CN112036611A - Power grid optimization planning method considering risks - Google Patents

Power grid optimization planning method considering risks Download PDF

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CN112036611A
CN112036611A CN202010806172.3A CN202010806172A CN112036611A CN 112036611 A CN112036611 A CN 112036611A CN 202010806172 A CN202010806172 A CN 202010806172A CN 112036611 A CN112036611 A CN 112036611A
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power
load
cost
planning
risk
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CN112036611B (en
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田鑫
张栋梁
吴军
甘佩莹
鉴庆之
李文升
赵龙
郑志杰
王艳
孙东磊
杨斌
魏佳
张家宁
薄其滨
王轶群
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State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
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    • G06N3/00—Computing arrangements based on biological models
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention relates to the power grid optimization technology, in particular to a risk-related power grid optimization planning method, which respectively carries out probability modeling on wind power, photovoltaic output and load so as to depict the random volatility of the wind power, photovoltaic output and load and provide energy storage charging and discharging strategies; secondly, load reduction minimization is taken as a target, and load flow calculation is carried out on the power system according to a probability optimal load flow method based on Latin hypercube sampling; and finally, analyzing the life cycle cost of the planning project, further constructing a power grid two-layer planning model based on an improved whale optimization algorithm, and recording the risk quantization into a risk loss cost into an objective function. And the power grid optimization planning considering the risks is developed, so that the adaptability of the grid planning scheme in a new situation is improved, and the safety risks brought to the system operation by uncertain factors are reduced from the source.

Description

Power grid optimization planning method considering risks
Technical Field
The invention belongs to the technical field of power grid optimization, and particularly relates to a risk-related power grid optimization planning method.
Background
At present, due to the fact that environmental protection, energy demand, macro policy and other multi-party promotion, the permeability of new energy is continuously improved, the cross-region interconnection of a power grid enables a grid structure to be complicated, controllable resources such as flexible loads and energy storage are continuously developed, and multiple risks are brought to a power system by the uncertain factors of multi-main-body development. As a core link of an electric power system, whether a power grid structure can resist risks caused by uncertain factors is a key for determining whether the system can maintain safe and reliable operation. In the existing research, the theory and method about power grid optimization planning tend to be mature, but uncertainty factors considered by a planning model are not comprehensive enough, the influence of multiple main bodies such as sources, networks, loads, storages and the like and interaction thereof on planning is not considered at the same time, and the research for reflecting the influence of the uncertainty factors on planning economy from the perspective of planning risks is rare. Therefore, power grid optimization planning research considering risks needs to be intensively developed, on one hand, safe and efficient grid connection of new energy can be promoted, and the improvement of the consumption rate of clean energy is facilitated; on the other hand, the structure rigidity of the power grid is improved, and risk accidents caused by uncertain factors are better resisted.
Disclosure of Invention
The invention aims to provide a risk-considered power grid optimization planning method.
In order to achieve the purpose, the invention adopts the technical scheme that: a risk-related power grid optimization planning method comprises the following steps:
step 1, modeling uncertainty factors of power supply, load and energy storage links in an electric power system;
step 2, carrying out load flow calculation on the power system according to a probability load flow method and an optimal load shedding model based on direct current load flow;
step 3, carrying out life cycle cost decomposition on the power grid planning project, and dividing the life cycle cost into investment period cost and operation period cost;
step 4, constructing a target function of power grid optimization planning, and establishing a risk-considering power grid two-layer planning model;
and 5, solving the planning model by adopting an improved whale optimization algorithm, and outputting a net rack planning scheme corresponding to the global optimal individual position.
In the above power grid optimization planning method taking risk into consideration, the modeling of uncertainty factors of a power supply link in the power system in step 1 includes fan output and photovoltaic output; the modeling of the uncertainty factors of the load link comprises the fluctuating load and the interruptible load participating in the response of the demand side; the modeling of the uncertainty factor of the energy storage link comprises energy storage charge and discharge strategies.
In the above power grid optimization planning method taking risk into consideration, the probabilistic power flow calculation method in step 2 adopts a monte carlo simulation method based on latin hypercube sampling; the optimal load shedding model based on the direct current power flow is modeled by applying a Yalmip language on a Matlab platform and solved by using Gurobi and Cplex solvers; the power flow calculation of the power system comprises the following specific steps:
step 2.1, inputting the original data of the system to be evaluated: grid network structure and line parameters, the installed number, capacity and installation site of traditional generator set and new energy, load access condition, energy storage capacity, installation site, maximum charge-discharge power and interruptible load ratio;
2.2, modeling treatment is carried out on the active power and the load uncertainty of the new energy;
2.3, extracting the output sequence of wind power and photovoltaic power by using a Latin hypercube sampling method within the given iteration times to generate a load sequence, and further forming the running state of energy storage;
step 2.4, performing direct current load flow calculation, recording a calculation result, judging whether the active power of the line exceeds the limit, switching to an optimal load shedding program if the active power of the line exceeds the limit, and calculating the minimum load shedding amount under the situation;
step 2.5, fitting the probability density distribution function of the line with power by using a difittool in a Matlab tool box to obtain a proper probability distribution curve and related parameters;
and 2.6, calculating various risk indexes according to the statistic value and the processing value of the probability load flow calculation result, performing risk index calibration right by using an entropy method, calculating the overall safety and economic risk of the system, and evaluating the risk level of the system.
In the above power grid optimization planning method taking risk into consideration, the investment cost in step 3 includes equipment procurement cost, installation and debugging cost, overhaul, operation and maintenance cost, equipment decommissioning cost, and fixed contract cost; the operation period cost comprises the cost of grid loss, the cost of risk loss and the cost of power generation and pollution discharge.
In the above risk-related power grid optimization planning method, the power grid two-layer planning model in step 4, on the premise of satisfying given constraint conditions, adds the risk cost and the pollution discharge cost of the system into the planned total life cycle cost, so that the annual value of the system is the lowest; and the upper layer and the lower layer are optimized through alternate iteration to obtain the optimal power grid planning scheme.
In the above risk-related power grid optimization planning method, the upper model aims at the lowest LCC annual value including risk cost, pollution discharge cost and the like, the decision variables include a power grid network frame and a new energy grid-connected point, and the required planning scheme is assigned to the lower layer.
In the above risk-related power grid optimization planning method, the lower layer model considers uncertainty models of power supply, load and energy storage links under the condition of satisfying the safe operation constraint condition, and performs operation risk assessment based on probability load flow with the goal of minimizing the total load reduction amount, and checks the adaptability of the power grid structure to uncertainty factors, and transfers the calculated operation cost to the upper layer optimization objective function.
In the above risk-considering power grid optimization planning method, the specific steps of solving the planning model by the whale optimization algorithm in step 5 are as follows:
step 5.1, inputting relevant parameters of power grid planning and basic parameters of a WOA algorithm, including the dimensionality of an optimization variable, the number of individuals of an optimization agent population and the total number of iterations;
step 5.2, randomly generating the position of the initial search agent, and initializing;
step 5.3, initializing iteration times, setting the value to be 1, selecting an individual with the minimum fitness value, wherein the position of the individual is the optimal point of the current optimization;
step 5.4, randomly generating the probability p, judging the size of the probability p, selecting a corresponding individual position updating formula according to the judgment result and the coefficient vector obtained by calculation, and updating the position of the individual;
step 5.6, adding 1 to the iteration frequency, judging whether the value of the iteration frequency reaches the preset iteration frequency, and if not, continuing the updating process;
and 5.7, outputting the global optimal individual position when the preset iteration times are reached, wherein the corresponding net rack planning scheme is the optimal scheme obtained by optimization.
The invention has the beneficial effects that: 1. according to the invention, a double-layer planning model is introduced to optimize the power grid planning scheme, the optimization result takes safety and economy into account, and the whole and comprehensive performance is achieved. 2. The invention improves the WOA optimization algorithm, improves the global and local search capability of the algorithm and increases the optimization speed of the algorithm. 3. The method has the advantages of strong feasibility and higher precision, and is favorable for improving the clean energy consumption rate of the power grid and reducing the risk of the power grid caused by uncertain factors.
Drawings
Fig. 1 is a flow chart of a power grid optimization planning method considering risks according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a power system probabilistic power flow calculation provided by an embodiment of the invention;
FIG. 3 is a schematic diagram of risk-aware grid planning life cycle cost that you also provide in accordance with an embodiment of the present invention;
fig. 4 is a schematic diagram of a risk-taking into account power grid two-tier planning model according to an embodiment of the present invention;
fig. 5 is a flow chart of power grid planning based on an improved whale algorithm according to an embodiment of the invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
In the power system, the development of multiple main bodies such as a power supply, a power grid, loads, energy storage and the like is uncertain, so that the system faces the background of multiple risks, in order to improve the capability of the grid frame for resisting risk accidents from a planning design source, the embodiment provides a power grid two-layer planning model considering the risks, the consumption of new energy is promoted from the planning design source, the adverse effect caused by uncertain factors is reduced, and the adaptability of a power grid planning scheme under a new development situation is improved.
The method carries out probability modeling on wind power, photovoltaic output and load respectively to depict random volatility of the wind power, photovoltaic output and load, and provides energy storage charging and discharging strategies; secondly, load reduction minimization is taken as a target, and load flow calculation is carried out on the power system according to a probability optimal load flow method based on Latin hypercube sampling; and finally, analyzing the life cycle cost of the planning project, further constructing a power grid two-layer planning model based on an improved whale optimization algorithm, and recording the risk quantization into a risk loss cost into an objective function.
The embodiment is realized by the following technical scheme, and the power grid optimization planning method considering the risk specifically comprises the following steps:
modeling uncertainty factors in links such as a power supply, a load, energy storage and the like in the power system;
carrying out load flow calculation on the power system by adopting a probability load flow method and an optimal load shedding model based on direct current load flow;
carrying out life cycle cost decomposition on a power grid planning project, and dividing the life cycle cost into investment period cost and operation period cost;
constructing an objective function of power grid optimization planning, and establishing a risk-considering power grid two-layer planning model;
and solving the planning model by adopting an improved whale optimization algorithm, and outputting a net rack planning scheme corresponding to the global optimal individual position, namely an optimal scheme obtained by optimization.
In addition, uncertainty factors for modeling in a power supply link are fan output and photovoltaic output; the uncertain factors for modeling in the load link are the fluctuating load and the interruptible load participating in the response of the demand side; the uncertainty factor for modeling in the energy storage link is the charge and discharge strategy of energy storage.
Moreover, the probability trend method adopts a Monte Carlo simulation method based on Latin hypercube sampling; the optimal load shedding model based on the direct current power flow is a typical mixed integer linear programming problem, is modeled by applying a Yalmip language on a Matlab platform, and is solved by using a solver such as a Gurobi solver and a Cplex solver; the specific steps of the power flow calculation of the power system are as follows:
inputting raw data of a system to be evaluated: grid network structure and line parameters, the installed number, capacity and installation site of traditional generator set and new energy, load access condition, energy storage capacity, installation site and maximum charge-discharge power, interruptible load ratio and the like;
modeling the active power and load uncertainty of the new energy;
within given iteration times, a latin hypercube sampling method is used for extracting the output sequence of wind power and photovoltaic power to generate a load sequence, and further an energy storage running state is formed;
performing direct current load flow calculation, recording a calculation result, judging whether the active power of the line exceeds the limit, switching to an optimal load shedding program if the active power of the line exceeds the limit, and calculating the minimum load shedding amount under the situation;
fitting the probability density distribution function of the line with power by using difittool in a Matlab toolbox to obtain a proper probability distribution curve and related parameters;
and calculating various risk indexes according to the statistic and the processing value of the probability load flow calculation result, performing risk index calibration weight by using an entropy method, calculating the overall safety and economic risk of the system, and evaluating the risk level of the system.
Moreover, the investment cost comprises equipment purchasing cost, installation and debugging cost, maintenance cost, equipment decommissioning cost and fixed contract cost; the operation period cost comprises the cost of grid loss, the cost of risk loss and the cost of power generation and pollution discharge.
And on the premise of meeting given constraint conditions, the power grid two-layer planning model accounts the risk cost and the pollution discharge cost of the system into the planned whole life cycle cost, so that the equal annual value of the system is the lowest, and the selected optimal net rack planning scheme balances the economy, the safety and the environmental protection of the system. And the upper layer and the lower layer are optimized through alternate iteration to obtain the optimal power grid planning scheme.
And the upper layer model aims at the lowest LCC annual value including risk cost, pollution discharge cost and the like, decision variables comprise a power grid net rack and a new energy grid-connected point, and the required planning scheme is assigned to the lower layer.
And under the condition that the lower layer model meets the safe operation constraint condition, considering uncertainty models of links such as a power supply, a load, energy storage and the like, carrying out operation risk assessment based on probability load flow by taking the minimum total load reduction amount as a target, checking the adaptability of the power grid structure to uncertainty factors, and transmitting the calculated operation period cost to the upper layer optimization objective function.
The specific steps of power grid planning based on the improved whale algorithm are as follows:
inputting relevant parameters of power grid planning and basic parameters of an improved WOA algorithm, wherein the relevant parameters comprise the dimensionality of an optimization variable, the individual number of an optimization agent population, the total number of iterations and the like;
randomly generating the position of an initial search agent, and initializing;
initializing iteration times, setting the value of the iteration times as 1, selecting an individual with the minimum fitness value, wherein the position of the individual is the current optimal point for optimization;
randomly generating probability p, judging the probability p, selecting a corresponding individual position updating formula according to the judgment result and the coefficient vector obtained by calculation, and updating the position of the individual;
adding 1 to the iteration times, judging whether the value of the iteration times reaches the preset iteration times, and if not, continuing the updating process;
and when the preset iteration times are reached, outputting the global optimal individual position, wherein the corresponding net rack planning scheme is the optimal scheme obtained by optimizing.
In specific implementation, as shown in fig. 1, a risk-related power grid optimization planning method includes the following steps:
s1: modeling uncertainty factors in links such as a power supply, a load, energy storage and the like in the power system;
modeling uncertainty factors in the power link in S1 is:
the fan output model:
adopting Weibull distribution to establish a probability model of wind speed, wherein the probability density function expression of the probability model is as follows:
Figure BDA0002629197200000071
in the formula, v is the actually measured wind speed of the wind power plant construction land; k is a shape parameter, c is a scale parameter, and the values of the two parameters are determined by the local wind speed change characteristics.
Active power output of the wind turbine generator:
Figure BDA0002629197200000081
in the formula, vi、vr、voRespectively carrying out cut-in wind speed, rated wind speed and cut-out wind speed on the wind driven generator; prRated power for the fan; A. b, C are corresponding characteristic parameters, the values of which are all dependent on the wind resource characteristics of the wind farm construction site:
Figure BDA0002629197200000082
Figure BDA0002629197200000083
Figure BDA0002629197200000084
photovoltaic output model:
the output power of photovoltaic power generation is related to the solar radiation intensity, and the probability density function of the photovoltaic power generation is as follows:
Figure BDA0002629197200000085
wherein r is the intensity of solar radiation; r ismaxIs the maximum radiation intensity; alpha and Beta are Beta distribution shape parameters respectively; is a Gamma function. The alpha, beta parameters are determined by the measured light intensity r and the local maximum illumination intensity rmaxThe desired μ and its standard deviation σ of the ratio are found:
Figure BDA0002629197200000086
general mathematical expressions for the above model:
Figure BDA0002629197200000091
in the formula, RiThe actual solar radiation intensity at a certain moment; rrThe active output of the photovoltaic cell is equal to the light radiation corresponding to the rated value, and the value is generally 1000 (W/m)2) (ii) a In general, RcThe value was set to 150 (W/m)2)；PnThe rated power is photovoltaic power generation.
Modeling uncertainty factors in the load link in S1 is as follows:
uncertainty load model:
the load fluctuation is described by normal distribution, and the probability density distribution is as follows:
Figure BDA0002629197200000092
in the formula (I), the compound is shown in the specification,
Figure BDA0002629197200000093
is the standard deviation of the load;
Figure BDA0002629197200000094
is the load mean.
Node load at a certain time t:
Figure BDA0002629197200000095
interruptible load model:
the load is set to a certain proportion.
Modeling of uncertainty factors in the energy storage link in S1 is as follows:
the linear relation between the generated active power and the initial storage electric quantity of the device is as follows:
Figure BDA0002629197200000096
in the formula, Prel(t) the releasable active electric quantity in the t time period; esto(t) storing the initial electric storage capacity of the battery in the t-th time period; eminThe minimum remaining capacity allowed for the energy storage device; Δ T is the sampling period; pdch,maxThe maximum charge and discharge power of the energy storage device. Initial electric energy storage E of time slot t +1sto(t+1)：
Figure BDA0002629197200000101
In the formula, Pbat(t) is the charge and discharge power of the battery in the t-th period. And (3) recording the surplus power generated by new energy sources such as wind power, photovoltaic and the like in the system at the time t as:
ΔGwind(t)＝PWTG(t)-PL(t)·ηwind
in the formula,. DELTA.Gwind(t) the new energy excess power at time t; pWTG(t) and PL(t) actual output and real-time load values of the new energy at the moment t are respectively; etawindThe system is a new energy installation proportion of the system.
Surplus power Delta G of new energywindWhen the (t) > 0, energy storage charging:
Figure BDA0002629197200000102
surplus power Delta G of new energywind(t) < 0, energy storage discharge:
Figure BDA0002629197200000103
in the formula, Pch,max,Pdch,maxThe energy storage maximum charging and discharging power is respectively.
S2: carrying out load flow calculation on the power system by adopting a probability load flow method and an optimal load shedding model based on direct current load flow;
the principle of latin hypercube sampling of the probabilistic trend method described in S2 is as follows:
let X1,X2,…,XKIs K input quantities in a research object and all obeys a certain probability distribution, wherein any random variable XkThe cumulative probability distribution function of (2):
Yk＝Dk(Xk)
performing Latin hypercube sampling on all input random variables: curve Yk＝Dk(Xk) Is equally divided into N layers which do not overlap each other, due to YkThe value range of (1) is 0-1.0, so that each interval has the same probability of 1/N. Randomly selecting a point in each interval, selecting the midpoint as sampling point, and using Yk＝Dk(Xk) Solving X by the inverse function ofkThe expression of the nth sample value in the sample sequence:
Figure BDA0002629197200000111
and performing decorrelation processing on the sampling sequence, and randomly generating a K multiplied by N order sequence matrix H, wherein each row in the matrix determines the position of element arrangement corresponding to the initial sampling sequence X so as to ensure the independence between variables. Assuming that a K × N order matrix is generated, where the elements in each row are randomly arranged 1,2, …, N, and then forward and backward iterations are performed:
and forward iteration:
for j＝2,3,…,k
for j＝1,2,…,j-1
Hk←takeout(Hk,Hj)
Hk←rank(Hk)
and (3) reverse iteration:
for j＝K-1,K-2,…,1
for j＝K,K-1,…,j+1
Hk←takeout(Hk,Hj)
Hk←rank(Hk)
wherein, the step of using the step of the step; takeout (H)k,Hj) Represents a pair vector Hk,HjDeveloping a Linear regression Hk＝a+bHjThe residual error of (a); rank (H)k) Representing the elements in the pair in order from small to large. Alternating and iterating in the positive direction and the negative direction until the root mean square value rho representing the matrix column correlationrmsAnd stabilizing or reaching the preset iteration number.
The optimal load shedding model based on the direct current power flow in the step S2 is represented as follows:
a direct current power flow model:
PN＝Bθ
branch tide:
pij＝(θi-θj)/xij＝aijθ/xij
substitution can obtain:
pij＝aijB-1PN/xij
expressed as a matrix multiplication:
PL＝BLAB-1PN＝MPN
in the formula, θ represents a phase angle of the node voltage; b is a node admittance matrix, and a reference node is not counted; pNInjecting power for the system node; x is the number ofijReactance for branch ij; p is a radical ofijIs the active power flowing through branch ij; thetai,θjThe phase angles of the voltages of the nodes i and j are respectively; a isijA branch correlation vector corresponding to a branch ij in the node correlation matrix; pLRepresenting the active power flow column vector of the branch; b isLIs a pair of angle matrixes, the diagonal elements of which are the admittances of the branches, A is a branch-node incidence matrix, and M is called a transformation matrix.
The optimal load shedding model is as follows:
Figure BDA0002629197200000121
PL＝BLAB-1(PG-PD+C)
Figure BDA0002629197200000131
PGi,min≤PGi≤PGi,max
0≤Ci≤YCi·PDi
-PLi,max≤PLi≤PLi,max
the constraint conditions are direct current flow equality constraint, generator active power constraint, load reduction constraint and branch active constraint respectively; the total number of the nodes of the system is n, and C is a vector formed by load shedding of each node; pLActive vectors for the branches; b isLThe method is characterized in that the method is a pair of angle arrays, diagonal elements of the angle arrays are admittance of branches, A is a branch-node incidence matrix, and B is a node admittance matrix; PG and PD are respectively the active power and the active load vector of the generator; y isCiThe load is a variable of 0 to 1, the value of 1 represents that the node i needs to cut the load, and the value of 0 represents that the load cutting amount is not more than the load of the node i.
As shown in fig. 2, the specific steps of performing the power flow calculation on the power system by using the probabilistic power flow method and the optimal load shedding model based on the dc power flow in S2 are as follows:
s2.1, inputting the original data of the system to be evaluated: grid network structure and line parameters, the installed number, capacity and installation site of traditional generator set and new energy, load access condition, energy storage capacity, installation site and maximum charge-discharge power, interruptible load ratio and the like;
s2.2, modeling treatment is carried out on the active power and the load uncertainty of the new energy;
s2.3, extracting the output sequence of wind power and photovoltaic power by using a Latin hypercube sampling method within the given iteration times to generate a load sequence, and further forming the running state of energy storage;
s2.4, performing direct current load flow calculation, recording a calculation result, judging whether the active power of the line exceeds the limit, switching to an optimal load shedding program if the active power of the line exceeds the limit, and calculating the minimum load shedding amount under the situation;
s2.5, fitting the probability density distribution function of the line with power by using a difittool in a Matlab tool box to obtain a proper probability distribution curve and related parameters;
and S2.6, calculating various risk indexes according to the statistic value and the processing value of the probability load flow calculation result, performing risk index calibration right by using an entropy method, calculating the overall safety and economic risk of the system, and evaluating the risk level of the system.
As shown in fig. 3, S3: carrying out life cycle cost decomposition on a power grid planning project, and dividing the life cycle cost into investment period cost and operation period cost;
life cycle cost C as described in S3totalThe calculation is as follows:
Ctotal＝Cinv+Cope
in the formula, CtotalThe total cost for the life cycle; cinvFor investment period cost, considering the cost conversion of all equipment from initial construction operation to return operation scrapping, and considering the fixed cost of interruptible load contract; copeThen is the operating period cost including grid loss costs, risk loss costs, and power generation emission costs.
Investment period cost:
Cinv＝Cdevice+Ccom
in the formula, CdeviceCost of equipment for planning construction, CcomThe costs are compensated for the interruptible load.
Cdevice＝Cpur+Cinst+Cre+Cscr
In the formula, CpurThe purchase cost of newly building equipment for the system; cinstAll costs required in the installation and debugging processes of the equipment; creThe cost of equipment operation and regular maintenance is saved; cscrThe scrapping cost of the equipment in the retired period is calculated according to the difference between the equipment dismantling cost, the transportation cost and the depreciation residual value.
Ccom＝kc·ηc·PL
In the formula, kcFixing compensation cost for interruptible load agreed by contract; etacIs the duty ratio of interruptible load; pLIs the total load level of the system.
Operating period cost:
Csystem＝Closs+Crisk+Cemi
Figure BDA0002629197200000151
Crisk＝Cloadloss+Cneloss
Figure BDA0002629197200000152
Figure BDA0002629197200000153
Figure BDA0002629197200000154
in the formula, CriskIs a risk fee; clossThe cost for the network loss; cloadlossPunishment is carried out for load shedding; cnelossPunishment caused by abandoned wind and abandoned light; clostIs the electricity price (ten thousand yuan/MWh), NlFor the total number of transmission lines, P, of the power gridloss,ijThe power loss of the line j in the ith sampling state; f. ofloadThe value of interruptible load and non-interruptible load is different for the load shedding punishment coefficient; Ω is the set of states in which load shedding operations occur, Ploadcut,iThe total amount of load reduction in state i, Pl1,iAt state i, the interruptible load, Δ T, is cut offiFor the duration of the state i, different power outage compensation coefficients are adopted for interruptible load and non-interruptible load, and k is taken respectivelyl1,kl2；fnePunishment coefficients of wind abandonment and light abandonment; k is the number of main pollution gas types generated in the coal-electricity production process; pgen,tIs the actual output of the thermal power generating unit at the moment t, lambdagas,kAnd the emission coefficient of the kth gas generated by the unit power generation amount of the thermal power generating unit.
When node voltage UnWhen the line active network loss is all 1, the line active network loss can be approximately calculated according to the following formula:
Figure BDA0002629197200000155
in the formula, PijThe active power flowing through the line j in the ith sampling state;
Figure BDA0002629197200000156
for power factor, this embodiment takes 0.95; rjIs a resistance parameter of line j.
As shown in fig. 4, S4: constructing an objective function of power grid optimization planning, and establishing a risk-considering power grid two-layer planning model;
the objective function of the risk-taking-into-account power grid two-layer planning model in S4 is:
objective function of upper-layer network frame planning model:
LCC annual value is minimal. And converting the initial equipment investment and the final equipment depreciation cost to the same horizontal year to obtain the annual value of the total life cycle cost, etc.:
min f1＝R(Cpur+Cinst)+Cre+D·Cscr+Ccom+Closs+Crisk+Cemi
Figure BDA0002629197200000161
Figure BDA0002629197200000162
wherein R is the annual capital recovery; d is the repayment fund coefficient; r is the discount rate, representing the rate of converting the expected cost or profit into the present value of capital, which is 7.0% in the present embodiment; n is the service life of the equipment, 20 is taken in the embodiment.
Objective function of the lower optimization model:
the total amount of load shedding is minimal.
The constraint conditions of the risk-considering power grid two-layer planning model in the S4 are as follows:
constraint conditions of the upper optimization model:
line extension constraint:
0≤Bij·Zij≤Zijmax
in the formula, BijA 0-1 decision variable of the transmission line ij; eijThe number of lines expanded among the nodes ij is shown; zijmaxThe maximum number of lines allowed to be expanded between nodes ij.
Constraint of line connectivity:
and (3) judging the connectivity of the graph by using graph theory knowledge through a Warshall algorithm:
Φ(Z)＝1
in the formula, phi (Z) is a criterion of network connectivity, and the value of 1 represents that the network has only one connected domain, namely the network frame topology has connectivity.
And (3) new energy grid connection point constraint:
the grid-connected scheme of the new energy needs to meet the constraint of a grid-connected point.
Constraint conditions of the lower optimization model are as follows:
and the method comprises the following steps of power balance constraint based on direct current power flow, generator capacity upper limit constraint, line transmission capacity constraint and node maximum load shedding amount constraint.
As shown in fig. 5, S5: solving the planning model by adopting an improved whale optimization algorithm, and outputting a net rack planning scheme corresponding to the global optimal individual position, namely an optimal scheme obtained by optimization;
the principle of the whale optimization algorithm in the S5 is as follows:
after the random initialization of the position of the search agent X, the updating mechanism of the position of the search agent has two stages of global random search and Bubble-net hunting strategies:
global random search:
the location update strategy at this stage is
Figure BDA0002629197200000171
Occurs under the conditions of (a). Update formula for individual location X:
Figure BDA0002629197200000172
Figure BDA0002629197200000173
Figure BDA0002629197200000174
wherein t is the current iteration number,
Figure BDA0002629197200000175
is an individual position vector randomly selected in the search agent;
Figure BDA0002629197200000176
and
Figure BDA0002629197200000177
respectively updating the position vectors before and after the individual in the search agent;
Figure BDA0002629197200000178
and
Figure BDA0002629197200000179
is a coefficient vector; denotes an element-by-element multiplication operation. In the iterative process, a is linearly reduced from 2 to 0;
Figure BDA00026291972000001710
to be in the value range of [0,1]Random vector of (2).
The Bubble-net hunting strategy:
when in use
Figure BDA00026291972000001711
In time, the position update of an individual in a search agent comprises two modes of a shrink wrapping mechanism and a spiral position update, which are similar to the bunble-net hunting strategy of whale:
the shrink wrapping mechanism:
the position of an optional individual in the search agent is taken as the current best solution, and other individuals update the position of the individual towards the direction of the best solution:
Figure BDA0002629197200000181
Figure BDA0002629197200000182
wherein t is the current iteration number,
Figure BDA0002629197200000183
a position vector corresponding to the current best solution;
Figure BDA0002629197200000184
and
Figure BDA0002629197200000185
the position vectors before and after updating for the individuals in the search agent are respectively.
Spiral updating position:
computing is located in
Figure BDA0002629197200000186
Whale and fish bait
Figure BDA0002629197200000187
The distance between preys. A helical equation is established between the positions of whales and prey, simulating the helical motion of an whale with an anvil:
Figure BDA0002629197200000188
Figure BDA0002629197200000189
in the formula (I), the compound is shown in the specification,
Figure BDA00026291972000001810
distance from the ith individual to the prey (current best solution); b is a logarithmic spiral shape coefficient, and the value is generally constant; l is in the range of [ -1,1 [)]The random number in (c).
When updating the position of the search agent, one of the modes is selected according to a certain probability, and the selection is generally 0.5:
Figure BDA00026291972000001811
the improvement process of the whale optimization algorithm in the S5 is as follows:
introducing an adaptive weight factor w improvement
Figure BDA00026291972000001812
And
Figure BDA00026291972000001813
the change process of (2):
Figure BDA00026291972000001814
in the formula, N is the maximum iteration number; k is a given adjustment factor, and 2 is taken.
The individual position updating formula after the weight factor w is adopted becomes:
Figure BDA0002629197200000191
Figure BDA0002629197200000192
carrying out differential random variation processing on the updated individuals to prevent the individuals from converging and becoming premature, wherein the variation strategy is as follows:
Figure BDA0002629197200000193
in the formula, r1,r2Is the interval [0,1]A random number within;
Figure BDA0002629197200000194
are randomly selected individuals in the population.
Converting the real number individuals into integers for optimizing, and performing rounding operation after the position of the search agent is updated:
X'(t)＝round(X(t))
wherein X' (t) is the individual position after the rounding operation; round () is a rounded rounding function.
The specific steps of the power grid planning based on the improved whale algorithm in the S5 are as follows:
s5.1, inputting relevant parameters of power grid planning and basic parameters of a WOA algorithm, including dimensionality of optimization variables, individual number of optimization agent populations, total iteration times and the like;
s5.2, randomly generating the position of the initial search agent, and initializing;
s5.3, initializing the iteration times, setting the value to be 1, selecting an individual with the minimum fitness value, wherein the position of the individual is the optimal point of the current optimization;
s5.4, randomly generating the probability p, judging the size of the probability p, selecting a corresponding individual position updating formula according to the judgment result and the coefficient vector obtained by calculation, and updating the position of the individual;
s5.5, adding 1 to the iteration frequency, judging whether the value of the iteration frequency reaches the preset iteration frequency, and if not, continuing the updating process;
and S5.6, outputting the global optimal individual position when the preset iteration times are reached, wherein the corresponding net rack planning scheme is the optimal scheme obtained by optimization.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
Although specific embodiments of the present invention have been described above with reference to the accompanying drawings, it will be appreciated by those skilled in the art that these are merely illustrative and that various changes or modifications may be made to these embodiments without departing from the principles and spirit of the invention. The scope of the invention is only limited by the appended claims.

Claims (8)

1. A risk-related power grid optimization planning method is characterized by comprising the following steps:
step 1, modeling uncertainty factors of power supply, load and energy storage links in an electric power system;
step 2, carrying out load flow calculation on the power system according to a probability load flow method and an optimal load shedding model based on direct current load flow;
step 3, carrying out life cycle cost decomposition on the power grid planning project, and dividing the life cycle cost into investment period cost and operation period cost;
step 4, constructing a target function of power grid optimization planning, and establishing a risk-considering power grid two-layer planning model;
and 5, solving the planning model by adopting an improved whale optimization algorithm, and outputting a net rack planning scheme corresponding to the global optimal individual position.
2. The method according to claim 1, wherein the modeling of uncertainty factors of power supply links in the power system according to step 1 includes fan output and photovoltaic output; the modeling of the uncertainty factors of the load link comprises the fluctuating load and the interruptible load participating in the response of the demand side; the modeling of the uncertainty factor of the energy storage link comprises energy storage charge and discharge strategies.
3. The method for planning a power grid optimally in consideration of risks according to claim 1, wherein the probabilistic power flow calculation method in the step 2 adopts a Monte Carlo simulation method based on Latin hypercube sampling; the optimal load shedding model based on the direct current power flow is modeled by applying a Yalmip language on a Matlab platform and solved by using Gurobi and Cplex solvers; the power flow calculation of the power system comprises the following specific steps:
step 2.1, inputting the original data of the system to be evaluated: grid network structure and line parameters, the installed number, capacity and installation site of traditional generator set and new energy, load access condition, energy storage capacity, installation site, maximum charge-discharge power and interruptible load ratio;
2.2, modeling treatment is carried out on the active power and the load uncertainty of the new energy;
2.3, extracting the output sequence of wind power and photovoltaic power by using a Latin hypercube sampling method within the given iteration times to generate a load sequence, and further forming the running state of energy storage;
step 2.4, performing direct current load flow calculation, recording a calculation result, judging whether the active power of the line exceeds the limit, switching to an optimal load shedding program if the active power of the line exceeds the limit, and calculating the minimum load shedding amount under the situation;
step 2.5, fitting the probability density distribution function of the line with power by using a difittool in a Matlab tool box to obtain a proper probability distribution curve and related parameters;
and 2.6, calculating various risk indexes according to the statistic value and the processing value of the probability load flow calculation result, performing risk index calibration right by using an entropy method, calculating the overall safety and economic risk of the system, and evaluating the risk level of the system.
4. The method according to claim 1, wherein the investment cost in step 3 includes equipment procurement cost, installation and debugging cost, overhaul and maintenance cost, equipment decommissioning cost and fixed contract cost; the operation period cost comprises the cost of grid loss, the cost of risk loss and the cost of power generation and pollution discharge.
5. The method for planning power grid optimization considering risks according to claim 1, wherein the power grid two-tier planning model in step 4, under the premise of satisfying given constraint conditions, puts the risk cost and pollution discharge cost of the system into the planned whole life cycle cost, so that the annual value is the lowest; and the upper layer and the lower layer are optimized through alternate iteration to obtain the optimal power grid planning scheme.
6. The method as claimed in claim 5, wherein the upper layer model aims at the lowest LCC annual value including risk cost, pollution discharge cost and the like, the decision variables comprise grid racks and new energy grid connection points, and the required planning scheme is assigned to the lower layer.
7. The method as claimed in claim 6, wherein the lower model considers uncertainty models of power supply, load and energy storage links under the condition of satisfying the safe operation constraint, performs operation risk assessment based on probability load flow with the goal of minimizing the total load reduction amount, checks the adaptability of the power grid structure to uncertainty factors, and transmits the calculated operation period cost to the upper optimization objective function.
8. The risk-aware grid optimization planning method according to claim 1, wherein the whale optimization algorithm in step 5 solves the planning model by the following steps:
step 5.1, inputting relevant parameters of power grid planning and basic parameters of a WOA algorithm, including the dimensionality of an optimization variable, the number of individuals of an optimization agent population and the total number of iterations;
step 5.2, randomly generating the position of the initial search agent, and initializing;
step 5.3, initializing iteration times, setting the value to be 1, selecting an individual with the minimum fitness value, wherein the position of the individual is the optimal point of the current optimization;
step 5.4, randomly generating the probability p, judging the size of the probability p, selecting a corresponding individual position updating formula according to the judgment result and the coefficient vector obtained by calculation, and updating the position of the individual;
step 5.6, adding 1 to the iteration frequency, judging whether the value of the iteration frequency reaches the preset iteration frequency, and if not, continuing the updating process;
and 5.7, outputting the global optimal individual position when the preset iteration times are reached, wherein the corresponding net rack planning scheme is the optimal scheme obtained by optimization.
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