CN116402307A - Power grid planning capacity analysis method considering operation characteristics of schedulable flexible resources - Google Patents
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Abstract
The invention relates to a power grid planning capacity analysis method considering the operation characteristics of schedulable flexible resources, which comprises the following steps: analyzing the operation characteristics of the schedulable flexible resource; identifying key schedulable flexible resources; according to the guiding mechanism for planning various schedulable flexible resources facing the power grid; and accessing the schedulable flexible resources with different capacities into the power distribution network according to a guiding mechanism for planning various schedulable flexible resources for the power grid, performing collaborative optimization of the schedulable flexible resources and the power grid planning layout, and finally analyzing the influence of the capacities on the power grid planning problem. The invention establishes a schedulable flexible resource guiding mechanism, explores a collaborative optimization technology of schedulable flexible resources and power grid planning layout, realizes effective promotion of the schedulable flexible resources to participate in power grid operation and planning, fully exerts the value of the schedulable flexible load resources, reduces the power grid planning capacity requirement of the power system and improves the utilization efficiency of power grid planning.
Description
Technical Field
The invention relates to the technical field of demand analysis of power grid planning, in particular to a power grid planning capacity analysis method considering the operation characteristics of schedulable flexible resources.
Background
Under the large background of global energy resource shortage, environmental pollution and the like, the electric automobile with green low-carbon sustainable new energy power generation and clean emission is greatly developed, becomes the consensus of countries around the world, and brings out a large number of related supporting policies. With the blowout type development of distributed new energy power generation and the development of energy storage technology, the proportion of the energy storage system with the dual attribute characteristics of source-load in the power grid load is continuously increased. On the other hand, the application of demand response means such as time-of-use electricity price and the like enables part of traditional loads to adjust the electricity demand of the load according to excitation or electricity price information, so that the load has the capacity of interaction with a power grid and adjustability, namely 'flexible load'.
Distributed renewable energy sources represented by photovoltaic power generation and wind power generation, electric vehicles with dual attributes of source-charge, energy storage systems and novel flexible loads with self-regulating capability can be collectively called as schedulable flexible resources in a power grid. Different schedulable flexible resources have different characteristics, such as distributed new energy power generation, and the output has the problems of fluctuation, intermittence, low controllability and the like. The capacity of the planning layout of the power system is increased, and the utilization efficiency of the power grid equipment is reduced, so that the operation economy of the power grid is greatly reduced. Therefore, analysis of the operating characteristics and operating principles of different schedulable flexible resources is an important basis for developing further research and fully utilizing the schedulable flexible resources.
In recent years, the peak of power load and new energy source are continuously increased, and great challenges are presented to the regulation capability of a power system; meanwhile, the schedulable flexible resources represented by demand side management, electric vehicles, energy storage and the like are developed under the support of related policies. Various schedulable flexible resources are accessed, so that the difficulty of power grid planning is increased, and the utilization efficiency of equipment is reduced. The new forms of safe, economical operation of the grid and planning of the grid have higher requirements and challenges. Many enterprises in foreign countries have long started research and application of flexible resource project scheduling problems, and data accumulation is relatively sufficient, but most of research is limited to flexible resource project scheduling in an electric power market, connection with power grid planning and layout is to be enhanced, load characteristics of flexible resources are not uniformly analyzed and classified, and specific analysis is not made on relevance of different flexible resources and power grid development planning. The development of the domestic electric power market just starts, the electric power spot market is imperfect, and how to link flexible resource scheduling and development planning layout of the power grid is a problem which needs to be solved. Therefore, the power grid planning research under the participation of the flexible load needs to be developed, and the research on the operation characteristics, the guiding mechanism and the relevance of the schedulable flexible resource and the power grid planning and the collaborative optimization technology is developed respectively.
Disclosure of Invention
The invention aims to solve the defects of capacity increase of a power system planning layout, reduction of power grid equipment utilization efficiency and the like caused by the fact that various schedulable flexible resources are connected into a power grid, and aims to provide a power grid planning capacity analysis method which is used for realizing cooperation of various flexible resources and power grid planning in time and space, fully utilizing the schedulable flexible resources to the greatest extent and improving the running reliability and economy of the power grid and considering the running characteristics of the schedulable flexible resources.
In order to achieve the above purpose, the present invention adopts the following technical scheme: a power grid planning capacity analysis method taking into account the operational characteristics of schedulable flexible resources, the method comprising the sequential steps of:
(1) Analyzing the operating characteristics of the schedulable flexible resource: dividing the schedulable flexible resources into energy one-way interactive flexible resources and energy two-way interactive flexible resources, wherein the energy one-way interactive flexible resources comprise flexible loads and schedulable new energy sources, and the energy two-way interactive flexible resources comprise energy storage devices, electric automobiles and micro-grids;
(2) Identifying a schedulable flexible resource: according to the operation characteristics of each schedulable flexible resource, a two-stage robust optimization model of power grid planning for accounting for the uncertainty of new energy output and a two-stage robust optimization model of power grid planning for accounting for the uncertainty of electric automobile output are established, the association degree of the two models is analyzed, and then key schedulable flexible resources are identified;
(3) According to the operation characteristics of each schedulable flexible resource and the key schedulable flexible resource, a guiding mechanism for planning each schedulable flexible resource for the power grid is provided;
(4) Analyzing the influence on the power grid planning: and accessing the schedulable flexible resources with different capacities into the power distribution network according to a guiding mechanism for planning various schedulable flexible resources for the power grid, performing collaborative optimization of the schedulable flexible resources and the power grid planning layout, and finally analyzing the influence of the capacities on the power grid planning problem.
The step (1) specifically comprises the following steps:
(1a) Analyzing the operating characteristics of the energy storage device: the energy storage device can stabilize the fluctuation of new energy output due to the bidirectional flow characteristic of the energy storage device, has the millisecond-level rapid, stable and accurate charge and discharge power regulation characteristic, and can promote the instantaneous, short-time and time period balance capacity of the power system;
(1b) Analyzing the operation characteristics of the schedulable new energy: by adopting distributed installation, the node voltage value can be effectively improved, the power flow distribution is improved, the load shedding operation is reduced, and the power supply reliability is improved; the problem that the voltage at the new energy access position is too high or the adjacent line is overloaded exists, the operation stability of the power distribution network is affected, and the power supply reliability is reduced; the operation has uncertainty of output, and the start and stop of the new energy and unstable power output can generate fluctuation on voltage and impact on the power supply voltage of the power distribution network user;
(1c) Analyzing the operation characteristics of the electric automobile: the electric automobile has space-time uncertainty, the load is obviously increased, the peak-valley difference of the load is enlarged, the difficulty of power grid control is increased, the capacity of the distribution transformer is enlarged, and the cost of the power grid is increased; the energy can be transmitted in two directions, the network loss of the power distribution network can be reduced by orderly charging and discharging, the node voltage waveform of the power distribution network can be improved by participating in scheduling, the power flow of a line is influenced, and the network loss of the line is further influenced;
(1d) Analyzing the operation characteristics of the flexible load: the flexible load has the energy storage characteristic, so that the load in the peak period of electricity consumption can be reduced, the peak-valley difference of the load of the power grid is reduced, and the line loss of the power grid can be reduced by orderly participating in scheduling; the flexible load can reduce the characteristics to increase the load, reduce the maximum reactive load, increase the critical voltage, start and stop orderly, reduce the power grid line loss and reduce the peak-valley difference;
(1e) Analyzing the operating characteristics of the micro-grid: the micro-grid contains various schedulable flexible resources and has the dual attribute of source load; by introducing the energy storage device, the power shortage in the power consumption peak period can be relieved; the new energy with high permeability causes impact of the micro-grid on normal operation of the main network, and supplies power to the island area, thereby being a powerful supplement to the traditional power supply mode.
The step (2) specifically comprises the following steps:
(2a) Introducing a robustness index Γ wt And Γ pv The uncertainty of new energy output is characterized, as key operation characteristics, the initial investment cost and operation and maintenance cost of the power grid are used as targets, and a two-stage robust optimization model of power grid planning is established, wherein the uncertainty of new energy output is considered;
uncertainty scene set of wind turbines:
uncertainty scene set for photovoltaic power plants:
wherein R is NW×T For wind and photovoltaic powerAn allowable range; NW is the number of units T of the fan and the photovoltaic unit as the total time period number;
and->
The predicted output values of the wind turbine generator and the distributed photovoltaic power station in the t period are respectively; p (P) wt,t And P pv,t The actual output values of the wind turbine generator system and the distributed photovoltaic power station in the t period are respectively; ΔP wt,t And DeltaP pv,t The fluctuation quantity of the wind turbine generator and the distributed photovoltaic power station in the t period; />
And->
The maximum fluctuation amount of the wind turbine generator and the distributed photovoltaic power station in the t period is set;
(2b) Taking the space-time uncertainty of the electric automobile as a key operation characteristic, adopting a normal distribution model to represent the characteristic, and establishing a power grid planning two-stage robust optimization model considering the output uncertainty of the electric automobile, wherein the model comprises two charging modes of conventional charging and quick charging:
Conventional charging: the formula (3) and the formula (4) are a charge quantity model and a discharge charge quantity model, the formula (5) is a normal distribution model of charge starting time, and the formula (6) is a normal distribution model of daily driving mileage:
wherein E is CCS (t)、E CCS (t-1) is the charge quantity of the electric automobile at the moment t and the moment t-1; t is t c For the actual charge duration, t d Is the actual discharge time length; t is t max_c For maximum charge duration, t max_d Is the maximum discharge duration; e (E) max The maximum charge amount is the maximum charge amount when the electric automobile is fully charged; mu (mu) x Sum sigma x Respectively represent the expected and standard deviation, mu, of the charge start time x x ＝17.6，σ x ＝3.4；μ L Sum sigma L The expected and variance, μ, of the logarithm lnL of the mileage L, respectively L ＝3.2，σ L ＝0.88；f s (x) A normal distribution model expression for the charging start time; f (f) L (L) is a daily driving mileage normal distribution model expression;
and (3) quick charging: equations (7), (8) and (9) are fast charge user queuing M/G/k models, and equations (10) and (11) are fast charge station charging models:
wherein t is BCS Charging time for the fast charge user;
rated capacity for fast charge user battery; b (B) i,BCS The residual electric quantity is the residual electric quantity when the ith fast charge user arrives at the integrated station; />
Rated charging power for the fast charging device; e (E) T At t BCS Is not limited to the desired one; d (D) T At t BCS Is a variance of (2); p (P) BCS (t) is the total charging power of the fast charging station during the t-th period; p (P) i,BCS (t) is the charging power of the ith fast charge user during the t period; η (eta) BCS Charging power of the quick charging equipment for the quick charging station; omega i,BCS (t) is a binary variable representing the state of charge of the ith fast charge user at the t period, 1 if charging is occurring, or 0 if not; e (B) i,BCS ) The method comprises the steps that the expected residual electric quantity is obtained when a total fast charge user arrives at an integral station; d (B) i,BCS ) The variance of the residual electric quantity when the total fast charge user arrives at the integral station is obtained;
(2c) The method comprises the steps of obtaining a data set under multiple scenes of power grid planning through a power grid planning two-stage robust optimization model considering new energy output uncertainty and a power grid planning two-stage robust optimization model considering electric automobile output uncertainty, and calculating the association degree by adopting a data mining algorithm: comprehensively analyzing the degree of association between each cost X and Y under n scenes by adopting a gray association theory to obtain the degree of association between each cost and the total cost, comprehensively analyzing the degree of association between new energy output fluctuation and each cost under n scenes by adopting an entropy weight method, comprehensively weighting to obtain the degree of association between the new energy output fluctuation and the total cost, and identifying whether the flexible resource is a key schedulable flexible resource or not.
The step (3) specifically comprises the following steps:
(3a) Analyzing a multi-element operation mode of the schedulable flexible resource;
(3b) According to the analysis of the operation mode, response characteristic and flexible resource optimization scheduling model of the schedulable flexible resources, a flexible load guiding mechanism facing power grid planning, an electricity price guiding mechanism based on user side energy storage price optimization, an electric vehicle charging load guiding mechanism based on space-time electricity price optimization, an electricity price guiding mechanism based on micro-grid system response characteristic and a rewarding and punishment mechanism of the schedulable new energy participating in power grid scheduling are provided;
(3c) Analysis is performed by researching a flexible load guiding mechanism facing power grid planning: the method comprises the steps of constructing a peak-valley time division model based on an improved boundary moving technology, adding constraint conditions, solving optimal time division by taking a Dunn index as an objective function, constructing the peak-valley time division model based on the improved boundary moving technology, and after carrying out peak-valley time-of-use electricity price on load curve peak, valley time, a user describes electricity consumption in each time by adjusting own electricity consumption mode as follows:
in which Q p 、Q f 、Q v The electricity consumption before the time-sharing electricity price is carried out in the peak, flat and valley time periods is respectively represented; q (Q) p0 ，Q f0 ，Q v0 Respectively representing the electricity consumption of each period after the time-sharing electricity price is implemented; ΔQ p ，ΔQ f ，ΔQ v Respectively representing the variation of the electricity consumption before and after the time-sharing electricity price, E is an electricity price elastic matrix lambda pp 、λ pf 、λ pv Respectively the self-elasticity coefficient lambda of each period fp 、λ ff 、λ fv The cross elastic coefficients of any two time periods are respectively; lambda (lambda) vp 、λ vf 、λ vv The electricity consumption is the peak-valley period;
the constraint conditions of the peak-to-valley period division model based on the improved boundary moving technology are as follows:
(3c1) User benefit constraints: before and after the time-sharing electricity price is implemented, the electricity charge of the user is not increased;
(3c2) Peak, plateau, valley period electricity price constraints: after the time-sharing electricity price is implemented, the peak period electricity price is larger than the ordinary period electricity price, and the ordinary period electricity price is larger than the valley period electricity price;
(3c3) Grid company benefit constraints: the implementation of the time-sharing electricity price can reduce the investment cost of the power supplier, and the overall benefit of the power supplier is not reduced;
(3c4) Marginal electricity price constraint: the electricity price in the valley period should not be lower than the marginal electricity price;
the objective function of the peak-to-valley period division model based on the improved boundary movement technique is as follows:
F(p)＝αF 1 (p)+βF 2 (p)+H (17)
wherein p represents a decision variable; alpha and beta are weight coefficients of an objective function; h is a penalty function term.
The step (4) specifically comprises the following steps:
(4a) Dividing the area to be planned into 3 areas, and firstly setting three schemes: the first scheme considers that the whole area to be planned is the common load; the second scheme is to access a schedulable new energy source and an energy storage device with the capacity of 10MW in three areas; the third scheme is that a schedulable new energy source and an energy storage device with the capacity of 15MW are accessed in three areas, and 10% of the common loads are considered to be flexible loads and electric automobile charging piles; because the capacity of the transformer substation is directly related to the cost, the transformer substation constant volume matching index based on the cost and capable of scheduling the flexible resource access is provided for measuring the influence of the access capacity on the transformer substation planning, and the transformer substation constant volume matching index capable of scheduling the flexible resource access is as follows:
Wherein, eta represents constant volume matching index; c (C) t The method comprises the steps of planning cost for a transformer substation considering schedulable flexible resource access; c t Planning cost for a conventional transformer substation without considering schedulable flexible resource access;
(4b) Calculating the equivalent capacity of the schedulable flexible resource, and updating the capacitance-to-load ratio formula of the transformer station according to the equivalent capacity;
objective function:
where C is the total cost, including distribution grid planning cost and operating cost, N T Representing the number of substations; omega represents the number of network frame lines; c (C) Tp (Si) is the investment cost of the transformer substation, r is the discount rate, m is the depreciation age of the transformer substation, C j To build the cost of a primary power supply circuit, C rT (Si) is the operation and maintenance cost of the transformer substation, n year Is a simulated year; x is x i For the binary decision variable, when the value is 1, the ith line is selected, and when the value is 0, the ith line is not selected; x is x j For the binary decision variable, the value is 1, which indicates that the jth line is selected, and the value is 0, which indicates that the jth line is not selected; c loss Cost per network loss; ΔP i Active loss of the ith line;
the capacity-to-load ratio formula:
wherein R is s Representing the capacity-to-load ratio; s is S i Representing the capacity of the i-th transformer; p (P) max Representing the peak load value of the area to be planned; m is m s The number of the transformer substations; n is the number of transformers used by each transformer substation; lambda is the load-synchronous rate; p (P) mgeq Equivalent capacity for micro-grid, including distributed power supply output P dg And micro-grid flexible resource output Fl mg ；
(4c) Collaborative optimization of schedulable flexible resources and grid planning layout: establishing a double-layer planning model of the power distribution network considering flexible resource access, taking investment and total operation cost as objective functions, wherein an upper half formula of a formula (21) is an upper layer model and is used for planning the power distribution network, and a lower half formula of the formula (21) is a lower layer model and is used for calculating operation cost of the power distribution network:
wherein f 0 、f 1 The method comprises the steps of respectively obtaining objective functions of an upper model and a lower model, wherein the upper model is an investment decision model, and the lower model is an operation scheduling model; x is x ov 、x iv Decision variables of the upper model and the lower model are respectively; x is x iv,s The decision variable of the lower model in the s scene is obtained; h (-) and G (-) correspond to the constraint conditions of the upper layer model and the lower layer model respectively, including equality constraint and inequality constraint;
objective function:
minC＝C p +C r (22)
wherein C is the total cost, including planning investment cost C p And operating cost C r The method comprises the steps of carrying out a first treatment on the surface of the First term in investment planning costs
For investment costs of the substation, the second item->
Planning costs for grid construction, where C Tp (S i ) Is the investment cost of the transformer substation, m is the depreciation age of the transformer substation,
Line flexibility constraint conditions are increased to ensure adequacy of network frame transmission capacity in the flexible resource operation process:
0≤FL l ≤βFL max (25)
equation (25) is a line flexibility constraint, where FL l The capacity is adjusted for the used flexibility of the first line, namely the used transmission capacity of the line; beta is a margin coefficient, and the value range is 0,1]An inner part; FL (FL) max The maximum flexibility adjustment capability of the line is provided;
because the grid planning part in the power distribution network planning model has a nonlinear mathematical model and the solving scale is large, a genetic algorithm is adopted for solving; in the process of solving grid planning by using a genetic algorithm, the phenomenon that the output grid structure is crossed occurs in the power distribution network planning, which is not allowed in the actual planning operation process, and the fitness function after line crossing judgment is considered is as follows:
wherein F is the cost after the line crossing judgment is considered, and J is the conventional constraint condition number; k (K) j A penalty function for the jth conventional constraint in the planning process; a, a linecross Determining binary variables for the line crossings; k' is a penalty function corresponding to the line crossing.
In the step (2 a), the two-stage robust optimization model of power grid planning which accounts for the uncertainty of new energy output specifically refers to: establishing a two-stage robust optimization model with the lowest total cost of the micro-grid, wherein the first-stage objective function is the lowest initial investment cost in the micro-grid, and the second-stage objective function is the lowest scheduling operation cost in the micro-grid;
Initial investment cost C in first-stage objective function inv The investment cost for the wind turbine generator, the distributed photovoltaic power station, the energy storage device and the miniature gas turbine generator is as follows:
in the method, in the process of the invention,
maximum battery capacity for the energy storage device; c (C) bat Investment cost for unit power of the energy storage device; p (P) i max And ci is the maximum technical output and the investment cost of unit power of the ith power equipment respectively; f (F) CRE (ri, yi) is annual resource gold recovery; r is (r) i And Y i The discount rate and discount years of the ith power equipment are respectively, wherein the discount years of the energy storage device are floating charge life;
scheduling operating cost C in second stage objective function open The running cost of the micro gas turbine, the electricity purchasing and selling cost of the micro power grid and the maintenance cost of equipment are as follows:
in the method, in the process of the invention,
C grid 、C op the running cost of the micro gas turbine, the electricity purchasing and selling cost of the micro power grid and the maintenance cost of equipment are respectively; c fuel,t Fuel cost for period t; p (P) G,t Real-time output of the micro gas turbine for a period t; k (k) n,t And c n,t N-th pollutant discharge amount and treatment unit price of the micro gas turbine with t time periods respectively; c buy,t And c sell,t Respectively representing the electricity purchasing price of the period t; />
And->
The power of the micro-grid for selling electricity to the power distribution network in the period t is respectively obtained; / >
The maintenance cost unit price of the energy storage device, the wind turbine generator, the distributed photovoltaic power station and the micro-combustion unit; p (P) i,t The output of the energy storage device, the wind turbine generator, the distributed photovoltaic power station and the micro-combustion unit in the t period is represented;
constraints include the following:
wherein the power balance constraint is:
the micro-combustion unit is constrained as follows:
the energy storage device is constrained as follows:
the state of charge constraints are:
SOC min ≤SOC t ≤SOC max (37)
SOC beg ＝SOC end (38)
the switching power constraint is:
wherein P is wt,t 、P pv,t 、
And P G,t The output of the wind turbine generator, the distributed photovoltaic power station, the energy storage device and the micro-combustion unit in the period t, P load,t Load for t period; />
For charging power of energy-storage device t period epsilon ch And epsilon dis The ratio of the maximum charge and discharge power of the stored energy to the maximum capacity of the storage battery is respectively; />
And->
Respectively representing the minimum and maximum output values of the micro-combustion unit in the t period; x-shaped articles bat Indicating the charge and discharge states of the stored energy, SOC min And SOC (System on chip) max The lower limit and the upper limit of the charge state of the storage battery are respectively defined, and the SOC is not less than 20% in order to prevent the discharge depth from being too large and ensure the service life of the storage battery to be too fast; SOC (State of Charge) beg And SOC (System on chip) end Indicating that the state of charge of the battery is the same throughout the scheduling period T,/and->
And->
Respectively representing the upper limit of the electric power purchased and sold by the micro-grid to the power distribution network; x-shaped articles M,t An integer variable of 0 to 1, wherein when the value of the integer variable is 1, the micro-grid purchases electricity to the power distribution network in a t period; / >
Indicating the maximum capacity of the energy storage device.
In step (2 b), the two-stage robust optimization model for power grid planning, which takes into account the uncertainty of the output of the electric vehicle, specifically refers to: determining a power station to which the electric vehicle goes according to the selection cost of the electric vehicle to each charging station, and then calculating the charging load to a power distribution network; the charging price is changed to guide the electric vehicle to charge to a power station closer to the new energy, and the specific form is as follows:
three layers of robust optimization models are established, and the objective functions are as follows:
wherein D is a new energy output scene set, and P (ζ) is the probability of output of each scene;
expressed as the square of the voltage amplitude at node i at time t; />
The square of the current amplitude of nodes i to j at time t; />
Representing the active power of nodes i to j at time t; />
Representing the active power of nodes i to j at time t; Φ represents the feasible region of the decision variables;
the constraint conditions are as follows:
charging station selection constraints:
in the method, in the process of the invention,
the comprehensive cost from the ith electric automobile to the jth charging station at the moment t; />
The charging electricity price of the j-th charging station at the moment t; />
The estimated waiting time of the jth charging station at the moment t; omega 1 ，ω 2 And omega 3 Is a weight coefficient; />
Is the lowest comprehensive cost; n is the number of charging stations, when u (i,j) When=0, it represents that vehicle i is not selectedCharging station j, when u (i,j) When=1, charging station j is selected on behalf of vehicle i; m is an arbitrarily large positive number; />
An equivalent distance from the current position to the jth charging station for the ith vehicle at the moment t;
electric automobile state of charge constraint:
wherein V is i Is the running speed of the ith vehicle; t (T) i The time for generating a charging willingness of the vehicle owner is represented; ts. i The method comprises the steps of starting charging time for an electric automobile; t (T) ch,i Charging the electric automobile for a long time; gamma ray i =1 represents t 0 The electric automobile is in a charging state at moment, gamma i =0 indicates that it is not in a charged state;
the travel time of the ith vehicle at the moment t; />
The estimated waiting time of the ith charging station at the moment t;
power station charging price constraint:
load flow balance constraint:
charging station power balance constraints:
wherein k is 3 And k 4 Is a coefficient of proportionality and is used for the control of the power supply,
for the electricity price at the time t of the station, +.>
The charging electricity price of the ith charging station at the moment t; omega shape a For the node set between the kth electric vehicle and the ith charging station, Ω b For the collection of all nodes in the power distribution network, rij and Xij are the resistance and reactance values of the branch ij respectively, pt ij and Qt ij respectively represent the active and reactive power flows of the branch ij, pt i.D and Qt i.D respectively represent the active load and reactive load of the node i at the moment t, and Pt i.en and Qt i.en respectively represent the active and reactive power injection of the power supply at the node i at the moment t; / >
And->
Active and reactive power injection of the charging station at the node i at the moment t is respectively shown; pt cs.j represents the active power output of charging station j at time t, p c The charging power of a single charging pile is N is the sum of the number of charging vehicles at all times in a day, u (i,j) Indicating the selection of charging station j by electric vehicle i, < >>
And the charging state of the electric automobile i at the time t is shown, and lambda is the power factor of the charging pile. />
In step (3 b), the flexible load guiding mechanism facing the grid planning refers to: the electricity price type guiding mechanism is adopted to guide the schedulable flexible resource, the basis is that the schedulable flexible resource is scientifically and reasonably divided into peak-average-valley time periods according to the load characteristics of users, and corresponding time-sharing electricity prices are formulated so as to guide the users, and the purpose of peak clipping and valley filling is achieved by combining the control of the load;
the electricity price type guiding mechanism based on the user side energy storage price optimization refers to: the response characteristic of the user side energy storage power station is reflected in that the energy storage device can store low power and high power as much as possible; the time-sharing electricity price is adopted to guide the energy storage at the user side, so that the electricity consumption of the user can be reduced, the peak-valley difference of the load of the power system can be reduced, the utilization rate of power generation and transmission equipment can be improved, and the equipment investment can be delayed, thereby realizing win-win;
The electric automobile charging load guiding mechanism based on space-time electricity price optimization refers to: the charging load of the electric automobile has certain randomness in time and also shows larger flow characteristic in space; the network-accessible electric automobile is also used as a mobile energy storage device to realize the reverse feeding of electric energy to the system; based on the time-space response characteristic of the electric automobile, the time-sharing electricity price is adopted to guide the electric automobile to participate in peak clipping and valley filling in time and space, and meanwhile, the network loss is reduced, and the economical efficiency and the reliability of a power grid are improved;
the electricity price type guiding mechanism based on the response characteristic of the micro-grid system refers to: the micro-grid system is guided to interact with the large power grid through time-sharing electricity price, and electricity is purchased from the large power grid when the micro-grid energy output is insufficient and the electricity price is low; when the energy output of the micro-grid meets the load demand, and the surplus exists, the surplus electric quantity is sold to a large power grid, and the higher electricity price can obtain the income, so that the economic cost of the micro-grid system is reduced;
the rewarding and punishing mechanism for the participation of the schedulable new energy in the power grid scheduling refers to: the enthusiasm of the new energy to participate in peak shaving can be guided by a reward and punishment policy, the capacity of the power system for absorbing the new energy is improved by phase change, the stable operation of the power system is effectively ensured, and the reduction of peak shaving pressure of the power system and the benefits of new energy generators are considered; guiding new energy manufacturers to reasonably allocate and store according to the actual conditions of the new energy manufacturers, thereby reducing the peak regulation pressure of the power grid.
According to the technical scheme, the beneficial effects of the invention are as follows: firstly, researching influence characteristics and correlation of the schedulable flexible load and the power grid planning, establishing a sound schedulable flexible resource guiding mechanism, exploring a collaborative optimization technology of the schedulable flexible resource and the power grid planning layout, effectively promoting the schedulable flexible resource to participate in the power grid operation and planning, fully playing the value of the schedulable flexible load resource, reducing the power grid planning capacity requirement of the power system and improving the utilization efficiency of the power grid planning; secondly, the invention converts partial load from 'rigid' to 'flexible' through the regulation and control means such as time-sharing electricity price, the electricity consumption is changed in a designated time period or different time periods according to the power grid demand, the power grid interactivity of the flexible load is effectively utilized to realize optimal regulation and control, the electric automobile, the flexible load, the energy storage system and the like are accessed into the power grid in a large scale, peak clipping and valley filling can be realized through reasonably guiding the schedulable flexible resources, and the peak-valley difference of the power grid is reduced; thirdly, the energy storage system with the dual attributes of source and load and the electric automobile are used as power sources to be connected into a power grid, a traditional power grid planning method based on artificial experience is abandoned, a novel power grid planning model is provided, the planning result is reasonably improved, the utilization rate of equipment is improved, flexible resources are reasonably utilized to coordinate and absorb new energy power generation represented by photovoltaic power generation and wind power generation, the influence of randomness and fluctuation of the new energy power generation is effectively relieved, and the safety and stability of power grid operation are improved.
Drawings
FIG. 1 is a schematic diagram of an influence mechanism of a schedulable flexible resource on power grid planning, which is obtained by the invention;
FIG. 2 is a schematic diagram of the effect of a pair of substation planning costs according to the solution of the present invention;
FIG. 3 is a schematic diagram of the effect of scheme II on the planning cost of a transformer substation;
fig. 4 is a schematic diagram of the effect of the scheme three on the planning cost of the transformer substation;
FIG. 5 is a schematic diagram showing the effect of the permeability of the schedulable flexible resources on the planning of the grid frame;
FIG. 6 is a schematic diagram showing the influence of the permeability of the schedulable flexible resources on the transformer substation planning;
FIG. 7 is a diagram showing the load level change before and after the time-of-use electricity price obtained by the invention;
FIG. 8 is a graph showing the load level change after a 10% increase in the flexible load ratio obtained by the present invention.
Detailed Description
A power grid planning capacity analysis method taking into account the operational characteristics of schedulable flexible resources, the method comprising the sequential steps of:
(1) Analyzing the operating characteristics of the schedulable flexible resource: dividing the schedulable flexible resources into energy one-way interactive flexible resources and energy two-way interactive flexible resources, wherein the energy one-way interactive flexible resources comprise flexible loads and schedulable new energy sources, and the energy two-way interactive flexible resources comprise energy storage devices, electric automobiles and micro-grids;
(2) Identifying a schedulable flexible resource: according to the operation characteristics of each schedulable flexible resource, a two-stage robust optimization model of power grid planning for accounting for the uncertainty of new energy output and a two-stage robust optimization model of power grid planning for accounting for the uncertainty of electric automobile output are established, the association degree of the two models is analyzed, and then key schedulable flexible resources are identified; by analyzing the operation characteristics of each schedulable flexible resource, the influence mechanism of the schedulable flexible resource on the power grid planning is obtained, as shown in fig. 1.
(3) According to the operation characteristics of each schedulable flexible resource and the key schedulable flexible resource, a guiding mechanism for planning each schedulable flexible resource for the power grid is provided;
(4) Analyzing the influence on the power grid planning: and accessing the schedulable flexible resources with different capacities into the power distribution network according to a guiding mechanism for planning various schedulable flexible resources for the power grid, performing collaborative optimization of the schedulable flexible resources and the power grid planning layout, and finally analyzing the influence of the capacities on the power grid planning problem.
The step (1) specifically comprises the following steps:
(1a) Analyzing the operating characteristics of the energy storage device: the energy storage device can stabilize the fluctuation of new energy output due to the bidirectional flow characteristic of the energy storage device, has the millisecond-level rapid, stable and accurate charge and discharge power regulation characteristic, and can promote the instantaneous, short-time and time period balance capacity of the power system;
(1b) Analyzing the operation characteristics of the schedulable new energy: by adopting distributed installation, the node voltage value can be effectively improved, the power flow distribution is improved, the load shedding operation is reduced, and the power supply reliability is improved; the problem that the voltage at the new energy access position is too high or the adjacent line is overloaded exists, the operation stability of the power distribution network is affected, and the power supply reliability is reduced; the operation has uncertainty of output, and the start and stop of the new energy and unstable power output can generate fluctuation on voltage and impact on the power supply voltage of the power distribution network user;
(1c) Analyzing the operation characteristics of the electric automobile: the electric automobile has space-time uncertainty, the load is obviously increased, the peak-valley difference of the load is enlarged, the difficulty of power grid control is increased, the capacity of the distribution transformer is enlarged, and the cost of the power grid is increased; the energy can be transmitted in two directions, the network loss of the power distribution network can be reduced by orderly charging and discharging, the node voltage waveform of the power distribution network can be improved by participating in scheduling, the power flow of a line is influenced, and the network loss of the line is further influenced;
(1d) Analyzing the operation characteristics of the flexible load: the flexible load has the energy storage characteristic, so that the load in the peak period of electricity consumption can be reduced, the peak-valley difference of the load of the power grid is reduced, and the line loss of the power grid can be reduced by orderly participating in scheduling; the flexible load can reduce the characteristics to increase the load, reduce the maximum reactive load, increase the critical voltage, start and stop orderly, reduce the power grid line loss and reduce the peak-valley difference;
(1e) Analyzing the operating characteristics of the micro-grid: the micro-grid contains various schedulable flexible resources and has the dual attribute of source load; by introducing the energy storage device, the power shortage in the power consumption peak period can be relieved; the new energy with high permeability causes impact of the micro-grid on normal operation of the main network, and supplies power to the island area, thereby being a powerful supplement to the traditional power supply mode.
The step (2) specifically comprises the following steps:
(2a) Introducing a robustness index Γ wt And Γ pv The uncertainty of new energy output is characterized, as key operation characteristics, the initial investment cost and operation and maintenance cost of the power grid are used as targets, and a two-stage robust optimization model of power grid planning is established, wherein the uncertainty of new energy output is considered;
uncertainty scene set of wind turbines:
uncertainty scene set for photovoltaic power plants:
wherein R is NW×T The allowable range of wind power and photovoltaic output is set; NW is the number of units T of the fan and the photovoltaic unit as the total time period number;
and->
The predicted output values of the wind turbine generator and the distributed photovoltaic power station in the t period are respectively; p (P) wt,t And P pv,t The actual output values of the wind turbine generator system and the distributed photovoltaic power station in the t period are respectively; ΔP wt,t And DeltaP pv,t The fluctuation quantity of the wind turbine generator and the distributed photovoltaic power station in the t period; / >
And->
For period tMaximum fluctuation of the wind turbine generator and the distributed photovoltaic power station;
(2b) Taking the space-time uncertainty of the electric automobile as a key operation characteristic, adopting a normal distribution model to represent the characteristic, and establishing a power grid planning two-stage robust optimization model considering the output uncertainty of the electric automobile, wherein the model comprises two charging modes of conventional charging and quick charging:
conventional charging: the formula (3) and the formula (4) are a charge quantity model and a discharge charge quantity model, the formula (5) is a normal distribution model of charge starting time, and the formula (6) is a normal distribution model of daily driving mileage:
wherein E is CCS (t)、E CCS (t-1) is the charge quantity of the electric automobile at the moment t and the moment t-1; t is t c For the actual charge duration, t d Is the actual discharge time length; t is t max_c For maximum charge duration, t max_d Is the maximum discharge duration; e (E) max The maximum charge amount is the maximum charge amount when the electric automobile is fully charged; mu (mu) x Sum sigma x Respectively represent the expected and standard deviation, mu, of the charge start time x x ＝17.6，σ x ＝3.4；μ L Sum sigma L The expected and variance, μ, of the logarithm lnL of the mileage L, respectively L ＝3.2，σ L ＝0.88；f s (x) A normal distribution model expression for the charging start time; f (f) L (L) is the daily mileageA normal distribution model expression;
and (3) quick charging: equations (7), (8) and (9) are fast charge user queuing M/G/k models, and equations (10) and (11) are fast charge station charging models:
Wherein t is BCS Charging time for the fast charge user;
rated capacity for fast charge user battery; b (B) i,BCS The residual electric quantity is the residual electric quantity when the ith fast charge user arrives at the integrated station; />
Rated charging power for the fast charging device; e (E) T At t BCS Is not limited to the desired one; d (D) T At t BCS Is a variance of (2); p (P) BCS (t) is the total charging power of the fast charging station during the t-th period; p (P) i,BCS (t) is the charging power of the ith fast charge user during the t period; η (eta) BCS Charging power of the quick charging equipment for the quick charging station; omega i,BCS (t) is a binary variable representing the state of charge of the ith fast charge user at the t-th time period, 1 if charging is occurring,otherwise, 0; e (B) i,BCS ) The method comprises the steps that the expected residual electric quantity is obtained when a total fast charge user arrives at an integral station; d (B) i,BCS ) The variance of the residual electric quantity when the total fast charge user arrives at the integral station is obtained;
(2c) The method comprises the steps of obtaining a data set under multiple scenes of power grid planning through a power grid planning two-stage robust optimization model considering new energy output uncertainty and a power grid planning two-stage robust optimization model considering electric automobile output uncertainty, and calculating the association degree by adopting a data mining algorithm: comprehensively analyzing the degree of association between each cost X and Y under n scenes by adopting a gray association theory to obtain the degree of association between each cost and the total cost, comprehensively analyzing the degree of association between new energy output fluctuation and each cost under n scenes by adopting an entropy weight method, comprehensively weighting to obtain the degree of association between the new energy output fluctuation and the total cost, and identifying whether the flexible resource is a key schedulable flexible resource or not.
The step (3) specifically comprises the following steps:
(3a) Analyzing a multi-element operation mode of the schedulable flexible resource;
(3b) According to the analysis of the operation mode, response characteristic and flexible resource optimization scheduling model of the schedulable flexible resources, a flexible load guiding mechanism facing power grid planning, an electricity price guiding mechanism based on user side energy storage price optimization, an electric vehicle charging load guiding mechanism based on space-time electricity price optimization, an electricity price guiding mechanism based on micro-grid system response characteristic and a rewarding and punishment mechanism of the schedulable new energy participating in power grid scheduling are provided;
(3c) Analysis is performed by researching a flexible load guiding mechanism facing power grid planning: the method comprises the steps of constructing a peak-valley time division model based on an improved boundary moving technology, adding constraint conditions, solving optimal time division by taking a Dunn index as an objective function, constructing the peak-valley time division model based on the improved boundary moving technology, and after carrying out peak-valley time-of-use electricity price on load curve peak, valley time, a user describes electricity consumption in each time by adjusting own electricity consumption mode as follows:
in which Q p 、Q f 、Q v The electricity consumption before the time-sharing electricity price is carried out in the peak, flat and valley time periods is respectively represented; q (Q) p0 ，Q f0 ，Q v0 Respectively representing the electricity consumption of each period after the time-sharing electricity price is implemented; ΔQ p ，ΔQ f ，ΔQ v Respectively representing the variation of the electricity consumption before and after the time-sharing electricity price, E is an electricity price elastic matrix lambda pp 、λ pf 、λ pv Respectively the self-elasticity coefficient lambda of each period fp 、λ ff 、λ fv The cross elastic coefficients of any two time periods are respectively; lambda (lambda) vp 、λ vf 、λ vv The electricity consumption is the peak-valley period;
the constraint conditions of the peak-to-valley period division model based on the improved boundary moving technology are as follows:
(3c1) User benefit constraints: before and after the time-sharing electricity price is implemented, the electricity charge of the user is not increased;
(3c2) Peak, plateau, valley period electricity price constraints: after the time-sharing electricity price is implemented, the peak period electricity price is larger than the ordinary period electricity price, and the ordinary period electricity price is larger than the valley period electricity price;
(3c3) Grid company benefit constraints: the implementation of the time-sharing electricity price can reduce the investment cost of the power supplier, and the overall benefit of the power supplier is not reduced;
(3c4) Marginal electricity price constraint: the electricity price in the valley period should not be lower than the marginal electricity price;
the objective function of the peak-to-valley period division model based on the improved boundary movement technique is as follows:
F(p)＝αF 1 (p)+βF 2 (p)+H (17)
wherein p represents a decision variable; alpha and beta are weight coefficients of an objective function; h is a penalty function term.
And the particle swarm optimization algorithm is adopted to optimize the time-of-use electricity price of the flexible loads with different proportions, and the optimization result is shown in figure 7. From fig. 8, it can be seen that under the joint participation of the flexible load and the time-sharing electricity price, the precision and the efficiency of time division can be effectively improved, and the proposed flexible load optimal guiding mechanism based on direct load control and the time-sharing electricity price can effectively reduce peak-valley difference, so as to achieve the effects of smoothing load curves and peak regulation.
The step (4) specifically comprises the following steps:
(4a) Dividing the area to be planned into 3 areas, and firstly setting three schemes: the first scheme considers that the whole area to be planned is the common load; the second scheme is to access a schedulable new energy source and an energy storage device with the capacity of 10MW in three areas; the third scheme is that a schedulable new energy source and an energy storage device with the capacity of 15MW are accessed in three areas, and 10% of the common loads are considered to be flexible loads and electric automobile charging piles; because the capacity of the transformer substation is directly related to the cost, the transformer substation constant volume matching index based on the cost and capable of scheduling the flexible resource access is provided for measuring the influence of the access capacity on the transformer substation planning, and the transformer substation constant volume matching index capable of scheduling the flexible resource access is as follows:
wherein, eta represents constant volume matching index; c (C) t The method comprises the steps of planning cost for a transformer substation considering schedulable flexible resource access; c t Planning cost for a conventional transformer substation without considering schedulable flexible resource access;
(4b) Calculating the equivalent capacity of the schedulable flexible resource, and updating the capacitance-to-load ratio formula of the transformer station according to the equivalent capacity;
objective function:
where C is the total cost, including distribution grid planning cost and operating cost, N T Representing the number of substations; omega represents the number of network frame lines; c (C) Tp (Si) is the investment cost of the transformer substation, r is the discount rate, m is the depreciation age of the transformer substation, C j To build the cost of a primary power supply circuit, C rT (Si) is the operation and maintenance cost of the transformer substation, n year Is a simulated year; x is x i For the binary decision variable, when the value is 1, the ith line is selected, and when the value is 0, the ith line is not selected; x is x j For the binary decision variable, the value is 1, which indicates that the jth line is selected, and the value is 0, which indicates that the jth line is not selected; c loss Cost per network loss; ΔP i Active loss of the ith line;
the capacity-to-load ratio formula:
wherein R is s Representing the capacity-to-load ratio; s is S i Representing the capacity of the i-th transformer; p (P) max Representing the peak load value of the area to be planned; m is m s The number of the transformer substations; n is the number of transformers used by each transformer substation; lambda is the load-synchronous rate; p (P) mgeq Equivalent capacity for micro-grid, including distributed power supply output P dg And micro-grid flexible resource output Fl mg ；
(4c) Collaborative optimization of schedulable flexible resources and grid planning layout: establishing a double-layer planning model of the power distribution network considering flexible resource access, taking investment and total operation cost as objective functions, wherein an upper half formula of a formula (21) is an upper layer model and is used for planning the power distribution network, and a lower half formula of the formula (21) is a lower layer model and is used for calculating operation cost of the power distribution network:
Wherein f 0 、f 1 The method comprises the steps of respectively obtaining objective functions of an upper model and a lower model, wherein the upper model is an investment decision model, and the lower model is an operation scheduling model; x is x ov 、x iv Decision variables of the upper model and the lower model are respectively; x is x iv,s The decision variable of the lower model in the s scene is obtained; h (-) and G (-) correspond to the constraint conditions of the upper layer model and the lower layer model respectively, including equality constraint and inequality constraint;
objective function:
minC＝C p +C r (22)
wherein C is the total cost, including planning investment cost C p And operating cost C r The method comprises the steps of carrying out a first treatment on the surface of the First term in investment planning costs
For investment costs of the substation, the second item->
Planning costs for grid construction, where C Tp (S i ) Is the investment cost of the transformer substation, m is the depreciation age of the transformer substation,
line flexibility constraint conditions are increased to ensure adequacy of network frame transmission capacity in the flexible resource operation process:
0≤FL l ≤βFL max (25)
equation (25) is a line flexibility constraint, where FL l The capacity is adjusted for the used flexibility of the first line, namely the used transmission capacity of the line; beta is a margin coefficient, and the value range is 0,1]An inner part; FL (FL) max The maximum flexibility adjustment capability of the line is provided;
because the grid planning part in the power distribution network planning model has a nonlinear mathematical model and the solving scale is large, a genetic algorithm is adopted for solving; in the process of solving grid planning by using a genetic algorithm, the phenomenon that the output grid structure is crossed occurs in the power distribution network planning, which is not allowed in the actual planning operation process, and the fitness function after line crossing judgment is considered is as follows:
Wherein F is the cost after the line crossing judgment is considered, and J is the conventional constraint condition number; k (K) j A penalty function for the jth conventional constraint in the planning process; a, a linecross Determining binary variables for the line crossings; k' is a penalty function corresponding to the line crossing.
In the step (2 a), the two-stage robust optimization model of power grid planning which accounts for the uncertainty of new energy output specifically refers to: establishing a two-stage robust optimization model with the lowest total cost of the micro-grid, wherein the first-stage objective function is the lowest initial investment cost in the micro-grid, and the second-stage objective function is the lowest scheduling operation cost in the micro-grid;
initial investment cost C in first-stage objective function inv The investment cost for the wind turbine generator, the distributed photovoltaic power station, the energy storage device and the miniature gas turbine generator is as follows:
in the method, in the process of the invention,
maximum battery capacity for the energy storage device; c (C) bat Investment cost for unit power of the energy storage device; p (P) i max And ci is the maximum technical output and the investment cost of unit power of the ith power equipment respectively; f (F) CRE (ri, yi) is annual resource gold recovery; r is (r) i And Y i The discount rate and discount years of the ith power equipment are respectively, wherein the discount years of the energy storage device are floating charge life;
Scheduling operating cost C in second stage objective function open The running cost of the micro gas turbine, the electricity purchasing and selling cost of the micro power grid and the maintenance cost of equipment are as follows:
in the method, in the process of the invention,
C grid 、C op the running cost of the micro gas turbine, the electricity purchasing and selling cost of the micro power grid and the maintenance cost of equipment are respectively; c fuel,t Fuel cost for period t; p (P) G,t Real-time output of the micro gas turbine for a period t; k (k) n,t And c n,t N-th pollutant discharge amount and treatment unit price of the micro gas turbine with t time periods respectively; c buy,t And c sell,t Respectively representing the electricity purchasing price of the period t; />
And->
The power of the micro-grid for selling electricity to the power distribution network in the period t is respectively obtained; />
The maintenance cost unit price of the energy storage device, the wind turbine generator, the distributed photovoltaic power station and the micro-combustion unit; p (P) i,t The output of the energy storage device, the wind turbine generator, the distributed photovoltaic power station and the micro-combustion unit in the t period is represented;
constraints include the following:
wherein the power balance constraint is:
the micro-combustion unit is constrained as follows:
the energy storage device is constrained as follows:
the state of charge constraints are:
SOC min ≤SOC t ≤SOC max (37)
SOC beg ＝SOC end (38)
the switching power constraint is:
wherein P is wt,t 、P pv,t 、
And P G,t The output of the wind turbine generator, the distributed photovoltaic power station, the energy storage device and the micro-combustion unit in the period t, P load,t Load for t period; / >
For charging power of energy-storage device t period epsilon ch And epsilon dis The ratio of the maximum charge and discharge power of the stored energy to the maximum capacity of the storage battery is respectively; />
And->
Respectively representing the minimum and maximum output values of the micro-combustion unit in the t period; x-shaped articles bat Indicating the charge and discharge states of the stored energy, SOC min And SOC (System on chip) max The lower limit and the upper limit of the charge state of the storage battery are respectively defined, and the SOC is not less than 20% in order to prevent the discharge depth from being too large and ensure the service life of the storage battery to be too fast; SOC (State of Charge) beg And SOC (System on chip) end Indicating that it is accumulated in the scheduling period TThe states of charge of the batteries are the same starting and ending +.>
And->
Respectively representing the upper limit of the electric power purchased and sold by the micro-grid to the power distribution network; x-shaped articles M,t An integer variable of 0 to 1, wherein when the value of the integer variable is 1, the micro-grid purchases electricity to the power distribution network in a t period; />
Indicating the maximum capacity of the energy storage device.
In step (2 b), the two-stage robust optimization model for power grid planning, which takes into account the uncertainty of the output of the electric vehicle, specifically refers to: determining a power station to which the electric vehicle goes according to the selection cost of the electric vehicle to each charging station, and then calculating the charging load to a power distribution network; the charging price is changed to guide the electric vehicle to charge to a power station closer to the new energy, and the specific form is as follows:
three layers of robust optimization models are established, and the objective functions are as follows:
Wherein D is a new energy output scene set, and P (ζ) is the probability of output of each scene;
expressed as the square of the voltage amplitude at node i at time t; />
The square of the current amplitude of nodes i to j at time t; />
Representing the active power of nodes i to j at time t; />
Representing the active power of nodes i to j at time t; Φ represents the feasible region of the decision variables;
the constraint conditions are as follows:
charging station selection constraints:
in the method, in the process of the invention,
from the ith electric automobile to the t moment the integrated cost of the j-th charging station; />
For time t j charging electricity price of the charging station; />
The estimated waiting time of the jth charging station at the moment t; omega 1 ，ω 2 And omega 3 Is a weight coefficient; />
Is the lowest comprehensive cost; n is the number of the charging stations, when u is (i,j) When=0, it represents that vehicle i does not select charging station j, when u (i,j) When=1, charging station j is selected on behalf of vehicle i; m is an arbitrarily large positive number; />
An equivalent distance from the current position to the jth charging station for the ith vehicle at the moment t;
electric automobile state of charge constraint:
wherein V is i Is the running speed of the ith vehicle; t (T) i The time for generating a charging willingness of the vehicle owner is represented; ts. i The method comprises the steps of starting charging time for an electric automobile; t (T) ch,i Charging the electric automobile for a long time; gamma ray i =1 represents t 0 The electric automobile is in a charging state at moment, gamma i =0 indicates that it is not in a charged state;
the travel time of the ith vehicle at the moment t; />
The estimated waiting time of the ith charging station at the moment t;
power station charging price constraint:
load flow balance constraint:
charging station power balance constraints:
wherein k is 3 And k 4 Is a coefficient of proportionality and is used for the control of the power supply,
for the electricity price at the time t of the station, +.>
The charging electricity price of the ith charging station at the moment t; omega shape a For the node set between the kth electric vehicle and the ith charging station, Ω b For the collection of all nodes in the power distribution network, rij and Xij are the resistance and reactance values of the branch ij respectively, pt ij and Qt ij respectively represent the active and reactive power flows of the branch ij, pt i.D and Qt i.D respectively represent the active load and reactive load of the node i at the moment t, and Pt i.en and Qt i.en respectively represent the active and reactive power injection of the power supply at the node i at the moment t; />
And->
Active and reactive power injection of the charging station at the node i at the moment t is respectively shown; pt cs.j represents the active power output of charging station j at time t, p c The charging power of a single charging pile is N is the sum of the number of charging vehicles at all times in a day, u (i,j) Indicating the selection of charging station j by electric vehicle i, < >>
And the charging state of the electric automobile i at the time t is shown, and lambda is the power factor of the charging pile.
In step (3 b), the flexible load guiding mechanism facing the grid planning refers to: the electricity price type guiding mechanism is adopted to guide the schedulable flexible resource, the basis is that the schedulable flexible resource is scientifically and reasonably divided into peak-average-valley time periods according to the load characteristics of users, and corresponding time-sharing electricity prices are formulated so as to guide the users, and the purpose of peak clipping and valley filling is achieved by combining the control of the load;
the electricity price type guiding mechanism based on the user side energy storage price optimization refers to: the response characteristic of the user side energy storage power station is reflected in that the energy storage device can store low power and high power as much as possible; the time-sharing electricity price is adopted to guide the energy storage at the user side, so that the electricity consumption of the user can be reduced, the peak-valley difference of the load of the power system can be reduced, the utilization rate of power generation and transmission equipment can be improved, and the equipment investment can be delayed, thereby realizing win-win;
the electric automobile charging load guiding mechanism based on space-time electricity price optimization refers to: the charging load of the electric automobile has certain randomness in time and also shows larger flow characteristic in space; the network-accessible electric automobile is also used as a mobile energy storage device to realize the reverse feeding of electric energy to the system; based on the time-space response characteristic of the electric automobile, the time-sharing electricity price is adopted to guide the electric automobile to participate in peak clipping and valley filling in time and space, and meanwhile, the network loss is reduced, and the economical efficiency and the reliability of a power grid are improved;
The electricity price type guiding mechanism based on the response characteristic of the micro-grid system refers to: the micro-grid system is guided to interact with the large power grid through time-sharing electricity price, and electricity is purchased from the large power grid when the micro-grid energy output is insufficient and the electricity price is low; when the energy output of the micro-grid meets the load demand, and the surplus exists, the surplus electric quantity is sold to a large power grid, and the higher electricity price can obtain the income, so that the economic cost of the micro-grid system is reduced;
the rewarding and punishing mechanism for the participation of the schedulable new energy in the power grid scheduling refers to: the enthusiasm of the new energy to participate in peak shaving can be guided by a reward and punishment policy, the capacity of the power system for absorbing the new energy is improved by phase change, the stable operation of the power system is effectively ensured, and the reduction of peak shaving pressure of the power system and the benefits of new energy generators are considered; guiding new energy manufacturers to reasonably allocate and store according to the actual conditions of the new energy manufacturers, thereby reducing the peak regulation pressure of the power grid.
Firstly, comparing the investment cost of the transformer substation in fig. 2 and fig. 3, it can be known that whether the flexible resource can be scheduled to be accessed or not does not affect the load partition and the substation location and volume-fixing result, and the cost change of the transformer substation and the system capacity are in a step-like relationship because the total capacity of the micro-grid in the area to be planned is smaller, so that the operation cost of the transformer substation in the final two schemes is the same. As can be seen from comparing the line investment cost and the network loss cost in fig. 3, due to the existence of the micro-grid, a reverse power flow occurs in the power distribution network, and the planning process needs to be constrained by the reverse power flow, so that the total length of the line is prolonged, the investment cost is increased, and the line investment cost and the micro-grid capacity show a positive correlation. However, when the reverse power flow generated by the micro-grid is not limited in all time periods, the partial load can be directly supplied by the micro-grid, so that the total length of the system circuit is reduced, and the circuit investment cost is reduced. As can be seen from comparing the line investment cost and the net loss cost in fig. 4, after the capacity of the schedulable flexible resources in the area to be planned is increased, the net rack planning cost is not greatly affected, because the original planning model already considers the flexible resource line transmission margin, and the cost is only slightly increased. After the capacity of the schedulable flexible resource is increased, the network loss cost is further reduced, so that the access of the schedulable flexible resource can be obtained, and the system running cost can be effectively reduced.
By analyzing fig. 5 and 6 and the foregoing, access of the schedulable flexible resource can reduce line loss, reduce system operation cost, and improve economy. However, attention is paid to the influence of the reverse power flow problem on investment cost, and the influence mainly depends on the output power and the upstream load power of the micro-grid, so that the node containing the micro-grid is arranged upstream of the topological structure as much as possible or the upstream load capacity of the node containing the micro-grid is increased, and the reverse power flow is prevented from being out of limit. The access types and the capacity of the schedulable flexible resources are in a step relation to the influence of the transformer substation planning cost, the planning cost is reduced only by accumulating the access types and the capacity of the schedulable flexible resources to a certain extent, and the transformer substation planning is performed again when the access proportion of the schedulable flexible resources is more than 20%.
In summary, the invention researches the influence characteristics and the correlation of the schedulable flexible load and the power grid planning, establishes a sound schedulable flexible resource guiding mechanism, explores a collaborative optimization technology of the schedulable flexible resource and the power grid planning layout, realizes the effective promotion of the schedulable flexible resource to participate in the power grid operation and planning, fully exerts the value of the schedulable flexible load resource, reduces the power grid planning capacity requirement of the power system and improves the utilization efficiency of the power grid planning; according to the invention, partial load is converted from 'rigidity' to 'flexibility' through regulation and control means such as time-sharing electricity price, the electricity consumption is transferred between specified time intervals or different time intervals according to the power grid demand, the power grid interactivity of the flexible load is effectively utilized to realize optimal regulation and control, the electric vehicle, the flexible load, the energy storage system and the like are accessed into the power grid in a large scale, peak clipping and valley filling can be realized through reasonably guiding the schedulable flexible resources, and the peak valley difference of the power grid is reduced; according to the invention, an energy storage system with the dual attribute of source-charge and an electric automobile are used as power sources to be connected into a power grid, a traditional power grid planning method based on artificial experience is abandoned, a novel power grid planning model is provided, the planning result is reasonably improved, the utilization rate of equipment is improved, flexible resources are reasonably utilized to coordinate and absorb new energy power generation represented by photovoltaic power generation and wind power generation, the influence of randomness and fluctuation of the new energy power generation is effectively relieved, and the safety and stability of power grid operation are improved.
Claims (8)
1. A power grid planning capacity analysis method considering the operation characteristics of schedulable flexible resources is characterized in that: the method comprises the following steps in sequence:
(1) Analyzing the operating characteristics of the schedulable flexible resource: dividing the schedulable flexible resources into energy one-way interactive flexible resources and energy two-way interactive flexible resources, wherein the energy one-way interactive flexible resources comprise flexible loads and schedulable new energy sources, and the energy two-way interactive flexible resources comprise energy storage devices, electric automobiles and micro-grids;
(2) Identifying a schedulable flexible resource: according to the operation characteristics of each schedulable flexible resource, a two-stage robust optimization model of power grid planning for accounting for the uncertainty of new energy output and a two-stage robust optimization model of power grid planning for accounting for the uncertainty of electric automobile output are established, the association degree of the two models is analyzed, and then key schedulable flexible resources are identified;
(3) According to the operation characteristics of each schedulable flexible resource and the key schedulable flexible resource, a guiding mechanism for planning each schedulable flexible resource for the power grid is provided;
(4) Analyzing the influence on the power grid planning: and accessing the schedulable flexible resources with different capacities into the power distribution network according to a guiding mechanism for planning various schedulable flexible resources for the power grid, performing collaborative optimization of the schedulable flexible resources and the power grid planning layout, and finally analyzing the influence of the capacities on the power grid planning problem.
2. The power grid planning capacity analysis method considering the operation characteristics of schedulable flexible resources according to claim 1, wherein: the step (1) specifically comprises the following steps:
(1a) Analyzing the operating characteristics of the energy storage device: the energy storage device can stabilize the fluctuation of new energy output due to the bidirectional flow characteristic of the energy storage device, has the millisecond-level rapid, stable and accurate charge and discharge power regulation characteristic, and can promote the instantaneous, short-time and time period balance capacity of the power system;
(1b) Analyzing the operation characteristics of the schedulable new energy: by adopting distributed installation, the node voltage value can be effectively improved, the power flow distribution is improved, the load shedding operation is reduced, and the power supply reliability is improved; the problem that the voltage at the new energy access position is too high or the adjacent line is overloaded exists, the operation stability of the power distribution network is affected, and the power supply reliability is reduced; the operation has uncertainty of output, and the start and stop of the new energy and unstable power output can generate fluctuation on voltage and impact on the power supply voltage of the power distribution network user;
(1c) Analyzing the operation characteristics of the electric automobile: the electric automobile has space-time uncertainty, the load is obviously increased, the peak-valley difference of the load is enlarged, the difficulty of power grid control is increased, the capacity of the distribution transformer is enlarged, and the cost of the power grid is increased; the energy can be transmitted in two directions, the network loss of the power distribution network can be reduced by orderly charging and discharging, the node voltage waveform of the power distribution network can be improved by participating in scheduling, the power flow of a line is influenced, and the network loss of the line is further influenced;
(1d) Analyzing the operation characteristics of the flexible load: the flexible load has the energy storage characteristic, so that the load in the peak period of electricity consumption can be reduced, the peak-valley difference of the load of the power grid is reduced, and the line loss of the power grid can be reduced by orderly participating in scheduling; the flexible load can reduce the characteristics to increase the load, reduce the maximum reactive load, increase the critical voltage, start and stop orderly, reduce the power grid line loss and reduce the peak-valley difference;
(1e) Analyzing the operating characteristics of the micro-grid: the micro-grid contains various schedulable flexible resources and has the dual attribute of source load; by introducing the energy storage device, the power shortage in the power consumption peak period can be relieved; the new energy with high permeability causes impact of the micro-grid on normal operation of the main network, and supplies power to the island area, thereby being a powerful supplement to the traditional power supply mode.
3. The power grid planning capacity analysis method considering the operation characteristics of schedulable flexible resources according to claim 1, wherein: the step (2) specifically comprises the following steps:
(2a) Introducing a robustness index Γ wt And Γ pv The uncertainty of new energy output is characterized, as key operation characteristics, the initial investment cost and operation and maintenance cost of the power grid are used as targets, and a two-stage robust optimization model of power grid planning is established, wherein the uncertainty of new energy output is considered;
Uncertainty scene set of wind turbines:
uncertainty scene set for photovoltaic power plants:
wherein R is NW×T The allowable range of wind power and photovoltaic output is set; NW is the number of units T of the fan and the photovoltaic unit as the total time period number;
and->
The predicted output values of the wind turbine generator and the distributed photovoltaic power station in the t period are respectively; p (P) wt,t And P pv,t The actual output values of the wind turbine generator system and the distributed photovoltaic power station in the t period are respectively; ΔP wt,t And DeltaP pv,t The fluctuation quantity of the wind turbine generator and the distributed photovoltaic power station in the t period; />
And->
The maximum fluctuation amount of the wind turbine generator and the distributed photovoltaic power station in the t period is set;
(2b) Taking the space-time uncertainty of the electric automobile as a key operation characteristic, adopting a normal distribution model to represent the characteristic, and establishing a power grid planning two-stage robust optimization model considering the output uncertainty of the electric automobile, wherein the model comprises two charging modes of conventional charging and quick charging:
conventional charging: the formula (3) and the formula (4) are a charge quantity model and a discharge charge quantity model, the formula (5) is a normal distribution model of charge starting time, and the formula (6) is a normal distribution model of daily driving mileage:
wherein E is CCS (t)、E CCS (t-1) is the charge quantity of the electric automobile at the moment t and the moment t-1; t is t c For the actual charge duration, t d Is the actual discharge time length; t is t max_c For maximum charge duration, t max_d Is the maximum discharge duration; e (E) max The maximum charge amount is the maximum charge amount when the electric automobile is fully charged; mu (mu) x Sum sigma x Respectively represent the expected and standard deviation, mu, of the charge start time x x ＝17.6，σ x ＝3.4；μ L Sum sigma L The expected and variance, μ, of the logarithm lnL of the mileage L, respectively L ＝3.2，σ L ＝0.88；f s (x) A normal distribution model expression for the charging start time; f (f) L (L) is a daily driving mileage normal distribution model expression;
and (3) quick charging: equations (7), (8) and (9) are fast charge user queuing M/G/k models, and equations (10) and (11) are fast charge station charging models:
wherein t is BCS Charging time for the fast charge user;
rated capacity for fast charge user battery; b (B) i,BCS The residual electric quantity is the residual electric quantity when the ith fast charge user arrives at the integrated station; />
Rated charging power for the fast charging device; e (E) T At t BCS Is not limited to the desired one; d (D) T At t BCS Is a variance of (2); p (P) BCS (t) is the total charging power of the fast charging station during the t-th period; p (P) i,BCS (t) is the charging power of the ith fast charge user during the t period; η (eta) BCS Charging power of the quick charging equipment for the quick charging station; omega i,BCS (t) is a binary variable representing the state of charge of the ith fast charge user at the t period, 1 if charging is occurring, or 0 if not; e (B) i,BCS ) The method comprises the steps that the expected residual electric quantity is obtained when a total fast charge user arrives at an integral station; d (B) i,BCS ) The variance of the residual electric quantity when the total fast charge user arrives at the integral station is obtained;
(2c) The method comprises the steps of obtaining a data set under multiple scenes of power grid planning through a power grid planning two-stage robust optimization model considering new energy output uncertainty and a power grid planning two-stage robust optimization model considering electric automobile output uncertainty, and calculating the association degree by adopting a data mining algorithm: comprehensively analyzing the degree of association between each cost X and Y under n scenes by adopting a gray association theory to obtain the degree of association between each cost and the total cost, comprehensively analyzing the degree of association between new energy output fluctuation and each cost under n scenes by adopting an entropy weight method, comprehensively weighting to obtain the degree of association between the new energy output fluctuation and the total cost, and identifying whether the flexible resource is a key schedulable flexible resource or not.
4. The power grid planning capacity analysis method considering the operation characteristics of schedulable flexible resources according to claim 1, wherein: the step (3) specifically comprises the following steps:
(3a) Analyzing a multi-element operation mode of the schedulable flexible resource;
(3b) According to the analysis of the operation mode, response characteristic and flexible resource optimization scheduling model of the schedulable flexible resources, a flexible load guiding mechanism facing power grid planning, an electricity price guiding mechanism based on user side energy storage price optimization, an electric vehicle charging load guiding mechanism based on space-time electricity price optimization, an electricity price guiding mechanism based on micro-grid system response characteristic and a rewarding and punishment mechanism of the schedulable new energy participating in power grid scheduling are provided;
(3c) Analysis is performed by researching a flexible load guiding mechanism facing power grid planning: the method comprises the steps of constructing a peak-valley time division model based on an improved boundary moving technology, adding constraint conditions, solving optimal time division by taking a Dunn index as an objective function, constructing the peak-valley time division model based on the improved boundary moving technology, and after carrying out peak-valley time-of-use electricity price on load curve peak, valley time, a user describes electricity consumption in each time by adjusting own electricity consumption mode as follows:
in which Q p 、Q f 、Q v The electricity consumption before the time-sharing electricity price is carried out in the peak, flat and valley time periods is respectively represented; q (Q) p0 ，Q f0 ，Q v0 Respectively representing the electricity consumption of each period after the time-sharing electricity price is implemented; ΔQ p ，ΔQ f ，ΔQ v Respectively representing the variation of the electricity consumption before and after the time-sharing electricity price, E is an electricity price elastic matrix lambda pp 、λ pf 、λ pv Self-elastic system for each time periodNumber lambda fp 、λ ff 、λ fv The cross elastic coefficients of any two time periods are respectively; lambda (lambda) vp 、λ vf 、λ vv The electricity consumption is the peak-valley period;
the constraint conditions of the peak-to-valley period division model based on the improved boundary moving technology are as follows:
(3c1) User benefit constraints: before and after the time-sharing electricity price is implemented, the electricity charge of the user is not increased;
(3c2) Peak, plateau, valley period electricity price constraints: after the time-sharing electricity price is implemented, the peak period electricity price is larger than the ordinary period electricity price, and the ordinary period electricity price is larger than the valley period electricity price;
(3c3) Grid company benefit constraints: the implementation of the time-sharing electricity price can reduce the investment cost of the power supplier, and the overall benefit of the power supplier is not reduced;
(3c4) Marginal electricity price constraint: the electricity price in the valley period should not be lower than the marginal electricity price;
the objective function of the peak-to-valley period division model based on the improved boundary movement technique is as follows:
F(p)＝αF 1 (p)+βF 2 (p)+H (17)
wherein p represents a decision variable; alpha and beta are weight coefficients of an objective function; h is a penalty function term.
5. The power grid planning capacity analysis method considering the operation characteristics of schedulable flexible resources according to claim 1, wherein: the step (4) specifically comprises the following steps:
(4a) Dividing the area to be planned into 3 areas, and firstly setting three schemes: the first scheme considers that the whole area to be planned is the common load; the second scheme is to access a schedulable new energy source and an energy storage device with the capacity of 10MW in three areas; the third scheme is that a schedulable new energy source and an energy storage device with the capacity of 15MW are accessed in three areas, and 10% of the common loads are considered to be flexible loads and electric automobile charging piles; because the capacity of the transformer substation is directly related to the cost, the transformer substation constant volume matching index based on the cost and capable of scheduling the flexible resource access is provided for measuring the influence of the access capacity on the transformer substation planning, and the transformer substation constant volume matching index capable of scheduling the flexible resource access is as follows:
Wherein, eta represents constant volume matching index; c (C) t The method comprises the steps of planning cost for a transformer substation considering schedulable flexible resource access; c t Planning cost for a conventional transformer substation without considering schedulable flexible resource access;
(4b) Calculating the equivalent capacity of the schedulable flexible resource, and updating the capacitance-to-load ratio formula of the transformer station according to the equivalent capacity;
objective function:
where C is the total cost, including distribution grid planning cost and operating cost, N T Representing the number of substations; omega represents the number of network frame lines; c (C) Tp (Si) is the investment cost of the transformer substation, r is the discount rate, m is the depreciation age of the transformer substation, C j To build the cost of a primary power supply circuit, C rT (Si) is the operation and maintenance cost of the transformer substation, n year Is a simulated year; x is x i For the binary decision variable, when the value is 1, the ith line is selected, and when the value is 0, the ith line is not selected; x is x j For the binary decision variable, the value is 1, which indicates that the jth line is selected, and the value is 0, which indicates that the jth line is not selected; c loss Cost per network loss; ΔP i Active loss of the ith line;
the capacity-to-load ratio formula:
wherein R is s Representing the capacity-to-load ratio; s is S i Representing the capacity of the i-th transformer; p (P) max Representing the peak load value of the area to be planned; m is m s The number of the transformer substations; n is the number of transformers used by each transformer substation; lambda is the load-synchronous rate; p (P) mgeq Equivalent capacity for micro-grid, including distributed power supply output P dg And micro-grid flexible resource output Fl mg ；
(4c) Collaborative optimization of schedulable flexible resources and grid planning layout: establishing a double-layer planning model of the power distribution network considering flexible resource access, taking investment and total operation cost as objective functions, wherein an upper half formula of a formula (21) is an upper layer model and is used for planning the power distribution network, and a lower half formula of the formula (21) is a lower layer model and is used for calculating operation cost of the power distribution network:
wherein f 0 、f 1 The method comprises the steps of respectively obtaining objective functions of an upper model and a lower model, wherein the upper model is an investment decision model, and the lower model is an operation scheduling model; x is x ov 、x iv Decision variables of the upper model and the lower model are respectively; x is x iv,s The decision variable of the lower model in the s scene is obtained; h (-) and G (-) correspond to the constraint conditions of the upper layer model and the lower layer model respectively, including equality constraint and inequality constraint;
objective function:
minC＝C p +C r (22)
wherein C is the total cost, including planning investment cost C p And operating cost C r The method comprises the steps of carrying out a first treatment on the surface of the First term in investment planning costs
For investment costs of the substation, the second item->
Planning costs for grid construction, where C Tp (S i ) The investment cost of the transformer substation is calculated, and m is the depreciation age of the transformer substation>
Representing line loss cost;
Line flexibility constraint conditions are increased to ensure adequacy of network frame transmission capacity in the flexible resource operation process:
0≤FL l ≤βFL max (25)
equation (25) is a line flexibility constraint, where FL l The capacity is adjusted for the used flexibility of the first line, namely the used transmission capacity of the line; beta is a margin coefficient, and the value range is 0,1]An inner part; FL (FL) max The maximum flexibility adjustment capability of the line is provided;
because the grid planning part in the power distribution network planning model has a nonlinear mathematical model and the solving scale is large, a genetic algorithm is adopted for solving; in the process of solving grid planning by using a genetic algorithm, the phenomenon that the output grid structure is crossed occurs in the power distribution network planning, which is not allowed in the actual planning operation process, and the fitness function after line crossing judgment is considered is as follows:
wherein F is the cost after the line crossing judgment is considered, and J is the conventional constraint condition number; k (K) j A penalty function for the jth conventional constraint in the planning process; a, a linecross Determining binary variables for the line crossings; k' is a penalty function corresponding to the line crossing.
6. A power grid planning capacity analysis method taking into account the operational characteristics of schedulable flexible resources as recited in claim 3, wherein: in the step (2 a), the two-stage robust optimization model of power grid planning which accounts for the uncertainty of new energy output specifically refers to: establishing a two-stage robust optimization model with the lowest total cost of the micro-grid, wherein the first-stage objective function is the lowest initial investment cost in the micro-grid, and the second-stage objective function is the lowest scheduling operation cost in the micro-grid;
Initial investment cost C in first-stage objective function inv The investment cost for the wind turbine generator, the distributed photovoltaic power station, the energy storage device and the miniature gas turbine generator is as follows:
in the method, in the process of the invention,
maximum battery capacity for the energy storage device; c (C) bat Investment cost for unit power of the energy storage device; p (P) i max And c i The maximum technical output and the investment cost of unit power of the ith power equipment are respectively; f (F) CRE (ri, yi) is annual resource gold recovery; r is (r) i And Y i Respectively the discount rate and discount of the ith power equipmentThe present years, wherein the folded years of the energy storage device are floating charge life;
scheduling operating cost C in second stage objective function open The running cost of the micro gas turbine, the electricity purchasing and selling cost of the micro power grid and the maintenance cost of equipment are as follows:
in the method, in the process of the invention,
C grid 、C op the running cost of the micro gas turbine, the electricity purchasing and selling cost of the micro power grid and the maintenance cost of equipment are respectively; c fuel,t Fuel cost for period t; p (P) G,t Real-time output of the micro gas turbine for a period t; k (k) n,t And c n,t N-th pollutant discharge amount and treatment unit price of the micro gas turbine with t time periods respectively; c buy,t And c sell,t Respectively representing the electricity purchasing price of the period t; />
And->
Respectively, micro-grid is aligned in t period The power of electricity purchasing and selling of the power grid; />
The maintenance cost unit price of the energy storage device, the wind turbine generator, the distributed photovoltaic power station and the micro-combustion unit; p (P) i,t The output of the energy storage device, the wind turbine generator, the distributed photovoltaic power station and the micro-combustion unit in the t period is represented;
constraints include the following:
wherein the power balance constraint is:
the micro-combustion unit is constrained as follows:
the energy storage device is constrained as follows:
the state of charge constraints are:
SOC min ≤SOC t ≤SOC max (37)
SOC beg ＝SOC end (38)
the switching power constraint is:
wherein P is wt,t 、P pv,t 、
And P G,t The output of the wind turbine generator, the distributed photovoltaic power station, the energy storage device and the micro-combustion unit in the period t, P load,t Load for t period; />
For charging power of energy-storage device t period epsilon ch And epsilon dis The ratio of the maximum charge and discharge power of the stored energy to the maximum capacity of the storage battery is respectively; />
And->
Respectively representing the minimum and maximum output values of the micro-combustion unit in the t period; x-shaped articles bat Indicating the charge and discharge states of the stored energy, SOC min And SOC (System on chip) max The lower limit and the upper limit of the charge state of the storage battery are respectively defined, and the SOC is not less than 20% in order to prevent the discharge depth from being too large and ensure the service life of the storage battery to be too fast; SOC (State of Charge) beg And SOC (System on chip) end Indicating that the state of charge of the battery is the same throughout the scheduling period T,/and->
And->
Respectively representing the upper limit of the electric power purchased and sold by the micro-grid to the power distribution network; x-shaped articles M,t An integer variable of 0 to 1, wherein when the value of the integer variable is 1, the micro-grid purchases electricity to the power distribution network in a t period; />
Indicating the maximum capacity of the energy storage device.
7. A power grid planning capacity analysis method taking into account the operational characteristics of schedulable flexible resources as recited in claim 3, wherein: in step (2 b), the two-stage robust optimization model for power grid planning, which takes into account the uncertainty of the output of the electric vehicle, specifically refers to: determining a power station to which the electric vehicle goes according to the selection cost of the electric vehicle to each charging station, and then calculating the charging load to a power distribution network; the charging price is changed to guide the electric vehicle to charge to a power station closer to the new energy, and the specific form is as follows:
three layers of robust optimization models are established, and the objective functions are as follows:
wherein D is a new energy output scene set, and P (ζ) is the probability of output of each scene;
expressed as the square of the voltage amplitude at node i at time t; />
The square of the current amplitude of nodes i to j at time t; />
Representing the active power of nodes i to j at time t;
representing the active power of nodes i to j at time t; Φ represents the feasible region of the decision variables;
the constraint conditions are as follows:
charging station selection constraints:
in the method, in the process of the invention,
the comprehensive cost from the ith electric automobile to the jth charging station at the moment t; / >
The charging electricity price of the j-th charging station at the moment t; />
The estimated waiting time of the jth charging station at the moment t; omega 1 ，ω 2 And omega 3 Is a weight coefficient;
is the lowest comprehensive cost; n is the number of charging stations, when u (i,j) When=0, it represents that vehicle i does not select charging station j, when u (i,j) When=1, charging station j is selected on behalf of vehicle i; m is an arbitrarily large positive number; />
An equivalent distance from the current position to the jth charging station for the ith vehicle at the moment t;
electric automobile state of charge constraint:
wherein V is i Is the running speed of the ith vehicle; t (T) i The time for generating a charging willingness of the vehicle owner is represented; ts. i The method comprises the steps of starting charging time for an electric automobile; t (T) ch,i Charging the electric automobile for a long time; gamma ray i =1 represents t 0 The electric automobile is in a charging state at moment, gamma i =0 indicates that it is not in a charged state;
the travel time of the ith vehicle at the moment t; />
The estimated waiting time of the ith charging station at the moment t;
power station charging price constraint:
load flow balance constraint:
charging station power balance constraints:
wherein k is 3 And k 4 Is a coefficient of proportionality and is used for the control of the power supply,
for the electricity price at the time t of the station, +.>
The charging electricity price of the ith charging station at the moment t; omega shape a For the node set between the kth electric vehicle and the ith charging station, Ω b For the collection of all nodes in the power distribution network, rij and Xij are the resistance and reactance values of the branch ij respectively, pt ij and Qt ij respectively represent the active and reactive power flows of the branch ij, pt i.D and Qt i.D respectively represent the active load and reactive load of the node i at the moment t, and Pt i.en and Qt i.en respectively represent the active and reactive power injection of the power supply at the node i at the moment t; />
And->
Active and reactive power injection of the charging station at the node i at the moment t is respectively shown; pt cs.j represents the active power output of charging station j at time t, p c The charging power of a single charging pile is N is the sum of the number of charging vehicles at all times in a day, u (i,j) Indicating the selection of charging station j by electric vehicle i, < >>
And the charging state of the electric automobile i at the time t is shown, and lambda is the power factor of the charging pile.
8. The power grid planning capacity analysis method considering the operation characteristics of schedulable flexible resources according to claim 4, wherein: in step (3 b), the flexible load guiding mechanism facing the grid planning refers to: the electricity price type guiding mechanism is adopted to guide the schedulable flexible resource, the basis is that the schedulable flexible resource is scientifically and reasonably divided into peak-average-valley time periods according to the load characteristics of users, and corresponding time-sharing electricity prices are formulated so as to guide the users, and the purpose of peak clipping and valley filling is achieved by combining the control of the load;
The electricity price type guiding mechanism based on the user side energy storage price optimization refers to: the response characteristic of the user side energy storage power station is reflected in that the energy storage device can store low power and high power as much as possible; the time-sharing electricity price is adopted to guide the energy storage at the user side, so that the electricity consumption of the user can be reduced, the peak-valley difference of the load of the power system can be reduced, the utilization rate of power generation and transmission equipment can be improved, and the equipment investment can be delayed, thereby realizing win-win;
the electric automobile charging load guiding mechanism based on space-time electricity price optimization refers to: the charging load of the electric automobile has certain randomness in time and also shows larger flow characteristic in space; the network-accessible electric automobile is also used as a mobile energy storage device to realize the reverse feeding of electric energy to the system; based on the time-space response characteristic of the electric automobile, the time-sharing electricity price is adopted to guide the electric automobile to participate in peak clipping and valley filling in time and space, and meanwhile, the network loss is reduced, and the economical efficiency and the reliability of a power grid are improved;
the electricity price type guiding mechanism based on the response characteristic of the micro-grid system refers to: the micro-grid system is guided to interact with the large power grid through time-sharing electricity price, and electricity is purchased from the large power grid when the micro-grid energy output is insufficient and the electricity price is low; when the energy output of the micro-grid meets the load demand, and the surplus exists, the surplus electric quantity is sold to a large power grid, and the higher electricity price can obtain the income, so that the economic cost of the micro-grid system is reduced;
The rewarding and punishing mechanism for the participation of the schedulable new energy in the power grid scheduling refers to: the enthusiasm of the new energy to participate in peak shaving can be guided by a reward and punishment policy, the capacity of the power system for absorbing the new energy is improved by phase change, the stable operation of the power system is effectively ensured, and the reduction of peak shaving pressure of the power system and the benefits of new energy generators are considered; guiding new energy manufacturers to reasonably allocate and store according to the actual conditions of the new energy manufacturers, thereby reducing the peak regulation pressure of the power grid.
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| CN118017524A (en) * | 2024-04-10 | 2024-05-10 | 湖南大学 | Real-time high-efficiency energy control method for through-type traction power supply system |
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| CN117154801A (en) * | 2023-11-01 | 2023-12-01 | 国网冀北电力有限公司 | Method and device for determining energy storage configuration and output scheme of power grid system |
| CN117154801B (en) * | 2023-11-01 | 2024-01-26 | 国网冀北电力有限公司 | Method and device for determining energy storage configuration and output scheme of power grid system |
| CN118017524A (en) * | 2024-04-10 | 2024-05-10 | 湖南大学 | Real-time high-efficiency energy control method for through-type traction power supply system |
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