CN119154284B - A dispatching method for virtual power plants to participate in inertia and reserve markets - Google Patents
A dispatching method for virtual power plants to participate in inertia and reserve markets Download PDFInfo
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Abstract
本发明公开了一种虚拟电厂参与惯量和备用市场的调度方法，包括以下步骤：(1)建立电力系统模型和虚拟电厂模型；(2)基于所述电力系统模型和所述虚拟电厂模型，建立不确定性功率输出模型；(3)基于所述电力系统模型和所述虚拟电厂模型，建立电力系统的频率响应模型；(4)基于所述不确定性功率输出模型和所述频率响应模型，建立同步发电机与虚拟电厂的优化调度模型；(5)求解所述优化调度模型，得到虚拟电厂的调度指令。
The present invention discloses a scheduling method for a virtual power plant to participate in an inertia and reserve market, comprising the following steps: (1) establishing a power system model and a virtual power plant model; (2) establishing an uncertainty power output model based on the power system model and the virtual power plant model; (3) establishing a frequency response model of the power system based on the power system model and the virtual power plant model; (4) establishing an optimal scheduling model for a synchronous generator and a virtual power plant based on the uncertainty power output model and the frequency response model; (5) solving the optimal scheduling model to obtain a scheduling instruction for the virtual power plant.
Description
Technical Field
The invention relates to a scheduling method for participation inertia and reserve markets of a virtual power plant, and belongs to the technical field of virtual power plants.
Background
Renewable energy sources and electrochemical energy storage that are grid-connected through power electronic inverters have the characteristics of flexibility and vulnerability and cannot spontaneously provide inertia and droop response as conventional synchronous generators. Therefore, the frequency safety of the power system is affected due to the large quantity of grid connection of the power electronic inverter power supply, and the burden of operation management of the power system is increased due to the large-scale distributed source load resources. In addition, the fluctuation of renewable energy power generation brings uncertainty to the dispatching of the power system, and the effective consumption of new energy is influenced, so that the reliability of the dispatching operation of the power system is influenced. The above problems present challenges for safe and reliable operation of power systems containing high proportions of renewable energy.
To cope with the safety problem, various inverter power control methods have been proposed in the academia and industry, such as droop control, virtual inertia control, virtual synchronous generator control, virtual oscillator control, so that an inverter-based power supply can autonomously perform virtual inertia response and frequency response. Wind power and photovoltaic can participate in the frequency response of the system through the inverter power control method, and battery energy storage can simultaneously provide standby and participate in the frequency response. In order to solve the problem of uncertainty of renewable energy sources, the existing method is used for compensating the new energy source power deviation of uncertainty in real-time operation by a reserved unit standby method, and the problem of uncertainty optimization scheduling of a power system is solved by uncertainty optimization methods such as robust optimization, random optimization, opportunity constraint optimization and the like. Based on the above technology, operators of the power system can guide controllable generators, renewable energy sources or energy storage to provide power reserve and virtual inertia response through setting reserve markets and inertia markets, so that the reliability and safety of the operation of the power system are improved.
In prior art research, the scheduling of units participating in the power system backup and inertia markets is typically based on a power system safety constraint scheduling method in the day before or during the day. The system operator solves the scheduling instructions according to the bids of the controllable synchronous generator and inverter power supplies, and the spare capacity and inertia response capacity of each unit and each power supply are determined. The prior art comprises a market participation scheduling method of synchronous generators and various power supplies, including application of wind power, photovoltaic, electric automobiles and batteries in standby and inertia markets. However, ubiquitous source load resources with small capacity and different characteristics are difficult to meet market participation conditions, and the regulation potential of flexible resources cannot be fully exerted. The virtual power plant (Virtual power plant, VPP) technology is used as an effective technical approach for large-scale and small-capacity distributed resource operation management, can coordinate distributed source load resources to create economic value and support efficient operation of an electric power system, but an optimal scheduling method for participation of the virtual power plant in standby and inertia markets is not yet available.
The existing dispatching method for the virtual power plant to participate in the standby and inertia markets is mainly constructed based on a traditional power system mainly comprising synchronous generators, and along with the increase of the permeability of new energy and the withdrawal of a traditional unit, a feasible optimized dispatching method for participating in the standby and inertia markets is designed according to the running characteristics of distributed resources in the virtual power plant.
Disclosure of Invention
In view of the above, the invention provides a scheduling method for virtual power plant participation inertia and standby markets aiming at the running characteristics of distributed resources in a virtual power plant, so as to solve the problem that the existing virtual power plant scheduling model does not have feasibility in participating in the standby and inertia markets of a novel power system.
In order to solve the problems, the invention provides the following technical scheme:
A scheduling method of virtual power plant participation inertia and standby market comprises the following steps of (1) establishing a power system model and a virtual power plant model, (2) establishing an uncertainty power output model based on the power system model and the virtual power plant model, (3) establishing a frequency response model of a power system based on the power system model and the virtual power plant model, (4) establishing an optimal scheduling model of a synchronous generator and the virtual power plant based on the uncertainty power output model and the frequency response model, and (5) solving the optimal scheduling model to obtain scheduling instructions of the virtual power plant.
Further, the step (1) of establishing a power system model comprises the steps of describing the power system by using a graph model of node interconnection and line interconnection, and respectively describing line parameters, a generator, a wind power plant, a virtual power plant and position information of loads by using a node admittance matrix, a generator node position matrix, a wind power plant node position matrix, a virtual power plant node position matrix and a load node position matrix, so as to construct the power system model, wherein a scheduling problem time period set of a power transmission network is defined as T, and the scheduling problem time period set is indexed by time T.
Further, the virtual power plant model in step (1) is described by an active power output, a power conditioning reserve, and a virtual inertia response capacity of the virtual power plant, and building the virtual power plant model includes:
based on the power prediction results of the distributed photovoltaic and the adjustable load, the charging and discharging power and the electric energy level of the stored energy are considered, and the upper limit and the lower limit of the active power output of the virtual power plant at the time t are obtained in an aggregation mode;
based on the demand response range of the adjustable load, the standby capacity of the stored energy and the electric energy level of the stored energy, the power up-regulation maximum value and the power down-regulation maximum value set by the virtual power plant at the time t are obtained in an aggregation mode;
And based on the virtual inertia control coefficient and the rated capacity of the distributed energy storage, the virtual inertia response capacity upper limit of the virtual power plant at the time t is obtained in an aggregation mode.
Further, the step (2) of establishing an uncertainty power output model comprises the steps of describing random errors of predicted power and actual power based on vectors formed by uncertainty variables, and representing the power adjustment quantity of uncertainty based on affine proportion control strategies of synchronous generators and virtual power plants.
Further, the step (3) of establishing the frequency response model comprises the steps of establishing a primary frequency modulation output model of the synchronous generator, solving the total inertia capacity of the electric power system, establishing the frequency response model by utilizing a swinging equation constructed based on the primary frequency modulation output model and the total inertia capacity, and deducing a frequency safety index.
Further, the step (4) of establishing the optimal scheduling model comprises the steps of (4.1) establishing an objective function of participation of the synchronous generator and the virtual power plant in optimal scheduling, (4.2) establishing power regulation constraint of the synchronous generator based on the uncertainty power output model, (4.3) establishing power regulation constraint of the virtual power plant based on the virtual power plant model and the uncertainty power output model, (4.4) establishing line flow constraint of the power system, and (4.5) establishing frequency safety constraint of the power system based on the frequency safety index.
Further, the objective function in the step (4.1) comprises synchronous generator operation cost and virtual power plant operation cost, wherein the synchronous generator operation cost comprises power generation fuel cost, primary frequency modulation standby cost and up-regulation and down-regulation power regulation standby cost at all scheduling moments, and the virtual power plant operation cost comprises power generation operation cost, inertia response cost and up-regulation and down-regulation power regulation standby cost at all scheduling moments.
Further, the power regulation constraint of the synchronous generator in the step (4.2) comprises constraint of a power generation control participation factor of the synchronous generator, system balance constraint of power generation control, upper and lower limit constraint of output power of the synchronous generator, power up-regulation and down-regulation standby of the power generation control of the synchronous generator and upper and lower limit constraint of primary frequency regulation standby, reliability distribution robustness constraint of power up-regulation and down-regulation standby of the power generation control of the synchronous generator, wherein the sum of the power generation control participation factors of all the synchronous generators and all the virtual power plants is limited to be 1;
The power regulation constraint of the virtual power plant in the step (4.3) comprises a constraint of a power generation control participation factor of the virtual power plant, a constraint of virtual inertia response capacity of the virtual power plant, an upper limit constraint and a lower limit constraint of output power of the virtual power plant, an upper limit constraint and a lower limit constraint of power up-regulation and power down-regulation of the virtual power plant, and a reliability distribution robust opportunity constraint of power up-regulation and power down-regulation of the virtual power plant.
Further, the step (4.4) of establishing a line flow constraint of the power system comprises the steps of firstly establishing a line flow model of the power system, and then establishing the line flow constraint based on the line flow model so as to ensure that the probability of the line flow between an upper limit and a lower limit of the flow is within a set range;
The step (4.5) of establishing the frequency safety constraint of the power system comprises the steps of obtaining a frequency minimum point safety threshold of the power system according to the frequency safety index, limiting the frequency of the power system not to be lower than the frequency minimum point safety threshold under power loss disturbance, limiting the total inertia of the power system not to be lower than an inertia level corresponding to the frequency change rate safety threshold, and limiting quasi-steady-state frequency deviation of the power system.
Further, the step (5) specifically comprises (5.1) solving moment information of uncertainty probability distribution based on historical data of random variables existing in constraint of the optimal scheduling model, and constructing fuzzy sets of the random variable probability distribution, (5.2) converting distribution robust opportunity constraint containing uncertainty vectors described by the fuzzy sets into a standard form to obtain non-convex distribution robust opportunity constraint, (5.3) reforming the non-convex distribution robust opportunity constraint obtained in the step (5.2) into convex linear constraint or second order cone constraint based on inequality approximation, and (5.4) solving the optimal scheduling model containing the convex linear constraint or second order cone constraint by a solver to obtain a scheduling instruction of the virtual power plant.
The technical scheme of the invention has the beneficial effects that:
1) The optimal scheduling model can enable the virtual power plant to participate in energy, inertia and standby markets, and the flexibility of distributed source load resources is aggregated and utilized;
2) The frequency response model of the power system disclosed by the invention establishes frequency safety constraint by taking conditions such as load damping, frequency modulation response dead zone and the like into consideration, and has higher accuracy;
3) The distributed robust opportunity constraint optimization method is based on probability information of independent uncertainty, has better economical efficiency compared with the robust optimization method, has higher calculation efficiency compared with the scene-based uncertainty optimization method, can enable the synchronous generator and the virtual power plant to compensate the uncertainty of the new energy output power through optimal scheduling, and improves the running reliability of the system.
Drawings
FIG. 1 is a flow chart of a method for scheduling virtual power plant participation inertia and standby markets according to an embodiment of the present invention.
FIG. 2 is a frame diagram of a virtual power plant participating in a standby and inertia market in accordance with an embodiment of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and specific embodiments, examples. The examples are provided for illustration only and are not intended to be limiting in any way.
The embodiment of the invention provides a scheduling method of virtual power plant participation inertia and reserve market, referring to fig. 1, the scheduling method comprises the following steps (1) to (5):
step (1), an electric power system model and a virtual power plant model are established, and the specific process is as follows:
(1.1) Power System model:
Consider a power transmission network in which I, E, G, W, V, L represents a node set, a power transmission line set, a synchronous generator set, a wind farm set, a virtual power plant set, and a load set in a power system, respectively, i and j represent indexes of nodes, and ij, g, w, v, l represents indexes of a power transmission line, a synchronous generator set, a wind farm, a virtual power plant, and a load, respectively. One power source or load of the synchronous generator set, the wind power plant and the virtual power plant exists on each node i or j, and a line ij represents a power transmission line connected with the node i and the node j in the power system. Based on the above, the power system can be described by using the graph models (I, E) of the interconnection of the nodes and the lines, and the line parameters, the generators, the wind farm, the virtual power plants and the position information of the loads are respectively described by using the node admittance matrix, the generator node position matrix, the wind farm node position matrix, the virtual power plant node position matrix and the load node position matrix, so as to construct the power system model. Wherein, the scheduling problem time period set of the power transmission network is defined as T, and is indexed by T.
(1.2) Virtual plant model:
Distributed resources in the power system can be divided into distributed photovoltaic, distributed energy storage and adjustable load types, and distributed in a power distribution network of a single transmission network load node, wherein the distributed resources on the single load node are managed in an aggregate way by a Commercial virtual power plant (Commercial VPP) to participate in power system scheduling and market transaction.
The distributed source load resource has the characteristics of multiple points, wide range and various characteristics, and the virtual power plant needs to aggregate the belonging distributed source load resource model when modeling, so as to obtain the integral output model of the virtual power plant. The distributed source load resource aggregation of the virtual power plant at the present stage usually adopts a geometric method to describe the aggregated feasible region, and the aggregation of the output power feasible region of the distributed source load resource, the aggregation of the power standby feasible region and the aggregation of the virtual inertia feasible region can be carried out in the self-dispatching link of the virtual power plant. The specific technical means of the distributed source load resource aggregation can adopt the existing mode, and the invention does not limit the method. The virtual power plant model of the embodiment of the invention is described by its active power output, power regulation reserve and virtual inertia response capacity, so that the building of the virtual power plant model in this step specifically includes:
(1.2.1) solving the upper limit of active power output of the virtual power plant at the time t by adopting the existing geometric aggregation method based on the power prediction result of the distributed photovoltaic and the adjustable load and considering the charge-discharge power and the electric energy level of the stored energy And lower limit of;
(1.2.2) Solving the power up-regulation maximum value set by the virtual power plant at the time t by adopting the existing geometric aggregation method based on the demand response range of the adjustable load, the standby capacity of the stored energy and the electric energy level of the stored energyAnd power down maximum;
(1.2.3) Solving the virtual inertia response capacity upper limit of the virtual power plant at the time t by adopting the existing geometric aggregation method based on the virtual inertia control coefficient and rated capacity of the distributed energy storage。
In the embodiment of the invention, the uncertainty fluctuation of the distributed photovoltaic output power in the virtual power plant is assumed to be compensated and regulated through the energy storage and the load in the virtual power plant, and is carried out in a self-dispatching link of the virtual power plant.
And (2) establishing an uncertainty power output model based on the power system model and the virtual power plant model. The random error of the wind power plant predicted power and the actual output power is described based on a vector formed by uncertainty variables, and then the power adjustment quantity of uncertainty is represented based on an affine proportion control strategy of a synchronous generator and a virtual power plant, so that the establishment of an uncertainty power output model is realized.
Firstly, the operating mechanism of the power system can schedule the synchronous generator set and the controllable source load based on the predicted values of the load and wind power, however, the actual output power of the wind power plant can deviate from the predicted power value with random errors. The uncertainty power output error of the wind power plant w at the scheduling time t is ζ w,t, and the uncertainty power output of the wind power plant can be modeled as follows:
wherein, Representing the output power of the wind farm w,Representing deterministic wind power predicted power. Global uncertainty power bias for power system at time tUncertainty power output errors from all wind farmsThe summation is expressed as:
。
In order to ensure the power supply reliability of the power system, the synchronous generator and the virtual power plant reserve active power reserve, and the uncertainty power output error of wind power is compensated in a proportional control mode in real time based on an automatic power generation control strategy. Considering the uncertainty output (or "uncertainty power adjustment") of the power generation control, based on affine proportional control strategies of the synchronous generator, the virtual power plant, the uncertainty power output of the synchronous generator and the virtual power plant can be modeled as:
wherein, Representing the uncertainty power output of the synchronous generator,Representing a deterministic synchronous generator planned output power,Representing an uncertainty power adjustment of a synchronous generator, whereinA participation factor for power generation control of the synchronous generator; representing the uncertainty power output of the virtual power plant, Representing a deterministic virtual power plant planned output power,Representing the virtual power plant uncertainty power adjustment contribution,The participation factors are controlled for the generation of the virtual power plant.
And (3) establishing a frequency response model of the power system based on the power system model and the virtual power plant model. The method mainly comprises the steps of firstly establishing a primary frequency modulation output model of a synchronous generator, then solving the total inertia capacity of the electric power system, and finally establishing a frequency response model of the electric power system and deducing a frequency safety index by utilizing a swing equation constructed based on the primary frequency modulation output model and the total inertia capacity.
When the power system frequency fluctuates due to a fault power disturbance, the synchronous generator may provide a primary frequency response through the droop response of its governor. To simplify the calculation and obtain a solvable result, we model the frequency response of the synchronous generator as a linear response model, assuming that the ramp time constant of the synchronous generator frequency response power output isConsider the frequency modulation response dead time corresponding to the dead zone of the frequency response (generally 0.03-0.05 Hz)The climbing time constant means that the generator is selfFrom moment on, the regulated power is linearly increased from 0 to the maximum regulated power, the maximum power regulation of the frequency response is obtained by frequency modulationLimited, primary frequency modulation output model of synchronous generatorThe expression is as follows:
wherein, Is a time variable of the frequency response.
The synchronous generator can spontaneously provide rotary inertia under the starting state, and the synchronous generatorIs recorded as inertia of (2)Virtual power plants may provide virtual inertia responses through internal distributed energy storage, expressed asTotal inertia capacity of power systemThe inertia of all synchronous generators and the virtual power plant inertia are added to be obtained:
In the invention, the power loss caused by the grid fault in the worst case is modeled as a step power loss disturbance The amplitude isFrequency response model for power systemThis can be expressed by the following swing equation:
Wherein D is the load damping coefficient of the power system, and L t is the total load demand of the power system.
The Frequency safety of the power system usually focuses on three indexes of a maximum Frequency change rate (Rate of Change of Frequency, roCoF), a Frequency minimum point (Frequency nadir) and a Quasi-steady-state Frequency deviation (Quasi STEADY STATE Frequency deviation), which correspond to the safety performance of the power system Frequency at the moment of fault occurrence, the transient process and the Quasi-steady-state process respectively. Based on the frequency response model, we can solve for these three frequency safety indicators.
Maximum rate of frequency changeAt the moment of occurrence of the disturbance, the magnitude of the disturbance related to the fault power and the inertia level of the power system is expressed as:
substituting the primary frequency modulation output model of the synchronous generator into the frequency response model of the power system to obtain the piecewise nonlinear expression. At the position of At a certain moment in the process, the frequency adjustment output power of all generators of the power system reaches a power disturbance value, and at the moment, the system frequency obtains the lowest point. Taking primary frequency modulation output model of synchronous generator in time periodSubstituting the swing equation of the frequency response model of the power system, and solving a differential equation corresponding to the frequency deviation change rate equal to 0Obtaining the time variable expression of the lowest frequency pointAnd then willSubstitution intoFrequency response model for a time periodThe expression can obtain the expression with the lowest frequency point. For the nonlinear frequency lowest point expression, the nonlinear frequency lowest point expression can be obtained by solving a piecewise linearization methodIs a linear approximation of the expression of (c).
Quasi-steady state frequency deviationFor the frequency deviation of the power system after the frequency response of all the generators is completely output, the result is determined by the sum of the frequency adjustment reserve (frequency modulation reserve) of all the generators, and the sum of the frequency adjustment reserve of all the synchronous generators is recorded asQuasi-steady state frequency deviation of power systemThe computational expression is:
The frequency response model and the frequency safety index of the power system established in the step are used in the frequency safety constraint condition of the power system in the subsequent step (4), and the frequency safety constraint condition is used for describing the frequency safety requirement of the power system.
And (4) establishing an optimal scheduling model of the synchronous generator and the virtual power plant based on the uncertainty power output model and the frequency response model. The method specifically comprises the following steps:
(4.1) establishing an objective function of participation of the synchronous generator and the virtual power plant in optimal scheduling;
(4.2) establishing a power regulation constraint of the synchronous generator based on the uncertainty power output model;
(4.3) establishing a power regulation constraint of the virtual power plant based on the virtual power plant model and the uncertainty power output model;
(4.4) establishing line power flow constraints of the power system;
and (4.5) establishing frequency safety constraint of the power system based on the frequency safety index obtained in the step (3).
The optimal scheduling model in the step is a core part of the method provided by the invention and mainly comprises an objective function (4.1) and constraint conditions (4.2) - (4.5). The method has the effects that the operation cost, operation constraint and safety constraint of the power system, the synchronous generator and the virtual power plant are considered, an optimal scheduling model capable of solving is established, and operation instructions corresponding to the participation inertia of the virtual power plant and the standby market are obtained through solving. The following describes the steps (4.1) to (4.5) in detail.
(4.1) Establishing an objective function of participation of the synchronous generator and the virtual power plant in optimization scheduling:
The objective functions of the synchronous generator and the virtual power plant participating in optimized dispatching comprise synchronous generator operation cost and virtual power plant operation cost, wherein the synchronous generator operation cost comprises power generation fuel cost, primary frequency modulation standby cost and up-regulation and down-regulation power regulation standby cost at all dispatching moments, and the virtual power plant operation cost comprises power generation operation cost, inertia response cost and up-regulation and down-regulation power regulation standby cost at all dispatching moments.
The objective of optimizing the scheduling in the present invention is to minimize the objective function, expressed as follows:
wherein, ,,,Respectively synchronous generatorsThe power generation cost coefficient, the primary frequency modulation standby cost coefficient, the power generation control up-regulation standby cost coefficient and the down-regulation standby cost coefficient;And And the power for the power generation control of the synchronous generator is adjusted upwards and downwards for standby.,,,The method comprises the steps of respectively aggregating a power generation cost coefficient of a virtual power plant v at a time t, an aggregated virtual inertia control cost coefficient, an aggregated power generation control up-regulation standby cost coefficient and a down-regulation standby cost coefficient;And And (5) up-regulating and down-regulating the power of the virtual power plant for standby.
(4.2) Establishing a power regulation constraint of the synchronous generator based on the uncertainty power output model:
the constraint of the synchronous generator power generation control participation factor is expressed as:
the system balance constraint of the power generation control limits the sum of the power generation control participation factors of all synchronous generators and all virtual power plants to 1, i.e. balances the global uncertainty power deviation 。
The upper and lower limit constraints of the synchronous generator output power are expressed as:
wherein, AndIs the upper limit and the lower limit of the power output of the synchronous generator.
The power up-regulation and down-regulation reserve of the synchronous generator power generation control and the upper limit and lower limit constraint of the primary frequency modulation reserve are expressed as follows:
wherein, AndAnd the maximum value is respectively the maximum value for power up-regulation and the maximum value for power down-regulation of the power generation control of the synchronous generator.
The reliability distribution robust opportunity constraint for power up-regulation and down-regulation of synchronous generator power generation control is expressed as:
the meaning of the reliability distribution robust opportunity constraint is interpreted as for an uncertainty vector In fuzzy setAll probability distributions withinEnsuring that the probability of the establishment of the constraint condition in the bracket is greater than a certain lower-bound probabilityI.e. uncertainty output of synchronous generator power generation controlThe probability of being smaller than the up-regulation and larger than the down-regulation is at leastThe feasibility probability threshold is constrained for the backup reliability set in advance.
(4.3) Establishing a power regulation constraint of the virtual power plant based on the virtual power plant model and the uncertainty power output model:
The constraint of the power generation control participation factor of the virtual power plant is expressed as:
the constraints of the virtual inertia response capacity of the virtual power plant are expressed as:
The upper and lower limit constraints of the virtual power plant output power are expressed as:
wherein, AndThe upper limit and the lower limit of the predicted power output of the virtual power plant at the time t are respectively set.
The upper and lower limit constraints for the power up-regulation and down-regulation of the virtual power plant are expressed as:
wherein, AndThe power up maximum value and the power down maximum value are respectively set by the virtual power plant at the time t.
The reliability distribution robustness opportunity constraint for the power up-regulation and down-regulation of the virtual power plant is as follows:
for uncertainty vectors In fuzzy setAll probability distribution conditions in the virtual power plant, and the two constraint conditions ensure uncertainty output of power regulation of the virtual power plantThe probability of being smaller than the up-regulation and larger than the down-regulation is at leastThe feasibility probability threshold is constrained for the backup reliability set in advance.
(4.4) Establishing line power flow constraints of the power system:
First, establishing a line power flow of a power system The model is represented as follows:
wherein, Shift factor matrix, subscript for representing power systemRepresenting the first of the matrixA row;, , the power injection matrixes of the synchronous generator, the virtual power plant and the wind power plant are respectively; And the output power vector of the synchronous generator at the moment t, the output power vector of the virtual power plant, the output power vector of the wind farm and the load demand vector are respectively.
Based on the line flow model, a flow constraint is then established, which is expressed as follows:
for uncertainty vectors In fuzzy setAll probability distribution conditions in the circuit are guaranteedUpper limit ofAnd lower limit ofProbability therebetween is at leastAnd (5) restricting a feasibility probability threshold value for the power flow which is set in advance.
And (4.5) establishing frequency safety constraint of the power system based on the frequency safety index obtained in the step (3).
The frequency minimum point constraint of the power system is obtained by solving the method in the step (3), and the original frequency minimum point expression is a nonlinear function, so that the optimal scheduling model can be solved, and the approximate linear expression set of the constraint is obtained by utilizing a piecewise linearization method and is expressed as follows:
wherein S is defined as a multidimensional space feasible domain of a nonlinear frequency lowest point function, the feasible domain S is divided into a plurality of sub-feasible domains by a piecewise linearization method, an original function on the current sub-feasible domain is approximated by a linear expression, ,Respectively decision variables,Is used for the linear coefficient of (c) in the linear coefficient of (c),The above three parameters are constant terms that can be solved by a least squares method of linear approximation. The frequency nadir constraint limits the system frequency to be not lower than the system frequency nadir safety threshold under power loss disturbance。
The constraint of the frequency change rate of the power system is actually a limitation on the inertia of the system, so that the total inertia of the system is not lower than the safety threshold of the frequency change rateThe corresponding inertia level is expressed as:
The constraint of the quasi-steady-state frequency deviation of the power system is actually the constraint of primary frequency modulation standby of the generator, and is expressed as follows:
wherein, Is a safe threshold for quasi-steady state frequency deviation.
And (5) solving an optimal scheduling model of the synchronous generator and the virtual power plant to obtain a scheduling instruction of the virtual power plant. The method mainly comprises the following steps:
(5.1) solving moment information of uncertainty probability distribution based on historical data of random variables existing in constraint of an optimized scheduling model, and constructing a fuzzy set of random variable probability distribution;
(5.2) converting the distribution robust opportunity constraint containing the uncertainty vector described by the fuzzy set into a standard form to obtain a non-convex distribution robust opportunity constraint;
(5.3) reforming the non-convex distributed robust opportunity constraint obtained in step (5.2) into a convex linear constraint or a second order cone constraint based on an inequality approximation;
And (5.4) solving an optimized dispatching model containing the convex linear constraint or the second order cone constraint by using a solver to obtain dispatching instructions of the virtual power plant.
The random power deviation vector (uncertainty vector) of all wind farms of the power system is recorded as(Note: here T represents the transpose, |w| is the number of wind farms). Based on moment information of the random power deviation vector, fuzzy sets of random power deviation probability distribution can be establishedThe definition is as follows:
wherein, Is a random power deviation vectorIs to take the first moment information of (a)Historical sample data means of (a);Is a random vectorWherein T in the upper right corner represents the transpose, takenCovariance of historical sample data of (a)。
For fuzzy setDescribed uncertainty-containing vectorsThe distributed robustness opportunity constraint of (2) may be modeled as the distributed robustness opportunity constraint of the following criteria:
wherein a (x) and b (x) are mapping functions of the decision variable x, the T in the upper right corner of a (x) representing the transpose, The allowable out-of-limit probability is a constraint condition set in advance.
The reliability distribution robust opportunity constraint of the synchronous generator power adjustment reserve, the reliability distribution robust opportunity constraint of the virtual power plant power adjustment reserve and the line tide constraint related in the step (4) can be expressed as the standard distribution robust opportunity constraint. Based on the inequality approximation, the non-convex distributed robust opportunity constraints can be reformed into convex linear constraints or second order cone constraints, expressed as follows:
By using the formula, the distributed robust opportunity constraint which cannot be directly solved in the steps (4.2) - (4.3) can be reformed into convex linear constraint or second order cone constraint.
Furthermore, the optimal scheduling model containing linear constraints or Second order cone constraints is a Second order cone programming (Second-order cone programming) problem, and can be solved by using a commercial solver such as CPLEX, GUROBI, MOSEK and other software.
The solving result of the optimized scheduling model comprises each scheduling momentAny synchronous generatorActive power output of (1), synchronous generator power generation control participation factor, synchronous generator primary frequency modulation standby, synchronous generator power generation control power up-regulation and down-regulation standby, expressed asThe solving result related to the virtual power plant comprises each scheduling momentAny virtual power plantActive power output of (1), virtual power plant generation control participation factor, virtual power plant inertia response capacity, virtual power plant generation control power up-regulation and down-regulation reserve, expressed as. After the dispatching mechanism of the power system solves the optimized dispatching model, dispatching instructions can be issued to the synchronous generator and the virtual power plant in the system at different dispatching moments t based on the solving results, and then dispatching of the synchronous generator and the virtual power plant can be achieved. The virtual power plant outputs corresponding virtual inertia and reserved reserve based on the scheduling result, and can participate in reserve and inertia markets in the electric power market.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several equivalent substitutions and obvious modifications can be made without departing from the spirit of the invention, and the same should be considered to be within the scope of the invention.
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