WO2015069056A1 - Method and system for managing power - Google Patents

Abstract

A method and system for managing power are disclosed. The power management method, according to the present invention, comprises the steps of: calculating an available resource on the basis of price data; modeling a task of a production process within industrial equipment so as to minimize energy costs by reflecting the calculated available resource therein. Thus, the charging/discharging of an energy storage device and produced power of an energy generation device are controlled and an operating point of a schedule task of a production process is selected so as to minimize energy costs according to a change in the price data.

Description

Power Management Method and System

The present invention relates to a power management method and system that can manage the power demand of the production process of the industrial equipment by applying a smart grid system to the industrial equipment to minimize the operating cost of the industrial equipment.

Recently, smart grid technology has been selected as a next generation core business in many countries and actively researched. The smart grid system aims to save power, and generally uses a method of turning on / off a specific device as shown in Korean Patent Laid-Open No. 2012-0097551 for power saving. However, existing smart grid systems do not provide an appropriate way of distributing energy to efficiently use the supplied energy.

In addition, current power outages are frequently occurring because a large number of people use a lot of energy at a certain time. Thus, there is a need for a way to reduce people's energy use during these times, but currently does not exist.

The present invention is to provide a power management method and system that can manage the power demand of the production process by applying a smart grid system to the industrial equipment to minimize the operating cost of the industrial equipment.

According to an aspect of the present invention, by applying a smart grid system to an industrial facility is provided a method that can manage the power demand of the production process to minimize the operating cost of the industrial facility.

To this end, the power management method according to the present invention comprises the steps of: calculating available resources based on price data; And modeling a task of a production process in an industrial facility to minimize energy costs by reflecting the calculated available resources.

In addition, the power management system according to the present invention includes an industrial facility having a production process including at least one task of consuming consumer goods to produce production goods; And an energy management device that calculates power based on price data, and models a task of the production process to minimize energy costs by reflecting the calculated power.

In addition, the power management method according to the present invention comprises the steps of modeling available resources based on price data; Modeling a schedule task capable of adjusting power demand among tasks which are processing operations performed in a production process in an industrial facility; And managing power demand by selecting an operating point of the modeled schedule task to minimize energy costs by reflecting the modeled available resources.

In addition, the energy management apparatus according to the present invention comprises a resource modeling unit for modeling available resources based on price data; A task modeling unit modeling a task which is a processing operation performed in a production process in an industrial facility; And a resource management unit for managing power demand by selecting an operating point of the modeled task to minimize energy costs by reflecting the modeled available resources.

By providing a power management method and system according to an embodiment of the present invention, by applying a smart grid system to the industrial equipment it is possible to manage the power demand of the production process to minimize the operating cost of the industrial equipment.

1 is a block diagram schematically showing the configuration of a smart grid system of an industrial facility according to an embodiment of the present invention.

2 is a flow chart illustrating a method for energy management for industrial equipment according to an embodiment of the present invention.

3 is a flowchart illustrating available resource modeling according to an embodiment of the present invention.

4 is a view for explaining the charging and discharging in the energy storage device (ESS) according to an embodiment of the present invention.

5 is a view for explaining the operation point of the schedule task according to an embodiment of the present invention.

6 is a block diagram schematically illustrating an internal configuration of an energy management apparatus according to an embodiment of the present invention.

7 shows a state-task network (STN) of an oxygen production plant to which the power management method of the present invention has been applied.

8 is a diagram illustrating a concept of MILD (Mixed Integer Linear Programming).

As the inventive concept allows for various changes and numerous embodiments, particular embodiments will be illustrated in the drawings and described in detail in the written description.

However, this is not intended to limit the present invention to specific embodiments, it should be understood to include all transformations, equivalents, and substitutes included in the spirit and scope of the present invention.

In the following description of the present invention, if it is determined that the detailed description of the related known technology may obscure the gist of the present invention, the detailed description thereof will be omitted.

Terms such as first and second may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another.

The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting of the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise.

In this application, the terms "comprise" or "have" are intended to indicate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, and one or more other features. It is to be understood that the present invention does not exclude the possibility of the presence or the addition of numbers, steps, operations, components, components, or a combination thereof.

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

1 schematically illustrates a smart grid system of an industrial installation according to the present invention.

The smart grid system of the industrial facility shown in FIG. 1 performs power management corresponding to the power demand for the production process 140 in the industrial facility such as steel, cement, paper production, and the like.

The production process 140 may perform a certain task using a feed or an intermediate to produce an intermediate or a final product, and each task may include a plurality of operations.

In Fig. 1, raw materials or intermediates or intermediates or final products using the same are shown in a circle. The portions 1, 2, 3, and 4, which are dotted circles, are raw materials, and the portions a, b, c, d, and e that are solid circles are intermediate materials. The insets 1, 2, 3 and 4 represent the final product.

Tasks are also marked with rectangles to indicate processing operations. Tasks include non-schedulable tasks (NST) and schedulable tasks (ST). The portion of the double rectangle represents non-scheduled tasks A, C, E, and F, and the portion of the single rectangle represents schedule tasks B, D, and G.

A non-scheduled task is a task that is not scheduled for demand and needs to be satisfied immediately regardless of whether electricity is cheap or expensive, and a scheduled task is a task capable of adjusting a schedule for demand. For example, non-scheduled tasks include furnace tasks in steel production or assembly tasks in automobile production, and scheduled tasks include hot / cold water or packaging tasks.

The smart grid system of the industrial equipment uses a lot of power in each task of the production process during the time when the unit price is relatively low based on the unit price provided by the utility company 150 and each task during the time when the unit price is relatively high Energy can be managed to consume less power.

Here, the unit price may be provided for each time period in which the price for power use is changed from the utility company, or may be provided with price data including the unit price for power use for each time period.

In order to facilitate understanding and explanation, it is assumed herein that the unit price for the use of power is changed every time period.

Smart grid system according to an embodiment of the present invention can manage the electricity demand (electricity demand) of each task in the production process to minimize the operating cost while satisfying the market demand.

As shown in FIG. 1, the smart grid system of a production facility may include an energy management device 110, an energy storage device 120, and an energy generation device 130.

The Energy Management System (EMS) 110 receives and stores price data on a unit price from the utility company 150 and stores a state-task network (STN) for the production process 140 of an industrial facility. : Models each task of the production process 140 to meet market demand in a state of receiving and storing a state-task network, modeling available resources based on price data, and modeling available resources In this way, you can manage the power demand of the production process by selecting the operating point of each task modeled to minimize the cost.

Here, the STN is composed of a task node and a state node, the task node represents a processing operation, and the state node refers to raw materials, intermediate materials, and final products.

The energy storage system (ESS) 120 is a means for storing resources at a low unit price based on price data, and providing resources to a production process of an industrial facility at a high unit price.

Energy Generating System (EGS) 130 is a means for producing available resources and providing them to the production process of industrial facilities.

2 is a flow chart illustrating an energy management method for a production process of an industrial facility according to an embodiment of the present invention, FIG. 3 is a flow chart illustrating available resource modeling according to an embodiment of the present invention, and FIG. FIG. 5 is a view illustrating a first available resource modeling according to an embodiment of the present invention, and FIG. 5 is a view illustrating an operation point of a schedule task according to an embodiment of the present invention.

It is assumed that the energy management device 110 receives and stores price data on a unit price from the utility company 150, and receives and stores a state-task network (STN) for the production process 140. Shall be.

In operation 210, the energy management apparatus 110 models each task of the production process 140 to produce a final product that satisfies the market demand.

In more detail, the energy management device 110 may model each task of the production process 140 based on price data. For example, the energy management device 110 may model each task such that the production process 140 consumes a lot of power when the unit price for the power for each time period provided by the utility company is low. If expensive, each task can be modeled such that production process 140 consumes less power.

According to one embodiment of the present invention, a task refers to a processing operation performed in the production process 140.

Each task of the production process 140 is divided into a scheduled task ST and a non-scheduled task NST.

The power demand consumed in the non-scheduled task may be determined a priori at the first start, but then may be predicted with reference to the previous history for each time period.

The schedule task supports a plurality of operating points as shown in FIG. 5, and the power demand consumed for each time period is a priori determined.

According to an embodiment of the present invention, the operation point refers to a detailed processing operation set in consideration of the consumer goods consumed in the task and the production goods output from the task. The production process of an industrial facility includes a plurality of tasks, and the operating point is selected for each schedule task in consideration of market demand and cost.

As shown in FIG. 5, the schedule task may produce consumer goods by processing consumer goods at any operating point. The consumer goods are represented by the input state (State s1) and the produce goods are represented by the output state (State s2). Here, the consumer goods are raw materials or intermediate goods to be produced through the task to make the production goods, the production goods are intermediate goods or final goods produced through the task of consuming the consumer goods.

The power demand consumed at multiple operating points in the schedule task is different. For this reason, the input state amount (consumption amount) and the output state amount (production amount) consumed at each operation point may respectively differ.

That is, the plurality of operating points in the schedule task may produce more production by consuming more consumer goods as the demand for power increases. On the other hand, operating points with lower power demands can consume less consumer goods and produce less production.

Accordingly, the energy management device 110 may model the schedule task with reference to the price data and the market demand.

That is, the energy management device 110 may be modeled so that an operating point with high power demand is selected and operated when the unit price is low, and may be modeled so that an operating point with low power demand is selected and operated when the unit price is relatively high. Accordingly, the energy management apparatus 100 may reduce the overall energy cost by making the intermediate material having high power consumption in advance when the electric charge is low and using the intermediate material prepared in advance when the electric charge is high.

In addition, any one of a plurality of operating points supporting the schedule task according to an embodiment of the present invention may be selected.

Of course, a plurality of operating points supporting schedule tasks may be selected and operated according to an implementation method, but in the present specification, each schedule task selects and operates only one operating point for convenience of understanding and description. Let's assume you can.

To this end, the present invention is a generalized demand response algorithm that can determine the optimal scheduling of the schedule task (ST) and in-facility Distributed Energy Resource (DER) to minimize the energy cost of industrial equipment Algorithm) is proposed.

The demand response algorithm (DR algorithm) can be made using a mixed integer linear programming (MILP) as shown in FIG. Inputs include day-ahead hourly electricity price, STN of the production process, operation data for each task (supported operating points, consumption and production speeds of related states, power demand at each operating point, etc.), and storage of each state. Information (initial storage in each state, lower and upper storage requirements, etc.), operation information of the ESS (energy storage capacity, maximum charging and discharging speed, charging and discharging efficiency, etc.), operating information of the EGS (operating range and related costs of electric power production, etc.) ).

All input is made using a MILP with an objective function and a set of constraints. The main features of MILP are:

(a) Decision variables can be continuous or discontinuous

(b) The objective function and constraints must be linear

The objective function is defined to minimize the energy costs of industrial installations. Linear constraints are specified for process, ESS, EGS and power sales or purchases. Constraints on process modeling include constraints such as operation, material balance, power, and storage. Constraints on the ESS include constraints such as power balance, capacity, charge and discharge. Finally, there are constraints on EGS and power sales / purchases.

When the MILP problem is solved, the output calculates the operating point of the selected ST, the rate of charge / discharge of the ESS, the power production of the EGS, the purchase or sale of electricity, and the total energy cost of the industrial facility at each time interval.

Modeling of the schedule task ST of the production process 140 includes constraints on material balance, power balance, storage and operation.

1) material balance

The material balance is shown in Equation 1a, where the storage (S _{s, t} ) of each state s at time t is stored at t-1 (S _{s, t-1} ) and at the time interval (t-1, t) It is equal to the result of subtracting the total amount of state s consumed in the time interval (t-1, t) from the sum of the total amount of state s produced.

Equation 1a

Where PA _{s, i, t-1} and CA _{s, i, t-1} are the amounts produced and consumed by task i in the time interval (t-1, t).

2) power balance

The total power demand E _t during the time interval t, t + 1 is equal to the sum of the power demands e _{i, t} of each task, as shown in Equation 1b.

Equation 1b

3) save balance

In order to ensure the reliability of the production process, as shown in Equation 1c, the storage (S _{s, t} ) of each state should not be less than the minimum value (LB _s ) or more than the maximum value (UB _s ).

Equation 1c

4) Operation Constraints

Referring to FIG. 5, a task ST _i is shown which consumes state s1 to produce state s2 and supports M operating points. For each operating point m, the consumptions ca _{i, m, s1} of the state _s1 , the outputs pa _{i, m, s2} of the state s2 _, and the power demand e _{i, m} are known a priori at each time interval. Task i wants to lower the power consumption when the electricity price is high and to increase the power consumption when the electricity price is low.

Each operating point of task i is related to the binary variables z _{i, m, t} during the time interval (t, t + 1). Z _{i, m, t} = 1 if task i operates at the m operating point during the time interval (t, t + 1); otherwise z _{i, m, t} = 0. During each time interval, ST _i operates only at one operating point due to the constraint of Equation 1d.

Equation 1d

That is, during task interval (t, t + 1) _, only one z _{i, m, t} in task i is 1 and the rest are all 0.

Accordingly, the power demand of the task i during the (t, t + 1) time interval may be derived as in Equation 1 below.

Equation 1

는 각 동작 점의 동작 상태를 나타내며, e_i,m는 각 동작 점의 전력 수요를 나타낸다.Where m represents an operating point,

Denotes an operating state of each operating point, and e _{i, m} denotes a power demand of each operating point.

In addition, the amount of consumption consumed by the task i during the (t, t + 1) time interval may be represented by Equation 2 below, and the amount of produced product produced by the task i may be expressed by Equation 3 below.

Equation 2

Equation 3

The energy management apparatus 110 may determine the amount of consumables and products by referring to the unit price to meet the market demand, model each task of the production process to satisfy the determined amount of consumer goods and the products, and power required by each modeled task. Demand can be derived.

In operation 215, the energy management apparatus 110 models the available resources based on the price data. Here, the available resources include resources stored in the energy storage device 120 and resources generated by the energy generation device 130.

Hereinafter, for convenience of understanding and explanation, the resource of the energy storage device will be referred to as a first available resource, and the resource generated by the energy generating device will be referred to as a second available resource.

A method of modeling available resources will be described in detail with reference to FIG. 3. In FIG. 3, the method of modeling the first available resource is described as prior art for convenience of understanding and description. However, the modeling of the first available resource and the second available resource may be performed in parallel, respectively. Of course.

In operation 310, the energy management apparatus 110 models the first available resource.

That is, in the modeling of the first available resource, the resource is stored in the energy storage device 120 when the unit price is low based on the price data, and the resource stored in the energy storage device 120 is stored when the unit price is high. Model it for production.

Modeling of the first available resource, that is, energy storage device (ESS) modeling includes energy balance, charge and discharge constraints, and capacity constraints.

1) Energy Balance Constraints

The amount of the first available resource, that is, the amount of resources S _{ESS, t} stored at time t, is equal to the amount of resources S _ESS, t-1 stored at the previous time _t-1 and the current time interval t-1, It is equal to the amount obtained by subtracting the amount of resources charged by t) multiplied by the charge efficiency α and subtracting the amount of resources discharged in the current time interval t, t-1 divided by the discharge efficiency β. If this is expressed as an equation with reference to FIG. 4, Equation 4 is obtained.

Equation 4

Where α denotes charging efficiency, β denotes discharging efficiency, E _{CESS, t-1} denotes the amount of charged resources in (t-1, t) time interval, and E _{DESS , t-1} represents the amount of resources discharged in the (t-1, t) time interval.

2) Charge / Discharge Constraints

The energy storage device 120 does not simultaneously charge (store) and discharge resources during the same period of time, as represented by Equation 4a.

Equation 4a

Where z _{c, t} and z _{d, t} are binary variables. If the ESS charges electricity in the time interval (t, t + 1), z _{c, t} = 1, otherwise z _{c, t} = 0. Also, if the ESS discharges electricity in the time interval (t, t + 1), then z _{d, t} = 1, otherwise z _{d, t} = 0.

The amount of electricity charged and discharged at each time interval (E _{CESS, t} , E _{DESS, t} ) is limited by the maximum charge rate (CH _SE ) and the maximum discharge rate (DCH _SE ). It can be expressed as Equation 6.

Equation 5

Here, z _{c, t} represents the state of charge and CH _SE represents the maximum charge rate.

Equation 6

Here, z _{d, t} represents a discharge state and DCH _SE represents a maximum discharge rate.

In Equation 5, E _{cESS, t} = 0 when z _{c, t} = 0, and E _{DESS, t} = 0 when z _{d, t} = 0 in Equation 6.

3) capacity limitation condition

As shown in Equation 6a, the amount of energy S _{ESS, t} stored at each time cannot exceed the maximum storage capacity C _SE .

Equation 6a

That is, the energy management device 110 charges resources to the energy storage device when the unit price is low based on the price data, and provides the resources stored in the energy storage device to the production process of the industrial equipment when the unit price is high. Can be modeled At this time, the maximum amount of resources stored in the energy storage device is limited by the storage capacity of the energy storage device. That is, the maximum amount of resources stored in the energy storage device does not exceed the maximum storage capacity of the energy storage device.

In operation 320, the energy management device 110 models a second available resource, that is, an energy generation device (EGS).

According to an embodiment of the present invention, the second available resource is a resource produced by the energy generating device 130 and is divided into a non-scheduled production resource and a scheduled production resource.

Unscheduled production resources represent resources produced by unscheduled energy generating devices, such as solar, wind, and waste heat power plants.

In addition, the schedule production resource represents a resource produced by a schedule energy generating device such as diesel generation.

Non-scheduled production resources are predicted for each hour, and scheduled production resources can be modeled to reflect price data. In this case, the non-scheduled production resource may be estimated at each time with reference to the previous history.

In addition, the schedule production resource may be modeled in consideration of the cost of producing the schedule production resource compared to the price data.

For example, the schedule production resource may be controlled at each time in consideration of the cost required to produce the schedule production resource based on the price data. For example, if price data is very inexpensive, scheduled production resources can be modeled so that they are not produced. On the other hand, if the price data is very expensive, the schedule production resource may be modeled to produce up to the maximum production capacity.

For example, suppose that the price data of resources provided by the utility company 150 is very expensive, and the power demand of the production process is high. In this case, when the cost required to produce the schedule production resource is cheaper than the price data, the schedule production resource may be modeled to be produced.

According to one embodiment of the invention, the schedule production resource may also be modeled in consideration of the first available resource. That is, when price data is expensive, the schedule production resource may be modeled to be produced by the shortage of the first available resource.

As described above, the second available resource is equal to the sum of the scheduled production resource and the non-scheduled production resource. This is expressed as an equation (7).

Equation 7

Since the schedule production resource is accompanied by the consumption of raw materials for the production of resources, the production amount of the schedule production resource can be expressed as Equation (8).

Equation 8

Where CR _t represents the amount of raw material consumed for the production of periodic production resources within a time interval, and γ represents the production efficiency.

In operation 220, the energy management apparatus 110 selects an operating point of each modeled task to minimize costs by reflecting the modeled available resources to manage power demand of the production process of the industrial equipment.

Smart grid technology allows consumers not only to buy electricity from a utility company, but also to sell surplus electricity to the utility company when electricity is available. Hereinafter, power sales and purchase modeling will be described.

According to an embodiment of the present invention, the power demand E _{DM, t} of the production process is derived using the power demand of each task, the first available resource and the second available resource. This may be expressed as an equation (9).

Equation 9

Where E _t represents the power demand of each task, E _{CESS, t} represents the first available resource charged, E _{DESS, t} represents the discharged first available resource, and E _{EGS, t} represents the second Represents an available resource.

If the power demand (E _{DM, t} ) of the industrial equipment is positive, it is necessary to purchase power from the utility company, and if it is negative, it is possible to sell surplus power to the utility company.

The energy management device 110 may manage a shortage to be provided through a utility company when the total power demand of the industrial facility is insufficient by only the first available resource and the second available resource.

In addition, the energy management apparatus 110 may manage the surplus resources to be sold through a utility company when the first available resources and the second available resources are larger than the total power demand of the industrial equipment.

In Equations 9a to 9c, z _{p, t} = 1 when the industrial equipment purchases electricity during the time interval (t, t + 1) and z _{s, t} = 1 when selling electricity. Where B is large and sufficient positive number.

Equation 9a

Equation 9b

Equation 9c

If the available resources are not sufficient to handle all processes, E _{DM, t} becomes a positive equation (9a) z _{p, t} = 1, (9b) is satisfied regardless of z _{s, t} , 9c becomes z _{c, t} = 0. This indicates that industrial equipment purchases electricity from utility companies. If the available resources are greater than the power demand of the industrial plant, E _{DM, t} becomes negative. Since B is a large and sufficient positive number, equation (9a) is satisfied irrespective of z _{p, t} , equation (9b) is z _{s, t} = 1, and equation (9c) is z _{p, t} = 0. This indicates that industrial equipment sells power to utility companies.

Table 1-1 shows the relationship between E _{p, t} , E _{s, t} and E _{DM, t} .

Table 1-1

[Correction under Rule 91 26.11.2014]

E _{p, t} and E _{s, t} are power purchases and sales in time intervals (t, t + 1), respectively. If both values are negative and E _{p, t} = 0, then there is no energy purchase; if E _{s, t} = 0 _, there is no energy sale. If E _{DM, t} is not negative and E _{p, t} = E _{DM, t} , E _{s, t} = 0, this means that the industrial facility purchases power from the utility company. If E _{DM, t} is negative and E _{p, t} = 0, E _{s, t} = -E _{DM, t,} then this is an industrial facility selling power from a utility company.

Equation 9d is applied between the variables shown in Table 1-1.

Equation 9d

When the power demand of the entire industrial facility is derived as described above, the energy management apparatus 110 selects the operating point of each modeled task to minimize the cost by reflecting the modeled available resources.

For example, when the unit price is low based on the price data, the energy management device 110 selects an operating point with high power demand for each modeled task to reflect the modeled available resources so that a lot of resources are consumed. Can manage On the other hand, when the unit price is expensive based on the price data, the energy management device 110 selects an operating point with less power demand for each modeled task reflecting the available modeled resources, so that less resource is consumed. Can manage.

As such, the energy management device 110 may manage the power demand of the industrial equipment so that the energy cost according to the operation of the industrial equipment is minimized to reflect the modeled available resources. Here, the energy cost may be derived by subtracting the cost of selling resources from the cost of purchasing resources, and adding the costs according to resource production when the resources are produced.

If this is expressed as an equation, it may be expressed as in Equation 10.

Equation 10

는 전력 구매비용을 나타내는 것으로, pp_t는 시간 간격(t, t+1)에서의 구매 단위가격이고, E_p,t는 전력 구매량이다.

는 전력 판매비용을 나타낸 것으로, ps_t는 시간 간격(t, t+1)에서의 판매 단위가격이고, E_s,t는 전력 판매량이다. here,

Is the power purchase cost, pp _t is the purchase unit price in the time interval (t, t + 1), E _{p, t} is the power purchase amount.

Is the power selling cost, ps _t is the selling unit price in the time interval t, t + 1, and E _{s, t} is the selling amount of electricity.

는 에너지 생산 비용을 나타내는 것으로, p_f는 스케줄 에너지 생성 장치(EGS f)에 의해 소비되는 원자재의 가격이다.

Is the cost of energy production, and p _f is the price of the raw material consumed by the scheduled energy generating device (EGS f).

부분은 스케줄 에너지 생성 장치의 동작 개시 및 중단 비용을 나타낸다. Last

The portion represents the start and stop cost of operation of the schedule energy generating device.

6 is a block diagram schematically illustrating an internal configuration of an energy management apparatus according to an embodiment of the present invention.

Referring to FIG. 6, the energy management apparatus 110 according to an exemplary embodiment may include an input unit 610, a task modeling unit 615, a resource modeling unit 620, a resource management unit 625, and a memory 630. And a control unit 635.

The input unit 610 is a means for receiving various information for power management of an industrial facility.

For example, the input unit 610 may include price data including a unit price, state-task network (STN) of the industrial equipment, operation information of the energy storage device (ESS) and the energy generation device (EGS), and each task. You can receive the operation information, various information about each state (state).

The task modeling unit 615 is to model each task of the industrial equipment to meet the market demand by using the STN, each task operation information, the various information about the status of the industrial equipment input through the input unit 610 It is meant for.

The task modeling unit 615 performs modeling using a MILP having an objective function and a series of constraints, and thus, an optimal operating point in the schedule task may be selected to minimize energy costs.

Accordingly, the task modeling unit 615 may perform modeling so that an intermediate material having high power demand is produced in advance when the electric charge is low, and an optimal operating point in the schedule task is selected so that the intermediate material produced in advance when the electric charge is high is used.

The resource modeling unit 620 is a means for modeling available resources based on price data. The resource modeling unit 620 performs modeling on the energy storage device (ESS) and the energy generation device (EGS), and when the power charge is low, the energy is stored in the ESS, and when the power charge is high, the stored energy is used or the EGS is Make it possible to produce energy.

The resource manager 625 is a means for managing the power demand of the industrial equipment by selecting an operating point of each task modeled to minimize the cost by reflecting the modeled available resources.

The memory 630 is a means for storing various information necessary for operating the energy management device 110 according to an embodiment of the present invention, various data necessary for managing power demand of an industrial facility, and the like.

The controller 635 is an internal component of the energy management apparatus 110 according to an embodiment of the present invention (eg, the input unit 610, the task modeling unit 615, the resource modeling unit 620, and the resource management unit). 625, the memory 630, etc.).

The results of applying the above-described power management method and system according to the present invention to Oxygen Generation Plants will now be described.

7 shows the STN of the oxygen production plant.

In FIG. 7, there are four oxygen generating devices (OGS) that produce oxygen from air. Assume that OSG # 1 is a non-scheduled task with a fixed operating point, and the other OSG is a scheduled task with a plurality of operating points (see Table 1).

Table 1

[Correction under Rule 91 26.11.2014]

Oxygen demand is 16000 Nm ³ per hour. Oxygen storage range is 2000-18000 Nm ³ .

In addition, there are three cold water systems (WCS) required by the OSG. Every WCS is a scheduled task with three operating points (see Table 2).

TABLE 2

[Correction under Rule 91 26.11.2014]

During each time interval, cold water demand varies with the operating state of the OGS. Cold water storage ranges from 150 to 850 m ³ .

It is assumed that the maximum storage capacity of the energy storage device is 6000 kWh and the maximum charge / discharge at 1500 kWh at time intervals. The charging and discharging efficiency is assumed to be 90%. Energy-generating devices use electricity to generate electricity (see Table 3).

TABLE 3

[Correction under Rule 91 26.11.2014]

[Correction under Rule 91 26.11.2014]
FIG. 9 estimates the amount of power produced by the solar energy generating device at each time interval, and FIG. 10 shows the purchase price and the selling price per hour.

[Correction under Rule 91 26.11.2014]
Table 4 shows the operating points of OSG # 2, # 3, # 4 and WCS # 1, # 2, # 3 during each time interval.

[Correction under Rule 91 26.11.2014]

[Correction under Rule 91 26.11.2014]
Table 4

[Correction under Rule 91 26.11.2014]

[Correction under Rule 91 26.11.2014]

[Correction under Rule 91 26.11.2014]
In OSG # 3, operating point 8 operates at time interval 0, producing 6000 Nm ³ of oxygen, consuming 70 m ³ of cold water, and requiring 2100 kWh of power (see Table 1).

[Correction under Rule 91 26.11.2014]
11 shows the overall power demand of OSG # 2, # 3, # 4 and WCS # 1, # 2, # 3 during each time interval.

[Correction under Rule 91 26.11.2014]
Power demand increased at time intervals 3, 4, and 6, where electricity was cheap. In this case, excess portions of oxygen and cold water were produced and oxygen and cold water storage increased (see FIGS. 12 and 13).

[Correction under Rule 91 26.11.2014]
However, the demand for electricity decreased during the expensive time intervals 14, 15 and 16. Stored oxygen and cold water were consumed during this time interval, but the amount of stored oxygen and cold water did not exceed the storage range.

[Correction under Rule 91 26.11.2014]
Referring to FIG. 11, it can be seen that the electricity price is low at the time interval 5 but the power demand is not increased. This is because the amount of stored oxygen and cold water is close to the upper storage limit and it is cheaper to shift the power demand to time interval 6 because the electrical value of time interval 6 is lower than the electrical value of time interval 5.

[Correction under Rule 91 26.11.2014]
14 shows the power demand of the energy storage device during the time interval.

[Correction under Rule 91 26.11.2014]
Positive numbers indicate charging and negative numbers indicate discharge. The energy storage device was charged when the electrical value was low and discharged when the electrical value was high.

[Correction under Rule 91 26.11.2014]
The maximum amount charged or discharged by the energy storage device during each time interval was 1500 kWh (see Table 3). The energy storage device reached a maximum storage capacity of 6000 kWh at time interval 6 and discharged all energy at time interval 16. The total discharge energy was less than the total charge energy because of the energy loss during charging and discharging.

[Correction under Rule 91 26.11.2014]
15 shows the power produced by the schedule energy generation device during each time interval.

[Correction under Rule 91 26.11.2014]
Scheduled energy generating devices produced power at time intervals 13 to 17, during which time the electricity was expensive and power demand decreased at peak times.

[Correction under Rule 91 26.11.2014]
Figure 16 shows the overall power demand of the industrial plant for each time interval.

[Correction under Rule 91 26.11.2014]
(a) shows the demand according to the electric value, and (b) shows the demand including the energy storage device (ESS). It can be seen that the power demand has increased at time intervals 1, 2, 3, 4, and 6, because the energy storage has charged the energy when the electricity price is low. On the other hand, at time intervals 13, 14, 15 and 16, the power demand has decreased because the energy storage device has discharged energy when the electricity is expensive.

[Correction under Rule 91 26.11.2014]
(c) shows the demand in which an energy storage device (ESS) and an energy generation device (EGS) are included. In the case of (c), the power demand was further reduced because the solar energy generating device produced energy at time intervals 6 to 18 and the schedule energy generating device generated energy at time intervals 13 to 17. The power demand is negative in time intervals 14 to 16, which means that industrial equipment can sell surplus power to utility companies.

[Correction under Rule 91 26.11.2014]
As such, the operating cost of the schedule tasks OGS # 2, # 3, # 4 and WCS # 1, # 2, # 3 can be reduced according to the change in the electric value per hour, and the energy storage device can be reduced. Saving energy when electricity is expensive By storing electricity in advance when electricity is low, energy costs can be reduced, and energy generating devices can save energy costs by producing additional electricity at a lower cost. .

[Correction under Rule 91 26.11.2014]
Table 5 shows the total energy costs for each case.

[Correction under Rule 91 26.11.2014]
As shown in Table 5, the total energy cost is $ 10,842 for a fixed price (the average price of a variable price), $ 10,594.6 for an hourly variable price, $ 10,028 for an energy storage device, energy storage and If all energy generation units are included, the energy savings are further reduced to $ 8,695.4.

[Correction under Rule 91 26.11.2014]
Table 5

[Correction under Rule 91 26.11.2014]

[Correction under Rule 91 26.11.2014]
On the other hand, the power distribution method according to an embodiment of the present invention can be implemented in the form of program instructions that can be executed through various electronic means for processing information can be recorded in the storage medium. The storage medium may include program instructions, data files, data structures, etc. alone or in combination.

[Correction under Rule 91 26.11.2014]
The program instructions recorded in the storage medium may be those specially designed and constructed for the present invention, or may be known and available to those skilled in the software art. Examples of storage media include magnetic media such as hard disks, floppy disks and magnetic tape, optical media such as CD-ROMs, DVDs, and magnetic-optical media such as floppy disks. hardware devices specifically configured to store and execute program instructions such as magneto-optical media and ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine code generated by a compiler, but also devices that process information electronically using an interpreter, for example, high-level language code that can be executed by a computer.

[Correction under Rule 91 26.11.2014]
The hardware device described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

[Correction under Rule 91 26.11.2014]
Although the above has been described with reference to a preferred embodiment of the present invention, those skilled in the art to which the present invention pertains without departing from the spirit and scope of the present invention as set forth in the claims below It will be appreciated that modifications and variations can be made.

[Correction under Rule 91 26.11.2014]
The power management method according to the present invention can be utilized in the field of smart grid systems, and particularly can be effectively used in the demand side of industrial equipment.

Claims (20)

Calculating available resources based on price data; And Modeling a task of a production process in an industrial facility to minimize energy costs by reflecting the calculated available resources, The task refers to a processing operation performed in the production process. The method of claim 1, The task includes a non-scheduled task that is not adjusted for power demand and a schedule task capable of adjusting for power demand. The method of claim 2, The schedule task includes a plurality of operating points, The operation point is a detailed processing operation set in consideration of the consumer goods consumed in the schedule task and the production goods output from the schedule task, wherein the power demands at the plurality of operation points are different from each other. The method of claim 3, wherein The modeling of the task may include modeling the operating point of the schedule task to be selected such that power demand of the schedule task increases when the price data is low and power demand of the schedule task decreases when the price data is high. Power management method. According to claim 1, Computing the available resources Calculating a first available resource charged or discharged to the energy storage device; And Calculating a second available resource produced by the energy generating device. The method of claim 5, Computing the available resource, And storing electricity in the energy storage device when the unit price is low based on the price data, and discharging electricity stored in the energy storage device when the unit price is high. The method of claim 5 The energy generation device includes a non-scheduled energy generation device that is not adjusted for power generation and a schedule energy generation device that can adjust for power generation. The method of claim 7, wherein Computing the available resources Predicting power generated by the schedule energy generating device based on a past history, and calculating power generated by the schedule energy generating device based on the price data. The method of claim 8, The power generated by the schedule energy generating device is calculated by further considering the cost of power generation by the schedule energy generating device. The method of claim 1, The energy cost is subtracted from the purchase cost of purchasing the available resource, the sale cost of selling the available resource, When the production resource is included in the available resources, the power management method characterized in that it is derived by adding the costs associated with the production of resources. A recording medium product having recorded thereon a program code for performing the method according to any one of claims 1 to 10. An industrial facility having a production process comprising at least one task of consuming consumer goods to produce produce; And An energy management apparatus for calculating power based on price data, and modeling a task of the production process to minimize energy costs by reflecting the calculated power, The task refers to a processing operation performed in the production process. The method of claim 12, The task is a schedule task capable of adjusting the power demand, including a plurality of operating points that are set in consideration of input consumer goods and output production goods, the power demand is different from each other, The energy management apparatus may model the operating point of the schedule task to be selected such that the power demand of the schedule task increases when the price data is low and the power demand of the schedule task decreases when the price data is high. Power management system. The method of claim 12, An energy storage device for charging or discharging power; And Further comprising an energy generating device for producing electric power, The energy storage device charges power when the unit price is low based on the price data, and discharges stored power when the unit price is high, The energy generating device calculates power to be generated based on the prediction based on the past history and the price data. The method of claim 14, The energy management device subtracts the selling cost of selling the power stored in the energy storage device from the purchase cost of purchasing power, and if the power included by the energy generating device is included in the power, the cost of power generation is added to the energy. A power management system, characterized in that the cost is calculated. Modeling available resources based on price data; Modeling a schedule task capable of adjusting power demand among tasks which are processing operations performed in a production process in an industrial facility; And Power management by selecting an operating point of the modeled schedule task to minimize energy costs by reflecting the modeled available resources. The method of claim 16, The modeling of the available resource may include modeling an energy storage device, wherein the modeling of the energy storage device includes an energy balance constraint, a charge / discharge constraint treaty, and a capacity constraint. The method of claim 16, The modeling of the schedule task may include applying a mixed integer linear programming (MILP) including a material balance, a power balance, a storage balance, and an operation constraint of the schedule task. The method of claim 16, The modeling of the schedule task may include generating intermediate materials having high power demand in advance when the price data is low, and selecting an optimal operating point so that intermediate materials prepared in advance are used when the price data is high. Way. A resource modeling unit for modeling available resources based on price data; A task modeling unit modeling a task which is a processing operation performed in a production process in an industrial facility; And And a resource manager to select an operating point of the modeled task so that the energy cost is minimized by reflecting the modeled available resources.

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