Control design assistance method and control design assistance device

Abstract

The present invention generates control logic for an energy supply system provided with a plurality of energy facilities within a short period at low cost. This control design assistance method for assisting in the designing of control logic for a real system comprises: a first step for creating, in a control simulator, first control logic for deciding on load distribution between individual facilities forming the real system by importing specification data pertaining to each of the facilities; a second step for deciding on a control parameter in the first control logic by analyzing the variability characteristics of the prediction error of a constraint condition; a third step for simulating, by using the control simulator, the load distribution between the facilities that is based on an operation plan and past data or simulated data pertaining to the constraint condition; and a fourth step for constructing second control logic for deciding on load distribution between the facilities of the real system by setting, for a control device for the real system, a control parameter and specification data of which the efficacy has been verified in the third step.

Description

Control design support method and control design support device

[0001] The present invention relates to a control design support method and control design support device that constructs a control logic for adjusting the actual energy load distribution in real time based on various actual measurement values ​​in an energy supply system that includes multiple energy facilities such as electric power facilities that supply electric power, thermal facilities that supply heat, and thermoelectric power facilities that supply both electric power and heat, in relation to the energy load distribution plan for each facility.

[0002] The recent trend toward decarbonization has created a demand for energy supply systems that can meet demand for electricity and heat while suppressing carbon dioxide emissions. This type of energy supply system is typically realized by combining a variety of energy facilities. Known examples of power generation facilities incorporated into energy supply systems include power generation facilities that utilize renewable energy sources such as solar and wind power, and storage and discharge facilities such as storage batteries that are often used in conjunction with such power generation facilities. Known examples of heat and power generation facilities or heat generation facilities incorporated into energy supply systems include gas engines and boilers that emit carbon dioxide, and hydrogen-mixed combustion engines, hydrogen boilers, and fuel cells that do not emit carbon dioxide. Known examples of equipment that are often used in conjunction with hydrogen-consuming heat and power generation facilities include water electrolysis systems that consume electricity to produce hydrogen from water.

[0003] In addition, customer demands for the combination of energy equipment that makes up an energy supply system are becoming more diverse, and incorporating various types of equipment with different characteristics also complicates the design of the control logic for controlling the energy supply system.

[0004] Here, Patent Document 1 is known as a document that illustrates an example of the control logic of an energy supply system. For example, the abstract of this document states that the problem to be solved is to provide power utilization equipment that can respond to various supply and demand requirements for power exchanged with an external power system of the power utilization equipment using a simple configuration without taking into account the responsiveness of the power output device, and as a solution, states that "the power utilization equipment includes at least one power output device connected to the external power system via a predetermined first connection point, a power converter connected to a second connection point that is closer to the external power system than the first connection point, a battery connected to the power converter, and a controller. The controller includes a first command generation unit that generates a first command to increase or decrease the output power from the power output device based on the first power deviation, and a second command generation unit that generates a second command to adjust the output power based on a second power deviation obtained by subtracting the exchanged power from a transfer power target value. The second command generation unit generates a charge command to charge the battery if the second power deviation exceeds an upper limit value determined based on a power transfer request value, and generates a discharge command to discharge the battery if the second power deviation is less than a lower limit value."

[0005] In this way, in Patent Document 1, the control logic is such that the power output device is instructed to increase or decrease the output power based on the first power deviation, and if the second power deviation exceeds a predetermined upper limit value, the storage battery is instructed to charge, and if the second power deviation is below a predetermined lower limit value, the storage battery is instructed to discharge.This allows the power exchanged with the external power system of the power utilization facility to respond to various supply and demand requirements without taking into consideration the responsiveness of the power output device.

[0006] JP 2023-13243 A

[0007] However, in Patent Document 1, the upper and lower load limit settings when determining the load allocation of multiple power facilities are determined only based on the fluctuation range, such as the maximum and minimum values ​​of power demand, and do not take into account the fluctuation characteristics of the power supply side when renewable energy power generation facilities are included, or the fluctuation characteristics of power demand.As a result, the upper and lower limit settings must be set with more leeway than necessary, which results in a narrower load range that each power facility can assume.Furthermore, Patent Document 1 does not particularly describe any ideas for speeding up the design of a power system consisting of multiple power facilities.

[0008] Therefore, an object of the present invention is to provide a control design support method and a control design support device that generate control logic for an energy supply system equipped with multiple energy facilities in a short period of time and at low cost by taking into account the fluctuation characteristics of the energy supply and demand sides and optimizing the upper and lower limit settings of the load distribution of each energy facility.

[0009] In order to solve the above problems, the control design support method of the present invention supports the design of a control logic for an actual system that supplies load according to demand, and includes the following steps: a first step of creating a first control logic in a control simulator that determines the load allocation of each piece of equipment by importing specification data of each piece of equipment that constitutes the actual system; a second step of determining control parameters in the first control logic by analyzing the fluctuation characteristics of prediction errors of constraint conditions; a third step of simulating, using the control simulator, the load allocation of each piece of equipment based on past data or simulated data of the constraint conditions and an operation plan; and a fourth step of constructing a second control logic that determines the load allocation of each piece of equipment of the actual system by setting, in a control device of the actual system, the specification data and the control parameters whose validity has been verified in the third step.

[0010] According to the control design support method and control design support device of the present invention, it is possible to generate control logic for an energy supply system equipped with a plurality of energy facilities in a short period of time and at low cost.

[0011] 1 is a configuration diagram of an energy supply system. An example of an operation plan optimized by the planning device 2. A diagram showing the flow of data in control logic. A diagram showing a control design support device and its peripheral devices according to the first embodiment. A diagram showing a network defining the connection relationships of facilities in the first embodiment. A diagram showing data defining a gas engine module. A diagram showing data defining a storage battery module. A diagram showing data defining a PV module. A diagram showing time series data of PV power generation amount that serves as a constraint condition of the investment simulator. A diagram showing data defining a grid power module. A diagram showing data defining a power demand module. A diagram showing time series data of power demand that serves as a constraint condition of the investment simulator. A diagram showing data defining the conditions of the investment simulator. A diagram showing analysis results of the investment simulator. A diagram showing data defining the analysis conditions of the planning simulator. A diagram showing time series data of PV power generation amount that serves as a constraint condition of the planning simulator. A diagram showing time series data of power demand that serves as a constraint condition of the planning simulator. A diagram showing the configuration of a control simulator. A diagram showing analysis conditions of the control simulator. A diagram showing time series data of PV power generation amount that serves as a constraint condition of the control simulator. A diagram showing time series data of power demand that serves as a constraint condition of the control simulator. 1 is a diagram showing a simulation result of a control simulator. 2 is a diagram showing a graph of a simulation result of a control simulator. 3 is a diagram showing a method for evaluating a margin when determining a control parameter. 4 is a diagram showing a network that defines the connection relationship of equipment in Example 2. 5 is a diagram showing data that defines a gas engine module.

[0012] Hereinafter, an embodiment of a control design support device according to the present invention will be described with reference to the drawings.

[0013] <Energy Supply System> First, the configuration of an energy supply system 100, which is a practical system, will be described with reference to Figures 1 to 3. In this embodiment, electric power is also referred to as a load.

[0014] 1 is a configuration diagram of an energy supply system 100. As shown in the figure, the energy supply system 100 includes a control device 1, a planning device 2, a gas engine 3, a storage battery 4, a photovoltaic power generation facility 5 (hereinafter referred to as "PV 5"), and sensors 6 and 7. Each of these will be explained in turn below.

[0015] <<Electric Power Equipment>> The gas engine 3, storage battery 4, and PV 5 are electric power equipment that work in conjunction with the grid power and supply power to various types of demand equipment via shared power lines. In addition, sensor 6 measures the amount of power taken in from the grid, and sensor 7 measures the total value of power consumption (electricity demand) of the demand equipment.

[0016] <<Planning Device 2>> The planning device 2 is a device for transmitting, as an operation plan, to the control device 1, a load allocation of each power facility that has been optimized based on a target function f that is specified in advance by a designer or the like, using predicted values ​​of power demand and PV power generation as constraint conditions (inputs). Specifically, the planning device 2 is a computer that includes hardware such as a calculation device such as a CPU, a storage device such as a semiconductor memory, and a communication device. The calculation device executes a predetermined program to realize each function, but the following description will omit such well-known techniques as appropriate.

[0017] Here, the objective function f is a cost function, CO ２ It is a function that takes into account both costs and CO emissions. ２ This function calculates the load allocation of each power facility over time to minimize emissions. The reason why only the power generation amount of PV5 is treated as a constraint among the power facilities is that the PV power generation amount is affected by the weather, and the upper limit of the power generation amount cannot be controlled arbitrarily.

[0018] 2 is an example of an operation plan optimized by the planning device 2, and is a table expressing the load of each power facility for one day, January 1, 2023, as time-series planned value data at 30-minute intervals. For example, the data row from 12:00 to 12:30 in the operation plan specifies an operation plan in which the grid power outputs 200 kW of power, the gas engine 3 outputs 300 kW of power, 500 kW of power is input (charged) to the storage battery 4, and the PV 5 outputs 1500 kW of power. This data row specifies that if the operation of each power facility is controlled according to each planned value, the cost and CO2 for the same time period will be reduced. ２ This shows that the planning device 2 predicts that the amount of emissions can be minimized.

[0019] <<Control Device 1>> The control device 1 is a device for controlling each piece of power equipment that constitutes the actual system based on the operation plan from the planning device 2. However, the operation plan is merely prediction data and does not guarantee minimization of costs, etc., so in order to minimize costs, etc. in the actual environment, it is necessary to adjust the contents of the operation plan according to the current situation.

[0020] Therefore, the control logic L1 implemented in the control device 1 calculates the load distribution of each power facility in real time based on the operation plan (planned value) planned by the planning device 2 and the actual value of the power demand measured by the sensor 7, and outputs a load command value according to each load distribution to each power facility, thereby reducing the actual cost and CO ２ The control device 1 controls each power facility so as to minimize the amount of emissions. The control device 1 having such a function is specifically a computer similar to the planning device 2.

[0021] Figure 3 is a diagram showing the flow of data during load distribution adjustment in the control logic L1 of the control device 1. The control logic L1 illustrated here is intended to control the energy supply system 100 of Figure 1, which is equipped with four types of power sources, and is therefore composed of four blocks B1 to B4, the same number as the number of power sources.

[0022] Block B1 is a control logic corresponding to grid power, which is a power source with the highest usage priority (priority P1). In this block B1, a power value a obtained by subtracting the sum of the power generation plan value of the gas engine 3 and the storage battery 4 at the current time calculated from the operation plan and the actual power generation value of the PV 5 from the actual measured value of the demand power measured by the sensor 7 is input to a range limiter for grid power. This range limiter is configured to limit the power value a to a lower limit E Ｌ and upper limit E Ｕ If the power value a is within the range of the lower limit E Ｌ If it is less than the lower limit E Ｌ is output, and the power value a is the upper limit value E Ｕ If it is equal to or greater than the upper limit E Ｕ The output of the range limiter of block B1 is technically a power generation output request value for the grid power, but since the grid power is outside the scope of control by the control device 1, this output is actually useless data.

[0023] Furthermore, block B1 outputs the deviation amount when the power value a falls outside the range of the range limiter as a correction value to block B2. Note that the correction value (deviation amount from the range limiter) output by block B1 is the upper limit value E of the range limiter. Ｕ Max(aE U ,0) and the lower limit value E of the range limiter Ｌ Min(aE L ,0).

[0024] Block B2 is a control logic corresponding to the storage battery 4, which is a power source with the second highest usage priority (priority P2). Block B2 calculates a power value a obtained by adding together the power generation plan value of the storage battery 4 at the current time calculated from the operation plan and the correction value output by block B1, and calculates the power value a by adjusting the lower limit E of the range limiter of the storage battery 4. Ｌ and upper limit E Ｕ The block B2 outputs the power output requirement value for the storage battery 4 (i.e., the load allocation of the storage battery 4) within the range of the range limiter. In addition, the block B2 outputs the deviation amount when the power value a falls outside the range limiter to the block B3 as a correction value.

[0025] Block B3 is a control logic corresponding to the gas engine 3, which is a power source with a usage priority (priority P3) second only to priority P2. This block B3 first generates a power value a by adding together the power generation plan value of the gas engine 3 at the current time calculated from the operation plan and the correction value output by block B2. Next, the power storage limiter monitoring unit of block B3 monitors the power storage amount of the storage battery 4 and performs the following process.

[0026] That is, if the amount of charge stored in the storage battery 4 is less than a predetermined amount, the charge amount limiter monitoring unit outputs the power value a to the range limiter and outputs the power value 0 to the block B4. Then, the range limiter, by the above-mentioned mechanism, outputs the lower limit value E Ｌ and upper limit E Ｕ The block B3 outputs the power generation output requirement value for the gas engine 3 (i.e., the load allocation of the gas engine 3) within the range of the range limiter. At this time, the block B3 outputs the deviation amount when the power value a falls outside the range limiter range as a correction value to the block B4.

[0027] On the other hand, if the amount of charge stored in the storage battery 4 is equal to or greater than the predetermined amount, the charge amount limiter monitoring unit outputs a power value of 0 to the range limiter and a power value of a to the block B4. Ｌ and upper limit E Ｕ The block B3 outputs the power generation output requirement value for the gas engine 3 within the range of the range limiter. At this time, the block B3 outputs the deviation amount when the power value 0 is outside the range of the range limiter and the sum of the power value a as a correction value to the block B4.

[0028] Block B4 is a control logic corresponding to PV5, which is a power source with the second highest usage priority (priority P4) after priority P3. Block B4 calculates the power value a obtained by adding the actual power output measurement value output by the sensor in PV5 and the correction value output by block B3, and calculates the power value a by adjusting the lower limit E of the range limiter of PV5. Ｌ and upper limit E Ｕ The power generation output demand value for PV5 (i.e., the load allocation of PV5) is output within the range of

[0000] . Note that even if a power generation output demand value greater than the actual measured power generation output value of PV5 is generated, the amount of power generated by PV5 cannot be increased in response to that. Therefore, the power generation output demand value for PV5 is intended to be used solely as a command to suppress the actual measured power generation output value of PV5.

[0029] By the processing of each block described above, the load distribution of the plurality of facilities is adjusted according to the error between the environment assumed when the operation plan is generated by the planning device 2 and the actual environment, and the cost and CO ２ Emissions can be minimized.

[0030] As shown in FIG. 2, the data rows of the operation plan are spaced at 30-minute intervals. Therefore, if the processing cycle of the control logic L1 is set to one minute, the data row for 0:00 should be acquired for the period from 0:00 to 0:29, and the data row for 0:30 should be acquired for the period from 0:30 to 0:59.

[0031] <Control Design Support Device 10, Planning Simulator 20, Investment Simulator 30> Next, the control design support device 10, planning simulator 20, and investment simulator 30 of the present invention will be described in detail.

[0032] FIG. 4 shows the configuration of the control device 1 and the control design support device 10, and the connection with peripheral devices (planning simulator 20, investment simulator 30) that cooperate with them.

[0033] The control design support device 10 is a device for supporting the design of the control logic L1 to be implemented in the control device 1, and is composed of a control simulator 11 and a control parameter determination unit 12. The control logic L11 implemented in the control simulator 11 is logic that executes the same processing (see FIG. 3) as the control logic L1 to be implemented in the control device 1. The control parameter determination unit 12 is a functional unit that determines the control parameters of both control logics.

[0034] The plan simulator 20 is a simulator that executes the same processing as the planning device 2, thereby generating an operation plan based on desk calculations that minimizes short-term operation costs and the like.

[0035] The relationship between the control simulator 11 (after the control parameters have been determined) and the planning simulator 20 in Fig. 4 is equivalent to the relationship between the control device 1 (after the control parameters have been determined) and the planning device 2 in Fig. 1. Therefore, the simulation results obtained by the control simulator 11 incorporating the operation plan optimized by the planning simulator 20 are equivalent to the operation of the actual system obtained by the control device 1 incorporating the operation plan optimized by the planning device 2.

[0036] Next, the investment simulator 30 will be described. The plan simulator 20 described above is a simulator that generates an operation plan that minimizes short-term operation costs, etc., but the investment simulator 30 is a simulator that generates an operation plan that minimizes long-term operation costs or CO ２ This is a simulator that calculates emissions and determines equipment specifications based on the calculation results, and is a functional unit that does not exist in the actual system shown in Figure 1.

[0037] Here, the equipment specifications determined by the investment simulator 30 refer to the combination of the type of power equipment (gas engine, storage battery, PV, etc.) selected and the capacity of each power equipment (load range: minimum output to rated output, storage capacity, etc.). In other words, in the case of power equipment specifications, the investment simulator 30 calculates the cost and CO2 for the required annual pattern of power demand. ２ The equipment specifications are identified by calculating the amount of emission reduction and determining the type and capacity of the power equipment accordingly.

[0038] The calculation itself in the investment simulator 30 is basically the same as the optimization process in the planning device 2 and the planning simulator 20. In other words, constraints such as power demand and PV power generation amount are given to the investment simulator 30, and the cost and CO ２ The process involves determining an operation pattern (time-series load distribution) that minimizes a target function defined by emissions. However, there are the following differences between the processes of the plan simulator 20 and the investment simulator 30. (1) The input power demand and PV power generation amount are short-term (e.g., one day) time-series data in the plan simulator 20, whereas they are long-term (e.g., one year) time-series data in the investment simulator 30. (2) The calculated costs and CO ２ (3) In the plan simulator 20, emissions are short-term (e.g., one day) costs, etc., whereas in the investment simulator 30, they are long-term (e.g., one year) costs, etc.,. (4) In the plan simulator 20, only operating costs (OPEX) are calculated, whereas in the investment simulator 30, equipment purchase costs (CAPEX) are also included. (4) The plan simulator 20 does not have a function for determining equipment specifications, but the investment simulator 30 has a function for verifying individual equipment specifications according to the calculated values ​​of the objective function obtained when the equipment specifications are changed, and for determining the optimal equipment specifications.

[0039] Fig. 5 shows a network N that defines the connection relationships of the power equipment used during simulation in the investment simulator 30. The network N illustrated here corresponds to the actual system in Fig. 1, and is composed of a gas engine module M3 corresponding to the gas engine 3, a storage battery module M4 corresponding to the storage battery 4, a PV module M5 corresponding to the PV 5, a grid power module M6 corresponding to the grid power measured by the sensor 6, a power demand module M7 corresponding to the power demand measured by the sensor 7, and a power connection module M1 that relays input and output between each module.

[0040] If such modules are prepared for each facility, any energy supply system 100 can be expressed in network form by appropriately combining the types and numbers of facilities. Details of the data that is set for each module, such as input / output characteristics and upper and lower output limits, will be described below.

[0041] <<Data for Gas Engine Module>> Fig. 6 is an example of data D3 set in the gas engine module M3. As shown in Fig. 5, the gas engine module M3 is configured to calculate the amount of gas (Nm ３ ) is input, and the power (kWh), cost (¥), and CO2 emissions (tons) are output. Therefore, the following information is registered in data D3.

[0042] The input / output characteristics of the output D31 (power) are Y = 5X, and the gas is 1 Nm ３ The relationship is set such that 5 kWh of power is output (generated) per hour. The upper limit of the output D31 (power) is set to the rated output (1000 kW) for one hour.

[0043] The input / output characteristics of output D32 (cost) are Y = 0.08X, and gas 1 Nm ３ The relationship is set such that the unit price is 0.08 yen.

[0044] Output D33 (CO ２ The input / output characteristic of the gas volume is Y = 0.002X, and the gas volume is 1 Nm ３ 0.002 tonnes of CO per ２A relationship is established in which the

[0045] The data D3 also includes the equipment cost of the gas engine 3, 100 million yen.

[0046] <<Data for Storage Battery Module>> Fig. 7 shows an example of data D4 set for the storage battery module M4. As shown in Fig. 5, the storage battery module M4 inputs and outputs power (kWh). Therefore, the following information is registered in the data D4:

[0047] The output characteristic of output D41 (power) is Y = 0.9X, and a relationship is set such that the efficiency during charging and discharging is 90%. In addition, 500 kW is set as the upper limit corresponding to the discharge rated value, and -500 kW is set as the lower limit corresponding to the charge rated value. Discharging is expressed as a positive value because it supplies power to demand like other power facilities, while charging is expressed as a negative value because it takes in other power.

[0048] The storage battery 4 differs from other power facilities in that it has the function of storing power. That is, in the storage battery 4, the amount of power charged is added to the stored power amount, and the amount of power discharged is subtracted from the stored power amount. In this data D4, 4000 kWh is set as the upper limit of the stored power amount, and the storage battery 4 is controlled so that the stored power amount does not increase beyond this. In addition, in data D4, 50 million yen is set as the equipment cost of the storage battery 4.

[0049] <<Data for PV Module>> Figure 8 shows an example of data D5 set for PV module M5. As shown in Figure 5, PV module M5 outputs power (kWh). As described above, PV5 is an uncontrollable power facility and is a constraint when determining an operation plan through optimization. Therefore, in the input / output characteristics section of data D5, this constraint is given in file format (e.g., file pv.csv) rather than as a formula. Equipment costs are also set for PV5.

[0050] FIG. 9 shows an example of the contents of the file pv.csv, which describes the constraints. In this example, time-series data of PV power generation amount every 30 minutes is written in CSV (comma separated values) format. As is clear from FIG. 9, in the file pv.csv, the data in the first column is date and time data, and the data in the second column is power generation amount data. In this example, one day's worth of data is considered one unit, and one year's worth of data (365 days) is considered one file.

[0051] <<Data for Grid Power Module>> Fig. 10 shows an example of data D6 set in the grid power module M6. As shown in Fig. 5, the grid power module M6 receives power (kWh) as an input, and outputs data such as power (kWh), cost (¥), and CO ２ The discharge amount (tons) is output. Therefore, the following information is registered in data D6.

[0052] The input / output characteristics of output D61 (power) are Y=X, and a relationship is set in which the input and output are equal. The upper limit of output D61 (power) is set to 2000 kW, and the lower limit is set to 0 kW. The upper and lower limits are set to the upper limit of the grid power, that is, one hour's worth of the physical capacity of the power receiving equipment, 2000 kW.

[0053] The input / output characteristics of output D62 (cost) vary depending on the time period, with Y = 20X for the time period from 7:00 to 22:00 and Y = 15X for other time periods. These input / output characteristics indicate that the electricity rate during the day is 20 yen / kWh and the electricity rate at night is 15 yen / kWh.

[0054] Output D63 (CO ２ The input characteristic of CO emissions is Y = 0.45X, which means that 0.45 tonnes of CO per kWh of input electricity is ２ This indicates that is emitted.

[0055] <<Data for Power Demand Module>> Figure 11 shows data D7 set in the power demand module M7. As shown in Figure 5, power (kWh) is input to the power demand module M7. As with the PV5 described above, power demand is also uncontrollable and is a constraint when determining an operation plan through optimization. Therefore, in the input / output characteristics section of data D7, this constraint is given in file format (for example, file demand.csv) rather than as a formula.

[0056] FIG. 12 shows an example of the contents of the file demand.csv, which describes constraints. In this example, time-series data for power demand every 30 minutes is written in CSV format. As is clear from FIG. 12, in the file demand.csv, the data in the first column is date and time data, and the data in the second column is power demand data. In this example, one day's worth of data is considered one unit, and one year's worth of data (365 days) is considered one file.

[0057] The above is the definition of each module shown in Figure 5. Because the input / output characteristics of each module are expressed in a linear form, any output variable can be defined for any equipment. By adopting such a versatile method, it becomes possible to perform analysis under conditions of equipment configuration and equipment specifications (capacity, etc.) that meet customer requirements. The reason for using a linear form for the input / output characteristics is to apply linear programming to the optimization process of the operation plan, which will be described later.

[0058] <<Processing by Investment Simulator 30>> Next, the analysis method by the investment simulator 30 will be described in detail with reference to FIGS. 13 and 14. FIG.

[0059] 13 shows an example of definition data D8 that defines the conditions of the investment simulator 30. In this definition data D8, the sum of the output D62 (cost) of the grid power module M6 and the output D32 (cost) of the gas engine module M3 is defined as the objective function of the optimization process. This allows the investment simulator 30 to obtain an operation plan that minimizes the total cost of electricity and gas through optimization on a daily basis. By repeating this process for 365 days, an operation plan for one year can be obtained.

[0060] 14 is an example of an analysis result D9 output by the investment simulator 30. Compared to the operation plan of FIG. 2 output by the planning device 2, the analysis result D9 of FIG. 14 has the following characteristics: cost data is also output; and the results for one year (365 days if it is not a leap year, and 366 days if it is a leap year) are output. Therefore, by aggregating this analysis result D9, the operating cost (total value of gas and electricity costs) for any one year can be calculated.

[0061] Furthermore, since the module data (data D3, D4, and D5) for each piece of power equipment also defines the equipment costs, if the power equipment to be used can be identified, the equipment costs can also be obtained. Therefore, the investment simulator 30 can determine conditions that optimize operating costs and equipment costs while changing the type and capacity of the equipment. When changing the type of equipment, the definition data for the equipment configuration shown in FIG. 5 can be changed, and when changing the specifications of each piece of equipment, such as capacity, the setting data for each module can be changed.

[0062] One way to use the investment simulator 30 is to present to the customer the changes in equipment costs and annual operating costs when the equipment conditions are changed. In this case, the customer can select the equipment configuration and equipment specifications that best suit their needs based on this cost information. In designing the planning device 2 and the control device 1, the efficiency of the design work can be improved by utilizing the planning / control simulator based on this data.

[0063] <<Processing by the Planning Simulator 20>> Next, an analysis by the planning simulator 20 and the control simulator 11, which is carried out after selecting the equipment configuration and equipment specifications of the actual system based on the simulation results of the investment simulator 30, will be described.

[0064] The internal processing in the planning simulator 20 is the same as that in the investment simulator 30. Therefore, the planning device 2, the planning simulator 20, and the investment simulator 30 implement the same optimization processing.

[0065] Fig. 15 shows definition data D10 that defines the analysis conditions of the plan simulator 20. The objective function in this definition data D10 is the sum of the grid power cost and the gas engine cost, similar to the definition data D8 in Fig. 13 that defines the conditions of the investment simulator 30. In addition, in the definition data D10, an upper limit of 1900 kW and a lower limit of 100 kW are set for the grid power. On the other hand, in the definition data D6 for the grid power module M6 shown in Fig. 10, an upper limit of 2000 kW and a lower limit of 0 kW are set.

[0066] The difference between the two is that the upper and lower limits in Figure 10 are physical values ​​determined by equipment specifications, while the upper and lower limits in Figure 15 are values ​​used to determine an operation plan that achieves system operation with a margin of error. In other words, the plan simulator 20 using the definition data D10 in Figure 15 generates an operation plan for grid power within the upper and lower limits (100 to 1900 kW) that provide a 100 kW margin relative to the physical upper and lower limits (0 to 2000 kW) of grid power. The purpose of this is to avoid problems caused by high-frequency fluctuations contained in the actual values ​​of power demand and PV power generation.

[0067] In other words, if an operation plan is created using a load close to the upper or lower physical limits of the equipment, the load of the equipment may reach the upper or lower limits in the event of a sudden change in the constraint conditions, which may cause a disruption to the power supply (for example, a power outage).In this embodiment, to prevent such a situation, a margin is provided in the load range used when creating the operation plan.The same applies to the capacity of the storage battery 4, and an operation plan is created by providing a margin of 100 kWh for the physical capacity of 4000 kWh shown in data D4 in Figure 7.

[0068] 16 and 17 show examples of time-series data D11 of PV power generation and time-series data D12 of power demand, in which the daily PV power generation and power demand are defined at 30-minute intervals.

[0069] The plan simulator 20 uses the above-described conditions to calculate the load of each facility so as to minimize costs under the environment of the time-series data D11 of power demand and the time-series data D12 of PV power generation, and outputs the calculated load as an operation plan. The operation plan by the plan simulator 20 is also output in the same format as the operation plan by the planning device 2, as shown in FIG.

[0070] <<Processing by Control Simulator 11>> Next, the processing by the control simulator 11 will be described. Fig. 18 is a diagram showing details of the control simulator 11. As shown here, the control simulator 11 is implemented with a control logic L11 that performs the same processing as the control logic L1 in Fig. 3. However, a difference from the configuration of the control device 1 is that the control simulator 11 is implemented with equipment models (a grid power model 11a, a gas engine model 11b, a storage battery model 11c, and a PV model 11d) that simulate the control operation of each piece of equipment.

[0071] The actual control device 1 incorporated in the energy supply system 100 is connected to the control unit of each power facility and operates each power facility by outputting a control signal to each control unit. The control simulator 11, which simulates this, outputs a control signal to each facility model instead of the actual power facility, and each facility model calculates a control operation in response to the control signal. Each facility model then obtains upper and lower limit values ​​for the load from the facility specification data defined during analysis by the investment simulator 30, and outputs a load within that range. Just as upper and lower limit values ​​are set for each facility model, upper and lower limit values ​​for each facility are also set in the control logic L11 of the control simulator 11.

[0072] Here, the control logic L11 of the control simulator 11 has the same function as the control logic L1 of the control device 1 illustrated in Fig. 3. That is, in the blocks B1 to B4 of the control logic L11, the range limiters in each block are set with upper and lower limit values ​​defined in the equipment specification data of the investment simulator 30.

[0073] Furthermore, parameters not present in the control logic L1 are additionally set in the control logic L11 of the control simulator 11. Definition data D13 defining the analysis conditions of the control simulator 11 is shown in FIG.

[0074] As shown here, in the definition data D13, a control priority P (P1 to P4 in this example) is set for each piece of power equipment. This defines the order of each block in the control logic L11. Other parameters that are additionally set in the definition data D13 include upper and lower limit values ​​for the grid power module M6. This is a load range that takes into account a margin, similar to the condition settings of the planning simulator 20 described above. For example, for the physical upper and lower limit values ​​of the grid power of 0-2000 kW, a margin of 100 kW is taken into account in the control operation, and the load range is set to 100-1900 kW.

[0075] As mentioned above, the default values ​​of the upper and lower limits of each block of the control logic L1 illustrated in Fig. 3 are the same as those in the equipment specification data of the investment simulator 30. However, if upper and lower limits are set in the definition data D13 as shown in Fig. 19, they are overwritten with those values.

[0076] 19, the start-up time and time constant are set for the gas engine module M3. Therefore, the control simulator 11 can perform an analysis that also takes into account the response delay of the equipment. Here, the start-up time and the delay time constant are set in the equipment model of the gas engine 3 as parameters related to the response time, and are reflected in the response calculation. Note that, since there is no response delay to control signals in equipment other than the gas engine, these parameters are not set.

[0077] 20 and 21 show the time-series data D14 of PV power generation and the time-series data D15 of power demand, which are input to the control simulator 11. The difference between the time-series data used in the plan simulator 20 and the investment simulator 30 (see FIGS. 9, 12, 16, and 17) and the time-series data used in the control simulator 11 is that the time intervals of the former are as long as 30 minutes, while the time intervals of the latter are as short as 1 second.

[0078] The purpose of the analysis by the control simulator 11 is to verify whether the network N equivalent to the actual system will supply the power demand without excess or shortage even when time-series data of constraint conditions (PV power generation amount, power demand) including instantaneous fluctuations that may actually occur is input, in other words, whether the set equipment specifications, parameters, etc. are effective in avoiding a power system interruption (power outage) caused by an instantaneous gap between demand and supply. Therefore, by using actually measured past data or simulated data created to approximate actual fluctuations as the PV power generation amount time-series data D14 and the power demand time-series data D15, the control robustness of the energy supply system 100 in an environment where the weather suddenly changes and the PV power generation amount suddenly changes is verified.

[0079] Fig. 22 is an example of an operation plan for each facility output by the control simulator 11. The difference from the operation plan by the planning device 2 illustrated in Fig. 2 is that the time intervals are shorter, at one second. The time intervals of the operation plan in Fig. 22 correspond to the time intervals of the time series data (Figs. 20 and 21) input to the control simulator 11. The structure of the control logic L11 of the control simulator 11 is similar to the control logic L1 of the control device 1 illustrated in Fig. 3, and the time series data of the PV power generation amount and power demand shown in Figs. 20 and 21 are input here to calculate a control signal for each power facility.

[0080] An example output from the control simulator 11 is shown in Figure 23. In the figure, the dotted line indicates the operation plan imported from the plan simulator 20, and the solid line indicates the results of control calculations performed by the control simulator 11. However, the solid lines for (a) power demand and (e) PV power generation amount do not indicate calculated values, but rather indicate the data (constraint conditions) given as input. For other equipment, the results are those of control signals calculated by the control logic L11 and responses calculated by the equipment model (as mentioned above, the gas engine 3 has a response delay).

[0081] In Figure 23, when a sudden fluctuation in (a) power demand or (e) PV power generation occurs, the system first responds by adjusting (b) grid power, which has priority P1. However, if it is determined that (b) grid power exceeds the upper or lower limit according to the planned value of (a) power demand, (c) the charge / discharge amount of the storage battery 4, which has priority P2, is adjusted from the planned value. Furthermore, if it is determined that the charge / discharge amount of the storage battery 4 also exceeds the upper or lower limit, or if it is determined that the upper or lower limit of the storage capacity is reached, (d) the load of the gas engine 3, which has priority P3, is adjusted from the planned value. Through the above process, the equipment load is adjusted from the planned value so that it satisfies the respective upper and lower limits. Note that in Figure 23, the graph (f) supply / demand gap shows the difference between power demand and power supply (total for each equipment). This graph shows that the gap is zero throughout the time period, confirming that the control operation for this condition can be performed without interrupting the power grid (power outage).

[0082] <<Processing by the Control Parameter Determination Unit 12>> Finally, the processing by the control parameter determination unit 12 included in the control design support device 10 will be described with reference to FIGS. 4 and 24. As described above, the upper and lower limit values ​​set in the control logic L11 of the control simulator 11 are determined by taking into account a margin relative to the physically operable load range in preparation for fluctuations in power demand and PV power generation. For example, if the control period of the control device 1 is set to one minute, if a sudden fluctuation occurs and the upper and lower limit values ​​of the equipment with priority P1 are exceeded within one minute, load adjustment will no longer be possible, and a power outage will occur due to the power supply-demand gap. Therefore, to be able to respond to sudden fluctuations, equipment with priority P1 in particular is operated with a margin, avoiding operation at a load close to the upper and lower limit values. Below, a method for determining this margin amount in the control parameter determination unit 12 will be described.

[0083] When the planning device 2 creates an operation plan, it sets predicted values ​​of power demand and PV power generation as constraints. If there is no difference between the predicted values ​​and the actual values, no supply-demand gap will occur if the equipment load is operated according to the operation plan. However, in reality, there will be a prediction error, which will require adjustment from the operation plan. In other words, an appropriate margin amount can be determined based on the characteristics of the prediction error.

[0084] Figure 24 shows the process for determining the margin (control parameter) from the prediction error, using PV power generation as an example. Given (a) a predicted value and (b) a measured value of PV power generation, the difference between them is the (c) prediction error. Next, a frequency filter is used to decompose the fluctuation characteristics of the (c) prediction error into (d) a high-frequency component and (e) a low-frequency component. The (d) high-frequency component is a short-period fluctuation characteristic, while the (e) low-frequency component is a long-period fluctuation characteristic. The time interval for the (a) predicted value of PV power generation is determined by the time interval of the weather information used as input for the prediction process, which is usually 30 minutes to 1 hour. Therefore, changes in PV power generation shorter than this interval appear as prediction errors. Furthermore, if the weather information used as input differs from the actual weather, naturally, this will appear as prediction errors longer than the 30-minute to 1-hour interval.

[0085] (e) The supply-demand gap caused by a low-frequency (long-cycle) prediction error can be addressed by load adjustment by the control device 1, so there is no problem. On the other hand, (d) the supply-demand gap caused by a high-frequency (short-cycle) prediction error may not be addressed by load adjustment by the control device 1, and must be addressed by a margin determined by the control parameter determination unit 12. In other words, the control parameter determination unit 12 determines the margin based on the fluctuation range of the high-frequency prediction error (d). In the example of FIG. 24 , the high-frequency prediction error (d) has a fluctuation range of ±50 kW, so the control parameter determination unit 12 determines the margin to be, for example, double that, 100 kW, in consideration of some margin.

[0086] For example, in the definition data D13 in Figure 19, the upper and lower limits of the grid power are set to 100 to 1900 kW, which is a value that takes into account a 100 kW margin relative to the physical capacity of the power receiving equipment, 2000 kW. As a result, when block B1 of control logic L1 shown in Figure 3 determines that the grid power load exceeds the upper limit of 1900 kW, it increases the discharge amount of the storage battery 4, which is equipment with a priority of P2 or higher, or increases the load on the gas engine 3, thereby adjusting the power supply from the grid power to be below 1900 kW. This process enables the system to operate with a margin of error so that the grid power does not approach the physical upper limit of 2000 kW.

[0087] Using the method described above, control parameters such as upper and lower limits are set in the control logic L11 of the control simulator 11. The same control logic is used for the control simulator 11 and the control device 1. Therefore, the control parameters are set after checking the control operation under the same conditions as in the actual system using the planning simulator 20 and the control simulator 11. In other words, the design of the control device 1 consists only of adjusting and setting the control parameters, which simplifies the design work. This reduces design costs and shortens the work time.

[0088] Next, a second embodiment of the present invention will be described with reference to Figures 25 and 26. Only the differences from the first embodiment will be described here. Note that the load in this embodiment also includes heat.

[0089] FIG. 25 shows a network N in Example 2. The difference from the network N in Example 1 described in FIG. 5 is that heat is also controlled in addition to electric power. The gas engine module M3a here is assumed to be a cogeneration type facility that outputs heat in addition to electricity. Therefore, compared to the gas engine module M3 in FIG. 5, heat has been added to the output. A boiler module M8 has also been added as a facility that outputs heat. Furthermore, a heat connection module M9 and a heat demand module M10 have also been added.

[0090] Figure 26 shows data D3a for the gas engine module M3a, which defines the equipment specifications for the gas engine 3 of this embodiment. The difference from data D3 (Figure 6) for the gas engine module M3 of Example 1 is that data on heat is defined as output D34. As described above, the definition of equipment specifications is general-purpose, and can be defined in the same format for both power and heat.

[0091] The same applies to the boiler module M8, and it is only necessary to define the linear expression representing the input / output characteristics and the upper and lower limit values ​​according to the boiler specifications. Also, for control, the control logic L1 in FIG. 3 of the first embodiment can be used as is.

[0092] As described above, in the system according to the present invention, the simulation condition definitions and control logic are general-purpose, and the same processing can be applied to both power and heat.

[0093] 100: Energy supply system 1: Control device, 2: Planning device, 3: Gas engine, 4: Storage battery, 5: Solar power generation facility, 6, 7: Sensor, 10: Control design support device, 11: Control simulator, 12: Control parameter determination unit, 20: Planning simulator, 30: Investment simulator

Claims

1. A control design support method for supporting the design of a control logic for an actual system that supplies load according to demand, comprising: a first step of creating, in a control simulator, a first control logic that determines the load allocation of each piece of equipment by importing specification data of each piece of equipment that constitutes the actual system; a second step of determining control parameters in the first control logic by analyzing the fluctuation characteristics of prediction errors of constraint conditions; a third step of simulating, using the control simulator, the load allocation of each piece of equipment based on past data or simulated data of the constraint conditions and an operation plan; and a fourth step of constructing, in a control device of the actual system, a second control logic that determines the load allocation of each piece of equipment of the actual system by setting, in the control device of the actual system, the specification data and the control parameters whose validity has been verified in the third step.

2. A control design support method according to claim 1, wherein the specification data is data indicating the load range or capacity range in which each piece of equipment can physically operate.

3. A control design support method according to claim 2, characterized in that in the second step, a frequency filter is used to decompose the prediction error of the constraint condition into high-frequency components and low-frequency components, and the control parameters are determined based on the amplitude of the high-frequency components.

4. A control design support method according to claim 3, characterized in that the load is electric power, each facility is an electric power facility that supplies electric power, the constraint conditions are electric power demand and the amount of power generated by a photovoltaic power generation facility, the effectiveness is that no power outages occur, and the control parameters are data on the amount of margin relative to the specification data of each facility.

5. A control design support device for supporting the design of a control logic for an actual system that supplies load according to demand, comprising: a control simulator that implements a first control logic that determines the load allocation of each piece of equipment that constitutes the actual system based on specification data of the equipment; and a control parameter determination unit that determines control parameters in the first control logic by analyzing the fluctuation characteristics of prediction errors of constraint conditions, wherein the control simulator simulates the load allocation of each piece of equipment based on past data or simulated data of the constraint conditions and an operation plan, and the control parameter determination unit constructs a second control logic for determining the load allocation of each piece of equipment in the actual system by setting the specification data and the control parameters, the validity of which has been verified in the simulation, in a control device of the actual system.

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