Country light-biogas virtual power plant energy optimization operation method and system considering energy storage degradation

Abstract

The invention discloses a rural light-biogas virtual power plant energy optimization operation method and system considering energy storage degradation. The method comprises the steps that an energy storage degradation cost model and virtual power plant basic operation constraints are established; the virtual power plant upper layer model prediction control layer considers the dynamic energy storage degradation cost, the electricity purchasing and selling cost and the total operation and maintenance cost to carry out virtual power plant energy operation optimization, and an upper layer optimization scheduling instruction is obtained; and inputting into a lower-layer model prediction controller, comprehensively considering deviation cost and energy storage scheduling cost of an upper-layer reference value to carry out source load power fluctuation stabilization to obtain a lower-layer operation result, and feeding back the lower-layer operation result to an updated upper-layer model prediction control layer to carry out virtual power plant energy loop iteration operation optimization. According to the method, the rural photovoltaic and biogas resources are flexibly adjusted, the method is suitable for a rural power distribution network to which a large number of renewable energy sources are accessed, and the average degradation cost of an energy storage battery and the average operation cost of the system are reduced on the premise of efficiently absorbing new energy.

Description

Technical Field

[0001] The present invention belongs to the technical field of comprehensive energy utilization, and relates to an energy optimization operation method and system for a rural solar-biogas virtual power plant taking into account energy storage degradation. Background Art

[0002] With the development of renewable energy in my country, the proportion of photovoltaic and biogas power generation connected to rural areas has increased year by year. After a large number of new energy sources are connected, the source-load balance capacity of rural power grids is poor, and there is a lack of effective adjustment methods to maximize the economic benefits of new energy access. At the same time, the access to new energy has increased the complexity and uncertainty of rural power grids and introduced many flexible resources.

[0003] As a means of regulation that can effectively aggregate flexible resources, virtual power plants are an important way to achieve efficient utilization of rural solar-biogas resources and improve the safety and economy of rural power grid operation. However, existing research on rural virtual power plants usually only considers the economic effects of real-time energy management system operation under different resources, loads and environmental conditions, while ignoring or only assuming fixed prices as operating costs. Unlike power generation resources, the frequent short-term scheduling of virtual power plants will have a more serious impact on the long-term life of the entire system. For example, frequent charging and discharging will greatly shorten the battery life of the energy storage part of the virtual power plant; in addition, the contradiction between economy and stability will further complicate the energy management of virtual power plants. Therefore, it is of great significance to study the energy optimization scheduling method of rural solar-biogas virtual power plants considering energy storage degradation. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides an energy optimization operation method and system for a rural photovoltaic-biogas virtual power plant taking into account energy storage degradation, which can flexibly adjust rural photovoltaic and biogas resources and is suitable for rural distribution networks with a large amount of renewable energy access. Under the premise of efficiently absorbing new energy, it can reduce the average degradation cost of energy storage batteries and the average operating cost of the system.

[0005] The present invention adopts the following technical solution.

[0006] A first aspect of the present invention provides an energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation, comprising the following steps:

[0007] Establish energy storage degradation cost model and basic operating constraints of virtual power plants;

[0008] The upper model predictive control layer of the virtual power plant takes into account the dynamic energy storage degradation cost, electricity purchase and sales cost and total operation and maintenance cost based on the energy storage degradation cost model, optimizes the energy operation of the virtual power plant based on the basic operation constraints of the virtual power plant, and obtains the upper optimization dispatching instructions;

[0009] The upper-level optimization scheduling instructions are input into the lower-level model predictive controller. The lower-level model predictive controller comprehensively considers the deviation cost of the upper-level reference value and the energy storage scheduling cost, and smoothes the source and load power fluctuations based on the basic operating constraints of the virtual power plant to obtain the lower-level operation results. The lower-level operation results are fed back to the updated upper-level model predictive control layer to optimize the iterative operation of the virtual power plant energy cycle.

[0010] Preferably, the energy storage degradation cost model is as follows:

[0011]

[0012] In the formula, C BDC (t,d B (Δt)) is the degradation cost of the energy storage battery at time t;

[0013] Δt is the time interval between discharge events;

[0014] C B Replacement cost of energy storage batteries;

[0015] P B (t) is the power of the energy storage battery at time t within Δt;

[0016] L B (d B (Δt)) is the depth of charge and discharge d B Energy storage battery life at (Δt);

[0017] d B (Δt) is the charge and discharge depth of the energy storage battery within Δt;

[0018] E BA (t) is the actual capacity of the energy storage battery at time t;

[0019] η Bc and η Bd are the charging and discharging efficiency coefficients of the energy storage battery respectively.

[0020] Preferably, the basic operating constraints of the virtual power plant include the photovoltaic output constraints of the virtual power plant, the output constraints of the biogas gas turbine, the charging and discharging operation constraints of the energy storage battery, the charge state constraints of the energy storage battery, the power capacity constraints of the power grid, energy storage and biogas, and the power balance constraints.

[0021] Preferably, the photovoltaic output constraint of the virtual power plant is:

[0022] 0≤P PV (t)≤P PVmax (2)

[0023] Where P PV(t) is the power of the photovoltaic power generation system of the virtual power plant at time t;

[0024] P PVmax is the maximum power of the photovoltaic power generation system of the virtual power plant;

[0025] The biogas gas turbine output constraint is:

[0026] 0≤P Bi (t)≤P Bimax (3)

[0027] Where P Bi (t) represents the power of the biogas gas turbine of the virtual power plant at time t;

[0028] P Bimax is the maximum power of the biogas gas turbine of the virtual power plant;

[0029] The charge and discharge operation constraints of the energy storage battery are:

[0030]

[0031] Where P Chr (t) is the charging power of the energy storage battery of the virtual power plant at time t;

[0032] P Disc (t) is the discharge power of the energy storage battery of the virtual power plant at time t;

[0033] The maximum charging and discharging power of the energy storage battery;

[0034] δ BS It is the charging and discharging efficiency of energy storage batteries;

[0035] E BS (t) is the electromotive force of the energy storage battery;

[0036] are the highest and lowest electromotive force of the energy storage battery respectively;

[0037] The energy storage battery state of charge constraint is:

[0038]

[0039] In the formula, are the maximum and minimum charge factors of the energy storage battery respectively;

[0040] t u ,t l are the time points of the upper and lower time domains, respectively;

[0041] The power capacity constraints of the grid, energy storage and biogas are:

[0042]

[0043] Where P M (t), P B (t) grid exchange and energy storage battery discharge respectively;

[0044] The power balance constraint is:

[0045] P L (t) = P M (t)+P B (t)+P PV (t)+P Bi (t), t∈{t 1 ,t u} (10)

[0046] Where P L (t), P M (t), P PV (t), P B (t), P Bi (t) are load, grid exchange, photovoltaic, energy storage battery discharge, and biogas power generation respectively.

[0047] Preferably, the objective function of the upper model predictive control layer for optimizing the energy operation of the virtual power plant is:

[0048]

[0049] In the formula, F u Represents the objective function of the virtual power plant upper layer model for optimizing the energy operation of the virtual power plant;

[0050] Indicates t u The cost of purchasing and selling electricity for the virtual power plant at the time;

[0051] Indicates t u Total operation and maintenance costs of photovoltaic, biogas and energy storage at all times;

[0052] Indicates t u Dynamic energy storage degradation cost at each moment;

[0053] T u Represents the prediction length of the upper model.

[0054] Preferably, The calculation formulas are:

[0055]

[0056] In the formula, C buy (t u) and P buy (t u ) are t u The electricity purchase price and power at the time;

[0057] C sell (t u ) and P sell (t u ) are t u The electricity price and power sold at the time;

[0058] r PV 、r Bi 、r B are the operation and maintenance cost coefficients of photovoltaic, biogas and energy storage batteries respectively;

[0059] P PV (t u ) is t u The power of the photovoltaic power generation system of the virtual power plant at every moment;

[0060] P Bi (t u ) indicates t u The power of the biogas gas turbine of the virtual power plant at any moment;

[0061] P B (t u ) indicates t u The power of the energy storage battery of the virtual power plant at all times;

[0062] g(t u ) is the state transition variable of the charge and discharge events in two consecutive time intervals;

[0063] t u ,t u -1 moment of energy storage battery degradation cost;

[0064] E B (t u ), E B (t u -1) are t u ,t u -1 The amount of power that the energy storage battery loses at one moment.

[0065] Preferably,

[0066] Where P B (t u -1) indicates t u -1 The power of the virtual power plant energy storage battery at moment 1.

[0067] Preferably, the objective function of the lower-layer model predictive controller for smoothing source-load power fluctuations is:

[0068]

[0069] In the formula, F l Predict the objective function of the controller for the underlying model;

[0070] is the deviation cost of the upper reference value;

[0071] The dispatch cost of energy storage;

[0072] T l Represents the prediction length of the underlying model.

[0073] Preferably, The calculation formulas are:

[0074]

[0075] In the formula, They are the prediction deviations of biogas, energy storage, and grid exchange power, respectively;

[0076] is the prediction deviation penalty coefficient for biogas, energy storage, and grid exchange power;

[0077] is the energy storage dispatch cost coefficient;

[0078] Provide energy storage after dispatch;

[0079] To provide energy storage before dispatch;

[0080] Δt l is the time interval of the underlying model.

[0081] Preferably, The calculation formula is:

[0082]

[0083] Where P Bi (t l ), P B (t l ), P M (t l ) are t l The output of biogas, energy storage and power grid at all times;

[0084] are t obtained by upper layer optimization respectively. lReference values ​​of biogas, energy storage and power grid output at all times.

[0085] A second aspect of the present invention provides an energy optimization operation system of a rural solar-biogas virtual power plant considering energy storage degradation, comprising:

[0086] Cost model and constraint building module, used to establish energy storage degradation cost model and basic operation constraints of virtual power plants;

[0087] The upper model predictive control module is used for the upper model predictive control layer of the virtual power plant. Based on the energy storage degradation cost model, the dynamic energy storage degradation cost, the purchase and sale cost of electricity, and the total operation and maintenance cost are considered, and the energy operation of the virtual power plant is optimized based on the basic operation constraints of the virtual power plant to obtain the upper optimization scheduling instructions;

[0088] The lower-level model predictive control module is used to input the upper-level optimization scheduling instructions into the lower-level model predictive controller. The lower-level model predictive controller comprehensively considers the deviation cost of the upper-level reference value and the energy storage scheduling cost, and smoothes the source and load power fluctuations based on the basic operating constraints of the virtual power plant to obtain the lower-level operation results, and feeds back the lower-level operation results to update the upper-level model predictive control layer to optimize the iterative operation of the virtual power plant energy cycle.

[0089] A terminal comprises a processor and a storage medium; the storage medium is used to store instructions;

[0090] The processor is configured to operate according to the instructions to execute the steps of the method.

[0091] A computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0092] Compared with the prior art, the beneficial effects of the present invention include at least:

[0093] (1) The present invention takes energy storage degradation into consideration and improves the energy supply elasticity of rural solar virtual power plants under different operating conditions by aggregating flexible control resources in rural areas. It also reduces the average degradation cost of energy storage batteries and the average operating cost of the system while ensuring that rural microgrids can safely and efficiently absorb new energy. It improves the new energy absorption capacity in rural areas and can effectively improve the economic benefits of production and life in rural areas.

[0094] (2) The upper-level model objective function takes into account the energy storage degradation cost on the basis of the basic electricity purchase and sales cost and system operation and maintenance cost, and realizes the coordinated optimization of multiple cost factors, so that the virtual power plant can better control cost expenditure during operation, extend battery life, and improve the long-term economic benefits of the virtual power plant.

[0095] (3) The dynamic energy storage degradation cost calculation model of the present invention clarifies the calculation method of the energy storage battery degradation cost in continuous time, dynamically estimates the battery degradation degree and corresponding cost, and improves the accuracy of battery degradation cost prediction.

[0096] (4) The objective function of the lower-level model comprehensively considers the prediction error of the upper-level model (the deviation cost of the upper-level reference value) and the energy storage dispatching cost, so that the sum of the impact cost of the prediction error fluctuation and load power change on the system and the energy storage response cost to cope with power fluctuation is minimized, balancing the dispatching efficiency and the cost of energy storage, making the system more robust while minimizing the dispatching cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 It is a flow chart of an energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to the present invention;

[0098] Figure 2 It is a hierarchical structure of energy optimization operation of the rural solar-biogas virtual power plant of the present invention;

[0099] Figure 3 It is a flow chart of the energy optimization operation of the rural solar-biogas virtual power plant based on the hierarchical structure of the present invention. DETAILED DESCRIPTION

[0100] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0101] Embodiment 1 of the present invention provides an energy optimization operation method of a rural solar-biogas virtual power plant taking into account energy storage degradation, such as Figure 1 As shown, the following steps are included:

[0102] S1. Establish energy storage degradation cost model and basic operation constraints of virtual power plants;

[0103] Further preferably, according to the battery energy storage cost degradation caused by the charge and discharge depth and service life, an energy storage degradation cost is established, and the energy storage degradation cost is a degradation conversion model that takes into account the battery charge and discharge depth and battery life;

[0104] The energy storage degradation cost model established specifically considers the discharge event starting from time t. The corresponding battery degradation cost within a time interval Δt is as follows:

[0105]

[0106] Where, d B (Δt) is the charge and discharge depth of the battery within the time interval Δt, C BDC (t,d B (Δt)) is the battery degradation cost corresponding to the discharge event, C B is the battery replacement cost, P B (t) is the average power in the discharge event time interval Δt, L B (d B (Δt)) is the depth of charge and discharge d B Battery life at (Δt), E BA (t) is the actual capacity at time t, η Bc and η Bd are the charging and discharging efficiency coefficients, respectively.

[0107] After the event, the actual capacity of the battery is reduced proportionally within the time t+Δt as follows:

[0108]

[0109] In the formula, E B.rated is the rated capacity of the battery.

[0110] Further preferably, the basic operation constraints of the virtual power plant established include photovoltaic output constraints of the virtual power plant, biogas gas turbine output constraints, energy storage battery charging and discharging operation constraints, energy storage battery charge state constraints, power capacity constraints of the power grid, energy storage and biogas, and power balance constraints;

[0111] Specifically, based on the basic output and operation constraints, constraint models for the virtual power plant's photovoltaic output, biogas gas turbine output, and energy storage battery charging and discharging are established, mainly including photovoltaic and biogas output constraints, energy storage operation constraints, power balance constraints, etc. during the operation of the virtual power plant. Its composition will meet the conditions required for the safe and stable operation of the virtual power plant under different operating conditions.

[0112] The output constraints of the PV system of the virtual power plant are as follows:

[0113] 0≤P PV (t)≤P PVmax (3)

[0114] Where P PV (t) is the power of the photovoltaic power generation system at time t, P PVmax is the maximum power of the photovoltaic power generation system.

[0115] The output constraints of biogas gas turbine are as follows:

[0116] 0≤P Bi (t)≤P Bimax (4)

[0117] Where P Bi (t) represents the power of the biogas gas turbine at time t; P Bimax is the maximum power of the gas turbine;

[0118] The charging and discharging operation constraints of energy storage batteries are as follows:

[0119]

[0120] Where P Chr (t) is the charging power, P Disc (t) is the discharge power, is the maximum charge and discharge power, δ BS is the charging and discharging efficiency of the energy storage battery, E BS (t) is the electromotive force of the energy storage battery, are the highest and lowest electromotive force respectively.

[0121] In order to prevent the battery from overcharging and discharging, the battery state of charge constraint is considered, which can be expressed as:

[0122]

[0123] In the formula, are the maximum and minimum charge factors respectively.

[0124] t u ,t l are the time points of the upper and lower time domains, respectively.

[0125] The inequality constraints in the model mainly include the power capacity limits of the utility grid, energy storage, and biogas:

[0126]

[0127] Where P M (t), P B (t) are grid exchange and energy storage battery discharge respectively.

[0128] The virtual power plant model needs to meet the power balance constraints:

[0129] P L (t) = P M (t)+P B (t)+P PV (t)+P Bi (t), t∈{t 1 ,t u} (11)

[0130] S2: The upper model predictive control layer of the virtual power plant optimizes the energy operation of the virtual power plant based on the energy storage degradation cost model, taking into account the dynamic energy storage degradation cost, electricity purchase and sales cost, and total operation and maintenance cost, and obtains the upper optimization dispatching instructions based on the basic operation constraints of the virtual power plant;

[0131] S3. Input the upper-level optimization scheduling instructions into the lower-level model predictive controller. The lower-level model predictive controller comprehensively considers the deviation cost of the upper-level reference value and the energy storage scheduling cost, and smoothes the source-load power fluctuation based on the basic operating constraints of the virtual power plant to obtain the lower-level operation results. The lower-level operation results are fed back to the updated upper-level model predictive control layer to optimize the iterative operation of the virtual power plant energy cycle.

[0132] During the specific implementation, the initial operating state and target of the system are used as the initial state of the upper-level model, and the historical energy consumption data of the virtual power plant corresponding to different production links of rural photovoltaic and biogas need to be collected, including historical temperature, equipment operating conditions, photovoltaic and biogas output data, user load data and electricity purchase and sales price data; the collected historical energy consumption of the virtual power plant is preprocessed, and the processed historical data is mainly used to establish the virtual power plant system and train prediction capabilities; temperature and equipment operating conditions mainly affect the power generation capacity of photovoltaic and biogas equipment, as well as the needs of some loads (such as fans, refrigeration equipment), which are included in the virtual power plant system.

[0133] Analyze the energy and load elements of the rural virtual power plant, extract the core energy flow characteristics, and collect different energy data through edge-cloud collaboration, as follows:

[0134] Sensors and equipment deployed at key nodes of the virtual power plant can collect data such as temperature, equipment operating conditions, initial data of solar power output, and initial data of user load in rural production links. They can also connect to remote IO stations or other controllers to collect data and obtain electricity purchase and sales price data.

[0135] Then, the controller connects to switches and routers via industrial Ethernet and sends the collected data to the cloud platform. For places where it is inconvenient to deploy wired communication networks, wireless communication can be used.

[0136] The information collection and control equipment of the virtual power plant includes dispatching communication equipment, dispatching automation equipment, relay protection equipment, safety automatic devices, power regulation and voltage regulation equipment, etc.

[0137] Preprocess the collected historical energy consumption of virtual power plants, including normalization, denoising and supplementation of missing data; specifically, normalize the collected historical energy consumption data of virtual power plants to obtain historical energy consumption data of different energy consumption links, and then preprocess to obtain an initial data set, and set and initialize relevant parameters;

[0138] The collected historical data (including energy consumption data such as electricity and heat energy consumed in all aspects of production and life of the rural solar-biogas virtual power plant) are normalized using the weighted FCM clustering method, and clustered according to different production conditions (season, temperature, different types of production activities, etc.), providing a basis for energy consumption prediction of different operating conditions of the virtual power plant.

[0139] Furthermore, the Savitazky-Golay algorithm is used to denoise the original data, and its expression is:

[0140]

[0141] Where, X j+1 is the data before the j+1th denoising, X j * is the jth denoised data; C i is the weight factor of the i-th value in the smoothing window, k is the width of the smoothing window, j is the initial value subscript, m is the width of half the smoothing window, and the algorithm window covers 2m+1 adjacent point values.

[0142] Furthermore, the missing data of the original data are supplemented by the neighboring interpolation method. The specific method is to use the data at the moment closest to the missing data to estimate the data at the missing time. For example, when the sampling rate is at the hourly level, the data of the previous hour and the next hour are used for missing interpolation. In principle, all the data required for prediction need to be supplemented.

[0143] Further preferably, the upper-layer optimization scheduling instructions are transmitted to the lower-layer model prediction controller, and the demand response instructions are fed back based on the load characteristics of the area to achieve source-load power fluctuation smoothing; the lower-layer operation results are fed back to the upper-layer model prediction layer, so as to perform rolling optimization under flexible adjustment of demand response and achieve the optimal overall operating cost considering the energy storage cost on a long time scale.

[0144] The hierarchical structure and process of the two-layer energy management method are as follows: Figure 2 , Figure 3 As shown, where T u and T 1 denote the prediction lengths in the upper and lower layers, respectively.

[0145] The upper layer consists of l ∈{1,…,T u} is composed of a nonlinear rolling model predictive controller, the lower layer consists of u ∈{1,…,T 1} is composed of a quadratic model predictive controller.

[0146] Δt u and Δtl They represent the time intervals of the upper and lower layers respectively. The control behavior in each time interval is obtained by solving the objective function of each layer, so that the decision of one layer affects the other layer.

[0147] Based on the given forecast data and the load response aggregation of the region, in the upper layer T u The optimal schedule is developed, but only at time Δt u The scheduling inside will be used as a reference value to control the scheduling behavior of the lower layer;

[0148] The lower layer optimizes itself through energy storage, and minimizes system power fluctuations while responding to regional flexible demand. u After that, the lower layer sends the updated state variables back to the upper layer and starts the next Δt u The scheduling problem.

[0149] Further preferably, the upper model predictive control layer of the virtual power plant considers the cost of electricity purchase and sale, equipment operation and maintenance cost, and energy storage degradation cost, and optimizes the energy operation of the virtual power plant based on the basic operation constraints of the virtual power plant (i.e., taking formulas (3)-(11) as the constraints of the objective function), and obtains the upper optimization scheduling instruction

[0150] is the optimized P Bi (t u ), P B (t u ), P M (t u ), in order to make the objective function F u middle The smallest group P Bi (t u ), P B (t u ), P M (t u ).

[0151] The objective function of the upper model predictive control layer for optimizing the energy operation of the virtual power plant is:

[0152]

[0153] In the formula, F u The objective function representing the upper-level energy optimization operation of the virtual power plant; represents the cost of purchasing and selling electricity of the virtual power plant; represents the operation and maintenance costs of photovoltaic, biogas and energy storage; Represents the energy storage degradation cost.

[0154] The cost of purchasing and selling electricity for a virtual power plant is:

[0155]

[0156] In the formula, C buy (t u ) and P buy (t u ) are t u The electricity purchase price and power at the time, C sell (t u ) and P sell (t u ) are t u The electricity price and power sold at the time, P M is the total exchange power of the grid.

[0157] In formula (14), P buy P sell and P M The relationship is: P M (t u )=P buy (t u )-P sell (t u ), P M is the total exchange power of the grid.

[0158] The operation and maintenance costs of photovoltaic, biogas and energy storage are:

[0159]

[0160] In the formula, r PV 、r Bi 、r B They are the operation and maintenance cost coefficients of photovoltaic, biogas and energy storage devices respectively.

[0161] Considering each time interval Δt u Battery degradation costs in It can only be determined after the charge or discharge event is completed, so it is necessary to first determine the battery P B (t u ) power flow. Introduced as an auxiliary binary variable g(t u ) is used to represent the state transition of charging and discharging events between two consecutive time intervals.

[0162]

[0163] Battery degradation cost over consecutive time intervals Through g(t u ) to indicate:

[0164]

[0165] in, t u ,t u -1 moment energy storage degradation cost;

[0166] E B (t u ), E B (t u -1) are t u ,t u -1 The amount of power that the energy storage battery loses at one moment.

[0167] Calculate using formula (1): Substitute formula (1) C BDC (t,d B t, d in (Δt) B (Δt) is replaced by Perform calculations, Indicates the depth of charge and discharge d B (Δt).

[0168] E B (t u ), E B (t u -1) is calculated using formula (2), that is, after the event, the actual capacity of the energy storage battery is reduced proportionally within the time t+Δt as follows: In the formula, E B.rated is the rated capacity of the energy storage battery.

[0169] The lower-level model predictive controller considers the penalty cost caused by the deviation from the upper-level reference value and the energy storage dispatch cost of smoothing power fluctuations, inputs the upper-level optimization dispatch instruction into the lower-level model predictive controller, and smoothes the source-load power fluctuation based on the basic operation constraints of the virtual power plant of S4 (i.e., the constraints of formulas (3)-(11) as the objective function). Through the energy storage response, the impact of the prediction error fluctuation and the load power change on the system is minimized (the deviation of the upper-level model prediction is compensated by increasing or decreasing the output of the energy storage battery to smooth the power fluctuation), and the lower-level operation result is obtained. The lower-level operation results are returned to update the upper-level model prediction control layer to optimize the energy operation status of the virtual power plant.

[0170] P Bi (t l ), P B (t l ), P M (t l ) is the optimal value, even if the objective function F l middle The smallest group PBi (t l ), P B (t l ), P M (t l );

[0171] Lower layer operation results Input to the upper model of the virtual power plant as its objective function and P in the constraints Bi (t u ), P B (t u ), P M (t u ).

[0172] The objective function of the lower-level model predictive controller for smoothing source-load power fluctuations is:

[0173]

[0174] In the formula, is the deviation cost of the upper reference value, is the energy storage dispatch cost.

[0175] The deviation cost of the upper reference value is:

[0176]

[0177] In the formula, are the prediction deviations of biogas, energy storage, and grid exchange power, respectively. is the prediction deviation penalty coefficient.

[0178]

[0179] Where P Bi (t l ), P B (t l ), P M (t l ) are respectively biogas, energy storage, and power grid at t l The effort of the moment, are t obtained by upper layer optimization respectively. l Reference values ​​of biogas, energy storage and power grid output at all times.

[0180] The energy storage dispatch cost is:

[0181]

[0182] In the formula, is the energy storage dispatch cost coefficient, For the energy storage output after dispatch, To provide energy storage before dispatch.

[0183] The lower-level operation results are fed back to the upper-level model prediction layer, so as to carry out rolling optimization under flexible adjustment of demand response, and achieve the optimal overall operation cost considering energy storage costs over a long time scale. After the relevant steps are executed, the virtual power plant can continuously optimize the energy consumption structure and respond to load demand in a timely manner, thereby increasing the proportion of new energy consumption in rural areas.

[0184] On the whole, the present invention can provide a reliable reference for the operation of rural virtual power plants, effectively reduce the economic cost caused by energy storage degradation according to the energy storage characteristics, and improve the flexibility and accuracy of energy supply design, thereby avoiding energy waste to the greatest extent; on this basis, different types of demand responses of rural solar-biogas virtual power plants are achieved through double-layer energy optimization, further forming an energy optimization operation method of rural solar-biogas virtual power plants that takes into account energy storage degradation.

[0185] Embodiment 2 of the present invention provides an energy optimization operation system of a rural solar-biogas virtual power plant taking into account energy storage degradation, including:

[0186] Cost model and constraint building module, used to establish energy storage degradation cost model and basic operation constraints of virtual power plants;

[0187] The upper model predictive control module is used for the upper model predictive control layer of the virtual power plant. Based on the energy storage degradation cost model, the dynamic energy storage degradation cost, the purchase and sale cost of electricity, and the total operation and maintenance cost are considered, and the energy operation of the virtual power plant is optimized based on the basic operation constraints of the virtual power plant to obtain the upper optimization scheduling instructions;

[0188] The lower-layer model prediction control module is used to input the upper-layer optimization scheduling instructions into the lower-layer model prediction controller. The lower-layer model prediction controller comprehensively considers the deviation cost of the upper-layer reference value and the energy storage scheduling cost, and smoothes the source-load power fluctuation based on the basic operation constraints of the virtual power plant, obtains the lower-layer operation results, and feeds back the lower-layer operation results to the updated upper-layer model prediction control layer to optimize the iterative operation of the virtual power plant energy cycle. Embodiment 3 of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0189] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0190] Compared with the prior art, the beneficial effects of the present invention include at least:

[0191] The present invention takes energy storage degradation into consideration and improves the energy supply elasticity of rural photovoltaic virtual power plants under different operating conditions by aggregating flexible regulation resources in rural areas. It also reduces the average degradation cost of energy storage batteries and the average operating cost of the system while ensuring that rural microgrids can safely and efficiently absorb new energy. It improves the new energy absorption capacity in rural areas and can effectively improve the economic benefits of production and life in rural areas.

[0192] The objective function of the upper-level model takes into account the energy storage degradation cost on the basis of the basic electricity purchase and sales cost and system operation and maintenance cost, and realizes the coordinated optimization of multiple cost factors, so that the virtual power plant can better control cost expenditure during operation, extend battery life, and improve the long-term economic benefits of the virtual power plant.

[0193] The dynamic energy storage degradation cost calculation model of the present invention clarifies the calculation method of the energy storage battery degradation cost in a continuous time, dynamically estimates the battery degradation degree and the corresponding cost, and improves the accuracy of battery degradation cost prediction.

[0194] The objective function of the lower-level model comprehensively considers the prediction error of the upper-level model (deviation cost of the upper-level reference value) and the energy storage dispatch cost, so that the sum of the impact cost of prediction error fluctuations and load power changes on the system and the energy storage response cost to cope with power fluctuations is minimized, balancing the dispatch efficiency and the cost of energy storage, making the system more robust while minimizing the dispatch cost.

[0195] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0196] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0197] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0198] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0199] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the operation of a rural solar-biogas virtual power plant considering energy storage degradation, characterized in that: include: Establish energy storage degradation cost model and basic operating constraints of virtual power plants; The upper model predictive control layer of the virtual power plant takes into account the dynamic energy storage degradation cost, electricity purchase and sales cost and total operation and maintenance cost based on the energy storage degradation cost model, optimizes the energy operation of the virtual power plant based on the basic operation constraints of the virtual power plant, and obtains the upper optimization dispatching instructions; The upper-level optimization scheduling instructions are input into the lower-level model predictive controller. The lower-level model predictive controller comprehensively considers the deviation cost of the upper-level reference value and the energy storage scheduling cost, and smoothes the source and load power fluctuations based on the basic operating constraints of the virtual power plant to obtain the lower-level operation results. The lower-level operation results are fed back to the updated upper-level model predictive control layer to optimize the iterative operation of the virtual power plant energy cycle.

2. According to claim 1, a method for optimizing the operation of a rural solar-biogas virtual power plant considering energy storage degradation is characterized in that: The energy storage degradation cost model is as follows: In the formula, C BDC (t,d B (Δt)) is the degradation cost of the energy storage battery at time t; Δt is the time interval between discharge events; C B Replacement cost of energy storage batteries; P B (t) is the power of the energy storage battery at time t within Δt; L B (d B (Δt)) is the depth of charge and discharge d B Energy storage battery life at (Δt); d B (Δt) is the charge and discharge depth of the energy storage battery within Δt; E BA (t) is the actual capacity of the energy storage battery at time t; η Bc and η Bd are the charging and discharging efficiency coefficients of the energy storage battery respectively.

3. According to claim 1, a method for optimizing the operation of a rural solar-biogas virtual power plant considering energy storage degradation is characterized in that: The basic operating constraints of the virtual power plant include the photovoltaic output constraints of the virtual power plant, the output constraints of the biogas gas turbine, the charging and discharging operation constraints of the energy storage battery, the charge state constraints of the energy storage battery, the power capacity constraints of the power grid, energy storage and biogas, and the power balance constraints.

4. The energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to claim 3 is characterized in that: The photovoltaic output constraint of the virtual power plant is: 0≤P PV (t)≤P PVmax (2) Where P PV (t) is the power of the photovoltaic power generation system of the virtual power plant at time t; P PVmax is the maximum power of the photovoltaic power generation system of the virtual power plant; The biogas gas turbine output constraint is: 0≤P Bi (t)≤P Bimax (3) Where P Bi (t) represents the power of the biogas gas turbine of the virtual power plant at time t; P Bimax is the maximum power of the biogas gas turbine of the virtual power plant; The charge and discharge operation constraints of the energy storage battery are: Where P Chr (t) is the charging power of the energy storage battery of the virtual power plant at time t; P Disc (t) is the discharge power of the energy storage battery of the virtual power plant at time t; The maximum charging and discharging power of the energy storage battery; δ BS It is the charging and discharging efficiency of energy storage batteries; E BS (t) is the electromotive force of the energy storage battery; are the highest and lowest electromotive force of the energy storage battery respectively; The energy storage battery state of charge constraint is: In the formula, are the maximum and minimum charge factors of the energy storage battery respectively; t u ,t l are the time points of the upper and lower time domains, respectively; The power capacity constraints of the grid, energy storage and biogas are: Where P M (t), P B (t) grid exchange and energy storage battery discharge respectively; The power balance constraint is: P L (t)=P M (t)+P B (t)+P PV (t)+P Bi (t), t∈{t1,t u } (10) Where P L (t), P M (t), P PV (t), P B (t), P Bi (t) are load, grid exchange, photovoltaic, energy storage battery discharge, and biogas power generation respectively.

5. According to claim 1, a method for optimizing the operation of a rural solar-biogas virtual power plant considering energy storage degradation is characterized in that: The objective function of the upper model predictive control layer for optimizing the energy operation of the virtual power plant is: In the formula, F u Represents the objective function of the virtual power plant upper layer model for optimizing the energy operation of the virtual power plant; Indicates t u The cost of purchasing and selling electricity for the virtual power plant at the time; Indicates t u Total operation and maintenance costs of photovoltaic, biogas and energy storage at all times; Indicates t u Dynamic energy storage degradation cost at each moment; T u Represents the prediction length of the upper model.

6. The energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to claim 5 is characterized in that: The calculation formulas are: In the formula, C buy (t u ) and P buy (t u ) are t u The electricity purchase price and power at the time; C sell (t u ) and P sell (t u ) are t u The electricity price and power sold at the time; r PV 、r Bi 、r B are the operation and maintenance cost coefficients of photovoltaic, biogas and energy storage batteries respectively; P PV (t u ) is t u The power of the photovoltaic power generation system of the virtual power plant at every moment; P Bi (t u ) indicates t u The power of the biogas gas turbine of the virtual power plant at any moment; P B (t u ) indicates t u The power of the energy storage battery of the virtual power plant at all times; g(t u ) is the state transition variable of the charge and discharge events in two consecutive time intervals; t u ,t u -1 moment of energy storage battery degradation cost; E B (t u ), E B (t u -1) are t u ,t u -1 The amount of power that the energy storage battery loses at one moment.

7. The energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to claim 6 is characterized in that: g(t u ) is calculated as: Where P B (t u -1) indicates t u -1 The power of the virtual power plant energy storage battery at moment 1.

8. The energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to claim 1 is characterized in that: The objective function of the lower-level model predictive controller for smoothing source-load power fluctuations is: In the formula, F l Predict the objective function of the controller for the underlying model; is the deviation cost of the upper reference value; The dispatch cost of energy storage; T l Represents the prediction length of the underlying model.

9. The energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to claim 8 is characterized in that: The calculation formulas are: In the formula, They are the prediction deviations of biogas, energy storage, and grid exchange power, respectively; is the prediction deviation penalty coefficient for biogas, energy storage, and grid exchange power; is the energy storage dispatch cost coefficient; Provide energy storage after dispatch; To provide energy storage before dispatch; Δt l is the time interval of the underlying model.

10. The energy optimization operation method of a rural solar-biogas virtual power plant considering energy storage degradation according to claim 9 is characterized in that: The calculation formula is: Where P Bi (t l ), P B (t l ), P M (t l ) are t l The output of biogas, energy storage and power grid at all times; are t obtained by upper layer optimization respectively. l Reference values ​​of biogas, energy storage and power grid output at all times.

11. A rural solar-biogas virtual power plant energy optimization operation system considering energy storage degradation, using the method described in any one of claims 1 to 10, characterized in that: The system comprises: Cost model and constraint building module, used to establish energy storage degradation cost model and basic operation constraints of virtual power plants; The upper model predictive control module is used for the upper model predictive control layer of the virtual power plant. Based on the energy storage degradation cost model, the dynamic energy storage degradation cost, the purchase and sale cost of electricity, and the total operation and maintenance cost are considered, and the energy operation of the virtual power plant is optimized based on the basic operation constraints of the virtual power plant to obtain the upper optimization scheduling instructions; The lower-level model predictive control module is used to input the upper-level optimization scheduling instructions into the lower-level model predictive controller. The lower-level model predictive controller comprehensively considers the deviation cost of the upper-level reference value and the energy storage scheduling cost, and smoothes the source and load power fluctuations based on the basic operating constraints of the virtual power plant to obtain the lower-level operation results, and feeds back the lower-level operation results to update the upper-level model predictive control layer to optimize the iterative operation of the virtual power plant energy cycle.

12. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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