Multi-resource collaborative power system short-time dynamic supply and demand balance evaluation method and device

Abstract

The invention discloses a multi-resource collaborative power system short-time dynamic supply and demand balance evaluation method and device, and the method comprises the steps: constructing a target function based on the power generation cost of an adjustable generator set, the starting and stopping cost of the adjustable generator set, the wind and light abandoning penalty cost, the load shedding cost and the load transfer cost, and determining a corresponding initial constraint condition; determining a prediction error constraint based on meteorological elements, a frequency support constraint of an adjustable generator set and a set fault constraint, carrying out optimization replacement on an initial constraint condition, and giving an optimization constraint condition; based on the objective function and the initial constraint condition, determining a unit commitment start-stop decision; and performing short-time dynamic supply and demand balance evaluation on the power system based on a unit combination start-stop decision in combination with an objective function and optimization constraint adjustment. According to the invention, more accurate and timely information support can be provided for scheduling and operation of a power system, and the short-time supply and demand balance demand and capability in a high-proportion new energy access scene can be effectively evaluated.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system dispatching, and specifically relates to a method and device for evaluating short-term dynamic supply and demand balance of a power system with multi-resource coordination. Background Art

[0002] The problem of power system balance is particularly prominent under a high proportion of renewable energy. Photovoltaic output drops rapidly from 16:00. Sudden weather events such as extreme heat without wind and cold waves may cause a sharp drop in wind and solar output. Other power sources need to quickly change from a reduced output state to a peak state. However, due to the ramp performance of coal-fired power units, there may be a gap in the future supply and demand balance without sufficient flexible adjustment resources. The basic idea of ​​power and electricity balance analysis for new power systems is to "collect multi-source heterogeneous data, calculate the supply and demand balance state, and update decisions based on indicators." At present, research on the supply and demand balance of power systems mainly focuses on source-grid-load-storage resource modeling, power and electricity balance simulation calculation methods, and evaluation index systems.

[0003] Most of the modeling methods for source, grid, load and storage resources are relatively mature. In power source modeling, the models for thermal power and hydropower participating in power and electricity balance are relatively mature. For new energy, for example, (1) a probability distribution model is used, and the wind speed is assumed to obey the Weibull distribution. The wind speed curve is generated through the Weibull distribution, and the power distribution is obtained through the wind speed-power relationship; (2) the Coupla generation method is used to generate the new energy output curve based on historical curve speculation. For grid-side modeling, the DC power flow calculation method is generally used to simplify the calculation and ensure calculation efficiency. In load modeling, the load forecasting method is very mature. Through classic methods such as time series forecasting method, accurate load curve forecasting results can be obtained as load models to directly participate in power and electricity balance. The research on grid-side energy storage is relatively mature, and there are many basic charging and discharging models for energy storage.

[0004] The power and electricity balance simulation calculation methods mainly include optimization model-based methods and heuristic algorithms. Among them, the optimization model-based methods can be divided into robust optimization methods, stochastic programming methods, and distributed robust optimization methods. The power and electricity balance indicators provide an important tool for evaluating the operation and planning of power systems, and play an important role in evaluating the power system's supply and absorption capacity, operating efficiency, and economy.

[0005] However, most existing methods are based on hourly or 15-minute unit combination models, and the resulting startup methods are difficult to meet the safety and stability constraints caused by minute-level fluctuations, resulting in greater operational risks for decision-making plans. The uncertainty and volatility of new energy may lead to a series of problems such as grid frequency equipment disconnection. The current methods do not consider frequency safety constraints and unit failures, and the stable operation reliability of the power system is insufficient. Summary of the invention

[0006] In view of the defects existing in the above-mentioned prior art, the present invention provides a method and device for evaluating the short-term dynamic supply and demand balance of a power system with multi-resource collaboration. It can provide more accurate and timely information support for the dispatch and operation of the power system, and effectively evaluate the short-term supply and demand balance requirements and capabilities in the scenario of high proportion of new energy access.

[0007] In a first aspect, the present invention provides a method for evaluating short-term dynamic supply and demand balance of a power system with multi-resource coordination, comprising:

[0008] The objective function is constructed based on the power generation cost of the adjustable generator set, the start-up and shutdown cost of the adjustable generator set, the penalty cost for abandoning wind and solar power, the load shedding cost, and the load transfer cost;

[0009] Determine the initial constraints of the adjustable generator set power generation cost, adjustable generator set start-up and shutdown cost, wind and solar abandonment penalty cost, load shedding cost, and load transfer cost;

[0010] Determine the forecast error constraints based on meteorological elements, the frequency support constraints of adjustable generator sets, and the unit failure constraints, and optimize and replace the initial constraints to give the optimized constraints;

[0011] Determine the start and stop decision of unit combination based on the objective function and initial constraints;

[0012] Based on the start-stop decision of unit combination, and combined with the objective function and optimization constraint adjustment, the short-term dynamic supply and demand balance of the power system is evaluated.

[0013] Furthermore, the objective function satisfies the following relationship:

[0014]

[0015]

[0016] In the formula, C t is the total cost of the power system during period t∈[1,T], is the power generation cost of the adjustable generator set, is the start-stop cost of the adjustable generator set, Penalty costs for abandoning wind power. is the load shedding cost, is the load transfer cost, and c is the diagonal matrix of power generation cost parameters of adjustable generator sets, a i 、c i are the power generation cost parameters of the ith adjustable generator set, b T is the power generation cost parameter vector of the adjustable generator set within the T time period, P t adis the output decision variable of the adjustable generator set, N is the number of adjustable generator sets, is the output decision variable of the Nth adjustable generator set, and are the start / stop operation indicator variables of the i-th adjustable generator set in period t, and are the start-stop costs of adjustable generator set i, τ curt P is the penalty cost coefficient for abandoning wind and solar power, t curt is the total abandoned wind and solar power of all new energy units, is the abandoned wind and solar power of renewable energy unit j at time t, M is the number of renewable energy units, is the set of nodes in the power system, is the load shedding amount of each type of non-transferable load at node m in period t, is the load shedding cost of each type of non-transferable load at node m in period t, φ m represents the cost coefficient of load transfer, t' represents the time after load transfer, is the load transferred from node m from time t to time t'.

[0017] Furthermore, the initial constraints of the adjustable generator set power generation cost, the adjustable generator set start-up and shutdown cost, the wind and solar power abandonment penalty cost, the load shedding cost and the load transfer cost include determining the load shedding amount constraint, the load transfer amount constraint, the node power balance constraint, the adjustable generator set start-up and shutdown state constraint, the adjustable generator set technical constraint and the energy storage state constraint.

[0018] Furthermore, the initial constraints include:

[0019]

[0020]

[0021] In the formula, is the proportion of each type of non-transferable load of node m in period t, is the proportion of the transferable load of node m in period t, is the load value of node m at time t, is the load transferred from node m from time t to time t', t,t'∈[1,T], - is the load transferred to node m from time t to time t', is the active power injected into node m during period t, θ m,t and θ n,t They represent the voltage phase angles of node m and node n during period t, respectively, and x mnis the reactance of the branch between node m and node n, is the maximum transmission power of the branch between node m and node n, is the charging and discharging power of energy storage unit k, when When is discharge, When charging, is the set of energy storage units connected to node m, S is the set of energy storage units connected to all nodes, is the power generation of adjustable generator set i in time period t, is the net load of node m in period t, is the total load shedding of all types of non-transferable loads at node m in period t, u i,t Indicates the switch state of the adjustable generator set i at time t, 0 means off, 1 means on, and are the indicator variables for the start and shutdown operation of the adjustable generator set i at time t, respectively, [·] + =max[·,0], are the lower and upper output limits of the adjustable generator set i, respectively. are the positive climbing limit and negative climbing limit of the adjustable generator set i, and are the energy storage charging power and discharging power of energy storage unit k in period t, E k,t is the energy storage capacity of energy storage unit k during period t, are the charging efficiency and discharging efficiency, respectively. They are the upper limits of charging and discharging speeds respectively.

[0022] Furthermore, the initial constraints are optimized, including:

[0023] Based on the Gaussian distribution of wind power output and photovoltaic output, determine the Gaussian distribution function of the total output of renewable energy;

[0024] Based on the Gaussian distribution of load in the power system and the probability distribution function of the total output of renewable energy, the mean and standard deviation of the net load are determined;

[0025] Based on the influence of meteorological factors on the output of wind turbines, Poisson distribution is used to count and fit the time when the output of wind turbines is zero in different wind speed ranges in the historical data of wind turbine output, and the probability of wind turbine output being zero under different wind speeds is given.

[0026] According to the probability of zero output of wind turbine generator set under different wind speeds, the spare capacity corresponding to the wind turbine generator set shutdown is determined;

[0027] The determined reserve capacity is modeled based on chance constraints and combined with the mean and standard deviation of the net load to obtain the optimized initial constraints through mixed integer linear programming.

[0028] Furthermore, the determined spare capacity is modeled based on opportunity constraints, including:

[0029]

[0030] p ws =P ws (y ws ≤δ|s wind =ws).

[0031] In the formula, β + and β - are the upper and lower backup constraint violation probabilities, i.e., risk tolerance, is the net load of node m in period t, is the mean value of wind power output, Represents the sum of the maximum outputs of the enabled adjustable generator sets connected to node m, It represents the minimum output sum of the adjustable generator sets connected to node m. is the positive spare capacity of node m at time t, is the negative spare capacity of node m at time t, is the spare capacity corresponding to the wind turbine generator set shutdown, p ws is the probability of zero output of wind turbine generator set under different wind speeds, P ws is the output of all wind turbines at each moment, p ws,jt is the output of a wind turbine at a certain moment, y ws is the random variable of wind turbine output under wind speed ws, δ is the output threshold of wind turbine, ws is the wind speed range set, and Pr[] represents the probability that the constraint condition is satisfied.

[0032] Furthermore, the mean and standard deviation of the net load satisfy the following relationship:

[0033]

[0034] In the formula, is the mean value of the net load, is the standard deviation of the net load, is the mean value of Gaussian distribution of load in the power system, is the standard deviation of the Gaussian distribution of loads in the power system, is the total load shedding of all types of non-transferable loads at node m in period t, is the mean of the Gaussian distribution function of the total output of renewable energy, is the standard deviation of the Gaussian distribution function of the total output of renewable energy, is the mean value of wind power output, is the standard deviation of wind power output, is the average photovoltaic output, is the standard deviation of PV output;

[0035] The optimized initial constraints satisfy the following relationship:

[0036]

[0037] In the formula, E[·] represents the expectation of the random variable, Φ is the Gaussian distribution function, and Φ -1 (1-β + ) and Φ -1 (1-β - ) are given probabilities 1-β + and 1-β - The corresponding quantile of the normal distribution.

[0038] Furthermore, the frequency support constraint of the adjustable generator set satisfies the following relationship:

[0039]

[0040] ΔP f,max =max(ΔP f,max1 ,ΔP f,max2 )

[0041] ΔP f,max2 =f(θ max )

[0042]

[0043] ΔP net =ΔP d -(ΔP wind +ΔP pv )

[0044] In the formula, ΔP G is the change in power generation of conventional adjustable generator sets after external interference, ΔP net is the fluctuation of net load, ΔP d is the load fluctuation, ΔP wind is the fluctuation of wind power output, ΔP pv is the fluctuation of photovoltaic output, Δω max Indicates the maximum acceptable frequency error, ω n is the natural frequency of the power system, θ maxis the maximum acceptable slope at time 0, D is the active frequency response coefficient of the load, 1 / R represents the pure gain of the speed regulator, K is the power generation coefficient of the conventional synchronous unit, ΔP f,max is the maximum acceptable power difference, ΔP f,max2 is the maximum acceptable power difference considering the slope constraint at time 0, ΔP f,max1 is the maximum acceptable power difference considering the frequency error constraint, P m L is the load power of node m.

[0045] Furthermore, the short-term dynamic supply and demand balance of the power system is evaluated, including:

[0046] The initial unit combination start-up and shutdown decisions obtained by solving the objective function and initial constraints through the solver are used as boundary conditions and substituted into the short-term supply and demand balance model formed by the objective function and optimization constraints. The solutions are respectively performed under no faults and under different unit fault constraints to obtain the power system power shortage and power supply margin indicators under no faults as well as the power system power shortage and power supply margin indicators corresponding to multiple unit faults.

[0047] Furthermore, the power shortage and power supply adequacy indicators of the power system satisfy the following relationship:

[0048]

[0049] In the formula, Q t The power system is short of electricity, M argin is the power supply adequacy index, P i max is the maximum output of adjustable generator set i.

[0050] In a second aspect, a short-term dynamic supply and demand balance assessment device for a power system with multi-resource collaboration is also provided, which adopts the above-mentioned short-term dynamic supply and demand balance assessment method for a power system with multi-resource collaboration, and the device includes:

[0051] A target determination module is used to construct an objective function based on the power generation cost of the adjustable generator set, the start and stop cost of the adjustable generator set, the penalty cost for abandoning wind and solar power, the load shedding cost, and the load transfer cost;

[0052] A constraint determination module, which is used to determine the initial constraints of the adjustable generator set power generation cost, the adjustable generator set start-up and shutdown cost, the wind and solar abandonment penalty cost, the load shedding cost and the load transfer cost;

[0053] The constraint optimization module is used to determine the forecast error constraints based on meteorological elements, the frequency support constraints of the adjustable generator sets and the unit failure constraints, and optimize and replace the initial constraints to give the optimized constraints;

[0054] A decision determination module, which is used to determine the start and stop decision of the unit combination based on the objective function and the initial constraint conditions;

[0055] The decision-making evaluation module is used to evaluate the short-term dynamic supply and demand balance of the power system based on the start and stop decisions of the unit combination, combined with the objective function and optimization constraint adjustment.

[0056] The present invention provides a method and device for evaluating short-term dynamic supply and demand balance of a power system with multi-resource coordination, which at least has the following beneficial effects:

[0057] (1) Incorporating multiple resources (such as energy storage, renewable energy, traditional power sources, demand response, etc.) into the same evaluation framework enables collaborative analysis and optimization of multiple resources; it can provide more accurate and timely information support for the dispatch and operation of the power system, and effectively evaluate the short-term supply and demand balance requirements and capabilities in scenarios with a high proportion of new energy access.

[0058] (2) Based on the power generation margin index, a long / short-term coupled collaborative optimization evaluation model is constructed based on the unit combination (supply and demand balance) model under long (short) time scales. This model takes into account constraints such as generation, transmission and transformation unit failures, system frequency, and prediction errors, providing a reliable method for evaluating power supply margin. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flowchart of a multi-resource coordinated short-term dynamic supply and demand balance evaluation method for a power system provided by the present invention;

[0060] Figure 2 A flowchart for optimizing initial constraints provided in one embodiment of the present invention;

[0061] Figure 3 A schematic diagram of a simplified aggregation frequency model provided for an embodiment of the present invention;

[0062] Figure 4 A schematic diagram of a multi-resource coordinated short-term dynamic supply and demand balance assessment device for an electric power system provided by the present invention. DETAILED DESCRIPTION

[0063] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0065] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

[0066] like Figure 1 As shown, the present invention provides a multi-resource coordinated short-term dynamic supply and demand balance evaluation method for a power system, comprising:

[0067] The objective function is constructed based on the power generation cost of the adjustable generator set, the start-up and shutdown cost of the adjustable generator set, the penalty cost for abandoning wind and solar power, the load shedding cost, and the load transfer cost;

[0068] Determine the initial constraints of the adjustable generator set power generation cost, adjustable generator set start-up and shutdown cost, wind and solar abandonment penalty cost, load shedding cost, and load transfer cost;

[0069] Determine the forecast error constraints based on meteorological elements, the frequency support constraints of adjustable generator sets, and the unit failure constraints, and optimize and replace the initial constraints to give the optimized constraints;

[0070] Determine the start and stop decision of unit combination based on the objective function and initial constraints;

[0071] Based on the start-stop decision of unit combination, and combined with the objective function and optimization constraint adjustment, the short-term dynamic supply and demand balance of the power system is evaluated.

[0072] The objective function of the economic dispatch model of the unit combination of the present invention usually includes the power generation cost and start-up and shutdown cost of the adjustable generator set, the cost of abandoning wind and solar power, the penalty cost of load shedding and the cost of load time transfer, so the total cost of the power system is:

[0073]

[0074] in is the power generation cost in the period t∈[1,T], which is usually a quadratic function of the output of the adjustable generator set:

[0075]

[0076] The expression of the start-stop cost of adjustable generator sets is:

[0077]

[0078] The penalty cost for abandoning wind and solar power of new energy units is:

[0079]

[0080] set up is a node in the power system grid topology, is the set of nodes in the power system, is the load vector of node m. At time t, the predicted amount of each type of load in node m can be allocated in proportion. represents the proportion of non-interruptible load, Respectively represent the proportion of different levels of interruptible load, Represents the proportion of transferable load, satisfying

[0081] Non-interruptible loads correspond to extremely high load shedding costs Other interruptible but non-transferable loads correspond to different load shedding costs. To simplify the model, the load shedding costs of interruptible loads are divided into three levels: and Therefore, the load shedding cost can be expressed as:

[0082]

[0083] Since load shifting requires compensation to users, the shifting costs need to be modeled:

[0084]

[0085] In the formula, C t is the total cost of the power system during period t∈[1,T], is the power generation cost of the adjustable generator set, is the start-stop cost of the adjustable generator set, Penalty costs for abandoning wind power. is the load shedding cost, is the load transfer cost, and is a real number set, c is the diagonal matrix of the power generation cost parameters of the adjustable generator set, a i 、c i are the power generation cost parameters of the ith adjustable generator set, b T is the power generation cost parameter vector of the adjustable generator set within the T time period, P tad is the output decision variable of the adjustable generator set, N is the number of adjustable generator sets, is the output decision variable of the Nth adjustable generator set, and are the start / stop operation indicator variables of the i-th adjustable generator set in period t, and are the start-stop costs of adjustable generator set i, τ curt P is the penalty cost coefficient for abandoning wind and solar power, t curt is the total abandoned wind and solar power of all new energy units, is the abandoned wind and solar power of renewable energy unit j at time t, M is the number of renewable energy units, is the set of nodes in the power system, is the load shedding amount of each type of non-transferable load at node m in period t, is the load shedding cost of each type of non-transferable load at node m in period t, φ m represents the cost coefficient of load transfer, t' represents the time after load transfer, is the load transferred from node m from time t to time t'. The adjustable generator set has two states: on and off. The energy storage unit has three states: charging, discharging, and shutting down. The output value of the new energy unit is determined by natural conditions and is a fixed curve.

[0086] The initial constraints on the power generation cost of the adjustable generator set, the start-up and shutdown cost of the adjustable generator set, the penalty cost for abandoning wind and solar power, the load shedding cost and the load transfer cost include determining the load shedding amount constraint, the load transfer amount constraint, the node power balance constraint, the start-up and shutdown state constraint of the adjustable generator set, the technical constraint of the adjustable generator set and the energy storage state constraint.

[0087] In actual application scenarios, the initial constraints include:

[0088] Load shedding for non-transferable loads is a decision variable (l∈{0,1,2,3}), which must satisfy:

[0089]

[0090] For transferable loads, is the decision variable (t, t′∈[1,T]), which should satisfy that the outgoing load at each moment is less than or equal to the transferable load at that moment:

[0091]

[0092] The transferred load includes the transferred out load and the transferred in load;

[0093] In UHV transmission lines, resistance is much smaller than reactance, so for nodes in UHV networks, The power flow during period t can be approximated using the injection of active power:

[0094]

[0095] The node power balance constraint can be expressed as:

[0096]

[0097] Usually the startup state of the adjustable generator set can be represented by a 0-1 decision variable, that is, u i,t ∈{0,1} represents the switch state of the adjustable generator set i at time t, 0 means shutdown, 1 means on. Therefore, the start-stop operation indicator variable and It can be expressed as:

[0098]

[0099] The technical constraints of adjustable generator sets mainly include the maximum / minimum output constraints of the sets, climbing constraints, etc. The maximum / minimum output constraints of the sets can be expressed as:

[0100]

[0101] The upward and downward climbing constraints of the unit can be expressed as:

[0102]

[0103] Energy storage state constraints include energy storage capacity constraints, maximum charging and discharging power constraints, etc. Energy storage charging power of energy storage device k∈S in period t and discharge power are decision variables, and And define the energy storage capacity E k,t As an auxiliary variable, the real-time energy of energy storage device k is:

[0104]

[0105] The charging and discharging of energy storage devices must meet the following constraints:

[0106]

[0107] In the formula, is the proportion of each type of non-transferable load of node m in period t, is the proportion of the transferable load of node m in period t, is the load value of node m at time t, is the load transferred from node m from time t to time t', t,t'∈[1,T], - is the load transferred to node m from time t to time t', is the active power injected into node m during period t, θ m,t and θ n,t They represent the voltage phase angles of node m and node n during period t, respectively, and x mn is the reactance of the branch between node m and node n, is the maximum transmission power of the branch between node m and node n, is the charging and discharging power of energy storage unit k, when When is discharge, When charging, is the set of energy storage units connected to node m, S is the set of energy storage units connected to all nodes, is the power generation of adjustable generator set i in time period t, is the net load of node m in period t, is the total load shedding of all types of non-transferable loads at node m in period t, u i,t Indicates the switch state of the adjustable generator set i at time t, 0 means off, 1 means on, and are the indicator variables for the start and shutdown operation of the adjustable generator set i at time t, respectively, [·] + =max[·,0], are the lower and upper output limits of the adjustable generator set i, respectively. are the positive climbing limit and negative climbing limit of the adjustable generator set i, and are the energy storage charging power and discharging power of energy storage unit k in period t, E k,t is the energy storage capacity of energy storage unit k during period t, are the charging efficiency and discharging efficiency, respectively. They are the upper limits of charging and discharging speeds respectively.

[0108] like Figure 2 As shown, the present invention optimizes the initial constraint conditions, which may include optimizing the initial constraint conditions through prediction error constraints, specifically:

[0109] Based on the Gaussian distribution of wind power output and photovoltaic output, determine the Gaussian distribution function of the total output of renewable energy;

[0110] Based on the Gaussian distribution of load in the power system and the probability distribution function of the total output of renewable energy, the mean and standard deviation of the net load are determined;

[0111] Based on the influence of meteorological factors on the output of wind turbines, Poisson distribution is used to count and fit the time when the output of wind turbines is zero in different wind speed ranges in the historical data of wind turbine output, and the probability of wind turbine output being zero under different wind speeds is given.

[0112] According to the probability of zero output of wind turbine generator set under different wind speeds, the spare capacity corresponding to the wind turbine generator set shutdown is determined;

[0113] The determined reserve capacity is modeled based on chance constraints and combined with the mean and standard deviation of the net load to obtain the optimized initial constraints through mixed integer linear programming.

[0114] In actual application scenarios, the prediction error constraints are determined as follows:

[0115] In extreme weather conditions, the uncertainty of meteorological factors will affect the accuracy of source load prediction. Therefore, the present invention considers that the actual source load at each moment follows the Gaussian distribution. is the load forecast value of node m at time t, and it obeys Gaussian distribution.

[0116] G and H represent the set of adjustable generating units and the set of new energy generating units in the power system respectively. and represents the set of adjustable generator sets and new energy generator sets connected to node m, Respectively represent the adjustable generator set and new energy generator set of node m. Similar to the load, the output of new energy also satisfies the Gaussian distribution, among which the output of wind power Photovoltaic output The probability distribution of wind power output and photovoltaic output is affected by weather events and may have a certain correlation within a certain time and space range. Wind power output and photovoltaic output are independent random variables, so the probability distribution of total renewable energy output can be obtained as in:

[0117]

[0118] However, in extreme weather conditions, excessive wind speeds may cause the wind turbine to be switched off, resulting in a wind turbine output of 0. Most current wind power output prediction models cannot directly determine this situation. In addition, since excessive wind speeds are not the only reason for a wind turbine to be switched off, it is inaccurate to determine whether to switch off based solely on wind speed and perform probabilistic modeling.

[0119] Therefore, the present invention adopts Poisson distribution to count and fit the number of hours with zero output under different wind speed ranges in historical data, and obtains the probability that the output of the wind turbine generator set is zero under the wind speed ws:

[0120] p ws =P ws (y ws ≤δ|s wind =ws). (17)

[0121] The wind speed can be divided into intervals, that is, ws∈{ws1,ws2,…}, where ws1 represents the wind speed range [0,1], ws2 represents the wind speed range [1,2], and the unit is m / s, and so on. ws It indicates the probability of cutting the generator within a certain wind speed range.

[0122] Also consider that the total load random variable and the renewable energy output random variable are independent of each other, so the total load of node m minus the net load of renewable energy output is a random variable and obeys the Gaussian distribution in:

[0123]

[0124] Since the risk of power failure is usually borne by the positive reserve, the power failure probability p of wind turbine j is considered ws After that, the standby capacity needs to be increased due to machine cutting:

[0125]

[0126] Introducing chance constraints to model spare capacity:

[0127]

[0128] Formula (20) can be written in equivalent form:

[0129]

[0130] Equation (21) can be solved by mixed-integer linear programming (MILP). The node power balance constraint is changed to:

[0131]

[0132] In the formula, β + and β - are the upper and lower backup constraint violation probabilities, i.e., risk tolerance, is the net load of node m in period t, is the mean value of wind power output, Represents the sum of the maximum outputs of the enabled adjustable generator sets connected to node m, It represents the minimum output sum of the adjustable generator sets connected to node m. is the positive spare capacity of node m at time t, is the negative spare capacity of node m at time t, is the spare capacity corresponding to the wind turbine generator set shutdown, p ws is the probability of zero output of wind turbine generator set under different wind speeds, P ws is the output of all wind turbines at each moment, p ws,jt is the output of a wind turbine at a certain moment, y ws is the random variable of wind turbine output under wind speed ws, δ is the output threshold of wind turbine, ws is the wind speed range set, Pr[] represents the probability of constraint condition being satisfied, is the mean value of the net load, is the standard deviation of the net load, is the mean value of Gaussian distribution of load in the power system, is the standard deviation of the Gaussian distribution of loads in the power system, is the total load shedding of all types of non-transferable loads at node m in period t, is the mean of the Gaussian distribution function of the total output of renewable energy, is the standard deviation of the Gaussian distribution function of the total output of renewable energy, is the mean value of wind power output, is the standard deviation of wind power output, is the average photovoltaic output, is the standard deviation of photovoltaic output, E[·] represents the expectation of the random variable, Φ is the Gaussian distribution function, Φ -1 (1-β + ) and Φ -1 (1-β - ) are given probabilities 1-β + and 1-β - The corresponding quantile of the normal distribution;

[0133] Determination of frequency support constraints:

[0134] With the rapid development of wind power and photovoltaic power, the sharp decline in the proportion of hydropower and thermal power in the power generation structure will lead to changes in the frequency characteristics of the future power system. After a high proportion of new energy is connected, the demand for flexible resource support capabilities for short-term supply and demand balance has increased significantly. In addition to the inertia of the unit to redistribute disturbances, the short-term adjustment capability of the adjustable generator set is also required to support it. Therefore, it is very important to give the adjustment capacity requirements of the adjustable generator set under the high proportion of new energy access.

[0135] The power disturbance of high proportion of renewable energy comes from renewable energy units and loads:

[0136] ΔP net =ΔP d-(ΔP wind +ΔP pv ) (twenty two)

[0137] The frequency model of the power system is a closed-loop control system, which mainly includes synchronous generators and loads, turbines and their speed regulators. For power systems with high penetration of renewable energy, without considering the frequency control capability of renewable energy generation, it is necessary to introduce variables that characterize the penetration rate based on the above model, such as Figure 3 A simplified aggregate frequency model of a power system with high penetration of renewable energy is shown.

[0138] K represents the power generation coefficient of a conventional synchronous unit (adjustable generator set):

[0139]

[0140] The open-loop transfer function of the closed-loop control system is:

[0141]

[0142] The corresponding closed-loop system characteristic equation is:

[0143] s 2 +2ζω n s+ω n 2 =0 (25)

[0144] in,

[0145]

[0146] According to the Routh criterion, its coefficient should satisfy ζ>0. According to the definition, M, D, and TR are all positive numbers. In the thermal power system, since the steam turbine 0<a<1, when the thermal power generation coefficient K>0, if the thermal power has sufficient frequency regulation capacity, the closed-loop stability condition can be met. In the hydropower system, since the turbine a=-2, in order to meet the closed-loop stability condition, when R<-aT / M, the hydropower generation coefficient should meet:

[0147]

[0148] For the support capacity of the adjustable generator set, the slope at time 0 and the frequency error constraint need to be considered to obtain the maximum support capacity. According to the frequency error transfer function of the power disturbance:

[0149]

[0150] The steady-state frequency error of the power step response is:

[0151]

[0152] Considering the frequency error constraint, the maximum acceptable power difference is:

[0153]

[0154] Similarly, the maximum acceptable power difference considering the slope constraint at time 0 can be obtained by derivation of the time domain form of the dynamic frequency error of the power step response:

[0155] ΔP f,max2 =f(θ max ) (31)

[0156] θ max is the maximum acceptable slope value at time 0. The maximum acceptable power difference can be obtained by combining equations (29) and (30):

[0157] ΔP f,max =max(ΔP f,max1 ,ΔP f,max2 ) (32)

[0158] It can be concluded that the up and down adjustment support capacity requirements of thermal power generating units can be expressed as:

[0159]

[0160] In the formula, Δω max Indicates the maximum acceptable frequency error, θ max is the maximum acceptable slope at time 0, D is the active frequency response coefficient of the load, T is the equivalent inertia time constant of the turbine, K is the power generation coefficient of the conventional synchronous unit, is the load power of node m, ΔP f,max is the maximum acceptable power difference, ΔP f,max2 is the maximum acceptable power difference considering the slope constraint at time 0, ΔP f,max1 The maximum acceptable power difference considering the frequency error constraint, M is the rotor time constant related to the moment of inertia, a is the turbine characteristic coefficient, for a turbine a = -2, for a steam turbine 0 < a < 1. 1 / R is the pure gain of the governor. ΔP G is the change in power generation of conventional adjustable generator sets after external interference, ΔP net is the fluctuation of net load, ΔP d is the load fluctuation, ΔP wind is the fluctuation of wind power output, ΔP pv is the fluctuation of photovoltaic output, ω n is the natural frequency of the power system (the frequency of the power system's free oscillation without damping), and ζ is the damping ratio, that is, the speed at which the oscillation of the power system decays after being disturbed.

[0161] Constraints for unit failures to be determined:

[0162] By using the N-1 principle, we can obtain the average / maximum power shortage of the power system when a single-point failure occurs in the adjustable generator set, and realize the evaluation of the power shortage in the single-point failure scenario. Assuming that there are N adjustable generator sets in the power system, the power shortage in the corresponding scenario is calculated and evaluated for each unit failure. The fault state of the adjustable generator set i can be represented by a 0-1 decision variable, that is, ε i,t ∈{0,1} represents the fault state of the adjustable generator set i at time t, 0 represents fault and 1 represents normal. Therefore, the start-stop variable u i,t Need to be updated to:

[0163]

[0164] u i,t,new Start and shut down plans for new units.

[0165] The unit combination start and shutdown plan u i,t The long-time unit commitment model is solved by using Python to call the IBM ILOG CPLEX solver.

[0166] The difference between the constraints of the multiple-constraint short-term supply and demand balance model and the constraints of the long-term unit commitment model is that the constraints of the multiple-constraint short-term supply and demand balance model add the results of prediction error constraints and frequency safety constraints under various unit failure conditions on the basis of the constraints of the long-term unit commitment model.

[0167] Conduct short-term dynamic supply and demand balance assessment on the power system, including:

[0168] The initial unit combination start-up and shutdown decisions obtained by solving the objective function and initial constraints through the solver are used as boundary conditions and substituted into the short-term supply and demand balance model formed by the objective function and optimization constraints. The solutions are respectively performed under no faults and under different unit fault constraints to obtain the power system power shortage and power supply margin indicators under no faults as well as the power system power shortage and power supply margin indicators corresponding to multiple unit faults.

[0169] In actual application scenarios, short-term supply and demand evaluation indicators are calculated to effectively evaluate the short-term supply and demand balance requirements and capabilities under scenarios with a high proportion of new energy access. Based on the power generation margin indicators, a long / short-term coupled collaborative optimization evaluation model is constructed based on the unit combination (supply and demand balance) model under long (short) time scales.

[0170] Python is used to call the IBM ILOG CPLEX solver to solve the long-term unit commitment model. The obtained unit combination start-up and shutdown plan is brought into the short-term supply and demand balance model as a boundary condition. Under different unit failure constraints, N types of unit failure results (N adjustable generator sets, each adjustable generator set has a single failure, which is N types of unit failure results) and 1 type of no-fault comparison result are obtained. The sum of the load shedding of all nodes in time period t is used as the power shortage indicator of the power system:

[0171]

[0172] This is used to analyze the power shortage of the system at each moment after a single unit fails, and the power supply margin index is the power supply margin index of the started adjustable generator set minus the maximum load:

[0173]

[0174] In the formula, Q t The power system is short of electricity, M argin is the power supply adequacy index, P i max is the maximum output of adjustable generator set i, and is the installed capacity.

[0175] The start-stop decision of the unit combination includes the start-stop status of the unit, specifically, the start-stop status of the adjustable generator set. The start-stop status of the unit determines the size of the spare capacity, but the power shortage that may be generated is different under short-term balance and long-term balance. Therefore, the present invention first obtains the start-stop status of the unit through long-term balance, and then uses the short-term supply and demand balance model to evaluate the start-stop status results, thereby realizing the short-term dynamic supply and demand balance evaluation of the power system.

[0176] like Figure 4 As shown, the present invention also provides a short-term dynamic supply and demand balance assessment device for a multi-resource coordinated power system, which adopts the above-mentioned short-term dynamic supply and demand balance assessment method for a multi-resource coordinated power system, and the device includes:

[0177] A target determination module is used to construct an objective function based on the power generation cost of the adjustable generator set, the start and stop cost of the adjustable generator set, the penalty cost for abandoning wind and solar power, the load shedding cost, and the load transfer cost;

[0178] A constraint determination module, which is used to determine the initial constraints of the adjustable generator set power generation cost, the adjustable generator set start-up and shutdown cost, the wind and solar abandonment penalty cost, the load shedding cost and the load transfer cost;

[0179] The constraint optimization module is used to determine the forecast error constraints based on meteorological elements, the frequency support constraints of the adjustable generator sets and the unit failure constraints, and optimize and replace the initial constraints to give the optimized constraints;

[0180] A decision determination module, which is used to determine the start and stop decision of the unit combination based on the objective function and the initial constraint conditions;

[0181] The decision-making evaluation module is used to evaluate the short-term dynamic supply and demand balance of the power system based on the start and stop decisions of the unit combination, combined with the objective function and optimization constraint adjustment.

[0182] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A multi-resource coordinated short-term dynamic supply and demand balance evaluation method for a power system, characterized in that: include: The objective function is constructed based on the power generation cost of the adjustable generator set, the start-up and shutdown cost of the adjustable generator set, the penalty cost for abandoning wind and solar power, the load shedding cost, and the load transfer cost; Determine the initial constraints of the adjustable generator set power generation cost, adjustable generator set start-up and shutdown cost, wind and solar abandonment penalty cost, load shedding cost and load transfer cost; Determine the forecast error constraints based on meteorological elements, the frequency support constraints of adjustable generator sets, and the unit failure constraints, and optimize and replace the initial constraints to give the optimized constraints; Based on the objective function and the initial constraints, determining the start and stop decision of the unit combination; Based on the start-stop decision of the unit combination, and in combination with the objective function and optimization constraint adjustment, a short-term dynamic supply and demand balance evaluation of the power system is performed.

2. The method for evaluating short-term dynamic supply and demand balance of a power system according to claim 1, characterized in that: The objective function satisfies the following relationship: In the formula, C t is the total cost of the power system during period t∈[1,T], T is the total time, is the power generation cost of the adjustable generator set, is the start-stop cost of the adjustable generator set, Penalty costs for abandoning wind power. is the load shedding cost, is the load transfer cost, c is the diagonal matrix of the power generation cost parameters of the adjustable generator set, is a set of real numbers, a i 、c i are the power generation cost parameters of the ith adjustable generator set, b T is the power generation cost parameter vector of the adjustable generator set within the T time period, P t ad is the output decision variable of the adjustable generator set, N is the number of adjustable generator sets, is the output decision variable of the Nth adjustable generator set, and are the start / stop operation indicator variables of the i-th adjustable generator set in period t, and are the start-stop costs of adjustable generator set i, τ curt P is the penalty cost coefficient for abandoning wind and solar power, t curt is the total abandoned wind and solar power of all new energy units, is the abandoned wind and solar power of renewable energy unit j at time t, M is the number of renewable energy units, Q is the set of nodes in the power system, is the load shedding amount of each type of non-transferable load at node m in period t, is the load shedding cost of each type of non-transferable load at node m in period t, φ m represents the cost coefficient of load transfer, t' represents the time after load transfer, is the load transferred from node m from time t to time t'.

3. The method for evaluating short-term dynamic supply and demand balance of a power system according to claim 2, characterized in that: The initial constraints of the adjustable generator set power generation cost, adjustable generator set start-up and shutdown cost, wind and solar power abandonment penalty cost, load shedding cost and load transfer cost include determining load shedding amount constraints, load transfer amount constraints, node power balance constraints, adjustable generator set start-up and shutdown state constraints, adjustable generator set technical constraints and energy storage state constraints.

4. The method for evaluating short-term dynamic supply and demand balance of a power system according to claim 2 or 3, characterized in that: The initial constraints further include: In the formula, is the proportion of each type of non-transferable load of node m in period t, is the proportion of the transferable load of node m in period t, is the load value of node m at time t, is the load transferred from node m from time t to time t', is the load transferred to node m from time t to time t', is the active power injected into node m during period t, θ m,t and θ n,t They represent the voltage phase angles of node m and node n during period t, respectively, and x mn is the reactance of the branch between node m and node n, is the maximum transmission power of the branch between node m and node n, is the charging and discharging power of energy storage unit k, when When is discharge, When charging, S m is the set of energy storage units connected to node m, S is the set of energy storage units connected to all nodes, is the power generation of adjustable generator set i in time period t, is the net load of node m in period t, is the total load shedding of all types of non-transferable loads at node m in period t, u i,t Indicates the switch state of the adjustable generator set i at time t, 0 means off, 1 means on, and are the indicator variables for the start and shutdown operation of the adjustable generator set i at time t, respectively, [·] + =max[·,0], are the lower and upper output limits of the adjustable generator set i, respectively. are the positive climbing limit and negative climbing limit of the adjustable generator set i, and are the energy storage charging power and discharging power of energy storage unit k in period t, E k,t is the energy storage capacity of energy storage unit k during period t, are the charging efficiency and discharging efficiency, respectively. They are the upper limits of charging and discharging speeds respectively.

5. The method for evaluating short-term dynamic supply and demand balance of a power system according to claim 1 or 3, characterized in that: Optimize the initial constraints, including: Based on the Gaussian distribution of wind power output and photovoltaic output, determine the Gaussian distribution function of the total output of renewable energy; Based on the Gaussian distribution of load in the power system and the probability distribution function of the total output of renewable energy, the mean and standard deviation of the net load are determined; Based on the influence of meteorological factors on the output of wind turbines, Poisson distribution is used to count and fit the time when the output of wind turbines is zero in different wind speed ranges in the historical data of wind turbine output, and the probability of wind turbine output being zero under different wind speeds is given. According to the probability of zero output of wind turbine generator set under different wind speeds, the spare capacity corresponding to the wind turbine generator set shutdown is determined; The determined reserve capacity is modeled based on chance constraints and combined with the mean and standard deviation of the net load to obtain the optimized initial constraints through mixed integer linear programming.

6. The method for evaluating short-term dynamic supply and demand balance of a power system according to claim 5, characterized in that: The modeling of the determined spare capacity based on the opportunity constraint further comprises: p ws =P ws (y ws ≤δ|s wind =ws). In the formula, β + and β - are the upper and lower backup constraint violation probabilities, i.e., risk tolerance, is the net load of node m in period t, is the mean value of wind power output, Represents the sum of the maximum outputs of the enabled adjustable generator sets connected to node m, It represents the minimum output sum of the adjustable generator sets connected to node m. is the positive spare capacity of node m at time t, is the negative spare capacity of node m at time t, is the spare capacity corresponding to the wind turbine generator set shutdown, p ws is the probability of zero output of wind turbine generator set under different wind speeds, P ws is the output of all wind turbines at each moment, p ws,jt is the output of a wind turbine at a certain moment, y ws is the random variable of wind turbine output under wind speed ws, δ is the output threshold of wind turbine, ws is the wind speed range set, and Pr[] represents the probability that the constraint condition is satisfied.

7. The method for evaluating short-term dynamic supply and demand balance of a power system according to claim 5, characterized in that: The mean and standard deviation of the net load satisfy the following relationship: In the formula, is the mean value of the net load, is the standard deviation of the net load, is the mean of the Gaussian distribution of load in the power system, is the standard deviation of the Gaussian distribution of loads in the power system, is the total load shedding of all types of non-transferable loads at node m in period t, is the mean of the Gaussian distribution function of the total output of renewable energy, is the standard deviation of the Gaussian distribution function of the total output of renewable energy, is the mean value of wind power output, is the standard deviation of wind power output, is the average photovoltaic output, is the standard deviation of PV output; The optimized initial constraints satisfy the following relationship: In the formula, E[·] represents the expectation of the random variable, Φ is the Gaussian distribution function, and Φ -1 (1-β + ) and Φ -1 (1-β - ) are given probabilities 1-β + and 1-β - The corresponding quantile of the normal distribution.

8. The method for evaluating short-term dynamic supply and demand balance of a power system according to claim 1 or 3, characterized in that: The frequency support constraints of the adjustable generator set satisfy the following relationship: ΔP f,max =max(ΔP f,max1 ,ΔP f,max2 ) ΔP f,max2 =f(θ max ) ΔP net =ΔP d -(ΔP wind +ΔP pv ) In the formula, ΔP G is the change in power generation of conventional adjustable generator sets after external interference, ΔP net is the fluctuation of net load, ΔP d is the load fluctuation, ΔP wind is the fluctuation of wind power output, ΔP pv is the fluctuation of photovoltaic output, Δω max Indicates the maximum acceptable frequency error, ω n is the natural frequency of the power system, θ max is the maximum acceptable slope at time 0, D is the active frequency response coefficient of the load, 1 / R represents the pure gain of the speed regulator, K is the power generation coefficient of the conventional synchronous unit, ΔP f,max is the maximum acceptable power difference, ΔP f,max2 is the maximum acceptable power difference considering the slope constraint at time 0, ΔP f,max1 To be the maximum acceptable power difference considering the frequency error constraint, is the load power of node m.

9. The method for evaluating short-term dynamic supply and demand balance of a power system according to claim 1, characterized in that: Conduct short-term dynamic supply and demand balance assessment on the power system, including: The initial unit combination start-up and shutdown decisions obtained by solving the objective function and initial constraints through the solver are used as boundary conditions and substituted into the short-term supply and demand balance model formed by the objective function and optimization constraints. The solutions are respectively performed under no faults and under different unit fault constraints to obtain the power system power shortage and power supply margin indicators under no faults as well as the power system power shortage and power supply margin indicators corresponding to multiple unit faults.

10. A short-term dynamic supply and demand balance assessment device for a multi-resource coordinated power system, characterized in that: The method for evaluating short-term dynamic supply and demand balance of a power system with multi-resource coordination as claimed in any one of claims 1 to 9 is adopted, and the device comprises: A target determination module is used to construct an objective function based on the power generation cost of the adjustable generator set, the start and stop cost of the adjustable generator set, the penalty cost for abandoning wind and solar power, the load shedding cost, and the load transfer cost; A constraint determination module, which is used to determine the initial constraints of the adjustable generator set power generation cost, the adjustable generator set start-up and shutdown cost, the wind and solar abandonment penalty cost, the load shedding cost and the load transfer cost; The constraint optimization module is used to determine the forecast error constraints based on meteorological elements, the frequency support constraints of the adjustable generator sets and the unit failure constraints, and optimize and replace the initial constraints to give the optimized constraints; A decision determination module, which is used to determine the start and stop decision of the unit combination based on the objective function and the initial constraint conditions; The decision-making evaluation module is used to evaluate the short-term dynamic supply and demand balance of the power system based on the start and stop decisions of the unit combination, combined with the objective function and optimization constraint adjustment.

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