Distributed energy system operation regulation and control method based on multi-element energy storage cooperation

Abstract

The invention discloses a multi-element energy storage collaborative distributed energy system operation regulation and control method, and the method comprises the steps: considering an optimal scheduling strategy of a power-energy type electricity storage, heat storage, cold storage and other technologies for cooperatively regulating and controlling a multi-energy load curve, and constructing a generalized technical model of energy equipment and system operation constraints; designing an energy system operation data preprocessing mechanism and a weight determination model under a given historical window; an energy system scheduling sequence of bipartite dynamic regression coordinated electric heating unit operation is provided, and an economic operation planning model is constructed; the model is applied to a historical window, optimal parameters of key regulation and control nodes are searched and determined through a heuristic algorithm so as to guide future time window scheduling and complete scheduling closed loop, and hyper-parameters of the method, such as historical and future window vision fields, time nodes and weights, are determined based on multiple scheduling closed loop behaviors so as to complete method closed loop. According to the method, a commercial solver is not needed, the economic operation rule of the distributed system is directly mined from historical operation, the method is used for determining the on-off and output plan of each device of the system in a future time sequence, and engineering application is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system regulation and control, and in particular to an operation regulation and optimization strategy for a distributed power source. Background Art

[0002] Developing a distributed energy system with a high proportion of integrated renewable energy can achieve distributed consumption of renewable energy and coordinate the flexible storage and conversion of multiple energy streams to solve the intermittent and volatile problems of renewable energy and loads, and improve the cleanliness, reliability, economy, and energy efficiency of energy supply. However, the technical characteristics of the various devices that constitute the distributed system are different, there are complex coupling relationships between them, and the system often operates in an uncertain source-load fluctuation environment, which brings great challenges to the energy management of the system, and the energy management strategy directly determines the actual performance that the system can achieve.

[0003] Most of the existing distributed energy system management strategies lack the consideration of the global operation of source-load-storage coordination. Although some strategies consider the development of prediction technology for uncertain factors such as source and load to achieve forward-looking system regulation, data prediction is unreliable and the prediction error itself is also a disturbance to the system operation. The contradiction of supply and demand mismatch in the uncertain operation environment is becoming increasingly prominent. Given that time series data has a large correlation in temporal distribution and energy storage equipment can coordinate the temporal correlation operation of the system, it is of great practical value to establish a data mining mechanism based on the active and coordinated energy storage and release of multi-energy storage systems to dynamically obtain the energy storage system operation plan that maximizes the performance of the distributed system, and then determine the overall system operation plan, so as to avoid the influence of uncertain factors, achieve coordinated operation that maximizes the utilization of system resources, and improve the system's adaptability to renewable energy.

[0004] The Chinese patent application number CN 202311526205.9 and the name "A global optimization method and system for energy-saving operation of a distributed energy system" discloses a technical solution, which includes the following steps: comprehensively considering the active loss and reactive loss of the distributed energy system, constructing a first objective function with optimal network loss and a constraint condition of real-time power flow balance; based on the first objective function, statically reconstructing the distributed energy system to obtain the optimal switching state of the distributed energy system; taking the maximization of the active output of the distributed energy system as the second objective function, and based on the optimal switching state of the distributed energy system, calculating the optimal output composition of the distributed energy system; taking the minimization of the reactive output of the distributed energy system as the third objective function, and based on the optimal switching state and output composition of the distributed energy system, calculating the optimal installation quantity and operating capacity of the reactive compensation equipment of the distributed energy system. This patent can realize the economical operation of distributed energy and reduce network operation losses. However, the patent document adopts a particle swarm algorithm to statically reconstruct the distributed energy system, optimize the switch state of the distributed power system, obtain the optimal state of the system switch, and ensure the overall stability of the system. However, it does not take into account the historical cyclical operation law of the system, nor the coordinated regulation between source, load and storage, and fully utilizes the ability of the multi-energy storage system to adjust the multi-energy load in time and coordinate the operation between source and load, thus making its regulation biased. Summary of the invention

[0005] In order to address the defects of the prior art, the present invention provides a distributed energy system operation and control method based on multi-energy storage synergy. The method directly mines the economic and technical operation laws of the system from historical operation data, determines the future system switch and output configuration, and improves the system's adaptability to the integrated variable renewable energy and multi-energy loads and the economy of operation while fully exploring the synergy potential and technical advantages of energy storage.

[0006] The objective of the present invention is achieved through the following technical solutions:

[0007] A distributed energy system operation control method with multi-energy storage coordination includes the following steps:

[0008] S1: Construct a generalized technical model of energy equipment and system operation constraints;

[0009] S2: Collect historical operation data of energy systems and design preprocessing mechanisms and historical scenario weight allocation models;

[0010] S3: Propose an energy system scheduling sequence and model for coordinating the operation of electric heating units based on the operation instructions of electric-to-cooling / heating equipment, dynamically adjusting at the midpoint between the upper and lower bounds to avoid energy redundancy through binary dynamic regression;

[0011] S4: Construct a distributed system economic operation planning model and determine the key parameters of key control nodes;

[0012] S5: Optimize the algorithm for historical scenarios based on the scheduling sequence and model to determine the optimal parameters for regulation;

[0013] S6: Determine the system switching and output plan for the future window based on the scheduling sequence and optimal parameters to complete the scheduling closed loop;

[0014] S7: Based on the operation data of multiple scheduling closed loops obtained by rolling execution of S2 to S6, a scheduling model hyperparameter search method is constructed, wherein the hyperparameters include parameters such as window matrix field of view, number of window included cycles, time resolution combination and window weight allocation, to complete the method closed loop.

[0015] In the above-mentioned distributed energy system operation control method with multi-energy storage coordination, in step S1, the generalized technical model includes a power link, a multi-energy conversion link and a multi-energy storage link, wherein:

[0016] 1) Power link

[0017] The power link is divided into cogeneration and non-cogeneration. Typical cogeneration includes gas turbines or internal combustion engines and their heat recovery components, PVT; non-cogeneration includes photovoltaics, wind turbines, solar collectors, boilers and other equipment that only produces electricity or heat. Its model is:

[0018]

[0019]

[0020] Where: OUP and INP represent the energy output and input power of the equipment respectively, the subscript k / m represents the cogeneration / non-cogeneration equipment, and K and M are the sets of corresponding equipment in the distributed system, and the superscript t indicates the time; is the power generation / heating efficiency of the equipment under rated conditions, Fitting model for the performance coefficient of power generation / heating equipment, in the factor set FS affecting equipment performance m The performance coefficient is obtained under the input of ; γ k / m Is a Boolean variable that indicates the start and stop of the system. are the upper and lower limits of the equipment's operating output, and Provide up and down climbing constraints for equipment output;

[0021] 2) Multi-energy conversion link

[0022] Typical equipment in the conversion link includes an electrothermal driven heat pump to convert multiple energy flows from the power link and adjust energy distribution. Its model is as follows:

[0023]

[0024]

[0025]

[0026] Where: O is the set of multi-energy conversion link equipment, and the subscripts etc / eth / htc / … respectively represent the working conditions of electricity to heat / electricity to cold / heat to cold / …;

[0027] 3) Multi-energy storage link

[0028] Typical equipment in the multi-energy storage link includes power storage, energy storage, heat storage, and cold storage technology; the power storage includes chemical batteries, compressed air energy storage, etc. The same thermal storage technology can achieve heat storage or cold storage due to the adjustment of the medium and its working temperature, and can be switched in a planned manner. The model is as follows:

[0029]

[0030]

[0031]

[0032] Where: L is the collection of energy storage devices, STH / SE is the heat storage / electricity storage of the energy storage device, DIS / CHA represents the energy discharge / charging power, and the subscript pis / eis identifies the power type / energy type storage device. is a Boolean variable, indicating that the energy storage device has only one function at the same time, and ΔT is the duration or step size.

[0033] In addition, the system operation constraints include the cooling-heating-electricity balance, which is expressed as follows:

[0034]

[0035] Where: γ o It is a Boolean variable, indicating that the energy from the electric heat conversion device cannot drive the heat-to-cooling device to provide cooling.

[0036] In the above-mentioned multi-energy storage coordinated distributed energy system operation control method, in step S2, the data preprocessing mechanism and historical scene weight allocation include:

[0037] S2-1), the data preprocessing mechanism is to construct a historical window matrix, continuously monitor and collect high time resolution data of the system's historical operation, the data is several seconds or tens of seconds, and construct three matrices of dual time scales:

[0038] S2-2. Assign weights to each period of the historical window: As the time distance between two periods increases, the correlation between the historical operation period and the future operation period becomes weaker, and the reference for the scheduling plan of the future operation window becomes weaker. Therefore, the following model is constructed to assign weights:

[0039]

[0040] Where: is the weight quota of the i-th historical scheduling period (corresponding to the i-th row of the matrix in step S2-1), and w i is the final weight value, and θ is the control parameter of the weight quota model. By adjusting θ, the allocation ratio of the weight quota in Z historical scheduling cycles can be controlled.

[0041] The above-mentioned multi-energy storage coordinated distributed energy system operation and control method, in the step S3, proposes an energy system scheduling sequence for coordinating the operation of electric heating units based on the operation instructions of the electric-to-cold / heat equipment, dynamically adjusting the midpoint between the upper and lower bounds, and avoiding energy redundancy through binary dynamic regression, including two parts corresponding to the dual time scale in step S2, namely, any time step with low time resolution and any random fluctuation event with high time resolution are processed as the subject respectively.

[0042] In the above-mentioned multi-energy storage coordinated distributed energy system operation control method, in the step S4, the constructed economic operation planning model includes an objective function, a constraint function and a decision variable set composed of key control parameters, which are listed as follows:

[0043] 1) Planning model:

[0044] The optimization model of distributed energy system is expressed as:

[0045]

[0046] fee∈[fuel, electricity purchase and sale, equipment operation and maintenance, emissions, …](29)

[0047] In the formula: the operating costs of the system include the purchase of fuel generated during the dispatch period, the purchase and sale of electricity from the power grid, the operation and maintenance of equipment operation, and emissions such as carbon emissions. Penalty costs will also be considered to adjust the dispatch direction: such as wind and solar power abandonment and energy efficiency; the constraints of the model include the equation part h(DEC)=0 and the inequality part g(DEC)<0, which are mainly the equipment technology model and system energy flow balance model constructed in step S1, where DEC is the decision variable set.

[0048] 2) Decision variables

[0049] Referring to step S3, the decision variables mainly include the critical start-up threshold of the power generation unit in a scheduling cycle, the charging and discharging plan of the energy storage and cold and hot energy storage equipment at each time step in the cycle, and the three key state points of the power storage equipment at each time step in the cycle:

[0050]

[0051] In step S5, the operation planning model constructed in step S4 is applied to the three matrices under the dual time scale constructed in step S2, and a set of key control parameters (Equation (30)) are determined by a heuristic search algorithm such as genetic or particle swarm, so that the system controls the operation of each scheduling period system in the historical window in turn according to the set of control parameters and the strategy described in step S3, and the weighted cumulative sum of the operating costs of each scheduling period is minimized as shown in equation (28).

[0052] In the above-mentioned multi-energy storage coordinated distributed energy system operation control method, in step S6, the preferred control parameters obtained in step S5 are combined with the strategy described in step S4 to guide the system operation in a future scheduling cycle, which can be regarded as i = 0. Then, the features and methods described in steps S2 to S6 are repeated to guide the system operation in the next future scheduling cycle, and the cycle is rolled to form a scheduling closed loop.

[0053] In the above-mentioned multi-energy storage coordinated distributed energy system operation and control method, in step S7, when the number of scheduling closed-loop scrolling times reaches a threshold, a combination of factors not limited to the step configuration of the scheduling cycle, the number of scheduling cycles that constitute the historical window, the parameters of the weight model, and the scheduling time scale are selected as decision variables. Combined with the optimization of steps S2 to S6, a further layer of search optimization is performed with the goal of minimizing the sum of the system operating costs of the i=0 row in each scroll, and the hyperparameters of the control method are updated in time to adapt to the latest operating environment of the system, forming a method closed loop.

[0054] Beneficial Effects

[0055] Compared with the prior art methods, the beneficial effects and advantages of the present invention are:

[0056] 1. The present invention establishes a source-load-storage coordinated control framework, taking the active storage and release of the multi-energy storage system as the starting point, giving full play to the ability of the multi-energy storage system to adjust the multi-energy load in time sequence and coordinate the source-load operation, improving the utilization rate of each system equipment and the adaptability to renewable energy, and can guide the formulation of the operation plan of the distributed energy system;

[0057] 2. The present invention relies on the constructed generalized distributed energy system model, extracts the periodic operation rules of system equipment from the historical operation data of the system in recent several cycles, and uses it for system scheduling in a near future cycle. It also timely nests the outer optimization to determine the hyperparameters of the method. Through the double closed loop of system operation and control method, the proposed method has wide adaptability and strong and flexible system management capabilities.

[0058] The present invention is further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Schematic diagram of the operation and control process of multi-energy storage coordination. DETAILED DESCRIPTION

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0061] The present invention provides an operation control and optimization strategy based on the coordination of multiple energy storage, which helps to extract the periodic operation rules of system equipment from historical operation data and use them for system scheduling in the near future. It also timely adjusts the window matrix field of view of the scheduling model, the number of cycles included in the window, the time resolution combination, the window weight allocation and other hyperparameters to form a double closed loop of system operation and control methods.

[0062] The present invention comprises the following steps:

[0063] S1: Construct a generalized technical model of energy equipment and system operation constraints;

[0064] S2: Collect historical operation data of energy systems and design preprocessing mechanisms and historical scenario weight allocation models;

[0065] S3: Propose an energy system scheduling sequence and model for coordinating the operation of electric heating units based on the operation instructions of electric-to-cooling / heating equipment, dynamically adjusting at the midpoint between the upper and lower bounds to avoid energy redundancy through binary dynamic regression;

[0066] S4: Construct a distributed system economic operation planning model and determine the key parameters of key control nodes;

[0067] S5: Optimize the algorithm for historical scenarios based on the scheduling sequence and model to determine the optimal parameters for regulation;

[0068] S6: Determine the system switching and output plan for the future window based on the scheduling sequence and optimal parameters to complete the scheduling closed loop;

[0069] S7: Based on the operation data of multiple scheduling closed loops obtained by rolling execution of S2 to S6, a scheduling model hyperparameter search method is constructed, wherein the hyperparameters include parameters such as window matrix field of view, number of window included cycles, time resolution combination and window weight allocation, to complete the method closed loop.

[0070] In the step S1, the generalized technical model includes a power link, a multi-energy conversion link, and a multi-energy storage link:

[0071] In step S1, the generalized technical model includes a power link, a multi-energy conversion link, and a multi-energy storage link, which are described as follows:

[0072] 1) Power link

[0073] The power link is divided into cogeneration and non-cogeneration. Typical cogeneration includes gas turbines or internal combustion engines and their heat recovery components, PVT; non-cogeneration includes photovoltaics, wind turbines, solar collectors, boilers and other equipment that only produces electricity or heat. Its model is:

[0074]

[0075]

[0076] Where: OUP and INP represent the energy output and input power of the equipment respectively, the subscript k / m represents the cogeneration / non-cogeneration equipment, and K and M are the sets of corresponding equipment in the distributed system, and the superscript t indicates the time; is the power generation / heating efficiency of the equipment under rated conditions, Fitting model for the performance coefficient of power generation / heating equipment, in the factor set FS affecting equipment performance m The performance coefficient is obtained under the input of ; γ k / m Is a Boolean variable that indicates the start and stop of the system. are the upper and lower limits of the equipment's operating output, and Provide up and down climbing constraints for equipment output;

[0077] 2) Multi-energy conversion link

[0078] Typical equipment in the conversion link includes an electrothermal driven heat pump to convert multiple energy flows from the power link and adjust energy distribution. Its model is as follows:

[0079]

[0080]

[0081] Where: O is the set of multi-energy conversion link equipment, and the subscripts etc / eth / htc / … respectively represent the working conditions of electricity to heat / electricity to cold / heat to cold / …;

[0082] 3) Multi-energy storage link

[0083] Typical equipment in the multi-energy storage link includes power storage, energy storage, heat storage, and cold storage technology; the power storage includes chemical batteries, compressed air energy storage, etc. The same thermal storage technology can achieve heat storage or cold storage due to the adjustment of the medium and its working temperature, and can be switched in a planned manner. The model is as follows:

[0084]

[0085]

[0086] Where: L is the collection of energy storage devices, STH / SE is the heat storage / electricity storage of the energy storage device, DIS / CHA represents the energy discharge / charging power, and the subscript pis / eis identifies the power type / energy type storage device. is a Boolean variable, indicating that the energy storage device has only one function at the same time, and ΔT is the duration or step size.

[0087] In addition, the system operation constraints include the cooling-heating-electricity balance, which is expressed as follows:

[0088]

[0089] Where: γ o It is a Boolean variable, indicating that the energy from the electric heat conversion device cannot drive the heat-to-cooling device to provide cooling.

[0090] In step S2, the data preprocessing mechanism and historical scene weight allocation include:

[0091] S2-1), the data preprocessing mechanism is to construct a historical window matrix, continuously monitor and collect high time resolution data of the system's historical operation, the data is several seconds or tens of seconds, and construct the following three matrices of dual time scales:

[0092]

[0093]

[0094]

[0095] Where: the superscript renew represents the energy output of renewable energy sources such as wind and solar that are difficult to control. and are the net value matrices of electricity and heating / cooling loads at low time resolutions (e.g., several minutes, more than ten minutes, and tens of minutes), respectively, where the number of columns H represents the period of the historical window, and the number of rows Z is the number of periods constituting the historical window; is the nth monitoring data covered by the ith time step in the jth cycle, and In order to monitor the mean of the net value data matrix, this operation filters out random fluctuations to a certain extent and retains the time series coupling characteristics. For net cooling and heating loads with large inertia, high-frequency quantities can no longer be retained, and the operation plan can be specified based on the low-frequency matrix only. For electric energy that requires instantaneous balance, a high-frequency random fluctuation matrix with a corresponding high time resolution (such as several seconds or tens of seconds) is constructed.

[0096] S2-2) Assign weights to each period of the historical window: As the time distance between two periods increases, the correlation between the historical operation period and the future operation period becomes weaker, and the reference for the scheduling plan of the future operation window becomes weaker. Therefore, the following model is constructed to assign weights:

[0097]

[0098] Where: is the weight quota of the i-th historical scheduling period (corresponding to the i-th row of the matrix in step S2-1), and w i is the final weight value, and θ is the control parameter of the weight quota model. By adjusting θ, the allocation ratio of the weight quota in Z historical scheduling cycles can be controlled; the change trend and amplitude of the weight quota with the increase of the number of historical cycles can also be adjusted, for example, the quota decreases quickly first and then slowly, or vice versa.

[0099] In step S3, an energy system scheduling sequence is proposed based on the operation instructions of the electric-to-cold / heat equipment, which is dynamically adjusted at the midpoint between the upper and lower bounds to avoid energy redundancy and coordinate the operation of the electric-to-heat unit. The sequence includes two parts corresponding to the dual time scale in step S2, which are described in detail as follows:

[0100] 1) Any time step with low temporal resolution:

[0101] Step S3-1: Setting the upper and lower critical states of power storage at time step t and target status If the state of this type of device at time step t Reaching the upper critical state Then To determine the discharge power position for the discharge target If you touch the Nether state The charging power occupancy is determined by the same logic Otherwise, the charge and discharge power occupancy is 0. The occupancy refers to the maximum charge and discharge power range of the device. Adjust to The charge and discharge power to be performed.

[0102] Step S3-2: Superimpose the charging and discharging occupancy of the power storage device and the charging and discharging plan of other energy storage devices on the net electric and thermal load to adjust the net load curve:

[0103]

[0104] Step S3-3: According to the technical sequence of electricity-to-cooling and heat-to-cooling, it is distributed to the multi-energy conversion equipment in sequence, and the amount of electricity and heat required to meet the cooling load is determined, and then added to and

[0105] Step S3-4: According to the technical sequence of cogeneration power equipment (initial value is set to 0), power-to-heat equipment, and power equipment for heat supply only, the amount of electricity required for heating is determined and added to

[0106] Step S3-5: Setting the startup critical grid electricity price of the cogeneration / non-cogeneration power generation equipment, This is only a preferred option, and other similar thresholds can also be set. Determine the heat output of the cogeneration power equipment according to the technical sequence of cogeneration / non-cogeneration power equipment (the former is preferred in the cogeneration scenario, otherwise the latter), power grid, etc. In addition, when the electricity price is lower than The power equipment is limited to the off state when If it is less than 0, the discharge of energy storage equipment will be weakened and / or the charging power will be increased, and the future discharge plan of such equipment will be changed in conjunction, and the adjustment amount will be evenly distributed to the remaining time steps of the scheduling period.

[0107] Step S3-6: For the electric heat / cooling device, in a scenario with a relatively larger heat load, such as winter, the heating operation is performed, and vice versa, the cooling operation is performed. In steps 4.3 to 4.5, energy redundancy is inevitable; if the heat is redundant, the operating power of the electric heat / cooling device determined in steps 4.3 and 4.4 is used as the upper limit, and the shutdown state is used as the lower limit. The bisection point of the upper and lower limits, i.e., the mean, is used to limit the output power of the electric heat / cooling device, and steps 4.3, 4.5, and 4.4 are executed; if the heat is still redundant, the bisection point is set as the upper limit, and if only heat power is provided, the output power of the electric heat / cooling device is limited. If the output of the equipment increases, the bisection point is set as the lower bound, and the bisection point is recalculated, and steps 4.3, 4.5, and 4.4 are executed again; the iteration is repeated until convergence and regression; convergence conditions: heat redundancy or the output increment of the heating power equipment is less than the threshold, or the electric heat / cooling equipment is shut down; if the equipment is shut down and there is heat redundancy, the energy release of the heat storage / cooling equipment at the current time step t is weakened or / and the charging power is enhanced, and the future energy release plan of this type of equipment is changed in conjunction, and the adjustment amount is evenly distributed to the remaining time steps of the scheduling period (t~H).

[0108] Step S3-7: Through the above steps, the operation plan of each device in the distributed system can be obtained for renewable energy output, energy demand, and energy price scenarios at any time step, and the system enters the stage of smoothing high-frequency random fluctuations in electricity.

[0109] 2) Any random fluctuation event with high time resolution:

[0110] Step S3-8: Taking the power occupancy of the power storage device determined by the low time resolution scheduling as the base point, that is, the power storage device determined in step S3-1 under the nth fluctuation monitoring event at the tth time step according to the state of the power storage device, j=t; pull high or lower Limits of the power curve:

[0111]

[0112] Where: and is the maximum charge and discharge power of the power storage device under the current monitoring event, which can be calculated according to equations (8)-(12);

[0113] Step S3-9: Raise the electric power within the limit of the power storage device to suppress downward fluctuations in electric energy, or lower the electric power to suppress upward fluctuations. The random fluctuations exceeding the limit are allocated to the energy storage device or the power grid in sequence.

[0114] In step S4, the constructed economic operation planning model includes three parts: objective function, constraint function and decision variable set composed of key control parameters. The details are as follows:

[0115] 1) Planning model:

[0116] The optimization model of distributed energy system is expressed as:

[0117]

[0118] fee∈[fuel, electricity purchase and sale, equipment operation and maintenance, emissions, …](29)

[0119] In the formula: the operating costs of the system include the purchase of fuel generated during the dispatch period, the purchase and sale of electricity from the power grid, the operation and maintenance of equipment operation, and emissions such as carbon emissions. Penalty costs will also be considered to adjust the dispatch direction: such as wind and solar power abandonment and energy efficiency; the constraints of the model include the equation part h(DEC)=0 and the inequality part g(DEC)<0, which are mainly the equipment technology model and system energy flow balance model constructed in step S1, where DEC is the decision variable set.

[0120] 2) Decision variables

[0121] Referring to step S3, the decision variables mainly include the critical start-up threshold of the power generation unit in a scheduling cycle, the charging and discharging plan of the energy storage and cold and hot energy storage equipment at each time step in the cycle, and the three key state points of the power storage equipment at each time step in the cycle:

[0122]

[0123] In step S5, the operation planning model constructed in step S4 is applied to the three matrices under the dual time scale constructed in step S2, and a set of key control parameters (Equation (30)) are determined by a heuristic search algorithm such as genetic or particle swarm, so that the system controls the operation of each scheduling period system in the historical window in turn according to the set of control parameters and the strategy described in step S3, and the weighted cumulative sum of the operating costs of each scheduling period is minimized as shown in equation (28).

[0124] In step S6, the preferred control parameters obtained in step S5 are combined with the strategy in step S4 to guide the system operation in a future scheduling cycle, which can be regarded as i = 0. Then, the features and methods described in steps S2 to S6 are repeated to guide the system operation in the next future scheduling cycle, and the cycle is rolled to form a scheduling closed loop.

[0125] In step S7, when the number of scheduling closed-loop scrolling times reaches a threshold, a combination of factors not limited to the step configuration of the scheduling cycle, the number of scheduling cycles that make up the historical window, the parameters of the weight model, and the scheduling time scale are selected as decision variables. Combined with the optimization of steps S2 to S6, a further layer of search optimization is performed with the goal of minimizing the sum of the system operating costs of the i=0 row in each scrolling, and the hyperparameters of the control method are updated in time to adapt to the latest operating environment of the system, forming a method closed loop.

[0126] In summary, the operation control and optimization strategy based on the coordination of multiple energy storage provided by the present invention is helpful to extract the periodic operation rules of system equipment from the periodic historical operation data of the system, and use it for system scheduling in the near future. It also embeds an optimization layer to timely adjust the hyperparameters of the method, forming a double closed loop of system operation and control methods, and improving the adaptability and matching of the methods.

Claims

1. A distributed energy system operation control method with multi-energy storage coordination, characterized in that: The steps include: S1: Construct a generalized technical model of energy equipment and system operation constraints; S2: Collect historical operation data of energy systems and design preprocessing mechanisms and historical scenario weight allocation models; S3: Propose an energy system scheduling sequence and model for coordinating the operation of electric heating units based on the operation instructions of electric-to-cooling / heating equipment, dynamically adjusting at the midpoint between the upper and lower bounds to avoid energy redundancy through binary dynamic regression; S4: Construct a distributed system economic operation planning model and determine the key parameters of key control nodes; S5: Optimize the algorithm for historical scenarios based on the scheduling sequence and model to determine the optimal parameters for regulation; S6: Determine the system switching and output plan for the future window based on the scheduling sequence and optimal parameters to complete the scheduling closed loop; S7: Based on the operation data of multiple scheduling closed loops obtained by rolling execution of S2 to S6, a scheduling model hyperparameter search method is constructed, wherein the hyperparameters include parameters such as window matrix field of view, number of window included cycles, time resolution combination and window weight allocation, to complete the method closed loop.

2. The distributed energy system operation control method based on multi-energy storage coordination according to claim 1 is characterized in that: In step S1, the generalized technical model includes a power link, a multi-energy conversion link and a multi-energy storage link, wherein: 1) Power link The power link is divided into cogeneration and non-cogeneration. Typical cogeneration includes gas turbines or internal combustion engines and their heat recovery components, PVT; non-cogeneration includes photovoltaics, wind turbines, solar collectors, boilers and other equipment that only produces electricity or heat. Its model is: Where: OUP and INP represent the energy output and input power of the equipment respectively, the subscript k / m represents the cogeneration / non-cogeneration equipment, and K and M are the sets of corresponding equipment in the distributed system, and the superscript t indicates the time; is the power generation / heating efficiency of the equipment under rated conditions, Fitting model for the performance coefficient of power generation / heating equipment, in the factor set FS affecting equipment performance m The performance coefficient is obtained under the input of ; γ k / m Is a Boolean variable that indicates the start and stop of the system. are the upper and lower limits of the equipment's operating output, and Provide up and down climbing constraints for equipment output; 2) Multi-energy conversion link Typical equipment in the conversion link includes an electrothermal driven heat pump to convert multiple energy flows from the power link and adjust energy distribution. Its model is as follows: Where: O is the set of multi-energy conversion link equipment, and the subscripts etc / eth / htc / … respectively represent the working conditions of electricity to heat / electricity to cold / heat to cold / …; 3) Multi-energy storage link Typical equipment in the multi-energy storage link includes power storage, energy storage, heat storage, and cold storage technology; the power storage includes chemical batteries, compressed air energy storage, etc. The same thermal storage technology can achieve heat storage or cold storage due to the adjustment of the medium and its working temperature, and can be switched in a planned manner. The model is as follows: Where: L is the collection of energy storage devices, STH / SE is the heat storage / electricity storage of the energy storage device, DIS / CHA represents the energy discharge / charging power, and the subscript pis / eis identifies the power type / energy type storage device. is a Boolean variable, indicating that the energy storage device has only one function at the same time, and ΔT is the duration or step length; In addition, the system operation constraints include the cooling-heating-electricity balance, which is expressed as follows: Where: γ o It is a Boolean variable, indicating that the energy from the electric heat conversion device cannot drive the heat-to-cooling device to provide cooling.

3. The distributed energy system operation control method based on multi-energy storage coordination according to claim 1 is characterized in that: In step S2, the data preprocessing mechanism and historical scene weight allocation include: S2-1), the data preprocessing mechanism is to construct a historical window matrix, continuously monitor and collect high time resolution data of the system's historical operation, the data is several seconds or tens of seconds, and construct three matrices of dual time scales: S2-2. Assign weights to each period of the historical window: As the time distance between two periods increases, the correlation between the historical operation period and the future operation period becomes weaker, and the reference for the scheduling plan of the future operation window becomes weaker. Therefore, the following model is constructed to assign weights: Where: is the weight quota of the i-th historical scheduling period (corresponding to the i-th row of the matrix in step S2-1), and w i is the final weight value, θ is the control parameter of the weight quota model. By adjusting θ, the allocation ratio of the weight quota in Z historical scheduling cycles can be controlled.

4. The distributed energy system operation control method based on multi-energy storage coordination according to claim 1 is characterized in that: In step S3, an energy system scheduling sequence is proposed based on the operation instructions of the electric-to-cold / heat equipment, dynamically adjusted at the midpoint between the upper and lower limits, and coordinated with the operation of the electric-to-heat unit to avoid energy redundancy, including two parts corresponding to the dual time scale in step S2, namely, any time step with low time resolution and any random fluctuation event with high time resolution are processed as the subject respectively.

5. The distributed energy system operation control method based on multi-energy storage coordination according to claim 1 is characterized in that: In step S4, the constructed economic operation planning model includes an objective function, a constraint function and a decision variable set composed of key control parameters, which are listed as follows: 1) Planning model: The optimization model of distributed energy system is expressed as: fee∈[fuel, electricity purchase and sale, equipment operation and maintenance, emissions, …](29) In the formula: the operating costs of the system include the purchase of fuel generated during the dispatch period, the purchase and sale of electricity from the power grid, the operation and maintenance of equipment operation, and the cost of emissions such as carbon emissions. Penalty costs will also be considered to adjust the dispatch direction: such as wind and solar abandonment and energy efficiency; the constraints of the model include the equation part h(DEC)=0 and the inequality part g(DEC)<0, which are mainly the equipment technology model and system energy flow balance model constructed in step S1, where DEC is the decision variable set; 2) Decision variables Referring to step S3, the decision variables mainly include the critical start-up threshold of the power generation unit in a scheduling cycle, the charging and discharging plan of the energy storage and cold and hot energy storage equipment at each time step in the cycle, and the three key state points of the power storage equipment at each time step in the cycle: In step S5, the operation planning model constructed in step S4 is applied to the three matrices under the dual time scale constructed in step S2, and a set of key control parameters (Equation (30)) are determined by a heuristic search algorithm such as genetic or particle swarm, so that the system controls the operation of each scheduling period system in the historical window in turn according to the set of control parameters and the strategy described in step S3, and the weighted cumulative sum of the operating costs of each scheduling period is minimized as shown in equation (28).

6. The distributed energy system operation control method based on multi-energy storage coordination according to claim 1 is characterized in that: In step S6, the preferred control parameters obtained in step S5 are combined with the strategy described in step S4 to guide the system operation in a future scheduling cycle, which can be regarded as i=0; then, the features and methods described in steps S2 to S6 are repeated to guide the system operation in the next future scheduling cycle, and the cycle is rolled to form a scheduling closed loop.

7. The distributed energy system operation control method based on multi-energy storage coordination according to claim 1 is characterized in that: In step S7, when the number of scheduling closed-loop scrolling times reaches a threshold, a combination of factors not limited to the step configuration of the scheduling cycle, the number of scheduling cycles that make up the historical window, the parameters of the weight model, and the scheduling time scale are selected as decision variables. Combined with the optimization of steps S2 to S6, a further layer of search optimization is performed with the goal of minimizing the sum of the system operating costs of the i=0 row in each scrolling, and the hyperparameters of the control method are updated in time to adapt to the latest operating environment of the system, forming a method closed loop.

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