the standard equipment technology is converted to the efficiency equipment technology. The unfinished coefficient refers to the floor area of the part where the efficiency improvement means is not yet applied or the percentage of furniture, that is, 1 minus the floor area of the part to which the efficiency improvement device technology has already been applied or the ratio of the furniture. Since the part of the improved device technology is applied to the penetration rate of the device, the incomplete coefficient is 1 minus the device penetration rate.

The adjustment factor refers to an adjustment value for an error that occurs during the calculation of the potential amount. The reference device EUI / UEC refers to the unit power consumption (using the rated capacity and annual usage time of the device) of the currently deployed reference device technology.

In addition, the DSM device technology EUI / UEC refers to the amount of power consumption (using the rated capacity of the device for improving efficiency and the annual usage time of the replaceable reference device technology) of the efficiency improving device technology to be distributed.

Economic potential refers to current and future retail electricity rates, current and future generation prices, environmental factors, and potential benefit estimates from reduced supply. In other words, economic potential refers to the technical potential of a cost-effective total energy efficiency improvement technology in consideration of supply-side alternatives or energy prices. The economic potential is an index to take into account because the cost of many energy efficiency technologies is initially higher than standard energy efficiency technologies.

16 to 19 are diagrams illustrating the flow of the latent amount calculation method. This section describes the procedure for estimating potential based on residential end-use.

For the purpose of estimating the potential, the entire household using electricity will be surveyed. To this end, the results of the home appliance and household power consumption survey (KEPCO) is utilized. The households in the market unit (single-family houses, apartments, townhouses, etc.) according to the type of housing among the total residential households are examined. It estimates the number of households by using the composition ratio by type of housing in the building statistics. In addition, the number of households is estimated by household distribution according to the results of the domestic electricity consumption behavior survey (Korea Electric Power Exchange), and the standard equipment technology is selected to investigate the UEC and market share.

Next, the reference device technology is selected based on the DSM target device technology list (for residential lighting). For example, a general 60W incandescent lamp (class 3 or less incandescent lamp), 32mm 40W general fluorescent lamps and magnetic ballast lamps (class 3 or less fluorescent lamps) lamps and the like. Investigate and estimate market share for the selected reference device technology. To this end, the results of incandescent and fluorescent lamps are used in statistical databases such as the total number of household appliances and the number of household appliances in the household power consumption survey (Korea Electric Power Exchange) and the household appliance usage survey (Ministry of Commerce, Industry and Energy). When the proportion of incandescent bulbs with 3,9 million units is about 70% of the sales volume of products below the 3rd grade, the number of incandescent bulbs is estimated to be 2,70,000 units.

Next, the UEC of the reference device technology is calculated. Power consumption per unit of reference device technology, usage time per unit of standard device technology, number of installations of major devices, reference device technology, through power consumption survey by major devices and data provided by manufacturers Estimate the usage rate of each time zone.

As a result, it can be set as UEC per device technology = power consumption per device technology x average annual use time per device technology, UEC per device = UEC per device technology x number of device technology per household x use rate per household.

Next, the selection and market share of the target DSM device technology, UEC, technical feasibility, applicability, reduction rate, incomplete coefficient investigation and estimation are estimated.

Select DSM device technology through DSM target device technology list. For example, a target device technology for replacing an incandescent lamp includes a 26mm 32W high efficiency fluorescent lamp, an electronic ballast, and a lamp having a high efficiency ballast.

Investigate and estimate market share of selected DSM device technologies. As shown in FIG. 16, the distribution level of main household appliances in the household appliance usage survey research (Ministry of Commerce, Industry and Energy) and the sales volume of each grade of the electricity usage survey for electricity demand management (1999) were used. Estimate.

Next, UEC calculation of DSM device technology is performed. As shown in FIG. 17, the saturation degree (1-supply rate) was measured based on the prevalence of the estimated DSM device technology and applied to the number of device technology used per household. As the saturation increases, the number of households used per household decreases and the number of households used increases, thus reducing the technical potential. At this time, the UEC per device technology = power consumption per device technology x the average annual use time per device technology, UEC per device = UEC per device technology x number of device technology use per household x use rate per household.

Next, as illustrated in FIG. 18, the technical feasibility coefficient is estimated by using a database in which coefficients for feasibility are estimated based on data provided by the manufacturer. The technical feasibility factor refers to the ratio of technically not having problems installing DSM device technology, and is divided into stages based on the maturity level of the DSM technology (for example, stage 1 20%, stage 2 40%, stage 3 60%). , Step 4 80%, step 5 100%).

Subsequently, the applicability factor is estimated, which is based on the distribution rate of the estimated reference device technology, which is based on the device technology preference survey. Say).

The reduction rate is then calculated, which is the ratio of power consumption of DSM device technology to reference device technology power consumption. Next, to estimate the incomplete coefficient, 1 is obtained by subtracting the diffusion rate of DSM device technology by the diffusion rate of the reference device technology as a factor. Example: If the CFL's 2003 availability rate is 5% of the total applicable households, then the incomplete factor is 5%), so the incomplete factor is the rate at which DSM equipment technology is to be deployed.

Finally, to estimate the adjustment factor, based on the data provided by the manufacturer, a DB is estimated by estimating the coefficient that can correct the difference between the actual power consumption and the calculated power consumption. For example, if the estimated total residential lighting power consumption is 1000 GWh, but the model is 700 GWh, the adjustment factor is used to match the forecasted consumption and then reflected in the calculation of potential for each device technology.

Through this process, the maximum technical potential (energy) is obtained. To this end, indicators such as the total residential consumer demand forecast, the UEC per base equipment technology consumer forecast, savings forecast, applicable coefficient forecast, incomplete coefficient forecast, viable coefficient forecast, and energy forecast among potential adjustment factors are required. Is defined as the product of

In addition, the maximum technical potential (load) has indicators such as the maximum demand for residential use, the prospect of peak technology acceptance rate of standard equipment, the prospect of savings rate, the prospect of applicable coefficient, the prospect of unfinished coefficient, the feasibility coefficient, the potential adjustment coefficient, and the load forecast. Required and defined as the product of all these variables.

In order to estimate the technical potential for each stage, indicators of DSM device technology life expectancy and DSM device technology penetration forecast are needed. Here, the DSM device technology spread outlook / DSM device technology life span = DSM device technology replacement spread (within device life), defined as DSM device technology replacement / DSM device technology total supply = DSM device technology replacement rate Maximum power saving potential of DSM device technology * Defined as the annual replacement rate of DSM device technology = the technical potential level.

The calculation of economic potential includes indicators such as reference equipment technology installation cost, reference device technology O & M cost, DSM device technology installation cost, DSM device technology O & M cost, avoidance equipment cost, avoidance energy cost, reference device technology life, and DSM device technology life. Is needed. Annual equalization standard equipment technology installation cost + annual equalization standard equipment technology O & M cost = annual equalization standard equipment technology cost, and annual equalization standard equipment technology cost is calculated as the present cost according to the life of DSM equipment technology. In the same way, the present-day cost of DSM device technology is calculated. Here, it is defined as the DSM device technology suspension cost-the reference device technology suspension cost = the DSM device technology replacement cost. Retrofit cost = DSM device technology suspension cost, reference device technology UEC x reduction rate x avoidance energy cost x Defined as suspension coefficient = benefit (energy), reference device technology use power x peak acceptance rate x reduction rate x avoidance facility cost x suspension coefficient = Defined as convenience (load). Defined as Benefit (Energy) + Benefit (Load) = Total Benefit. Defined as gross benefit / cost = TRC ratio, the economic potential is estimated when this indicator is greater than 1, and if there is an economic benefit.

20 to 24 illustrate a user interface screen provided by the latent amount calculation module 14 of the web server 20 of FIG. 2. As shown in FIG. 20, the basic input for calculating the potential amount includes a standard demand prediction for the existing device and a prediction number of DSM device distribution. Estimation of DSM equipment dissemination is implemented with the same interface as estimation of equipment dissemination rate in the standard demand forecasting system, and it can be estimated using diffusion equations such as simple growth rate, regression analysis growth rate, Gompertz model, Bass model, Lawrence-Lawton model.

21 shows the result of calculating the maximum technical potential amount. According to the above-described analytical model, the applicable coefficient, the incomplete coefficient, the feasibility coefficient, etc. are predicted and the maximum technical potential is calculated through this. The user may modify or supplement these coefficients.

22 is a screen showing a result of estimating the prospects for the replacement number in order to calculate the technical potential amount for each step. This estimates the technical potential level.

To estimate economic potential, cost indicators should be calculated and compared with avoided costs. FIG. 23 shows the cost indicator calculation result screen so that the cost indicators for the reference device and the DSM device can be inquired.

After the cost indicators and the avoidance cost calculations are completed, the refining potential can be calculated as shown in FIG. If necessary, the user may repeat the calculation process for each of the above maximum technical potential, staged technical potential, and economic potential.

25 to 27 illustrate the flow of the method for calculating the avoidance cost. Avoidance costs are incremental costs of alternative power or incremental costs incurred when a utility company generates electricity on its own or purchases power from another generator in the absence of power purchases from an accredited facility. The components that comprise are avoided generation cost, avoided transmission and distribution cost, avoided loss cost, and avoided environmental cost. Avoidance generation costs are further divided into avoidance equipment costs and avoidance energy costs.

Fig. 25 illustrates a procedure for estimating the avoidance facility cost. The avoidance cost represents the money saved by the utility to avoid having to build new power generation equipment, which also represents the savings from avoiding the purchase of equipment from other utilities. It can be said that it represents the money you make by selling excess equipment to other utilities.

27 illustrates a procedure for estimating the avoided energy cost. Avoided energy costs are factors that vary with the operation of the power system and include fuel, some of the operating and maintenance costs, and other costs that may vary, such as lime or reagents required for the operation of sulfur dioxide scrubbers.

Fig. 26 illustrates a procedure for estimating avoided transmission and distribution costs. Avoidance transmission and distribution costs represent the construction and maintenance costs of transmission and distribution facilities required as the system expands. Avoidance cost represents the cost derived from the losses incurred in the transmission and distribution stage.

In addition, there are avoided environmental costs, which are inevitable by-products of electricity generation, resulting in air pollution, land-use transformations, water pollution and other pollutants that can damage the environment. Represents the cost of responding to environmental impacts that would change with the participation of new power plants.

* Hereinafter, with reference to FIG. 25, the avoidance generation cost calculation method is demonstrated. The working capital included in the existing calculation method is deleted and the net expected return is regarded as the opportunity cost of the investment. In calculating expected return, we reflected the reality by using the pre-tax utility rate instead of the social discount rate used in the conventional method. In addition, the exclusion of the corporate tax items included in the existing method fully reflected the principle of the CUB method centering on the cost of alternative facilities.

In the model shown, avoided generation cost = annualized net present value of annual cost (actual discount rate applied), annual power generation cost = pensionized facility cost + fixed O & M cost, equalized power generation cost = equipment cost Therefore, we used indicators such as the annualized value (applied to the real power company's discount rate), the unit cost (KRW / KW) = the total construction cost divided by the generation capacity.

In the model for estimating the avoidance transmission / distribution cost shown in FIG. 26, the avoidance transmission / distribution cost = the annualized net present value of the pensionized transmission / distribution cost, and the pensionization transmission / distribution cost = the value of the equipment price equalized annually according to the equipment life (nominal power company discount rate) (Applied), equipment cost = past power distribution equipment incremental cost divided by increment of load.

27 shows a model for estimating the avoidance energy cost. In this case, the fuel cost and environmental cost are converted from the current cost-based calculation method to the fuel cost basis, and the avoided energy cost = the annualized net present value of the annual cost (actual discount rate applied) and the total energy cost (KRW / KWh) = Parameters such as fuel cost + variable O & M cost, fuel cost (won / KWh) = fuel cost (calorie cost, won / Gcal) x heat consumption (thermal efficiency, Kcal / KWh) were used.

28 to 31 illustrate screens provided by the avoidance cost estimation module 16. The input user interface screen for calculating the avoidance cost is illustrated in FIG. 28 and has a structure managed for each project. To calculate the avoidance cost, select the power generation (grenade coal, anthracite coal, heavy oil, LNG, nuclear power), input data on equipment, maintenance cost, finance, tax, working capital, and power generation, and press the result calculation button. The avoidance cost is calculated by the built-in algorithm, and the result screen is displayed. For the avoidance cost calculation, socioeconomic indicators as shown in FIG. 29, facility characteristic indicators as shown in FIG. 30, and facility and fuel cost indicators as shown in FIG. 31 are entered. Requires the following indicators: For equipment, data such as equipment capacity, total construction cost, construction cost unit, operation time point, etc. are required, and for operation maintenance cost, operation maintenance cost (won / kW year) and cost reference year (early year) are required. In addition, economic life period (years), depreciation period (years), depreciation method (flat / definitive method), depreciable assets (%), investment remuneration rate (%), discount rate (%), debt ratio (%) and Financial data such as the interest rate (%) is required, and other costs such as taxes and insurance premiums, working capital and power generation costs, fuel unit price (KRW / kcal), unit price base year (year), and fuel calorific value (kcal / kgl) Data such as average thermal efficiency (%), facility reserve (%), in-house consumption rate (%), transmission loss ratio (%) and distribution loss ratio (%) will be needed.

Through such data, avoidance energy cost, avoidance facility cost, avoidance transmission cost, avoidance distribution cost, avoidance environment cost and the like are calculated and provided through the user interface.

32 illustrates a cost effectiveness analysis procedure performed by the cost effectiveness analysis module 18 in the web server 20 of FIG. The cost effectiveness analysis module 18 is a model developed for cost effectiveness analysis of a given DSM program. The DSM program is a key component of energy-saving electricity use technology or electric equipment, which is generally emerging with technological development, and market access such as institutional devices, incentives, and fee benefits such as regulations and guidelines. The focus is on maximizing the outcomes of the program, as well as dissemination plans.

For the analysis of the DSM program, starting with the investigation and collection of all the DSM device technologies (details and devices) that can be considered at the present time, the technologies that can be applied realistically through consistent and systematic analysis and procedures are provided. Select. The economic effects of the selected DSM program are analyzed through the cost-effectiveness model.

The first step in the cost effectiveness analysis is the analysis of the results of the resource assessment. In other words, after calculating the power saving potential for the DSM device technology, the economic evaluation is conducted through cost-effectiveness test, and the cost effect is analyzed based on the DSM device technology that has passed such economics.

In the case of resource evaluation, economic evaluation is based on the TRC test (total resource test), and cost-effectiveness tests other than the TRC test are conducted as necessary. In fact, by selecting a device technology that can be programmed by DSM, the power saving potential estimated only for cost-effective device technology is an economic potential.

The flow will be described with reference to FIG. 32. First, evasion cost, which is a representative benefit index, is calculated. The basic inputs for estimating avoidance costs are the financial characteristics of the avoidance facilities (construction costs, fixed operating costs, fuel costs, depreciation costs, environmental costs, application discount rates, analysis periods, etc.), and the technical characteristics of the avoidance facilities (power system, life span, Equipment capacity, equipment reserve ratio, loss rate, thermal efficiency, utilization rate), and after evaluating the avoidance cost of power generation and transmission and distribution facilities according to the calculation algorithm, the avoidance energy cost based on fuel cost and environmental cost is calculated. Calculate

Next, the device technology cost is calculated. Based on the predicted number of DSM device technologies, the number of participants is calculated when the actual program is executed. Based on this, the present value of total cost during the analysis period according to the exchange or replacement with the device technology is calculated along with the purchase cost, installation cost, lifetime, discount rate, and power saving power of the DSM device technology.

The next point to consider is management costs, and when the DSM program is implemented, costs are incurred for management and promotion. These administrative costs are only incurred during the implementation of the program and use the present value of the total costs of labor, program administration and promotion.

In addition, the rebate cost is calculated. This is an incentive fee paid to customers participating in the DSM program and is a benefit indicator for participant testing. Use the present value of the total cost paid for the amount of power saved by the DSM program design.

In addition, fee changes are taken into account, and participant tests are used as benefit indicators, just like rebate costs. It is the current price of electricity rates, which decreased due to the decrease in power consumption. It is divided into energy saving part and load saving part.

A cost-effectiveness test is performed based on these data. The cost effectiveness of the program is tested based on the California Standard Practice Manual. The California Standards Assessment Method consists of four tests, Participant Test (P Test), Customer Impact Test (RIM Test), Utility Cost Test (UC Test), and Total Resource Cost Test (TRC Test). If the benefits of the analysis outweigh the costs, the evaluation results are continued by the program.

33 through 35 illustrate screens of a user interface provided by the cost effectiveness analysis module 18 in the web server 20 of FIG. 2. As shown in FIG. 33, the cost effectiveness analysis module 18 performs a cost effectiveness analysis process for each specific project.

The implemented analysis program is generally composed of overview screen, data input screen, and result screen, and each screen is explained using lighting equipment as an example. First, select New from the project screen (not shown), and then select Cost-Effective Analysis-High-Efficiency Lighting Equipment through the wizard of FIG. 33 to enter the initial screen of the Cost-Effectiveness Analysis Program for High-Efficiency Lighting Equipment. Process the basic entries in the fixture initial screen. The basic input section is composed of program name, program type, DSM device technology selection, usage selection, market unit selection, avoidance cost setting, electricity rate, and the DSM device technology selection screen and program type designation screen as an example. And FIG. 35.

As such, upon completion of the item for device technology dissemination, the system provides a cost screen for estimating and confirming the cost. The cost part is largely divided into equipment technology cost, management cost, and rebate cost according to the model described above.

Once the dissemination and cost are estimated, the system performs a final B / C test and outputs the results. This part also performs B / C tests on all inputs according to the logic inside the system built by the model described above. Specifically. The details of rate reduction and B / C test are provided, and the details of rate reduction include items such as rate reduction, total energy reduction, total load reduction, and total rate reduction.

In addition, B / C Test item summarizes cost / benefit by power generation facilities and reports the results of various tests through B / C summary. The test used here focuses on the TRC test and the RIM test.

In the above description of the preferred embodiment of the present invention, the present invention is not limited to the above-described embodiments and drawings, and the general knowledge in the technical field to which the present invention belongs without departing from the technical spirit of the present invention. Since various changes and modifications may be made by those having the scope of the present invention, the scope of the present invention should be determined by the appended claims and their equivalents.

By using the present invention, it is possible to construct a database for the demand management evaluation and a reasonable analysis system, and to facilitate the establishment of an energy supply and demand policy by allowing the user connected to the Internet to easily perform the demand management evaluation. .

In addition, according to the present invention, it is possible to predict the demand for the reference devices and to calculate the potential of the high-efficiency devices that can be replaced, thereby immediately evaluating the effects of the replacement.

In addition, according to the present invention, it is possible to calculate a benefit index for individual demand management programs and analyze the cost effect immediately.

Furthermore, according to the present invention, it is possible to increase investment efficiency by providing reliable market information in consideration of investment efficiency to the government, a dedicated agency, a power company, a customer, and a related equipment supplier.

Claims (4)

A DB server for classifying, storing, and managing statistical data on power demand for each period, load device type, and customer type; And a web server interworking with the DB server and providing an authenticated user with access through a network to provide an evaluation result of the economics of the DSM (demand management) program based on statistical data of the DB server on a user interface screen. , The web server, A reference demand prediction module based on the statistical data, a reference demand prediction module for presenting an estimate of demand for power amount and load for each power consumption unit during the reference period as reference demand; A potential calculation module for calculating potential for each energy saving amount and load reduction amount that can be achieved when the predetermined load is replaced with the DSM device; An avoidance cost estimating module for estimating an incremental cost of alternative power or an incremental cost required by the power supply company for supplying additional power as an avoidance cost; And A cost effectiveness analysis module for analyzing and providing the evaluation result as a cost effect based on the reference demand, the potential amount, and the avoidance cost, The reference demand is estimated according to the classification of the device used and the technology of the device, the predicted sector of the amount or load, the market unit by type and the forecast period, The potential amount is calculated with respect to the device classification and device technology of the replacement target device to a high-efficiency device, And said avoidance cost is calculated according to socioeconomic indicators, facility characteristic indexes, and facility and fuel cost indexes. The method of claim 1, wherein the statistical data, At least one or more of the quantity or ratio of each item of the market unit by type during the forecast period, the power consumption according to the user classification and the device technology selected by the user, the usage time, the usage rate, the diffusion rate, and the number of units supplied, Demand management evaluation system via the Internet, characterized in that the update by a predetermined certified statistical organization connected through the Internet. The method of claim 1, The user interface screen, And a menu for selecting any one of the prediction of the reference demand, the calculation of the potential amount, the calculation of the avoidance cost and the analysis of the cost effect. The method of claim 1, wherein in the calculation of the latent amount, At the very least, the available coefficient, which is the ratio of the actual applicable part to the penetration rate of the device, the reduction factor, which is the ratio of the power consumption reduced when the device technology is converted to the efficiency-enhancing device technology, and the efficiency improvement device technology is not applied Technical potential based on incomplete coefficients, which are proportions to the total coverage of the uncovered portion, and A demand management evaluation system via the Internet, characterized in that the economic potential is calculated in consideration of the required device cost and the life of the device.

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