KR20210061823A - Energy management system and method for minimizing the power purchase cost of microgrid using the same - Google Patents

Abstract

The present invention relates to a method for an energy management system to calculate the cost of purchasing electricity in a microgrid. The method includes the steps of: predicting a first amount of power to be generated by an energy generating device constituting the microgrid; predicting a second amount of power to be used in a load constituting the microgrid; and calculating the minimum electricity purchase cost to purchase at a main grid which provides power to the microgrid or an adjacent microgrid adjacent to the microgrid. Therefore, it is possible to stably operate the energy in the microgrid.

Description

Energy management system and method for minimizing the power purchase cost of microgrid using the same}

The present invention relates to an energy management system for minimizing power purchase cost of a microgrid and a method of determining power purchase cost using the same.

Recently, with the spread and spread of new and renewable energy in industrial and residential complexes, small-scale electric power communities are being built that can self-sufficiency of electric power such as micro grids. At this time, the power generated by the renewable energy generating device constituting the microgrid is not produced in a certain amount depending on the situation.

Therefore, it is necessary to be able to monitor and control the renewable energy generating device and energy storage device so that power can be stably supplied within the microgrid. Nevertheless, if it is difficult to meet the power demand by the microgrid itself by predicting the amount of power generation and demand within the microgrid, power must be purchased through a utility (eg, KEPCO) or an adjacent microgrid.

Economic dispatch or generator operation plans by minimizing power generation costs using traditional fossil energy have been established academically. And, based on this, the actual system is implemented and operated.

The ideal microgrid is equipped with renewable energy facilities and energy storage devices, so that electricity can be produced and consumed within the microgrid. However, the production of new and renewable energy is affected by the environment such as the weather, and the energy storage device is also in a situation where it is not possible to sufficiently store energy due to insufficient capacity.

Therefore, insufficient power must be supplied through the main grid (eg, the utility grid) or adjacent microgrids. However, there are disadvantages in which it is difficult to predict how much insufficient power should be supplied from the main grid or adjacent microgrids, and what is the minimum power purchase cost to stably supply the supplied power.

Accordingly, the present invention provides an energy management system capable of minimizing power purchase cost and a method of determining power purchase cost using the same in order to prepare for a case where a microgrid needs to purchase power from the outside, such as a main grid or an adjacent microgrid. do.

The present invention relates to an energy management system for minimizing power purchase cost of a microgrid and a method of determining power purchase cost using the same.

The energy management system for managing the energy of the microgrid is a reference information generation module that generates reference information for calculating the power purchase cost of the microgrid, based on the reference information, according to the amount of power purchased for a certain period and the amount of power purchased. An optimization module for optimizing an affected demand response cost, and a calculation module for calculating a minimum power purchase cost to be purchased by the microgrid from the outside based on the optimized power purchase amount and demand response cost, and the reference information, , The energy management system interlocks with the main grid supplying power from the outside to the microgrid and adjacent microgrids.

According to an embodiment, the microgrid includes an energy generating device that generates power inside the microgrid, a load that consumes power generated inside the microgrid, a load that consumes power supplied from the outside, and the power generated inside the microgrid. It may include an energy storage device for storing the remaining power consumed by the load.

According to an embodiment, the reference information generation module may include an energy output prediction module that predicts an amount of power output to be generated during a preset period in the energy generation device.

According to an embodiment, the reference information generation module may further include a load usage prediction module that predicts a predetermined amount of power usage to be used in the future based on the amount of power currently being used by the load.

According to an embodiment, the reference information generation module further includes a microgrid measurement module that measures a power usage pattern in the microgrid, and the power usage pattern includes an amount of power used in the microgrid and a current time. And, the power operation information corresponding to the current time may be included among the power operation information previously set and stored in the microgrid.

According to an embodiment, the reference information generation module may further include a demand response planning module that checks load reduction plan information scheduled in the microgrid.

According to an embodiment, the calculation module determines a purchase cost to purchase power from the main grid, a purchase cost to purchase power from the adjacent microgrids, or a demand response cost to minimize a power purchase period, a purchase amount, and a purchase time. I can.

The method of calculating the power purchase cost of the microgrid by the energy management system includes: predicting a first amount of power to be generated by an energy generating device constituting the microgrid, and predicting a second amount of power to be used by a load constituting the microgrid. Step, and when the power shortage in the microgrid is predicted based on the predicted first power amount and the second power amount, the minimum power purchase cost to be purchased from a main grid providing power to the microgrid or an adjacent microgrid adjacent to the microgrid. And calculating.

According to an embodiment, the calculating of the minimum power purchase cost includes a power purchase cost to be purchased from the main grid at an arbitrary time, a power purchase cost to be purchased from the adjacent microgrid at the arbitrary time, and the random time. A value obtained by adding the cost for the demand response in the microgrid to the minimum may be determined as the minimum power purchase cost.

According to an embodiment, the step of predicting the second amount of power further comprises measuring a power use pattern of the microgrid, wherein the power use pattern includes an amount of power used in the microgrid and a current time. And, the power operation information corresponding to the current time may be included among the power operation information previously set and stored in the microgrid.

According to an embodiment, the step of predicting the second amount of power may further include checking information on a load reduction plan scheduled in the microgrid.

According to the present invention, energy in the microgrid can be stably operated, and insufficient energy can be purchased economically through utilities or adjacent microgrids.

In addition, it is possible to reduce the load according to the demand response.

In addition, it is possible to minimize the microgrid operation cost by minimizing the power purchase cost and the demand response cost based on the optimization method.

1 is an exemplary diagram of a microgrid system according to an embodiment of the present invention.
2 is a structural diagram of an energy management system according to an embodiment of the present invention.
3 is a flowchart of a method of determining a power purchase cost according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art may easily implement the present invention. However, the present invention may be implemented in various different forms and is not limited to the embodiments described herein. In the drawings, parts irrelevant to the description are omitted in order to clearly describe the present invention, and similar reference numerals are attached to similar parts throughout the specification.

Throughout the specification, when a part "includes" a certain component, it means that other components may be further included rather than excluding other components unless specifically stated to the contrary.

Hereinafter, an energy management system and method for minimizing power purchase cost of a microgrid according to an embodiment of the present invention will be described in detail with reference to the drawings.

1 is an exemplary diagram of a microgrid system according to an embodiment of the present invention.

As shown in FIG. 1, the microgrid 100 is a renewable energy generating device 10 that generates power through renewable energy such as sunlight and wind power, and the power generated by the renewable energy generating device 10. It consists of an energy storage device 20 for storing, and a load 30 that consumes power.

Power generated by the renewable energy generating device 10 is provided to the load 30 to be consumed by the load 30, and the remaining power is stored in the energy storage device 20. Power stored in the energy storage device 20 may supply power to the load 30 when the renewable energy generating device 10 cannot generate power.

At this time, the microgrid 100 is the main grid (or, also referred to as'utility') 200, which supplies power to the load 30 due to uncertainty in the output of renewable energy, or an adjacent microgrid (hereinafter referred to as'adjacent Insufficient power may be supplied from (referred to as'microgrid') 300.

In order to provide stable power supply/consumption operation of the microgrid and purchase power from outside, the amount of generation and demand of renewable energy must be predicted in advance. This is predicted by the energy management system 400 interlocked with the microgrid 100.

The energy management system 400 can manage to stably supply power to the load 30 using the energy storage device 20 while minimizing the power purchase cost for a certain period based on the predicted generation amount and demand amount information. have.

The energy management system 400 calculates the sum of the real-time power rates of the main grid 200, which is a direct power purchase place, the adjacent microgrid 300, and the cost for the power demand response (DR) at the load 30. The cost of purchasing electricity can be minimized with the optimization technique that minimizes it.

The power demand response means that when the demand for power increases in the load 30, the consumer reacts to reduce power use. The number of consumers, that is, the number of loads 30 is large, and it is very difficult to manually adjust a large number of loads 30. Therefore, it is not an exaggeration to say that there is no method other than planned outages that can be adjusted by the power grid operator on the current power system.

In particular, with a simple electricity tariff system, consumers are not motivated to respond voluntarily. Therefore, in the United States and Europe, a method in which a power grid operator provides a strong compensation for the demand response while generating a basic demand response based on various tariff plans, and further, more active demand response services are applied through sales in the market.

In an embodiment of the present invention, the energy management system 400 may model a demand response within the microgrid 100 like a virtual power plant. Therefore, an optimization technique that minimizes the cost of a virtual generator can be applied.

The structure of the energy management system 400 in such an environment will be described with reference to FIG. 2.

2 is a structural diagram of an energy management system according to an embodiment of the present invention.

As shown in FIG. 2, the energy management system 400 includes a reference information generation module 410 that generates reference information provided when calculating the power purchase cost, in order to calculate the purchase cost based on the reference information, for a certain period of time. And an optimization module 420 for optimizing the amount of electricity purchased and the demand response cost affected thereto, and a calculation module 430 for calculating the power purchase cost of the microgrid based on the optimized information.

The reference information generation module 410 includes a renewable energy output prediction module 411, a load usage prediction module 412, a microgrid measurement module 413, and a demand response planning module 414.

The new and renewable energy output prediction module 411 predicts the degree of new and renewable energy output from the new and renewable energy generating device 10. In the embodiment of the present invention, an example of predicting the output of power using sunlight among various renewable energy sources will be described.

The new and renewable energy output prediction module 411 interlocks with the new and renewable energy generation device 10, which is a photovoltaic power generation facility, to obtain facility information such as the installation location, capacity, azimuth, and elevation angle of the new and renewable energy generation device 10. To collect. In addition, the new and renewable energy output prediction module 411 provides environmental information such as the position of the sun, amount of sunlight, sunrise time, and sunset time by time slot, to a specific server (for example, a weather information providing server, etc.) (drawings, not shown). Receive from

The new and renewable energy output prediction module 411 predicts the amount of power output of the new renewable energy to be output from the new renewable energy generation device 10 based on the facility information and the environment information. Since the renewable energy output prediction module 411 predicts the amount of power output using facility information and environment information is various, and the information used in the prediction is also various, the embodiment of the present invention is not limited to any one method and information. Does not.

The load usage prediction module 412 interoperates with the load 30 and predicts a planned amount of power usage of the load to be used by the load 30 in the future based on the amount of power used by the load 30. Alternatively, based on the power usage information used by the load 30 in the past, the expected amount of power use may be predicted.

The power consumption can be predicted by the power load prediction method using an artificial neural network. Specifically, a pattern for each day of the week is derived by using the history information of power consumption. Correlation analysis is performed between patterns of power consumption by day of the week and surrounding environment information (temperature, wind speed, humidity, dew point, and seasonal information). In this case, the Pearson correlation coefficient can be used. A learning model (learning model for each day of the week) can be derived using the Inkyung Neural Network as input data of the impact and history information of each environmental information, and prediction can be performed based on the learning model.

Since the load usage prediction module 412 may perform a method of predicting the planned amount of load strategy use in various ways, it is not limited to any one method in the embodiment of the present invention.

The microgrid measurement module 413 measures the power usage pattern in the current microgrid. Here, the power usage pattern includes the amount of power used in the microgrid, the current time, and power operation information corresponding to the current time among power operation information previously set and stored in the microgrid.

In addition, the microgrid measurement module 413 checks power usage patterns of other adjacent microgrids located around the microgrid. In the exemplary embodiment of the present invention, the power operation information set in the microgrid is stored in the microgrid measurement module 413 as an example, but may be received from an external information storage means.

The demand response planning module 414 checks load reduction plan information scheduled in the microgrid. Here, the load reduction plan information includes the load reduction time point and the reduction amount information at that time point. To this end, in the embodiment of the present invention, the load reduction plan information of all microgrids is stored and managed in the demand response planning module 414 as an example, but the load reduction planning module installed in the microgrid (not shown in the drawing) ).

The energy management system 400 manages the total power consumption of the microgrid 100, and in the upper EMS (Ex. Korea is KPX, power exchange, not indicated in this patent), the generation amount of the entire grid (domestic example, national system) If the demand for electricity is large, plan to reduce demand. In the summer when peak power use occurs, power to demand-responsive customers (demand-responsive customers, that is, customers performing power reduction, in this patent, microgrids operated as energy management systems) contracted to reduce power consumption during peak hours The reduction target amount is indicated. The transfer is the total amount of electricity that the customer needs to reduce, and the customer decides which load to reduce in detail at the corresponding time (peak time), whether the reduction is possible, and the reduction schedule for each detailed load. The demand response planning module selects the load for demand reduction in the microgrid, determines the reduction amount by load, and schedule (reduction time and reduction amount by load) to achieve the goal of the power reduction amount required by the upper energy management system. Carry out.

There is a problem that it is difficult to limit the demand response only to the economics of MG. If the demand response is carried out, it is an advantage for the entire grid to reduce the operation of the corresponding generator. In other words, minimizing the cost of demand response is the same as minimizing the power generation cost of the grid. It can be seen as minimizing MG's external purchase power and minimizing power generation costs from a grid standpoint.

The optimization module 420 receives the reference information generated by the reference information generation module 410 as an input. And, in order to minimize the operating cost of the microgrid during a predetermined operating period other than a specific point in time, and to minimize the cost of purchasing power from the outside and the cost according to the demand response of the load, it is received from the reference information generation module 410. Among the reference information, the amount of power purchased from outside (eg, the main grid or adjacent microgrid) for a certain period of time and the demand response cost affected by the purchased power are optimized.

The optimization module 420 determines the optimization time of the demand response and the amount of power reduction, and determines the amount of power purchased outside of the corresponding time zone (total purchase amount, whether to purchase from a utility or from an adjacent microgrid is undecided). The demand response cost is the loss cost for the act of forcibly reducing the power consumption by requesting demand monitoring while using the load within the microgrid. A cost optimization technique using Linear Program (LP) and/or an optimization technique based on the linear condition of a linear function (objective function) can be applied.

When the planned (current) future demand response (power reduction amount) of time t is performed on the microgrid (#1), the economic cost of performing the demand response, the cost of purchasing power from an external utility, and the external microgrid (#2) In order to calculate the power purchase cost from ), it is necessary to estimate the amount of renewable generation and load in the time t (in the future). These two are unknown numbers that are not planned, so predictions are required. There are various techniques for predicting loads and renewables, and artificial neural networks, regression analysis, and learning prediction using ARIMA statistical models can be used.

The optimization module 420 uses an optimization technique in order to optimize the purchased power purchase amount and the demand response cost. Here, the optimization technique may be any one of various techniques used in the power system, and is not limited to a specific technique in the embodiment of the present invention.

The calculation module 430 derives the power purchase amount and the power supply period from the external/microgrid for each time point in a certain period based on the result optimized by the optimization module 420. Here, the calculation module 430 minimizes the purchase cost by using a purchase cost minimization algorithm.

The calculation module 430 optimizes the amount of external power purchases in each time period determined by the optimization module. Determine whether to purchase electricity from utilities and adjacent microgrids for each time period (utility and power purchase costs for adjacent microgrids vary by time) or optimize. The forecasts of renewable power generation and loads by time of the future may not be 100% consistent. Renewable and load predicted values derived from the prediction unit and real-time measured values using the microgrid measurement module unit are used to check errors, maintain the planned optimal demand response, and if an error occurs, the optimization module is external. The purchase amount is recalculated and the calculation unit determines the external purchase in real time, and calculates and corrects the external power purchase location and purchase amount for each time period.

Purchasing cost minimization means minimizing the cost of purchasing external power for a certain period in order to prepare for the case of purchasing power from the main grid 200 and the adjacent microgrid 300 due to insufficient power in the microgrid 100. Means to do. In order to minimize the purchase cost, the calculation module 430 has equations and inequality constraints. A function defining the constraints will be described with reference to the following equation.

Equation 1 is a function defined by the power purchase determination module 430 in order to minimize the cost of the microgrid, and Equations 2 to 11 are energy usage, SOC (Social Overhead Capital), cost, etc. of resource elements in the microgrid. This is a function that defines the constraints of.

Variables of functions corresponding to each equation defined above may be defined as shown in Table 1 below.

variable meaning

Unit kW power purchase cost from utility u at time t [KRW/kW]

Unit kWh electricity purchase cost [KRW/kW] purchased from external microd m at time t

The amount of power supplied by the utility u into the microgrid at time t [kW]

The amount of power supplied by the external microgrid m into the microgrid at time t [kW]

The maximum amount of power the utility u can supply in the microgrid at time t [kW]

The maximum amount of power the external microgrid m can supply in the microgrid at time t [kW]

The amount of power the energy storage device charges at time t [kW]

The minimum amount of charge the energy storage device can charge in a unit time [kW]

The maximum amount of charge the energy storage device can charge per unit time [kW]

The amount of power the energy storage device discharges at time t [kW]

Minimum charge amount [kW] that the energy storage device can discharge per unit time

The maximum amount of charge the energy storage device can discharge in a unit time [kW]

The amount of energy in the energy storage device at time t

Minimum charge amount of energy storage device [kW]

Maximum charging amount of energy storage device [kW]

Charging efficiency of energy storage device [%]

Discharge efficiency of energy storage device [%]

Unit time (t-(t-1))

The amount of electricity generated by renewable energy at time t [kW]

The amount of power consumed by the load at time t [kW] (meaning the amount of load before DR is reflected)

Cost function for DR within microgrid at time t [circle]

Load reduction amount by DR in microgrid at time t [kW]

Pre-specified percentage of nominal load

Constant term of a linear function that determines the amount of power consumed against price

Slope coefficient of a linear function that determines the amount of power consumed against price

Marginal cost to determine the amount of power consumption

A method of minimizing power purchase cost by managing energy in the microgrid using the energy management system 400 described above will be described with reference to FIG. 3.

3 is a flowchart of a method of determining a power purchase cost according to an embodiment of the present invention.

As shown in FIG. 3, the energy management system 400 predicts the degree of output of new and renewable energy to be output from the new and renewable energy generating device 10 in the microgrid (S100). If the renewable energy generating device 10 is a photovoltaic power generation facility, the energy management system 400 includes not only facility information such as the location where the renewable energy generating device 10 is installed, output capacity, etc., but also Based on environmental information (for example, the position of the sun by time zone, amount of sunlight, sunrise time, sunset time, etc.), the degree of renewable energy output on the day is predicted.

Here, since the energy management system 400 uses facility information and environment information to predict the output of new and renewable energy on the day in various ways, it is not limited to any one method in the embodiment of the present invention. In addition, in the embodiment of the present invention, it is described as an example of predicting the output of new and renewable energy on the day, but it is also possible to predict the output of new and renewable energy for a certain period.

After estimating the degree of renewable energy output, the energy management system 400 predicts a planned amount of power use of the load to be used by the load 30 in the future based on the amount of power used by the load 30 in the microgrid (S110). Alternatively, based on the power usage information used by the load 30 in the past, the expected amount of power use may be predicted.

The energy management system 400 collects power use patterns of other adjacent microgrids located in the vicinity together with the current power use pattern in the microgrid (S120). Here, the power usage pattern includes the amount of power used in the microgrid, the current time, and power operation information corresponding to the current time among power operation information previously set and stored in the microgrid.

The energy management system 400 checks the load reduction plan information scheduled in the microgrid (S130). Here, the load reduction plan information includes the load reduction time point and the reduction amount information at that time point.

The energy management system 400 employs a method of optimizing the power purchase amount and demand response cost among new and renewable energy output prediction information, power usage prediction information in the load, microgrid measurement information, and demand response information collected through steps S100 to S130. Optimized by using (S140). That is, based on the predicted new and renewable energy output prediction information in step S100 and the power consumption prediction information in the load predicted in step S110, it is predicted whether the microgrid has insufficient power.

If it is predicted that power will be insufficient, the energy management system 400 calculates the power purchase cost of the microgrid using the optimized power purchase amount and the demand response cost (S150). When calculating the power purchase cost in step S150, the energy management system 400 calculates the purchase cost minimization algorithm.

),인접 마이크로그리드로부터 상기 임의의 시각에 구매할 전력 구매 비용(

), 그리고 시간 t에 마이크로그리드 내 수요 반응에 대한 비용 함수(

)의 합이 최소가 되도록(

) 전력 구매 비용을 계산한다.In other words, the energy management system 400 is the cost of purchasing electricity to be purchased from the main grid 200 at an arbitrary time (

), the cost of purchasing power to purchase from the adjacent microgrid at the above arbitrary time (

), and the cost function for the demand response in the microgrid at time t (

So that the sum of) is minimal (

) Calculate the cost of purchasing electricity.

In addition, the energy management system 400 may also determine a power purchase strategy for the microgrid based on the calculated power purchase cost. Here, the power purchase strategy refers to a purchase amount of power and a period of time to purchase power after a certain point in time.

In this way, when the power purchase cost is determined using the energy management system 400, energy in the microgrid can be stably operated, and insufficient energy can be economically purchased through the main grid or adjacent microgrids.

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

400: energy management system
410: reference information generation module
420: optimization module
430: calculation module

Claims (11)

As an energy management system that manages the energy of the microgrid,
A reference information generation module that generates reference information for calculating the power purchase cost of the microgrid,
An optimization module that optimizes the purchase amount of electricity purchased for a certain period based on the reference information and the demand response cost affected by the purchase amount of electricity, and
A calculation module that calculates the minimum purchase cost of power to be purchased from the outside by the microgrid based on the optimized power purchase amount, demand response cost, and the reference information
Including,
The energy management system is an energy management system that interlocks with a main grid supplying power from the outside to the microgrid and adjacent microgrids. The method of claim 1,
The microgrid,
An energy generating device that generates power inside the microgrid,
A load that consumes the power generated inside and the power supplied from the outside, and
An energy storage device that stores the remaining power consumed by the load among the internally generated power
Energy management system comprising a. The method of claim 2,
The reference information generation module,
An energy output prediction module for predicting an amount of power output to be generated during a preset period in the energy generating device,
Energy management system comprising a. The method of claim 3,
The reference information generation module,
A load usage prediction module that predicts a planned amount of power usage to be used in the future based on the amount of power currently being used by the load,
Energy management system further comprising a. The method of claim 4,
The reference information generation module,
Microgrid measurement module that measures the power usage pattern in the microgrid
Including more,
The power use pattern includes an amount of power used in the microgrid, a current time, and power operation information corresponding to the current time among power operation information previously set and stored in the microgrid. The method of claim 5,
The reference information generation module,
Demand response planning module that checks load reduction plan information scheduled in the microgrid
Energy management system further comprising a. The method of claim 1,
The calculation module,
An energy management system for determining a power purchase period, a purchase amount, and a purchase time so that a purchase cost to purchase power from the main grid, a purchase cost to purchase power from the adjacent microgrids, or a demand response cost is minimized. As a method for the energy management system to calculate the cost of purchasing power for the microgrid,
Predicting a first amount of power to be generated by an energy generating device constituting the microgrid,
Estimating a second amount of power to be used in the load constituting the microgrid, and
When the power shortage in the microgrid is predicted based on the predicted first power amount and the second power amount, calculating a minimum power purchase cost to purchase from a main grid providing power to the microgrid or an adjacent microgrid adjacent to the microgrid. step
Power purchase cost calculation method comprising a. The method of claim 8,
The step of calculating the minimum power purchase cost,
The power purchase cost to be purchased from the main grid at a certain time, the power purchase cost to be purchased from the adjacent microgrid at the random time, and the sum of the cost for the demand response in the microgrid at the random time becomes the minimum. Power purchase cost calculation method for determining a value as the minimum power purchase cost. The method of claim 8,
Predicting the second amount of power,
Measuring the power usage pattern of the microgrid
Including more,
The power usage pattern is calculated as the power purchase cost including the amount of power used in the microgrid, the current time, and power operation information corresponding to the current time among the power operation information previously set and stored in the microgrid. Way. The method of claim 8,
Predicting the second amount of power,
Checking the load reduction plan information scheduled in the microgrid
Power purchase cost calculation method further comprising.

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