Novel medium-and-long-term electric power and electric quantity balance analysis method for electric power system based on panoramic time sequence deduction

Abstract

The invention discloses a novel medium-and-long-term electric power and electric quantity balance analysis method for an electric power system based on panoramic time sequence deduction. The method comprises the following steps: 1) constructing a daily granularity time sequence simulation model; 2) solving the daily granularity time sequence simulation model to obtain a medium and long term adjustment resource allocation plan and a daily granularity electric power and electric quantity balance analysis result; 3) adaptively dividing the optimization period into a plurality of sub-periods through an adaptive period decoupling algorithm, and speculating the power of each sub-period at the initial moment to realize time sequence decoupling among the sub-periods; 4) constructing an hour granularity time sequence simulation model; and 5) carrying out hour-level parallel time sequence production simulation on each sub-period, solving the hour granularity time sequence simulation model in parallel, generating a sub-period equipment operation scheme, and carrying out quantitative analysis on the long-term electric power and electric quantity imbalance risk in the novel electric power system. The method gives full play to the flexibility of short-term resource adjustment, achieves the efficient consumption of new energy and the reliable supply of power, and accurately quantifies the risk of supply-demand imbalance.

Description

Technical Field

[0001] This invention relates to the field of power balance analysis technology, specifically to a novel long-term power balance analysis method for power systems based on panoramic time-series simulation. Background Technology

[0002] The increasing proportion of renewable energy in the power system poses challenges to reliable power supply and full utilization of renewable energy. Power balance analysis is the foundation for rationally planning the layout of power sources and scientifically arranging operation modes. It is highly correlated with important indicators of new power systems such as supply and demand guarantee capacity and renewable energy utilization rate, and is of great significance to the safe and stable operation of high-proportion new power systems.

[0003] Compared to traditional power systems, the power balance problem in new power systems exhibits the following characteristics:

[0004] 1) Rapid fluctuations: The source-load fluctuation rate of a high-proportion renewable energy power system is large, and the system regulation rate is difficult to keep up with the rapid changes in source-load. The analysis method that simply considers the matching ability of the system peak-shaving and valley-shaving depth with the source-load fluctuation range is difficult to meet the balance analysis requirements of a high-proportion renewable energy power system.

[0005] 2) Adjustment time lag: Due to the limitations of the equipment's flexible adjustment capabilities, the adjustment measures adopted at the current moment to cope with source load fluctuations will have a continuous impact on the system's supply and demand balance capability in the future, requiring a time-series refined analysis of the supply and demand balance capability.

[0006] 3) Seasonal mismatch: The differences in meteorological conditions in different seasons cause the characteristics of the source load to exhibit seasonal features. By making reasonable and flexible allocation of long-term resources throughout the year, cross-seasonal synergy and mutual assistance are of great significance to improving the system's supply and demand capacity.

[0007] Existing methods for power balance analysis include power balance tables, stochastic production simulation, and time-series production simulation. Among them, time-series production simulation can reflect the time-series operating characteristics of the system and is suitable for accurate power balance analysis of power systems with a high proportion of renewable energy.

[0008] Medium- and long-term time-series production simulations are computationally complex and difficult to solve. While some scholars have researched ways to improve the efficiency of these simulations, existing literature has accelerated the process through model simplification and piecewise optimization. However, model simplification overlooks the adjustment time lag and rapid fluctuations of high-proportion renewable energy systems, failing to fully exploit the flexible adjustment capabilities of equipment and resulting in inaccurate analytical conclusions. Piecewise optimization methods do not adequately consider the seasonal mismatch characteristics of high-proportion renewable energy systems, making it difficult to effectively allocate flexible resources across different time periods throughout the year. Summary of the Invention

[0009] The purpose of this invention is to provide a novel long-term power balance analysis method for power systems based on panoramic time-series simulation, comprising the following steps:

[0010] 1) Construct a daily granular time-series simulation model for conducting full-cycle operation simulation of the power system.

[0011] 2) Solve the daily granular time series simulation model to obtain the medium- and long-term regulation resource allocation plan and the daily granular power balance analysis results.

[0012] 3) Based on the daily granular power balance analysis results, the optimization cycle is adaptively divided into several sub-periods by an adaptive time period decoupling algorithm, and the power at the initial moment of each sub-period is predicted to achieve time decoupling between sub-periods.

[0013] 4) For each sub-period, the medium- and long-term adjustment resource allocation plan is used as the boundary, and the hourly granular time series simulation model is constructed by combining the time series decoupling results.

[0014] 5) Conduct hourly parallel time-series production simulations for each sub-period, solve hourly granular time-series simulation models in parallel, generate equipment operation plans for each sub-period, and quantitatively analyze the long-term power imbalance risk in the new power system.

[0015] Furthermore, the constraints of the daily granular time series simulation model include DC power flow constraints at daily power shortage risk moments, power balance constraints at daily peak-shaving shortage risk moments, daily power balance constraints, thermal power operation constraints, hydropower operation constraints, wind and solar capacity constraints, and seasonal energy storage operation constraints.

[0016] Furthermore, the daily granular time-series simulation model is solved using a solver.

[0017] The solvers include Gurobi and Cplex.

[0018] The medium- and long-term regulation resource allocation plan includes the annual daily granular unit combination plan, reservoir capacity scheduling plan, and seasonal energy storage scheduling plan.

[0019] The daily granular power balance analysis results include daily power deficit, daily peak-shaving deficit, and daily unbalanced power.

[0020] Furthermore, the steps for achieving temporal decoupling between sub-time periods using the adaptive time-period decoupling algorithm are as follows:

[0021] 3.1) By analyzing the daily granular power balance analysis results, an adaptive sub-period division optimization model is constructed.

[0022] 3.2) Based on the constrained PELT algorithm that considers the minimum and maximum length of sub-periods, solve the adaptive sub-period division optimization model to obtain each sub-period after division.

[0023] 3.3) Predict the power at the initial moment of each sub-time period after the division, and realize the temporal decoupling between the sub-time periods, as shown below:

[0024] 3.3.1) Determine the daily imbalance in electricity consumption Is it located in the interval? If yes, proceed to step 3.3.2. If not, determine the daily unbalanced electricity consumption. Is it greater than If yes, proceed to step 3.3.4; otherwise, proceed to step 3.3.5. Here, k represents the sub-time period index. This represents the (k-1)th sub-time period. This represents the mapping from a sub-period to a day. Indicates the first Daily imbalance of electricity. This is a preset threshold.

[0025] 3.3.2) Determine the daily power shortage Is it located in the interval? If yes, proceed to step 3.3.3). If not, determine the daily power shortage. Is it greater than If yes, proceed to step 3.3.4; otherwise, proceed to step 3.3.5. Indicates the first Japan's daily electricity shortage, This is a preset threshold.

[0026] 3.3.3) Let the k-th sub-time period be... Power at the initial moment ,in, , They represent the first The minimum and maximum power values ​​at the moment when a power shortage risk occurs at a thermal power unit node.

[0027] 3.3.4) Let the k-th sub-time period be... Power at the initial moment .

[0028] 3.3.5) Let the k-th sub-time period be... Power at the initial moment .

[0029] Furthermore, the steps for solving the adaptive sub-time period division optimization model are as follows:

[0030] 3.2.1) Initialize the candidate segmentation point set C, the cost function F[0], and the segmentation point set R[0] after partitioning, with initialization time t=1.

[0031] 3.2.2) Initialize the cost function for time t. Candidate segmentation points and segmentation point set Initialize candidate segmentation points .

[0032] 3.2.3) Determine candidate segmentation points Has the maximum number of candidate segment points been reached? If yes, proceed to step 3.2.5. If not, determine the candidate segment points. to candidate segmentation point Is the sub-time period located within the sub-time period length range? If yes, proceed to step 3.2.4; otherwise, let... And return to step 3.2.3). Wherein, , These represent the minimum and maximum lengths of the sub-time period, respectively.

[0033] 3.2.4) Calculate candidate segmentation points Cost And determine the cost Is it less than the cost function? If so, then update the cost function. Candidate segmentation points and segmentation point set and order If not, then return to step 3.2.3; otherwise, let (Return to step 3.2.3). Wherein, Indicates the first Cost function of candidate segmentation points. For penalty parameters, This represents the piecewise cost function.

[0034] 3.2.5) Update the cost function at time t and the segmented point set after partitioning And will include candidate segmentation points that exceed the sub-time period length interval. and cost Greater than the cost function Candidate segmentation points Remove the candidate segmentation point set C, and add time t to the candidate segmentation point set C.

[0035] 3.2.6) Determine whether time t has reached the total number of days in the optimization cycle. If so, output the set of segmented points after partitioning. If not, then let t = t + 1 and return to step 3.2.2).

[0036] Furthermore, the constraints of the hourly granular time series simulation model include DC power flow constraints, thermal power unit operation constraints, hydropower operation constraints, wind and solar capacity constraints, seasonal energy storage constraints, and electrochemical energy storage operation constraints.

[0037] Furthermore, the hourly granular time series simulation model is solved using a commercial solver.

[0038] The commercial solvers include Gurobi and Cplex.

[0039] The technical effectiveness of this invention is undeniable. It proposes a panoramic simulation approach to power balance analysis, employing "full-cycle operation simulation - adaptive time-period decoupling - parallel time-series flexible adjustment." This approach fully considers the characteristics of high-proportion renewable energy power systems. It flexibly adjusts in stages to address the different response speeds and spatiotemporal adjustment capabilities of medium- and long-term time-series coupled equipment and short-term time-series coupled equipment, thereby improving the computational efficiency of medium- and long-term power balance. A full-cycle daily granularity supply-demand balance characteristic analysis process is established, efficiently allocating medium- and long-term adjustment resources to address the seasonal mismatch characteristics of new power systems. Based on the characteristics of daily granularity supply-demand imbalance, an improved Pruned Exact Linear Time (PELT) algorithm is adopted to adaptively divide sub-periods, achieving time-series decoupling of sub-periods and introducing parallel computing methods to improve solution efficiency. Using the medium- and long-term adjustment resource allocation plan as the boundary, and considering the rapid fluctuations in source and load and the time lag in adjustment characteristics of new power systems, hourly time-series simulations are conducted in parallel. This fully leverages the flexibility of short-term adjustment resources, achieving efficient renewable energy consumption and reliable power supply, and accurately quantifying the risk of supply-demand imbalance.

[0040] This invention establishes a three-stage technical architecture of "daily granular resource allocation - adaptive time-period decoupling - refined imbalance assessment," and proposes a medium- to long-term power balance analysis method based on panoramic time-series simulation. First, a daily granular operational simulation model is established to reduce the dimensionality of the medium- to long-term regulatory resource time-series allocation process and determine the medium- to long-term regulatory resource allocation plan. Second, the characteristics of supply and demand imbalance throughout the entire cycle are captured, and an adaptive sub-period division method and time-series decoupling strategy are established to enable parallel computation of hourly simulations. Using the medium- to long-term regulatory resource allocation plan as the boundary, an hourly operational simulation model is established to accurately assess the risk of supply and demand imbalance. Attached Figure Description

[0041] Figure 1 A schematic diagram of a new power system long-term power balance analysis method based on panoramic time-series simulation;

[0042] Figure 2 A flowchart for the constrained PELT algorithm;

[0043] Figure 3 A graph showing the annual power shortage curve for the IEEE-14 Node system;

[0044] Figure 4 This is a graph showing the annual water discharge rate of the IEEE-14 node system.

[0045] Figure 5 The annual wind curtailment curve for the IEEE-14 Node system;

[0046] Figure 6 The graph shows the annual light curtailment curve for the IEEE-14 node system.

[0047] Figure 7 A graph showing the supply and demand imbalance of the IEEE-118 node system; Figure 7 (a) is a graph showing the annual power shortage; Figure 7 (b) is a graph showing the annual water discharge rate; Figure 7 (c) is the annual wind curtailment curve; Figure 7 (d) is the annual light curvature curve. Detailed Implementation

[0048] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0049] Example 1:

[0050] See Figures 1 to 7 A novel long-term power balance analysis method for power systems based on panoramic time-series simulation includes the following steps:

[0051] 1) Construct a daily granular time-series simulation model for conducting full-cycle operation simulation of the power system.

[0052] The new power system is a new era power system that prioritizes ensuring energy and power security, aims to meet the power demands of high-quality economic and social development, focuses on building a high-proportion renewable energy supply and consumption system, relies on multi-directional coordination and flexible interaction between power generation, grid, load, and storage, uses a robust, intelligent, and flexible power grid as its hub platform, and is based on technological and institutional innovation. It is an important component of the new energy system and a key carrier for achieving the "dual carbon" goal. This section primarily emphasizes the characteristics of a high proportion of renewable energy.

[0053] 2) Solve the daily granular time series simulation model to obtain the medium- and long-term regulation resource allocation plan and the daily granular power balance analysis results.

[0054] 3) Based on the daily granular power balance analysis results, the optimization cycle is adaptively divided into several sub-periods by an adaptive time period decoupling algorithm, and the power at the initial moment of each sub-period is predicted to achieve time decoupling between sub-periods.

[0055] 4) For each sub-period, the medium- and long-term adjustment resource allocation plan is used as the boundary, and the hourly granular time series simulation model is constructed by combining the time series decoupling results.

[0056] 5) Conduct hourly parallel time-series production simulations for each sub-period, solve hourly granular time-series simulation models in parallel, generate equipment operation plans for each sub-period, and quantitatively analyze the long-term power imbalance risk in the new power system.

[0057] Example 2:

[0058] A novel long-term power balance analysis method for power systems based on panoramic time-series simulation is described in Example 1. Further, the objective function of the daily granular time-series simulation model is as follows:

[0059] (1)

[0060] (2)

[0061] (3)

[0062] (4)

[0063] (5)

[0064] (6)

[0065] In the formula, This indicates the operating cost of thermal power plants in the first phase. This indicates a penalty for a period of power outage. This indicates the penalty for wind curtailment in the first phase. This indicates the first phase of the penalty for abandoning light. This indicates a phase one of water abandonment penalties. This represents the index of the power generation node. This indicates the number of thermal power units. Represents a date index. This indicates the total number of days in the optimization period. This represents the cost function for thermal power generation. Indicates the first The node of the thermal power unit is at the Daily power shortage risk moment power. Indicates the first The node of the thermal power unit is at the The daily peak-shaving gap risk is a constant factor affecting power. Indicates the first The node of the thermal power unit is at the The number of hours used per day. Indicates the first The maximum power of each thermal power unit node. This indicates the total number of hours. Indicates the first The start-up cost of each thermal power unit node. Indicates the first The node of the thermal power unit is at the The power-on logo for the day. Indicates the first Shutdown cost of each thermal power unit node. Indicates the first The node of the thermal power unit is at the The shutdown icon for the day. This indicates an optimization of the scene index. This indicates the number of optimized scenarios. Represents a set of nodes. This indicates the cost of penalties for shortcomings. Indicates the first The first scenario The node at the th The risk of power shortages is ever-present. Indicates the first The first scenario The node at the th Daily power shortage. Indicates the first The probability of each scenario. This indicates the total number of wind farms. This indicates the penalty cost for wind curtailment. Indicates the first The first scenario The wind farm node at the first The predicted maximum power for the day. Indicates the first The first scenario The wind farm node at the first The daily peak-shaving gap risk is a constant factor affecting power. Indicates the first The first scenario The wind farm node at the first The number of hours used per day. Indicates the first The first scenario The wind farm node at the first Daily projected power generation. This indicates the total number of photovoltaic power plants. This indicates the cost of penalties for abandoning light. Indicates the first The first scenario The photovoltaic power station node at the first The predicted maximum power for the day. Indicates the first The first scenario The photovoltaic power station node at the first The daily peak-shaving gap risk is a constant factor affecting power. Indicates the first The first scenario The photovoltaic power station node at the first The number of hours used per day. Indicates the first The first scenario The photovoltaic power station node at the first Daily projected power generation. This indicates the total number of hydroelectric power stations. Indicates the first The first scenario The hydropower station node at the first Daily water discharge.

[0066] Example 3:

[0067] The main technical contents of the new long-term power balance analysis method in the power system based on panoramic time series simulation are described in any one of Examples 1 to 2. Furthermore, the constraints of the daily granular time series simulation model include DC power flow constraints at the daily power shortage risk moment, power balance constraints at the daily peak-shaving shortage risk moment, daily power balance constraints, thermal power operation constraints, hydropower operation constraints, wind and solar capacity constraints, and seasonal energy storage operation constraints.

[0068] Example 4:

[0069] A novel long-term power balance analysis method for power systems based on panoramic time-series simulation, the main technical contents of which are described in any one of Examples 1 to 3. Furthermore, the DC power flow constraints at the daily power shortage risk moment are as follows:

[0070] (7)

[0071] (8)

[0072] In the formula, , Both represent the generator node index. Represents a date index. Represents a set of nodes. Represents a node To the node Line admittance. , They represent the first The first scenario , The node at the th The risk of a daily power shortage is ever-present. Indicates the first The first scenario The node at the th At times when the daily power shortage risk exists, thermal power, hydropower, and wind power are injected with power. Indicates the first The first scenario The node at the th Maximum daily load power. Indicates the first The first scenario The node at the th Daily power shortage risk and potential power outage. Represents a node To the node Line capacity.

[0073] The power balance constraints at the daily peak-shaving gap risk moment are as follows:

[0074] (9)

[0075] In the formula, This indicates the number of thermal power units. This indicates the total number of hydroelectric power stations. Indicates the first The power generation efficiency of each hydropower station node. This indicates the total number of wind farms. This indicates the total number of photovoltaic power plants. Indicates the first The node of the thermal power unit is at the The daily peak-shaving gap risk is a constant factor affecting power. Indicates the first The first scenario The wind farm node at the first The daily peak-shaving gap risk is a constant factor affecting power. Indicates the first The first scenario The photovoltaic power station node at the first The daily peak-shaving gap risk is a constant factor affecting power. Indicates the first The hydropower station node at the first Power generation flow rate at times of daily peak-shaving deficit risk. Indicates the first Each scene The node at the th The load during times of daily peak-shaving gap risk.

[0076] The daily electricity consumption balance constraints are as follows:

[0077] (10)

[0078] In the formula, Indicates the first The node of the thermal power unit is at the The number of hours used per day. Indicates the first The maximum power of each thermal power unit node. Indicates the first The hydropower station node at the first The number of hours used per day. Indicates the first The maximum power generation flow of each hydropower station node. Indicates the first The first scenario The wind farm node at the first The number of hours used per day. Indicates the first The first scenario The wind farm node at the first Daily power generation. Indicates the first The first scenario The photovoltaic power station node at the first The number of hours used per day. Indicates the first The first scenario The photovoltaic power station node at the first Daily power generation. This indicates the total number of seasonal energy storage systems. , They represent the first The seasonal energy storage system node at the first Daily charging and discharging power. Indicates the first The scene in Daily power shortage. Indicates the first The scene in Daily load electricity.

[0079] The operating constraints for thermal power plants are as follows:

[0080] (11)

[0081] (12)

[0082] (13)

[0083] (14)

[0084] (15)

[0085] (16)

[0086] In the formula, Indicates the first The node of the thermal power unit is at the Daily power shortage risk moment power. , They represent the first The minimum and maximum power values ​​at the moment when a power shortage risk occurs at a thermal power unit node. Indicates the first The node of the thermal power unit is at the The power-on logo for the day. Indicates the first The node of the thermal power unit is at the The shutdown icon for the day. Represents a date index. , They represent the first Minimum start-up time and minimum shutdown time for each thermal power unit node. , They represent the first The node of the thermal power unit is at the , The on / off status of the day. Indicates the first The node of the thermal power unit is at the The on / off status of the day.

[0087] The hydropower operation constraints are as follows:

[0088] (17)

[0089] (18)

[0090] (19)

[0091] (20)

[0092] (twenty one)

[0093] In the formula, , They represent the first The first scenario The hydropower station node at the first , Daily storage capacity. Indicates the first The first scenario The hydropower station node at the first Daily water volume. Indicates the first The first scenario The hydropower station node at the first Daily water discharge. , They represent the first The minimum and maximum reservoir capacity of each hydropower station node. This indicates the total number of days in the optimization period. , They represent the first The first scenario The reservoir capacity of each hydropower station node on days 0 and D. Indicates the first The initial reservoir capacity of each hydropower station node. Indicates the first The hydropower station node at the first Power generation flow at times of daily power shortage risk. Indicates the first The hydropower station node at the first Power generation flow rate at times of daily peak-shaving deficit risk.

[0094] The wind and solar capacity constraints are as follows:

[0095] (twenty two)

[0096] (twenty three)

[0097] In the formula, Indicates the first The first scenario The wind farm node at the first The predicted maximum power for the day. Indicates the first The first scenario The photovoltaic power station node at the first The predicted maximum power for the day.

[0098] The seasonal energy storage operation constraints are as follows:

[0099] (twenty four)

[0100] (25)

[0101] (26)

[0102] (27)

[0103] (28)

[0104] (29)

[0105] In the formula, Indicates the first The maximum discharge power of each seasonal energy storage system node. This indicates the total number of hours. Indicates the first The seasonal energy storage system node at the first Daily discharge state variables. Indicates the first The maximum charging power of each seasonal energy storage system node. Indicates the first The seasonal energy storage system node at the first Daily charging state variables. , They represent the first The seasonal energy storage system node at the first , Japan's SOC. Indicates the first Discharge efficiency of seasonal energy storage system nodes. Indicates the first Charging efficiency of seasonal energy storage system nodes. , They represent the first The lower and upper limits of SOC for each seasonal energy storage system node. , They represent the first The SOC of a seasonal energy storage system node on days 0 and D. Indicates the first Initial SOC of a seasonal energy storage system node.

[0106] Example 5:

[0107] The main technical contents of the new long-term power balance analysis method in the power system based on panoramic time series simulation are described in any one of Examples 1 to 4. Furthermore, the daily granular time series simulation model is solved using a solver.

[0108] The solvers include Gurobi and Cplex.

[0109] The medium- and long-term regulation resource allocation plan includes the annual daily granular unit combination plan, reservoir capacity scheduling plan, and seasonal energy storage scheduling plan.

[0110] The daily granular power balance analysis results include the daily power deficit, daily peak-shaving deficit, and daily unbalanced power, as shown below:

[0111] (30)

[0112] (31)

[0113] (32)

[0114] In the formula, This indicates the daily power shortage on day d. This indicates an optimization of the scene index. This indicates the number of optimized scenarios. Indicates the first The probability of each scenario. This represents the index of the power generation node. Represents a set of nodes. Indicates the first The first scenario The node at the th The risk of power shortages is ever-present. This represents the daily unbalanced electricity consumption on day d. Indicates the first The first scenario The node at the th Daily power shortage. This indicates the total number of wind farms. Indicates the first The first scenario The wind farm node at the first The predicted maximum power for the day. Indicates the first The first scenario The wind farm node at the first The daily peak-shaving gap risk is a constant factor affecting power. This indicates the total number of photovoltaic power plants. Indicates the first The first scenario The photovoltaic power station node at the first The predicted maximum power for the day. Indicates the first The first scenario The photovoltaic power station node at the first The daily peak-shaving gap risk is a constant factor affecting power. This indicates the total number of hydroelectric power stations. Indicates the first The first scenario The hydropower station node at the first Daily water discharge. Indicates the first The power generation efficiency of each hydropower station node. This indicates the daily peak-shaving gap on day d.

[0115] Example 6:

[0116] The novel long-term power balance analysis method for power systems based on panoramic time-series simulation, with its main technical contents described in any one of Examples 1 to 5, further includes the following steps for achieving time-series decoupling between sub-time periods using an adaptive time-series decoupling algorithm:

[0117] 3.1) By analyzing the daily granular power balance analysis results, an adaptive sub-period division optimization model is constructed, as shown below:

[0118] (33)

[0119] (34)

[0120] (35)

[0121] In the formula, This represents the segmentation point index. m is the number of segmentation points. For penalty parameters, This represents the piecewise cost function. , They represent the first , The day is divided into segments based on the number of segmentation points. , They represent the first , The eigenvectors of the day. Represents a date index. Indicates the first The eigenvectors of the day. Represents the norm. This indicates the total number of days in the optimization period. , These represent the minimum and maximum lengths of the sub-time period, respectively.

[0122] 3.2) Based on the constrained PELT algorithm that considers the minimum and maximum length of sub-periods, solve the adaptive sub-period division optimization model to obtain each sub-period after division.

[0123] 3.3) Predict the power at the initial moment of each sub-time period after the division, and realize the temporal decoupling between the sub-time periods, as shown below:

[0124] 3.3.1) Determine the daily imbalance in electricity consumption Is it located in the interval? If yes, proceed to step 3.3.2. If not, determine the daily unbalanced electricity consumption. Is it greater than If yes, proceed to step 3.3.4; otherwise, proceed to step 3.3.5. Here, k represents the sub-time period index. This represents the (k-1)th sub-time period. This represents the mapping from a sub-period to a day. Indicates the first Daily imbalance of electricity. This is a preset threshold.

[0125] 3.3.2) Determine the daily power shortage Is it located in the interval? If yes, proceed to step 3.3.3). If not, determine the daily power shortage. Is it greater than If yes, proceed to step 3.3.4; otherwise, proceed to step 3.3.5. Indicates the first Japan's daily electricity shortage, This is a preset threshold.

[0126] 3.3.3) Let the k-th sub-time period be... Power at the initial moment ,in, , They represent the first The minimum and maximum power values ​​at the moment when a power shortage risk occurs at a thermal power unit node.

[0127] 3.3.4) Let the k-th sub-time period be... Power at the initial moment .

[0128] 3.3.5) Let the k-th sub-time period be... Power at the initial moment .

[0129] Example 7:

[0130] A novel long-term power balance analysis method for power systems based on panoramic time-series simulation, the main technical contents of which are described in any one of Examples 1 to 6. Furthermore, the steps for solving the adaptive sub-time period division optimization model are as follows:

[0131] 3.2.1) Initialize the candidate segmentation point set C, the cost function F[0], and the segmentation point set R[0] after partitioning, with initialization time t=1.

[0132] 3.2.2) Initialize the cost function for time t. Candidate segmentation points and segmentation point set Initialize candidate segmentation points .

[0133] 3.2.3) Determine candidate segmentation points Has the maximum number of candidate segment points been reached? If yes, proceed to step 3.2.5. If not, determine the candidate segment points. to candidate segmentation point Is the sub-time period located within the sub-time period length range? If yes, proceed to step 3.2.4; otherwise, let... And return to step 3.2.3). Wherein, , These represent the minimum and maximum lengths of the sub-time period, respectively.

[0134] 3.2.4) Calculate candidate segmentation points Cost And determine the cost Is it less than the cost function? If so, then update the cost function. Candidate segmentation points and segmentation point set and order If not, then return to step 3.2.3; otherwise, let (Return to step 3.2.3). Wherein, Indicates the first Cost function of candidate segmentation points. For penalty parameters, This represents the piecewise cost function.

[0135] 3.2.5) Update the cost function at time t and the segmented point set after partitioning And will include candidate segmentation points that exceed the sub-time period length interval. and cost Greater than the cost function Candidate segmentation points Remove the candidate segmentation point set C, and add time t to the candidate segmentation point set C.

[0136] 3.2.6) Determine whether time t has reached the total number of days in the optimization cycle. If so, output the set of segmented points after partitioning. If not, then let t = t + 1 and return to step 3.2.2).

[0137] Example 8:

[0138] A novel long-term power balance analysis method for power systems based on panoramic time-series simulation, the main technical contents of which are described in any one of Examples 1 to 7. Furthermore, the objective function of the hourly granular time-series simulation model is as follows:

[0139] (36)

[0140] (37)

[0141] (38)

[0142] (39)

[0143] (40)

[0144] (41)

[0145] In the formula, k represents the sub-time period index. This represents the operating cost of thermal power plants in the k-th sub-period. This represents the penalty for power outage in the k-th sub-time period. This represents the wind curtailment penalty for the k-th sub-period. This represents the penalty for light abandonment in the k-th sub-time period. This represents the penalty for water wastage during the k-th sub-period. This represents the index of the power generation node. This indicates the number of thermal power units. t represents time. This represents the k-th sub-time period. This represents the cost function for thermal power generation. Indicates the first The power of each thermal power unit node at time t. This indicates an optimization of the scene index. This indicates the number of optimized scenarios. Indicates the first The probability of each scenario. Represents a set of nodes. This indicates the cost of penalties for shortcomings. Indicates the first The first scenario The power of each node at time t. This indicates the total number of wind farms. This indicates the penalty cost for wind curtailment. Indicates the first The first scenario The predicted power of each wind farm node at time t. Indicates the first The first scenario The power of a wind farm node at time t. This indicates the total number of photovoltaic power plants. This indicates the cost of penalties for abandoning light. Indicates the first The first scenario The predicted power of a photovoltaic power station node at time t. Indicates the first The first scenario The power of a photovoltaic power station node at time t. This indicates the total number of hydroelectric power stations. This indicates the cost of penalties for abandoning water. Indicates the first The first scenario The water discharge flow rate of each hydropower station node at time t.

[0146] Example 9:

[0147] The main technical contents of the new long-term power balance analysis method in the power system based on panoramic time series simulation are described in any one of Examples 1 to 8. Furthermore, the constraints of the hourly granular time series simulation model include DC power flow constraints, thermal power unit operation constraints, hydropower operation constraints, wind and solar capacity constraints, seasonal energy storage constraints, and electrochemical energy storage operation constraints.

[0148] The DC power flow constraints are as follows:

[0149] (42)

[0150] (43)

[0151] In the formula, , Both represent the generator node index. t represents time. Represents a set of nodes. Represents a node To the node Line admittance. , They represent the first The first scenario , The angle of attack of a node at time t. Indicates the first The first scenario The power injected into each node at time t. Indicates the first The first scenario The load power of each node at time t. Indicates the first The first scenario The power shortage of each node at time t. Represents a node To the node Line capacity.

[0152] The operating constraints of the thermal power units are as follows:

[0153] (44)

[0154] (45)

[0155] In the formula, , They represent the first The minimum and maximum power of each thermal power unit node. , They represent the first The power of each thermal power unit node at times t and t-1. Indicates the first The uphill limit for each thermal power unit node. Indicates the first Downhill ramp limit for each thermal power unit node. This represents the mapping from a sub-period to a day. , They represent the first The node of the thermal power unit is at the The power off and power on indicators for each day. Indicates the first The node of the thermal power unit is at the The on / off status of the day.

[0156] The hydropower operation constraints are as follows:

[0157] (46)

[0158] (47)

[0159] (48)

[0160] (49)

[0161] In the formula, Indicates the first The power generation flow of a hydropower station node at time t. Indicates the first The maximum power generation flow of each hydropower station node. , They represent the first The first scenario The reservoir capacity of each hydropower station node at times t-1 and t. Indicates a unit of time. Represents a date index. Indicates the first The first scenario The hydropower station node at the first Daily water flow rate. Indicates the first The first scenario The hydropower station node at the first Daily water discharge rate. , They represent the first The minimum and maximum reservoir capacity of each hydropower station node. This represents the k-th sub-time period. This indicates the first phase determined by the [specific term / representation]. The power generation flow of a hydropower station node on day d.

[0162] The wind and solar capacity constraints are as follows:

[0163] (50)

[0164] (51)

[0165] In the formula, Indicates the first The first scenario The predicted power of each wind farm node at time t. Indicates the first The first scenario The power of a wind farm node at time t. Indicates the first The first scenario The predicted power of a photovoltaic power station node at time t. Indicates the first The first scenario The power of a photovoltaic power station node at time t.

[0166] The seasonal energy storage constraints are as follows:

[0167] (52)

[0168] (53)

[0169] (54)

[0170] (55)

[0171] (56)

[0172] (57)

[0173] In the formula, Indicates the first The first scenario The discharge power of a seasonal energy storage system node at time t. Indicates the first The maximum discharge power of each seasonal energy storage system node. Indicates the first The first scenario The discharge state variables of a seasonal energy storage system node at time t. Indicates the first The first scenario The charging power of a seasonal energy storage system node at time t. Indicates the first The maximum charging power of each seasonal energy storage system node. Indicates the first The first scenario The charging state variables of a seasonal energy storage system node at time t. , They represent the first The first scenario The SOC of a seasonal energy storage system node at times t-1 and t. Indicates the first Discharge efficiency of seasonal energy storage system nodes. Indicates the first Charging efficiency of seasonal energy storage system nodes. , They represent the first The lower and upper limits of SOC for each seasonal energy storage system node. Indicates the first The first scenario The SOC of a seasonal energy storage system node at the end of the kth sub-period Tk. Indicates the first The seasonal energy storage system node at the first Japan's SOC.

[0174] The operational constraints of the electrochemical energy storage are as follows:

[0175] (58)

[0176] (59)

[0177] (60)

[0178] (61)

[0179] (62)

[0180] (63)

[0181] In the formula, Indicates the first The first scenario The discharge power of a node in an electrochemical energy storage system at time t. Indicates the first The maximum discharge power of each electrochemical energy storage system node. Indicates the first The first scenario The discharge state variables of a node in an electrochemical energy storage system at time t. Indicates the first The first scenario The charging power of each node in an electrochemical energy storage system at time t. Indicates the first The maximum charging power of each electrochemical energy storage system node. Indicates the first The first scenario The charging state variables of a node in an electrochemical energy storage system at time t. , They represent the first The first scenario The SOC of an electrochemical energy storage system node at times t-1 and t. Indicates the first The discharge efficiency of each node in an electrochemical energy storage system. Indicates the first The charging efficiency of each electrochemical energy storage system node. , They represent the first The lower and upper limits of SOC for each node in an electrochemical energy storage system. Indicates the first Initial SOC of an electrochemical energy storage system node. This indicates the total number of hours. This indicates rounding down to the nearest integer. This is the starting time of each day. Indicates the first The first scenario Each electrochemical energy storage system node at the beginning of each day SOC.

[0182] Example 10:

[0183] The main technical contents of the new power system long-term power balance analysis method based on panoramic time series simulation are described in any one of Examples 1 to 9. Furthermore, the hourly granular time series simulation model is solved by a commercial solver.

[0184] The commercial solvers include Gurobi and Cplex.

[0185] Example 11:

[0186] See Figures 1 to 7 A novel long-term power balance analysis method for power systems based on panoramic time-series simulation includes the following steps:

[0187] 1) A panoramic simulation approach for power balance analysis, consisting of "full-cycle time-series simulation - adaptive time-period decoupling - parallel time-series flexible adjustment," is proposed. (See [link to relevant documentation]). Figure 1 This study summarizes the multi-timescale response characteristics of different flexible devices, establishes a phased time-series simulation framework, and reduces the dimensionality of the medium- to long-term adjustment of resource time-series allocation processes, thereby ensuring both full-cycle resource optimization deployment and high-precision time-series adjustment. It captures the full-cycle supply-demand imbalance characteristics and, based on an adaptive sub-period division method and time-series decoupling strategy, achieves decoupled parallel adjustment of sub-periods, effectively improving the efficiency of power balance analysis.

[0188] 2) In the first stage, a full-cycle operation simulation model is proposed. Based on the established operating boundaries and wind-solar-hydro load scenarios, and considering the seasonal mismatch characteristics of the new power system, daily granular time-series operation simulations are carried out to verify the daily power shortage, peak-shaving shortage, and unbalanced power, and to formulate an annual daily granular unit combination plan. Storage capacity scheduling plan and seasonal energy storage dispatch plan The daily granular power balance analysis results were obtained, namely the daily power deficit. Daily peak shaving gap Daily imbalance electricity (Record that the power shortage is positive and the power wasted is negative).

[0189] 3) In the second stage, an adaptive time-period decoupling method is proposed, based on the results obtained in the first stage. , and As a feature, an improved PELT algorithm is proposed, which checks the time period length constraint and introduces a pruning strategy, adaptively dividing the first-stage optimization period D into... Each sub-time period is denoted as ,based on , and The unit power at the initial moment of the sub-period is inferred to achieve time decoupling between sub-periods.

[0190] 4) In the third stage, a parallel timing flexible adjustment model is proposed, which divides the hourly granularity optimization cycle according to the SP obtained in stage two. Each sub-period, with each period derived from Phase One. , and Taking into account the short-term equipment flexibility adjustment capability, hourly parallel time-series production simulations are carried out for each sub-period, and flexible equipment operation plans for each sub-period are formulated to achieve rapid tracking of source-load fluctuations and accurately quantify the medium- and long-term power imbalance risks.

[0191] The main steps of the first phase of full-cycle operation simulation are as follows:

[0192] 1) To address the seasonal mismatch characteristics of the new power system, it is necessary to conduct full-cycle operation simulations and formulate medium- and long-term regulation resource allocation plans, such as annual unit combination plans, reservoir capacity scheduling plans, and seasonal energy storage scheduling plans. Daily granular time-series simulations can balance the computational efficiency and analytical accuracy of full-cycle simulations, but they cannot accurately describe the operational status at every moment within a day. Therefore, verification is performed on the daily power shortage risk moments, daily peak-shaving shortage risk moments, and daily power balance. The objective function is as follows:

[0193] (1)

[0194] (2)

[0195] (3)

[0196] (4)

[0197] (5)

[0198] (6)

[0199] in =24; This refers to the operating cost of thermal power plants in the first phase. This is a penalty for a phase of power outage; This is a penalty for wind curtailment in the first phase. This is the first stage of the Light Abandonment Penalty; This is a phase one of water abandonment penalties; This refers to the number of thermal power units. This is a function of the cost of thermal power. The power output of thermal power unit i at the time of power shortage risk on day d; The power output of thermal power unit i at the risk moment of peak-shaving deficit on day d; The number of utilization hours of thermal power unit i on day d; The start-up cost of thermal power unit i; Cost of shutting down thermal power units; This is the start-up indicator for thermal power unit i on day d; This is the start-up indicator for thermal power unit i on day d; To optimize the number of scenes; The probability of scene SCE; The cost of punishing shortcomings; Represents a set of nodes; The power shortage power at the time of the power shortage risk of node i on day d in scenario sce; The power shortage of node i in scenario sce on day d; The cost of curtailing wind power; For scenario sce wind farm i, predict the maximum power on day d; For the peak-shaving gap risk moment of the wind farm on day d in scenario sce wind farm; For scenario sce wind farm i, the number of utilization hours on day d; Predicted power generation of wind farm i on day d in scenario sce; The cost of penalties for abandoning light; The predicted maximum power of the photovoltaic power station i in scenario sce on day d; For the peak-shaving gap risk moment of the photovoltaic power station i on the dth day in scenario sce; For the sce photovoltaic power station i, the number of utilization hours on the dth day; Predicted power generation of the SCE photovoltaic power station on day d; The cost of penalties for abandoning water; The water discharge volume of the sce hydropower station on day d is the amount of water discharged.

[0200] 2) Establish DC power flow constraints at the time of daily power shortage risk:

[0201] (7)

[0202] (8)

[0203] in For the line admittance ij; For the scenario sce node i, the angle of attack at the moment of power shortage risk on day d; Inject power into thermal power, hydropower and wind power at the time of power shortage risk on day d of scenario sce node i, and take the minimum predicted power of wind power on day d of scenario sce; The maximum load power of node i in scenario sce on day d; Let ij be the line capacity. Equation (7) represents the node power balance constraint at the time of daily power shortage risk; Equation (8) represents the line capacity constraint.

[0204] 3) Establish power balance constraints at daily peak-shaving gap risk moments:

[0205] (9)

[0206] in The number of utilization hours of hydropower unit i on day d; The maximum power generation flow of hydropower unit i; The charging amount for seasonal energy storage on day d; This represents the discharge volume of seasonal energy storage on day d. The load power of scenario sce on day d.

[0207] 4) Establish daily electricity balance constraints:

[0208] (10)

[0209] in The number of utilization hours of hydropower unit i on day d; The maximum power generation flow of hydropower unit i; The charging amount for seasonal energy storage on day d; This represents the discharge volume of seasonal energy storage on day d. The load power of scenario sce on day d.

[0210] 5) Establish constraints for thermal power plant operation:

[0211] (11)

[0212] (12)

[0213] (13)

[0214] (14)

[0215] (15)

[0216] (16)

[0217] in The minimum start-up time for thermal power unit i; Let i be the minimum shutdown time of thermal power unit i. Equations (12) and (13) represent thermal power capacity constraints; Equations (14) and (15) represent thermal power start-up and shutdown state constraints; (15) represents the minimum start-up time constraint of thermal power; Equation (16) represents the minimum shutdown time constraint of thermal power.

[0218] 6) Establish constraints for hydropower operation:

[0219] (17)

[0220] (18)

[0221] (19)

[0222] (20)

[0223] (twenty one)

[0224] in The reservoir capacity of the hydropower station on day d; The water inflow to hydropower station i on day d; Let i be the minimum reservoir capacity of the hydropower station. This represents the maximum reservoir capacity of hydropower station i. Let i be the initial reservoir capacity of hydropower station i. Equations (17) and (18) represent the hydropower generation flow constraints; Equation (19) represents the hydropower reservoir capacity balance constraints; Equation (20) represents the upper and lower limits of reservoir capacity constraints; Equation (21) represents the initial state constraints of reservoir capacity.

[0225] 7) Establish wind and solar capacity constraints:

[0226] (twenty two)

[0227] (twenty three)

[0228] 8) Establish seasonal energy storage operation constraints:

[0229] (twenty four)

[0230] (25)

[0231] (26)

[0232] (27)

[0233] (28)

[0234] (29)

[0235] in For seasonal energy storage i, the discharge state variable on day d; For seasonal energy storage i, the charging state variable on day d; The maximum discharge power of seasonal energy storage i; Maximum charging power for seasonal energy storage; For seasonal energy storage i discharge efficiency; Improving charging efficiency for seasonal energy storage; This represents the lower limit of seasonal energy storage iSOC. This is the seasonal upper limit for iSOC (In-Storage Operating Value). Let i be the initial SOC of seasonal energy storage. Equation (24) represents the discharge power constraint of seasonal energy storage; Equation (25) represents the charging power constraint of seasonal energy storage; Equation (26) represents the charging and discharging state constraint of seasonal energy storage; Equation (27) represents the SOC balance constraint of seasonal energy storage; Equation (28) represents the upper and lower limits constraint of seasonal energy storage SOC; Equation (29) represents the initial state constraint of seasonal energy storage.

[0236] 9) The full-cycle operation simulation model can be solved using commercial solvers such as Gurobi and Cplex. By solving the daily granular time-series simulation model, the... , and ,in Analysis yielded , and ,in

[0237] (30)

[0238] (31)

[0239] (32)

[0240] The main steps of the second-stage adaptive time-period decoupling are as follows:

[0241] 1) Adaptive time-period decoupling: By analyzing the power supply and demand imbalance characteristics obtained from the full-cycle operation simulation, the complete analysis cycle is divided into several sub-periods, and the temporal decoupling between the sub-periods is realized, which reduces the problem scale, lays the foundation for subsequent parallel analysis, and effectively improves the solution efficiency.

[0242] 2) Taking into account , and Three features form the feature vector on day d. Based on the feature vectors within each sub-period With the objective of minimizing the sum of distances to the center, the following adaptive sub-time period division optimization model is established:

[0243] (33)

[0244] (34)

[0245] (35)

[0246] In the formula, m is the number of segment points; β is the penalty parameter used to prevent overfitting and control the number of segment points; C(·) is the segmentation cost function; The minimum length of the sub-period; The maximum length of the sub-period is given by equation (34). Equation (35) represents the orderliness of the segmentation points.

[0247] 3) A constrained PELT algorithm considering the minimum and maximum lengths of sub-time periods is proposed. While ensuring global optimality, the computational complexity is made close to linear, and it is used for adaptive time period division. The algorithm flow is as follows: Figure 2 As shown. The proposed constrained PELT algorithm checks whether the sub-period length constraint is satisfied before calculating the cost function. If the sub-period length constraint is violated, the cost calculation is skipped. The sub-period length constraint is satisfied without increasing the computational complexity. The steps are as follows: [1] Initialize F[0], R[0] and candidate segmentation point set C; [2] For time t, initialize , and [3] For each candidate point τ in C, check whether the sub-period length constraint is satisfied. If it is satisfied, calculate the cost. If not satisfied, proceed to step [5]; [4] Determine if cost is less than If so, then update. , and Conversely, proceed to step [5]; [5] Update , , trim C, add t to C; [6] If t=D, output the set of segment points C, otherwise return [2].

[0248] 4) To achieve each sub-time period Timing decoupling requires determining the time period of the thermal power unit. Power at the initial moment To meet the unit's ramp-up constraints. Sub-period The simulation results for the entire lifecycle at the final time step are inferred and determined based on the following process:

[0249] [1] If > Then it can be considered that in the first There is a general power shortage throughout the day, during which thermal power units maintain a high output level for most of the time. It can be inferred that thermal power units operate at high output levels during the sub-period. Power at initial time = ,in This indicates an optimized hour-to-day mapping within a given time period; It is a positive number, representing the set threshold. This represents the maximum power of thermal power unit i. Conversely, if... <- Then it can be considered that in the first There is generally a peak-shaving gap during the day, during which thermal power units maintain a low output level most of the time, which can be inferred = ,in Let i be the minimum power of thermal power unit i. If - ≤ ≤ Then proceed to step [2].

[0250] [2] If > and < Then it can be considered that in the first There is a general power shortage during the day, and thermal power units maintain a high output level for most of the time, which can be inferred. = ,in It is a positive number, representing the set threshold. Conversely, > and < Then it can be considered that in the first There is a general peak-shaving gap during the day, which can be inferred. = For the remaining cases, proceed to step 3).

[0251] [3] At this time, the thermal power unit did not show obvious high or low output characteristics, so it can be taken as .

[0252] The main steps of the third-stage parallel timing adjustment are as follows:

[0253] 1) Parallel time-series flexible adjustment stages consider the time-series operation characteristics of short-term adjustment resources, accurately simulating the power system's time-series balance process and quantifying the risk of supply-demand imbalance. The results from stage one... , and Using the boundaries and combining the temporal decoupling results, each sub-time period is defined. An hourly-granular time-series simulation model is established, with the objective function as follows:

[0254] (36)

[0255] (37)

[0256] (38)

[0257] (39)

[0258] (40)

[0259] (41)

[0260] in Sub-period Thermal power plant operating costs; Sub-period Penalty for power outage; Sub-period Punishment for abandoning wind; For time period Punishment for abandoning light; For time period Punishment for abandoning water; Let i be the power of the thermal power unit at time t; Let be the power shortage at node i at time t; Predict the power output of the wind farm at time t in scenario sce; Let sce be the power of the wind farm at time t in scenario i; Predict the power output of the photovoltaic power station at time t in scenario sce; Let sce be the power of the photovoltaic power station at time t in scenario i; Let be the discharge flow rate of the hydropower station at time t in scenario sce.

[0261] 2) Establish DC power flow constraints:

[0262] (42)

[0263] (43)

[0264] in For scene sce node i at time t, the angle of attack is... Power is injected into thermal power, hydropower, wind power, photovoltaic power, and energy storage at time t, when the power shortage risk occurs at node i in scenario sce; Let t be the load power at node i in scenario sce at time t.

[0265] 3) Establish operating constraints for thermal power units:

[0266] (44)

[0267] (45)

[0268] in The ramp-up limit for thermal power unit i; Let i be the downhill ramp limit for thermal power unit i. Equation (45) represents the ramp constraint for thermal power.

[0269] 4) Establish constraints for hydropower operation:

[0270] (46)

[0271] (47)

[0272] (48)

[0273] (49)

[0274] in Let i be the power generation flow rate of the reservoir at time t; Let be the inflow rate of the reservoir at time t (i); Δt represents the unit time period (1 hour). Equation (49) represents the sub-period of stage two. The power generation from internal hydropower is consistent with the optimization results of Phase 1.

[0275] 5) Establish wind and solar capacity constraints:

[0276] (50)

[0277] (51)

[0278] 6) Establish seasonal energy storage constraints:

[0279] (52)

[0280] (53)

[0281] (54)

[0282] (55)

[0283] (56)

[0284] (57)

[0285] in For the seasonal energy storage scenario sce, the discharge power at time t is; The charging power at time t for seasonal energy storage in scenario sce; This indicates the SOC state at time t for seasonal energy storage in scenario sce; For scenario sce seasonal energy storage at time t, the discharge state variable is... Let i be the charging state variable for seasonal energy storage in scenario sce at time t. Equation (57) represents the second sub-period of stage. The internal seasonal energy storage scheduling plan is consistent with the optimization results of Phase 1.

[0286] 7) Establish operational constraints for electrochemical energy storage:

[0287] (58)

[0288] (59)

[0289] (60)

[0290] (61)

[0291] (62)

[0292] (63)

[0293] in The discharge power at time t for the electrochemical energy storage of scenario sce; The charging power at time t for the electrochemical energy storage of scenario sce; This represents the SOC state at time t in scenario sce electrochemical energy storage; Let i be the discharge state variable at time t in the scenario sce electrochemical energy storage. For scenario sce electrochemical energy storage at time t, the charging state variable is defined. For electrochemical energy storage, i discharge efficiency; The charging efficiency of electrochemical energy storage; This represents the lower limit of iSOC for electrochemical energy storage. This represents the upper limit of the electrochemical energy storage iSOC. Let i be the initial SOC of the electrochemical energy storage. Equation (63) represents the initial SOC constraint of the electrochemical energy storage.

[0294] 8) Parallel timing flexible adjustment models can be solved using commercial solvers such as Gurobi and Cplex.

[0295] Example 12:

[0296] A novel long-term power balance analysis method based on panoramic time-series simulation is proposed for power systems. The main technical content is described in Example 11. Furthermore, this example verifies the effectiveness and timeliness of the proposed method using an improved IEEE-14 node system. System power parameters and source-load data are referenced from a province in western China. The following four examples illustrate the accuracy and efficiency of the proposed panoramic time-series simulation-adaptive time period division-time period decoupling and flexible adjustment approach for power balance analysis:

[0297] Case 1: The power balance analysis method proposed in this invention, which is "full-cycle operation simulation - adaptive time period division - time period decoupling and flexible adjustment";

[0298] Case 2: Adaptive Compact Panorama Time Series (CPTS) method;

[0299] Case 3: Fast Finite Stochastic Dual Dynamic Integer Programming (FFSDDIP) method;

[0300] Case 4: Accelerated Analytical Target Cascading (A-ATC) method.

[0301] Table 1 Performance Comparison of Case 1-Case 4

[0302]

[0303] Table 1 shows the power shortage, power abandonment, and solution time for Cases 1-4. Case 4, due to the quadratic term in the convergence penalty term of its subproblem and the need for repeated iterations between the subproblem and the coordinator to ensure the temporal coupling constraint holds, incurs significant computational cost. Case 3, requiring repeated forward and backward processes to achieve convergence, also incurs high computational cost. The method proposed in this invention, by using the annual daily granular unit combination plan, reservoir capacity scheduling plan, and seasonal energy storage scheduling plan formulated during the full-cycle operation simulation phase as time-period decoupling and flexible adjustment of phase boundaries, ensures the holding of the temporal coupling constraint, reduces the problem size, avoids the tedious iterative solution process, and greatly improves computational efficiency. The adaptive compact panoramic time series in Case 2 loses a significant amount of scene information, which greatly affects computational accuracy. The method proposed in this invention can fully utilize scene information. Case 3 only considers the limited forward phase, insufficiently taking into account the time lag of the new power system's regulation and neglecting the risk of power imbalance in future periods; Case 4 struggles to coordinate annual flexibility resources and does not adequately consider the seasonal mismatch of the new power system. The method proposed in this invention, however, can coordinate the allocation of medium- and long-term flexible regulation resources and perform refined simulations of these resources throughout the optimization cycle, effectively mitigating the risk of medium- and long-term supply-demand imbalance in the new power system and promoting reliable power supply and full utilization of renewable energy. The annual power gap curves, annual hydropower curtailment curves, annual wind power curtailment curves, and annual solar power curtailment curves obtained from Cases 1-4 are shown below. Figures 3-6 As shown in the figure, the power shortages in Cases 2-4 are concentrated at the end of the year, while Case 1 has a smaller power shortage at the end of the year, but a larger power shortage around the 6000h hour compared to Cases 2-4. Comparing the wind and solar curtailment curves, it can be found that Cases 2 and 3 have more curtailment at 6000-7000h, while Case 1 only has some wind curtailment at the end of the year. This indicates that Case 1 can effectively achieve the rational allocation of medium- and long-term regulation resources throughout the entire cycle. By storing sufficient wind and solar resources across seasons during the 6000h-7000h period, it can meet the large load demand at the end of the year and achieve the goal of reducing the annual supply-demand imbalance gap.

[0304] Example 13:

[0305] A novel long-term power balance analysis method based on panoramic time-series simulation is proposed for power systems. The main technical content is described in Example 11. Furthermore, the effectiveness and scalability of the proposed method are verified using an improved IEEE-118 node system. System power parameters and source-load data are referenced from a province in western China. The imbalance quantities, computational efficiency, and supply-demand imbalance curves verified using the IEEE-118 node system are shown in Tables 2 and 3, respectively. Figure 7 As shown.

[0306] Table 2 Imbalanced Charges and Calculation Time for the IEEE-118 Node System

[0307]

[0308] The power balance analysis method proposed in this invention, which combines "full-cycle operation simulation - adaptive time-period decoupling - parallel timing flexible adjustment," is scalable and applicable to general new power systems. As shown in Table 2, increasing the system scale significantly impacts the solution efficiency of the power balance analysis. Figure 7 The data shows a visible power shortage throughout the year, which is more pronounced in spring and winter. The number of periods of power curtailment also increases significantly, indicating a substantial risk of supply-demand imbalance.

Claims

1. A novel long-term power balance analysis method for power systems based on panoramic time-series simulation, characterized in that, Includes the following steps: 1) Construct a daily-granular time-series simulation model for conducting full-cycle operation simulation of the power system; 2) Solve the daily granular time series simulation model to obtain the medium- and long-term regulation resource allocation plan and the daily granular power balance analysis results; 3) Based on the daily granular power balance analysis results, the optimization cycle is adaptively divided into several sub-periods by an adaptive time period decoupling algorithm, and the power at the initial moment of each sub-period is predicted to achieve time-series decoupling between sub-periods; 4) For each sub-period, the medium- and long-term adjustment resource allocation plan is used as the boundary, and the hourly granular time series simulation model is constructed by combining the time series decoupling results; 5) Conduct hourly parallel time-series production simulations for each sub-period, solve hourly granular time-series simulation models in parallel, generate equipment operation plans for each sub-period, and quantitatively analyze the long-term power imbalance risk in the new power system.

2. The novel long-term power balance analysis method for power systems based on panoramic time-series simulation as described in claim 1, characterized in that, The objective function of the daily granular time-series simulation model is shown below: （1） （2） （3） （4） （5） （6） In the formula, This indicates the operating cost of a thermal power plant in its first phase. This indicates a penalty for a period of power outage; This indicates the penalty for wind curtailment in the first phase; This indicates the first phase of the penalty for abandoning light; This indicates a phase one of water abandonment penalties; Indicates the generator node index; Indicates the number of thermal power units; Indicates date index; This indicates the total number of days in the optimization period; Represents the cost function of thermal power; Indicates the first The node of the thermal power unit is at the Daily power shortage risk at any given moment; Indicates the first The node of the thermal power unit is at the Daily peak-shaving gap risk moment power; Indicates the first The node of the thermal power unit is at the Hours used per day; Indicates the first The maximum power of each thermal power unit node; Indicates the total number of hours; Indicates the first The start-up cost of a single thermal power unit node; Indicates the first The node of the thermal power unit is at the The power-on indicator for the day; Indicates the first Shutdown cost of a single thermal power unit node; Indicates the first The node of the thermal power unit is at the The device is off on the day; This indicates an optimization of the scene index; Indicates the number of optimized scenarios; Represents a set of nodes; This indicates the cost of penalties for shortcomings; Indicates the first The first scenario The node at the th The risk of power shortages is ever-present; Indicates the first The first scenario The node at the th Daily power shortage; Indicates the first The probability of each scenario; Indicates the total number of wind farms; This indicates the penalty cost for wind curtailment; Indicates the first The first scenario The wind farm node at the first The predicted maximum power for the day; Indicates the first The first scenario The wind farm node at the first Daily peak-shaving gap risk moment power; Indicates the first The first scenario The wind farm node at the first Hours used per day; Indicates the first The first scenario The wind farm node at the first Daily projected power generation; Indicates the total number of photovoltaic power plants; This indicates the cost of penalties for abandoning light; Indicates the first The first scenario The photovoltaic power station node at the first The predicted maximum power for the day; Indicates the first The first scenario The photovoltaic power station node at the first Daily peak-shaving gap risk moment power; Indicates the first The first scenario The photovoltaic power station node at the first Hours used per day; Indicates the first The first scenario The photovoltaic power station node at the first Daily projected power generation; Indicates the total number of hydroelectric power stations; Indicates the first The first scenario The hydropower station node at the first Daily water discharge.

3. The novel long-term power balance analysis method for power systems based on panoramic time-series simulation as described in claim 1, characterized in that, The constraints of the daily granular time series simulation model include DC power flow constraints at daily power shortage risk moments, power balance constraints at daily peak-shaving shortage risk moments, daily power balance constraints, thermal power operation constraints, hydropower operation constraints, wind and solar capacity constraints, and seasonal energy storage operation constraints.

4. The novel long-term power balance analysis method for power systems based on panoramic time-series simulation as described in claim 3, characterized in that, The DC power flow constraints at the time of daily power shortage risk are as follows: （7） （8） In the formula, , Both represent power generation node indices; Indicates date index; Represents a set of nodes; Represents a node To the node The line admittance; , They represent the first The first scenario , The node at the th The risk of a daily power shortage is ever-present. Indicates the first The first scenario The node at the th At a critical moment when the daily power shortage risk exists, thermal power, hydropower and wind power are injected into the power supply. Indicates the first The first scenario The node at the th Maximum daily load power; Indicates the first The first scenario The node at the th Daily power shortage risk; power outages at any time. Represents a node To the node Line capacity; The power balance constraints at the daily peak-shaving gap risk moment are as follows: （9） In the formula, Indicates the number of thermal power units; Indicates the total number of hydroelectric power stations; Indicates the first The power generation efficiency of each hydropower station node; Indicates the total number of wind farms; Indicates the total number of photovoltaic power plants; Indicates the first The node of the thermal power unit is at the Daily peak-shaving gap risk moment power; Indicates the first The first scenario The wind farm node at the first Daily peak-shaving gap risk moment power; Indicates the first The first scenario The photovoltaic power station node at the first Daily peak-shaving gap risk moment power; Indicates the first The hydropower station node at the first Power generation flow during periods of daily peak-shaving deficit risk; Indicates the first Each scene The node at the th Load during times of daily peak-shaving gap risk; The daily electricity consumption balance constraints are as follows: （10） In the formula, Indicates the first The node of the thermal power unit is at the Hours used per day; Indicates the first The maximum power of each thermal power unit node; Indicates the first The hydropower station node at the first Hours used per day; Indicates the first The maximum power generation flow of each hydropower station node; Indicates the first The first scenario The wind farm node at the first Hours used per day; Indicates the first The first scenario The wind farm node at the first Daily power generation; Indicates the first The first scenario The photovoltaic power station node at the first Hours used per day; Indicates the first The first scenario The photovoltaic power station node at the first Daily power generation; This indicates the total number of seasonal energy storage systems; , They represent the first The seasonal energy storage system node at the first Daily charging and discharging power; Indicates the first The scene in Daily power shortage; Indicates the first The scene in Daily load power; The operating constraints for thermal power plants are as follows: （11） （12） （13） （14） （15） （16） In the formula, Indicates the first The node of the thermal power unit is at the Daily power shortage risk at any given moment; , They represent the first Minimum and maximum power values ​​at the moment of power shortage risk for each thermal power unit node; Indicates the first The node of the thermal power unit is at the The power-on indicator for the day; Indicates the first The node of the thermal power unit is at the The device is off on the day; Indicates date index; , They represent the first Minimum start-up time and minimum shutdown time for each thermal power unit node; , They represent the first The node of the thermal power unit is at the , Daily power on / off status; Indicates the first The node of the thermal power unit is at the Daily power on / off status; The hydropower operation constraints are as follows: （17） （18） （19） （20） （21） In the formula, , They represent the first The first scenario The hydropower station node at the first , Daily storage capacity; Indicates the first The first scenario The hydropower station node at the first Daily water volume; Indicates the first The first scenario The hydropower station node at the first Daily water wastage; , They represent the first The minimum and maximum reservoir capacity of each hydropower station node; This indicates the total number of days in the optimization period; , They represent the first The first scenario The reservoir capacity of each hydropower station node on days 0 and D; Indicates the first Initial reservoir capacity of each hydropower station node; Indicates the first The hydropower station node at the first Power generation flow at times of daily power shortage risk; Indicates the first The hydropower station node at the first Power generation flow during periods of daily peak-shaving deficit risk; The wind and solar capacity constraints are as follows: （22） （23） In the formula, Indicates the first The first scenario The wind farm node at the first The predicted maximum power for the day; Indicates the first The first scenario The photovoltaic power station node at the first The predicted maximum power for the day; The seasonal energy storage operation constraints are as follows: （24） （25） （26） （27） （28） （29） In the formula, Indicates the first Maximum discharge power of each seasonal energy storage system node; Indicates the total number of hours; Indicates the first The seasonal energy storage system node at the first Daily discharge state variables; Indicates the first Maximum charging power of each seasonal energy storage system node; Indicates the first The seasonal energy storage system node at the first Daily charging status variables; , They represent the first The seasonal energy storage system node at the first , SOC of the day; Indicates the first Discharge efficiency of seasonal energy storage system nodes; Indicates the first Charging efficiency of seasonal energy storage system nodes; , They represent the first Lower and upper limits of SOC for each seasonal energy storage system node; , They represent the first The SOC of a seasonal energy storage system node on days 0 and D; Indicates the first Initial SOC of a seasonal energy storage system node.

5. The novel long-term power balance analysis method for power systems based on panoramic time-series simulation as described in claim 1, characterized in that, The daily granularity time series simulation model is solved using a solver; The solvers include Gurobi and Cplex; The medium- and long-term regulation resource allocation plan includes an annual daily granular unit combination plan, a reservoir capacity scheduling plan, and a seasonal energy storage scheduling plan. The daily granular power balance analysis results include the daily power deficit, daily peak-shaving deficit, and daily unbalanced power, as shown below: （30） （31） （32） In the formula, This indicates the daily power shortage on day d; This indicates an optimization of the scene index; Indicates the number of optimized scenarios; Indicates the first The probability of each scenario; Indicates the generator node index; Represents a set of nodes; Indicates the first The first scenario The node at the th The risk of power shortages is ever-present; This represents the daily unbalanced electricity volume on day d; Indicates the first The first scenario The node at the th Daily power shortage; Indicates the total number of wind farms; Indicates the first The first scenario The wind farm node at the first The predicted maximum power for the day; Indicates the first The first scenario The wind farm node at the first Daily peak-shaving gap risk moment power; Indicates the total number of photovoltaic power plants; Indicates the first The first scenario The photovoltaic power station node at the first The predicted maximum power for the day; Indicates the first The first scenario The photovoltaic power station node at the first Daily peak-shaving gap risk moment power; Indicates the total number of hydroelectric power stations; Indicates the first The first scenario The hydropower station node at the first Daily water wastage; Indicates the first The power generation efficiency of each hydropower station node; This indicates the daily peak-shaving gap on day d.

6. The novel long-term power balance analysis method for power systems based on panoramic time-series simulation as described in claim 1, characterized in that, The steps for achieving temporal decoupling between sub-time periods using the adaptive time-period decoupling algorithm are as follows: 3.1) By analyzing the daily granular power balance analysis results, an adaptive sub-period division optimization model is constructed, as shown below: （33） （34） （35） In the formula, This represents the segmentation point index; m is the number of segmentation points; For penalty parameters, Represents the piecewise cost function; , They represent the first , The day is divided into segments based on each segmentation point; , They represent the first , The eigenvectors of the day; Indicates date index; Indicates the first The eigenvectors of the day; Represents the norm; This indicates the total number of days in the optimization period; , These represent the minimum and maximum lengths of the sub-time period, respectively. 3.2) Based on the constrained PELT algorithm considering the minimum and maximum lengths of sub-periods, solve the adaptive sub-period partitioning optimization model to obtain the partitioned sub-periods; 3.3) Predict the power at the initial moment of each sub-time period after the division, and realize the temporal decoupling between the sub-time periods, as shown below: 3.3.1) Determine the daily imbalance in electricity consumption. Is it located in the interval? If yes, proceed to step 3.3.2; otherwise, determine the daily unbalanced electricity consumption. Is it greater than If yes, proceed to step 3.3.4; otherwise, proceed to step 3.3.

5. Here, k represents the sub-time period index. This represents the (k-1)th sub-time period; This represents the mapping from a sub-period to a day; Indicates the first Daily imbalance electricity; The preset threshold; 3.3.2) Determine the daily power shortage Is it located in the interval? If yes, proceed to step 3.3.3); otherwise, determine the daily power shortage. Is it greater than If yes, proceed to step 3.3.4; if no, proceed to step 3.3.

5. Indicates the first Japan's daily electricity shortage, The preset threshold; 3.3.3) Let the k-th sub-time period be... Power at the initial moment ,in, , They represent the first Minimum and maximum power values ​​at the moment of power shortage risk for each thermal power unit node; 3.3.4) Let the k-th sub-time period be... Power at the initial moment ; 3.3.5) Let the k-th sub-time period be... Power at the initial moment .

7. The novel long-term power balance analysis method for power systems based on panoramic time-series simulation as described in claim 6, characterized in that, The steps for solving the adaptive sub-time period division optimization model are as follows: 3.2.1) Initialize the candidate segmentation point set C, the cost function F[0], and the segmentation point set R[0] after partitioning, at initialization time t=1; 3.2.2) Initialize the cost function for time t. Candidate segmentation points and segmentation point set Initialize candidate segmentation points ; 3.2.3) Determine candidate segmentation points Has the maximum number of candidate segment points been reached? If yes, proceed to step 3.2.5; otherwise, determine the candidate segment points. to candidate segmentation point Is the sub-time period located within the sub-time period length range? If yes, proceed to step 3.2.4; otherwise, let... , and return to step 3.2.3); where, , These represent the minimum and maximum lengths of the sub-time period, respectively. 3.2.4) Calculate candidate segmentation points Cost And determine the cost Is it less than the cost function? If so, then update the cost function. Candidate segmentation points and segmentation point set and order If not, then return to step 3.2.3; otherwise, let (Return to step 3.2.3); where, Indicates the first Cost function of candidate segmentation points; For penalty parameters, Represents the piecewise cost function; 3.2.5) Update the cost function at time t and the segmented point set after partitioning And will include candidate segmentation points that exceed the sub-time period length interval. and cost Greater than the cost function Candidate segmentation points Remove the candidate segmentation point set C, and add time t to the candidate segmentation point set C; 3.2.6) Determine whether time t has reached the total number of days in the optimization cycle. If so, output the set of segmented points after partitioning. If not, then let t = t + 1 and return to step 3.2.2).

8. The novel long-term power balance analysis method for power systems based on panoramic time-series simulation as described in claim 1, characterized in that, The objective function of the hourly granular time series simulation model is shown below: （36） （37） （38） （39） （40） （41） In the formula, k represents the sub-time period index; This represents the operating cost of thermal power plants in the k-th sub-period. This represents the penalty for power outage in the k-th sub-time period; This represents the wind curtailment penalty for the k-th sub-period; This represents the penalty for light loss in the k-th sub-time period; This represents the penalty for water wastage during the k-th sub-period. Indicates the generator node index; Indicates the number of thermal power units; t represents time; This represents the k-th sub-time period; Represents the cost function of thermal power; Indicates the first The power of each thermal power unit node at time t; This indicates an optimization of the scene index; Indicates the number of optimized scenarios; Indicates the first The probability of each scenario; Represents a set of nodes; This indicates the cost of penalties for shortcomings; Indicates the first The first scenario The power of each node at time t; Indicates the total number of wind farms; This indicates the penalty cost for wind curtailment; Indicates the first The first scenario The predicted power of each wind farm node at time t; Indicates the first The first scenario The power of each wind farm node at time t; Indicates the total number of photovoltaic power plants; This indicates the cost of penalties for abandoning light; Indicates the first The first scenario The predicted power of a photovoltaic power station node at time t; Indicates the first The first scenario The power of a photovoltaic power station node at time t; Indicates the total number of hydroelectric power stations; This indicates the cost of penalties for abandoning water; Indicates the first The first scenario The water discharge flow rate of each hydropower station node at time t.

9. The novel long-term power balance analysis method for power systems based on panoramic time-series simulation as described in claim 1, characterized in that, The constraints of the hourly granular time series simulation model include DC power flow constraints, thermal power unit operation constraints, hydropower operation constraints, wind and solar capacity constraints, seasonal energy storage constraints, and electrochemical energy storage operation constraints. The DC power flow constraints are as follows: （42） （43） In the formula, , Both represent the generator node index; t represents time; Represents a set of nodes; Represents a node To the node The line admittance; , They represent the first The first scenario , The angle of attack of a node at time t; Indicates the first The first scenario The power injection power of each node at time t; Indicates the first The first scenario The load power of each node at time t; Indicates the first The first scenario The power shortage of each node at time t; Represents a node To the node Line capacity; The operating constraints of the thermal power units are as follows: （44） （45） In the formula, , They represent the first Minimum and maximum power of each thermal power unit node; , They represent the first The power of each thermal power unit node at times t and t-1; Indicates the first Uphill ramp limit for each thermal power unit node; Indicates the first Downhill ramp limit for each thermal power unit node; This represents the mapping from a sub-period to a day; , They represent the first The node of the thermal power unit is at the The power-off and power-on indicators for each day; Indicates the first The node of the thermal power unit is at the Daily power on / off status; The hydropower operation constraints are as follows: （46） （47） （48） （49） In the formula, Indicates the first The power generation flow of each hydropower station node at time t; Indicates the first The maximum power generation flow of each hydropower station node; , They represent the first The first scenario The reservoir capacity of each hydropower station node at times t-1 and t; Indicates a unit of time; Indicates date index; Indicates the first The first scenario The hydropower station node at the first Daily water flow rate; Indicates the first The first scenario The hydropower station node at the first Daily water discharge rate; , They represent the first The minimum and maximum reservoir capacity of each hydropower station node; This represents the k-th sub-time period; This indicates the first phase determined by the [specific term / representation]. The power generation flow of each hydropower station node on day d; The wind and solar capacity constraints are as follows: （50） （51） In the formula, Indicates the first The first scenario The predicted power of each wind farm node at time t; Indicates the first The first scenario The power of each wind farm node at time t; Indicates the first The first scenario The predicted power of a photovoltaic power station node at time t; Indicates the first The first scenario The power of a photovoltaic power station node at time t; The seasonal energy storage constraints are as follows: （52） （53） （54） （55） （56） （57） In the formula, Indicates the first The first scenario The discharge power of a seasonal energy storage system node at time t; Indicates the first Maximum discharge power of each seasonal energy storage system node; Indicates the first The first scenario Discharge state variables of a seasonal energy storage system node at time t; Indicates the first The first scenario The charging power of a seasonal energy storage system node at time t; Indicates the first Maximum charging power of each seasonal energy storage system node; Indicates the first The first scenario The charging state variables of a seasonal energy storage system node at time t; , They represent the first The first scenario The SOC of a seasonal energy storage system node at times t-1 and t; Indicates the first Discharge efficiency of seasonal energy storage system nodes; Indicates the first Charging efficiency of seasonal energy storage system nodes; , They represent the first Lower and upper limits of SOC for each seasonal energy storage system node; Indicates the first The first scenario The SOC of a seasonal energy storage system node at the end of the k-th sub-period Tk; Indicates the first The seasonal energy storage system node at the first SOC of the day; The operational constraints of the electrochemical energy storage are as follows: （58） （59） （60） （61） （62） （63） In the formula, Indicates the first The first scenario The discharge power of a node in an electrochemical energy storage system at time t; Indicates the first The maximum discharge power of each electrochemical energy storage system node; Indicates the first The first scenario Discharge state variables of a node in an electrochemical energy storage system at time t; Indicates the first The first scenario The charging power of an electrochemical energy storage system node at time t; Indicates the first The maximum charging power of each electrochemical energy storage system node; Indicates the first The first scenario The charging state variables of a node in an electrochemical energy storage system at time t; , They represent the first The first scenario The SOC of each node in an electrochemical energy storage system at times t-1 and t; Indicates the first Discharge efficiency of individual electrochemical energy storage system nodes; Indicates the first The charging efficiency of each electrochemical energy storage system node; , They represent the first Lower and upper limits of SOC for each node in an electrochemical energy storage system; Indicates the first Initial SOC of each electrochemical energy storage system node; Indicates the total number of hours; Indicates rounding down; This is the initial time of each day; Indicates the first The first scenario Each electrochemical energy storage system node at the beginning of each day SOC.

10. The novel long-term power balance analysis method for power systems based on panoramic time-series simulation as described in claim 1, characterized in that, The hourly granular time series simulation model was solved using a commercial solver; The commercial solvers include Gurobi and Cplex.

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