Flexibility management system and flexibility management method

Abstract

To utilize the degree of freedom in operating DER so as to reduce energy costs.SOLUTION: A flexibility management system 100 comprises: an initial solution calculation unit 112 that calculates, on the basis of operation condition data stored in an operation condition database 111, an initial solution for an operation plan which includes a demand curve corresponding to an operation plan of distributed energy resources and the operation plan; a suboptimal solution calculation unit 113 that calculates a plurality of suboptimal solutions different from existing solutions which are for the operation plan and which include the initial solution; and an operation plan variation database 114 that stores, as existing solutions, the initial solution and the plurality of suboptimal solutions.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a flexibility management system and a flexibility management method. [Background technology]

[0002] Flexibility management of distributed energy resources (DERs) involves sharing flexibility information among multiple energy systems and operating them in a coordinated manner.

[0003] Distributed energy resources (DERs) include, for example, storage batteries and generators. Storage batteries and generators are DERs that can generate negawatts and can control the power output to air conditioners, elevators, and other systems. Furthermore, by utilizing the flexibility of DERs regarding power generation and demand, demand systems such as factory systems that can adjust production processes and transportation systems that can adjust electric vehicle (EV) operations can shift demand in line with fluctuations in electricity market prices and reduce energy costs. Furthermore, shifting demand in multiple demand systems in line with the power generation curve of renewable energy (a type of DER) can expand the use of renewable energy. However, because DERs are operated separately in multiple energy systems, it is difficult to calculate DER operation plans across multiple energy systems in a unified manner.

[0004] As a method for sharing DER flexibility information across systems and collaboratively calculating DER operation plans, a method using response time, duration, output, etc. as indicators, as seen in adjustment power trading, is known. For example, Patent Document 1 discloses an energy management method using activation time and reduced energy cost as indicators.

[0005] Patent Document 1 describes an energy management method in which "multiple operation plans with different activation times of adjustment power are generated, and an operation plan is selected based on the activation probability of adjustment power at the activation time and the reduced energy cost of the operation plan." [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Publication No. 2020-137395 Summary of the Invention [Problem to be solved by the invention]

[0007] As mentioned above, an energy management method has been disclosed that shares DER flexibility information across systems and uses activation times and reduced energy costs as indicators. However, for example, air conditioning and transportation systems are subject to operational constraints related to their intended purposes. Due to these operational constraints, creating flexibility using simple indicators such as activation times cannot fully utilize the operational flexibility of DERs, and energy costs cannot be sufficiently reduced.

[0008] The present invention has been made to solve the above problems, and an object of the present invention is to utilize the operational flexibility of DERs to reduce energy costs. [Means for solving the problem]

[0009] The flexibility management system of the present invention includes an initial solution calculation unit that calculates an initial solution for an operation plan including an operation plan for a distributed energy resource and a demand curve corresponding to the operation plan based on operating condition data stored in an operating condition database, a quasi-optimal solution calculation unit that calculates multiple quasi-optimal solutions that differ from existing solutions for the operation plan including the initial solution, and an operation plan variation database that stores the initial solution and the multiple quasi-optimal solutions as existing solutions. The above-described flexibility management system is one aspect of the present invention, and a flexibility management method reflecting one aspect of the present invention is configured in the same manner as the above-described flexibility management system. [Effects of the Invention]

[0010] According to the present invention having the above configuration, it is possible to utilize the operational flexibility of DERs and reduce energy costs. Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a block diagram showing the functional configuration of a flexibility management system according to a first embodiment of the present invention. [Figure 2] 1 is a block diagram showing a hardware configuration of a flexibility management system according to a first embodiment of the present invention. [Figure 3] 4 is a flowchart showing the procedure of an operation plan variation generation process in the flexibility management system according to the first embodiment of the present invention. [Figure 4] FIG. 2 is a sequence diagram showing energy system cooperation in the flexibility management system according to the first embodiment of the present invention. [Figure 5] 4 is a graph showing an example of calculation of operation plan variations in the flexibility management system according to the first embodiment of the present invention. [Figure 6] 4 is a graph showing an example of calculation of an coupled operation plan in the flexibility management system according to the first embodiment of the present invention. [Figure 7] FIG. 2 is a diagram showing an example of data of flexibility information in the flexibility management system according to the first embodiment of the present invention. [Figure 8] FIG. 1 is a diagram showing an application example in which the flexibility management system according to the first embodiment of the present invention is applied to a coupled analysis of a linear operation planning system and a non-linear operation planning system. [Figure 9]FIG. 1 is a diagram showing an example of application in which the flexibility management system according to the first embodiment of the present invention is applied to a hierarchical EMS composed of a higher-level EMS and a lower-level EMS. [Figure 10] FIG. 10 is a block diagram showing the functional configuration of a flexibility management system according to a second embodiment of the present invention. [Figure 11] FIG. 10 is a diagram showing energy system coordination through a combination of demand curve variations in a flexibility management system according to a second embodiment of the present invention. [Figure 12] FIG. 10 is a sequence diagram showing energy system cooperation in a flexibility management system according to a second embodiment of the present invention. [Figure 13] FIG. 10 is a diagram showing a configuration example in which energy system coordination is applied to a plurality of consumers in a flexibility management system according to a second embodiment of the present invention. [Figure 14] FIG. 10 is a diagram showing a configuration example in which energy system coordination is applied to multiple tenants at the same power receiving point in a flexibility management system according to a second embodiment of the present invention. [Figure 15] FIG. 10 is a diagram showing a configuration example in which energy system coordination is applied to a plurality of consumers who receive power supply from the same electricity retailer in a flexibility management system according to a second embodiment of the present invention. [Figure 16] FIG. 10 is a diagram showing an example of application in which a flexibility management system according to a second embodiment of the present invention is applied to a regional EMS and a plurality of consumer EMSs. [Figure 17] FIG. 10 is a diagram showing an application example in which a flexibility management system according to a second embodiment of the present invention is applied to an electricity retailer EMS and a plurality of consumer EMSs. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In this specification and drawings, components having substantially the same functions or configurations are designated by the same reference numerals, and redundant description will be omitted.

[0013] First Embodiment [Example of functional configuration of flexibility management system] First, an example of the functional configuration of a flexibility management system 100 according to a first embodiment of the present invention will be described. FIG. 1 is a block diagram showing an example of the functional configuration of the flexibility management system 100 according to this embodiment. As shown in FIG. 1, the flexibility management system 100 includes an energy system 10 and an energy system 11. The energy systems 10 and 11 are connected via a network (not shown) so that they can send and receive information data to and from each other. The network is configured, for example, by a closed circuit network such as a dedicated line, or a public line such as the Internet.

[0014] (1) Functional configuration of Energy System 11 The energy system 11 is an example of a first energy system, and as shown in Fig. 1 , includes an operating condition DB (Data Base) 111, an initial solution calculation unit 112, a sub-optimal solution calculation unit 113, an operation plan variation DB 114, a communication unit 115, and an operation plan execution unit 116. The operating condition DB 111, the initial solution calculation unit 112, the operation plan variation DB 114, and the communication unit 115 are connected in this order. The operating condition DB 111, the sub-optimal solution calculation unit 113, the operation plan variation DB 114, and the operation plan execution unit 116 are also connected in this order. The communication unit 115 is connected to the operation plan execution unit 116.

[0015] The operating condition DB (operating condition database) 111 stores operating condition data including usage prediction data of the energy system 11 and operating parameters related to objective functions and constraints associated with the equipment operation plan. Taking an ice thermal storage air conditioning system as an example, the usage prediction data is time-series data of air conditioning loads such as the required amount of cold and hot energy. Taking an ice thermal storage air conditioning system as an example, the operating parameters are data related to equipment specifications such as the operating efficiency of the air conditioning system, rated power consumption, and rated thermal storage capacity of the thermal storage tank, as well as data related to costs such as the unit price per kWh of electricity and the CO2 emission coefficient.

[0016] The initial solution calculation unit 112 calculates an initial solution for an operation plan including an operation plan for a distributed energy resource and a demand curve corresponding to the operation plan, based on the operating condition data stored in the operating condition DB 111. The initial solution calculation unit 112 calculates an initial solution using an objective function (objective function for calculating the initial solution) and constraint conditions (constraint conditions for calculating the initial solution) related to the optimization of the operation plan for the equipment, using an optimization method such as mathematical programming, based on the usage forecast data and the operating parameters. The initial solution for the operation plan calculated by the initial solution calculation unit 112 is stored in the operation plan variation DB 114.

[0017] Here, taking an ice thermal storage air conditioning system as an example, the operation plan is a plan that defines, in the form of time-series data, when and at what output various operation modes will be executed to satisfy the air conditioning load. The various operation modes include, for example, air conditioning operation, heat storage operation (an operation mode in which heat is stored in a heat storage tank), simultaneous air conditioning and heat storage operation (an operation mode in which air conditioning is performed while heat is stored), and heat storage-based air conditioning operation (an operation mode in which heat stored in a heat storage tank is used for air conditioning). The power demand curve associated with the operation plan is also determined in the form of time-series data. For example, the initial solution calculation unit 112 creates a plan for the next day every day. When creating an air conditioning operation plan, the initial solution calculation unit 112 may update (calculate) the 24-hour operation plan every hour, for example, depending on the weather forecast for the day.

[0018] The suboptimal solution calculation unit 113 calculates multiple suboptimal solutions that differ from existing solutions of the operation plan including the initial solution. The calculation process of the suboptimal solutions is similar to the calculation process of the initial solution. The suboptimal solution calculation unit 113 adds a term that evaluates the orthogonality between the demand curve of the suboptimal solution and the demand curve of the existing solution to the objective function for initial solution calculation to set it as the objective function for suboptimal calculation, and adds a constraint on the amount of change in the objective function for initial solution calculation to the constraint conditions for initial solution calculation to set it as the constraint conditions for suboptimal calculation. The suboptimal solution calculation unit 113 calculates the suboptimal solution using the objective function for suboptimal calculation and the constraint conditions for suboptimal calculation. The multiple suboptimal solutions calculated by the suboptimal solution calculation unit 113 are stored in the operation plan variation DB 114.

[0019] The operation plan variation DB (operation plan variation database) 114 stores the initial solution calculated by the initial solution calculation unit 112 and a plurality of quasi-optimal solutions calculated by the quasi-optimal solution calculation unit 113.

[0020] The communication unit 115 transmits demand curve variations including demand curves of all existing solutions stored in the operation plan variation DB 114 as flexibility information to the energy system 10. In addition, the communication unit 115 receives demand curve selection information (see FIG. 7 ) described later from the energy system 10 and outputs it to the operation plan execution unit 116.

[0021] The operation plan execution unit 116 reads out an operation plan corresponding to the demand curve from the operation plan variation DB 114 based on the demand curve selection information received from the energy system 10, and executes operation of the energy system 11 in accordance with the read operation plan.

[0022] (2) Functional configuration of energy system 10 The energy system 10 may be the same type of energy system as the energy system 11, or may be a different type of energy system. In the following, an example will be described in which the energy system 10 is a BEMS (Building Energy Management System) equipped with a storage battery. In this example, the energy system 10, which is a BEMS, is a higher-level system, and the energy system 11, which is responsible for air conditioning, is a lower-level system (see FIG. 9 described later).

[0023] As shown in FIG. 1 , the energy system 10 includes an operating condition DB 101, a communication unit 102, an external demand curve variation DB 103, a coupled solution calculation unit 104, an operation plan DB 105, and an operation plan execution unit 106. The operating condition DB 101, the coupled solution calculation unit 104, and the communication unit 102 are connected in this order. The operating condition DB 101, the coupled solution calculation unit 104, the operation plan DB 105, and the operation plan execution unit 106 are connected in this order. The communication unit 102, the external demand curve variation DB 103, and the coupled solution calculation unit 104 are connected in this order. The coupled solution calculation unit 104 is also connected to the communication unit 102.

[0024] The operating condition DB (operating condition database) 101 stores operating condition data including usage forecast data of the energy system 10 and operating parameters related to the objective function and constraint conditions associated with the equipment operation plan. However, the usage forecast data recorded in the operating condition DB 101 is time-series data on the building's power demand other than air conditioning, such as lighting load and outlet load. The operating parameters are data on equipment specifications such as the rated capacity and rated output of the storage battery, and data on costs such as the unit price per unit and CO2 emission coefficient.

[0025] The communication unit 102 receives demand curve variation, which is flexibility information, from the communication unit 115 of the energy system 11 and outputs it to the external demand curve variation DB 103. In addition, the communication unit 102 acquires selection information of the demand curve selected by the coupled solution calculation unit 104 and transmits it to the communication unit 115 of the energy system 11.

[0026] The external demand curve variation DB103 does not need to store all of the time series data related to the operation plan of the energy system 11 (for example, time series data related to the execution of each operation mode), but only stores the demand curve variation of the energy system 11 received by the communication unit 102.

[0027] The coupled solution calculation unit 104 selects one demand curve from the received demand curve variations based on the operating condition data stored in the operating condition DB, sets the selected demand curve as a constraint condition, and calculates an operation plan using an objective function used to calculate an operation plan for the distributed energy resources of the system. The coupled solution calculation unit 104 also calculates an operation plan for the storage battery, which is a DER of the energy system 10, that minimizes objective functions such as electricity rates and CO2 emissions. The operation plan here is time-series data that determines the charge / discharge amount of the storage battery. For the selected demand curve, the coupled solution calculation unit 104 outputs selection information of the demand curve to the communication unit 102. The coupled solution calculation unit 104 also outputs the calculated operation plan to the operation plan DB 105.

[0028] The operation plan DB 105 stores the operation plan calculated by the coupled solution calculating unit 104. Note that the coupled solution calculating unit 104 calculates only one operation plan, and therefore the operation plan DB 105 stores only one operation plan.

[0029] The operation plan execution unit 106 acquires an operation plan for the energy system 10 (storage battery) from the operation plan DB 105, and executes operation of the energy system 10 in accordance with the operation plan.

[0030] [Example of hardware configuration for flexibility management system] Next, the hardware configuration of the flexibility management system will be described. Fig. 2 is a diagram showing an example of the hardware configuration of the flexibility management system according to this embodiment. The flexibility management device 21 shown in Fig. 2 is a computer device that realizes the functions of the energy system 11 shown in Fig. 1. The flexibility management device 20 shown in Fig. 2 is a computer device that realizes the functions of the energy system 10 shown in Fig. 1. The flexibility management device 21 is connected to managed facility 215 that is the management target of the energy system 11. The flexibility management device 20 is connected to managed facility 205 that is the management target of the energy system 10.

[0031] The flexibility management device 21 includes a computing device 211 , a storage device 212 , a communication device 213 , and an input / output device 214 .

[0032] The arithmetic device 211 is configured by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), etc. The arithmetic device 211 executes the arithmetic processing of the initial solution calculation unit 112, the quasi-optimal solution calculation unit 113, and the operation plan execution unit 116 of the energy system 11.

[0033] The storage device 212 is configured as a computer-readable non-transitory recording medium, such as a storage device such as an HDD (Hard Disk Drive), that stores a program for the arithmetic processing executed by the arithmetic device 211. The storage device 212 includes an operating condition DB 111 and an operation plan variation DB 114 for the energy system 11. The storage device 212 also stores programs and data for the arithmetic device 211 to control each part, an OS (Operating System), a controller, etc. The storage device 212 is not limited to an HDD, and may be, for example, a recording medium such as an SSD (Solid State Drive), a CD (Compact Disc)-ROM, or a DVD (Digital Versatile Disc)-ROM.

[0034] The communication device 213 is configured with a NIC (Network Interface Card), a modem, etc., and establishes a connection with the flexibility management device 20 and transmits and receives various data.

[0035] The input / output device 214 is composed of a display unit including a display device such as an LCD (Liquid Crystal Display) or an organic EL (Electro-Luminescence) display, and an operation unit including a touch sensor. The display unit and operation unit are integrally formed, for example, as a touch panel. The operation unit outputs, for example, usage prediction data and operation parameters input by the user to the operation condition DB 111. The display unit is used, for example, to visualize data in the operation plan variation DB 114. The operation unit can also be composed of a mouse, a tablet, or the like, and configured separately from the display unit.

[0036] The flexibility management device 20 includes a computing device 201 , a storage device 202 , a communication device 203 , and an input / output device 204 .

[0037] The arithmetic device 201 is configured by, for example, a CPU, an MPU, etc. The arithmetic device 201 executes the arithmetic processing of the coupled solution calculation unit 104 and the operation plan execution unit 106 of the energy system 10.

[0038] The storage device 202 is configured as a computer-readable non-transitory recording medium, such as a storage device such as an HDD, that stores a program for the arithmetic processing executed by the arithmetic device 201. The storage device 202 includes an operating condition DB 101, an external demand curve variation DB 103, and an operation plan DB 105 of the energy system 10. The storage device 202 also stores programs for the arithmetic device 211 to control each part, an OS, programs for a controller, and data. The storage device 202 is not limited to an HDD, and may be a recording medium such as an SSD, a CD-ROM, or a DVD-ROM.

[0039] The communication device 203 is composed of a NIC, a modem, etc., and establishes a connection with the flexibility management device 21 and transmits and receives various data.

[0040] The input / output device 204 is composed of a display unit including a display device such as an LCD or an organic EL display, and an operation unit including a touch sensor. The display unit and operation unit are integrally formed, for example, as a touch panel. The operation unit outputs, for example, usage prediction data and operation parameters input by the user to the operation condition DB 101. The display unit is used, for example, to visualize data in the operation plan DB 105. The operation unit can also be composed of a mouse, a tablet, or the like, and configured separately from the display unit.

[0041] [Operation plan variation generation process steps] Next, the procedure of the operation plan variation generation process in the flexibility management system 100 will be described. FIG. 3 is a flowchart showing the procedure of the operation plan variation generation process in the flexibility management system 100 according to this embodiment. When the energy system 11 is, for example, an ice thermal storage air conditioning system, the process described below is executed every hour in response to updates of the weather forecast for the day, for example. Note that the execution cycle of the flexibility management process is not limited to one hour and may be changed as needed.

[0042] First, the initial solution calculation unit 112 of the energy system 11 resets the data stored in the operation plan variation DB 114, that is, the existing solution set (step S10).

[0043] Next, the initial solution calculating unit 112 performs an operation plan variation generation process, which will be described later, to calculate an initial solution for the operation plan (step S11).

[0044] Next, the initial solution calculating unit 112 adds the calculated initial solution of the operation plan to the existing solution set stored in the operation plan variation DB 114 (step S12).

[0045] Next, the quasi-optimal solution calculation unit 113 performs an operation plan variation generation process, which will be described later, to calculate a quasi-optimal solution for the operation plan, that is, a new solution (step S13).

[0046] Next, the sub-optimal solution calculation unit 113 calculates the inner product of the new solution and the existing solution stored in the operation plan variation DB 114 (step S14).

[0047] Next, the sub-optimal solution calculation unit 113 determines whether or not all of the calculated inner products of the new solution and the existing solutions are equal to or less than the inner product threshold value (step S15).

[0048] In the process of step S15, if the quasi-optimal solution calculation unit 113 determines that there is a solution whose inner product between the new solution and the existing solution exceeds the inner product threshold (No in S15), the operation plan variation generation process ends.

[0049] On the other hand, in the process of step S15, if the quasi-optimal solution calculation unit 113 determines that all of the inner products between the new solution and the existing solutions are equal to or less than the inner product threshold value (Yes determination in S15), the quasi-optimal solution calculation unit 113 performs the process of step S16. In the process of step S16, the quasi-optimal solution calculation unit 113 adds the new solution to the set of existing solutions stored in the operation plan variation DB 114.

[0050] Next, the sub-optimal solution calculation unit 113 determines whether the number of existing solutions stored in the operation plan variation DB 114 is equal to or less than the number threshold value (step S17).

[0051] In the process of step S17, if the quasi-optimal solution calculation unit 113 determines that the number of existing solutions exceeds the number threshold (determination of No in S17), the operation plan variation generation process ends.

[0052] On the other hand, in the process of step S15, if the quasi-optimal solution calculation unit 113 determines that the number of existing solutions is equal to or less than the number threshold (YES determination in S17), it returns to the process of step S13 and repeatedly executes the processes of steps S13 to S17.

[0053] Next, a description will be given of energy system coordination in the flexibility management system 100. Fig. 4 is a sequence diagram of energy system coordination in the flexibility management system 100 according to this embodiment.

[0054] First, in the energy system 11, an operation plan and a demand curve of a DER are calculated in the operation plan variation generation process 411, which is a calculation process of the initial solution calculation unit 112 and the sub-optimal solution calculation unit 113 described in the flowchart of Fig. 3. The calculated demand curve variation is transmitted to the energy system 10 as flexibility information.

[0055] Next, in the energy system 10, an operation plan for the DERs of the energy system 10 is calculated, including the selection of a demand curve, in a coupled operation planning process 401, which is a calculation process of the coupled solution calculation unit 104. The demand curve selection information is returned to the energy system 11.

[0056] Next, in the energy system 11, the operation plan corresponding to the demand curve selected by the energy system 10 is executed in an operation plan execution process 412. Meanwhile, in the energy system 10, the operation plan calculated in the coupled operation plan process 401 is executed in an operation plan execution process 402.

[0057] 4, the operation plan variation generation process 411 and the coupled operation planning process 401 are executed periodically, for example, once a day, to calculate an operation plan for up to 24 hours ahead. The operation plan execution process 412 and the operation plan execution process 402 according to the calculated operation plan are executed at shorter intervals, for example, every 30 minutes. If the usage prediction data recorded in the operating condition DB 111 and the operating condition DB 101 is updated every moment, the execution intervals of the operation plan variation generation process 411 and the coupled operation planning process 401 are not limited to once a day. By executing the operation plan variation generation process 411 and the coupled operation planning process 401 in accordance with updates to the usage prediction data, it is possible to achieve energy system coordination while reflecting more real-time operating conditions.

[0058] [Operation plan variation generation process] Next, a description will be given of an operation plan variation generation process in the initial solution calculation unit 112 and the sub-optimal solution calculation unit 113 of the energy system 11. In the following, an example will be described in which the operation plan variation generation process is executed every hour.

[0059] First, we will explain the usage prediction data and operation parameters recorded in the operation condition DB 111 of the energy system 11, as well as the design variables used in the operation plan variation generation process. In each parameter explained below, "f" is a subscript that represents the time frame number.

[0060] (1) Usage forecast data Q ac_load f : Air conditioning load (kWh), i.e., the amount of cooling required per time frame. P ac_rated_cooling : Rated cooling power consumption (kW), that is, the rated value of air conditioning power consumption. E ac_efficiency_cooling Cooling operation efficiency, that is, the efficiency of the heat pump during air conditioning operation. Here, the cooling operation efficiency is a constant, but it may also be time-series data set according to the temperature. E ac_efficiency_ice : Ice thermal storage operation efficiency, that is, the heat pump efficiency during thermal storage operation. Here, the ice thermal storage operation efficiency is a constant, but it may also be time-series data set according to the air temperature. C ac_rated_ice : The rated cold heat capacity (kWh) of the heat storage tank, i.e., the amount of cold heat stored in the heat storage tank. H ac_rated_ice : The rated cold energy output (kW) of the heat storage tank, that is, the amount of cold energy that can be extracted from the heat storage tank per unit time. K ac_partial_cooling : Partial cooling coefficient.

[0061] (2) Operating parameters K ac_loading_ice : Cooling heat sharing coefficient, that is, a coefficient that limits the air conditioning load that can be shared by the heat dissipation amount of the heat storage tank to a predetermined ratio or less. K ac_loss_ice : Natural heat dissipation rate of cold, that is, the natural heat dissipation rate per unit time of the heat storage tank. K orthogonal : Orthogonality weighting coefficient, i.e., a coefficient that weights the orthogonality of solution variations in the objective function. K degrade : Electricity cost increase tolerance coefficient, that is, a coefficient that restricts the rate of increase in electricity cost of a suboptimal solution relative to the optimal solution (initial solution).

[0062] (3) Design variables w demand_variation f : The total value of electricity demand (kWh) per time frame (non-negative continuous variable). w ac_direct f : Air conditioning power demand (kWh) per heat pump time frame when only air conditioning operation is performed (non-negative continuous variable). w ac_direct_with_charge f : Air conditioning power demand (kWh) per time frame of the heat pump when air conditioning operation and heat storage operation are performed simultaneously (non-negative continuous variable). w ac_charge f : Heat pump thermal storage electricity demand (kWh) per time frame (non-negative continuous variable). q ac_charge f: The amount of heat stored in the thermal storage tank per time frame (kWh) (non-negative continuous variable). q ac_discharge f : Heat release from the thermal storage tank per time frame (kWh) (non-negative continuous variable). s ac f : A binary variable that switches the heat storage operation mode. In the case of heat storage operation, s ac f is 1, and in the case of heat dissipation operation, s ac f is 1. u ac f : A binary variable that controls whether or not the heat storage operation mode can be switched. ac f When is 1, switching from heat dissipation to heat storage is possible, and u ac f If is 0, switching from heat dissipation to heat storage is not possible. c ac f : The heat storage state (kWh) of the heat storage tank in each time frame.

[0063] In the operation plan variation generation process, the objective function used in the equipment operation plan is, for example, a function whose objective is to minimize the electricity charge or a function whose objective is to minimize CO2 emissions. In the following explanation, an objective function whose objective is to minimize the electricity charge will be explained as an example. Equation (1) is a calculation formula for calculating the objective function.

[0064]

number

[0065] In equation (1), F is a set of time frames; for example, for a 24-hour operation plan, F = {1, 2, 3, ..., 24}. M is the existing solution set, and the subscript m is the number assigned to the elements of the existing solution set. YTOU f is the unit price per time frame. The first term in equation (1) is the sum of the unit price multiplied by the power demand, and the second term is a linear weighting of the sum of the dot products between the new solution and the existing solution. Equation (1) minimizes the sum of the first and second terms.

[0066] The first term in equation (1) is the electricity cost for the target period (F) of the operation plan. Minimizing the first term is the design variable w of the demand curve that minimizes the electricity cost. demand_variation f When CO2 emissions are used as the objective function, instead of the metered rate, the first term may be set to the CO2 emission coefficient, which represents the amount of CO2 emissions per kWh, multiplied by the electricity demand, to minimize CO2 emissions.

[0067] The second term is the design variable w of the demand curve. demand_variation f and the demand curve W of the existing solution stored in the operation plan variation DB 114. demand_existing m,f The inner product of the vector normalized to norm 1 is the total value in the existing solution set (M). demand_variation f and existing solution W demand_existing m,f is treated as an |F|-dimensional vector (|F| is the number of elements in set F). By considering it as a vector, the smaller the inner product, the stronger the orthogonality of the two vectors, and the more different they are. The larger the inner product, the weaker the orthogonality of the two vectors, and the more similar they are. Minimizing the second term means optimizing the demand curve so that it becomes more orthogonal to the demand curve of the existing solution, i.e., so that the new solution differs from the existing solution.

[0068] Since the initial solution calculation unit 112 calculates an initial solution for the operation plan, no existing solutions exist at the time of calculation of the initial solution, i.e., the existing solution set M is an empty set. Therefore, in the operation plan variation generation process in the initial solution calculation unit 112, equation (1) consists of only the first term and becomes an objective function that minimizes the power cost (power fee).

[0069] On the other hand, in the operation plan variation generation process in the quasi-optimal solution calculation unit 113, since an existing solution exists, the design variable w demand_variation f The objective function to optimize is as follows. The second term of equation (1) may be transformed into the following equation (2).

[0070]

number

[0071] Equation (2) is the design variable w demand_variation f and existing solution W demand_existing m,f The inner product is then taken after averaging every two frames. By lowering the time resolution of the second term in this way, the difference in the demand curve between the new solution and the existing solution, that is, the time span of the demand shift, can be made larger.

[0072] The constraints used in the operation plan variation generation process will be described below.

[0073] Design variable w demand_variation f can be expressed by the following equation (3).

[0074]

number

[0075] In equation (3), w ac_direct f is the air conditioning power demand when only air conditioning is in operation. ac_direct_with_charge f is the air conditioning power demand when air conditioning operation and heat storage operation are performed simultaneously. ac_charge f However, this is the demand for stored heat electricity.

[0076] In the operation plan variation generation process in the quasi-optimal solution calculation unit 113, the constraint condition expressed by the following formula (4) is applied.

[0077]

number

[0078] In equation (4), the left side is the first term of the objective function, which represents the power cost. optimal is the power cost in the initial solution. K degrade is the electricity cost increase allowance coefficient. optimal Electricity bill increase allowance coefficient K degradeBy multiplying by , it is possible to restrict the increase in power cost relative to the initial solution in calculating the suboptimal solution.

[0079] Furthermore, the combined power demand for air conditioning and thermal storage can be restricted to the rated power consumption or less using the following formula (5).

[0080]

number

[0081] Furthermore, using the following formula (6), the air conditioning load can be met by the sum of the air conditioning heat quantity of the heat pump and the heat radiation quantity of the heat storage tank.

[0082]

number

[0083] In equation (6), the first term on the left side is the heat pump air conditioning power demand w ac_direct f Cooling efficiency E ac_efficiency_cooling The second term on the left side multiplies the air conditioning power demand of the heat pump when air conditioning operation and thermal storage operation are performed simultaneously by the ice thermal storage operation efficiency. ac_discharge f is the amount of heat released from the heat storage tank.

[0084] Furthermore, the air conditioning load that can be shared by the amount of heat released from the heat storage tank can be restricted using the following formula (7).

[0085]

number

[0086] In addition, the binary variable s ac f Depending on the ac_direct f is set to 0, and during heat dissipation operation, ac_direct_with_charge f can be constrained to be 0.

[0087]

number

[0088]

number

[0089] Using equations (8) and (9), w ac_direct f and w ac_direct_with_charge f By overlapping the two in equation (3) to make them exclusive, it is possible to handle the design variables whose coefficients (operating efficiency) change depending on the thermal storage operation mode within the framework of mixed integer linear programming. ac_partial_cooling By multiplying the right-hand side of the equation by this, when air conditioning operation and heat storage operation are performed simultaneously, the power consumption allocated to air conditioning operation can be restricted to a predetermined ratio of the rated power consumption or less. This corresponds to an equipment restriction that prevents the air conditioning capacity from being increased arbitrarily during heat storage operation, because the refrigerant is supplied to the indoor unit and the heat storage tank through the same circuit.

[0090] In addition, the binary variable s for switching the thermal storage operation mode is calculated using the following equations (10) and (11). ac f Depending on the amount of heat released during heat storage operation, q ac_discharge f is set to 0, and the heat storage amount during heat dissipation operation q ac_chargee f can be constrained to be 0.

[0091]

number

[0092]

number

[0093] Using equations (10) and (11), it is possible to prevent a physically impossible solution in which heat is stored and released simultaneously. In addition, the upper limits of the heat storage amount and heat release amount are set to the rated cooling capacity C ac_rated_ice , rated cooling output H ac_rated_ice can be constrained to

[0094] In addition, the heat storage state c of the heat storage tank is calculated using the following equation (12): ac f The rated cooling capacity C ac_rated_ice The following constraints can be applied:

[0095]

number

[0096] In addition, the natural heat dissipation (c ac f-1 ×K ac_loss_ice ) and the heat input / output (q ac_charge f -q ac_discharge f ) and the time change in the heat storage state is c ac f can be constrained.

[0097]

number

[0098] In addition, the amount of heat stored (w ac_charge f-1 ×E ac_efficienc_ice ) and thermal storage power demand (q ac_charge f ) can be associated and constrained.

[0099]

number

[0100] Furthermore, restrictions on switching of the heat storage operation mode can be imposed using the following equations (15) and (16).

[0101]

number

[0102]

number

[0103] Using equation (15), the heat storage state cac f If is not 0, the binary variable u ac f Limit to 0, and c ac f If is 0, u ac f can be set to any value (can be 0 or 1). ac f-1 If you want to set 0, (s ac f , s ac f-1 ) can be limited to (1,1), (0,0), and (0,1). In other words, continuation of heat storage operation, continuation of heat dissipation operation, and change from heat storage operation to heat dissipation operation are allowed. On the other hand, if change from heat dissipation operation to heat storage operation is not allowed, u in equation (16) can be limited to (1,1), (0,0), and (0,1). ac f-1 By setting to 1, (s ac f , s ac f-1 ) can be limited to the combinations (1,1), (0,0), (0,1), (1,0). In other words, any continuation or change of the operating state is allowed. ac f-1 If is not 0, the operation cannot be switched from heat radiation to heat storage, and the heat storage state c ac f-1 When is 0, the system switches from heat radiation operation to heat storage operation. Physically, this corresponds to an equipment constraint that ice making cannot start if ice melting is not complete, and ice making can start only when ice melting is complete.

[0104] The design variables w are optimized based on the objective functions and constraints of the above equations (1) to (16). demand_variation f is the demand curve for the initial solution and the suboptimal solution. A general mixed integer linear programming solver can be used for the optimization calculation. Among other design variables, w ac_direct f , w ac_direct_with_charge f , w ac_charge f , q ac_discharge f is time-series data representing the operation plans of the initial solution and the sub-optimal solution.

[0105] In utilizing the flexibility of an energy system with complex operational constraints, it is not easy to digitize and share objective functions and constraint conditions across systems. In contrast, the initial solution calculation unit 112 and the quasi-optimal solution calculation unit 113 of the energy system 11 according to this embodiment digitize flexibility in a simple representation format, namely, variations in demand curves, thereby enabling the flexibility to be shared and effectively utilized across systems.

[0106] Furthermore, in the present invention, there is no need to modify the constraints related to the original DER operation plan when generating demand curve variations. The newly added constraint is equation (4), which is a constraint on the cost increase of the new solution relative to the initial solution. Furthermore, the first term in equation (1) is the objective function related to the original DER operation plan. The second term in equation (1) was added to evaluate orthogonality when generating demand curve variations, and is also the difference between the initial solution and the suboptimal solution.

[0107] Furthermore, by adding a term for evaluating the orthogonality of the new solution to the existing solution to the objective function for calculating the initial solution (corresponding to the second term in equation (1)), and by adding a constraint regarding the cost increase of the new solution relative to the initial solution (corresponding to equation (4)), the DER operation plan calculation process described by the objective function and constraints can be extended to an operation plan variation generation process.

[0108] Next, the determination of whether the inner product of the new solution and the existing solution is equal to or less than the inner product threshold, that is, the processing of step S15 shown in Fig. 3, will be described. As shown in the following equation (17), the demand curve w demand_variation f , and the demand curve W of the existing solution demand_existing m,f are each normalized to a norm of 1, and then the inner product is calculated. The inner product of the new solution and each element m (existing solution) of the existing solution set M is calculated using equation (17). Each calculated inner product is compared with an inner product threshold in the processing of step S15 shown in FIG. 3, and if it is determined that all the inner products are equal to or less than the inner product threshold, the new solution is stored in the operation plan variation DB114.

[0109]

number

[0110] Note that the new solution is not normalized in the above-mentioned equation (1). This is because normalization would make the objective function nonlinear and increase the amount of optimization calculations. On the other hand, normalization in equation (17) makes the inner product the cosine of both vectors, which can be compared with a threshold set within the range of -1 to 1. Furthermore, equation (17) is a calculation for determining the inner product, and does not affect the linearity of the optimization calculation of the new solution.

[0111] [Coupled operation planning process] Next, a specific example of a coupled operation planning process in the coupled solution calculating unit 104 of the energy system 10 will be described. The following describes an example of a coupled operation planning process with charge / discharge operation of a storage battery (energy system 10).

[0112] First, the constants and design variables used in the coupled operation planning process will be described. The constants used in the coupled operation planning process are stored in the operating condition DB 101 of the energy system 10. Here, the coupled operation planning process is executed, for example, every hour, and in each parameter described below, "f" is a subscript representing the time frame number.

[0113] (1) Constants used in the coupled operation planning process Y TOU_0 f : Time-of-use charge (\ / kWh), that is, the unit price for the amount of electricity. TOU_0 f is the Y in the operation plan variation generation process of the energy system 11. TOU f It may be the same as or different from. C bess_rated : Battery rated capacity (kWh), i.e., the amount of electricity that can be stored in the battery. P bess_rated : Battery rated output (kW), that is, the rated power for charging and discharging the battery. Ebess_efficiency : Charging / discharging efficiency, that is, the charging / discharging efficiency taking into account the conversion loss and internal resistance loss of the storage battery. K bess_loss : Natural discharge rate, that is, the natural discharge rate of the storage battery per unit time. W demand_variation n,f : Demand curve variation (kWh), that is, time-series data of the demand curve variation calculated by the energy system 11 and stored in the external demand curve variation DB 103. n is the identification number of the variation. W demand_others f : Other demand curves (kWh), that is, time series data of other demand curves stored in the operating condition DB 101. Other demand curves include, for example, lighting loads and outlet loads.

[0114] (2) Design variables used in the coupled operation planning process w demand_total f : The total value of electricity demand per time frame (kWh). w bess_charge f : The charge amount (kWh) per time frame of the battery (non-negative continuous variable). w bess_discharge f : The amount of discharge (kWh) per time frame of the battery (non-negative continuous variable). s bess f : A binary variable that switches the charge / discharge mode (1: charge operation, 0: discharge operation). s demand_variation n : A binary variable that controls the selection of demand curve variations (1: selected, 0: not selected). c bess f : State of charge (kWh) of the storage battery in each time frame. Note that the state of charge (SoC) is generally expressed as a percentage, but here it is expressed in kWh.

[0115] In the coupled operation planning process, the objective function used in the operation plan of the energy system 10 is, for example, the total electricity price (unit price of metered charge × electricity demand) which is the target to be minimized in the following equation (18).

[0116]

number

[0117] w in equation (18) demand_total f can be expressed by the following equation (19).

[0118]

number

[0119] As shown in equation (19), w demand_total f can be expressed as a linear sum of the selected demand curve variation (first term on the right-hand side), other demand curves (second term on the right-hand side), charging amount (third term on the right-hand side), and discharging amount (fourth term on the right-hand side). N in equation (19) is the set of external demand curve variations.

[0120] The constraint conditions used in the coupled operation planning process will be explained using the following formulas (20) to (24).

[0121]

number

[0122] Equation (20) is a binary variable s demand_variation n By constraining the sum of these variables to 1, one of the variables is constrained to 1 and the remaining variables are constrained to 0. The s corresponding to the demand curve selected by the above equations (18) and (19) is demand_variation n is 1. demand_variation n This is the demand curve selection information returned to the energy system 11.

[0123] Furthermore, in the coupled operation planning process, the following equations (21) and (22) are used to restrict the generation of physically infeasible solutions for simultaneously charging and discharging.

[0124]

number

[0125]

number

[0126] In equations (21) and (22), the binary variable s bess f Depending on the bess_discharge f is 0, and during discharge operation, w bess_charge f is constrained to be 0.

[0127] In addition, in the coupled operation planning process, the state of charge of the storage battery is restricted to be equal to or less than the rated capacity using the following equation (23).

[0128]

number

[0129] Furthermore, in the coupled operation planning process, the amount of change in the state of charge is restricted using the following equation (24).

[0130]

number

[0131] Equation (24) constrains the amount of change in the state of charge by natural discharge over time (second term on the right-hand side) and the input and output of power due to charging and discharging operations (third term on the right-hand side).

[0132] The constraints are not limited to the above-mentioned various constraints, and for example, demand_total f An upper limit may be set for the contracted power, the power receiving capacity, etc. By the operation planning process using the objective function and constraint conditions described above, one optimal external demand curve variation is selected in minimizing the objective function, and at the same time, an operation plan for the energy system 10 (storage battery) is calculated.

[0133] In the above-described operation planning process, there is no need to modify the constraints related to the DER's original operation plan when selecting an external demand curve variation. The newly added constraint, i.e., the external demand curve, is constrained to one by Equation (20). The objective function is composed of a term related to the selection of the demand curve (the first term on the right-hand side of Equation (19)) and a term related to the DER's original operation plan (the second and subsequent terms on the right-hand side of Equation (19)). In other words, by adding a term related to the selection of the demand curve to the objective function of the original operation plan (corresponding to the first term on the right-hand side of Equation (19)) and adding a constraint that only one demand curve is selected (corresponding to Equation (20)), the DER operation plan calculation process can be expanded into a coupled operation planning process that combines the selection of an external demand curve variation.

[0134] The demand curve selection information is transmitted to the energy system 11 by the communication unit 102 of the energy system 10, and the operation plan of the energy system 10 is stored in the operation plan DB 105. In the energy system 11, the operation plan execution unit 116 acquires, from the operation plan variation DB 114, an operation plan linked to the demand curve selected by the coupled solution calculation unit 104 of the energy system 10, and executes the operation plan. Through such an operation, the operation plan of the energy system 11 is coupled with the operation plan of the energy system 10, enabling cooperative operation of DERs across the two energy systems.

[0135] Next, a description will be given of an example of calculation of operation plan variations in the initial solution calculation unit 112 and the sub-optimal solution calculation unit 113 of the energy system 11. Fig. 5 is a graph showing an example of calculation of operation plan variations in the flexibility management system 100 according to this embodiment.

[0136] Fig. 5(A) shows the time-of-day charge Y TOU f 5A is a diagram showing an example of the time change characteristics of the energy consumption rate (A) and the energy consumption rate (E) of the energy consumption rate (E).

[0137] Fig. 5(B) shows an example of an initial solution of an operation plan variation calculated by the initial solution calculation unit 112 and stored in the operation plan variation DB 114. Figs. 5(C) and 5(D) show examples of quasi-optimal solutions of an operation plan variation calculated by the quasi-optimal solution calculation unit 113 and stored in the operation plan variation DB 114. The horizontal axis of Fig. 5(B) to Fig. 5(D) represents time (Hour), and the vertical axis represents power demand (\ / kWh).

[0138] The bar 511 shown in FIG. 5(B) represents the design variable w ac_direct f The bar 512 indicates the air conditioning power demand when only air conditioning operation is performed, which corresponds to the design variable w ac_direct_with_charge f The bar 513 indicates the air conditioning power demand when air conditioning operation and heat storage operation are performed simultaneously, which corresponds to the design variable w ac_charge f The curve 510 (bold line) represents the sum of the power demands represented by the bars 511 to 513, i.e., the design variable w demand_variation f 5(C) and 5(D) are the same as those in FIG. 5(B), and therefore, redundant explanations will be omitted. Curve 510, curve 520, and curve 530 are demand curve variations transmitted from the energy system 11 to the energy system 10.

[0139] 5(E) to 5(G) are diagrams showing heat demand and supply corresponding to FIGS. 5(B) to 5(D), respectively. The horizontal axis of FIGS. 5(E) to 5(G) represents time (hour), and the vertical axis represents heat demand and supply. Here, cold demand and supply are shown as negative values, that is, the signs are reversed from those in equation (6). Bars 541 represent w ac_direct f ×E ac_efficiency_cooling (see equation (6)). ac_direct_with_charge f ×E ac_efficiency_ice (see equation (6)). ac_discharge f (see equation (6)). Curve 540 (see bold line) represents the sum of the cold energy supplies shown by bars 541 to 543, that is, Q ac_load f(see equation (6)). Curve 544 (see dashed line) shows the air conditioning load corresponding to the design variable c ac f This corresponds to the change in the heat storage state of the heat storage tank (see equations (13) and (14)), i.e., the change in which heat is stored in synchronization with the demand for heat storage power (see bar 513) and heat is released in synchronization with the supply of cold heat by heat release from the heat storage tank (see bar 543).

[0140] Comparing Figures 5(B) to 5(D) or Figures 5(E) to 5(G) reveals that curves 510, 520, and 530 differ due to changes in the timing of heat storage and heat release. It can also be seen that curves 510, 520, and 530 are all operation plans that shift demand from high-price or high-load time periods to low-price or low-load time periods in light of the time-of-day rates shown in Figure 5(A). The increase in electricity cost of the suboptimal solution compared to the initial solution is 0.01%. Thus, the energy system 11 according to this embodiment can generate variations of operation plans with different demand curves, i.e., operation plans with small cost fluctuations and different demand curves, without substantially increasing the costs of the energy system 11 (such as electricity costs and CO2 emissions). 5(A) to 5(G) may be displayed by the input / output device 214 of the flexibility management device 21 to present each variation information shown in FIG. 5 to the manager of the energy system 11.

[0141] Variations of operation plans that do not change costs can be generated by simply setting constraints to suppress cost increases, as in equation (4). However, this method only yields the same solution even if optimization calculations are repeated. To generate different solutions, the optimization conditions must be changed. In this embodiment, the generation of different solutions is automated by applying linear algebra to evaluate the orthogonality between the existing solution and the new solution in the objective function of equation (1). Therefore, in the energy system 11 according to this embodiment, the operator does not have to arbitrarily set conditions when changing the optimization conditions.

[0142] Next, a calculation example of a coupled operation plan in the coupled solution calculation unit 104 of the energy system 10 will be described. Fig. 6 is a graph showing a calculation example of a coupled operation plan in the flexibility management system 100 according to this embodiment. Fig. 6(A) is the same as Fig. 5(A), so a duplicated explanation will be omitted.

[0143] FIG. 6B shows an example of an operation plan calculated by the coupled solution calculation unit 104 of the energy system 10. A curve 611 (see thin line) represents the operation plan calculated by the coupled solution calculation unit 104 of the energy system 10. demand_variation n,f (see equation (19)). Here, it is assumed that the curve 510 shown in FIG. 5(B) is selected as the curve 611. That is, the design variable s demand_variation n is 1. Bar 612 represents the constant W demand_others f (see equation (19)). Bar 613 indicates the design variable w bess_charge f (see equation (19)). Bars 614 are displayed as negative values, and the design variable w bess_discharge f The curve 610 (see the thick line) is a demand curve obtained by adding up the power demands indicated by the curve 611 and the bars 612 to 614. demand_total f The curve 615 (dashed line) shows the total power demand corresponding to the design variable c bess f This shows the state of charge of the storage battery corresponding to (see equation (24)).

[0144] As can be seen by comparing Figures 6(A) and 6(B), the total electricity demand (curve 610) is high during the low-price time slots of the time-of-day rates (see around 11:00 to around 14:00 in Figure 6(A)). The total electricity demand (curve 610) is low during the high-price time slots of the time-of-day rates (see around 17:00 to around 20:00 in Figure 6(A)). In other words, the flexibility management system 100 shifts demand from the high-price time slots of the time-of-day rates to the low-price time slots by combining the selection of a demand curve variation for the energy system 11 with the operation plan of the storage battery associated with the energy system 10. As a result, the flexibility management system 100 can reduce electricity costs.

[0145] Next, a description will be given of the linkage data between the energy system 11 and the energy system 10. Fig. 7 is a diagram showing an example of linkage data between systems in the flexibility management system 100 according to this embodiment.

[0146] 7 stores information data of demand curve variations stored in the operation plan variation DB of the energy system 11. The information of the demand curve variations stored in the table 71 is transmitted as flexibility information from the energy system 11 to the energy system 10. The flexibility information includes identification information of the demand curve variation and time-series data representing the demand curve corresponding to the demand curve variation.

[0147] As shown in FIG. 7, table 71 has a time frame field and a variation ID field. The variation ID is identification information for the demand curve variation and is indicated by, for example, an ID number (such as "1," "2," or "3"). Table 71 stores the power demand for each demand curve variation for each time frame, i.e., time-series data representing the demand curve. For example, in time frame "9," the power demands for variation "1," variation "2," and variation "3" are "24," "420," and "24," respectively.

[0148] Here, variation "1," variation "2," and variation "3" correspond to variation_1 to variation_3 shown in FIGS. 5(B) to 5(D), respectively. The costs of variation "1," variation "2," and variation "3" are stored in table 72 shown in FIG. 7. Table 72 stores the costs of variation "1," variation "2," and variation "3," for example, "69594," "69601," and "69601," respectively. The information stored in table 72 is transmitted to the energy system 10 as cost data corresponding to the demand curve variation, together with the information stored in table 71. The energy system 10 may perform a screening process based on the cost data stored in table 72. An example of the screening process is a process of excluding a variation whose cost increase relative to the minimum-cost variation exceeds a predetermined ratio from targets for selection in the coupled operation planning process.

[0149] The table 73 stores demand curve selection information. The table 73 includes identification information of a demand curve variation and information indicating whether the demand curve variation has been selected. As shown in FIG. 7, the table 73 stores selection information corresponding to a variation ID. The selection information can be, for example, "1" indicating that a demand curve variation has been selected, or "0" indicating that a demand curve variation has not been selected. For example, in the data example shown in the table 73, the selection information corresponding to the variation "1" is "1". That is, the variation "1" is the demand curve variation selected by the energy system 10. The information stored in the table 73 is transmitted as demand curve selection information from the energy system 10 to the energy system 11. The energy system 11 receives the table 73 and executes the operation plan associated with the variation ID for which the selection information is "1".

[0150] In the explanation of Figures 5 and 6, the time-of-use charge (Y TOU f) and the time-of-use charges (Y TOU_0 f ) are the same, the present invention is not limited to this. The time-of-day charge applied to the energy system 11 and the time-of-day charge applied to the energy system 10 may be different. For example, consider a case where the DER included in the energy system 11 has a high degree of freedom in operation and the DER included in the energy system 10 has a low degree of freedom in operation, and the same variable charge is applied to both. In this case, the energy system 11 can reduce electricity costs by demand shifting, but the energy system 10 cannot keep up with the variable charge, and electricity costs increase, which may offset the effect of reducing electricity costs by the energy system 11. In order to prevent such a situation from occurring, for example, it is possible to set the time-of-day charge (Y TOU f ) is a variable tariff such as a market-linked tariff, and the time-of-use tariff (Y TOU_0 f ) may be a fixed fee that does not vary over time. By applying different time-of-day rates to both systems, it is possible to avoid an increase in the electricity cost of the energy system 10, and it is possible to suppress the combined electricity cost of the energy systems 11 and 10.

[0151] Next, an application example of the flexibility management system 100 will be described. Generally, storage batteries and cogeneration systems are composed of DERs with linear or linearly approximable characteristics, so the amount of calculation required to optimize a linear operation plan is small. On the other hand, chargers linked to EV (Electric Vehicle) vehicle dispatch plans are composed of DERs that are subject to nonlinear constraints, so the amount of calculation required to optimize a nonlinear operation plan is large. When coupling operation plans for a system that includes a nonlinear DER (e.g., a charger linked to an EV vehicle dispatch plan) with a system that includes a linear DER (e.g., a storage battery), a nonlinear operation plan problem with many variables must be solved, which lengthens the time required to calculate the operation plan.

[0152] The above problem can be solved by applying the flexibility management system 100 of the present invention. FIG. 8 is a diagram showing an application example in which the flexibility management system 100 according to this embodiment is applied to a coupled analysis of a linear operation planning system and a nonlinear operation planning system. The linear operation planning system 140 shown in FIG. 8 corresponds to the energy system 10 in FIG. 1. The nonlinear operation planning system 141 shown in FIG. 8 corresponds to the energy system 11 in FIG. 1. The nonlinear operation planning system 141 transmits demand curve variations to the linear operation planning system 140. The linear operation planning system 140 selects one of the demand curve variations received from the nonlinear operation planning system 141, calculates an operation plan, and transmits selection information of the selected demand curve to the nonlinear operation planning system 141. The nonlinear operation planning system 141 executes an operation plan corresponding to the demand curve selected by the linear operation planning system 140.

[0153] As a method for coupling the linear operation planning system 140 and the nonlinear operation planning system 141, the Benders decomposition method, which converges to an optimal solution by alternately performing calculations, is known. Although the present invention does not provide a strict optimal solution in coupled analysis like the Benders decomposition method, it is possible to shorten the calculation time without performing convergence calculations by adding constraints and objective functions specific to the generation and selection of demand curve variations to the original optimization problem.

[0154] There is also coupled operation between a higher-level EMS (Energy Management System) that manages the entire building or premises of a consumer, such as a BEMS (Building Energy Management System) or FEMS (Factory Energy Management System), and a lower-level EMS (Equipment EMS) that manages the air conditioning system, power supply system, etc. installed at the consumer. In some cases, the higher-level EMS and the lower-level EMS are integrated, but if they are different systems, a problem occurs in that the operation plans of the two cannot be directly coordinated.

[0155] The above problem can be solved by applying the flexibility management system 100 of the present invention. FIG. 9 is a diagram showing an example of application of the flexibility management system 100 according to this embodiment to a hierarchical EMS composed of a higher-level EMS and a lower-level EMS. The higher-level EMS 150 shown in FIG. 9 corresponds to the energy system 10 in FIG. 1. The lower-level EMS 151 shown in FIG. 9 corresponds to the energy system 11 in FIG. 1. The lower-level EMS 151 transmits demand curve variations to the higher-level EMS 150. The higher-level EMS 150 selects one of the demand curve variations received from the lower-level EMS 151, calculates an operation plan, and transmits selection information of the selected demand curve to the lower-level EMS 151. The lower-level EMS 151 executes the operation plan corresponding to the demand curve selected by the higher-level EMS 150. In this way, by applying the flexibility management system 100 to a hierarchical EMS composed of a higher-level EMS and a lower-level EMS, it is possible to couple the operation plans of the higher-level EMS and the lower-level EMS even when the higher-level EMS and the lower-level EMS are not integrated.

[0156] 9 shows an example of a system configuration in which there is only one lower-level EMS 151, but the present invention is not limited to this, and there may be multiple lower-level EMSs 151. The coupling of operation plans between multiple lower-level EMSs 151 and the upper-level EMS 150 is similar to the coupling of operation plans in the flexibility management system 200 according to the second embodiment described later, and will be described in detail in the second embodiment.

[0157] [effect] As described above, in the flexibility management system 100 according to this embodiment, the energy system 11 incorporating a DER transmits demand curve variations to the energy system 10 as flexibility information. The energy system 10 incorporating a DER selects one of the demand curve variations received from the energy system 11, calculates an operation plan, and returns selection information of the selected demand curve to the energy system 11. The energy system 11 then executes an operation plan corresponding to the demand curve selected by the energy system 10. By coupling the operation plans in this manner, the flexibility management system 100 can fully utilize the operational flexibility of the DER and sufficiently reduce energy costs. Furthermore, the flexibility management system 100 can shorten the calculation time of the operation plan without performing convergence calculations by adding constraints and objective functions specific to the generation and selection of demand curve variations to the optimization problem. Furthermore, by applying the flexibility management system 100 to a hierarchical EMS consisting of a higher-level EMS and a lower-level EMS, the operation plans of both the higher-level EMS and the lower-level EMS can be coupled even when the higher-level EMS and the lower-level EMS are not integrated.

[0158] Second Embodiment The following describes a flexibility management system 200 according to a second embodiment of the present invention. Fig. 10 is a block diagram showing the functional configuration of the flexibility management system 200 according to this embodiment.

[0159] As shown in FIG. 10, the flexibility management system 200 includes an energy coordination system 80 and energy systems 81 to 83. Each of the energy systems 81 to 83 is an example of a first energy system and has a configuration similar to that of the energy system 11 shown in FIG. 1, so a repeated description will be omitted. Note that the DERs included in each of the energy systems 81 to 83 are not necessarily of the same type. Each of the energy systems 81 to 83 may be any system, such as a building system, factory system, or transportation system, which is a complex combination of an air conditioning system, a cogeneration system, a heat pump water heater, a storage battery, and a charger linked to an EV vehicle. Furthermore, the number of energy systems having a configuration similar to that of the energy system 11 is not limited to three and may be any number.

[0160] The energy coordination system 80 may have the same configuration as the energy system 10, or may be a system that does not include DERs. In this embodiment, an example in which the energy coordination system 80 has a different configuration from the energy system 10 will be described.

[0161] Energy coordination system 80 aims to coordinate energy systems 81 to 83. The operator of energy coordination system 80 is, for example, an energy manager who oversees multiple buildings and plants, a regional system operator such as a microgrid, or a retail electricity supplier. The operator of energy coordination system 80 is, for example, an aggregator of adjustment or supply capacity, an EaaS (Energy as a Service) / EFaaS (Energy & Facility Management as a Service) operator that provides and operates DERs, an EV charging operator, or an EV fleet operator.

[0162] As can be seen from a comparison between Fig. 10 and Fig. 1, the energy coordination system 80 does not include the operation plan DB 105 and the operation plan execution unit 106 shown in Fig. 1. In addition, the energy coordination system 80 includes a coordination solution calculation unit 804. Here, a duplicated description of each component that is the same as the component shown in Fig. 1 will be omitted.

[0163] The communication unit 102 of the energy coordination system 80 receives flexibility information, that is, demand curve variations, from each of the energy systems 81 to 83 and stores the information in the external demand curve variation DB103.

[0164] The cooperative solution calculation unit 804 is connected to the operating condition DB 101 and the external demand curve variation DB 103. The cooperative solution calculation unit 804 is also connected to the communication unit 102. Based on the operating condition data stored in the operating condition DB 101, the cooperative solution calculation unit 804 selects and combines one demand curve from the corresponding demand curve variations for each of the multiple first energy systems (energy systems 81 to 83), and calculates a cooperative operation plan using an objective function associated with the cooperative operation plan.

[0165] The cooperative solution calculation unit 804 transmits demand curve selection information indicating the selected demand curve to each energy system (energy systems 81 to 83). On the other hand, each energy system executes an operation plan for its own system corresponding to the selected demand curve based on the demand curve selection information received from the energy cooperative system 80.

[0166] [Cooperative operation planning process] Next, a cooperative operation planning process for calculating a cooperative operation plan in the cooperative solution calculation unit 804 will be described. In this embodiment, it is assumed that the objective function for calculating the initial solution and the objective function for suboptimal calculation in the energy systems 81 to 83 are functions whose objective is to minimize the electricity fee. It is also assumed that the objective function used to calculate the cooperative operation plan in the energy cooperative system 80 is a function whose objective is to minimize CO2 emissions. The constants used in the cooperative operation planning process include, for example, the constants described below.

[0167] Y TOU_cooperation f : Time-of-use cost, i.e., the cost per unit of electricity. Y TOU_cooperation fare stored in the operating condition DB 101. Here, an example will be described in which the time period cost is a CO2 emission coefficient (kg-CO2 / kWh) that changes for each time period depending on the power source configuration. The estimation range of the CO2 emission coefficient may be, for example, the entire power system, a specific power transmission and distribution system area, a consumer with an on-site renewable energy power source, or a multi-site energy system consisting of multiple consumers and renewable energy power sources.

[0168] W demand_variation e,n,f : Demand curve variation (kWh), that is, time series data of the demand curve variation calculated in the energy systems 81 to 83. W demand_variation e,n,f That is, the flexibility information is stored in the external demand curve variation DB 103. Here, the subscript e is the identification number of each energy system connected to the energy coordination system 80. The flexibility information has the same format as that shown in FIG. 7. However, in order to distinguish between each energy system (energy systems 81 to 83), the flexibility information includes the identification number of each energy system. That is, in this embodiment, the flexibility information includes identification information of each energy system, identification information of the demand curve variation, and time-series data representing the demand curve corresponding to the demand curve variation.

[0169] The design variables used in the coordinated operation planning process are as follows: w demand_cooperation f : The total value of electricity demand per time frame (kWh). s demand_variation e,n : A binary variable (1: selected, 0: not selected) that manages the selection of the demand curve variation for each of the energy systems 81 to 83.

[0170] The objective function used in the coordinated operation planning process is to minimize the CO2 emission coefficient expressed by the following equation (25).

[0171]

number

[0172] The constraints used in the coordinated operation planning process will be explained using equations (26) and (27).

[0173]

number

[0174] w in equation (26) demand_cooperation f is a value obtained by adding up the demand curve variations selected for each of the energy systems 81 to 83. E is a set of each energy system (energy systems 81 to 83) connected to the energy coordination system 80.

[0175]

number

[0176] Equation (27) is a binary variable s corresponding to each of the energy systems 81 to 83. demand_variation e,n By constraining the sum of the variables to 1, one variable is constrained to 1 and the remaining variables are constrained to 0. By using equation (27), the demand curve selected for each energy system is constrained to one. The binary variable s demand_variation e,n is demand curve selection information returned from the energy coordination system 80 to each of the energy systems 81 to 83. The demand curve selection information has the same format as that shown in FIG. 7. However, in order to distinguish between the energy systems 81 to 83, the demand curve selection information includes an identification number for each energy system. That is, in this embodiment, the demand curve selection information includes identification information for each energy system, identification information for a demand curve variation, and information indicating whether the demand curve variation has been selected.

[0177] The cooperative solution calculation unit 804 selects one variation of the optimal demand curve for each of the energy systems 81 to 83 in order to minimize the objective function (for example, CO2 emissions) based on the objective function and constraint conditions of equations (25) to (27). Demand curve selection information is transmitted to the energy systems 81 to 83 via the communication unit 102. The energy systems 81 to 83 each execute an operation plan corresponding to the demand curve selected by the energy cooperative system 80.

[0178] As described in the first embodiment, the energy systems 81 to 83 execute an operation plan variation generation process using the electricity price as an objective function, and the energy coordination system 80 executes a coordinated operation planning process using the CO2 emissions as an objective function. In this way, it is possible to coordinate multiple energy systems so as to minimize the electricity price of each energy system (energy systems 81 to 83) and also minimize the CO2 emissions of the aggregate of the energy systems.

[0179] Next, an example of energy system coordination based on a combination of demand curve variations of the energy systems 81 to 83 will be described. FIG. 11 is a diagram showing energy system coordination based on a combination of demand curve variations in the flexibility management system 200 according to the present embodiment. Graphs 91, 92, and 93 shown in FIG. 11 show examples of demand curve variations of the energy systems 81, 82, and 83, respectively. Graph 90 is, for example, a power generation curve for solar power generation. The coordination solution calculation unit 804 selects one of the demand curve variations transmitted from each of the energy systems 81 to 83. The coordination solution calculation unit 804 selects, for example, a demand curve 94 in graph 91, a demand curve 95 in graph 92, and a demand curve 96 in graph 93.

[0180] The cooperative solution calculation unit 804 makes the selected combination of the demand curve 94, the demand curve 95, and the demand curve 96 approach the power generation curve of the solar power generation shown in the graph 90. In this way, the amount of solar power generation used can be increased.

[0181] Next, a description will be given of a sequence of energy system coordination in the flexibility management system 200. Fig. 12 is a sequence diagram showing energy system coordination in the flexibility management system 200 according to this embodiment.

[0182] First, operation plan variation generation processes 1011, 1021, and 1031 are executed in each of the energy systems 81 to 83, and the calculated demand curve variations are transmitted to the energy coordination system 80 as flexibility information. Next, the energy coordination system 80 executes a coordinated operation plan process 1001 to select one demand curve for each energy system. Next, the energy coordination system 80 transmits demand curve selection information for each energy system to each piece of flexibility information. Next, each energy system executes operation plan execution processes 1012, 1022, and 1032 to execute an operation plan linked to the demand curve based on the received demand curve selection information.

[0183] Next, a configuration example of a flexibility management system 200a in which energy system coordination is applied to a plurality of consumers will be described. Fig. 13 is a diagram showing a configuration example in which energy system coordination is applied to a plurality of consumers in the flexibility management system 200a according to this embodiment. Flexibility management devices 1101, 1102, and 1103 have the same configuration as the flexibility management device 21 shown in Fig. 2, and are computer devices that manage energy systems 81, 82, and 83, respectively.

[0184] Managed facilities 1115, 1125, and 1135 are managed facilities (DERs) of energy systems 81, 82, and 83, respectively, and are connected to the power grid 1105 via power receiving points. Consumers 1110, 1120, and 1130 correspond to energy systems 81, 82, and 83, respectively. Consumer 1110 has a flexibility management device 1101 and managed facility 1115. Consumer 1120 has a flexibility management device 1102 and managed facility 1125. Consumer 1130 has a flexibility management device 1103 and managed facility 1135. The flexibility management devices 1101, 1102, and 1103 are each connected to the flexibility coordination management device 110 via a network 1104. The network 1104 is configured, for example, by a closed circuit network such as a dedicated line, or a public line such as the Internet. The flexibility coordination management device 110 is a computer device that implements the functions of the energy coordination system 80 .

[0185] The supply contracts between the consumers 1110, 1120, and 1130 and the retail electricity supplier are made individually for each consumer. The pricing may also differ depending on the size of the consumer and the rate plan selected. In this case, in the operation plan variation generation process, the flexibility management devices 1101, 1102, and 1103 each determine the time-of-day rate Y TOU fThe differences are as follows. On the other hand, the flexibility coordination management device 110 executes the coordinated operation planning process using CO2 emissions as the objective function, as described above, thereby enabling the expansion of renewable energy usage without compromising the economic rationality of each consumer. For example, if the power system 1105 is a single distribution system and a renewable energy source 1106 is connected to the power system 1105, the present invention can promote local production and consumption of renewable energy and eliminate reverse power flow congestion at distribution substations. The required CO2 emission coefficient for each distribution system varies depending on the amount of renewable energy generated and the demand curve, and therefore cannot be determined in advance. In contrast, for example, CO2 emissions can be approximately estimated from the statistics of the CO2 emission coefficient of electricity supplied by a retail electricity supplier and the predicted power generation of renewable energy sources, and then applied to the objective function of equation (25).

[0186] The energy coordination system 80 may procure electricity at the market price in the electricity trading market or at a market-linked price linked to the electricity trading market. In this case, each energy system (energy systems 81 to 83) sets the time-of-day electricity rate as a price signal to encourage demand shift to low-price time periods in the objective function for the initial solution calculation and the objective function for the pre-suboptimal calculation. The energy coordination system 80 sets the market price or the market-linked price in the objective function used to calculate the coordinated operation plan. Below, a specific example in which the energy coordination system 80 procures electricity at the market price in the electricity trading market or at a market-linked price linked to the electricity trading market will be described using Figures 14 and 15.

[0187] Fig. 14 is a diagram showing an example configuration in which energy system coordination is applied to multiple tenants at the same power receiving point in a flexibility management system 200b according to this embodiment. The configurations of the flexibility management devices 1201, 1202, and 1203 and the managed facilities 1215, 1225, and 1235 are similar to those of these components shown in Fig. 13, and therefore redundant explanations will be omitted. Furthermore, the flexibility management devices 1201, 1202, and 1203 are each connected to the flexibility coordination management device 120 via a network 1104. The flexibility coordination management device 120 is a computer device that implements the functions of the energy coordination system 80.

[0188] As shown in FIG. 14 , building 125 has common area 1210, tenant area 1220, and tenant area 1230. Furthermore, managed facilities 1215, 1225, and 1235 are connected to an on-premise power grid 1105. The power demand of each of managed facilities 1215, 1225, and 1235 is measured by on-premise meters 1216, 1226, and 1236. On-premise power grid 1105 is connected to power grid 1105 via power receiving point 127. Note that, when building 125 is located downstream from a single power receiving point, it may be a single building or multiple buildings. When building 125 consists of multiple buildings, each tenant area may be a single building.

[0189] Here, it is assumed that the supply contract between the building 125 and the electricity retailer is made collectively by the manager of the building 125, with each receiving point being a unit. The manager charges for each tenant's electricity demand (measured value) measured by the corresponding on-site meter. The electricity demand for the common area 1210 is subject to the manager's expenses, but will be described here in the same way as the tenant's.

[0190] If the supply contract for building 125 is, for example, a market-linked tariff, the administrator can reduce payments to the retail electricity supplier by encouraging each tenant to shift their demand to lower-price time slots. While a time-of-day rate can be set as an example of a price signal to encourage tenants to shift their demand to lower-price time slots, the time-of-day rate that benefits each tenant may differ depending on the tenant's business type and equipment configuration. For example, the flexibility of DERs can be effectively utilized by offering tenant unit 1220 a time-of-day rate that combines an incentive for low-price time slots and a penalty for high-price time slots. On the other hand, even if tenant unit 1230 is offered a time-of-day rate that combines an incentive for low-price time slots and a penalty for high-price time slots, the flexibility cannot be utilized due to equipment constraints during high-price time slots, and the penalty may actually increase the cost burden.

[0191] The trade-off described above can be resolved by presenting different time-of-day rates to each tenant unit. Specifically, each of the flexibility management devices 1201, 1202, and 1203 presents a different time-of-day rate Y to each tenant unit in the operation plan variation generation process. TOU f As an example of setting the time-of-day charges, for example, a time-of-day charge with a large difference is applied to the tenant unit 1220, and a time-of-day charge with a small difference is applied to the tenant unit 1230. Also, for example, the time division for switching the metered charge unit price of the time-of-day charge may be adjusted for each tenant unit. On the other hand, in the cooperative operation planning process of the flexibility cooperative management device 120, the time-of-day cost (Y TOU_cooperation f ) is set as the daily market-linked tariff. By doing so, it is possible to encourage demand shifts in a way that benefits each tenant, and by combining this demand curve variation, it becomes possible for the administrator to reduce payments to the retail electricity supplier.

[0192] Fig. 15 is a diagram showing an example of a configuration in which energy system coordination is applied to multiple consumers who receive power supply from the same electricity retailer in the flexibility management system 200c according to this embodiment. As can be seen by comparing Fig. 15 with Fig. 13, the difference from the example configuration shown in Fig. 13 is that the flexibility coordination management device 130 belongs to the electricity retailer 1300. Note that the power receiving point of the consumer is not limited to a specific area of ​​the power grid.

[0193] For a retail electricity supplier 1300 that procures from the energy trading market at market unit prices, encouraging consumers 1310, 1320, and 1330 to shift their demand to lower price time periods leads to a reduction in market procurement costs. Time-of-use charges can be set as an example of a price signal to consumers, but depending on the consumer's business type and facility configuration, a time-of-use charge that reduces the cost burden for one consumer may actually increase the cost burden for another consumer.

[0194] The trade-off described above can be resolved by presenting different time-of-day rates to each consumer. Specifically, each of the flexibility management devices 131, 132, and 133 presents a different time-of-day rate Y to each consumer in the operation plan variation generation process. TOU f On the other hand, in the cooperative operation planning process of the flexibility cooperative management device 130, the time period cost (Y TOU_cooperation f ) as the market unit price, it is possible to encourage demand shifts in a way that benefits each consumer, and by combining this demand curve variation, it is possible to reduce the market procurement costs of the 1,300 electricity retailers.

[0195] Next, an application example in which the flexibility management system 200 is applied to a regional EMS and multiple consumer EMSs will be described. Fig. 16 is a diagram showing an application example in which the flexibility management system 200 according to this embodiment is applied to a regional EMS and multiple consumer EMSs. The regional EMS 160 shown in Fig. 16 corresponds to the energy coordination system 80 shown in Fig. 10. The multiple consumer EMSs 161 shown in Fig. 16 correspond to the energy systems 81 to 83 shown in Fig. 10. The consumer EMS 161 transmits, for example, demand curve variation and cost data (see Fig. 7) calculated using the electricity rate as an objective function to the regional EMS 160.

[0196] The regional EMS 160 selects one demand curve from the received demand curve variations and transmits it to each consumer EMS 161, using, for example, CO2 emissions as an objective function. If the regional EMS 160 selects a demand curve variation that does not minimize the cost, the operator of the regional EMS compensates for the increased cost, and the regional EMS 160 notifies (transmits) the cost increase settlement data to each consumer EMS 161. This allows the optimal demand curve variation to be selected and combined from a regional perspective without compromising the economic rationality of the consumer. The operator of the regional EMS may be, for example, a local government, a chamber of commerce, a cooperative, or a regional grid operator. By using the consumer EMS 161's electricity rate as an objective function and the regional EMS 160's CO2 emissions as an objective function, consumers can contribute to regional CO2 reduction within the scope of their DER operational flexibility while pursuing their own economic rationality.

[0197] Next, an application example in which the flexibility management system 200 is applied to an electricity retailer EMS and multiple consumer EMSs will be described. FIG. 17 is a diagram showing an application example in which the flexibility management system 200 according to this embodiment is applied to an electricity retailer EMS and multiple consumer EMSs. The electricity retailer EMS 170 shown in FIG. 17 corresponds to the energy coordination system 80 shown in FIG. 10. The consumer EMS 171 shown in FIG. 17 corresponds to the energy system 81 shown in FIG. 10. The consumer EMS 171 transmits, for example, demand curve variation and cost data (see table 72 in FIG. 7) calculated using the electricity charge based on the rate setting in the supply contract as an objective function to the electricity retailer EMS 170.

[0198] The electricity retailer EMS170 selects one demand curve from the received demand curve variations and transmits it to each consumer EMS171, using, for example, market procurement, whose procurement unit price fluctuates over time, or relative procurement of renewable energy, whose procurement amount fluctuates over time, as an objective function. The electricity retailer EMS170 can reduce procurement costs and improve the supply rate of renewable energy by combining market procurement, whose procurement unit price fluctuates over time, or relative procurement of renewable energy, whose procurement amount fluctuates over time, within the range of the operation freedom of each consumer EMS171's DER.

[0199] Furthermore, if the electricity retailer EMS 170 selects a demand curve variation that does not have the lowest cost, the electricity retailer EMS 170 establishes a mechanism for compensating for the increased cost and notifies (transmits) the increased cost settlement data to each consumer EMS 171. In this way, the electricity retailer EMS 170 can select and combine demand curve variations that contribute to reducing procurement costs and improving the renewable energy supply rate, without impairing the economic rationality of each consumer EMS 171. In particular, with regard to procurement costs, if the reduction in the procurement costs exceeds the increased cost to be compensated to the consumer, it is an economically rational choice for the electricity retailer.

[0200] [effect] As described above, in the flexibility management system 200 according to this embodiment, the cooperative solution calculation unit 804 of the energy cooperative system 80 selects and combines demand curve variations received from the energy systems 81 to 83, thereby enabling cooperative operation of DERs across multiple energy systems. Furthermore, the cooperative solution calculation unit 804 notifies the regional EMS 160 of the cost increase compensated by the operator of the regional EMS, thereby enabling the selection and combination of optimal demand curve variations from a regional perspective without impairing the economic rationality of consumers. Furthermore, the cooperative solution calculation unit 804 selects and combines demand curve variations that contribute to reducing procurement costs and increasing the renewable energy supply rate, thereby enabling the reduction of procurement costs and the expansion of renewable energy use.

[0201] It should be noted that the present invention is not limited to the above-described embodiments, and various other applications and modifications are possible as long as they do not deviate from the gist of the present invention as set forth in the claims. For example, the above-described embodiments have described in detail and specifically the configuration of the flexibility management system in order to clearly explain the present invention, and are not necessarily limited to having all of the described configurations. Furthermore, it is possible to replace part of the configuration of the embodiments described here with the configuration of other embodiments, and it is also possible to add the configuration of one embodiment to the configuration of another embodiment. Furthermore, it is also possible to add, delete, or replace part of the configuration of each embodiment with other configurations. In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and do not necessarily show all the control lines and information lines in the product. In reality, it can be assumed that almost all components are interconnected.

[0202] Furthermore, in each of the above-described embodiments, when referring to the number of elements (including the number, numerical value, amount, range, etc.), unless otherwise specified or when it is clearly limited to a specific number in principle, it is not limited to that specific number and may be more or less than the specific number.

[0203] Furthermore, the system or device in each of the above-described embodiments may be a physical computer system (one or more physical computers), or may be a system built on a group of computing resources (plural computing resources) such as a cloud platform. The computer system or group of computing resources may include one or more interface devices (including, for example, a communication device and an input / output device), one or more storage devices (including, for example, a memory (main memory) and an auxiliary storage device), and one or more arithmetic devices.

[0204] Furthermore, in each of the above-described embodiments, when a function is realized by executing a program by a computing device, the function may be at least a part of one or more computing devices, since the specified processing is performed using a storage device and / or an interface device, etc. Processing described using a function as the subject may be processing performed by a system including one or more computing devices.

[0205] In each of the above-described embodiments, the program may be installed from a program source. The program source may be, for example, a program distribution computer or a computer-readable storage medium (e.g., a computer-readable non-transitory storage medium). The description of each function is an example, and multiple functions may be combined into one function, or one function may be divided into multiple functions. [Explanation of symbols]

[0206] 10,11,81,82,83...Energy system, 100,200...Flexibility management system, 101,105,111...Operating condition DB, 102,115...Communication unit, 103...External demand curve variation DB, 104...Coupled solution calculation unit, 106...Operation plan execution unit, 112...Initial solution calculation unit, 113...Sub-optimal solution calculation unit, 114...Operation plan variation DB, 116...Operation plan execution unit, 80...Energy coordination system, 804...Cooperative solution calculation unit, 20,21...Flexibility management device, 201,211...Calculation unit, 202...Storage device, 203,213...Communication device, 204,214...Input / output device, 205,215...Facility to be managed

Claims

1. an initial solution calculation unit that calculates an initial solution of an operation plan including an operation plan of a distributed energy resource and a demand curve corresponding to the operation plan based on operation condition data stored in an operation condition database; a quasi-optimal solution calculation unit that calculates a plurality of quasi-optimal solutions that differ from an existing solution of the operation plan that includes the initial solution; an operation plan variation database that stores the initial solution and a plurality of the sub-optimal solutions as the existing solutions. Flexibility Management System.

2. The first energy system including the initial solution calculation unit, the quasi-optimal solution calculation unit, and the operation plan variation database uses demand curve variations including the demand curves of all the existing solutions as flexibility information. The flexibility management system of claim 1 .

3. The initial solution calculation unit calculates the initial solution using an objective function for initial solution calculation and a constraint condition for initial solution calculation related to optimization of the operation plan, The quasi-optimal solution calculation unit adds a term that evaluates the orthogonality between the demand curve of the quasi-optimal solution and the demand curve of the existing solution to the objective function for initial solution calculation to set it as an objective function for quasi-optimal calculation, adds a constraint on the amount of change in the objective function for initial solution calculation to the constraint conditions for initial solution calculation to set it as a constraint condition for quasi-optimal calculation, and calculates the quasi-optimal solution using the objective function for quasi-optimal calculation and the constraint conditions for quasi-optimal calculation. The flexibility management system of claim 2.

4. The second energy system that receives the flexibility information from the first energy system includes a coupled solution calculation unit that selects one of the demand curve variations received based on operating condition data stored in an operating condition database of its own system, uses the selected demand curve as a constraint condition, and calculates an operation plan using an objective function used to calculate an operation plan for a distributed energy resource of its own system, transmits demand curve selection information indicating the selected demand curve to the first energy system, and executes the operation plan calculated by the coupled solution calculation unit, The first energy system executes an operation plan of the first energy system corresponding to the selected demand curve based on the demand curve selection information received from the second energy system.

4. The flexibility management system of claim 3.

5. the objective function for initial solution calculation and the objective function for sub-optimal calculation are functions for minimizing electricity charges or functions for minimizing CO2 emissions, the flexibility information includes identification information of the demand curve variation and time-series data representing a demand curve corresponding to the demand curve variation; The demand curve selection information includes identification information of the demand curve variation and information indicating whether the demand curve variation has been selected.

5. The flexibility management system of claim 4.

6. The energy coordination system receives the flexibility information from each of the plurality of first energy systems, and includes a coordination solution calculation unit that selects and combines one demand curve from the corresponding demand curve variations for each of the plurality of first energy systems based on operating condition data stored in an operating condition database of the system itself, and calculates the coordination operation plan using an objective function associated with the coordination operation plan, and transmits demand curve selection information indicating the selected demand curve to each of the corresponding first energy systems, Each of the first energy systems executes an operation plan for its own system corresponding to the selected demand curve based on the demand curve selection information received from the energy coordination system.

4. The flexibility management system of claim 3.

7. the objective function for calculating the initial solution and the objective function for calculating the sub-optimal solution are functions that aim to minimize electricity charges, and the objective function used in calculating the cooperative operation plan is a function that aims to minimize CO2 emissions, the flexibility information includes identification information of the first energy system, identification information of the demand curve variation, and time-series data representing a demand curve corresponding to the demand curve variation; The demand curve selection information includes identification information of the first energy system, identification information of the demand curve variation, and information indicating whether the demand curve variation has been selected.

7. The flexibility management system of claim 6.

8. When the energy cooperation system procures electricity at a market price in the electricity trading market or a market-linked price linked to the electricity trading market, each of the plurality of first energy systems sets a time-of-day rate unit price of electricity as a price signal that prompts a demand shift to a low-price time period in the objective function for the initial solution calculation and the objective function for the sub-optimal calculation; The energy coordination system sets the market unit price or the market-linked unit price as an objective function used to calculate the coordinated operation plan.

7. The flexibility management system of claim 6.

9. calculating an initial solution for an operation plan including an operation plan for a distributed energy resource and a demand curve corresponding to the operation plan based on operating condition data stored in an operating condition database; calculating a plurality of sub-optimal solutions that differ from an existing solution of the operation plan that includes the initial solution; storing the initial solution and a plurality of the sub-optimal solutions as the existing solutions. Flexibility management methods.

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