A new energy power system dispatching method considering quasi-linear demand response

Abstract

The application provides a new energy power system scheduling method considering quasi-linear demand response, comprising the following steps: based on historical predicted output and historical actual output of new energy, a prediction box is divided, and an uncertainty set is obtained; based on the uncertainty set, an uncertainty scenario is constructed, and a severe scenario is generated; a system load quasi-linear model under the severe scenario is constructed, and the system load quasi-linear model is solved to obtain a system load quasi-line; based on historical node load data, a prediction response interval of each node is obtained; a node load quasi-linear model is constructed, and the node load quasi-linear model is solved to obtain an optimal load response and a node load quasi-line; and the power supply capacity of the system and the new energy consumption rate are improved.

Description

Technical Field

[0001] This invention relates to the field of power system demand response technology, and more specifically, to a new energy power system dispatching method that takes into account quasi-linear demand response. Background Technology

[0002] Currently, most studies on large-scale quasi-line demand response in power systems only consider increasing the system's renewable energy absorption rate as the objective. They calculate the net load curve through simulated unit combinations and then use the inverted net load curve as the quasi-line that users should achieve in demand response. However, the uncertainty of renewable energy output is not considered in the quasi-line formulation process, which is detrimental to scenarios with large renewable energy fluctuations. Therefore, modeling the uncertainty of renewable energy output and establishing a robust quasi-line model that considers this uncertainty can improve the ability to cope with the randomness of renewable energy. However, current quasi-line demand response is not coupled with dispatching decisions. Because the system's demand for quasi-line demand response is not calculated, over-response can easily occur, failing to consider the coordination between quasi-line demand response and unit combination. Since the system's adjustability varies at different times, the degree of user participation in demand response also varies. However, current quasi-line demand response compensation mechanisms are all-time oriented and lack research on high-proportion renewable energy dispatching methods that consider the coordination of nodal load quasi-line and unit combination and take into account time-sharing compensation mechanisms.

[0003] To overcome the shortcomings of existing technologies, this invention provides a new energy power system dispatching method that considers quasi-linear demand response, thereby improving the system's power supply capacity and new energy absorption rate. Summary of the Invention

[0004] The purpose of this invention is to provide a new energy power system dispatching method considering quasi-linear demand response, comprising: dividing the forecast bins based on the historical predicted output and historical actual output of new energy sources, and obtaining an uncertainty set; constructing uncertainty scenarios based on the uncertainty set, and generating severe scenarios; constructing a system load quasi-linear model under the severe scenarios, and solving the system load quasi-linear model to obtain the system load quasi-linearity; obtaining the predicted response interval of each node based on historical node load data; constructing a node load quasi-linear model, and solving the node load quasi-linear model to obtain the optimal load response and node load quasi-linearity.

[0005] Furthermore, it also includes: evaluating the user's response effectiveness based on the user's participation load in demand response, and obtaining evaluation results; and determining the total compensation amount for the user based on the evaluation results using a time-sharing compensation method.

[0006] Furthermore, the expression for determining the total compensation amount for the user is as follows:

[0007] ;

[0008] ;

[0009] in, This represents the response similarity of the k-th node at time t. The curve profile representing the load response of node k; The nodal directrix representing node k; This represents the time-sharing compensation for the k-th node; The time variable is represented; T represents the total number of time periods in the scheduling cycle. This represents the compensation coefficient for time period t.

[0010] Furthermore, obtaining the uncertainty set includes: normalizing the historical predicted output and the historical actual output to obtain standard historical predicted output and standard historical actual output, respectively; dividing the prediction bins based on the standard historical actual output to obtain multiple prediction interval data bins, and determining the historical error data probability in each data bin; constructing a probability histogram based on the historical error data probability, and fitting the probability histogram with a general distribution model to obtain the probability distribution model of the system error in each data bin; the general distribution model includes a probability density function, a cumulative distribution function, and an inverse function of the cumulative distribution function; the probability distribution model of the system error includes a system probability density function, a system cumulative distribution function, and an inverse function of the system cumulative distribution function.

[0011] Furthermore, the expression for the probability of the historical error data is:

[0012] ;

[0013] in, This represents the probability of historical error data at time t; This represents the actual historical output at time t; This represents the historical predicted output at time t;

[0014] The expression for the system probability density function is:

[0015] ;

[0016] The expression for the cumulative distribution function of the system is:

[0017] ;

[0018] The expression for the inverse function of the system's cumulative distribution function is:

[0019] ;

[0020] in, , and These represent the first shape parameter, the second shape parameter, and the third shape parameter of the general distribution model, respectively. Represents the system probability density function; Represents the system's cumulative distribution function; It represents the inverse function of the system's cumulative distribution function; Indicates the independent variable; This represents the dependent variable.

[0021] Furthermore, the expression for generating the harsh scenario is:

[0022] ;

[0023] in, This represents the first extreme wind power output scenario, i.e., the scenario with the maximum wind power output. Indicates the maximum wind power output; The second extreme wind power output scenario is the scenario with the minimum wind power output. Indicates the minimum wind power output; This represents the first extreme wind power generation slope scenario, i.e., the maximum positive wind power generation slope; and These are the positive and negative slope coefficients, respectively, when t is an odd number. , When t is even, , ; This represents the second extreme wind power generation slope scenario, namely, the maximum negative wind power generation slope.

[0024] Furthermore, the system load guideline model includes a system load objective function and system load constraints; the expression for the system load objective function is:

[0025] ;

[0026] ;

[0027] ;

[0028] ;

[0029] in, Indicates the scene number; Indicates the number of scenes; Indicates the worst-case scenario; This indicates taking the minimum value; This indicates the operating cost of thermal power units; This indicates the cost of curtailing wind and solar power. Indicates the cost of load shedding; , and Let represent the first cost coefficient, the second cost system, and the third cost coefficient of the i-th thermal power unit, respectively; This represents the output of the i-th thermal power unit; The time variable is represented; T represents the total number of time periods in the scheduling cycle. This indicates the total number of thermal power units in the system; This represents the penalty coefficient for curtailment of renewable energy. and These represent the maximum output values ​​of wind power and photovoltaic power at time t, respectively; the system load constraints include thermal power unit operation constraints, ramp-up constraints, unit start-up and shutdown time constraints, and node power balance constraints.

[0030] The expression for the operating constraints of the thermal power unit is:

[0031] ;

[0032] in, and Let represent the minimum and maximum active power output of the i-th generator unit, respectively; This represents the active power output of the i-th generating unit during time period t; The time variable is represented; T represents the total number of time periods in the scheduling cycle. This indicates the total number of thermal power units in the system;

[0033] The expression for the climbing constraint is:

[0034] ;

[0035] in, This represents the downhill ramp rate of unit i; This represents the active power output of the i-th generating unit during time period t; This represents the active power output of the i-th generating unit during the time period t-1; This represents the ramp rate of unit i;

[0036] The expression for the unit start-up and shutdown time constraint is:

[0037] ;

[0038] ;

[0039] in, Indicates the minimum continuous operating time of the unit; Indicates the start / stop time of unit i; This indicates the start-up and shutdown status of unit i during time period t; and These represent the start-up and shutdown status of unit i during time periods t and t-1, respectively. Indicates the minimum continuous downtime of the generating unit;

[0040] The expression for the node power balance constraint is:

[0041] ;

[0042] Where i represents the unit variable; Represents the output of the i-th thermal power unit; j represents the variable of the new energy unit; Indicates the number of new energy generating units; Indicates the output of the new energy generating units; Represents node variables; Indicates the number of load nodes; This represents the day-ahead forecast load of the k-th node; The node load guideline for node k; This represents the value of the rigid load at the k-th node during time period t;

[0043] Furthermore, obtaining the predicted response interval for each node includes: filtering similar time periods for the predicted period of each node from the historical node load data; determining the load elasticity coefficient of each node in the predicted period based on the historical load of each time period in the similar time periods; dividing the nodes based on the load elasticity coefficient to obtain multiple levels of node sets, and setting different demand response potential coefficients for each level of node set; and obtaining the predicted response interval for each node based on the predicted load of each node and the demand response potential coefficient.

[0044] Furthermore, the expression for the load elasticity coefficient is as follows:

[0045] ;

[0046] in, This represents the load elasticity coefficient for time period t; Indicates the number of load nodes participating in demand response; Represents node variables; This represents the normalized value of the system load in the t-th time period on day i; This represents the normalized average of the load during the t-th time period within a specified number of similar days;

[0047] The expression for the predicted response interval is:

[0048] ;

[0049] ;

[0050] ;

[0051] ;

[0052] in, and These represent the upper and lower limits of the adjustable load response of the k-th node during time period t, respectively. Indicates the demand response potential coefficient; This represents the output of the k-th adjustable load during time period t; This represents the total load value of the adjustable load of the k-th node over T time periods; This represents the day-ahead forecast value of the k-th adjustable load during time period t.

[0053] Furthermore, the nodal load guideline model includes a nodal load objective function and nodal load constraints; the expression for the nodal load objective function is:

[0054] ;

[0055] ;

[0056] ;

[0057] ;

[0058] ;

[0059] ;

[0060] in, This indicates taking the minimum value; This indicates the operating cost of thermal power units; Represents the start-up and shutdown costs of the generating unit; This indicates the cost of curtailing wind and solar power. Indicates the cost of load shedding; The deviation penalty between the node load baseline and the global load baseline; , and Let represent the first cost coefficient, the second cost system, and the third cost coefficient of the i-th thermal power unit, respectively; This represents the output of the i-th thermal power unit; The time variable is represented; T represents the total number of time periods in the scheduling cycle. and Let represent the start-up and shutdown costs of the i-th generating unit, respectively; and These represent the states of the i-th generator unit at time t and time t-1, respectively. This indicates the total number of thermal power units in the system; This represents the penalty coefficient for curtailment of renewable energy. x and These represent the maximum output values ​​of wind power and photovoltaic power at time t, respectively. and Let represent the wind power and solar power output at time t, respectively; Indicates the loss-of-load penalty coefficient; This represents the maximum load at time t; This represents the load value at time t; This is the deviation penalty coefficient between the load baseline and the global load baseline; Indicates the number of load nodes participating in demand response;

[0061] The nodal load constraints include thermal power unit operation constraints, ramping constraints, unit start-up and shutdown time constraints, nodal power balance constraints, standard line demand response constraints, and line capacity constraints.

[0062] The expression for the operating constraints of the thermal power unit is:

[0063] ;

[0064] in, and Let represent the minimum and maximum active power output of the i-th generator unit, respectively; This represents the active power output of the i-th generating unit during time period t; The time variable is represented; T represents the total number of time periods in the scheduling cycle. This indicates the total number of thermal power units in the system;

[0065] The expression for the climbing constraint is:

[0066] ;

[0067] in, This represents the downhill ramp rate of unit i; This represents the active power output of the i-th generating unit during time period t; This represents the active power output of the i-th generating unit during the time period t-1; This represents the ramp rate of unit i;

[0068] The expression for the unit start-up and shutdown time constraint is:

[0069] ;

[0070] ;

[0071] in, Indicates the minimum continuous operating time of the unit; Indicates the start / stop time of unit i; This indicates the start-up and shutdown status of unit i during time period t; and These represent the start-up and shutdown status of unit i during time periods t and t-1, respectively. Indicates the minimum continuous downtime of the generating unit;

[0072] The expression for the node power balance constraint is:

[0073] ;

[0074] Where i represents the unit variable; Represents the output of the i-th thermal power unit; j represents the variable of the new energy unit; Indicates the number of new energy generating units; Indicates the output of the new energy generating units; Represents node variables; Indicates the number of load nodes; This represents the day-ahead forecast load of the k-th node; The node load guideline for node k; This represents the value of the rigid load at the k-th node during time period t;

[0075] The expression for the directrix demand response constraint is:

[0076] ;

[0077] ;

[0078] ;

[0079] in, This represents the normalized value of the adjustable load of the k-th node after time period t; and These represent the upper and lower limits of the adjustable load response of the k-th node during time period t, respectively.

[0080] The expression for the line capacity constraint is:

[0081] ;

[0082] in, and Let represent the lower and upper limits of the transmission power of the i-th line, respectively; This represents the power flowing through the i-th line during time period t; This indicates the total number of system lines.

[0083] The technical solutions of the embodiments of the present invention have at least the following advantages and beneficial effects:

[0084] This invention considers a high-proportion renewable energy system scheduling method with quasi-linear demand response. By introducing historical renewable energy data into a data bin to construct an uncertainty set, robust quasi-linear calculation is performed to consider the uncertainty of renewable energy output. The node load quasi-linearity is formulated considering the differences in node load response capabilities. At the same time, the quasi-linearity is coupled with scheduling to determine the system demand, thereby improving the system's power supply capacity and renewable energy absorption rate.

[0085] This invention provides different incentives for compensation based on the different time periods during which users participate in demand response. This time-sharing compensation method can facilitate load response and improve the efficiency of load response. Attached Figure Description

[0086] Figure 1 This invention provides a new energy power system dispatching method that considers quasilinear demand response;

[0087] Figure 2 This is a schematic diagram illustrating the working principle of the alignment setting and scheduling decision-making in this invention. Detailed Implementation

[0088] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0089] Figure 1 This invention provides a new energy power system dispatching method that considers quasilinear demand response. Figure 2 This is a schematic diagram illustrating the working principle of the alignment setting and scheduling decision-making in this invention. Figure 1 and Figure 2 As shown, the dispatching method for new energy power systems considering quasi-linear demand response includes the following:

[0090] Step 110: Based on the historical predicted output and historical actual output of new energy sources, divide the prediction bins and obtain the uncertainty set.

[0091] Historical predicted power output can refer to previously predicted renewable energy output. For example, the predicted wind power output before the current time. Historical actual power output can also refer to previously predicted renewable energy output. For example, the wind power output before the current time. A prediction box can refer to dividing historical data into multiple segments based on power magnitude, obtaining the historical actual power output and its corresponding historical predicted power output within different power ranges. An uncertainty set can be used to represent the probability and situation of renewable energy power generation. For example, statistically calculating wind power prediction data and actual measurement data over the past year, due to the large difference in wind power error caused by different wind speed levels, a prediction box is introduced to determine a suitable uncertainty set. First, the wind power prediction data and actual measurement data are normalized to fall within the range [0,1]. Then, this range is divided into 10 intervals to ensure that each prediction interval contains sufficient data.

[0092] The obtained uncertainty set includes:

[0093] The historical predicted output and the historical actual output are normalized to obtain the standard historical predicted output and the standard historical actual output, respectively.

[0094] Standard historical forecast output refers to the normalized historical forecast output. Standard historical actual output refers to the normalized historical actual output.

[0095] Based on the standard historical actual output, prediction bins are divided to obtain multiple prediction intervals, and the probability of historical error data in each data bin is determined. The expression for the probability of historical error data is:

[0096] ;

[0097] in, This represents the probability of historical error data at time t; This represents the actual historical output at time t; This represents the historical predicted output at time t.

[0098] The data bin is used to distinguish the data for each forecast interval. The forecast bin includes the standard historical actual power output and the standard historical forecast power output for the corresponding forecast interval.

[0099] A probability histogram is constructed based on the historical error data, and a general distribution model is used to fit the probability histogram to obtain the probability distribution model of the systematic error within each data bin; the general distribution model includes a probability density function, a cumulative distribution function, and an inverse function of the cumulative distribution function; the probability distribution model of the systematic error includes a system probability density function, a system cumulative distribution function, and an inverse function of the system cumulative distribution function.

[0100] The expression for the system probability density function is:

[0101] ;

[0102] The expression for the cumulative distribution function of the system is:

[0103] ;

[0104] The expression for the inverse function of the system's cumulative distribution function is:

[0105] ;

[0106] in, , and These represent the first shape parameter, the second shape parameter, and the third shape parameter of the general distribution model, respectively. Represents the system probability density function; Represents the system's cumulative distribution function; It represents the inverse function of the system's cumulative distribution function; Indicates the independent variable; This represents the dependent variable.

[0107] Step 120: Based on the set of uncertainties, construct an uncertainty scenario and generate a severe scenario.

[0108] Uncertainty scenarios can refer to various uncertainties in new energy power generation. For example, for wind power generation, uncertainties can include variations in wind strength. For photovoltaic power generation, uncertainties can include factors such as sunlight intensity and shadow area. Adverse scenarios can refer to extreme scenarios in new energy power generation. For example, for wind power generation, adverse scenarios can include extreme wind output scenarios (i.e., the overall wind force is either at its minimum or maximum) and extreme wind power ramps (i.e., wind power fluctuates between the minimum and maximum values ​​of the wind power uncertainty set).

[0109] The expression for generating the harsh scenario is:

[0110] ;

[0111] in, This represents the first extreme wind power output scenario, i.e., the scenario with the maximum wind power output. Indicates the maximum wind power output; The second extreme wind power output scenario is the scenario with the minimum wind power output. Indicates the minimum wind power output; This represents the first extreme wind power generation slope scenario, i.e., the maximum positive wind power generation slope; and These are the positive and negative slope coefficients, respectively, when t is an odd number. , When t is even, , ; This represents the second extreme wind power slope scenario, i.e., the maximum negative wind power slope. The extreme maximum output scenario and the extreme minimum output scenario are opposite, and the extreme slope scenarios for wind power also show completely opposite trends.

[0112] By introducing four extreme cases, we can avoid the over-conservatism of the solution.

[0113] Step 130: Construct a system load baseline model under the severe scenario, and solve the system load baseline model to obtain the system load baseline. The objective function of the system load baseline model is to minimize system operating costs, considering wind and solar curtailment penalties and load shedding penalties, using unit output as the decision variable, to optimize the economic efficiency of system operating costs, thereby improving the system's ability to absorb new energy and supply power.

[0114] The system load guideline model includes a system load objective function and system load constraints; the expression for the system load objective function is:

[0115] ;

[0116] ;

[0117] ;

[0118] ;

[0119] in, Indicates the scene number; Indicates the number of scenes; Indicates the worst-case scenario; This indicates taking the minimum value; This indicates the operating cost of thermal power units; This indicates the cost of curtailing wind and solar power. Indicates the cost of load shedding; , and Let represent the first cost coefficient, the second cost system, and the third cost coefficient of the i-th thermal power unit, respectively; This represents the output of the i-th thermal power unit; The time variable is represented; T represents the total number of time periods in the scheduling cycle. This indicates the total number of thermal power units in the system; This represents the penalty coefficient for curtailment of renewable energy. and These represent the maximum output values ​​of wind power and photovoltaic power at time t, respectively; the system load constraints include thermal power unit operation constraints, ramp-up constraints, unit start-up and shutdown time constraints, and node power balance constraints.

[0120] The system load constraints include thermal power unit operation constraints, ramp-up constraints, unit start-up and shutdown time constraints, and node power balance constraints.

[0121] The expression for the operating constraints of the thermal power unit is:

[0122] ;

[0123] in, and Let represent the minimum and maximum active power output of the i-th generator unit, respectively; This represents the active power output of the i-th generating unit during time period t; The time variable is represented; T represents the total number of time periods in the scheduling cycle. This indicates the total number of thermal power units in the system;

[0124] The expression for the climbing constraint is:

[0125] ;

[0126] in, This represents the downhill ramp rate of unit i; This represents the active power output of the i-th generating unit during time period t; This represents the active power output of the i-th generating unit during the time period t-1; This represents the ramp rate of unit i;

[0127] The expression for the unit start-up and shutdown time constraint is:

[0128] ;

[0129] ;

[0130] in, Indicates the minimum continuous operating time of the unit; Indicates the start / stop time of unit i; This indicates the start-up and shutdown status of unit i during time period t; and These represent the start-up and shutdown status of unit i during time periods t and t-1, respectively. Indicates the minimum continuous downtime of the generating unit;

[0131] The expression for the node power balance constraint is:

[0132] ;

[0133] Where i represents the unit variable; Represents the output of the i-th thermal power unit; j represents the variable of the new energy unit; Indicates the number of new energy generating units; Indicates the output of the new energy generating units; Represents node variables; Indicates the number of load nodes; This represents the day-ahead forecast load of the k-th node; The node load guideline for node k; This represents the value of the rigid load of the k-th node during time period t. Since user participation in demand response (DR) is voluntary, users who register for DR are considered adjustable loads, while those who do not register are considered rigid loads. For each load node, its load can consist of both rigid and adjustable loads.

[0134] The system load baseline can refer to the reference load curve for the time period to be predicted. The system can consume electricity according to this system load curve to better participate in load response. The time to be predicted can be the next day.

[0135] Step 140: Based on historical node load data, obtain the predicted response range for each node. Considering that each node will exhibit different response potential when participating in demand response, the demand response potential capacity for each time period is assessed based on the variance of historical load data for similar days of each node when formulating the node load baseline.

[0136] Historical node load data can refer to the load status of a node at various historical moments. For example, the load value of a node in the power system within one year prior to the current time. Predicted response range can refer to the response range of a node. For example, the upper and lower limits of a node's participation in load response.

[0137] The process of obtaining the predicted response interval for each node includes:

[0138] The system filters similar time periods for the predicted period from the historical node load data for each node. The predicted period can be the next day. Similar time periods can refer to historical periods with similar electricity consumption to the predicted period. For example, the total load for the next day is predicted, and similar days are found from historical data based on the total load.

[0139] Based on the historical load of each time period within the similar time periods, the load resilience coefficient of each node in the predicted time period is determined. The load resilience coefficient represents the node's ability to participate in load response. The mean squared error of the load in each time period within the similar days is used as the load resilience coefficient to measure the magnitude of the load resilience in that time period; a larger variance indicates greater load variation and a higher shaping capability. Historical load can refer to the load value of a node at each moment. The expression for the load resilience coefficient is:

[0140] ;

[0141] in, This represents the load elasticity coefficient for time period t; Indicates the number of load nodes participating in demand response; Represents node variables; This represents the normalized value of the system load in the t-th time period on day i; This represents the normalized average load of the t-th time period within a specified number of similar days.

[0142] Based on the load resilience coefficient, nodes are divided into multiple levels of node sets, and different demand response potential coefficients are set for each level of node set. The demand response potential coefficient can refer to a pre-defined value representing the ability of different nodes to respond to load changes. For example, the calculated load resilience coefficients can be sorted, and then divided into four levels according to the demand response potential from largest to smallest. Different demand response capability levels represent the corresponding demand response potential of the load during that time period. The demand response capability levels can include: Level 1, with a corresponding demand response potential coefficient of a; Level 2, with a corresponding demand response potential coefficient of b; Level 3, with a corresponding demand response potential coefficient of c; and Level 4, with a corresponding demand response potential coefficient of d.

[0143] Based on the predicted load of each node and the demand response potential coefficient, the predicted response interval for each node is obtained. The expression for the predicted response interval is:

[0144] ;

[0145] ;

[0146] ;

[0147] ;

[0148] in, and These represent the upper and lower limits of the adjustable load response of the k-th node during time period t, respectively. Indicates the demand response potential coefficient; This represents the output of the k-th adjustable load during time period t; This represents the total load value of the adjustable load of the k-th node over T time periods; This represents the day-ahead forecast value of the k-th adjustable load during time period t.

[0149] Step 150: Construct a node load baseline model and solve the node load baseline model to obtain the optimal load response and node load baseline. The objective function of the node load baseline model includes unit operating costs, unit start-up and shutdown costs, curtailment penalties for solar and wind power, load loss penalties, and demand response compensation. Time-sharing compensation is not considered when making scheduling decisions to reduce the amount of computation and shorten the computation time. Moreover, selecting an appropriate cost coefficient can mitigate the errors of the two methods to some extent.

[0150] Optimal load response refers to the optimal load quantity participating in the load response. Under optimal load response, the power grid system can operate stably with the lowest cost. Node load profiles can be used to guide the electricity consumption of users at each node. Typically, node load profiles can be sent to users at the corresponding nodes in advance to guide their electricity consumption. For example, the node load profile for the following day can be sent to users the day before, and users can then consume electricity according to the received node load profile the following day.

[0151] The nodal load guideline model includes a nodal load objective function and nodal load constraints; the expression for the nodal load objective function is:

[0152] ;

[0153] ;

[0154] ;

[0155] ;

[0156] ;

[0157] ;

[0158] ;

[0159] in, This indicates taking the minimum value; This indicates the operating cost of thermal power units; Represents the start-up and shutdown costs of the generating unit; This indicates the cost of curtailing wind and solar power. Indicates the cost of load shedding; The deviation penalty between the node load baseline and the global load baseline; , and Let represent the first cost coefficient, the second cost system, and the third cost coefficient of the i-th thermal power unit, respectively; This represents the output of the i-th thermal power unit; The time variable is represented; T represents the total number of time periods in the scheduling cycle. and Let represent the start-up and shutdown costs of the i-th generating unit, respectively; and These represent the states of the i-th generator unit at time t and time t-1, respectively. This indicates the total number of thermal power units in the system; This represents the penalty coefficient for curtailment of renewable energy. x and These represent the maximum output values ​​of wind power and photovoltaic power at time t, respectively. and Let represent the wind power and solar power output at time t, respectively; Indicates the loss-of-load penalty coefficient; This represents the maximum load at time t; This represents the load value at time t; This is the deviation penalty coefficient between the load baseline and the global load baseline; Indicates the number of load nodes participating in demand response;

[0160] The nodal load constraints include thermal power unit operation constraints, ramping constraints, unit start-up and shutdown time constraints, nodal power balance constraints, standard line demand response constraints, and line capacity constraints.

[0161] The expression for the operating constraints of the thermal power unit is:

[0162] ;

[0163] in, and Let represent the minimum and maximum active power output of the i-th generator unit, respectively; This represents the active power output of the i-th generating unit during time period t; The time variable is represented; T represents the total number of time periods in the scheduling cycle. This indicates the total number of thermal power units in the system;

[0164] The expression for the climbing constraint is:

[0165] ;

[0166] in, This represents the downhill ramp rate of unit i; This represents the active power output of the i-th generating unit during time period t; This represents the active power output of the i-th generating unit during the time period t-1; This represents the ramp rate of unit i;

[0167] The expression for the unit start-up and shutdown time constraint is:

[0168] ;

[0169] ;

[0170] in, Indicates the minimum continuous operating time of the unit; Indicates the start / stop time of unit i; This indicates the start-up and shutdown status of unit i during time period t; and These represent the start-up and shutdown status of unit i during time periods t and t-1, respectively. Indicates the minimum continuous downtime of the generating unit;

[0171] The expression for the node power balance constraint is:

[0172] ;

[0173] Where i represents the unit variable; Represents the output of the i-th thermal power unit; j represents the variable of the new energy unit; Indicates the number of new energy generating units; Indicates the output of the new energy generating units; Represents node variables; Indicates the number of load nodes; This represents the day-ahead forecast load of the k-th node; The node load guideline for node k; This represents the value of the rigid load at the k-th node during time period t;

[0174] The expression for the directrix demand response constraint is:

[0175] ;

[0176] ;

[0177] ;

[0178] in, This represents the normalized value of the adjustable load of the k-th node after time period t; and These represent the upper and lower limits of the adjustable load response of the k-th node during time period t, respectively.

[0179] The expression for the line capacity constraint is:

[0180] ;

[0181] in, and Let represent the lower and upper limits of the transmission power of the i-th line, respectively; This represents the power flowing through the i-th line during time period t; This indicates the total number of system lines.

[0182] In some embodiments, the method further includes evaluating the user's response effectiveness based on the load of user participation in demand response, and obtaining evaluation results. Based on the characteristics of time-of-use pricing, different importance levels are set for user response compensation in different time periods, and compensation is calculated time-of-use. The aim is to provide higher incentives to guide users to respond actively during critical periods of power consumption and supply security, while giving users greater flexibility during other periods when the system's adjustment capacity is relatively abundant. To avoid excessive calculation time, the demand response cost in the objective function does not consider time-of-use compensation.

[0183] User-involved load response can refer to the amount of load response participated in by users at each node. For example, the amount of electricity a user consumes less. The evaluation result can refer to the similarity between the user's load curve and the node's load baseline.

[0184] Based on the evaluation results, the total compensation for the user is determined using a time-sharing compensation method.

[0185] The total compensation amount refers to the compensation paid by users for load response. The expression for determining the total compensation amount for the users is:

[0186] ;

[0187] ;

[0188] in, This represents the response similarity of the k-th node at time t. The curve profile representing the load response of node k; The nodal directrix representing node k; This represents the time-sharing compensation for the k-th node; The time variable is represented; T represents the total number of time periods in the scheduling cycle. This represents the compensation coefficient for time period t.

[0189] The above are merely preferred embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A dispatching method for a new energy power system considering quasi-linear demand response, characterized in that, include: Based on the historical predicted and actual power output of new energy sources, prediction bins are divided, and an uncertainty set is obtained, including: The historical predicted output and the historical actual output are normalized to obtain the standard historical predicted output and the standard historical actual output, respectively. Based on the standard historical actual output, prediction bins are divided to obtain multiple prediction intervals, and the probability of historical error data in each data bin is determined: ； in, This represents the probability of historical error data at time t; This represents the actual historical output at time t; This represents the historical predicted output at time t; A probability histogram is constructed based on the historical error data, and a general distribution model is used to fit the probability histogram to obtain the probability distribution model of the systematic error within each data bin. The general distribution model includes a probability density function, a cumulative distribution function, and an inverse function of the cumulative distribution function. The probability distribution model of the systematic error includes a system probability density function, a system cumulative distribution function, and an inverse function of the system cumulative distribution function. The expression of the system probability density function is as follows: ； The expression for the cumulative distribution function of the system is: ； The expression for the inverse function of the system's cumulative distribution function is: ； in, , and These represent the first shape parameter, the second shape parameter, and the third shape parameter of the general distribution model, respectively. Represents the system probability density function; Represents the system's cumulative distribution function; It represents the inverse function of the system's cumulative distribution function; Indicates the independent variable; Indicates the dependent variable; Based on the aforementioned set of uncertainties, an uncertainty scenario is constructed, and a severe scenario is generated; A system load guideline model under the aforementioned severe scenario is constructed, and the system load guideline model is solved to obtain the system load guideline; Based on historical node load data, the predicted response range for each node is obtained; Construct a node load guideline model and solve the node load guideline model to obtain the optimal load response and node load guideline.

2. The new energy power system dispatching method considering quasi-linear demand response according to claim 1, characterized in that, Also includes: Based on the user participation load in demand response, the effectiveness of user response is evaluated, and the evaluation results are obtained. Based on the evaluation results, the total compensation for the user is determined using a time-sharing compensation method.

3. The new energy power system dispatching method considering quasi-linear demand response according to claim 2, characterized in that, The expression for determining the total compensation amount for the user is: ； ； in, This represents the response similarity of the k-th node at time t. The curve profile representing the load response of node k; The nodal directrix representing node k; This represents the time-sharing compensation for the k-th node; The time variable is represented; T represents the total number of time periods in the scheduling cycle. This represents the compensation coefficient for time period t.

4. The new energy power system dispatching method considering quasi-linear demand response according to claim 1, characterized in that, The expression for generating the harsh scenario is: ; in, This represents the first extreme wind power output scenario, i.e., the scenario with the maximum wind power output. Indicates the maximum wind power output; The second extreme wind power output scenario is the scenario with the minimum wind power output. Indicates the minimum wind power output; This represents the first extreme wind power generation slope scenario, i.e., the maximum positive wind power generation slope; and These are the positive and negative slope coefficients, respectively, when t is an odd number. , When t is even, , ; This represents the second extreme wind power generation slope scenario, namely, the maximum negative wind power generation slope.

5. The new energy power system dispatching method considering quasi-linear demand response according to claim 1, characterized in that, The system load guideline model includes a system load objective function and system load constraints; the expression for the system load objective function is: ； ； ； ； in, Indicates the scene number; Indicates the number of scenes; Indicates the worst-case scenario; This indicates taking the minimum value; This indicates the operating cost of thermal power units; This indicates the cost of curtailing wind and solar power. Indicates the cost of load shedding; , and Let represent the first cost coefficient, the second cost system, and the third cost coefficient of the i-th thermal power unit, respectively; This represents the output of the i-th thermal power unit; The time variable is represented; T represents the total number of time periods in the scheduling cycle. This indicates the total number of thermal power units in the system; This represents the penalty coefficient for curtailment of renewable energy. and These represent the maximum output values ​​of wind power and photovoltaic power at time t, respectively. and Let represent the wind power and solar power output at time t, respectively; Indicates the loss-of-load penalty coefficient; This represents the maximum load at time t; This represents the load value at time t; The system load constraints include thermal power unit operation constraints, ramp-up constraints, unit start-up and shutdown time constraints, and node power balance constraints. The expression for the operating constraints of the thermal power unit is: ； in, and Let represent the minimum and maximum active power output of the i-th generator unit, respectively; This represents the active power output of the i-th generating unit during time period t; The time variable is represented; T represents the total number of time periods in the scheduling cycle. This indicates the total number of thermal power units in the system; The expression for the climbing constraint is: ; in, This represents the downhill ramp rate of unit i; This represents the active power output of the i-th generating unit during time period t; This represents the active power output of the i-th generating unit during the time period t-1; This represents the ramp rate of unit i; The expression for the unit start-up and shutdown time constraint is: ; ; in, Indicates the minimum continuous operating time of the unit; Indicates the start / stop time of unit i; This indicates the start-up and shutdown status of unit i during time period t; and These represent the start-up and shutdown status of unit i during time periods t and t-1, respectively. Indicates the minimum continuous downtime of the generating unit; The expression for the node power balance constraint is: ; Where i represents the unit variable; Represents the output of the i-th thermal power unit; j represents the variable of the new energy unit; Indicates the number of new energy generating units; Indicates the output of the new energy generating units; Represents node variables; Indicates the number of load nodes; This represents the day-ahead forecast load of the k-th node; The node load guideline for node k; This represents the value of the rigid load of the k-th node during time period t.

6. The new energy power system dispatching method considering quasi-linear demand response according to claim 1, characterized in that, The process of obtaining the predicted response interval for each node includes: Filter similar time periods for the predicted time period of each node from the historical node load data; Based on the historical load of each time period in the similar time period, the load elasticity coefficient of each node in the time period to be predicted is determined respectively; Based on the load resilience coefficient, the nodes are divided into multiple levels of node sets, and different demand response potential coefficients are set for each level of node set. Based on the predicted load of each node and the demand response potential coefficient, the predicted response range of each node is obtained.

7. The new energy power system dispatching method considering quasi-linear demand response according to claim 6, characterized in that, The expression for the load elasticity coefficient is: ； in, This represents the load elasticity coefficient for time period t; Indicates the number of load nodes participating in demand response; Represents node variables; This represents the normalized value of the system load in the t-th time period on day i; This represents the normalized average of the load during the t-th time period within a specified number of similar days; The expression for the predicted response interval is: ； ； ; ； in, and These represent the upper and lower limits of the adjustable load response of the k-th node during time period t, respectively. Indicates the demand response potential coefficient; This represents the output of the k-th adjustable load during time period t; This represents the total load value of the adjustable load of the k-th node over T time periods; This represents the day-ahead forecast value of the k-th adjustable load during time period t.

8. The new energy power system dispatching method considering quasi-linear demand response according to claim 1, characterized in that, The nodal load guideline model includes a nodal load objective function and nodal load constraints; the expression for the nodal load objective function is: ； ； ； ； ； ； in, This indicates taking the minimum value; This indicates the operating cost of thermal power units; Represents the start-up and shutdown costs of the generating unit; This indicates the cost of curtailing wind and solar power. Indicates the cost of load shedding; The deviation penalty between the node load baseline and the global load baseline; , and Let represent the first cost coefficient, the second cost system, and the third cost coefficient of the i-th thermal power unit, respectively; This represents the output of the i-th thermal power unit; The time variable is represented; T represents the total number of time periods in the scheduling cycle. and Let represent the start-up and shutdown costs of the i-th generating unit, respectively; and Let these represent the states of the i-th generating unit at time t and time t-1, respectively; This indicates the total number of thermal power units in the system; This represents the penalty coefficient for curtailment of renewable energy. x and These represent the maximum output values ​​of wind power and photovoltaic power at time t, respectively. and Let represent the wind power and solar power output at time t, respectively; Indicates the loss-of-load penalty coefficient; This represents the maximum load at time t; This represents the load value at time t; This is the deviation penalty coefficient between the load baseline and the global load baseline; Indicates the number of load nodes participating in demand response; The nodal load constraints include thermal power unit operation constraints, ramping constraints, unit start-up and shutdown time constraints, nodal power balance constraints, standard line demand response constraints, and line capacity constraints. The expression for the operating constraints of the thermal power unit is: ； in, and Let represent the minimum and maximum active power output of the i-th generator unit, respectively; This represents the active power output of the i-th generating unit during time period t; The time variable is represented; T represents the total number of time periods in the scheduling cycle. This indicates the total number of thermal power units in the system; The expression for the climbing constraint is: ; in, This represents the downhill ramp rate of unit i; This represents the active power output of the i-th generating unit during time period t; This represents the active power output of the i-th generating unit during the time period t-1; This represents the ramp rate of unit i; The expression for the unit start-up and shutdown time constraint is: ; ; in, Indicates the minimum continuous operating time of the unit; Indicates the start / stop time of unit i; This indicates the start-up and shutdown status of unit i during time period t; and These represent the start-up and shutdown status of unit i during time periods t and t-1, respectively. Indicates the minimum continuous downtime of the generating unit; The expression for the node power balance constraint is: ; Where i represents the unit variable; Represents the output of the i-th thermal power unit; j represents the variable of the new energy unit; Indicates the number of new energy generating units; Indicates the output of the new energy generating units; Represents node variables; Indicates the number of load nodes; This represents the day-ahead forecast load of the k-th node; The node load guideline for node k; This represents the value of the rigid load at the k-th node during time period t; The expression for the directrix demand response constraint is: ； ； ； in, This indicates that the guideline can respond to the lower limit; This represents the normalized value of the adjustable load of the k-th node after time period t; This indicates the upper limit of the responsiveness of the guideline; The expression for the line capacity constraint is: ; in, and Let represent the lower and upper limits of the transmission power of the i-th line, respectively; This represents the power flowing through the i-th line during time period t; This indicates the total number of system lines.

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