An optimization scheduling method based on a two-layer collaborative architecture of urban energy internet

Abstract

The present invention discloses an optimization scheduling method based on a two-layer collaborative architecture of urban energy Internet, which reasonably integrates cloud supervision and weak centralization ideas, has broad adaptability to the large-scale development needs of urban energy Internet, and also applies the collaboratively optimized two-layer control architecture to typical control scenarios of urban energy Internet, so that it can expand its application space and exert its application value as soon as possible in the process of adapting to energy technology changes, and has good technical adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of urban energy internet, and in particular to an optimization scheduling method based on a double-layer collaborative architecture of urban energy internet. Background Art

[0002] Under the dual pressures of the substantial growth in energy demand and the increasing urgency of environmental protection, improving energy efficiency and promoting the rational consumption of renewable energy are the only way to reform the energy structure. With the increasing urbanization level in my country, the energy Internet system integrating "Internet +" has become a new form of energy industry development under the background of new towns. It will solve the problems of low comprehensive energy efficiency, insufficient new energy consumption capacity, and poor demand response interaction of traditional energy systems in accordance with the basic principles of green, low-carbon and sustainable development. Breaking the original mode of separate planning, design and independent operation of a single energy system, carrying out integrated planning and design of urban energy Internet, and realizing the coordinated optimization and regulation of various heterogeneous energy flows such as electricity, gas and heat are the primary tasks of urban energy Internet construction.

[0003] The traditional centralized optimization and control strategy is formulated under the premise of a single decision-making subject, and often uses the energy management system or local substation control center as the centralized management layer. However, the number of distributed energy entities in the new urban energy Internet is increasing, and the electricity, gas, and heat subsystems also belong to different regulatory departments and operators; the engineering data of the urban energy Internet is massive and heterogeneous, and the data processing capabilities and real-time requirements are extremely high. At the same time, the privacy and security of energy entities also need to be considered, which makes the traditional centralized control unsustainable. For example, the central air-conditioning of a commercial building is unlikely to accept the intrusive control of the upper layer; some users are unwilling to upload complete energy consumption data for privacy reasons. Since centralized optimization and control faces severe challenges in privacy, reliability, economy, flexibility, feasibility, etc., distributed control is gradually being applied to urban energy Internet, which allows neighboring distributed resources to exchange information. However, due to the differences in the types, energy consumption characteristics, and optimization decision-making capabilities of various energy entities, it is impossible to require all energy entities to be consistent with the global optimization goals at all times. In addition, there will be a single interest subject that only cares about its own optimization goals, thus affecting the overall economy and security of the system. Therefore, the optimal control mode of urban energy Internet still needs in-depth research, and different control methods need to be reasonably integrated to improve the overall performance of the system. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an optimization scheduling method based on the two-layer collaborative architecture of urban energy Internet, and to apply the proposed architecture specifically to typical control scenarios of urban energy Internet, so that it can expand its application space and exert its application value as soon as possible in the process of adapting to energy technology changes, and have good technical adaptability.

[0005] In order to solve the above technical problems, the present invention provides an optimization scheduling method based on a two-layer collaborative architecture of urban energy internet, comprising the following steps:

[0006] (1) Analyze the physical architecture and decision-making structure of the urban energy Internet, sort out the types of energy entities, control objectives and control requirements in the urban energy Internet, and divide the typical control scenarios of the urban energy Internet. Based on the energy management system and virtual controller, build a centralized-distributed two-layer collaborative optimization control architecture of the urban energy Internet;

[0007] (2) According to the characteristics of different energy entities, virtual controllers are deployed using real deployment or virtual deployment;

[0008] (3) According to the optimization operation objectives of different energy entities in typical scenarios, a mathematical model for optimization and control under the centralized-distributed dual-layer collaborative optimization and control architecture of urban energy Internet is established;

[0009] (4) Based on the established mathematical model of optimization and control, the interaction process between the energy management system and virtual control is designed, and a solution method for the mathematical model of optimization and control based on the Bellman principle is proposed.

[0010] Preferably, in step (1), the urban energy Internet presents the physical architecture characteristics of multiple distributed energy entities freely interconnected; the decision-making structure characteristics of the urban energy Internet are manifested in that each energy entity is subordinate to different interest entities, makes autonomous decisions, and is not subject to centralized decision-making and direct regulation by the power grid; the regulation requirements include but are not limited to real-time requirements, privacy requirements, and global requirements; the regulation scenarios include but are not limited to short-term optimization scheduling operation and energy market transaction bidding.

[0011] Preferably, in step (1), an energy management system is deployed in the supervision center of the top layer of the urban energy Internet, and a virtual controller is deployed in each distributed energy entity at the bottom layer; a virtual controller can be deployed in energy entities including but not limited to small distributed photovoltaic, wind power, thermal power source, gas supply source, electricity / heat / gas pipeline network and industrial / commercial / residential, and multiple virtual devices together constitute the distributed decision-making layer of the centralized-distributed two-layer collaborative optimization control architecture of the urban energy Internet; large centralized generator sets, thermal power plants, natural gas sources, high-voltage transmission networks, heat / gas pipelines and other control systems are connected to the traditional urban energy management system to form a centralized supervision and management layer of the centralized-distributed two-layer collaborative optimization control architecture of the urban energy Internet; the centralized supervision and management layer communicates with all virtual controllers of the distributed decision-making layer, and the instructions are directly transmitted; under this architecture, the energy management system only verifies the local decision-making and control quantities uploaded by each virtual controller, and sends them for execution after confirmation or correction.

[0012] Preferably, in step (2), the autonomous control authority of each energy subject is divided into levels according to the type of energy subject, control requirements, control objectives and importance, and two modes of real deployment and virtual deployment of virtual controllers are set to determine whether the execution of the decision made by the virtual controller of each energy subject at the bottom layer needs to be supervised and approved by the energy management system of the top-level supervision center after the decision is autonomously generated by the virtual controller of each energy subject at the bottom layer; the deployment mode of the virtual controller is selected by comprehensively considering multiple factors, including the optimization goals of various stakeholders, asset owners, and user privacy wishes; the real deployment mode refers to the configuration of computing resources on the distributed resource side of the bottom layer, which not only has distributed computing functions, but also undertakes distributed control functions, that is, truly realizes self-decision-making and self-control; the virtual deployment mode refers to the configuration of computing resources in the top-level energy management system, and the top layer provides computing services for distributed decision-making. Under this deployment mode, the virtual controller does not have the authority to directly implement distributed control, and needs to generate a control strategy and upload it to the energy management system for supervision and approval.

[0013] Preferably, in step (3), in a typical scenario, two virtual controllers are set, virtual controller 1 represents a microgrid system, and virtual controller 2 represents a distributed multi-energy system, wherein the objective function of the microgrid is to comprehensively consider the use of energy storage systems to achieve peak load shaving and valley filling of the power grid and distributed clean energy consumption, and the objective function of the distributed energy system is to simultaneously consider NO x and CO2 emissions; pollutants in distributed multi-energy systems mainly come from gas turbines, gas boilers, internal combustion engines, fuel cells, purchased electricity, etc., considering NO x and CO2 emissions;

[0014] The objective function of the microgrid system is to optimize the peak load shaving effect and the utilization rate of clean energy:

[0015] minf=-(ω1f1+ω2f2)

[0016] In the formula, f is the optimization effect evaluation index of the system, f1 is the peak shaving and valley filling effect, f2 is the consumption and utilization rate of clean energy, ω1 and ω2 are the weight coefficients of the first two respectively;

[0017] The constraints of the microgrid include:

[0018] (1) The constraints of photovoltaic power generation are:

[0019]

[0020] In the formula, Respectively represent the lower and upper limits of photovoltaic output, P capa Indicates the rated power of the photovoltaic unit, θ, S pv , η pvThey represent local solar radiation, solar panel area and solar energy conversion efficiency respectively;

[0021] (2) The battery charging and discharging constraints are:

[0022] S t+1 =S t (1-ε)+P c η c

[0023] S t+1 =S t (1-ε)+P d η d

[0024] Where P c , η c Represent the charging power and charging efficiency of the battery, P d , η d They represent the discharge power and discharge efficiency of the battery respectively, and ε represents the leakage rate of the battery;

[0025] (3) During the entire planning period, the sum of the battery charge capacity should be equal to the sum of the battery discharge capacity:

[0026]

[0027] In the formula, ΔW(t cha ) indicates that at t cha The charge amount at the time, ΔW(t discha ) indicates that at t discha The discharge amount at the moment;

[0028] The objective function of the distributed multi-energy system is:

[0029]

[0030] Where D total represents the environmental assessment index of this distributed multi-energy system, D co2 represents the CO2 emissions of this system in a scheduling cycle, Indicates the NO of this system in a scheduling cycle x Emission, ω1, ω2 are the weight coefficients of the first two, V ANG Indicates the natural gas heat consumed (GJ / a), E pur Indicates the amount of electricity purchased (MW·h);

[0031] The energy constraints of distributed multi-energy systems include:

[0032] (1) The electric energy conservation constraint is:

[0033]

[0034] Where n is the number of distributed generation, P DG,i represents the electric power of the i-th distributed generation system, P grid Represents the exchange power with the grid, P d P represents the power generation output of the trigeneration device, c Represents the electric power of the battery energy storage system, P load Indicates the current electrical load;

[0035] (2) The cold energy conservation constraint is

[0036]

[0037] In the formula, k is the number of refrigeration equipment, C i represents the cooling power of the i-th refrigeration equipment, C load Indicates the current cooling load;

[0038] (3) The thermal energy conservation constraint is

[0039]

[0040] Where m is the number of gas turbines, H i represents the thermal power of the i-th gas turbine, H e Indicates the thermal power generated by other heat-generating equipment, H ss Represents the thermal power of the heat storage device, H load Indicates the current heat load;

[0041] (4) The output power constraint of the gas turbine is

[0042]

[0043] -D ngas Δt≤ΔP gas ≤U pgas Δt

[0044] In the formula, They represent the lower and upper limits of the actual output power of the gas turbine, ΔP gas represents the variable power of the gas turbine, Δt represents the scheduling time interval, and D ngas represents the ramp rate of the gas turbine, U pgas Indicates the ramp down rate of the gas turbine.

[0045] Preferably, in step (4), considering the problem that some energy entities are unable to disclose detailed operation data due to privacy security, state variables or key indicators that can evaluate the optimized operation status of each energy entity are extracted for the top-level supervision center to supervise / assess / serve the bottom-level energy entities; under the framework of two-layer collaborative optimization and control, the bottom-level energy entities only use the state variables and key indicators used for supervision / assessment / service to interact between the top-level energy management system and the bottom-level virtual controllers, instead of uploading detailed data of the controlled energy entities; the virtual controller is the "decision maker" or "executor" of the operation optimization of each distributed energy entity, and is the core equipment of the two-layer optimization and control system, with two deployment modes: virtual deployment and real deployment.

[0046] Preferably, in step (4), due to the different types of energy entities, differences in optimization operating conditions and decision-making capabilities, and different degrees of optimization goal pursuit, each energy entity is not always able to accurately and timely complete its own local optimization and keep consistent with the global goal; based on the Bellman principle, a complete optimization process can be decomposed into a series of single-stage decision problems, and then the transfer and constraint relationships between the stages are used to solve the single-stage optimization problems one by one; through this conversion, the distributed optimization problem is transformed into a dynamic programming problem, that is, first solve the local optimal solution of the local subsystem, and then gradually expand to the global optimal solution of the global system; this method optimizes traditional complex decisions into verification-type or empowerment-type approval decisions, and the centralized decision-making function of the top level is significantly weakened; since there is no need to use complex optimization decision tools, the top level only needs to apply conventional decision results to monitor the direction of the entire system state, so that the amount of centralized optimization calculation tasks of the system is significantly reduced; in a typical scenario on a university campus, the total power generation of distributed generation and the power difference of the battery energy storage system are processed as information interaction variables, and virtual controller 1 and virtual controller 2 both fully consider their own local optimal goals, and gradually solve the optimization problem of the entire system by continuously expanding the scope boundary of the local system.

[0047] The beneficial effects of the present invention are as follows: (1) The architecture system proposed in the present invention reasonably integrates the ideas of centralized supervision and weak centralization, inherits the advantages of centralized and distributed optimization, and takes into account the differentiated optimization operation conditions and target pursuit levels of different energy entities or distributed subsystems in the urban energy Internet. It can not only meet the real-time requirements of optimization and regulation of multiple distributed energy entities and reduce the computing pressure of the cloud platform, but also provide key monitoring / supervision / assessment / guidance and other information services at the top level. In this way, it can avoid or limit the irrational behavior of energy entities and guide them to mobilize and utilize their own limited conditions according to their capabilities to actively and quickly optimize themselves, while at the same time enabling the urban energy Internet as a whole to have a higher optimization capability; (2) The present invention will transmit The complex decision-making of the system is optimized into verification-type or empowerment-type approval decisions, and the centralized decision-making function of the top level is significantly weakened; since there is no need to use complex optimization decision-making tools, the top level only needs to apply conventional decision results to monitor the trend of the entire system status, which significantly reduces the amount of centralized optimization computing tasks of the system, and grants the distributed independent decision-making authority of the underlying VSC, so as to achieve the appropriate sinking of the computing tasks, which is more in line with the actual application needs of the urban energy Internet with the characteristics of multi-energy coupling and multi-participating entities; (3) The present invention specifically applies the proposed architecture to the typical control scenarios of the urban energy Internet, so that in the process of adapting to the changes in energy technology, it can expand the application space and give play to the application value as soon as possible, and has good technical adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0049] Figure 2 Schematic diagram of the centralized-distributed dual-layer collaborative optimization architecture in the present invention.

[0050] Figure 3 Schematic diagram of a two-layer collaborative optimization architecture for a typical control scenario in the present invention.

[0051] Figure 4 It is a schematic diagram of the virtual deployment method of the virtual controller in the present invention.

[0052] Figure 5 It is a schematic diagram of the actual deployment method of the virtual controller in the present invention. DETAILED DESCRIPTION

[0053] like Figure 1 As shown, an optimization scheduling method based on a two-layer collaborative architecture of urban energy Internet includes the following steps:

[0054] (1) Analyze the physical architecture and decision-making structure of the urban energy Internet, sort out the types of energy entities, control objectives and control requirements in the urban energy Internet, and divide the typical control scenarios of the urban energy Internet. Based on the energy management system and virtual controller, build a centralized-distributed two-layer collaborative optimization control architecture of the urban energy Internet;

[0055] (2) According to the characteristics of different energy entities, virtual controllers are deployed in real or virtual deployment modes;

[0056] (3) According to the optimization operation objectives of different energy entities in typical scenarios, a mathematical model for optimization and control under the centralized-distributed dual-layer collaborative optimization and control architecture of urban energy Internet is established;

[0057] (4) Based on the established mathematical model of optimization and control, the interactive process of energy management system and virtual control is designed, and a solution method of the mathematical model of optimization and control based on Bellman principle is proposed;

[0058] The urban energy internet presents the physical architecture characteristics of multiple distributed energy entities freely interconnected; the decision-making structure characteristics of the urban energy internet are that each energy entity is subordinate to different interest entities, makes autonomous decisions, and is not subject to centralized decision-making and direct regulation by the power grid; the regulation requirements include but are not limited to real-time requirements, privacy requirements, and global requirements; the regulation scenarios include but are not limited to short-term optimization scheduling and operation, and energy market transaction bidding. Taking the integrated energy system of a university campus as an example, the system mainly includes a trigeneration system composed of gas turbines, heat boilers, steam turbines, etc., photovoltaic power generation and energy storage. At the same time, the integrated energy system is connected to the main grid, and the main grid compensates for the shortfall in electricity.

[0059] An energy management system is deployed in the regulatory center at the top level of the urban energy Internet, and a virtual controller is deployed in each distributed energy entity at the bottom level, thereby establishing a centralized-distributed two-layer collaborative optimization architecture. Figure 2As shown. A virtual controller can be deployed in energy entities including but not limited to small distributed photovoltaic, wind power, thermal power sources, gas supply sources, electricity / heat / gas pipeline networks, and industrial / commercial / residential energy entities. Multiple virtual controllers together constitute the distributed decision-making layer of the centralized-distributed two-layer collaborative optimization and control architecture of the urban energy Internet. Control systems such as large centralized generators, thermal power plants, natural gas sources, high-voltage transmission networks, and heat / gas pipelines are connected to the traditional urban energy management system to form a centralized supervision and management layer of the centralized-distributed two-layer collaborative optimization and control architecture of the urban energy Internet. The centralized supervision and management layer communicates with all virtual controllers of the distributed decision-making layer, and instructions are directly delivered. Under this architecture, the energy management system only verifies the on-site decision-making and control quantities uploaded by each virtual controller, and sends them for execution after confirmation or correction. Taking the integrated energy system of a university campus as an example, its two-layer collaborative optimization architecture is as follows Figure 3 shown.

[0060] According to the type of energy subject, regulation requirements, regulation objectives and importance, the autonomous regulation authority of each energy subject is divided into levels, and two modes of real deployment and virtual deployment of virtual controllers are set to determine whether the execution of the decision of the virtual controller of each energy subject at the bottom layer needs to be supervised and approved by the energy management system of the top-level regulatory center after the decision is made autonomously. The deployment mode of the virtual controller is selected based on multiple factors, including the optimization goals of each stakeholder, the asset owner, and the user's privacy wishes. The real deployment mode refers to the configuration of computing resources on the distributed resource side of the bottom layer. In addition to the distributed computing function, it also assumes the distributed control function, that is, truly realizing self-decision-making and self-control. The virtual deployment mode refers to the configuration of computing resources in the top-level energy management system, and the top-level provides computing services for distributed decision-making. Under this deployment mode, the virtual controller does not have the authority to directly implement distributed control, and needs to generate regulation strategies and upload them to the energy management system for supervision and approval. For example, for managers or users who have control needs for their own integrated energy systems, the real deployment mode of virtual controllers is adopted; for third parties that do not involve privacy and only provide data value-added services, the virtual deployment mode of virtual controllers can be adopted. The schematic diagram of the two deployment modes, virtual deployment and real deployment, is as follows Figure 4 and Figure 5 In a typical university campus control scenario, since the school needs to have actual control over its own equipment, a real deployment method is adopted.

[0061] In a typical scenario, two virtual controllers are set up. Virtual controller 1 represents the microgrid system, and virtual controller 2 represents the distributed multi-energy system. Compared with the centralized optimization, the optimization objectives of the two are no longer the same. The objective function of the microgrid is to comprehensively consider the use of energy storage systems to achieve peak load shaving and valley filling of the power grid and distributed clean energy consumption, and the objective function of the distributed energy system is to simultaneously consider NOx The pollutants of the distributed multi-energy system mainly come from gas turbines, gas boilers, internal combustion engines, fuel cells, purchased electricity, etc. The present invention mainly considers NO x and CO2 emissions, NO emissions of each major equipment x The emissions are shown in Table 1.

[0062] Table 1 NO of each main equipment x Emissions

[0063]

[0064]

[0065] Microgrid system: It consists of 1MW photovoltaic power generation, 500kW / 3MWh battery energy storage system and 0.6MW electric load. The local power balance of this system is mainly achieved by adjusting the battery energy storage. Similarly, the missing power is supplemented by the main grid.

[0066] In this system, the main objective function of the optimized operation is to comprehensively consider the peak shaving and valley filling effect and the utilization rate of clean energy. The objective function is as follows:

[0067] minf=-(ω1f1+ω2f2)

[0068] In the formula, f is the optimization effect evaluation index of the system, f1 is the peak-shaving and valley-filling effect, f2 is the clean energy consumption utilization rate, ω1 and ω2 are the weight coefficients of the first two. Here, the objective function should be to maximize the optimization effect evaluation index, but in order to make the objective function conform to the habit of solving the minimum value, it is necessary to add a negative sign and then solve the minimum value.

[0069] The constraints of the microgrid include:

[0070] (1) The upper limit of photovoltaic power generation is related to the local solar radiation, solar panel area and solar energy conversion efficiency at that time. At the same time, the maximum output of photovoltaic power generation should also be less than the rated power of the photovoltaic unit. The constraints are as follows:

[0071]

[0072] In the formula, Respectively represent the lower and upper limits of photovoltaic output, P capa Indicates the rated power of the photovoltaic unit, θ, S pv , η pv They represent local solar radiation, solar panel area and solar energy conversion efficiency respectively.

[0073] (2) The battery has two states: charging and discharging. The constraints are as follows:

[0074] S t+1 =S t (1-ε)+P c η c

[0075] S t+1 =S t (1-ε)+P d η d

[0076] Where P c , η c Represent the charging power and charging efficiency of the battery, P d , η d They represent the discharge power and discharge efficiency of the battery respectively, and ε represents the leakage rate of the battery.

[0077] (3) During the entire planning period, the sum of the battery charge capacity should be equal to the sum of the battery discharge capacity:

[0078]

[0079] In the formula, ΔW(t cha ) indicates that at t cha The charge amount at the time, ΔW(t discha ) indicates that at t discha The discharge amount at the moment.

[0080] Distributed multi-energy system: gas is supplied by the natural gas system and then transmitted to the trigeneration device, through which electricity, heat and cold energy are output to meet the university's electricity load, heat load and cold load. The missing electric power is supplemented by the main power grid.

[0081] The objective function of the distributed multi-energy system is:

[0082]

[0083] Where D total represents the environmental assessment index of this distributed multi-energy system, D co2 represents the CO2 emissions of this system in a scheduling cycle, Indicates the NO of this system in a scheduling cycle x Emission, ω1, ω2 are the weight coefficients of the first two respectively. ANG Indicates the natural gas heat consumed (GJ / a), E pur Indicates the amount of electricity purchased (MW·h).

[0084] The energy constraints of distributed multi-energy systems include:

[0085] (1) Distributed generation, electricity purchase, combined cooling, heating and power generation, electricity storage and electricity load should meet the basic energy conservation law:

[0086]

[0087] Where n is the number of distributed generation, P DG,i represents the electric power of the i-th distributed generation system, P grid Represents the exchange power with the grid, P d P represents the power generation output of the trigeneration device, c Represents the electric power of the battery energy storage system, P load It should be noted that here, the total power generation of distributed generation and the power difference of the battery energy storage system are processed as information interaction variables, which are considered to be a constant value of 0.6MW.

[0088] (2) The production and consumption of cold energy should always be balanced:

[0089]

[0090] In the formula, k is the number of refrigeration equipment, C i represents the cooling power of the i-th refrigeration equipment, C load Indicates the current cooling load

[0091] (3) Gas turbines, other heating equipment, heat storage and heat loads should meet the basic thermal energy conservation requirements:

[0092]

[0093] Where m is the number of gas turbines, H i represents the thermal power of the i-th gas turbine, H e Indicates the thermal power generated by other heat-generating equipment, H ss Represents the thermal power of the heat storage device, H load Indicates the current heat load.

[0094] (4) The actual output power of the gas turbine should be within the normal operating range and satisfy the inequality constraints. In addition, the rate of change of its power should also satisfy the corresponding ramp constraints. The constraints are as follows:

[0095]

[0096] -D ngas Δt≤ΔP gas ≤U pgas Δt

[0097] In the formula, They represent the lower and upper limits of the actual output power of the gas turbine, ΔPgas represents the variable power of the gas turbine, Δt represents the scheduling time interval, and D ngas represents the ramp rate of the gas turbine, U pgas Indicates the ramp down rate of the gas turbine.

[0098] Considering that some energy entities cannot disclose detailed operation data due to privacy security, we can consider using a big data correlation analysis method to conduct correlation mining on the operation data of energy entities, extract state variables or key indicators that can evaluate the optimized operation status of each energy entity, and use them for the top-level supervision center to supervise / assess / service the bottom-level energy entities. Under the framework of two-layer collaborative optimization and control, the bottom-level energy entities only interact with the state variables and key indicators used for supervision / assessment / service between the top-level energy management system and the bottom-level virtual controllers, instead of uploading detailed data of the controlled energy entities. The virtual controller is the "decision maker" or "executor" of the operation optimization of each distributed energy entity, and is the core equipment of the two-layer optimization and control system. It has two deployment modes: virtual deployment and real deployment. For control scenarios with high requirements for real-time and privacy, the bottom-level distributed control mode with unsupervised or moderate supervision of key indicators is mainly adopted; for control scenarios with high requirements for system optimization, a collaborative service mechanism is adopted to conduct centralized assessment of target-related indicators on some bottom-level subsystems that need to be supervised at the top level; under the top-level supervision / assessment / service environment, the bottom-level energy entities seek common ground while reserving differences and make autonomous optimization according to their capabilities.

[0099] Due to the different types of energy entities, differences in optimization operating conditions and decision-making capabilities, and different degrees of optimization goal pursuit, each energy entity is not always able to accurately and timely complete its own local optimization and keep consistent with the global goal. Based on the Bellman principle, a complete optimization process can be decomposed into a series of single-stage decision problems, and then the transfer and constraint relationships between the stages are used to solve the single-stage optimization problems one by one. Through this conversion, the distributed optimization problem is transformed into a dynamic programming problem, that is, the local optimal solution of the local subsystem is first solved, and then gradually expanded to the global optimal solution of the global system. This method optimizes traditional complex decisions into verification-type or empowerment-type approval decisions, and the centralized decision-making function of the top level is significantly weakened; since there is no need to use complex optimization decision-making tools, the top level only needs to apply conventional decision results to monitor the direction of the entire system state, which significantly reduces the amount of centralized optimization calculation tasks of the system. In a typical scenario on a university campus, the total power generation of distributed generation and the power difference of the battery energy storage system are processed as information interaction variables. Virtual controller 1 and virtual controller 2 both fully consider their own local optimal goals, and gradually solve the optimization problem of the entire system by continuously expanding the scope boundary of the local system.

Claims

1. An optimization scheduling method based on a two-layer collaborative architecture of urban energy internet, characterized in that: The steps include: (1) Analyze the physical architecture and decision-making structure of the urban energy Internet, sort out the types of energy entities, control objectives and control requirements in the urban energy Internet, and divide the typical control scenarios of the urban energy Internet. Based on the energy management system and virtual controller, build a centralized-distributed two-layer collaborative optimization control architecture of the urban energy Internet; (2) According to the characteristics of different energy entities, virtual controllers are deployed using real deployment or virtual deployment; (3) According to the optimization operation objectives of different energy entities in typical scenarios, an optimization control mathematical model under the centralized-distributed two-layer collaborative optimization control architecture of urban energy Internet is established; in typical scenarios, two virtual controllers are set up, virtual controller 1 represents the microgrid system, and virtual controller 2 represents the distributed multi-energy system. The objective function of the microgrid is to comprehensively consider the use of energy storage systems to achieve peak shaving and valley filling of the power grid and distributed clean energy consumption, and the objective function of the distributed energy system is to simultaneously consider NO x and CO2 emissions; pollutants in distributed multi-energy systems mainly come from gas turbines, gas boilers, internal combustion engines, fuel cells, purchased electricity, and NO x and CO2 emissions; The objective function of the microgrid system is to optimize the peak load shaving effect and the utilization rate of clean energy: minf=-(ω1f1+ω2f2) In the formula, f is the optimization effect evaluation index of the system, f1 is the peak shaving and valley filling effect, f2 is the consumption and utilization rate of clean energy, ω1 and ω2 are the weight coefficients of the first two respectively; The constraints of the microgrid include: (a) The constraints of photovoltaic power generation are: In the formula, Respectively represent the lower and upper limits of photovoltaic output, P capa Indicates the rated power of the photovoltaic unit, θ, S pv , η pv They represent local solar radiation, solar panel area and solar energy conversion efficiency respectively; (b) The battery charging and discharging constraints are: S t+1 =S t (1-e)+P c or c S t+1 =S t (1-e)+P d or d Where P c , η c Represent the charging power and charging efficiency of the battery, P d , η d They represent the discharge power and discharge efficiency of the battery respectively, and ε represents the leakage rate of the battery; (c) During the entire planning period, the sum of the charges on the battery should be equal to the sum of the discharges: In the formula, ΔW(t cha ) indicates that at t c h a The charge amount at the time, ΔW(t discha ) indicates that in td isc h a The discharge amount at the moment; The objective function of the distributed multi-energy system is: minD total =ω1D co2 +ω2D NOx =ω1(0.05982V ANG +0.096081E pur )+ω2D NOx Where D total represents the environmental assessment index of this distributed multi-energy system, D co2 represents the CO2 emissions of this system in a scheduling cycle, D NOx Indicates the NO of this system in a scheduling cycle x Emission, ω1, ω2 are the weight coefficients of the first two, V ANG Indicates the heat of natural gas consumed, its unit is GJ / a, E pur Indicates the amount of electricity purchased, the unit is MW·h; The energy constraints of distributed multi-energy systems include: (a) The electric energy conservation constraint is Where n is the number of distributed generation, P DG,i represents the electric power of the i-th distributed generation system, P grid Represents the exchange power with the grid, P d P represents the power generation output of the trigeneration device, c Represents the electric power of the battery energy storage system, P load Indicates the current electrical load; (b) The cold energy conservation constraint is In the formula, k is the number of refrigeration equipment, C i represents the cooling power of the i-th refrigeration equipment, C loa d represents the current cooling load; (c) The thermal energy conservation constraint is Where m is the number of gas turbines, H i represents the thermal power of the i-th gas turbine, H e Indicates the thermal power generated by other heat-generating equipment, H ss Represents the thermal power of the heat storage device, H load Indicates the current heat load; (d) The output power constraint of the gas turbine is In the formula, They represent the lower and upper limits of the actual output power of the gas turbine, ΔP gas represents the variable power of the gas turbine, Δt represents the scheduling time interval, and D ngas represents the ramp rate of the gas turbine, U pgas represents the ramp down rate of the gas turbine; (4) Based on the established mathematical model of optimization and control, the interaction process between the energy management system and virtual control is designed, and a solution method for the mathematical model of optimization and control based on the Bellman principle is proposed.

2. The optimization scheduling method based on the double-layer collaborative architecture of urban energy internet according to claim 1 is characterized in that: In step (1), the urban energy Internet presents the physical architecture characteristics of multiple distributed energy entities freely interconnected; the decision-making structure characteristics of the urban energy Internet are that each energy entity is subordinate to different interest entities, makes autonomous decisions, and is not subject to centralized decision-making and direct regulation by the power grid; the regulation requirements include real-time requirements, privacy requirements, and global requirements; Control scenarios include short-term optimized scheduling operations and energy market transaction bidding.

3. The optimization scheduling method based on the double-layer collaborative architecture of urban energy Internet according to claim 1 is characterized in that: In step (1), an energy management system is deployed in the supervision center of the top layer of the urban energy Internet, and a virtual controller is deployed in each distributed energy entity at the bottom layer; a virtual controller is deployed in small distributed photovoltaic, wind power, thermal power source, gas supply source, electricity / heat / gas pipeline network and industrial / commercial / residential energy entities, and multiple virtual controllers together constitute the distributed decision-making layer of the centralized-distributed two-layer collaborative optimization control architecture of the urban energy Internet; large centralized generator sets, thermal power plants, natural gas sources, high-voltage transmission networks, and heat / gas pipeline control systems are connected to the traditional urban energy management system to form a centralized supervision and management layer of the centralized-distributed two-layer collaborative optimization control architecture of the urban energy Internet; the centralized supervision and management layer communicates with all virtual controllers of the distributed decision-making layer, and the instructions are directly transmitted; under this architecture, the energy management system only verifies the local decision-making and control quantities uploaded by each virtual controller, and sends them for execution after confirmation or correction.

4. The optimization scheduling method based on the double-layer collaborative architecture of urban energy internet according to claim 1 is characterized in that: In step (2), the autonomous control authority of each energy subject is divided into levels according to the type of energy subject, control requirements, control objectives and importance, and two modes of real deployment and virtual deployment of virtual controllers are set to determine whether the execution of the decision made by the virtual controller of each energy subject at the bottom layer needs to be supervised and approved by the energy management system of the top-level supervision center after the decision is made autonomously by the virtual controller of each energy subject at the bottom layer; the deployment mode of the virtual controller is selected by comprehensively considering multiple factors, including the optimization goals of various stakeholders, asset owners, and user privacy wishes; the real deployment mode refers to the configuration of computing resources on the distributed resource side of the bottom layer, which not only has distributed computing functions, but also undertakes distributed control functions, that is, truly realizes self-decision-making and self-control; the virtual deployment mode refers to the configuration of computing resources in the top-level energy management system, and the top layer provides computing services for distributed decision-making. Under this deployment mode, the virtual controller does not have the authority to directly implement distributed control, but needs to generate a control strategy and upload it to the energy management system for supervision and approval.

5. The optimization scheduling method based on the double-layer collaborative architecture of urban energy internet according to claim 1 is characterized in that: In step (4), considering that some energy entities cannot disclose detailed operation data due to privacy security, state variables or key indicators that can evaluate the optimized operation status of each energy entity are extracted for the top-level supervision center to supervise / assess / serve the bottom-level energy entities; under the framework of two-layer collaborative optimization and control, the bottom-level energy entities only use the state variables and key indicators used for supervision / assessment / service to interact between the top-level energy management system and the bottom-level virtual controllers; the virtual controller is the "decision maker" or "executor" of the operation optimization of each distributed energy entity, and is the core equipment of the two-layer optimization and control system, with two deployment modes: virtual deployment and real deployment.

6. The optimization scheduling method based on the double-layer collaborative architecture of urban energy internet according to claim 1 is characterized in that: In step (4), due to the different types of energy entities, differences in optimization operation conditions and decision-making capabilities, and different degrees of optimization goal pursuit, each energy entity is not always able to accurately and timely complete its own local optimization and keep consistent with the global goal; Based on the Bellman principle, a complete optimization process is decomposed into a series of single-stage decision-making problems, and then the transfer and constraint relationships between the stages are used to solve the single-stage optimization problems one by one; through this transformation, the distributed optimization problem is transformed into a dynamic programming problem, that is, first solving the local optimal solution of the local subsystem, and then gradually expanding to the global optimal solution of the global system.

Images