CN113867291A - Energy storage and heat exchange optimal scheduling method - Google Patents
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Abstract
The invention discloses an energy storage heat exchange optimal scheduling method, which comprises the following steps: the cloud processing computing system detects the total heat energy absorption value of the current operation period in real time and obtains the total heat energy absorption value in the current operation period; detecting historical heat energy consumption values in the same operation period in the current season, and calculating to obtain an average historical heat energy consumption value; the cloud processing computing system determines energy storage heat exchange optimization scheduling execution operation according to the relation proportion of the total heat energy absorption value and the historical heat energy consumption average value in the current operation period: the scheduling method fully considers the relation between the actual total heat energy absorption value and the historical heat energy consumption average value of the energy storage system, realizes intelligent management and control, and guarantees the long-term use reliability and safety of the system.
Description
Technical Field
The invention relates to the field of intelligent energy storage control, in particular to an energy storage heat exchange optimal scheduling method.
Background
Solar energy is a renewable energy source, which refers to the thermal radiation energy of the sun, and the main expression is the solar ray. With the ever-decreasing consumption of fossil fuels, solar energy has become an important component of energy used by humans and is constantly being developed. The solar energy is utilized in a photo-thermal conversion mode and a photoelectric conversion mode, and solar power generation is a new renewable energy source. Solar energy in a broad sense also includes wind energy, chemical energy, water energy, etc. on the earth.
In spring, summer and autumn, the energy absorption system can absorb solar energy and convert the solar energy into heat energy, the heat energy is stored through the energy storage system or the cross-season energy storage system, and the stored heat energy is extracted when the heat energy is in winter and is used for heating in winter and heating water all year round. However, research finds that in the process of energy storage and heat exchange optimal configuration, the energy storage and heat exchange configuration in the prior art is not controlled intelligently, and only the residual heat energy is directly converted and stored in the energy storage system, so that the aim of saving energy cannot be achieved in the specific heat exchange configuration, and finally the capacity of the energy storage system reaches the limit quickly, and the comprehensive energy control effect is influenced; meanwhile, except for the situation that heat energy is needed locally, the external energy consumption unit of the system may also need heat energy supply, so the system in the prior art cannot implement intelligent management and control on the external energy consumption unit and the local energy storage system.
Therefore, how to ensure the sustainable energy storage operation of the energy storage system and effectively save energy is an urgent problem to be solved by those skilled in the art.
Disclosure of Invention
The invention aims to provide an energy storage heat exchange optimization scheduling method, which solves the technical problems pointed out in the prior art.
The invention provides an energy storage heat exchange optimal scheduling method, which comprises the following operation steps:
the cloud processing computing system detects the total heat energy absorption value of the current operation period in real time and obtains the total heat energy absorption value in the current operation period;
the cloud processing computing system acquires historical heat energy consumption values in the same operation period in the current season detected by the historical database and calculates to obtain historical heat energy consumption average values;
the cloud processing computing system determines energy storage heat exchange optimization scheduling execution operation according to the relation proportion of the total heat energy absorption value and the historical heat energy consumption average value in the current operation period:
if the ratio of the total heat energy absorption value in the current operation period to the historical heat energy consumption average value is larger than a first preset ratio, the total heat energy absorption value in the current operation period is multiplied by the first preset ratio to obtain an energy storage value, and then the heat energy transmission corresponding to the energy storage value is transmitted to an energy storage system; then, the residual heat obtained by subtracting the stored energy value from the total heat energy absorption value in the current operation period is conveyed to an external energy utilization unit;
and if the ratio of the total heat energy absorption value to the historical heat energy consumption average value in the current operation period is detected to be less than or equal to a first preset ratio, all the total heat energy in the current operation period is transmitted to the energy storage system.
Preferably, as one possible embodiment; before the cloud processing computing system determines the energy storage heat exchange optimization scheduling execution operation according to the relation proportion of the total heat energy absorption value and the historical heat energy consumption average value in the current operation period, the method further comprises the step of setting a first preset proportion.
Preferably, as one possible embodiment; the setting of the first preset proportion specifically comprises the following operation steps:
judging the current season in which the current operation cycle is positioned, and setting different first preset proportion numerical values according to different current seasons:
if the current season is one of spring, summer and autumn, a first preset proportion corresponding to summer is taken; the first preset proportion corresponding to summer is 1: 3; if the current season is the winter season, a first preset proportion corresponding to the winter season is adjusted; the first preset ratio corresponding to winter is 1: 4.
Preferably, as one possible embodiment; before the residual heat obtained by subtracting the stored energy value and the like from the total heat energy absorption value in the current operation period is conveyed to the external energy consumption units, the method also comprises the step of detecting the required values of a plurality of external energy consumption units to realize the dynamic conveying adjustment of the external energy consumption units.
Preferably, as one possible embodiment; the method for detecting the required values of the plurality of external energy consumption units to realize the dynamic conveying adjustment of the external energy consumption units comprises the following operation steps:
detecting the required values of a plurality of external energy consumption units, sequencing the required values of the plurality of external energy consumption units, and sequentially arranging the required values according to the sequence from the large value to the small value to obtain an external energy consumption unit required list;
and carrying out successive transmission on the external energy units from the high-order position to the low-order position of the external energy unit demand list.
Preferably, as one possible embodiment; the cloud processing computing system obtains historical heat energy consumption values in the same operation period in the current season detected by the historical database and calculates the historical heat energy consumption average values, and the cloud processing computing system comprises the following operation steps:
the cloud processing computing system randomly detects N historical heat energy consumption values in the same operation period in the current season in a historical database, wherein the N historical heat energy consumption values are obtained;
and summing the N historical heat consumption values to obtain a total historical data value, and dividing the total historical data value by N to obtain an average historical heat consumption value.
Preferably, as one possible embodiment; the value range of N is 1000-10000.
Preferably, as one possible embodiment; the method comprises the steps of conveying all the total heat energy in the current operation period to the energy storage system, detecting the limit capacity of the energy storage system in real time, and alarming when the current energy storage capacity of the energy storage system reaches the corresponding limit capacity of the energy storage system.
Preferably, as one possible embodiment; when the current energy storage capacity of the energy storage system reaches the limit capacity of the corresponding energy storage system, alarming operation is carried out, and the method specifically comprises the following operation steps:
when the current energy storage capacity of the energy storage system reaches the limit capacity of the corresponding energy storage system, sending a closing instruction to the intermediate heat exchange equipment to control the current energy storage system to be closed to receive heat energy operation;
and sending alarm information to the mobile terminal to implement intelligent alarm operation, wherein the alarm information comprises the number of the current energy storage system, the limit capacity of the current energy storage system and the position information of the current energy storage system.
Preferably, as one possible embodiment; the alarm information also comprises the number of the intermediate heat exchange equipment corresponding to the current energy storage system and the position information of the current intermediate heat exchange equipment; the position information is GPS positioning information.
The application provides an energy storage heat exchange optimization scheduling method, has the technical effect that:
analyzing the energy storage heat exchange optimal scheduling method provided by the embodiment of the invention, the method comprises the following operation steps: the cloud processing computing system detects the total heat energy absorption value of the current operation period in real time and obtains the total heat energy absorption value in the current operation period; the cloud processing computing system acquires historical heat energy consumption values in the same operation period in the current season detected by the historical database and calculates to obtain historical heat energy consumption average values; the cloud processing computing system determines energy storage heat exchange optimization scheduling execution operation according to the relation proportion of the total heat energy absorption value and the historical heat energy consumption average value in the current operation period:
if the ratio of the total heat energy absorption value in the current operation period to the historical heat energy consumption average value is larger than a first preset ratio, the total heat energy absorption value in the current operation period is multiplied by the first preset ratio to obtain an energy storage value, and then the heat energy transmission corresponding to the energy storage value is transmitted to an energy storage system; then, the residual heat obtained by subtracting the stored energy value from the total heat energy absorption value in the current operation period is conveyed to an external energy utilization unit;
and if the ratio of the total heat energy absorption value to the historical heat energy consumption average value in the current operation period is detected to be less than or equal to a first preset ratio, all the total heat energy in the current operation period is transmitted to the energy storage system.
When the intelligent control method is applied specifically, when the ratio between the total heat energy absorption numerical value in the current operation period and the historical heat energy consumption average numerical value is larger than a first preset ratio, the system determines that the relative ratio of the total heat energy absorption numerical value in the current operation period is higher, and the intelligent control method is particularly suitable for energy storage;
and if the ratio of the total heat energy absorption value to the historical heat energy consumption average value in the current operation period is detected to be less than or equal to a first preset ratio, all the total heat energy in the current operation period is transmitted to the energy storage system. At this time, the system determines that the total heat energy absorption value in the current operation period has a lower specific gravity relative to the historical consumption value, and is not suitable for conveying heat energy to the outside.
Drawings
Fig. 1 is a schematic main flow chart of an energy storage heat exchange optimization scheduling method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a specific implementation flow of the energy storage heat exchange optimization scheduling method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a further specific implementation flow of the energy storage heat exchange optimization scheduling method according to the embodiment of the present invention;
fig. 4 is a schematic diagram of a further specific implementation flow of the energy storage heat exchange optimization scheduling method according to the embodiment of the present invention;
fig. 5 is a schematic diagram of another specific implementation flow of the energy storage heat exchange optimization scheduling method according to the embodiment of the present invention.
Detailed Description
In order to facilitate an understanding of the invention, the invention is described in more detail below with reference to the accompanying drawings and specific examples. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It is to be noted that, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The first embodiment is as follows:
the invention relates to a heat energy system which is an improved heat energy system and comprises a photovoltaic end, a plurality of energy storage systems, a plurality of intermediate heat exchange devices and different heat exchange controllers arranged on the intermediate heat exchange devices; each energy storage system is connected with the intermediate heat exchange equipment in a one-to-one correspondence manner, namely each intermediate heat exchange equipment can carry out heat exchange treatment on the corresponding energy storage system;
referring to fig. 1, a first embodiment of the present invention provides an energy storage heat exchange optimization scheduling method, including the following operation steps:
step S10, the cloud processing computing system detects the total heat energy absorption value of the current operation period in real time and obtains the total heat energy absorption value in the current operation period;
step S20, the cloud processing computing system acquires a historical database to detect historical heat energy consumption values in the same operation period in the current season and calculates to obtain an average historical heat energy consumption value;
and step S30, the cloud processing computing system determines the energy storage heat exchange optimization scheduling execution operation according to the relation proportion of the total heat energy absorption value and the historical heat energy consumption average value in the current operation period:
if the ratio of the total heat energy absorption value in the current operation period to the historical heat energy consumption average value is larger than a first preset ratio, the total heat energy absorption value in the current operation period is multiplied by the first preset ratio to obtain an energy storage value, and then the heat energy transmission corresponding to the energy storage value is transmitted to an energy storage system; then, the residual heat obtained by subtracting the stored energy value from the total heat energy absorption value in the current operation period is conveyed to an external energy utilization unit; it should be noted that, in the technical solution of the present application, when the ratio between the total heat energy absorption value in the current operation period and the historical heat energy consumption average value is greater than a first preset ratio, the system determines that the relative ratio of the total heat energy absorption value in the current operation period is high, and is particularly suitable for energy storage, most of the heat energy should be directly output to the energy storage system, and then a small part of the remaining heat energy is transmitted to an external energy consumption unit, and the intelligent control method can conditionally perform energy storage control; the energy storage of the energy storage system is mainly used for supplying heat to local energy consumption units, and meanwhile, a small part of heat in a certain proportion is allocated to be supplied to external energy consumption units for use, so that overall intelligent management is realized;
and if the ratio of the total heat energy absorption value to the historical heat energy consumption average value in the current operation period is detected to be less than or equal to a first preset ratio, all the total heat energy in the current operation period is transmitted to the energy storage system. At this time, the system determines that the total heat energy absorption value in the current operation period has a lower specific gravity relative to the historical consumption value, and is not suitable for conveying heat energy to the outside.
Referring to fig. 2, before step S30 is executed, the method further includes setting a first preset ratio. The setting of the first preset proportion specifically comprises the following operation steps:
step S31, judging the current season of the current operation cycle, setting different first preset proportion values according to the difference of the current season:
step S32, if the current season is one of spring, summer and autumn, a first preset proportion corresponding to summer is taken; the first preset proportion corresponding to summer is 1: 3; if the current season is the winter season, a first preset proportion corresponding to the winter season is adjusted; the first preset ratio corresponding to winter is 1: 4.
It should be noted that, in the above specific technical solution, researchers consider the first preset ratio to be a key adjusting ratio, which is different in specific implementation; for example: setting different first preset proportion numerical values according to different current seasons, and if the current season is one of spring, summer and autumn, calling a first preset proportion corresponding to summer; the first preset proportion corresponding to summer is 1:3, namely, the situation that the total heat energy absorption value is relatively high currently can be determined only when the proportion between the total heat energy absorption value and the historical heat energy consumption average value in the current operation period is detected to be larger than the first preset proportion (1:3), and the method is particularly suitable for storing heat energy and supplying heat energy by an external energy consumption unit.
However, if the current season is the winter season, a first preset proportion corresponding to the winter season is adjusted; the first preset ratio corresponding to winter is 1: 4. When the ratio of the total heat energy absorption value to the historical heat energy consumption average value in the current operation period is determined to be larger than a first preset ratio (1:4), the current situation that the total heat energy absorption value is relatively high can be directly determined; in this case, the heat exchanger is particularly suitable for storing heat energy and supplying heat energy to an external energy unit. If the ratio is lower than or equal to the first preset ratio (1:4), no heat energy is supplied to the external energy consumption unit at the moment, and the whole stored energy supplied to the energy storage system is mainly used for supplying heat to the local energy consumption unit.
Preferably, as one possible embodiment; before the residual heat obtained by subtracting the stored energy value and the like from the total heat energy absorption value in the current operation period is conveyed to the external energy consumption units, the method also comprises the step of detecting the required values of a plurality of external energy consumption units to realize the dynamic conveying adjustment of the external energy consumption units.
In the above-described embodiment, the required values of the plurality of external energy consumption units are detected in consideration of the large number of external energy consumption units, and the heat energy is supplied as required.
Referring to fig. 3, the detecting the required values of a plurality of external energy consumption units to realize the dynamic transportation adjustment of the external energy consumption units specifically includes the following operation steps:
step S33, detecting the required values of a plurality of external energy consumption units, sequencing the required values of the plurality of external energy consumption units, and sequentially arranging the required values according to the sequence from the large value to the small value to obtain an external energy consumption unit required list;
and step S34, carrying out successive conveying on the external energy units from the high-order position to the low-order position of the external energy unit demand list.
It should be noted that, in the above specific technical solution, the required values of a plurality of external energy consumption units are detected, the required values of the plurality of external energy consumption units are sorted, and the required values are sequentially arranged according to the order of the required values to obtain an external energy consumption unit demand list; then the following operation steps are carried out; the operation steps are as follows: and carrying out successive transmission on the external energy units from the high-order position to the low-order position of the external energy unit demand list.
Referring to fig. 4, in the execution process of step S20, the cloud processing computing system obtains the historical database to detect the historical thermal energy consumption value in the same operation cycle in the current season, and calculates to obtain the average historical thermal energy consumption value, including the following operation steps:
step S21: the cloud processing computing system randomly detects N historical heat energy consumption values in the same operation period in the current season in a historical database, wherein the N historical heat energy consumption values are obtained;
step S22: and summing the N historical heat consumption values to obtain a total historical data value, and dividing the total historical data value by N to obtain an average historical heat consumption value. The value range of N is 1000-10000.
It should be noted that, in the above specific technical solution, the cloud processing computing system randomly detects N historical thermal energy consumption values in the same operation period in the current season in the historical database, and simultaneously fully considers various complex situations, so that a large number of historical thermal energy consumption values are retrieved. The value range of N is 1000-10000, and the average value of the historical heat energy consumption is finally obtained.
Preferably, as one possible embodiment; the method comprises the steps of conveying all the total heat energy in the current operation period to the energy storage system, detecting the limit capacity of the energy storage system in real time, and alarming when the current energy storage capacity of the energy storage system reaches the corresponding limit capacity of the energy storage system.
It should be noted that, in the above specific technical solution, the limit capacity of the energy storage system is detected in real time, so that the energy storage system can be monitored conveniently, and finally, the limit capacity of the energy storage system is prevented from reaching an early warning value.
Referring to fig. 5, when the current energy storage capacity of the energy storage system reaches the limit capacity of the corresponding energy storage system, an alarm operation is performed, which specifically includes the following operation steps:
step S35, when the current energy storage capacity of the energy storage system reaches the limit capacity of the corresponding energy storage system, sending a closing instruction to the intermediate heat exchange equipment to control the current energy storage system to receive heat energy operation;
and S36, sending alarm information to the mobile terminal to implement intelligent alarm operation, wherein the alarm information comprises the number of the current energy storage system, the limit capacity of the current energy storage system and the position information of the current energy storage system.
It should be noted that, in the above specific technical solution, the mobile terminal sends alarm information to implement intelligent alarm operation, and the main purpose is to ensure that the mobile terminal implements real-time online monitoring; the alarm information comprises the number of the current energy storage system, the limit capacity of the current energy storage system and the position information of the current energy storage system.
Preferably, as one possible embodiment; the alarm information also comprises the number of the intermediate heat exchange equipment corresponding to the current energy storage system and the position information of the current intermediate heat exchange equipment; the position information is GPS positioning information.
It should be noted that, in the above specific technical solution, the alarm information further includes the number of the intermediate heat exchange device corresponding to the current energy storage system, and the position information of the current intermediate heat exchange device; the position information is GPS positioning information, and the on-line monitoring and the on-line identification of the energy storage system are implemented by utilizing the alarm information, so that the maintenance processing is finally and conveniently carried out.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; the technical solutions described in the foregoing embodiments can be modified by those skilled in the art, or some or all of the technical features can be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An energy storage heat exchange optimal scheduling method is characterized by comprising the following operation steps:
the cloud processing computing system detects the total heat energy absorption value of the current operation period in real time and obtains the total heat energy absorption value in the current operation period;
the cloud processing computing system acquires historical heat energy consumption values in the same operation period in the current season detected by the historical database and calculates to obtain historical heat energy consumption average values;
the cloud processing computing system determines energy storage heat exchange optimization scheduling execution operation according to the relation proportion of the total heat energy absorption value and the historical heat energy consumption average value in the current operation period:
if the ratio of the total heat energy absorption value in the current operation period to the historical heat energy consumption average value is larger than a first preset ratio, the total heat energy absorption value in the current operation period is multiplied by the first preset ratio to obtain an energy storage value, and then the heat energy transmission corresponding to the energy storage value is transmitted to an energy storage system; then, the residual heat obtained by subtracting the stored energy value from the total heat energy absorption value in the current operation period is conveyed to an external energy utilization unit;
and if the ratio of the total heat energy absorption value to the historical heat energy consumption average value in the current operation period is detected to be less than or equal to a first preset ratio, all the total heat energy in the current operation period is transmitted to the energy storage system.
2. The energy storage heat exchange optimal scheduling method of claim 1, wherein before the cloud processing computing system determines the energy storage heat exchange optimal scheduling execution operation according to the relation ratio of the total heat energy absorption value and the historical heat energy consumption average value in the current operation cycle, the method further comprises setting a first preset ratio.
3. The energy storage heat exchange optimal scheduling method of claim 2, wherein the setting of the first preset ratio specifically comprises the following operation steps:
judging the current season in which the current operation cycle is positioned, and setting different first preset proportion numerical values according to different current seasons:
if the current season is one of spring, summer and autumn, a first preset proportion corresponding to summer is taken; the first preset proportion corresponding to summer is 1: 3; if the current season is the winter season, a first preset proportion corresponding to the winter season is adjusted; the first preset ratio corresponding to winter is 1: 4.
4. The energy storage heat exchange optimal scheduling method of claim 2, wherein before the step of delivering the residual heat obtained by subtracting the energy storage value from the total heat energy absorption value in the current operation cycle to the external energy consumption units, the method further comprises the step of detecting the required values of the plurality of external energy consumption units to realize the dynamic delivery adjustment.
5. The energy storage heat exchange optimal scheduling method according to claim 4, wherein the detection of the required values of the plurality of external energy consumption units to achieve the dynamic transmission adjustment thereof specifically comprises the following operation steps:
detecting the required values of a plurality of external energy consumption units, sequencing the required values of the plurality of external energy consumption units, and sequentially arranging the required values according to the sequence from the large value to the small value to obtain an external energy consumption unit required list;
and carrying out successive transmission on the external energy units from the high-order position to the low-order position of the external energy unit demand list.
6. The energy storage heat exchange optimal scheduling method of claim 2, wherein the cloud processing computing system obtains a historical database to detect a historical heat energy consumption value in the same operation period in the current season and calculates to obtain an average historical heat energy consumption value, and the method comprises the following operation steps:
the cloud processing computing system randomly detects N historical heat energy consumption values in the same operation period in the current season in a historical database, wherein the N historical heat energy consumption values are obtained;
and summing the N historical heat consumption values to obtain a total historical data value, and dividing the total historical data value by N to obtain an average historical heat consumption value.
7. The energy storage heat exchange optimization scheduling method of claim 6, wherein the value range of N is between 1000-10000.
8. The energy storage heat exchange optimal scheduling method of claim 7, wherein the total heat energy in the current operation period is completely transmitted to the energy storage system, and further comprising detecting the limit capacity of the energy storage system in real time and alarming when the current energy storage capacity of the energy storage system reaches the corresponding limit capacity of the energy storage system.
9. The energy storage heat exchange optimal scheduling method according to claim 8, wherein when the current energy storage capacity of the energy storage system reaches the limit capacity of the corresponding energy storage system, an alarm operation is performed, and the method specifically comprises the following operation steps:
when the current energy storage capacity of the energy storage system reaches the limit capacity of the corresponding energy storage system, sending a closing instruction to the intermediate heat exchange equipment to control the current energy storage system to be closed to receive heat energy operation;
and sending alarm information to the mobile terminal to implement intelligent alarm operation, wherein the alarm information comprises the number of the current energy storage system, the limit capacity of the current energy storage system and the position information of the current energy storage system.
10. The energy storage heat exchange optimal scheduling method of claim 9, wherein the alarm information further includes a number of an intermediate heat exchange device corresponding to the current energy storage system, and position information of the current intermediate heat exchange device; the position information is GPS positioning information.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140094980A1 (en) * | 2012-02-27 | 2014-04-03 | Kabushiki Kaisha Toshiba | Electric/thermal energy storage schedule optimizing device, optimizing method and optimizing program |
| CN107423852A (en) * | 2017-07-24 | 2017-12-01 | 华北电力大学（保定） | A kind of light storage combined plant optimizing management method of meter and typical scene |
| US20190165580A1 (en) * | 2017-11-27 | 2019-05-30 | Ihi Inc. | System and method for optimal control of energy storage system |
| CN109858759A (en) * | 2018-12-29 | 2019-06-07 | 陕西鼓风机（集团）有限公司 | A kind of industrial park comprehensive energy balance dispatching method |
| JP2019126186A (en) * | 2018-01-17 | 2019-07-25 | 日本電気株式会社 | Energy storage system, energy storage device, energy storage control device, energy storage method, and energy storage control program |
| CN111487939A (en) * | 2020-04-17 | 2020-08-04 | 内蒙古润泰新能源科技有限公司 | Intelligent system for heating, power supply and refrigeration integrated natural energy and control method |
| CN111738502A (en) * | 2020-06-15 | 2020-10-02 | 上海交通大学 | Multi-energy complementary system demand response operation optimization method for promoting surplus wind power consumption |
| CN112381300A (en) * | 2020-11-17 | 2021-02-19 | 国网北京市电力公司 | Energy utilization system, energy analysis method and device based on energy utilization system |
-
2021
- 2021-10-08 CN CN202111173006.5A patent/CN113867291B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140094980A1 (en) * | 2012-02-27 | 2014-04-03 | Kabushiki Kaisha Toshiba | Electric/thermal energy storage schedule optimizing device, optimizing method and optimizing program |
| CN107423852A (en) * | 2017-07-24 | 2017-12-01 | 华北电力大学（保定） | A kind of light storage combined plant optimizing management method of meter and typical scene |
| US20190165580A1 (en) * | 2017-11-27 | 2019-05-30 | Ihi Inc. | System and method for optimal control of energy storage system |
| CN111492559A (en) * | 2017-11-27 | 2020-08-04 | 石川岛美州集团有限公司 | System and method for optimal control of an energy storage system |
| JP2019126186A (en) * | 2018-01-17 | 2019-07-25 | 日本電気株式会社 | Energy storage system, energy storage device, energy storage control device, energy storage method, and energy storage control program |
| CN109858759A (en) * | 2018-12-29 | 2019-06-07 | 陕西鼓风机（集团）有限公司 | A kind of industrial park comprehensive energy balance dispatching method |
| CN111487939A (en) * | 2020-04-17 | 2020-08-04 | 内蒙古润泰新能源科技有限公司 | Intelligent system for heating, power supply and refrigeration integrated natural energy and control method |
| CN111738502A (en) * | 2020-06-15 | 2020-10-02 | 上海交通大学 | Multi-energy complementary system demand response operation optimization method for promoting surplus wind power consumption |
| CN112381300A (en) * | 2020-11-17 | 2021-02-19 | 国网北京市电力公司 | Energy utilization system, energy analysis method and device based on energy utilization system |
Non-Patent Citations (1)
| Title |
|---|
| 郑国太 等: ""基于供需能量平衡的用户侧综合能源系统电/热储能设备综合优化配置"", 《电力系统保护与控制》, vol. 46, no. 16, pages 16 - 26 * |
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