CN115526401A - Novel power supply optimal planning method for electric power system based on digital twinning - Google Patents
Novel power supply optimal planning method for electric power system based on digital twinning Download PDFInfo
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Abstract
The invention provides a novel power supply optimal planning method for an electric power system based on digital twins, which solves the load fluctuation of a power grid and improves the absorption capacity of the power grid by proportioning the power supply capacity. The strategy solves the problem of uncertainty of new energy output by establishing a five-dimensional new power system model and modeling wind power and photovoltaic units by using a mechanism-data driven hybrid model, establishes a structure planning model and a system planning model of a virtual power system, simulates the system planning modeling by digital twins, feeds back a calculation result to a planning main body to guide planning decision, realizes automatic updating iteration of model structure and parameters by real-time communication and interaction of sensors and physical entity information, verifies the effect of a planning scheme by combining indexes such as system planning cost, wind curtailment quantity and the like, provides detailed information of operating points of the power system, and searches for the optimal occupation ratio of each type of power supply by using multiple times of simulation after updating parameters.
Description
Technical Field
The invention relates to a novel power supply optimal planning method for an electric power system, so as to stabilize load fluctuation of a power grid and improve the consumption capacity of the power grid.
Background
The core problem of the novel power system is the multilayer mixed integer nonlinear programming problem, and the problems of construction cost, resource assessment, environmental cost and the like of a plurality of systems need to be considered, wherein the most important problem is to solve the problem of multi-system coupling. The novel power system is different from the traditional power system in that the ratio of the new energy to the power grid is higher than fifty percent. Because of the influence of factors such as wind speed, wind direction and solar radiation intensity, huge uncertainty and volatility are brought to wind power and photovoltaic power, huge problems are brought in the planning of new energy access to a power grid, and the problems are mainly reflected in poor electric energy quality, operation stability of a power system, reduction of safety, difficulty in power transmission and the like, so that accurate prediction of output of new energy is guaranteed to be the basis of power supply planning of a novel power system. The novel power system planning comprises two parts, wherein one part is used for planning the occupation ratio of various power supply types of a power system, and the other part is used for considering the type of the commissioning equipment from space and time after the occupation ratio is determined.
The digital twin is a virtual model constructed from multiple physical fields, multiple space-time, multiple angles, multiple subjects fused and the like in a digital space, the virtual model monitors the dynamic evolution process of the physical model in real time through a sensor, data analysis, processing, prediction, diagnosis and the like are carried out by combining big data, and the operation and decision of a physical entity are controlled in the whole life cycle of a product through the digital model.
A novel power system digital model is established in a digital twin, and the digital twin has main functions in the planning of a novel power system: modeling and simulating system planning, and feeding back a result to a main body planning to guide planning decision, wherein the model interactively stores a large amount of data in real time along with communication-physical entities, the physical information comprises regional perennial power supply planning information, regional power supply installed capacity and quantity, regional user electricity utilization information, wind power photovoltaic power information, regional generator set information and the like, a structure planning model and a system planning model are established in a virtual model, continuous self-learning is carried out in big data, and virtual-real updating iteration is carried out to obtain a wind-light output predicted value; load prediction and power output balance, system stability and wind-abandoning light-abandoning quantity of a project area are calculated in a structure planning model, an optimal power occupation scheme of various types of power supplies of a novel power system is obtained, the consumption capacity of the power system is improved, and safer and more stable operation of a power grid system is guaranteed.
Disclosure of Invention
Firstly, constructing a novel power system framework under a digital twin, wherein the novel power system framework consists of five-dimensional models: the method comprises the steps of establishing a structure planning model and a system planning model of the virtual power system, establishing a digital twin model for system planning modeling simulation, feeding back a calculation result to a planning main body to guide planning decision, realizing automatic updating and iteration of a model structure and parameters through real-time communication interaction of a sensor and physical entity information, calculating wind-light power prediction by using an algorithm and combining a mass data optimization virtual model, determining the proportion of each type of power supply, evaluating the operation stability of a novel power system planning scheme by using the digital twin model, checking the effect of the planning scheme by combining indexes such as system planning cost, light curtailment quantity and the like, providing detailed information of the operation point of the power system, and searching the optimal proportion of each type of power supply by using multiple times of simulation after parameter updating.
The technical problem of the invention is mainly realized by the following technical scheme:
a power optimal planning method frame of a novel power system power optimal planning method based on digital twinning is characterized in that: comprises the following steps of (a) carrying out,
step 1: step 1, establishing a digital twin five-dimensional space model, as shown in fig. 1:
U DT ＝(PE,VM,DD,CN,SS) (1)
in the formula: PE is a physical entity, VM is a virtual model, DD is a digital twin database, CN is data connection, and SS is a service; a physical entity system may consist of a device or product, a physical system, an activity process, or even an organization. They act according to the laws of physics and cope with uncertain circumstances. Firstly, obtaining novel power system structure planning and system planning information, wherein the information comprises installed capacity and quantity of regional power supplies, power system frequency, voltage amplitude, phase angle, power grid architecture, load type and the like. The virtual model is real-time mapping of a physical entity and can be formulated by a geometric model, a behavior model or a rule model, the digital twin database comprises static attribute data and dynamic data attributes, part of data is generated by virtual simulation, a simulation result is reflected, and a system planning main body is guided by the result to carry out further planning; the service module comprises application services such as load side demand and power system monitoring, power transmission line diagnosis, prediction and health management.
Step 2: modeling a virtual power system by using a mechanism-data hybrid model, modeling mechanisms of a wind turbine generator and a photovoltaic generator, and correcting model errors by data driving in a parallel mode. The wind turbine generator mainly comprises blades, a rotating wheel, a controller, a generator and the like, and according to a leaf element momentum theory, the wind turbine generator modeling method is used for modeling the torque of the wind wheel:
in the formula:
wind wheel torques of all wind turbine generators in the region y; r and r0 are respectively the radius of the wind wheel and the radius of the hub; c is the chord length of the phyllanthus; a is the inclination angle of the wind direction relative to the wind wheel; ρ is the gas density; v tot Is the relative velocity of the leaf element; l 1 ,l d The gas lift coefficient and the resistance coefficient of the phyllanthin are taken as the parameters;
the mechanism modeling of the photovoltaic unit is as follows:
in the formula: i is the theoretical output current of the photovoltaic cell; i is sc ,U oc ,I m ,U m ,P max ,K I ,K V Short-circuit current, open-circuit voltage, maximum power point electric power, maximum power point voltage, maximum power and electric temperature system tested by photovoltaic cell manufacturers under IEC conditionsNumber and voltage temperature coefficients, etc.;
in practical engineering, due to the influence of adverse conditions such as climate and unit service life, the deviation between the output result and the actual value is large because an operation curve of a photovoltaic unit model and a wind turbine unit model cannot be predicted, so that an empirical model can be established for correcting the deviation in data driving in a parallel mode, the data driving is modeled by a neural network, a hybrid model parallel mode is shown in fig. 2, and the operation parameter of physical equipment is Y 0 The primary output parameter of the mechanism model is Y 1 The data-driven model output is d y ，Y 2 And outputting the mixed model. In the whole model operation, let ERROR coefficient ERROR = Y 0 -Y 1 -d y As minimum as possible, i.e. the function satisfies Y as much as possible 0 ＝d y +Y 1 ＝Y 2 And when the neural network training is finished, the output of the wind turbine generator and the photovoltaic generator after error correction is finally obtained.
The main performance parameters of the wind turbine generator are as follows: wind wheel rotational speed, generator rotational speed and generator power, photovoltaic unit main parameter is: outputting voltage and current as network output layer; the wind turbine generator system has the following influence parameters: wind speed, wind direction, blade cross-section, horizontal axis wind speed, photovoltaic unit influence parameter: illumination intensity, humidity, photovoltaic cell life, module connection loss, the above-mentioned parameter is the network input layer.
The updating process of the electric power system model based on the digital twins is mainly divided into two processes, wherein in one process, a physical system object directly transmits digital twins information to the model through a sensor technology, the model carries out super-parameter iterative computation under an intelligent neural network algorithm, so that the regular change of electric quantities such as voltage, frequency, transmission line loss and the like of a novel electric power system full life cycle is realized through a real-time evolution model, a digital twins database is compared with measured data through historical operating data, and the electric power system planning parameters in a certain area are predicted by using the intelligent neural network algorithm, so that the process reflects the predictability of the digital twins; the other process is that the influence of a power system model under extreme conditions is deduced manually, the boundary conditions which are not beneficial to the stable operation of the power system under the rare conditions in reality are assumed manually, the key information of the power system under various adverse conditions is changed through an intelligent neural algorithm, the information obtained under the assumed conditions is compared in the intelligent artificial algorithm, and an optimal planning scheme is selected, and the process reflects the hypothesis of digital twinning;
and 3, step 3: the novel structure planning modeling of the power system establishes a target function of a structure planning model of the virtual power system by taking economy, abandoned wind and abandoned light quantity and system fluctuation rate as references, and the total operation cost is as follows:
in the formula: t is the planning interval year;
the system construction, system operation and maintenance, unit fuel and environmental pollution cost in the t year are respectively expressed as follows:
in the formula: c is a novel power system power supply type;
O t,c,i representing new increase of a certain class in planning yearInstalled capacity and total installed capacity of a unit i of a type power supply c;
represents the construction cost of a certain type of newly added power supply capacity;
represents the operating cost of a certain type of power supply and the i variable operating power cost;
representing the cost of the power consumption fuel of the power supply c unit i;
representing the cost of electricity consumption carbon emission and the environmental cleaning cost of a certain power supply c unit i; f t,c,i ＝B t,c,i O t,c,i Representing the annual output of a certain type of power supply m unit i; b is t,c,i Representing the total number of annual operating hours of a certain type of power m unit i.
The wind and light abandoning amount is:
wherein, the first and the second end of the pipe are connected with each other,
is the abandoned wind power of the wind power plant after the installed capacity after T interval years,
the light power is the light power of the installed capacity of the photoelectric plant after T interval years;
the fluctuation rate of the wind, light, fire and storage combined power generation system is as follows:
wherein the content of the first and second substances,
and the new installed capacity generating capacity of the wind power plant, the photovoltaic power plant and the stored energy after T intervals.
The structure planning of novel electric power system establishes the constraint condition, its characterized in that, resource development constraint:
K Gt +Y m,i ≥K pt +Y m,i+1 (1.1)
in the formula: k Gt 、K pt Representing the total power consumption and the maximum consumption of the unit in a certain place; y is m,i 、Y m,i+1 Representing the construction time of a certain type of power supply c unit i and i + 1;
the environmental constraints are:
in the formula: f e,t,c,i 、F tot Representing an upper limit on the amount of exhaust gas discharged and dischargeable.
And (3) new energy ratio constraint:
in the formula:
representing the energy consumption of the new energy unit;
representing the new energy ratio; s. the tot Represents the total amount of energy consumption;
the power balance constraint is:
wherein, P load,t Is the total load value of the regional power grid,
for newly adding the output data of the installed capacity of the thermal power in the T interval years, and the method comprises the following steps:
in the formula:
adding a predicted value and a predicted error of the capacity of the wind power system;
the capacity of the newly added photovoltaic power generation system is predicted value and prediction error;
the unit output constraint conditions are as follows:
wherein d is 0 、d 1 、d 2 Respectively is the state coefficient P of peak regulation of the thermal power generating unit in a certain area i (a)、P i (b) Respectively is the lower limit and the upper limit of the output of the peak regulating generator set in different states of the thermal power generating unit, P i (max)、P i (min) respectively representing the upper limit and the lower limit of the output of the regional normal peak regulating thermal power generating unit;
the minimum start-stop time constraint of the thermal power generating unit is as follows:
wherein, the first and the second end of the pipe are connected with each other,
is the current working operation time of t-1 moments of a first group of thermal power generating units in a certain area,
for the current work stop time of the ith group of thermal power generating units at the t-1 th moment in a certain region,
is the starting operation instantaneous time of the ith thermal power generating unit at the tth moment of a certain region, namely the minimum starting time,
is the minimum time from running to stopping of the first group of thermal power generating units in a certain area, u i,t The state variation of the ith thermal power generating unit in the operation period at the tth moment in a certain area is shown, wherein 1 represents the operation of the thermal power generating unit, and 0 represents the shutdown of the thermal power generating unit;
and (3) climbing restraint of the thermal power generating unit:
in the formula:
respectively providing maximum downward and upward climbing power of a newly added thermal power unit in a certain area during capacity working;
the upper limit and the lower limit of the charge and discharge power of the energy storage battery are as follows:
in the formula:
respectively representing the minimum charge-discharge and maximum power of each energy storage battery in a certain area;
novel reserve restraint of electric power system:
in the formula: r 1,t 、R 0,t Respectively representing the positive spare capacity and the negative spare capacity required by the generator sets of other power systems after wind and light power converters are ignored in a certain area; r res,1,t 、R res,0,t Dividing the capacity into a positive spare capacity and a negative spare capacity required during the safe and stable operation of a novel power system representing a certain area;
respectively, represent the confidence level of the unit equipment backup requirement that the new power system must meet.
And 4, step 4: and carrying out simulation analysis by combining the steps, further optimizing indexes such as economy, wind curtailment and light curtailment of the structure planning by utilizing an optimization algorithm based on digital twin modeling driving, carrying out weighted sum on each index by considering multiple indexes such as the economy, the wind curtailment and the system fluctuation rate level of the structure planning model, and optimizing decision variables such as the model number and the number of the equipment and the optimized operation mode of the system equipment under different working conditions. On the basis of inheriting a nonlinear model after system coupling, firstly, coding decision variables by using an intelligent optimization algorithm, searching according to algorithm rules in a solving area, further calculating to obtain decision variables corresponding to positions solved for multiple times, wherein the decision variables represent calculation basic conditions provided by power planning based on digital twins, and finally, carrying out next calculation by using an optimization algorithm according to indexes such as calculation construction cost, absorption capacity and the like of the system planning model after checking is completed through checking structural planning constraints. As shown in fig. 3.
During solving, the virtual model is solved by adopting an improved particle swarm-genetic algorithm, and the algorithm solving flow is as follows:
(1) Firstly, initializing each parameter and particle population;
(2) Each particle performs a large range of motion in the search space;
(3) Judging the constraint condition, if the particle does not meet the constraint condition, returning to the step (2), recalculating the speed and the searching direction of the particle, and updating the particle;
(4) Comparing and updating the individual optimal solution and the global optimal solution of all the particles;
(5) Judging whether the convergence condition of the particle swarm algorithm is met, if the global optimal solution cannot be updated after continuous 25 iterations, determining convergence, indicating that the optimal solution is obtained, transmitting the optimal solution to a genetic algorithm for next-step solving, wherein the genetic algorithm takes all particles as chromosomes, the dimensionalities of the particles as genes of the chromosomes, inputting the historical optimal solutions of all the particles, and moving the initial chromosome set which is taken as the genetic algorithm to the genetic algorithm; if not, returning to the step (2);
(6) Simulating the planning scheme of the virtual-novel power system built by the cloudsps platform to operate so as to select and operate the planning scheme;
(7) Starting cross and variation operation on the virtual-novel power system planning scheme;
(8) Judging whether the constraint condition is met, if so, returning to the step (7);
(9) Judging whether the convergence condition of the genetic algorithm is met, when the global optimal solution is not carried out after continuous 25 iterations, considering the convergence, and if the convergence condition is not met, returning to the step (6); and if so, outputting a final result.
Therefore, the invention has the following advantages: (1) The method comprises the following steps that a visualization model built based on a Cloudsps platform can simulate the operation and energy flow of novel power system equipment such as a photovoltaic generator set, a wind generating set and a thermal generator set according to real-time collected actual scene data; (2) According to the real-time data of the twin database, the actual condition of the result load is optimized, and the planning scheme of the novel power system can be reflected in real time. (3) By adopting a data driving method and combining real-time data of a twin database, the output of the wind generating set and the photovoltaic set can be fully reflected according to the change characteristics of the ring factors, the expression of the model is more accurate, the energy equipment can more flexibly deal with the load change, the investment cost is further optimized, and the fluctuation rate of a novel power system is reduced.
Drawings
Fig. 1 is a digital twinning five-dimensional space diagram.
Fig. 2 is a hybrid model parallel mode.
FIG. 3 is an optimization algorithm based on a digital twin modeling drive.
Fig. 4 is a load prediction diagram.
FIG. 5 is a wind-solar prediction graph.
Fig. 6 is a new energy occupancy map.
Fig. 7 is a graph showing the change in the wind curtailment rate.
Detailed Description
The technical solution of the present invention is further described below by way of examples with reference to the accompanying drawings.
Firstly, the existing installed capacity of new energy, the future installed capacity, power type parameters, wind and light prediction parameters and environment cleaning cost are assumed, wind and light power prediction is contrastively analyzed in a digital twin database, and a conversion model of numerical weather forecast and wind power and photovoltaic output prediction is constructed by adopting a GLSTM neural network. And comparing data in a digital twin database, calculating the values to obtain a prediction deviation if searching to obtain the actual wind-solar output value and the wind-solar output prediction under similar conditions, and compensating the weight by the prediction deviation. And further optimizing the neural network algorithm for the digital twin modeling drive by combining equipment constraint and the like, calculating the optimal combination of the power supply in the novel power system, obtaining the optimal planning scheme of the novel power system, and improving the absorption capacity, the power grid safety and the stability of the novel power system.
To verify the advantages of the present invention, the following simulation analysis was performed:
taking a certain area in China as an example for calculation, assuming that the planning year is 2035 years, the maximum load of the area in 2022 years is 1678 thousands KW, the average load is 1022 thousands KW, and by inputting the characteristic parameters of the highest temperature, the lowest temperature and the weather, the load value of the area within the planning year range is predicted by using a neural network, as shown in FIG. 4, assuming that the spare rate of power supply equipment of the area is set to 8%.
Relevant data such as wind direction, temperature, wind speed and the like are selected according to local numerical weather forecast and input into a digital twin database, and output prediction is carried out on certain time period of wind power and photovoltaic by combining a neural network prediction scheme, as shown in fig. 5. After prediction, planning information is transmitted to a digital twin database through a communication network, data processing, data normalization and data analysis are further transmitted to a digital twin model, decision variables of the model are optimized in a neural network algorithm, power supply occupation ratios of various types every year in a planning year are obtained through digital twin optimization, as shown in fig. 6, and changes of new energy occupation ratios and abandoned wind and abandoned light quantities are obtained through analysis, as shown in fig. 7.
The above embodiments are only intended to illustrate the technical solution of the present invention and not to limit the same, and those skilled in the art to which the present invention pertains can make various modifications or compensations to the described embodiments without departing from the scope of the present invention as defined by the appended claims.
Claims (7)
1. A novel power supply optimal planning method of a power system based on digital twinning is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
establishing a digital twin five-dimensional space model;
modeling a virtual power system by using a mechanism-data hybrid model, modeling mechanisms of a wind turbine generator and a photovoltaic generator, and correcting model errors by data driving in a parallel mode;
establishing a target function and a constraint condition of a structure planning model of the virtual power system according to the economy, the wind curtailment and light curtailment amount and the system fluctuation rate as references;
and solving an objective function by using an optimization algorithm based on digital twin modeling drive, and performing weighted sum on each index, wherein optimization decision variables comprise equipment models, quantity and system equipment optimization operation modes under different working conditions.
2. The optimal planning method for the power supply of the novel power system based on the digital twin as claimed in claim 1, is characterized in that: establishing digital twin five-dimensional space model
U DT ＝(PE,VM,DD,CN,SS) (1)
In the formula: PE is a physical entity, VM is a virtual model, DD is a digital twin database, CN is a data connection, and SS is a service.
3. The optimal planning method for the power supply of the novel power system based on the digital twin as claimed in claim 1, is characterized in that: modeling a virtual power system by using a mechanism-data hybrid model, modeling mechanisms of a wind turbine generator and a photovoltaic generator, correcting model errors by data driving in a parallel mode, and modeling wind wheel torque according to a leaf element momentum theory:
in the formula:
wind wheel torques of all wind turbine generators in the region y; r and r0 are respectively the radius of the wind wheel and the radius of the hub; c is the chord length of the chlorophyll; a is the inclination angle of the wind direction relative to the wind wheel; ρ is the gas density; v tot Is the relative velocity of the phyllanthin; l 1 ,l d The gas lift coefficient and the resistance coefficient of the phyllanthin are taken as the parameters;
the mechanism modeling of the photovoltaic unit is as follows:
in the formula: i is the theoretical output current of the photovoltaic cell; i is sc ,U oc ,I m ,U m ,P max ,K I ,K V Short circuit current, open circuit voltage, maximum for photovoltaic cell manufacturers testing under IEC conditionsPower point power, maximum power point voltage, maximum power, electrical temperature coefficient, and voltage temperature coefficient.
4. The optimal planning method for the power supply of the novel power system based on the digital twin as claimed in claim 1 is characterized in that: establishing an objective function of a structure planning model of the virtual power system according to the reference of economy, the light curtailment amount of the abandoned wind and the system fluctuation rate, wherein the total operation cost is as follows:
in the formula: t is the planning interval year;
the system construction, system operation and maintenance, unit fuel and environmental pollution cost in the t year are respectively expressed as follows:
in the formula: c is a novel power system power supply type;
O t,c,i the installed capacity and the total installed capacity of a certain type of power supply c unit i are newly increased in the planning year;
represents the construction cost of a certain type of newly added power supply capacity;
represents the operating cost of a certain type of power supply and the i variable operating power cost;
representing the cost of the power consumption fuel of the power supply c unit i;
representing the cost of electricity consumption carbon emission and the cost of environmental cleaning of a certain power supply c unit i; f t,c,i ＝B t,c,i O t,c,i Representing the annual output of a certain type of power supply m unit i; b is t,c,i Represents the total annual operating hours of a certain type of power supply m unit i;
the wind and light abandoning amount is:
wherein, P i w,aban (T) is the power of the abandoned wind after the installed capacity of the wind power plant after T years of interval, P i pv,aban (T) is the abandoned optical power of the installed capacity of the photovoltaic plant after T interval years;
the fluctuation rate of the wind, light, fire and storage combined power generation system is as follows:
wherein, P i w (t)、P i pv (t)、P i C (T) New wind, photovoltaic and energy storage plants after T years intervalsAnd (4) installed capacity power generation.
5. The optimal planning method for the power supply of the novel power system based on the digital twin as claimed in claim 1, is characterized in that: and (3) establishing constraint conditions for the structural planning of the novel power system, wherein the constraint conditions comprise resource development constraint, environment constraint, new energy ratio constraint, power balance constraint, unit output constraint, thermal power unit minimum start-stop time constraint, thermal power unit climbing constraint, energy storage battery charge-discharge power upper and lower limit constraint and novel power system standby constraint.
6. The optimal planning method for the power supply of the novel power system based on the digital twin as claimed in claim 1, is characterized in that: the resource development constraints are based on the following formulas:
K Gt +Y m,i ≥K pt +Y m,i+1 (0.1)
in the formula: k Gt 、K pt Representing the total power consumption and the maximum consumption of the unit in a certain place; y is m,i 、Y m,i+1 Representing the construction time of a certain type of power supply c unit i and i + 1;
the environmental constraints are based on the following formula:
in the formula: f e,t,c,i 、F tot Represents the discharge amount and the upper limit of the discharge amount of the waste gas;
the new energy ratio constraint is based on the following formula:
in the formula:
representing the energy consumption of the new energy unit;
representing the new energy ratio; s tot Represents the total amount of energy consumption;
the power balance constraint is based on the following equation:
wherein, P load,t For the total load value, P, of the regional power grid i g (T) is the output data of newly-increased thermal power installed capacity in T interval years, and:
in the formula: p i w,yc (t)、ΔP i w,wc (t) a predicted value and a predicted error of the capacity of the newly-increased wind power system are obtained; p i pv,yc (t)、ΔP i pv,wc (t) a predicted value and a predicted error of the capacity of the newly added photovoltaic power generation system are obtained;
the unit output constraint is based on the following formula:
wherein d is 0 、d 1 、d 2 Respectively is the state coefficient P of peak regulation of thermal power generating unit in a certain region i (a)、P i (b) Respectively as the lower limit and the upper limit of the output power of the peak regulating generator set in different states of the thermal power generating unit P i (max)、P i (min) respectively representing the upper limit and the lower limit of the output of the normal peak-shaving thermal power generating unit in the region;
the minimum start-stop time constraint of the thermal power generating unit is based on the following formula:
wherein the content of the first and second substances,
is the current working operation time at t-1 moments of a first group of thermal power generating units in a certain area,
the current work stop time of the ith thermal power generating unit at the t-1 th moment in a certain region,
is the starting operation instantaneous time of the ith thermal power generating unit at the tth moment of a certain region, namely the minimum starting time,
is the minimum time from running to stopping of the first group of thermal power generating units in a certain area, u i,t The state variation of the ith thermal power generating unit in the operation period at the tth moment in a certain area is shown, wherein 1 represents the operation of the thermal power generating unit, and 0 represents the shutdown of the thermal power generating unit;
the ramp constraint of the thermal power generating unit is based on the following formula:
in the formula:
respectively providing maximum downward and upward climbing power of a newly added thermal power unit in a certain area during capacity working;
the upper limit and the lower limit of the charging and discharging power of the energy storage battery are based on the following formula:
in the formula:
respectively representing the minimum charge-discharge power and the maximum power of each energy storage battery in a certain area;
the novel power system backup constraints are based on the following formula:
in the formula: r 1,t 、R 0,t Respectively representing the positive spare capacity and the negative spare capacity required by the generator sets of other power systems after wind and light power converters are ignored in a certain area; r res,1,t 、R res,0,t Dividing the capacity into a positive spare capacity and a negative spare capacity required during the safe and stable operation of a novel power system representing a certain area;
respectively, represent the confidence level of the unit equipment backup requirement that the new power system must meet.
7. The power optimal planning method of the novel power system based on the digital twin power supply optimal planning method according to claim 1, wherein in the solving, the virtual model is solved by adopting an improved particle swarm-genetic algorithm, and the algorithm solving process is as follows:
(1) Firstly, initializing each parameter and particle population;
(2) Each particle performs a large range of motion in the search space;
(3) Judging the constraint condition, if the particle does not meet the constraint condition, returning to the step (2), recalculating the speed and the searching direction of the particle, and updating the particle;
(4) Comparing and updating the individual optimal solution and the global optimal solution of all the particles;
(5) Judging whether the convergence condition of the particle swarm algorithm is met, if the global optimal solution cannot be updated after continuous 25 iterations, determining convergence, indicating that the optimal solution is obtained, transmitting the optimal solution to a genetic algorithm for next-step solving, wherein the genetic algorithm takes all particles as chromosomes, the dimensionalities of the particles as genes of the chromosomes, inputting the historical optimal solutions of all the particles, and moving the initial chromosome set which is taken as the genetic algorithm to the genetic algorithm; if not, returning to the step (2);
(6) Simulating and operating a planning scheme of a virtual-novel power system built by the cloudsps platform so as to select and operate the planning scheme;
(7) Starting cross and variation operation on the virtual-novel power system planning scheme;
(8) Judging whether the constraint condition is met, if so, returning to the step (7);
(9) Judging whether the convergence condition of the genetic algorithm is met, when the global optimal solution is not carried out after continuous 25 iterations, considering the convergence, and if the convergence condition is not met, returning to the step (6); and if so, outputting a final result.
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