CN103475013A - Method and system for comprehensively optimizing energy storing power station planning and operating - Google Patents

Abstract

The invention discloses a method for comprehensively optimizing energy storing power station planning and operating. The method comprises the first step of constructing a real-part matrix G and a virtual matrix B of a node admittance matrix used for carrying out load flow calculation according to network parameters of an electric power system and determining the upper limit value and the lower limit value of voltage of all nodes and the upper limit value and the lower limit value of all line power flow when the electric power system operates safely and stably, the second step of determining the upper limit value and the lower limit value of work output of a common electric generator, determining generating cost parameters of the common electric generator, determining the upper limit value and the lower limit value of the work output of a wind turbine generator or a wind power motor field and determining the penalty electricity price for draught fan fault according to the power source parameters of the electric power system, the third step of determining the capacity price, the power price and the average service life of an energy storage system and daily maintenance charge according to parameters of the energy storage system, the fourth step of determining the typical day wind power predicted data of the wind turbine generator or the wind power motor field, determining the day load data, determining the backup data of rotating of the electric power system and determining the transmission loss price of the electric power system according to scheduling operating data of the electric power system, and the last step of determining an inner layer optimization model and an outer layer optimization model according to the data collected in the first step, the second step, the third step and the fourth step.

Description

Energy-accumulating power station planning and operation comprehensive optimization method and system

Technical field

The invention belongs to generation of electricity by new energy and control field, relate in particular to a kind of energy-accumulating power station planning and operation comprehensive optimization method and system based on dual-layer optimization theoretical (BLP)

Background technology

Since entering the new century, the situation of fossil energy shortage and environmental pollution is more and more serious, impel power industry to find the reproducible clean energy resource of exploitation and substitute existing chemical energy source, Optimization of Energy Structure, wherein, wind-powered electricity generation starts to be subject to people's attention gradually as a kind of reproducible clean energy resource of extensive existence.But along with the increase year by year of peak load regulation network difficulty, and some regional rack construction lags far behind power construction, the phenomenon that China's wind energy turbine set is abandoned wind is day by day serious, has caused great energy waste, has also caused huge loss to wind-powered electricity generation electricity power enterprise.For this reason, the space-time that extensive energy-storage system is used to adjust electric power system power supply and load is matching properties not, alleviates and abandons the wind phenomenon, improves system wind-powered electricity generation access capability.Certainly, to the research of extensive energy-storage system, still in the starting stage, what in application, at first need to pay close attention to is exactly the planning problem of energy-accumulating power station.Yet the planning problem of energy-accumulating power station should be take the actual motion effect as basis, the argument programming problem does not have practical significance separately.Therefore, the planning problem of energy-accumulating power station and the unified consideration of operation problem could need to be met to the requirement of energy-accumulating power station planning operation, this also meets the development trend of generation of electricity by new energy and control.

Summary of the invention

(1) technical problem that will solve

The technical problem to be solved in the present invention is: a kind of simple and practical energy-accumulating power station planning and operation comprehensive optimization method and system are provided, for the problems such as layout, capacity configuration, power configuration of resolving the energy-accumulating power station planning process, and analyze the expansion Optimization of Unit Commitment By Improved in the energy-accumulating power station running.

(2) technical scheme

For addressing the above problem, the invention provides a kind of energy-accumulating power station planning and operation comprehensive optimization method, comprise step: energy-accumulating power station planning and operation comprehensive optimization method, it is characterized in that, comprise step:

S1. according to the power system network parameter, build the node admittance matrix real-part matrix G and the imaginary-part matrix B that calculate for trend, the bound of the bound of each node voltage and each Line Flow while determining system safety stable operation;

S2. according to the electric power system power parameter, determine the meritorious bound of exerting oneself of conventional generator, determine conventional generator cost of electricity-generating parameter, determine the meritorious bound of exerting oneself in wind-powered electricity generation unit or wind-powered electricity generation airport, determine and abandon wind punishment electricity price;

S3. according to the energy-storage system parameter, determine energy storage system capacity price, power price and average life, determine the day maintenance cost of energy-storage system;

S4. according to the power system dispatching service data, determine wind-powered electricity generation unit or wind-powered electricity generation airport typical case day wind power prediction data, determine this daily load data, determine electric power system spinning reserve data, determine the network loss electricity price of system;

The data of S5. collecting according to step S1～S4, determine the internal layer Optimized model;

The data of S6. collecting according to step S1～S4, determine outer Optimized model.

Preferably, determine that the internal layer Optimized model adopts formula (1):

${\min C}_{\mathrm{operation}}=f_{\mathrm{gen}}\left(P_{i_G}\right)+f_{\mathrm{pun}}(P_{i_G},P_{i_W},P_{i_S})$

$f_{\mathrm{gen}}\left(P_{i_G}\right)=\underset{i_G=1}{\overset{{\mathrm{\α}}_G}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}(a_{i_G}{P}_{i_{G}t}^2+b_{i_G}{P}_{i_{G}t}+c_{i_G})$

$f_{\mathrm{pun}}(P_{i_G},P_{i_W},P_{i_s})=\underset{i_W=1}{\overset{{\mathrm{\α}}_W}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{\mathrm{\η}}^W(P_{i_{W}t}^{\max }-P_{i_{W}t})\·\mathrm{\Δt}$

$s.t.a.\left\{\begin{array}{c}\underset{i_G=1}{\overset{{\mathrm{\α}}_G}{\mathrm{\Σ}}}{I}_{i_{G}t}{P}_{i_{G}t}+\underset{i_W=1}{\overset{{\mathrm{\α}}_W}{\mathrm{\Σ}}}{P}_{i_{W}t}+\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{P}_{i_{S}t}-P_{\mathrm{Lt}}=0\\ \underset{i_G}{\overset{{\mathrm{\α}}_G}{\mathrm{\Σ}}}{I}_{i_{G}t}{P}_{i_G}^{\max }+\underset{i_W=1}{\overset{{\mathrm{\α}}_W}{\mathrm{\Σ}}}{P}_{i_{W}t}+\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{P}_{i_{S}t}-P_{\mathrm{Lt}}\≥R_t\end{array}\right.---\left(1\right)$

$b.\left\{\begin{array}{c}{P}_{i_G}^{\min }\≤P_{i_{G}t}\≤P_{i_G}^{\max }\\ 0\≤P_{i_{W}t}\≤P_{i_{W}t}^{\max }\\ {-P}_{i_S}^{c\max }\≤P_{i_{S}t}\≤P_{i_S}^{d\max }\\ S_{i_S}^{\min }\≤S_{i_{S}t}\≤S_{i_S}^{\max }\end{array}\right.$

$c.\{S_{i_{S}t+1}=S_{i_{S}t}-P_{i_{S}t}\·\mathrm{\Δt}(1\≤t\≤T)$

Wherein, C _operation: system operation cost; f _gen: the cost of electricity-generating of conventional power generation usage unit;

F _pun: abandon wind punishment; α _g: the number of conventional power generation usage unit in system;

I _g: the sequence number of each conventional power generation usage unit in system, and i _g=1,2 ..., α _g; α _w: the number of system apoplexy group of motors; i _w: the sequence number of system apoplexy group of motors, and i _w=1,2 ..., α _w; α _s: the number of generating set in system; i _s: the sequence number of each generating set in system, and i _s=1,2 ..., α _s;

conventional generator is meritorious exerts oneself;

the wind-powered electricity generation unit is meritorious exerts oneself;

the power of energy-storage system;

with

the cost of electricity-generating coefficient of generator,

when generator output is the time cost;

t+1 is the state-of-charge of energy-storage system constantly,

t is the state-of-charge of energy-storage system constantly;

T: participate in the discrete time point sum calculated; Δ t: the temporal resolution while calculating operating cost; T: discrete markers in a day, and t=1,2 ..., T;

exerting oneself of day part conventional power generation usage unit;

exerting oneself of day part wind-powered electricity generation unit; exerting oneself of day part energy-storage system;

the state-of-charge of energy-storage system;

the on off state of day part conventional power generation usage unit;

wind-powered electricity generation unit typical case day wind power prediction data, P _ltmean this daily load data; η ^w:

Abandon wind punishment electricity price;

energy-storage system maximum charge power;

the maximum discharge power of energy-storage system;

the energy-storage system minimum capacity;

the energy-storage system heap(ed) capacity;

wind-powered electricity generation unit day breeze power prediction data.

3, method as claimed in claim 1 or 2, is characterized in that, described definite outer Optimized model adopts formula (2):

${\min C}_{\mathrm{plan}}=C_{\mathrm{investment}}(P_{i_S},S_{i_S},T_{i_S},L_{i_S})+C_{\mathrm{operation}}+{\mathrm{\η}}^{\mathrm{loss}}{W}_{\mathrm{loss}}$

$C_{\mathrm{investment}}=\frac{{\mathrm{\η}}^p\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{P}_{i_S}+{\mathrm{\η}}^S\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{S}_{i_S}}{T_{i_S}}+\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{M}_{i_S}$

$s.t.a.\left\{\begin{array}{c}{P}_{i_{G}t}+P_{i_{W}t}+P_{i_{S}t}-P_{{\mathrm{Li}}_{B}t}=V_{i_{B}t}\underset{j_B\∈i_B}{\mathrm{\Σ}}{V}_{j_{B}t}(G_{i_B{j}_B}{\cos \mathrm{\θ}}_{i_B{j}_B}+B_{i_B{j}_B}{\sin \mathrm{\θ}}_{i_B{j}_B})\\ W_{\mathrm{loss}}=\mathrm{\Δt}\·\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i_B=1}{\overset{{\mathrm{\α}}_B}{\mathrm{\Σ}}}{V}_{i_{B}t}\underset{j_B\∈i_B}{\mathrm{\Σ}}{V}_{j_{B}t}{G}_{i_B{j}_B}{\cos \mathrm{\θ}}_{i_B{j}_B}\end{array}\right.---\left(2\right);$

$b.\left\{\begin{array}{c}{V_{i_B}}^{\min }\≤V_{i_{B}t}\≤{V_{i_B}}^{\max }\\ {P_{{\mathrm{li}}_B{j}_B}}^{\min }\≤P_{{\mathrm{li}}_B{j}_{B}t}\≤{P_{{\mathrm{li}}_B{j}_B}}^{\max }\end{array}\right.$

$c.\{{P_{i_G}}^{\min }\≤P_{i_{G}t}\≤{P_{i_G}}^{\max },{P_{i_W}}^{\min }\≤P_{i_{W}t}\≤{P_{i_W}}^{\max }$

Wherein,

C _plan: the total cost of energy-storage system investment and operation; t is i constantly _bthe voltage of node;

C _investment: day investment and the maintenance cost of energy-storage system equivalence; C _operation: system operation cost; each node voltage during system safety stable operation

lower limit;

each node voltage during system safety stable operation the upper limit; α _b: the number of node in system; i _b: the sequence number of each node in system, and i _b=1,2 ..., α _b; j _b: the sequence number of each node in system, and j _b=1,2 ..., α _b; Li _bj _b: from node i _bto node j _binterconnection; α _g: the number of conventional power generation usage unit in system; i _g: the sequence number of each conventional power generation usage unit in system, and i _g=1,2 ..., α _g; α _w: the number of system apoplexy group of motors; i _w: the sequence number of system apoplexy group of motors, and i _w=1,2 ..., α _w; α _s: the number of generating set in system; i _s: the sequence number of each generating set in system, and i _s=1,2 ..., α _s;

the wind-powered electricity generation unit is meritorious exerts oneself;

the power of energy-storage system; T: participate in the discrete time point sum calculated; Δ t: the temporal resolution while calculating operating cost; T: discrete markers in a day, and t=1,2 ..., T; exerting oneself of day part conventional power generation usage unit;

exerting oneself of day part wind-powered electricity generation unit;

exerting oneself of day part energy-storage system; W _loss: the day network loss of system;

the power of energy-storage system;

the capacity of energy-storage system; the day maintenance cost of energy-storage system; the average life of energy-storage system; η ^s: the capacity price of energy-storage system; η ^p: the power price of energy-storage system; η ^loss: the network loss electricity price of system;

t is i constantly _bthe load of Nodes;

t is j constantly _bthe voltage of Nodes;

with

power flow equation coefficient matrix the inside and i _bnode and j _bthe part that node is corresponding,

i _bnode voltage and j _bphase difference between node voltage;

t is constantly from i _bnode is to j _bpower on the interconnection of node.

On the other hand, the present invention also provides a kind of energy-accumulating power station planning and operation complex optimum system, it is characterized in that, comprise: the first module, be used for according to the power system network parameter, build the node admittance matrix real-part matrix G and the imaginary-part matrix B that calculate for trend, the bound of the bound of each node voltage and each Line Flow while determining system safety stable operation; The second module, for according to the electric power system power parameter, determine the meritorious bound of exerting oneself of conventional generator, determines conventional generator cost of electricity-generating parameter, determines the meritorious bound of exerting oneself in wind-powered electricity generation unit or wind-powered electricity generation airport, determines and abandon wind punishment electricity price; The 3rd module, for according to the energy-storage system parameter, determine energy storage system capacity price, power price and average life, determines the day maintenance cost of energy-storage system; Four module, for according to the power system dispatching service data, determine wind-powered electricity generation unit or wind-powered electricity generation airport typical case day wind power prediction data, determines this daily load data, determines electric power system spinning reserve data, determines the network loss electricity price of system; The 5th module, for the data of collecting according to step the first module～four module, determine the internal layer Optimized model; The 6th module, for the data of collecting according to step the first module～four module, determine outer Optimized model.

(3) beneficial effect

The present invention contains the electric power system of wind-powered electricity generation and energy storage in typical case's operating cost of day by calculating, the operation problem of energy storage is embedded to planning problem, considers influencing each other between planning problem and operation problem, has following beneficial effect:

1) proposed first to abandon for minimizing the energy-storage system plan model of wind, can obtain optimum energy-accumulating power station power configuration, capacity configuration and position configuration conclusion, instruct the planning process of energy-accumulating power station, the development in prediction electrical network future, improve the wind-powered electricity generation access capability, there is significant economic benefit and environmental benefit;

2) proposed first to abandon for minimizing the energy-storage system moving model of wind, can obtain the plan of exerting oneself of optimum conventional unit, wind-powered electricity generation unit and energy-accumulating power station, instruct the running of energy-accumulating power station, instruct the economic dispatch process of electrical network, can improve to greatest extent the economy of operation of power networks.

The accompanying drawing explanation

Fig. 1 is energy-accumulating power station planning according to the present invention and operation comprehensive optimization method flow chart.

Embodiment

Below in conjunction with drawings and Examples, the specific embodiment of the present invention is described in further detail.Following examples are used for the present invention is described, but are not used for limiting the scope of the invention.

Model of the present invention is considered energy-accumulating power station day-to-day operation parameter and the impact of mode on planning, the thought of dual-layer optimization in Optimum Theory is introduced to electric power system.As shown in Figure 1, the energy-accumulating power station planning based on the dual-layer optimization theory according to a kind of foundation of the present invention comprises step with the operation Integrated Optimization Model:

S1. according to the power system network parameter, build the node admittance matrix real-part matrix G and the imaginary-part matrix B that calculate for trend, each node voltage while determining system safety stable operation

bound

with each Line Flow

bound wherein, i _band j _bfor the sequence number of each node in system, and i _b, j _b=1,2 ..., α _b, α _bfor the number of node in system, li _bj _bexpression is from node i _bto node j _binterconnection;

S2. according to the electric power system power parameter, determine that conventional generator is meritorious to exert oneself

bound

determine conventional generator cost of electricity-generating parameter

with determine that wind-powered electricity generation unit (field) is meritorious and exert oneself

bound

determine and abandon wind punishment electricity price η ^w.Wherein, i _gfor the sequence number of each conventional power generation usage unit in system, and i _g=1,2 ..., α _g, α _gfor the number of conventional power generation usage unit in system, i _wfor the sequence number of system apoplexy group of motors (field), and i _w=1,2 ..., α _w, α _wnumber for system apoplexy group of motors (field);

S3. according to the energy-storage system parameter, determine energy storage system capacity price η ^s, power price η ^pand average life determine the day maintenance cost of energy-storage system

wherein, i _sfor the sequence number of each generating set in system, and i _s=1,2 ..., α _s, α _snumber for generating set in system;

S4. according to the power system dispatching service data, determine wind-powered electricity generation unit (field) typical case day wind power prediction data

determine these daily load data P _lt, determine electric power system spinning reserve data R _t, determine the network loss electricity price η of system ^loss.Wherein, t is markers discrete in a day, and t=1,2 ..., T, the discrete time point sum of T for participate in calculating, if every 1h calculates once, T=24, if every 15min calculating is once, T=96;

The data of S5. collecting according to step S1～S4, determine the internal layer Optimized model, and mathematic(al) representation is as (1).Wherein, C _operationmean system operation cost, by f _genand f _puntwo parts form, f _genthe cost of electricity-generating that means the conventional power generation usage unit, f _punmean to abandon wind punishment, temporal resolution when Δ t means to calculate operating cost, if every 1h calculates once, Δ t=1, if every 15min calculates once, Δ t=1/4;

with

mean respectively exerting oneself and the state-of-charge of energy-storage system of day part conventional power generation usage unit, wind-powered electricity generation unit (field), energy-storage system; meaning the on off state of day part conventional power generation usage unit, is the 0-1 variable, and 1 means start, and 0 means shutdown, is variable to be solved.

mean respectively energy-storage system maximum charge power, maximum discharge power, minimum capacity and heap(ed) capacity, the optimum results for skin is optimized, be passed to internal layer optimization by skin optimization;

${\min C}_{\mathrm{operation}}=f_{\mathrm{gen}}\left(P_{i_G}\right)+f_{\mathrm{pun}}(P_{i_G},P_{i_W},P_{i_S})$

$f_{\mathrm{gen}}\left(P_{i_G}\right)=\underset{i_G=1}{\overset{{\mathrm{\α}}_G}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}(a_{i_G}{P}_{i_{G}t}^2+b_{i_G}{P}_{i_{G}t}+c_{i_G})$

$f_{\mathrm{pun}}(P_{i_G},P_{i_W},P_{i_s})=\underset{i_W=1}{\overset{{\mathrm{\α}}_W}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{\mathrm{\η}}^W(P_{i_{W}t}^{\max }-P_{i_{W}t})\·\mathrm{\Δt}$

$s.t.a.\left\{\begin{array}{c}\underset{i_G=1}{\overset{{\mathrm{\α}}_G}{\mathrm{\Σ}}}{I}_{i_{G}t}{P}_{i_{G}t}+\underset{i_W=1}{\overset{{\mathrm{\α}}_W}{\mathrm{\Σ}}}{P}_{i_{W}t}+\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{P}_{i_{S}t}-P_{\mathrm{Lt}}=0\\ \underset{i_G}{\overset{{\mathrm{\α}}_G}{\mathrm{\Σ}}}{I}_{i_{G}t}{P}_{i_G}^{\max }+\underset{i_W=1}{\overset{{\mathrm{\α}}_W}{\mathrm{\Σ}}}{P}_{i_{W}t}+\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{P}_{i_{S}t}-P_{\mathrm{Lt}}\≥R_t\end{array}\right.$

$b.\left\{\begin{array}{c}{P}_{i_G}^{\min }\≤P_{i_{G}t}\≤P_{i_G}^{\max }\\ 0\≤P_{i_{W}t}\≤P_{i_{W}t}^{\max }\\ {-P}_{i_S}^{c\max }\≤P_{i_{S}t}\≤P_{i_S}^{d\max }\\ S_{i_S}^{\min }\≤S_{i_{S}t}\≤S_{i_S}^{\max }\end{array}\right.$

$c.\{S_{i_{S}t+1}=S_{i_{S}t}-P_{i_{S}t}\·\mathrm{\Δt}(1\≤t\≤T)---\left(1\right)$

Wherein, C _operation: system operation cost; f _gen: the cost of electricity-generating of conventional power generation usage unit;

F _pun: abandon wind punishment; α _g: the number of conventional power generation usage unit in system;

I _g: the sequence number of each conventional power generation usage unit in system, and i _g=1,2 ..., α _g; α _w: the number of system apoplexy group of motors; i _w: the sequence number of system apoplexy group of motors, and i _w=1,2 ..., α _w; α _s: the number of generating set in system; i _s: the sequence number of each generating set in system, and i _s=1,2 ..., α _s; conventional generator is meritorious exerts oneself;

the wind-powered electricity generation unit is meritorious exerts oneself;

the power of energy-storage system;

with

the cost of electricity-generating coefficient of generator,

when generator output is

the time cost;

t+1 is the state-of-charge of energy-storage system constantly,

t is the state-of-charge of energy-storage system constantly;

T: participate in the discrete time point sum calculated; Δ t: the temporal resolution while calculating operating cost; T: discrete markers in a day, and t=1,2 ..., T;

exerting oneself of day part conventional power generation usage unit; exerting oneself of day part wind-powered electricity generation unit;

exerting oneself of day part energy-storage system;

the state-of-charge of energy-storage system; the on off state of day part conventional power generation usage unit;

wind-powered electricity generation unit typical case day wind power prediction data, P _ltmean this daily load data; η ^w: abandon wind punishment electricity price; energy-storage system maximum charge power;

the maximum discharge power of energy-storage system;

the energy-storage system minimum capacity;

the energy-storage system heap(ed) capacity;

wind-powered electricity generation unit day breeze power prediction data.

The data of S6. collecting according to step S1～S4, determine outer Optimized model, and mathematic(al) representation is as (2).Wherein,

c _operationwith identical in (1), be the optimum results that internal layer is optimized, pass to outer optimization by internal layer optimization.C _investmentthe day investment and the maintenance cost that mean the energy-storage system equivalence, W _lossthe day network loss of expression system.

the power, capacity and the layout configurations that mean respectively energy-storage system are variable to be solved.

${\min C}_{\mathrm{plan}}=C_{\mathrm{investment}}(P_{i_S},S_{i_S},T_{i_S},L_{i_S})+C_{\mathrm{operation}}+{\mathrm{\η}}^{\mathrm{loss}}{W}_{\mathrm{loss}}$

$C_{\mathrm{investment}}=\frac{{\mathrm{\η}}^p\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{P}_{i_S}+{\mathrm{\η}}^S\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{S}_{i_S}}{T_{i_S}}+\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{M}_{i_S}$

$s.t.a.\left\{\begin{array}{c}{P}_{i_{G}t}+P_{i_{W}t}+P_{i_{S}t}-P_{{\mathrm{Li}}_{B}t}=V_{i_{B}t}\underset{j_B\∈i_B}{\mathrm{\Σ}}{V}_{j_{B}t}(G_{i_B{j}_B}{\cos \mathrm{\θ}}_{i_B{j}_B}+B_{i_B{j}_B}{\sin \mathrm{\θ}}_{i_B{j}_B})\\ W_{\mathrm{loss}}=\mathrm{\Δt}\·\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i_B=1}{\overset{{\mathrm{\α}}_B}{\mathrm{\Σ}}}{V}_{i_{B}t}\underset{j_B\∈i_B}{\mathrm{\Σ}}{V}_{j_{B}t}{G}_{i_B{j}_B}{\cos \mathrm{\θ}}_{i_B{j}_B}\end{array}\right.---\left(2\right);$

$b.\left\{\begin{array}{c}{V_{i_B}}^{\min }\≤V_{i_{B}t}\≤{V_{i_B}}^{\max }\\ {P_{{\mathrm{li}}_B{j}_B}}^{\min }\≤P_{{\mathrm{li}}_B{j}_{B}t}\≤{P_{{\mathrm{li}}_B{j}_B}}^{\max }\end{array}\right.$

$c.\{{P_{i_G}}^{\min }\≤P_{i_{G}t}\≤{P_{i_G}}^{\max },{P_{i_W}}^{\min }\≤P_{i_{W}t}\≤{P_{i_W}}^{\max }$

C _plan: the total cost of energy-storage system investment and operation;

t is i constantly _bthe voltage of node;

C _investment: day investment and the maintenance cost of energy-storage system equivalence; C _operation: system operation cost; each node voltage during system safety stable operation

lower limit;

each node voltage during system safety stable operation

the upper limit; α _b: the number of node in system; i _b: the sequence number of each node in system, and i _b=1,2 ..., α _b; j _b: the sequence number of each node in system, and j _b=1,2 ..., α _b; Li _bj _b: from node i _bto node j _binterconnection; α _g: the number of conventional power generation usage unit in system; i _g: the sequence number of each conventional power generation usage unit in system, and i _g=1,2 ..., α _g; α _w: the number of system apoplexy group of motors; i _w: the sequence number of system apoplexy group of motors, and i _w=1,2 ..., α _w; α _s: the number of generating set in system; i _s: the sequence number of each generating set in system, and i _s=1,2 ..., α _s;

the wind-powered electricity generation unit is meritorious exerts oneself;

the power of energy-storage system; T: participate in the discrete time point sum calculated; Δ t: the temporal resolution while calculating operating cost; T: discrete markers in a day, and t=1,2 ..., T;

exerting oneself of day part conventional power generation usage unit; exerting oneself of day part wind-powered electricity generation unit;

exerting oneself of day part energy-storage system; W _loss: the day network loss of system;

the power of energy-storage system;

the capacity of energy-storage system;

the day maintenance cost of energy-storage system;

the average life of energy-storage system; η ^s: the capacity price of energy-storage system; η ^p: the power price of energy-storage system; η ^loss: the network loss electricity price of system;

t is i constantly _bthe load of Nodes;

t is j constantly _bthe voltage of Nodes;

with power flow equation coefficient matrix the inside and i _bnode and j _bthe part that node is corresponding,

i _bnode voltage and j _bphase difference between node voltage;

t is constantly from i _bnode is to j _bpower on the interconnection of node.

On the other hand, the present invention also provides a kind of energy-accumulating power station planning and operation complex optimum system, comprise: the first module, be used for according to the power system network parameter, build the node admittance matrix real-part matrix G and the imaginary-part matrix B that calculate for trend, the bound of the bound of each node voltage and each Line Flow while determining system safety stable operation; The second module, for according to the electric power system power parameter, determine the meritorious bound of exerting oneself of conventional generator, determines conventional generator cost of electricity-generating parameter, determines the meritorious bound of exerting oneself in wind-powered electricity generation unit or wind-powered electricity generation airport, determines and abandon wind punishment electricity price; The 3rd module, for according to the energy-storage system parameter, determine energy storage system capacity price, power price and average life, determines the day maintenance cost of energy-storage system; Four module, for according to the power system dispatching service data, determine wind-powered electricity generation unit or wind-powered electricity generation airport typical case day wind power prediction data, determines this daily load data, determines electric power system spinning reserve data, determines the network loss electricity price of system; The 5th module, for the data of collecting according to step the first module～four module, determine the internal layer Optimized model; The 6th module, for the data of collecting according to step the first module～four module, determine outer Optimized model.

Energy-accumulating power station based on dual-layer optimization theory planning of the present invention and operation Integrated Optimization Model can the computer-aided traffic control system for each provincial straight tune wind energy turbine set of China and Wind-Electric Power Stations among, for the planning of instructing energy-accumulating power station, with operation, control, can greatly reduce the wind-powered electricity generation amount of abandoning, improve the wind-powered electricity generation access capability, there is great economic and social benefit.

The above is only the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the prerequisite that does not break away from the technology of the present invention principle; can also make some improvement and replacement, these improvement and replacement also should be considered as protection scope of the present invention.

Claims (6)

1. an energy-accumulating power station is planned and the operation comprehensive optimization method, it is characterized in that, comprises step:

S1. according to the power system network parameter, build the node admittance matrix real-part matrix G and the imaginary-part matrix B that calculate for trend, the bound of the bound of each node voltage and each Line Flow while determining system safety stable operation;

S2. according to the electric power system power parameter, determine the meritorious bound of exerting oneself of conventional generator, determine conventional generator cost of electricity-generating parameter, determine the meritorious bound of exerting oneself in wind-powered electricity generation unit or wind-powered electricity generation airport, determine and abandon wind punishment electricity price;

S3. according to the energy-storage system parameter, determine energy storage system capacity price, power price and average life, determine the day maintenance cost of energy-storage system;

S4. according to the power system dispatching service data, determine wind-powered electricity generation unit or wind-powered electricity generation airport typical case day wind power prediction data, determine this daily load data, determine electric power system spinning reserve data, determine the network loss electricity price of system;

The data of S5. collecting according to step S1～S4, determine the internal layer Optimized model;

The data of S6. collecting according to step S1～S4, determine outer Optimized model.

2. the method for claim 1, is characterized in that, described definite internal layer Optimized model adopts formula (1):

${\min C}_{\mathrm{operation}}=f_{\mathrm{gen}}\left(P_{i_G}\right)+f_{\mathrm{pun}}(P_{i_G},P_{i_W},P_{i_S})$

$f_{\mathrm{gen}}\left(P_{i_G}\right)=\underset{i_G=1}{\overset{{\mathrm{\α}}_G}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}(a_{i_G}{P}_{i_{G}t}^2+b_{i_G}{P}_{i_{G}t}+c_{i_G})$

$f_{\mathrm{pun}}(P_{i_G},P_{i_W},P_{i_s})=\underset{i_W=1}{\overset{{\mathrm{\α}}_W}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{\mathrm{\η}}^W(P_{i_{W}t}^{\max }-P_{i_{W}t})\·\mathrm{\Δt}$

$s.t.a.\left\{\begin{array}{c}\underset{i_G=1}{\overset{{\mathrm{\α}}_G}{\mathrm{\Σ}}}{I}_{i_{G}t}{P}_{i_{G}t}+\underset{i_W=1}{\overset{{\mathrm{\α}}_W}{\mathrm{\Σ}}}{P}_{i_{W}t}+\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{P}_{i_{S}t}-P_{\mathrm{Lt}}=0\\ \underset{i_G}{\overset{{\mathrm{\α}}_G}{\mathrm{\Σ}}}{I}_{i_{G}t}{P}_{i_G}^{\max }+\underset{i_W=1}{\overset{{\mathrm{\α}}_W}{\mathrm{\Σ}}}{P}_{i_{W}t}+\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{P}_{i_{S}t}-P_{\mathrm{Lt}}\≥R_t\end{array}\right.---\left(1\right)$

$b.\left\{\begin{array}{c}{P}_{i_G}^{\min }\≤P_{i_{G}t}\≤P_{i_G}^{\max }\\ 0\≤P_{i_{W}t}\≤P_{i_{W}t}^{\max }\\ {-P}_{i_S}^{c\max }\≤P_{i_{S}t}\≤P_{i_S}^{d\max }\\ S_{i_S}^{\min }\≤S_{i_{S}t}\≤S_{i_S}^{\max }\end{array}\right.$

$c.\{S_{i_{S}t+1}=S_{i_{S}t}-P_{i_{S}t}\·\mathrm{\Δt}(1\≤t\≤T)$

Wherein, C _operation: system operation cost; f _gen: the cost of electricity-generating of conventional power generation usage unit;

F _pun: abandon wind punishment; α _g: the number of conventional power generation usage unit in system;

I _g:the sequence number of each conventional power generation usage unit in system, and i _g=1,2 ..., α _g; α _w: the number of system apoplexy group of motors; i _w: the sequence number of system apoplexy group of motors, and i _w=1,2 ..., α _w; α _s: the number of generating set in system; i _s: the sequence number of each generating set in system, and i _s=1,2 ..., α _s;

conventional generator is meritorious exerts oneself;

the wind-powered electricity generation unit is meritorious exerts oneself;

the power of energy-storage system;

with

the cost of electricity-generating coefficient of generator,

when generator output is

the time cost;

t+1 is the state-of-charge of energy-storage system constantly,

t is the state-of-charge of energy-storage system constantly;

T: participate in the discrete time point sum calculated; Δ t: the temporal resolution while calculating operating cost; T: discrete markers in a day, and t=1,2 ..., T;

exerting oneself of day part conventional power generation usage unit;

exerting oneself of day part wind-powered electricity generation unit; exerting oneself of day part energy-storage system;

the state-of-charge of energy-storage system;

the on off state of day part conventional power generation usage unit;

wind-powered electricity generation unit typical case day wind power prediction data, P _ltmean this daily load data; η ^w: abandon wind punishment electricity price;

energy-storage system maximum charge power;

the maximum discharge power of energy-storage system;

the energy-storage system minimum capacity;

the energy-storage system heap(ed) capacity;

wind-powered electricity generation unit day breeze power prediction data.

3. method as claimed in claim 1 or 2, is characterized in that, described definite outer Optimized model adopts formula (2):

${\min C}_{\mathrm{plan}}=C_{\mathrm{investment}}(P_{i_S},S_{i_S},T_{i_S},L_{i_S})+C_{\mathrm{operation}}+{\mathrm{\η}}^{\mathrm{loss}}{W}_{\mathrm{loss}}$

$C_{\mathrm{investment}}=\frac{{\mathrm{\η}}^p\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{P}_{i_S}+{\mathrm{\η}}^S\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{S}_{i_S}}{T_{i_S}}+\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{M}_{i_S}$

$s.t.a.\left\{\begin{array}{c}{P}_{i_{G}t}+P_{i_{W}t}+P_{i_{S}t}-P_{{\mathrm{Li}}_{B}t}=V_{i_{B}t}\underset{j_B\∈i_B}{\mathrm{\Σ}}{V}_{j_{B}t}(G_{i_B{j}_B}{\cos \mathrm{\θ}}_{i_B{j}_B}+B_{i_B{j}_B}{\sin \mathrm{\θ}}_{i_B{j}_B})\\ W_{\mathrm{loss}}=\mathrm{\Δt}\·\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i_B=1}{\overset{{\mathrm{\α}}_B}{\mathrm{\Σ}}}{V}_{i_{B}t}\underset{j_B\∈i_B}{\mathrm{\Σ}}{V}_{j_{B}t}{G}_{i_B{j}_B}{\cos \mathrm{\θ}}_{i_B{j}_B}\end{array}\right.---\left(2\right);$

$b.\left\{\begin{array}{c}{V_{i_B}}^{\min }\≤V_{i_{B}t}\≤{V_{i_B}}^{\max }\\ {P_{{\mathrm{li}}_B{j}_B}}^{\min }\≤P_{{\mathrm{li}}_B{j}_{B}t}\≤{P_{{\mathrm{li}}_B{j}_B}}^{\max }\end{array}\right.$

$c.\{{P_{i_G}}^{\min }\≤P_{i_{G}t}\≤{P_{i_G}}^{\max },{P_{i_W}}^{\min }\≤P_{i_{W}t}\≤{P_{i_W}}^{\max }$

Wherein,

C _plan: the total cost of energy-storage system investment and operation;

t is i constantly _bthe voltage of node;

C _investment: day investment and the maintenance cost of energy-storage system equivalence; C _operation: system operation cost; each node voltage during system safety stable operation

lower limit;

each node voltage during system safety stable operation the upper limit; α _b: the number of node in system; i _b: the sequence number of each node in system, and i _b=1,2 ..., α _b; j _b: the sequence number of each node in system, and j _b=1,2 ..., α _b; Li _bj _b: from node i _bto node j _binterconnection; α _g: the number of conventional power generation usage unit in system; i _g: the sequence number of each conventional power generation usage unit in system, and i _g=1,2 ..., α _g; α _w: the number of system apoplexy group of motors; i _w: the sequence number of system apoplexy group of motors, and i _w=1,2 ..., α _w; α _s: the number of generating set in system; i _s: the sequence number of each generating set in system, and i _s=1,2 ..., α _s;

the wind-powered electricity generation unit is meritorious exerts oneself;

the power of energy-storage system; T: participate in the discrete time point sum calculated; Δ t: the temporal resolution while calculating operating cost; T: discrete markers in a day, and t=1,2 ..., T;

exerting oneself of day part conventional power generation usage unit;

exerting oneself of day part wind-powered electricity generation unit;

exerting oneself of day part energy-storage system; W _loss: the day network loss of system; the power of energy-storage system;

the capacity of energy-storage system; the day maintenance cost of energy-storage system;

the average life of energy-storage system; η ^s: the capacity price of energy-storage system; η p: the power price of energy-storage system; η ^loss: the network loss electricity price of system;

t is i constantly _bthe load of Nodes;

t is j constantly _bthe voltage of Nodes;

with

power flow equation coefficient matrix the inside and i _bnode and j _bthe part that node is corresponding,

i _bnode voltage and j _bphase difference between node voltage; t is constantly from i _bnode is to j _bpower on the interconnection of node.

4. an energy-accumulating power station is planned and operation complex optimum system, it is characterized in that, comprising:

The first module, for according to the power system network parameter, build the node admittance matrix real-part matrix G and the imaginary-part matrix B that calculate for trend, the bound of the bound of each node voltage and each Line Flow while determining system safety stable operation;

The second module, for according to the electric power system power parameter, determine the meritorious bound of exerting oneself of conventional generator, determines conventional generator cost of electricity-generating parameter, determines the meritorious bound of exerting oneself in wind-powered electricity generation unit or wind-powered electricity generation airport, determines and abandon wind punishment electricity price;

The 3rd module, for according to the energy-storage system parameter, determine energy storage system capacity price, power price and average life, determines the day maintenance cost of energy-storage system;

Four module, for according to the power system dispatching service data, determine wind-powered electricity generation unit or wind-powered electricity generation airport typical case day wind power prediction data, determines this daily load data, determines electric power system spinning reserve data, determines the network loss electricity price of system;

The 5th module, for the data of collecting according to step the first module～four module, determine the internal layer Optimized model;

The 6th module, for the data of collecting according to step the first module～four module, determine outer Optimized model.

5. system as claimed in claim 4, is characterized in that, described definite internal layer Optimized model adopts formula (1):

${\min C}_{\mathrm{operation}}=f_{\mathrm{gen}}\left(P_{i_G}\right)+f_{\mathrm{pun}}(P_{i_G},P_{i_W},P_{i_S})$

$f_{\mathrm{gen}}\left(P_{i_G}\right)=\underset{i_G=1}{\overset{{\mathrm{\α}}_G}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}(a_{i_G}{P}_{i_{G}t}^2+b_{i_G}{P}_{i_{G}t}+c_{i_G})$

$f_{\mathrm{pun}}(P_{i_G},P_{i_W},P_{i_s})=\underset{i_W=1}{\overset{{\mathrm{\α}}_W}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{\mathrm{\η}}^W(P_{i_{W}t}^{\max }-P_{i_{W}t})\·\mathrm{\Δt}$

$s.t.a.\left\{\begin{array}{c}\underset{i_G=1}{\overset{{\mathrm{\α}}_G}{\mathrm{\Σ}}}{I}_{i_{G}t}{P}_{i_{G}t}+\underset{i_W=1}{\overset{{\mathrm{\α}}_W}{\mathrm{\Σ}}}{P}_{i_{W}t}+\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{P}_{i_{S}t}-P_{\mathrm{Lt}}=0\\ \underset{i_G}{\overset{{\mathrm{\α}}_G}{\mathrm{\Σ}}}{I}_{i_{G}t}{P}_{i_G}^{\max }+\underset{i_W=1}{\overset{{\mathrm{\α}}_W}{\mathrm{\Σ}}}{P}_{i_{W}t}+\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{P}_{i_{S}t}-P_{\mathrm{Lt}}\≥R_t\end{array}\right.$ （1）

$b.\left\{\begin{array}{c}{P}_{i_G}^{\min }\≤P_{i_{G}t}\≤P_{i_G}^{\max }\\ 0\≤P_{i_{W}t}\≤P_{i_{W}t}^{\max }\\ {-P}_{i_S}^{c\max }\≤P_{i_{S}t}\≤P_{i_S}^{d\max }\\ S_{i_S}^{\min }\≤S_{i_{S}t}\≤S_{i_S}^{\max }\end{array}\right.$

$c.\{S_{i_{S}t+1}=S_{i_{S}t}-P_{i_{S}t}\·\mathrm{\Δt}(1\≤t\≤T)$

Wherein, C _operation: system operation cost; f _gen: the cost of electricity-generating of conventional power generation usage unit;

F _pun: abandon wind punishment; α _g: the number of conventional power generation usage unit in system;

I _g: the sequence number of each conventional power generation usage unit in system, and i _g=1,2 ..., α _g; α _w: the number of system apoplexy group of motors; i _w: the sequence number of system apoplexy group of motors, and i _w=1,2 ..., α _w; α _s: the number of generating set in system; i _s: the sequence number of each generating set in system, and i _s=1,2 ..., α _s; conventional generator is meritorious exerts oneself;

the wind-powered electricity generation unit is meritorious exerts oneself;

the power of energy-storage system;

with

the cost of electricity-generating coefficient of generator,

when generator output is

the time cost;

t+1 is the state-of-charge of energy-storage system constantly,

t is the state-of-charge of energy-storage system constantly;

T: participate in the discrete time point sum calculated; Δ t: the temporal resolution while calculating operating cost; T: discrete markers in a day, and t=1,2 ..., T;

exerting oneself of day part conventional power generation usage unit;

exerting oneself of day part wind-powered electricity generation unit;

exerting oneself of day part energy-storage system;

the state-of-charge of energy-storage system;

the on off state of day part conventional power generation usage unit;

wind-powered electricity generation unit typical case day wind power prediction data, P _ltmean this daily load data; η ^w: abandon wind punishment electricity price;

energy-storage system maximum charge power;

the maximum discharge power of energy-storage system; the energy-storage system minimum capacity;

the energy-storage system heap(ed) capacity;

wind-powered electricity generation unit day breeze power prediction data.

6. system as described as claim 4 or 5, is characterized in that, described definite outer Optimized model adopts formula (2):

${\min C}_{\mathrm{plan}}=C_{\mathrm{investment}}(P_{i_S},S_{i_S},T_{i_S},L_{i_S})+C_{\mathrm{operation}}+{\mathrm{\η}}^{\mathrm{loss}}{W}_{\mathrm{loss}}$

$C_{\mathrm{investment}}=\frac{{\mathrm{\η}}^p\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{P}_{i_S}+{\mathrm{\η}}^S\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{S}_{i_S}}{T_{i_S}}+\underset{i_S=1}{\overset{{\mathrm{\α}}_S}{\mathrm{\Σ}}}{M}_{i_S}$

$s.t.a.\left\{\begin{array}{c}{P}_{i_{G}t}+P_{i_{W}t}+P_{i_{S}t}-P_{{\mathrm{Li}}_{B}t}=V_{i_{B}t}\underset{j_B\∈i_B}{\mathrm{\Σ}}{V}_{j_{B}t}(G_{i_B{j}_B}{\cos \mathrm{\θ}}_{i_B{j}_B}+B_{i_B{j}_B}{\sin \mathrm{\θ}}_{i_B{j}_B})\\ W_{\mathrm{loss}}=\mathrm{\Δt}\·\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i_B=1}{\overset{{\mathrm{\α}}_B}{\mathrm{\Σ}}}{V}_{i_{B}t}\underset{j_B\∈i_B}{\mathrm{\Σ}}{V}_{j_{B}t}{G}_{i_B{j}_B}{\cos \mathrm{\θ}}_{i_B{j}_B}\end{array}\right.---\left(2\right);$

$b.\left\{\begin{array}{c}{V_{i_B}}^{\min }\≤V_{i_{B}t}\≤{V_{i_B}}^{\max }\\ {P_{{\mathrm{li}}_B{j}_B}}^{\min }\≤P_{{\mathrm{li}}_B{j}_{B}t}\≤{P_{{\mathrm{li}}_B{j}_B}}^{\max }\end{array}\right.$

$c.\{{P_{i_G}}^{\min }\≤P_{i_{G}t}\≤{P_{i_G}}^{\max },{P_{i_W}}^{\min }\≤P_{i_{W}t}\≤{P_{i_W}}^{\max }$

Wherein,

C _plan: the total cost of energy-storage system investment and operation; t is i constantly _bthe voltage of node;

C _investment: day investment and the maintenance cost of energy-storage system equivalence; C _operation: system operation cost;

each node voltage during system safety stable operation

lower limit;

each node voltage during system safety stable operation

the upper limit; α _b: the number of node in system; i _b:

The sequence number of each node in system, and i _b=1,2 ..., α _b; j _b: the sequence number of each node in system, and j _b=1,2 ..., α _b; Li _bj _b: from node i _bto node j _binterconnection; α _g: the number of conventional power generation usage unit in system; i _g: the sequence number of each conventional power generation usage unit in system, and i _g=1,2 ..., α _g; α _w: the number of system apoplexy group of motors; i _w: the sequence number of system apoplexy group of motors, and i _w=1,2 ..., α _w; α _s: the number of generating set in system; i _s: the sequence number of each generating set in system, and i _s=1,2 ..., α _s;

the wind-powered electricity generation unit is meritorious exerts oneself;

the power of energy-storage system; T: participate in the discrete time point sum calculated; Δ t: the temporal resolution while calculating operating cost; T: discrete markers in a day, and t=1,2 ..., T;

exerting oneself of day part conventional power generation usage unit;

exerting oneself of day part wind-powered electricity generation unit;

exerting oneself of day part energy-storage system; W _loss: the day network loss of system;

the power of energy-storage system;

the capacity of energy-storage system;

the day maintenance cost of energy-storage system;

the average life of energy-storage system; η ^s: the capacity price of energy-storage system; η ^p: the power price of energy-storage system; η ^loss: the network loss electricity price of system;

t is i constantly _bthe load of Nodes;

t is j constantly _bthe voltage of Nodes; with

power flow equation coefficient matrix the inside and i _bnode and j _bthe part that node is corresponding,

i _bnode voltage and j _bphase difference between node voltage; t is constantly from i _bnode is to j _bpower on the interconnection of node.

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