User side novel subject regulation and control method and system based on equivalent aggregation

Abstract

The invention provides a user side novel main body regulation and control method and system based on equivalent aggregation, and belongs to the technical field of electric power regulation and control. Comprising the following steps: analyzing operation characteristics and physical characteristics of distributed photovoltaic and energy storage to construct a value evaluation model of the distributed photovoltaic and energy storage; an autoregression sliding model is adopted to describe the randomness of the distributed energy sources so as to determine output prediction errors of the distributed energy sources, and the output conditions of the distributed energy sources are simulated; and constructing an equivalent aggregation model based on a multi-objective planning method to aggregate various distributed energy sources and realize regulation and control of the user-side novel main body. According to the invention, reasonable regulation and control of the user-side novel main body can be realized, energy configuration can be optimized, energy utilization efficiency can be improved, the acceptance capability of the power grid to distributed energy can be enhanced, and the regulation and control problem faced by the power industry can be better solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power control, and in particular relates to a new user-side subject control method and system based on equivalent aggregation. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of distributed energy resources (such as photovoltaics and energy storage), how to effectively manage and regulate these resources has become a major challenge facing power systems. However, existing technologies and methods have certain shortcomings in dealing with the randomness, volatility, and multi-objective optimization of distributed energy. For example:

[0004] (1) Most existing methods achieve optimal scheduling of distributed energy by directly building a microgrid scheduling model. However, this method often only focuses on scheduling within the microgrid, lacks consideration of the regulation of new entities on the user side, and does not fully consider the randomness and volatility of distributed energy, making it difficult to cope with complex grid environments in practical applications.

[0005] (2) Existing methods also include aggregating multiple distributed energy resources to achieve optimal scheduling of the power grid based on distributed energy aggregation. However, although this method takes the aggregation of distributed energy into account, it cannot establish an effective equivalent aggregation model and lacks sufficient consideration of multi-objective optimization problems, such as economic efficiency and environmental benefits, resulting in unsatisfactory scheduling results.

[0006] (3) Another existing method is to optimize the scheduling of distributed energy by establishing a stochastic programming model based on the randomness of distributed energy. However, although this method takes the randomness of distributed energy into account, it does not fully consider the physical and operational characteristics of distributed energy, and cannot establish an effective value assessment model, making it difficult to achieve low-cost and efficient management in practical applications. Summary of the Invention

[0007] In order to overcome the shortcomings of the above-mentioned existing technologies, the present invention provides a new user-side subject control method and system based on equivalent aggregation, which can realize reasonable control of new user-side subjects, optimize energy configuration, improve energy utilization efficiency, and enhance the power grid's ability to accept distributed energy, so as to better solve the control problems faced by the power industry.

[0008] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0009] A first aspect of the present invention provides a new user-side subject control method based on equal value aggregation.

[0010] A new user-side subject control method based on equal-value aggregation, comprising:

[0011] Analyze the operational and physical characteristics of distributed photovoltaics and energy storage to build a value assessment model for distributed photovoltaics and energy storage;

[0012] An autoregressive sliding model is used to describe the randomness of distributed energy resources to determine the output forecast error of distributed energy resources. Based on the percentage of the output forecast error, the output of distributed energy resources is simulated.

[0013] Combining the value assessment model of distributed photovoltaic and energy storage and the output of the obtained distributed energy, an equivalent aggregation model is constructed based on the multi-objective planning method to aggregate various types of distributed energy and realize the regulation of new entities on the user side.

[0014] Furthermore, the operational and physical characteristics of distributed photovoltaics are analyzed, including:

[0015] Based on the PV cell equivalent circuit model, the equivalent relationship between output voltage and output current is determined, and the obtained equivalent relationship is approximately derived to construct a PV array model. Under the constructed PV array model, the normal distribution method is used to characterize the allowable deviation of photovoltaic output under photovoltaic prediction. Based on the obtained allowable deviation of photovoltaic output and the functional relationship of photovoltaic predicted output, a confidence interval is determined. Within the confidence interval, the output of any photovoltaic array in distributed photovoltaic at any time is analyzed.

[0016] Furthermore, the operational and physical characteristics of energy storage are analyzed, including:

[0017] The energy storage working condition is described according to the state of charge of the energy storage. Specifically, under the condition that the energy storage operation constraints are met, the energy storage working condition is described according to the ratio of the current remaining power of the energy storage battery to the rated capacity.

[0018] Furthermore, an autoregressive sliding model is used to describe the randomness of distributed energy resources to determine the output forecast error of distributed energy resources, including:

[0019] A random time series is used to represent the output of distributed energy at each moment. The realization of each random time series is used as a scenario. Specifically, the corresponding parameters in the distributed energy under the random time series are used to replace the inherent parameters in the autoregressive sliding model to update the autoregressive sliding model; the updated autoregressive sliding model is recursively used to obtain the percentage of prediction error.

[0020] Furthermore, based on the percentage of the output prediction error, the output of distributed energy is simulated, including:

[0021] According to the percentage of the obtained output prediction error, the size and direction of the power fluctuation at each moment in each scenario are determined; the obtained scenarios are reduced based on the heuristic synchronous back-substitution reduction method, and a scenario tree is generated; based on the generated scenario tree, the characteristics of random changes in the output of distributed power sources in multiple consecutive time periods are simulated.

[0022] Furthermore, an equivalent aggregation model is constructed based on a multi-objective programming method, including:

[0023] Based on the day-ahead optimization scheduling model of the virtual power plant under the distributed energy prediction error, a day-ahead optimization total target set is generated; under the said day-ahead optimization total target set, the multi-objective operation optimization model is optimized and the constraints are set; the final multi-objective operation optimization model and the constraints are combined to construct an equivalent aggregation model.

[0024] Furthermore, the total target set for day-ahead optimization includes the comprehensive revenue of the virtual power plant within one day, the utilization ratio of flexibility margin, environmental benefits, fluctuation of renewable energy, and user service quality feedback.

[0025] The second aspect of the present invention provides a new user-side subject control system based on equal value aggregation.

[0026] A new user-side subject control system based on equal value aggregation, including:

[0027] The value assessment model building module is configured to: analyze the operating characteristics and physical characteristics of distributed photovoltaics and energy storage to build a value assessment model for distributed photovoltaics and energy storage;

[0028] The distributed energy output simulation module is configured to: describe the randomness of distributed energy using an autoregressive sliding model to determine the output prediction error of the distributed energy; and simulate the output of the distributed energy based on the percentage of the output prediction error;

[0029] The new user-side subject control module is configured to: combine the value assessment model of the established distributed photovoltaic and energy storage and the output of the obtained distributed energy, and build an equivalent aggregation model based on the multi-objective planning method to aggregate various types of distributed energy and realize the control of the new user-side subject.

[0030] The third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of a new user-side subject control method based on equal-value aggregation as described in the first aspect of the present invention.

[0031] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and runnable on the processor. When the processor executes the program, it implements the steps of a new user-side subject control method based on equal-value aggregation as described in the first aspect of the present invention.

[0032] One or more of the above technical solutions have the following beneficial effects:

[0033] (1) This invention combines the established distributed photovoltaic and energy storage value assessment model with the output of distributed energy resources, and constructs an equivalent aggregation model based on a multi-objective planning method to aggregate various distributed energy resources and achieve the regulation of new user-side entities. This model not only focuses on the scheduling within the microgrid, but also fully considers the regulation of new user-side entities. Compared with existing technologies, it can achieve effective regulation even in more complex grid environments.

[0034] (2) The present invention analyzes the operational and physical characteristics of distributed photovoltaics and energy storage to construct a value assessment model for distributed photovoltaics and energy storage; uses an autoregressive sliding model to describe the randomness of distributed energy to determine the output prediction error of distributed energy; and simulates the output of distributed energy based on the percentage of the output prediction error. The present invention not only constructs an equivalent aggregation model based on a multi-objective planning method, but also fully considers multiple factors such as its economic and environmental benefits by constructing a value assessment model for distributed photovoltaics and energy storage, thus avoiding the technical problem of unsatisfactory scheduling effects caused by a single objective in the prior art.

[0035] (3) The present invention uses an autoregressive sliding model to describe the randomness of distributed energy, recursively obtains the percentage of its output prediction error, and then accurately simulates the output of distributed energy. Based on the multi-objective planning method, an equivalent aggregation model is established, which comprehensively considers economic efficiency, flexibility margin utilization ratio, environmental benefits, renewable energy fluctuations, and user service quality feedback. Compared with existing technologies, it can better achieve reasonable regulation of new user-side entities while ensuring low-cost and efficient management.

[0036] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0038] Figure 1This is a flowchart of a new user-side subject control method based on equal-value aggregation in Example 1 of the present invention.

[0039] Figure 2 Schematic diagram of the PV cell equivalent circuit model in Example 1 of the present invention.

[0040] Figure 3 This is a schematic diagram of a photovoltaic output when the confidence level is 0.9 in the first embodiment of the present invention.

[0041] Figure 4 Schematic diagram of the value assessment process of the photovoltaic-energy storage module in Example 1 of the present invention.

[0042] Figure 5 Schematic diagram of a common scene set of various structures in embodiment 1 of the present invention; wherein, Figure 5 (a) is a schematic diagram of the general scene structure. Figure 5 (b) is a schematic diagram of the fan-shaped scene structure. Figure 5 (c) in the figure is a schematic diagram of the tree-shaped scene structure.

[0043] Figure 6 This is a schematic diagram of a typical day optimized load curve in Example 1 of the present invention. DETAILED DESCRIPTION

[0044] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0045] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0046] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0047] The overall idea proposed by the present invention is: The present invention provides a new user-side subject control method based on equivalent aggregation. First, the physical and operational characteristics of distributed photovoltaic and energy storage modules are analyzed, and then a value assessment model is established based on these characteristics. Next, the autoregressive moving average model is used to characterize the uncertainty of distributed energy. This link fully considers the random fluctuation characteristics of distributed energy and the laws affected by climatic conditions, and then progressively calculates the proportion of its power generation forecast error. The application of this model helps to accurately and meticulously simulate the output of distributed energy, laying the foundation for subsequent equivalent aggregation work. Finally, by aggregating multiple factors, comprehensively considering the economy, the proportion of flexibility margin utilization, environmental benefits, renewable energy fluctuations, and user service quality feedback, constructing the objective function and the constraints of each objective, and using the multi-objective planning method to aggregate and optimize the new user-side subject.

[0048] Example 1

[0049] This embodiment discloses a new user-side subject control method based on equal value aggregation.

[0050] like Figure 1 As shown, a new user-side subject control method based on equal value aggregation includes:

[0051] Step S1: Analyze the operational characteristics and physical characteristics of distributed photovoltaics and energy storage to construct a value assessment model for distributed photovoltaics and energy storage;

[0052] Step S2: using an autoregressive sliding model to describe the randomness of distributed energy resources to determine the output prediction error of distributed energy resources; simulating the output of distributed energy resources based on the percentage of the output prediction error;

[0053] Step S3: Combining the established distributed photovoltaic and energy storage value assessment model and the obtained distributed energy output, an equivalent aggregation model is constructed based on a multi-objective planning method to aggregate various distributed energy sources and realize the regulation of new user-side entities.

[0054] Based on the above process, the present invention can achieve the rational regulation of new user-side entities, optimize energy allocation, improve energy utilization efficiency, and enhance the grid's ability to accommodate distributed energy, thus better addressing the regulation issues facing the power industry. To facilitate understanding of the technical solution of the present invention, the following further explains and illustrates the specific implementation methods of the technical solution of the present invention.

[0055] In step S1, the operating characteristics and physical characteristics of distributed photovoltaics and energy storage are analyzed to construct a value assessment model for distributed photovoltaics and energy storage.

[0056] The goal of analyzing and modeling the physical characteristics of distributed photovoltaic and energy storage modules is to help aggregators better understand the characteristics of different resources and build a model that comprehensively considers the characteristics of each distributed energy resource. Such a model can help aggregators better understand, manage, and optimize these distributed energy resources. This process can be achieved through the following methods:

[0057] Step S1-1: Photovoltaic module modeling and physical characteristics analysis

[0058] Based on the PV cell equivalent circuit model, the equivalent relationship between output voltage and output current is determined, and the obtained equivalent relationship is approximated to construct a PV array model. Under the constructed PV array model, the normal distribution method is used to characterize the allowable deviation of photovoltaic output under photovoltaic prediction. Based on the obtained allowable deviation of photovoltaic output, a confidence interval is determined in combination with the functional relationship of photovoltaic predicted output. Within the confidence interval, the output of any photovoltaic array in distributed photovoltaic at any time is analyzed. This can be achieved specifically through the following process:

[0059] Solar photovoltaic cells are a type of photoelectric semiconductor unit that uses sunlight to generate electricity directly. They are devices that directly convert sunlight energy into electrical energy through the photoelectric effect or photochemical effect. Currently, crystalline silicon solar photovoltaic cells based on the photoelectric effect are the most mature and widely used, and are the mainstream type of photovoltaic cells. According to relevant electronic theories, an ideal crystalline silicon PV cell can be regarded as consisting of a photocurrent source connected in parallel with a forward-biased diode. Due to the loss of the cell itself, series and parallel resistances should also be included. Therefore, PV cells can be used as follows: Figure 2 The equivalent circuit shown is Figure 2 The direction indicated by the arrow is used as the reference direction. The relationship between the output voltage and the output current can be expressed as:

[0060]

[0061] Among them, I p is the photocurrent, which is proportional to the light intensity; I d is the current flowing through the diode, which is related to the voltage across the diode and the reverse saturation current; I sat is the saturation current in the diode direction; A is the diode curve factor, and A∈(1,2); q=1.6×10 -19 is the electron charge; K = 1.38 × 10 -23 J / K is the Boltzmann constant; T is the absolute temperature, R s is the battery series resistance; R sh is the parallel resistance of the battery, U and I are the operating voltage and current respectively.

[0062] The PV cell model used in engineering is based on the external characteristic equivalence. During simulation, only the main parameters provided by the manufacturer need to be input: short-circuit current (I sc )Open circuit voltage (U oc ), maximum power point (MPP) current (I m )、MPP voltage (U m ), maximum power (P m ), we can get technical index parameters close to the battery physical model. Therefore, under the premise of meeting the engineering application accuracy, after derivation, formula (1) can be approximated as:

[0063]

[0064] in,

[0065] PV cells are connected in series and parallel to form a PV array. Ignoring the component link loss and different characteristics, the electrical characteristics of the PV array only need to be scaled by the PV cell characteristics, that is, the voltage is multiplied by the number of series connections, and the current is multiplied by the number of parallel connections. Therefore, using the I sc 、U oc , I m 、U m 、P m , the PV array model can be derived from formula (2).

[0066] For the convenience of modeling, the operation and maintenance costs of the photovoltaic array during operation are ignored, and only its working output and its error are considered. Specifically, it is assumed that the error of photovoltaic prediction follows the general normal distribution N(0, σ(P PVF )); where σ(P PVF ) represents the allowable deviation of photovoltaic output under photovoltaic prediction, and its definition is:

[0067]

[0068] Among them, P PVF is the predicted photovoltaic output; k PV is the correlation coefficient, which indicates the relationship between the predicted PV output and its allowable deviation, and is set to 9%; It is the theoretical maximum value of photovoltaic output.

[0069] It is understandable that dispatchers often hope that the error of photovoltaic prediction is as small as possible, so they will give a confidence interval. Specifically, assuming that the confidence level δ PV , the functional relationship of photovoltaic predicted output is f PV (x), then its confidence interval It can be calculated by the following formula:

[0070]

[0071] Among them, Inf{} represents the lower bound of the confidence interval, δ PV Indicates the confidence level.

[0072] like Figure 3 The figure shows the output of a photovoltaic system with a confidence level of 0.9. The time period shown is 24 hours, with the horizontal axis in hours (h) and the vertical axis in power (kW). The power forecast value is generated according to the prediction algorithm, that is, the predicted power curve fluctuates between 2:00 and 24:00, with its peak power between 180kW and 200kW and the valley power close to 0kW. Based on the preset confidence level, the confidence interval of the predicted power is calculated. The confidence interval is determined by the lower bound (Inf) and the upper bound (Sup) of the confidence interval to represent the prediction error range. Therefore, the photovoltaic output should meet the following constraints:

[0073]

[0074] in, is the output of the ith photovoltaic array at time t; and are the minimum output and maximum output of the ith photovoltaic array at time t after considering the error.

[0075] Step S1-2: Energy storage module modeling and operation law analysis

[0076] The energy storage working condition is described based on the state of charge of the energy storage. Specifically, under the condition that the energy storage operation constraints are met, the energy storage working condition is described based on the ratio of the current remaining power of the energy storage battery to the rated capacity. This can be achieved through the following process:

[0077] A notable characteristic of the power system is that electricity cannot be stored in large quantities and must be used immediately upon generation. However, this applies to the overall, long-term scenario. On shorter timescales, electricity can still be stored through methods such as electrochemical energy storage. With improvements in various battery chemistries, including flow batteries, nickel-based batteries, and lithium batteries, as well as the development of various energy storage battery planning and management solutions, the application of energy storage in power systems has become increasingly widespread and mature.

[0078] In this embodiment, the energy storage working condition is described by the state of charge (SOC) of the energy storage. The SOC represents the ratio of the current remaining power of the energy storage battery to its rated capacity, which is expressed as follows:

[0079]

[0080] in, is the state of charge of the i-th group of energy storage batteries at time t; and are the charging power and discharging power of the i-th group of energy storage batteries at time t; η ESS is the charge and discharge efficiency of the energy storage battery, that is, the loss of the energy storage battery during operation is taken into account; Δt is the charge and discharge time period, which is 1h.

[0081] The following constraints should be met during operation:

[0082]

[0083] in, and It is a 0 / 1 variable, when it is 0, it means no charging or discharging, and when it is 1, it means charging or discharging; and are the minimum power and maximum power generated by the energy storage battery respectively; and are the minimum power and maximum power for charging the energy storage battery respectively; and are the lower and upper limits of the state of charge, respectively.

[0084] Step S1-3: Construct a value assessment model for distributed photovoltaic and energy storage, namely, a module cost model, a user net benefit calculation model, and a user cost-profit ratio model.

[0085] Distributed photovoltaic modules combined with energy storage technology achieve bidirectional current transmission through power electronic bidirectional converters. They not only act as energy storage batteries but also have the high-stability power supply characteristics of an uninterruptible power supply (UPS), enhancing the grid's ability to integrate renewable energy.

[0086] A distributed photovoltaic energy storage module consists of photovoltaic panels, battery packs, a bidirectional control unit, a grid-connected inverter, and end users. After the photovoltaic system is installed at the user end, if the photovoltaic power generation exceeds the power demand, part of the power is directly supplied to the user, and the remaining power is transmitted to the grid. Conversely, if the photovoltaic power generation is insufficient to meet the power demand, the grid will provide the required power.

[0087] In a distributed photovoltaic energy storage module, when the photovoltaic output power is greater than the load demand, the photovoltaic power supply is used to power the load, and the excess power can be used to charge the energy storage module; when the photovoltaic output power is less than the load demand, the battery is used to charge the load first, and the insufficient power is provided by the grid. The initial cost of the distributed photovoltaic module is defined as C PV , the installed capacity of the photovoltaic power generation module is W, and the installed cost of the photovoltaic module per unit capacity is C PPV , the operating life of the photovoltaic module is n. The annual operating cost of the energy storage module is C ESS , the unit charge and discharge cost is CEP , the battery purchase cost is C E , the battery cell capacity is Q, the battery charge and discharge efficiency is η, and the number of battery cycles is n E The total charge and discharge of the battery in a year is Q Σ The initial cost of the photovoltaic-energy storage module is C. The module cost model is shown in formula (8):

[0088]

[0089] Assuming that the photovoltaic and energy storage modules installed by residents have a service life of 20 years, in order to more reasonably calculate the net benefits of users, the economic evaluation is conducted by considering the discount rate. After the installation of the distributed photovoltaic system, the total net benefits of the user within the service life of the system are defined as R. The benefits of the user in the Nth year are defined as T N The discount rate is defined as i, and the return converted to the investment year after considering the discount rate is R N The user net income calculation model is shown in formula (9):

[0090]

[0091] Cost-profit ratio is an important indicator of profitability, which refers to the ratio of profit R to cost C. Define the user's cost-profit ratio R a As shown in formula (10):

[0092]

[0093] The economic evaluation process of distributed photovoltaic-energy storage modules is as follows: Figure 4 As shown, specifically, first, the annual solar radiation data for the target area is obtained, and based on the efficiency model of the unit kilowatt photovoltaic system, the photovoltaic output power PV is calculated. Then, the annual user-side load data is collected and combined with the photovoltaic output power PV to calculate the key energy indicators E1 and E2 of the energy storage system. E1 is the total amount of electricity generated when the output power of the distributed photovoltaic module exceeds the load power during the day, and E2 is the total amount of electricity generated when the output power of the distributed photovoltaic module is less than the load power during the day. After that, the judgment conditions are set. If all the above conditions are met, the subsidy income T3 is triggered, and the time-of-use electricity fee of the energy storage system is calculated.

[0094] In step S2, an autoregressive sliding model is used to describe the randomness of distributed energy to determine the output prediction error of distributed energy; and the output of distributed energy is simulated according to the percentage of the output prediction error.

[0095] Because distributed power sources, such as photovoltaic arrays, are directly affected by weather conditions and are extremely difficult to accurately predict, it is necessary to consider various possible scenarios to analyze the error in the predicted distributed power output. The output of distributed energy at each moment can be represented by a random time series, and the realization of each random time series can be considered as a scenario. Based on this characteristic, the Auto-Regressive Moving Average Model (ARMA) is used to describe the randomness of distributed power sources. After replacing the letters in the general model with the corresponding parameters, the model is as follows:

[0096]

[0097] Among them, VP t The output power prediction error and predicted power P of distributed energy output at time t in the span of t time periods t The ratio of , 1 moment is 0; ε t is an independent error term at time t. In this embodiment, it is assumed that ε~N(0,σ 2 ), the value is 0 at the moment 1; and θ j is a non-zero undetermined coefficient; and σ, and θ j It can be obtained based on actual experience evaluation; p and q are the autoregressive order and moving average order of the model, respectively. To simplify the model, both are taken as 1 in this embodiment.

[0098] The percentage of prediction error can be obtained by recursion through the above formula, and the power fluctuation magnitude and direction at each moment in each scenario can be obtained by the following formula:

[0099] P t (k) = P t gVp t (k) (12)

[0100] Among them, P t (k) is the power fluctuation of the distributed generation at time t in the kth scenario, and a positive value indicates an upward trend; VP t (k) is the prediction error percentage of distributed power at time t in the kth scenario obtained by the previous formula.

[0101] So, we can get N k There are scenarios showing the fluctuation of the output power of distributed power generation, and the probability of each scenario is 1 / N. kThis value is usually large, so it is necessary to reduce the scenarios by scenario reduction to improve the computing efficiency of the computer. This embodiment reduces the obtained scenarios by using a heuristic synchronous back-substitution reduction method, and generates a scenario tree to further simulate the random changes in the output of distributed power sources in multiple consecutive periods. The variable advantages of the scenario tree can be achieved by Figure 5 The comparison shows that among them, Figure 5 (a) in the figure is a general scene structure. Figure 5 (b) in the figure is a fan-shaped scene structure. Figure 5 (c) in the figure is a tree-like scenario structure. Unlike the single scenarios in general and fan-shaped scenario structures, the scene points in the scenario tree are more interconnected. The information becomes increasingly detailed as time goes on, which is similar to the random and variable output of distributed power sources. Therefore, the scenario tree can simulate the output of distributed power sources more accurately and precisely.

[0102] In step S3, combining the established distributed photovoltaic and energy storage value assessment model and the obtained distributed energy output, an equivalent aggregation model is constructed based on a multi-objective planning method to aggregate various distributed energy sources and achieve regulation of new user-side entities. This can be achieved through the following process:

[0103] Step S3-1: Based on the virtual power plant day-ahead optimization scheduling model under distributed energy forecast errors, generate the day-ahead optimization total target set, namely:

[0104] minF all ={f1,f2,f3,f4,f5} (13)

[0105] Among them, F all is the total target set for day-ahead optimization; f1 is the comprehensive benefit of the virtual power plant within one day, that is, the economic efficiency of the virtual power plant operation; f2 is the utilization ratio of flexibility margin; f3 is the environmental benefit; f4 is the fluctuation of renewable energy; and f5 is the user service quality feedback.

[0106] The following is an explanation of each sub-goal:

[0107] A. Comprehensive income of virtual power plant in one day

[0108] The economic goal of the new user-side entity operation should be to maximize revenue. After considering factors such as distributed energy and operating costs, the model is modified to the following:

[0109]

[0110] R t =λ t P t (15)

[0111]

[0112] in:

[0113]

[0114] Among them, R t is the total revenue obtained by the virtual power plant after trading with the large power grid at time t; C t P is the total cost of the virtual power plant at time t; t The remaining salable power after internal power consumption of the new entity on the user side; etc. is the active power output of the corresponding micro gas turbine, generator set, photovoltaic array, wind turbine, energy storage device, etc. numbered i at time t; P t DR 、P t AC 、P t D 、P t H etc. are the power consumption of data center servers, air conditioning equipment, heating users, and electric boilers at time t; are the operation and maintenance costs of the micro gas turbine, generator set, energy storage device, photovoltaic array, and wind turbine numbered i at time t; C DC (ΔP t DR ) is the cost of shedding load in the data center at time t. t 、 etc. are the actual electricity price of the power grid at time t and the operation and maintenance cost coefficients of the micro gas turbine, generator set, energy storage equipment, photovoltaic array, wind turbine, etc.; a and b are the fitting coefficients between the cost of switching servers in the data center and the power of the switched servers.

[0115] B. Flexibility margin utilization ratio

[0116] The flexibility margin utilization ratio indicates the sensitivity of the virtual power plant when adjusting power through controllable adjustment means. The smaller the ratio, the easier it is for the virtual power plant to achieve stable operation through its own conditions, that is, it has greater flexibility. Therefore, the target for this indicator should be:

[0117]

[0118] C. Environmental benefits

[0119] The environmental benefits of virtual power plants during operation can be enhanced directly by reducing carbon emissions. This embodiment, based on the output allocation method, uses the emission performance concept to represent carbon emissions by taking a certain proportion of the operating power of micro gas turbines and generator sets. In addition, improving the utilization rate of renewable energy can also effectively reduce carbon emissions. This indicator is reflected by the amount of wind and solar power curtailment. The ideal state is to have no wind and solar power curtailment, that is, both wind turbines and photovoltaic arrays can make the best use of natural resources. However, in reality, truly zero wind and solar power curtailment is not realistic, so it can only be regarded as a goal to minimize it. The comprehensive goals of environmental benefits are as follows:

[0120]

[0121] in, and are the predicted theoretical maximum power generation of the wind turbine and photovoltaic array numbered i at time t; and are the coefficients for converting the operating power of the micro gas turbine and the generator set into carbon emissions, respectively, which are 0.1 and 0.2 in this embodiment.

[0122] D. Fluctuation of renewable energy

[0123] Since sunlight, natural wind and other factors may change suddenly, in order to prevent wind turbines and photovoltaic arrays from causing excessive power fluctuations due to sudden environmental changes, which may in turn damage the equipment life, this embodiment also aims to minimize power fluctuations, namely:

[0124]

[0125] E. User service quality feedback

[0126] Although the data center is one of the coupling points between the power system and the thermal system, it is convenient to combine the two systems through load switching, but improving service to users remains one of its important goals, including the user group that sends computing service requests to it and the user group that needs to use the waste heat generated by it for heating. Therefore, improving the quality of service to users should be a key goal. The main heat recovered from waste heat comes from the energy dissipated by the working energy of the data center servers. Therefore, this embodiment converts the service quality to the user into the amount of data service requests that users can solve in real time, that is, minimize the number of servers to ensure service efficiency, that is:

[0127]

[0128] Step S3-2: Optimize the multi-objective operation optimization model and set constraints within the overall day-ahead optimization objective set. Combine the resulting multi-objective operation optimization model and constraints to construct an equivalent aggregation model. The constraints include virtual power plant power balance constraints, power constraints on the power source side, power constraints on the load side / heat source side, and energy storage battery constraints.

[0129] A. Virtual power plant power balance constraints, namely:

[0130]

[0131] Among them, P t grid is the power amount traded with the large power grid at time t. It is stipulated that when buying, it is regarded as the power supply side and the value in the above formula is negative; when selling, it is regarded as the load side and the value in the above formula is positive.

[0132] B. Power constraints on the power supply side, namely:

[0133]

[0134] in, is the rated power of the micro gas turbine numbered i; and are the lower and upper limits of the operating power of the generator set numbered i; and The maximum power fluctuation values ​​set for the wind turbine and photovoltaic array numbered i are 30% of their rated powers in this embodiment.

[0135] C. Power constraints on the load side / heat source side, namely:

[0136]

[0137] in, is the amount of power consumed by the data that the data center must process at time t, that is, the total amount of non-transferable load; The maximum operating power of the data center; The amount of heat required to maintain the heating temperature for users; θ H ,θ DC and θ MT is the conversion coefficient of active power to heat, which is 0.9, 0.8, and 0.8 respectively in this embodiment; P t H,min and P t H,max The minimum and maximum power supply for electric boiler operation. t ref 、N S 、 and Based on queuing theory, the specific formula is as follows:

[0138]

[0139] in, is the maximum data delay that users can accept after considering QoS. The power consumption of the data center can be deduced as follows:

[0140]

[0141] P t DR =P t ref -ΔP t DR (29)

[0142]

[0143] Among them, P t ref is the total power consumed by the data center when all servers are working at time t; and are the maximum and minimum operating powers of the servers, respectively. For ease of understanding, this embodiment assumes that the maximum and minimum operating powers of all servers are the same; P t DR is the total power consumed by the data center after switching servers at time t; ΔP t DR is the power consumed by the server switched on at time t. In addition, the amount of load switched off in the data center should also satisfy the overall invariance, that is, it should be:

[0144]

[0145] D. Energy storage battery constraints, namely:

[0146]

[0147] Among them, P SESS,max is the maximum capacity of the energy storage device; P SESS,min is the minimum state of charge of the energy storage device to extend its service life. In this embodiment, it is 0.1 of the maximum capacity. Vt is the time period, which is 1h here. k SESS In order to extend the service life of energy storage equipment, the proportional coefficient between the charging and discharging power at each moment and its maximum capacity is generally set to 0.2.

[0148] In order to further demonstrate the significant advantages of the method provided by the present invention, this embodiment combines Figure 6 Take the following application example:

[0149] Figure 6 The figure shows the power amount of the two types of loads in each time period and the operating status of the loads under the microgrid energy management method proposed in the present invention. It can be seen that by transferring most of the transferable loads during the day to the night, this is beneficial for the data center as both an electric load and a heat source, because it can provide more heat for heat users in the low temperature environment at night through waste heat recovery.

[0150] Combine Figure 6 The application example is based on a microgrid in an industrial park. The park includes a distributed photovoltaic power station (5MW total installed capacity), an energy storage system (2MWh lithium battery storage), a micro gas turbine (1MW), and adjustable loads such as a data center and electric boilers. The park aims to achieve the following goals: maximize renewable energy utilization (with photovoltaic power generation as a priority); reduce energy costs (through peak-valley arbitrage and demand response); and meet carbon emission constraints (reducing annual carbon emissions by 20%).

[0151] In this application scenario, the configuration and parameter design are as follows:

[0152] ①Distributed resource configuration

[0153] A. Photovoltaic modules:

[0154] Equivalent circuit model parameters, open circuit voltage (V OC )=600V, short-circuit current (I SC )=10A,MPP power(P mpp )=5kW / group.

[0155] Output prediction error model: the confidence level is 0.9, and the allowable deviation coefficient η = 9%.

[0156] B. Energy storage module:

[0157] Capacity: 2MWh, SOC range (0.2-0.9), charge and discharge efficiency η = 90%. Charge and discharge power constraint: Maximum charge and discharge power 500kW.

[0158] Micro gas turbine: rated power 1MW, carbon emission coefficient 0.2kg / kWh.

[0159] ②Load side parameters

[0160] Data center: base load 500kW, adjustable load 200kW (achieved through server switching).

[0161] Quality of service constraint: data request latency ≤ 100ms.

[0162] Electric boiler: heating power range is 200-800kW, conversion efficiency is 80%.

[0163] Under the above application scenario, configuration and parameter design, the control method and implementation process are as follows:

[0164] Step 1: Distributed Resource Modeling and Economic (Value) Assessment: Generate a PV output scenario tree based on the PV equivalent circuit model and output forecast error. Calculate the full lifecycle cost of the energy storage module using a 5% discount rate to determine the economic threshold for energy storage participation in scheduling.

[0165] Step 2: Randomness Description and Scenario Generation: Using the ARMA model to predict PV output errors, 100 initial scenarios were generated and then reduced to 10 representative scenarios through synchronous back-substitution. Example scenarios included: sunny (probability 0.6, error ±5%), cloudy (probability 0.3, error ±15%), and rainy (probability 0.1, error ±30%).

[0166] Step 3: Multi-objective optimization scheduling: objective function (based on Equations (13)-(22)).

[0167] Economic efficiency: Maximize the park's net profit (electricity sales revenue - gas turbine fuel cost - energy storage loss cost).

[0168] Environmental protection: Minimize carbon emissions (limit the micro gas turbine output to ≤300kW).

[0169] Flexibility: Control the energy storage SOC between 0.4-0.8 to cope with sudden fluctuations.

[0170] Constraints:

[0171] Power balance (based on formula (23)): PV + energy storage + gas turbine = data center + electric boiler + other loads in the park.

[0172] Energy storage SOC dynamic constraint (based on formula (32)): charging and discharging power ≤ 500kW, SOC final value ≥ 0.5.

[0173] Step 4: Equal-value aggregation and autonomous scheduling.

[0174] Photovoltaic, energy storage, and data center loads are aggregated into a "virtual autonomous body", which is equivalent to a dispatchable 2MW virtual machine group.

[0175] Autonomous strategy: When photovoltaic output is high, priority is given to power supply and energy storage charging; during peak electricity price periods (14:00-16:00), energy storage is discharged and non-critical loads in the data center are reduced.

[0176] 4. Implementation Effect

[0177] Economical: The park's average daily electricity costs were reduced by 18%, and the peak-valley arbitrage benefits from energy storage increased by 12%.

[0178] Environmental protection: The photovoltaic absorption rate increased from 75% to 92%, and carbon emissions decreased by 22%.

[0179] Stability: Through equal-value aggregation, the response time of power grid dispatch instructions is shortened to 5 minutes and the volatility is reduced by 40%.

[0180] Example 2

[0181] This embodiment discloses a new user-side subject control system based on equal value aggregation.

[0182] A new user-side subject control system based on equal value aggregation, including:

[0183] The value assessment model building module is configured to: analyze the operating characteristics and physical characteristics of distributed photovoltaics and energy storage to build a value assessment model for distributed photovoltaics and energy storage;

[0184] The distributed energy output simulation module is configured to: describe the randomness of distributed energy using an autoregressive sliding model to determine the output prediction error of the distributed energy; and simulate the output of the distributed energy based on the percentage of the output prediction error;

[0185] The new user-side subject control module is configured to: combine the value assessment model of the established distributed photovoltaic and energy storage and the output of the obtained distributed energy, and build an equivalent aggregation model based on the multi-objective planning method to aggregate various types of distributed energy and realize the control of the new user-side subject.

[0186] Through the above-mentioned systematic modular design, a unified response optimization model for multi-resource aggregation can be constructed. Using equivalent aggregation technology, a distributed energy dispatch and operation framework can be constructed across different levels, driving the system dispatch and management model to a bottom-up hierarchical cluster aggregation model, optimizing grid regulation, improving the distributed energy acceptance capacity, and achieving green and low-carbon dispatch. At the same time, distributed energy resources such as photovoltaics and energy storage can be coupled with user-side adjustable loads to form micro-autonomous entities, achieving internal autonomous dispatch. The plasticity of adjustable loads can be used to stabilize the random volatility of distributed photovoltaics, enhancing the grid's acceptance of new energy.

[0187] Example 3

[0188] The purpose of this embodiment is to provide a computer-readable storage medium.

[0189] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a new user-side subject control method based on equal-value aggregation as described in the first embodiment of the present disclosure.

[0190] Example 4

[0191] The purpose of this embodiment is to provide an electronic device.

[0192] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of a new user-side subject control method based on equal-value aggregation as described in the first embodiment of the present disclosure are implemented.

[0193] The steps involved in the apparatuses of Examples 2, 3, and 4 above correspond to those of Method Example 1. For detailed implementations, please refer to the relevant description of Example 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any method of the present invention.

[0194] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0195] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A new user-side subject control method based on equal value aggregation, characterized in that: include: Analyze the operational and physical characteristics of distributed photovoltaics and energy storage to build a value assessment model for distributed photovoltaics and energy storage; An autoregressive sliding model is used to describe the randomness of distributed energy resources to determine the output forecast error of distributed energy resources. Based on the percentage of the output forecast error, the output of distributed energy resources is simulated. Combining the value assessment model of distributed photovoltaic and energy storage and the output of the obtained distributed energy, an equivalent aggregation model is constructed based on the multi-objective planning method to aggregate various types of distributed energy and realize the regulation of new entities on the user side.

2. A new user-side subject control method based on equal value aggregation according to claim 1, characterized in that: Analyze the operating characteristics and physical characteristics of distributed photovoltaics, including: Based on the PV cell equivalent circuit model, the equivalent relationship between output voltage and output current is determined, and the obtained equivalent relationship is approximately derived to construct a PV array model. Under the constructed PV array model, the normal distribution method is used to characterize the allowable deviation of photovoltaic output under photovoltaic prediction. Based on the obtained allowable deviation of photovoltaic output and the functional relationship of photovoltaic predicted output, a confidence interval is determined. Within the confidence interval, the output of any photovoltaic array in distributed photovoltaic at any time is analyzed.

3. The user-side new subject control method based on equal value aggregation according to claim 1 is characterized in that: Analyze the operational and physical characteristics of energy storage, including: The energy storage working condition is described according to the state of charge of the energy storage. Specifically, under the condition that the energy storage operation constraints are met, the energy storage working condition is described according to the ratio of the current remaining power of the energy storage battery to the rated capacity.

4. The user-side new subject control method based on equal value aggregation according to claim 1 is characterized in that: An autoregressive sliding model is used to describe the randomness of distributed energy resources to determine the output forecast error of distributed energy resources, including: A random time series is used to represent the output of distributed energy at each moment. The realization of each random time series is used as a scenario. Specifically, the corresponding parameters in the distributed energy under the random time series are used to replace the inherent parameters in the autoregressive sliding model to update the autoregressive sliding model; the updated autoregressive sliding model is recursively used to obtain the percentage of prediction error.

5. The user-side new subject control method based on equal value aggregation according to claim 1 is characterized in that: Based on the percentage of the generated output prediction error, simulate the output of distributed energy, including: According to the percentage of the obtained output prediction error, the size and direction of the power fluctuation at each moment in each scenario are determined; the obtained scenarios are reduced based on the heuristic synchronous back-substitution reduction method, and a scenario tree is generated; based on the generated scenario tree, the characteristics of random changes in the output of distributed power sources in multiple consecutive time periods are simulated.

6. The user-side new subject control method based on equal value aggregation according to claim 1 is characterized in that: Constructing an equivalent aggregation model based on a multi-objective programming method, including: Based on the day-ahead optimization scheduling model of the virtual power plant under the distributed energy prediction error, a day-ahead optimization total target set is generated; under the said day-ahead optimization total target set, the multi-objective operation optimization model is optimized and the constraints are set; the final multi-objective operation optimization model and the constraints are combined to construct an equivalent aggregation model.

7. A new user-side subject control method based on equal value aggregation according to claim 6, characterized in that: The total target set for day-ahead optimization includes the comprehensive revenue of the virtual power plant within a day, the utilization rate of flexibility margin, environmental benefits, renewable energy fluctuations, and user service quality feedback.

8. A new user-side subject control system based on equal value aggregation, characterized in that: include: The value assessment model building module is configured to: analyze the operating characteristics and physical characteristics of distributed photovoltaics and energy storage to build a value assessment model for distributed photovoltaics and energy storage; The distributed energy output simulation module is configured to: describe the randomness of distributed energy using an autoregressive sliding model to determine the output prediction error of the distributed energy; and simulate the output of the distributed energy based on the percentage of the output prediction error; The new user-side subject control module is configured to: combine the value assessment model of the established distributed photovoltaic and energy storage and the output of the obtained distributed energy, and build an equivalent aggregation model based on the multi-objective planning method to aggregate various types of distributed energy and realize the control of the new user-side subject.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of the new user-side subject control method based on equal value aggregation as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the new user-side subject control method based on equal value aggregation as described in any one of claims 1 to 7 are implemented.

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