CN109114662B - Heating control method and system of electrothermal energy storage device based on multiple intelligent agents - Google Patents
Heating control method and system of electrothermal energy storage device based on multiple intelligent agents Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
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Abstract
The invention provides a heating control method of an electrothermal energy storage device based on multiple intelligent agents, which comprises the following steps: analyzing the characteristics of the electric heating energy storage device, and establishing a heat supply model, an electric load demand model and a heat storage model; providing an electric heating energy storage device control model of a plurality of agents; constructing constraint conditions of an electrothermal energy storage device according to the heating load model, the electrical load demand model and the heat storage model, and constructing a target function of a single electrothermal energy storage device agent according to the electrothermal energy storage device control model of the multi-agent; and optimizing the heating control of the multi-agent electrothermal energy storage device according to the constraint conditions of the electrothermal energy storage device and the objective function of the single electrothermal energy storage device agent. The heating control method of the electrothermal energy storage device based on the multi-agent fully utilizes the peak clipping and valley filling characteristics of the electrothermal energy storage device, and realizes peak clipping and valley filling of the whole electrothermal energy storage system.
Description
Technical Field
The invention relates to the field of control of heating systems, in particular to a heating control method of an electrothermal energy storage device based on multiple intelligent agents.
Background
Along with the continuous aggravation of haze weather in the heating period in winter, the nation starts to widely popularize the concept of replacing coal by electricity, replacing oil by electricity and coming from a distance, governments, electric power companies and heating companies in various places start the engineering project of changing coal into electricity to relieve atmospheric pollution, save energy, reduce consumption and gradually popularize and apply electric heating, and the concept will undoubtedly become one of important means for reducing urban environmental pollution. In recent years, with the rapid development of the electric power industry in China, a heating mode by utilizing heat generated by electric energy is more and more approved by people in terms of cleanness and convenience, the development of electric heating becomes one of the heating trends in winter, and meanwhile, the comprehensive consideration of the electric power structure in China is that Chinese provides a 'peak clipping and valley filling' measure in the electric power industry: the peak-valley electricity price is implemented, the peak time is advocated to use less electricity, and the valley time is advocated to use more electricity.
Therefore, a heat storage management mode of the electrothermal energy storage device based on multiple intelligent agents is needed to be provided, the peak clipping and valley filling characteristics of the electrothermal energy storage device are fully utilized, and the 'peak clipping and valley filling' of the whole electrothermal energy storage system is realized, so that the problem which needs to be solved by technical personnel in the field is urgently solved.
Disclosure of Invention
In order to solve the defects in the prior art, a heating control method and a heating control system of an electrothermal energy storage device based on multiple intelligent agents are provided.
The invention provides a heating control method of an electrothermal energy storage device based on multiple intelligent agents, which comprises the following steps:
step 1: analyzing the characteristics of the electric heating energy storage device, and establishing a heat supply model, an electric load demand model and a heat storage model;
step 2: providing an electric heating energy storage device control model of a plurality of agents;
and step 3: constructing constraint conditions of an electrothermal energy storage device according to the heating load model, the electrical load demand model and the heat storage model, and constructing a target function of a single electrothermal energy storage device agent according to the electrothermal energy storage device control model of the multi-agent;
and 4, step 4: and optimizing the heating control of the multi-agent electrothermal energy storage device according to the constraint conditions of the electrothermal energy storage device and the objective function of the single electrothermal energy storage device agent.
Preferably, the heat supply model of step 1 is as follows:
in the formula: phd_t: the heat supply load of the electric heating energy storage device at the moment t; n: the number of buildings in the heat supply range of the single electric heating energy storage device; q. q.sm: heat dissipation index per unit area of mth building; sm: surface area of the mth building; t isinside_t: the indoor temperature of the heat supply building at the moment t; t isoutside_t: and (4) supplying the outdoor temperature of the building at the time t.
Preferably, the establishing of the power load demand model in step 1 includes: the condition that the electric load demand of the electric heating energy storage device in the valley period should meet is shown as the following formula:
in the formula: phd_t: the heat supply load of the electric heating energy storage device at the moment t;
the electric load of the electric heating energy storage device is used at the time of t time at the night off-peak time; phd_max: maximum power of the electric heat energy storage device; delta Pnd_t: the heat loss power of the electric heating energy storage device during heat storage in the non-valley period; t is td_s、td_e、tnd: the start time, the end time of the nighttime valley period, and the end time of the non-valley period, respectively.
Preferably, the heat storage model in step 1 is represented by the following formula:
in the formula: qhd_t: the heat storage capacity of the electrothermal energy storage device at the moment t;
the electric load of the electric heating energy storage device is used at the time of t time at the night off-peak time; phd_t: and (4) heating load of the electrothermal energy storage device at the moment t.
And 2, providing a control model of the multi-agent electric heating energy storage device, wherein the control model comprises the steps of intelligently designing a dispatching management system of the electric heating energy storage device according to a multi-agent system, and forming the multi-agent electric heating energy storage device control model.
Preferably, the multi-agent electrothermal energy storage device control model comprises: a power grid agent GA, a regional agent RA, a community agent SA and an electric heating energy storage device agent EA; the GA, the RA, the SA and the EA are reserved and uploaded by the EA, are integrated by the SA, are subjected to calculation optimization through cooperation of the RA and the EA, are adjusted by the GA, and finally are subjected to cooperative work to prepare a control strategy.
Preferably, the constraint conditions in step 3 include: power constraint, heat storage constraint and power fluctuation constraint.
Preferably, the power constraint is as follows:
Phd_min≤Phd_t≤Phd_max (5)
in the formula: phd_t: the heat supply load of the electric heating energy storage device at the moment t; phd_minAnd Phd_max: respectively a lower limit and an upper limit of the power of the electric heating energy storage device.
Preferably, the stored heat amount is constrained as shown by the following formula:
Qhd_t≤Qhd_max (6)
in the formula: qhd_t: the heat storage capacity of the electrothermal energy storage device at the moment t; qhd_max: the maximum heat storage capacity of the heat accumulator of the electric heat energy storage device.
Preferably, the power fluctuation constraint is as follows:
in the formula: phd,t: the heat supply load of the electric heating energy storage device at the moment t; phd,t-1: the heat supply load of the electric heating energy storage device at the time t-1;
and
and the electric heating energy storage device raises and lowers the response speed limit of power.
Preferably, the objective function F in step 3 is represented by the following formula:
F＝min[max(Lj,r)-min(Lj,r)] (8)
in the formula: l isj,r: and scheduling actual load data of the electric heating energy storage device intelligent body in the j time period.
Preferably, the optimizing the heating control of the electrothermal energy storage device of the multi-agent in the step 4 includes:
4-1, initializing relevant parameters of the electric heating energy storage device;
4-2, initializing iteration parameters;
4-3, iteration is carried out according to the number of the time sections, and the electric heating energy storage device is distributed to each time section according to the access time;
4-4, calculating the heat storage and heat supply time period suitable for the heat storage and heat supply time period; 4-5, calculating and superposing the total load L after the heat supply load of the current electric heating energy storage device is superposedj,rAnd maximum load L of dispatching centerj,maxMaking a comparison if Lj,r＜Lj,maxIf so, producing a heat storage plan of the electric heating energy storage device and updating the scheduled load information, otherwise, exiting the cycle;
and 4-6, circularly iterating to T-24 according to the time period, and sequentially obtaining a heat storage plan of each electrothermal energy storage device within 1 day along with the advance of time.
Preferably, the relevant parameters of the step 4-1 include the number of the electrothermal energy storage devices, the starting time and the ending time of the night valley period, and the power upper and lower limit parameters.
The invention provides a heating control system of an electrothermal energy storage device based on multiple intelligent agents, which comprises: the system comprises a first building module, a second building module, a third building module, a fourth building module and an optimization module;
the first construction module is used for constructing a heat supply model, an electricity load demand model and a heat storage model according to the operating characteristics of the electric heating energy storage device;
the second construction module is used for constructing an electrothermal energy storage device control model of the multi-agent according to the multi-agent system;
the third construction module is used for constructing the constraint condition of the electrothermal energy storage device according to the first construction module;
the fourth construction module is used for constructing a target function of the intelligent body of the single electrothermal energy storage device according to the second construction module;
and the optimization module is used for optimizing the heating control of the multi-agent electrothermal energy storage device according to the constraint conditions of the electrothermal energy storage device of the third construction module and the objective function of the single electrothermal energy storage device agent of the fourth construction module.
Preferably, the first construction module comprises a construction heat supply sub-module, a construction electric load demand sub-module and a construction heat storage sub-module;
the construction of the heat supply submodule is as follows: for constructing a heat supply model according to the following formula:
in the formula: phd_t: the heat supply load of the electric heating energy storage device at the moment t; n: the number of buildings in the heat supply range of the single electric heating energy storage device; q. q.sm: heat dissipation index per unit area of mth building; sm: surface area of the mth building; t isinside_t: the indoor temperature of the heat supply building at the moment t; t isoutside_t: the outdoor temperature of the heat supply building at the moment t;
the construction of the electric load demand submodule comprises the following steps: the method is used for constructing the power load demand model according to the following formula:
in the formula: phd_t: the heat supply load of the electric heating energy storage device at the moment t;
the electric load of the electric heating energy storage device is used at the time of t time at the night off-peak time; phd_max: maximum work of electric heat energy storage deviceRate; delta Pnd_t: the heat loss power of the electric heating energy storage device during heat storage in the non-valley period; t is td_s、td_e、tnd: respectively the start time and the end time of the valley period and the end time of the non-valley period at night;
the construction heat storage quantity sub-module: for constructing a heat storage amount model according to the following formula:
in the formula: qhd_t: the heat storage capacity of the electrothermal energy storage device at the moment t;
the electric load of the electric heating energy storage device is used at the time of t time at the night off-peak time; phd_t: and (4) heating load of the electrothermal energy storage device at the moment t.
Preferably, the third building block comprises: the device comprises a power constraint submodule, a heat storage quantity constraint submodule and a power fluctuation constraint submodule;
the power constraint sub-module: for constructing a power constraint according to the following formula:
Phd_min≤Phd_t≤Phd_max
in the formula: phd_t: the heat supply load of the electric heating energy storage device at the moment t; phd_minAnd Phd_max: the lower limit and the upper limit of the power of the electric heating energy storage device are respectively set;
the heat storage amount restriction sub-module: for constructing a heat storage constraint according to the following equation:
Qhd_t≤Qhd_max
in the formula: qhd_t: the heat storage capacity of the electrothermal energy storage device at the moment t; qhd_max: the maximum heat storage capacity of a heat accumulator of the electric heating energy storage device;
the power fluctuation constraint submodule: for constructing a power fluctuation constraint according to the following formula:
in the formula: phd,t: the heat supply load of the electric heating energy storage device at the moment t; phd,t-1: the heat supply load of the electric heating energy storage device at the time t-1;
and
and the electric heating energy storage device raises and lowers the response speed limit of power.
Preferably, the fourth building block: for constructing an objective function according to the following formula:
F＝min[max(Lj,r)-min(Lj,r)]
in the formula: l isj,r: and scheduling actual load data of the electric heating energy storage device intelligent body in the j time period.
Preferably, the optimization module includes: the device comprises an initialization submodule, a distribution submodule, a calculation submodule and a comparison submodule;
the initialization submodule: the relevant parameters and the iteration parameters are used for initializing the electrothermal energy storage device;
the allocation submodule: the electric heating energy storage device is used for distributing the electric heating energy storage device to each time interval according to the access time;
the calculation submodule: used for calculating the heat storage and heat supply time interval suitable for the device and calculating the total load L after the heat supply load of the current electric heat energy storage device is superposedj,r；
The comparison submodule: total load L after heat supply load of current electric heat energy storage devicej,rAnd maximum load L of dispatching centerj,maxA comparison is made.
Compared with the closest prior art, the technical scheme provided by the invention has the following beneficial effects:
the heating control method of the electric heating energy storage device comprehensively considers all influence factors in the operation process of the electric heating energy storage device, analyzes the demand model of all the influence factors, provides the control model of the electric heating energy storage device, optimizes the heating control of the multi-agent electric heating energy storage device according to the constraint conditions of the electric heating energy storage device and the objective function of a single electric heating energy storage device agent, and achieves the effects of energy conservation, consumption reduction, peak clipping and valley filling.
The control strategy provided by the invention introduces a multi-agent thought, and a single electric heating energy storage device is regarded as a single agent system, so that a multi-agent system is formed, the system meets the operation indexes of the electric heating energy storage device required by the heat storage of a user, and the optimal control is carried out on the heating system based on the multi-agent electric heating energy storage device from the aspect of peak clipping and valley filling.
Drawings
FIG. 1 is a schematic diagram of a prior art electrothermal energy storage apparatus;
FIG. 2 is a block diagram of a prior art multi-agent system;
FIG. 3 is a diagram of a heat storage management architecture of a multi-agent based electrothermal energy storage device according to the present invention;
FIG. 4 is a flow chart of a heating control method of the multi-agent electrothermal energy storage device provided by the invention;
FIG. 5 is a flow chart of the optimal control of the multi-agent electrothermal energy storage device provided by the invention.
Detailed Description
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
Referring to the attached drawing 1, the electric heat energy storage device uses electricity as energy, converts the electricity into heat energy, and outputs certain steam, high-temperature water or organic heat carrier through conversion in the device to store the heat energy. The electric heating energy storage device is a high-efficiency and energy-saving heating device, which is started at the low-ebb time of a power grid, heats and stores water in a water tank, and is closed at the peak time of the power grid. The electric heating energy storage device utilizes the electric energy in the valley period as the energy source, realizes the measures of 'peak clipping and valley filling', improves the heat supply efficiency of the device, and reduces the heat supply power error of the heating device. Therefore, a heat supply model is established by considering factors influencing the heat supply of the electric heating energy storage device; analyzing the operating characteristics of the electric heating energy storage device, and establishing a load demand model of the electric heating energy storage device by considering the working principle and the operating mode of the electric heating energy storage device.
Referring to fig. 2, the general structure of an intelligent system mainly includes a decision module, a data collection module, a communication module, an execution module, a knowledge base, and a knowledge update module.
A knowledge base: the knowledge base is used for storing the existing knowledge of the intelligent agent and receiving the knowledge updated in real time. The knowledge consists of description data and information data. The description data refers to the self and environmental states of the intelligent agent, and comprises an environment model, a control object model, a target, an information processing model, an algorithm model, a task acceptance and decomposition model and the like of the intelligent agent; and the information data is a record of the monitoring object of the intelligent agent and other received object information.
A data collection module: and collecting and integrating the environmental information and a database of the system, screening control object information, and influencing the action of the decision module.
A decision module: and by analyzing the data in the knowledge base and the data collection module, the behavior of the intelligent agent is decided according to the preset and self-reasoning ability.
An execution module: and performing action on the control object according to the deployment of the decision module.
A communication module: and the intelligent agent is communicated with other intelligent agents or intelligent agents and is responsible for sending and receiving information.
Referring to fig. 3, the management scheme divides the power system into 3 levels: the 1 st layer is a power transmission network layer; the 2 nd layer is a power distribution network layer above the transformer; the 3 rd layer is an electricity utilization layer below the transformer. On the level below the transformer, the working state of the electric heating energy storage device is managed by adopting a multi-agent coordination control strategy. The electric heating energy storage device is used as an intelligent agent with adaptability, and the electric heating energy storage devices under the same transformer are a heat supply group. Dividing 1 day into a plurality of time intervals as required, when the electric heating energy storage device is connected to a power distribution network, the intelligent agent obtains load information of the connected transformer in the time interval and estimated load information of the connected transformer in subsequent time intervals, simultaneously combines the heating demand and available time intervals of users, formulates a heat supply plan of the intelligent agent, issues updated user load information to other intelligent agents, sequentially generates respective working plans according to the connection time, and jointly completes the whole 'peak shifting and valley filling' task of the electric heating energy storage device.
Referring to fig. 4 and 5, the invention further provides a heating control method of the multi-agent electric heating energy storage device, which specifically comprises the following steps:
step 1: analyzing the operating characteristics of the electric heating energy storage device, and establishing a load demand model of the electric heating energy storage device by considering the working principle and the operating mode of the electric heating energy storage device;
step 2: providing an electrothermal energy storage device control model based on multiple agents, analyzing the composition of the multiple agent system and the control strategy of the multiple agent system in the electrothermal energy storage device heating system;
and step 3: establishing an optimized control mathematical model of the electric heating energy storage device, analyzing and sorting main variables and parameters of the single electric heating energy storage device, analyzing and integrating parameters of the electric heating energy storage device and variables in the operation process of the electric heating energy storage device, and establishing a load control constraint condition and a target function of an intelligent agent of the single electric heating energy storage device;
and 4, step 4: an electric heating energy storage device optimization control research strategy based on multiple intelligent agents is formulated, and the purposes of energy saving, consumption reduction and peak clipping and valley filling are achieved.
The specific steps of step 1 are as follows:
step 1.1: analyzing the heat supply amount of the electrothermal energy storage device:
the heat supply load has many factors, including temperature, weather, solar radiation, wind power, building heat insulation characteristics, and the like, but is mainly affected by temperature. For analysis, a daily heat load prediction function is adopted, the temperature is used as a variable, and the heat supply amount of the electric heating energy storage device is a function of the outdoor temperature:
wherein: phd_tThe heat supply load of the electrothermal energy storage device at the moment t, n is the number of buildings in the heat supply range of a single electrothermal energy storage device, qmIs a heat dissipation index of the unit area of the mth building, and can be regarded as a constant, S, without considering the influence of wind speed and solar radiation on the heat dissipation index of the mth buildingmIs the surface area of the m-th building, Tinside_tAccording to the indoor air quality standard which is established by the state in 2002, GB/T18883-2002, the indoor temperature of a building is supplied at the moment T. The winter heating standard is regulated to be 16-24 ℃, and the indoor temperature meeting the standard is the comfortable indoor temperature, so the T isinside_tTaking the median value of 20 ℃, Toutside_tThe outdoor temperature of the heating building is supplied at the moment t.
Step 1.2: establishing an electrical load demand equation of the electrothermal energy storage device by analyzing the operation mode of the electrothermal energy storage device:
the electric heating energy storage device mainly runs at the valley period at night, and heat storage is carried out while basic heating is met. Firstly, in the valley period, the electric heat energy storage device is put into operation, one part of generated heat directly supplies heat to users to meet the basic heat demand, and the other part heats water in the heat accumulator to store the heat to meet the heat supply demand of the non-valley period; and secondly, at the non-valley period, the electric heating energy storage device stops running, and hot water in the heat accumulator is used for supplying heat to users. Accordingly, the electric load of the electric heating energy storage device furnace in the valley period should satisfy the following conditions:
wherein:
for the electric load of the electric heat energy storage device at the time of t period of the night valley period, Phd_maxFor maximum power, Δ P, of an electrothermal energy storage devicend_tThe heat loss power t of the electrothermal energy storage device during the heat storage in the non-valley periodd_s、td_e、tndThe start time, the end time of the nighttime valley period, and the end time of the non-valley period, respectively.
Step 1.3: the heat storage model of the electric heating energy storage device is as follows:
wherein: qhd_tThe heat storage capacity of the electrothermal energy storage device at the moment t is shown.
In the step 2: the optimal control model of the electrothermal energy storage device based on the multi-agent is characterized in that key parts in a system for dispatching and controlling the electrothermal energy storage device are designed into intelligent agents according to a multi-agent system and a complex adaptation theory, and a four-layer multi-agent system is formed. The system members are as follows: a grid Agent ga represents an uppermost grid in a system architecture, a regional Agent ra (regional Agent) represents a partitioned power scheduling information system, a Sub-distributed Agent SA (Sub-distributed Agent) represents a small-block information integration and integration system, and an electric heating energy storage device Agent ea (electric heating energy storage device) represents an electric heating energy storage device.
The GA represents the uppermost grid of the system, which represents the interest of the utility company. Under the multi-agent-based electric heating energy storage device dispatching management structure, the aim is to ensure the stability of a power grid, reduce the running loss of the power grid and maximize the benefits of a power company.
RA is a middle-layer agent under the jurisdiction of GA, is positioned at a key node of the multi-agent system, is an executor of GA formulation strategy, and is responsible for guiding the heat supply and storage period of the electric heating energy storage device, and is equivalent to the 'sales agent' of GA.
The SA is a cell agent under the jurisdiction of RA, and is an agent that mainly provides an auxiliary function. The SA is directly connected with each electric heating energy storage device and is responsible for integrating user demand uploading information and constraint monitoring. In fact, because RA receives huge data, when the heat supply required by the user is too much, the user cannot process the heat in time, and the problem of dimension disaster may occur. Therefore, the SA is necessary to be an auxiliary agent.
Wherein, when the integration information becomes the matrix, for preventing the emergence of dimension calamity, SA can be with the electric heat energy storage device of reservation and the required heat supply information whole one-tenth matrix of uploading in real time, later upload RA again, the matrix form as follows:
wherein A islAn m multiplied by n matrix uploaded for the ith SA, i is the number of the electrothermal energy storage devices, j is an event number, and PijThe power charged in the jth time period is predicted for the ith electrothermal energy storage device.
EA is the intelligent system implanted on the electric heat energy storage device, and represents the electric heat energy storage device to act and select. Among the objectives studied, the electrothermal energy storage device is planned with a view to minimizing the peak-to-valley difference. According to the heating requirement and the current charge state of the electric heating energy storage device issued by the upper level, the EA can independently make a selection to complete a task.
In the multi-agent system, the agents cooperate and negotiate with each other to complete the cooperative optimization task. Although the four-layer Agent has a secondary relation logically, the priority is not divided into upper and lower parts. Its importance throughout the multi-agent system is equal.
The four-layer agent is reserved and uploaded by EA, integrated by SA, cooperated by RA and EA for calculation and optimization, adjusted by GA, and finally collaboratively made into a control strategy.
In the step 3: the electric heating energy storage device has certain peak regulation capacity, can participate in power grid dispatching under the condition of being equipped with an intelligent load control device, and needs to meet corresponding constraint conditions in the operation process.
Step 3.1: power constraint of the electrothermal energy storage device:
heating load P of electrothermal energy storage device at time thd_tIs within its working range, i.e.:
Phd_min≤Phd_t≤Phd_max (5)
wherein: phd_minAnd Phd_maxRespectively a lower limit and an upper limit of the power of the electric heating energy storage device.
Step 3.2: heat storage restraint for electrothermal energy storage device
The electric heat energy storage device has the highest outlet water temperature, and if the water temperature in the heat accumulator exceeds 95 ℃, the electric heat energy storage device can reduce the load operation, so the heat storage amount is within the specified range under the condition of meeting the heat supply requirement in the next day, namely:
Qhd_t≤Qhd_max (6)
wherein: qhd_maxThe maximum heat storage capacity of the heat accumulator of the electric heating energy storage device.
Step 3.3: power fluctuation restraint of electrothermal energy storage device
The power adjustability of the electrothermal energy storage device is high, but in order to ensure the safe and stable operation of the electrothermal energy storage device, the power fluctuation of the electrothermal energy storage device should be limited within a certain range, namely:
wherein:
and
is the response speed limit of the power rising and falling of the electric heating energy storage device.
Step 3.4: establishing an objective function:
in order to achieve the goal of maximizing the interest of the utility company, the ideal load curve should be the distribution of new load peaks due to the concentrated operation of the electrothermal energy storage device to the adjacent load valley periods, i.e. the effect of so-called "peak clipping and valley filling", i.e. the following optimization goals are achieved:
F＝min[max(Lj,r)-min(Lj,r)] (8)
wherein: actual load data within a time period is scheduled for EV access.
The specific steps of step 4 are as follows:
step 4.1: firstly, relevant parameters of the electrothermal energy storage device are initialized. The method comprises the following steps of (1) parameters including the number of the electric heating energy storage devices, the starting time and the ending time of a night valley period (the period of time that the electric heating energy storage devices can store heat can be obtained), power upper and lower limits and the like;
step 4.2: initializing iteration parameters, namely dividing one day into 24 hours, wherein the number of the electric heating energy storage devices which are connected to a power grid to operate in each time period is as follows;
step 4.3: iteration is carried out according to the number of the time sections, and the electric heating energy storage device is distributed to each time section according to the access time;
step 4.4: iteration is carried out in each time period according to the number of the electrothermal energy storage devices, and each electrothermal energy storage device calculates the heat storage and heat supply time period suitable for the electrothermal energy storage device per se by combining and superimposing scheduling load information of the heat supply load of the electrothermal energy storage device before the electrothermal energy storage device is connected in the time period according to parameters such as the starting time, the ending time and the heat supply demand of the nighttime valley time period;
step 4.5: calculating the total load L after the heat supply load of the current electric heat energy storage device is superposedj,rAnd maximum load L of dispatching centerj,maxMaking a comparison if Lj,r＜Lj,maxProducing a heat storage plan of the electric heating energy storage device and updating the scheduled load information; otherwise, the circulation is exited (namely, the heat accumulation is not continued in the period);
step 4.6: and circularly iterating to T-24 according to the time period, and sequentially obtaining a heat storage plan of each electrothermal energy storage device within 1 day along with the advance of time.
In the heating control method of the multi-agent electric heating energy storage device, each electric heating energy storage device can make a heat storage plan by taking 'peak clipping and valley filling' as a target under the condition of meeting the heat demand of a user according to the heat demand of the electric heating energy storage device and the load information of a dispatching center.
In this embodiment, a heating control system of an electrothermal energy storage device based on multi-agent includes: the system comprises a first building module, a second building module, a third building module, a fourth building module and an optimization module;
the first construction module is used for constructing a heat supply model, an electric load demand model and a heat storage model according to the operating characteristics and the working principle of the electric heating energy storage device;
the second construction module is used for constructing an electrothermal energy storage device control model of the multi-agent according to the multi-agent system;
the third construction module is used for constructing the constraint condition of the electric heating energy storage device according to the first construction module;
the fourth construction module is used for constructing a target function of the intelligent agent of the single electrothermal energy storage device according to the second construction module;
and the optimization module is used for optimizing the heating control of the multi-agent electrothermal energy storage device according to the constraint conditions of the electrothermal energy storage device of the third construction module and the objective function of the single electrothermal energy storage device agent of the fourth construction module.
The first construction module comprises a construction heat supply submodule, a construction electric load demand submodule and a construction heat storage submodule;
constructing a heat supply submodule: for constructing a heat supply model according to the following formula:
in the formula: phd_t: the heat supply load of the electric heating energy storage device at the moment t; n: the number of buildings in the heat supply range of the single electric heating energy storage device; q. q.sm: heat dissipation index per unit area of mth building; sm: surface area of the mth building; t isinside_t: the indoor temperature of the heat supply building at the moment t; t isoutside_t: the outdoor temperature of the heat supply building at the moment t;
constructing an electric load demand submodule: the method is used for constructing the power load demand model according to the following formula:
in the formula: phd_t: the heat supply load of the electric heating energy storage device at the moment t;
the electric load of the electric heating energy storage device is used at the time of t time at the night off-peak time; phd_max: maximum power of the electric heat energy storage device; delta Pnd_t: the heat loss power of the electric heating energy storage device during heat storage in the non-valley period; t is td_s、td_e、tnd: respectively the start time and the end time of the valley period and the end time of the non-valley period at night;
constructing a heat storage sub-module: for constructing a heat storage amount model according to the following formula:
in the formula: qhd_t: the heat storage capacity of the electrothermal energy storage device at the moment t;
the electric load of the electric heating energy storage device is used at the time of t time at the night off-peak time; phd_t: and (4) heating load of the electrothermal energy storage device at the moment t.
The third building block comprises: the device comprises a power constraint submodule, a heat storage quantity constraint submodule and a power fluctuation constraint submodule;
a power constraint submodule: for constructing a power constraint according to the following formula:
Phd_min≤Phd_t≤Phd_max
in the formula: phd_t: the heat supply load of the electric heating energy storage device at the moment t; phd_minAnd Phd_max: the lower limit and the upper limit of the power of the electric heating energy storage device are respectively set;
a heat storage amount restraint submodule: for constructing a heat storage constraint according to the following equation:
Qhd_t≤Qhd_max
in the formula: qhd_t: the heat storage capacity of the electrothermal energy storage device at the moment t; qhd_max: the maximum heat storage capacity of a heat accumulator of the electric heating energy storage device;
a power fluctuation constraint submodule: for constructing a power fluctuation constraint according to the following formula:
in the formula: phd,t: the heat supply load of the electric heating energy storage device at the moment t; phd,t-1: the heat supply load of the electric heating energy storage device at the time t-1;
and
and the electric heating energy storage device raises and lowers the response speed limit of power.
A fourth building block: for constructing an objective function according to the following formula:
F＝min[max(Lj,r)-min(Lj,r)]
in the formula: l isj,r: and scheduling actual load data of the electric heating energy storage device intelligent body in the j time period.
The optimization module comprises: the device comprises an initialization submodule, a distribution submodule, a calculation submodule and a comparison submodule;
initializing a submodule: the system comprises a plurality of electric heating energy storage devices, a plurality of control modules and a control module, wherein the control module is used for initializing relevant parameters and iteration parameters of the electric heating energy storage devices, and the relevant parameters comprise the number of the electric heating energy storage devices, the starting time and the ending time of the low valley period at night and power upper and lower limit parameters;
an allocation submodule: the electric heating energy storage device is used for distributing the electric heating energy storage device to each time interval according to the access time;
a calculation submodule: for computing fitnessCombining self heat storage and heat supply time periods, and calculating the total load L after the heat supply load of the current electric heat energy storage device is superposedj,r；
A comparison submodule: total load L after heat supply load of current electric heat energy storage devicej,rAnd maximum load L of dispatching centerj,maxMaking a comparison if Lj,r＜Lj,maxAnd producing a heat storage plan of the electric heating energy storage device and updating the scheduled load information, otherwise, exiting the cycle.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing 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 data processing 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 data processing 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.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (14)
1. A heating control method of an electrothermal energy storage device based on multiple intelligent agents is characterized by comprising the following steps:
step 1: analyzing the characteristics of the electric heating energy storage device, and establishing a heat supply model, an electric load demand model and a heat storage model;
step 2: providing an electric heating energy storage device control model of a plurality of agents;
and step 3: constructing constraint conditions of an electrothermal energy storage device according to the heating load model, the electrical load demand model and the heat storage model, and constructing a target function of a single electrothermal energy storage device agent according to the electrothermal energy storage device control model of the multi-agent;
and 4, step 4: optimizing the heating control of the electrothermal energy storage devices of the multiple agents according to the constraint conditions of the electrothermal energy storage devices and the objective function of the agent of the single electrothermal energy storage device;
the step 2 of providing the control model of the multi-agent electric heating energy storage device comprises the steps of intelligently designing a dispatching management system of the electric heating energy storage device according to a multi-agent system, and forming the multi-agent electric heating energy storage device control model;
correspondingly, the multi-agent electrothermal energy storage device control model comprises: a power grid agent GA, a regional agent RA, a community agent SA and an electric heating energy storage device agent EA; the GA, the RA, the SA and the EA are reserved and uploaded by the EA, are integrated by the SA, are subjected to calculation optimization by cooperation of the RA and the EA, are adjusted by the GA, and finally cooperate to make a control strategy;
correspondingly, the step 4 of optimizing the heating control of the multi-agent electrothermal energy storage device comprises:
4-1, initializing relevant parameters of the electric heating energy storage device;
4-2, initializing iteration parameters;
4-3, iteration is carried out according to the number of the time sections, and the electric heating energy storage device is distributed to each time section according to the access time;
4-4, calculating the heat storage and heat supply time period suitable for the heat storage and heat supply time period;
4-5, calculating and superposing the total load L after the heat supply load of the current electric heating energy storage device is superposedj,rAnd maximum load L of dispatching centerj,maxMaking a comparison if Lj,r＜Lj,maxIf so, producing a heat storage plan of the electric heating energy storage device and updating the scheduled load information, otherwise, exiting the cycle;
4-6, circularly iterating to T-24 according to the time period, and sequentially obtaining a heat storage plan of each electrothermal energy storage device within 1 day along with the advance of time;
and the related parameters of the step 4-1 comprise the number of the electric heating energy storage devices, the starting time and the ending time of the low valley period at night and the upper and lower limit parameters of power.
2. The heating control method of an electrothermal energy storage device according to claim 1, wherein the heating load model of step 1 is as follows:
in the formula: phd_t: the heat supply load of the electric heating energy storage device at the moment t; n: number of buildings in heat supply range of single electric heating energy storage device；qm: heat dissipation index per unit area of mth building; sm: surface area of the mth building; t isinside_t: the indoor temperature of the heat supply building at the moment t; t isoutside_t: and (4) supplying the outdoor temperature of the building at the time t.
3. The heating control method of an electrothermal energy storage device according to claim 1, wherein the establishing of the electricity load demand model of step 1 comprises: the condition that the electric load demand of the electric heating energy storage device in the valley period should meet is shown as the following formula:
in the formula: phd_t: the heat supply load of the electric heating energy storage device at the moment t;
the electric load of the electric heating energy storage device is used at the time of t time at the night off-peak time; phd_max: maximum power of the electric heat energy storage device; delta Pnd_t: the heat loss power of the electric heating energy storage device during heat storage in the non-valley period; t is td_s、td_e、tnd: the start time, the end time of the nighttime valley period, and the end time of the non-valley period, respectively.
4. The heating control method of an electrothermal energy storage device according to claim 1, wherein the heat storage amount model of step 1 is represented by the following equation:
in the formula: qhd_t: the heat storage capacity of the electrothermal energy storage device at the moment t;
the electric load of the electric heating energy storage device is used at the time of t time at the night off-peak time; phd_t: and (4) heating load of the electrothermal energy storage device at the moment t.
5. The heating control method of an electrothermal energy storage device according to claim 1, wherein the constraint condition in step 3 includes: power constraint, heat storage constraint and power fluctuation constraint.
6. The method of claim 5, wherein the power constraint is as follows:
Phd_min≤Phd_t≤Phd_max (5)
in the formula: phd_t: the heat supply load of the electric heating energy storage device at the moment t; phd_minAnd Phd_max: respectively a lower limit and an upper limit of the power of the electric heating energy storage device.
7. A heating control method of an electrothermal energy storage device according to claim 5, wherein the stored heat amount constraint is as follows:
Qhd_t≤Qhd_max (6)
in the formula: qhd_t: the heat storage capacity of the electrothermal energy storage device at the moment t; qhd_max: the maximum heat storage capacity of the heat accumulator of the electric heat energy storage device.
8. The method of claim 5, wherein the power fluctuation constraint is as follows:
in the formula: phd,t: the heat supply load of the electric heating energy storage device at the moment t; phd,t-1: the heat supply load of the electric heating energy storage device at the time t-1;
and
and the electric heating energy storage device raises and lowers the response speed limit of power.
9. The heating control method of an electrothermal energy storage device according to claim 1, wherein the objective function F in the step 3 is represented by the following formula:
F＝min[max(Lj,r)-min(Lj,r)] (8)
in the formula: l isj,r: and scheduling actual load data of the electric heating energy storage device intelligent body in the j time period.
10. A multi-agent based heating control system for an electrothermal energy storage device, the system comprising: the system comprises a first building module, a second building module, a third building module, a fourth building module and an optimization module;
the first construction module is used for constructing a heat supply model, an electricity load demand model and a heat storage model according to the operating characteristics of the electric heating energy storage device;
the second construction module is used for constructing an electrothermal energy storage device control model of the multi-agent according to the multi-agent system;
the third construction module is used for constructing the constraint condition of the electrothermal energy storage device according to the first construction module;
the fourth construction module is used for constructing a target function of the intelligent body of the single electrothermal energy storage device according to the second construction module;
and the optimization module is used for optimizing the heating control of the multi-agent electrothermal energy storage device according to the constraint conditions of the electrothermal energy storage device of the third construction module and the objective function of the single electrothermal energy storage device agent of the fourth construction module.
11. A heating control system for an electrothermal energy storage device according to claim 10, wherein the first construction module comprises a construction heat supply sub-module, a construction electrical load demand sub-module, and a construction heat storage sub-module;
the construction of the heat supply submodule is as follows: for constructing a heat supply model according to the following formula:
in the formula: phd_t: the heat supply load of the electric heating energy storage device at the moment t; n: the number of buildings in the heat supply range of the single electric heating energy storage device; q. q.sm: heat dissipation index per unit area of mth building; sm: surface area of the mth building; t isinside_t: the indoor temperature of the heat supply building at the moment t; t isoutside_t: the outdoor temperature of the heat supply building at the moment t;
the construction of the electric load demand submodule comprises the following steps: the method is used for constructing the power load demand model according to the following formula:
in the formula: phd_t: the heat supply load of the electric heating energy storage device at the moment t;
the electric load of the electric heating energy storage device is used at the time of t time at the night off-peak time; phd_max: maximum power of the electric heat energy storage device; delta Pnd_t: the heat loss power of the electric heating energy storage device during heat storage in the non-valley period; t is td_s、td_e、tnd: respectively the start time and the end time of the valley period and the end time of the non-valley period at night;
the construction heat storage quantity sub-module: for constructing a heat storage amount model according to the following formula:
in the formula: qhd_t: the heat storage capacity of the electrothermal energy storage device at the moment t;
the electric load of the electric heating energy storage device is used at the time of t time at the night off-peak time; phd_t: and (4) heating load of the electrothermal energy storage device at the moment t.
12. The heating control system of an electrothermal energy storage device of claim 10, wherein the third building block comprises: the device comprises a power constraint submodule, a heat storage quantity constraint submodule and a power fluctuation constraint submodule;
the power constraint sub-module: for constructing a power constraint according to the following formula:
Phd_min≤Phd_t≤Phd_max
in the formula: phd_t: the heat supply load of the electric heating energy storage device at the moment t; phd_minAnd Phd_max: the lower limit and the upper limit of the power of the electric heating energy storage device are respectively set;
the heat storage amount restriction sub-module: for constructing a heat storage constraint according to the following equation:
Qhd_t≤Qhd_max
in the formula: qhd_t: the heat storage capacity of the electrothermal energy storage device at the moment t; qhd_max: the maximum heat storage capacity of a heat accumulator of the electric heating energy storage device;
the power fluctuation constraint submodule: for constructing a power fluctuation constraint according to the following formula:
in the formula: phd,t: the heat supply load of the electric heating energy storage device at the moment t; phd,t-1: the heat supply load of the electric heating energy storage device at the time t-1;
and
and the electric heating energy storage device raises and lowers the response speed limit of power.
13. A heating control system for an electrothermal energy storage device according to claim 10, wherein the fourth construction module: for constructing an objective function according to the following formula:
F＝min[max(Lj,r)-min(Lj,r)]
in the formula: l isj,r: and scheduling actual load data of the electric heating energy storage device intelligent body in the j time period.
14. A heating control system for an electrothermal energy storage device according to claim 10, wherein the optimization module comprises: the device comprises an initialization submodule, a distribution submodule, a calculation submodule and a comparison submodule;
the initialization submodule: the relevant parameters and the iteration parameters are used for initializing the electrothermal energy storage device;
the allocation submodule: the electric heating energy storage device is used for distributing the electric heating energy storage device to each time interval according to the access time;
the calculation submodule: used for calculating the heat storage and heat supply time interval suitable for the device and calculating the total load L after the heat supply load of the current electric heat energy storage device is superposedj,r；
The comparison submodule: total load L after heat supply load of current electric heat energy storage devicej,rAnd maximum load L of dispatching centerj,maxA comparison is made.
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| CN110230842A (en) * | 2019-03-25 | 2019-09-13 | 国网辽宁省电力有限公司 | A kind of heat storage electric boiler " peak load shifting " control method based on multiple agent |
| CN110567046A (en) * | 2019-09-25 | 2019-12-13 | 辽宁斯宝达节能科技开发有限公司 | Intelligent management system method for multi-mode heating equipment |
| CN111006299B (en) * | 2019-12-31 | 2021-08-20 | 国网北京市电力公司 | Control method and device for heat accumulating type electric heating |
| CN111981559A (en) * | 2020-08-21 | 2020-11-24 | 新疆中兴能源有限公司 | Wind-solar complementary power generation heat accumulation type electric heating system |
| CN112413702B (en) * | 2020-10-23 | 2021-11-26 | 国网天津市电力公司电力科学研究院 | Method and system for matching heat accumulating type electric heating load with distribution network area |
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