CN-115759656-B - Robust and random combined service area micro-energy network multi-granularity cooperative regulation and control method

CN115759656BCN 115759656 BCN115759656 BCN 115759656BCN-115759656-B

Abstract

The invention provides a multi-granularity cooperative regulation and control method for a service area micro energy network by combining robustness and randomness. The method comprises the steps of establishing a fine granularity model and a coarse granularity model of a micro-energy network of a service area, carrying out robust optimal scheduling design of the micro-energy network of the service area by utilizing the fine granularity model, carrying out random optimal scheduling design of the micro-energy network of the service area by utilizing the coarse granularity model, combining the robust optimal scheduling design and the random optimal scheduling design of the micro-energy network of the service area under a rolling optimization framework, and respectively using a robust optimization part and a random optimization part for a near period and a far period of the rolling optimization to obtain a multi-granularity-source load storage collaborative scheduling scheme combining the robust optimization and the random optimization. The invention constructs a distributed resource fine-granularity model-fine model of each link of source load storage of the service area micro energy network, combines the two kinds of granularity models of the scheduling resources of each link of source load storage of the service area micro energy network according to the requirement of optimization calculation, and is used for the operation optimization of the micro energy network.

Inventors

XIA MINGCHAO
SONG YUGUANG
CHEN QIFANG

Assignees

北京交通大学

Dates

Publication Date: 20260512
Application Date: 20221124

Claims (4)

1. A robust and random combined service area micro energy network multi-granularity cooperative regulation and control method is characterized by comprising the following steps: establishing a fine granularity model of a micro energy network in a service area; Establishing a service area micro energy network coarse granularity model; carrying out robust optimization scheduling design of the micro energy network of the service area by utilizing the micro energy network fine granularity model of the service area; carrying out random optimization scheduling design of the micro-energy network of the service area by utilizing the coarse-granularity model of the micro-energy network of the service area; combining a robust optimization scheduling design and a random optimization scheduling design of the micro energy network in the service area under a rolling optimization framework, using a robust optimization part for an adjacent period of rolling optimization, and using a random optimization part for a farther period of rolling optimization to obtain a multi-granularity source load storage collaborative scheduling scheme combining the robust optimization and the random optimization; the establishing of the service area micro energy network fine granularity model comprises the following steps: The service area micro energy network fine granularity model comprises a charging, exchanging and storing integrated unit, a heat pump unit, a hydrogen energy unit, a heat energy storage unit, an absorption refrigeration unit and a system energy balance constraint; 1) Charging, replacing and storing integrated unit The charging and replacing integrated unit comprises a quick charging part and a power replacing part, and the state of the quick charging pile is expressed as follows: Wherein, the Indicating the number of fast-fill piles in an occupied state, Representing the number of quick filling piles in an idle state; the state transition equation of the fast filling pile is expressed as: Wherein, the Representing a matrix of the fast-fill system, Representing the input matrix of the fast-fill system, Representing a fast-fill input variable; System matrix of fast charging part Expressed as: Input matrix of fast charging part Expressed as: Wherein, the , The rest is zero; the input variables of the fast-fill part satisfy the constraint: Charging energy corresponding to the fast charging part And average charging power Expressed as: The power conversion part consists of a power conversion battery pack and is divided into two combinations of a full power part and a flexible charging and discharging part; The equation of the charge of the power exchanging part is expressed as: Wherein, the For the full power collection capacity, In order to flexibly collect the amount of electricity, In order to provide the power for the power conversion service, To flexibly scale the set to the full power set, And The charge-discharge power is represented by the number of the electrodes, A charge-discharge state, a charge of 1, a discharge of 0, And The charge-discharge efficiency is represented by the ratio, Indicating the power demand in a waiting state, Indicating a charging/battery change demand; 2) Heat pump unit Constructing an operation model of the heat pump unit based on the first-order equivalent thermal parameter model; Wherein, the The indoor temperature is indicated to be the indoor temperature, And Indicating the indoor heat capacity and heat resistance parameters, Indicating the thermal disturbance of the room in the room, Representing the thermal power of the heat pump, Indicating the total heat load demand; 3) Hydrogen energy unit Electric hydrogen production part: the fuel cell portion, modeled based on the manner of piecewise linearization: Wherein, the Indicating the rate of electrical hydrogen production, Represents the electric hydrogen production power, Which is indicative of the power of the hydrogen compressor, The quality of the hydrogen in the gas storage tank is indicated, And Indicating the rate of hydrogen gas ingress and egress, Indicating the hydrogen consumption rate of the fuel cell, Represents the generated power of the fuel cell, Indicating the heat-generating power of fuel cell, meter The segment variables of the fuel cell performance curve are shown, 、 And Represents the relative hydrogen consumption per unit value, the electricity generation per unit value and the relative heat generation per unit value of the fuel cell, 、 And Indicating the hydrogen consumption, electricity production and heat production ratings of the fuel cell, Representing the total period of optimization; 4) Thermal energy storage unit The thermal energy storage is used for storing and releasing heat energy, and is used for absorbing renewable energy sources and providing heat energy support; Wherein, the Representing the energy of the stored heat energy, And The exothermic power and the heat storage power of the heat storage are represented; 5) Absorption refrigeration unit The absorption refrigeration unit converts heat energy into cold energy, and modeling is performed based on a piecewise linearization method, as follows: Wherein, the And Represents the output cold power and the input hot power of the absorption chiller, And The per unit value of the output refrigeration power and the per unit value of the input heat power of the absorption refrigeration machine are shown, Representing the segmentation variable; 6) System energy balance constraint Electric power balance: thermodynamic equilibrium: hydrogen balance: Wherein, the And Indicating the output of the photovoltaic and blower, The electric power of purchase is indicated, Representing the management of the feed-in grid, And Represents the heat pump electric power and the thermal power, , Representing the coefficient of performance of the heat pump, Representing the electrical load required for basic operation of the service area, Discarding electric power representing renewable energy sources; The establishing of the service area micro energy network coarse-grained model comprises the following steps: The service area micro energy network coarse-grained model comprises a charging, exchanging and storing integrated unit, a heat pump unit, a hydrogen energy unit, a heat energy storage unit, an absorption refrigeration unit and a system energy balance constraint; 1) Charging, replacing and storing integrated unit Neglecting the occupation state of the quick-charging pile under the coarse-grained model, combining the full-power set stored by the battery and the flexible set for unified treatment, and simplifying the model to be: Wherein, the Representing the total number of fast-fill piles, Indicating the energy state of the battery; The rest is consistent with the fine granularity model; 2) Heat pump unit Establishing a coarse-grained model of the heat pump cluster from the viewpoint of equivalent thermal energy storage; Wherein, the Representing the equivalent thermal energy storage of the service area heat pump operating thermal area, And Represents the average value of equivalent heat capacity and equivalent thermal resistance of the heat pump operation heat region, Indicating the total thermal interference power of the thermal zone, Representing the total number of hot areas, And Representing the upper and lower boundary coefficients of equivalent heat energy; 3) Hydrogen energy unit Ignoring the intermediate process of the electric hydrogen production and the fuel cell, and regarding the hydrogen energy unit as an energy storage; Wherein, the Represents the energy of the equivalent hydrogen energy unit, Represents the heating value of the hydrogen gas, Represents the energy release power of the hydrogen energy, An electric power duty ratio coefficient representing the discharge power of the hydrogen energy; 4) Thermal energy storage unit The coarse grain model and the fine grain model of the thermal energy storage unit are kept consistent; 5) Absorption refrigeration unit Representing an absorption equation by using a conversion coefficient; 6) System energy balance constraint In the coarse-grained model, the intermediate process of electric hydrogen production and fuel cells in a hydrogen energy system is ignored, the compressor operation electric power in the hydrogen balance relation and the electric power balance is hidden in a formula, and the new energy balance is as follows: the thermodynamic equilibrium is consistent with that in the fine-grained model.
2. The method of claim 1, wherein utilizing the service area micro energy grid fine-grained model for robust optimal scheduling design of a service area micro energy grid comprises: Dividing a rolling scheduling time domain into two parts, wherein one part is adjacent time period, the scheduling period is shorter, the other part is distant time period, the scheduling period is longer, robust optimization is adopted for scheduling in the adjacent time period, random optimization based on opportunity constraint is adopted for scheduling in the distant time period, and a service area micro energy network fine granularity model is adopted for the robust optimization part; a. Optimization target setting The self-sufficient energy supply level of the micro energy network in the service area is maximized, the dependence on the energy of the external power network is reduced, and an objective function is established, wherein the objective function is expressed as follows: b. adjacent period scheduling based on robust optimization Scheduling of adjacent time periods in a rolling scheduling time domain adopts robust optimization, and scheduling period The scheduling time scale is set to ; The uncertainty of the photovoltaic, fan out and vehicle charging demand is expressed based on the form of a budget uncertainty set, expressed as: Wherein, the 、 And Representing predicted photovoltaic, fan and charge demand values, 、 And Representing the maximum deviation possible for the predicted photovoltaic, fan and charging demands, derived from historical deviation data of the prediction system, 、 And Uncertainty variables representing photovoltaic, fan and charging requirements, 、 And Representing the corresponding uncertainty adjustment parameter, Representing a current scheduling period; By introducing auxiliary variables For absolute value Linearizing to give , Converting the model; Based on the established uncertainty set, the schedule of the adjacent time period in the rolling schedule time domain is designed into a robust optimized form: constraint conditions are that the fine-grained model, ; Wherein, the The decision variables contained in the fine-grained model of the formula are represented, Representing the possible fields to which it corresponds, Decision variables representing an uncertainty set of formulas, Representing its corresponding feasible region.
3. The method of claim 1, wherein utilizing the service area micro energy network coarse-grained model to perform random optimization scheduling design of a service area micro energy network comprises: The method comprises the steps of designing the scheduling of a longer period in a rolling scheduling time domain into a random optimization form based on opportunity constraint based on a service area micro-energy network coarse granularity model: The opportunity constraint is designed as the charge-discharge power constraint of the storage battery, and the power imbalance caused by the opportunity constraint is represented by the charge-discharge power change: Wherein, the And The charge-discharge power is represented by the number of the electrodes, A charge-discharge state, a charge of 1, a discharge of 0, Representing confidence that the opportunity constraint is broken; the opportunity constraint deterministic is represented with scene constraints: Wherein, the The superscript indicates that the scene is, Representing the total number of scenes, Representing the total period of the random optimization, And Representing the maximum charge power and the maximum discharge power which can be achieved by all modules of the battery; The scheduling of the longer period in the rolling scheduling time domain is designed into a random optimization form, expressed as: Constraint conditions are a service area micro energy network coarse-grained model, Wherein, the Representation of Average values under individual scenes are marked with superscripts in constraint conditions of random optimization 。
4. The method of claim 3, wherein the robust optimization scheduling design and the random optimization scheduling design for the micro-energy network of the service area are combined under a rolling optimization framework, the robust optimization part is used for a close period of rolling optimization, the random optimization part is used for a far period of rolling optimization, and the multi-granularity-source load storage collaborative scheduling scheme combining the robust optimization and the random optimization is obtained, and the method comprises the following steps: The cooperative optimization of the two optimization strategies on the service area micro energy network operation on two time scales and two model granularities is realized by combining the robust optimization with the random optimization on the rolling time domain; The objective function of multi-granularity collaborative scheduling combining robust optimization with random optimization is expressed as: constraint of coupling points: Heat pump coupling: Thermal energy storage coupling: Hydrogen energy system coupling: Filling, replacing and storing integrated coupling: And under the common constraint of a fine-granularity optimization model constraint condition, a coarse-granularity optimization model constraint condition and a multi-granularity model coupling point constraint condition, solving an objective function of multi-granularity collaborative scheduling combined by the robust optimization and the random optimization to obtain a multi-granularity source load storage collaborative scheduling scheme.

Description

Robust and random combined service area micro-energy network multi-granularity cooperative regulation and control method Technical Field The invention relates to the technical field of micro-energy control, in particular to a multi-granularity cooperative regulation and control method for a micro-energy network of a service area by combining robustness and randomness. Background The international energy agency indicates that the carbon emissions of the transportation sector account for about 25% of the world's carbon emissions, and the energy consumption accounts for about 33% of the world's. Therefore, besides the great development of electric vehicles and hydrogen fuel cell vehicles, the construction of clean low-carbon traffic infrastructure and traffic facility resources and energy is of great significance in improving the energy consumption ratio of renewable energy sources and promoting the collaborative decarburization of various departments. The high-speed service area is often far away from the energy hub in road traffic, and a self-generating and self-using distributed renewable energy consumption mode is built according to local resource endowment of the high-speed service area, so that the load energy supply requirement of a non-network area can be reduced, and the carbon emission reduction process can be accelerated. The micro energy network is used as a special distributed energy system, can integrate various renewable energy sources, energy storage, distributed power sources, local loads and control units, and is very suitable for the running mode requirement of the high-speed service area energy system. A random and adjustable robust mixed day-ahead scheduling model with a demand response participating in wind power consumption in the prior art adopts a combined optimization thought of combining robust optimization and random optimization, and the key point is to determine 'optimal conversion time'. Aiming at the characteristic that the closer the wind power is to the running point, the higher the accuracy is, and the advantages of a random optimization and adjustable robust optimization method are combined, the wind power consumption model with demand response participation is provided, and the random and adjustable robust together form the day-ahead dispatching. The model effectively combines random optimization and adjustable robust optimization by determining the optimal conversion time, highlights the characteristics that long-term scheduling is long in robustness and short-term scheduling is dominant in economy, and achieves source-load interaction. The existing research on the high-speed service area energy system is mostly aimed at a service area charging station part, lacks the research on the complete energy system of the high-speed service area, and cannot fully improve the operation efficiency of the service area energy system and the capability of absorbing renewable energy. The existing research considers different energy system compositions and builds different types of energy unit models. Because of the non-linear and discontinuous characteristics of the energy units and the operation links thereof, the operation form of the energy units is difficult to accurately describe by the oversimplified modeling method in the form of an energy router, the solving pressure is increased by the fully refined modeling method, and the high-efficiency requirements of on-line optimization and operation of the energy system in the service area are difficult to meet. The existing single robust optimization method can lead to over conservation of the optimal scheduling result, while the calculation result of the random optimization method increases the flexibility of scheduling operation but the reliability of operation is also reduced, and meanwhile, the calculation load of the random optimization method is generally larger. Disclosure of Invention The embodiment of the invention provides a multi-granularity cooperative regulation and control method for a service area micro energy network by combining robustness and randomness so as to realize the operation optimization of a server micro energy network. In order to achieve the above purpose, the present invention adopts the following technical scheme. A robust and random combined service area micro energy network multi-granularity cooperative regulation and control method comprises the following steps: establishing a fine granularity model of a micro energy network in a service area; Establishing a service area micro energy network coarse granularity model; carrying out robust optimization scheduling design of the micro energy network of the service area by utilizing the micro energy network fine granularity model of the service area; carrying out random optimization scheduling design of the micro-energy network of the service area by utilizing the coarse-granularity model of the micro-energy network of the service area; The robust optimizatio