JP2005102364A - Distributed energy community control system, central controller, decentralized controller, and their control method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To compute an optimum operation plan efficiently with a small amount of computation even if the number of units of distributed power sources increases in a distributed energy community. <P>SOLUTION: A group of consumers having distributed power units and energy storages are divided into n pieces of consumer groups 1<SB>1</SB>-1<SB>n</SB>, and each consumer group 1<SB>1</SB>-1<SB>n</SB>is provided with decentralized controllers 1<SB>10</SB>, 1<SB>20</SB>, ... 1<SB>n0</SB>. Each decentralized controller 1<SB>10</SB>-1<SB>1n</SB>makes optimum operation plans for the distributed power unit and the energy storage contained in the consumer group in question. A central controller 21 provided within a center 2 transmits control information including the amount of control capable of adjusting the supply power in each consumer group to each distributed controller 1<SB>10</SB>-1<SB>1n</SB>so as to control the surplus or supplement a shortage in the quantity of energy supply of the entire community. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a control device and a control method for an energy community having a distributed power supply device and / or an energy saving device and comprising a large number of consumers connected to an electric power system.

  As a method of performing low-cost operation control of a community having a plurality of power generation devices, the energy balance of the entire community can be adjusted in a single or a set of control systems, such as the power demand supply control system described in Patent Document 1. There is a method to control in consideration.

As a method for creating a power generation plan, in addition to the mathematical programming method that has been used in the past, tabu search and genetics such as those used in the generator operation planning method described in Patent Document 2. Metaheuristic techniques such as genetic algorithms. In recent years, with the spread of distributed power sources, etc., the need to consider nonlinear and discontinuous characteristics and constraints has increased, and the metaheuristic method is a high-speed approximate solution of the global optimal solution at a relatively high speed regardless of the function form. Therefore, it has come to be used for power generation planning.
JP 2002-010500 A JP 2001-258157 A

  The control method of the above-described conventional power supply and demand control system increases the amount of calculation required to calculate a low-cost optimal operation plan as the number of distributed power sources increases, and also includes the constraint conditions of energy storage devices such as storage batteries. Since the search for the optimal operation plan becomes difficult as the complexity of the system becomes complicated, there is a problem that the accuracy as the optimal solution of the obtained operation plan is deteriorated.

  In addition, when targeting a community with many controllable distributed power sources and performing control while creating an optimal operation plan in real time, local search may end within the obtained time, etc. Because of this problem, it was necessary to devise in order to always obtain a global optimum solution stably.

  An object of the present invention is to provide a control system and method capable of efficiently calculating an optimum operation plan with a small amount of calculation even when the number of distributed power supplies increases.

  In order to achieve the above object, the distributed energy community control system according to the present invention is configured such that consumers are grouped into a plurality of consumer groups, and the optimal distributed power supply devices and energy storage devices included in each of these consumer groups. One or more distributed control devices having means for creating an operation plan are provided for each customer group, and control information including a control amount capable of adjusting power supplied to each customer group is transmitted to each of these distributed control devices. And a central control device that complementarily controls the surplus or shortage of the energy supply amount of the entire community.

  Here, the “control amount” is “a (virtual) time series power price pattern in the community”. For example, it is a (planned) power price up to 24 hours after changing every 30 minutes. It is also a function for “increase / decrease depending on the amount of power received / received” for each customer group. As a simple example, the unit price differs depending on the power received (buying power) and the power transmitted (power sold) For example, the power loss is proportional to the square of the amount of power received and received, and the reduced power is multiplied by the unit price to obtain the cost of receiving and transmitting power). The central controller determines (updates) every predetermined time (every time the prediction data is updated) while using the solution of the optimum operation plan created by each distributed controller for this power price pattern.

  As a specific control method of the central control unit, a method of adjusting the power price pattern so that the power supply and demand in the community is balanced in all time zones, and an optimization method such as metaheuristics are used. There is a method for searching for a power price pattern that minimizes the energy cost. The former method is effective when energy self-sufficiency is aimed at within the community without relying on external power, or when it will minimize energy costs. The latter method is effective when aiming at further minimization of energy costs by power trading under the electricity liberalization market.

  As described above, according to the present invention, by distributing the control devices for each customer group, it is possible to efficiently calculate their optimum operation plans regardless of an increase in the number of distributed power supply devices to be controlled. . Even if the central control device goes down or communication is interrupted, control can be performed according to an operation plan that is close to optimum.

  By making the control amount transmitted from the central control device to each distributed power supply device into an in-community electricity price pattern that increases or decreases depending on the amount of received and transmitted power, it is possible to easily perform operation control that balances energy supply and demand in consideration of transmission loss. it can.

  By optimizing the in-community electricity price pattern at the central controller, it is possible to create an optimal operation plan that minimizes the energy cost of the entire community.

  By monitoring and managing the optimum search process of each distributed control device by the central control device, it is possible to always calculate a stable and highly accurate optimum operation plan.

  Next, embodiments of the present invention will be described with reference to the drawings.

  FIG. 1 shows a configuration of a distributed energy community control system according to an embodiment of the present invention.

The distributed energy community control system customer groups 1 each consumer 1 _{1 1 1,} 1 _12, and distributed control system 1 ₁₀ for controlling the distributed power supply and energy storage devices.., Customer groups 1 ₂ each customer 1 _21, 1 _22, the distributed control system 1 ₂₀ for controlling the distributed power supply and energy storage devices.., customer groups 1 each consumer 1 _{n n1,} 1 _n2, ... of The distributed control device 1 _n0 that controls the distributed power supply device and the energy storage device, and the central control device 21 installed in the center 2 are configured.

The distributed control devices 1 ₁₀ , 1 ₁₁ ,..., 1 _1n have communication means with the central control device 21 and optimally operate the distributed power supply devices and energy storage devices of each customer in the customer group. . The central control device 21 monitors the energy supply and demand status in the energy community, and each consumer group 1 ₁ , 1 ₂ from the commercial power supply 3 for each distributed control device 1 ₁₀ , 1 _{20 ,.} ,..., 1 _n are controlled.

  In the center 2, a fuel cell 22 as a power generator and a storage battery 23 as an energy storage device, which are controlled by a central control device 21, are installed. Moreover, although one distributed control apparatus is installed in each consumer group, a plurality of distributed control apparatuses may be installed for each consumer group.

  Consumers are grouped so as to reduce power distribution loss and the like within the group. Moreover, since the prediction accuracy improves as the power load demand increases, the size of the consumer group is determined in consideration of the accuracy of the demand prediction. For example, a group of customers accommodated in one transformer is considered as one group. In addition, the apartment house may be one consumer group, or each floor may be a consumer group.

FIG. 2 shows a schematic flow of a first processing example performed by the central control device 21 and the distributed control devices 1 ₁₀ to 1 _n0 in this embodiment, and FIG. 3 shows a detailed flow thereof.

The central control device 21 first predicts the electric power and / or heat load demand of each consumer using weather information, event information, etc., for consumers having a power generator using natural energy such as sunlight or wind power. Then, power generation prediction is performed, and these prediction results are transmitted to the respective distributed control devices 1 ₁₀ to 1 _n0 (steps 111 and 112). Alternatively, information necessary for prediction may be transmitted, and prediction may be performed in each of the distributed control devices 1 ₁₀ to 1 _n0 . Here, the heat load refers to the amount of heat used for hot water supply from a hot water storage tank, for example.

Next, the central controller 21 transmits a control signal pattern corresponding to the in-community power price to each of the distributed power supply devices 1 ₁₀ to 1 _n0 (step 113). This power price is a time-series function that varies with time. In addition, in order to consider power transmission loss in detail, in the case of receiving power, the power price is calculated by adding the power transmission loss according to the power, and in the case of power transmission, the power transmission loss is determined according to the power. The power price for the subtracted power amount. When power transmission loss is simply taken into account, this power price may be a function that becomes cheaper as the amount of power transmitted from the customer group increases and increases as the amount of power received by the customer group increases. Alternatively, it is possible to simply provide a difference between the power purchase price and the power sale price. By setting as described above, it is possible to control the supply and demand of energy in the community in consideration of power transmission loss.

The distributed control devices 1 ₁₀ to 1 _n0 installed in each consumer group receive the daily power / heat load demand forecast and the power generation forecast and the planned value of the in-community power price transmitted from the central control device 21. (Steps 211 and 212), and from these, an operation plan for power generation of the fuel cell, which is a controllable distributed power source in the own group, and charge / discharge of the storage battery is created (Steps 213 to 216). The termination condition of step 215 is a timer time, the number of repetitions, or the like. This operation plan is created, for example, on a daily basis or 24 hours from the current time, and is updated as needed by forecasting demand and correcting the planned price. The obtained operation plan with the best evaluation value is stored in the memory.

  In creating an operation plan, for example, an operation plan pattern that minimizes the objective function is searched for by using, as an objective function, a cost obtained by adding / subtracting a power cost for receiving and transmitting power within the community to a daily energy cost, ie, fuel cost. The fuel cost is calculated from the fuel flow rate with respect to the generated power in the generated power target pattern by modeling the efficiency characteristics, start-up characteristics, response characteristics, etc. of the fuel cell. Also, the power pattern obtained by subtracting the predicted data of the power load from the power supplied from the fuel cell and storage battery by this operation plan pattern becomes the power pattern to be received and transmitted, and this power pattern is substituted into the function of the power value pattern to calculate the power cost. Is calculated. The storage battery and hot water tank are modeled in consideration of charge / discharge loss, heat dissipation loss, and the like, and the remaining capacity is calculated. The amount of heat obtained by subtracting the prediction data of the thermal load (hot water supply amount) from the exhaust heat amount pattern by the fuel cell model when operating with the operation plan pattern is integrated into the hot water tank model. In order to balance the storage battery and hot water tank in one day, the difference in charge amount and hot water amount in one day is added to the objective function as a penalty function. Further, overcharge or overdischarge of the storage battery may be handled as a constraint condition or may be added as a penalty function. As described above, the calculation is omitted as the case where the distribution loss in the demand group is small, but an objective function considering the distribution loss can be used.

  Various optimal algorithms can be applied as search methods. By using metaheuristic techniques such as tabu search and genetic algorithm, a highly accurate approximate solution of the global optimal solution can be obtained within a realistic time. be able to.

Each of the distributed control devices 1 ₁₀ to 1 _n0 transmits the search result of the optimum operation plan to the central control device 21 (step 220). The central controller 21 receives the optimum operation plan from each of the distributed controllers 1 ₁₀ to 1 _n0 (step 121), and when each distributed power source is operated according to these operation plans, the energy supply and demand in the community at each hour The balance is calculated (step 122). Then, it is determined whether the surplus or shortage of energy in each time zone is within an allowable range (step 123). If it is not within the allowable range, a pattern in which the control amount corresponding to the in-community power price in that time zone is adjusted is transmitted by the time of the next optimum search start (step 113). If it is within the allowable range, the power generation forecast is corrected. (Step 112). However, when the energy cost of the entire community does not increase due to the purchase and sale of external power, or when the center 2 has the fuel cell 22 and the storage battery 23 and can be handled by these controls, there is no need for adjustment. In addition, when the power supply in the community is insufficient even during the time period when the power price in the community is higher than the power purchase price of external power, it is possible to reduce the energy cost by purchasing external power. The central controller 21 can make a simple determination. That is, by setting the community power price within a range that is lower than the power purchase price of external power and higher than the power sale price, low-cost energy management that effectively uses external power can be performed. In this way, energy management that maintains an energy balance within the community, such as preventing reverse power flow to the outside, can be easily performed while effectively using inexpensive late-night power and power trading under power liberalization.

By repeating the above processing at regular time intervals, each of the distributed control devices 1 ₁₀ to 1 _n0 controls the distributed power source and the like according to the latest optimum operation plan. Even if the central control device 21 goes down or communication is interrupted, each distributed control device 1 ₁₀ to 1 _n0 performs an optimal calculation using past prediction data, so that a distributed power source can be operated according to an operation plan as close to optimal as possible. Etc. can be controlled.

FIG. 4 shows a schematic flow of a second processing example performed by the central control device 21 and each of the distributed control devices 1 ₁₀ to 1 _n0 in this embodiment, and FIG. 5 shows a detailed flow thereof.

The central control device 21 uses the weather information, event information, and the like to perform power / heat load demand and power generation prediction of each consumer, and transmits these prediction results to the respective distributed control devices 1 ₁₀ to 1 _n0 (steps). 131, 132).

Next, the central control device 21 sets an in-community electric power price and transmits a control signal pattern corresponding to this to the distributed control devices 1 ₁₀ to 1 _n0 (step 133). Here, a plurality of power price pattern candidates are listed and transmitted at the same time or with a time difference. Further, when there are a plurality of distributed control devices in the customer group, different power price patterns may be transmitted to each of them.

In each of the distributed control devices 1 ₁₀ to 1 _n0 installed in each consumer group, the daily power / heat load demand forecast and the power generation forecast transmitted from the central control device 21 and the planned value of the electricity price in the community are set. From these (steps 211 and 212), an operation plan for generating power of the fuel cell, which is a controllable distributed power source in the own group, and charging / discharging of the storage battery is created on a daily basis (steps 213 to 216).

  In creating an operation plan, for example, an operation plan that minimizes this objective function using a meta-heuristic method with the cost obtained by adding / subtracting the energy cost of the day as the fuel cost and the power cost received and transmitted within the community as the objective function. Is searched (steps 214 to 216).

Each of the distributed control devices 1 ₁₀ to 1 _n0 transmits the search result of the optimum operation plan to the central control device 21 (step 220).

The central controller 21 receives the optimum operation plan (step 141), calculates the total energy cost when each distributed power source is operated with the optimum operation plan from each of the distributed control devices 1 ₁₀ to 1 _n0 , and evaluates it. (Step 142). The total energy cost here is calculated by adding and subtracting the power trading cost with the outside of the community to the total fuel cost in each consumer group. Since there are a plurality of transmitted power price patterns as described above, the total energy costs for each power price pattern are compared, a power price pattern that minimizes this is determined, and transmitted to each distributed power source (step 150). . Each of the distributed control devices 1 ₁₀ to 1 _n0 determines an optimum operation plan for the received power price pattern (step 230). If there is a surplus in computing capacity and communication volume in each of the distributed control devices 1 ₁₀ to 1 _n0, the power price pattern that minimizes the total energy cost while satisfying constraints such as the upper limit of trading power using the metaheuristic method An optimal search can also be performed (step 144). At this time, when the center 2 includes the fuel cell 22 and the storage battery 23, it is necessary to optimize the operation plan while satisfying the constraint conditions.

As described above, the central control device 21 sets the power price pattern in the community, which is the control information in the optimum search, while repeatedly or in parallel creating the optimum operation plan of the distributed power source in each of the distributed control devices 1 ₁₀ to 1 _n0 . To decide on. By using optimization methods such as meta-heuristics at this decision stage, it is possible to effectively use power transactions, etc. under the liberalization of electricity, and to achieve optimal energy that minimizes the total energy cost in the community while satisfying various constraints. Enable management.

  FIG. 6 shows another example of a method for creating an optimum operation plan performed in each distributed control apparatus.

First, each of the distributed control devices 1 ₁₀ to 1 _n0 sets an initial operation pattern for performing an optimal search (step 241). There may be a plurality of initial patterns depending on the optimum algorithm to be used, and it is preferable to create them using the optimum search result calculated last time. Next, the objective evaluation function such as the energy cost described above is calculated (step 242). It is determined whether there is an interrupt signal from the central controller 26 (step 243). If there is, return to Step 241. If not, the operation pattern to be evaluated next is determined using the optimal algorithm based on the calculated evaluation value, and stored in the memory (steps 244 and 245). As the optimum algorithm, tabu search, simulated annealing, genetic algorithm, PSO (Particle Swarm Optimization), or the like can be used. The optimum search as described above is repeated until a termination condition such as a timer or the number of repetitions is satisfied (step 244).

  As described above, one or more distributed control devices are installed in each group. When a plurality of distributed control devices are installed in the same group, an optimal solution can be calculated efficiently by performing an optimal search by changing the initial pattern and search parameters.

The central controller 21 monitors the search process in each of the distributed controllers 1 ₁₀ to 1 _n0 (step 151), and determines that an efficient search has not been performed, for example, the best solution has not been updated for a certain period of time. The distributed control device is notified (interrupted) of the best solution so far in the group (steps 152 and 153), and the optimum search is restarted using the best solution as an initial pattern of the operation plan. In addition to the best solution, a control parameter or the like that has obtained the best solution may be notified. On the contrary, when each distributed control device 1 ₁₀ to 1 _n0 does not update the best solution for a certain time or more in its optimum search, the central control device 26 can be requested and sent the best solution candidate and control parameter. The optimum search may be restarted using the optimum solution or the like. Alternatively, the best solution information may be transmitted and received between the distributed control devices. Here, the control parameters include, for example, the length of the tabu list when tabu search is used for the optimal algorithm. The taboo list is a memory for storing attributes indicating the past optimal search direction and the like, and adds a function for escaping from the local solution. When the optimal search in all the distributed control devices 1 ₁₀ to 1 _n0 in the customer group is completed, the central control device 21 notifies each distributed control device 1 ₁₀ to 1 _n0 of the operation pattern with the best evaluation value (step) 153), each distributed power source is controlled accordingly.

As described above, the central control device 21 monitors the optimum search of the operation plan in each of the distributed control devices 1 ₁₀ to 1 _{n0 and} can share the best solution in the group to efficiently perform the optimum search. it can. Even when there is only one distributed control device in the group, efficient search management can be performed in the same manner by searching for the optimum operation of adjacent groups in the distributed control device.

It is a block diagram of the distributed energy community control system of one Embodiment of this invention. It is a figure which shows the general | schematic flow of the 1st process example performed with the central control apparatus and the distributed control apparatus in the system of FIG. It is a figure which shows the detailed flow of FIG. It is a figure which shows the schematic flow of the 2nd process example performed with the central control apparatus and the distributed control apparatus in the system of FIG. It is a figure which shows the detailed flow of FIG. It is a figure which shows the other example of the preparation method of the optimal operation plan performed with each distributed control apparatus.

Explanation of symbols

1 ₁ to 1 _n consumer group 2 center 3 commercial power source 1 ₁₀ to 1 _n0 distributed control device 1 ₁₁ , 1 ₁₂ ,..., 1 ₂₁ , 1 ₂₂ ,..., 1 _n1 , 1 _n2 consumer 21 central control device 22 fuel Battery 23 Storage battery 110-113, 120-123, 130-133, 140-144, 151-153 Step 210-215, 220, 230, 241-246 Step

Claims (18)

In an energy community consisting of a large number of consumers with distributed power supplies and / or energy storage devices connected to the power grid,
The consumers are grouped into a plurality of consumer groups;
One or more units in each customer group, and a distributed control device that creates an optimal operation plan of the distributed power supply device and / or energy storage device included in the customer group based on the control amount;
A distributed energy community control system including a central control device that transmits control information including the control amount capable of adjusting power supplied to each consumer group to each distributed control device.   The central control unit transmits, as the control amount, a power price pattern in the community that increases or decreases depending on the amount of received and transmitted power to each distributed control device, and then the community based on the optimum operation plan transmitted from each distributed control device. The system according to claim 1, wherein the control amount is determined such that power supply and demand within the balance is balanced at each time.   The central control device transmits one or more power price pattern candidates in the community to each distributed control device as the control amount, and determines the control amount so that the total energy cost in the community is minimized. The system of claim 1.   The central control unit has means for monitoring a process in which each of the distributed control devices calculates an optimum operation plan, and means for notifying each of the distributed control devices of information used for calculating the optimum operation plan. The system of claim 1. Each of the distributed control devices includes means for creating an initial operation plan for searching for an optimal solution, means for changing the operation plan and determining an operation plan as a next evaluation candidate, and the evaluation obtained so far Means to store the operation plan with the best value,
5. The system according to claim 4, wherein the central control unit notifies each distributed control unit of an initial operation plan pattern and / or a control parameter as the information when the best solution is not updated for a predetermined time or longer in each distributed control unit. .   A large number of consumers having a distributed power supply device and / or energy storage device and connected to an electric power system are grouped into a plurality of customer groups, and each consumer group includes a distributed power supply device included in the customer group. And / or the distributed power community provided with one or more distributed control devices for creating an optimal operation plan of the energy storage device based on the control amount can adjust the power supply in each consumer group A central control device having means for transmitting control information including a control amount to each distributed control device.   As the control amount, the power price pattern in the community, which increases or decreases depending on the amount of received and transmitted power, is transmitted to each distributed control device, and then the power supply and demand in the community is changed based on the optimum operation plan transmitted from each distributed control device. The central control device according to claim 6, wherein the control amount is determined so as to balance in time.   7. The control amount is determined such that one or more power price pattern candidates in a community are transmitted to each distributed control device as the control amount, and the total energy cost in the community is minimized. Central control unit.   7. The method according to claim 6, further comprising means for monitoring a process in which each of the distributed control devices calculates an optimum operation plan, and means for notifying each of the distributed control devices of information used for calculating the optimum operation plan. Central control unit.   The central control device according to claim 9, wherein when each of the distributed control devices does not update the best solution for a predetermined time or longer, the central control device notifies the distributed control device of an initial operation plan pattern and / or a control parameter as the information.   One or more distributed energy communities, each of which has a distributed power supply device and / or energy storage device, in which a large number of customers connected to the power system are grouped into a plurality of customer groups, are provided. A distributed control device having means for creating an optimal operation plan of the distributed power supply device and / or energy storage device included in the consumer group based on a control amount capable of adjusting the power supply in each consumer group .   A means for creating an initial operation plan for searching for an optimal solution, a means for changing the operation plan to determine an operation plan as the next evaluation candidate, and an operation plan having the best evaluation value obtained so far The distributed control device according to claim 11, further comprising a storage unit. One or more units are provided in each consumer group of the distributed energy community, which has a distributed power supply device and / or energy storage device, and a large number of customers connected to the power system are grouped into a plurality of customer groups, A distributed control device that creates an optimal operation plan of the distributed power supply device and / or energy storage device included in the customer group based on the control amount, and the control amount that can adjust the power supplied to each customer group A distributed energy community control method performed by a central controller that transmits control information including
The central controller predicts demand for power and / or heat load and transmits predicted data to each of the distributed controllers;
Each of the distributed control devices receiving the prediction data;
The central controller sets an in-community power price and transmits it as a power price pattern to each of the distributed controllers;
Each of the distributed control devices receiving the power price pattern;
A step of setting an initial operation plan to be evaluated based on the prediction data and the power price pattern by each of the distributed control devices;
Each distributed control device calculates and evaluates the energy cost in the customer group based on the operation plan;
Each of the distributed control devices determines whether a predetermined termination condition is satisfied; and
When the termination condition is not satisfied, each distributed control device changes the operation plan by an optimal algorithm, and returns to the step of calculating and evaluating the energy cost; and
When the termination condition is satisfied, each of the distributed control devices selects an optimal operation plan from the evaluated operation plans, and transmits the selected operation plan to the central control device;
The central control unit receiving an optimal operation plan from each of the distributed control units;
The central control unit calculating a community energy supply-demand balance at each time based on the received optimal operation plan;
The central control unit determines whether the surplus / shortage of energy in each time zone is within an allowable range from the energy supply / demand balance in the community, and returns to the step of predicting the demand for electric power and heat load if within the allowable range. A distributed energy community control method comprising the step of returning to the step of setting an in-community power price if not within an allowable range. One or more units are provided in each consumer group of the distributed energy community, which has a distributed power supply device and / or energy storage device, and a large number of customers connected to the power system are grouped into a plurality of customer groups, A distributed control device that creates an optimal operation plan of the distributed power supply device and / or energy storage device included in the consumer group based on the control amount, and the control that can adjust the power supplied to each consumer group A distributed energy community control method performed by a central controller that transmits control information including a quantity to each distributed controller,
The central controller predicts demand for power and / or heat load, and transmits predicted data to each distributed controller;
Each of the distributed control devices receiving prediction data from the central control device;
Setting an initial in-community electricity price pattern to be evaluated by the central controller and transmitting to each of the distributed controllers;
Each distributed control device sets an initial operation plan to be evaluated based on the prediction data and the power pattern in the initial community; and
Each distributed control device calculating an energy cost in the customer group based on the operation plan; and
Each of the distributed control devices determines whether a predetermined termination condition is satisfied; and
If the termination condition is not satisfied, each distributed control device changes the operation plan by an optimal algorithm and returns to the step of calculating the energy cost; and
When the termination condition is satisfied, each of the distributed control devices selects an optimal operation plan from the evaluated operation plans, and transmits the selected operation plan to the central control device;
The central control unit receiving an optimal operation plan from each distributed control unit; and
The central controller calculating and evaluating a total energy cost in the community based on the optimal operational plan;
The central controller determining whether a predetermined termination condition is satisfied;
If the termination condition is not met, the central controller changes the power price pattern with an optimal algorithm and returns to receiving the optimal operation plan; and
When the termination condition is satisfied, the central controller determines an optimal power price pattern from the evaluated power price patterns, and transmits to each distributed control device;
A distributed energy community control method comprising the steps of: each of the distributed control devices receiving the optimal power price pattern and determining an optimal operation plan for the received power price pattern. One or more units are installed in each consumer group of the distributed energy community, which has a distributed power supply device and / or energy storage device, and a large number of customers connected to the power system are grouped into a plurality of customer groups. A distributed energy community control method in which a distributed control device creates an optimum operation plan of a distributed power supply device and / or an energy storage device included in the customer group based on control from a central control device,
Receiving forecast data in which the central controller predicts demand for power and / or heat load;
Receiving an electricity price pattern of an in-community electricity price set by the central controller;
Setting an initial operation plan to be evaluated based on the forecast data and the power price pattern;
Calculating and evaluating energy costs within the customer group;
Determining whether a predetermined termination condition is satisfied;
If the termination condition is not satisfied, the operation plan is changed by the optimal algorithm, and the step returns to the step of calculating and evaluating the energy cost,
A distributed energy community control method comprising a step of selecting an optimum operation plan from the evaluated operation plans and transmitting the selected operation plan to the central control device when an end condition is satisfied. One or more units are provided in each consumer group of the distributed energy community, which has a distributed power supply device and / or energy storage device, and a large number of customers connected to the power system are grouped into a plurality of customer groups, A distributed energy community control method in which a central control device controls a distributed control device that creates an optimum operation plan of distributed power supply devices and / or energy storage devices included in the customer group,
Predicting demand for power and / or heat load and transmitting forecast data to each of the distributed control devices;
Setting a power price in the community and transmitting it to each of the distributed control devices as a power price pattern;
Receiving an optimal operation plan from each of the distributed control devices;
Calculating the community energy supply-demand balance at each hour based on the optimal operation plan received;
From the energy supply / demand balance in the community, it is determined whether the surplus / shortage of energy in each time zone is within an allowable range, and if it is within the allowable range, the process returns to the step of predicting the demand for electric power and heat load. A decentralized energy community control method comprising the step of returning to the step of setting an intra-community electricity price. One or more units are installed in each consumer group of the distributed energy community, which has a distributed power supply device and / or energy storage device, and a large number of customers connected to the power system are grouped into a plurality of customer groups. A distributed energy community control method in which a distributed control device creates an optimum operation plan of a distributed power supply device and / or an energy storage device included in the consumer group under the control of a central control device,
Receiving forecast data in which the central controller predicts demand for power and / or heat load;
Receiving an initial in-community electricity price pattern set by the central controller;
Setting an initial operation plan to be evaluated based on the prediction data and the power pattern in the initial community;
Calculating energy costs within the customer group based on the operational plan;
Determining whether a predetermined termination condition is satisfied;
If the termination condition is not satisfied, the operation plan is changed by the optimal algorithm, and the step of returning to the step of calculating the energy cost,
When the termination condition is satisfied, selecting an optimum operation plan from the evaluated operation plans, and transmitting to the central control unit;
A distributed energy community control method comprising the steps of: each distributed control device receiving an optimal power price pattern from the central control device and determining an optimal operation plan for the received power price pattern. One or more units are provided in each consumer group of the distributed energy community, which has a distributed power supply device and / or energy storage device, and a large number of customers connected to the power system are grouped into a plurality of customer groups, A distributed energy community control method in which a central control device controls a distributed control device that creates an optimum operation plan of distributed power supply devices and / or energy storage devices included in the customer group,
Predicting demand for power and / or heat load and transmitting the forecast data to each distributed controller;
Setting an initial community power price pattern to be evaluated and transmitting to each of the distributed control devices;
Receiving an optimal operation plan from each distributed control device; and
Calculating and evaluating a total energy cost in the community based on the optimal operational plan;
Determining whether a predetermined termination condition is satisfied;
If the termination condition is not satisfied, changing the power price pattern by an optimal algorithm and returning to the step of receiving the optimal operation plan; and
A distributed energy community control method comprising a step of determining an optimal power price pattern from the evaluated power price patterns and transmitting the determined power price pattern to each distributed control device when the termination condition is satisfied.

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