CN112053024A - Optimized scheduling method based on town energy Internet double-layer collaborative architecture - Google Patents

Optimized scheduling method based on town energy Internet double-layer collaborative architecture Download PDF

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CN112053024A
CN112053024A CN202010661691.5A CN202010661691A CN112053024A CN 112053024 A CN112053024 A CN 112053024A CN 202010661691 A CN202010661691 A CN 202010661691A CN 112053024 A CN112053024 A CN 112053024A
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王�琦
张年初
何国鑫
李宁
张红颖
贾一超
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Nanjing Normal University
China Electric Power Research Institute Co Ltd CEPRI
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China Electric Power Research Institute Co Ltd CEPRI
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Abstract

The invention discloses an optimized scheduling method based on a double-layer collaborative framework of a town energy Internet, which reasonably integrates cloud supervision and weak centralization thought, has wide adaptability to the large-scale development requirement of the town energy Internet, and applies the collaborative optimized double-layer regulation framework to a typical regulation scene of the town energy Internet, so that the application space is expanded as soon as possible, the application value is exerted in the process of complying with the energy technology revolution, and the optimized scheduling method has good technical adaptability.

Description

Optimized scheduling method based on town energy Internet double-layer collaborative architecture
Technical Field
The invention relates to the technical field of town energy Internet, in particular to an optimized scheduling method based on a double-layer collaborative architecture of the town energy Internet.
Background
Under the double pressure of great increase of energy demand and increasingly urgent environmental protection, the energy utilization efficiency is improved, the reasonable consumption of renewable energy is promoted, and the energy source is a necessary way for the improvement of energy structure. With the increasing level of urbanization in China, an energy internet system integrating internet +' becomes a new state of energy industry development under the background of novel cities and towns, and the problems of low comprehensive energy efficiency, insufficient new energy consumption capability, poor demand-response interaction and the like of the traditional energy system can be solved according to the basic criteria of green, low carbon and sustainability. The integrated planning design of the town energy Internet is carried out by breaking through the mode of independent planning, independent design and independent operation of the original single energy system, the overall optimization regulation and control of various heterogeneous energy flows such as electricity, gas and heat are realized, and the integrated planning and control method is the primary task of the construction of the town energy Internet.
The traditional centralized optimization regulation and control strategy is made on the premise of a single decision-making main body, an energy management system or a local power transformation control center is often used as a centralized management layer, however, distributed energy main bodies in the novel town energy Internet are increasing day by day, and electric, gas and heat subsystems also belong to different supervision departments and operators; the urban energy Internet has massive and heterogeneous engineering data, has extremely high requirements on data processing capacity and real-time performance, and simultaneously needs to consider privacy safety of an energy main body, so that the traditional centralized regulation and control are difficult to continue. For example, a central air conditioner of a commercial building is less subject to intrusive control by upper floors; some users are reluctant to upload complete energy usage data for privacy reasons. Due to the serious challenges of privacy, reliability, economy, flexibility, feasibility and the like faced by centralized optimal regulation, distributed regulation is increasingly applied to the town energy Internet, which allows neighboring distributed resources to exchange information. However, since there are many differences in the types, energy usage characteristics, and optimization decision-making capabilities of the energy principals, it is impossible to require all the energy principals to be consistent with the global optimization objective at any time, and moreover, a single benefit principal only considers the own optimization objective, thereby affecting the overall economy and safety of the system. Therefore, the optimal regulation and control mode of the urban energy internet still needs to be deeply researched, and different regulation and control modes need to be reasonably integrated to improve the comprehensive performance of the system.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an optimized scheduling method based on a double-layer collaborative architecture of the town energy Internet, and the proposed architecture is specifically applied to a typical regulation and control scene of the town energy Internet, so that the application space is expanded as soon as possible, the application value is exerted in the process of complying with the energy technology revolution, and the optimized scheduling method has good technical adaptability.
In order to solve the technical problem, the invention provides an optimized scheduling method based on a town energy Internet double-layer collaborative framework, which comprises the following steps:
(1) analyzing a physical framework and a decision structure of the town energy Internet, carding the type, a regulation and control target and a regulation and control requirement of an energy main body in the town energy Internet, dividing a typical regulation and control scene of the town energy Internet, and building a centralized-distributed double-layer cooperative optimization regulation and control framework of the town energy Internet based on an energy management system and a virtual soft controller;
(2) according to the characteristics of different energy bodies, the virtual controller is deployed in an actual deployment mode or an imaginary deployment mode;
(3) establishing an optimized regulation mathematical model under a town energy Internet centralized-distributed double-layer collaborative optimized regulation architecture according to optimized operation targets of different energy main bodies in a typical scene;
(4) and designing an interaction flow of an energy management system and virtual control according to the established optimal regulation mathematical model, and providing an optimal regulation mathematical model solving method based on the Bellman principle.
Preferably, in the step (1), the town energy Internet presents the physical architecture characteristic of free interconnection of a plurality of distributed energy main bodies; the decision structure of the town energy Internet is characterized in that each energy subject belongs to different benefit subjects, makes an autonomous decision, and is not subjected to centralized decision and direct regulation and control of a power grid; regulatory requirements include, but are not limited to, real-time requirements, privacy requirements, global requirements; the regulation and control scenes comprise but are not limited to short-term optimization scheduling operation and energy market trading bidding.
Preferably, in the step (1), an energy management system is deployed in a supervision center at the top layer of the town energy Internet, and a virtual controller is deployed in each distributed energy main body at the bottom layer; the virtual controller can be deployed in energy bodies including but not limited to small distributed photovoltaic, wind power, a heat power source, a gas supply source, an electricity/heat/gas pipeline network, industry/business/residents and the like, and a plurality of virtual devices jointly form a distributed decision-making layer of a town energy Internet centralized-distributed double-layer collaborative optimization regulation and control framework; control systems such as a large centralized generator set, a thermal power plant, a natural gas source, a high-voltage power transmission network and a heat/gas transmission pipeline are connected into a traditional town energy management system to form a centralized supervision and management layer of a town energy Internet centralized-distributed double-layer collaborative optimization regulation and control framework; the centralized supervision management layer is communicated with all the virtual controllers of the distributed decision-making layer, and the instruction is direct; under the framework, the energy management system only checks the local decision-making regulating and controlling quantity uploaded by each virtual controller, and the local decision-making regulating and controlling quantity is issued and executed after being confirmed or corrected.
Preferably, in the step (2), according to the type, the regulation requirement, the regulation target and the importance degree of the energy body, the autonomous regulation authority of each energy body is classified, and two modes of real deployment and imaginary deployment of the virtual soft controller are set so as to determine whether the execution of the decision needs to be supervised and approved by the energy management system of the top-level supervision center after the virtual controller of each energy body at the bottom layer autonomously generates the decision; selecting a deployment mode of the virtual controller, and comprehensively considering multiple factors including optimization targets of all benefit agents, asset owners and user privacy willingness; the real deployment mode refers to that computing resources are configured on the bottom distributed resource side, and besides the distributed computing function, the real deployment mode also bears the distributed control function, namely the self decision and the self control are really realized; the virtual deployment mode refers to the fact that computing resources are configured in a top-level energy management system, a top level provides computing services of distributed decision, and under the deployment mode, a virtual controller does not have the right of directly executing distributed control, needs to generate a regulation and control strategy and uploads the regulation and control strategy to the energy management system for supervision and approval.
Preferably, in step (3), in a typical scenario, two virtual controllers are provided, the virtual controller 1 represents a microgrid system, the virtual controller 2 represents a distributed multi-energy system, wherein an objective function of the microgrid is to comprehensively consider the energy storage system to realize peak clipping and valley filling and distributed clean energy consumption of the power grid, and an objective function of the distributed energy system is to consider NO simultaneouslyxAnd CO2Environmental indicators of emissions; the pollutants of the distributed multi-energy system mainly come from gas turbines, gas boilers, internal combustion engines, fuel cells, purchased electricity and the like, and NO is consideredxAnd CO2Discharge capacity;
the objective function of the micro-grid system is the optimization of the peak clipping and valley filling effects and the consumption and utilization rate of clean energy:
minf＝-(ω1f1+ω2f2)
wherein f is the optimization effect evaluation index of the system, f1For peak clipping and valley filling effects, f2For the consumption and utilization rate of clean energy, omega1，ω2The weight coefficients of the first two are respectively;
the constraints of the microgrid include:
(1) the constraint condition of photovoltaic power generation is
Figure BDA0002578810490000031
Figure BDA0002578810490000032
In the formula (I), the compound is shown in the specification,
Figure BDA0002578810490000033
respectively representing the lower and upper limits of the photovoltaic output, PcapaRepresenting the rated power, theta, S, of the photovoltaic unitpv，ηpvRespectively representing local solar radiation, solar cell panel area and solar energy conversion efficiency;
(2) the constraint conditions of charging and discharging the storage battery are
St+1＝St(1-)+Pcηc
St+1＝St(1-)+Pdηd
In the formula, Pc，ηcRespectively representing the charging power and the charging efficiency, P, of the batteryd，ηdRespectively representing the discharge power and the discharge efficiency of the battery and representing the leakage rate of the battery;
(3) the sum of the charged amounts of the batteries should equal the sum of the discharged amounts throughout the planned cycle:
Figure BDA0002578810490000034
in the formula,. DELTA.W (t)cha) Is shown at tchaAmount of charge at time, Δ W (t)discha) Is shown at tdischaThe amount of discharge at a moment;
the objective function of the distributed multi-energy system is
minDtotal＝ω1Dco2+ω2DNOx＝ω1(0.05982VANG+0.096081Epur)+ω2DNOx
In the formula, DtotalRepresenting an environmental assessment index, D, of the distributed multi-energy systemco2Indicating the CO of the system during a scheduling period2Discharge amount, DNOxIndicating the system within a scheduling periodNO ofxDischarge amount, omega1，ω2The weight coefficients, V, of the first twoANGIndicating the heat of the natural gas consumed (GJ/a), EpurRepresents the amount of electricity purchased (MW & h);
the energy constraint conditions of the distributed multi-energy system comprise:
(1) the conservation of electric energy is constrained to
Figure BDA0002578810490000041
Where n is the number of distributed power generation, PDG,iRepresents the electric power of the ith distributed power generation system, PgridRepresenting the exchange of electric power, P, with the griddRepresents the generated output of the combined cooling heating and power device, PcRepresenting the electric power of the battery energy storage system, PloadRepresenting the current electrical load;
(2) the conservation of cold energy is constrained to
Figure BDA0002578810490000042
Where k is the number of refrigeration units, CiDenotes the cooling capacity of the i-th refrigerating appliance, CloadIndicating the current cooling load;
(3) conservation of heat energy is constrained to
Figure BDA0002578810490000043
Where m is the number of gas turbines, HiRepresents the thermal power of the ith gas turbine, HeRepresents the thermal power, H, emitted by other thermal devicesssIndicating the thermal power of the heat storage unit HloadRepresenting the current thermal load;
(4) the output power of the gas turbine is constrained to
Figure BDA0002578810490000044
-DngasΔt≤ΔPgas≤UpgasΔt
In the formula (I), the compound is shown in the specification,
Figure BDA0002578810490000045
respectively representing the lower and upper limits, Δ P, of the actual output power of the gas turbinegasRepresenting the varying power of the gas turbine, at representing the scheduling time interval, DngasIndicating the uphill rate, U, of the gas turbinepgasRepresenting the gas turbine's ramp down rate.
Preferably, in the step (4), considering the problem that detailed operation data cannot be disclosed due to privacy safety of part of energy subjects, extracting state variables or key indexes capable of evaluating the optimized operation state of each energy subject, and using the state variables or key indexes for supervision/assessment/service of a top-level supervision center on a bottom-level energy subject; under the framework of double-layer collaborative optimization regulation, each energy main body at the bottom layer only interacts state variables and key indexes for supervision/assessment/service between the top-layer energy management system and each virtual controller at the bottom layer instead of uploading detailed data of a controlled energy main body; the virtual controller is a 'decision maker' or 'executor' for operation optimization of each distributed energy main body, is core equipment of a double-layer optimization regulation and control system, and has two deployment modes of virtual deployment and real deployment.
Preferably, in the step (4), because of different types of the energy subjects, differences of optimized operation conditions and decision-making capabilities, and different pursuit degrees of optimization targets, each energy subject cannot always accurately and timely complete local optimization of itself and keep consistent with a global target; based on the Bellman principle, a complete optimization process can be decomposed into a series of single-stage decision problems, and then the single-stage optimization problems are solved one by using the transfer and constraint relations among the stages; through the conversion, the distributed optimization problem is converted into a dynamic programming problem, namely, the local optimal solution of the local subsystem is solved firstly, and then the global optimal solution of the global system is gradually expanded; the method optimizes the traditional complex decision into a checking type or an empowerment type approval decision, and the centralized decision function at the top layer is obviously weakened; because a complex optimization decision tool is not needed, the top layer only needs to apply a conventional decision result to monitor the trend of the state of the whole system, so that the centralized optimization calculation task amount of the system is obviously reduced; in a typical scene of a university campus, the total power generation amount of distributed power generation and the power difference value of a battery energy storage system are processed into information interaction variables, local optimal targets of the virtual controllers are fully considered by the virtual controller 1 and the virtual controller 2, and the optimization problem of the whole system is gradually solved by continuously expanding the range boundary of the local system.
The invention has the beneficial effects that: (1) the architecture system reasonably integrates the concentrative supervision and weak centralization ideas, inherits the advantages of centralized optimization and distributed optimization, considers the differentiated optimized operation conditions and target pursuit degrees of different energy main bodies or distributed subsystems in the town energy Internet, can meet the real-time requirement of the optimized regulation and control of a plurality of distributed energy main bodies, lightens the calculation pressure of a cloud platform, can avoid or limit the irrational behaviors of the energy main bodies under the environment that key monitoring/supervision/examination/guidance and other information services are provided at the top layer, guides the energy main bodies to move in an effort and utilizes respective limited conditions to actively and rapidly and autonomously seek the best, and simultaneously enables the whole town energy Internet to have higher tendency capability; (2) the traditional complex decision is optimized into a checking type or an empowerment type approval decision, and the centralized decision function at the top layer is obviously weakened; because a complex optimization decision tool is not needed, the top layer only needs to apply a conventional decision result to monitor the state trend of the whole system, so that the centralized optimization calculation task amount of the system is obviously reduced, the distributed independent decision authority of the VSC at the bottom layer is given, the proper sinking of the calculation task amount is realized, and the actual application requirements of the town energy Internet with the characteristics of multi-energy coupling and multi-participation main body are better met; (3) the structure is particularly applied to typical regulation and control scenes of the urban energy Internet, so that the application space is expanded as soon as possible and the application value is exerted in the process of complying with the energy technology revolution, and the structure has good technical adaptability.
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FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of a centralized-distributed two-layer cooperative optimization architecture according to the present invention.
Fig. 3 is a schematic diagram of a two-layer cooperative optimization architecture of a typical regulation scenario in the present invention.
FIG. 4 is a schematic diagram of the imaginary deployment of the virtual controller in the present invention.
FIG. 5 is a schematic diagram of the real deployment of the virtual controller in the present invention.
Detailed Description
As shown in fig. 1, an optimized scheduling method based on a double-layer collaborative architecture of a town energy internet includes the following steps:
(1) analyzing a physical framework and a decision structure of the town energy Internet, carding the type, a regulation and control target and a regulation and control requirement of an energy main body in the town energy Internet, dividing a typical regulation and control scene of the town energy Internet, and building a centralized-distributed double-layer cooperative optimization regulation and control framework of the town energy Internet based on an energy management system and a virtual soft controller;
(2) according to the characteristics of different energy bodies, the virtual controller is deployed in an actual deployment mode or an imaginary deployment mode;
(3) establishing an optimized regulation mathematical model under a town energy Internet centralized-distributed double-layer collaborative optimized regulation architecture according to optimized operation targets of different energy main bodies in a typical scene;
(4) designing an interaction flow of an energy management system and virtual control according to the established optimized regulation mathematical model, and providing an optimized regulation mathematical model solving method based on the Bellman principle;
the town energy Internet has the physical architecture characteristic that a plurality of distributed energy main bodies are freely interconnected; the decision structure of the town energy Internet is characterized in that each energy subject belongs to different benefit subjects, makes an autonomous decision, and is not subjected to centralized decision and direct regulation and control of a power grid; regulatory requirements include, but are not limited to, real-time requirements, privacy requirements, global requirements; the regulation and control scenes comprise but are not limited to short-term optimization scheduling operation and energy market trading bidding. Taking a comprehensive energy system of a university campus as an example, the system mainly comprises a combined cooling heating and power system composed of a gas turbine, a heat boiler, a steam turbine and the like, and photovoltaic power generation and energy storage. Meanwhile, the comprehensive energy system is connected with the main network, and the main network compensates the shortage electric quantity.
An energy management system is deployed in a supervision center at the top layer of the town energy Internet, and a virtual controller is deployed in each distributed energy main body at the bottom layer, so that the established centralized-distributed double-layer collaborative optimization architecture is shown in FIG. 2. One virtual controller can be deployed in energy bodies including but not limited to small distributed photovoltaic, wind power, heat power source, air supply source, electricity/heat/gas pipeline network, industry/business/residents and the like, and a plurality of virtual devices jointly form a distributed decision layer of a town energy Internet centralized-distributed double-layer collaborative optimization regulation and control framework. Control systems such as a large centralized generator set, a thermal power plant, a natural gas source, a high-voltage power transmission network and a heat/gas transmission pipeline are connected into a traditional town energy management system to form a centralized supervision and management layer of a town energy Internet centralized-distributed double-layer collaborative optimization regulation and control framework. The centralized supervision and management layer is communicated with all the virtual controllers of the distributed decision-making layer, and the instruction is direct. Under the framework, the energy management system only checks the local decision-making regulating and controlling quantity uploaded by each virtual controller, and the local decision-making regulating and controlling quantity is issued and executed after being confirmed or corrected. Taking an integrated energy system of a university campus as an example, a two-tier collaborative optimization architecture is shown in fig. 3.
And classifying the autonomous control authority of each energy body according to the type, the control requirement, the control target and the importance degree of the energy body, and setting real deployment and imaginary deployment modes of the virtual soft controller to determine whether the execution of the decision needs to be supervised and approved by an energy management system of a top-level supervision center after the virtual controller of each energy body at the bottom layer autonomously generates the decision. The deployment mode of the virtual controller selects and comprehensively considers multiple factors including optimization targets of all interest principals, asset owners, user privacy willingness and the like. The real deployment mode refers to that the computing resources are configured on the bottom distributed resource side, and besides the distributed computing function, the real deployment mode also bears the distributed control function, namely the self decision and the self control are really realized. The virtual deployment mode refers to the fact that computing resources are configured in a top-level energy management system, a top level provides computing services of distributed decision, and under the deployment mode, a virtual controller does not have the right of directly executing distributed control, needs to generate a regulation and control strategy and uploads the regulation and control strategy to the energy management system for supervision and approval. For example, a manager or a user who performs a control demand on the own integrated energy system adopts a real deployment mode of the virtual controller; and for a third party which does not relate to privacy and only provides data value-added services, the virtual controller can be deployed in an imaginary deployment mode. Schematic diagrams of both virtual deployment and real deployment are shown in fig. 4 and 5. In a typical regulation and control scene of a university campus, a real deployment mode is adopted because the school needs to have actual control right on own equipment.
In a typical scene, two virtual controllers are arranged, the virtual controller 1 represents a micro-grid system, the virtual controller 2 represents a distributed multi-energy system, and compared with centralized optimization, the optimization targets of the two virtual controllers are not the same, wherein the objective function of the micro-grid is to comprehensively consider the utilization of an energy storage system to realize peak clipping and valley filling and distributed clean energy consumption of the grid, and the objective function of the distributed energy system is to consider NO simultaneouslyxAnd CO2Environmental indicators of emissions. Pollutants of the distributed multifunctional system mainly come from a gas turbine, a gas boiler, an internal combustion engine, a fuel cell, purchased electricity and the like, and NO is mainly considered in the inventionxAnd CO2Emission, NO of the respective major equipmentxThe discharge amounts are shown in table 1.
TABLE 1 NO of the respective principal apparatusxDischarge capacity
Figure BDA0002578810490000081
Micro-grid system: the system consists of 1MW photovoltaic power generation, a 500kW/3MWh battery energy storage system and 0.6MW electric load. The local power balance of the system is mainly realized by adjusting the energy storage of the battery, and similarly, the part with electric power loss is supplemented by the power supply of the main power grid.
In the system, the main objective function of the optimized operation comprehensively considers the peak clipping and valley filling effects and the consumption and utilization rate of clean energy, and the objective function is as follows:
minf＝-(ω1f1+ω2f2)
wherein f is the optimization effect evaluation index of the system, f1For peak clipping and valley filling effects, f2For the consumption and utilization rate of clean energy, omega1，ω2The weighting coefficients of the first two are respectively. Here, the objective function should be a maximum optimization effect evaluation index, but in order to make the objective function conform to the habit of solving the minimum value, a negative sign needs to be added, and then the minimum value needs to be solved.
The constraints of the microgrid include:
(1) the upper limit of the output of the photovoltaic power generation is related to the local solar radiation, the area of the solar cell panel and the solar energy conversion efficiency at that time, and meanwhile, the maximum output of the photovoltaic power generation is also smaller than the rated power of the photovoltaic unit, and the constraint is as follows:
Figure BDA0002578810490000082
Figure BDA0002578810490000083
in the formula (I), the compound is shown in the specification,
Figure BDA0002578810490000084
respectively representing the lower and upper limits of the photovoltaic output, PcapaRepresenting the rated power, theta, S, of the photovoltaic unitpv，ηpvRespectively, local solar radiation, solar cell area and solar conversion efficiency.
(2) The battery has two states of charge and discharge, and the constraints are as follows:
St+1＝St(1-)+Pcηc
St+1＝St(1-)+Pdηd
in the formula, Pc，ηcRespectively representing the charging power and the charging efficiency, P, of the batteryd，ηdThe discharge power and discharge efficiency of the battery are respectively shown, and the leakage rate of the battery is shown.
(3) The sum of the charged amounts of the batteries should equal the sum of the discharged amounts throughout the planned cycle:
Figure BDA0002578810490000091
in the formula,. DELTA.W (t)cha) Is shown at tchaAmount of charge at time, Δ W (t)discha) Is shown at tdischaThe amount of discharge at that moment.
Distributed multi-energy system: the natural gas system supplies gas and then transmits the gas to the combined cooling heating and power device, the device outputs electric energy, heat energy and cold energy to meet the electric load, heat load and cold load of major schools, and the part with electric power deficiency is supplemented by the main power grid.
The objective function of the distributed multi-energy system is
minDtotal＝ω1Dco2+ω2DNOx＝ω1(0.05982VANG+0.096081Epur)+ω2DNOx
In the formula, DtotalRepresenting an environmental assessment index, D, of the distributed multi-energy systemco2Indicating the CO of the system during a scheduling period2Discharge amount, DNOxIndicating NO of the system during a scheduling periodxDischarge amount, omega1，ω2The weighting coefficients of the first two are respectively. VANGIndicating the heat of the natural gas consumed (GJ/a), EpurIndicating the amount of electricity purchased (MW · h).
The energy constraint conditions of the distributed multi-energy system comprise:
(1) the distributed generating capacity, the electricity purchasing quantity and the combined cooling heating and power generation device can meet the basic energy conservation requirements on power generation output, electricity storage and electric load:
Figure BDA0002578810490000092
where n is the number of distributed power generation, PDG,iRepresents the electric power of the ith distributed power generation system, PgridRepresenting the exchange of electric power, P, with the griddRepresents the generated output of the combined cooling heating and power device, PcRepresenting the electric power of the battery energy storage system, PloadRepresenting the current electrical load. It is noted that, here, the total power generation amount of the distributed power generation and the power difference value of the battery energy storage system are processed as information exchange variables, which are considered as constant values 0.6MW here.
(2) The production and consumption of cold energy should be balanced throughout:
Figure BDA0002578810490000093
where k is the number of refrigeration units, CiDenotes the cooling capacity of the i-th refrigerating appliance, CloadIndicating the current cooling load
(3) Gas turbines, other heat producing equipment, heat storage and heat load should meet basic heat energy conservation:
Figure BDA0002578810490000094
where m is the number of gas turbines, HiRepresents the thermal power of the ith gas turbine, HeRepresents the thermal power, H, emitted by other thermal devicesssIndicating the thermal power of the heat storage unit HloadRepresenting the current thermal load.
(4) The actual output power of the gas turbine should be within the normal operating range, satisfying the inequality constraint, and the rate of change of its power should also satisfy the corresponding ramp constraint, as follows:
Figure BDA0002578810490000101
-DngasΔt≤ΔPgas≤UpgasΔt
in the formula (I), the compound is shown in the specification,
Figure BDA0002578810490000102
respectively representing the lower and upper limits, Δ P, of the actual output power of the gas turbinegasRepresenting the varying power of the gas turbine, at representing the scheduling time interval, DngasIndicating the uphill rate, U, of the gas turbinepgasRepresenting the gas turbine's ramp down rate.
Considering the problem that detailed operation data cannot be disclosed due to privacy safety of part of energy bodies, a big data relevance analysis-based method can be considered to be adopted to conduct relevance mining on the operation data of the energy bodies, extract state variables or key indexes capable of evaluating the optimized operation state of each energy body, and use the state variables or key indexes for supervision/assessment/service of a top-level supervision center on a bottom-level energy body. Under the framework of double-layer collaborative optimization regulation and control, each energy main body at the bottom layer only interacts state variables and key indexes for supervision/assessment/service between the top-layer energy management system and each virtual controller at the bottom layer instead of uploading detailed data of the controlled energy main body. The virtual controller is a 'decision maker' or 'executor' for operation optimization of each distributed energy main body, is core equipment of a double-layer optimization regulation and control system, and has two deployment modes of virtual deployment and real deployment. Aiming at a regulation scene with higher requirements on instantaneity and privacy, a bottom-layer distributed regulation mode of an unsupervised or key index moderate supervision mode is mainly adopted; aiming at a regulation and control scene with high global requirement for system optimization, a cooperative service mechanism for carrying out target associated index centralized assessment on a part of bottom subsystems needing to be monitored at the top layer is adopted; under the environment of top layer supervision/assessment/service, all energy main bodies at the bottom layer seek for coexistence and existence, and independent optimization is flexibly carried out.
Due to the fact that the types of the energy bodies are different, the optimization operation conditions and the decision-making capability are different, and the pursuit degree of the optimization targets is different, the energy bodies cannot always accurately and timely complete local optimization of the energy bodies and keep the local optimization consistent with the global targets. Based on the Bellman principle, a complete optimization process can be decomposed into a series of single-stage decision problems, and then the single-stage optimization problems are solved one by using the transfer and constraint relations among the stages. Through the conversion, the distributed optimization problem is converted into a dynamic programming problem, namely, the local optimal solution of the local subsystem is solved firstly, and then the global optimal solution of the global system is gradually expanded. The method optimizes the traditional complex decision into a checking type or an empowerment type approval decision, and the centralized decision function at the top layer is obviously weakened; because a complex optimization decision tool is not needed, the top layer only needs to apply a conventional decision result to monitor the trend of the state of the whole system, and the centralized optimization calculation task amount of the system is obviously reduced. In a typical scene of a university campus, the total power generation amount of distributed power generation and the power difference value of a battery energy storage system are processed into information interaction variables, local optimal targets of the virtual controllers are fully considered by the virtual controller 1 and the virtual controller 2, and the optimization problem of the whole system is gradually solved by continuously expanding the range boundary of the local system.

Claims (7)

1. An optimized scheduling method based on a double-layer collaborative architecture of a town energy Internet is characterized by comprising the following steps:
(1) analyzing a physical framework and a decision structure of the town energy Internet, carding the type, a regulation and control target and a regulation and control requirement of an energy main body in the town energy Internet, dividing a typical regulation and control scene of the town energy Internet, and building a centralized-distributed double-layer cooperative optimization regulation and control framework of the town energy Internet based on an energy management system and a virtual soft controller;
(2) according to the characteristics of different energy bodies, the virtual controller is deployed in an actual deployment mode or an imaginary deployment mode;
(3) establishing an optimized regulation mathematical model under a town energy Internet centralized-distributed double-layer collaborative optimized regulation architecture according to optimized operation targets of different energy main bodies in a typical scene;
(4) and designing an interaction flow of an energy management system and virtual control according to the established optimal regulation mathematical model, and providing an optimal regulation mathematical model solving method based on the Bellman principle.
2. The optimized dispatching method based on the double-layer collaborative architecture of the town energy Internet as claimed in claim 1, wherein in the step (1), the town energy Internet presents the physical architecture characteristics of the free interconnection of a plurality of distributed energy bodies; the decision structure of the town energy Internet is characterized in that each energy subject belongs to different benefit subjects, makes an autonomous decision, and is not subjected to centralized decision and direct regulation and control of a power grid; the regulation and control requirements comprise real-time requirements, privacy requirements and global requirements; the regulation and control scene comprises short-term optimized dispatching operation and energy market trading bidding.
3. The optimized dispatching method based on the town energy Internet double-layer collaborative architecture as claimed in claim 1, wherein in the step (1), an energy management system is deployed in a supervision center at the top layer of the town energy Internet, and a virtual controller is deployed in each distributed energy main body at the bottom layer; the virtual controller can be deployed in energy bodies such as small distributed photovoltaic, wind power, a heat power source, an air supply source, an electricity/heat/air pipeline network, industry/business/residents and the like, and a plurality of virtual devices jointly form a distributed decision-making layer of a town energy Internet centralized-distributed double-layer collaborative optimization regulation and control framework; a large centralized generator set, a thermal power plant, a natural gas source, a high-voltage power transmission network and a heat/gas transmission pipeline control system are connected into a traditional town energy management system to form a centralized supervision and management layer of a town energy Internet centralized-distributed double-layer collaborative optimization regulation and control framework; the centralized supervision management layer is communicated with all the virtual controllers of the distributed decision-making layer, and the instruction is direct; under the framework, the energy management system only checks the local decision-making regulating and controlling quantity uploaded by each virtual controller, and the local decision-making regulating and controlling quantity is issued and executed after being confirmed or corrected.
4. The optimized scheduling method based on the town energy Internet double-layer collaborative architecture as claimed in claim 1, wherein in the step (2), according to the type, regulation requirement, regulation objective and importance degree of the energy subject, the autonomous regulation authority of each energy subject is classified in a grade manner, and two manners of real deployment and imaginary deployment of the virtual soft controller are set so as to determine whether the execution of the decision needs to be supervised and approved by the energy management system of the top-level supervision center after the virtual controller of each energy subject on the bottom layer autonomously generates the decision; selecting a deployment mode of the virtual controller, and comprehensively considering multiple factors including optimization targets of all benefit agents, asset owners and user privacy willingness; the real deployment mode refers to that computing resources are configured on the bottom distributed resource side, and besides the distributed computing function, the real deployment mode also bears the distributed control function, namely the self decision and the self control are really realized; the virtual deployment mode refers to the fact that computing resources are configured in a top-level energy management system, a top level provides computing services of distributed decision, and under the deployment mode, a virtual controller does not have the right of directly executing distributed control, needs to generate a regulation and control strategy and uploads the regulation and control strategy to the energy management system for supervision and approval.
5. The optimized dispatching method based on the double-layer collaborative architecture of the town energy Internet as claimed in claim 1, wherein in the step (3), in a typical scenario, two virtual controllers are provided, the virtual controller 1 represents a microgrid system, the virtual controller 2 represents a distributed multi-energy system, wherein an objective function of the microgrid is to comprehensively consider the utilization of an energy storage system to realize peak clipping and valley filling and distributed clean energy consumption of the power grid, and an objective function of the distributed energy system is to consider NO simultaneouslyxAnd CO2Environmental indicators of emissions; the pollutants of the distributed multi-energy system mainly come from gas turbines, gas boilers, internal combustion engines, fuel cells, purchased electricity and the like, and NO is consideredxAnd CO2Discharge capacity;
the objective function of the micro-grid system is the optimization of the peak clipping and valley filling effects and the consumption and utilization rate of clean energy:
minf＝-(ω1f1+ω2f2)
wherein f is the optimization effect evaluation index of the system, f1For peak clipping and valley filling effects, f2For the consumption and utilization rate of clean energy, omega1，ω2The weight coefficients of the first two are respectively;
the constraints of the microgrid include:
(a) the constraint condition of photovoltaic power generation is
Figure FDA0002578810480000021
Figure FDA0002578810480000022
In the formula (I), the compound is shown in the specification,
Figure FDA0002578810480000023
respectively representing the lower and upper limits of the photovoltaic output, PcapaRepresenting the rated power, theta, S, of the photovoltaic unitpv，ηpvRespectively representing local solar radiation, solar cell panel area and solar energy conversion efficiency;
(b) the constraint conditions of charging and discharging the storage battery are
St+1＝St(1-)+Pcηc
St+1＝St(1-)+Pdηd
In the formula, Pc，ηcRespectively representing the charging power and the charging efficiency, P, of the batteryd，ηdRespectively representing the discharge power and the discharge efficiency of the battery and representing the leakage rate of the battery;
(c) the sum of the charged amounts of the batteries should equal the sum of the discharged amounts throughout the planned cycle:
Figure FDA0002578810480000031
in the formula,. DELTA.W (t)cha) Is shown at tchaAmount of charge at time, Δ W (t)discha) Is shown at tdischaThe amount of discharge at a moment;
the objective function of the distributed multi-energy system is
min Dtotal＝ω1Dco2+ω2DNOx＝ω1(0.05982VANG+0.096081Epur)+ω2DNOx
In the formula, DtotalRepresenting an environmental assessment index, D, of the distributed multi-energy systemco2Indicating the CO of the system during a scheduling period2Discharge amount, DNOxIndicating NO of the system during a scheduling periodxDischarge amount, omega1，ω2The weight coefficients, V, of the first twoANGIndicating the heat of the natural gas consumed (GJ/a), EpurRepresents the amount of electricity purchased (MW & h);
the energy constraint conditions of the distributed multi-energy system comprise:
(a) the conservation of electric energy is constrained to
Figure FDA0002578810480000032
Where n is the number of distributed power generation, PDG,iRepresents the electric power of the ith distributed power generation system, PgridRepresenting the exchange of electric power, P, with the griddRepresents the generated output of the combined cooling heating and power device, PcRepresenting the electric power of the battery energy storage system, PloadRepresenting the current electrical load;
(b) the conservation of cold energy is constrained to
Figure FDA0002578810480000033
Where k is the number of refrigeration units, CiDenotes the cooling capacity of the i-th refrigerating appliance, CloadIndicating the current cooling load;
(c) conservation of heat energy is constrained to
Figure FDA0002578810480000034
Where m is the number of gas turbines, HiRepresents the thermal power of the ith gas turbine, HeRepresents the thermal power, H, emitted by other thermal devicesssIndicating the thermal power of the heat storage unit HloadRepresenting the current thermal load;
(d) the output power of the gas turbine is constrained to
Figure FDA0002578810480000041
-DngasΔt≤ΔPgas≤UpgasΔt
In the formula (I), the compound is shown in the specification,
Figure FDA0002578810480000042
respectively representing the lower and upper limits, Δ P, of the actual output power of the gas turbinegasRepresenting the varying power of the gas turbine, at representing the scheduling time interval, DngasIndicating the uphill rate, U, of the gas turbinepgasRepresenting the gas turbine's ramp down rate.
6. The optimized scheduling method based on the town energy Internet double-layer collaborative architecture as claimed in claim 1, wherein in the step (4), considering the problem that a part of energy subjects cannot disclose detailed operation data due to privacy security, state variables or key indexes capable of evaluating the optimized operation state of each energy subject are extracted and used for supervision/assessment/service of a top-level supervision center on a bottom-level energy subject; under the framework of double-layer collaborative optimization regulation and control, only state variables and key indexes for supervision/assessment/service are interacted between the top-layer energy management system and each virtual controller at the bottom layer by each energy main body at the bottom layer; the virtual controller is a 'decision maker' or 'executor' for operation optimization of each distributed energy main body, is core equipment of a double-layer optimization regulation and control system, and has two deployment modes of virtual deployment and real deployment.
7. The optimized scheduling method based on the double-layer collaborative architecture of the town energy Internet as claimed in claim 1, wherein in the step (4), each energy subject is not always able to complete its local optimization accurately and timely and keep consistent with the global objective due to different types of each energy subject, differences of optimized operation conditions and decision-making capability, and different pursuit degree of optimization objectives; decomposing a complete optimization process into a series of single-stage decision problems based on the Bellman principle, and then solving the single-stage optimization problems one by using the transfer and constraint relations among the stages; through the conversion, the distributed optimization problem is converted into a dynamic programming problem, namely, the local optimal solution of the local subsystem is solved firstly, and then the global optimal solution of the global system is gradually expanded.
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