JP5921390B2 - Energy management system, energy management method, program, and server device - Google Patents

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 a customer side such as a subscriber's house.

  With the recent increase in environmental conservation awareness and concerns over power shortages, zero energy homes (ZEH) or net zero energy homes are attracting attention. A net zero energy house means a house where annual primary energy consumption is almost zero on the net, and indispensable for realizing this is a photovoltaic power generation (PV) system, storage battery. Or a distributed power source such as a fuel cell (FC) and a home energy management system (HEMS).

  FC is particularly promising among distributed power sources because it can generate power stably day and night regardless of the weather and can supply heat energy using exhaust heat. However, reverse power flow from FC to the commercial power grid is not allowed under contract with the power company. Therefore, several techniques for preventing reverse power flow of power generated by FC have been proposed.

JP 2009-296097 A JP 2002-48005 A JP 2010-273407 A JP 2011-181377 A

  As a measure for preventing the reverse power flow, there is a technique in which surplus power generated by the FC is consumed by a dummy load or a reverse power flow prevention heater. This wastes energy. There is also a technique for charging the storage battery with surplus power, but it may be considered that the battery is already fully charged when it needs to be charged and cannot be charged.

  Even if the reverse power flow prevention heater and the storage battery are used in combination, it takes a long time to change the output of the FC. Therefore, if the amount of power demand is long, the amount of surplus power generated exceeds the storage battery capacity. In such a case, it is necessary to stop the operation of the FC or to consume surplus power with the reverse flow prevention heater. Even if the reverse power flow is prevented by disconnecting from the system by a relay, the surplus power generated is currently wasted.

  An object is to provide an energy management system, an energy management method, a program, and a server device that can prevent surplus power from being consumed unnecessarily.

  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 includes an energy production device including a power generation unit that generates power, a fixed-capacity storage battery capable of storing surplus power generated by prohibiting reverse power flow to the power system, and an electric device including an energy consuming device. Acquire related data from the client 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 electric equipment based on the energy demand and the energy production amount in order to optimize the energy balance in the building under the condition of minimizing the surplus power discarded when the storage battery is fully charged. . A control part transmits the control information for controlling an electric equipment to a client apparatus based on the calculated operation plan.

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 illustrating a main part of the HEMS according to the embodiment. FIG. 4 is a diagram for explaining the control target model 300g. FIG. 5 is a flowchart illustrating a processing procedure in the embodiment. FIG. 6 is a conceptual diagram illustrating an example of gene design of the genetic algorithm according to the embodiment. FIG. 7 is a flowchart illustrating an example of the flow of the optimization calculation according to the embodiment. FIG. 8 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. Of course, a fuel cell that generates power using non-renewable energy can be connected to the power transmission and distribution network.

  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 customer-participation-type energy supply through bidirectional cooperation between the customer and the 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. In the following, a subscriber who subscribes to an energy management service is taken as an example of a consumer who is supplied with power from a distribution network. The customer is equipped with electrical equipment, and the electrical equipment is also supplied with power from the distribution network.

  The client system is formed with a home gateway (HGW) 7 as a core. The home gateway 7 is a client device installed in the subscriber home 100 and can receive various services from the cloud 300.

  The 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, the power (AC voltage) supplied from the power system 6 is distributed to each home via a power pole transformer 61 and the like, and the distribution board of the subscriber home 100 via a watt-hour meter (smart meter) 19. 20 is supplied. The watt-hour meter 19 measures the amount of power generated by the energy production equipment provided in the subscriber's home 100, the amount of power consumed by the subscriber's home 100, the amount of power flowing from the power grid 6, or the amount of power flowing backward to the power grid 6. It has a function. 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 electric power to the electrical equipment (lighting, air conditioner, heat pump water heater (HP), etc.) 5 connected to the distribution board 20 and the power conditioning system (PCS) 104 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 subscriber's house. A device that consumes power, a device that generates power, and a device that consumes and generates power, including the PV system 101 and the fuel cell (hereinafter referred to as FC unit) 103, all correspond 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.

  A solar panel is installed on the roof or outer wall of the subscriber's house 100 to form a 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 subscriber house 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. On the other hand, the FC unit 103 is a power generation unit that produces electric power from city gas or LP gas (liquefied propane gas).

  Since the power generated by the FC unit 103 is prohibited from flowing back to the power system 6, surplus power may be generated. The surplus power can be charged in the storage battery 102, but the capacity of the storage battery 102 is fixed and cannot be charged unlimitedly beyond the limit. When the storage battery 102 is fully charged, surplus power is converted into heat or the like 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 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 the electric power sent from the electric power grid | system 6 can be charged to the storage battery 102 at midnight. Furthermore, the PCS 104 includes an inverter (not shown), converts the DC power supplied from the storage battery 102 or the FC unit 103 into AC power, and supplies the AC power to the distribution line 21. As a result, the electric device can also receive power supply from the storage battery 102 and the FC unit 103 via the PCS 104.

In short, the PCS 104 has a function as a power converter for transferring energy between the storage battery 102, the FC unit 103, and the distribution line 21. The PCS 104 also has a function of stably controlling the storage battery 102 and the FC unit 103.
A communication line such as a LAN (Local Area Network) is arranged in the subscriber's house 100, and a 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 intention of the subscriber in the control of the system. To do. 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, for example, an LCD (Liquid Crystal Display) on the operating status and power consumption of the electric device 5, the PV device 101, the storage battery 102, and the FC unit 103, and notifies the subscriber (user) by voice guidance or the like. The terminal 105 includes an operation panel, and accepts various operations and setting inputs by the subscriber.

  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 of the FC unit 103 and various 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 subscriber home 100 from the home gateway 7 of each subscriber home 100 regularly or irregularly. In addition, the collection unit 300 a acquires user operation history and the like on the terminal 105 from the terminal 105. Note that the collection unit 300a and the terminal 105 can directly communicate with each other via the communication line 40.

  The acquired data is stored as data 300h in the database DB. The data 300h includes the power demand amount of each subscriber house 100, the power consumption amount of each electrical device 5, the hot water supply amount, the operating state, the remaining charge and charge / discharge power of the storage battery 102, the power generation amount of the PV system 101, and the like. In addition, weather data provided by the Japan Meteorological Agency can be included in the data 300h.

  The prediction unit 300b predicts the energy demand amount and the energy production amount in the subscriber home 100 based on the data 300h acquired by the collection unit 300a. The prediction unit 300 predicts, for example, the power demand amount of the subscriber's house 100, the hot water supply demand amount, the PV power generation amount, and the like.

  The calculation unit 300c calculates an operation plan of the electric device 5 (including the PV system 101, the storage battery 102, and the FC unit 103) based on the control target model 300g and the predicted energy demand and energy production. That is, the calculation unit 300c calculates, for example, the charge / discharge schedule of the storage battery 102 or the power generation schedule of the FC unit 103 based on, for example, the power demand, the hot water supply demand, and the PV power generation.

That is, the calculation unit 300c determines an operation plan for the electrical device 5 in order to optimize the energy balance in the subscriber's house 100. 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.
Furthermore, the calculation unit 300c calculates an operation plan according to a predetermined constraint condition. In this embodiment, the calculation unit 300c calculates the operation plan based on the condition that the surplus power discarded with the full charge of the storage battery 102 is minimized.

  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 a charge / discharge of the storage battery 102, an operation, an operation / stop instruction for power generation of the FC unit 103, an output target value, and the like 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 subscriber's home 100 includes an interface unit (user interface 105a in FIG. 3) for reflecting the subscriber's intention in the control of the electric device 5 based on the control information transmitted from the control unit 300d. The user interface 105 a includes a display for displaying a charge / discharge schedule of the storage battery 102 and a power generation schedule of the FC unit 103. The subscriber 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, the intention of the subscriber can be reflected in the execution of the schedule.

In addition, the subscriber 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 user interface 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.

  FIG. 3 is a functional block diagram illustrating a main part of the HEMS according to the embodiment. In FIG. 3, from the PCS 104 of the subscriber's home 100, the electric device 5, the storage battery 102, the FC unit 103, the watt-hour meter 19, and the distribution board 20, the electric power consumption, operating state, and storage battery of each electric device 5 every predetermined time. Data such as the remaining charge amount 102 and charge / discharge power amount 102, the power demand amount of the subscriber's house 100, the hot water supply demand amount, and the PV power generation amount are transmitted to the cloud 300 regularly or irregularly via the home gateway 7. The

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

  The prediction unit 300b provided for each subscriber uses the weather data such as weather forecast in addition to the power demand, hot water demand, and PV power generation among the collected data, for each 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.

And the calculation part 300c provided for every subscriber controls operation | movement control of the electric equipment 5 based on the energy demand for every predetermined time calculated by prediction calculation, an energy supply amount, an energy unit price, the control object model 300g, etc. The optimum scheduling related to the above is executed.
The prediction unit 300b and the calculation unit 300c can be implemented, for example, as functional objects provided exclusively for each subscriber. That is, the functions of the prediction unit 300b and the calculation unit 300c can be provided for each subscriber. 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.

  Alternatively, the prediction unit 300b and the calculation unit 300c can be implemented as functional objects provided for a plurality of subscribers. That is, the calculation by the prediction unit 300b and the calculation unit 300c can be executed in a unit in which a plurality of subscribers are collected. 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. The control target model 300g includes each component of the power system 6, the FC unit 103, the storage battery 102, 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. The variables shown in FIG. 4 are shown in the following table.

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 storage battery 102 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).

Formula (6) shows the power supply-demand balance. P _D (t) indicates the power demand of the subscriber's house 100, P _C (t) indicates purchased power or sold power, and P _PV (t) indicates the power generation amount of _PV 101. Expressions (7) and (8) indicate the constraint condition that the reverse power flow from the FC main body 220 and the storage battery 102 is prohibited. Equation (9) shows the constraint condition of the capacity of the storage battery 102.

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 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 102 is determined 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 part 300c calculates the schedule for every predetermined time of the electric power generation amount of the fuel cell 103, and the charging / discharging amount of the storage battery 102 so that a utility bill may be minimized (step S1-3). The calculated schedule is stored in the database DB.

  Next, the system transmits a message signal indicating the charge / discharge amount schedule of the storage battery or the schedule of the power generation amount of the fuel cell 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 device operation scheduler (control unit 300d) sends control information for controlling the electric device 5 of the subscriber home 100 according to the created schedule via the IP network 200 to the home of the subscriber home 100. It transmits to the gateway 7 (step S1-6). The control information includes, for example, charge / discharge of the storage battery 102, an operation / stop instruction for power generation of the fuel cell 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 generates a charge / discharge of the storage battery 102 or an operation / stop instruction for power generation of the FC unit 103, an output target value, and the like for each schedule time interval from the result of the optimal scheduling, and joins it. It transmits to the home gateway 7 of the person's house 100. The subscriber instructs the system via the user interface 105a whether or not 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 power generation amount P _FC (t) of the FC unit 103 and the charge / discharge power P _SB (t) of the storage battery 102 are incorporated into the gene. The operation plan of the storage battery 102 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 in order to correspond to the decrease in the utility cost balance C and the increase in the fitness Fit in consideration of the possibility that the utility bill C will become negative if the PV power generation amount greatly exceeds the power demand of the subscriber's home 100. is there. In the embodiment, a function satisfying f (C)> 0 is used.

Give power demand, hot water demand, PV power generation amount, electricity unit price, gas unit price, and PV purchase price to the above formula, and repeat genetic operations such as mutation, crossing, dredging, etc. to maximize Fit Thus, it is possible to obtain a series of the power generation amount P _FC (t) of the FC unit 103 and the charge / discharge P _SB (t) of the storage battery 102 so that the utility bill C is reduced.

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 results of the daily power demand and hot water supply demand of the subscriber's house 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 PV power generation amount, the power demand amount, and the hot water supply demand amount in the subscriber's house 100 are predicted, and the optimization function that minimizes the evaluation function under the set constraint condition based on the predicted value. By executing the calculation, energy management is performed so as to minimize the energy cost. That is, by optimizing the operation plan of the FC unit 103 and the charging / discharging schedule of the storage battery 102 based on the model in which the power generation amount of the FC unit 103 is variable, the reverse power flow prevention heater 222 is not operated wastefully, Costs can be reduced.

  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.

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 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, supply and demand plans such as FC power generation schedules and storage battery charge / discharge schedules can be created in consideration of the overall balance.
Therefore, it is possible to avoid a case where the storage battery 102 is fully charged and the surplus power of the FC unit 103 cannot be charged, or a case where the remaining charge is insufficient when the storage battery 102 should 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 prevent excess power from being consumed wastefully.
In addition, although embodiment demonstrated using a genetic algorithm, a genetic algorithm is not the only solution as a solution for calculating an operation plan. It is possible to calculate an optimal operation plan using various other algorithms.

  Although an embodiment of the present invention has been described, this embodiment is presented as an example and is not intended to limit the scope of the invention. The novel embodiment can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the scope of the invention. This embodiment and its modifications 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 ... Subscriber house, 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 ... Distribution board, 21 ... distribution line, 5 ... electrical equipment, 104 ... power conditioning system (PCS), 101 ... PV system, 102 ... storage battery 102 ... fuel cell, 25 ... home network, 105 ... terminal, 105a ... user interface, DESCRIPTION OF SYMBOLS 200 ... IP network, 300a ... Collection part, 300b ... Prediction part, 300c ... Calculation part, 300d ... Control part, 300g ... Control object model, 300h ... Data, 40 ... Communication line, 220 ... FC main body, 221 ... Auxiliary boiler, 222 ... Reverse flow prevention heater, 223 ... Hot water storage tank

Claims (24)

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 for acquiring data relating to an electric device including an energy production device including a fuel cell, a storage battery capable of storing electric power, and an energy consuming device from a 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;
Optimum energy balance in the building under conditions that minimize the surplus power that is discarded due to the fact that the power generated by the fuel cell is prohibited from flowing back into the grid due to the full charge of the storage battery A calculation unit for calculating an operation plan of the electrical device based on the energy demand and the energy production,
An energy management system comprising: a control unit that transmits control information for controlling the electric device to the client device based on the calculated operation plan.   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 control target model includes the system, the storage 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 optimizes an objective function including variables related to the system, the storage battery, the fuel cell, the auxiliary boiler, the reverse flow prevention heater, and the hot water tank, and calculates the operation plan. 3. The energy management system according to 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. 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 data related to electrical equipment including energy production equipment including fuel cells, storage batteries capable of storing power, and energy consuming equipment from the client device,
Predicting energy demand and energy production in the building where the client device is installed based on the data;
Optimum energy balance in the building under conditions that minimize the surplus power that is discarded due to the fact that the power generated by the fuel cell is prohibited from flowing back into the grid due to the full charge of the storage battery To calculate the operation plan of the electrical equipment based on the energy demand and energy production,
An energy management method for transmitting control information for controlling the electric device to the client device based on the calculated operation plan.   The energy management method according to claim 9, 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 is based on the data held in a database holding the acquired data and a control target model of the electric device and the control target model of the fuel cell, the system, the storage battery, Optimize the objective function including variables related to the fuel cell and the auxiliary boiler, reverse power flow prevention heater, and hot water tank provided in the fuel cell, calculate the operation plan,
The energy management method according to claim 10, wherein the control target model includes the system, the storage battery, the fuel cell, and the auxiliary boiler, the reverse power flow prevention heater, and the hot water tank provided in the fuel cell .   The energy management method according to claim 11, wherein the objective function includes an electricity rate unit price, a gas rate unit price, and a power sale price as variables.   The energy management method according to claim 11, wherein the calculating includes optimizing the objective function by a genetic algorithm. A program executed by a computer,
The program is
Acquire data related to electrical equipment including energy production equipment including fuel cells, storage batteries capable of storing power, and energy consuming equipment from client devices,
Predicting energy demand and energy production in the building where the client device is installed based on the data;
Optimum energy balance in the building under conditions that minimize the surplus power that is discarded due to the fact that the power generated by the fuel cell is prohibited from flowing back into the grid due to the full charge of the storage battery To calculate the operation plan of the electrical equipment based on the energy demand and energy production,
The program which transmits the control information for controlling the said electric equipment to the said client apparatus based on the said calculated operation plan.   The program according to claim 14, wherein the calculating calculates the operation plan under a condition that also includes limiting a change with time of the power generation amount of the fuel cell to a predetermined range. The calculating is based on the data held in a database holding the acquired data and a control target model of the electric device and the control target model of the fuel cell, the system, the storage battery, Optimize the objective function including variables related to the fuel cell and the auxiliary boiler, reverse power flow prevention heater, and hot water tank provided in the fuel cell, calculate the operation plan,
The program according to claim 15, wherein the control target model includes the system, the storage battery, the fuel cell, and the auxiliary boiler provided in the fuel cell, the reverse power flow prevention heater, and the hot water storage tank .   The program according to claim 16, wherein the objective function includes a unit price of electricity charge, a unit price of gas charge, and a power sale price as variables.   The program according to claim 16, wherein the calculating optimizes the objective function by a genetic algorithm. In a server device that can communicate with a client device,
An acquisition unit for acquiring data relating to an electrical device including an energy production device including a fuel cell, a storage battery capable of storing electric power, and an energy consuming 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;
Optimum energy balance in the building under conditions that minimize the surplus power that is discarded due to the fact that the power generated by the fuel cell is prohibited from flowing back into the grid due to the full charge of the storage battery A calculation unit for calculating an operation plan of the electrical device based on the energy demand and the energy production,
A server device comprising: a control unit that transmits control information for controlling the electric device to the client device based on the calculated operation plan.   The server device according to claim 19, wherein the calculation unit calculates the operation plan under a condition that also includes limiting a change in 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 server device according to claim 20, 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 control target model includes the system, the storage 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 optimizes an objective function including variables related to the system, the storage battery, the fuel cell, the auxiliary boiler, the reverse flow prevention heater, and the hot water tank, and calculates the operation plan. The server device according to 21.   23. The server device according to claim 22, wherein the objective function includes an electricity bill unit price, a gas bill unit price, and a power sale price as variables.   The server device according to any one of claims 22 and 23, wherein the calculation unit optimizes the objective function by a genetic algorithm.

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