Electric power and electric quantity balance analysis method and system for new energy high-proportion regional power grid

Abstract

The invention relates to an electric power and electric quantity balance analysis method and system for a new energy high-proportion regional power grid. The method comprises the following steps: acquiring basic data of a source side, a grid side, a load side and a storage side; respectively calculating a balance quantitative index and an electric power quantity index according to the basic data; wherein the balance quantitative indexes comprise source-side flexibility and source-side stability, grid-side voltage quality and grid-side loss, load-side voltage qualification rate and supply-demand balance degree, charge-discharge cycle efficiency and storage-side utilization rate; the electric power and electric quantity indexes comprise electric power balance and electric quantity balance; calculating a single-side performance index according to the balance quantitative index, and calculating a power and electric quantity balance index according to the power and electric quantity index; calculating a comprehensive evaluation score according to the single-side performance index and the electric power and electric quantity balance index; and analyzing the balance degree of the power quantity according to the comprehensive evaluation score. The intelligent and high-precision electric power and electric quantity balance analysis method is constructed, the accuracy of electric power and electric quantity balance analysis is effectively improved, and the operation efficiency and the safety level of a regional power grid can be enhanced.

Description

Technical Field

[0001] This invention belongs to the field of power analysis technology, specifically relating to a power balance analysis method and system for power grids in areas with a high proportion of new energy sources. Background Technology

[0002] With the ongoing transformation of the energy structure and the construction of new power systems, the large-scale grid connection of renewable energy and the influx of a high proportion of intermittent loads are leading to increased random fluctuations in both electricity supply and demand. This significantly increases the difficulty of power balance analysis, as traditional methods relying on manual experience or static models are insufficient to meet real-time and dynamic requirements. In this context, building an intelligent and high-precision power balance analysis and forecasting system has become a key factor in enhancing the operational efficiency and safety of regional power grids. Deep learning technology has demonstrated outstanding advantages in the field of time-series forecasting.

[0003] Power balance refers to the real-time matching of instantaneous power generation and power consumption (including losses) in a power system, ensuring grid frequency stability and safe equipment operation. Energy balance refers to the long-term equilibrium of total electricity production and consumption (including losses) over a specific time period (e.g., day, month, year). The main differences lie in the time scale, objective orientation, and technical means. Power balance relies on real-time dispatch and rapid response technologies (such as energy storage frequency regulation); imbalances can directly lead to power outages. Energy balance, on the other hand, requires long-term planning and market mechanism coordination (such as unit combination optimization); imbalances can lead to energy shortages or economic losses. The two are closely related: power balance provides real-time assurance for energy balance, while energy balance provides resource support for power balance. Modern power systems need to achieve dual objectives through the coordinated efforts of "source-grid-load-storage," seeking the optimal balance between dynamic security response and long-term low-carbon planning to support the efficient, safe, and sustainable development of new power systems.

[0004] As the penetration rate of renewable energy in the system continues to increase, the power balance characteristics of the power system undergo significant changes. In scenarios with a low proportion of renewable energy, power balance analysis can usually be simplified to the analysis of typical and extreme scenarios on a monthly, weekly, or daily basis. With the increase in the proportion of renewable energy, the number of typical scenarios increases dramatically, rendering the traditional typical scenario method inapplicable. Wind and solar energy, limited by their natural properties, are difficult to predict and control effectively. Affected by the random fluctuations of renewable energy, the peak-valley characteristics of the system's daily net load curve change significantly, with less pronounced peak-valley characteristics and different curve shapes for systems with different proportions of wind and solar energy. In systems with a high proportion of renewable energy, the reduced operation of conventional generators leads to decreased control capabilities on the generation side, making it more difficult for the system to track load and renewable energy fluctuations, facing the dual challenges of power supply and clean energy consumption. During periods of low load, renewable energy output is high, making peak shaving difficult for the power system; during periods of high load, renewable energy output is low, forcing the grid to implement orderly power consumption measures. Effectively addressing the uncertainty of renewable energy output and ensuring reliable power supply and maximum clean energy consumption becomes significantly more challenging.

[0005] The new power system presents new requirements for power balance. With increasing time scales, the accuracy of renewable energy power forecasting decreases significantly, highlighting the daily uncertainty and seasonal imbalance of renewable energy in the system. The response characteristics of various heterogeneous adjustable resources (source, grid, and storage) differ significantly across time scales, making multi-time-scale matching between system uncertainty and adjustability difficult. Furthermore, mathematical models for long-term, cross-day regulation are challenging to solve. As the proportion of renewable energy continues to increase, both supply and demand, as well as system regulation resources, exhibit high uncertainty. The system balance mechanism is shifting from "deterministic generation tracking uncertain loads" to "two-way matching of uncertain generation and uncertain loads."

[0006] Under the new power system framework, as the proportion of renewable energy integration gradually increases, the volatility and uncertainty of the system's supply and demand balance are also rising. This places higher demands on the rationality and accuracy of modeling for various resource models, including power sources, grids, loads, and storage. On the one hand, the characteristic parameters of various resources should comprehensively and accurately reflect their actual physical characteristics. On the other hand, their operation and control strategies should be realistic, fully considering their operating patterns and characteristics, and combining them with actual production to provide scientific and reasonable operation and control strategies. This will fully reflect the changes and characteristics of various resources accompanying the construction of the new power system, effectively ensuring the accuracy of power balance simulation in the new power system.

[0007] In new power systems, power balance analysis in regions with a high proportion of renewable energy requires consideration of complex constraints during system planning or operation, based on new source-grid-load-storage characteristic models. The optimization objectives are to determine the operating status of the source-grid-load-storage system under the conditions of maximum renewable energy absorption and maximum load supply capacity, thereby reflecting the system balance, strengthening weak links, and sustainably maintaining supply-demand balance. The power balance analysis framework originates from the actual planning and operation rules of the power system. With the accelerated construction of new power systems, current balance analysis calculations tend to exhibit new characteristics such as integrated processes, diversified scales, and refined granularity. Because power balance analysis in new power systems with a high proportion of renewable energy grids requires processing heterogeneous data from different sources, calculating power flow according to different time scales, and analyzing the power balance status, the analysis must be combined with the desired simulation time scale and period, different optimization objectives, and updated decisions based on corresponding indicators after balance analysis.

[0008] Power balance assessment is a crucial aspect of power system planning and operation, reflecting the system's performance in matching supply and demand in both electricity and energy consumption through quantitative indicators. Power system evaluation takes a multi-dimensional approach, incorporating grid constraints, economic constraints, environmental constraints, and social constraints to form a comprehensive quantitative assessment system. The continuous development of new power systems and the integration of high proportions of renewable energy have made the dynamic balance problem of power systems increasingly complex. New power systems require more refined quantitative evaluation indicators for power balance. Summary of the Invention

[0009] Based on the aforementioned shortcomings and deficiencies in the existing technology, one of the objectives of this invention is to at least solve one or more of the aforementioned problems in the existing technology. In other words, one of the objectives of this invention is to provide a power balance analysis method and system for power grids in areas with a high proportion of new energy sources that meets one or more of the aforementioned requirements.

[0010] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0011] A method for power balance analysis of power grids in areas with a high proportion of renewable energy sources includes the following steps:

[0012] S1. Collect basic data from the source side, grid side, load side, and storage side;

[0013] S2. Based on the basic data from the source side, grid side, load side, and storage side, calculate the balance quantification indicators and power quantity indicators for each of the following: The balance quantification indicators for the source side include source side flexibility and source side stability; the balance quantification indicators for the grid side include grid side voltage quality and grid side losses; the balance quantification indicators for the load side include load side voltage qualification rate and supply-demand balance; the balance quantification indicators for the storage side include charge-discharge cycle efficiency and storage side utilization rate; and the power quantity indicators include power balance and power quantity balance.

[0014] S3. Calculate the single-side performance index based on the balance quantification index of the source side, grid side, load side, and storage side, and calculate the power balance index based on the power quantity index.

[0015] S4. The comprehensive evaluation score is calculated based on the single-sided performance index and the power balance index.

[0016] S5. Analyze the balance of electricity consumption based on the comprehensive evaluation score.

[0017] As a preferred embodiment, the basic data on the source side includes historical photovoltaic power output time series, historical wind power output time series, and conventional power source parameters; wherein, the conventional power source parameters include coal power and hydropower, and the parameters include ramp rate and minimum technical output.

[0018] The basic data on the network side includes power grid topology, transmission capacity, and congestion data;

[0019] The basic data on the load side includes historical load data, forecast curves, and interruptible load potential.

[0020] The basic data on the energy storage side includes energy storage configuration, which includes power, capacity, efficiency, and charging / discharging strategy.

[0021] As a preferred embodiment, the formula for calculating the source-side flexibility is:

[0022]

[0023] Among them, △P range =P max -P min Indicates the adjustment range; △P rate =max(|P t -P t-1 | represents the maximum adjustment rate; α and β are constant coefficients; This represents the average power, obtained through cumulative averaging in time-series simulation.

[0024] The formula for calculating the source-side stability is:

[0025]

[0026] in, The coefficient of variation is σ, which represents the standard deviation of the output power of the i-th source-side device. i With mean μ i The ratio; This represents the average coefficient of variation, where N is the number of source-side devices.

[0027] As a preferred embodiment, the grid-side voltage quality is calculated as follows:

[0028] Q1 = 1 - RMSE;

[0029] in, The V obtained from the time series simulation is a per-unit value;

[0030] The formula for calculating the network-side loss is:

[0031]

[0032] in, Indicates loss rate The mean over the time step; P loss This indicates that the total system loss includes transformer losses and line losses; P in This indicates the input power.

[0033] As a preferred embodiment, the formula for calculating the load-side voltage qualification rate is:

[0034]

[0035] Specifically, by using the indicator function I(x), the time point when the load bus meets the requirement that the voltage deviation is within 5% is recorded as 1, and the time point when it does not meet the requirement is recorded as 0; in the time dimension, the pass rate of each bus is calculated; finally, the pass rate of all load buses is averaged to obtain the load-side voltage pass rate.

[0036] The formula for calculating the supply-demand balance is:

[0037]

[0038] Among them, P in,t P represents the input energy at time t. load,t This represents the total load demand at time t; for each time point, the absolute value of the deviation of the supply-demand ratio from 1 is taken, and the average deviation over the time dimension T is calculated.

[0039] As a preferred embodiment, the formula for calculating the charge-discharge cycle efficiency is:

[0040]

[0041] Among them, Edischarge E represents the total discharge capacity. charge Indicates the total charging capacity;

[0042] The formula for calculating the storage utilization rate is as follows:

[0043]

[0044] Among them, E max This represents the theoretical maximum charge / discharge energy, expressed as the rated power C. p With duration T total The product of; E actual This represents the actual charging and discharging energy.

[0045] As a preferred embodiment, the formula for calculating the power balance is:

[0046]

[0047] Among them, P Imbalance，t =P gen，t +P storage，t +P balance，t -P load，t -P loss，t The imbalance at time t represents the difference between the output of the generator, photovoltaic system, energy storage, and balancing node, and the load and losses. The absolute value of the imbalance is then multiplied by the total load P. load，t The ratio, as a relative imbalance, is then taken as the mean over the time dimension T.

[0048] The formula for calculating the power balance is:

[0049]

[0050] Among them, E Imbalance For unbalanced electricity, E load This is the rated discharge capacity.

[0051] As a preferred embodiment, the formula for calculating the unilateral performance index is:

[0052]

[0053] in, These are the weights corresponding to each indicator;

[0054] The formula for calculating the power balance index is as follows:

[0055]

[0056] in, These are the weights for power balance and energy balance, respectively.

[0057] As a preferred option, the formula for calculating the comprehensive evaluation score is:

[0058]

[0059] in, These are the weights of the single-sided performance index and the power balance index, respectively.

[0060] This invention also provides a power balance analysis system for power grids in areas with a high proportion of renewable energy, applying the power balance analysis method described in any of the preceding solutions. The power balance analysis system includes:

[0061] The data acquisition module is used to collect basic data from the source side, grid side, load side, and storage side.

[0062] The calculation module is used to calculate the balance quantification index and power quantity index of the source side, grid side, load side and storage side respectively based on the basic data of the source side, grid side, load side and storage side. It is also used to calculate the single-side performance index based on the balance quantification index of the source side, grid side, load side and storage side, calculate the power quantity balance index based on the power quantity index, and calculate the comprehensive evaluation score based on the single-side performance index and the power quantity balance index.

[0063] The analysis module is used to analyze the balance of electricity consumption based on the comprehensive evaluation score.

[0064] Compared with the prior art, the beneficial effects of this invention are:

[0065] This invention constructs an intelligent and high-precision power balance analysis method, which effectively improves the accuracy of power balance analysis and enhances the operational efficiency and safety level of regional power grids. Attached Figure Description

[0066] Figure 1 This is a flowchart of the multi-scale power balance analysis of the power system according to the present invention;

[0067] Figure 2 This is a flowchart of the power balance analysis of the present invention;

[0068] Figure 3 This is a flowchart illustrating the operation of the time series module of the present invention.

[0069] Figure 4 This is a layered structure design diagram for the power balance analysis of the present invention;

[0070] Figure 5 This is a radar chart for the comprehensive evaluation of the indicators of this invention. Detailed Implementation

[0071] To more clearly illustrate the embodiments of the present invention, specific implementation methods will be described below with reference to the accompanying drawings. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without any creative effort.

[0072] The power balance of new power systems is characterized by multi-period coordination, multi-regional complementarity, and multi-entity collaboration. The analytical models for power balance inevitably involve large scales and complex coupling relationships. Therefore, to achieve efficient balance analysis, decoupling analysis based on the balance mechanism and model characteristics is essential. Considering that the balance control responsibilities and controllable resources of each region and entity are relatively clear, and the coupling characteristics are relatively well-defined, a good foundation for decoupling exists. However, the deep coupling of the same balance resource across multiple time scales presents challenges to decoupling analysis. Therefore, multi-period coordination decoupling is a key challenge, taking into account the balance characteristics of multi-regional complementarity and multi-entity collaboration. It is necessary to establish analytical models and methods for different time scales, consider multi-period collaborative relationships and constraints, clarify key interactive information, and establish a coordination and decoupling framework for medium- and long-term and short-term balances.

[0073] Specifically, firstly, this study investigates medium- to long-term production simulation techniques based on probabilistic matching of supply and demand resources: It constructs supply constraints for stochastic resources such as hydropower and solar power, establishing a probabilistic description model of primary energy supply and a dynamic quantification model of carbon emissions for long timescales; it utilizes efficient Monte Carlo simulation and stochastic sequence generation techniques to establish multi-scenario continuous load correction curves, employs an improved general outage table and Markov state transition matrix to construct a multi-state model of power generation equipment considering resource volatility and component failure probability; and it combines parallel dynamic programming with the equivalent power function method to construct a two-layer framework combining deterministic and stochastic production simulation, establishing an automatic boundary verification and adjustment mechanism for medium- to long-term operations. Then, based on the medium- to long-term power decomposition results, this study investigates short-term production simulation techniques considering the synergy of multiple flexible resources: it conducts differentiated modeling for the characteristics of various flexible resources, carries out research on multi-granularity time-series production simulation techniques considering the synergy of multiple flexible resources, and, based on the synergistic adjustment mechanism of multiple flexible resources and the influence mechanism of multiple types of market transactions, combines sequence operation theory to achieve one-to-one matching of supply and demand, obtain the probability distribution of system supply, and calculate the system balance margin. Finally, this study investigates a power balance analysis technique that considers multi-cycle coordination and decoupling: based on the coupling characteristics of multi-cycle balance states and optimization strategies, short-term scenario verification and correction are considered in medium- and long-term power balance, while medium- and long-term decomposition strategy probabilistic modeling is considered in short-term production simulation; alternating correction techniques for multi-cycle balance analysis problems are studied to form a unified power balance analysis model; and a solution algorithm based on decomposition coordination and heuristic fusion is proposed, combining model characteristics and solution requirements, to achieve accurate and efficient multi-cycle power balance analysis.

[0074] Regarding the design of evaluation indicators for power balance: Power balance is the result of the combination of power output and load demand. New power systems will face dynamic, diverse, and differentiated operating scenarios. Traditional deterministic evaluation indicators may not only fail to fully reflect the system's balance but also struggle to reconcile multiple objectives such as sufficiency and economy. Therefore, the evaluation indicators for power balance in new power systems should, based on traditional indicators, focus on adapting to and describing probabilistic balance characteristics, and should possess characteristics such as simplicity, completeness, and practicality.

[0075] The power balance analysis method for power grids in areas with a high proportion of renewable energy sources according to embodiments of the present invention includes the following steps:

[0076] S1. Collect basic data from the source side, grid side, load side, and storage side;

[0077] S2. Based on the basic data from the source side, grid side, load side, and storage side, calculate the balance quantification indicators and power quantity indicators for each of the following: The balance quantification indicators for the source side include source side flexibility and source side stability; the balance quantification indicators for the grid side include grid side voltage quality and grid side losses; the balance quantification indicators for the load side include load side voltage qualification rate and supply-demand balance; the balance quantification indicators for the storage side include charge-discharge cycle efficiency and storage side utilization rate; and the power quantity indicators include power balance and power quantity balance.

[0078] S3. Calculate the single-side performance index based on the balance quantification index of the source side, grid side, load side, and storage side, and calculate the power balance index based on the power quantity index.

[0079] S4. The comprehensive evaluation score is calculated based on the single-sided performance index and the power balance index.

[0080] S5. Analyze the balance of electricity consumption based on the comprehensive evaluation score.

[0081] Specifically, such as Figure 1 As shown, firstly, based on the power balance calculation results, a probabilistic evaluation method for power balance margin under multiple stochastic scenarios is studied. A balance margin index is proposed as the core indicator of power balance status, intuitively reflecting the system's balance capacity and serving as the basis for other auxiliary evaluation indicators. The margin index is divided into two typical indicators: power supply margin M. P The power supply margin M represents the level at which the power supply capacity exceeds the maximum load within a certain period T. E It represents the level at which the power supply capacity exceeds the total load power within a certain period T.

[0082] The calculation formulas for the above two key indicators are as follows:

[0083]

[0084] in, P represents the maximum available output of thermal power, hydropower, other conventional generating units, and energy storage in the regional power grid at time t. t R Let P be the available power output of the new energy source at time t. t I P is the output of external power to the grid at time t. t L Let P be the load value at time t. t O Let E be the load supplied by the power grid at time t; HG E represents the amount of hydropower available to the hydropower unit during time period T; ESLossThis represents the amount of electricity lost during charging and discharging of energy storage within time period T. If there is no power shortage or power supply shortage, the margin index will be greater than 1; conversely, if the above problems exist, the margin index will be less than 1. Therefore, this index can concisely and clearly reflect the balance state.

[0085] The two indicators above provide deterministic assessment results under a specific scenario. To address probabilistic assessments under multiple stochastic balance scenarios, the probability that the balance margin of electricity and power consumption does not meet operational requirements (i.e., the probability that the balance margin of electricity and power consumption is less than 1) can be expressed as follows:

[0086] α(M P )=Pr(M P <1) (3-3)

[0087] α(M E )=Pr(M E <1) (3-4)

[0088] Based on the core indicator of balance margin, this paper further proposes auxiliary assessment indicators for power balance status. It analyzes the relationship between these indicators and the balance margin, considering factors such as system imbalance risk, balance adjustment costs, and flexibility requirements. These indicators can serve as supplementary indicators for characterizing power balance status, providing support for quantitative assessment of power balance and auxiliary dispatch decisions.

[0089] The preliminary process for power balance analysis in this embodiment of the invention is as follows: Figure 2 As shown, the main steps include the following five parts:

[0090] Step 1: Data preparation and scenario construction;

[0091] Scene design: typical day / season, extreme weather (low wind and light + high load), high penetration rate scene.

[0092] Input data:

[0093] (a) Source side: Historical output time series of photovoltaic / wind power (15min / 1h resolution), parameters of conventional power sources (coal power, hydropower, etc.) (ramp-up rate, minimum technical output);

[0094] (b) Network side: Grid topology, transmission capacity, congested data;

[0095] (c) Load side: historical load data, forecast curves, interruptible load potential;

[0096] (d) Storage side: Energy storage configuration (power / capacity, efficiency, charge / discharge strategy).

[0097] Step 2: Time series simulation;

[0098] (a) Model construction: Power balance was simulated in time intervals of 15 min to 1 h.

[0099] (b) Constraints: Ramp-up limits for conventional power sources, SOC limits for energy storage, and transmission capacity limits.

[0100] Step 3: Multi-temporal and spatial scale balance margin index;

[0101] I. Time Scale:

[0102] (a) Short-term (hourly): Peak shaving margin (available flexible resource power / net load fluctuations);

[0103] (b) Medium to long term (daily / monthly): Power shortage rate (power shortage / total power demand).

[0104] II. Spatial Scale:

[0105] (a) Node margin: the percentage of remaining transmission capacity at critical nodes;

[0106] (b) Regional margin: The degree of matching between regional net output and load (e.g., net load peak-to-valley difference rate).

[0107] Step 4: Quantitative analysis of individual components;

[0108] 1) Photovoltaic / Wind Power: Penetration limits, curtailment rate, and capacity reliability.

[0109] 2) Conventional power supply: minimum technical output ratio and frequency modulation contribution.

[0110] 3) Energy storage: charging and discharging frequency, dynamic changes in SOC, and peak shaving and valley filling efficiency.

[0111] 4) Load: Peak-to-valley difference rate, demand response potential.

[0112] 5) Power grid: percentage of periods of congestion, and cross-regional power support.

[0113] Step 5: Output electrical penalty power balance analysis and optimization suggestions;

[0114] 1) Increase energy storage configuration (power / capacity optimization).

[0115] 2) Improve the flexibility of conventional power sources (such as deep peak shaving of coal-fired power).

[0116] 3) Dynamic allocation of power transmission rights.

[0117] Among them, after the power grid is constructed, such as Figure 2As shown, time series simulation is required to simulate the dynamic processes of the system network. This time series simulation is based on Pandapower's basic and advanced time series computations. Pandapower's time series module (the `run_timeseries` function) is specifically designed for simulating time-based operations and is associated with the control module. In the time series, the simulation controller updates the values ​​of different elements in each time step of the loop, achieving a dynamic effect by adjusting different time lengths and time step sizes. When the `run_timeseries` function is called, a loop is started, iterating through each `time_step`. In each step, the controller's running loop starts a control loop for each controller. The control loop element variables are updated by the added controllers. The operation flow of the time series module is as follows: Figure 3 As shown.

[0118] The simulation method for source-grid-load-storage time series simulation in this embodiment of the invention is as follows:

[0119] After obtaining the time-series input data and formulating the corresponding operating strategy, time-series simulation can be performed. There are two main methods for time-series simulation. The first method involves obtaining the relevant input data by executing the corresponding time step according to the operating strategy before the simulation, then importing the data from each side into a constant controller, setting the output writer, and finally running the time-series simulation. The simulation function is the default power flow calculation function. The second method involves using a custom controller, writing the component's operating strategy into it, importing the input data of the custom controller with the operating strategy into the custom controller separately, and importing the remaining input data into the constant controller. Then, setting the output writer and finally running the time-series simulation. In this design, the first method was chosen, primarily for the following reasons:

[0120] 1) The controller running in the first method only includes a constant controller. You only need to import the data from each side and add an output writer to run the time series. The operation is simpler than the second method, and the effect is the same. The only difference is that you need to control the consistency of the time step when you make the running strategy in advance.

[0121] 2) The first approach is more suitable for the system design requirements of this project. The operating strategy essentially refers to the component acquiring relevant data from the input data within a given time step, and then using this output data as the input data for the time series. The second approach excludes the data acquired by the operating strategy from the input data in the time series simulation; instead, it uses it as output data accompanying the time series simulation. If the power network or input data is changed, the time series simulation will use this custom controller in the operating system, leading to unwanted results and reducing the system's versatility.

[0122] 3) Select Method 1 and pre-determine the execution strategy. Use the obtained data as input for the time series simulation, and import the data from each side into the constant controller. This controller is used in the time series module to read data from the DataSource and write it to the network. Its code form, including key parameters, is as follows:

[0123] ConstControl(net,element,variable,element_index,data_source,profile_name)

[0124] In the parameters of this controller, net represents the imported power network; element represents the element in the power network that needs to be controlled; variable represents the controlled variable of the control element; element_index represents the ID of the controlled element; data_source represents the data source, which here represents the input data read; profile_name represents the configuration name of the element in the data source.

[0125] After reading the power network and locking the controlled components and their controlled variables, the controller reads the configuration names and related time series data that need to be assigned to the controlled components and their variables from the input data. The controller writes to the net using the time_step attribute of the time series function. Therefore, the time series length of the data source should be consistent with the time length of the time series simulation, otherwise an error will occur.

[0126] After successfully importing the constant controller, you can set the OutputWriter. The OutputWriter is used to store and format specific outputs from time series calculations. It also stores the variables needed after time series simulation, based on the length of the time_step. It needs to be written before the time series simulation function, and its code form including key parameters is as follows:

[0127] ow=OutputWriter(net,output_path,output_file_type)

[0128] ow.log_variable(table,variable)

[0129] In the parameters of the output writer, output_path indicates the path of the folder where the output is written, output_file_type indicates the type of output file to use, and here we choose to export as a table file. log_variables represents the output writer; table represents the DataFrame table where the components are located after running the simulation, for example, res_line is the time series simulation result table of the line; variable represents the specific variables based on the table, for example, pl_mw represents the active power loss of the line.

[0130] After configuring the output writer, the time series function should be executed. If the time series function is used first, and then the output writer is configured, the simulation results will not be recorded. The time series function allocates component variables for each time step according to the `time_step` attribute based on the parameters provided in the controller. It calls the `run` keyword to run the function for each time step, defaulting to the `runpp()` function, which performs regular power flow calculations. Its code form, including key parameters, is as follows:

[0131] run_timeseries(net,time_steps,verbose=True)

[0132] In time series functions, the time_steps attribute represents the time step size, while verbose indicates whether to print a progress bar.

[0133] The power balance analysis system design for power grids in areas with a high proportion of new energy sources in this invention adopts the Analytic Hierarchy Process (AHP) primarily because the evaluation of source-grid-load-storage balance capacity involves multiple levels of indicators. AHP, through its tree-like decomposition of target layer, criterion layer, and indicator layer, can hierarchically structure the system, making it clearer and more observable. Based on the principle that the construction of a quantitative evaluation system should follow the combination of qualitative analysis and quantitative calculation, ensuring the scientific nature of indicator selection and the operability of the calculation methods, the hierarchical tree decomposition is continued, employing a layered structure such as... Figure 4 As shown.

[0134] 1) Target layer: Comprehensive evaluation score of dynamic balance capacity of source, grid, load and storage, used to evaluate the overall coordinated operation level of the system.

[0135] 2) Criterion layer: includes two major categories: single-sided performance indicators and power balance indicators. The single-sided performance indicators are further divided into four subcategories: source side, grid side, load side, and storage side.

[0136] 3) Indicator layer: The indicator layer is a detailed refinement of the criteria layer. The design adopts 10 specific evaluation indicators. Among them, each of the 4 subcategories selects two refined indicators, while the power balance is divided into two indicators: power balance and power balance.

[0137] Once the hierarchical design is clear, a judgment matrix should be established for each level. The judgment matrix is ​​constructed by comparing the importance of indicators at the same level pairwise, using a 1-9 scale, where the earlier the number, the more important it is.

[0138] Here, we choose to transform the judgment matrices of each sub-category indicator layer, power balance indicator layer, and sub-category criterion layer into all-1 matrices, considering it as an ideal case. The main reason for this design is to simplify the overall calculation, directly transforming the original calculation method based on the judgment matrix into a weighted calculation method. After this design, the weights between the two indicators in all indicator layers are equally distributed and directly set to 0.5; while the weights of the source-grid-load-storage sub-category criterion layers are distributed at 0.25, and the weight of the power balance layer is 1; for the target layer, considering the large number of indicators on one side, the weight of the indicators on one side is set to 0.6, and the weight of the balance indicator is set to 0.4.

[0139] After the standard judgment matrix is ​​designed, a consistency check is required. The consistency check verifies whether there are contradictions in the judgment logic, which is especially important in systems with a large number of indicators. It is used to quantify inconsistencies and ensure the reliability of the judgment matrix. The main process includes:

[0140] 1) First, calculate the largest eigenvalue. The calculation formula is shown in Formula 4-1:

[0141]

[0142] Where A represents the judgment matrix, w represents the weight vector matrix, and n represents the order of the matrix;

[0143] 2) Calculate the consistency index (CI). The calculation formula is shown in Formula 4-2:

[0144]

[0145] 3) Calculate the consistency index RI by looking up the value in a table based on the matrix order;

[0146] 4) Calculate the consistency ratio CR, using the formula shown in Formula 4-3:

[0147]

[0148] 5) The judgment criterion CR is acceptable if it is less than 0.1; otherwise, the judgment matrix needs to be modified.

[0149] By setting all judgment matrices to all 1s, i.e., CR is 0 in the ideal case, which meets the consistency requirement, the calculation process of consistency check can be reduced.

[0150] After the consistency test is completed, the evaluation system's indicators can be calculated and the results analyzed.

[0151] The analysis system of this invention takes into account the characteristics of the new power system in its index design. It designs quantitative indicators for four dimensions—source, grid, load, and storage—as well as the power balance characteristics. Each indicator has a clear mathematical calculation method. The quantitative processing of the index system mainly includes the following steps:

[0152] 1) Indicator Calculation Design. The calculation formula is designed by using the results of time series simulation as input.

[0153] 2) Dimensionless processing of indicators. To eliminate the influence of dimensional differences between different indicators, all indicators are normalized so that the values ​​of each indicator are within the range of [0,1].

[0154] 3) Weighted aggregation of indicators. Based on the weighting system determined by the analytic hierarchy process, the indicators at each level are weighted and calculated to obtain the final comprehensive evaluation score.

[0155] The following are the specific quantitative calculation methods for each indicator:

[0156] Source-side indicators are divided into source-side flexibility and source-side stability indicators, and their calculation formulas are Formula 4-4 and Formula 5-4, respectively.

[0157] As shown in Equation 4-5.

[0158]

[0159] In the flexibility formula, △P range =P max -P min Indicates the adjustment range; △P rate =max(|P t -P t-1 |) represents the maximum adjustment rate; α and β are constant coefficients, and both are selected to be 0.5 in the design; This represents the average power; dividing by P is to eliminate the influence of dimensions.

[0160]

[0161] In the stability formula, The coefficient of variation (the ratio of the standard deviation of the output of the i-th source-side device to its mean) is used to eliminate the influence of dimensions. G2 represents the average coefficient of variation. When the average coefficient of variation approaches 0, the volatility is low, while G2 approaches 1, indicating better stability.

[0162] 1) The grid-side indicators are divided into grid-side voltage quality and grid-side loss, and the calculation formulas are shown in Formula 4-6 and Formula 4-7, respectively.

[0163] Q1 = 1 - RMSE (4-6)

[0164] Grid-side voltage quality using root mean square error V obtained from time series simulation is a per-unit value.

[0165]

[0166] Among the network-side loss indicators, P represents the loss rate, calculated as the average over the time step; loss This indicates that the total system loss includes transformer losses and line losses; P in This represents the input power; the lower the loss, the closer the indicator is to 1.

[0167] 2) Load-side indicators are divided into load-side voltage qualification rate and supply-demand balance, and the calculation formulas are shown in Formula 4-8 and Formula 4-9 respectively:

[0168]

[0169] In the load-side voltage qualification rate, the indicator function I(x) is used to select the time point when the load bus meets the voltage deviation within 5% and is recorded as 1, and the time point when it does not meet the requirement is recorded as 0; in the time dimension, the qualification rate of each bus is calculated; finally, the qualification rate of all load buses is averaged to obtain the overall voltage qualification rate.

[0170]

[0171] In the calculation of supply and demand balance, P in,t P represents the input energy at a certain point in time. load,t This represents the total load demand at that point in time; the absolute value of the deviation of the supply-demand ratio from 1 at each moment is taken to calculate the average deviation over the time dimension; if the supply-demand ratio is always 1, the balance is 1.

[0172] 3) Storage-side indicators are divided into charge-discharge cycle efficiency and storage-side utilization rate, and the calculation formulas are shown in Formula 4-10 and Formula 4-11, respectively:

[0173]

[0174] In charge-discharge cycle efficiency, the total discharge capacity is divided by the total charge capacity.

[0175]

[0176] In terms of storage side utilization, E max E represents the theoretical maximum charge / discharge energy. max =C p ·T total The product of rated power and duration; E actual This represents the actual charging and discharging energy.

[0177] 4) Electricity and power indicators are divided into power balance and energy balance, and the calculation formulas are Formula 4-12 and Formula 5-14 respectively.

[0178] As shown in Equation 4-13.

[0179]

[0180] In power balance calculations, P Imbalance =P gen +P storage +P balance -P load -P loss The unbalance is represented by the difference between the output of generators, photovoltaics, energy storage, and balance nodes and the load and losses. The ratio of the unbalance to the total load is taken as the relative unbalance, and then the average value is taken. The smaller the average relative unbalance, the closer the power balance index is to 1.

[0181]

[0182] In the power balance calculation, similar to the power balance calculation, a relative imbalance is set. The smaller the relative imbalance, the closer the index is to 1.

[0183] 5) After obtaining the indicators of each indicator layer, the criterion layer is calculated. The criterion layer calculation includes one-sided performance calculation and power calculation, as shown in Formula 4-14 and Formula 4-15.

[0184]

[0185] In the calculation of the single-sided performance index, each refined index of the original sub-category index side is multiplied by its corresponding weight, which is 0.5 in this case; finally, it is divided by 4, that is, each sub-category side is multiplied by a weight of 0.25.

[0186]

[0187] In the calculation of the power balance index, each detailed index of the original sub-category index is multiplied by its corresponding weight, and the weight here is 0.5.

[0188] 6) Finally, the target layer is calculated, and the calculation formula is shown in 4-16.

[0189]

[0190] The overall target layer calculation involves single-sided performance indicators and power balance indicators, with weights of 0.6 and 0.4, respectively.

[0191] As an example, the analysis results obtained by applying the above analysis method of the embodiments of the present invention are shown in Table 1; the radar chart of the comprehensive evaluation of the main indicators is shown in Table 1. Figure 5 As shown, data analysis reveals that some indicators scored higher than others; the closer the overall evaluation score is to 100%, the higher the overall balance.

[0192] Among these indicators, grid-side loss, supply-demand balance, power balance, and energy balance scored relatively high because their calculations all involve balancing input and output power. During the power flow calculation of the entire power network, the network is automatically balanced through slack nodes and parallel reactive power compensators, so the entire network is essentially in a balanced state over time, resulting in high scores. Source-side flexibility and energy storage utilization scored relatively low. Source-side flexibility involves changes in the adjustment range and speed. Regarding energy storage utilization, according to the designed calculation method, the theoretical maximum charging and discharging energy will be higher than the actual energy, and the energy storage points are in a resting state during certain time periods in the actual curve.

[0193] Table 1 Analysis Results

[0194]

[0195] The analysis method in this invention is designed according to the steps of the Analytic Hierarchy Process (AHP) and adopts ideal conditions in many aspects. However, the rationality and universality of the designed indicators are not particularly high. Therefore, for actual network conditions, under the premise of data science in all aspects, a better indicator design can also obtain a more realistic indicator evaluation.

[0196] Based on the above-mentioned power balance analysis method for power grids in areas with a high proportion of new energy sources, the power balance analysis system provided in this embodiment of the invention includes the following functional modules: acquisition module, calculation module, and analysis module.

[0197] The acquisition module in this embodiment of the invention is used to acquire basic data from the source side, grid side, load side, and storage side;

[0198] The calculation module of this invention is used to calculate the balance quantification index and power quantity index of the source side, grid side, load side and storage side respectively based on the basic data of the source side, grid side, load side and storage side. It is also used to calculate the single-side performance index based on the balance quantification index of the source side, grid side, load side and storage side, calculate the power quantity balance index based on the power quantity index, and calculate the comprehensive evaluation score based on the single-side performance index and the power quantity balance index.

[0199] The analysis module in this embodiment of the invention is used to analyze the balance of power consumption based on the comprehensive evaluation score;

[0200] The specific processing procedures of the above functional modules can be found in the detailed description of the power balance analysis method described above, and will not be repeated here.

[0201] The above description is merely a detailed explanation of preferred embodiments and principles of the present invention. For those skilled in the art, there may be changes in specific implementation methods based on the ideas provided by the present invention, and these changes should also be considered within the scope of protection of the present invention.

Claims

1. A method for power balance analysis in power grids in areas with a high proportion of new energy sources, characterized in that, Includes the following steps: S1. Collect basic data from the source side, grid side, load side, and storage side; S2. Based on the basic data from the source side, grid side, load side, and storage side, calculate the balance quantification indicators and power quantity indicators for each of the following: The balance quantification indicators for the source side include source side flexibility and source side stability; the balance quantification indicators for the grid side include grid side voltage quality and grid side losses; the balance quantification indicators for the load side include load side voltage qualification rate and supply-demand balance; the balance quantification indicators for the storage side include charge-discharge cycle efficiency and storage side utilization rate; and the power quantity indicators include power balance and power quantity balance. S3. Calculate the single-side performance index based on the balance quantification index of the source side, grid side, load side, and storage side, and calculate the power balance index based on the power quantity index. S4. The comprehensive evaluation score is calculated based on the single-sided performance index and the power balance index. S5. Analyze the balance of electricity consumption based on the comprehensive evaluation score.

2. The power balance analysis method according to claim 1, characterized in that, The basic data on the source side includes historical power output time series of photovoltaic power, historical power output time series of wind power, and conventional power source parameters; among them, conventional power sources include coal power and hydropower, and the parameters include ramp rate and minimum technical output. The basic data on the network side includes power grid topology, transmission capacity, and congestion data; The basic data on the load side includes historical load data, forecast curves, and interruptible load potential. The basic data on the energy storage side includes energy storage configuration, which includes power, capacity, efficiency, and charging / discharging strategy.

3. The power balance analysis method according to claim 1 or 2, characterized in that, The formula for calculating the source-side flexibility is: Among them, △P range =P max -P min Indicates the adjustment range; △P rate =max(|P t -P t-1 | represents the maximum adjustment rate; α and β are constant coefficients; This represents the average power, obtained through cumulative averaging in time-series simulation. The formula for calculating the source-side stability is: in, The coefficient of variation is σ, which represents the standard deviation of the output power of the i-th source-side device. i With mean μ i The ratio; This represents the average coefficient of variation, where N is the number of source-side devices.

4. The power balance analysis method according to claim 3, characterized in that, The calculation method for the grid-side voltage quality is as follows: Q1 = 1 - RMSE; in, The V obtained from the time series simulation is a per-unit value; The formula for calculating the network-side loss is: in, Indicates loss rate The mean over the time step; P loss This indicates that the total system loss includes transformer losses and line losses; P in This indicates the input power.

5. The power balance analysis method according to claim 4, characterized in that, The formula for calculating the load-side voltage qualification rate is as follows: Specifically, by using the indicator function I(x), the time point when the load bus meets the requirement that the voltage deviation is within 5% is recorded as 1, and the time point when it does not meet the requirement is recorded as 0; in the time dimension, the pass rate of each bus is calculated; finally, the pass rate of all load buses is averaged to obtain the load-side voltage pass rate. The formula for calculating the supply-demand balance is: Among them, P in,t P represents the input energy at time t. load,t This represents the total load demand at time t; for each time point, the absolute value of the deviation of the supply-demand ratio from 1 is taken, and the average deviation over the time dimension T is calculated.

6. The power balance analysis method according to claim 5, characterized in that, The formula for calculating the charge-discharge cycle efficiency is as follows: Among them, E discharge E represents the total discharge capacity. charge Indicates the total charging capacity; The formula for calculating the storage utilization rate is as follows: Among them, E max This represents the theoretical maximum charge / discharge energy, expressed as the rated power C. p With duration T total The product of; E actual This represents the actual charging and discharging energy.

7. The power balance analysis method according to claim 6, characterized in that, The formula for calculating the power balance is: Among them, P Imbalance，t =P gen，t +P storage，t +P balance，t -P load，t -P loss，t The imbalance at time t represents the difference between the output of the generator, photovoltaic system, energy storage, and balancing node, and the load and losses. The absolute value of the imbalance is then multiplied by the total load P. load，t The ratio, as a relative imbalance, is then taken as the mean over the time dimension T. The formula for calculating the power balance is: Among them, E Imbalance For unbalanced electricity, E load This is the rated discharge capacity.

8. The power balance analysis method according to claim 7, characterized in that, The formula for calculating the unilateral performance index is as follows: in, These are the weights corresponding to each indicator; The formula for calculating the power balance index is as follows: in, These are the weights for power balance and energy balance, respectively.

9. The power balance analysis method according to claim 8, characterized in that, The formula for calculating the comprehensive evaluation score is as follows: in, These are the weights of the single-sided performance index and the power balance index, respectively.

10. A power balance analysis system for power grids in areas with a high proportion of new energy sources, employing the power balance analysis method as described in any one of claims 1-9, characterized in that, The power balance analysis system includes: The data acquisition module is used to collect basic data from the source side, grid side, load side, and storage side. The calculation module is used to calculate the balance quantification index and power quantity index of the source side, grid side, load side and storage side respectively based on the basic data of the source side, grid side, load side and storage side. It is also used to calculate the single-side performance index based on the balance quantification index of the source side, grid side, load side and storage side, calculate the power quantity balance index based on the power quantity index, and calculate the comprehensive evaluation score based on the single-side performance index and the power quantity balance index. The analysis module is used to analyze the balance of electricity consumption based on the comprehensive evaluation score.

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