KR102238624B1 - System and method for operating virtual power plant based on multi-objective function - Google Patents

Abstract

The present invention relates to a virtual power plant operating system having distributed resources including new and renewable energy, and more particularly, extracting multiple objective functions for maximizing profits and minimizing costs by providing an independent control means for each distributed resource, Based on this, the present invention relates to a virtual power plant operating system based on a multi-objective function that enables stable operation of a virtual power plant in consideration of profits and costs by creating a strategy for operating distributed resources and a method of operating the same.

Description

System and method for operating virtual power plant based on multi-objective function

The present invention relates to a virtual power plant operating system having distributed resources including new and renewable energy, and more particularly, extracting multiple objective functions for maximizing profits and minimizing costs by providing an independent control means for each distributed resource, Based on this, the present invention relates to a virtual power plant operating system based on a multi-objective function that enables stable operation of a virtual power plant in consideration of profits and costs by creating a strategy for operating distributed resources and a method of operating the same.

Distributed Energy Resource (DER), which is installed and operated in small and medium-sized near demand sites, is being actively introduced to complement the existing power supply method centered on central power plants.

Distributed power can be installed in a short period of time at the required scale in the required area and can contribute to the short-term stabilization of the power system because the generator can be started within a short period of time. It can be used to improve system reliability and power quality.

These distributed power sources are recently being constructed as power generation facilities using eco-friendly energy sources such as hydropower, wind power, and solar power, or charging/discharging facilities such as energy storage devices. It is more preferred because it can be installed and has the least place restrictions, and it can be installed and operated in various ways from small power generation facilities to large power generation facilities in the form desired by the operator.

A control strategy that can effectively connect with the operation of the existing power system must be established so that the role of the existing central power supply power plant can be replaced by the above-described distributed power supply. A typical implementation of this distributed power grid connection strategy is the Virtual Power Plant (VPP). The virtual power plant is an integrated management system for operating various types of distributed power scattered in the power system as a single power generation system using advanced information and communication technology and automatic control technology for the purpose of participating in the wholesale power market and system operation. it means.

The above-described virtual power plant predicted the amount of power produced by the distributed power source using modeling for various distributed power sources, and controlled the power bidding in the power trading market according to the predicted amount of power production.

In this regard, in the prior art literature, in constructing a core virtual generator (VPP) operation solution, the concept of a multi-agent system (MAS) is applied to enable efficient and flexible system operation through the gaming relationship between components. The operating system is disclosed.

According to the prior art document, for the load, the bid price and the auction bid for the energy consumption are input from the subagents, and the VPP operating device determines the energy consumption to be supplied at the corresponding bid price, and the power supply device is determined. Control to supply energy usage. And, within the preset energy usage range corresponding to the energy usage price bid for the two subagents entering the non-cooperative game, each price of the two subagents corresponding to the optimal operating point at which the net profit is the highest at the corresponding bid price. It is configured to determine the amount of energy to be provided.

At this time, the above-described virtual generator operating system predicts the generation capacity and generation cost of distributed power in advance and participates in the bidding, and the weather information is applied to the new and renewable energy to predict and apply the generation capacity and power generation cost.

However, considering that the prediction accuracy of meteorological information gradually decreases due to environmental problems, etc., it is difficult to apply new and renewable energy whose power generation capacity and power generation cost are uncertain.

In addition, as the usage increases, the net profit increases proportionally, but when the inflection point is exceeded, the efficiency decreases due to an increase in cost. As a result, there is a problem that the gain of the virtual power plant becomes negative.

In addition, in the game theory applied in the prior art literature, there may be cases where the user does not accept the determination of energy usage, and if all of them enter as non-cooperative games, there may be cases in which the profits become negative.

In addition, in general, virtual power plants incur costs for operation and maintenance of distributed power sources, and prior art documents simply apply only operating rules that maximize profits, but do not consider such costs.

In particular, when operating distributed resources through transactions with the power exchange, bidding is restricted when the average reduction fulfillment rate is less than 70% on transaction days more than three times, so a more stable virtual power plant operation method considering risks is required.

Korean Patent Publication No. 10-1472582

The present invention was conceived in view of the above-described problems, and an object of the present invention is to maximize profits and minimize costs by providing independent control means for each distributed resource in a virtual power plant operating system having distributed resources including new and renewable energy. It is to provide a virtual power plant operating system based on multi-objective function optimization that extracts multiple objective functions for and enables efficient management of distributed resources based on this and an operating method thereof.

In order to achieve the above objects, a virtual power plant operating system based on a multi-objective function according to the present invention includes: at least one load, at least one power generation source including a renewable energy facility, and a distributed resource including an energy storage system; A VPP monitoring device connected to the distributed resource to monitor each state of the distributed resource and provide distributed resource information collected for each distributed resource to a VPP operating device; And power sales price, determine an objective function corresponding to profit and cost based on distributed resource information and power sales price provided from the VPP monitoring device, and apply a preset weight for each objective function to determine the profit and cost. It is composed of a VPP operating device that transmits the distributed resource operation strategy that maximizes the difference to the distributed resource through the VPP monitoring device, and each of the distributed resources diagnoses the state of the distributed resource and predicts the amount of individual power. It is characterized by including independent resource control units for calculating and transmitting the life cost to the VPP monitoring device, and operating and controlling the distributed resource based on the distributed resource operation strategy received from the VPP monitoring device.

Here, the VPP operating device applies a preset weight to each objective function, weights the objective function of the maximum profit to which the weight is applied and the objective function of the minimum cost to set it as the final objective function, and maximizes the final objective function. It is desirable to be configured to create a distributed resource management strategy.

In addition, the profit objective function is composed of one or a combination of two or more of profit types including power sales profit, power generation source government subsidy profit, and energy storage system government subsidy profit, and the cost objective function is the lifetime of distributed resources. It is made as a cost, but it is preferable that the lifetime cost is calculated as the reciprocal of the maximum lifetime in the lifetime prediction curve of the distributed resource.

In addition, the weight increases the probability that the item will be selected by setting the weight of the item with high profit to the profit objective function, and the item is selected by setting the weight of the item with high cost to the cost objective function. It is preferably set to lower the probability of becoming.

In addition, in the cost objective function, the weight of the life cost of the energy storage system is applied differently according to the size of the state of charge, but the weight is set higher when the value is greater than that value than when the value is less than a certain value of the state of charge, and the constant value is 70~ It is preferable to adjust to any one of 90% of the range.

In addition, it is preferable that the VPP operating apparatus predicts the selling price of electricity, compares and analyzes the predicted selling price in the previous time block with the currently predicted selling price, and changes and sets the weight for the profit objective function.

In order to achieve the above objects, a method for operating a virtual power plant based on a multi-objective function according to the present invention includes at least one load, at least one power generation source including a new and renewable energy facility, and distributed resources including an energy storage system. In the virtual power plant operating method based on a multi-objective function of a virtual power plant operating system comprising a VPP operating device that is combined to perform power management, wherein each of the above-described distributed resources has a resource control unit that manages the corresponding distributed resource. , A first step of collecting power amount, life cost, and operation status information for the distributed resource from each resource control unit of the load, power generation source and energy storage system in the VPP operating device, and predicting the selling price of power in the VPP operating device. The second step, a third step of setting a weight for an objective function corresponding to profit and cost in the VPP operating device, each distributed resource information collected in the first step in the VPP operating device, and determined in the second step The fourth step of predicting VPP expected profit and VPP cost based on the selling price, and generating a profit objective function and a cost objective function based on each predicted value, and the profit objective function generated in the fourth step in the VPP operating device. And a fifth step of determining the objective function of the maximum profit and the minimum cost by applying a predetermined weight to the cost objective function and generating a distributed resource operation strategy corresponding thereto.

Here, in the second step, it is preferable that the VPP operating device predicts the selling price of electricity, compares and analyzes the predicted selling price in the previous time block with the currently predicted selling price, and changes and sets the weight for the profit objective function.

In addition, the profit objective function is composed of one or a combination of two or more of profit types including power sales profit, power generation source government subsidy profit, and energy storage system government subsidy profit, and the cost objective function is the lifetime of distributed resources. It is made as a cost, but it is preferable that the lifetime cost is calculated as the reciprocal of the maximum lifetime in the lifetime prediction curve of the distributed resource.

In addition, in the third step, the weight of the item with high profit is set larger than the weight of the item with high profit for the benefit objective function, and the probability of the item being selected is increased, and the weight of the item with high cost is set higher with respect to the cost objective function. It is preferable that the item is set to lower the probability of being selected.

In addition, in the cost objective function, the weight of the life cost of the energy storage system is applied differently according to the size of the state of charge, but when the weight is higher than the value of the state of charge, the weight is set higher than that of the state of charge, and the constant value is It is preferable to adjust to any one of the range of 70 to 90%.

In addition, in the fifth step, the VPP operating device applies a preset weight to each objective function, weighted the objective function of the maximum profit to which the weight is applied and the objective function of the minimum cost, and sets it as the final objective function. It is desirable to be configured to create a distributed resource management strategy to maximize the function.

According to the virtual power plant operating system based on a multi-objective function and its operating method according to the present invention, in the virtual power plant system operated by providing a renewable energy facility such as solar energy as a power source, the maximum profit objective function and the minimum cost By creating an operating strategy for distributed resources using multiple objective functions including an objective function, there is an effect that it is possible to manage not only the operating profit of the virtual power plant but also the risk of error due to cost occurrence at the same time.

1 is a diagram showing a schematic configuration of a virtual power plant operating system according to the present invention.
FIG. 2 is a block diagram showing the functional separation of the internal configuration of the VPP operating device shown in FIG. 1.
3 is a view showing the internal configuration of each resource control unit shown in FIG. 1 divided into functional modules.
4 is a view showing the internal configuration of the operation strategy establishment unit shown in FIG. 2 divided into functional modules.
5 is a flow chart for explaining the operation of the virtual power plant operating system shown in FIG.

The present invention can be implemented in various other forms without departing from the technical idea or main features thereof. Accordingly, the embodiments of the present invention are merely illustrative in all respects and should not be interpreted as limiting.

Terms such as first and second may be used to describe various elements, but the elements should not be limited by the terms.

The above terms are used only for the purpose of distinguishing one component from another component. For example, without departing from the scope of the present invention, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element.

When a component is referred to as being "connected" or "connected" to another component, it is understood that it may be directly connected or connected to the other component, but other components may exist in the middle. It should be.

On the other hand, when a component is referred to as being "directly connected" or "directly connected" to another component, it should be understood that there is no other component in the middle.

The terms used in the present application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise.

In the present application, terms such as "comprise" or "include" and "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification. It is to be understood that the possibility of the presence or addition of other features or numbers, steps, actions, components, parts, or combinations thereof beyond that is not preliminarily excluded.

Unless otherwise defined, all terms used herein including technical or scientific terms have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

Terms such as those defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and should not be interpreted as an ideal or excessively formal meaning unless explicitly defined in the present application. Does not.

Hereinafter, in order to describe in detail enough that a person of ordinary skill in the art can easily implement the present invention, the most preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. .

1 is a diagram showing a schematic configuration of a virtual power plant operating system based on a multi-objective function according to the present invention.

As shown in Figure 1, the virtual power plant operating system based on the multi-objective function according to the present invention is a virtual power plant (VPP: Virtual Power Plant, hereinafter referred to as "VPP") operating device 100, and a VPP monitoring device 200 ) And distributed resources 300.

The VPP operating device 100 creates an operating strategy for maximizing the operating profit of the virtual power plant and minimizing the cost based on the state of the distributed resources 300. And, based on this operation strategy, along with participating in the bidding, the distributed resource 300 is operated.

At this time, the VPP operating device 100 generates an optimal operating strategy for the distributed resources 300 in response to the power demand required from the power exchange. Here, the power exchange may include a number of trading markets such as a capacity market, a power market, and an auxiliary service market. The capacity market is a market in which electricity is traded on a monthly or yearly basis, the power market is a market in which electricity is traded on an hourly or daily basis, and the auxiliary service market may mean a market in which electricity is traded in seconds or minutes.

That is, the VPP operating device 100 determines the power selling price, determines an objective function corresponding to the profit and cost based on the distributed resource information and the power selling price provided from the VPP monitoring device, and is preset for each objective function. A distributed resource operation strategy that maximizes the difference between profit and cost by applying a weight is transmitted to the distributed resource 300 through the VPP monitoring device 200.

The VPP monitoring device 200 monitors the power generation capacity and operating state of the distributed resource 300 and transmits information on the distributed resource 300 to the VPP operating device 100. In addition, the VPP monitoring device 200 operates the distributed resource 300 according to a distributed resource management strategy provided from the VPP operating device 100.

In the present invention, the VPP monitoring device 200 transmits the distributed resource operation strategy information to the distributed resource 300 so that the distributed resource 300 can independently perform an operation operation based on the corresponding operation strategy.

In addition, in the present invention, the VPP monitoring device 200 may collect each state information from the distributed resource 300 and transmit it to the VPP operating device 100.

The distributed resource 300 includes at least one load 310, at least one power generation source 320 including a renewable energy facility, and an energy storage system connected to the load 310 and the power generation source 320 (ESS: Energy Storage System, 330).

The load 310 is a device that consumes power, and is a distributed resource in a passive sense in that it creates surplus power in a manner that does not consume power.

The power generation source 320 includes, for example, a power generation device using a renewable energy source such as cogeneration, microturbine, solar power, wind power, biomass, fuel cells, and the like.

At this time, the load 310 and the power generation source 320 are connected to the energy storage system 330, store power generated by the power generation source 320, and supply them to the load 310 when necessary. In other words, an energy storage system (ESS) is a system that increases energy efficiency by storing generated electrical energy and supplying it at the most necessary time. It simultaneously performs the role of load and power according to charging and discharging of electrical energy. It is a two-way power facility.

In addition, in the present invention, the distributed resource 300 includes a resource control unit 400 for each distributed resource.

The resource control unit 400 collects state information of the corresponding distributed resource 300, predicts the amount of power generation or demand, and predicts the life cost.

The resource control unit 400 is connected to each resource in a one-to-one correspondence to independently manage and control the corresponding resource. That is, the load control module 410 is coupled to the load 310, the power generation control module 420 is coupled to the power source 320, and the storage control module 430 is coupled to the energy storage device 330, respectively. . At this time, for L loads 310, M power generation sources 320, and N energy storage devices 330, L load control modules 410, M power generation control modules 420, and N storage control modules 430 may be provided.

FIG. 2 is a block diagram showing a functionally separated internal configuration of the VPP operating device 100 shown in FIG. 1.

As shown in FIG. 2, the VPP operating apparatus 100 includes a price prediction unit 110, a weight determination unit 120, and an operation strategy establishment unit 130.

The price prediction unit 110 predicts a selling price for power generated by the distributed resources 300. At this time, the price prediction unit 110 predicts the system marginal price (SMP) of the power exchange.

The weight determination unit 120 determines weights for the profit objective function and the cost objective function. At this time, the weight increases the probability that the item will be selected by setting the weight of the item with high profit to the profit objective function, and the item is selected by setting the weight of the item with high cost to the cost objective function. It is set to lower the probability.

In addition, the weight for each objective function is adjusted by comparing the predicted selling price in the previous time block with the current predicted selling price result. These weights can be adjusted linearly or non-linearly according to the difference from the previous predicted selling price, and can also be adjusted by changing conditions.

In the present invention, the profit objective function may be composed of one or a combination of two or more of profit types including profits from electricity sales, government subsidies from power generation sources, and government subsidies from energy storage systems. It can be done at a lifetime cost.

The weight determination unit 120 may determine by applying an ESS Renewable Energy Certificate (ESS REC) weight to the ESS government subsidy profit, and applying a PV or WT REC weight to the power generation government subsidy profit. That is, by setting the weight of the item having a high REC weight to be high, the probability of being selected is increased.

In addition, the weight determination unit 120 may determine the weight by applying the average SMP value for a month to the power sales profit, and calculate the other subsidiary profit as 1/000 of the REC spot market price or fixed contract price per month to determine the weight. have.

In addition, the weight determination unit 120 may apply different weights of the energy storage system 330 among distributed resources by subdividing the weight of the energy storage system 330 according to the size of the state of charge. In other words, based on a certain value of the state of charge (e.g., applying differently to one of the ranges of 70 to 90%), the weight is set so that the life cost is higher when it is 70 to 90% or more than when it is less than 70 to 90%. I can. In general, the charge/discharge curve of the energy storage system 330 starts from any one of the ranges of 70 to 90%, and if it is less than that, it indicates a linear charge curve, and in the case of more than that, in consideration of the characteristic in which the charge curve is inflected. When establishing an operation strategy, charging and discharging are mainly used to the extent that the life cost is low.

For example, when applying a certain value of the state of charge as 80%, when the charge/discharge cost in the range of 0 to 80% of the energy storage system 330 is W80, the charge/discharge cost in the range exceeding 80% is “W80× (N is a number exceeding 1)". That is, when considering that the cost objective function aims at minimizing cost, when the state of charge of the energy storage system 330 is 80% or more, the weight is set higher than when the state of charge is less than 80%, so the selection probability is low. You lose.

The operation strategy establishment unit 130 generates a virtual power plant operation strategy in consideration of the maximum profit and minimum cost through a weighted sum of a plurality of objective functions and weight sets.

3 is a diagram showing the internal configuration of each resource control unit 400 shown in FIG. 1 divided into functional modules.

At this time, each resource control unit, that is, the load control module 410, the power generation control module 420, and the storage control module 42 perform the same function for the resources they manage.

As shown in FIG. 3, each resource control unit includes an electric energy estimation module 401, a condition diagnosis module 402, and a life cost calculation module 403.

The power amount prediction module 401 predicts the amount of generation or demand of distributed resources. That is, the power generation control module 420 and the storage control module 430 predict the amount of power generation, and the load control module 410 predicts the amount of demand.

The state diagnosis module 402 diagnoses the operating state of distributed resources. For example, it collects state information including power generation time, power generation efficiency, operation characteristics, charging and discharging time, and load patterns.

The lifetime cost calculation module 403 calculates the lifetime cost of the distributed resource. Here, the life cost may be calculated as the reciprocal of the maximum life distance in the life prediction curve generated based on the state of the distributed resource. The life prediction curve of distributed resources can be generated in various ways using a known algorithm.

4 is a diagram showing the internal configuration of the operation strategy establishment unit 130 shown in FIG. 2 divided into functional modules.

4, the operation strategy establishment unit 130 includes a VPP profit calculation module 131, a VPP cost calculation module 132, an objective function generation module 133, and a strategy establishment module 134 do.

The VPP profit calculation module 131 calculates the expected VPP profit based on the amount of power generated by the distributed resources 300. This can be calculated by calculating the expected profits for each distributed resource, and then summing them.

The VPP cost calculation module 132 calculates the VPP cost generated by the distributed resource 300. This can be calculated by summing the lifetime cost for each distributed resource.

The objective function generation module 133 determines a maximum profit objective function and a minimum cost objective function. Here, the maximum profit objective function is an objective function that maximizes the expected VPP profit, and the minimum cost objective function is an objective function that minimizes the VPP cost.

The profit objective function may be composed of one or a combination of two or more of profit types including profits from electricity sales, government subsidies from power generation sources, and government subsidies from energy storage systems.

In addition, the cost objective function may be formed as a lifetime cost of distributed resources. Here, the life cost is calculated as the reciprocal of the maximum life distance in the life prediction curve of the distributed resource.

The strategy establishment module 134 determines the maximum benefit objective function and the minimum cost objective function by applying a preset weight to each benefit and cost objective function, and weights the weighted maximum benefit objective function and the minimum cost objective function to final In addition to setting it as an objective function, a distributed resource management strategy that maximizes this final objective function is created.

That is, the strategy establishment module 134 generates a distributed resource operation strategy in which the difference between profit and cost is maximized.

At this time, the strategy establishment module 134 compares the previous final objective function with the current final objective function and maximizes the final objective function by adjusting the weight or the objective function by operating characteristic until the current final objective function becomes larger than the previous final objective function. can do.

Then, the strategy establishment module 134 sets an operating strategy for having a maximized final objective function as a bidding strategy. That is, the strategy establishment module 134 participates in electric power bidding at the sales price predicted at the date and time of maximizing the objective function, and in the case of a successful bid, distributed resources can be operated according to the corresponding operation strategy.

Next, a method of operating a virtual power plant operating system based on a multi-objective function configured as described above will be described with reference to the drawings shown in FIG. 5.

First, at least one load 310 and at least one power generation source 320 including a renewable energy facility are connected to the VPP operating device 100, and an energy storage system 330 is combined.

In the above-described state, the VPP operating device 100 includes the amount of power for each distributed resource from the resource control unit 400 of the distributed resource 300 consisting of the load 310, the power generation source 320, and the energy storage system 330, and Collect life cost and operational status information (ST100).

In addition, the VPP operating device 100 predicts the selling price of electricity, which is an optimal operating standard (ST200).

Subsequently, the VPP operating device 100 sets a weight for an objective function corresponding to profit and cost (ST300). In this case, the weight may be automatically changed and set by comparing and analyzing the predicted selling price in the previous time block with the currently predicted selling price.

In the above-described state, the VPP operating device 100 predicts the expected VPP profit and VPP cost based on the distributed resource information provided from each resource control unit 400 and the selling price, and the profit objective function and cost based on each predicted value. Create an objective function (ST400).

Subsequently, the VPP operating device 100 applies a preset weight to each objective function, determines the maximum profit objective function and the minimum cost objective function, and weights the maximum profit objective function and the minimum cost objective function to which the weight is applied to the final objective. In addition to generating the function, an operation strategy for each distributed resource that maximizes the final objective function is generated using a preset heuristic algorithm (ST500, ST600). At this time, the VPP operating apparatus 100 may generate a final objective function in response to a difference between the maximum profit objective function and the minimum cost objective function.

Thereafter, the VPP operating device 100 transmits the corresponding operating strategy information to the resource control unit 400 of each distributed resource 300, and the resource control unit 400 of each distributed resource 300 is the VPP operating device 100 In addition to participating in the electric power bidding based on the operation strategy provided from ), the distributed resource 300 is operated in consideration of both profit and cost (ST700).

That is, according to an embodiment of the present invention, in a virtual power plant system operated with a renewable energy facility such as solar energy as a power generation source, distributed resources based on a multi-objective function including the maximum profit and the minimum cost By creating an optimal operating strategy for the power generation, it is possible to operate the virtual power plant more stably by simultaneously managing the cost incurred as well as the profits from power generation.

The embodiments of the present invention described above may also be implemented in the form of a recording medium including instructions executable by a computer, such as a program module executed by a computer. Such recording media include computer-readable media, and computer-readable media may be any available media that can be accessed by a computer, and include both volatile and nonvolatile media, removable and non-removable media. In addition, computer-readable media includes computer storage media, which are volatile and nonvolatile embodied in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. , Removable and non-removable media are included.

The scope of the present invention is indicated by the claims to be described later rather than the detailed description, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be construed as being included in the scope of the present invention. do.

100: VPP operating device, 200: VPP monitoring device,
300: distributed resource, 400: resource control unit,
310: load, 320: power generation source,
330: energy storage system (ESS), 410: load control module,
420: power generation control module, 430: storage control module.

Claims (12)

Distributed resources including at least one load, at least one power generation source including new and renewable energy facilities, and an energy storage system;
A VPP monitoring device connected to the distributed resource to monitor each state of the distributed resource and provide distributed resource information collected for each distributed resource to the VPP operating device; And
The power sales price is determined, the objective function corresponding to the profit and cost is determined based on the distributed resource information and the power sales price provided from the VPP monitoring device, and the difference between the profit and the cost by applying a preset weight for each objective function. It is configured to include a VPP operating device that transmits the distributed resource operation strategy to maximize the distributed resource to the distributed resource through the VPP monitoring device,
Each of the distributed resources diagnoses the state of the distributed resource, predicts the amount of individual power, calculates the life cost of the distributed resource and transmits it to the VPP monitoring device, and distributes the corresponding distribution based on the distributed resource operation strategy received from the VPP monitoring device. It is composed of each having an independent resource control unit for operating and controlling resources
The profit objective function is composed of one or a combination of two or more of profit types including profits from electricity sales, government subsidies from power generation sources, and government subsidies from energy storage systems,
The cost objective function is composed of the life cost of the distributed resource, and the life cost is calculated as an reciprocal of the maximum life distance in the life prediction curve of the distributed resource.
The method of claim 1,
The VPP operating device applies a preset weight for each objective function, weighted the objective function of the maximum profit to which the weight is applied and the objective function of the minimum cost, and sets it as the final objective function, and distributed resources to maximize the final objective function. A virtual power plant operating system based on a multi-objective function, characterized in that it is configured to generate an operating strategy.
delete The method of claim 1,
The weight increases the probability that the item is selected by setting the weight of the item with high profit to the profit objective function,
A virtual power plant operating system based on a multi-objective function, characterized in that the weight of the item having a higher cost is set to be larger for the cost objective function, thereby lowering the probability that the item will be selected.
The method of claim 4,
In the cost objective function, the life cost of the energy storage system is weighted differently according to the size of the state of charge,
A virtual power plant operating system based on a multi-objective function, characterized in that the weight is set higher when the value is higher than that of the state of charge than when the state of charge is less than a predetermined value, and the predetermined value is in any one of a range of 70 to 90%.
The method of claim 1,
The VPP operating device predicts the selling price of electricity, compares and analyzes the predicted selling price in the previous time block with the currently predicted selling price, and changes and sets the weight for the profit objective function. Power plant operating system.
At least one load, at least one power generation source including a renewable energy facility, and a VPP operating device that performs power management by being combined with distributed resources including an energy storage system, wherein each of the above-described distributed resources corresponds to In the virtual power plant operating method based on a multi-objective function of a virtual power plant operating system configured by each having a resource control unit for managing distributed resources,
A first step of collecting, in the VPP operating device, information on an amount of power, life cost, and operation status of a corresponding distributed resource from a load, a power generation source, and each resource control unit of an energy storage system;
A second step of predicting a selling price of electricity in the VPP operating device;
A third step of setting a weight for an objective function corresponding to profit and cost in the VPP operating device;
The VPP operating device predicts the expected VPP profit and VPP cost based on the distributed resource information collected in the first step and the selling price determined in the second step, and the profit objective function and the cost objective function based on each predicted value. A fourth step of generating; And
The VPP operating device determines the objective function of the maximum profit and the minimum cost by applying a preset weight to the profit objective function and the cost objective function generated in the fourth step, and creates a distributed resource operation strategy corresponding thereto. Including a fifth step;
The profit objective function is composed of one or a combination of two or more of profit types including profits from electricity sales, government subsidies from power generation sources, and government subsidies from energy storage systems,
The cost objective function is composed of the life cost of the distributed resource, and the life cost is calculated as an inverse number of the maximum life distance in the life prediction curve of the distributed resource.
The method of claim 7,
In the second step, the VPP operating device predicts the selling price of electricity, compares and analyzes the predicted selling price in the previous time block and the currently predicted selling price to change and set the weight for the profit objective function. A method of operating a virtual power plant based on a function.
delete The method of claim 7,
In the third step, the weight of the item with a higher profit is set to a larger weight with respect to the profit objective function to increase the probability that the item is selected
A method of operating a virtual power plant based on a multi-objective function, characterized in that the weight of the item having a higher cost is set to be larger for the cost objective function to lower the probability of the item being selected.
The method of claim 7,
In the cost objective function, the life cost of the energy storage system is weighted differently according to the size of the state of charge,
A method for operating a virtual power plant based on a multi-objective function, characterized in that the weight when the value is higher than that of the state of charge is applied higher than when the state of charge is less than a predetermined value, and the predetermined value is any one of a range of 70 to 90%.
The method of claim 7,
In the fifth step, the VPP operating system applies a preset weight to each objective function, weighted the objective function of the maximum profit to which the weight is applied and the objective function of the minimum cost, and sets it as the final objective function. A method of operating a virtual power plant based on a multi-objective function, characterized in that it is configured to generate a distributed resource operation strategy to maximize.

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