DAY-AHEAD SCHEDULING METHOD AND APPARATUS FOR POWER SYSTEM, ELECTRONIC DEVICE AND STORAGE MEDIUM

Abstract

The present application provides a day-ahead scheduling method and apparatus for a power system, an electronic device, and a storage medium, where the method includes: obtaining power system prediction parameters in a to-be-scheduled time period, where the power system prediction parameters include thermal power unit parameters, renewable energy station parameters, load parameters, energy storage station (ESS) parameters, node parameters, a transmission line parameter and other power gird parameters; constructing a day-ahead scheduling optimization model and generating multiple renewable energy power scenarios based on the power system prediction parameters; solving the day-ahead scheduling optimization model to obtain day-ahead scheduling results; verifying the day-ahead scheduling results based on the power system prediction parameters and the multiple renewable energy power scenarios; and in case that the verifying the day-ahead scheduling results successes, outputting the day-ahead scheduling results. In the method, the impact of the renewable energy station grid-connected proportion instructions on the renewable energy power scenarios is characterized by generating multiple renewable energy power scenarios, the voltage security for nodes in the power system is ensured by constructing the day-ahead scheduling optimization model, the feasibility of intraday power grid operation is ensured by verifying the day-ahead scheduling result, and the power system is safely and economically scheduled under the condition of voltage security.

Claims

1. A day-ahead scheduling method for a power system, comprising: obtaining power system prediction parameters in a to-be-scheduled time period, wherein the power system prediction parameters comprise thermal power unit parameters, renewable energy station parameters, load parameters, energy storage station (ESS) parameters, node parameters, a transmission line parameter and other power gird parameters; constructing a day-ahead scheduling optimization model and generating multiple renewable energy power scenarios based on the power system prediction parameters; solving the day-ahead scheduling optimization model to obtain day-ahead scheduling results; verifying the day-ahead scheduling results based on the power system prediction parameters and the multiple renewable energy power scenarios; and in case that verifying the day-ahead scheduling results successes, outputting the day-ahead scheduling results.

2. The method of claim 1, wherein constructing the day-ahead scheduling optimization model based on the power system prediction parameters comprises: constructing an objective function for power system day-ahead scheduling optimization problem and objective constraints corresponding to the objective function based on the power system prediction parameters.

3. The method of claim 2, wherein the objective function for power system day-ahead scheduling optimization problem is as follows: ${.Math.}_t{.Math.}_{i{G}_{}}\left\{c_i^g{p}_i^g\left(t\right)+c_i^{\mathrm{spare}}\left[r_i^{g,\mathrm{up}}\left(t\right)+r_i^{g,\mathrm{dn}}\left(t\right)\right]\right\}+\underset{\begin{array}{c}{p}_i^g\left(t\right),r_i^{g,\mathrm{up}}\left(t\right),r_i^{g,\mathrm{dn}}\left(t\right),w_j^{\mathrm{pption}}\left(t\right),p_d^{\mathrm{dr}}\left(t\right),\\ p_{j,y}^{\mathrm{re},\mathrm{cut}}\left(t\right),p_{j,y}^{\mathrm{re}}\left(t\right),p_{d,y}^{\mathrm{load}}\left(t\right),p_{d,y}^{\mathrm{load},\mathrm{cut}}\left(t\right),p_{i,y}^{\mathrm{gc}}\left(t\right),q_{i,y}^{\mathrm{gc}}\left(t\right),\\ p_{k,y}^{e,\mathrm{ch}}\left(t\right),p_{k,y}^{e,\mathrm{dc}}\left(t\right),E_{k,y}\left(t\right),q_{k,y}^e\left(t\right),p_{l,y}^{\mathrm{brch}}\left(t\right),q_{l,y}^{\mathrm{brch}}\left(t\right),\\ s_y^1\left(t\right)~s_y^4\left(t\right),s_y^5\left(t\right)~s_y^{12}\left(t\right),v_{b,y}^{\mathrm{amp}}\left(t\right),p_{b,y}^{\mathrm{bus}}\left(t\right),q_{b,y}^{\mathrm{bus}}\left(t\right),{}_{b,y}\left(t\right),\\ r_{i,y}^{\mathrm{gc},\mathrm{use}}\left(t\right),q_{i,y}^{\mathrm{gc},\mathrm{use}}\left(t\right),q_{k,y}^{e,\mathrm{use}}\left(t\right),{}_y,d,i,j,k,t,y=1,.Math.,.Math.Y_n^{}.Math.\end{array}}{\min }\underset{t}{.Math.}\underset{i{R}_{}}{.Math.}{c}_j^{\mathrm{pption}}.Math.\frac{{\stackrel{}{W}}_j^{\mathrm{pption}}-w_j^{\mathrm{pption}}\left(t\right)}{{\stackrel{}{W}}_j^{\mathrm{pption}}-{\underset{\_}{W}}_j^{\mathrm{pption}}}+\underset{t}{.Math.}\underset{d{D}_{}}{.Math.}{c}_d^{\mathrm{dr}}{p}_d^{\mathrm{dr}}\left(t\right)+\underset{y{Y}_n^{}}{.Math.}{}_y$ $s.t.\mathrm{objective}\mathrm{constraints}$ wherein is a set of scheduling time periods, .sub.G is a set of thermal power units, c.sub.i.sup.g is a cost per unit power output, p.sub.i.sup.g(t) is active pre-output power of a thermal power unit i in a time period t, c.sub.i.sup.spare is a regulation spare cost for regulating up and down per unit, r.sub.i.sup.g,up(t) is an uphill capacity of the thermal power unit i in the time period t, r.sub.i.sup.g,dn(t) is a downhill capacity of the thermal power unit i in the time period t, w.sub.j.sup.pption(t) is a renewable energy station grid-connected proportion of a renewable energy station j in the time period t, p.sub.d.sup.dr(t) is a protocol capacity of a demand side response of a load d in the time period t, p.sub.j,y.sup.re,cut(t) is a renewable energy power reduction value of the renewable energy station j in the time period t, p.sub.j,y.sup.re(t) is a renewable energy power grid-connected value of the renewable energy station j in the time period t, p.sub.d,y.sup.load(t) is a load power supply value of the load d in the time period t, p.sub.d,y.sup.load,cut(t) is a demand side response usage value of the load d in the time period t, p.sub.i,y.sup.gc(t) is unit real-time active power output of the thermal power unit i in the time period t, q.sub.i,y.sup.gc(t) is unit real-time reactive power output of the thermal power unit i in the time period t, p.sub.k,y.sup.e,ch(t) is active charging power of an energy storage station k in a scenario y and the time period t, p.sub.k,y.sup.e,dc(t) is active discharging power of the energy storage station k in the scenario y and the time period t, E.sub.k,y(t) is stored energy of the energy storage station k in the scenario y and the time period t, q.sub.k,y.sup.e(t) is reactive power injected by the energy storage station to the power grid, p.sub.l,y.sup.brch(t) is active power flow of a transmission line l in the scenario y and the time period t, q.sub.l,y.sup.brch(t) is reactive power flow of the transmission line l in the scenario y and the time period t, s.sub.y.sup.1(t)s.sub.y.sup.2(t) are slack variables of active power balance in the power system, s.sub.y.sup.3(t)s.sub.y.sup.4(t) are slack variables of reactive power balance in the power system, s.sub.y.sup.5(t)s.sub.y.sup.12(t) are line capacity slack variables considering active power flow and reactive power flow, v.sub.b,y.sup.amp(t) is a node voltage of a node b in the scenario y and the time period t, p.sub.b,y.sup.bus(t) is an active power input value of the node b in the scenario y and the time period t, q.sub.b,y.sup.bus(t) is a reactive power input value of the node b in the scenario y and the time period t, .sub.b,y(t) is a node phase angle value of the node b in the scenario y and the time period t, r.sub.i,y.sup.gc,use(t) is an auxiliary variable for obtaining the active power spare usage amount, q.sub.i,y.sup.gc,use(t) is an auxiliary variable for obtaining the reactive power spare usage amount, q.sub.k,y.sup.e,use(t) is an auxiliary variable for obtaining the reactive power spare usage amount of the energy storage station, .sub.R is a set of the renewable energy stations, c.sub.j.sup.pption is a unit cost of the renewable energy station grid-connected proportion regulation instruction, W.sub.j.sup.pption is a upper limit of the renewable energy station grid-connected proportion of the renewable energy station j in the time period t, W.sub.j.sup.pption is a lower limit of the renewable energy station grid-connected proportion of the renewable energy station j in the time period t, .sub.D is a set of the loads, c.sub.d.sup.dr is a cost of the demand side response per unit capacity, .sub.Y.sup.n is a set of typical renewable energy scenarios, .sub.y is a variable of power system operating cost in the scenario y, d is an index of the loads, i is a thermal power unit number, j is an index of the renewable energy stations, k is an index of the energy storage stations, t is a scheduling time point, y is an index of the renewable energy scenarios, l is an index of the transmission lines, and |.sub.Y.sup.n| is a number of elements comprised in the set of typical renewable energy scenarios .sub.Y.sup.n.

4. The method of claim 3, wherein the objective constraints comprise voltage security constraints, and the voltage security constraints comprise: a constraint of node voltage amplitude: ${\underset{\_}{V}}_b^{\mathrm{amp}}{v}_{b,y}^{\mathrm{amp}}\left(t\right){\stackrel{}{V}}_b^{\mathrm{amp}},t,y=1,.Math.,.Math.Y_n^{}.Math.,b,$ wherein V.sub.b.sup.amp and V.sub.b.sup.b are a lower limit and an upper limit of the node b in the scenario y and the time period t respectively, b is an index of the nodes, and is a set of the nodes; a constraint of node active power: $p_{b,y}^{\mathrm{bus}}\left(t\right)=-{\mathrm{BF}}_{b,d}^D.Math.p_{d,y}^{\mathrm{load}}\left(t\right)+{\mathrm{BF}}_{b,i}^G.Math.p_{i,y}^{\mathrm{gc}}\left(t\right)+{\mathrm{BF}}_{b,j}^R.Math.p_{j,y}^{\mathrm{re}}\left(t\right)+{\mathrm{BF}}_{b,k}^{\mathrm{ESS}}.Math.\left[-p_{k,y}^{e,\mathrm{ch}}\left(t\right)+p_{k,y}^{e,\mathrm{dc}}\left(t\right)\right],b,d,i,j,k,l,t,y=1,.Math.,.Math.Y_n^{}.Math.,$ wherein BF.sub.b,d.sup.D is a position correlation matrix of the load d with respect to the node b, BF.sub.b,i.sup.G is a position correlation matrix of the thermal power unit i with respect to the node b, BF.sub.b,j.sup.R is a position correlation matrix of the renewable energy station j with respect to the node b, BF.sub.b,k.sup.ESS is a position correlation matrix of the energy storage station k with respect to the node b; a constraint of node reactive power: $q_{b,y}^{\mathrm{bus}}\left(t\right)=-{\mathrm{BF}}_{b,d}^D.Math.Q_{d,t}^D+{\mathrm{BF}}_{b,i}^G.Math.q_{i,y}^{\mathrm{gc}}\left(t\right)+{\mathrm{BF}}_{b,k}^{\mathrm{ESS}}.Math.q_{k,y}^e\left(t\right),b,d,i,j,k,l,t,y=1,.Math.,.Math.Y_n^{}.Math.,$ wherein Q.sub.d,t.sup.D is a reactive power demand of the load d in the time period t; a constraint of a relationship among node active power, a node voltage amplitude and a node voltage phase angle: $p_{b1,y}^{\mathrm{bus}}\left(t\right)={.Math.}_{b2}{G}_{b1,b2}^{\mathrm{NET}}{v}_{b2,y}^{\mathrm{amp}}\left(t\right)-{.Math.}_{b2}{B}_{b1,b2}^{\mathrm{NET}*}{}_{b2,y}\left(t\right),b1,t{T}_{\mathrm{NOW}}^{},y=1,.Math.,.Math.Y_n^{}.Math.,$ wherein p.sub.b1,y.sup.bus(t) is an active power input value of a node b1 in the scenario y and the time period t, G.sub.b1,b2.sup.NET is a real part of an element corresponding to a row b1 and a column b2 in a node admittance matrix Y.sup.NET, v.sub.b2,y.sup.amp(t) is a node voltage of a node b2 in the scenario y and the time period t, B.sub.b1,b2.sup.NET* is an imaginary part of the elements of corresponding to the row b1 and the column b2 in a node admittance matrix Y.sup.NET*, .sub.b2,y(t) is a node phase angle of the node b2 in the scenario y and the time period t, b1 and b2 are nodes, .sub.T.sup.NOW is a set of scheduling time periods; a constraint of a relationship among node reactive power, the node voltage amplitude and the node voltage phase angle: $q_{b1,y}^{\mathrm{bus}}\left(t\right)=-\underset{b2}{.Math.}{B}_{b1,b2}^{\mathrm{NET}}{v}_{b2,y}^{\mathrm{amp}}\left(t\right)-\underset{jN}{.Math.}{G}_{b1,b2}^{\mathrm{NET}}{}_{b2,y}\left(t\right),b1,t{T}_{\mathrm{NOW}}^{},y=1,.Math.,.Math.Y_n^{}.Math.,$ wherein q.sub.b1,y.sup.bus(t) is a reactive power input value of the node b1 in the scenario y and the time period t, B.sub.b1,b2.sup.NET is an imaginary part of the element corresponding to the row b1 and the column b2 in a node admittance matrix Y.sup.NET; a constraint of a relationship among active power flow of a transmission line, the voltage amplitude, and the voltage phase angle of a start node and an end node corresponding to the transmission line: $p_{l,y}^{\mathrm{brch}}\left(t\right)=g_{b1,b2}^{\mathrm{NET}}\left[v_{b1,y}^{\mathrm{amp}}\left(t\right)-v_{b2,y}^{\mathrm{amp}}\left(t\right)\right]-u_{b1,b2}^{\mathrm{NET}}\left[{}_{b1,y}\left(t\right)-{}_{b2,y}\left(t\right)\right],b1,l,t{T}_{\mathrm{NOW}}^{},y=1,.Math.,.Math.Y_n^{}.Math.;$ wherein g.sub.b1,b2.sup.NET is conductance of the transmission line l, v.sub.b1,y.sup.amp(t) is node voltage of a node b1 in the scenario y and the time period t, .sub.b1,y(t) is a node phase angle of the node b1 in the scenario y and the time period t; a constraint of a relationship among reactive power flow of a transmission line, the voltage amplitude and the voltage phase angle of a start node and an end node corresponding to the transmission line: $q_{l,y}^{\mathrm{brch}}\left(t\right)=-u_{b1,b2}^{\mathrm{NET}}\left[v_{b1,y}^{\mathrm{amp}}\left(t\right)-v_{b2,y}^{\mathrm{amp}}\left(t\right)\right]-g_{b1,b2}^{\mathrm{NET}}\left[{}_{b1,y}\left(t\right)-{}_{b2,y}\left(t\right)\right],b1,l,t{T}_{\mathrm{NOW}}^{},y=1,.Math.,.Math.Y_n^{}.Math.;\mathrm{and}$ a constraint of an operating range of the node voltage phase angle: ${}_{\mathrm{slack},y}\left(t\right)=0,{}_{b,y}\left(t\right)\left[{\underset{\_}{}}_b^{\mathrm{angle}},{\stackrel{}{}}_b^{\mathrm{angle}}\right],b,t,y=1,.Math.,.Math.Y_n^{}.Math.;$ wherein .sub.slack,y(t) is a phase angle of a slack node, .sub.b.sup.angle and .sub.b.sup.angle are a lower limit and an upper limit of the node phase angle of the node b in the scenario y and the time period t respectively.

5. The method of claim 1, wherein the step of generating multiple renewable energy power scenarios comprises: for a renewable energy station j in a scheduling time period t, generating a random number R.sub.rand.sup.j,t in an interval [0,1] that obeys uniform distribution, then there must be an integer M.sub.0 (1M.sub.0M.sub.GMM) so that the following formula is workable: $R_{\mathrm{rand}}^{j,t}\left[\underset{m=0}{\overset{M_0-1}{.Math.}}{}_{j,t,m},\underset{m=0}{\overset{M_0}{.Math.}}{}_{j,t,m}\right];$ determining a renewable energy output probability distribution with a mean of P.sub.j,t,M.sub.0.sup. and a variance of P.sub.j,t,M.sub.0.sup.; sampling randomly from the renewable energy output probability distribution to obtain the multiple renewable energy power scenarios; wherein renewable energy output probability distribution is represented by a Gaussian mixture model (GMM), M.sub.GMM is a number of Gaussian components in the renewable energy output probability distribution, and .sub.j,t,m is a weight coefficient of an m-th Gaussian component in the renewable energy output probability distribution.

6. The method of any of claims 1 to 5, verifying the day-ahead scheduling results based on the power system prediction parameters and the multiple renewable energy power scenarios comprises: for each renewable energy power scenario, constructing an intraday scheduling optimization model based on the day-ahead scheduling results and the power system prediction parameters; solving the intraday scheduling optimization model to obtain a multi-stage optimal solution of variables in the renewable energy power scenario; calculating a penalty cost corresponding to the day-ahead scheduling results based on the multi-stage optimal solution of the variables; and verifying the day-ahead scheduling results based on the penalty cost.

7. The method of claim 6 further comprises: in case that the verifying the day-ahead scheduling results fails, updating a cutting plane set based on the penalty cost corresponding to the day-ahead scheduling results; optimizing the day-ahead scheduling results based on an updated cutting plane set, and verifying an optimized day-ahead scheduling results; and in case that the verifying the optimized day-ahead scheduling results successes, outputting an optimized day-ahead scheduling results.

8. A day-ahead scheduling apparatus for a power system, comprising: a power system prediction parameter obtaining module, used for obtaining power system prediction parameters in a to-be-scheduled time period, wherein the power system prediction parameters comprise thermal power unit parameters, renewable energy station parameters, load parameters, energy storage station (ESS) parameters, node parameters, a transmission line parameter and other power gird parameters; a renewable energy power scenario generating module, used for constructing a day-ahead scheduling optimization model and generating multiple renewable energy power scenarios based on the power system prediction parameters; a day-ahead scheduling optimization model solving module, used for solving the day-ahead scheduling optimization model to obtain day-ahead scheduling results; day-ahead scheduling results verifying module, used for verifying the day-ahead scheduling results based on the power system prediction parameters and the multiple renewable energy power scenarios; and day-ahead scheduling results outputting module, used for, in case that the verifying the day-ahead scheduling results successes, outputting the day-ahead scheduling results.

9. An electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, causes the electronic device to perform the method of any of claims 1 to 7.

10. A non-transitory computer readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the method of any of claims 1 to 7.

Description

BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to illustrate the solutions of the embodiments more clearly according to the present application and the related art, the accompanying drawings used in the description for the embodiments or the related art are briefly introduced below. It should be noted that the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings may be obtained according to these drawings without creative effort.

[0048] FIG. 1 is a schematic flowchart of a day-ahead scheduling method for a power system according to an embodiment of the present application;

[0049] FIG. 2 is a schematic overall flowchart of a day-ahead scheduling method for a power system according to an embodiment of the present application;

[0050] FIG. 3 is a schematic structural diagram of a day-ahead scheduling apparatus for a power system according to an embodiment of the present application; and

[0051] FIG. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The solutions according to the present application are clearly described below in combination with the accompanying drawings in the embodiments of the present application. It should be noted that the described embodiments are some embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0053] In order to solve the problem that the decision-dependent uncertainty for renewable energy and the voltage security of power system are not fully considered in traditional power system scheduling modes, the present application provides a day-ahead scheduling method for a power system in which the decision-dependent uncertainty for renewable energy and the voltage security of power system are considered. FIG. 1 shows a schematic flowchart of a day-ahead scheduling method for a power system according to an embodiment of the present application.

[0054] As shown in FIG. 1, the method includes the following steps. [0055] S110: obtaining power system prediction parameters in a to-be-scheduled time period.

[0056] It should be noted that, when the power system day-ahead scheduling is performed, a data preparation operation needs to be performed first. In an embodiment, the power system prediction parameters in a to-be-scheduled time period are obtained, where the power system prediction parameters include thermal power unit parameters, renewable energy station parameters, load parameters, energy storage station (ESS) parameters, node parameters, a transmission line parameter and other power gird parameters.

[0057] The to-be-scheduled time period may be set based on an actual situation. For example, the to-be-scheduled time period is a next day, and the step S110 is to obtain the power system prediction parameters within the next day.

1) Thermal Power Unit Parameters.

[0058] An index of the thermal power unit is denoted as i, parameters of the thermal power unit include an active power output range [P.sub.i.sup.gen, P.sub.i.sup.gen] and a reactive output range [Q.sub.i.sup.gen, Q.sub.i.sup.gen] of a thermal power unit i, an operating status U.sub.i(t) of the thermal power unit i in a to-be-scheduled time period t, a ramp up speed R.sub.i.sup.ramp,+, a ramp down speed R.sub.i.sup.ramp,, an up regulation thermal spare capacity R.sub.i.sup.spare,+, a down regulation thermal spare capacity R.sub.i.sup.spare,, a power output c.sub.i.sup.g, an regulation spare cost c.sub.i.sup.spare for regulating up and down per unit, a generation shift factor SF.sub.l,i.sup.g of the thermal power unit i with respect to the transmission line l, and a position correlation matrix BF.sub.b,i.sup.g of the thermal power unit i to the node b.

[0059] A set of the thermal power units i is denoted as .sub.G, and the number of the thermal power units is denoted as N.sub.G.

2) Renewable Energy Station Parameters.

[0060] An index of the renewable energy station is denoted as j, parameters of the renewable energy station include a grid-connected proportion regulation range [W.sub.j.sup.pption, W.sub.j.sup.pption] of the renewable energy station, a unit cost c.sub.j.sup.pption of the renewable energy station grid-connected proportion regulation instruction, a unit power cut cost c.sub.j.sup.recut of the renewable energy station j, a generation shift factor SF.sub.l,j.sup.renew of the renewable energy station j with respect to the transmission line l, and a position correlation matrix BF.sub.b,j.sup.renew of the renewable energy station j with respect to the node b.

[0061] It should be noted that, in the present application, in case that Gaussian mixture model (GMM) is used to represent the probability distribution of the renewable energy output, an active GMM parameter set of the renewable energy station j in the time period t is:

.sub.j,t.sup.R={.sub.j,t,m.sup.R,P.sub.j,t,m.sup.R,,P.sub.j,t,m.sup.R,,m=1, . . . ,M.sub.GMM}, [0062] where M.sub.GMM is the number of Gaussian components, .sub.j,t,m.sup.R is a weight coefficient of an m-th Gaussian component (0<.sub.j,t,m.sup.R<1 and .sub.m=1, . . . , M.sub.GMM.sub.j,t,m.sup.R=1), P.sub.j,t,m.sup.R, is a mean value of the m-th Gaussian component, and P.sub.j,t,m.sup.R, is a variance of the m-th Gaussian component.

[0063] A set of the renewable energy stations j is denoted as .sub.R, and the number of the renewable energy stations j is denoted as N.sub.R.

3) Load Parameters.

[0064] An index of the load is denoted as d, parameters of the load include an active power demand P.sub.d,t.sup.D and a reactive power demand Q.sub.d,t.sup.D of the load d in the time period t, a generation shift factor SF.sub.l,d of the load d with respect to the transmission line l, a position correlation matrix BF.sub.b,d.sup.D of the load d with respect to the node b, a protocol capacity upper limit P.sub.d,t.sup.dr of demand side response power of the load d in the time period t, and a unit demand side response cost c.sub.d.sup.ldcut of the load d in the time period t.

[0065] A set of the loads is denoted as .sub.D, and the number of the loads is denoted as N.sub.D.

4) ESS Parameters.

[0066] An index of the energy storage station is denoted as k, the parameters of the energy storage station include an upper limit of active charging and discharging power P.sub.k.sup.ESS of the energy storage station k, an upper limit of reactive charging and discharging power Q.sub.k.sup.ESS of the energy storage station k, an upper limit S.sub.k.sup.ESS of apparent power charging and discharging power of the energy storage station k, a capacity range [E.sub.k.sup.ESS, .sub.k.sup.ESS] of the energy storage station k, a charging efficiency .sub.k.sup.CH of the energy storage station k, a discharging efficiency .sub.k.sup.DC of the energy storage station k, an initial power value Et of the energy storage station k, a unit charging and discharging power cost c.sub.k.sup.ESS of the energy storage station k, a generation shift factor SE.sub.l,k.sup.ESS of the energy storage k with respect to the line l, and a position correlation matrix BF.sub.b,k.sup.ESS of the energy storage k with respect to the node b.

[0067] A set of the energy storage is denoted as .sub.ESS, and the number of the energy storage is denoted as N.sub.ESS.

5) Node Parameters.

[0068] An index of the node is denoted as b, parameters of the node include a voltage amplitude allowable variation range [V.sub.b.sup.amp, V.sub.b.sup.amp] of the node b, and a phase angle allowable variation range [.sub.b.sup.angle, .sub.b.sup.angle] of the node b.

[0069] A set of the nodes is denoted as , and the number of the nodes is denoted as N.sub.B.

[0070] In addition, the parameters of the node further include a node admittance matrix Y.sup.NET of the power grid, a node admittance matrix Y.sub.NET* without considering bypass capacitance, a real part G.sub.b1,b2.sup.NET of an element corresponding to a row b1 and a column b2 in the node admittance matrix Y.sup.NET, an imaginary part B.sub.b1,b2.sup.NET of the element corresponding to the row b1 NET of an and the column b2 in the node admittance matrix Y.sup.NET, and an imaginary part B.sub.b1,b2.sup.NET* of an element corresponding to a row b1 and a column b2 in the node admittance matrix Y.sup.NET*.

6) Transmission Line Parameter.

[0071] An index of the transmission line is denoted as, a parameter of the transmission line includes a maximum power transmission capacity S.sub.l.sup.L of the transmission line l.

[0072] A set of the transmission line is denoted as , and the number of the transmission line is denoted as N.sub.L.

[0073] It should be note that, in case that two nodes corresponding to the transmission line l are b1 and b2, a conductance and a susceptance of the transmission I may be denoted as g.sub.b1,b2.sup.NET and u.sub.b1,b2.sup.NET respectively.

7) Other Power Grid Parameters.

[0074] The other power grid parameters include a unit scheduling cost c.sup.S of active power balance emergency regulation resources and transmission line congestion emergency regulation resources, and a unit scheduling cost c.sup.SL of reactive power balance emergency regulation resources and the transmission line congestion emergency regulation resources. [0075] S120: constructing a day-ahead scheduling optimization model and generating multiple renewable energy power scenarios based on the power system prediction parameters. [0076] S130: solving the day-ahead scheduling optimization model to obtain day-ahead scheduling results.

[0077] It should be noted that, on the basis of obtaining the power system prediction parameters in the to-be-scheduled time period in step S110, further, in step S120, the day-ahead scheduling optimization model is constructed based on the obtained power system prediction parameters, and multiple renewable energy power scenarios are generated based on the obtained power system prediction parameters, and in step S130, the day-ahead scheduling optimization model is solved to obtain the day-ahead scheduling result.

[0078] It should be noted that, in this embodiment, the process of the power system day-ahead scheduling involves a three-layer structure, including an upper level, a middle level and a lower level.

[0079] In the upper level, the day-ahead scheduling optimization model is constructed based on the power system prediction parameters, the constructed day-ahead scheduling optimization model is solved to obtain the day-ahead scheduling results.

[0080] In the middle level, multiple renewable energy power scenarios are generated based on the power system prediction parameters.

[0081] In the lower level, the day-ahead scheduling results transmitted from the upper level and the multiple renewable energy power scenarios transmitted from the middle level are received, and the day-ahead scheduling results are verified based on the power system prediction parameters and the multiple renewable power scenarios.

[0082] In step S120, the day-ahead scheduling optimization model is constructed base on the power system prediction parameters, where the day-ahead scheduling optimization model includes an objective function for power system day-ahead scheduling optimization problem and objective constraints corresponding to the objective function.

[0083] Accordingly, constructing the day-ahead scheduling optimization model includes constructing the objective function for power system day-ahead scheduling optimization problem and the objective constraints corresponding to the objective function, and there are multiple objective constraints (elaborated in the below embodiments).

[0084] Further, in step S130, multiple renewable energy power scenarios may be generated based on the power system prediction parameters. In an embodiment, since an active power GMM parameter set .sub.j,t.sup.R of the renewable energy station j (j=1, . . . , N.sub.R) is: .sub.j,t.sup.R={.sub.j,t,m.sup.R,P.sub.j,t,m.sup.R,,P.sub.j,t,m.sup.R,,m=1, . . . , M}, denoting

[00011]${}_{j,t,m}^R{.Math.}_{m=0}=0,$

the multiple renewable energy power scenarios h in a renewable energy power scenario set .sub.RO.sup.n may be generated by the following mode: [0085] for the renewable energy station j in the time period t, a random number R.sub.rand.sup.j,t is generated in an interval [0,1] that obeys uniform distribution, then there must be an integer M.sub.0 (1M.sub.0M.sub.GMM) so that the following formula is workable:

[00012]$\begin{array}{cc}{R}_{\mathrm{rand}}^{j,t}\left[{.Math.}_{m=0}^{M_0-1}{}_{j,t,m},{.Math.}_{m=0}^{M_0}{}_{j,t,m}\right],& \left(1\right)\end{array}$ [0086] a renewable energy output probability distribution with a mean value of P.sub.j,t,M.sub.0.sup. and a variance of P.sub.j,t,M.sub.0.sup. is determined, and a random sampling is performed from the renewable energy output probability distribution to obtain the multiple renewable energy power scenarios, an active power output of the renewable energy station j in the time period t may be obtained, and a renewable energy power scenario p.sub.j,t.sup.renew,h is generated.

[0087] The renewable energy output probability distribution is represented by a GMM, M.sub.GMM is the number of Gaussian components in the renewable energy output probability distribution, and .sub.j,t,m is a weight coefficient of an m-th Gaussian component in the renewable energy output probability distribution.

[0088] Further, the renewable energy power scenario p.sub.j,t.sup.renew,h (t, j.sub.R, h=1, . . . , N.sub.RO) of the scenario h in the renewable energy power scenario set N.sub.RO may be obtained by traversing both the time period t and the renewable energy station j. The N.sub.RO renewable energy scenarios P.sub.j,t.sup.renew,h (multiple renewable energy power scenarios) constitute the renewable energy power scenario set .sub.RO.sup.n.

[0089] h is an index of each of the renewable energy scenarios in the renewable energy power scenario set .sub.RO.sup.n.

[0090] On the basis of constructing the day-ahead scheduling optimization model in step S120, in step S130, the day-ahead scheduling optimization model is solved to obtain the day-ahead scheduling results.

[0091] The day-ahead scheduling results include a pre-output value {tilde over (P)}.sub.i.sup.g,n(t) of the thermal power unit, an uphill spare tuning value {tilde over (R)}.sub.i.sup.g,up,n(t) of the thermal power unit, a downhill spare tuning value {tilde over (R)}.sub.i.sup.g,dn,n(t) of the thermal power unit, a renewable energy station grid-connected proportion instruction {tilde over (W)}.sub.j.sup.pption,n(t), and a protocol capacity {tilde over (P)}.sub.d.sup.dr,n(t) of a demand side response.

[0092] It should be noted that, there is no strict logical sequence between the step of constructing the day-ahead scheduling optimization model in step S120 and the step of generating multiple renewable energy power scenarios, and neither there is a strict logical sequence between the step of generating multiple renewable energy power scenarios in step S130 and S120. [0093] S140: verifying the day-ahead scheduling results based on the power system prediction parameters and the multiple renewable energy power scenarios. [0094] S150: in case that verifying the day-ahead scheduling results successes, outputting the day-ahead scheduling results.

[0095] It should be noted that, in step S140, the lower level verifies, based on the power system prediction parameters in the to-be-scheduled time period obtained in step S110 and the multiple renewable energy power scenarios generated in step S120, the day-ahead scheduling results obtained by solving the day-ahead scheduling optimization model in step S130.

[0096] In case that the verifying the day-ahead scheduling results successes, the day-ahead scheduling results obtained in step S130 are regarded as final decision values of the power system and output; in case that the verifying the day-ahead scheduling results fails, a new round of iterating and updating is performed, which includes updating the day-ahead scheduling optimization model and repeatedly performing the steps from S120 to S140 until verifying the day-ahead scheduling results successes.

[0097] It should be noted that, the verifying the day-ahead scheduling results in this step is to correct day-ahead spare decision values of the power system, namely the day-ahead scheduling results, thereby ensuring the feasibility of day-ahead scheduling for the power system.

[0098] In this embodiment, by obtaining the power system prediction parameters in the to-be-scheduled time period; constructing the day-ahead scheduling optimization model and generating the multiple renewable energy power scenarios based on the power system prediction parameters; solving the day-ahead scheduling optimization model to obtain the day-ahead scheduling results; verifying the day-ahead scheduling results based on the power system prediction parameters and the multiple renewable energy power scenarios; the day-ahead scheduling results is output in case that the verifying the day-ahead scheduling results successes. In the method, the impact of the renewable energy station grid-connected proportion instructions on the renewable energy power scenarios is characterized by generating multiple renewable energy power scenarios, so that the decision-dependent uncertainty for renewable energy is taken into account in the day-ahead scheduling for power system, the voltage security for nodes in the power system is ensured by constructing the day-ahead scheduling optimization model, and the feasibility of intraday power grid operation is ensured by verifying the day-ahead scheduling result, which solves the problem that the decision-dependent uncertainty for renewable energy and the voltage security of power system are not fully considered in traditional power system scheduling modes, and the power system is safely and economically scheduled under the condition of voltage security.

[0099] On the basis of the above embodiment, the day-ahead scheduling optimization model is constructed based on the power system prediction parameters. In an embodiment, the day-ahead scheduling optimization model includes the objective function and the corresponding constraints, and constructing the day-ahead scheduling optimization model includes constructing the objective function for the power system day-ahead scheduling optimization problem and the objective constraints corresponding to the objective function.

[0100] The objective function (2) for the power system day-ahead scheduling optimization problem is as follows:

[00013]$\begin{array}{cc}\left\{c_i^g{p}_i^g\left(t\right)+c_i^{\mathrm{spare}}\left[r_i^{g,\mathrm{up}}\left(t\right)+r_i^{g,\mathrm{dn}}\left(t\right)\right]\right\}+\underset{\begin{array}{c}{p}_i^g\left(t\right),r_i^{g,\mathrm{up}}\left(t\right),r_i^{g,\mathrm{dn}}\left(t\right),w_j^{\mathrm{pption}}\left(t\right),p_d^{\mathrm{dr}}\left(t\right),\\ p_{j,y}^{\mathrm{re},\mathrm{cut}}\left(t\right),p_{j,y}^{\mathrm{re}}\left(t\right),p_{d,y}^{\mathrm{load}}\left(t\right),p_{d,y}^{\mathrm{load},\mathrm{cut}}\left(t\right),p_{i,y}^{\mathrm{gc}}\left(t\right),q_{i,y}^{\mathrm{gc}}\left(t\right),\\ p_{k,y}^{e,\mathrm{ch}}\left(t\right),p_{k,y}^{e,\mathrm{dc}}\left(t\right),E_{k,y}\left(t\right),q_{k,y}^e\left(t\right),p_{l,y}^{\mathrm{brch}}\left(t\right),q_{l,y}^{\mathrm{brch}}\left(t\right),\\ s_y^1\left(t\right)s_y^4\left(t\right),s_y^5\left(t\right)s_y^{12}\left(t\right),v_{b,y}^{\mathrm{amp}}\left(t\right),p_{b,y}^{\mathrm{bus}}\left(t\right),q_{b,y}^{\mathrm{bus}}\left(t\right),{}_{b,y}\left(t\right),\\ r_{i,y}^{\mathrm{gc},\mathrm{use}}\left(t\right),q_{i,y}^{\mathrm{gc},\mathrm{use}}\left(t\right),q_{k,y}^{e,\mathrm{use}}\left(t\right),{}_y,d,i,j,k,t,y=1,...,.Math.Y_n^{}.Math.\end{array}}{\min }\underset{t}{.Math.}\underset{j{R}_{}}{.Math.}{c}_j^{\mathrm{pption}}.Math.\frac{{\stackrel{\_}{W}}_j^{\mathrm{pption}}-w_j^{\mathrm{pption}}\left(t\right)}{{\stackrel{\_}{W}}_j^{\mathrm{pption}}-{\underset{\_}{W}}_j^{\mathrm{pption}}}+\underset{t}{.Math.}\underset{d{D}_{}}{.Math.}{c}_d^{\mathrm{dr}}{p}_d^{\mathrm{dr}}\left(t\right)+\underset{y{Y}_n^{}}{.Math.}{}_y& \left(2\right)\end{array}$$s.t.\mathrm{objective}\mathrm{constraints}$

where is a set of scheduling time periods, .sub.G is a set of thermal power units, c.sub.i.sup.g is a cost per unit power output, p.sub.i.sup.g(t) is active pre-output power of a thermal power unit i in a time period t, c.sub.i.sup.spare is a regulation spare cost for regulating up and down per unit, r.sub.i.sup.g,up(t) is an uphill capacity of the thermal power unit i in the time period t, r.sub.i.sup.g,dn(t) is a downhill capacity of the thermal power unit i in the time period t, w.sub.j.sup.pption(t) is a renewable energy station grid-connected proportion of a renewable energy station j in the time period t, p.sub.d.sup.dr(t) is a protocol capacity of a demand side response of a load d in the time period t, p.sub.j,y.sup.re,cut(t) is a renewable energy power reduction value of the renewable energy station j in the time period t, p.sub.j,y.sup.re(t) is a renewable energy power grid-connected value of the renewable energy station j in the time period t, p.sub.d,y.sup.load (t) is a load power supply value of the load d in the time period t, p.sub.d,y.sup.load,cut(t) is a demand side response usage value of the load d in the time period t, p.sub.i,y.sup.gc(t) is unit real-time active power output of the thermal power unit i in the time period t, q.sub.i,y.sup.gc(t) is unit real-time reactive power output of the thermal power unit i in the time period t, p.sub.k,y.sup.e,ch(t) is active charging power of an energy storage station k in a scenario y and the time period t, p.sub.k,y.sup.e,dc(t) is active discharging power of the energy storage station k in the scenario y and the time period t, E.sub.k,y(t) is stored energy of the energy storage station k in the scenario y and the time period t, q.sub.k,y.sup.e(t) is reactive power injected by the energy storage station to the power grid, p.sub.i,y.sup.brch(t) is reactive power flow of a transmission line l in the scenario y and the time period t, q.sub.l,y.sup.brch(t) is reactive power flow of the transmission line l in the scenario y and the time period t, s.sub.y.sup.1(t)s.sub.y.sup.2(t) are slack variables of active power balance in the power system, s.sub.y.sup.3 (t)s.sub.y.sup.4(t) are slack variables of reactive power balance in the power system, s.sub.y.sup.5(t)s.sub.y.sup.12(t) are line capacity slack variables considering active power flow and reactive power flow, v.sub.b,y.sup.amp (t) is a node voltage of a node b in the scenario y and the time period t, p.sub.b,y.sup.bus (t) is an active power input value of the node b in the scenario y and the time period t, q.sub.b,y.sup.bus(t) is a reactive power input value of the node b in the scenario y and the time period t, .sub.b,y(t) is a node phase angle value of the node b in the scenario y and the time period t, r.sub.i,y.sup.gc,use(t) is an auxiliary variable gc,use for obtaining the active power spare usage amount, q.sub.i,y.sup.gc,use(t) is an auxiliary variable for obtaining the reactive power spare usage amount, q.sub.k,y.sup.e,use(t) is an auxiliary variable for obtaining the reactive power spare usage amount of the energy storage station, .sub.R is a set of the renewable energy stations, c.sub.j.sup.pption is a unit cost of the renewable energy station grid-connected proportion regulation instruction, W.sub.j.sup.pption is a upper limit of the renewable energy station grid-connected proportion of the renewable energy station j in the time period t, W.sub.j.sup.pption is a lower limit of the renewable energy station grid-connected proportion of the renewable energy station j in the time period t, .sub.D is a set of the loads, c.sub.d.sup.dr is a cost of the demand side response per unit capacity, .sub.Y.sup.n is a set of typical renewable energy scenarios, .sub.y is a variable of power system operating cost in the scenario y, d is an index of the loads, i is a thermal power unit number, j is an index of the renewable energy stations, k is an index of the energy storage stations, t is a scheduling time point, y is an index of the renewable energy scenarios, l is an index of the transmission lines, and |.sub.Y.sup.n| is a number of elements included in the set of typical renewable energy scenarios .sub.Y.sup.n.

[0101] Further, there are multiple objective constraints in the objective function, including objective constraints (3) to (52), which are expanded as follows.

1) Constraints (3) to (5) of Active Pre-Output, Ramp Up, and Ramp Down of the Thermal Power Unit:

[00014]$\begin{array}{cc}{U}_i\left(t\right){\underset{\_}{P}}_i^{\mathrm{gen}}{p}_i^g\left(t\right)U_i\left(t\right){\stackrel{\_}{P}}_i^{\mathrm{gen}},i{G}_{},t& \left(3\right)\end{array}$$\begin{array}{cc}{p}_i^g\left(t+1\right)-p_i^g\left(t\right)U_i\left(t\right)R_i^{\mathrm{ramp},+}+\left[1-U_i\left(t\right)\right].Math.{\stackrel{\_}{P}}_i^{\mathrm{gen}},i{G}_{},t& \left(4\right)\end{array}$$\begin{array}{cc}{p}_i^g\left(t\right)-p_i^g\left(t+1\right)U_i\left(t+1\right)R_i^{\mathrm{ramp},-}+\left[1-U_i\left(t+1\right)\right].Math.{\stackrel{\_}{P}}_i^{\mathrm{gen}},i{G}_{},t& \left(5\right)\end{array}$

where the variable p.sub.i.sup.g(t) represents the active pre-output power of the thermal power unit i in the time period t, the formulas (3) to (5) are constraints of the upper and lower limit of the pre-output of the thermal power unit, a constraint of the ramp up of the pre-output power of the thermal power unit, and a constraint of the ramp down of the pre-output power of the thermal power unit respectively.

2) Constraints (6) to (8) of Uphill Capacity and Downhill Capacity of the Thermal Power Unit:

[00015]$\begin{array}{cc}{U}_i\left(t\right){\underset{\_}{P}}_i^{\mathrm{gen}}+r_i^{g,\mathrm{dn}}\left(t\right)p_i^g\left(t\right)U_i\left(t\right){\stackrel{\_}{P}}_i^{\mathrm{gen}}-r_i^{g,\mathrm{up}}\left(t\right),i{G}_{},t,& \left(6\right)\end{array}$$\begin{array}{cc}0r_i^{g,\mathrm{up}}\left(t\right)U_i\left(t\right)R_i^{\mathrm{spare},+},i{G}_{},t,& \left(7\right)\end{array}$$\begin{array}{cc}0r_i^{g,\mathrm{dn}}\left(t\right)U_i\left(t\right)R_i^{\mathrm{spare},-},i{G}_{},t,& \left(8\right)\end{array}$

where the variables r.sub.i.sup.g,up(t) and r.sub.i.sup.g,dn(t) are the uphill capacity and the downhill capacity of the thermal power unit i in the time period t respectively, the formula (6) is a constraint of relationship between the uphill capacity, the downhill capacity, and the pre-output of the thermal power unit, the formula (7) is a physical constraint of the uphill capacity of the thermal power unit, and the formula (8) is a physical constraint of the downhill capacity of the thermal power unit.

3) A Constraint (9) of Renewable Energy Station Grid-Connected Proportion:

[00016]$\begin{array}{cc}{w}_j^{\mathrm{pption}}\left(t\right)\left[{\underset{\_}{W}}_j^{\mathrm{pption}},{\stackrel{\_}{W}}_j^{\mathrm{pption}}\right],j{R}_{},t,& \left(9\right)\end{array}$

where the variable w.sub.j.sup.pption(t) represents the renewable energy station grid-connected proportion of the renewable energy station j in the time period t, the formula (9) represents that decision on the variable w.sub.j.sup.pption(t) is made within the range from W.sub.j.sup.pption to W.sub.j.sup.pption.

4) A Constraint (10) of Protocol Capacity of Demand Side Response:

[00017]$\begin{array}{cc}0p_d^{\mathrm{dr}}\left(t\right){\stackrel{\_}{P}}_{d,t}^{\mathrm{dr}},d{D}_{},t,& \left(10\right)\end{array}$

where the variable p.sub.d.sup.dr(t) is the protocol capacity of the demand side response of the load d in the time period t, Pd,t.sup.dr is an upper limit of the protocol capacity of the demand side response of the load d in the time period t.

5) Constraints (11) to (51) on Feasible Cut of the Day-Ahead Scheduling.

[0102] It should be noted that, some iteration parameters need to be used and n is denoted as the number of iterations in this embodiment, it is initialized that n=1.

[0103] In the process of an n-th iteration, the typical renewable energy scenario set used in the upper level is .sub.Y.sup.n, and the renewable energy power scenarios in .sub.Y.sup.n are denoted as P.sub.j,t.sup.renew,y

(j=1, . . . ,N.sub.R,t=1, . . . ,N.sub.T,y=1, . . . ,|.sub.Y.sup.n|),

where y is an index of the renewable energy scenarios, and |.sub.Y.sup.n| is the number of elements included in the set |.sub.Y.sup.n|.

[0104] In case that n=1, it is initialized that |.sub.Y.sup.n|=1, and the renewable energy output scenarios P.sub.j,t.sup.renew,y|.sub.y=1 (j=1, . . . , N.sub.R, t=1, . . . , N.sub.T) in .sub.Y.sup.n is set to be .sub.m=1, . . . ,M.sub.j,t,m.sup.RP.sub.j,t,m.sup.R,.

[0105] In the n-th iteration, for each of the renewable energy output scenario P.sub.j,t.sup.renew,y (j=1, . . . , N.sub.R, t=1, . . . , N.sub.T) in .sub.Y.sup.n, the constraints (11) to (51) need to be added.

[00018]$\begin{array}{cc}0p_{j,y}^{\mathrm{re},\mathrm{cut}}\left(t\right)F_j\left[w^{\mathrm{pption}}\left(t\right)\right].Math.P_{j,t}^{\mathrm{renew},y},j{R}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(11\right)\end{array}$$\begin{array}{cc}{p}_{j,y}^{\mathrm{re}}\left(t\right)+p_{j,y}^{\mathrm{re},\mathrm{cut}}\left(t\right)=F_j\left[w^{\mathrm{pption}}\left(t\right)\right].Math.P_{j,t}^{\mathrm{renew},y},j{R}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(12\right)\end{array}$$\begin{array}{cc}{F}_j\left[w^{\mathrm{pption}}\left(t\right)\right]={.Math.}_{e=1}^{N_R}{a}_{j,e}^{\mathrm{pption}}{w}_e^{\mathrm{pption}}\left(t\right)+b_j^{\mathrm{pption}},j{R}_{},t& \left(13\right)\end{array}$

where in constraint (11), the variables p.sub.j,y.sup.re,cut(t) and p.sub.j,y.sup.re(t) are the renewable energy power reduction value and the renewable energy power grid-connected value of the renewable energy station j in the scenario y and the time period t.

[0106] The constraints (11) and (12) are a constraint of an interval of the renewable energy power reduction value and a constraint of a relationship between the renewable energy power reduction value, the renewable energy power grid-connected value, and the renewable energy active power scenario, respectively.

[0107] In the constraints (11) to (13), the vector w.sup.pption(t) is defined as w.sup.pption(t)=[w.sub.1.sup.pption(t), . . . , w.sub.j.sup.pption(t), . . . ,

[00019]$w_{N_R}^{\mathrm{pption}}$

(t)], and the function F.sub.j[w.sup.pption(t)] may be used to represent the different impacts of changes in grid-connected proportion instruction on the renewable energy active power output scenario of the renewable energy station j. The expression of the function F.sub.j[w.sup.pption(t)] is shown in the constraint (13).

[0108] In the constraint (13), the coefficient a.sub.j,e.sup.pption represents the impact of changes in grid-connected proportion instruction of a renewable energy station e on the renewable energy active power scenario of the renewable energy station j, the coefficient b.sub.j.sup.pption is the constant term coefficient of the function F.sub.j[w.sup.pption(t)].

[00020]$\begin{array}{cc}0p_{d,y}^{\mathrm{load},\mathrm{cut}}\left(t\right)p_d^{\mathrm{dr}}\left(t\right),d{D}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(14\right)\end{array}$$\begin{array}{cc}{p}_{d,y}^{\mathrm{load}}\left(t\right)+p_{d,y}^{\mathrm{load},\mathrm{cut}}\left(t\right)=P_{d,t}^D,d{D}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(15\right)\end{array}$

where the variables P.sub.d,y.sup.load,cut(t) and p.sub.d,y.sup.load(t) are demand side response usage value and load power supply value of the load d in the scenario y and the time period t respectively, the constraint (14) is a constraint on an interval of the demand side response usage value, and the constraint (15) is a constraint on a relationship between the load power supply, the demand side response usage value and the load prediction value.

[00021]$\begin{array}{cc}{U}_i\left(t\right){\underset{\_}{P}}_i^{\mathrm{gen}}{p}_{i,y}^{\mathrm{gc}}\left(t\right)U_i\left(t\right){\stackrel{\_}{P}}_i^{\mathrm{gen}},i{G}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(16\right)\end{array}$$\begin{array}{cc}{p}_i^g\left(t\right)-r_i^{g,\mathrm{dn}}\left(t\right)p_{i,y}^{\mathrm{gc}}\left(t\right)p_i^g\left(t\right)+r_i^{g,\mathrm{up}}\left(t\right),i{G}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(17\right)\end{array}$$\begin{array}{cc}{p}_{i,y}^{\mathrm{gc}}\left(t+1\right)-p_{i,y}^{\mathrm{gc}}\left(t\right)U_i\left(t\right)R_i^{\mathrm{ramp},+}+\left[1-U_i\left(t\right)\right].Math.{\stackrel{\_}{P}}_i^{\mathrm{gen}},i{G}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(18\right)\end{array}$$\begin{array}{cc}{p}_{i,y}^{\mathrm{gc}}\left(t\right)-p_{i,y}^{\mathrm{gc}}\left(t+1\right)U_i\left(t+1\right)R_i^{\mathrm{ramp},-}+\left[1-U_i\left(t+1\right)\right].Math.{\stackrel{\_}{P}}_i^{\mathrm{gen}},i{G}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(19\right)\end{array}$

where the variable p.sub.i,y.sup.gc(t) represents real-time active power output of the thermal power unit i in the time period t, the constraints (16) and (17) are constraints of an interval of the real-time active power output of the thermal power unit, and the constraints (18) and (19) are constraints of a ramp up speed and a ramp down speed of the real-time active power output of the thermal power unit.

[00022]$\begin{array}{cc}{U}_i\left(t\right){\underset{\_}{Q}}_i^{\mathrm{gen}}{q}_{i,y}^{\mathrm{gc}}\left(t\right)U_i\left(t\right){\stackrel{\_}{Q}}_i^{\mathrm{gen}},i{G}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(20\right)\end{array}$

where the variable q.sub.i,y.sup.gc(t) represents the real-time reactive power output of the thermal power unit i in the period t, and the constraint (20) is a constraint of an interval of the real-time reactive power output of the thermal power unit.

[00023]$\begin{array}{cc}0p_{k,y}^{e,\mathrm{ch}}\left(t\right){\stackrel{\_}{P}}_k^{\mathrm{ess}},k{E}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(21\right)\end{array}$$\begin{array}{cc}0p_{k,y}^{e,\mathrm{dc}}\left(t\right){\stackrel{\_}{P}}_k^{\mathrm{ess}},k{E}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(22\right)\end{array}$$\begin{array}{cc}{E}_{k,y}\left(t\right)=E_{k,y}\left(t-1\right)+{}_k^{\mathrm{ch}}{p}_{k,y}^{e,\mathrm{ch}}\left(t\right)-\frac{p_{k,y}^{e,\mathrm{dc}}\left(t\right)}{{}_k^{\mathrm{dc}}},k{E}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(23\right)\end{array}$$\begin{array}{cc}{\underset{\_}{E}}_k^{\mathrm{ess}}\left(t\right)=E_{k,y}\left(t\right){\stackrel{\_}{E}}_k^{\mathrm{ess}},k{E}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(24\right)\end{array}$$\begin{array}{cc}{E}_{k,y}\left(0\right)=E_k^{\mathrm{init}},E_{k,y}\left(N_T\right)=E_k^{\mathrm{end}},k{E}_{},y=1,.Math.,.Math.Y_n^{}.Math.& \left(25\right)\end{array}$$\begin{array}{cc}-{\stackrel{\_}{Q}}_k^{\mathrm{ess}}{q}_{k,y}^e\left(t\right){\stackrel{\_}{Q}}_k^{\mathrm{ess}},k{E}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(26\right)\end{array}$$\begin{array}{cc}-\sqrt{2}.Math.{\stackrel{\_}{S}}_k^{\mathrm{ess}}{p}_{k,y}^{e,\mathrm{ch}}\left(t\right)-p_{k,y}^{e,\mathrm{dc}}\left(t\right)+q_{k,y}^e\left(t\right)\sqrt{2}.Math.{\stackrel{\_}{S}}_k^{\mathrm{ess}},k{E}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(27\right)\end{array}$$\begin{array}{cc}-\sqrt{2}.Math.{\stackrel{\_}{S}}_k^{\mathrm{ess}}{p}_{k,y}^{e,\mathrm{ch}}\left(t\right)-p_{k,y}^{e,\mathrm{dc}}\left(t\right)-q_{k,y}^e\left(t\right)\sqrt{2}.Math.{\stackrel{\_}{S}}_k^{\mathrm{ess}},k{E}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(28\right)\end{array}$

where p.sub.k,y.sup.e,ch(t) represents active charging power of the energy storage station k in a scenario y and the time period t, p.sub.k,y.sup.e,dc(t) represents active discharging power of the energy storage station k in the scenario y and the time period t, E.sub.k,y(t) represents the stored energy of the energy storage station k in the scenario y and the time period t, and q.sub.k,y.sup.e(t) represents the reactive power injected by the energy storage station to the power grid.

[0109] The constraints (21) and (22) are constraints of a charging power range of the energy storage station and a discharging power range of the energy storage station respectively, the constraint (23) is a constraint of a time period coupling of the stored energy of the energy storage station, the constraint (24) is a constraint of an upper limit and a lower limit of the stored energy of the energy storage station, the constraint (25) is a constraint of a value of the stored electric quantity energy at the beginning and end of a day for the energy storage station, the constraint (26) is a constraint of a range of the reactive power injected by the energy storage station k to the power grid in the scenario y and the time period t, and the constraints (27) and (28) are constraints of operating ranges of the active power and the reactive power of the energy storage station.

[00024]$\begin{array}{cc}{p}_{i,y}^{\mathrm{gc}}\left(t\right)+{.Math.}_{j{}_R}{p}_{j,y}^{\mathrm{re}}\left(t\right)+{.Math.}_{k{}_E}\left[p_{k,y}^{e,\mathrm{ch}}\left(t\right)-p_{k,y}^{e,\mathrm{dc}}\left(t\right)\right]+s_y^1\left(t\right)-s_y^2\left(t\right)={.Math.}_{d{N}_D}{p}_{d,y}^{\mathrm{load}}\left(t\right),t,y=1,.Math.,.Math.{}_Y^n.Math.& \left(29\right)\end{array}$$\begin{array}{cc}{q}_{i,y}^{\mathrm{gc}}\left(t\right)+{.Math.}_{k{}_E}{q}_{k,y}^e\left(t\right)+s_y^3\left(t\right)-s_y^4\left(t\right)={.Math.}_{d{N}_D}{Q}_{d,t}^D,t,y=1,.Math.,.Math.{}_Y^n.Math.& \left(30\right)\end{array}$$\begin{array}{cc}.Math.p_{l,y}^{\mathrm{brch}}\left(t\right)+s_{l,y}^5\left(t\right)-s_{l,y}^6\left(t\right).Math.{\stackrel{\_}{S}}_l^L,t,y=1,.Math.,.Math.{}_Y^n.Math.,l& \left(31\right)\end{array}$$\begin{array}{cc}.Math.q_{l,y}^{\mathrm{brch}}\left(t\right)+s_{l,y}^7\left(t\right)-s_{l,y}^8\left(t\right).Math.{\stackrel{\_}{S}}_l^L,t,y=1,.Math.,.Math.{}_Y^n.Math.,l& \left(32\right)\end{array}$$\begin{array}{cc}.Math.p_{l,y}^{\mathrm{brch}}\left(t\right)+q_{l,y}^{\mathrm{brch}}\left(t\right)+s_{l,y}^9\left(t\right)-s_{l,y}^{10}\left(t\right).Math.\sqrt{2}.Math.{\stackrel{\_}{S}}_l^L,t,y=1,.Math.,.Math.{}_Y^n.Math.,l& \left(33\right)\end{array}$$\begin{array}{cc}.Math.p_{l,y}^{\mathrm{brch}}\left(t\right)-q_{l,y}^{\mathrm{brch}}\left(t\right)+s_{l,y}^{11}\left(t\right)-s_{l,y}^{12}\left(t\right).Math.\sqrt{2}.Math.{\stackrel{\_}{S}}_l^L,t,y=1,.Math.,.Math.{}_Y^n.Math.,l& \left(34\right)\end{array}$$\begin{array}{cc}{s}_y^1\left(t\right),s_y^2\left(t\right),s_y^3\left(t\right),s_y^4\left(t\right)0,t,y=1,.Math.,.Math.{}_Y^n.Math.& \left(35\right)\end{array}$$\begin{array}{cc}{s}_{l,y}^5\left(t\right),s_{l,y}^6\left(t\right),s_{l,y}^7\left(t\right),s_{l,y}^8\left(t\right),s_{l,y}^9\left(t\right),s_{l,y}^{10}\left(t\right),s_{l,y}^{11}\left(t\right),s_{l,y}^{12}\left(t\right)0,t,y=1,.Math.,.Math.{}_Y^n.Math.,l& \left(36\right)\end{array}$

where the variables p.sub.l,y.sup.brch(t) and q.sub.l,y.sup.brch(t) represent the active power flow and the reactive power flow of the transmission line l in the scenario y and the time period t, the variables s.sub.y.sup.1(t) and s.sub.y.sup.2(t) represent the slack variables of the active power balance in the power system, the variables s.sub.y.sup.3(t) and s.sub.y.sup.4(t) represent the slack variables of the reactive power balance in the power system, and the variables s.sub.y.sup.5(t)s.sub.y.sup.12(t) are the line capacity slack variables considering active power flow and reactive power flow.

[0110] Corresponding to the above variables, the constraints (29) and (30) are constraints of the active power balance in the power system and the reactive power balance in the power system respectively, the constraints (31) to (34) are capacity constraints of the transmission line considering active power flow and reactive power flow, and the constraints (35) to (36) are non-negative constraints of the slack variables.

[00025]$\begin{array}{cc}{\underset{\_}{V}}_b^{\mathrm{amp}}{v}_{b,y}^{\mathrm{amp}}\left(t\right){\stackrel{\_}{V}}_b^{\mathrm{amp}},t,y=1,.Math.,.Math.Y_n^{}.Math.,b& \left(37\right)\end{array}$

where the variable v.sub.b.sup.amp(t) is the node voltage of the node b in the scenario y and the time period t, and the constraint (37) is an amplitude constraint of the node voltage.

[00026]$\begin{array}{cc}{p}_{b,y}^{\mathrm{bus}}\left(t\right)=-{\mathrm{BF}}_{b,d}^D.Math.p_{d,y}^{\mathrm{load}}\left(t\right)+{\mathrm{BF}}_{b,i}^G.Math.p_{i,y}^{\mathrm{gc}}\left(t\right)+{\mathrm{BF}}_{b,f}^R.Math.p_{j,y}^{\mathrm{re}}\left(t\right)+{\mathrm{BF}}_{b,k}^{\mathrm{ESS}}.Math.\left[-p_{k,y}^{e,\mathrm{ch}}\left(t\right)+p_{k,y}^{e,\mathrm{dc}}\left(t\right)\right],b,d,i,j,k,l,t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(38\right)\end{array}$$\begin{array}{cc}{q}_{b,y}^{\mathrm{bus}}\left(t\right)=-{\mathrm{BF}}_{b,d}^D.Math.Q_{d,t}^D+{\mathrm{BF}}_{b,i}^G.Math.q_{i,y}^{\mathrm{gc}}\left(t\right)+{\mathrm{BF}}_{b,k}^{\mathrm{ESS}}.Math.q_{k,y}^e\left(t\right),b,d,i,j,k,l,t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(39\right)\end{array}$$\begin{array}{cc}{p}_{b1,y}^{\mathrm{bus}}\left(t\right)={.Math.}_{b2}{G}_{b1,b2}^{\mathrm{NET}}{v}_{b2,y}^{\mathrm{amp}}\left(t\right)-{.Math.}_{b2}{B}_{b1,b2}^{\mathrm{NET}*}{}_{b2,y}\left(t\right),b1,t{T}_{\mathrm{NOW}}^{},y=1,.Math.,.Math.Y_n^{}.Math.& \left(40\right)\end{array}$$\begin{array}{cc}{q}_{b1,y}^{\mathrm{bus}}\left(t\right)=-{.Math.}_{b2}{B}_{b1,b2}^{\mathrm{NET}}{v}_{b2,y}^{\mathrm{amp}}\left(t\right)-{.Math.}_{jN}{G}_{b1,b2}^{\mathrm{NET}}{}_{b2,y}\left(t\right),b1,t{T}_{\mathrm{NOW}}^{},y=1,.Math.,.Math.Y_n^{}.Math.& \left(41\right)\end{array}$$\begin{array}{cc}{p}_{l,y}^{\mathrm{brch}}\left(t\right)=g_{b1,b2}^{\mathrm{NET}}\left[v_{b1,y}^{\mathrm{amp}}\left(t\right)-v_{b2,y}^{\mathrm{amp}}\left(t\right)\right]-u_{b1,b2}^{\mathrm{NET}}\left[{}_{b1,y}\left(t\right)-{}_{b2,y}\left(t\right)\right],b1,l,t{T}_{\mathrm{NOW}}^{},y=1,.Math.,.Math.Y_n^{}.Math.& \left(42\right)\end{array}$$\begin{array}{cc}{q}_{l,y}^{\mathrm{brch}}\left(t\right)=-u_{b1,b2}^{\mathrm{NET}}\left[v_{b1,y}^{\mathrm{amp}}\left(t\right)-v_{b2,y}^{\mathrm{amp}}\left(t\right)\right]-g_{b1,b2}^{\mathrm{NET}}\left[{}_{b1,y}\left(t\right)-{}_{b2,y}\left(t\right)\right],b1,l,t{T}_{\mathrm{NOW}}^{},y=1,.Math.,.Math.Y_n^{}.Math.& \left(43\right)\end{array}$$\begin{array}{cc}{}_{\mathrm{slack},y}\left(t\right)=0,{}_{b,y}\left(t\right)\left[{\underset{\_}{}}_b^{\mathrm{angle}},{\stackrel{\_}{}}_b^{\mathrm{angle}}\right],b,t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(44\right)\end{array}$

where p.sub.b,y.sup.bus(t) and q.sub.b,y.sup.bus(t) represent the active power input value and the reactive power input value of the node b in the scenario y and the time period t, .sub.b,y(t) represents the node phase angle value of the node b in the scenario y and the time period t, and in .sub.b,y(t), a phase angle of a slack node is represented by .sub.slack,y(t).

[0111] The constraints (38) and (39) are constraints on the node active power and the node reactive power respectively, the constraint (40) is a constraint of a relationship between the node reactive power, the node voltage amplitude and the node voltage phase angle, the constraint (42) is a constraint of a relationship between the active power flow of the transmission line, the voltage amplitude of the starting and end nodes corresponding to the transmission line and the voltage phase angle of the starting and end nodes corresponding to the transmission line, the constraint (43) is a constraint of a relationship between the reactive power flow of the transmission line, the voltage amplitude of the starting and end nodes corresponding to the transmission line and the voltage phase angle of the starting and end nodes corresponding to the transmission line, and the constraint (44) is a constraint of an operation range of the node voltage phase angle.

[0112] The above formulas (37) to (44) are the voltage security constraints among the objective constraints. In an embodiment, a linear flow mode considering the power system active and reactive power flow is used to construct the voltage amplitude, the voltage phase angle and the constraint of the relationship between the active power and the reactive power of the power device, and the constraints of the voltage amplitude of node and the operation ranges of the voltage phase angle of the node are added, and through the day-ahead scheduling method for the power system according to the present embodiment, the security of the voltage amplitude and phase angle of the power system may be ensured.

[0113] Compared with the traditional optimization mode based on direct current (DC) power flow, this embodiment may better ensure the voltage security of the power system and satisfy the active power balance and reactive power balance of the power system simultaneously.

[00027]$\begin{array}{cc}{r}_{i,y}^{\mathrm{gc},\mathrm{use}}\left(t\right)p_{i,y}^{\mathrm{gc}}\left(t\right)-p_i^g\left(t\right),i{G}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(45\right)\end{array}$$\begin{array}{cc}{r}_{i,y}^{\mathrm{gc},\mathrm{use}}\left(t\right)p_{i,y}^{\mathrm{gc}}\left(t\right)-p_i^g\left(t\right),i{G}_{},t,y=1,.Math.,.Math.y_n^{}.Math.& \left(46\right)\end{array}$$\begin{array}{cc}{q}_{i,y}^{\mathrm{gc},\mathrm{use}}\left(t\right)q_{i,y}^{\mathrm{gc}}\left(t\right),i{G}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(47\right)\end{array}$$\begin{array}{cc}{q}_{i,y}^{\mathrm{gc},\mathrm{use}}\left(t\right)-q_{i,y}^{\mathrm{gc}}\left(t\right),i{G}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(48\right)\end{array}$$\begin{array}{cc}{q}_{k,y}^{e,\mathrm{use}}\left(t\right)q_{k,y}^e\left(t\right),i{G}_{},t,y=1,.Math.,.Math.y_n^{}.Math.& \left(49\right)\end{array}$$\begin{array}{cc}{q}_{k,y}^{e,\mathrm{use}}\left(t\right)-q_{k,y}^e\left(t\right),i{G}_{},t,y=1,.Math.,.Math.Y_n^{}.Math.& \left(50\right)\end{array}$

where the variables r.sub.i,y.sup.gc,use(t), q.sub.i,y.sup.gc,use(t) and q.sub.k,y.sup.e,use(t) are auxiliary variables for obtaining the active power spare usage amount, the reactive power usage amount and the reactive power usage amount of the energy storage station respectively.

[0114] The constraints (45) to (50) are constraints of auxiliary variables for calculating the active power spare usage amount, the reactive power spare usage amount, and the reactive power spare usage amount of the energy storage station for the thermal power unit.

[00028]$\begin{array}{cc}{}_y=\left[c_i^g{p}_{i,y}^{\mathrm{gc}}\left(t\right)+c_i^g{q}_{i,y}^{\mathrm{gc},\mathrm{use}}\left(t\right)+c_i^{\mathrm{spare}}{r}_{i,y}^{\mathrm{gc},\mathrm{use}}\left(t\right)\right]+& \left(51\right)\end{array}$${.Math.}_t{.Math.}_{j{}_R}{c}_j^{\mathrm{recut}}{p}_{j,y}^{\mathrm{re},\mathrm{cut}}\left(t\right)+{.Math.}_t{.Math.}_{k{}_E}{c}_k^{\mathrm{ess}}[p_{k,y}^{e,\mathrm{ch}}\left(t\right)+$$p_{k,y}^{e,\mathrm{dc}}\left(t\right)+q_{k,y}^{e,\mathrm{use}}\left(t\right)]++{.Math.}_t{.Math.}_{d{}_D}{c}_d^{\mathrm{dr}}{p}_{d,y}^{\mathrm{load},\mathrm{cut}}\left(t\right)+$${.Math.}_t{c}^S\left[s_y^1\left(t\right)+s_y^2\left(t\right)+s_y^3\left(t\right)+s_y^4\left(t\right)\right]+{.Math.}_t{.Math.}_{l{}_L}{c}^{\mathrm{SL}}[s_{l,y}^5\left(t\right)+$$s_{l,y}^6\left(t\right)+s_{l,y}^7\left(t\right)+s_{l,y}^8\left(t\right)+s_{l,y}^9\left(t\right)+s_{l,y}^{10}\left(t\right)+s_{l,y}^{11}\left(t\right)+s_{l,y}^{12}\left(t\right)]$$y=1,.Math.,.Math.{}_Y^n.Math.$

where the variable .sub.y represents the power system operation cost in the scenario y.

[0115] The constraint (51) is a constraint equation for the power system intraday operation.

6) Reserve Tuning Causality Cutting Plane Set Constraint (52).

[0116] In the successive iterations of this embodiment, the lower level feeds back/transmits and updates the cutting plane set .sub.CUT.sup.n(t) to the upper level, and the variables p.sub.i.sup.g(t), r.sub.i.sup.g,up(t), r.sub.i.sup.g,dn(t), w.sub.j.sup.pption(t) and p.sub.d.sup.dr(t) need to satisfy the constraint shown in formula (52):

[00029]$\begin{array}{cc}\left\{\begin{array}{c}{p}_1^g\left(t\right),.Math.,p_i^g\left(t\right),.Math.,p_{N_G}^g\left(t\right),\\ r_1^{g,\mathrm{up}}\left(t\right),.Math.,r_i^{g,\mathrm{up}}\left(t\right),.Math.,r_{N_G}^{g,\mathrm{up}}\left(t\right),\\ r_1^{g,\mathrm{dn}}\left(t\right),.Math.,r_i^{g,\mathrm{dn}}\left(t\right),.Math.,r_{N_G}^{g,\mathrm{dn}}\left(t\right),\\ w_1^{\mathrm{pption}}\left(t\right),.Math.,w_j^{\mathrm{pption}}\left(t\right),.Math.,w_{N_R}^{\mathrm{pption}}\left(t\right),\\ p_1^{\mathrm{dr}}\left(t\right),.Math.,p_d^{\mathrm{dr}}\left(t\right),.Math.,p_{N_D}^{\mathrm{dr}}\left(t\right),t\end{array}\right\}{\mathrm{CUT}}_n^{},& \left(52\right)\end{array}$

in case that n=1, .sub.CUT.sup.n(t) represent all the feasible region of the above decision variables; in case that n2, the expression of .sub.CUT.sup.n(t) is referred to formula (107).

[0117] The above formulas (3) to (52) are the necessary objective constraints to solve the objective function.

[0118] It should be noted that, the objective function of the day-ahead optimization model is to minimize the thermal power unit pre-output power cost, the thermal power unit spare cost, the renewable energy grid-connected proportion instruction cost, the demand side response protocol cost and the scheduling cost.

[0119] In an embodiment, a preliminary expression of the objective function of the day-ahead optimization model is shown as formula (53).

[00030]$\begin{array}{cc}\underset{t}{.Math.}\underset{i{G}_{}}{.Math.}\left\{c_i^g{p}_i^g\left(t\right)+c_i^{\mathrm{spare}}\left[r_i^{g,\mathrm{up}}\left(t\right)+r_i^{g,\mathrm{dn}}\left(t\right)\right]\right\}+& \left(53\right)\end{array}$$\underset{\begin{array}{c}{p}_i^g\left(t\right),r_i^{g,\mathrm{up}}\left(t\right),r_i^{g,\mathrm{dn}}\left(t\right),w_j^{\mathrm{pption}}\left(t\right),\\ p_d^{\mathrm{dr}}\left(t\right),p_{j,y}^{\mathrm{re},\mathrm{cut}}\left(t\right),p_{j,y}^{\mathrm{re}}\left(t\right),p_{d,y}^{\mathrm{load}}\left(t\right),\\ p_{d,y}^{\mathrm{load},\mathrm{cut}}\left(t\right),p_{i,y}^{\mathrm{gc}}\left(t\right),q_{i,y}^{\mathrm{gc}}\left(t\right),\\ p_{k,y}^{e,\mathrm{ch}}\left(t\right),p_{k,y}^{e,\mathrm{dc}}\left(t\right),E_{k,y}\left(t\right),\\ q_{k,y}^e\left(t\right),p_{l,y}^{\mathrm{brch}}\left(t\right),q_{l,y}^{\mathrm{brch}}\left(t\right),\\ s_y^1\left(t\right)~s_y^4\left(t\right),s_y^5\left(t\right)~s_y^{12}\left(t\right),\\ v_b^{\mathrm{amp}}\left(t\right),p_{b,y}^{\mathrm{bus}}\left(t\right),q_{b,y}^{\mathrm{bus}}\left(t\right),{}_{b,y}\left(t\right),\\ r_{i,y}^{\mathrm{gc},\mathrm{use}}\left(t\right),q_{i,y}^{\mathrm{gc},\mathrm{use}}\left(t\right),q_{k,y}^{e,\mathrm{use}}\left(t\right),\\ {}_y,d,i,j,k,t,y=1,.Math.,.Math.Y_n^{}.Math.\end{array}}{\min }\underset{t}{.Math.}\underset{j{R}_{}}{.Math.}{c}_j^{\mathrm{pption}}.Math.\frac{{\stackrel{\_}{W}}_j^{\mathrm{pption}}-w_j^{\mathrm{pption}}\left(t\right)}{{\stackrel{\_}{W}}_j^{\mathrm{pption}}-{\underset{\_}{W}}_j^{\mathrm{pption}}}+$$\underset{t}{.Math.}\underset{d{D}_{}}{.Math.}{c}_d^{\mathrm{dr}}{p}_d^{\mathrm{dr}}\left(t\right)+\frac{1}{.Math.Y_n^{}.Math.}\underset{y{Y}_n^{}}{.Math.}{}_y$