Demand management-based distributed resource integrated operation system

Abstract

To provide a custom-made type port folio for managing a demand in accordance with an individual user load generation standard.SOLUTION: A distributed resource integration management system of a demand management board, contains: a communication part that receives consideration object information including a load power and user characteristic information including a setting time for calculating a load increment / decrement number momentum of the load power; a holding part that holds data containing a load power received during the setting time; a calculation part that calculates the load increment / decrement number momentum by using the load power during the setting time, and calculates a number momentum reflection prediction load power by using the load increment / decrement number momentum; and a control part that is for generating a DR event or controlling an ESS as reaching the number momentum reflection prediction load power to at least one critical value corresponded to a constant standard of a peak power.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a demand management-based distributed resource integrated operation system, and more particularly to a distributed resource integrated operation system that is capable of preparing for peak power by generating a DR event or controlling the charging and discharging of an ESS battery as a predicted load power reflecting a load fluctuation trend reaches a certain level of peak power. [Background technology]

[0002] Demand for electricity is increasing every year due to industrial development, and the amount of electricity used by numerous consumers is increasing exponentially as electronic devices continue to increase. However, there is a practical limit to the increase in electricity production facilities. As a result, the national government is continuously promoting policies to encourage and support the installation of new and renewable energy sources and Energy Storage Systems (ESS) in consumer spaces such as individual homes, apartment buildings, office spaces, and small-scale factories as part of its energy policies.

[0003] After the ESS is constructed, the customer-specific current demand management uses real-time load power to manage the charging and discharging schedule of the energy storage device when the reference load power is exceeded, even though the amount of power usage and usage patterns vary depending on the characteristics of the installation location, such as a home or a factory facility.

[0004] Or, since it is only used to store and discharge the battery power of the ESS according to the peak time period, it is not possible to respond to an unexpected maximum peak, and since the load generation level for each consumer is not applied, demand management has been inefficient.

[0005] Therefore, a method needs to be presented that can effectively operate a DR program by scheduling battery charging and discharging while also conducting DR (Demand Response) events for each consumer in an environment where distributed resources such as new and renewable distributed power sources, ESS, and EVs are activated. Summary of the Invention [Problem to be solved by the invention]

[0006] The present invention is directed to solving the above-mentioned problems, and a demand management-based distributed resource integrated operation system according to an embodiment of the present invention can generate a DR event or control the charging and discharging of an ESS battery when a predicted load power reflecting a load fluctuation trend reaches a certain level of peak power.

[0007] In addition, the demand management-based distributed resource integrated operation system according to an embodiment of the present invention can provide a customized portfolio for demand management according to the load generation level of each consumer. [Means for solving the problem]

[0008] In order to achieve the above-mentioned object, a demand management based distributed resource integrated operation system according to one embodiment of the present invention includes a communication unit that receives consideration information including load power and consumer characteristic information including a set time for calculating a load increase / decrease trend rate of the load power; a storage unit that stores data including the load power received during the set time; a calculation unit that calculates a load increase / decrease trend rate using the load power during the set time and calculates a trend-reflecting expected load power using the load increase / decrease trend rate; and a control unit that generates a DR event or controls the ESS when the trend-reflecting expected load power reaches at least one critical value corresponding to a certain level of peak power.

[0009] In an embodiment, the consumer characteristic information further includes at least one of past power data, a supply and demand plan, DR participation information, a criticality rate (%) for each step, and a battery charging rate (%) for each step.

[0010] In one embodiment, the calculation unit further includes a peak power calculation unit that predicts maximum demand based on the past power data and the supply and demand plan, and calculates peak power.

[0011] In an embodiment, the at least one critical value is determined by a critical rate (%) for each step input according to a consumer load characteristic.

[0012] In one embodiment, the control unit is classified into operation monetization, optimization / storage, peak approach, and peak step according to the at least one critical value.

[0013] In an embodiment, the calculation unit further includes a DR power prediction unit that calculates a DR potential power by summing up an amount of power available for a DR event using the DR participation information.

[0014] In one embodiment, the consideration information further includes at least one of TOU rate plan information, a maximum battery charge capacity of the ESS, and a battery charge status of the ESS.

[0015] In one embodiment, the calculation unit further includes a current TOU calculation unit that compares the TOU rate plan information based on season and time period with the current date and time, and calculates the current rate plan time period (TOU) based on whether the current date and time period is a light load, heavy load, or maximum load rate plan time period.

[0016] In one embodiment, the control unit determines whether a preset condition is satisfied for each step depending on the load power, the DR potential power, the current rate plan time of day (TOU), and the battery charge state of the ESS, and determines whether to charge / discharge the ESS or generate a DR event accordingly.

[0017] In an embodiment, the preset condition may be set using at least one of the peak power, the step judgment threshold value, TOU, the DR potential power, the maximum battery capacity of the ESS, and the trend-reflecting battery charging rate.

[0018] In one embodiment, the DR event may request at least one of load power curtailment, load transfer, and direct load control to be handled according to priority. Effect of the Invention

[0019] The demand management-based distributed resource integrated operation system according to the present invention as described above can prepare for peak power by generating a DR event or controlling the charging and discharging of the ESS battery when the predicted load power reflecting the load fluctuation trend reaches a certain level of peak power.

[0020] In addition, the demand management-based distributed resource integrated operation system according to the present invention can provide a customized portfolio for demand management according to the load generation level of each consumer. [Brief description of the drawings]

[0021] [Figure 1] 1 is a schematic diagram illustrating a demand management-based distributed resource management system according to an embodiment of the present invention. [Diagram 2] 1 is a diagram showing a configuration of a demand management device according to an embodiment of the present invention. [Diagram 3] FIG. 2 is a diagram showing in more detail the configuration of a demand management device according to an embodiment of the present invention. [Figure 4] 4 is a flow chart showing operations performed by a demand management device according to an embodiment of the present invention. [Diagram 5] 1 is a flow chart illustrating a method for determining a step according to an embodiment of the present invention. [Figure 6] 1 is a flow chart illustrating the logic of an operation monetization step according to one embodiment of the present invention. [Figure 7] 1 is a flow diagram illustrating the logic of an optimization / storage step according to one embodiment of the present invention. [Figure 8] FIG. 13 is a diagram illustrating the logic of a peak proximity step according to an embodiment of the present invention. [Figure 9]FIG. 13 is a diagram illustrating the logic of a peak step according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] In the following, a preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings in order to clarify the technical concept of the present invention. In the description of the present invention, if a detailed description of related known functions or components is deemed to unnecessarily obscure the gist of the present invention, the detailed description will be omitted. In the drawings, components having substantially the same functional configurations are given the same reference numbers and characters as much as possible, even if they are shown in different drawings. For convenience of description, the apparatus and the method are described together when necessary.

[0023] FIG. 1 is a schematic diagram of a demand management-based distributed resource management system according to an embodiment of the present invention.

[0024] Referring to FIG. 1, a demand management-based distributed resource management system according to one embodiment of the present invention includes a power grid operator device 10, a system 20, a power meter 30, an energy storage device 40 (hereinafter referred to as 'ESS'), a load 50, a user terminal 60 and a demand management device 100.

[0025] The power grid operator device 10 means a device operated by a power grid operator that provides electric power in addition to a power plant. The power grid operator device 10 supplies necessary electric power to a building that has already subscribed or a building or facility located in a specific city or region via the grid 20, receives information related to electric power usage from the watt-hour meter 30, and can process billing for the electric power usage. The power grid operator device 10 can also settle the bill for the electric power sold by the ESS 40 to the grid 20. For example, the power grid operator device 10 may be a device operated by a power plant, an electric power company, or the like.

[0026] The grid 20 may be a commercial power grid to which businesses that operate power plants and sell the generated electricity, such as the Korea Electric Power Corporation, are connected. Electric power is supplied from the grid 20 to the load 50, and the ESS 40 may charge the electric power from the grid 20 or discharge the electric power stored in the ESS 40 to the load 50 according to the control of the demand management device 100.

[0027] The watt-hour meter 30 is configured for each consumer and can measure the amount of power used by each consumer in real time. That is, the watt-hour meter 30 is connected to a plurality of loads 50_1, 50_2, 50_N provided in each consumer. The watt-hour meter 30 can provide the measured power usage information to the demand management device 100 in real time.

[0028] Each consumer may be an individual residence, a multi-generational residence, a building, an office space, a small industrial facility, etc., and each consumer includes an ESS 40 and a number of loads 50_1, 50_2, 50_N.

[0029] The ESS 40 can store power supplied from the grid 20 or discharge the charged power to the load 50. The ESS 40 can also participate in power trading by discharging the charged power to the grid 20. That is, a user may be a producer-consumer or a participating consumer who simultaneously plays the role of producer and consumer. According to an embodiment, the ESS 40 can be used as an energy storage device for distributed resources such as new renewable energy, EVs, etc.

[0030] The load 50 refers to a plurality of loads provided at each consumer. The load 50 consumes power supplied from the grid 20 or the ESS 40. For example, various electrical equipment provided at consumers such as factories and homes is an example of the load. In addition, the operation of each of the plurality of loads 50_1, 50_2, 50_N is controlled by the demand management device 100 in response to a DR event.

[0031] The user terminal 60 is a terminal owned by a manager who operates a consumer, and can provide consumer characteristic information based on the load usage pattern of the consumer to the demand management device 100. In addition, the user terminal 60 can receive a DR request for power reduction of the load 50 provided in the consumer from the demand management device 100.

[0032] The demand management device 100 can calculate a trend-reflected predicted load power according to a load pattern of the consumer provided from the power meter 30. The demand management device 100 can generate a DR event or control the charging and discharging of the ESS 40 as the trend-reflected predicted load power reaches at least one critical value corresponding to a certain level of peak power. The demand management device 100 can transmit a DR request to the user terminal 60 or control the load 50 through the generated DR event.

[0033] The demand management-based distributed resource operation system monitors power in real time via the power meter 30, and based on the monitoring results, generates a DR event or controls the power of the ESS 40 to prepare for peak power for each consumer.

[0034] FIG. 2 is a diagram showing the configuration of a demand management device according to an embodiment of the present invention.

[0035] Referring to FIG. 2, the demand management device according to an embodiment of the present invention includes a communication unit 110, a storage unit 130, a calculation unit 150, and a control unit 170.

[0036] The communication unit 110 receives consideration information including load power and consumer characteristic information including a set time for calculating the load power load change trend rate. That is, the communication unit 110 receives the load power, which is the amount of power used, in real time from the power meter 30, and receives the set time H for calculating the load power load change trend rate from the user terminal 60.

[0037] The storage unit 130 may store data including the load power received during the set time H. For example, if the set time H for calculating the load increase / decrease trend rate of the load power received from the user terminal 60 is 3 hours, the storage unit 130 may store the load power for 3 hours prior to the current time.

[0038] The calculation unit 150 may calculate a load increase / decrease trend rate (%) using the load power during the set time H, and may calculate the expected load power reflected in the trend using the load increase / decrease trend rate. That is, the calculation unit 150 may calculate the load increase / decrease trend rate (%) using the load power during the set time H stored in the storage unit 130. For example, if the time range set by the customer is three hours, the calculation unit 150 may calculate the load increase / decrease trend rate (%) using load power data for three hours prior to the current time. In addition, the calculation unit 150 may calculate the expected load power reflected in the trend (kW) using the calculated load increase / decrease trend rate (%). This will be described in detail with reference to FIG. 3 and FIG. 4.

[0039] The control unit 170 may generate a DR event or control the ESS 40 as the trend-reflecting expected load power (kW) reaches at least one critical value corresponding to a certain level of peak power. For example, if the peak power is 100 kW and the critical value is a value corresponding to 80% of the peak power, the control unit 170 may generate a DR event or control the ESS to discharge to the load when the trend-reflecting expected load power reaches 80 kW. Here, the peak power may be a maximum limit value for peak restriction according to a load pattern and a Time-Of-Use (TOU). Alternatively, the peak power may be set as a peak value of a peak time predicted by season.

[0040] In one embodiment, the DR event may request at least one of load power reduction, load transfer, and direct load control (DLC) to be handled according to priority. To this end, the controller 170 may transmit a DR request to the user terminal 60 to reduce the power usage of the load 50 and directly control the operation of the load 50. The configuration and operation of the demand management device 100 will be described in more detail below with reference to Fig. 3 to Fig. 9. Before the description, symbols and their meanings are defined as shown in Table 1.

[0041] [Table 1]

[0042] Fig. 3 is a more detailed diagram showing the configuration of a demand management device according to an embodiment of the present invention, and Fig. 4 is a flow chart showing the operation performed by the demand management device according to an embodiment of the present invention.

[0043] Referring to FIG. 3, a demand management system according to one embodiment of the present invention includes a communication unit 110, a storage unit 130, a calculation unit 150 and a control unit 170. The storage unit 130 includes a consumer information DB 131, a consideration information DB 133, and a step-by-step logic DB 135. The calculation unit 150 includes an expected power calculation unit 151, a peak power calculation unit 153, a current TOU determination unit 155, and a DR power prediction unit 157.

[0044] 4, the communication unit 110 receives consumer characteristic information from the user terminal 60 (S410). The consumer characteristic information is stored in the consumer information DB 131. The consumer characteristic information includes a set time H for calculating an increase / decrease trend rate of the load power.

[0045] In one embodiment, the consumer characteristic information includes past power data, a supply and demand plan, DR participation information, and a criticality rate (%) α n , Step-by-step trend reflecting battery criticality rate (%) β n As described later, the step judgment criticality rate α n Using this, the threshold value Z for each step is calculated. n It is possible to determine the battery charging rate β according to each step. n Using step-by-step trend reflecting battery capacity Qβ n can be determined.

[0046] In addition, the DR participation information may include information for power shedding, information for load transfer, and information for direct load control DLC. The information for power shedding includes a load shedding capacity according to a time period, and the information for direct load control DLC includes load control information according to a priority level of each load.

[0047] In addition, the communication unit 110 receives consideration subject information from the power grid operator device 20, the watt-hour meter 30, and the ESS 40 (S420). The consideration subject information is stored in the consideration subject information DB 133. The consideration subject information includes the load power received from the watt-hour meter 30.

[0048] In one embodiment, the consideration information further includes at least one of TOU rate plan information, the maximum battery charge capacity Q of the ESS 40, and the battery charge state B(t) of the ESS 40. That is, the communication unit 110 can receive information on the maximum battery charge capacity Q of the ESS 40 and the battery charge state B(t) of the ESS 40 from the ESS 40. In addition, the communication unit 110 can receive TOU rate plan information (a rate plan in which rates are differentiated according to seasons and time periods) from the power grid operator device 20. According to an embodiment, the peak power (kW) can be received from the power grid operator device 20.

[0049] In addition, the TOU rate plan refers to a rate plan in which off-peak, mid-peak, and on-peak rates are metered according to the season and time period. For example, in the summer (June 1st to August 31st), off-peak rates can be applied from 23:00 to 09:00, on-peak rates can be applied from 10:00 to 12:00 and 13:00 to 17:00, and on-peak rates can be applied from 23:00 to 12:00 and 13:00 to 17:00, respectively, and on-peak rates can be applied from 23:00 to 09:00.

[0050] The predicted power calculation unit 151 calculates the trend-reflected predicted load power P LFirst, the expected power calculation unit 151 can calculate a load increase / decrease trend rate (%) ∂(tH) for a time range H set by the user. For example, if the time range set by the user is three hours, the expected power calculation unit 151 can calculate a load increase / decrease trend rate (%) using load power data for three hours prior to the current time stored in the consideration information DB 133.

[0051] That is, the expected power calculation unit 151 can analyze the trend of the load power and predict how it will decrease or increase in the future. For example, the load power after a preset time a can be predicted by analyzing the increase / decrease trend of the load for three hours prior to the current time. As an example, when the preset time a is 15 minutes, the expected load power after 15 minutes can be calculated using the load increase / decrease trend rate (%).

[0052] The expected power calculation unit 151 calculates the real-time load power P L Using (t) and the load increase / decrease trend rate ∂(tH), the trend-reflecting predicted load power P L In this case, the predicted load power (kW) reflecting the trend can be calculated as load power (kW) + (load power (kW) × load increase / decrease trend rate (%)) as shown in the following formula (1).

[0053]

number

[0054] The peak power calculation unit 153 calculates the peak power P PEAK (S440) The peak power calculation unit 153 can calculate the peak power based on the load pattern of the consumer and the peak value at the peak time predicted by season.

[0055] In one embodiment, the peak power calculation unit 153 can predict maximum demand based on past power data and a supply and demand plan, and calculate peak power (kW). The peak power calculation unit 153 can predict a peak based on various conditions, such as when it is expected to exceed the maximum demand in the supply and demand plan, when it is expected that the previous maximum power will be updated, etc. Alternatively, the peak power can be determined based on a peak value of a peak time period expected by season. In the present invention, peak power (kW) means a maximum limit value for peak restriction according to a load pattern and TOU.

[0056] The control unit 170 may classify steps according to the degree of load generation (S450). Since the degree of load generation differs by country / industry / TOU, the steps are adjusted according to the mutual influence with other elements.

[0057] In one embodiment, the control unit 170 calculates the trend-reflecting predicted load power P L For (t+a), it can be classified into an operational profit step, an optimization / storage step, a peak approach step, and a peak step according to the threshold values ​​Z1, Z2, and Z3 for each step. This is explained in detail in Figure 5.

[0058] The current TOU calculation unit 155 compares the TOU rate plan information according to season and time period with the current date and time, and calculates the rate plan (TOU) for the current time period based on whether the current date and time period are in a rate plan time period of light load, medium load, or maximum load (S460). For example, if the current date is August 12 and the current time is 11:00, the rate plan for the maximum load time period is applied.

[0059] The DR power prediction unit 157 uses the DR participation information received from the user terminal 60 to sum up the amount of power that can be actively used in the DR event, and calculates the DR potential power (kW) P DRFor example, if it is determined that the customer can reduce power by 100 kW from 1:00 to 2:00 and that the customer can reduce power by 50 kW by priority-based load control, the DR potential power (kW) from 1:00 to 2:00 is 150 kW.

[0060] The control unit 170 determines whether a preset condition is met according to the determined step (S480), and generates a DR event or controls charging / discharging of the battery (S490). A logic for each step is stored in the step logic DB 135 of the storage unit 130, and the control unit 170 can apply a condition for each step by using the logic for each step.

[0061] In one embodiment, the control unit 170 determines whether or not preset conditions are satisfied for each step depending on the load power, the DR potential power, the current time slot rate plan (TOU), and the battery charge state of the ESS, and accordingly determines whether or not to charge / discharge the ESS or whether or not to generate a DR event.

[0062] In one embodiment, the preset conditions for each step may be set using at least one of the peak power, a threshold value for judging each step, TOU, DR potential power, a maximum battery capacity of the ESS, and a battery charging rate reflecting a trend for each step.

[0063] In other words, the control unit 170 may control the charging / discharging of the battery of the ESS 40, transmit a DR request to the user terminal 60 due to a DR event, or control the operation of the load 50 depending on whether the preset conditions for each step are satisfied (S490).

[0064] In one embodiment, the DR event may be a message requesting that at least one of load power reduction, load transfer, and direct load control be processed according to a priority. In this case, the loads 50 may be controlled using information for direct load control DLC received from the user terminal 60. For example, the control unit 170 may control the loads 50 according to information according to the priority of each load 50.

[0065] In one embodiment, when the determined step is an 'operation monetization step', the control unit 170 may control the battery to discharge and supply power to the load 50 or generate a selective DR event depending on whether a preset condition is satisfied. In this case, when the control unit 170 controls the battery to discharge, the power may be sold to the grid. This will be described in detail with reference to FIG. 6.

[0066] In one embodiment, when the determined step is an 'optimization / storage step', the control unit 170 may control the battery to discharge and supply to the load 50 or generate a selective DR event depending on whether a preset condition is satisfied. This will be described in detail with reference to FIG. 7.

[0067] In one embodiment, when the determined step is a 'peak approach step', the control unit 170 may control the battery to discharge and supply to the load 50 or generate a selective DR event depending on whether a preset condition is satisfied, as will be described in detail with reference to FIG.

[0068] In one embodiment, when the determined step is the 'peak step', the control unit 170 may control the battery to discharge and supply to the load 50 or generate a mandatory DR event depending on whether a preset condition is satisfied. This will be described in detail with reference to FIG.

[0069] FIG. 5 is a flow chart illustrating a method for determining a step according to an embodiment of the present invention.

[0070] Referring to FIG. 5, the control unit 170 calculates the peak power P PEAK , real-time load power P L (t), predicted load power P L (t+a) and the n-th step judgment critical rate α n can be input (S510).

[0071] The control unit 170 calculates the trend-reflected predicted load power P L (t+a) is the peak power P PEAK As the temperature reaches at least one of the critical values ​​Z1, Z2, and Z3 corresponding to a certain level, the steps can be divided (S520).

[0072] In one embodiment, at least one of the threshold values ​​Z1, Z2, and Z3 is determined by the step determination threshold rates (%) α1, α2, and α3 input according to the consumer load characteristics. In this case, the step determination threshold values ​​for distinguishing each step can be determined by the following formula (2). That is, each step determination threshold value Z n may be a value obtained by multiplying the peak power by the criticality rate determined for each step.

[0073]

number

[0074] In one embodiment, the control unit 170 may classify the trend-reflected predicted load power into an operation profit step, an optimization / storage step, a peak approach step, and a peak step according to at least one critical value. That is, the step-by-step regions may be set according to the values ​​satisfied by the following equation (3). As described above, in equation (3), P L +P L ∂(tH) is the predicted load power P L (t+a)

[0075]

number

[0076] For example, if the first to third step judgment critical rates set by the customer are 20%, 50%, and 80%, respectively, when the predicted load power reflecting the trend is within 20% of the peak power standard, it is judged as the operation profitability step, when the predicted load power reflecting the trend is greater than 20% but within 50% of the peak power standard, it is judged as the optimization / storage step, when the predicted load power reflecting the trend is greater than 50% but within 80% of the peak power standard, it is judged as the peak approach step, and when the predicted load power reflecting the trend is greater than 80% of the peak power standard, it is judged as the peak step.

[0077] FIG. 6 is a flow diagram illustrating the logic of the operation monetization step according to one embodiment of the present invention.

[0078] Referring to FIG. 6, the control unit 170 controls the load power P L (t), peak power P PEAK The current time slot rate plan (TOU), the maximum battery charge capacity Q of the ESS, the battery charge state B(t) of the ESS, the first step judgment criticality rate α1, and the trend-reflecting battery charge rate β1 for the operation monetization step can be input (S610).

[0079] The control unit 170 receives information (load power P L Based on the current time (t), the current rate plan (TOU), and the ESS battery charge state B(t)), it can be determined whether the pre-set conditions are met. The conditions set in the operational monetization step are the first step judgment threshold Z1 (=P PEAK These are pre-set conditions using α1), the maximum battery capacity Q of the ESS, and the trend-reflecting battery capacity Qβ1 for the operational monetization step.

[0080] If the conditions preset in the operation monetization step are met, the control unit 170 can control the battery to be charged or discharged and the battery to be supplied to the load 50 or provided to the grid 20 for power trading.

[0081] That is, the control unit 170 can control the battery to be charged (S650) if the 'load power' is the same as or smaller than the 'first step judgment threshold value' (S620), the 'current time period rate plan' is the 'light load (off-peak) time period rate plan' (S630), and the 'ESS battery charging state' is the same as or smaller than the 'battery capacity reflecting trend for operation monetization step' (S640).

[0082] In addition, if the 'load power' is the same as or smaller than the 'first step judgment threshold value' (S620), the 'current time period rate plan' is the 'light load (off-peak) time period rate plan' (S630), and the 'ESS battery charge state' is greater than the 'battery capacity reflecting trend for operation monetization step' (S640), the control unit 170 can control the battery to be discharged and supplied to the load 50 or provided to the grid 20 for power trading (S652).

[0083] FIG. 7 is a flow diagram illustrating the logic of the optimization / storage steps according to one embodiment of the present invention.

[0084] Referring to FIG. 7, in the 'optimization / storage step', the control unit 170 controls the load power P L (t), peak power P PEAK , the current time slot price plan (TOU) for the current time, and the DR potential power P DR The maximum battery charge capacity Q of the ESS, the battery charge state B(t) of the ESS, the second step judgment critical rate α2, and the trend-reflecting battery charge rate β2 for the optimization / storage step can be input (S710).

[0085] The control unit 170 receives information (load power P L(t), current time slot price plan (TOU), DR potential power P DR , it can be judged whether the preset conditions are met based on the battery charge state B(t) of the ESS. The conditions set in the optimization / storage step are the second step judgment critical value Z2 (=P PEAK α2), the maximum battery capacity Q of the ESS, and the trend-reflecting battery capacity Qβ2 for the optimization / storage step are pre-set conditions.

[0086] If the preset condition is met in the optimization / storage step, the control unit 170 can control the battery to discharge and supply power to the load 50, or generate a selective DR event.

[0087] That is, the control unit 170 can control the battery to be discharged (S760) if the 'load power' is the same as or smaller than the 'second step judgment threshold' (S720), the 'current rate plan time period' is a 'light load (Off-Peak) time period' (S730), the 'DR potential power' is the same as or smaller than the value obtained by subtracting the 'load power' from the 'second step judgment threshold' (S740), and the 'ESS battery charge state' is the same as or larger than the 'battery capacity reflecting trend for optimization / storage step' (S750).

[0088] In addition, the control unit 170 may generate a selective DR event (S762) if the 'load power' is equal to or smaller than the 'second step determination threshold' (S720), the 'current time slot rate plan' is the 'mid-peak time slot rate plan' (S732), the 'DR potential power' is equal to or larger than the value obtained by subtracting the 'load power' from the 'second step determination threshold' (S742), and the 'ESS battery charge state' is equal to or smaller than the 'battery capacity reflecting trend for optimization / storage step' (S752). The selective DR can be selectively applied from among load reduction, load transfer, and direct load control (DLC).

[0089] In addition, the control unit 170 may control the battery to discharge and generate a selective DR event (S764) if the 'load power' is equal to or smaller than the 'second step determination threshold' (S720), the 'current time slot rate plan' is the 'mid-peak time slot rate plan' (S732), the 'DR potential power' is smaller than the 'second step determination threshold' minus the 'load power' (S742), and the 'ESS battery charge state' is equal to or larger than the 'optimization / storage step trend-reflecting battery capacity' (S754). The selective DR event may be selectively applied from among load reduction, load transfer, and direct load control DLC. In this case, the control unit 170 may request that the load reduction, load transfer, and direct load control DLC be processed in order of priority.

[0090] FIG. 8 is a diagram illustrating the logic of the peak proximity step according to one embodiment of the present invention.

[0091] Referring to FIG. 8, in the case of the 'peak approach step', the control unit 170 determines the load power P L (t), peak power P PEAK , the current time slot price plan (TOU) for the current time, and the DR potential power P DR The maximum battery charge capacity Q of the ESS, the battery charge state B(t) of the ESS, the second step judgment critical rate α2, and the trend-reflecting battery charge rate β3 for the peak approach step can be input (S810).

[0092] The control unit 170 receives information (load power P L (t), current time slot price plan (TOU), DR potential power P DR , the ESS battery charge state B(t)) to determine whether the preset conditions are met. The conditions set in the peak approach step are the second step judgment threshold value Z2 (=P PEAK These are conditions that have already been set using α2), the maximum battery capacity Q of the ESS, and the trend-reflecting battery capacity Qβ3 for the peak approach step.

[0093] If a preset condition is met in the peak approach step, the control unit 170 can control the battery to discharge and supply power to the load 50, or generate a selective DR event.

[0094] That is, the control unit 170 can generate a selective DR event (S860) when the 'load power' is equal to or greater than the 'second step judgment threshold' (S820), the 'current time slot rate plan' is a 'mid-load (Mid-Peak) time slot rate plan' or an 'on-peak) time slot rate plan' (S830), the 'DR potential power' is equal to or greater than the value obtained by subtracting the 'load power' from the 'second step judgment threshold' (S840), and the 'ESS battery charge state' is equal to or less than the 'peak approach step trend-reflecting battery capacity' (S850).

[0095] In addition, the control unit 170 may generate a selective DR event and control the battery to be discharged (S862) if the 'load power' is equal to or greater than the 'second step judgment threshold' (S820), the 'current time slot rate plan' is a 'mid-load (Mid-Peak) time slot rate plan' or an 'on-peak) time slot rate plan' (S830), the 'DR potential power' is smaller than the 'second step judgment threshold' minus the 'load power' (S840), and the 'ESS battery charge state' is equal to or greater than the 'peak approach step trend-reflecting battery capacity' (S852).

[0096] FIG. 9 is a diagram for explaining the logic of the peak step according to an embodiment of the present invention.

[0097] Referring to FIG. 9, in the case of the 'peak step', the control unit 170 determines the load power P L (t), peak power P PEAKThe current time slot rate plan (TOU), the maximum battery charge capacity Q of the ESS, the battery charge state B(t) of the ESS, the critical rate α3 for judging the third step, and the trend-reflecting battery charge rate β4 for the peak step can be input (S810).

[0098] The control unit 170 receives information (load power P L Based on the peak time, the current time rate plan (TOU), and the ESS battery charge state B(t), it can be determined whether the pre-set conditions are met. The conditions set in the peak step are determined based on the third step judgment threshold Z3 (=P PEAK α3), the ESS battery maximum capacity Q, and the peak step trend reflecting battery capacity Qβ4 are used as pre-set conditions. At this time, the DR potential power P DR can determine whether a condition is met regardless of the value.

[0099] If a preset condition is met in the peak step, the control unit 170 can control the battery to discharge and supply power to the load 50, or generate a mandatory DR event.

[0100] That is, the control unit 170 can generate a mandatory type DR event (S950) when the 'load power' is equal to or greater than the 'third step judgment threshold value' (S920), the 'current time slot rate plan' is the 'mid-peak time slot rate plan' or the 'on-peak time slot rate plan' (S930), and the 'ESS battery charge state' is equal to or less than the 'peak step trend reflected battery capacity' (S940). At least one mandatory type DR event must be executed for each of load reduction, load transfer, and load direct control DLC.

[0101] In addition, if the 'load power' is greater than the 'third step judgment threshold value' (S920), the 'current time slot rate plan' is a 'mid-load (Mid-Peak) time slot rate plan' or an 'on-peak) time slot rate plan' (S930), and the 'ESS battery charge state' is greater than the 'peak step trend-reflecting battery capacity' (S940), the control unit 170 can generate a mandatory DR event and control the battery to be discharged (S952).

[0102] The present invention has been described in detail above with reference to the preferred embodiment shown in the drawings. Such embodiment is merely illustrative and should not be considered as limiting the present invention, and should be considered as descriptive rather than restrictive. The true technical scope of the present invention is determined by the technical ideas of the appended claims, not the above description. Even if specific terms are used in this specification, they are used merely for the purpose of explaining the concept of the present invention, and are not used to limit the meaning or the scope of the present invention described in the claims. Each step of the present invention does not necessarily have to be performed in the order described, and may be performed in parallel, alternatively, or separately. A person having ordinary skill in the art to which the present invention belongs can make various modifications and equivalent other embodiments without departing from the essential technical idea of ​​the present invention claimed in the claims. Equivalents should be understood to include not only currently published equivalents, but also equivalents developed in the future, i.e., all components invented to perform the same function regardless of structure. [Explanation of symbols]

[0103] 10 Power grid operator equipment 20 lines 30 Energy meter 40 ESS 50_1,50_2,50_N load 60 terminals 100 Demand management device 110 Communications Department 130 Preservation Department 131 Consumer information DB 133 Information to be considered DB 135 Step-by-step logic database 150 Calculation section 151 Estimated power calculation unit 153 Peak power calculation unit 155 Current TOU Judgment Department 157 DR Power Prediction Department 170 Control section

Claims

1. a communication unit that receives consumer characteristic information including consideration information including load power and a set time for calculating a load increase / decrease trend rate of the load power; a storage unit for storing data including the load power received during the set time; A calculation unit that calculates a load increase / decrease trend rate by using the load power during the set time and calculates a trend-reflecting predicted load power by using the load increase / decrease trend rate; and a control unit for generating a DR event or controlling the ESS when the trend-reflected predicted load power reaches at least one critical value corresponding to a certain level of peak power; A demand management-based distributed resource integrated operation system including:

2. The consumer characteristic information is The demand management-based distributed resource integrated operation system of claim 1, further comprising at least one of past power data, supply and demand plan, DR participation information, criticality rate (%) for each step, and battery charging rate (%) for each step.

3. The calculation unit is 3. The demand management-based distributed resource integrated operation system according to claim 2, further comprising a peak power calculation unit that predicts maximum demand based on the past power data and the supply and demand plan and calculates peak power.

4. The at least one critical value is 3. The demand management-based distributed resource integrated operation system according to claim 2, wherein the step judgment criticality rate (%) is determined according to the customer load characteristics.

5. The control unit is The demand management-based distributed resource integrated operation system according to claim 4, characterized in that the system is classified into operation profit, optimization / storage, peak approach, and peak step according to at least one of the critical values.

6. The calculation unit is 6. The system of claim 5, further comprising a DR power forecasting unit that calculates DR potential power by summing up the amount of power available for the DR event using the DR participation information.

7. The information to be considered is The demand management-based distributed resource integration and operation system according to claim 6, further comprising at least one of TOU fee plan information, a maximum battery charge capacity of the ESS, and a battery charge state of the ESS.

8. The calculation unit is The demand management-based distributed resource integrated operation system of claim 7, further comprising a current TOU calculation unit that compares the TOU rate plan information according to season and time period with the current date and time, and calculates the current rate plan time period (TOU) by determining whether the current date and time period is a light load, heavy load, or maximum load rate plan time period.

9. The control unit is 9. The demand management-based distributed resource integrated operation system of claim 8, further comprising: determining whether a preset condition is satisfied for each step according to the load power, the DR potential power, the current rate plan time period (TOU), and the battery charge state of the ESS; and determining whether or not ESS charging / discharging is required or whether or not a DR event is required to be generated.

10. The preset conditions are: The demand management-based distributed resource integrated operation system of claim 9, characterized in that the peak power, the critical value for each step, TOU, the DR potential power, the maximum battery capacity of the ESS, and the trend-reflecting battery charging rate are set using at least one of them.

11. The DR event is 10. The demand management-based distributed resource integration operation system according to claim 9, further comprising a request to process at least one of load power reduction, load transfer, and direct load control according to priority.

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