KR101899052B1 - Real-Time Demand Bidding method and system for Energy Management in Discrete Manufacturing Facilities - Google Patents

Abstract

The present invention provides a real time demand bidding method and system for energy management in a discrete manufacturing facility. To this end, the present invention provides a real-time demand bidding method for energy management of a discrete manufacturing system in which each machine processes an independent operation at a predetermined time interval, comprising the steps of: Calculating a first optimal machine operation schedule indicating whether each machine is in operation at a first maximum profit and time interval; and when the real time demand bidding event is notified from the utility company, the discrete manufacturing system Calculating a second maximum profit and a second optimal machine operation schedule; if the second maximum profit is greater than the first maximum profit, the discrete manufacturing system participates in a real-time demand bidding event and bids with an optimal load reduction amount; If the bid is approved by the utility company, The acid manufacturing system includes operating each machine with the second optimal machine operation schedule.

Description

TECHNICAL FIELD [0001] The present invention relates to a real-time demand bidding method and system for energy management in a discrete manufacturing facility,

The present invention provides a real time demand bidding method and system for energy management in a discrete manufacturing facility.

Demand Bidding (DB) is the most efficient Demand Response (DR) method to flatten the demand curve in the Smart Grid. DR induces changes in demand side consumption to prevent grid instability and large voltage fluctuations due to unbalanced power generation and consumption. DR can be classified as incentive-based DR and price-based DR. Incentive-based DR provides incentives to consumers by reducing the load on the power system's peak period. Price-based DR (time-based DR) allows consumers to change energy usage patterns in response to time-varying charges.

Previous research on DR has focused on price-based DR in most residential and commercial areas. However, since 42.6% of the world's electricity is consumed by the industry, DR in the industrial sector is particularly important in reducing peak demand for the power grid. The preceding article cited obstacles to DR practices in industrial facilities, which were categorized into two aspects. First, when modeling the DR problem in the industrial sector, machinery interdependencies of the production line must be considered. For example, if the raw material supply machine does not supply the raw material, the machine can not start operation. Modeling machine-to-machine interdependencies increases the complexity of mathematical models when compared to DRs in residential and commercial areas. Second, among other industrial applications, there are significant changes in industrial processes and load. Therefore, it is difficult to design a standard model for industrial DR. Current industrial DRs have specific applications, and specific models have been reported for industrial refrigeration systems, meat industry, cement industry, and food industry. All of these approaches are manual DR, a manual decision to respond to DR events, to optimize load reduction rules, actively adjust production and energy planning, or limit the scope of real-time load blocking.

In 2013, the automatic DR method of the refinery was proposed based on the day-to-day real-time price (DA-RTP), where the interdependence of the operating constraints of the process was considered. However, the production model is not realistic because it does not consider storing intermediate goods. In 2014, an automatic DR method that classifies a processing task as a non-scheduleable task or a schedule-adjustable task has been announced. Here, the DR method was evaluated by taking oxygen production based on DA-RTP as an example. One limitation is that the mathematical model is tailored to the continuous process and does not take into account the machine's sequence of operations. The problem with current DA-RTP is that if the electricity cost falls within an hour, many consumers suddenly consume energy, leading to a new peak on the demand side. In order to overcome these problems, a cooperative DR method to prevent non - cooperative behavior of consumers including penalty mechanisms has been proposed and applied to industrial refrigeration warehouses.

There are three limitations to the current approach. First, most of the studies are tailored to continuous processes, and there is no universal automatic DR model for discrete manufacturing. Second, most industrial DR schemes take day-ahead DR into account, but real-time DR has more potential to maintain power grid balance due to operational constraints in production and uncertainty in forecasts. Third, all previous reports on industry DR focusing on price-based DR are US statistics, but show that incentive-based DR contributes to 93% of the peak load, while price-based DR is only 7%. Using a comprehensive model based on psychology or economics, the researchers found that consumers 'responses to price-based DR and consumers' responses to incentive-based DR are significantly different, and incentive-based DRs have a greater impact on consumer behavior. While there have been a lot of studies on price - based DR, there have been few studies on incentive - based DR. Therefore, a broad survey of incentive-based DRs with applications for industrial consumers is required.

Demand Bidding (DB) is an important form of incentive-based DR. There are two types of demand in the wholesale and retail electricity markets, as shown in Figure 1.

As shown in FIG. 1, an independent system operator (ISO) 40 intermediates the supply and demand of energy between the utility company 10 and the power generation company 30 in the wholesale electricity market. Demand bidding in the wholesale electricity market refers to the process by which utility companies (10) propose demand bidding to wholesale markets for energy. Demand bidding in the retail market refers to incentive-based DR algorithms based on day-ahead prices, where utility companies offer incentives to large-scale consumers (20) to suggest capacity-auction bids and induce load reductions do. These two types of demand bidding differ considerably in terms of purpose and scope of application.

U.S. Published Patent Application No. 2013-328961 U.S. Published Patent Application No. 2012-098902

It is an object of the present invention to provide a method and system in which a power consumer can receive a demand bidding event in real time and quickly participate in a demand bidding.

Another object of the present invention is to provide a method and system that can maximize the production profit of a discrete manufacturing system participating in a demand bidding event.

To this end, the real-time demand bidding method according to the present invention is a real-time demand bidding method for energy management of a discrete manufacturing system in which each machine processes an independent operation at a predetermined time interval, Calculating a first optimal machine operation schedule indicating the presence or absence of an operation of each machine at a first maximum profit and time interval in the absence of the discrete manufacturing system; Wherein the system comprises: calculating a second maximum profit and a second optimal machine operation schedule for event participation; if the second maximum profit is greater than the first maximum profit, the discrete manufacturing system participates in a real- Bidding at a reduced rate, If the bid is approved from the discrete manufacturing system includes the steps of operating each machine to the second optimum machine operation schedule.

In addition, the discrete manufacturing system according to the present invention includes a plurality of machines for performing independent operations at predetermined time intervals to output a resultant, a plurality of buffers for storing raw materials, intermediate materials, or final products from each machine, A discrete manufacturing system comprising a control device for controlling independent operation, the control device being operable to determine whether the machine is operating in a first maximum profit and time interval in the absence of a real time demand bidding event from a utility company supplying power, Calculating a first optimal machine operation schedule and calculating a second maximum profit and a second optimal machine operation schedule for event participation when a real time demand bidding event is notified from the utility company, If it is bigger than profit, it bids with the optimal load reduction amount for real-time demand bidding event, And when the bidding is approved by the utility company, each of the machines operates according to the second optimal machine operation schedule.

Also, in the real-time automatic demand bidding method for energy management of the discrete manufacturing system according to the present invention, a utility company that supplies power supplies a demand bidding event including an event time, a minimum energy reduction amount, and an incentive ratio at a time interval of 1 hour or less The discrete manufacturing system calculating the maximum profit for determining the participation of the demand bidding event in consideration of the event time, the minimum energy reduction amount and the incentive ratio; And the discrete manufacturing system participates in the demand bidding event if it is greater than the maximum profit calculated before the event.

In addition, the automatic demand bidding system for energy management in the discrete manufacturing system according to the present invention includes a utility company that notifies a demand bidding event including an event time, a minimum energy reduction amount and an incentive ratio at a time interval of one hour or less, The maximum profit for determining participation in the demand bidding event is calculated in consideration of the time, the minimum energy reduction amount, and the incentive ratio, and if the maximum profit is larger than the maximum profit calculated before the demand bidding event, Manufacturing system.

As described above, according to the real-time automatic demand bidding method of the discrete manufacturing system according to the present invention, the utility company can prevent the uncertainty due to the prediction error and secure the stability of the power supply in real time.

In addition, the consumer of the discrete manufacturing system can automatically bid the demand only when the maximum profit is secured while adjusting the energy use schedule in real time, thereby maximizing the production profit of the discrete manufacturing system.

Fig. 1 shows the energy demand in the electric wholesale market and the retail market. Fig.
FIG. 2 is a diagram showing an interaction between a utility company and a consumer in a DA-DB; FIG.
3 is a diagram illustrating interaction between a utility company and a consumer in an RT-DB according to the present invention.
4 is a view showing a configuration of a discrete manufacturing system according to the present invention.
5 is a flowchart showing an automatic demand bidding process for energy management in a discrete manufacturing system according to the present invention.
6 is a view showing a configuration of a lithium-ion battery module presented as a case study of the present invention.
7 is a view showing a configuration of an assembling system of a lithium-ion battery module as a case study of the present invention.
8 is a diagram illustrating a machine operation schedule in the absence of an RT-DB event;
FIG. 9 is a graph showing electricity price and energy consumption in the absence of an RT-DB event.
10 is a diagram showing a machine operation schedule in the presence of an RT-DB event;
11 is a graph showing the electricity price and energy consumption when the incentive rate of the RT-DB event is 0.54 $ / kWh.
12 is a graph showing the electricity price and energy consumption when the incentive rate of the RT-DB event is 0.45 $ / kWh.
FIG. 13 is a graph showing profit according to an incentive ratio; FIG.
14 is a graph showing the load reduction according to the incentive ratio.
15 is a graph showing the production amount according to the incentive ratio.
16 is a diagram showing a power consumption pattern of a machine in a discrete manufacturing system according to the present invention.

The present invention proposes a DB program which means incentive-based DR in the retail market. Since the DB program according to the present invention is an incentive-based DR, it differs from the price-based DR. Incentive-based DR can be a compensation-based program or price-based DR can be a penalty-based program. Compensation-based programs are more attractive to industrial consumers, especially because they can reduce costs and provide compensation because of reduced load. Recently, DA-DB was released at PG & E and Southern California Edison, but the DB for implementing incentive-based DR is relatively new and little has been reported for DB. DA-DB related reports focus on hotel energy management and residential energy management.

The present invention discloses a real-time DB (RT-DB) program. The RT-DB program according to the present invention notifies the time spent in the day before notification to 10 minutes. This prevents uncertainty due to forecast errors and enables real-time operation of demand side energy consumption management and grid stability. Utility companies will announce RT-DBs in peak and grid outages and industrial consumers will decide whether to participate in RT-DB events or how much to reduce their bids to maximize profits. When a utility company approves a bid, industrial consumers adjust their production and energy plans to reduce the load.

The automatic RT-DB according to the present invention is designed to perform optimal load reduction bidding decision and active adjustment of production and energy planning. The automatic RT-DB according to the present invention was developed based on a general discrete manufacturing production model. Optimization problems are formulated to maximize producer profit while minimizing energy costs. The optimization problem is transformed into the MILP (Mixed Integer Linear Programming) problem, and when the MILP problem is solved, the automatic RT-DB algorithm can adjust the production and energy planning to determine the optimal bid. Algorithm performance is evaluated using a lithium ion battery module manufacturing plant as an example. The simulation results show that the automatic RT-DB algorithm effectively reduces the load during RT-DB events while maximizing the benefit of the manufacturer.

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

First, introduce the current DA-DB program and explain how it can be extended to RT-DB program. In general, the DA-DB program is a voluntary DR scheme that provides the opportunity to receive incentives in return for load reductions during peak load periods.

Figure 2 shows the interaction between a utility company and a large consumer.

2, if the utility company 10 satisfies at least one of the following conditions at 12:00 noon on the previous day, the DA-DB event is notified to the consumer 20.

(1) If the next day's development resources are not expected to be sufficient

(2) If transmission or distribution reliability problems arise on the next day

(3) If the system operator's predicted load exceeds 43,000MW the next day

(4) If the system operator is expected to issue a warning notice or issue a warning or high level notice the next day

(5) If the predicted temperature of the next day exceeds a certain value

The DA-DB event notification may include a DA-DB event duration (e.g., 4-6 pm), a minimum load reduction required at entry (e.g., 10 kW reduction every hour), an incentive rate And $ 0.5 when reduced).

After the DA-DB event notification, the consumer decides whether to participate in the DA-DB program and how much to reduce the utility bids for utility companies. Bidding can be done from 12:00 to 4:00 pm. The consumer receives a bid approval or rejection after 5 pm. The next day, when the expected conditions of the grid are met and there is a problem with the stability of the grid, the utility company calls the bidding registered consumer to reduce the load according to the load reduction bidding contract. The consumer will receive another amount of compensation for the actual load reduction. However, there is no cost penalty if the load reduction fails. At time t , the load reduction is given by Equation (1).

Where CB (t) is the average energy consumption over the last 10 days corresponding to the consumer default. E (t) is the actual energy consumption.

Consumer compensation during time t is given by Equation (2).

Where I (t) is the incentive rate.

One of the obvious limitations of the DA-DB program is that its efficiency depends on the grid prediction technique. If a missed load peak or grid emergency occurs, it will be too late to generate a DA-DB event. To overcome these limitations and realize a DB program in near real time (i.e., 10-15 minutes), an RT-DB program according to the present invention is proposed.

FIG. 3 shows the interaction between a utility company and a consumer in an RT-DB program according to the present invention.

Referring to FIG. 3, the RT-DB according to the present invention can reduce the known time and the determination time to 10 minutes.

The time of 10 minutes is sufficient for the industrial consumer to determine the optimal load reduction bidding in response to the RT-DB program by using the real time communication capability, the automatic control system and the automatic RT-DB algorithm according to the present invention. Also, the RT-DB algorithm according to the present invention can be applied to a conventional DA-DB through a slight modification.

Discrete Manufacturing System Model

The present invention constitutes a mathematical model to be applied to a discrete fabrication facility. The assembly line is a production system that includes processing and parts merging.

Figure 4 shows the structure of a discrete manufacturing assembly system used in an actual manufacturing environment.

4, m _ij is a machine and i is an i th serial production line branch. j represents the jth machine in the jth serial production line branch. The machine m _ij, i> 0 is a component machine. Machine m ₀₁ is an assembly machine. Machine m _ij , i = 0; j> 1 is an additional processing machine for assembled products. B _ij is a buffer that stores the intermediate product provided by the machine m _ij .

In this discrete manufacturing assembly system, each machine m _ij can be scheduled (i.e., on or off) on the time axis. Here, the time axes are divided into the same operation time intervals. Between two successive operations of the machine, the length of the time interval T _S. The length of the time interval is specified for each application. For other industrial applications, the length of the time interval can be 10 minutes, 15 minutes, 60 minutes, and so on. In the present invention, T _S = 10 minutes. If the time interval is too short, some machines may not complete the operation processing. However, if the time interval is too long, it may fail to respond to the RT-DB event because the energy usage schedule can not be adjusted in real time. The total number of time intervals on the schedule linear axis is denoted by S. In the case of T _s = 10 minutes in the 24-hour schedule linear axis, S is expressed by Equation (3).

Next, the assembly system model will be described. The assembly system model considers two models, the production model and the energy model.

A. Production model

machine

i) Each machine m _ij has a fixed time to process one part and is called the cycle time CT _ij .

ii) In discrete manufacturing, each machine operates in an impulse mode taking maximum efficiency into account. That is, it operates at 100% when needed and 0% when it is not needed. can be expressed by the following equation (4 ) by the decision variable x _ij (t) of the machine m _ij at the tth time interval.

buffer

Each buffer B _ij has a capacity CAP _ij .

Starvation Rule

i) Buffer B _ij , i = 0, ..., S ; If j = 1, ..., M _i -1 is empty, the machine m _{i ( j +1)} is exhausted.

ii) The machines m _i1 , i = 1, ..., S are not exhausted.

iii) If at least one of the buffers B _iM , i = 1, ..., S is empty, then the assembly machine m ₀₁ is depleted.

iv) The depleted machine stops processing operations and enters a low-power state for energy conservation.

Blocking Rule

i) Buffer B _ij , i = 0, ..., S; j = 1, ..., M _i Is full, the machine m _ij is shut off.

ii) The blocked machine enters the low power state to stop the operation processing and prevent buffer overflow.

B. Production Model Constraints

During a given time interval, the machine m _ij processes the quantities n _ij of the various parts as shown in equation (5).

The buffer storage at the end of the time interval t is given by Equations (6) to (8).

Here, α _ij is the buffer B _ij It is a factor that indicates the number of parts required for the machine behind to produce one part.

Starvation Rule

During the time period t, the buffer satisfies the equation (9), where N ⁰ is a natural number set including zero.

이다. 이 규칙은 다음과 같이 표현될 수 있다. A time interval is empty and the last B _ij (t -1) If supplying the material to the machine m _{i (j + 1)} machine m _{i (j + 1)} of the (t -1) is exhausted during the time interval t Will be. In other words,

to be. This rule can be expressed as:

This can be expressed by Equation (10).

이면

이다.

이면 수학식 9로부터

이다.

이면 수학식 10으로부터

이다. 이로부터 수학식 4의

에 근거하여

값을 결정할 수 있다.

If

to be.

From equation (9)

to be.

From equation (10)

to be. From this,

On the basis of

Value can be determined.

가 비어 있고 조립 기계 m ₀₁ 에 재료를 제공할 수 없으면, 조립 기계 m ₀₁ 는 시간 간격 t 동안 고갈될 것이다. 즉,

이다. 이 법칙은 다음과 같이 표현될 수 있다. At the end of the time interval ( t- 1)

If there is blank and to provide the material to the assembly machine ₀₁ m, ₀₁ m assembly machine will run out during a time period t. In other words,

to be. This rule can be expressed as:

This can be expressed by Equation (11).

Blocking Rule

During the time interval t , the buffer has the same condition as in equation (12).

가 가득 차면 기계 m _ij 는 동작 차단된다. 즉,

이다. 이 법칙은 다음과 같이 표현될 수 있다. At the end of the time interval ( t- 1)

The machine m _ij is shut off. In other words,

to be. This rule can be expressed as:

This can be expressed by Equation (13).

C. Energy model

A typical power consumption pattern of a machine in a discrete manufacturing facility is shown in FIG.

Figure 16 shows a typical power consumption pattern of a machine. Initially, the machine starts operating (1) and enters a low-power state (2). If the machine is scheduled to process the machining material during the first time, the operation will rise (③) and enter the operating state (④). In this operating state, the machine repeats the processing material. The average time for processing the material to be processed is called the cycle time. During the second time, if the machine is not scheduled to process the processing material, the operation will fall (⑤) and enter the low power state (②). In the present invention, the first time is referred to as "machine ON" and the second time is referred to as "machine OFF". During the "machine on" time interval, the total electrical consumption of the machine can be measured as Eon . During the "machine off" time interval, the total consumption of the machine can be measured as Eoff . The right part of FIG. 16 shows the equivalent power consumption, which is the same as the gray part on the left.

, 기계 m _ij 의 시간 간격 당 전기 소비는 Eon _ij 이다. 만약 기계 m _ij 가 시간 간격 t 동안 "오프" 스케줄이면

, 저 전력 상태로 들어가 기본 기능만을 유지하게 되고 이때 기계 m _ij 의 시간 간격당 전기 소비는 Eoff _ij 이다. 시간 간격 t 동안 기계 m _ij 의 에너지 소비는

이다. 이 공식에서

(기계 m _ij "온")이면 Eon _ij 이 되고,

(기계 m _ij "오프")이면 Eoff _ij 가 된다. If the machine m _ij is an " on " schedule for a time interval t

, The electrical consumption per time interval of the machine m _ij is Eon _ij . If the machine m _ij is the " off " schedule for the time interval t

, It enters the low power state and maintains only the basic function, where the electrical consumption per time interval of the machine m _ij is Eoff _ij . The energy consumption of machine m _ij over time interval t is

to be. In this formula

(Machine m _ij " on "), Eon _ij ,

(Machine m _ij " off "), it becomes Eoff _ij .

이다. 시간 간격 t 동안 생산라인의 총 전기 비용은

이다. 여기서 p(t)는 시간 간격 t 동안 전기 값이다. 스케줄 직선 축(T)에서 총 전기 비용은

이다.During the time interval t , the total energy consumption of all machines in the production line is

to be. The total electricity cost of the production line during the time interval t

to be. Where p (t) is an electrical value during the time interval t . The total electricity cost on the schedule linear axis (T)

to be.

D. Energy Model Constraints

The total energy consumed by the plant during the time interval t is kept below the limit E _max . That is, Equation 14 is obtained.

Where E _max > 0 is determined by the limit of the transmission line in the area.

Optimization problems and RT-DB algorithms

A. Formulation of optimization problem

The microeconomic theory, like commodity consumers, increases demand to the point where electricity consumers are equal to the additional costs that they can derive. For example, a producer would not produce a product if it does not generate sales revenue because of the electricity cost of production. In other words, the factory will decide whether to produce in the direction of maximizing profits and when and how to produce it. The optimization objective used here is to maximize profits.

를 생산하고 m개의 입력 단위

를 사용한다고 가정한다. 출력제품의 값이

이고 입력 비용이

이라고 하면, 회사가 얻는 이익 π은 수학식 15와 같다. Profit is the total income minus costs. The factory has n output units

And m input units

. The value of the output product

And input costs

, The profit π obtained by the company is expressed by Equation (15).

는 출력제품의 총 수입이고,

는 총 입력 비용이다. 생산 출력물의 가격은 시장 가격으로 책정된다. 입력 비용은 원자재, 에너지 및 인건비를 포함하여 생산과 관련된 모든 비용을 포함한다. here,

Is the total revenue of the output product,

Is the total input cost. The prices of production output are set at market prices. Input costs include all costs associated with production, including raw materials, energy and labor costs.

It is assumed that the factory participates in the RT-DB program. If there is no RT-DB event, the optimization objective function is as shown in equation (16).

The first term is the import amount, the second term is the input cost excluding the electricity cost, and the third term is the electricity cost.

를 이용하여 목적(즉, 이익)이 최대화될 수 있다. This optimization problem follows the constraints of equations (4) to (14). These constraints and the optimization objective function of equation (16) create a Mixed Integer Linear Programming (MILP) problem. Through the MILP solution, the decision variable

Can be used to maximize the purpose (i. E., Profit).

When an RT-DB event occurs and the factory participates in the RT-DB event, the optimization objective function is as shown in equation (17).

는 RT-DB 이벤트 T' 동안의 부하 감소이고,

는 기본 부하이고,

는 RT-DB 이벤트 동안 스케줄 조정되는 에너지 소비이다. The first through third terms are the same as in Equation 16, and the fourth term represents the incentive of the RT-DB event. here,

Is the load reduction during the RT-DB event T '

Is the basic load,

Is the energy consumption that is scheduled during the RT-DB event.

Since the RT-DB event also specifies the minimum load reduction LR _min , the constraint is not equal to Equations 4 to 14,

와 입찰할 부하 감소

를 변경함으로써 이익이 최대화되도록 최적화 목적 함수를 최적화한다. The constraints of equations (4) through (14) and (18) and the optimization objective function of (17) produce a Mixed Integer Linear Programming (MILP) problem. Through the MILP solution, the decision variable during the RT-DB event

Reduce the burden to bid with

To optimize the optimization objective function to maximize profits.

B. Automatic RT-DB Algorithm

Based on the above mathematical model, the present invention proposes an automatic RT-DB algorithm as shown in Fig. The algorithm according to the present invention was implemented using a Manufacturing Operations Management System (MOMS), which allows an industrial consumer to determine whether to participate in RT-DB events and to submit utilities optimal tuning capacity bids and adjustments to production and energy plans. . The algorithm in accordance with the present invention enables an industrial consumer to actively generate an optimal operation schedule of a machine that intelligently interacts with utility companies in response to RT-DB events as well as maximizing profits.

3 illustrates the interaction between the utility company 10 and the consumer 20 in the RT-DB event. In explaining the automatic RT-DB algorithm shown in FIG. 5, the consumer 20 of FIG. Manufacturing system. The RT-DB event processing unit may be a utility company 10, a control unit of a discrete manufacturing system, a computer or a hardware device. However, for convenience of explanation, the utility company 10 and the discrete- Manufacturing system.

In FIG. 5, when the day begins discrete manufacturing system RT-DB offers a MILP problem in the absence of Eq. (4-14, 16) released, the biggest objective function value of π ₁ (maximum profit) and the set of decision variables S ₁ (24-hour optimum machine operation schedule) (S10).

The discrete manufacturing system checks whether the RT-DB event is notified from the utility company (S12). If the RT-DB event is notified from the utility company, the discrete manufacturing system solves the MILP problem (Equations 4-14, 17-18) , The largest objective function value π ₂ (maximum profit), the set of decision variables S ₂ , and the optimal load reduction amount LR (S14).

The next discrete manufacturing system compares the maximum profit π ₂ obtained for the RT-DB event participation and the maximum profit π ₁ obtained without the RT-DB event (S 16). If π ₂ is larger than π ₁ , the RT- And submits the optimal load reduction amount LR and bids the utility company (S18).

Discrete manufacturing system when the confirmation that the bid has been approved by the utility company (S20), the bid is approved, so that the production system operating in accordance with the determined set of variables S ₂ (optimum machine operation schedule) (S22).

If the RT-DB event is not notified by the utilities company, or if π ₂ is less than π ₁ , or if the bid is not approved, the discrete manufacturing system is set to operate the production system according to the decision variable set S ₁ (S24).

Next, the discrete manufacturing system checks whether the day is over (S26). If the day is over, the RT-DB algorithm is terminated. If the day is not over yet, the discrete manufacturing system checks the RT-DB event notification every fixed time (10 minutes) .

simulation

The present invention proposes modeling for a discrete manufacturing system participating in an RT-DB event assuming a large capacity lithium-ion manufacturing plant.

A. Case Study: Lithium-Ion Battery Manufacturing

The manufacture of large capacity lithium-ion batteries is an example of typical discrete manufacturing and has four manufacturing processes, including assembly, injection, formation, and rating.

6, the battery module includes a plurality of battery cells A, auxiliary components, namely, an intermediate frame B, a cooling fin C, a foam D, a frame E ). These components are assembled and coupled into the battery module through continuous operation.

Injection: The cell is filled with electrolyte and sealed.

Formation: The cell is cycled through at least once precisely controlled charge or discharge to activate the working material and transform it into a usable form.

Rating: The rating for the battery module is evaluated according to performance based on discharge, resistance and capacity measurements.

This process can be classified into 10 tasks, and appropriate equipment is assigned to each task as shown in Table 1.

Task Task description Machine One Assembly of components A & D m ₁₁ 2 Assembly of components (AD) & A m ₁₂ 3 Assembly of components B & (ADA) & C m ₁₃ 4 Assembly of components E & A m ₂₁ 5 Assembly of components (EA) & C m ₂₂ 6 Assembly of components A & E m ₃₁ 7 Assembly of (EAC) & (BADAC) & (AE) m ₀₁ 8 Filling m ₀₂ 9 Formation m ₀₃ 10 Grading m ₀₄

7 shows the arrangement structure of the battery module assembly system together with the power, cycle time, and buffer capacity of each machine. In the low power state, the power of each machine is 0.5W. Some machines are arranged in parallel to increase the production capacity of some parts of the process. Table 2 lists raw materials and labor costs as well as the market price of the output.

Item Unit price ($) Battery cell (A) 20 Intermediate frames (B) 2.5 Cooling fins (C) 0.5 Compression foams (D) 0.6 Frame (E) 3.5 Labor cost for each battery module 26 Large-capacity lithium-ion battery module 121

Other parameters used in this case study are listed in Table 3.

Parameter Description Value E max Maximum power drawn by the factory 500 kW p (t) Hourly electricity price DA-RTP data

B. Results

The MILP solution, Lingo 12.0, is used to solve the optimization problem according to the present invention. First, a scenario without an RT-DB event will be described.

Scenario 1: There is no RT-DB event

At the beginning of the day, the MILP problem, represented by equations (4), (16), must be solved to obtain an optimized 24 hour machine operation schedule, as shown in FIG. 8, the horizontal axis represents the schedule time for 24 hours. In this case study, there are six time slots at each time because the time slot is 10 minutes. The vertical axis represents all machines in the production line. A gray box means that the machine is "on" in that time slot. A white box means that the machine is "off". The energy consumption of the production line per hour in accordance with this schedule is shown in Fig. Energy consumption was lower during peak times (14-17 th time). This alleviates the grid load during peak load times and saves consumers electricity costs. The 24 hour production and maximum profit based on this optimized production schedule are shown in the second column of Table 4.

Scenario 1 Scenario 2 Scenario 3 Profit ($) 2776.86 2780.51 2776.10 Production volume 828 units 786 units 822 units

) Load reduction bid
(

)
- 16 ^th hour: 212
17 ^th hour: 35.3 16 ^th hour: 22.5
17 ^th hour: 12.8 Join the RT-DB event  - Yes No

scenario 2: 16 If there is RT-DB event at the -17th time ( incentive  ratio 0.54 $ / kWh )

Now consider RT-DB events. When the 16th time starts, the RT-DB event occurs. RT-DB event notification includes three elements: RT-DB event time (16th to 17th time), minimum load reduction to participate in RT-DB program (10kW), and incentive rate (0.54 $ / kWh). Solving the MILP problem according to equations (4-14) and (17-18), a new optimal schedule is obtained for the 16th to 24th time as shown in FIG. During RT-DB events (ie, 16-17th time), most machines are stopped and provide load reduction. The energy consumption per hour of the production line from the new schedule is calculated as shown in FIG. The red line indicates that the RT-DB event has participated. During the 16th and 17th hours, the load was significantly reduced compared to the original energy scheme, shown in blue. New optimal load reduction bids, yields, and profits are listed in the third column of Table 4. The benefit of the new schedule was $ 2,780.51, which is larger than the original schedule of $ 2,776.86. The factory is expected to participate in the RT-DB event with a new schedule as the profits increase.

scenario 3: 16 In the seventeenth hour incentive  ratio 0.45 $ / kWh RT-DB events with

Scenario 3 is the same as Scenario 2 except that the incentive rate is low. The optimum operation plan of the machine is equally obtained and the energy consumption per hour is shown in FIG. It can be seen in Figure 12 that the energy consumption is slightly reduced in the new schedule during the RT-DB event. In the last column of Table 4, load reduction bids, new yields, and profits are listed. In scenario 3, the industrial consumer should not participate in the RT-DB event because the re-adjusted plan profit ($ 2,776.10) is less than the original plan's profit ($ 2,776.86).

In order to determine the relationship between the incentive rate and the response of the industrial consumer, additional simulations were performed according to incentive rates varying from 0.35-0.7 $ / kWh with 0.05 $ / kWh steps.

Figure 13 shows the benefit for the rebalancing energy for RT-DB event participation as a function of the incentive rate. The larger the incentive rate, the greater the benefit from participating in the RT-DB event. When the incentive rate is less than 0.51 $ / kWh, as shown by the red dot, the profit from RT-DB participation is less than the original energy plan profit ($ 2,776.86). Therefore, participating in RT-DB events is uneconomical.

Figure 14 shows the load reduction of the industrial consumer as a function of the incentive rate. When the incentive rate is less than 0.51 $ / kWh, it is uneconomical to participate in the RT-DB event, so the consumer must perform the original energy plan, which means there is no load reduction. When the incentive rate exceeds 0.51 $ / kWh, the industrial consumer must participate in the RT-DB event and the load reduction increases until the limit of no reduction capacity is reached for the 16th to 17th hour as a function of the incentive rate something to do.

Figure 15 shows the yield as a function of the incentive rate. Industrial consumers should not participate in RT-DB events when the incentive rate is less than 0.51 $ / kWh, as production is unchanged compared to the original energy plan (828 units) listed in the second column of Table 4. When the incentive rate exceeds 0.51 $ / kWh, industrial consumers will have to participate in the RT-DB event, which will reduce production. If the incentive rate is greater than 0.55 $ / kWh, the rebalanced load is reduced to the minimum amount during the 16th to 17th hours, and the output can no longer be reduced.

Table 5 shows the computation time required to solve the optimization problem using Lingo 12.0 software on the PC.

Solve the optimization
problem without the
RT-DB event Solve the optimization
problem with an
RT-DB event Computing time (s)  56 Average: 91.4
(Max: 119, Min: 21)

The time required to solve the optimization problem when there was no RT-DB event was 56 seconds, and the time required to solve the optimization problem was 91.4 seconds on average with the RT-DB event. This shows that mathematical problems can be solved in a timely manner to satisfy the real-time demands of RT-DB programs (ie, 10 minutes prior notification).

The discrete manufacturing production model and automatic RT-DB algorithm have been described so far. Optimization problems have been formulated to maximize manufacturing profits. By solving these optimization problems, an automatic RT-DB algorithm is executed to determine optimal load reduction bids and generate adjusted production and energy plans. The performance of the algorithm was verified through a case study of a lithium-ion battery module manufacturing plant. The simulation results show that the algorithm according to the present invention maximizes the benefit of the manufacturer while effectively reducing the load during RT-DB events. The present invention can be considered as a win-win strategy for both electric users and utility companies. We also discussed the relationship between incentive ratios and consumer demand elasticities, including analysis of changes in output and profit.

Meanwhile, the real-time demand bidding method according to the present invention can be implemented by a software program and recorded in a predetermined recording medium readable by a computer.

For example, the recording medium may be a hard disk, a flash memory, a RAM, a ROM, or the like embedded in each reproduction apparatus, or an external optical disk such as a CD-R or a CD-RW, a compact flash card, a smart media, have.

The functional operations and implementations described in the present specification may be embodied in digital electronic circuitry, computer software, firmware, or hardware, or a combination of any of the foregoing. The implementations described in the present disclosure may be implemented as one or more computer program products, that is, one or more modules associated with computer program instructions encoded on a program storage medium of the type for control of, or for execution by, Lt; / RTI >

While the drawings of the present invention depict operational processes, it should be understood that such operations should be performed in the order shown, in order to obtain the desired results, or that all illustrated operations should be performed. In certain cases, multitasking and parallel processing may be advantageous.

The foregoing description is merely illustrative of the present invention, and various modifications may be made by those skilled in the art without departing from the spirit of the present invention.

Accordingly, the embodiments disclosed in the specification of the present invention are not intended to limit the present invention. The scope of the present invention should be construed according to the following claims, and all the techniques within the scope of equivalents should be construed as being included in the scope of the present invention.

10: Utility company 20: Consumer
30: Generation company 40: Independent system operator

Claims (21)

A method for real-time demand bidding for energy management of a discrete manufacturing system in which each machine processes independent operation at a predetermined time interval,
The discrete manufacturing system calculates the first maximum profit so that the value obtained by subtracting the input cost and the input cost including the raw material cost and the labor cost from the output product import amount is maximized in a state where there is no real time demand bidding event from a utility company that supplies electric power Generating a first machine operation schedule by adjusting a determination variable that determines whether each machine is operating according to a depletion rule and a shutdown rule so that a first maximum profit is generated;
When the real-time demand bidding event is notified from the utility company, the discrete manufacturing system calculates the maximum value obtained by subtracting the input cost including the raw material cost and the labor cost from the output product import amount plus the incentive based on the energy reduction amount And generating a second machine operation schedule by adjusting a determination variable that determines whether or not each machine operates according to a depletion rule and a shutdown rule so that a second maximum profit is generated;
If the second maximum profit is greater than the first maximum profit, the discrete manufacturing system participates in a real time demand bidding event and bids with a load reduction amount used to calculate the second maximum profit,
And when the bidding is approved by the utilities company, the discrete manufacturing system includes operating each machine with the second machine operation schedule. The method according to claim 1,
Wherein the depletion rule stops operation when each buffer that is previously located in each machine constituting the discrete manufacturing system is empty and switches to a low power state for energy saving. The method according to claim 1,
Wherein the depletion rule stops operation if any of the plurality of buffers previously located in the assembling machine constituting the discrete manufacturing system is empty and switches to a low power state for energy saving. The method according to claim 1,
Wherein the blocking rule switches to a low power state to stop operation if each buffer next to each machine constituting the discrete manufacturing system is full and to prevent overflow of the buffer. The method according to claim 1,
Wherein the electricity cost is determined by the electricity price in the first machine operation schedule or the second machine operation schedule and the time interval. The method according to claim 1,
Wherein the energy reduction amount is a value obtained by subtracting the energy usage amount adjusted by the second machine operation schedule from the basic energy usage amount of the discrete manufacturing system. delete The method according to claim 1,
Wherein the discrete manufacturing system does not participate in a real time demand bidding event and the first discretionary bidding event does not participate in a real time demand bidding event when the utility company does not have a real time demand bidding event or the second maximum profit is less than the first maximum profit, Further comprising operating each machine with a machine operation schedule. A plurality of machines for performing independent operations at predetermined time intervals to output the results, a plurality of buffers for storing raw materials, intermediate materials or final products from the respective machines, and a control device for controlling independent operations of the respective machines In the discrete manufacturing system,
The controller calculates a first maximum profit so that a value obtained by subtracting an input cost and an input cost including a raw material cost and a labor cost from an output product import amount in a state where there is no real time demand bidding event from a utility company that supplies power, Generating a first machine operation schedule by adjusting a determination variable that determines whether or not each machine operates in accordance with a depletion rule and a blocking rule so as to obtain a first maximum profit,
When the real-time demand bidding event is notified from the utilities company, in order to participate in the event, the second maximum value is set such that the value obtained by subtracting the input cost including the raw material cost and labor cost from the output product import amount plus the incentive based on the energy reduction amount, Generating a second machine operation schedule by adjusting a determination variable that determines whether or not each machine operates in accordance with a depletion rule and a blocking rule so as to calculate a profit and a second maximum profit,
If the second maximum profit is greater than the first maximum profit, bid with a load reduction amount used to calculate the second maximum profit in a real time demand bidding event,
And if the bidding is approved by the utility company, each machine operates according to the second machine operation schedule. 10. The method of claim 9,
If there is no real time demand bidding event from the utility company or if the second maximum profit is less than the first maximum profit or the utility company does not approve the bidding, Wherein each machine is operated on a machine operation schedule. 10. The method of claim 9,
Wherein each machine stops operation and switches to a low power state if the previously located buffer is empty or the next located buffer is full. 10. The method of claim 9,
Wherein the assembling machine for collecting a plurality of output materials from the plurality of machines stops the operation and switches to a low power state if any of the plurality of previously located buffers is empty. delete delete delete delete delete delete delete delete delete

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