KR102247711B1 - Method and server for deriving investment and design plans through milp model within national-level complex renewable energy supply system - Google Patents

Abstract

A method of performing a simulation related to the operation of a hybrid renewable energy supply system on a computing system is shown. A method of performing a simulation for a complex renewable energy supply system includes: classifying a specific area selected as an analysis target into a plurality of detailed areas, dividing a period set as an analysis period into a random time interval; Collecting economic factor information including at least one of a tax rate, a discount rate, an interest rate, and a capital cost coefficient related to an energy source; Collecting energy demand information for a period set as the analysis period; Collecting area information including at least one of resource holding status, land cost, and distance between the detailed areas for the plurality of detailed areas; Collecting technology and economy factor information including at least one of energy conversion efficiency, energy storage capacity, facility life, investment cost, and operating cost; And, based on the collected economic factor information, energy demand information, regional information, and technology economy factor information, the total net flow (TNF) is calculated to maximize the NPV (Net Present Value). It may be configured including the step of.

Description

METHOD AND SERVER FOR DERIVING INVESTMENT AND DESIGN PLANS THROUGH MILP MODEL WITHIN NATIONAL-LEVEL COMPLEX RENEWABLE ENERGY SUPPLY SYSTEM}

Various embodiments of the present invention relate to a technology for deriving an investment and design plan through mixed integer linear programming within a national-level complex renewable energy supply system.

Due to concerns about the depletion of fossil fuels and the impact of fossil fuel use on the environment, research on sustainable energy systems that rely on the use of renewable energy sources is becoming increasingly popular.

The market size for renewable energy production facilities and systems continues to grow, and large investments in this regard continue to be made.

The energy supply system using renewable energy as the main energy source has advantages in terms of environmental aspects and energy security when compared to the energy supply system mainly using fossil fuels.

However, in order to successfully implement a supply system based on a renewable energy source, there were obstacles to be solved first. Not only was it necessary to integrate with existing infrastructure, but also to expand new infrastructure to produce, store, and transport energy, as well as the fact that renewable energy was only available intermittently.

Therefore, in order to analyze these problems, various studies related to renewable energy sources have been conducted. However, the studies focus on the analysis of each energy source such as wind power, solar power, or biomass, or on the analysis of one of electricity, hydrogen or liquid fuel, which is the type of energy generated through this. In most cases. Therefore, these studies have individually addressed detailed topics such as determining the timing of investments, optimizing energy production plans, or determining the construction location of facilities.

In such an environment, a comprehensive decision-making approach was needed that could comprehensively determine which technology to choose, where the installation location of the facility would be suitable, and at what timing is the best investment to proceed.

Korean Patent Registration No. 10-1993421

It is an object of various embodiments of the present invention to perform a simulation related to the operation of a hybrid renewable energy supply system.

An object of the present invention is to provide information on investment timing, installation location, and the like for energy-related facilities in order to derive results in the form of maximum economic efficiency based on collected information.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems that are not mentioned will be clearly understood by those skilled in the art from the following description.

According to one of various embodiments of the present invention for solving the above-described problem, a method of performing a simulation for a complex renewable energy supply system performed on a computing system is to classify a specific area selected as an analysis target into a plurality of detailed areas. And dividing the period set as the analysis period into a random time interval and classifying the period; Collecting economic factor information including at least one of a tax rate, a discount rate, an interest rate, and a capital cost coefficient related to an energy source; Collecting energy demand information for a period set as the analysis period; Collecting area information including at least one of resource holding status, land cost, and distance between the detailed areas for the plurality of detailed areas; Collecting technology and economy factor information including at least one of energy conversion efficiency, energy storage capacity, facility life, investment cost, and operating cost; And, based on the collected economic factor information, energy demand information, regional information, and technology economy factor information, the total net flow (TNF) is calculated to maximize the NPV (Net Present Value). It may be configured including the step of.

The energy demand information may include energy demand information for each of electricity, hydrogen, and liquid fuel.

The step of calculating the TNF to maximize the NPV may be characterized in that it is performed through mixed integer linear programming (MILP).

The step of calculating TNF to maximize the NPV may be characterized by using the following equation.

(Ω = tax rate, Λ = depreciation rate, r = interest rate)

The TNF may be characterized in that it is calculated through the following equations.

(TB = gross profit, TC = total cost, TSP = profit from energy sales, TSC = residual value of the facility, TBC = _{profit from sales of CO 2} , FIC = cost of facility investment, TIC = cost of transportation investment, FOC = cost of facility operation, TOC = Transportation operating cost, TFC = feedstock cost, TLC = land cost)

The TSP may be calculated through the following equation.

(I ^E = electricity, I ^H = hydrogen, I ^F = liquid fuel, J ^EP = electricity production facility, J ^HP = hydrogen production facility, J ^FP = liquid fuel production facility, G = area, Φ = Sales price, P _ijgt = amount of i produced from facility j in region g during period t)

_{Calculation of P ijgt} for power generation through wind power may be characterized by being calculated through the following equation.

(Cp(v _t ) = power factor, ρ _h = air density at height h, β _j = area the blade sweeps, v _gt = wind speed in g area at time t, η _j = power production efficiency, N _ijgt = number of wind turbines installed in area g during period t)

_{Calculation of P ijgt} for power generation through biomass may be characterized by being calculated through the following equation.

(μ _i = HHV of biomass (High heating value: high heating value, ξ _i = energy required for drying biomass, γ _i = moisture content of biomass, o _i = target moisture content for drying biomass, θ _i = Parasitic factor, Q _ijlg'gt = transport rate of biomass transported from region g'to region j production facility in region g via l transport mode during t)

The TIC may be calculated through the following equations.

(ζ _t = number of days in t period, δ _l = l availability of transport mode, χ _l = l capacity of transport mode, κ _gg' = transport distance between g and g', Φ _l = l average speed of transport mode , λ _l = l The time required for loading/unloading in the mode of transport, Q _i(j)lgg't = during the time of t, the energy or biomass is transported from the g'area to the j facility in the g area through the mode of transportation. speed)

According to another embodiment of the present invention, the management server performing a simulation related to the operation of the complex renewable energy supply system classifies a specific area selected as an analysis target into a plurality of detailed areas, and sets a period set as the analysis period to an arbitrary An analysis preparation performing unit that divides into time intervals and classifies; An economic factor management unit that collects economic factor information including at least one of a tax rate, discount rate, interest rate, and capital cost factor related to an energy source; An energy demand management unit collecting energy demand information for a period set as the analysis period; An area information management unit collecting area information including at least one of resource holding status, land cost, and distances between the detailed areas for the plurality of detailed areas; A technology economy factor management unit that collects technology economy factor information including at least one of energy conversion efficiency, energy storage capacity, facility life, investment cost, and operation cost; And, based on the collected economic factor information, energy demand information, regional information, and technology economy factor information, the total net flow (TNF) is calculated to maximize the NPV (Net Present Value). It may be configured to include a simulation performing unit.

According to an embodiment of the present invention, the decision maker should select the ratio of each energy-related facility based on what criteria, which of various technologies for producing specific energy should be selected, how should the investment timing and region be selected? In relation to this, various simulation results can be provided.

According to an exemplary embodiment of the present invention, as mixed integer linear programming is utilized in addition to setting and limiting various variables, a more accurate and comprehensive simulation result may be provided.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a view for explaining a process of operating a hybrid renewable energy supply system according to an embodiment of the present invention.
2 is a block diagram schematically showing the configuration of a management server according to an embodiment of the present invention.
3 is a map of Korea's traffic-related energy demand within a CRES estimated by a management server according to an embodiment of the present invention.
4 is a map showing the result of an optimized setting of an energy supply network of CRES, generated by a simulation of a management server according to an embodiment of the present invention.
FIG. 5 is a result of showing the scale of infrastructure to be installed in each area for each specific period within the CRES, generated by simulation of the management server according to an embodiment of the present invention.

The terms used in the present specification are for describing exemplary embodiments and are not intended to limit the present invention. In this specification, the singular form also includes the plural form unless specifically stated in the phrase. As used herein, “comprises” and/or “comprising” do not exclude the presence or addition of one or more other elements other than the mentioned elements. Throughout the specification, the same reference numerals refer to the same elements, and "and/or" includes each and all combinations of one or more of the mentioned elements. Although "first", "second", and the like are used to describe various elements, it goes without saying that these elements are not limited by these terms. These terms are only used to distinguish one component from another component. Therefore, it goes without saying that the first component mentioned below may be the second component within the technical idea of the present invention.

When a part of the specification is said to "include" a certain component, it means that other components may be further included rather than excluding other components unless specifically stated to the contrary. In addition, terms such as "... unit" and "module" described in the specification mean a unit that processes at least one function or operation, which may be implemented as hardware or software or a combination of hardware and software. .

1 is a view for explaining a process of operating a hybrid renewable energy supply system according to an embodiment of the present invention.

According to an embodiment, the collection and analysis of all information related to the complex renewable energy supply system may be performed through a separate management server. Such a management server may be included inside the complex renewable energy supply system, but may be located outside the complex renewable energy supply system.

In the management server, data collection and analysis for the complex renewable energy supply system may be performed, and in detail, optimization analysis for various options within the system may be performed.

Hereinafter, for convenience of description in the present specification, the composite renewable energy supply system will be described as CRES, and the renewable energy source will be described as RES.

RES-based systems rely on various technological processes to perform conversion, storage, and transport of energy in the system, respectively, so the analysis can be very complicated. In addition, planning for a system at the national level can be a very difficult challenge as long-distance transport or long-term planning must be considered, and the area to be covered is also huge.

To make this analysis easier to perform, several simplifications can be performed. For example, the target period for analysis may be divided and classified at arbitrary time intervals, and a specific region selected as the analysis target may be divided into a plurality of sub-regions and classified. For each time interval, decision makers can build various types of energy-related facilities and transport platforms in at least some of the different locations.

Looking at the items for which simplification is performed, economic factors such as tax rates, discount rates, interest rates, and cost of capital coefficients can be set to be constant during the target period for analysis. In addition, information on the energy demand for the period set as the analysis period can be set in advance, and for each area classified as a detailed area, regional information such as the resource holding status of the area, land cost, and distance between detailed areas. Can be set.

In addition, technological and economic factors such as energy conversion efficiency, energy storage capacity, lifespan of energy-related facilities, investment costs, and operating costs may also be preset to be constant during the period to be analyzed. Reference market prices for power, hydrogen, liquid fuel, and carbon dioxide must also be set, and additionally, fluctuations in the market price of carbon dioxide may be considered. The amount of carbon dioxide emitted from industrial facilities or the amount of carbon dioxide emitted from other activities can also be set to a certain value.

When the conditions of the above-described parameters are satisfied, the management server may derive analysis results for various items by performing a simulation on the CRES. For example, items from which the analysis result is derived may include the type of RES used as primary energy and the RES utilization strategy for the number of locations, and may include information on the location, number, and type of energy-related facilities to be installed. have. In addition, energy distribution and operation strategies such as the type and amount of energy to be transported and the mode of transport to be used for transport can be derived, and the capital investment power for each period and the timing and scale of investments in each region can also be derived. have.

CRES can cover a variety of energy supply pathways from the primary energy source to the final energy demand. Such a route may be implemented through a combination of various energy conversion technologies, storage technologies, and transportation technologies.

Referring to FIG. 1, a network diagram showing the flow of major technological processes and energy and raw materials within the CRES is shown.

Each skill is presented as a box, but may not be limited to a single task. For example, pyrolysis techniques include (i) crushing and cleaning of supplied biomass, (ii) pretreatment for hydrolysis, (iii) pyrolysis for liquid fuel production, and (iv) hydrocracking for liquid fuel production. It can be done through.

Referring to FIG. 1, in the process of analyzing CRES through the present invention, three types of primary energy sources may be considered. The primary energy sources to be considered may be wind, solar and biomass, and such energy sources may be converted into electricity or hydrogen or converted into liquid fuel through various technological processes.

In FIG. 1, the type of energy source and the energy production process through it, energy storage process, and energy demand are indicated as individual items, and the energy transport process is indicated in the form of arrows connecting each item.

Referring to FIG. 1, it is possible to grasp the relationship between technology and energy produced through it. For example, electric energy produced through wind turbines, solar heat, or biomass may be stored in EES (Electrical Energy Storage) and then supplied to a place where there is a demand for electric energy, and a place where electrolysis is performed. It can also be supplied as.

According to an embodiment of the present invention, a transport-related analysis result may be derived by a CRES analysis performed in a management server, and the transport-related analysis result includes an amount of energy or energy sources to be transported, types of various transport modes, and It can be influenced by numbers, etc.

2 is a block diagram schematically showing the configuration of the management server 100 according to an embodiment of the present invention.

Referring to FIG. 2, the management server 100 includes an analysis preparation execution unit 110, an economic factor management unit 120, an energy demand management unit 130, a regional information management unit 140, a technology economy factor management unit 150, and a simulation. It may be configured to include the execution unit 160, the communication unit 170, the storage unit 180, and the control unit 190.

The management server 100 may receive various types of information and setting values from an external user terminal and an external server. The external user terminal may be configured as a terminal operated by the management entity of the management server 100, and the management entity may input setting values necessary for the simulation of CRES through its own user terminal. Such a user terminal may be one of digital devices equipped with a memory means such as a mobile phone, a smart phone, a PDA, a tablet PC, and a personal computer (desktop, notebook, etc.) and equipped with a microprocessor to have computing power.

The analysis preparation execution unit 110 may manage details that must be set before the simulation is performed.

According to an embodiment, the analysis preparation execution unit 110 may set a specific area as an analysis target and classify the set specific area into a plurality of detailed areas. In addition, the analysis preparation execution unit 110 may set a period to be an analysis target, and divide the period set as the analysis target period into arbitrary time intervals and classify it.

The region classification and period classification of the analysis preparation execution unit 110 may be selected by an input of a management subject of the management server 100.

Looking at an example in which the analysis preparation execution unit 110 sets a specific region as an analysis target and classifies the set specific region into a plurality of detailed regions, all regions of the Republic of Korea may be set as the analysis target, and each administration of the Republic of Korea Zones can be classified into sub-regions. 3 to 5 to be described later, the territory of the Republic of Korea is classified into sub-regions of Incheon, Seoul, Gyeonggi, Chungnam, Daejeon, Jeonbuk, Gwangju, Jeonnam, Gangwon, Chungbuk, Gyeongbuk, Daegu, Gyeongnam, Ulsan, and Busan. You can check.

Looking at an example in which the analysis preparation execution unit 110 sets a period to be analyzed and divides the period set as the analysis target period into arbitrary time intervals, the analysis preparation execution unit 110 is from 2014 to 2050. It is possible to set up to 36 years as an analysis target period, and classify it into three-year increments, which can be classified into 13 analysis periods.

The economic factor management unit 120 may set a tax rate, discount rate, interest rate, and capital cost coefficient related to an energy source. Such a setting may be set by input of a management entity through a user terminal, and a tax rate, discount rate, interest rate, and capital cost coefficient for a specific time may be received from an external server and set as a value for the period to be analyzed.

The energy demand management unit 130 may collect energy demand information for a period set as an analysis target period by the analysis preparation execution unit 110. The energy demand information may include energy demand information for each period and region, and may also include energy demand information for each of electricity, hydrogen, and liquid fuel.

The energy demand management unit 130 may generate a predicted value using past energy demand information and utilize the generated predicted value in setting energy demand information for each period, region, and type for a period set as an analysis target period.

When the analysis preparation execution unit 110 sets a specific area as an analysis target and classifies the set specific area into a plurality of detailed areas, the area information management unit 140 provides the status of resource possession, land cost, and detailed area for each detailed area. You can set the distance between them.

According to an embodiment of the present invention, the distance between the detailed areas managed by the area information management unit 140 may be set differently according to the location of the energy-related facilities installed in each area, even if they are between the same detailed areas. For example, various types of power generation facilities may exist in various locations in one sub-region, and locations where storage facilities and demand exist may also exist in various locations, so the distance between sub-regions is power generation facilities, storage facilities, and energy sources. In addition, it can be set according to the situation according to the location of the demand.

The technology economy factor management unit 150 may set technology economy factors such as energy conversion efficiency, energy storage capacity, lifespan of a facility, investment cost, and operation cost.

As for the energy conversion efficiency, efficiency figures can be managed for each type of energy conversion, and the energy storage capacity, life of the facility, investment cost, and operation cost can also be determined differently depending on the situation of each energy-related facility.

The simulation performing unit 160 may perform simulation on various items to be determined within the CRES, and various intermediate variables may be set and calculated during this process.

The simulation execution unit 160 may calculate power generated by the electricity production system for each energy source. In detail, the simulation performing unit 160 may calculate the amount of power produced through wind, solar, biomass, and the like.

Calculation of power generation through wind power performed by the simulation performing unit 160 may be calculated through Equation 1 as follows.

In Equation 1, the left side refers to the amount of electricity produced through wind power, and the variables on the right side are Cp(v _t ) = power coefficient, ρ _h = air density at height h, β _j = area that the blade sweeps, v _gt = wind speed in g area at time t, η _j = power production efficiency, N _ijgt = This can be matched with the number of wind turbines installed in the g area during the t period.

Calculation of power generation through biomass performed by the simulation performing unit 160 may be calculated through the following equation.

In Equation 2, the left side means the amount of electricity produced through the biomass, and the variables on the right side are μ _i = HHV (High heating value: high heating value, ξ _i = the amount of energy required for biomass drying, γ) _i = moisture content of the biomass, o _i = target moisture content to dry the biomass, θ _i = parasitic factor, Q _ijlg'gt = during the t period l from the g'area to the j production facility in the g area via transport mode It can be matched such as the transport speed of the biomass being transported.

The simulation performing unit 160 may calculate the amount produced from each of the plurality of energy sources in this manner in relation to hydrogen production and liquid fuel production, not electricity production.

The simulation performing unit 160 may limit and set various items and variables in order to simulate a model for optimizing a network in the CRES.

The simulation performing unit 160 may preferentially set a variable such that the energy demand for a specific detailed region by time is satisfied. The types of energy can be classified into electricity, hydrogen, and liquid fuel. The simulation performing unit 160 may set variables and items equal to the amount of energy transferred from other regions to the corresponding region through various types of transport modes in the specific type of energy demand for a specific region during a specific period.

The simulation performing unit 160 may calculate flows of energy and energy sources. For example, electric energy, the simulation execution unit 160, the total amount of electric energy produced during a specific period in a specific area, the amount of electric energy used to charge the EES facility, and electricity used to produce hydrogen through electrolysis. It can be assumed to be equal to the sum of the amounts of energy.

The simulation performing unit 160 may set the simulation model such that the total amount of hydrogen produced by the hydrogen production system in a specific region during a specific period is the same as the amount transferred from the region to the hydrogen tank in another region. Similarly, the simulation execution unit 160 may set a simulation model so that the amount of liquid fuel produced in the entire CRES is the same as the amount used, and the total amount of carbon dioxide emitted from each energy production is discharged within the entire CRES. The simulation model can be set to be the same as the amount of carbon dioxide produced. In addition, the simulation performing unit 160 may set the simulation model so that the total amount of carbon dioxide discharged from each region is the same as the amount collected by the carbon dioxide capture facility in the CRES.

In relation to the biomass, the simulation performing unit 160 may set the simulation model such that the total amount of usable biomass is equal to or greater than the consumption amount of the total biomass.

The simulation performing unit 160 may set a simulation model such that the flow rate of energy and energy sources is limited to the minimum and maximum capacity of an energy-related facility. In addition, the simulation performing unit 160 may set a simulation model such that the amount of energy stored in the energy storage system is limited by the minimum and maximum storage capacity of the facility.

As described above, the simulation performing unit 160 may set various limits for energy and energy sources in setting a simulation model, and such limitations may be implemented through an equation.

The simulation performing unit 160 may also manage the life of the facility in relation to various energy-related facilities considered within the CRES. For example, a facility that has reached the end of its life can be considered as non-existent for energy production, storage, use, etc.

According to an embodiment of the present invention, based on the collected economic factor information, energy demand information, regional information, and technology economy factor information, the simulation performing unit 160 has a maximum net present value (NPV). The TNF (Total Net Flow: Net Cash Flow) can be calculated.

In this process, the simulation execution unit 160 may use Mixed Integer Linear Programming (MILP) to calculate TNF for the NPV to be maximized. Mixed-integer linear programming is a method for deriving a model solution in which some of the variables used in the simulation model are integers. That is, mixed-integer linear programming may be described as a technique for solving a problem of determining variables that cause a linear function to have a minimum or maximum while satisfying a linear inequality or a linear equation, which is a finite number of constraints.

The simulation performing unit 160 may calculate the TNF at which the NPV is maximum by using Equation 3 as follows.

The variables on the right side of Equation 3 may correspond to Ω = tax rate, Λ = depreciation rate, and r = interest rate.

According to an embodiment of the present invention, in Equation 3, TNF may be calculated through Equation 4 as follows.

Various variables included in Equation 4 above are TB = total profit, TC = total cost, TSP = energy sales profit, TSC = residual price of the facility, TBC = CO ₂ sales profit, FIC = facility investment cost, TIC = transportation investment. Cost, FOC = facility operating cost, TOC = transport operating cost, TFC = feedstock cost, TLC = land cost.

In Equation 4, the calculation of the TSP corresponding to the energy sales profit may be calculated through Equation 5 below.

In Equation 5, the variables on the right side are respectively I ^E = electricity, I ^H = hydrogen, I ^F = liquid fuel, J ^EP = electricity production facility, J ^HP = hydrogen production facility, J ^FP = liquid fuel production facility, G = Area, Φ = Sales price, P _ijgt = can be matched such as the amount of i produced from the j facility in the g area during the t period.

In Equation 5, P _ijgt calculation for power generation through wind power may be calculated through Equation 1 described above, and P _ijgt calculation for power production through biomass may be calculated through Equation 2 above. I can.

In Equation 4, the calculation of the TIC corresponding to the transport investment cost may be calculated through Equation 6 as follows.

Various variables included in Equation 6 are ζ _t = number of days within t period, δ _l = l availability efficiency of transport mode, χ _l = l capacity of transport mode, κ _gg' = transport distance between g and g' , Φ _l = l average speed of transport mode, λ _l = l time required for loading/unloading in transport mode, Q _i(j)lgg't = during t time l from region g'to region g via transport mode j Can be responded to, such as the rate at which energy or biomass is transported to the facility.

Various variables included in Equation 4 may be calculated in various ways as can be referred to through Equations 1, 2, 5, and 6.

The simulation performing unit 160 may calculate the TNF that maximizes the NPV by using the above-described equations, and in this process, it is possible to derive values that various variables must have.

In this way, according to the optimization solution derived by the simulation performing unit 160, the CRES manager can determine the type of energy-related facilities, installation time, installation location, installation scale, transportation method, investment scale, etc. on a reasonable basis. do.

The communication unit 170 enables the management server 100 to communicate with a complex renewable energy supply system, other user terminals, or an external server. The communication network used by the communication unit 170 to perform communication may be configured regardless of its communication mode such as wired or wireless, for example, a local area network (LAN) or a metropolitan area network (MAN: Metropolitan). Area Network) and a wide area network (WAN).

The storage unit 180 serves to store information collected, generated, and operated in various configuration units of the management server 100. That is, the storage unit 180 may store information on various energy-related facilities, information on regional characteristics of detailed regions, information on energy demand for each region according to energy types, and information on economic factors or technological and economic factors. The storage unit 180 may include, for example, a memory, a cache, a buffer, and the like, and may be formed of software, firmware, hardware, or a combination of at least two or more of them.

The control unit 190 includes an analysis preparation execution unit 110, an economic element management unit 120, an energy demand management unit 130, a regional information management unit 140, a technology and economy element management unit 150, a simulation execution unit 160, and a communication unit. A function of controlling the data flow between the 170 and the storage unit 180 may be performed. That is, the control unit 190 according to an embodiment of the present invention includes an analysis preparation execution unit 110, an economic element management unit 120, an energy demand management unit 130, a regional information management unit 140, and a technology economy element management unit 150. ), the simulation execution unit 160, the communication unit 170, and the storage unit 180 can be controlled to perform their own functions, respectively.

In FIG. 2, the analysis preparation execution unit 110, the economic factor management unit 120, the energy demand management unit 130, the regional information management unit 140, the technology and economy factor management unit 150, the simulation execution unit 160, and the communication unit 170 ) Is a configuration that functionally classifies the control unit 190, and thus may be integrated and configured as one control unit 190.

3 is a map of Korea's traffic-related energy demand within the CRES estimated by the management server 100 according to an embodiment of the present invention.

Referring to FIG. 3, an optimization model for energy demand may be derived according to the simulation result of the management server 100 described through FIG. 2.

FIG. 3 shows the energy demand for transportation in Korea expected in 2050, and FIG. 3 shows the results for three types of energy: electricity, hydrogen, and liquid fuel.

According to an embodiment, the management server 100 includes market share information of an internal combustion locomotive (ICEV), an electric vehicle (EV), and a fuel vehicle (FCV) and related to each energy source in order to generate an energy demand map as shown in FIG. 3. Simulation can be performed based on tax rate information and the like.

The management server 100 may reflect regional characteristics in order to calculate the energy demand for each detailed region shown in FIG. 3. The regional characteristics reflected by the management server 100 may include a population, the number of registered transportation means, an average annual mileage, and the like.

4 is a map showing the result of the optimized setting of the energy supply network of CRES generated by the simulation of the management server 100 according to an embodiment of the present invention.

FIG. 4 is an analysis target of Korea in 2050, and referring to FIG. 4, the results can be derived as 7935 large wind turbines are installed in Korea in 2050 to produce 10,408 GWh of electric power per year.

Referring to FIG. 4, it can be seen that the wind turbine is installed only in the Gangwon region, which is the R9 region among the detailed regions in Korea, which may be due to a regional characteristic with high wind speed. Within CRES, solar and bio-systems may not be the means of generating electricity, which may be due to relatively high production costs, transportation costs and investment costs compared to wind power generation.

As such, in the simulation result shown in FIG. 4, only one method may be selected in relation to hydrogen production and liquid fuel production. For example, in hydrogen production, an electrolysis method may be selected, and in liquid fuel production, a biomass pyrolysis method may be selected.

Looking at the simulation results of FIG. 4, in relation to hydrogen production, a large-capacity electrolysis facility may be installed to be distributed in various regions. This may be because the transport cost of hydrogen is high, so it is economically efficient to distribute hydrogen production facilities in various areas rather than concentrated in a certain area.

Some regions can produce more hydrogen than demand, and likewise, some regions can produce less hydrogen than demand. Referring to FIG. 4, a detailed area classified as R9 may have a relatively low demand for hydrogen, but a simulation result can be derived that the most hydrogen cracking facilities are installed due to low land costs. Similarly, a simulation result can be derived that a sub-region classified as R2 has a high demand for hydrogen, but no hydrogen production facilities are installed due to very high land costs.

Referring to FIG. 4, in the production of liquid fuel, the pyrolysis facility through biomass can be installed in R9, where transportation cost is low and land cost is low.

The management server 100 may perform calculation and analysis based on the above-described equations in deriving a simulation result for CRES, and mixed integer linear programming may be used in this process.

In deriving the simulation result for the CRES, the management server 100 may consider regional characteristics such as resource availability, land cost, and distance-based transportation cost for the demand level of detailed regions.

FIG. 5 is a result of showing the scale of infrastructure to be installed in each area for each specific period in CRES, generated by simulation of the management server 100 according to an embodiment of the present invention.

According to an embodiment of the present invention, the management server 100 may establish an investment strategy for the timing and location of the investment for the set analysis period. 5 shows some of Korea's investment strategies established for 2014 to 2050. In FIG. 5, the time intervals from 2014 to 2050 were classified into 13 time intervals with an interval of 3 years. That is, in FIG. 5, T1 may mean 2014 and T13 may mean 2050.

In FIG. 5, energy-related facilities installed in each region of Korea every six years and energy demand of each region are shown according to the simulation result. The size of energy-related facilities can be calculated by synthesizing the status of installed and closed facilities.

Looking at the example of FIG. 5, production facilities for electricity and liquid fuel may be installed only in R9 among the detailed areas. This may be due to the assumption that transportation costs for electricity and liquid fuel are based on the existing infrastructure, and that only operating costs are required without facility investment costs. In contrast, in the case of a hydrogen supply network, it is not centralized and can be distributed in various sub-regions because the facility investment cost for the transport system is required.

In FIG. 5, the number of installed energy-related facilities may be calculated differently according to energy demand at a specific time interval. According to an embodiment, from the simulation result of the management server 100, wind energy may be selected to be in charge of electricity production, and the electric energy produced through wind power generation meets the general power demand as well as hydrogen. It can also be used to meet the electric power demand of electrolysis production facilities to produce. Thus, the number of wind turbines that must be installed for wind power generation can be affected not only by the demand for electricity but also by the demand for hydrogen. Referring to FIG. 5, it can be seen that the amount of power produced by the yellow line is different from the amount of power demanded by the yellow area as time passes.

As described above, according to various embodiments of the present invention, simulation results for carrying out investment and design planning in the complex renewable energy supply system can be derived, and such simulation results are performed by setting various constraint conditions and mixed integer linear programming. It can be done through. Decision makers can make decisions about system design and investment based on the optimized numerical values through simulation results.

The steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or a combination thereof. Software modules include Random Access Memory (RAM), Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), Flash Memory, hard disk, removable disk, CD-ROM, or It may reside on any type of computer-readable recording medium well known in the art to which the present invention pertains.

In the above, embodiments of the present invention have been described with reference to the accompanying drawings, but those of ordinary skill in the art to which the present invention pertains can be implemented in other specific forms without changing the technical spirit or essential features. You will be able to understand. Therefore, the embodiments described above are illustrative in all respects, and should be understood as non-limiting.

100: management server
110: analysis preparation execution unit
120: Economic Factors Management Department
130: Energy demand management department
140: Regional Information Management Department
150: Technology and Economy Factors Management Department
160: simulation execution unit
170: communication department
180: storage unit
190: control unit

Claims (10)

In a computing system, a method of performing a simulation related to the operation of a complex renewable energy supply system,
Classifying a specific area selected as an analysis target into a plurality of detailed areas, dividing a period set as an analysis period into a random time interval, and classifying it;
Collecting economic factor information including at least one of a tax rate, a discount rate, an interest rate, and a capital cost coefficient related to an energy source;
Collecting energy demand information for a period set as the analysis period;
Collecting area information including at least one of resource holding status, land cost, and distance between the detailed areas for the plurality of detailed areas;
Collecting technology and economy factor information including at least one of energy conversion efficiency, energy storage capacity, facility life, investment cost, and operating cost; And
Based on the collected economic factor information, energy demand information, regional information, and technology economy factor information, it calculates the total net flow (TNF) that maximizes the NPV (Net Present Value). Includes steps,
The step of calculating TNF to maximize the NPV is characterized by utilizing the following equation,

(Ω = tax rate, Λ = depreciation rate, r = interest rate)
The TNF is characterized in that it is calculated through the following equations,

(TB = gross profit, TC = total cost, TSP = profit from energy sales, TSC = residual value of the facility, TBC = _{profit from sales of CO 2} , FIC = cost of facility investment, TIC = cost of transportation investment, FOC = cost of facility operation, TOC = Transportation operating cost, TFC = feedstock cost, TLC = land cost)
The TSP is characterized in that it is calculated through the following equation,

(I ^E = electricity, I ^H = hydrogen, I ^F = liquid fuel, J ^EP = electricity production facility, J ^HP = hydrogen production facility, J ^FP = liquid fuel production facility, G = area, Φ = Sales price, P _ijgt = amount of i produced from facility j in region g during period t)
How to conduct a simulation for a combined renewable energy supply system.
The method of claim 1,
The energy demand information,
A method of performing a simulation for a hybrid renewable energy supply system that includes energy demand information for each of electricity, hydrogen and liquid fuel.
The method of claim 1,
The step of calculating the TNF to maximize the NPV,
A method of performing a simulation for a complex renewable energy supply system, characterized in that it is performed through mixed integer linear programming (MILP).
delete delete delete The method of claim 1,
A method of performing a simulation for a hybrid renewable energy supply system, characterized in that the calculation of _{P ijgt} for power generation through wind power is calculated through the following equation.

(Cp(v _t ) = power factor, ρ _h = air density at height h, β _j = area the blade sweeps, v _gt = wind speed in g area at time t, η _j = power production efficiency, N _ijgt = number of wind turbines installed in area g during period t)
delete delete In the management server that performs a simulation related to the operation of the complex renewable energy supply system,
An analysis preparation execution unit that classifies a specific area selected as an analysis target into a plurality of detailed areas, and divides a period set as an analysis period into arbitrary time intervals;
An economic factor management unit that collects economic factor information including at least one of a tax rate, discount rate, interest rate, and capital cost factor related to an energy source;
An energy demand management unit collecting energy demand information for a period set as the analysis period;
An area information management unit collecting area information including at least one of resource holding status, land cost, and distances between the detailed areas for the plurality of detailed areas;
A technology economy factor management unit that collects technology economy factor information including at least one of energy conversion efficiency, energy storage capacity, facility life, investment cost, and operation cost; And
Based on the collected economic factor information, energy demand information, regional information, and technology economy factor information, it calculates the total net flow (TNF) that maximizes the NPV (Net Present Value). It includes a simulation execution unit,
The step of calculating TNF to maximize the NPV is characterized by utilizing the following equation,

(Ω = tax rate, Λ = depreciation rate, r = interest rate)
The TNF is characterized in that it is calculated through the following equations,

(TB = gross profit, TC = total cost, TSP = profit from energy sales, TSC = residual value of the facility, TBC = _{profit from sales of CO 2} , FIC = cost of facility investment, TIC = cost of transportation investment, FOC = cost of facility operation, TOC = Transportation operating cost, TFC = feedstock cost, TLC = land cost)
The TSP is characterized in that it is calculated through the following equation,

(I ^E = electricity, I ^H = hydrogen, I ^F = liquid fuel, J ^EP = electricity production facility, J ^HP = hydrogen production facility, J ^FP = liquid fuel production facility, G = area, Φ = Sales price, P _ijgt = amount of i produced from facility j in region g during period t)
Management server.

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