JP6294137B2 - Consumer equipment operation management system and method - Google Patents

Description

  The present invention relates to a consumer equipment operation management system and method for adjusting power demand of consumer equipment so that a predetermined equipment control amount can be obtained by demand response (hereinafter referred to as DR).

  In recent demand-side management technology development, the distributed energy sources subject to jurisdiction are integratedly managed, and power is exchanged with the grid side through the purchase and sale of surplus power and adjustment of the load, etc. Service to be operated as is being studied. It is also discussed that the service operation is performed by a third party other than the grid operator, and the operator who operates the service is called an aggregator. The target distributed energy source can be traced back to the integration of emergency generators and small hydropower in the old days, and in recent years the target equipment has expanded to consumer equipment because of the development of communication means. The target consumer devices include a distributed power source as a power supply source, a heat pump and an air conditioner as a load, and an electric vehicle and a storage battery as a source and load.

  As a background art in this technical field, for example, there is Non-Patent Document 1. This may be a function that predicts the maximum and minimum values of the control amount obtained from the distributed energy source as a distribution and uses the predicted distribution.

Electric Power Research Institute: “Enterprise Integration Functions for Distributed Energy Resources: Phase 1”, Technical Update, 2013.12

  However, in the prior art and Non-Patent Document 1 described above, in order to obtain a predetermined control amount, a plurality of consumer devices to be controlled by the DR are selected from a group of consumer devices to be managed. Due to the uncertainty of the quantity, there is a difference between the capacity of the device to be controlled and the actually obtained control amount, and the selection of the target device is not efficient and the accuracy is low.

  In order to solve the above problems, in a consumer equipment operation management system for creating a consumer equipment operation plan including a heat pump water heater, equipment information including an equipment operation schedule of the heat pump water heater, and the heat pump water heater Obtaining an adjustment amount indicating a control amount to be requested, and an operation plan creation unit that creates device operation constraints based on the device operation schedule, and based on the device operation schedule, the device operation constraints and the adjustment amount, A predicting unit that predicts the probability that the heat pump water heater can obtain the adjustment amount within a predetermined period, and the operation plan creation unit selects a heat pump water heater that performs control based on the probability, and Create an operational plan. A method invention corresponding to the system invention is also included.

  According to the present invention, it is possible to efficiently and highly accurately select a device to be a DR target.

It is an example of the whole system configuration including the configuration of a customer equipment operation management system. It is an example of the operation plan preparation flow of an uncertainty consideration type operation plan preparation part. It is an example of the operation plan created by the uncertainty-considered operation plan creation unit. It is an example of the preparation flow of the one-day plan of an uncertainty consideration type operation plan preparation part. It is an example of an apparatus driving schedule. It is an example of the optimization flow of the device control amount of an uncertainty consideration type operation plan creation part. It is an example of the heat pump water heater 107 using a heat pump. It is an example of the thermal storage cycle of the heat pump water heater 107. It is an example of a correction flow of the operation plan of an uncertainty consideration type operation plan creation part. It is a figure for demonstrating the uncertainty of apparatus control amount. It is a figure for demonstrating the state transition model of a heat pump. It is an example of the prediction flow of DR success probability.

  Embodiments of a consumer equipment control system and control system to which the present invention is applied will be described below with reference to the drawings.

  FIG. 1 shows an example of the overall system configuration including a configuration diagram of the customer equipment operation management system 101. The market trading unit 102 conducts trading with various markets 114 such as an ancillary market, a capacity market, and a spot market, and determines a heat pump control amount (hereinafter referred to as DR adjustment amount) by DR and its consideration (hereinafter referred to as DR consideration). . The aggregator needs to create an operation plan for the heat pump to be managed so as to satisfy the DR adjustment amount. When creating the operation plan, first, the DR success probability prediction unit 103 predicts probabilistically for each heat pump whether or not a predetermined control amount can be obtained in a control cycle in which DR is performed. Hereinafter, the predicted value is referred to as DR success probability. Using the predicted DR success probability, the uncertainty-considered operation plan creation unit 104 creates an operation plan so that the probability that the DR adjustment amount is obtained as a DR control result is sufficiently high in operation. The operation execution unit 105 outputs a heat pump control command to the device management unit 106 in accordance with the operation plan. The equipment management unit 106 rewrites the equipment operation schedule set in each heat pump water heater 107 in accordance with the control command, or directly outputs a control command to each heat pump water heater 107 via the communication unit 108. Each heat pump water heater 107 is controlled. The system connection unit 109 monitors the connection between the heat pump water heater 107 and the power system, and transmits the measured power amount and the like to the device management unit 106 via the communication unit 108. The device management unit 106 notifies the charge calculation unit 110 of the control results in DR, such as the control amount of each heat pump water heater 107 (hereinafter referred to as device control amount), based on the amount of power. The fee calculation unit 110 calculates the contribution of each heat pump water heater 107 based on the notified control result, and calculates an execution fee to be paid to each customer from the DR consideration. Information necessary for these is held in various databases (hereinafter referred to as DB). The transaction management DB 111 holds customer transaction information such as the execution fee and contract period, and market transaction information such as the DR adjustment amount and the DR consideration. The device information DB 113 stores the device status such as the remaining hot water storage amount and DR success probability of each heat pump water heater 107, the device operation schedule of each heat pump water heater 107, the change history of the device operation schedule, the coefficient of performance of each heat pump water heater 107, It holds consumer equipment information such as equipment specifications such as the size of the hot water storage tank and area information where each heat pump water heater 107 is installed. The measurement value DB 112 holds measurement value information such as the amount of power measured by the system connection unit 109 and the hot water temperature in the hot water storage tank of each heat pump water heater 107 used by the DR success probability prediction unit 103 for prediction.

  The constituent elements of these consumer equipment operation management systems 101 may be all managed by an aggregator, or some or all of them may be managed by a system operator such as a power distribution company.

  Each unit such as the DR success probability prediction unit 103 and the uncertainty-considered operation plan creation unit 104 is processed by a processor such as a CPU, and various databases are stored in a storage medium such as a memory or a hard disk.

  FIG. 2 is an example of an operation plan creation flow of the uncertainty-considered operation plan creation unit 104. In step S201, the uncertainty-considered operation plan creation unit 104 creates an operation plan as a one day advance plan at a predetermined time such as 18:00 on the day before the DR. The DR day is a monitoring cycle such as a 30-minute cycle, and the uncertainty-considering operation plan creation unit 104 repeats the correction of the operation plan in step S202.

  FIG. 3 shows an example of an operation plan. The operation plan includes a consumer device list 301 and a device operation mode 304 of each consumer device at each time 303 for each control cycle. The device operation mode 304 includes a heat storage consumption operation 305 that consumes heat storage in a shower, a customer heat storage operation 306 that performs a heat storage operation according to a device operation schedule set by a consumer, and a DR heat storage operation that performs a heat storage operation controlled by DR. There are 307.

  FIG. 4 is an example of a flow in which the uncertainty-considering operation plan creation unit 104 creates a one-day advance plan in step S201. The uncertainty-considered operation plan creation unit 104 acquires device information from the device information DB 113 in step S401. In step S402, the uncertainty-considered operation plan creation unit 104 creates a device operation constraint using a device operation schedule that is a part of the device information. The device operation restrictions are a heat storage time zone of each device and a heat storage time required in the heat storage time zone. In step S403, the uncertainty-considered operation plan creation unit 104 acquires the DR adjustment amount from the transaction management DB 111. The DR adjustment amount is a time at which DR necessary for adjusting the supply and demand balance of the power system is performed, and a target value of the total device control amount at the time. In addition, the uncertainty-considered operation plan creation unit 104 includes a device operation schedule that is a part of the device information acquired in step S401, a device operation constraint created in step S402, and the DR adjustment amount acquired in step S403. In step S405, the device control amount is optimized in consideration of the DR success probability, and an operation plan is created.

  FIG. 5 is an example of an equipment operation schedule used by the uncertainty-considering operation plan creation unit 104 in step S402. For a certain consumer device, there are each time 501 for each control cycle, an operation mode 502 at each time set by the consumer, and a section 505 indicating a schedule unit of one cycle. In the operation mode 502, the heat storage consumption operation 503 and the customer heat storage operation 504 are set. A section 505 indicates a time period from the end of the heat storage consumption operation 503 to the time when the next heat storage consumption operation 503 ends. Since the consumer device needs to perform a heat storage operation before the heat storage consumption operation 503 starts in the section, the uncertainty-considered operation plan creation unit 104 sets each of each device as a device operation constraint in step S402. A heat storage time zone excluding the heat storage consumption operation 503 and a heat storage time required in the heat storage time zone are created from the device operation schedule. In the example of FIG. 5, the apparatus operation restriction regarding the section 2 (506) is that the heat storage time zone is from 10:00 to 17:00, and the heat storage time is 1 hour.

  FIG. 6 is an example of an optimization flow of the device control amount of the uncertainty-considered operation plan creation unit 104. The uncertainty-considered operation plan creation unit 104 uses the device operation schedule acquired as device information in step S401, and the DR adjustment amount that the sum of the device control amounts is acquired in step S403 in accordance with the device operation constraint created in step S402. In step S601, the device control amount of the consumer device is searched so as to satisfy. The search is performed by changing some or all of the customer heat storage operation 504 to the DR heat storage operation in the example of the equipment operation schedule of FIG. In step S604, the uncertainty-considered operation plan creation unit 104 requests the DR success probability prediction unit 103 to predict the DR success probability of each heat pump water heater 107 with respect to the device control amount after the search. The DR success probability is acquired for time 303. The uncertainty-considered operation plan creation unit 104 evaluates the DR objective function in step S605 using the device control amount after the search, and confirms the DR constraint condition in step S602. The DR success probability acquired in step S604 by the uncertainty-considered operation plan creation unit 104 may be used directly for the objective function or may be used as a constraint condition. For example, in order to minimize the difference between the DR adjustment amount acquired in Step S403 and the overall capacity of the heat pump secured as the DR target device, the variance of the DR success probability may be minimized. In addition, when an operation plan that ensures the device control amount equal to or greater than the DR adjustment amount acquired in step S403 is made, the expected value of the device control amount considering the confidence interval of the DR success probability is the DR adjustment amount. The constraint condition may be set so as to be larger than that. The objective function and the constraint condition can be set in addition to these, and the uncertainty of the device control amount can be taken into account by using the DR success probability for at least one objective function or constraint condition. If there is no problem with the constraint condition in step S602 (S603), the uncertainty-considered operation plan creation unit 104 temporarily stores the device control amount and the evaluation result of the objective function in step S605 in step S606. While further searching for the device control amount of the consumer device in step S601 can be expected to improve the objective function evaluation result in step S605, the uncertainty-considered operation plan creation unit in step S607 Step 104 repeats steps S601 to S606. For the iterative calculation from step S601 to step S606 in S607, a general optimization method may be used. For example, a mixed integer programming method, a particle swarm optimization method, or the like may be considered, and iterative sufficient for optimization. Specify the number of times. In step S608, the uncertainty-considered operation plan creation unit 104 selects an optimal device control amount with the best evaluation result of the objective function in step S605 from among a plurality of sets of device control amounts temporarily stored in step S606. . In step S609, the uncertainty-considered operation plan creation unit 104 creates an operation plan based on the optimal device control amount.

  FIG. 7 shows an example of a heat pump water heater 107 using a heat pump. The heat pump water heater 701 includes a heat pump 702 and a hot water storage tank 703. The heat pump 702 takes in heat from the outside world, heats the water pumped from the hot water storage tank 703 by heat exchange, and returns it to the hot water storage tank 703. The hot water storage tank 703 is filled with hot water or water, and hot water is supplied from an upper part having a high temperature, and water is supplied from a lower part having a low temperature. One or more temperature sensors 504 are provided in the hot water storage tank, and the average water temperature in the hot water storage tank 703 can be measured based on the water temperature measured by the hot water storage tank 703. Shown here is an example of the heat pump water heater 107, and even if the heat pump water heater has another structure, the present invention can be applied if the average water temperature of the hot water storage tank 703 can be measured.

  FIG. 8 shows an example of a heat storage cycle of the heat pump water heater 107. The heat pump water heater 107 is on standby with a certain amount of hot water in the hot water storage tank 703 in preparation for a consumer to use a small amount of hot water during normal operation. Hereinafter, this state is referred to as a standby at normal time 802. Here, when the heat pump water heater 107 performs the heat storage operation (306, 307), the amount of hot water in the hot water storage tank 703 increases by the control amount specified by the time or the average water temperature in the hot water storage tank 703. This state in which the amount of hot water is increasing is referred to as heat storage 803. After the end of heat storage, the customer is on standby until they use hot water. This state is referred to as a stand-by standby 804. When the consumer consumes hot water during the heat storage consumption operation 305, the amount of hot water in the hot water storage tank 703 decreases. The state where the amount of hot water is reduced is defined as heat consumption 805. After the heat consumption 805, the process returns to the stand-by state 802, and an operation for securing a certain amount of hot water in the hot water storage tank 703 is performed.

  FIG. 9 is an example of an operation plan correction flow of the uncertainty-considered operation plan creation unit 104. In step S901, the uncertainty-considered operation plan creation unit 104 acquires the device operation restriction created in step S402. Next, the uncertainty-considered operation plan creation unit 104 acquires the DR adjustment amount from the transaction management DB 111 in step S902. Further, in step S903, the uncertainty-considered operation plan creation unit 104 obtains device information from the device information DB 113 as a device information history such as the remaining amount of hot water stored in each heat pump water heater 107, the coefficient of performance of each heat pump water heater 107, and the like. Consumer equipment information such as equipment specifications such as the size of a hot water storage tank and regional information where each heat pump water heater 107 is installed is acquired. In step S904, the uncertainty-considered operation plan creation unit 104 requests the DR success probability prediction unit 103 to predict the DR success probability of each heat pump water heater 107 for a plurality of types of device control amounts assumed in the operation plan. The DR success probability is acquired for each time 303 and each device control amount as a prediction result. Finally, the uncertainty-considered operation plan creation unit 104 corrects the device control amount of each heat pump water heater 107 in step S905 and updates the operation plan. More specifically, the DR success probability acquired in step S904 is monitored, and the device of the device having the high DR success probability is satisfied so as to satisfy the device operation restriction acquired in step S901 and the DR adjustment amount acquired in step S902. The control amount and the device control amount of the device having the low DR success probability are exchanged, or the device control amount of the device having the low DR success probability is reduced.

  If the DR success probability can be predicted according to the heat pump usage status of the customer, this configuration allows the uncertainty of the device control amount of the heat pump to be a DR target according to the customer heat pump usage status on the DR day. Can be reduced.

  FIG. 10 is a diagram for explaining the uncertainty of the device control amount. In an ideal load pattern example 1001 for a heat pump water heater for one day, heat is stored by a heat storage operation 504 at night from 0 o'clock and is consumed by a heat storage consumption operation 503 from 7 o'clock such as a morning shower. Further, heat is stored again by the heat storage operation 504 from 14:00, and heat is consumed by the heat storage consumption operation 503 from 17:00 such as cooking in the evening. Under ideal conditions, the heat storage schedule of the device set in advance corresponds to the actual heat consumption, and the heat storage amount and the heat consumption amount are equal. However, in actual device operation, the consumer does not necessarily consume heat according to the heat storage schedule, and the heat storage amount and the heat consumption amount may not be equal as in Example 1002 in which the DR heat storage operation stops. If the error 1003, which is the difference between the heat storage amount and the heat consumption at this time, is large, the DR start time 1004 is reached and the DR heat storage operation 307 for the time specified by the DR is started. Before the time elapses, the DR heat storage operation stops (1005), and the expected device control amount may not be obtained. In order to handle the uncertainty of the device control amount, a state transition model for each heat storage cycle of the heat pump water heater 107 is used.

  FIG. 11 is a diagram for explaining a state transition model 1101 of the heat pump water heater 107. As described with reference to FIG. 8, the heat storage cycle of the heat pump water heater 107 has a standby state 802 during normal operation, a heat storage unit 803, a standby state 804 during heat storage state, and a heat consumption 805. Therefore, the order is changed in consideration of performing the heat storage operation in the DR heat storage operation 307, and the state transition model 1101 performs one cycle of the heat storage standby 1102, the heat consumption 1103, the stationary standby 1104, and the heat storage 1105. Each state has a probability distribution 1106 of hot water storage and hot water consumption as an output distribution. The hot water storage amount and hot water consumption probability distribution 1106 is obtained from the average hot water temperature of the hot water storage tank 703 obtained as past measurement data, and the actual hot water storage amount and hot water consumption result data is obtained. The amount of actual data is cut out for each cycle of the state transition model 1101, and the hot water storage is stored for each state transition model 1101 for each of a plurality of pieces of data having a similar time series pattern of the extracted remaining hot water storage and hot water consumption. A parameter for determining the shape of the probability distribution 1106 of the quantity and the hot water consumption is learned. Note that the remaining amount of hot water is the difference between the average hot water temperature of the hot water storage tank 703 and the average hot water temperature of the hot water storage tank 703 at each time in the stand-by state 1104. It is a fluctuation. Further, the probability distribution 1109 of the remaining heat storage amount at the start of the heat storage 1105 is learned for each state transition model 1101. For this, the remaining heat storage amount at the start of the heat storage 1105 of a plurality of data having similar time series patterns used when learning the probability distribution of the hot water storage amount and the hot water consumption amount of each state transition model 1101 is used. Since the heat storage controllable range 1108 is obtained by removing the heat storage remaining amount 1110 from the heat storage upper limit 1107 of each heat pump water heater 107, the probability of the heat storage controllable range 1108 can be obtained from the probability distribution 1109 of the heat storage remaining amount. If the device control amount of each heat pump water heater 107 is given, the probability that the device control amount falls within the heat storage controllable range 1108 is known.

  FIG. 12 is an example of a DR success probability prediction flow. In step S1201, the DR success probability prediction unit 103 obtains a state transition model after learning the probability distribution 1106 of the hot water storage amount and hot water consumption and the probability distribution 1109 of the remaining heat storage amount at the start of the heat storage 1105. Next, the DR success probability prediction unit 103 acquires or assumes the average hot water temperature of the hot water storage tank 703 of each heat pump water heater 107, and calculates the remaining hot water storage amount and the hot water consumption. In step S1203, the DR success probability prediction unit 103 predicts the remaining heat storage amount at the start of the heat storage 1105 in the heat storage cycle of each device at that time from the state transition model, the remaining hot water storage amount, and the hot water consumption. In step S1204, the DR success probability prediction unit 103 acquires the weather condition in which each heat pump water heater 107 is placed from the area information in which each heat pump water heater 107 acquired as device information in step S903 is installed. Based on the weather conditions, the device control amount of each heat pump water heater 107 is corrected using the coefficient of performance and tank capacity of each heat pump water heater 107 acquired as device information in step S903. In step S1205, the DR success probability prediction unit 103 obtains the probability that the corrected device control amount is obtained from the probability distribution 1109 of the remaining heat storage amount and sets it as the DR success probability.

  By taking the above configuration, it is possible to reduce the uncertainty of the device control amount of the heat pump that is the DR target, according to the heat pump usage situation of the consumer.

  In addition, this invention is not limited to an above-described Example, Various modifications are included. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described.

101 Consumer Equipment Operation Management System 102 Market Trading Department
103 DR success probability prediction unit 104 uncertainty considering operation plan creation unit 105 operation execution unit 106 device management unit 107 heat pump water heater 108 communication unit 109 system connection unit 110 charge calculation unit 111 transaction management DB
112 Measurement DB
113 Device information DB
701 Heat pump water heater 702 Heat pump 703 Hot water storage tank

Claims (10)

In a customer equipment operation management system that creates an operation plan for consumer equipment including heat pump water heaters,
Operation plan creation for acquiring device information including a device operation schedule of the heat pump water heater and an adjustment amount indicating a control amount required for the heat pump water heater, and creating device operation constraints based on the device operation schedule And
A prediction unit that predicts a probability that the heat pump water heater can obtain the adjustment amount within a predetermined period based on the device operation schedule, the device operation constraint, and the adjustment amount;
The said operation plan preparation part selects the heat pump water heater which controls based on the said probability, and produces the said operation plan, The consumer equipment operation management system characterized by the above-mentioned. In the customer operation management system according to claim 1,
The said operation plan preparation part selects the said heat pump water heater with the said probability higher than a predetermined value as a control object, The consumer equipment operation management system characterized by the above-mentioned. In the customer operation management system according to claim 1,
The customer equipment operation management system, wherein the operation plan creation unit performs the selection by minimizing the variance of the probability. In the customer operation management system according to claim 1,
The said prediction part calculates | requires the said probability based on the probability distribution of the heat storage residual amount of the hot water storage tank heat-stored by the said heat pump water heater, The consumer equipment operation management system characterized by these. In the consumer equipment operation management system according to claim 4,
The predicting unit cuts out the remaining hot water consumption and hot water consumption data of the hot water storage tank for each heat storage cycle consisting of the heat pump standby time of heat storage, heat consumption, steady state standby and heat storage state. A consumer equipment operation management system characterized by obtaining a probability distribution of amount and hot water consumption, and predicting the remaining heat storage based on the remaining hot water storage and the probability distribution of hot water consumption. In the consumer equipment operation management system according to claim 5,
The hot water storage remaining amount is obtained from the difference between the average hot water temperature of the hot water storage tank in the stationary standby and the average hot water temperature of the hot water storage tank at each time, and the hot water consumption is calculated every monitoring period of the remaining hot water storage amount. A customer equipment operation management system characterized by obtaining from the fluctuations of. In the consumer equipment operation management system according to claim 5,
The equipment information includes area information where the heat pump water heater is installed, and the prediction unit acquires weather conditions from the area information and corrects the adjustment amount based on the weather conditions. A consumer equipment operation management system. In the consumer equipment operation management system according to claim 1,
The operation plan creation unit repeatedly calculates the adjustment amount, and creates the operation plan based on the adjustment amount having a high evaluation value of an objective function obtained from the adjustment amount. . In the consumer equipment operation management system according to claim 8,
The operation plan creation unit may include a mixed integer programming method or a particle swarm optimization method in the iterative calculation. In the consumer equipment operation management method of creating a consumer equipment operation plan including a heat pump water heater,
Acquiring device information including a device operation schedule of the heat pump water heater, an adjustment amount indicating a control amount required for the heat pump water heater, and creating device operation constraints based on the device operation schedule;
Predicting the probability that the heat pump water heater will obtain the adjustment amount within a predetermined period based on the device operation schedule, the device operation constraint and the adjustment amount;
Selecting a heat pump water heater that performs control based on the probability and creating the operation plan.