CN116260165B

CN116260165B - Multi-micro-grid energy storage system scheduling method, device, equipment and medium

Info

Publication number: CN116260165B
Application number: CN202310220748.1A
Authority: CN
Inventors: 胡启冬; 陈梦; 金笑天; 芦新叶; 张瑜
Original assignee: Suzhou Shenlan Wanwei Energy Technology Co ltd
Current assignee: Suzhou Shenlan Wanwei Energy Technology Co ltd
Priority date: 2023-03-09
Filing date: 2023-03-09
Publication date: 2023-10-20
Anticipated expiration: 2043-03-09
Also published as: CN116260165A

Abstract

The specification provides a multi-microgrid energy storage system scheduling method, device, equipment and medium, comprising the following steps: acquiring data information of each micro-grid at the current moment; predicting target power generation capacity of each power generation system in each micro-grid at a specified time based on the power information in the data information; predicting the predicted power consumption of each power consumption system in each micro-grid corresponding to the specified time based on the power consumption in the data information; predicting the predicted refrigerating capacity and the predicted heat supply capacity of each combined cooling, heating and power supply system in each micro-grid corresponding to the specified time based on the data information; predicting the predicted refrigeration consumption and the predicted heat supply consumption of each micro-grid corresponding to the specified time based on the data information; and optimizing and scheduling each power generation system, each power consumption system and each micro-grid energy storage system based on the generated energy, the power consumption, the predicted refrigerating capacity, the predicted heat supply quantity, the predicted refrigerating consumption and the predicted heat supply consumption. And the operation efficiency of each micro-grid is improved.

Description

Multi-micro-grid energy storage system scheduling method, device, equipment and medium

Technical Field

The present disclosure relates to the technical field of micro-grids, and in particular, to a method, an apparatus, a device, and a medium for scheduling an energy storage system of a multi-micro-grid.

Background

Along with the rapid development of high quality of the economic society in China, the demand for green energy is continuously increased. Clean energy such as renewable energy sources has become an indispensable important part of energy supply in China, and micro-grids are regarded as effective solutions of renewable energy sources, which play an increasingly important role in improving power supply reliability, safety, consuming renewable energy sources and the like.

In recent years, a micro grid has undergone a new development history, and a micro grid group composed of a plurality of micro grid systems is gradually transformed from a single micro grid system. The micro-grid group not only has higher reliability, but also can effectively improve the in-situ digestion capability of renewable energy sources, but also brings new technical challenges. Because the power grid is large in scale, high in complexity and diversified, the power generation amount and the power consumption of micro-grids with various scales cannot be accurately predicted, the centralized scheduling method in the traditional power industry has difficulty in meeting the optimal scheduling requirement of the micro-grid group, and the overall benefit of the micro-grid group system and the benefit of a single micro-grid in the system are difficult to balance.

Disclosure of Invention

An aim of the embodiment of the specification is to provide a dispatching method, a dispatching device, dispatching equipment and dispatching media for an energy storage system of a plurality of micro-grids, so that the running efficiency of the micro-grids is improved.

In one aspect, embodiments of the present disclosure provide a method for scheduling a multi-microgrid energy storage system, the method comprising:

acquiring current power generation amount, current running unit number and current unit running state of each power generation system in different micro-grids in a target area at the current moment, current power consumption amount of each power consumption system in each micro-grid in the target area, current refrigerating capacity and current heat supply amount of each combined cooling, heating and power supply system in each micro-grid in the target area, and current refrigerating consumption amount and current heat supply consumption amount of each micro-grid in the target area;

inputting the current power generation amount, the current running unit number and the current unit running state of each power generation system in different micro-grids into a pre-established power generation amount prediction model, and predicting the target power generation amount of each power generation system in each micro-grid at a specified time; the generating capacity prediction model is obtained based on the historical generating capacity, the number of the historical operating units and the operating state training of the historical operating units of each generating system in each micro-grid;

Inputting the current power consumption of each power consumption system in each micro-grid into a pre-established power consumption prediction model, and predicting the predicted power consumption of each power consumption system in each micro-grid corresponding to the specified time; the power consumption prediction model is obtained based on historical power consumption training of each power consumption system in each micro-grid;

inputting the current refrigerating capacity and the current heating capacity of each combined cooling, heating and power supply system in each micro-grid into a pre-established energy generation prediction model, and predicting the predicted refrigerating capacity and the predicted heating capacity of each combined cooling, heating and power supply system in each micro-grid corresponding to the specified time; the energy generation prediction model is obtained based on the historical refrigerating capacity and the historical heating capacity training of each combined cooling heating and power system in each micro-grid;

inputting the current refrigeration consumption and the current heat supply consumption of each micro-grid into a pre-established energy consumption prediction model, and predicting the predicted refrigeration consumption and the predicted heat supply consumption of each micro-grid corresponding to the specified time; the energy consumption prediction model is obtained based on historical refrigeration consumption and historical heat supply consumption of each micro-grid through training;

And carrying out optimized scheduling on each power generation system, each power consumption system and each micro-grid energy storage system in each micro-grid in the target area based on the target power generation amount of each power generation system in each micro-grid, the predicted power consumption amount of each power consumption system in each micro-grid, the predicted refrigerating capacity and the predicted heat supply amount of each combined cooling and power supply system in each micro-grid, and the predicted refrigerating consumption amount and the predicted heat supply consumption amount of each micro-grid.

Optionally, the optimizing and scheduling each power generation system, each power consumption system and each micro-grid energy storage system in each micro-grid in the target area based on the target power generation amount of each power generation system in each micro-grid, the predicted power consumption amount of each power consumption system in each micro-grid, the predicted cooling capacity and the predicted heat supply amount of each combined cooling and power supply system in each micro-grid, and the predicted cooling consumption amount and the predicted heat supply consumption amount of each micro-grid includes:

an objective function is constructed by the minimum power generation and operation scheduling total cost, the maximum power generation utilization rate, the refrigeration capacity utilization rate of the maximum combined heat and power supply system, the heat supply utilization rate of the maximum combined heat and power supply system, the minimum electric quantity stored by the micro-grid energy storage system and the minimum electric quantity of the commercial power, the constraint condition that the electricity storage capacity of each energy storage system in each micro-grid is smaller than the rated electricity storage capacity and the electricity generation capacity utilization rate of each energy storage system in each micro-grid, the refrigeration capacity utilization rate of the combined cooling, heating and power supply system is in a second preset range, and the heat supply capacity utilization rate of the combined cooling, heating and power supply system is in a third preset range is used as the objective function;

And carrying out optimized scheduling on each power generation system, each power consumption system and each micro-grid energy storage system in each micro-grid in the target area by utilizing an optimized cooperative algorithm based on the target power generation amount, the predicted power consumption amount, the predicted refrigerating capacity, the predicted heat supply amount, the predicted refrigerating consumption amount and the predicted heat supply consumption amount. Optionally, the objective function is:the constraint conditions of the objective function are as follows: />Wherein K represents the total cost of power generation and operation scheduling of the multi-micro-grid system; k (K) ₀ Representing operational scheduling costs between different micro-grids; p represents the power generation amount utilization rate; e represents power consumption; d represents the generated energy; d (D) ₀ Representing the electric quantity of the mains supply; j represents the electric quantity stored by the energy storage system; q represents the refrigerating capacity utilization rate of a cold-heat-electricity triple supply system in the multi-micro-grid system; h represents the refrigeration consumption; f represents the refrigerating capacity; r represents the heat supply utilization rate of a cold-heat-electricity triple supply system in the multi-micro-grid system; i represents heat supply consumption; g represents heat supply amount; j represents the energy storage electric quantity of an energy storage system in the multi-micro-grid; a. b and n respectively represent different micro-grids. Optionally, the power generation amount prediction model is established by the following modes: acquiring the historical power generation amount, the number of the historical operation units and the operation state of the historical units of each power generation system in each micro-grid in the target area as a power generation amount sample The data; establishing the power generation amount prediction model by adopting a time sequence neural network algorithm; and taking the generated energy sample data as training data of the generated energy prediction model, selecting a root mean square error as a loss function of the generated energy prediction model, selecting an average absolute error as an evaluation index of the generated energy prediction model, selecting a gradient descent algorithm as an optimization rule of the generated energy prediction model, and training the generated energy prediction model until the generated energy prediction model meets preset requirements. Optionally, the power consumption prediction model is established by the following method: acquiring historical power consumption of each power consumption system in each micro-grid in the target area as power consumption sample data; establishing the power consumption prediction model by adopting a long-short-term memory neural network algorithm; and taking the power consumption sample data as training data of the power consumption prediction model, selecting root mean square error as a loss function of the power consumption prediction model, selecting average absolute error as an evaluation index of the power consumption prediction model, selecting a gradient descent algorithm as an optimization rule of the power consumption prediction model, and training the power consumption prediction model until the power consumption prediction model meets preset requirements. Optionally, the energy generation prediction model is established by:

Acquiring historical refrigerating capacity and historical heating capacity of a triple co-generation system in each micro-grid in the target area as energy generation sample data;

establishing the energy generation prediction model by adopting a time sequence neural network algorithm;

and taking the energy generation sample data as training data of the energy generation prediction model, selecting root mean square error as a loss function of the energy generation prediction model, selecting average absolute error as an evaluation index of the energy generation prediction model, selecting a gradient descent algorithm as an optimization rule of the energy generation prediction model, and training the energy generation prediction model until the energy generation prediction model meets preset requirements.

Optionally, the energy consumption prediction model is established by the following method:

acquiring historical refrigeration consumption and historical heat supply consumption of each micro-grid in the target area as consumption prediction sample data;

establishing the energy consumption prediction model by adopting a long-term and short-term memory neural network algorithm;

and taking the energy consumption sample data as training data of the energy consumption prediction model, selecting root mean square error as a loss function of the energy consumption prediction model, selecting average absolute error as an evaluation index of the energy consumption prediction model, selecting a gradient descent algorithm as an optimization rule of the energy consumption prediction model, and training the energy consumption prediction model until the energy consumption prediction model meets preset requirements.

In yet another aspect, the present invention provides a multi-microgrid energy storage system scheduling apparatus comprising: at least one processor and a memory for storing processor-executable instructions that when executed by the processor implement the method described above.

In yet another aspect, the present invention provides a computer readable storage medium, which when executed by a processor of an electronic device, causes the electronic device to perform a multi-microgrid energy storage system scheduling method as described above.

According to the multi-micro-grid energy storage system scheduling method, device, equipment and medium, the related data of each system in different micro-grids in the target area are collected in real time, the generated energy of the power generation systems of the different micro-grids are respectively predicted by using the intelligent learning algorithm, the power consumption data of the different micro-grids are collected in real time, the power consumption of the different micro-grids at the next moment is predicted, and the prediction accuracy of the generated energy and the power consumption of the multi-micro-grids is improved. Meanwhile, based on the fact that the refrigerating capacity and the heating capacity data of the cold-heat-power triple supply systems of different micro-grids are collected in real time, the refrigerating capacity and the heating capacity of the cold-heat-power triple supply systems of different micro-grids at the next moment are predicted, based on the fact that the refrigerating capacity and the heating capacity data of the different micro-grids are collected in real time, the refrigerating capacity and the heating capacity of the different micro-grids at the next moment are predicted, and the prediction precision of the refrigerating capacity and the heating capacity of the different micro-grid systems and the respective demand capacities of the different micro-grid systems is improved. In addition, the power generation and dispatching cost, the power generation utilization rate, the refrigerating capacity utilization rate, the heating capacity utilization rate and the energy storage system of different micro-grids are optimized by adopting a collaborative optimization algorithm, so that a theoretical basis is provided for the collaborative efficient high-quality operation dispatching of a plurality of micro-grids, and the reliability of a multi-micro-grid system is improved.

Drawings

In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some of the embodiments described in the present description, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic flow chart of a multi-microgrid energy storage system scheduling embodiment provided in an embodiment of the present disclosure;

FIG. 2 is a schematic block diagram illustrating one embodiment of a multi-microgrid energy storage system dispatching apparatus provided herein;

fig. 3 is a block diagram of a hardware architecture of a multi-microgrid energy storage system scheduling server in one embodiment of the present disclosure.

Detailed Description

In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.

Micro-Grid (Micro-Grid) is also translated into a Micro-Grid, and refers to a small power generation and distribution system consisting of a distributed power supply, an energy storage device, an energy conversion device, a load, a monitoring and protecting device and the like.

The proposal of the micro-grid aims to realize flexible and efficient application of the distributed power supply and solve the problem of grid connection of the distributed power supply with huge quantity and various forms. The development and extension of the micro-grid can fully promote the large-scale access of the distributed power supply and the renewable energy sources, realize the high-reliability supply of various energy forms of loads, and be an effective way for realizing an active power distribution network, so that the traditional power grid is transited to the intelligent power grid.

Fig. 1 is a schematic flow chart of a multi-microgrid energy storage system scheduling embodiment provided in an embodiment of the present disclosure. Although the description provides methods and apparatus structures as shown in the examples or figures described below, more or fewer steps or modular units may be included in the methods or apparatus, whether conventionally or without inventive effort. In the steps or the structures where there is no necessary causal relationship logically, the execution order of the steps or the module structure of the apparatus is not limited to the execution order or the module structure shown in the embodiments or the drawings of the present specification. The described methods or module structures may be implemented in a device, server or end product in practice, in a sequential or parallel fashion (e.g., parallel processor or multi-threaded processing environments, or even distributed processing, server cluster implementations) as shown in the embodiments or figures.

In a specific embodiment, as shown in fig. 1, in one embodiment of the multi-micro grid energy storage system scheduling provided in the present disclosure, the method may be applied to a terminal device such as a computer, a tablet computer, a server, a smart phone, etc., and the method may include the following steps:

step 102, obtaining the current power generation amount, the current running unit number and the current unit running state of each power generation system in different micro-grids in a target area at the current moment, the current power consumption amount of each power consumption system in each micro-grid in the target area, the current refrigerating capacity and the current heat supply amount of each combined heat and power supply system in each micro-grid in the target area, and the current refrigerating consumption amount and the current heat supply consumption amount of each micro-grid in the target area.

In a specific implementation, the power generation systems in the microgrid include, but are not limited to: wind power generation system, photovoltaic power generation system, gas power generation system, geothermal power generation system, cold and heat electricity trigeminy supply system and the commercial power system of switching on. Power consuming systems in a microgrid include, but are not limited to: the system comprises a refrigerating power consumption system for refrigerating a micro-grid, a heating power consumption system for refrigerating the micro-grid, a power consumption system of the micro-grid and the rest system formed by other power consumption equipment except the three systems.

It can be understood that the current power generation amount, the current running unit number and the current unit running state have mapping relations with the corresponding systems. The current power consumption also has a mapping relation with each power consumption system.

In a specific implementation process, the target area is a coverage area of a plurality of micro-grids, and the micro-grids can be understood as grids in scenes such as industrial parks, mines, hotels or farms, and the embodiment of the specification is not specifically limited, and can be specifically adjusted according to actual use scenes. The historical data can be understood as all data generated before the current moment, and the data such as the generated energy, the number of the running units and the running state of the units can be obtained in real time, and other data such as: the unit running time and the like are not particularly limited, and the embodiment of the specification can be specifically adjusted according to actual use scenes.

Step 104, inputting the current power generation amount, the current running unit number and the current unit running state of each power generation system in different micro-grids into a pre-established power generation amount prediction model, and predicting the target power generation amount of each power generation system in each micro-grid at a specified time; the generating capacity prediction model is obtained based on the historical generating capacity, the number of the historical operating units and the operating state training of the historical units of each generating system in each micro-grid.

In a specific implementation process, the power generation amount prediction model is established by the following modes:

acquiring historical power generation capacity, the number of historical operation units and the operation state of the historical units of each power generation system in each micro-grid in the target area as power generation capacity sample data;

establishing the power generation amount prediction model by adopting a time sequence neural network algorithm;

and taking the generated energy sample data as training data of the generated energy prediction model, selecting a root mean square error as a loss function of the generated energy prediction model, selecting an average absolute error as an evaluation index of the generated energy prediction model, selecting a gradient descent algorithm as an optimization rule of the generated energy prediction model, and training the generated energy prediction model until the generated energy prediction model meets preset requirements.

Specifically, the preset requirement may include that the training frequency reaches a preset frequency or that the model precision reaches a preset requirement.

In some embodiments of the present disclosure, the power generation amount prediction model may be trained according to the following steps:

1. the method comprises the steps of dividing historical generated energy, the number of running units and unit running state data of a wind power generation system, a photovoltaic power generation system, a geothermal power generation system and a combined cooling, heating and power generation system in different micro-grids into generated energy training sample data and generated energy test sample data according to time sequence, wherein the proportion of the generated energy training sample data to the generated energy test sample data is 8:2;

2. Constructing a time sequence neural network model; the time-series neural network can be a cyclic neural network (recurrent neural network, RNN) which is a type of neural network specifically designed to process variable-length sequence data.

3. Selecting root mean square error and average absolute error as a loss function and an evaluation index of the model; the root mean square error and the average absolute error may be selected as required, and are not particularly limited in the embodiment of the present specification.

4. Selecting a gradient descent algorithm as an optimization algorithm of the time sequence neural network model; among them, gradient descent (gradient descent) is widely used in machine learning, whether in linear regression or Logistic regression, and its main purpose is to find the minimum of an objective function or converge to the minimum by iteration.

5. Inputting the generated energy training sample data into a time sequence neural network model for training;

6. inputting generating capacity test sample data to evaluate the time sequence neural network model;

7. and optimizing the structure and super parameters of the model by adopting a grid search algorithm, and selecting the optimal parameters to construct the generating capacity prediction model.

In some embodiments of the present disclosure, the parameter prediction model may be constructed using a time-series deep learning algorithm, such as: the Transformer neural network model can analyze the relation between the historical data and the generating capacity parameter at the next moment by utilizing a time sequence deep learning algorithm, and further predicts the parameter of the generating capacity of the micro-grid at the next moment based on the generating capacity at the current moment, the number of running units and the running state parameter input model of the units acquired in real time.

Step 106, inputting the current power consumption of each power consumption system in each micro-grid into a pre-established power consumption prediction model, and predicting the predicted power consumption of each power consumption system in each micro-grid corresponding to the specified time; the power consumption prediction model is obtained based on historical power consumption training of each power consumption system in each micro-grid.

The power consumption prediction model is established by the following steps:

acquiring historical power consumption of each power consumption system in each micro-grid in the target area as power consumption sample data;

establishing the power consumption prediction model by adopting a long-short-term memory neural network algorithm;

and taking the power consumption sample data as training data of the power consumption prediction model, selecting root mean square error as a loss function of the power consumption prediction model, selecting average absolute error as an evaluation index of the power consumption prediction model, selecting a gradient descent algorithm as an optimization rule of the power consumption prediction model, and training the power consumption prediction model until the power consumption prediction model meets preset requirements.

Specifically, the preset requirements include that the training times reach the preset times or that the model precision reaches the preset requirements.

In particular, the specified time may be understood as the next moment or the next cycle. The specified time can be set in the power generation amount prediction model and the power consumption amount prediction model according to the need, namely, the corresponding specified time is set before model training, and it is understood that the shorter the specified time is, the more accurate the predicted power consumption amount and power generation amount are.

In a specific implementation process, based on the obtained historical power consumption data of different consumption demands of each micro-grid, a power consumption prediction model of systems such as electricity, heating and refrigeration in each micro-grid at the next moment can be built by combining a time sequence neural network algorithm, so that real-time dynamic prediction of power consumption of different consumption demands is realized.

In some embodiments of the present disclosure, the power consumption prediction model may be trained as follows:

1. the obtained historical data of the power consumption of each micro-grid are divided into power consumption training sample data and power consumption test sample data according to time sequence, wherein the proportion of the power consumption training sample data to the power consumption test sample data is 8:2;

2. constructing a time sequence neural network model; the method comprises the steps of carrying out a first treatment on the surface of the

4. Selecting a gradient descent algorithm as an optimization algorithm of the model; among them, gradient descent (gradient descent) is widely used in machine learning, whether in linear regression or Logistic regression, and its main purpose is to find the minimum of an objective function or converge to the minimum by iteration.

5. Inputting power consumption training sample data into a time sequence neural network model for training;

6. inputting power consumption test sample data to evaluate the time sequence neural network model;

7. and optimizing the structure and super parameters of the model by adopting a grid search algorithm, and selecting the optimal parameters to construct a power consumption prediction model.

In some embodiments of the present disclosure, the parameter prediction model may be constructed using a time-series deep learning algorithm, such as: the Transformer neural network model with the attention mechanism can analyze the relation between the historical data and the power consumption parameters at the next moment by utilizing a time sequence deep learning algorithm, and further predicts the power consumption parameters at the next moment based on the power consumption parameter input model at the current moment acquired in real time.

Step 108, inputting the current refrigerating capacity and the current heating capacity of each combined cooling, heating and power supply system in each micro-grid into a pre-established energy generation prediction model, and predicting the predicted refrigerating capacity and the predicted heating capacity of each combined cooling, heating and power supply system in each micro-grid corresponding to the specified time; the energy generation prediction model is obtained based on the historical refrigerating capacity and the historical heating capacity training of each combined cooling heating and power system in each micro-grid.

The energy generation prediction model is established by the following modes:

In a specific implementation process, based on the obtained historical refrigerating capacity and historical heat supply capacity of the triple co-generation systems in each micro-grid, an energy generation prediction model of each triple co-generation system at the next moment can be built by combining a time sequence neural network algorithm, and real-time dynamic prediction of the refrigerating capacity and heat supply capacity of each triple co-generation system is realized.

In some embodiments of the present description, the energy generation predictive model may be trained as follows:

1. dividing the acquired historical data of the refrigerating capacity and the heat supply capacity of the triple co-generation system of each micro-grid into energy generation training sample data and energy generation test sample data according to time sequence, wherein the proportion of the energy generation training sample data to the energy generation test sample data is 8:2;

5. Inputting energy generation training sample data into a time sequence neural network model for training;

6. inputting energy generation test sample data to evaluate the time sequence neural network model;

7. and optimizing the structure and super parameters of the model by adopting a grid search algorithm, and selecting the optimal parameters to construct an energy generation prediction model.

In some embodiments of the present disclosure, the parameter prediction model may be constructed using a time-series deep learning algorithm, such as: the relationship between the historical data and the parameters of the refrigerating capacity and the heating capacity at the next moment can be analyzed by utilizing a time sequence deep learning algorithm by using a transducer neural network model with an attention mechanism, and the parameters of the refrigerating capacity and the heating capacity at the next moment are further predicted based on the parameters of the refrigerating capacity and the heating capacity at the current moment acquired in real time and input into the model.

Step 110, inputting the current refrigeration consumption and the current heat supply consumption of each micro-grid into a pre-established energy consumption prediction model, and predicting the predicted refrigeration consumption and the predicted heat supply consumption of each micro-grid corresponding to the specified time; the energy consumption prediction model is obtained based on historical refrigeration consumption and historical heat supply consumption of each micro-grid through training.

The energy consumption prediction model is established by the following steps:

In a specific implementation process, an energy generation prediction model of each micro-grid at the next moment can be built by combining a time sequence neural network algorithm based on the acquired historical refrigeration consumption and historical heat supply consumption in each micro-grid, so that real-time dynamic prediction of the refrigeration capacity and heat supply capacity of each micro-grid is realized.

1. dividing the acquired historical data of the refrigerating capacity and the heat supply capacity of each micro-grid into energy consumption training sample data and energy generation test sample data according to time sequence, wherein the ratio of the energy consumption training sample data to the energy consumption test sample data is 8:2;

5. Inputting the energy consumption training sample data into a time sequence neural network model for training;

6. inputting energy consumption test sample data to evaluate the time sequence neural network model;

And 112, optimally scheduling each power generation system, each power consumption system and each micro-grid energy storage system in each micro-grid in the target area based on the target power generation amount of each power generation system in each micro-grid, the predicted power consumption amount of each power consumption system in each micro-grid, the predicted refrigerating capacity and the predicted heat supply amount of each combined cooling and power supply system in each micro-grid and the predicted refrigerating consumption amount and the predicted heat supply consumption amount of each micro-grid.

Specifically, the optimizing and scheduling each power generation system, each power consumption system and each micro-grid energy storage system in each micro-grid in the target area based on the target power generation amount of each power generation system in each micro-grid, the predicted power consumption amount of each power consumption system in each micro-grid, the predicted cooling capacity and the predicted heat supply amount of each combined cooling and power supply system in each micro-grid, and the predicted cooling consumption amount and the predicted heat supply consumption amount of each micro-grid includes:

And carrying out optimized scheduling on each power generation system, each power consumption system and each micro-grid energy storage system in each micro-grid in the target area by utilizing an optimized cooperative algorithm based on the target power generation amount, the predicted power consumption amount, the predicted refrigerating capacity, the predicted heat supply amount, the predicted refrigerating consumption amount and the predicted heat supply consumption amount.

In a specific implementation process, after the power generation amount of each micro-grid, the power consumption amount of each micro-grid, the predicted refrigerating capacity and the predicted heat supply amount of each combined heat and power supply system, the predicted refrigerating consumption amount and the predicted heat supply consumption amount of each micro-grid are predicted at the next moment, an objective function can be constructed by using the minimum power generation and operation scheduling cost, the highest power generation utilization rate, the optimal energy storage and the utilization of commercial power, the energy storage capacity of the micro-grid energy storage system, the electric consumption amount of electricity, the electric consumption amount of heating, the electric consumption amount of refrigeration and the residual electric consumption amount are taken as constraints, and a collaborative optimization algorithm (such as a fast elite non-dominant ranking genetic algorithm) is combined to realize real-time optimal scheduling of the micro-grid energy storage system.

Based on the above embodiments, in one embodiment of the present specification,

the objective function is:

The constraint conditions of the objective function are as follows:

wherein, K represents the total cost of power generation and operation scheduling of the multi-micro-grid system; k (K) ₀ Representing operational scheduling costs between different micro-grids; p represents the power generation amount utilization rate; e represents power consumption; d represents the generated energy; d (D) ₀ Representing the electric quantity of the mains supply; j represents the electric quantity stored by the energy storage system; q represents the refrigerating capacity utilization rate of a cold-heat-electricity triple supply system in the multi-micro-grid system; h representsRefrigeration consumption; f represents the refrigerating capacity; r represents the heat supply utilization rate of a cold-heat-electricity triple supply system in the multi-micro-grid system; i represents heat supply consumption; g represents heat supply amount; j represents the energy storage electric quantity of an energy storage system in the multi-micro-grid; a. b and n respectively represent different micro-grids.

In a specific implementation process, the generated energy of a power generation system in different micro-grids at the next moment and the power consumption at the next moment are predicted, meanwhile, the refrigerating capacity and the heating capacity of the different micro-grid triple co-generation system at the next moment and the refrigerating demand and the heating demand of different micro-grids at the next moment are predicted, then, objective functions can be constructed by using the lowest power generation and operation scheduling cost, the highest generated energy utilization rate, the highest refrigerating and heating capacity utilization rate and the optimal energy storage and commercial power utilization, the electric energy storage capacity of the different micro-grid energy storage systems, the power consumption, the heating and the refrigerating demands of the different micro-grids are used as constraints, and the real-time optimal scheduling of the multi-micro-grid power generation, energy storage, refrigeration and heating systems is realized by combining a collaborative optimization algorithm (such as a fast elite non-dominant sequencing genetic algorithm).

Taking three micro-grids of a target area as an example, the parameters of each micro-grid are as follows:

micro grid a:

wherein Da represents the total power generation amount of the micro grid a; da1, da2, da3 and Da4 respectively represent the generated energy of a wind power generation system, a photovoltaic power generation system, a geothermal power generation system and a combined cooling, heating and power supply system in the micro-grid a; ea represents the total power consumption of the micro grid a; ea1, ea2 and Ea3 respectively represent the electric quantity required by electricity consumption, the electric quantity required by heating consumption and the electric quantity required by refrigeration consumption in the micro-grid a; fa represents the total refrigeration capacity of the combined cold and heat power system of the micro grid a; fa1 represents the refrigerating capacity of the inter-cooling, heating, power generation and triple supply system of the micro grid a; ga represents the total heat supply quantity of the intercooling-heating-power triple-generation system of the micro grid a; ga1 represents the heat supply of an intercooling-heating-power triple-generation system of the micro grid a; ha represents the total refrigeration demand of the micro grid a; ha1 represents the total refrigeration demand of the micro grid a; ia represents the total heating demand of the micro grid a; ia1 represents the heating demand of the micro grid a; ja represents the storage capacity of the energy storage system in the micro grid a; ja1 represents the electric power storage amount of the energy storage system in the micro grid a; ka represents the total cost of the micro grid a; ka1, ka2, ka3, ka4, ka5, ka6, ka7 and Ka8 respectively represent the costs of wind power generation system, photovoltaic power generation system, geothermal power generation system, combined cooling, heating, power generation system, cooling, heating, energy storage and operation scheduling in the micro-grid a.

Micro grid b:

wherein Db represents the total power generation amount of the micro grid b; db1, db2, db3 and Db4 respectively represent the generated energy of a wind power generation system, a photovoltaic power generation system, a geothermal power generation system and a combined cooling, heating and power supply system in the micro-grid b; eb represents the total power consumption of the micro grid b; eb1, eb2 and Eb3 respectively represent the electric quantity required by electricity consumption, the electric quantity required by heating consumption and the electric quantity required by refrigeration consumption in the micro-grid b; fb represents the total refrigerating capacity of the intercooling-heating-power cogeneration system of the micro-grid b; fb1 represents the refrigerating capacity of an intercooling-heating-power triple-generation system of the micro-grid b; gb represents the total heat supply quantity of the inter-cooling, heating and power triple supply system of the micro grid b; gb1 represents the heat supply of the inter-cooling, heating and power triple supply system of the micro grid b; hb represents the total refrigeration demand of the micro grid b; hb1 represents the total refrigeration demand of the micro grid b; ib represents the total heating demand of the micro grid b; ib1 represents the heating demand of the micro grid b; jb represents the storage capacity of the energy storage system in the micro grid b; jb1 represents the amount of electricity stored in the energy storage system in the micro grid b; kb represents the total cost of the micro grid b; kb1, kb2, kb3, kb4, kb5, kb6, kb7 and Kb8 respectively represent the cost of wind power generation system, photovoltaic power generation system, geothermal power generation system, combined cooling, heating, power generation system, cooling, heating, energy storage and operation scheduling in the micro-grid b.

Micro grid b:

where Dc represents the total power generation of the micro grid c; dc1, dc2, dc3 and Dc4 respectively represent the generated energy of a wind power generation system, a photovoltaic power generation system, a geothermal power generation system and a combined cooling, heating and power supply system in the micro-grid c; ec represents the total power consumption of the micro grid c; ec1, ec2 and Ec3 respectively represent the electricity consumption required by electricity, the heat consumption required by electricity and the refrigeration consumption required by electricity in the micro-grid c; fc represents the total refrigeration capacity of the combined cold and heat power system of the micro grid c; fc1 represents the refrigerating capacity of an intercooling-heating-power triple-generation system of the micro grid c; gc represents the total heat supply quantity of the intercooling-heating-power triple-generation system of the micro-grid c; gc1 represents the heat supply of an intercooling-heating-power triple-generation system of the micro-grid c; hc represents the total refrigeration demand of the micro grid c; hc1 represents the total refrigeration demand of the micro grid c; ic represents the total heating demand of the micro grid c; ic1 represents the heating demand of the micro grid c; jc represents the storage capacity of the energy storage system in the microgrid c; jc1 represents the amount of electricity stored in the energy storage system in the micro grid c; kc represents the total cost of the micro grid c; kc1, kc2, kc3, kc4, kc5, kc6, kc7 and Kc8 respectively represent the cost of wind power generation system, photovoltaic power generation system, geothermal power generation system, combined cooling, heating, power generation system, combined cooling, heating, energy storage and operation scheduling in the micro-grid c.

In the present specification, each embodiment of the method is described in a progressive manner, and the same and similar parts of each embodiment are referred to each other, and each embodiment mainly describes differences from other embodiments. Reference is made to the description of parts of the method embodiments where relevant.

Based on the above-mentioned multi-micro-grid energy storage system dispatching method, one or more embodiments of the present disclosure further provide an apparatus for multi-micro-grid energy storage system dispatching. The system may include devices (including distributed systems), software (applications), modules, components, servers, clients, etc. that use the methods described in embodiments of the present description in combination with the necessary devices to implement the hardware. Based on the same innovative concepts, the embodiments of the present description provide means in one or more embodiments as described in the following embodiments. Because the implementation schemes and methods of the device for solving the problems are similar, the implementation of the device in the embodiments of the present disclosure may refer to the implementation of the foregoing method, and the repetition is omitted. As used below, the term "unit" or "module" may be a combination of software and/or hardware that implements the intended function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

Specifically, fig. 3 is a schematic block diagram of an embodiment of a multi-micro grid energy storage system dispatching device provided in the present specification, as shown in fig. 3, where the multi-micro grid energy storage system dispatching device provided in the present specification includes:

the data obtaining module 31 is configured to obtain a current power generation amount, a current number of operating units, a current operating state of the units, a current power consumption amount of each power consumption system in each micro grid in the target area, a current cooling capacity and a current heat supply amount of each combined cooling and heating power system in each micro grid in the target area, and a current cooling consumption amount and a current heat supply consumption amount of each micro grid in the target area;

the generating capacity prediction module 32 is configured to input the current generating capacity, the number of current running units and the running state of the current units of each generating system in the different micro-grids into a pre-established generating capacity prediction model, and predict the target generating capacity of each generating system in each micro-grid at a specified time; the generating capacity prediction model is obtained based on the historical generating capacity, the number of the historical operating units and the operating state training of the historical operating units of each generating system in each micro-grid;

The power consumption prediction module 33 is configured to input the current power consumption of each power consumption system in each micro-grid into a power consumption prediction model that is built in advance, and predict the predicted power consumption of each power consumption system in each micro-grid corresponding to the specified time; the power consumption prediction model is obtained based on historical power consumption training of each power consumption system in each micro-grid;

the energy generation prediction module 34 is configured to input a current cooling capacity and a current heating capacity of each combined cooling and heating system in each micro-grid into a pre-established energy generation prediction model, and predict a predicted cooling capacity and a predicted heating capacity of each combined cooling and heating system in each micro-grid corresponding to a specified time; the energy generation prediction model is obtained based on the historical refrigerating capacity and the historical heating capacity training of each combined cooling heating and power system in each micro-grid;

the energy consumption prediction module 35 is configured to input the current refrigeration consumption and the current heat supply consumption of each micro-grid into a pre-established energy consumption prediction model, and predict the predicted refrigeration consumption and the predicted heat supply consumption of each micro-grid corresponding to a specified time; the energy consumption prediction model is obtained based on historical refrigeration consumption and historical heat supply consumption of each micro-grid through training;

The scheduling module 36 is configured to optimally schedule each power generation system, each power consumption system, and each micro-grid energy storage system in each micro-grid in the target area based on the target power generation amount of each power generation system in each micro-grid, the predicted power consumption amount of each power consumption system in each micro-grid, the predicted cooling amount and the predicted heat supply amount of each combined cooling and power supply system in each micro-grid, and the predicted cooling consumption amount and the predicted heat supply consumption amount of each micro-grid.

According to the multi-microgrid energy storage system scheduling provided by the embodiment of the specification, the related data of each power generation system in the microgrid are collected in real time, the power generation amounts of different power generation systems are respectively predicted by using the trained power generation amount prediction model, and the prediction accuracy of the power generation amounts is improved. Meanwhile, based on the real-time collection of power consumption data of different power consumption systems, the power consumption of each power consumption system corresponding to the designated time is predicted, the prediction accuracy of the power consumption is improved, and further, the predicted target power generation amount and the predicted power consumption are utilized to perform optimal scheduling on each power generation system, each power consumption system and the micro-grid energy storage system in the micro-grid, so that a theoretical basis is provided for the micro-grid to cooperate with efficient high-quality operation scheduling, the operation cost of the micro-grid is further improved, and the operation efficiency of the micro-grid is improved.

The embodiment of the specification also provides a multi-micro-grid energy storage system dispatching device, which comprises: at least one processor and a memory for storing processor executable instructions, the processor implementing the multi-microgrid energy storage system scheduling method of the above embodiment when executing the instructions, such as:

It should be noted that the above description of the apparatus and system according to the method embodiments may also include other implementations. Specific implementation may refer to descriptions of related method embodiments, which are not described herein in detail.

The multi-micro-grid energy storage system dispatching device provided by the specification can also be applied to various grid systems. The system or server or terminal or device may be a separate server or may include a server cluster, a system (including a distributed system), software (applications), an actual operating device, a logic gate device, a quantum computer, etc. using one or more of the methods or one or more embodiments of the present description in combination with necessary hardware implementation. The detection system for reconciling discrepancy data may comprise at least one processor and a memory storing computer executable instructions that when executed by the processor perform the steps of the method described in any one or more of the embodiments described above.

The method embodiments provided in the embodiments of the present specification may be performed in a mobile terminal, a computer terminal, a server, or similar computing device. Taking the operation on the server as an example, fig. 3 is a block diagram of a hardware structure of a multi-micro grid energy storage system dispatching server in one embodiment of the present disclosure, and the computer terminal may be the multi-micro grid energy storage system dispatching server or the multi-micro grid energy storage system dispatching device in the above embodiment. The server 10 as shown in fig. 3 may include one or more (only one is shown in the figure) processors 100 (the processor 100 may include, but is not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA), a nonvolatile memory 200 for storing data, and a transmission module 300 for communication functions. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 3 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, server 10 may also include more or fewer components than shown in FIG. 3, for example, may also include other processing hardware such as a database or multi-level cache, a GPU, or have a different configuration than that shown in FIG. 3.

The nonvolatile memory 200 may be used to store software programs and modules of application software, such as program instructions/modules corresponding to the multi-microgrid energy storage system scheduling method in the embodiments of the present disclosure, and the processor 100 executes the software programs and modules stored in the nonvolatile memory 200 to perform various functional applications and resource data updates. The non-volatile memory 200 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid state memory. In some examples, the non-volatile memory 200 may further include memory located remotely from the processor 100, which may be connected to the computer terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, office and networks, mobile communication networks, and combinations thereof.

The transmission module 300 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission module 300 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission module 300 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.

The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

The method or apparatus according to the foregoing embodiments provided in the present specification may implement service logic through a computer program and be recorded on a storage medium, where the storage medium may be read and executed by a computer, to implement effects of the solutions described in the embodiments of the present specification, for example:

Acquiring the current power generation amount, the number of current running units and the running state of the current units of each power generation system in the micro-grid at the current moment, and the current power consumption of each power consumption system in the micro-grid;

inputting the current power generation amount, the current running unit number and the current unit running state into a pre-established power generation amount prediction model, and predicting a target power generation amount corresponding to the designated time; the power generation amount prediction model is obtained based on historical power generation amount, the number of the historical operating units and the operating state of the historical units in a training mode;

inputting the current power consumption into a pre-established power consumption prediction model, and predicting the predicted power consumption corresponding to the specified time; the power consumption prediction model is obtained based on historical power consumption training;

and carrying out optimized scheduling on each power generation system, each power consumption system and the micro-grid energy storage system in the micro-grid based on the target power generation amount and the predicted power consumption.

The storage medium may include physical means for storing information, typically by digitizing the information before storing it in an electronic, magnetic, or optical medium. The storage medium may include: means for storing information using electrical energy such as various memories, e.g., RAM, ROM, etc.; devices for storing information using magnetic energy such as hard disk, floppy disk, magnetic tape, magnetic core memory, bubble memory, and USB flash disk; devices for optically storing information, such as CDs or DVDs. Of course, there are other ways of readable storage medium, such as quantum memory, graphene memory, etc.

The method and the device for dispatching the multi-micro grid energy storage system provided by the embodiment of the specification can be implemented in a computer by executing corresponding program instructions by a processor, such as a c++ language of a windows operating system is used for implementation on a PC end, a linux system is used for implementation, or other programming languages such as android and iOS systems are used for implementation on an intelligent terminal, and processing logic based on a quantum computer is used for implementation.

It should be noted that, the descriptions of the apparatus, the computer storage medium, and the system according to the related method embodiments described in the foregoing description may further include other implementations, and specific implementation manners may refer to descriptions of corresponding method embodiments, which are not described herein in detail.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are referred to each other, and each embodiment is mainly described in a different manner from other embodiments. In particular, for a hardware + program class embodiment, the description is relatively simple as it is substantially similar to the method embodiment, and reference is made to the partial description of the method embodiment where relevant.

Embodiments of the present description are not limited to situations in which industry communication standards, standard computer resource data updates, and data storage rules must be met or described in one or more embodiments of the present description. Some industry standards or embodiments modified slightly based on the implementation described by the custom manner or examples can also realize the same, equivalent or similar or predictable implementation effect after modification of the above examples. Examples of data acquisition, storage, judgment, processing, etc., using these modifications or variations may still fall within the scope of alternative implementations of the examples of this specification.

In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable Gate Array, FPGA)) is an integrated circuit whose logic function is determined by the programming of the device by a user. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.

The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a car-mounted human-computer interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

Although one or more embodiments of the present description provide method operational steps as described in the embodiments or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one way of performing the order of steps and does not represent a unique order of execution. In actual implementation of the apparatus or the terminal product, the methods illustrated in the embodiments or the drawings may be performed sequentially or in parallel (e.g., in a parallel processor or a multi-threaded processing environment, or even in a distributed resource data update environment). The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, it is not excluded that additional identical or equivalent elements may be present in a process, method, article, or apparatus that comprises a described element. The terms first, second, etc. are used to denote a name, but not any particular order.

For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, when one or more of the present description is implemented, the functions of each module may be implemented in the same piece or pieces of software and/or hardware, or a module that implements the same function may be implemented by a plurality of sub-modules or a combination of sub-units, or the like. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable resource data updating apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable resource data updating apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable resource data updating apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable resource data updating apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, read only compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage, graphene storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

One skilled in the relevant art will recognize that one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, one or more embodiments of the present description can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

One or more embodiments of the present specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the present specification may also be practiced in distributed computing environments where tasks are performed by remote devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are referred to each other, and each embodiment is mainly described in a different manner from other embodiments. In particular, for system embodiments, the description is relatively simple as it is substantially similar to method embodiments, and reference is made to the section of the method embodiments where relevant. In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present specification. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

The foregoing is merely an example of one or more embodiments of the present specification and is not intended to limit the one or more embodiments of the present specification. Various modifications and alterations to one or more embodiments of this description will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of the present specification, should be included in the scope of the claims.

Claims

1. A multi-microgrid energy storage system scheduling method, the method comprising:

Optimizing and scheduling each power generation system, each power consumption system and each micro-grid energy storage system in each micro-grid in the target area based on the target power generation amount of each power generation system in each micro-grid, the predicted power consumption amount of each power consumption system in each micro-grid, the predicted refrigerating capacity and the predicted heat supply amount of each combined cooling and power supply system in each micro-grid, and the predicted refrigerating consumption amount and the predicted heat supply consumption amount of each micro-grid;

the optimizing and dispatching the power generation systems, the power consumption systems and the micro-grid energy storage systems in the target area based on the target power generation amount of the power generation systems in the micro-grids, the predicted power consumption of the power consumption systems in the micro-grids, the predicted refrigerating capacity and the predicted heat supply amount of the combined cooling and power supply systems in the micro-grids, the predicted refrigerating consumption amount and the predicted heat supply consumption amount of the micro-grids, comprises the following steps:

2. The method of claim 1, wherein,

wherein K represents the total cost of power generation and operation scheduling of the multi-micro-grid system; k (K) ₀ Representing operational scheduling costs between different micro-grids; p represents the power generation amount utilization rate; e represents power consumption; d represents the generated energy; d (D) ₀ Representing the electric quantity of the mains supply; j represents the electric quantity stored by the energy storage system; q represents the refrigerating capacity utilization rate of a cold-heat-electricity triple supply system in the multi-micro-grid system; h represents the refrigeration consumption; f represents the refrigerating capacity; r represents the heat supply utilization rate of a cold-heat-electricity triple supply system in the multi-micro-grid system; i represents heat supply consumption; g represents heat supply amount; j represents the energy storage electric quantity of an energy storage system in the multi-micro-grid; j (J) _MAX Representing rated electricity storage capacity of an energy storage system in the multi-micro-grid; a. b and n respectively represent different micro-grids.

3. The method of claim 1, wherein the power generation amount prediction model is established by:

4. The method of claim 1, wherein the power consumption prediction model is established by:

5. The method of claim 1, wherein the energy generation predictive model is established by:

6. The method of claim 1, wherein the energy consumption prediction model is established by:

And taking the consumption prediction sample data as training data of the energy consumption prediction model, selecting root mean square error as a loss function of the energy consumption prediction model, selecting average absolute error as an evaluation index of the energy consumption prediction model, selecting a gradient descent algorithm as an optimization rule of the energy consumption prediction model, and training the energy consumption prediction model until the energy consumption prediction model meets preset requirements.

7. A multi-microgrid energy storage system dispatching device, the device comprising:

the data acquisition module is used for acquiring the current power generation amount, the current running unit number and the current unit running state of each power generation system in different micro-grids in a target area at the current moment, the current power consumption amount of each power consumption system in each micro-grid in the target area, the current refrigerating capacity and the current heat supply amount of each combined heat and power supply system in each micro-grid in the target area, and the current refrigerating consumption amount and the current heat supply consumption amount of each micro-grid in the target area;

the generating capacity prediction module is used for inputting the current generating capacity, the number of the current running units and the running state of the current units of each generating system in different micro-grids into a pre-established generating capacity prediction model to predict the target generating capacity of each generating system in each micro-grid at a designated time; the generating capacity prediction model is obtained based on the historical generating capacity, the number of the historical operating units and the operating state training of the historical operating units of each generating system in each micro-grid;

The power consumption prediction module is used for inputting the current power consumption of each power consumption system in each micro-grid into a pre-established power consumption prediction model to predict the predicted power consumption of each power consumption system in each micro-grid corresponding to the specified time; the power consumption prediction model is obtained based on historical power consumption training of each power consumption system in each micro-grid;

the energy generation prediction module is used for inputting the current refrigerating capacity and the current heating capacity of each combined cooling, heating and power system in each micro-grid into a pre-established energy generation prediction model to predict the predicted refrigerating capacity and the predicted heating capacity of each combined cooling, heating and power system in each micro-grid corresponding to the specified time; the energy generation prediction model is obtained based on the historical refrigerating capacity and the historical heating capacity training of each combined cooling heating and power system in each micro-grid;

the energy consumption prediction module is used for inputting the current refrigeration consumption and the current heat supply consumption of each micro-grid into a pre-established energy consumption prediction model to predict the predicted refrigeration consumption and the predicted heat supply consumption of each micro-grid corresponding to the specified time; the energy consumption prediction model is obtained based on historical refrigeration consumption and historical heat supply consumption of each micro-grid through training;

The dispatching module is used for carrying out optimized dispatching on each power generation system, each power consumption system and each micro-grid energy storage system in each micro-grid in the target area based on the target power generation amount of each power generation system in each micro-grid, the predicted power consumption amount of each power consumption system in each micro-grid, the predicted refrigerating capacity and the predicted heat supply amount of each combined cooling and power supply system in each micro-grid and the predicted refrigerating consumption amount and the predicted heat supply consumption amount of each micro-grid;

8. A multi-microgrid energy storage system scheduling device, comprising: at least one processor and a memory for storing processor-executable instructions which, when executed, implement the method of any one of claims 1-6.

9. A computer readable storage medium, characterized in that instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the multi-microgrid energy storage system scheduling method of any one of claims 1 to 6.