CN103475013B - Energy-accumulating power station planning and operation comprehensive optimization method and system - Google Patents

Abstract

The invention discloses the planning of a kind of energy-accumulating power station and run comprehensive optimization method, comprise: S1. is according to power system network parameter, build the node admittance matrix real-part matrix G and imaginary-part matrix B that are used for Load flow calculation, the bound of each node voltage and the bound of each Line Flow during certainty annuity safe and stable operation; S2. according to electric power system power source parameter, determine that conventional generator is gained merit the bound of exerting oneself, determine conventional generator cost of electricity-generating parameter, determine to gain merit the bound of exerting oneself in Wind turbines or wind-powered electricity generation airport, determine to abandon wind punishment electricity price; S3. according to energy-storage system parameter, determine energy storage system capacity price, power price and average life, and day maintenance cost; S4. according to power system dispatching service data, determine Wind turbines or wind-powered electricity generation airport typical case's day wind power prediction data, determine this daily load data, determine electric power system spinning reserve data, the network loss electricity price of certainty annuity; According to the data that step S1 ~ S4 collects, determine internal layer Optimized model and outer Optimized model.

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, particularly relate to the planning of a kind of energy-accumulating power station based on dual-layer optimization theory (BLP) and run comprehensive optimization method and system

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 region rack construction lags far behind power construction, the phenomenon that China's wind energy turbine set abandons wind is day by day serious, causes great energy waste, also result in huge loss to wind-powered electricity generation electricity power enterprise.For this reason, large-scale energy storage system is used to the space-time not matching properties adjusting electric power system power source and load, alleviates and abandons wind phenomenon, improves system wind power integration ability.Certainly, be still in the starting stage to the research of large-scale energy storage system, what first need in application to pay close attention to is exactly the planning problem of energy-accumulating power station.But the planning problem of energy-accumulating power station should based on actual motion effect, and independent argument programming problem does not have practical significance.Therefore, need the planning problem of energy-accumulating power station and the unified consideration of operation problem, could meet 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: provide a kind of simple and practical energy-accumulating power station to plan and run comprehensive optimization method and system, for resolving the problem such as layout, capacity configuration, power configuration in energy-accumulating power station planning process, and analyze the expansion Optimization of Unit Commitment By Improved in energy-accumulating power station running.

(2) technical scheme

For solving the problem, the invention provides the planning of a kind of energy-accumulating power station and running comprehensive optimization method, comprising step: energy-accumulating power station is planned and run comprehensive optimization method, it is characterized in that, comprises step:

S1. according to power system network parameter, the node admittance matrix real-part matrix G and imaginary-part matrix B that are used for Load flow calculation is built, the bound of each node voltage and the bound of each Line Flow during certainty annuity safe and stable operation;

S2. according to electric power system power source parameter, determine that conventional generator is gained merit the bound of exerting oneself, determine conventional generator cost of electricity-generating parameter, determine to gain merit the bound of exerting oneself in Wind turbines or wind-powered electricity generation airport, determine to abandon wind punishment electricity price;

S3. according to 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 power system dispatching service data, determine Wind turbines or wind-powered electricity generation airport typical case's day wind power prediction data, determine this daily load data, determine electric power system spinning reserve data, the network loss electricity price of certainty annuity;

S5. according to the data that step S1 ~ S4 collects, internal layer Optimized model is determined;

S6. according to the data that step S1 ~ S4 collects, outer Optimized model is determined.

Preferably, determine that 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 Wind turbines in system; i _w: the sequence number of Wind turbines in system, 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; wind turbines is meritorious exerts oneself; the power of energy-storage system;

with the cost of electricity-generating coefficient of generator, when generator output is time cost;

the state-of-charge of t+1 moment energy-storage system, the state-of-charge of t energy-storage system;

T: participate in the discrete time point sum calculated; Δ t: calculate temporal resolution during operating cost; Markers discrete in t: one day, and t=1,2 ..., T; exerting oneself of day part conventional power generation usage unit; exerting oneself of day part Wind turbines; 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 turbines typical case day wind power prediction data, P _ltrepresent 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; energy-storage system minimum capacity; energy-storage system heap(ed) capacity; wind turbines day wind power prediction data.

3, method as claimed in claim 1 or 2, is characterized in that, describedly determines that 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: energy-storage system investment and the total cost run; t i _bthe voltage of node;

C _investment: the day investment of energy-storage system equivalence and maintenance cost; 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 system interior joint; 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 Wind turbines in system; i _w: the sequence number of Wind turbines in system, 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; wind turbines is meritorious exerts oneself; the power of energy-storage system; T: participate in the discrete time point sum calculated; Δ t: calculate temporal resolution during operating cost; Markers discrete in t: one day, and t=1,2 ..., T; exerting oneself of day part conventional power generation usage unit; exerting oneself of day part Wind turbines; 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 i _bthe load of Nodes; t j _bthe voltage of Nodes; with inside power flow equation coefficient matrix and i _bnode and j _bthe part that node is corresponding, i _bnode voltage and j _bphase difference between node voltage; t is 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 to plan and runs comprehensive optimization system, it is characterized in that, comprise: the first module, for according to power system network parameter, build the node admittance matrix real-part matrix G and imaginary-part matrix B that are used for Load flow calculation, the bound of each node voltage and the bound of each Line Flow during certainty annuity safe and stable operation; Second module, for according to electric power system power source parameter, determines that conventional generator is gained merit the bound of exerting oneself, determines conventional generator cost of electricity-generating parameter, determines to gain merit the bound of exerting oneself in Wind turbines or wind-powered electricity generation airport, determines to abandon wind punishment electricity price; 3rd module, for according to energy-storage system parameter, determines energy storage system capacity price, power price and average life, determines the day maintenance cost of energy-storage system; Four module, for according to power system dispatching service data, determines Wind turbines or wind-powered electricity generation airport typical case's day wind power prediction data, determines this daily load data, determine electric power system spinning reserve data, the network loss electricity price of certainty annuity; 5th module, for the data of collecting according to step the first module ~ the four module, determines internal layer Optimized model; 6th module, for the data of collecting according to step the first module ~ the four module, determines outer Optimized model.

(3) beneficial effect

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

1) propose first for reducing the energy-storage system plan model abandoning wind, optimum energy-accumulating power station power configuration, capacity configuration and position configuration conclusion can be obtained, instruct the planning process of energy-accumulating power station, the development of looking forward to the prospect electrical network future, improve wind power integration ability, there is significant economic benefit and environmental benefit;

2) propose first for reducing the energy-storage system moving model abandoning wind, the plan of exerting oneself of optimum conventional power unit, Wind turbines and energy-accumulating power station can be obtained, instruct the running of energy-accumulating power station, instruct the economic dispatch process of electrical network, the economy of operation of power networks can be improved to greatest extent.

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 for illustration of the present invention, but are not used for limiting the scope of the invention.

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

S1. according to power system network parameter, the node admittance matrix real-part matrix G and imaginary-part matrix B that are used for Load flow calculation is built, each node voltage during certainty annuity safe and stable operation bound with each Line Flow bound wherein, i _band j _bfor the sequence number of node each in system, and i _b, j _b=1,2 ..., α _b, α _bfor the number of system interior joint, li _bj _brepresent from node i _bto node j _binterconnection;

S2. according to electric power system power source parameter, determine that conventional generator is meritorious and exert oneself bound determine conventional generator cost of electricity-generating parameter with determine that Wind turbines (field) is meritorious to exert oneself bound determine to abandon wind punishment electricity price η ^w.Wherein, i _gfor the sequence number of conventional power generation usage unit each 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 Wind turbines in system (field), and i _w=1,2 ..., α _w, α _wfor the number of Wind turbines in system (field);

S3. according to energy-storage system parameter, energy storage system capacity price η is determined ^s, power price η ^pand average life determine the day maintenance cost of energy-storage system wherein, i _sfor the sequence number of generating set each in system, and i _s=1,2 ..., α _s, α _sfor the number of generating set in system;

S4. according to power system dispatching service data, Wind turbines (field) typical case day wind power prediction data are determined determine this daily load data P _lt, determine electric power system spinning reserve data R _t, the network loss electricity price η of certainty annuity ^loss.Wherein, t is markers discrete in a day, and t=1,2 ..., T, T are the discrete time point sum participating in calculating, if every 1h calculates once, then T=24, if every 15min calculates once, then T=96;

S5. according to the data that step S1 ~ S4 collects, determine internal layer Optimized model, mathematic(al) representation is as (1).Wherein, C _operationrepresent system operation cost, by f _genand f _puntwo parts form, f _genrepresent the cost of electricity-generating of conventional power generation usage unit, f _punrepresent and abandon wind punishment, Δ t represents temporal resolution when calculating operating cost, if every 1h calculates once, then and Δ t=1, if every 15min calculates once, then Δ t=1/4; with represent exerting oneself and the state-of-charge of energy-storage system of day part conventional power generation usage unit, Wind turbines (field), energy-storage system respectively; representing the on off state of day part conventional power generation usage unit, is 0-1 variable, and 1 represents start, and 0 represents shutdown, is variable to be solved. represent energy-storage system maximum charge power, maximum discharge power, minimum capacity and heap(ed) capacity respectively, be the optimum results that 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 Wind turbines in system; i _w: the sequence number of Wind turbines in system, 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; wind turbines is meritorious exerts oneself; the power of energy-storage system;

with the cost of electricity-generating coefficient of generator, when generator output is time cost;

the state-of-charge of t+1 moment energy-storage system, the state-of-charge of t energy-storage system;

T: participate in the discrete time point sum calculated; Δ t: calculate temporal resolution during operating cost; Markers discrete in t: one day, and t=1,2 ..., T; exerting oneself of day part conventional power generation usage unit; exerting oneself of day part Wind turbines; 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 turbines typical case day wind power prediction data, P _ltrepresent 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; energy-storage system minimum capacity; energy-storage system heap(ed) capacity; wind turbines day wind power prediction data.

S6. according to the data that step S1 ~ S4 collects, determine outer Optimized model, mathematic(al) representation is as (2).Wherein, c _operationidentical with (1), be the optimum results that internal layer is optimized, pass to outer optimization by internal layer optimization.C _investmentrepresent day investment and the maintenance cost of energy-storage system equivalence, W _lossthe day network loss of expression system. representing the power of energy-storage system, capacity and layout configurations respectively, is 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: energy-storage system investment and the total cost run; t i _bthe voltage of node;

C _investment: the day investment of energy-storage system equivalence and maintenance cost; 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 system interior joint; 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 Wind turbines in system; i _w: the sequence number of Wind turbines in system, 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; wind turbines is meritorious exerts oneself; the power of energy-storage system; T: participate in the discrete time point sum calculated; Δ t: calculate temporal resolution during operating cost; Markers discrete in t: one day, and t=1,2 ..., T; exerting oneself of day part conventional power generation usage unit; exerting oneself of day part Wind turbines; 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 i _bthe load of Nodes; t j _bthe voltage of Nodes; with inside power flow equation coefficient matrix and i _bnode and j _bthe part that node is corresponding, i _bnode voltage and j _bphase difference between node voltage; t is 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 to plan and runs comprehensive optimization system, comprise: the first module, for according to power system network parameter, build the node admittance matrix real-part matrix G and imaginary-part matrix B that are used for Load flow calculation, the bound of each node voltage and the bound of each Line Flow during certainty annuity safe and stable operation; Second module, for according to electric power system power source parameter, determines that conventional generator is gained merit the bound of exerting oneself, determines conventional generator cost of electricity-generating parameter, determines to gain merit the bound of exerting oneself in Wind turbines or wind-powered electricity generation airport, determines to abandon wind punishment electricity price; 3rd module, for according to energy-storage system parameter, determines energy storage system capacity price, power price and average life, determines the day maintenance cost of energy-storage system; Four module, for according to power system dispatching service data, determines Wind turbines or wind-powered electricity generation airport typical case's day wind power prediction data, determines this daily load data, determine electric power system spinning reserve data, the network loss electricity price of certainty annuity; 5th module, for the data of collecting according to step the first module ~ the four module, determines internal layer Optimized model; 6th module, for the data of collecting according to step the first module ~ the four module, determines outer Optimized model.

Energy-accumulating power station based on dual-layer optimization theory planning of the present invention may be used among the computer-aided traffic control system of each provincial straight tune wind energy turbine set of China and Wind-Electric Power Stations with operation Integrated Optimization Model, the planning being used to guide energy-accumulating power station controls with operation, greatly can reduce and abandon wind-powered electricity generation amount, improve wind power integration ability, 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 not departing from the technology of the present invention principle; can also make some improvement and replacement, these improve and replace and also should be considered as protection scope of the present invention.

Claims (2)

1. energy-accumulating power station planning and an operation comprehensive optimization method, is characterized in that, comprise step:

S1. according to power system network parameter, the node admittance matrix real-part matrix G and imaginary-part matrix B that are used for Load flow calculation is built, the bound of each node voltage and the bound of each Line Flow during certainty annuity safe and stable operation;

S2. according to electric power system power source parameter, determine that conventional generator is gained merit the bound of exerting oneself, determine conventional generator cost of electricity-generating parameter, determine to gain merit the bound of exerting oneself in Wind turbines or wind-powered electricity generation airport, determine to abandon wind punishment electricity price;

S3. according to 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 power system dispatching service data, determine Wind turbines or wind-powered electricity generation airport typical case's day wind power prediction data, determine this daily load data, determine electric power system spinning reserve data, the network loss electricity price of certainty annuity;

S5. according to the data that step S1 ~ S4 collects, internal layer Optimized model is determined;

S6. according to the data that step S1 ~ S4 collects, outer Optimized model is determined;

Described determine internal layer Optimized model adopt formula (1):

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

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

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

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

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

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

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 Wind turbines in system; i _w: the sequence number of Wind turbines in system, 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; wind turbines is meritorious exerts oneself; the power of energy-storage system;

with the cost of electricity-generating coefficient of generator, when generator output is the cost that conventional generator is gained merit when exerting oneself;

the state-of-charge of t+1 moment energy-storage system, the state-of-charge of t energy-storage system;

T: participate in the discrete time point sum calculated; Δ t: calculate temporal resolution during operating cost; Markers discrete in t: one day, and t=1,2 ..., T; exerting oneself of day part conventional power generation usage unit; exerting oneself of day part Wind turbines; 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 turbines typical case day wind power prediction data, P _ltrepresent 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; energy-storage system minimum capacity; energy-storage system heap(ed) capacity; R _t: electric power system spinning reserve data; the lower limit that rule generated power is exerted oneself; the upper limit that rule generated power is exerted oneself;

Described determine outer Optimized model adopt formula (2):

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

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

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

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

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

Wherein,

C _plan: energy-storage system investment and the total cost run; t i _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 security of system stable operation Lower limit; Each node voltage during security of system stable operation The upper limit; α _B: the number of system interior joint; 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 Wind turbines in system; i _W: the sequence number of Wind turbines in system, 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; Wind turbines is meritorious exerts oneself; The power of energy-storage system; T: participate in the discrete time point sum calculating; Δ t: temporal resolution when calculating operating cost; Discrete markers in t: one day, and t=1,2 ..., T; Exerting oneself of day part conventional power generation usage unit; Exerting oneself of day part Wind turbines; 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 i _BThe load of Nodes; T j _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 from i _BNode is to j _BPower on the interconnection of node; The layout configurations of energy-storage system; The lower limit of Line Flow; The upper limit of Line Flow; The lower limit that rule generated power is exerted oneself; The upper limit that rule generated power is exerted oneself; The lower limit that Wind turbines is meritorious exerts oneself; The upper limit that Wind turbines is meritorious exerts oneself.

2. energy-accumulating power station planning and an operation comprehensive optimization system, is characterized in that, comprising:

First module, for according to power system network parameter, builds the node admittance matrix real-part matrix G and imaginary-part matrix B that are used for Load flow calculation, the bound of each node voltage and the bound of each Line Flow during certainty annuity safe and stable operation;

Second module, for according to electric power system power source parameter, determines that conventional generator is gained merit the bound of exerting oneself, determines conventional generator cost of electricity-generating parameter, determines to gain merit the bound of exerting oneself in Wind turbines or wind-powered electricity generation airport, determines to abandon wind punishment electricity price;

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

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

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

6th module, for the data of collecting according to step the first module ~ the four module, determines outer Optimized model;

Described determine internal layer Optimized model adopt formula (1):

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

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

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

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

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

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 Wind turbines in system; i _w: the sequence number of Wind turbines in system, 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; wind turbines is meritorious exerts oneself; the power of energy-storage system;

with the cost of electricity-generating coefficient of generator, when generator output is the cost that conventional generator is gained merit when exerting oneself;

the state-of-charge of t+1 moment energy-storage system, the state-of-charge of t energy-storage system;

T: participate in the discrete time point sum calculated; Δ t: calculate temporal resolution during operating cost; Markers discrete in t: one day, and t=1,2 ..., T; exerting oneself of day part conventional power generation usage unit; exerting oneself of day part Wind turbines; 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 turbines typical case day wind power prediction data, P _ltrepresent 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; energy-storage system minimum capacity; energy-storage system heap(ed) capacity; R _t: electric power system spinning reserve data; the lower limit that rule generated power is exerted oneself; the upper limit that rule generated power is exerted oneself;

Described determine outer Optimized model adopt formula (2):

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

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

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

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

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

Wherein,

C _plan: energy-storage system investment and the total cost run; t i _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 security of system stable operation Lower limit; Each node voltage during security of system stable operation The upper limit; α _B: the number of system interior joint; 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 Wind turbines in system; i _W: the sequence number of Wind turbines in system, 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; Wind turbines is meritorious exerts oneself; The power of energy-storage system; T: participate in the discrete time point sum calculating; Δ t: temporal resolution when calculating operating cost; Discrete markers in t: one day, and t=1,2 ..., T; Exerting oneself of day part conventional power generation usage unit; Exerting oneself of day part Wind turbines; 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 i _BThe load of Nodes; T j _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 from i _BNode is to j _BPower on the interconnection of node; The layout configurations of energy-storage system; The lower limit of Line Flow; The upper limit of Line Flow; The lower limit that rule generated power is exerted oneself; The upper limit that rule generated power is exerted oneself; The lower limit that Wind turbines is meritorious exerts oneself; The upper limit that Wind turbines is meritorious exerts oneself.