KR102008962B1 - Optimized asset management system for water supply facilities - Google Patents

Abstract

According to the present invention, an optimal asset management system of a water supply facility includes: a data collection unit which obtains data from a GIS management program and a water supply network real-time monitoring and controlling system (SCADA); an input unit which receives the status data of tangible assets of a water supply facility, the asset diagnosis/evaluation data of the water supply facility, and the history data of the water supply facility based on the data of the data collecting unit; and a calculation unit which performs a) the status assessment of the tangible asset of the water supply facility, b) the estimation of the duration period of the tangible asset of the water supply facility, c) the estimation of the reinvestment priority of the tangible asset of the water supply facility, d) the estimation of the lifecycle cost of the tangible asset of the water supply facility, e) the prediction of the future fiscal balance of a water service provider, and f) the optimization of the fiscal balance of the water service provider associated with a future funding plan based on the data inputted in the input unit; and an output unit which outputs the future funding plan and future fiscal balance of the water service provider as a result of the performance of the calculation unit. By the above composition, the present invention has an effect of presenting an optimal asset management plan suitable for the service achievement target and requirements of a water service provider and customers by considering whether an asset management plan is able to achieve the target level of the water service provider wherein the asset management plan is derived through the optimization process in consideration of an optimal fiscal balance derived from optimization technique according to the target flow rate and the future funding plan of the water service provider.

Description

OPTIMIZED ASSET MANAGEMENT SYSTEM FOR WATER SUPPLY FACILITIES}

The present invention is an asset management system for optimally managing water supply facilities. Specifically, the status evaluation results and durable calculations of tangible assets derived from the present survey data, historical data, and diagnosis / evaluation data on the water supply facilities assets are calculated. Based on the results, it relates to a system for optimal asset management planning through optimization techniques within the annual budget available.

Currently, the aging water supply facilities continue to accumulate, and the accumulation of the aging water supply facilities causes a decrease in the flow rate and an increase in maintenance costs. Due to the ever-increasing maintenance costs, the financial conditions of Korean water companies continue to deteriorate, and due to the deteriorated financial conditions, the capital for investment in maintenance of old waterworks facilities is in short supply. As a result, Korea's aging water supply facilities continue to accumulate, and a vicious cycle that continues to increase in maintenance costs is being fixed. In particular, small-scale water supply projects with low financial revenues and low flow rates have led to deficits in the water supply business. In response, the Korean government launched a large-scale aging waterworks improvement project (local waterworks modernization project) to overcome this vicious cycle at the national level. In the prior art, there is disclosed a system for managing assets of waterworks in Korean Patent Publication No. 10-1567774 (20151111 publication, Waterworks Asset Management System).

Even if national funding projects are carried out, old water facilities accumulate over time. Therefore, the Korean government intends to institutionalize “waterworks asset management” introduced in advanced foreign countries, to proactively respond to old waterworks facilities and to improve the sustainability of waterworks projects. Currently, legislation and field application research are being conducted to systematically manage asset management of water supply facilities, and considering the continuity of asset management of waterworks facilities, development of an optimal asset management establishment system is essential.

In the present invention, the optimization technique (dynamic planning method) within the budget range available for each year based on the condition evaluation result and the endurance calculation result of the tangible asset derived by using the current survey data, historical data, diagnosis / evaluation data on the waterworks facilities. (DP, Dynamic Programming, GA, Genetic Algorithm, etc.) to develop the optimal asset management planning system derived through.

The present invention, data collection unit for obtaining data from the GIS management program and SCADA (waterworks pipe network real-time monitoring and control system); An input unit for receiving tangible asset status data of waterworks facilities, diagnosis / evaluation data of waterworks facilities, and historical data of waterworks facilities based on the data collection unit; And based on the data entered in the input section, a) assessing the condition of tangible assets of waterworks facilities, b) estimating the lifespan of tangible assets of waterworks facilities, c) estimating the reinvestment priorities of tangible assets of waterworks facilities, and d) estimating the lifecycle cost of tangible assets of waterworks facilities. e) a calculation unit for performing the water budget optimization of the water service provider in connection with the water budget forecasting of the water service provider; And an output unit for outputting a future investment plan and a future financial balance of the water service provider as a result of the operation of the operation unit.

The calculation of the tangible assets of the tap water facilities of the operation unit is a safety coefficient-based physical durability calculation model, and a safety factor derived as a ratio of the strength remaining in the facility and the stress acting on the facility as shown in Equation (1) below. SF (Safety Factor) calculation method is used.

(1)

(One)

The residual strength of the facility is calculated by the following equation (2) according to the annual corrosion rate after laying the water pipe.

(2)

(2)

Using the above equations (1) and (2) to predict the change of the safety coefficient over time, and determines the time when the safety coefficient is less than 1 as the end of the endurance of the facility.

The calculation of the tangible assets of tap water facilities of the operation unit is an accident history-based statistical durability calculation model, which is a linear model of equation (3), a power model of equation (4), an exponential model of equation (5), and equation ( Use any one of 6) cohort models.

(3)

(3)

(4)

(4)

(5)

(5)

(6)

(6)

The calculation of the priority of reinvestment of tangible assets of tap water facilities of the operation unit is based on the degree of data acquisition level of the target area, and the priority calculation is different.

If the data acquisition level is relatively low, priority is given to the facilities located at the top of the water supply process (priority in the order of intake, water, water, water, and water) and the same supply. Within the process, priority is given to the acquisition of tangible assets in the highest order.

If the data acquisition level is high, the priority of reinvestment of tangible assets of waterworks facilities is determined in order of high water supply risk.

The water supply risk is calculated by multiplying the probability of damage to the facility and the damage range when the facility is damaged as shown in Equation (7) below.

In case of damage to the facility, the extent of damage is estimated by the amount of supply shortage or compensation for damages due to supply shortage.

(7)

(7)

The life cycle cost of tangible assets of tap water of the operation unit is

Taking into account the costs of operating and reinvesting waterworks (improvement of facilities), the life-cycle cost is estimated as shown in Equation (8) below.

(8)

(8)

The future fiscal balance forecast of the water service provider of the operation unit,

We forecast the future financial balance through three categories of revenues consisting of water supply income, government subsidies, and financial institution borrowings, and three categories of expenses consisting of maintenance expenses, acquisition of tangible assets, and repayment of financial institution borrowings.

Annual revenue is calculated by Equation (9) below, and annual government subsidies are calculated by Equation (10) below.

(9)

(9)

(10)

10

Annual financial institution borrowings are calculated by Equation (11) below.

(11)

(11)

Annual maintenance fee is calculated by Equation (12) below.

(12)

(12)

The cost of acquiring tangible assets by year is calculated by the following equation (13).

(13)

(13)

Annual repayment of financial institution borrowings is calculated by the following equation (14).

(14)

(14)

The interest to be considered in annual repayment of financial institution borrowings is calculated by the following equation (15).

(15)

(15)

Optimizing the fiscal balance of the water service provider in connection with the future financing plan of the operation unit means deriving an optimal fiscal balance that can balance the profits and expenses during the analysis period according to the future financing plan. The optimal financial balance is derived by applying an optimization technique that aims to minimize the repayment of debt from financial institutions (the sum of borrowings and interest), and the optimization technique applies nonlinear planning or linear planning.

(16)

(16)

As a constraint for deriving an optimal fiscal balance that balances the revenue and expenditure,

Equations (17) that the water rate considered in calculating water supply revenues increase constantly during the analysis period and equations (18) that the government subsidies decrease uniformly during the analysis period are used as constraints.

(17)

(17)

(18)

(18)

According to the present invention, the asset management plan derived through the optimization process in consideration of the optimal fiscal balance derived according to the optimization technique according to the target flow rate and the future funding plan of the water service provider is the target level of the water service provider. By considering whether this can be achieved, the effect of presenting an optimal asset management plan suitable for the service provider's service achievement goals and requirements is generated.

1 is a flow balance optimization flow chart linked to the future funding plan of the water service provider according to an embodiment of the present invention,
2 is an overall flowchart of an optimal asset management system of waterworks according to an embodiment of the present invention,
3 is an example of a statistically durable calculation model based on accident history in the method for calculating the lifespan of tangible assets of running water according to an embodiment of the present invention.
4 is an example of investment priority selection based on a water supply risk calculation result according to an embodiment of the present invention.
5 is an example of deriving a reinvestment cost of a facility based on a life cycle cost calculation result according to an embodiment of the present invention.
6 is an exemplary graph of a result of predicting a future financial balance according to an embodiment of the present invention.

The objects, specific advantages and novel features of the present invention will become more apparent from the following detailed description and preferred embodiments in conjunction with the accompanying drawings. In addition, the terms used are terms defined in consideration of functions in the present invention, which may vary according to the intention or convention of the user operator. Therefore, the definitions of these terms should be made based on the contents throughout the present specification.

1 is a flow balance optimization flow chart linked to the future funding plan of the water service provider according to an embodiment of the present invention, Figure 2 is an overall flow chart of the optimal asset management system of water supply facilities according to an embodiment of the present invention.

[One. Method of Valuation of Tangible Assets of Waterworks Facilities]

The condition evaluation of tangible assets of waterworks facilities is carried out using the current status of tangible assets, diagnosis / evaluation data, and historical data. First, tangible assets are classified into buildings, structures, mechanical equipment, and electrical equipment according to the local government's settlement guidance. Here, the structure is divided into concrete structure, steel structure, water pipe.

The method for evaluating the condition of individual water supply pipelines is the evaluation method of the water pipe presented in the Ministry of Environment's Water Supply Pipe Network Diagnosis Manual (Ministry of Environment, 2007), or the detailed guidebook for the maintenance and performance evaluation of facilities (Waterworks) by the Ministry of Land, Infrastructure and Transport, and Korea Facility Safety Authority. Follow the method for condition assessment of individual members as presented in the Ministry of Land, Infrastructure and Transport and Korea Facility Safety Authority. The data used in the status evaluation are as shown in <Table 1>.

division Data name Remarks Ministry of Environment, (2007), Water Pipeline Diagnostics Manual Indirect evaluation Occurrence of complaints such as tube type, diameter, inner coating, outer coating, buried year, soil type, surrounding road, connection method, leakage / damage / valve replacement, water quality / water pressure Direct evaluation Actual inner diameter, pipe thickness, coating thickness, external corrosion depth, external corrosion circumference, external coating peeling, internal deposit thickness, internal corrosion depth, internal corrosion circumference, internal coating peeling, maximum scale thickness, water pressure Ministry of Land, Infrastructure and Transport, (2011), Detailed Guidance on Maintenance and Performance Evaluation of Facilities Pipeline accident history, years of passage, soil type around soil, soil resistivity, soil pH, sulfate ion and chlorine ion, generosity potential difference, inside and outside coating state, pipe corrosion, pipe thickness, pipe strain, pipe leakage

<Table 1> Data used for evaluating the condition of water supply pipeline

In the case of tangible assets such as (reinforced) concrete structures, steel structures, mechanical equipment, and electrical equipment, including water supply pipelines, the Ministry of Land, Infrastructure and Transport and the Korea Institute for Facility Safety, `` Detailed Guideline for the Maintenance and Performance Evaluation of Facilities (Waterworks) (Ministry of Land, Infrastructure and Transport, Korea Follow the methods for assessing the condition of individual members as outlined in the Korea Safety and Health Agency, 2011). The data used to assess the condition of tangible assets other than water pipelines are shown in <Table 2>.

Property, plant and equipment Data name Remarks (Rebar) concrete structure Sinking / injury, slope, activity, foundation scour,
Concrete cracking, peeling, peeling / layer separation, rebar exposure, leakage of concrete members, leakage of expansion joints, white condition, concrete breakage, dropping of members,
Carbonation Residual Depth, Total Chloride Ion Content Steel structures Steel corrosion, steel fatigue crack, steel deformation and displacement, coating state of steel Hardware Pump Pump bed foundation, pump vibration level, pump noise level valve Valve damage Electrical equipment Insulation deterioration, poor grounding, poor electrical equipment

<Table 2> Data to be used for condition assessment of tangible assets other than water supply pipeline

[2. Calculation method of durability of tangible assets of waterworks facilities]

Durable estimation methods of tangible assets of water supply facilities can be classified into three types according to the degree of data acquisition in the target area. If there is no data (accident history data, diagnosis and evaluation data, etc.) for calculating the service life of tangible assets in the target area, the useful life of the water supply facilities provided in Annex 2 to the Enforcement Regulations of the Local Public Enterprise Act shall be considered to be durable. The useful life of the water supply facilities as shown in Table 2 of the Enforcement Rules of the Local Public Enterprise Act is shown in <Table 3>.

Property, plant and equipment Service life Remarks Civil engineering facilities (reinforced concrete structures, steel structures) 30 years Stainless steel pipe, cast iron pipe, steel pipe 30 years PVC pipe, PE pipe 20 years Galvanized steel pipe 10 years Other tubes 20-30 years Water pipe accessories (mechanical equipment, electrical equipment) 20-30 years

<Table 3> Service life criteria of water and sewage construction

Next, if the accident history data for the water supply facilities (leakage recovery ledger for water supply pipelines, repair / repair ledger for other facilities) or diagnostic / evaluation data are insufficient to develop a predictive model, The results of the condition assessment derived from each method of facility classification are used. The following <Table 4> calculates the endurance life of tangible assets of waterworks facilities using the results of condition assessment, and this criterion can be applied differently according to the change trend and target region of condition assessment results repeatedly performed in the target area. .

Status grade Data name Remarks A Available for 30 years from the date of assessment. B Available for 20 years from the time of status assessment. C Can be used for 10 years from the time of status evaluation. D End of life is terminated at the time of health assessment. E End of life is terminated at the time of health assessment.

<Table 4> Examples of durable estimates of tangible assets of waterworks facilities

Finally, if the accident history data or the diagnosis / evaluation data for the water supply facilities are sufficient to develop a calculation model that is durable, the safety model based on the physically durable calculation model or the accident history based statistical durability calculation model are used as follows. Calculate the service life of water supply facilities.

First, the safety factor-based physical durability estimation model uses a safety factor (SF) method that derives the ratio of the strength remaining in the facility and the stress acting on the facility, as shown in Equation (1).

(1)

(One)

: 안전계수 (-)

: Factor of safety (-)

: 잔존강도 (kg_f/cm²)

: Residual strength (kg _f / cm ² )

: 작용응력 (kg_f/cm²)

: Working stress (kg _f / cm ² )

Although the stresses on the installation over time can be considered to be nearly similar, the residual strength of the installation gradually decreases over time. The prediction of residual strength of a facility over time can be used to simulate changes in the safety factor over time. Equation (2) shows an example of the equation that simulates how the residual strength changes according to the annual corrosion rate after laying the water supply pipe.

(2)

(2)

: 상수도관로의 부식비율 (%)

: Corrosion rate of water pipe line (%)

: 상수도관로 매설 후 경과연수 (년)

: Years spent after laying water pipe (year)

: 상수 (관종에 따라, 대상지역의 특성에 따라 상수가 달라짐.)

: Constant (Constant varies depending on the type of plant and the characteristics of the target area.)

If the prediction of the safety factor change over time using equations (1) and (2), it is possible to determine that the safety factor is less than 1 as the end of the service life of the facility.

Next, the statistically durable estimation model based on the accident history has the following models: equation (3) _linear model, equation (4) _squared model, equation (5) _index model, equation (6) _cohort model . The models presented are models for estimating the failure rate of a facility (the number of failures per year). The coefficients shown in the equation vary depending on the characteristics of the facility and the target area, and methods such as nonlinear regression or least-squares method can be used to obtain coefficients for the equation.

(3)

(3)

(4)

(4)

(5)

(5)

(6)

(6)

: t연도의 파손율(건/년),

= failure rate in t years (case / year),

: 현재 연도(년)

: Current year

: 시설물 설지 연도(년)

: Year of facility installation (year)

: 상수(시설물의 특성 및 대상지역의 특성에 따라 상수가 달라짐.)

: Constant (The constant varies depending on the characteristics of the facility and the target area.)

3 is an exemplary diagram of the accident history based statistical durable prediction model.

When estimating the service life limit through a statistical model for predicting the damage rate of a facility, the threshold break rate is used to consider the time when the facility exceeds the limit failure rate as the end of the service life of the facility. Calculate the endurance life. The marginal damage rate of the facility also depends on the characteristics of the target area and the target of the water service provider.

[3. Method for Estimating Priority of Reinvestment of Tangible Assets of Waterworks Facilities]

Reinvestment priorities for tangible assets of tap water are applied in two ways depending on the degree of data obtained in the target area. First, if the data acquisition level of the target area is low, priority is given to the facility located at the top of the supply process. In other words, priorities are given in the order of intake, water, water, water, and drainage. In the same supply process, priority is given to the acquisition of tangible assets.

Next, if the data acquisition level of the target area is high, the priority of reinvestment of tangible assets of tap water facilities is determined in order of high water supply risk. The water supply risk is calculated by multiplying the probability of damage to the facility and the range of impact (damage range) when the facility is damaged, as shown in equation (7). In the event of damage to a facility, the extent of damage (damage) can be estimated from the shortage of supplies or the compensation for damages from shortages.

(7)

(7)

: 물 공급 리스크 (m³/년 또는 원/년)

: Water supply risk (m ³ / year or KRW / year)

: Probability of Failure (시설물의 파손 발생 확률) (건/년)

: Probability of Failure (case / year)

: Consequence of Failure (시설물 파손에 따른 피해범위)

: Consequence of Failure

(m ³ / case or won / case)

Figure 4 is an example based on the above risk calculation results as an example of the investment priority calculation based on the water supply risk calculation results.

The probability of failure of a facility (PoF) utilizes the failure rate derived from the statistically durable estimation model presented in the previous step. The extent of impact from damage to a facility can be estimated through hydraulic analysis of the water supply network. In other words, it is possible to calculate the amount of supply shortage that can be derived by excluding the quantity that can be supplied from the quantity to be supplied, or use the value converted into the cost form through the damage (compensation) cost due to the shortage supply.

[4. Life Cycle Cost Estimation Method for Individual Tangible Assets of Waterworks Facilities]

The life-cycle cost of an individual tangible asset of a water supply can be defined as the sum of the costs incurred during its life, which includes planning, designing, constructing, operating and disposing of the facility. In the present invention, since it is impossible to estimate the exact cost of the installation planning and design of the facility, it is not considered, and in consideration of the cost of operating and reinvesting the facility (improving the facility), Estimate life cycle costs.

(8)

(8)

: 생애주기비용 (원)

: Life cycle cost (KRW)

: 시설물의 운영비용 (원)

: Operating cost of facility (KRW)

: 시설물의 재투자(개량)비용 (원)

: Reinvestment (improvement) cost of facility (KRW)

: 시설물 개수 (총 시설물의 수 = q)

: Number of facilities (total number of facilities = q)

: 연도 (년, 총 분석기간 = p)

: Year (year, total analysis period = p)

: 사회적 할인율 (%)

: Social discount rate (%)

5 is an example of deriving the cost of reinvestment (improvement) of facilities based on the results of the life cycle cost calculation.

The costs of operating each individual facility can be derived from past operating cost trends. The cost of reinvestment (improvement) of each individual facility can be estimated by taking into account the unit cost of improving tangible assets, and the unit of improvement cost is based on the values presented in the standard product.

[5. Forecast of future fiscal balance for water supply companies]

A financial statement of a water service provider consists of a balance sheet, cash flow and profit and loss statements.

In summary, the three statements can be divided into revenue and spending. In detail, the revenue section can be divided into water supply revenue, water supply construction profit, other operating income, interest income, other non-operating income, other accounting subsidies, government subsidies, financial institution borrowings, funds, facility contributions, local government contributions, and others. The expenditure division can be divided into labor expenses, maintenance expenses, provisions, interest paid, depreciation expenses, other non-operating expenses, tangible assets acquisition, intangible assets acquisition, non-operating equipment acquisition, investment assets acquisition, repayment of borrowings, and others.

Here, the three income categories of water supply income, government subsidies, and financial institution borrowings, and the three expense items of maintenance expenses, acquisition of tangible assets, and repayment of borrowings (including interest on borrowings) are the goals and futures set by the water service provider. It can be regarded as a value that changes according to the asset management plan linked to the funding plan. Other items are considerably less changed by the asset management plan, and can be regarded as similar to the current level in the future.

In the present invention, the future finance of the water service provider through the prediction of six items (water supply, government subsidies, financial institution borrowings, maintenance expenses, tangible asset acquisition, repayment of borrowings (including interest on borrowings)) among the financial items of the water service provider. Predict the balance.

Equation (9) shows an equation for predicting annual water income. Water supply revenue is the revenue from tap water, multiplied by the price of tap water. The amount of supply applied in this study is calculated by multiplying the annual projected flow rate of the water supply company by the production forecast value of the master plan for water maintenance in the study area. The tap water price unit is based on the price unit price of the target area. In the future, it is assumed that the tap price unit price increases by the annual inflation rate.

Equation (10) represents an equation for predicting annual government subsidies. State subsidies are expenses that do not have to be repaid. The government subsidy simulates that the water service provider becomes financially independent without the government subsidy after the end of the asset management plan. Therefore, the state subsidies are received from the base subsidy by decreasing the amount by a certain amount during the analysis period, and the state subsidies are set not to receive the national subsidies from the end of the analysis.

Equation (11) represents an equation for predicting annual financial institution borrowings. During the analysis period, if the deficit occurs when subtracting maintenance expenses and expenditures from the acquisition of tangible assets from the income from water supply and subsidies during the analysis period, it is assumed that the deficit is borrowed. Assume

Equation (12) represents an equation for predicting annual maintenance costs. The maintenance cost is calculated by additionally considering the manpower operation cost of the water purification facilities in the water supply network standard maintenance cost formula proposed by Kim et al (2017). As the old water pipeline increases, the flow rate decreases and the leak rate increases. An increase in leak rate means an increase in the quantity lost to the supply to the consumer. However, since the supply to be supplied to the consumer is determined by the forecast, it means that the quantity to be produced additionally increases. This can be regarded as an increase in maintenance costs, so if the flow rate decreases, the additional maintenance cost is calculated by multiplying the unit price by the water price to increase production.

Equation (13) represents an equation for deriving the cost of acquiring tangible assets for each year. The acquisition of property, plant and equipment is derived from the construction costs incurred when the replacement and replacement are carried out based on the service life of the water supply facilities derived in the previous step.

Equation (14) represents an equation for predicting annual borrowings repayments. Borrowing repayment was considered to be incurred as borrowings and interest expenses, with the remaining amount as repayment of borrowings when the maintenance costs and expenditures on the acquisition of tangible assets were subtracted from revenue from revenues and government subsidies, as opposed to borrowings. Equation (15) represents an equation for deriving the interest to be considered when repaying the borrowing in Equation (14).

(9)

(9)

(10)

10

(11)

(11)

(12)

(12)

(13)

(13)

(14)

(14)

(15)

(15)

: 분석연도 (년)

: Year of analysis (years)

: 분석종료연도 (년)

: Year of analysis end (year)

: m 연도의 수도요금 수입 (원)

: m Water rate income (year)

: m 연도의 공급수량 (m³/년)

m Supply of year (m ³ / year)

RWR _m : m% target flow rate in year

: m 연도의 수도요금 (원/m³)

: Water rate for year m (won / m ³ )

: m 연도의 국고보조금 (원)

: State subsidies for m years (KRW)

: 분석 시작 시점의 국고보조금 (원)

: State subsidies at the start of analysis (KRW)

: m 연도의 금융기관차입금 (원)

: m Loan of financial institution in year (KRW)

: m 연도의 유지관리비용 (원)

m Maintenance costs for year (KRW)

: m 연도의 유수수량 (m³)

: Flowing water in m year (m ³ )

: m 연도의 유수율 (%)

: m flow rate in year (%)

: m 연도의 급수전 밀도 (전/km²)

: m water supply density (year / km ² )

: m 연도의 21년 이상 상수도관로의 비율 (%)

:% of water pipes over 21 years in year m

: m 연도의 상수도관로 유지관리비용 원단위 (천원/m)

: m water supply pipe maintenance cost (unit: 1,000 won / m)

: m 연도의 정수처리시설 유지관리인력 비용 (백만원)

m Cost of water purification facility maintenance manpower (million won)

: 대상지역의 기준 유수율 (%)

: Standard flow rate of target area (%)

: m 연도의 유형자산취득 금액 (원)

m Acquisition of tangible assets for the year m (KRW)

: 상수도시설물 n의 취득시점에서의 유형자산취득 금액 (원)

: Amount of property, plant and equipment acquired at the time of acquisition of water supply facility n (KRW)

: 물가상승률 (%)

: Inflation rate (%)

: 상수도시설물 n의 취득시점으로부터 m 연도까지 경과연도 (년)

: Year passed from the acquisition of water supply facility n to year m (years)

: m 연도의 금융기관차입금 상환액 (원)

m Repayment of Financial Institution Borrowings for Year (KRW)

: m 연도의 차입금의 이자 (원)

: Interest on borrowings in m years (won)

: 금융기관 이자율 (%)

: Interest rate of financial institutions (%)

[6. How to Optimize Fiscal Balance of Water Service Providers Linked with Future Funding Plan]

The optimal financial balance in water supply can be defined as a balance between revenue and expenditure, ensuring business sustainability. The present invention derives an optimal fiscal balance to balance revenue and expenditure during the analysis period according to the future funding plan of the water service provider. The long-term funding plan considers three ways to increase water bills to increase water supply, increase loans by taking loans from banks, and increase subsidies with government support. Borrowings are assumed to occur only when they are inevitable, as in the fiscal balance forecasting method.

 Figure 1 shows a flow chart of the fiscal balance optimization of the water service provider linked to the future funding plan. In the “minimization of repayment and application of optimization technique” shown in FIG. Here, the optimization technique may be applied to nonlinear programming or linear programming.

(16)

(16)

Constraints to balance the fiscal balance can be applied differently depending on the financial status of the utility. Equation (17) and Equation (18) are just one example. Equation (17) assumes that the water rate taken into account when calculating the water supply revenue increases regularly during the analysis period. It expresses the assumption that

(17)

(17)

(18)

(18)

6 is an example of a result of predicting the future financial balance (optimal financial balance) using the above formula as an example of the result of the optimal financial balance deduction.

[7. Annual Investment available  Optimization Method of Asset Management Plan Considering Budget Scope and Life Cycle Cost of Individual Tangible Assets]

The period of improvement is determined by the service life of all tangible assets of the water supply facility. It is desirable that the water service provider respond to the demand for improvement from all assets. However, there are limitations in the budgetary use of water utilities, which makes it difficult to cope with the improvement of all tangible assets that occur annually. Therefore, there is a need to develop an improvement plan that can respond to the maximum improvement demand within the available budget. The available budget range depends on future funding plans.

In order to develop an optimal asset management plan, first, the heterogeneous water supply assets are classified into water supply pipeline assets and other assets (reinforced concrete structures, steel structures, machinery and electrical equipment).

Next, the timing of reinvestment of the property, plant and equipment is determined by the remaining life of the asset, and the remaining life of the asset is calculated by considering the service life of the facility calculated in the previous stage and the number of years that have elapsed up to this point.

When developing an asset management plan within the range of available budgets derived from a future funding plan, the investment priorities given in Eq. (7) should be taken into account in the preceding steps. If the demand for improvement over the budget range is available for each year, the demand for improvement over the budget range shall correspond to the following year.

The cost incurred in reinvestment (improvement) of a specific asset in the future shall be applied after deriving the cost for improvement by considering the inflation rate to the acquisition price at the time of asset acquisition as shown in Equation (13).

An optimal asset management plan is defined as a plan that minimizes the life cycle costs shown in Eq. (8) within the annual budget available. Optimization techniques to minimize life cycle costs can use dynamic programming and genetic algorithms.

After deriving the optimized asset management plan, when implementing the plan, it is examined whether the water service provider can achieve the target level set as the target, and if the target level is not achieved, the future financing plan scenario is reset. We then derive an asset management plan that can achieve the target level repeatedly.

[8. How to review your goal level]

Water service providers can set various goals according to the conditions of water service providers. The following <Table 5> shows various performance evaluation indicators that can be set by the water service provider, and establishes the most optimal asset management plan for the water service provider by examining whether the performance evaluation indicators can be achieved through the optimized asset management plan. do.

<Table 5> Performance Evaluation Indicators Available for Reviewing whether Water Works Providers Achieve Target Levels

[Optimization Methodology]

As described above, a flow chart of the budget balance of the water service provider in connection with the future financing plan is shown in FIG. 1, and the objective function of minimizing the repayment of borrowings (sum of borrowings and interest) in the “minimization of repayments, application of optimization techniques” The optimization technique is applied, and the optimization technique can be applied to nonlinear programming or linear programming.

a.Nonlinear Programming

Nonlinear programming (NLP) is a mathematical programming method in which a model is composed of objective functions and constraints like linear programming, but the objective function or constraints are represented as nonlinear functions rather than linear equations. Since nonlinear programming (NLP) is expressed as a nonlinear function, there is no efficient solution like the collective method of linear programming. However, nonlinear programming can be used to model problem situations where it is difficult to represent objective functions or constraints linearly, which can contribute to a more realistic abstraction of the problem. To solve nonlinear planning problems, we can generally use matrix calculation, Lagrange multipliermethod, and Kuhn-Tucker Theorem.

a-1) Matrix calculation

)는 함수값의 최대 증가율이 발생하는 방향을 나타낸다. Nonlinear programming (NLP) is generally solved by matrix calculation, and can be solved using gradients and Hessian to solve matrices. Gradient is the first-order partial derivative of a continuous and continuous differential function, and geometrically the gradient vector (

) Indicates the direction in which the maximum increase rate of the function value occurs.

Hessian is a quadratic matrix, and the object function that can be differentiated twice in succession can be solved through the Hessian matrix.

a-2) Lagrange Multiplier ( Lagrange multipliermethod )

Lagrange multipliermethod is a method that is applied to nonlinear planning model with equation constraint as shown in the following equation.Lagrange multipliermethod introduces Lagrange multiplier to the original model and connects the objective function and the constraint of the equation. It is a method to find the maximum value and the minimum value after converting to a non-linear plan model with no constraints.

In order to apply the Lagrange multiplier to the Lagrangian function, it is as follows.

를 결정변수

와 라그랑지 승수

에 대해 편미분 값이 0이 되어야 원래의 모형에서 최적해를 찾을 수 있다. The determinants of the objective function are Lagrange functions under equality constraints.

Is a determinant

And Lagrange multiplier

The partial derivative value of 0 must be zero to find the optimal solution in the original model.

a-3) Kuhn-Tucker Theorem

The Kuhn-Tucker Theorem is a method applied to nonlinear planning models under inequality constraints to represent the most common decision-making situations, but the method of finding the optimal solution is more complex than other methods. The Kuhn-Tucker Theorem defines the requirements for a given point to be optimal. If the nonlinear planning model under general inequality constraints is given by the following equation, then the Kuhn-Tucker Theorem is required. The conditions are the following six conditions.

이 원래 모형의 최적해라면, 다음과 같은 조건을 만족시키는 라그랑지 승수

이 존재한다.

If it is the optimal solution of this original model, Lagrange multiplier satisfying

This exists.

①

②

③

④

또는

⑤

or

또는

⑥

or

b. Linear programming

There are many models of optimization techniques, but one of the most complete techniques is linear programming (LP). In linear programming (LP), a single goal of maximization or minimization must be pursued, and the goal must be expressed in objective function and constraint expression. In addition, the objective function and the constraint expression are composed of determinants that are interrelated, and there is a condition that they must have linearity.

Mathematical programming is used among the major areas of business science to address whether best use of permitted and limited resources is possible. Mathematical programming is a method of pursuing the most efficient but optimal method for limited resources. It is also called optimization because it uses mathematical techniques to pursue the optimal.

Mathematical programming must satisfy the following three conditions:

에 의해 표현되어야 한다. ① structure of the problem is variable

Should be represented by

② There is a boundary condition in the problem and it must be expressed by the following formula.

③ There must be a goal to be obtained and this goal must be expressed by the variable of ①.

If the conditions ② and condition ③ are linear among the mathematical planning methods that satisfy the above three conditions, they are called linear programming method and nonlinear bivariate nonlinear planning method. If only integer solution is required in linear programming, it is called integer programming.

There are several solutions to the linear programming problem, but the most widely used solution is the simplex method. Collective law is an advanced solution based on simultaneous linear equations.

The collective method selects a random basis solution and finds the optimal solution in the end by continuing to find a basis solution that has a larger value of the objective function if the basis solution is not optimal. There are four steps to using the collective law and the process is as follows.

① [Step 1] Initial Solution

Find the first basis possible solution and find the basis for this solution.

② [Step 2] Group Judgment and Entry Variable Selection

Verify whether the current solution is optimal by applying Group Standard I. If it is optimal, then the solution at this time is optimal and finishes the whole process. Otherwise, the group criteria I are applied to select the entry variables from the non-base variables. Group Standard I is as follows.

③ [Step 3] Select Base Dropout Variable

From the baseline variables, apply group criteria II to select the baseline dropout variables.

④ Basis correction

Correct the basis and find a new solution. Then return to Step 2.

In order to use the linear programming method, it is necessary to pursue a single goal, and that the objective function and the constraint expression should be linear, and the correlation between the determinants of the objective function and the constraint expression should be correlated. If either equation of the objective function or constraint does not have linearity or there is little interrelationship between the constituent determinants, another optimization technique is used to find the optimal solution under these conditions.

c. Dynamic programming

Dynamic Programming (DP), developed by R. Bellman and GBDanizig, is a special technique for solving optimization problems and is a management science technique based on linear programming. It is a technique developed to solve the problem of multi-stage decision making that is not only one decision but a continuous demand. A series of decision-making problems can be said to be a mathematical planning model that can be divided into several stages by considering temporal or spatial correlations, broken down into partial problems, and then optimally solved for multi-step decision problems by sequentially optimizing the partial problems. have. Decision making at each stage is made using the principle of R. Bellman's optimality. R. Bellman's Principle of Optimality states that “the optimal policy (series of decisions) is the result of the initial decision, no matter what decision is made in the initial state. It must be optimal for the state obtained in. This means that the decision at a given stage is affected by the decision at the previous stage.

Basic principles and formulas of the dynamic programming method can be expressed by the following figure and equation.

(2.25)

(2.25)

: N단계에서의 목적함수

: Objective function at level N

: N단계에서의 설명변수

: Explanatory variable in step N

: N단계에서의 계수

: Coefficient in N levels

By repeating the ignition formula sequentially, it is possible to obtain R _N (C), which is the maximum value of the entire objective function R _N (X ₁ , X ₂ , ..., X _N ). Here you can see the principle of optimality: no matter what value X _N is (initial state C, final crystal X _N ), the state resulting from the initial decision, ie CX _N , is optimally distributed to N-1 steps. It is the optimal policy at the whole level. Thus, a function on the principle of optimality is expressed as an ignition equation, and in many cases, R ₁ (C), R ₂ (C),... … We then derive the optimal value by finding the sequential solution of R _N (C).

d. Genetic algorithm

In 1960, Rechenberg (1965) first proposed the Evolution Strategy in order to solve large-scale combination optimization and constrained discretization problems. In the early 1970s, John Holland developed a genetic algorithm based on biological evolution theory and genetics. (GA, Genetic Algorithm) began to be introduced and developed into Goldberg and Santani (1989). Genetic Algorithm (GA) is a theory that uses the principle that a good trait individual adapts well to the natural environment to produce a good descendant. It gives fitness based on objective function values and constraints. It mimics the evolutionary process itself, and the process of evolution survives the survival of the fittest, which means that only those who have adapted well to a given environment survive, leaving offspring and descendants adapting to the environment. On the contrary, it is the culmination of those who have not adapted to the environment. Individuals with high fitness can survive and have a high probability of generation, with crossovers and mutations forming the next generation of populations, leading to superior genetic traits in the next generation. After a few hundred or thousands of generations, only the most suitable individuals for a given environment survive. Genetic algorithms (GAs) are more versatile and useful because the use of well-suited populations is similar to the optimization method using derivatives, and the search for the optimal solution through hybridization and mutation is similar to conventional random search. In recent years, the development of computing technology has accelerated the search for the optimal solution, and many researches and applications have been made.

d-1) basic concepts of genetic operations

Genetic Algorithm (GA) is the principle of evolution of survival of the fittest and natural selection, so that a group of living organisms has a high probability of surviving an individual with suitable traits to survive in that environment. Through the process of crossover and mutation, they evolve in a better direction and individuals of inappropriate traits are gradually eliminated through the process of evolution. By repeating this evolutionary process, the main idea is that eventually individuals with the most appropriate traits will be formed.

d-2) Characteristics of genetic algorithm

In addition to the fact that genetic algorithm mimics natural localization, it has several features that distinguish it from existing search algorithms.

① Instead of directly using the parameter in question, it is appropriately represented by a symbolic representation.

② deal with population rather than a single point

Only require payoff or objective function values, no other supplementary information such as differentiability, continuity, unimodality, etc.

④ Use genetic operators

⑤ The results obtained are probabilistic rather than decisive

⑥ Very flexible even in very complex and large search spaces

d-3) genetic algorithm operators

1) Crossover

Natural organisms produce offspring through sexual mating, and mating is an algorithmic simulation of this process, partially altering two individual chromosomes to create new individuals. Crossing is a genetic operator that combines two other individuals to produce new individuals by inheriting them from offspring. Parents inherit the dominant traits that have survived the environment. At this time, the trait of the parent should be properly inherited to the offspring, which is called character preservingness or crossover neighborhood. Size of mating method There are one-point mating method and advantage mating method as shown in the following figure.

2) Mutation

As evolution evolves, through breeding and reproduction, groups produce stronger generations and chromosomes resemble each other. This is a good condition for finding the optimal solution at the end of the generation, but when it occurs early in the generation, gene diversity is ignored and the search area narrows and converges in unwanted years.

To solve this problem, mutations use a method of converting alleles and genes into individual alleles, with a constant probability of mutation. Random numbers in the range of [0, 1] are randomly generated when the number less than the set reference value is generated. Typical mutation probabilities are less than 0.05, which means that mutations occur randomly, thus reducing the optimal value. In many cases, the results of mutations are not a feasible solution. In many cases, the mutations that go wrong can be eliminated over time and the successful mutations that occur occasionally improve the optimal solution.

There are several methods used for mutations, such as inversion, displacement, duplicate, addition, and deletion0, and dynamic mutations that assign different probabilities to different generational fits. There is also a method such as (Dynamic Mutation).

3) Generation

Generation is a technique that mimics the survival of deficits and natural selection. The reproduction operator aims to strengthen the entire population based on the goodness-of-fit values. By dismissing the unselected groups and selecting strong individuals, the selected groups are widely distributed to the next generation. However, the major problems that arise in genetic algorithms that deal with fair sized populations are the lack of genetic diversity and a drop in selection pressure. Genetic diversity and selection pressures have opposing relationships, so they must be properly balanced when applied.

Roulette wheel selection, ranking-based selection, and tournament selection are used as the selection reproduction technique. Among the various methods, the following figure and the strength of the roulette wheel selection technique are used. It is used widely.

Although the preferred embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements of those skilled in the art using the basic concepts of the present invention defined in the following claims are also provided. It belongs to the scope of rights.

Claims (14)

A data collection unit for obtaining data from a GIS management program and a SCADA real-time monitoring and control system;
An input unit for receiving tangible asset status data of waterworks facilities, diagnosis / evaluation data of waterworks facilities, and historical data of waterworks facilities based on the data collection unit; And
Based on the data entered in the input section, a) assessing the condition of tangible assets of waterworks facilities, b) estimating the lifespan of tangible assets of waterworks facilities, c) calculating the priority of reinvestment of tangible assets of waterworks facilities, d) estimating the lifecycle cost of tangible assets of waterworks facilities, d) e) a calculation unit for performing the water budget prediction of the water service provider and f) deriving the water budget of the water service provider in connection with the future funding plan; And
And an output unit configured to output a future investment plan and a future financial balance of the water service provider as a result of the calculation unit.
The future fiscal balance forecast of the water service provider of the operation unit,
We forecast the future financial balance through three categories of revenues consisting of water supply income, government subsidies, and financial institution borrowings, and three categories of expenses consisting of maintenance expenses, acquisition of tangible assets, and repayment of financial institution borrowings.
Annual water supply revenue is calculated by the following equation (9), annual government subsidies are calculated by the following equation (10),
Optimal asset management system of water supply facilities.

(9)

10

: Year of analysis (years)

: Year of analysis end (year)

: m Water rate income (year)

m Supply of year (m ³ / year)
RWR _m : Target flow rate in _m years (%)

: Water rate for year m (won / m ³ )

: State subsidies for m years (KRW)

: State subsidies at the start of analysis (KRW)
The method of claim 1,
The calculation of the tangible assets of tap water facilities of the operation unit is a calculation model of physical durability based on the safety factor.
Using the safety factor (SF) calculation method derived from the ratio of the strength remaining in the facility and the stress acting on the facility, as shown in equation (1) below,
Optimal asset management system of water supply facilities.

(One)

: Factor of safety (-)

: Residual strength of facility (kg _f / cm ² )

: Working stress acting on the facility (kg _f / cm ² )
The method of claim 1,
The calculation of the tangible assets of tap water facilities of the operation unit is an accident history-based statistical durability calculation model, which is a linear model of equation (3), a power model of equation (4), an exponential model of equation (5), and equation ( 6) using any one of the cohort model,
Optimal asset management system of water supply facilities.

(3)

(4)

(5)

(6)

= failure rate in t years (case / year),

: Current year

: Year of facility installation (year)

: Constant (The constant varies depending on the characteristics of the facility and the target area.)
The method of claim 1,
The priority of reinvestment of tangible assets of tap water facilities of the operation unit is
The priority calculation is based on the degree of data acquisition in the target area.
If the data acquisition level is relatively low, priority is given to the facilities located at the top of the water supply process (priority in the order of intake, water, water, water, and water) and the same supply. Within the process, priority is given to the acquisition of tangible assets in the highest order.
Optimal asset management system of water supply facilities.
The method of claim 4, wherein
If the data acquisition level is high, the priority of reinvestment of tangible assets of waterworks facilities is determined in order of high water supply risk.
The water supply risk is calculated by multiplying the probability of damage to the facility and the damage range when the facility is damaged as shown in Equation (7) below.
In case of damage to the facility, the damage range is calculated by the amount of supply shortage or compensation for damages due to supply shortage.
Optimal asset management system of water supply facilities.

(7)

: Water supply risk (m ³ / year or KRW / year)

: Probability of Failure (case / year)

: Consequence of Failure (m ³ / case or won / case)
The method of claim 1,
The life cycle cost of tangible assets of tap water of the operation unit is
Considering the costs for operating and reinvesting waterworks (improvement of facilities), a life cycle cost is calculated as shown in Equation (8) below.
Optimal asset management system of water supply facilities.

(8)

: Life cycle cost (KRW)

: Operating cost of facility (KRW)

: Reinvestment (improvement) cost of facility (KRW)

: Number of facilities (total number of facilities = q)

: Year (year, total analysis period = p)

: Social discount rate (%)
delete delete delete The method of claim 1,
The cost required to acquire tangible assets for each year is calculated by the following equation (13),
Optimal asset management system of water supply facilities.

(13)

m Acquisition of tangible assets for the year m (KRW)

: Amount of property, plant and equipment acquired at the time of acquisition of water supply facility n (KRW)

: Inflation rate (%)

: Year passed from the acquisition of water supply facility n to year m (years)
delete delete The method of claim 1,
Optimizing the fiscal balance of the water service provider in conjunction with the future funding plan of the operation unit means deriving an optimal fiscal balance that can balance the profits and expenses during the analysis period according to the future funding plan,
As shown in Equation (16) below, the optimal financial balance is derived by applying an optimization technique that aims to minimize the repayment of debt from financial institutions (sum of borrowings and interest).
The optimization technique applies a nonlinear programming or linear programming,
Optimal asset management system of water supply facilities.

(16)

: Year of analysis end (year)

m Repayment of Financial Institution Borrowings for Year (KRW)

: Interest on borrowings in m years (won)
The method of claim 13,
As a constraint for deriving an optimal fiscal balance that balances the revenue and expenditure,
Equation (17) that the water rate taken into account when calculating water supply revenue increases constantly during the analysis period;
Using the equation (18) as a constraint that the government subsidies decrease uniformly during the analysis period,
Optimal asset management system of water supply facilities.

(17)

(18)

: Water rate for year m (won / m ³ )

: Year of analysis (years)

: State subsidies for m years (KRW)

: State subsidies at the start of analysis (KRW)

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