JP4245583B2 - Control device, control method, program, and recording medium of distributed energy system - Google Patents

Control device, control method, program, and recording medium of distributed energy system Download PDF

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JP4245583B2
JP4245583B2 JP2005118343A JP2005118343A JP4245583B2 JP 4245583 B2 JP4245583 B2 JP 4245583B2 JP 2005118343 A JP2005118343 A JP 2005118343A JP 2005118343 A JP2005118343 A JP 2005118343A JP 4245583 B2 JP4245583 B2 JP 4245583B2
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value
energy
predicted
prediction
evaluation value
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JP2006304402A (en
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朗 中澤
雅人 丸山
満 工藤
靖史 平岡
章 竹内
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日本電信電話株式会社
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  The present invention uses predicted values of energy generators and / or energy loads in an energy system having one or more energy generators, one or more energy storage devices, and one or more energy loads. The present invention relates to a control apparatus and method for a distributed energy system that creates an operation plan for an energy generation device and an energy storage device so that an evaluation value of the operation plan is the best.

  As a method for performing low-cost operation control by effectively using the energy of the distributed energy system, there is a “distributed energy community system and its control method” described in Patent Document 1. This is because the control center receives data on the power generation amount of the fuel cell, the energy storage amount of the storage battery, and the power consumption amount of the load from the control device of the distributed energy system via the communication line, and generates the generated power in each distributed energy system. This is a system for commanding a value and a received / transmitted power value and complementarily controlling power supply and demand through a power line between a plurality of distributed energy systems having different daily load characteristics of power demand.

  As a technique for predicting the energy demand necessary for controlling the energy system, there are techniques using regression analysis and a neural network. However, as in the above energy system, the demand for energy such as electric power / hot water supply for each household in a general household depends on the irregular living behavior of the consumer, and thus it is difficult to predict with high accuracy. The demand for hot water supply is more difficult to predict because there are many times when there is no demand and a demand peak occurs once.

On the other hand, a power generation system using natural energy such as sunlight and wind power makes a prediction using meteorological information such as temperature and weather, and therefore greatly depends on its prediction accuracy.
JP 2002-44870 A

  In the above energy system, for example, a day when there is almost no demand for hot water supply in a certain household occurs irregularly, or a day when the forecasted photovoltaic power generation is hardly obtained due to a sudden change in the weather forecast. There are concerns about large deviations in forecasts. When creating an operation plan for such a system, there is a problem that even if it is optimal for only one predicted pattern, the cost increases when the prediction is lost. In order to avoid this, if optimization is performed under a constraint condition that has an excessive margin, the optimality of a day with a small prediction error is reduced.

  As a method for taking into account the prediction uncertainty as described above, there are a method for calculating an evaluation value for a large number of prediction patterns, a method for calculating an expected value as a probability model, and the like. However, in actual operation, there are many systems that perform control by correcting an operation plan value in a time zone in which the prediction is out of date or rescheduling an optimal plan when it is determined that the prediction has been out of date. In the above-described conventional method, since the operation pattern is evaluated as planned, the evaluation function is not accurate with respect to the actual operation.

  An object of the present invention is to provide a control apparatus and method for a distributed energy system that creates an optimal operation plan that takes into account not only the accuracy and uncertainty of prediction but also the control operation corresponding to the prediction deviation.

In order to achieve the above object, a controller for a distributed energy system according to the present invention comprises:
A prediction unit that calculates a predicted deviation pattern in which the predicted value deviates by a certain value or more and its occurrence probability;
A simulation unit that performs a simulation in which an operation plan is corrected from a time at which it can be determined that the predicted deviation pattern occurs;
An evaluation value in this simulation is calculated, the evaluation value is weighted according to the occurrence probability of the predicted deviation pattern, and the evaluation value when the driving is performed according to the optimal driving plan under the condition of the prediction value is used. An evaluation value calculation unit that performs evaluation value calculation for adding values until a predetermined optimum search termination condition is satisfied;
An optimum operation plan creation unit that determines an operation plan having the best evaluation value among the evaluation values as an optimum operation plan is provided.

Further, the control method of the distributed energy system of the present invention includes:
A prediction step of calculating a predicted deviation pattern in which the predicted value deviates by a predetermined value or more and its occurrence probability;
A simulation step of performing a simulation in which the operation plan is corrected from a time at which it can be determined that the predicted deviation pattern occurs,
An evaluation value in this simulation is calculated, the evaluation value is weighted according to the occurrence probability of the predicted deviation pattern, and the evaluation value when the driving is performed according to the optimal driving plan under the condition of the prediction value is used. An evaluation value calculation step for performing evaluation value calculation for adding values until a predetermined optimum search termination condition is satisfied;
An optimum operation plan creating step for determining an operation plan having the best evaluation value among the evaluation values as an optimum operation plan;

  An evaluation value is calculated using a typical pattern and its occurrence probability that greatly deviate from the prediction used in the optimum operation plan, and a control operation simulation corresponding to the pattern. In other words, if a pattern that can suppress the deterioration of the evaluation value such as cost by increasing or decreasing the amount of power generation when the deviation from the prediction can be determined, the evaluation is commensurate with that, so it can be handled by the control even at the time of the deviation from the prediction. An optimal operation plan can be created. Further, since the evaluation is performed according to the occurrence probability of the predicted deviation pattern, the optimality is not lost even during predictive intermediate time.

  According to the embodiment of the present invention, the predicted value is that of an energy generator using natural energy, and the weather forecast and its results, the predicted value using the weather forecast and the measured value as a database for each season are stored. The predicting step calculates the probability of occurrence of the predicted deviation using the database and the weather information of the target date, and the simulation step performs simulation using the probability of occurrence of the predicted deviation pattern and the predicted value at the time of deviation.

  In order to create an optimal operation plan using the predicted deviation occurrence probability calculated using the weather information forecast and actual data accumulated by season, the degree of uncertainty of the weather forecast that varies depending on the season can be flexibly incorporated into the optimal evaluation. be able to.

  According to another embodiment of the present invention, the predicted value is that of the energy load, the demand data of this load is measured and accumulated, and the error between the actual value of the daily unit and the predicted value is a certain value or more. A step of registering actual measurement data of a certain day as irregular model data, and calculating a ratio of the irregular model data out of data used for demand prediction as an occurrence probability of the predicted deviation pattern; A simulation is performed using the probability of occurrence of irregularity and the predicted value at irregularity calculated using the irregular model data.

  Analyzing energy demand data, selecting the days when consumers' daily behavior was regular and irregular, and using the predicted pattern and occurrence probability, both patterns can be handled with control action It is possible to create an optimal operation plan.

  As described above, according to the present invention, it is possible to create an optimum operation plan that considers not only the accuracy and uncertainty of prediction but also the control operation corresponding to the prediction deviation.

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

FIG. 1 shows a configuration of a distributed energy community system according to an embodiment of the present invention. This distributed energy community system is composed of a plurality of consumers 1 1 , 1 2 ,..., 1 n and a control device 2.

Each consumer 1 1 to 1 n has a solar cell 11 and a fuel cell 12 as energy generators, a storage battery 13 and a hot water tank 15 as energy storage devices, a power load 14, and a heat load 16. The fuel cell 12, the storage battery 13, and the power load 14 are connected to the power system 3 by the power line 4. The hot water tank 15 and the heat load 16 are connected to the fuel cell 12 by a heat pipe 17. The energy generation device and the energy storage device are not limited to those described above, and other types of energy generation devices and energy storage devices may be used.

  The control device 2 measures the system power 3 and the load power 14 connected to the energy generation device and the energy storage device, and controls the power generation amount of the fuel cell 12 and the charge / discharge amount of the storage battery 13. The unit 21, the preparatory unit 22, the prediction DB (database) 23, the simulation unit 24, the evaluation value calculation unit 25, and the optimum operation plan creation unit 26 are included.

  The communication unit 21 can connect to the Internet or the like to obtain information on weather forecasts, energy prices, events, and the like. The predicting unit 22 uses these information and the like to predict the power generation amount of the solar cell 11, the demand of the power load 14, and the time series value of the demand of the hot water storage tank 15 that is a heat load, and stores the result in the prediction DB 23. . The optimum operation plan creation unit 26 creates a command schedule for power generation of the fuel cell 12, which is a controllable energy generation device and energy storage device, and charge / discharge of the storage battery 13. This is to search for the best combination of the power generation pattern and the charge / discharge pattern. The candidate power generation / charge / discharge schedule includes information on energy prices such as power and gas and the power / day of the prediction by the prediction unit 22. The evaluation value is calculated using the heat load demand forecast and the power generation forecast. In the evaluation calculation unit 25 that performs this, the simulation unit 24 is used to perform simulation calculation in consideration of the control operation at the time of predicted deviation, and the power generation / charge / discharge schedule with the best evaluation value such as cost is selected. This power generation / charge / discharge schedule is created, for example, on a daily basis, the current time, or 24 hours after the predetermined time elapses, and is updated as needed at predetermined time intervals, for example, every hour.

  FIG. 2 shows a processing flow of the control device 2 in the system of FIG.

  First, the process in the prediction unit 22 will be described. A time series value of the demand amount of the power load 14 and the thermal load 16 and the power generation amount of the solar cell 11 from the current next time zone to 24 hours ahead is predicted (step 102). The prediction result and the prediction information corresponding thereto are stored in the prediction DB 23 together with the actual measurement value. From the information in the prediction DB 23, a pattern in which the predicted value deviates by a certain value or more and its occurrence probability are calculated (step 103).

  Next, the optimum operation plan creation unit 26 sets a schedule (initial operation schedule) which is an initial solution (step 104). There may be a plurality of initial solutions using the schedule obtained as the optimal solution in the previous search.

  Here, this set of solutions is evaluated by the evaluation calculation unit 25. For example, an energy cost is an objective function, and a schedule that minimizes the value (evaluation value) of the objective function is an optimal solution. The energy cost includes an electric power cost, a fuel cost, and the like, and 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. The storage battery 13 and the hot water storage tank 15 are modeled in consideration of charging / discharging loss, heat dissipation loss, etc., and the remaining capacity is calculated in order to balance the storage battery 13 and the hot water storage tank 15 in one day. The difference in cost in one day is added to the objective function as a penalty function.

  Next, simulation when the predicted value deviates is performed by the simulation unit 24 (step 106). This is not only calculated by replacing the predicted pattern with a pattern that deviates, but also takes into account the control operation corresponding to the deviated pattern. In other words, the control operation performed in the target control system is simulated, such as correcting the operation plan value when the prediction is lost or rescheduling the optimum plan at regular intervals. However, if the optimal operation plan is simulated in the same way as the actual one, a huge amount of calculation may be required depending on the number of controlled objects, so the results of the same conditions are used from the actual operation plan data. May be simplified.

  At the time of the deviation from the prediction, the evaluation is performed in the same manner as the previous predictive middle, and a comprehensive evaluation value is calculated as follows.

(Evaluation value) = α × (Evaluation value at predictive time) + β × (Evaluation value when deviation from power generation prediction)
+ Γ × (Evaluation value when deviation from demand forecast) + ・ ・ ・
Here, α, β, and γ are weighting coefficients based on the occurrence probability of each prediction deviation. By doing so, among the operation plans having various patterns having the same evaluation value at the predictive intermediate time, the evaluation value with a good evaluation value at the time of prediction deviation is high. Further, even when there are a plurality of types of power generation prediction deviations and demand prediction probabilities, weighting addition may be performed similarly. It is possible to add in the same way also when the power generation prediction deviation and the demand prediction deviation occur simultaneously.

  Subsequently, a schedule to be evaluated next is determined using an optimization method (step 109). Since the present invention includes a control operation simulation, a metaheuristic method such as a tabu search or a genetic algorithm that can easily handle a nonlinear / discontinuous function is suitable as an optimization method to be applied.

  The optimum operation plan creation unit 26 performs the optimum search based on the calculation time, the number of iterations, or the optimum solution is not updated within a certain number of iterations, including the generation of the schedule to be evaluated and the prediction deviation simulation as described above. The schedule which is the solution of the best evaluation value obtained until the termination condition is satisfied is set as the optimum operation plan, and the output of each energy generator and energy storage device is commanded in accordance with this schedule (step 110).

  Hereinafter, the prediction deviation pattern and the calculation method of the occurrence probability will be described with some examples.

  In FIG. 3, the example of calculation of the prediction deviation probability in photovoltaic power generation amount prediction is shown. The probabilities for each combination of forecasts and actual conditions of monthly weather conditions as shown in FIG. 3 are tabulated using the prediction information DB 23. In a certain month, when the forecast for the afternoon of the next day is clear, when it is actually cloudy or rainy, the predicted deviation probability is set. It may be a little more detailed using a 3-hour forecast. In addition, calculation or correction may be performed using forecast information such as precipitation probability. By summing up every season, such as by month, it is possible to reflect the characteristics of the season where forecasts are easily lost. When the predicted value and the actual value data are accumulated for a certain period of time, as shown in FIG. 3, for example, the total power generation amount (kWh) per day is divided into several stages, and the predicted value and the actual value are constant. A case where a difference of more than the value occurs can be set as a predicted deviation probability.

  FIG. 4 shows an example of the creation of a predicted deviation pattern in the prediction of the amount of photovoltaic power generation and the simulation for correcting the optimum operation plan using the pattern. In solar power generation, for example, an average solar radiation amount pattern is calculated for each weather such as sunny, cloudy, and rainy every month, and the power generation amount is calculated based on this pattern. FIG. 4 shows an image of the correction of the optimum operation plan in a case where the forecast is clear and it becomes cloudy from the afternoon. The forecast deviation judgment time is considered to be around 11:00 when the weather forecast as shown in FIG. 4 is announced, but is not limited to this, and can be similarly implemented when judged from the amount of power generation in the morning. The simulation for correcting the optimum operation plan is performed from the time when it is assumed that the predicted deviation can be determined. In this example, as shown in FIG. 4, it is assumed that the operation pattern is corrected to increase the discharge amount and the charge amount of the storage battery 13 in response to the decrease in the amount of photovoltaic power generation. Rescheduling in actual control may be performed up to 24 hours ahead from this time, but in the calculation of the evaluation value in the prediction deviation simulation, evaluation is performed with the time width of the initial plan.

  FIG. 5 shows a calculation example of the predicted departure probability in the hot water supply demand prediction. As in the example of the hot water supply demand shown in FIG. 5, the irregular model data is selected from the correlation between the daily temperature and the accumulated amount of hot water demand for each consumer. The irregular model data may be selected from the difference between the predicted value and the actual value using this correlation. The forecast deviation probability of each consumer as shown in FIG. 5 is obtained by classifying by the season, day of the week, etc., or by using data for the past several tens of days from the target date used for demand prediction.

  FIG. 6 shows an example of a predicted deviation pattern in hot water supply demand prediction and a correction simulation of an optimum operation plan using the predicted deviation pattern. In the case of hot water supply demand, there may be a case where a typical irregular pattern is caused by going out or not bathing. In such a case, the predicted deviation pattern can be created relatively easily, for example, by averaging the actual values of the irregular model corresponding to the range used in the normal prediction model for each time period to create an irregular pattern. That's fine. In the simulation example shown in FIG. 6, it is assumed that control for reducing or stopping the output of the fuel cell 12 is performed when it is determined that the irregular pattern has almost no hot water supply demand that is normally generated. is doing. In this way, among the 24 hours when the initial optimum operation plan is created, a simulation is performed for the time zone in which the operation control is performed with correction, and the target evaluation value is calculated for the 24 hours when the operation plan is created.

  There are cases where a large number of prediction deviation patterns are assumed, but for example, when there are a large number of consumers, the simulation may be performed by limiting the prediction deviation probability to consumers having a certain value or more. Further, in the initial stage evaluation calculation in the optimum search, it is possible to take measures for reducing the calculation amount, such as omitting the prediction deviation simulation. As mentioned above, if the prediction deviation simulation is appropriately narrowed down to typical patterns, it is possible to cope with the prediction deviation without excessively increasing the amount of calculation and the calculation time, but also the optimality in the predictive middle. It is possible to create an optimal operation plan that will not be lost.

  The function of the control device 2 described above is executed by recording a program for realizing the function on a computer-readable recording medium, causing the computer to read the program recorded on the recording medium, and executing the program. It may be. The computer-readable recording medium refers to a recording medium such as a flexible disk, a magneto-optical disk, and a CD-ROM, and a storage device such as a hard disk device built in a computer system. Further, the computer-readable recording medium is a medium that dynamically holds the program for a short time (transmission medium or transmission wave) as in the case of transmitting the program via the Internet, and in the computer serving as a server in that case Such as a volatile memory that holds a program for a certain period of time.

It is a block diagram of the distributed energy community system of one Embodiment of this invention. It is a flowchart which shows the flow of a process of the control apparatus in the system of FIG. It is a figure which shows the example of calculation of the prediction deviation probability in photovoltaic power generation amount prediction. It is a figure which shows the example of a simulation which corrected the example of the prediction deviation pattern of photovoltaic power generation, and the optimal driving | operation plan with respect to it. It is a figure which shows the example of calculation of the irregular model in the hot water supply demand prediction, and its occurrence probability. It is a figure which shows the example of a simulation which corrected the example of the prediction deviation pattern of hot water supply demand, and the optimal driving | operation plan with respect to it.

Explanation of symbols

1 1 to 1 n consumer 2 control device 3 power system 4 power line 11 solar cell 12 fuel cell 13 storage battery 14 power load 15 hot water tank 16 thermal load 17 thermal piping 21 communication unit 22 prediction unit 23 prediction DB
24 simulation unit 25 evaluation calculation unit 26 optimum operation plan creation unit 23 prediction DB
101-110 steps

Claims (6)

  1. Operating in the energy system with one or more energy generators, one or more energy storage devices and one or more energy loads, using the energy generator and / or the predicted value of the energy loads In a control device for a distributed energy system that creates an operation plan for the energy generation device and the energy storage device so that the evaluation value of the plan is the best,
    A prediction unit that calculates a predicted deviation pattern in which the predicted value deviates by a certain value or more and its occurrence probability;
    A simulation unit that performs a simulation in which an operation plan is corrected from a time at which it can be determined that the predicted deviation pattern occurs;
    An evaluation value in this simulation is calculated, the evaluation value is weighted according to the occurrence probability of the predicted deviation pattern, and the evaluation value when the driving is performed according to the optimal driving plan under the condition of the prediction value is used. An evaluation value calculation unit that performs evaluation value calculation for adding values until a predetermined optimum search termination condition is satisfied;
    A control apparatus for a distributed energy system, comprising: an optimum operation plan creation unit that determines an operation plan having the best evaluation value among the evaluation values as an optimum operation plan.
  2. Operating in the energy system with one or more energy generators, one or more energy storage devices and one or more energy loads, using the energy generator and / or the predicted value of the energy loads In a control method of a distributed energy system for creating an operation plan of the energy generation device and the energy storage device so that an evaluation value of a plan is the best,
    A prediction step of calculating a predicted deviation pattern in which the predicted value deviates by a predetermined value or more and its occurrence probability;
    A simulation step of performing a simulation in which the operation plan is corrected from a time at which it can be determined that the predicted deviation pattern occurs,
    An evaluation value in this simulation is calculated, the evaluation value is weighted according to the occurrence probability of the predicted deviation pattern, and the evaluation value when the driving is performed according to the optimal driving plan under the condition of the prediction value is used. An evaluation value calculation step for performing evaluation value calculation for adding values until a predetermined optimum search termination condition is satisfied;
    A control method for a distributed energy system, comprising: an optimum operation plan creating step for determining an operation plan having the best evaluation value among the evaluation values as an optimum operation plan.
  3.   The predicted value is that of an energy generator using natural energy, and includes a step of accumulating a weather forecast and its results, a predicted value using the weather forecast, and this measured value as a database for each season, the predicting step 3. The occurrence probability of the predicted deviation pattern is calculated using the database and weather information of the target date, and the simulation step performs the simulation using the occurrence probability of the predicted deviation pattern and the predicted value at the time of deviation. A method for controlling a distributed energy system as described.
  4.   The predicted value is that of the energy load, demand data of the load is measured and accumulated, and the actual measurement data on the day when the error between the actual value of the daily unit and the predicted value is a certain value or more is used for the irregular model. A step of registering as data, wherein the prediction step calculates a ratio of the irregular model data among data used for demand prediction as an occurrence probability of the prediction deviation pattern, and the simulation step generates the prediction deviation pattern The method of controlling a distributed energy system according to claim 2, wherein the simulation is performed using a probability and a predicted value at irregular time calculated using the irregular model data.
  5.   The program for making a computer perform the control method of a distributed energy system of any one of Claim 2 to 4.
  6. A computer-readable recording medium on which the program according to claim 5 is recorded.

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