JP2014096866A - Energy management system, energy management method, program, and server device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an energy management system in which an on-vehicle battery is effectively utilized to achieve effective energy utilization.SOLUTION: The energy management system has a client and a server. The server has an acquisition section, a prediction section, a calculation section, and a control section. The acquisition section acquires data related to an electrical device which includes an energy production device having a power generation unit, a variable capacitance type power storage system in which surplus power can be stored, and a consumption device. The prediction section predicts energy demand amount and production amount in a building. The calculation section calculates operation plan of the electrical device to minimize surplus power to be discarded in accordance with full charging of the power storage system and optimize energy consumption in the building. The control section transmits control information of the electrical device to the client, on the basis of the operation plan. The power storage system includes a storage battery and an on-vehicle battery.

Description

  Embodiments described herein relate generally to an energy management system, an energy management method, a program, and a server device that manage an energy balance on the customer side.

  In recent years, attention has been focused on distributed power sources such as photovoltaic power generation (PV) systems, storage batteries, and fuel cells (FC), against the backdrop of growing awareness of environmental conservation and concerns over power shortages. . In order to effectively connect these new energy devices to an existing power supply system, a home energy management system (HEMS) has been widely spread.

  On the other hand, technological development related to electric vehicles is also active. The battery mounted on this type of vehicle has made significant progress compared to the conventional one, and it is considered that the power is supplied to the home at the time of a power failure, for example, by taking advantage of its performance.

JP 2012-85406 A JP 2012-120295 A

  A technology combining an in-vehicle battery of an electric vehicle (hereinafter referred to as EV) and HEMS is still in the process of development. In particular, there is a demand for an energy management method that comprehensively considers household electrical devices and in-vehicle batteries.

  An object is to provide an energy management system, an energy management method, a program, and a server device that make it possible to effectively use an in-vehicle battery, thereby effectively using energy.

  According to the embodiment, the energy management system includes a client device and a server device that can communicate with the client device. The server device includes an acquisition unit, a prediction unit, a calculation unit, and a control unit. The acquisition unit acquires, from the client device, data related to an electric device including an energy production device including a power generation unit that generates electric power, a capacity-change-type power storage system capable of storing electric power, and an energy consuming device. The prediction unit predicts the energy demand amount and the energy production amount in the building where the client device is installed based on the data. The calculation unit calculates the operation plan of the electrical equipment based on the energy demand and the energy production to optimize the energy consumption in the building under the condition of minimizing the surplus power discarded when the power storage system is fully charged To do. A control part transmits the control information for controlling an electric equipment to a client apparatus based on the calculated operation plan. The power storage system includes a storage battery installed in a building and an in-vehicle battery that can be electrically connected to the storage battery.

FIG. 1 is a diagram illustrating an example of a system according to the embodiment. FIG. 2 is a diagram illustrating an example of an energy management system according to the embodiment. FIG. 3 is a functional block diagram showing the main parts of the cloud computing system 300 and the HEMS according to the first embodiment. FIG. 4 is a diagram for explaining the control target model 300g according to the first embodiment. FIG. 5 is a flowchart showing a processing procedure in the first embodiment. FIG. 6 is a conceptual diagram showing an example of gene design of the genetic algorithm according to the first embodiment. FIG. 7 is a flowchart illustrating an example of the flow of the optimization calculation according to the first embodiment. FIG. 8 is a diagram for explaining the effects obtained by the first embodiment. FIG. 9 is a functional block diagram showing the main parts of the cloud computing system 300 and the HEMS according to the second embodiment. FIG. 10 is a diagram for explaining a control target model 300g according to the second embodiment. FIG. 11 is a conceptual diagram showing an example of gene design according to the second embodiment. FIG. 12 is a functional block diagram showing the main parts of the cloud computing system 300 and the HEMS according to the third embodiment. FIG. 13 is a diagram for explaining a control target model 300g according to the third embodiment. FIG. 14 is a diagram for explaining an effect obtained by the embodiment. FIG. 15 is a diagram for explaining an effect obtained by the embodiment.

  FIG. 1 is a diagram illustrating an example of a system according to the embodiment. FIG. 1 shows an example of a power transmission and distribution network known as a so-called smart grid. In an existing power distribution network, existing power plants such as nuclear power, thermal power, and hydropower are connected to a wide variety of consumers such as ordinary households, buildings, and factories through a power network. In the next-generation distribution network, in addition to these, distributed power sources that combine renewable energy such as sunlight and wind power and power storage devices as new power sources, and new traffic systems and charging stations as new demand will be used for power transmission and distribution. Connected to the net. These various elements are connected via a communication network and controlled in a centralized manner.

  Systems that intelligently distribute power are collectively referred to as an energy management system (EMS). EMS is classified into several types according to its size. For example, there are HEMS (Home Energy Management System) for general households and BEMS (Building Energy Management System) for buildings. In addition, there are CEMS (Community Energy Management System) for smaller systems or local communities, and FEMS (Factory Energy Management System) for large factories. These systems can be linked to achieve fine power control.

This type of system enables highly coordinated operation among existing power plants, distributed power sources, renewable energy sources such as solar and wind power, and consumers. As a result, a new and smart power supply service such as an energy supply system mainly composed of natural energy and a consumer-participation type energy supply and demand through two-way cooperation between a customer and a business operator is created.
FIG. 2 is a diagram illustrating an example of an energy management system according to the embodiment. The HEMS includes a client system and a cloud computing system (hereinafter abbreviated as a cloud) 300 as a server system capable of communicating with the client system. Below, an example of the energy management with respect to the consumer supplied with electric power from a power distribution network is taken up. The consumer is equipped with electrical equipment, and the electrical equipment is supplied with power from the distribution network.

  The client system is formed with a home gateway (HGW) 7 installed in the customer 100 as a core. The home gateway 7 has a function of communicating with the cloud computing system 300. For example, the home gateway 7 can request the cloud computing system 300 for a service for optimizing the energy consumption of the electric equipment of the consumer 100.

  A cloud computing system (hereinafter abbreviated as “cloud”) 300 includes a server computer SV and a database DB. The server computer SV may be single or plural. The database DB may be provided in one server computer SV or may be distributed in a plurality of server computers SV.

  In FIG. 2, electric power (AC voltage) supplied from the electric power system 6 is distributed to each home through a power pole transformer 61 and the like, and through a watt-hour meter (smart meter) 19, a distribution board 20 of the customer 100. To be supplied. The watt-hour meter 19 is a function for measuring the amount of power generated by energy production equipment provided in the customer 100, the amount of power consumed by the customer 100, the amount of power flowing from the power system 6, or the amount of power flowing backward to the power system 6. Is provided. As is well known, the power generated based on the renewable energy is allowed to flow back to the power system 6.

  The distribution board 20 supplies power to the electrical equipment (lighting, air conditioner, heat pump water heater (HP), etc.) 5 and the power conditioning system (PCS) 104 connected to the distribution board 20 via the distribution line 21. Supply. Moreover, the distribution board 20 is provided with the measuring device which measures the electric energy for every feeder.

  The electric device 5 is a device that can be connected to the distribution line 21 in the customer 100, including electric vehicles EV and PV system 101, devices that consume electric power, devices that generate electric power, and electric power Any device that consumes and generates the power corresponds to the electrical device 5. The electric device 5 is detachably connected to the distribution line 21 via an outlet (not shown), and is connected to the distribution board 20 via the distribution line 21.

  Solar panels are installed on the roof and outer wall of the customer 100 to form the PV system 101. The DC voltage generated in the PV system 101 is supplied to the PCS 104. The PCS 104 supplies this direct-current voltage to the storage battery 102 in order to charge the storage battery 102 that is deferred for each consumer 100. The PV system 101 is an energy creation device that produces energy for operating the electrical device 5 from renewable energy, and a wind power generation system and the like also fall within its category.

  In contrast, a fuel cell (FC) 103 is a power generation unit that produces electric power from fossil fuels such as city gas, that is, non-renewable energy. Since the electric power generated by the non-renewable energy is prohibited from flowing backward to the electric power system 6, surplus electric power may be generated. The surplus power can be charged to the storage battery 102 or the EV in-vehicle battery, but cannot be charged beyond the limit. When the storage battery 102 and the in-vehicle battery are fully charged, surplus power is converted into heat and discarded, resulting in wasted energy and costs (such as gas charges). In the embodiment, a technique capable of avoiding such a situation will be described.

  The storage capacity of the storage battery 102 installed at the consumer 100 is usually fixed. The same applies to in-vehicle batteries, which can be regarded as fixed-capacity power storage devices when viewed alone. However, if these power storage devices are generally regarded as a system, it can be considered as a power storage system whose capacity changes. The capacity of the power storage system increases or decreases depending on whether or not EV is connected to the distribution line 21 of the customer 100, and also changes depending on the number of EVs and the performance of the in-vehicle battery. In the embodiment, it is assumed that the capacity of the power storage system changes as described above.

  The PCS 104 includes a converter (not shown), converts AC power from the distribution line 21 into DC power, and supplies it to the storage battery 102 and EV. And the electric power sent from the electric power grid | system 6 can be charged to the storage battery 102 at midnight. Further, the PCS 104 includes an inverter (not shown), and converts DC power supplied from the storage battery 102, in-vehicle battery or fuel cell (hereinafter referred to as FC unit) 103 into AC power and supplies it to the distribution line 21. As a result, the electric device can also receive power supply from the storage battery 102 and the fuel cell 103 via the PCS 104.

  Further, the PCS 104 also distributes power to a charging outlet that can be connected to the EV. This makes it possible to charge and discharge the on-vehicle battery. In short, the PCS 104 has a function as a power converter for transferring energy between the storage battery 102, the EV, the fuel cell 103, and the distribution line 21. The PCS 104 also has a function of stably controlling the storage battery 102 and the fuel cell 103.

  The customer 100 is provided with a communication line such as a LAN (Local Area Network) and the home network 25 is formed. The home gateway 7 is detachably connected to both the home network 25 and the IP network 200 via a connector (not shown) or the like. As a result, the home gateway 7 can communicate with the watt-hour meter 19, the distribution board 20, the PCS 104, and the electrical device 5 connected to the home network 25. The home network 25 may be a wired line or a wireless line.

  The home gateway 7 includes a communication unit 7a as a processing function according to the embodiment. The communication unit 7 a transmits various data to the cloud 300 and receives various data from the cloud 300.

  The home gateway 7 is a computer having a Central Processing Unit (CPU) and a memory (not shown). The memory stores a program including instructions for communicating with the cloud 300, requesting the cloud 300 to calculate an operation plan related to the operation of the electric device, and reflecting the user's intention in the control of the system. . The functions of the home gateway 7 are realized by the CPU functioning based on various programs.

  That is, the home gateway 7 transmits various data to the cloud 300 and receives various data from the cloud 300. The home gateway 7 is a client device that can communicate with the cloud 300 and the server computer SV. The various data transmitted from the home gateway 7 includes request signals for requesting the cloud 300 to perform various calculations.

  The home gateway 7 is connected to the terminal 105 via a wired line or a wireless line. The home gateway 7 and the terminal 105 can be combined to realize the function as the client device. The terminal 105 may be a so-called touch panel or the like, for example, a general-purpose portable information device or a personal computer.

  The terminal 105 displays the operating status and power consumption of the electric device 5, the fuel cell 103, the storage battery 102, and the PV device 101 on, for example, an LCD (Liquid Crystal Display) or notifies a consumer (user) by voice guidance or the like. The terminal 105 includes an operation panel, and accepts various operations and setting inputs by a consumer.

  The IP network 200 is the so-called Internet or a VPN (Virtual Private Network) of a system vendor. The home gateway 7 can communicate with the server computer SV via the IP network 200 and exchange data with the database DB. The IP network 200 may include a wireless or wired communication infrastructure for forming a bidirectional communication environment between the home gateway 7 and the cloud 300.

  The cloud 300 includes a collection unit 300a, a prediction unit 300b, a calculation unit 300c, and a control unit 300d. Further, the control target model 300g and various types of data 300h are stored in the database DB of the cloud 300. The collection unit 300a, the prediction unit 300b, the calculation unit 300c, and the control unit 300d are functional objects distributed and arranged in a single server computer SV or the cloud 300. Those skilled in the art will readily understand how to implement these functional objects in the system.

  For example, the collection unit 300a, the prediction unit 300b, the calculation unit 300c, and the control unit 300d are realized as programs executed by the server computer SV of the cloud 300. This program can be executed by a single computer or can be executed by a system including a plurality of computers. Various functions according to the embodiment are realized by executing the instructions described in the program.

  The collection unit 300a acquires various data related to the electrical equipment 5 of the customer 100 from the home gateway 7 of each customer 100. The acquired data is stored as data 300h in the database DB. The data 300h includes the power demand amount of each customer 100, the power consumption amount of each electric device 5, the hot water supply amount, the operating state, the remaining charge and charge / discharge power of the storage battery 102 and the in-vehicle battery 106, the power generation amount of the PV system 101, etc. including. These data are used for prediction of energy demand, etc. as data relating to equipment connected to the distribution line 21 of the customer 100.

  The prediction unit 300b predicts the hourly energy demand of each electrical device 5 and the hourly total energy demand of the consumer 100 based on the data acquired by the collection unit 300a. Specifically, the prediction unit 300 predicts the power demand amount, the hot water supply demand amount, the PV power generation amount, etc. of the customer 100.

  The calculation unit 300c is configured to charge the storage battery 102 and the in-vehicle battery 106 from the control target model 300g including the storage battery 102, the in-vehicle battery 106, and the fuel cell 103 of the customer 100, and the predicted power demand, hot water supply demand, and PV power generation. The discharge schedule and the power generation schedule of the fuel cell 103 are calculated.

  That is, the calculation unit 300c determines the operation of the electric device 5 so as to optimize the energy in the customer 100 based on the predicted energy demand. That is, the calculation unit 300c calculates an operation plan related to the operation of the electrical device 5 that can optimize the energy balance of the customer 100 based on the predicted energy demand. This process is called optimal scheduling.

  The energy balance is, for example, a utility bill, and is an amount that is evaluated based on a balance between the cost of power energy consumed by the electrical equipment 5 and the power sales fee of energy generated mainly by the PV system 101. The calculated time-series operation plan of the electric device 5 is stored in the database DB.

  The control unit 300d generates control information for controlling the electric device 5 based on the calculated operation plan. That is, the controller 300d generates an operation / stop instruction, an output target value, and the like for charging / discharging and operation of the storage battery 102 and the in-vehicle battery 106 and power generation of the fuel cell 103 from the result of the optimal scheduling. Such control information is transmitted to the terminal 105 and the home gateway 7 via the communication line 40.

  The terminal 105 of the customer 100 includes an interface unit (interface unit 105a in FIG. 3) for reflecting the intention of the customer in the control of the electric device 5 based on the control information transmitted from the control unit 300d. The interface unit 105 a includes a display for displaying a charge / discharge schedule of the storage battery 102 and a power generation schedule of the fuel cell 103. The user can check the schedule by looking at the contents displayed on the display, and can select whether to allow or reject the execution of the displayed schedule. Thereby, a user's intention can be reflected in execution of a schedule.

In addition, the user can input an instruction (command) for requesting recalculation of the schedule to the cloud 300 or giving information necessary for this to the system via the interface unit 105a.
In the above configuration, it can be understood that the server computer is positioned as a master device, and the home gateway is positioned as a slave device that receives a control signal from the master device. Next, a plurality of embodiments will be described based on the above configuration.

[First Embodiment]
FIG. 3 is a functional block diagram illustrating a main part of the HEMS according to the first embodiment. In FIG. 3, the electric power consumption, operation state, remaining charge amount and charge / discharge power amount of the storage battery 102 for each electric device 5 of the customer 100, and remaining charge amount of the in-vehicle battery (designated 106) of EV Data such as the charge / discharge power amount, the operating status of the FC unit 103, the power demand amount of the customer 100, the hot water supply demand amount, and the PV power generation amount are regularly or irregularly transmitted to the cloud 300 via the home gateway 7. Is done.

  ECHONET (registered trademark), ECHONET Lite (registered trademark), and the like are known as in-home communication protocols in the customer 100. If it is household appliances 5 provided with such a communication function, home gateway 7 will collect various data via home network 25, and will transmit to cloud 300. In addition, by installing a measuring device having a communication function on the distribution board 20, the home gateway 7 can collect the power consumption and operating state of the electrical device 5. For DC devices such as the PV system 101, the on-vehicle battery 106, the storage battery 102, and the FC unit 103, data can be collected via the PCS 104.

  When the actual data exceeds or falls below the predetermined fluctuation amount related to each demand amount / power generation amount set via the user interface 105a, the home gateway 7 transmits the data to the cloud 300. Irregular means transmission at such timing. Also, the operation history of the terminal 105 is transmitted to the cloud 300. These data and information are stored in the database DB group.

  The prediction unit 300b provided for each consumer uses the power demand amount, the hot water supply demand amount, the PV power generation amount in the collected data, and weather data such as a weather forecast for every predetermined time on the target day. Predict power demand, hot water demand, and PV power generation. Meteorological data is distributed from other servers (such as the Japan Meteorological Agency) at several times a day. You may perform prediction calculation according to the timing which received this weather data.

  Then, the calculation unit 300c provided for each consumer controls the operation of the electric device 5 based on the energy demand amount, the energy supply amount, the energy unit price, the control target model 300g, etc. for each predetermined time calculated by the prediction calculation. The optimum scheduling related to the above is executed.

  The prediction unit 300b and the calculation unit 300c can be implemented as, for example, functional objects provided exclusively for each consumer. That is, the functions of the prediction unit 300b and the calculation unit 300c can be provided for each consumer. For example, such a form is possible by creating a plurality of threads in the program execution process. According to such a form, there is an advantage such as easy security.

  Or it is also possible to implement the prediction part 300b and the calculation part 300c as a functional object provided with respect to a some consumer. That is, the calculation by the prediction unit 300b and the calculation unit 300c can be executed in units of a plurality of consumers. According to such a form, it is possible to obtain merits such as saving of calculation resources.

FIG. 4 is a diagram for explaining the control target model 300g according to the first embodiment. The control target model 300g includes each component of the power system 6, the FC unit 103, the storage battery 102, the in-vehicle battery 106, the PV system 101, and the load (home appliance) 205. The FC unit 103 includes components of an FC main body 220, an auxiliary boiler 221, a reverse power flow prevention heater 222, and a hot water tank 223. Table 1 shows the variables shown in FIG.

The control target model 300g shows an input / output relationship of each component and a relational expression between input variables or output variables between the components. For example, the control target model 300g can be expressed by the following equations (1) to (10).

In the equation (1), the gas supply amount F (t) is shown as the sum of the supply amount F _FC (t) to _FC and the supply amount F _B (t) to the auxiliary boiler. The FC main body 220 generates power by P _FC (t) with respect to the gas supply amount of F _FC (t) and exhausts heat by Q _FC (t). The input / output characteristics of the FC main body 220, that is, the relationship between the gas supply amount, the power generation amount, and the exhaust heat amount in the FC main body 220 are approximated as shown in equations (2) and (3).

The reverse power flow prevention heater 222 converts the surplus power P _H (t) into heat of the amount of heat Q _H (t) and consumes it, that is, discards it so that the surplus power does not flow back into the power system 6. . The auxiliary boiler 221 supplies hot water supply Q _B (t) that cannot be covered by the hot water supply Q _ST (t) from the hot water storage tank 223 in the hot water supply demand.

The hot water storage amount H (t) of the hot water storage tank 223 includes the exhaust heat Q _FC (t) of the FC main body 220, the heat generation amount Q _H (t) of the reverse flow prevention heater 222, and hot water supply as shown in the equation (4). _Increase or decrease by Q _ST (t). The amount of heat lost due to heat dissipation or the like is expressed by hot water storage efficiency r. Equation (5) shows the capacity restriction of the hot water tank 223. The power storage system (storage battery 102, in-vehicle battery 106) can be modeled as a model in which the remaining charge S (t) increases or decreases _{depending on} the charge / discharge power P _SB (t). Particularly in the embodiment, the storage battery 102 and the in-vehicle battery 106 are individually modeled as shown in Table 1.

Formula (6) shows the power supply-demand balance. P _D (t) indicates the power demand of the customer 100, P _C (t) indicates purchased power or sold power, and P _PV (t) indicates the power generation amount of _PV 101. The constraint conditions for prohibiting the reverse power flow from the FC main body 220 and the storage battery 102 in the equations (7) and (8) are shown. Expression (9) shows the constraint condition of the capacity of the power storage system, that is, the sum of the capacity of the storage battery 102 and the capacity of the in-vehicle battery 106.

Expression (10) shows a constraint condition that the change of the power generation amount with respect to time of the FC unit 103 (FC main body 220) is limited within a predetermined range. In other words, the expression (10) indicates that the change in the power generation amount of the FC main body 220 from a certain time t-1 to the next time t is the lower limit of the decrease rate of the FC power generation amount −P _{FC_DOWN} and the increase rate of the FC power generation amount. This is a constraint condition that the upper limit _{PFC_UP} is limited.

  The control unit 300d (equipment operation scheduler) determines the charge / discharge schedule of the storage battery 102, the in-vehicle battery 106, or the operation / stop instruction for power generation of the FC unit 103, the output target value from the optimum scheduling result for a predetermined time. It transmits to the home gateway 7 of the customer 100 every interval (for example, 1 hour).

  The user interface 105a displays each schedule and various information received from the cloud 300 together with a confirmation message to notify the user. By selecting allow / deny for the confirmation message, the user can select whether or not to execute the calculated schedule. The user can also request the cloud 300 to modify the schedule using the user interface 105a.

Furthermore, it is possible to set information such as a scheduled use time zone of EV, a minimum remaining charge amount at the time of departure, and a planned travel distance using the user interface 105a. The condition regarding the operation of the EV is related to the equation (9). In the following equation (9) ′, the remaining charge S (t) of the power storage system is expressed as the sum of the remaining charge S _BAT (t) of the storage battery 102 and the remaining charge S _EV (t) of the in-vehicle battery 106. It shows that. Furthermore, a condition is imposed on S _BAT (t) and the remaining charge S _EV (t) that are not less than the minimum value S _{min and not} more than the maximum value S _max .

However, at the EV use start time, _SEV must be equal to or more than the set minimum charge remaining amount. At the EV use end time, _SEV is a value that is reduced by an amount commensurate with the planned travel distance.

The calculation unit 300c (FIGS. 2 and 3), when given the power / hot water demand and PV power generation, the electricity / gas unit price and the power purchase price under the above conditions, The schedule of power generation P _FC (t) of the FC unit 103 and charge / discharge P _SB (t) of the storage battery system is obtained so that the balance (energy cost) is minimized. For example, a genetic algorithm can be used as the optimization algorithm. Next, the operation of the above configuration will be described.

  FIG. 5 is a flowchart illustrating a processing procedure in the embodiment. The optimization calculation requires power demand prediction, hot water supply demand prediction, PV power generation prediction, and the like, and this optimization calculation is executed at the timing of several times a day when the prediction calculation is executed.

  In FIG. 5, the prediction unit 300b acquires the power demand amount, the hot water supply demand amount, and the PV power generation amount for each predetermined time from the database DB (step S1-1). In this step, not only current data but also past data such as data on the same day of the previous year may be acquired. Next, the prediction unit 300b predicts a power demand amount, a hot water supply demand amount, and a PV power generation amount for each predetermined time for calculating an operation plan (step S1-2).

  Next, the calculation unit 300c calculates a schedule for each predetermined time of the power generation amount of the FC unit 103 and the charge / discharge amounts of the storage battery 102 and the in-vehicle battery 106 so as to minimize the utility bill (step S1-3). The calculated schedule is stored in the database DB.

  Next, the system transmits a message signal including the charge / discharge schedule of the storage battery 102, the in-vehicle battery 106, and the power generation schedule of the FC unit 103 to the terminal 105 via the IP network 200. The terminal 105 decodes the message signal and displays various schedules on the interface (step S1-4). Routines related to message message transmission and display are executed periodically or in response to a request from the user.

  Next, the cloud 300 waits for the arrival of a permission message signal indicating that the execution of the device operation schedule is permitted by the user (step S1-5). If permitted, the equipment operation scheduler (control unit 300d) sends control information for controlling the electric equipment 5 of the customer 100 according to the created schedule via the IP network 200 to the home gateway 7 of the consumer 100. (Step S1-6). The control information includes, for example, charge / discharge of the storage battery 102 and the in-vehicle battery 106, an operation / stop instruction for power generation of the FC unit 103, an output target value, and the like. The above procedure is repeated every time interval of the schedule.

  The control unit (equipment operation scheduler) 300d determines the charge / discharge of the storage battery 102, the in-vehicle battery 106, the operation / stop instruction for power generation of the FC unit 103, the output target value, etc. for each schedule time interval from the result of the optimal scheduling. It is generated and transmitted to the home gateway 7 of the customer 100. The user instructs the system via the user interface 105a whether control is possible based on the transmitted control information.

FIG. 6 is a conceptual diagram illustrating an example of gene design of the genetic algorithm according to the embodiment. In the embodiment, the gene includes the power generation amount P _FC (t) of the FC unit 103, the charge / discharge power P _SB (t) of the storage battery 102, and the charge / discharge power P _EV (t) of the in-vehicle battery 106. . An operation plan for the storage battery 102, the in-vehicle battery 106 and the FC unit 103 for one day is an individual, and a generation is formed from a plurality of individuals.

Equation (11) shows the fitness Fit to be maximized. An operation plan can be calculated by optimizing this Fit as an objective function. The utility bill C is shown in Equation (12), and the cost g (P _FC , P _SB ) required for discontinuity in equipment operation is shown in Equation (13). The total from t = 0 to t = 23 in the utility bill C corresponds to obtaining the sum over 24 hours.

In equation (11), the fitness Fit is the cost g (P _FC , P _SB )> 0 for the discontinuity of the equipment operation to the monotonically increasing function f (C) with the daily energy bill C as a variable. Is added and expressed as the reciprocal thereof. This is for considering the possibility that the utility bill C will be negative if the PV power generation amount greatly exceeds the power demand of the customer 100, and to correspond to the decrease in the utility bill C and the increase in the fitness Fit. . In the embodiment, a function satisfying f (C)> 0 is used.

The power demand, hot water supply demand, PV power generation amount, unit price of electricity charge, unit price of gas charge, and PV purchase price are given to the above formula, and genetic operations such as mutation, crossing, dredging, etc. are repeated to maximize Fit. As a result, the power generation amount P _FC (t) of the FC unit 103 and the charge / discharge P _SB (t) of the power storage system, that is, P _BAT (t) and P _EV (t) are reduced so that the utility bill C is reduced. It becomes possible to ask.

FIG. 7 is a flowchart illustrating an example of the flow of the optimization calculation according to the embodiment. As described above, the calculation unit 300c performs an optimization operation using a genetic algorithm.
(Step S2-1) Generation of Initial Individual Group Based on random or past actual values, n initial individuals that satisfy the constraint condition are generated. Solids that do not satisfy the constraint condition are bit-inverted to modify the gene to satisfy the constraint condition.

(Step S2-2) End determination process This process repeats the processes of steps S2-3 to S2-6. Calculate the fitness of each individual and the average fitness for that generation. If the loop in step S2-2 reaches the specified number of times, the algorithm operation is terminated. Alternatively, the average fitness of the generation is compared with the average fitness of the previous two generations, and if the result is equal to or less than the arbitrarily set value ε, the algorithm is terminated.

(Step S2-3) 淘汰 Select individuals that do not satisfy the constraints. If there are more than a predetermined number of individuals, the number of individuals having poor fitness (small fitness) is selected.

(Step S2-4) Proliferation When the number of individuals is less than the predefined number of individuals, the individual with the best fitness is proliferated.

(Step S2-5) Crossover Pairing is performed at random. Pairing is performed as much as the ratio (crossover rate) to the total number of individuals, and gene loci are randomly selected for each pair and crossed at one point.

(Step S2-6) Mutation Individuals are randomly selected by the ratio (mutation rate) to the total number of individuals, and the genes at arbitrary (randomly determined) loci of each individual are bit-inverted.

  The procedure of (Step S2-2) to (Step S2-6) is repeated while incrementing the number of generations until the condition of the number of generations <the maximum number of generations is satisfied. If this condition is satisfied, the process ends after the result output (step S2-7).

  FIG. 8 is a diagram for explaining an effect obtained by the embodiment. FIG. 8 shows an example of an operation plan for one day of the storage battery 102 and the FC unit 103 calculated based on the prediction result of the daily power demand and hot water supply demand of the customer 100. The unit price of electricity was assumed to be 28 yen / kWh from 7:00 to 23:00 and 9 yen / kWh from 23:00 to 7:00 the next day. In FIG. 8, the calculation result using only the electric power demand, the hot water supply demand, and the electricity and gas unit price is shown without assuming the improvement of the utility bill due to the power sale.

  The operation plan of the storage battery 102 is charged in a time zone where the unit price of electricity is low (0: 00 to 6:00), and is a time zone where the unit price of electricity is high (7:00 to 10:00, 13: 00 to 22:00). ) To discharge. Thereby, since the purchased electric power in the time zone with a high electricity bill unit price decreases, an electricity bill can be reduced.

  The FC unit 103 is operated at the maximum output, and the storage battery 102 is charged with the surplus power generation amount during the time when the power generation amount exceeds the power demand (12: 0 to 14:00). Therefore, it is possible to prevent the generated power from being consumed (discarded) wastefully by the reverse flow prevention heater 222, and it is possible to reduce gas costs. It can be seen that the reverse power flow prevention heater 222 has moved without operating for 24 hours.

  As described above, in the embodiment, the optimization calculation that predicts the PV power generation amount, the power demand amount, and the hot water supply demand amount in the customer 100 and minimizes the evaluation function under the set constraint conditions based on the predicted value. By executing the above, energy management is performed so as to minimize the energy cost. That is, based on a model in which the power generation amount of the FC unit 102 is variable, the reverse power flow prevention heater 222 is wasted by optimizing the operation plan of the FC unit 103 and the charge / discharge schedule of the storage battery 102 and the on-vehicle battery 106. Without it, it becomes possible to reduce the utility cost.

  As shown in equations (11) and (12), the gas charge required for the operation of the FC unit 103 is included in the function indicating the fitness Fit to be maximized. As a result, a schedule that causes the reverse power flow prevention heater 222 to operate wastefully under the condition that there exists a possible solution acts in a direction that is deceived in the process of optimization calculation. That is, it is possible to reduce the number of cases where the reverse power flow prevention heater 222 is operated wastefully.

Further, according to the equation (10), the change amount of the power generation amount of the FC unit 103 from a certain time t-1 to the next time t is _calculated from the lower limit −P _{FC_DOWN} of the decrease rate of the power generation amount of the FC unit 103. Since the constraint condition is set to fall within the range of the upper limit P _{FC_UP} of the increase rate of the power generation amount, it is possible to generate a power generation schedule so that the amount of change in the power generation amount of the FC unit 103 does not exceed the ability to follow the power demand. Become. In other words, due to the above constraint conditions, it is possible to generate the power generation schedule of the FC unit 103 within a range that does not exceed the ability to follow the power demand. That is, the FC unit 103 can be operated according to the power generation schedule set by HEMS.

In particular, by combining the prediction procedure of step S1-2 and the optimal scheduling of step S1-3 (FIG. 5), it is possible to predict power demand, hot water supply demand prediction, and PV power generation prediction for a period of about one day. Accordingly, a supply and demand plan such as an FC power generation schedule and a charge / discharge schedule for the storage battery 102 and the in-vehicle battery 106 can be created in consideration of the overall balance.
Therefore, when both the storage battery 102 and the in-vehicle battery 106 are fully charged and the surplus power of the FC unit 103 cannot be charged, or when the remaining charge of the storage battery 102 and the in-vehicle battery 106 is insufficient and the power is to be self-supplied. It is possible to avoid the case where the battery cannot be discharged.

  As described above, according to the embodiment, it is possible to effectively use surplus power that cannot flow backward to the commercial power system without wasting it. Accordingly, it is possible to provide an energy management system, an energy management method, a program, and a server device that can effectively use a vehicle-mounted battery and prevent energy from being wasted.

[Second Embodiment]
FIG. 9 is a functional block diagram illustrating a main part of the HEMS according to the second embodiment. In FIG. 9, the same reference numerals are given to the portions common to FIG. 3, and only different portions will be described here.

  In the second embodiment, it is assumed that a plurality of EVs are connected to the distribution line 21 of the customer 100. FIG. 9 shows two EV in-vehicle batteries (reference numerals 106a and 106b). Accordingly, the calculation unit 300c calculates the operation schedule of each of the in-vehicle batteries 106a and 106b under comprehensive given conditions including other electric devices (storage battery 102). Below, two EVs are shown as EV1 and EV2, respectively.

FIG. 10 is a diagram for explaining a control target model 300g according to the second embodiment. In the second embodiment, the remaining charge S _EV1 (t) of the in-vehicle battery 106a and the remaining charge S _EV2 (t) of the in-vehicle battery 106b are incorporated in the control target model 300g.

  FIG. 11 is a conceptual diagram showing an example of gene design according to the second embodiment. As apparent from the comparison with FIG. 6, in the second embodiment, since the number of target elements increases in the individual of the gene compared to the first embodiment, various genes are generated. As a result, according to the second embodiment, not only can the response time at the time of a power failure be increased by increasing the capacity of the power storage system, but also power interchange between the two EVs at normal times is possible. Therefore, not only the effect obtained in the first embodiment but also the advantage that the flexibility of system operation can be improved.

[Third Embodiment]
FIG. 12 is a functional block diagram illustrating a main part of the HEMS according to the third embodiment. In FIG. 12, parts that are the same as those in FIG. 3 are given the same reference numerals, and only different parts will be described here. In the third embodiment, a gas engine generator (hereinafter referred to as GE) 107 using a prime mover is applied as an energy production device in place of the FC unit 103.

  FIG. 13 is a diagram for explaining a control target model 300g according to the third embodiment. Compared with FIG. 4, the FC main body 220 is replaced with a generator main body 224, and the FC unit 103 is represented as a GE unit 107.

  A gas engine generator has a characteristic that it is more responsive than a fuel cell, and has a particular advantage. For example, even if the power of the in-vehicle battery 106 is insufficient when suddenly going out in EV, there is an advantage that the insufficient power can be compensated without greatly affecting the optimum schedule from that point. In other words, according to the third embodiment, it is possible to construct a system that can flexibly cope with unexpected situations as well as the effects obtained by the first and second embodiments.

  The present invention is not limited to the above embodiment. For example, in the embodiment, the use of a genetic algorithm has been described, but the genetic algorithm is not the only solution for calculating an operation plan. It is possible to calculate an optimal operation plan using various other algorithms.

Moreover, the operation plan of the electric equipment is not limited to the graph shown in FIG. 8, and various patterns can be calculated. FIG. 14 and FIG. 15 are graphs showing another example of the operation pattern of the electric device 5 and also showing the charge / discharge amount of the EV and the purchased electric energy considering the EV.
FIG. 14 is a graph assuming that the EV is at home all day long, that is, connected to the distribution line 21. According to the storage battery charging power graph and the EV charging / discharging amount graph, power is supplied from the storage battery 102 in the morning (7:00 to 11:00), and from evening to night (17:00 to 22: 00), it can be seen that power is supplied from the in-vehicle battery 106. In addition, it can be seen that the storage battery 102 and the in-vehicle battery 106 are charged from midnight to dawn (22:00 to 5:00).

  FIG. 15 is a graph that assumes that the EV has gone out during the day, that is, it has left the distribution line 21. During the time period when EV is out and the in-vehicle battery 106 cannot be used (8:00 to 19:00), the battery 102 must be relied on, but when the EV returns and is reconnected to the distribution line 21, the operation plan is Recalculated immediately. And it turns out that the vehicle-mounted battery 106 is charged at night (22:00 to 6:00). In both graphs of FIGS. 14 and 15, the power consumption of the reverse power flow prevention heater 222 remains 0, indicating that the power is not wasted.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the scope of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof.

  DESCRIPTION OF SYMBOLS 100 ... Consumer, 300 ... Cloud computing system, 7 ... Home gateway, 7a ... Communication part, SV ... Server computer, DB ... Database, 6 ... Electric power system, 61 ... Transformer, 19 ... Electricity meter, 20 ... Minute Power board, 21 ... distribution line, 5 ... electrical equipment, 104 ... power conditioning system (PCS), 101 ... PV system, 102 ... storage battery, 103 ... fuel cell, EV ... electric car, 106 ... vehicle battery, 25 ... home network , 105 ... terminal, 200 ... IP network, 300a ... collection part, 300b ... prediction part, 300c ... calculation part, 300d ... control part, 300g ... control target model, 205 ... load (home appliance), 220 ... FC main body, 221 ... Auxiliary boiler, 222 ... Reverse power flow prevention heater, 223 ... Hot water storage tank, 300h ... Data, 4 ... communication line, 107 ... gas engine generator, 224 ... generator body

Claims (28)

In an energy management system comprising a client device and a server device capable of communicating with the client device,
The server device
An acquisition unit that acquires data relating to an electric device including an energy production device including a power generation unit that generates electric power, a capacity-change-type power storage system capable of storing electric power, and an energy consumption device from the client device;
A prediction unit that predicts energy demand and energy production in a building where the client device is installed, based on the data;
Calculate the operation plan of the electrical device based on the energy demand and energy production to optimize energy consumption in the building under the condition of minimizing surplus power discarded due to full charge of the power storage system A calculation unit to
A control unit that transmits control information for controlling the electrical device to the client device based on the calculated operation plan;
The power storage system includes:
A storage battery deferred in the building;
An energy management system including an in-vehicle battery that can be electrically connected to the storage battery. The power generation unit is a fuel cell;
2. The energy management system according to claim 1, wherein the calculation unit calculates the operation plan under a condition that also includes limiting a change of the power generation amount of the fuel cell with respect to time within a predetermined range. And a database for holding the acquired data and a control target model of the electrical device,
The energy management system according to claim 2, wherein the calculation unit calculates the operation plan based on data held in the database and a control target model of the fuel cell. The model to be controlled includes the system, the storage battery, the in-vehicle battery, the fuel cell, and an auxiliary boiler, a reverse power flow prevention heater, and a hot water tank provided in the fuel cell,
The calculation unit calculates the operation plan by optimizing an objective function including variables related to the system, the storage battery, the vehicle battery, the fuel cell, the auxiliary boiler, the reverse power flow prevention heater, and the hot water tank. The energy management system according to claim 3.   The energy management system according to claim 4, wherein the objective function includes an electricity bill unit price, a gas bill unit price, and a power sale price as variables.   The energy management system according to claim 4, wherein the calculation unit optimizes the objective function using a genetic algorithm.   The energy management system according to claim 1, wherein the client device includes an interface unit configured to reflect the intention of the power consumer in the control information transmitted from the control unit.   The energy management system according to claim 1, wherein at least one of the acquisition unit, the prediction unit, the calculation unit, and the control unit is a functional object distributed and arranged in a cloud computing system.   The energy management system according to claim 1, wherein the power generation unit is a power generator using a prime mover. An energy management method applicable to an energy management system comprising a client device and a server device capable of communicating with the client device,
The server device
Obtaining from the client device data related to an electrical device including an energy production device including a power generation unit that generates electric power, a capacity-change-type power storage system capable of storing electric power, and an energy consuming device,
Predicting energy demand and energy production in the building where the client device is installed based on the data;
Calculate the operation plan of the electrical device based on the energy demand and energy production to optimize energy consumption in the building under the condition of minimizing surplus power discarded due to full charge of the power storage system And
Based on the calculated operation plan, control information for controlling the electrical equipment is transmitted to the client device,
The power storage system includes:
A storage battery deferred in the building;
An energy management method including an in-vehicle battery electrically connectable to the storage battery. The power generation unit is a fuel cell;
11. The energy management method according to claim 10, wherein the calculating calculates the operation plan under a condition that also includes limiting a change of the power generation amount of the fuel cell with respect to time within a predetermined range.   The calculating includes optimizing an objective function including variables related to the system, the storage battery, the vehicle battery, the fuel cell, the auxiliary boiler, the reverse power flow prevention heater, and the hot water tank, and The energy management method according to claim 11, wherein the energy management method is calculated.   The energy management method according to claim 12, wherein the objective function includes an electricity bill unit price, a gas bill unit price, and a power sale price as variables.   The energy management method according to claim 12, wherein the calculating includes optimizing the objective function by a genetic algorithm.   The energy management method according to claim 10, wherein the power generation unit is a power generator using a prime mover. A program executed by a computer,
The program is
Acquire data from the client device regarding energy production equipment including a power generation unit that generates power, a capacity-change storage system that can store power, and electrical equipment including energy consuming equipment,
Predicting energy demand and energy production in the building where the client device is installed based on the data;
Calculate the operation plan of the electrical device based on the energy demand and energy production to optimize energy consumption in the building under the condition of minimizing surplus power discarded due to full charge of the power storage system And
Based on the calculated operation plan, control information for controlling the electrical equipment is transmitted to the client device,
The power storage system includes:
A storage battery deferred in the building;
And a vehicle-mounted battery that can be electrically connected to the storage battery. The power generation unit is a fuel cell;
The program according to claim 16, wherein the calculating calculates the operation plan under a condition including limiting a change with time of the power generation amount of the fuel cell to a predetermined range.   The calculating includes optimizing an objective function including variables related to the system, the storage battery, the vehicle battery, the fuel cell, the auxiliary boiler, the reverse power flow prevention heater, and the hot water tank, and The program according to claim 17, which calculates.   The program according to claim 18, wherein the objective function includes an electricity bill unit price, a gas bill unit price, and a power sale price as variables.   The program according to claim 18, wherein the calculating optimizes the objective function by a genetic algorithm.   The program according to claim 16, wherein the power generation unit is a power generator using a prime mover. In a server device that can communicate with a client device,
An acquisition unit that acquires data relating to an electric device including an energy production device including a power generation unit that generates electric power, a capacity-change-type power storage system capable of storing electric power, and an energy consumption device from the client device;
A prediction unit that predicts energy demand and energy production in a building where the client device is installed, based on the data;
Calculate the operation plan of the electrical device based on the energy demand and energy production to optimize energy consumption in the building under the condition of minimizing surplus power discarded due to full charge of the power storage system A calculation unit to
A control unit that transmits control information for controlling the electrical device to the client device based on the calculated operation plan;
The power storage system includes:
A storage battery deferred in the building;
A server device including an in-vehicle battery that can be electrically connected to the storage battery. The power generation unit is a fuel cell;
23. The server device according to claim 22, wherein the calculation unit calculates the operation plan under a condition including limiting the change of the power generation amount of the fuel cell with respect to time within a predetermined range. And a database for holding the acquired data and a control target model of the electrical device,
24. The server device according to claim 23, wherein the calculation unit calculates the operation plan based on data held in the database and a control target model of the fuel cell. The model to be controlled includes the system, the storage battery, the in-vehicle battery, the fuel cell, and an auxiliary boiler, a reverse power flow prevention heater, and a hot water tank provided in the fuel cell,
The calculation unit calculates the operation plan by optimizing an objective function including variables related to the system, the storage battery, the vehicle battery, the fuel cell, the auxiliary boiler, the reverse power flow prevention heater, and the hot water tank. The server device according to claim 24.   26. The server apparatus according to claim 25, wherein the objective function includes an electricity bill unit price, a gas bill unit price, and a power sale price as variables.   27. The server device according to claim 25, wherein the calculation unit optimizes the objective function using a genetic algorithm.   The server device according to claim 22, wherein the power generation unit is a power generator using a prime mover.

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