JP2012055078A - Energy management system and energy management method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an energy management system and an energy management method capable of absorbing and reducing a surplus power at power generation with natural energy in a community, and further capable of reducing at least one of electricity rate and usage of system power in heat storage operation by reflecting efficiency drop at the time of starting.SOLUTION: A community EMS2 includes a consumer 1 equipped with at least one of a PV facility 1a and an electric water heater 1b, and controls the electric water heater 1b of a community 10 with which a system power is supplied to the consumer 1 from a power system 100. It includes a surplus power predicting part for predicting a surplus power of the PV facility 1a, and a local production/local consumption planning part which generates a heat storage operation pattern of the electric water heater 1b so as to utilize the surplus power and the system power. The heat storage operation pattern is so generated that at least one of the use amount of the system power and the electricity rate posed according to use amount of the system power is reduced.

Description

  The present invention relates to an energy management system and an energy management method for managing energy in a community formed by gathering consumers.

In recent years, in consumers who consume electric power, the introduction of photovoltaic power generation (hereinafter referred to as “PV”) has progressed, and the number of systems using both the grid power supplied from the power company and the PV power generated at the consumer end has increased. Yes. From now on, it is assumed that the power flow situation of the distribution system will change with the region and time due to the deviation of PV introduction amount by region and the fluctuation of PV power generation due to the influence of weather. It is conceivable that a reverse power flow from the customer end to the power distribution system occurs during the time period. In addition, there is a concern that the voltage control of the distribution system and the supply / demand balance control of the entire system will be affected.
As one method of the countermeasure, adjusting the time zone of the heat storage operation of the electric water heater installed in the consumer end is examined. In other words, electric water heaters are usually operated using cheap electricity at night to store hot water in hot water storage tanks and use hot water in the daytime of the next day. If predicted, increase the demand for electricity by suppressing the heat storage operation at night the previous day and increasing the heat storage capacity of the next day, and performing the heat storage operation according to the time of day when the PV power generation amount is large on the next day. It has been studied to reduce the reverse power flow in the distribution system and the increase in the supply power of the entire system (for example, see Non-Patent Document 1).

The characteristics of this method are as follows.
In other words, when the voltage of the distribution system rises due to the reverse power flow generated by PV in the daytime, the PV power generation amount is automatically suppressed by the PV voltage increase suppression function, but the suppression amount is effective in the heat storage operation of the electric water heater. Can be used.
In general, there is a sufficient amount of hot water demand in the consumer after the daytime PV, and as described above, the heat storage operation time is changed from the night before the daytime to the daytime PV. Even if it is shifted, there is no shortage of hot water, and convenience for consumers is not impaired.
Moreover, the electric water heater already installed for hot water use can be utilized, and an initial cost can be held down.

"Solar power generation reverse power flow control method using cooperative control with consumer equipment-Operation planning method of heat pump type water heater considering the uncertainty of forecast-", Asari (Denki Lab), et al. Shu, 2009/08.

According to the technique described in Non-Patent Document 1, it is possible to reduce the electricity cost of a consumer while realizing the suppression of the reverse power flow of the electric power generated by PV and the prevention of running out of hot water in the hot water storage type electric water heater. it can.
However, the effect of the heat storage operation pattern of the electric water heater on the energy efficiency is not considered. For example, in a heat pump type electric water heater, the effect of the heat pump energy efficiency from the outside temperature, the effect of taking time to operate at the rated energy efficiency when starting the heat storage operation, and storing in the hot water storage tank after generating hot water It does not take into account the effects of heat dissipation loss that occurs according to the time to leave. And if energy efficiency is low, the electric power required to meet the same hot water demand will increase.

  Moreover, when only the PV power generation amount is insufficient for the heat storage operation of the electric water heater, the grid power is used. At this time, if the unit price of grid power is set to be different depending on the time zone, even if the total power amount of grid power used in the heat storage operation is the same, the use of grid power depends on the time zone during the heat storage operation. The charge amount (electricity charge) according to the amount changes. If the heat storage operation is performed during a time period in which the unit price is high, the electricity charge for the grid power will be high.

  Accordingly, the present invention provides an energy management system and energy management that can absorb and reduce surplus electric power generated by natural energy in a community by heat storage operation, and further reduce at least one of the amount of system power used and the electricity charge in the heat storage operation. It is an object to provide a method.

In order to solve the above problems, the present invention provides a means for predicting surplus power of power generation by natural energy, and the use of the predicted surplus power, and the heat storage operation of the heat storage device so as to use system power as necessary. An energy management system having a means for creating an operation pattern. And while reducing surplus electric power, an operation pattern is created so that at least one of the usage-amount of system power and the electricity bill imposed according to the usage-amount of system power may be reduced.
In addition, the energy management system of the present invention is characterized by creating an operation pattern in consideration of the efficiency of the heat storage device.
Moreover, this invention sets it as the energy management method when carrying out heat storage driving | operation of the heat storage apparatus with which a community is equipped. And a step of predicting surplus power due to natural energy based on at least a power supply amount due to natural energy and a power demand amount of the heat storage device, and an electricity charge imposed according to the amount of grid power used and the amount of grid power used Generating an operation pattern that uses the predicted surplus power so that at least one of the above is reduced.

According to the energy management system and the energy management method of the present invention, surplus power generated by natural energy in the community can be absorbed and reduced by the heat storage operation, and at least the amount of system power used and the electricity bill in the heat storage operation can be reduced. One side can be reduced.
Further, in consideration of the efficiency of the heat storage device, at least one of the amount of system power used and the electricity bill in the heat storage operation can be reduced.

It is a figure which shows one structural example of a community. It is a figure which shows one structural example of the functional block of a customer terminal. (A) is a functional block diagram of community EMS, (b) is a figure which shows an example of supply-and-demand prediction table data. It is a flowchart in which the community EMS executes an energy management program. (A) is a graph showing an example of the relationship between the outside air temperature Temp and the heat storage operation electric energy P_ec, (b) is a graph showing an example of the relationship between the outside air temperature Temp and the supply heat amount SHW_ec, and (c) is an outside air temperature Temp. It is a graph which shows an example of the relationship between heat dissipation rate LR_ec. It is a figure which shows an example of a thermal storage driving | operation pattern.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings as appropriate.
As shown in FIG. 1, a plurality of consumers 1 (four consumers 1 are shown in FIG. 1) gather to form one community 10, and a community EMS (energy management system) that manages the energy of the community 10. ) 2 is provided.
The consumer 1 is assumed to be a general household, a building, a factory, a store, a school, or the like.

  Electricity is supplied to the community 10 from an electric power system 100 operated by an electric power company. The power system 100 includes a distribution line 3 including a transformer (not shown) and the like, and power is supplied to each consumer 1 by the distribution line 3. Thus, the electric power supplied from the electric power grid | system 100 to the consumer 1 is called system electric power.

  In addition, although FIG. 1 illustrates the state in which four customers 1 are orderly connected to the distribution line 3 for the sake of simplicity, there may be a more complicated configuration in practice.

Further, the consumer 1 according to the present embodiment is provided with a photovoltaic power generation facility 1a, an electric water heater 1b, a power load device 1c such as an air conditioner, and the like. Hereinafter, the power load equipment 1c such as the solar power generation equipment 1a, the electric water heater 1b, and the air conditioner is collectively referred to as a consumer equipment. Further, the photovoltaic power generation is referred to as “PV”, the photovoltaic power generation facility 1 a is referred to as “PV facility”, and the electric power generated by the photovoltaic power generation facility 1 a is referred to as “PV power”.
The PV facility 1 a is a power generation facility that receives sunlight, which is natural energy, and converts it into electric power, which is hereinafter referred to as a first power supply source in the consumer 1. The electric power (PV electric power) generated by the PV facility 1a is mainly supplied to and consumed by the customer 1 equipped with the PV facility 1a. When surplus power that is not consumed (hereinafter referred to as PV surplus power) is generated, the power system 100 is reversely flowed.

  In addition, in this embodiment, although the electric power grid | system 100 becomes an electric power supply source different from PV installation 1a (1st electric power supply source), this is called the 2nd electric power supply source in the consumer 1 below.

  Further, as described above, the power grid 100 is operated by a power company, and the grid power supplied from the power grid 100 to the customer 1 is charged according to the amount of use. On the other hand, the PV power generated by the PV equipment 1a owned by the customer 1 is power that does not generate a charge amount (electricity charge) according to the amount used.

  The electric water heater 1b is a heat storage type hot water heater (heat storage device) that stores hot water as a heat quantity in a hot water storage tank (not shown), and is configured to boil hot water by a heat pump unit that is driven by electric power, for example, to become an electric load device. . In addition, there may be an electric water heater 1b for boiling hot water with an electric heater.

  The power load device 1c is a device that consumes power and becomes a load, and includes, for example, home appliances such as an air conditioner.

The customer terminal 1d monitors the status of customer equipment (such as the PV equipment 1a, the electric water heater 1b, and the power load equipment 1c) provided for each customer 1 and provided to the customer 1, and notifies the community EMS2. Each device is controlled in accordance with a command from the community EMS2.
For example, the customer terminal 1d starts the heat storage operation of the electric water heater 1b based on a command from the community EMS2.

The customer terminal 1d is configured as shown in FIG. 2, for example.
The input / output unit 11a includes a keyboard, a monitor, and the like (not shown). For example, the input / output unit 11a is an interface for inputting data to the customer terminal 1d by an operation of an administrator of the customer 1 (see FIG. 1) or displaying a situation. It is.
The community EMS communication unit 11b is an interface that is connected to the community EMS2 (see FIG. 1) via a network and performs data communication with the community EMS2.

  The local production for local consumption control unit 11c receives a local production for local consumption activation signal from the manager of the customer 1 (see FIG. 1) via the input / output unit 11a, or the community EMS2 via the community EMS communication unit 11b. When receiving a command (such as a local production for local consumption start command) from (see FIG. 1), control is performed by transmitting a control signal to each of the consumer devices via the consumer device communication unit 11d. The local production for local consumption activation signal and the local production for local consumption activation command may be a control signal for directly activating the control of the consumer device, or an information signal for prompting the consumer to activate the control of the consumer device (for example, Charge information). In the latter case, an administrator of the community 10 (see FIG. 1) may confirm the information signal and activate the control of the consumer device, or various conditions (for example, activate the control of the consumer device) , Conditions for associating control details of consumer equipment for each fee information content) are stored in the customer terminal 1d as table data, and local production / local consumption activation signals and local production / local consumption activation commands are stored in the table data. The configuration may be such that when the condition is matched, the control of the consumer device is started according to the table data.

  The consumer equipment communication unit 11d is connected to each of the consumer equipment (the PV equipment 1a, the electric water heater 1b, and other power load equipment 1c) provided in the consumer 1 via, for example, a LAN (Local Area Network). This is an interface for data communication with each consumer device.

  The electric water heater state monitoring unit 11e controls the state of the electric water heater 1b (for example, the amount of stored heat operation power, the amount of remaining heat, the amount of heating, the temperature of the water supply, etc.) of the electric water heater 1b via the consumer device communication unit 11d. The state data of the electric water heater 1b acquired from the unit (not shown) is transmitted to the community EMS2 (see FIG. 1) via the community EMS communication unit 11b. Here, the heat storage operation power amount is the amount of power consumed when the electric water heater 1b performs the heat storage operation (when the heat storage operation is not performed, “0”), and the remaining heat storage amount is the hot water storage of the electric water heater 1b. It is the value which calculated the calorie | heat amount which the warm water stored in a tank (not shown) has on the basis of the case where water temperature is "0 degreeC". The heating amount is the amount of heat obtained when the hot water in the hot water storage tank is heated by the heating unit of the electric water heater 1b (for example, a heat pump in the case of a heat pump type electric water heater). It is the temperature of the water replenished to the vessel 1b from the outside.

The electric water heater heat storage control unit 11f is a functional block that controls the heat storage operation of the electric water heater 1b. For example, based on a command from the local production / local consumption control unit 11c, A heat storage operation start signal and a heat storage operation stop signal are transmitted to the controller of the water heater 1b to adjust (control) the state of the heat storage operation of the electric water heater 1b.
The power demand state monitoring unit 11g measures the power demand amount in the customer 1, and transmits the measured result to the community EMS2 (see FIG. 1) via the community EMS communication unit 11b. As a method for measuring the power demand, for example, a method for measuring power at a power receiving point where system power is supplied from the power system 100 (see FIG. 1) to the customer 1, an electric water heater 1b provided in the customer 1, A method of collecting the power consumption in the other power load device 1c from the control unit of each device via the consumer device communication unit 11d and adding them may be considered.
The PV state monitoring unit 11h acquires the power generation amount (PV power generation amount) of the PV facility 1a from the control unit (not shown) of the PV facility 1a via the consumer device communication unit 11d, and acquires the acquired PV power generation amount. Is transmitted to the community EMS2 via the community EMS communication unit 11b.

  All customer terminals 1d provided in the community 10 (see FIG. 1) are connected to the community EMS2 (see FIG. 1) via a network (such as a wide area LAN) constructed in the community 10, for example.

  The community EMS2 (see FIG. 1) executes a predetermined program on a computer (not shown) to coordinately control one or a plurality of connected customer terminals 1d (see FIG. 1), and the community 10 (see FIG. 1). 3, for example, as shown in FIG. 3A, the input / output unit 20, the local production for local consumption planning unit 21, the supply and demand prediction unit 22, the customer terminal communication unit 23, and customer data A management unit 24 is included. The customer data management unit 24 includes device specification data 24a, customer performance data 24b, and supply / demand prediction table data 24c. Hereinafter, the program executed by the community EMS 2 to manage the energy of the community 10 is referred to as an energy management program.

The input / output unit 20 includes a keyboard, an HDD unit, a monitor, and the like (not shown), and is an interface for inputting / outputting data to / from the community EMS2 by operation of an administrator of the community 10 (see FIG. 1), for example.
The customer terminal communication unit 23 is an interface that is connected to the community EMS communication unit 11b (see FIG. 2) of the customer terminal 1d via a network and performs data communication with the customer terminal 1d.

  The supply and demand prediction unit 22 includes a weather condition management unit 22a, a PV power generation amount prediction unit 22b, a power demand prediction unit 22c, a hot water demand prediction unit 22d, and a surplus power prediction unit 22e. The community 10 (see FIG. 1) Predict PV surplus power at. Hereinafter, the predicted PV surplus power is referred to as a PV surplus power prediction value.

  The weather condition management unit 22a takes in data of predicted values such as weather, outside temperature, and solar radiation intensity at predetermined times from the present to the future from, for example, a weather forecast site, and manages them.

The predetermined time in this embodiment is 0 minutes and 30 minutes at each time from 0:00 to 24:00 (specifically, 23:59) (0: 0, 0:30, 10:00, .., 23:00, 23:30), hereinafter referred to as management target time. And the weather condition management part 22a acquires and manages the temporal change for every management object time (namely, every 30 minutes) of an outside air temperature predicted value and a solar radiation intensity predicted value (weather condition).
Note that the management target time is not limited to setting at 30-minute intervals, but may be set at intervals longer than 30 minutes or may be set at intervals shorter than 30 minutes.
Further, the time zone of the management target time is not limited to 0:00 to 24:00, but may be another time zone (for example, from 23:00 to 23:00 on the next day).

The PV power generation amount prediction unit 22b calculates a PV power generation amount prediction value at each management target time of each customer 1 (see FIG. 1). That is, the power supply amount of the PV equipment 1a (see FIG. 1) is predicted as a power prediction unit.
For example, the “irradiance intensity-PV power generation amount characteristic data” of the customer 1 is acquired from the device specification data 24a, and further, the irradiation intensity predicted value data at the management target time is acquired from the weather condition management unit 22a as weather prediction data. To do. Then, with reference to the acquired “solar radiation intensity-PV power generation characteristic data”, PV power generation characteristic data corresponding to the predicted solar radiation intensity value is obtained.
Then, the PV power generation amount prediction unit 22b further calculates a PV power generation amount predicted value (electric power supply) in the community 10 (see FIG. 1) by adding the PV power generation amount prediction value for each customer 1 for all the consumers 1. Predicted value).

With this configuration, the PV power generation amount prediction unit 22b can predict a temporal change in the PV power generation amount prediction value based on weather prediction data indicating the prediction of the weather situation (predicted solar radiation intensity value), and in the community 10 (see FIG. 1). A temporal change in the supply amount of the PV power can be predicted.
The solar radiation intensity-PV power generation amount characteristic data is data for associating the PV power generation amount with the solar radiation intensity, is preset as a specification of the PV facility 1a (see FIG. 1), and is customer data as the device specification data 24a. Accumulated in the management unit 24.

  The power demand prediction unit 22c calculates a predicted power demand amount for each management target time of each customer 1 (see FIG. 1). For example, the average value of the past power demand amount of the community 10 (see FIG. 1) at each time of the management target time is set as the predicted power demand amount value at that time in the community 10.

The hot water demand prediction unit 22d calculates a predicted hot water demand amount at each management target time of each customer 1 (see FIG. 1). For example, the average value of the past results of the hot water demand at each time of the management target time is set as the predicted hot water demand at that time.
In addition to the method described above, the method for predicting the hot water demand prediction value is, for example, the average value of a certain period of the past daily hot water demand (hot water demand per day) actual data, or The average value of only the days with the same day classification (for example, weekday and holiday classification) in the actual data is used as the daily hot water demand forecast value for the next day, and the value of 1/48 is the hot water at each management target time. The method of using the demand amount predicted value may be used.

  The surplus power prediction unit 22e outputs a PV surplus power prediction value in the community 10 (see FIG. 1) at each management target time. For example, the surplus power prediction unit 22e calculates the power demand prediction value in the community 10 at the time calculated by the power demand prediction unit 22c from the PV power generation prediction value in the community 10 at the time calculated by the PV power generation amount prediction unit 22b. The subtracted value is set as the PV surplus power predicted value in the community 10 at the time.

  Thus, the demand-and-supply prediction unit 22 predicts a temporal change in the PV surplus power prediction value in the community 10 (see FIG. 1) by the surplus power prediction unit 22e.

Note that the surplus power prediction unit 22e replaces the power demand prediction value predicted by the power demand prediction unit 22c and the PV power generation amount prediction value predicted by the PV power generation amount prediction unit 22b (power prediction unit). PV surplus power using the data corresponding to the predicted power demand value and the data corresponding to the predicted PV power generation amount input from the input / output unit 20 (see (a) of FIG. 3) by the administrator of 1) It is good also as a structure which calculates (predicts) a predicted value.
Moreover, in the case of this structure, it is good also as the demand-and-supply prediction part 22 which is not equipped with the electric power demand prediction part 22c and the PV electric power generation amount prediction part 22b.

In addition, the supply and demand prediction unit 22 acquires a solar radiation intensity predicted value, a PV power generation amount predicted value, a power demand predicted value, a hot water demand amount predicted value, and a PV surplus power predicted value at each management target time, and manages It arranges in time series for every time, and it accumulates in the customer data management part 24 as supply-and-demand prediction table data 24c. The supply and demand prediction table data 24c is configured as shown in FIG.
In addition, the data used as the item of the supply-and-demand prediction table data 24c shown to (b) of FIG. 3 shows the value totaled about all the consumers 1 (refer FIG. 1) which form the community 10 (refer FIG. 1).
The supply and demand prediction unit 22 creates supply and demand prediction table data 24c after that time for each management target time. For example, the supply and demand prediction unit 22 creates supply and demand prediction table data 24 c for 24 hours from 0:00 to 24:00 at 0:00 and accumulates it in the customer data management unit 24. Further, the supply and demand prediction unit 22 creates and stores the supply and demand prediction table data 24c for 23.5 hours from 0:30 to 24:00 in the customer data management unit 24 at 0:30.
In this way, the supply and demand prediction unit 22 creates 48 patterns of supply and demand prediction table data 24c over 48 times at intervals of 30 minutes per day.

  When the management target time is from 23:00 to 23:00 on the next day, the supply and demand prediction unit 22 creates supply and demand prediction table data 24c that covers 24 hours from 23:00 to 23:00 on the next day. In 30 minutes, supply and demand prediction table data 24c for 23.5 hours from 23:30 to 23:00 on the next day is created. In this way, the supply and demand prediction unit 22 creates the supply and demand prediction table data 24c according to the set management target time.

  The customer data management unit 24 is a storage device that accumulates data necessary for the community EMS2 to manage the energy of the community 10 (see FIG. 1) and can extract data as necessary. Stores and manages device specification data 24a, customer performance data 24b, and electricity rate unit price data in addition to the supply and demand prediction table data 24c. The customer performance data 24b is stored in the customer terminal 1d (see FIG. 1) of each customer 1 in the community 10 (see FIG. 1) via the customer terminal communication unit 23 at each management target time. 1) to obtain and store the current state data.

The equipment specification data 24a is data related to the specifications of consumer equipment (PV equipment 1a, electric water heater 1b, power load equipment 1c, etc.) provided in all the consumers 1 (see FIG. 1). The above-mentioned “solar radiation intensity-PV power generation amount characteristic data” is included as data indicating the specification of 1).
In addition, as the device specification data 24a, for example, for the electric water heater 1b (see FIG. 1), the power consumption during the heat storage operation and the average COP (Coefficient of Performance) data for the heat storage operation, It manages data representing characteristics related to changes in power consumption, supply heat, COP due to outside air temperature and changes at start-up in the heat storage operation of the container 1b (see FIG. 1).

  The COP of the electric water heater 1b (see FIG. 1) is a value indicating energy efficiency when the electric water heater 1b performs a heat storage operation, and is calculated as a ratio of the total amount of output energy to the total amount of input energy in the heat storage operation for a fixed time. The average COP is an average value of COPs under some preset conditions.

The customer performance data 24b includes customer supply / demand state data and customer heat storage device state data. The customer supply and demand state data includes the electric power demand, the PV power generation amount, the hot water demand amount, the heating amount, and the respective values for each management target time of each consumer 1 (see FIG. 1) in the community 10 (see FIG. 1). It is configured to include the total (total) in the community 10 of (electric power demand amount, PV power generation amount, hot water demand amount, heating amount) for each management target time.
The consumer heat storage device state data is the state quantity of the electric water heater 1b (see FIG. 1) at each management target time of each consumer 1 in the community 10 (heat storage operation electric energy, heating amount, supply water temperature, outside temperature, etc.) ), And data on the total heat storage operation electric energy and heating amount in the community 10.
The total (total) of power demand, PV power generation, hot water demand, heat storage operation power, and heating amount within the community 10 is determined by the customer data management unit 24 within the community 10 at each management target time. The electric power demand, the PV power generation amount, the hot water demand amount, and the heating amount collected from each consumer 1 are calculated for the respective consumers 1 in the community 10 together.

  Electricity unit price data is data indicating a unit price for each management target time of the grid power. The unit price is a unit price per unit amount (for example, 1 kWh) of the used electric energy. Electricity unit price data is appropriately updated by the administrator of the community 10 (see FIG. 1) via the input / output unit 20.

  The local production for local consumption planning unit 21 determines (creates) an operation pattern (heat storage operation pattern) when the electric water heater 1b performs a heat storage operation based on the PV power generation amount prediction value, the PV surplus power prediction value, the electricity rate unit price data, and the like. ) Details of the local production and local consumption planning unit 21 creating the heat storage operation pattern will be described later.

  The community EMS2 configured as shown in (a) of FIG. 3 is configured to execute an energy management program for managing the energy of the community 10 (see FIG. 1) when the management target time is reached. The energy management program includes a customer data management unit 24, a supply and demand prediction unit 22, a weather condition management unit 22a, a PV power generation amount prediction unit 22b, a power demand prediction unit 22c, a hot water demand prediction unit 22d, and a surplus power prediction unit 22e. The process is started, and then the process of the local production for local consumption planning unit 21 is started to create a heat storage operation pattern. And based on the heat storage operation pattern created by the process in the local production for local consumption planning unit 21, for each customer terminal 1d (see FIG. 1) of each customer 1 in the community 10 (see FIG. 1), An electric water heater control signal serving as a local production for local consumption start command to the electric water heater 1b (see FIG. 1) of the consumer 1 is transmitted.

As shown in FIG. 4, when the community EMS2 (see FIG. 1) executes the energy management program, the processing of each part of the supply and demand prediction unit 22, the customer data management unit 24, and the local production for local consumption planning unit 21 is performed as described above. It starts and calculates | requires PV electric power generation amount predicted value as a 1st step. That is, the power supply amount is predicted.
And community EMS2 calculates | requires the electric power demand amount predicted value in the community 10 (refer FIG. 1) as a 2nd step. That is, the power demand is predicted.
Furthermore, community EMS2 calculates | requires the PV surplus electric power prediction value in the community 10 by subtracting an electric power demand amount prediction value from a PV electric power generation amount prediction value as a 3rd step. That is, surplus power in the community 10 is predicted based on the predicted value of the power supply amount and the predicted value of the power demand amount, and a heat storage operation pattern is created as the fourth step.
And community EMS2 is based on the heat storage driving | operation pattern produced in the local production for local consumption plan part 21, with respect to the consumer terminal 1d (refer FIG. 1) of each consumer 1, local production for local consumption start instruction | command (electric water heater control) Signal).

  In the consumer terminal 1d of each customer 1 (see FIG. 2), the local production for local consumption control unit 11c (see FIG. 2) receives a local production for local consumption activation command (electric water heater) via the community EMS communication unit 11b (see FIG. 2). When the control signal is received, an activation instruction for the heat storage operation of the electric water heater 1b (see FIG. 2) is transmitted to the electric water heater heat storage control unit 11f (see FIG. 2) based on the signal. The electric water heater heat storage control unit 11f that has received the start command for the heat storage operation transmits a control signal (a heat storage operation start signal or a heat storage operation stop signal) to the electric water heater 1b via the consumer device communication unit 11d. The heat storage operation of the container 1b is controlled.

  Below, the procedure (4th step) in which the local production for local consumption plan part 21 of community EMS2 creates a heat storage driving | operation pattern is demonstrated (refer FIGS. 1-3) suitably.

The local production for local consumption planning unit 21 is configured to create a heat storage operation pattern assuming the following requirements 1 to 5.
Requirement 1: PV surplus power is absorbed and reduced as much as possible through heat storage operation.
Requirement 2: Of the electric power necessary for heat storage, the system power is used for the PV power that is insufficient.
Requirement 3: Keep the electricity cost of grid power required for heat storage operation as low as possible.
Requirement 4: Electricity consumption required for heat storage operation within the community is kept low.
Requirement 5: Avoid running out of hot water.

The local production for local consumption planning unit 21 assumes the above requirements and is selected and set in advance by an administrator of the community 10 among the following two objective functions (first objective function F1 and second objective function F2). The heat storage operation pattern is created so as to make the value of one objective function of the maximum possible.
F1: W1 × ΣU_ep [ti] −
W2 × {Σ (P_ec [ti] −
Min (EP_pv [ti], P_ec [ti])) ×
rate [ti]} → maximization F2: W1 × ΣU_ep [ti] −
W3 × {Σ (P_ec [ti] −
Min (EP_pv [ti], P_ec [ti]))} → maximization

ti is a parameter indicating the i-th management target time.
As described above, the management target time in this embodiment is set at an interval of 30 minutes from 0:00 to 23:30. For example, t0 is 0: 0, t1 is 0:30, and t2 is 1. It indicates the hour 0 minutes, and 23:30 is indicated by t47.
Further, the first symbol Σ of the first objective function F1 and the second objective function F2 indicates that the sum is obtained for all the management target times, and the second symbol of the first objective function F1 and the second objective function F2. Σ indicates that all customers 1 in the community 10 and all management target times are summed.

P_ec is the power consumption (heat storage operation power amount) consumed by the electric water heater 1b during the heat storage operation, and the heat storage operation indicating the state of the heat storage operation in the characteristic function FP_ec of the heat storage operation energy with the outside temperature Temp as a parameter. It is defined as a value obtained by multiplying the state variable S_ec and a coefficient k1_ec for reflecting the influence on the heat storage operation electric energy at the start of the heat storage operation. That is, the heat storage operation electric energy P_ec is expressed by the following equation (1).
P_ec = FP_ec (Temp) × S_ec × k1_ec (1)

The heat storage operation state variable S_ec is a variable that indicates the state of the heat storage operation of the electric water heater 1b for each customer 1, and indicates, for example, “1” during the heat storage operation and “0” during the heat storage operation stop. Thus, by indicating “0” when the heat storage operation is stopped, the heat storage operation electric energy P_ec during the heat storage operation stop can be set to zero.
Moreover, the characteristic function FP_ec of the heat storage operation electric energy is a characteristic function that associates the heat storage operation electric energy P_ec with the outside air temperature Temp, and in the present embodiment, the first efficiency characteristic indicating the efficiency of the electric water heater 1b and To do.

  For example, an administrator or the like of the community 10 (see FIG. 1) sets in advance, or, as shown in FIG. It may be obtained by approximation. For example, if the customer specification data 24a (see FIG. 3A) is stored in the customer data management unit 24 (see FIG. 3A), the characteristic function FP_ec (first efficiency characteristic) is obtained. Managed by the customer data management unit 24, for example, the local production for local consumption planning unit 21 (see FIG. 3A) can refer to the characteristic function FP_ec as necessary. Then, using the characteristic function FP_ec, it is possible to predict a change in the heat storage operation electric energy P_ec that changes according to a change in the outside air temperature Temp.

The coefficient k1_ec is a coefficient for reflecting the influence on the heat storage operation power amount at the start of the heat storage operation. For example, the following equation (2) expresses that the heat storage operation power amount becomes smaller at the time of the heat storage operation start. To do.
k1_ec [ti] = (S_ec [ti] +
S_ec [ti-1]) / 2 (2)
Here, the coefficient k1_ec [ti] is a value at the management target time ti, S_ec [ti-1] is the heat storage operation state at the management target time (ti-1), and S_ec [ti] is the management target time ti. This is the heat storage operation state.

  When the current time is 0:00 and “t0”, the data of “ti−1” may be, for example, 23:30 of the previous day, that is, the data of “t47” of the previous day, or may be set in advance. An initial value may be used.

U_ep is the PV surplus power absorption amount (use amount of PV surplus power) in the entire community 10, and the total value in the community 10 of the PV surplus power EP_pv of each customer 1 and the heat storage operation power amount of each customer 1 It is defined as a small value of the total value in the community 10 of P_ec.
That is, the PV surplus power absorption amount U_ep is expressed by the following equation (3). The two symbols Σ in the formula (3) both indicate the summation over all the consumers 1 in the community 10.
U_ep = Min (ΣEP_pv, ΣP_ec) (3)

  The rate is an electricity charge per unit power of the system power (a unit price), and is a coefficient for conversion to an electricity charge according to the usage amount of the system power.

From the above, ΣU_ep [ti] indicated in the first objective function F1 and the second objective function F2 indicates the sum of PV surplus power absorption amounts at all management target times in all electric water heaters 1b.
Further, Σ (P_ec [ti] −Min (EP_pv [ti], P_ec [ti])) × rate [ti] indicated by the first objective function F1 is the amount of power consumed by the electric water heater 1b that performs the heat storage operation. Of these, a value obtained by summing up the electricity charges required when system power is allocated to the amount of PV surplus power absorption amount over all the electric water heaters 1b and all the management target times is shown.
Further, Σ (P_ec [ti] −Min (EP_pv [ti], P_ec [ti])) indicated by the second objective function F2 is PV surplus power out of the amount of power consumed by the electric water heater 1b that performs the heat storage operation. The power demand amount (hereinafter referred to as “system power consumption amount”) that is allocated to the grid power for the shortage of the absorbed amount is a total value over all the electric water heaters 1b and all the management target times.

  W1 indicated by the first objective function F1 and the second objective function F2, W2 indicated by the first objective function F1, and W3 indicated by the second objective function F2 are respectively the PV surplus power absorption amount, the electricity rate, and the system power. It is a weighting factor that determines the weighting of consumption, and assumes “0” or a positive value, respectively.

In addition, as a constraint condition when the local production for local consumption planning unit 21 creates a heat storage operation pattern, the remaining heat storage amount (RHW_ec) of the hot water storage tank for storing hot water of the electric water heater 1b is the specified minimum amount (RHW_ec_min) and the specified maximum amount. (RHW_ec_max). That is, the relationship of the following formula (4) is established.
RHW_ec_min ≦ RHW_ec ≦ RHW_ec_max (4)

  The specified minimum amount (RHW_ec_min) and the specified maximum amount (RHW_ec_max) of the remaining amount of heat storage are values given in advance as the specifications of the electric water heater 1b, and are stored in the consumer data management unit 24 as device specification data 24a.

In the present embodiment, the heat storage remaining amount RHW_ec [ti] at the management target time ti is converted to the heat storage remaining amount RHW_ec [ti-1] at the (i-1) th management target time (ti-1) by the heat storage operation. From the heat quantity obtained by adding the supplied heat quantity SHW_ec [ti-1] supplied to the water heater 1b, the hot water demand DHW_ec [ti-1], the remaining heat storage amount RHW_ec [ti-1], and the heat dissipation loss rate LR_ec [ti- 1] and the amount of heat obtained by subtracting the product.
That is, the remaining heat storage amount RHW_ec [ti] is expressed by the following equation (5).
RHW_ec [ti] = RHW_ec [ti−1] +
SHW_ec [ti-1] -DHW_ec [ti-1]-
RHW_ec [ti-1] × LR_ec [ti-1] (5)

The supplied heat quantity SHW_ec [ti] supplied in the heat storage operation reflects the influence of the heat storage operation state variable S_ec [ti] and the start of the heat storage operation on the characteristic function FSHW_ec having the outside air temperature Temp [ti] as a parameter. It is calculated by multiplying the coefficient k2_ec [ti].
That is, the supplied heat amount SHW_ec [ti] is expressed by the following equation (6).
SHW_ec [ti] =
FSHW_ec (Temp [ti]) ×
S_ec [ti] × k2_ec [ti] (6)
Further, the characteristic function FSHW_ec is a characteristic function that associates the supplied heat amount SHW_ec with the outside air temperature Temp, like the characteristic function FP_ec of the heat storage operation electric energy, and in the present embodiment, the second function indicating the efficiency of the electric water heater 1b. Efficiency characteristics.

  For example, the administrator of the community 10 (see FIG. 1) sets in advance, or approximates past driving performance data and experimental data by the least square method or the like as shown in FIG. You may ask. For example, if the customer specification data 24a (see FIG. 3A) is stored in the customer data management unit 24 (see FIG. 3A), the characteristic function FSHW_ec (second efficiency characteristic) is obtained. Managed by the customer data management unit 24, for example, the local production for local consumption planning unit 21 (see FIG. 3A) or the like can refer to the characteristic function FSHW_ec as necessary. Then, using the characteristic function FSHW_ec, it is possible to predict a change in the supply heat amount SHW_ec that changes in accordance with a change in the outside air temperature Temp.

The coefficient k2_ec is a coefficient for reflecting the influence on the supply heat amount at the start of the heat storage operation. For example, the following equation (7) expresses that the supply heat amount becomes smaller at the start of the heat storage operation.
k2_ec [ti] =
((S_ec [ti-1] + S_ec [ti]) / 2) ×
((S_ec [ti-1] + S_ec [ti]) / 2) (7)
Here, the coefficient k2_ec [ti] is a value at the management target time ti.

  For example, when the heat storage operation of the electric water heater 1b is started between the management target time (ti-1) and ti, the coefficient k1_ec [ti] is 0.5 and the coefficient k2_ec [ti] is 0.25, so that the heat storage operation is performed. The effect of the decrease in efficiency at the time of start-up can be considered as the efficiency is reduced to ½.

  As described above, by incorporating the coefficient k1_ec [ti] and the coefficient k2_ec [ti], the objective function for maximizing the local production / local consumption planning unit 21 when generating the heat storage operation pattern is generated at the start of the heat storage operation. The reduction in efficiency of the heat storage operation is reflected, and the local production for local consumption planning unit 21 can create a heat storage operation pattern so as to suitably suppress the efficiency decrease that occurs at the start of the heat storage operation.

The heat dissipation loss rate LR_ec of the hot water storage tank is defined by a characteristic function FLR_ec using the outside air temperature Temp as a parameter. That is, the heat dissipation loss rate LR_ec is expressed by the following equation (8).
LR_ec [ti] = FLR_ec (Temp [ti]) (8)
In addition, the characteristic function FLR_ec is a characteristic function that associates the heat dissipation loss rate LR_ec with the outside air temperature Temp. In the present embodiment, the characteristic function FLR_ec is a third efficiency characteristic indicating the efficiency of the electric water heater 1b.

  For example, the administrator of the community 10 (see FIG. 1) sets in advance, or approximates by the least square method or the like based on past driving performance data and experimental data as shown in FIG. You may ask. For example, if the customer specification data 24a (see FIG. 3A) is stored in the customer data management unit 24 (see FIG. 3A), the characteristic function FLR_ec (third efficiency characteristic) is obtained. Managed by the customer data management unit 24, for example, the local production for local consumption planning unit 21 (see FIG. 3A) can refer to the characteristic function FLR_ec as necessary. Then, using the characteristic function FLR_ec, it is possible to predict a change in the heat dissipation loss rate LR_ec that changes according to a change in the outside air temperature Temp.

  The term RHW_ec [ti-1] × LR_ec [ti-1] included in the definition of the remaining heat storage amount RHW_ec [ti] of the hot water storage tank is multiplied by the heat dissipation loss rate LR_ec [ti-1] of the hot water storage tank. It is a term that reflects a decrease in the remaining heat storage due to heat dissipation loss of the electric water heater 1b. As a result, the difference between the heat dissipation loss of the electric water heater 1b and the heat dissipation loss due to the outside air temperature is reflected in the objective function to be maximized by the local production / local consumption planning unit 21. The heat storage operation pattern can be created so as to suitably suppress the heat dissipation loss.

  The local production for local consumption planning unit 21 is configured so that the value of the objective function set in advance among the two objective functions (the first objective function F1 and the second objective function F2) is as large as possible. The heat storage operation state variable S_ec, that is, the heat storage operation pattern is obtained so as to satisfy the constraint conditions.

  The first objective function F1 and the second objective function F2 are summed for all the electric water heaters 1b (see FIG. 1) provided in the community 10 (see FIG. 1) (heat storage operation electric energy, PV surplus power absorption amount, electricity The local production and local consumption planning unit 21 increases the value of the set objective function as much as possible to change the heat storage operation pattern of all the electric water heaters 1b in the community 10. Will be created.

  There is no limitation on the method of finding a heat storage operation pattern in which the local production and consumption planning unit 21 makes the objective functions F1 and F2 as large as possible. For example, a large number of heat storage operation pattern candidates are generated using random numbers, It is determined whether the constraint condition is satisfied, the objective function F1 or F2 is calculated for the candidate satisfying the constraint condition, the candidate having the maximum value is selected, and this is output as a solution. be able to.

In this way, the local production for local consumption planning unit 21 creates a heat storage operation pattern that satisfies the requirements 1 to 5 described above. FIG. 6 illustrates an image of the heat storage operation pattern.
The vertical axis is the electric energy, and the horizontal axis is the time. A curve a indicated by a thin solid line in FIG. 6 is a curve indicating a predicted value of the PV power generation amount, and a curve c indicated by a one-dot chain line is a curve indicating a predicted value of the power demand amount. And the curve b shown with a thick continuous line in FIG. 6 is a curve which shows the predicted value of PV surplus electric power.

The heat storage operation pattern is determined as shown by a broken line in FIG. In other words, the electric water heater 1b (see FIG. 1) is a time zone in which the PV power generation amount a is large and the power demand amount c is small between 7 o'clock and 23 o'clock, which is the daytime time zone, that is, the PV surplus power b is large. The heat storage operation pattern d is created so as to perform the heat storage operation. Thus, the PV surplus power can be absorbed by the heat storage operation of the electric water heater 1b, and the heat storage operation can be performed without using the system power as much as possible.
Therefore, PV surplus power can be used (absorbed) to the maximum, and system power consumption and electricity charges can be kept low.

Note that the heat storage operation pattern d shown in FIG. 6 is a pattern indicating the total heat storage operation electric energy of all the electric water heaters 1b (see FIG. 1) provided in the community 10 (see FIG. 1). 1b shows a state in which the heat storage operation is performed at the same timing.
For example, when the time of the heat storage operation required for all the electric water heaters 1b is different, the heat storage operation electric energy is shown stepwise in the portion indicating the heat storage operation of the heat storage operation pattern.

In addition, a data display device such as a monitor is provided in the input / output unit 20 (see FIG. 3A) of the community EMS2, and for example, at least one of the following first data to eighth data may be displayed. Good.
First data: data indicating the heat storage operation state for each consumer.
Second data: Data indicating the heat storage operation electric energy for each consumer.
Third data: Data indicating the heat storage operation heat supply amount for each consumer.
Fourth data: Daily data indicating the amount of surplus power absorption in the region.
Fifth data: daily data indicating the charge amount in the area.
Sixth data: Daily data indicating the amount of heat stored in the area.
Seventh data: Data indicating the resource breakdown of the daily heat storage operation electric energy.
Eighth data: Data indicating the electricity rate for each consumer.

  The data (first data) indicating the heat storage operation state for each consumer is data indicating the state of the heat storage operation for each customer 1, and is a heat storage operation state variable (S_ec). Then, for example, for each electric water heater 1b (see FIG. 1) of the consumer 1, the horizontal axis represents the management target time of the heat storage operation state variable (S_ec) for each management target time, and the vertical It displays on the input / output part 20 (refer (a) of FIG. 3) as a trend graph which takes a thermal storage driving | running state variable value on an axis | shaft.

  The data (second data) indicating the heat storage operation power amount for each consumer is data indicating the power amount required for the heat storage operation of the electric water heater 1b (see FIG. 1) for each customer 1 (see FIG. 1). The heat storage operation electric energy (P_ec [ti]) for each customer 1. And, for example, for each electric water heater 1b of the consumer 1 (see FIG. 1), for example, the horizontal axis represents the management target time of the heat storage operation electric energy (P_ec [ti]) for each management target time. And displayed on the input / output unit 20 (see FIG. 3A) as a trend graph with the vertical axis representing the heat storage operation electric energy.

  The data (third data) indicating the heat storage operation heat supply amount for each consumer is data indicating the heat amount stored in the electric water heater 1b (see FIG. 1) for each customer 1 (see FIG. 1) in the heat storage operation. The amount of heat supplied by the heat storage operation for each customer 1 (SHW_ec [ti]). And, for example, the time change of the supply heat amount (SHW_ec [ti]) by the heat storage operation for each management target time, for each electric water heater 1b (see FIG. 1) of the consumer 1, for example, the management target time on the horizontal axis. Is displayed on the input / output unit 20 (see (a) of FIG. 3) as a trend graph with the amount of heat supplied on the vertical axis.

The daily data (fourth data) indicating the surplus power absorption amount in the region is a value obtained by adding up the PV surplus power use amount in the community 10 (see FIG. 1), and the PV for each customer 1 (see FIG. 1). The surplus power absorption amount (U_ep [ti]) is obtained for all customers 1 and for all management target times. For example, the plan value and the past performance of the data obtained in this way are displayed on the input / output unit 20 (see FIG. 3A).
Note that the planned values for the day are, for example, the values calculated when the local production and consumption planning unit 21 (see FIG. 3A) of the community EMS2 sets the first objective function F1 and the second objective function F2. It is a value predicted based on it, and the past performance is, for example, a value indicating an average value of the performance values up to the previous day.

  The daily data (fifth data) indicating the charge amount in the area is the sum of the charge amount in the heat storage operation of the electric water heater 1b (see FIG. 1) within the community 10 (see FIG. 1), and the heat storage operation electric energy The value obtained by subtracting the PV surplus power absorption amount (U_ep [ti]) from (P_ec [ti]) and the unit price rate [ti] is multiplied by all the customers 1 (see FIG. 1), and all It is obtained by adding up the management target times. For example, the plan value and the past performance of the data obtained in this way are displayed on the input / output unit 20 (see FIG. 3A).

  The daily data (sixth data) indicating the amount of heat stored in the area is the sum of the amount of power used in the heat storage operation of the electric water heater 1b (see FIG. 1) within the community 10, and the consumer 1 (FIG. 1) for each of the consumers 1 and all the times to be managed, the heat storage operation electric energy (P_ec [ti]) for each) is obtained. For example, the plan value and the past performance of the data obtained in this way are displayed on the input / output unit 20 (see FIG. 3A).

Data indicating the resource breakdown of the daily heat storage operation electric energy (seventh data) is used for the own PV power, daily other PV power, and grid power of the daily heat storage operation power, that is, the heat storage operation. It is the data which shows the breakdown of the electric power supply source of electric power.
As for the own PV power, the amount of power obtained by applying the PV surplus power (EP_pv) of one consumer 1 (see FIG. 1) to the heat storage operation energy (P_ec [ti]) in the consumer 1 is shown in FIG. 1), the total amount of electric power for all consumers 1).
For other PV power in the area, for each management target time, obtain a smaller value among the total value of the PV power generation amount in the community 10 (see FIG. 1) and the total value of the heat storage operation power amount in the community 10, This is totaled for all the management target times of the day, and the amount of power obtained by subtracting the amount of power for its own PV power from there.
Further, the grid power is set to a power value required for the heat storage operation of the electric water heater 1b in the community 10, in addition to the own PV power and other PV power in the area.
For example, the time variation for each management target time of the own PV power, local PV power, and grid power defined in this way, for example, the management target time on the horizontal axis and the vertical axis It displays on the input / output part 20 (refer (a) of FIG. 3) as a trend graph which takes each electric energy.

The data (eighth data) indicating the electricity charge for each consumer indicates the charge amount corresponding to the amount of power used in the heat storage operation of the electric water heater 1b (see FIG. 1) for each consumer 1 (see FIG. 1). In the data, the electricity rate (real electricity rate) multiplied by the unit price rate [ti] in accordance with the amount of grid power used in the heat storage operation of the electric water heater 1b and the PV surplus power (EP_pv) of other customers 1 were used. In this case, it is calculated by multiplying the usage amount by a predetermined coefficient rate2 and the sum (effective electric charge + electric charge equivalent value) of the value corresponding to the electric charge of PV surplus power (electric charge equivalent value). It is assumed that the data is obtained.
In this case, the coefficient rate2 is calculated based on the amount of PV surplus power used when the surplus PV generated in the customer 1 is considered to be charged according to the amount used when another customer 1 uses it. Is a coefficient for conversion to a value equivalent to (electric charge equivalent value).
For example, the input / output unit 20 (FIG. 3 (a)) shows, for example, the planned value of the day and the past results of the data indicating the electricity rate (actual electricity rate + value corresponding to the electricity rate) calculated for each consumer. Display).

  Thus, for example, the administrator of the community 10 (see FIG. 1) can manage each demand by displaying at least one of the first data to the eighth data on the input / output unit 20 (see FIG. 3A). The state of the house 1 (see FIG. 1) and the state of the entire community 10 can be confirmed in detail.

As described above, the community EMS2 (see FIG. 1) according to the present embodiment uses the PV surplus power preferentially for the heat storage operation of the electric water heater 1b (see FIG. 1) and uses the grid power and charges. A heat storage operation pattern is created so as to keep at least one of them low. Then, the electric water heater 1b performs a heat storage operation according to the created heat storage operation pattern. With this configuration, the PV surplus power can be used (absorbed) to the maximum, and at least one of the usage amount of the system power and the charge amount (electricity charge) according to the usage amount of the system power can be kept low. .
As described above, according to the community EMS2 (see FIG. 1) according to the present embodiment, the PV surplus power in the region (community 10 (see FIG. 1)) is converted into the electric water heater 1b (see FIG. 1) in the region. ) To reduce the impact on the power system, further improve the energy efficiency of the heat storage operation of the electric water heater 1b in the area, and use the grid power usage or system in the area It is possible to reduce at least one of the power usage charges (electricity charges).

  Note that the design of the present invention can be changed as appropriate without departing from the spirit of the invention. For example, the structure provided with the storage battery which the consumer 1 which forms the community 10 shown in FIG. 1 does not illustrate may be sufficient. In this case, the PV surplus power can be stored in the storage battery, and the power stored in the storage battery can also be used for the heat storage operation. According to this configuration, the system power consumption can be further reduced.

  Moreover, although all the consumers 1 of the community 10 shown in FIG. 1 are equipped with the PV equipment 1a, the community 10 including the consumers 1 not equipped with the PV equipment 1a may be used.

  Moreover, as a 1st electric power supply source which converts natural energy into electric power, instead of PV equipment 1a (refer to Drawing 1) which converts solar energy into electric power, for example, wind power generation equipment (not shown) which converts wind power into electric power ). In this case, the supply and demand prediction table data 24c (see FIG. 3B) has predicted values of wind direction and wind power, and the community EMS2 (see FIG. 1) predicts the amount of power generation based on the predicted values of wind direction and wind power. If it is set as the structure to perform, the effect equivalent to the structure of this embodiment provided with the PV equipment 1a can be acquired. That is, the first power supply source need not be limited to the PV facility 1a as long as it is a power supply source (power generation facility) that converts natural energy into electric power.

1 Consumer 2 Community EMS (Energy Management System)
1a Photovoltaic power generation equipment (PV equipment, first power supply source)
1b Electric water heater (heat storage device)
10 Community 20 Input / output unit (data display device)
21 Local production for local consumption planning department (pattern making department)
22a Meteorological condition management unit 22b PV power generation amount prediction unit (power prediction unit)
22c Electric power demand prediction part 22e Surplus electric power prediction part 100 Electric power system (2nd electric power supply source)
F1 first objective function F2 second objective function

Claims (9)

Second power different from the first power supply source, including one or more consumers including at least one of a first power supply source that converts natural energy into power and a heat storage device that is driven by power. An energy management system for controlling a heat storage operation of the heat storage device provided in a community in which system power is supplied to the consumer from a supply source according to an operation pattern,
A local production for local consumption plan that uses the surplus power of the power supplied from the first power supply source and creates the operation pattern so as to perform the heat storage operation of the heat storage device using the grid power as necessary. With
The local production and local consumption planning unit reduces the surplus power, and reduces the operation pattern so that at least one of the usage amount of the grid power and the electricity charge imposed according to the usage amount of the grid power is reduced. An energy management system characterized by creating   The energy management system according to claim 1, further comprising a surplus power prediction unit that predicts surplus power of power supplied from the first power supply source.   The surplus power prediction unit predicts surplus power of the power based on at least one of prediction of power supply amount of the first power supply source and prediction of power demand amount in the community. The energy management system according to claim 2.   4. The apparatus according to claim 1, further comprising at least one of a power prediction unit that predicts a power supply amount of the first power supply source and a power demand prediction unit that predicts the power demand amount. 5. The energy management system according to claim 1.   The energy management system according to claim 4, wherein the power prediction unit predicts a power supply amount of the first power supply source based on weather prediction data obtained by predicting a weather condition of the community. A first efficiency characteristic that associates power consumption when the heat storage device performs a heat storage operation with an outside air temperature, and a second efficiency characteristic that associates the amount of heat supplied when the heat storage device performs a heat storage operation with an outside air temperature At least one of the efficiency characteristic and the third efficiency characteristic that associates the heat dissipation loss rate of the heat storage device with the outside air temperature is managed by the data management unit,
The local production for local consumption planning department
A prediction that reflects the reduction in efficiency that occurs during startup and uses the first efficiency characteristic of the power consumption that changes in response to a change in ambient temperature;
A prediction that reflects a decrease in efficiency that occurs at the time of start-up while using the second efficiency characteristic of the supply heat amount that changes according to a change in outside air temperature;
Performing at least one of the heat dissipation loss rate that changes according to a change in the outside air temperature and the prediction using the third efficiency characteristic;
Creating the operation pattern so as to reduce the usage amount of the system power that changes according to at least one of the change in the power consumption, the change in the supply heat amount, and the change in the heat dissipation loss rate. The energy management system according to claim 1, wherein: First data indicating the state of the heat storage operation for each consumer;
Second data indicating the amount of power required for the heat storage operation for each consumer;
Third data indicating, for each customer, the amount of heat stored in the heat storage device in the heat storage operation,
4th data which combined the usage-amount of the said surplus electric power in the said community,
Fifth data obtained by adding the charge amount in the heat storage operation in the community;
Sixth data obtained by adding up the amount of power used in the heat storage operation in the community;
Seventh data showing a breakdown of power supply sources of power used for the heat storage operation;
7. The data display device according to claim 1, further comprising: a data display device that displays at least one of eighth data indicating a billing amount in the heat storage operation for each customer. Energy management system. The natural energy is solar energy,
The energy management system according to any one of claims 1 to 7, wherein the first power supply source is a photovoltaic power generation facility that converts sunlight into electric power by a solar cell. Second power different from the first power supply source, including one or more consumers including at least one of a first power supply source that converts natural energy into power and a heat storage device that is driven by power. An energy management method for performing a heat storage operation of the heat storage device provided in a community in which system power is supplied from a supply source to the consumer,
A first step of predicting a power supply amount of the first power supply source;
A second step of predicting power demand in the community;
A third step of predicting surplus power of power supplied from the first power supply source based on the predicted value of the power supply amount and the predicted value of the power demand amount;
A fourth step of creating an operation pattern of the heat storage device,
The fourth step includes
Predicting a change in power consumption consumed by the heat storage device according to a change in outside air temperature when performing the heat storage operation;
Predicting a change in the amount of heat supplied to the heat storage device in the heat storage operation according to a change in outside air temperature; and
Predicting a change in heat dissipation loss of the heat storage device according to a change in outside air temperature;
Solving an objective function that changes in accordance with a change in the power consumption so as to satisfy a constraint condition that changes in accordance with a change in the supplied heat amount and a change in the heat dissipation loss,
It is a step of creating the operation pattern using the predicted surplus power so that at least one of the usage amount of the grid power and the electricity charge imposed according to the usage amount of the grid power is reduced. Energy management method characterized by

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