Trans-provincial and trans-regional electric power system tie line power scheduling optimization method

Abstract

The invention discloses a trans-provincial and trans-regional electric power system tie line power scheduling optimization method. Obtaining trans-provincial and trans-regional power basic data, and further constructing a tie line power transmission power constraint; constructing a model constraint condition based on trans-provincial and trans-regional power basic data and tie line power transmission power constraint, constructing a target function by taking minimization of the annual comprehensive operation cost of the system as an optimization target, and further constructing a trans-provincial and trans-regional joint optimization model; and solving the trans-provincial and trans-regional joint optimization model to obtain a power optimization result. The cross-provincial and cross-regional tie line power scheduling method effectively solves the problem of cross-provincial and cross-regional tie line power scheduling, remarkably reduces the total operation consumption of the system and the new energy power abandoning rate, enhances the economy and power supply reliability of a cross-provincial and cross-regional power system, realizes daily optimization configuration of inter-regional tie line power transmission power, and improves the power supply efficiency. The method has remarkable advantages in the aspects of improving system guarantee power supply reliability and reducing new energy waste.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of power system optimal dispatch method, and particularly relates to a power dispatch optimization method for inter-provincial and inter-regional power system tie lines. BACKGROUND

[0002] With the continuous increase of new energy penetration in power systems, the contradiction between wind and light resources and power load reverse distribution is increasingly prominent. Northwest, North China and other regions have rich renewable energy resources, but the local load is relatively limited. Large-scale wind and light power generation cannot be fully consumed locally, and must be transmitted to the central and eastern load centers relying on inter-provincial and inter-regional transmission channels. This spatial mismatch between resources and loads puts higher requirements on the balanced dispatching and stable operation of the existing power system.

[0003] Inter-provincial and inter-regional power systems face many challenges in actual operation. On the one hand, with the large-scale access of new energy generation (wind, solar, etc.), the volatility, intermittency and prediction uncertainty of its output pose potential threats to the safe and stable operation of the power system. On the other hand, the load characteristics of different provinces and regions differ greatly, with different peak and valley periods and load levels, which further exacerbates the imbalance between power supply and demand. Under this background, to achieve safe and economic operation of inter-provincial and inter-regional power systems, how to optimize the transmission power of inter-provincial and inter-regional tie lines becomes a problem to be solved.

[0004] Traditional power system planning methods often focus on the optimization of a single province or a local area, and are difficult to fully consider the complementary advantages of inter-provincial and inter-regional resources and the overall coordinated operation benefits. In terms of power optimization of inter-provincial and inter-regional tie lines, existing methods are mostly based on fixed transmission power or simple power distribution mode, and fail to fully tap the potential of tie lines in flexible allocation of power resources, response to new energy output fluctuations and load changes, and are difficult to effectively balance the interests between different provinces and achieve overall optimal operation of the system. SUMMARY

[0005] To solve the problems in the background art, the present application provides a power dispatch optimization method for inter-provincial and inter-regional power system tie lines.

[0006] The technical solution adopted by the present application is as follows: The present application comprises the following steps: S1, obtaining inter-provincial and inter-regional power basic data, and then constructing tie line transmission power constraints; S2, constructing model constraint conditions based on the inter-provincial and inter-regional power basic data and the tie line transmission power constraints, constructing an objective function with the minimum annual comprehensive operation cost of the system as the optimization target, and then constructing an inter-provincial and inter-regional joint optimization model; S3, solving the inter-provincial and inter-regional joint optimization model to obtain a power optimization result.

[0007] The inter-provincial and inter-regional power basic data in S1 includes an inter-provincial and inter-regional power system network structure, tie-line transmission capacity, new energy output in a preset time, load level, thermal power unit installed capacity, and thermal power unit operation parameters.

[0008] The tie-line transmission power constraint setting in S1 includes that the transmission power of each inter-provincial and inter-regional tie line at any time is not more than a preset maximum power and is not less than a preset minimum power, and the transmission power of the same inter-provincial and inter-regional tie line at any time period in the same day is the same.

[0009] The model constraint condition in S2 includes a tie-line transmission power constraint, a node power balance constraint, a line power flow constraint, an energy storage operation state constraint, a thermal power unit constraint, and a multi-energy complementary source and load constraint. (1) The tie-line transmission power constraint is set according to the following formula: P n Tie,min ≤P n,t Tie ≤P n Tie,max P n,s,1 Tie =P n,s,2 Tie =…=P n,s,24 Tie Wherein, n is the sequence number of the inter-provincial and inter-regional tie line in the system, t is the time period sequence number, s is the optimization typical day sequence number, P n Tie,max and P n Tie,min are the maximum and minimum transmission power of the nth inter-provincial and inter-regional tie line, P n,t Tie represents the transmission power of the nth inter-provincial and inter-regional tie line at the t time, P n,s,1 Tie to P n,s,24 Tie represent the power at each time in the s day. (2) The node power balance constraint is set according to the following formula: ∑ k1 P d,k1,t G +∑ k2 P d,k2,t H +∑ k3 P d,k3,t W +∑ k4 Pd,k4,t V +∑ k5 (P d,k5,t ES,dis -P d,k5,t ES,cha ) +∑ k6 (P d,k6,t PH,dis -P d,k6,t PH,cha )=P d,t -P d,t DR -P d,t CT +∑ L1 P d,L1,t line +∑ L2 P d,L2,t DC +∑ L3 P d,L3,t Tie wherein d represents the number of node sequences in the system, k1=1 to N G,d , k2=1 to N H,d , k3=1 to N W,d , k4=1 to N V,d , k5=1 to N ES,d , k6=1 to N PH,d , L1=1 to N line,d , L2=1 to N DC,d , L3=1 to N Tie,d , N G,d , N H,d , N W,d , N V,d , N ES,d , N PH,d , N line,d , N DC,d , N Tie,d respectively represent the number of thermal power generating units, the number of hydropower generating units, the number of wind farms, the number of photovoltaic power stations, the number of electrochemical energy storage power stations, the number of pumped storage power stations, the number of alternating current lines, the number of direct current lines, the number of tie lines at the dth node, P d,k1,t G , P d,k2,t H , P d,k3,t W , P d,k4,t V , P d,k5,t ES,cha , P d,k5,t ES,dis , P d,k6,t PH,cha , Pd,k6,t PH,dis Pd, t, g, Pd, t, h, Pd, t, p, Pd, t, chg, Pd, t, dis, Pd, t, pum, Pd, t, dis, respectively, are the power of thermal power unit, the power of hydropower unit, the power of wind farm, the power of photovoltaic power station, the charging power of electrochemical energy storage power station, the discharging power of electrochemical energy storage power station, the charging power of pumped storage power station and the discharging power of pumped storage power station of the dth node at t moment, P d,t d,t DR d,t CT Pd, t, d, Pd, t, dr, Pd, t, cl, respectively, are the load demand, the demand side response curtailment power and the load shedding power of the dth node at t moment, P d,L1,t line d,L2,t DC d,L3,t Tie Pd, t, ac, Pd, t, dc, Pd, t, cross, respectively, represent the power of alternating current transmission line, direct current transmission line and cross-province cross-region tie line connected with the dth node; (3) The line flow constraint is set according to the following formula: θ slack,t =0 -π≤θ L(to),t ≤π -π≤θ L(from),t ≤π P L,t line =(θ L(from),t -θ L(to),t ) / X line Wherein, L represents the sequence number of total alternating current lines in the system, θ slack,t is the reference voltage phase angle of the system balance node, θ L(from),t and θ L(to),t are the voltage phase angles of the starting point and the terminal point of the Lth line, P L,t line is the transmission power of the Lth line at t moment, X line is the reactance per unit of the line, P L,t line,max is the thermal stability limit power of the line; (4) The energy storage operating state constraint includes pumped storage constraint and electrochemical energy storage constraint; The pumped storage constraint is set according to the following formula: SOC i min ≤SOC i,n1,t ≤SOC i max SOC i,n1,1 =SOC i,n1,T =ξ i ​​​​P i,n1 PH,min ≤P i,n1,t PH,cha ≤P i,n1 PH,max P i,n1 PH,min ≤P i,n1,t PH,dis ≤P i,n1 PH,max SOC i,n1,t =SOC i,n1,t-1 +η i,cha P i,n1,t PH,cha -P i,n1,t PH,dis / η i,dis 0≤R i,n1,t ≤P i,n1 PH,max -P i,n1,t PH,dis SOC i,n1,t-1 -(P i,n1,t PH,dis +R i,n1,t ) / η i,dis ≥SOC i min where i is the sequence number of the region in the system, n1 is the sequence number of the pumped storage power station in the ith region, SOC i,n1,1 , SOC i,n1,t , and SOC i,n1,T are the state of charge of the nth1 pumped storage power station in the ith region at the initial time, at time t, and at the end of the scheduling period T, respectively, SOC i max , SOC i min , and ξ i are the upper limit, lower limit, and initial value of the state of charge, respectively, P i,n1,t PH,cha , P i,n1,t PH,dis , and R i,n1,t are the charging power, discharging power, and spinning reserve power of the nth1 pumped storage power station in the ith region at time t, P i,n1 PH,min and P i,n1 PH,max represent the lower limit and upper limit of the charging and discharging power of the nth1 pumped storage power station in the ith region, η i,cha , and η i,dis are the charging and discharging efficiencies of the pumped storage power station in the ith region. The electrochemical energy storage constraint is set according to the following formula: SOC i ’min ≤ SOC ’ i,n2,t ≤ SOC i ’max SOC ’ i,n2,1 = SOC ’ i,n2,T = ξ i ’ P i,n2 ES,min ≤ P i,n2,t ES,cha ≤ P i,n2 ES,max P i,n2 ES,min ≤ P i,n2,t ES,dis ≤ P i,n2 ES,max SOC ’ i,n2,t = (1-σ i ) SOC ’ i,n2,t-1 + η ’ i,cha P i,n2,t ES,cha - P i,n2,t ES,dis / η ’ i,dis 0 ≤ R ’ i,n2,t ≤ P i,n2 ES,max - P i,n2,t ES,dis (1-σ i ) SOC i,n2,t-1 - (P i,n2,t PH,dis + R ’ i,n2,t ) / η ’ i,dis ≥ SOC i ’min wherein n2 is the sequence number of the i-th regional electrochemical energy storage power station, SOC ’ i,n2,1 , SOC ’ i,n2,t , SOC ’i,n2,T Let SOC represent the state of charge (SOC) of the n2th electrochemical energy storage power station in the i-th region at the initial time, time t, and the end of the dispatch period T, respectively. i ’max SOC i ’min ξ i ’ These represent the upper limit, lower limit, and initial value of its state of charge, P. i,n2,t ES,cha P i,n2,t ES,dis R ’ i,n2,t P represents the charging power, discharging power, and spinning reserve power of the n2th electrochemical energy storage power station in the i-th region at time t. i,n2 ES,min P i,n2 ES,max η represents the lower and upper limits of the charging and discharging power of the n2th electrochemical energy storage power station in the i-th region. ’ i,cha η ’ i,dis σ i These are the charging efficiency, discharging efficiency, and self-discharge rate of the electrochemical energy storage power station in the i-th region, respectively. (5) The constraints of the thermal power unit shall be set according to the following formula: ∑ j P i,j,t G =P i,c,t Cluster O i,c,t =O i,c,t-1 +SU i,c,t -SD i,c,t O i,c,t ≥∑ τ1 SU i,c,t-τ1 O i,c,t ≥∑ j P i,j,t G,max -∑ τ2 SU i,c,t-τ2 C i,c,t SU =SC i,c,t SU SU i,c,t C i,c,t SD =SC i,c,t SD SU i,c,t αi,c min O i,c,t ≤P i,c,t Cluster ≤O i,c,t -λ i,c Ru O i,c,t ≤P i,c,t Cluster -P i,c,t-1 Cluster ≤λ i,c Rd O i,c,t ∑ c (O i,c,t -P i,c,t Cluster )+∑ n1 R i,n1,t +∑ n2 R ’ i,n2,t ≥λ i,W ∑ n3 P i,n3,t W +λ i,V ∑ n4 P i,n4,t V +λ i,L ∑ d P i,d,t +R i,N-1 ∑ c (P i,c,t Cluster -α i,c min O i,c,t )≥λ i,W ∑ n3 P i,n3,t W +λ V ∑ n4 P i,n4,t V +λ L ∑ d P i,d,t Where Cluster represents a thermal power unit cluster, j = 1 to N c,i G , c = 1 to N Cluster,i , n1 = 1 to N PH,i , n2 = 1 to N ES,i , n3 = 1 to N W,i , n4 = 1 to N V,i , d = 1 to N D,i , N c,iG Let N be the number of thermal power units in cluster c within the i-th region. Cluster,i N PH,i N ES,i N W,i N V,i N D,i P represents the number of thermal power unit clusters, pumped storage power stations, electrochemical energy storage power stations, wind farms, photovoltaic power stations, and load nodes in the i-th region, respectively. i,j,t G Let P be the output power of the j-th thermal power unit in the i-th region at time t. i,j G,max Let P be the installed capacity of the j-th thermal power unit in the i-th region. i,c,t Cluster Let O be the total output power of the c-th thermal power unit cluster in the i-th region at time t. i,c,t SU i,c,t SD i,c,t Let C represent the online capacity, operating capacity, and shutdown capacity of the c-th thermal power unit cluster in the i-th region at time t. i,c,t SU C i,c,t SD SC i,c,t SU SC i,c,t SD These represent the cluster's startup cost, shutdown cost, startup cost coefficient, and shutdown cost coefficient, respectively, P. i,n3,t W Let P be the actual power generation of the n3rd wind turbine in the i-th region at time t. i,n4,t V Let P be the actual power generation of the n4th photovoltaic unit in the i-th region at time t. i,d,t Let τ1 = 1 to T represent the load demand of the d-th node in the i-th region at time t. i,c On τ2=1 to T i,c Off T i,c On T i,c Off α i,c min , λ i,c Ru , λ i,c Rd Let λ represent the minimum start-up time, shutdown time, minimum load factor, maximum ramp rate, and maximum run-down rate of the c-th unit cluster within the i-th region. i,W , λ i,V , λ i,L Ri,N-1 respectively are the wind power prediction error coefficient, the photovoltaic power prediction error coefficient, the load reserve coefficient and the maximum single unit capacity of thermal power of the ith region; (6) The multi-energy complementary source and load constraints of the ith region are set according to the following formula: 0≤P i,n3,t W ≤Cap i,n3 W δ i,n3,t W 0≤P i,n4,t V ≤Cap i,n4 V δ i,n4,t V 0≤P i,n5,t H ≤Cap i,n5 H δ i,n5,t H 0≤P i,d,t DR ≤λ i,DR P i,d,t 0≤P i,d,t CT ≤λ i,DR P i,d,t Wherein, Cap i,n3 W is the installed capacity of the nth3 wind turbine, δ i,n3,t W is the normalized theoretical output of the nth3 wind turbine at t time, Cap i,n4 W is the installed capacity of the nth4 photovoltaic unit, δ i,n4,t V is the normalized theoretical output of the nth4 photovoltaic unit at t time, P i,n5,t H is the actual power generation of the nth5 hydroelectric unit in the region at t time, Cap i,n5 H is the installed capacity of the nth5 hydroelectric unit, δ i,n5,t H is the normalized theoretical output of the nth5 hydroelectric unit at t time, P i,d,t DR , P i,d,t CT are the demand side response reduction power and load shedding power of the dth node at t time, λ i,DR is the maximum adjustment ratio of demand side response, λi,CT Maximum load reduction rate.

[0010] The objective function in S2 is set according to the following formula: Min C Sys Ope =∑ i ∑ t [∑ n6 (V i,n6 Cg,G P i,n6,t G +C i,n6,t SU,G +C i,n6,t SD,G )+∑ d C i DR P i,d,t DR +∑ d C i CT P i,d,t CT +∑ n7 C i,n7 DC P i,n7,t DC +∑ n2 C ope,i ES (P i,n2,t ES,cha +P i,n2,t ES,dis )+∑ n1 C ope,i PH (P i,n1,t PH,cha +P i,n1,t PH,dis ) +∑ n3 C waste,i W (Cap i,n3 W δ i,n3,t W -P i,n3,t W )+∑ n4 C waste,i V (Cap i,n4 V δ i,n4,t V -P i,n4,t V )] +∑ i (∑ n2 Cpunish,i,n2 ES +∑ n1 C punish,i,n1 PH )+∑ n ∑ t C n Tie P n,t Tie In the formula, C Sys Ope is a system objective function, i = 1 to N, t = 1 to T, n = 1 to N Tie , n1 = 1 to N PH,i , n2 = 1 to N ES,i , n3 = 1 to N W,i , n4 = 1 to N V,i , n6 = 1 to N G,i , n7 = 1 to N DC,i , d = 1 to N D,i , N is the number of system areas, T is the total length of the optimization scheduling, N Tie is the number of interties in the system, N G,i , N D,i , N ES,i , N PH,i , N W,i , N V,i , N DC,i are the number of thermal power units, the number of nodes, the number of electrochemical energy storage power stations, the number of pumped storage power stations, the number of wind farms, the number of photovoltaic power stations and the number of DC lines in the i-th area, respectively, P i,n6,t G , C i,n6,t SU,G and C i,n6,t SD,G are the power generation, start-up cost and shutdown cost of the n6-th thermal power unit in the i-th area at the t-th time, respectively, V i,n6 Cg,G is the operation cost coefficient of the n6-th thermal power unit in the i-th area, P i,d,t DR and P i,d,t CT are the demand side response reduction power and load shedding power of the d-th node in the i-th area at the t-th time, respectively, C i DR and C i CT are the demand side response compensation rate and load shedding penalty coefficient of the i-th area, respectively, P i,n7,t DC and C i,n7 DCThe transmission power and transmission cost coefficient of the nth direct current of the ith region, respectively, P i,n2,t ES,cha and P i,n2,t ES,dis The charging and discharging power of the nth2 electrochemical energy storage power station of the ith region at time t, respectively, P i,n1,t PH,cha and P i,n1,t PH,dis The charging and discharging power of the nth1 pumped storage power station of the ith region at time t, respectively, C ope,i ES and C ope,i PH The operation loss parameters of the electrochemical energy storage and pumped storage per unit of electric quantity of the ith region, respectively, Cap i,n3 W Cap i,n3,t W and P i,n3,t W The maximum power generation and actual power generation of the nth3 wind farm of the ith region at time t, respectively, Cap i,n4 V Cap i,n4,t V and P i,n4,t V The maximum power generation and actual power generation of the nth2 photovoltaic power station of the ith region at time t, respectively, Cap i,n3 W and Cap i,n4 V The installed capacity of the nth3 wind farm and the nth4 photovoltaic power station of the ith region, respectively, Cap i,n3,t W and Cap i,n4,t V The normalized theoretical output of the nth3 wind farm and the nth4 photovoltaic power station of the ith region at time t, respectively, Cap waste,i W and Cap waste,i V The curtailment penalty coefficient of the wind power and photovoltaic power of the ith region, respectively, Cap punish,i,n1 PH and Cap punish,i,n2 ES The discharge compensation amount of the nth1 pumped storage power station and the nth2 electrochemical energy storage power station of the ith region at the peak of the net load, respectively, P n,t Tie and Cap n Tie The transmission power and transmission cost coefficient of the nth tie line, respectively.

[0011] The S3 is specifically: based on a network constraint relaxation cluster unit combination method, a cross-provincial and cross-regional joint optimization model is solved to obtain a power optimization result.

[0012] The power optimization result in the S3 includes: cross-provincial and cross-regional tie-line transmission power, output power of each regional thermal power unit and cluster start-stop state, power generation of hydropower units and new energy units, charge-discharge power of electrochemical energy storage and pumped storage power stations, demand side response reduction power and load shedding power of each node, transmission power and direction of each line.

[0013] The transmission power of the cross-provincial and cross-regional tie-line in the power optimization result is obtained in a daily optimization mode.

[0014] The present application has the following beneficial effects: The present application innovatively introduces tie-line transmission power constraints into a cross-provincial and cross-regional joint optimization model, realizes the collaborative optimization of power resources between provincial and cross-regional power grids, and enhances the flexibility and scheduling efficiency of power system operation. Through modeling and solving the cross-provincial and cross-regional joint optimization model, daily optimization configuration of tie-line transmission power between regions is realized, and the method has significant advantages in improving system power supply reliability and reducing new energy waste (reducing curtailment rate). BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 is a simplified structure diagram of the cross-provincial and cross-regional interconnection system in the present embodiment.

[0016] Figure 2 is a flowchart of a tie-line power dispatching optimization method of a cross-provincial and cross-regional power system.

[0017] Figure 3 is a daily tie-line transmission power diagram of the system in the present embodiment based on the optimization method.

[0018] Figure 4 is a monthly tie-line transmission power diagram of the system in the present embodiment based on the contrast monthly optimization mode.

[0019] Figure 5 is a tie-line transmission power diagram of the system in the present embodiment based on the contrast fixed power mode. DETAILED DESCRIPTION

[0020] The present application will be further described and explained in conjunction with the drawings and specific embodiments.

[0021] The present embodiment includes the following steps: S1, cross-provincial and cross-regional power basic data is obtained, and then tie-line transmission power constraints are constructed; S2, constructing a model constraint condition according to the cross-provincial and cross-regional power basic data and the tie-line transmission power constraint, constructing a target function with the optimization target of minimizing the annual comprehensive operation cost of the system, and then constructing a cross-provincial and cross-regional joint optimization model; S3, taking the transmission power of the cross-provincial and cross-regional tie-line in the tie-line transmission power constraint as a decision variable, solving the cross-provincial and cross-regional joint optimization model monthly in parallel to obtain a power optimization result, and the power system performs power dispatching according to the power optimization result.

[0022] The cross-provincial and cross-regional power basic data in S1 includes: the grid structure of the cross-provincial and cross-regional power system, the inter-regional tie-line transmission capacity, the new energy output in a preset time, the load level, the installed capacity of thermal power units, and the operation parameters of thermal power units.

[0023] The tie-line transmission power constraint setting in S1 includes: the transmission power of each cross-provincial and cross-regional tie-line at any time does not exceed the preset maximum power and is not less than the preset minimum power; the transmission power of the same cross-provincial and cross-regional tie-line at any time in the same day is the same.

[0024] The cross-provincial and cross-regional tie-line specifically refers to the power transmission line that crosses the power grid of different provincial administrative regions or different national regional dispatching control areas (such as North China, East China, Central China, Northwest China, Northeast China, and South China, etc.), and undertakes the functions of cross-regional power transmission and system interconnection. Its operation state and power transmission directly affect the optimal allocation of regional power resources, new energy consumption, and the safe and stable operation of the power grid.

[0025] The model constraint condition in S2 includes the tie-line transmission power constraint, the node power balance constraint, the line power flow constraint, the energy storage operation state constraint, the thermal power unit constraint, and the multi-energy complementary source and load constraint; (1) The tie-line transmission power constraint is set according to the following formula: P n Tie,min ≤P n,t Tie ≤P n Tie,max P n,s,1 Tie =P n,s,2 Tie =…=P n,s,24 Tie Wherein, n is the sequence number of the cross-provincial and cross-regional tie-line in the system, t is the time period sequence number, s is the optimization typical day sequence number, P n Tie,max and P n Tie,min are the maximum and minimum transmission power of the nth cross-provincial and cross-regional tie-line, P n,tTie Pn,t represents the transmission power of the nth inter-provincial and inter-regional tie line at the tth time point, P n,s,1 Tie to P n,s,24 Tie Pn,t represents the transmission power of the nth inter-provincial and inter-regional tie line at the tth time point, P (2) The node power balance constraint is set according to the following formula: ∑ k1 P d,k1,t G +∑ k2 P d,k2,t H +∑ k3 P d,k3,t W +∑ k4 P d,k4,t V +∑ k5 (P d,k5,t ES,dis -P d,k5,t ES,cha ) +∑ k6 (P d,k6,t PH,dis -P d,k6,t PH,cha )=P d,t -P d,t DR -P d,t CT +∑ L1 P d,L1,t line +∑ L2 P d,L2,t DC +∑ L3 P d,L3,t Tie wherein d represents the total node sequence number in the system, t is the time period sequence number, k1=1 to N G,d , k2=1 to N H,d , k3=1 to N W,d , k4=1 to N V,d , k5=1 to N ES,d , k6=1 to N PH,d , L1=1 to N line,d , L2=1 to N DC,d , L3=1 to N Tie,d , N G,d , N H,d , N W,d , N V,d , N ES,d , N PH,d , N line,d , NDC,d , N Tie,d , N d,k1,t G , N d,k2,t H , N d,k3,t W , N d,k4,t V , N d,k5,t ES,cha , N d,k5,t ES,dis , N d,k6,t PH,cha , N d,k6,t PH,dis , N d,t d,t DR , N d,t CT , N d,L1,t line , N d,L2,t DC , N d,L3,t Tie , N (3) Line flow constraints are set according to the following formula: θ slack,t =0 -π≤θ L(to),t ≤π -π≤θ L(from),t ≤π P L,t line =(θ L(from),t -θ L(to),t ) / X line Wherein, L represents the total number of AC line sequences in the system, θ slack,t is the reference voltage phase angle of the system balance node, θ L(from),t , θ L(to),t is the voltage phase angle of the starting point and the terminal point of the line L, P L,t line ​P line is the transmission power of line L at time t, X L,t line,max is the reactance per unit of the line, P L line,max is the thermal stability limit power of the line, and ΔP L(from),t is the relaxation variable introduced for flexible constraint. L(to),t slack,t ; The three formulas of the line flow constraint are as follows: (1) The range of the values of the phase angles θ line and θ L,t of the starting point and the ending point of the AC line L is limited, and the voltage phase angle θ line, of the balance node is always 0 as the reference point of the system.

[0026] (2) The transmission power P L,t at time t is calculated based on the reactance per unit X line,max of the AC line.

[0027] (3) The size of the transmission power of the AC line is limited, and the relaxation variable is introduced to flexibly constrain the thermal stability limit power P i of the line, and the relaxation variable is included in the objective function to realize the minimization processing, so as to ensure the feasible solution of the model.

[0028] (4) The operation state constraint of the energy storage includes the pumped storage constraint and the electrochemical energy storage constraint. The pumped storage constraint of the i-th region is set according to the following formula: SOC min i,n1,t ≤ SOC i max SOC i,n1,1 = SOC i,n1,T = ξ i P i,n1 PH,min ≤ P i,n1,t PH,cha ≤ P i,n1 PH,max P i,n1 PH,min ≤ P i,n1,t PH,dis ≤ P i,n1 PH,max SOC i,n1,t = SOC i,n1,t-1 + η i,cha P i,n1,t PH,cha - Pi,n1,t PH,dis / η i,dis 0≤R i,n1,t ≤P i,n1 PH,max -P i,n1,t PH,dis SOC i,n1,t-1 -(P i,n1,t PH,dis +R i,n1,t ) / η i,dis ≥SOC i min where i is the sequence number of the region in the system, n1 is the sequence number of the pumped storage power station in the ith region, t is the sequence number of the time period, SOC i,n1,1 , SOC i,n1,t , SOC i,n1,T are the state of charge of the nth1 pumped storage power station in the ith region at the initial time, at time t, and at the end of the scheduling period T, respectively, SOC i max , SOC i min , ξ i are the upper limit, lower limit and initial value of the state of charge, respectively, P i,n1,t PH,cha , P i,n1,t PH,dis , R i,n1,t are the charging power, discharging power and spinning reserve power of the nth1 pumped storage power station in the ith region at time t, P i,n1 PH,min and P i,n1 PH,max represent the lower limit and upper limit of the charging and discharging power of the nth1 pumped storage power station in the ith region, η i,cha , η i,dis are the charging and discharging efficiencies of the pumped storage power station in the ith region; The electrochemical energy storage constraint is set according to the following formula: SOC i ’min ≤SOC ’ i,n2,t ≤SOC i ’max SOC ’ i,n2,1 =SOC ’ i,n2,T =ξ i ’ P i,n2 ES,min≤P i,n2,t ES,cha ≤P i,n2 ES,max P i,n2 ES,min ≤P i,n2,t ES,dis ≤P i,n2 ES,max SOC ’ i,n2,t =(1-σ i )SOC ’ i,n2,t-1 +η ’ i,cha P i,n2,t ES,cha -P i,n2,t ES,dis / η ’ i,dis 0≤R ’ i,n2,t ≤P i,n2 ES,max -P i,n2,t ES,dis (1-σ i )SOC i,n2,t-1 -(P i,n2,t PH,dis +R ’ i,n2,t ) / η ’ i,dis ≥SOC i ’min wherein n2 is the sequence number of the i-th regional electrochemical energy storage power station, SOC ’ i,n2,1 , SOC ’ i,n2,t , SOC ’ i,n2,T are the state of charge of the n2-th electrochemical energy storage power station in the i-th region at the initial moment, at the moment t, and at the end of the scheduling period T, respectively, SOC i ’max , SOC i ’min , ξ i ’ are the upper limit, the lower limit and the initial value of the state of charge, respectively, P i,n2,t ES,cha , P i,n2,t ES,dis , R ’ i,n2,trespectively, the charging power, discharging power and rotating reserve power of the nth2 electrochemical energy storage power station of the ith region at time t, P i,n2 ES,min , P i,n2 ES,max denote the lower limit and upper limit of the charging and discharging power of the nth2 electrochemical energy storage power station of the ith region, η ’ i,cha , η ’ i,dis , σ i respectively, the charging efficiency, discharging efficiency and self-discharge rate of the electrochemical energy storage power station of the ith region; (5) The thermal power unit constraint of the ith region is set according to the following formula: ∑ j P i,j,t G =P i,c,t Cluster O i,c,t =O i,c,t-1 +SU i,c,t -SD i,c,t O i,c,t ≥∑ τ1 SU i,c,t-τ1 O i,c,t ≥∑ j P i,j,t G,max -∑ τ2 SU i,c,t-τ2 C i,c,t SU =SC i,c,t SU SU i,c,t C i,c,t SD =SC i,c,t SD SU i,c,t α i,c min O i,c,t ≤P i,c,t Cluster ≤O i,c,t -λ i,c Ru O i,c,t ≤P i,c,t Cluster -P i,c,t-1 Cluster ≤λ i,c Rd O i,c,t ∑ c (O i,c,t -P i,c,t Cluster )+∑ n1 R i,n1,t +∑ n2 R ’ i,n2,t ≥λ i,W ∑ n3 P i,n3,t W +λ i,V ∑ n4 P i,n4,t V +λ i,L ∑ d P i,d,t +R i,N-1 ∑ c (P i,c,t Cluster -α i,c min O i,c,t )≥λ i,W ∑ n3 P i,n3,t W +λ V ∑ n4 P i,n4,t V +λ L ∑ d P i,d,t wherein Cluster represents a thermal power unit cluster, c is the sequence number of the thermal power unit cluster, j is the sequence number of the unit in the thermal power unit cluster, t is the sequence number of the time period, j=1 to N c,i G , c=1 to N Cluster,i , n1=1 to N PH,i , n2=1 to N ES,i , n3=1 to N W,i , n4=1 to N V,i , d=1 to N D,i , N c,i G is the number of thermal power units in cluster c in the i-th region, N Cluster,i , N PH,i , N ES,i , N W,i , N V,i , N D,i are the number of thermal power unit clusters, pumped storage power stations, electrochemical energy storage power stations, wind farms, photovoltaic power stations and load nodes in the i-th region, respectively, P i,j,t GLet P be the output power of the j-th thermal power unit in the i-th region at time t. i,j G,max Let P be the installed capacity of the j-th thermal power unit in the i-th region. i,c,t Cluster Let O be the total output power of the c-th thermal power unit cluster in the i-th region at time t. i,c,t SU i,c,t SD i,c,t Let C represent the online capacity, operating capacity, and shutdown capacity of the c-th thermal power unit cluster in the i-th region at time t. i,c,t SU C i,c,t SD SC i,c,t SU SC i,c,t SD These represent the cluster's startup cost, shutdown cost, startup cost coefficient, and shutdown cost coefficient, respectively, P. i,n3,t W Let P be the actual power generation of the n3rd wind turbine in the i-th region at time t. i,n4,t V Let P be the actual power generation of the n4th photovoltaic unit in the i-th region at time t. i,d,t Let τ1 = 1 to T represent the load demand of the d-th node in the i-th region at time t. i,c On τ2=1 to T i,c Off T i,c On T i,c Off α i,c min , λ i,c Ru , λ i,c Rd Let λ represent the minimum start-up time, shutdown time, minimum load factor, maximum ramp rate, and maximum run-down rate of the c-th unit cluster within the i-th region. i,W , λ i,V , λ i,L R i,N-1 These are the wind power output prediction error coefficient, photovoltaic power output prediction error coefficient, load reserve coefficient, and maximum single-unit capacity of thermal power in the i-th region, respectively. (6) The multi-energy complementary source load constraints in the i-th region include wind power output constraints, photovoltaic power output constraints, hydropower output constraints, demand-side response and load shedding constraints; Wind power output constraints are set according to the following formula: 0≤P i,n3,t W ≤Capi,n3 W delta i,n3,t W The photovoltaic power output constraint is set according to the following formula: 0 < P i,n4,t V < Cap i,n4 V delta i,n4,t V The hydroelectric power output constraint is set according to the following formula: 0 < P i,n5,t H < Cap i,n5 H delta i,n5,t H The demand side response constraint is set according to the following formula: 0 < P i,d,t DR < lambda i,DR P i,d,t The load shedding constraint is set according to the following formula: 0 < P i,d,t CT < lambda i,DR P i,d,t wherein Cap i,n3 W is the installed capacity of the nth3 wind turbine, delta i,n3,t W is the normalized theoretical output of the nth3 wind turbine at time t, Cap i,n4 W is the installed capacity of the nth4 photovoltaic turbine, delta i,n4,t V is the normalized theoretical output of the nth4 photovoltaic turbine at time t, P i,n5,t H is the actual power generated by the nth5 hydroelectric turbine at time t in the region, Cap i,n5 H is the installed capacity of the nth5 hydroelectric turbine, delta i,n5,t H is the normalized theoretical output of the nth5 hydroelectric turbine at time t, P i,d,t DR , P i,d,t CT are the demand side response reduction power and load shedding power of the dth node at time t, lambda i,DR is the maximum adjustment ratio of demand side response, lambda i,CT is the maximum load reduction rate.

[0029] The annual comprehensive operation cost of the system includes the power generation of each regional generator unit and the number of start-stop, the demand side response adjustment amount, the load shedding amount, the DC loss, the operation state of the electrochemical energy storage and pumped storage, the wind / photovoltaic curtailment penalty amount, the tie-line transmission cost, etc. Among them, the number of cross-provincial and cross-regional tie lines is not included in the total AC / DC line number of the system. The objective function in S2 is set according to the following formula: Min C Sys Ope =∑ i ∑ t [∑ n6 (V i,n6 Cg,G P i,n6,t G +C i,n6,t SU,G +C i,n6,t SD,G )+∑ d C i DR P i,d,t DR +∑ d C i CT P i,d,t CT +∑ n7 C i,n7 DC P i,n7,t DC +∑ n2 C ope,i ES (P i,n2,t ES,cha +P i,n2,t ES,dis )+∑ n1 C ope,i PH (P i,n1,t PH,cha +P i,n1,t PH,dis ) +∑ n3 C waste,i W (Cap i,n3 W δ i,n3,t W -P i,n3,t W )+∑ n4 C waste,i V (Cap i,n4 V δ i,n4,t V -Pi,n4,t V )] +∑ i (∑ n2 C punish,i,n2 ES +∑ n1 C punish,i,n1 PH )+∑ n ∑ t C n Tie P n,t Tie wherein C Sys Ope is the system objective function, i = 1 to N, t = 1 to T, n = 1 to N Tie , n1=1 to N PH,i , n2=1 to N ES,i , n3=1 to N W,i , n4=1 to N V,i , n6=1 to N G,i , n7=1 to N DC,i , d = 1 to N D,i , N is the number of system areas, T is the total length of optimization scheduling, N Tie is the number of system interconnections, N G,i , N D,i , N ES,i , N PH,i , N W,i , N V,i , N DC,i are the number of thermal power units, the number of nodes, the number of electrochemical energy storage power stations, the number of pumped storage power stations, the number of wind farms, the number of photovoltaic power stations and the number of DC lines in the i-th area, P i,n6,t G , C i,n6,t SU,G and C i,n6,t SD,G are the power generation, start-up cost and shutdown cost of the n6-th thermal power unit in the i-th area at the t-th time, V i,n6 Cg,G is the operation cost coefficient (piecewise linearization approximation) of the n6-th thermal power unit in the i-th area, P i,d,t DR and P i,d,t CT are the demand-side response reduction power and load shedding power of the d-th node in the i-th area at the t-th time, C i DR and C i CTrespectively the demand side response compensation rate and the load shedding penalty coefficient of the i-th region, P i,n7,t DC and C i,n7 DC respectively the transmission power and the transmission cost coefficient of the n-th DC line of the i-th region, P i,n2,t ES,cha and P i,n2,t ES,dis respectively the charging and discharging power of the n-th electrochemical energy storage power station of the i-th region at time t (set to positive), P i,n1,t PH,cha and P i,n1,t PH,dis respectively the charging and discharging power of the n-th pumped storage power station of the i-th region at time t (set to positive), C ope,i ES and C ope,i PH respectively the operation loss parameters of the electrochemical energy storage and the pumped storage per unit of electric quantity of the i-th region, Cap i,n3 W δ i,n3,t W and P i,n3,t W respectively the maximum power generation and the actual power generation of the n-th wind farm of the i-th region at time t, Cap i,n4 V δ i,n4,t V and P i,n4,t V respectively the maximum power generation and the actual power generation of the n-th photovoltaic power station of the i-th region at time t, Cap i,n3 W and Cap i,n4 V respectively the installed capacity of the n-th wind farm and the n-th photovoltaic power station of the i-th region, δ i,n3,t W and δ i,n4,t V respectively the normalized theoretical output of the n-th wind farm and the n-th photovoltaic power station of the i-th region at time t, C waste,i W and C waste,i V respectively the curtailment penalty coefficient of the wind power and the photovoltaic power of the i-th region, C punish,i,n1 PH and C punish,i,n2 ES respectively the discharging compensation amount of the n-th pumped storage power station and the n-th electrochemical energy storage power station of the i-th region at the peak of the net load, P n,tTie and C n Tie respectively transmission power and transmission cost coefficient of the nth tie line.

[0030] S3 is specifically: based on the network-constrained relaxed clustered unit commitment (NC-RCUC) method, the cross-provincial and cross-regional joint optimization model is solved monthly in parallel to obtain the power optimization result, and the model complexity is reduced and the solving speed is accelerated.

[0031] The power optimization result in S3 is a full-year hourly time sequence, including: cross-provincial and cross-regional tie line transmission power, output power and cluster start-stop state of each regional thermal power unit, actual power generation of hydropower unit and new energy unit (wind power, photovoltaic), charge and discharge power of electrochemical energy storage and pumped storage power station, demand side response reduction power and load shedding power of each node, and transmission power and direction of each line.

[0032] The transmission power of the cross-provincial and cross-regional tie line in the power optimization result is obtained by daily optimization. Specifically, since the power supply and demand pattern and the new energy generation characteristics will change significantly with the seasons, the transmission power of the cross-provincial and cross-regional tie line needs to be dynamically adjusted according to different typical days to realize the optimal allocation and efficient consumption of regional power resources. The tie line power optimization method is a daily optimization mode, that is, the transmission power remains constant within a single day, and the transmission power can be flexibly changed between different days according to the actual load demand, and the power transmission at each time does not exceed the specified upper and lower limits.

[0033] The present application takes the basic data such as new energy output, load level, thermal power unit installed capacity and operation parameters as input, constructs a cross-provincial and cross-regional joint optimization model with the minimum total annual operation cost of the system as the target, and comprehensively considers the operation constraints such as regional node power balance, line power flow, energy storage operation state and new energy output; then a cross-provincial and cross-regional tie line power optimization method is proposed, which keeps the transmission power constant within a day and adjusts it flexibly according to the actual demand between different days; finally, the joint optimization model is solved by taking the tie line transmission power as the decision variable, aiming to enhance the economy and power supply reliability of the cross-provincial and cross-regional power system.

[0034] To verify the effectiveness of the present application, the cross-provincial and cross-regional interconnected system composed of three provinces (belonging to northwest, north China and east China regions respectively) in China is used to realize the above method, and the specific steps are not repeated, and the technical effects and implementation details are mainly given.

[0035] The cross-provincial and cross-regional joint optimization model in this embodiment is optimized and solved by building a Threads multi-thread parallel environment and calling a Gurobi commercial solver.

[0036] This application example is based on a cross-provincial and cross-regional interconnection system composed of three provinces in China (belonging to the northwest, north China and east China respectively), which contains 196 nodes, 424 AC lines and 9 UHV DC lines, of which 2 UHV DC lines are cross-provincial and cross-regional tie lines. The wind power, photovoltaic output and load characteristic data use the actual time series data of the three provinces in 2022. The network structure parameters of the test system are shown in Table 1.

[0037] Table 1 Network structure parameters of the test system Region Number of units Number of nodes Number of AC lines Number of UHVDC lines (excluding system tie lines) Province A 59 50 81 1 Province B 269 72 169 3 Province C 159 74 174 3 Figure 1 The simplified structure diagram of the cross-provincial and cross-regional interconnection system, the two cross-provincial and cross-regional tie lines are connected with province A and province B, and province A and province C, and the transmission capacity limit of each is 8000 MW. In the model building process, the transmission power of the two tie lines is taken as the decision variable for joint optimization. The transmission power of the remaining 7 UHV DC channels is set as the boundary condition of the model solution, and a fixed value is adopted according to the seasonal characteristics.

[0038] Figure 2 The flow chart of the tie line power dispatching optimization method of the cross-provincial and cross-regional power system, the cross-provincial and cross-regional joint optimization model is constructed by inputting the system network structure and new energy, load time series data, so as to determine the tie line power optimization method.

[0039] In order to verify the effectiveness of the method, the following two kinds of comparison benchmarks are set in the application example: a) Monthly optimization mode: the transmission power of the tie line remains constant within the month, and only adjustment between different months is allowed; b) Fixed power mode: multiple provinces make independent optimization decisions, and the transmission power of the tie line between regions is set to a fixed value. In order to ensure the feasibility of the comparison results, the annual total transmission power of each tie line in the fixed power mode and the method of the application should be equal.

[0040] Figure 3 The daily transmission power diagram of the system tie line based on the optimization method of the application is shown, the transmission power remains constant within a day, and can be flexibly changed between different days, Figure 3 The data is taken from the peak period of the tie line transmission power from the 150th to the 250th day of the year. Figure 4 The monthly transmission power diagram of the system tie line based on the comparison monthly optimization mode is shown, the transmission power of the tie line remains unchanged within the month, and only cross-month adjustment is allowed, which represents a lower frequency power adjustment mode. Figure 5 The transmission power diagram of the system tie line based on the comparison fixed power mode is shown, the transmission power of the tie line is set to a fixed value and is not adjusted.

[0041] The optimization method of the application can flexibly adjust the size according to the daily load characteristics and new energy output prediction, realize short-term flexible deployment of power resources, and monthly optimization mode can adapt to the trend change of monthly new energy generation and load demand, but the system flexibility level decreases.

[0042] Table 2 is the optimization results of the method of the application and the control group, which is used to compare the differences of the simulation results of the cross-provincial and cross-regional power system operation based on different optimization methods. Each optimization method in Table 2 adopts a monthly parallel optimization method, which can reduce the calculation complexity and speed up the solution efficiency, and the calculation step length of the production simulation is 1h, and the total period is 8760h. The new energy curtailment rate in the comparison index of Table 2 includes the wind power curtailment rate and the photovoltaic power curtailment rate.

[0043] Table 2 Optimization results of the method of the application and the control group

[0044] According to Table 2, the optimization method of the application has significant advantages in improving system power supply reliability and reducing new energy curtailment rate. In terms of power supply reliability, the method of the application greatly reduces the amount of less-used load, which is 2133GWh less than 12959GWh of the fixed power mode, with a decrease of 16.46%. This shows that the application significantly improves the system's power supply reliability and stability through more flexible power adjustment and more accurate supply-demand matching, and reduces the load reduction caused by power shortage. In terms of new energy consumption, the method of the application optimizes the transmission power of the tie line by day, fully utilizes the complementarity of wind and light resources and the difference in load characteristics in different regions, and significantly improves the consumption level and utilization efficiency of new energy.

[0045] The cross-provincial and cross-regional power system tie line power optimization method provided by the application takes new energy output and load level data as input, constructs a cross-provincial and cross-regional joint optimization model with the optimal system operation economy as the target, comprehensively considers the operation constraints such as regional node power balance, line flow, energy storage operation state and new energy output, and then proposes a tie line transmission power daily optimization method to optimize and solve with tie line power as the decision variable. The cross-provincial and cross-regional transmission channel plays an important role in the power balance of each province, and the optimization method of the application can realize reasonable power deployment between provinces, promote the complementary use of power surpluses and deficits in different regions, improve their ability to cope with power supply and demand imbalance, and enhance the stability and reliability of the power system.

[0046] The above specific embodiments are only used to explain the application, and are not used to limit the application. Those skilled in the art can make various modifications and improvements to the application without departing from the principles and core concepts of the application. Any equivalent replacement, modification and similar scheme based on the claims and description of the application are within the protection scope of the application.

Claims

1. A method for power dispatch optimization of tie lines of cross-provincial and cross-regional power systems, characterized in that, Comprise the following steps: S1, obtain the cross-provincial cross-regional power basic data, and then build the tie-line power transmission power constraint; S2, based on the cross-provincial cross-regional power basic data and the tie-line power transmission power constraint, build the model constraint condition, and build the objective function with the optimization target of minimizing the annual comprehensive operation cost of the system, and then build the cross-provincial cross-regional joint optimization model; S3, solve the cross-provincial cross-regional joint optimization model to obtain the power optimization result. 2.The method of claim 1, wherein: The cross-provincial cross-regional power basic data in S1 includes: cross-provincial cross-regional power system network structure, tie-line power transmission capacity, new energy output in a preset time, load level, thermal power unit installed capacity and thermal power unit operation parameters.

3. The method of claim 1, wherein the method further comprises: The tie-line power transmission power constraint setting in S1 includes: the transmission power of the same cross-provincial cross-regional tie-line in the same day is the same at any time; the transmission power of each cross-provincial cross-regional tie-line at any time does not exceed the preset maximum power and is not less than the preset minimum power.

4. The method of claim 1, wherein the method further comprises: The model constraint condition in S2 includes tie-line power transmission power constraint, node power balance constraint, line power flow constraint, energy storage operation state constraint, thermal power unit constraint and multi-energy complementary source and load constraint; (1) The tie-line power transmission power constraint is set according to the following formula: P n Tie,min ≤P n,t Tie ≤P n Tie,max P n,s,1 Tie =P n,s,2 Tie =…=P n,s,24 Tie wherein n is the sequence number of inter-provincial and inter-regional tie-line in the system, t is the sequence number of time period, s is the sequence number of optimization typical day, P n Tie,max and P n Tie,min are the maximum and minimum transmission power of the nth inter-provincial and inter-regional tie-line, respectively, P n,t Tie denotes the transmission power of the nth inter-provincial and inter-regional tie-line at the tth time, P n,s,1 Tie to P n,s,24 Tie denotes the power at each time in the s th day; (2) The node power balance constraint is set according to the following formula: ∑ k1 P d,k1,t G +∑ k2 P d,k2,t H +∑ k3 P d,k3,t W +∑ k4 P d,k4,t V +∑ k5 (P d,k5,t ES,dis -P d,k5,t ES,cha ) +∑ k6 (P d,k6,t PH,dis -P d,k6,t PH,cha )=P d,t -P d,t DR -P d,t CT +∑ L1 P d,L1,t line +∑ L2 P d,L2,t DC +∑ L3 P d,L3,t Tie Wherein, d represents the number of node sequence in the system, k1=1 to N G,d , k2=1 to N H,d , k3=1 to N W,d , k4=1 to N V,d , k5=1 to N ES,d , k6=1 to N PH,d , L1=1 to N line,d , L2=1 to N DC,d , L3=1 to N Tie,d , N G,d , N H,d , N W,d , N V,d , N ES,d , N PH,d , N line,d , N DC,d , N Tie,d The number of thermal power units, the number of hydropower units, the number of wind farms, the number of photovoltaic power stations, the number of electrochemical energy storage power stations, the number of pumped storage power stations, the number of AC lines, the number of DC lines, the number of tie lines at the dth node, respectively, P d,k1,t G , P d,k2,t H , P d,k3,t W , P d,k4,t V , P d,k5,t ES,cha , P d,k5,t ES,dis , P d,k6,t PH,cha , P d,k6,t PH,dis The power of thermal power units, the power of hydropower units, the power of wind farms, the power of photovoltaic power stations, the charging power of electrochemical energy storage power stations, the discharging power of electrochemical energy storage power stations, the charging power of pumped storage power stations and the discharging power of pumped storage power stations at the dth node at time t, respectively, P d,t , P d,t DR , P d,t CT The load demand, the demand side response reduction power and the load shedding power at the dth node at time t, respectively, P d,L1,t line , P d,L2,t DC , P d,L3,t Tie Indicate the power of the AC transmission line, the DC transmission line and the cross-provincial cross-regional tie line connected with the dth node. (3) The line power flow constraint is set according to the following formula: θ slack,t =0 - π < θ < π L(to),t ≤ π - π < θ < π L(from),t ≤ π P L,t line =(θ L(from),t - θ L(to),t ) / X line where L represents the sequence number of the total AC line in the system, θ slack,t is the reference voltage phase angle of the system equilibrium node L(from),t , θ L(to),t are the voltage phase angles of the starting point and the ending point of the Lth line respectively, P L,t line is the transmission power of the Lth line at time t, X line is the reactance per unit of the line, P L,t line,max is the thermal stability limit power of the line; (4) The energy storage operation state constraint includes pumped storage constraint and electrochemical energy storage constraint; The pumped storage constraint is set according to the following formula: SOC i min ≤ SOC i,n1,t ≤ SOC i max SOC i,n1,1 =SOC i,n1,T =ξ i P i,n1 PH,min ≤P i,n1,t PH,cha ≤P i,n1 PH,max P i,n1 PH,min ≤P i,n1,t PH,dis ≤P i,n1 PH,max SOC i,n1,t =SOC i,n1,t-1 +η i,cha P i,n1,t PH,cha -P i,n1,t PH,dis / η i,dis 0 < R i,n1,t ≤ P i,n1 PH,max - P i,n1,t PH,dis SOC i,n1,t-1 - (P i,n1,t PH,dis + R i,n1,t ) / η i,dis ≥ SOC i min wherein i is the sequence number of the region in the system, n1 is the sequence number of the pumped storage power station in the ith region, SOC i,n1,1 , SOC i,n1,t , SOC i,n1,T are the state of charge of the nth1 pumped storage power station in the ith region at the initial time, at time t, and at the end of the scheduling period T, respectively, SOC i max , SOC i min , ξ i are the upper limit, the lower limit, and the initial value of the state of charge thereof, respectively, P i,n1,t PH,cha , P i,n1,t PH,dis , R i,n1,t are the charging power, the discharging power, and the spinning reserve power of the nth1 pumped storage power station in the ith region at time t, respectively, P i,n1 PH,min and P i,n1 PH,max denote the lower limit and the upper limit of the charging and discharging power of the nth1 pumped storage power station in the ith region, η i,cha , η i,dis are the charging and discharging efficiencies of the pumped storage power station in the ith region; The electrochemical energy storage constraint is set according to the following formula: SOC i ’min ≤ SOC ’ i,n2,t ≤ SOC i ’max SOC ’ i,n2,1 =SOC ’ i,n2,T =ξ i ’ P i,n2 ES,min ≤P i,n2,t ES,cha ≤P i,n2 ES,max P i,n2 ES,min ≤P i,n2,t ES,dis ≤P i,n2 ES,max SOC ’ i,n2,t = (1 - σ i ) SOC ’ i,n2,t-1 + η ’ i,cha P i,n2,t ES,cha - P i,n2,t ES,dis / η ’ i,dis 0 < R ’ i,n2,t ≤ P i,n2 ES,max - P i,n2,t ES,dis (1 - σ i ) SOC i,n2,t-1 - (P i,n2,t PH,dis + R ’ i,n2,t ) / η ’ i,dis ≥ SOC i ’min wherein n2 is the sequence number of the i-th regional electrochemical energy storage power station, SOC ’ i,n2,1 , SOC ’ i,n2,t , SOC ’ i,n2,T are the state of charge of the n2-th electrochemical energy storage power station of the i-th region at the initial moment, at the moment t, and at the end of the scheduling period T, respectively, SOC i ’max , SOC i ’min , ξ i ’ are the upper limit, the lower limit and the initial value of the state of charge, respectively, P i,n2,t ES,cha , P i,n2,t ES,dis , R ’ i,n2,t are the charging power, the discharging power and the spinning reserve power of the n2-th electrochemical energy storage power station of the i-th region at the moment t, respectively, P i,n2 ES,min , P i,n2 ES,max represent the lower limit and the upper limit of the charging and discharging power of the n2-th electrochemical energy storage power station of the i-th region, η ’ i,cha , η ’ i,dis , σ i are the charging efficiency, the discharging efficiency and the self-discharge rate of the i-th regional electrochemical energy storage power station, respectively. (5) The thermal power unit constraint is set according to the following formula: ∑ j P i,j,t G =P i,c,t Cluster O i,c,t =O i,c,t-1 +SU i,c,t -SD i,c,t O i,c,t ≥∑ τ1 SU i,c,t-τ1 O i,c,t ≥∑ j P i,j,t G,max -∑ τ2 SU i,c,t-τ2 C i,c,t SU =SC i,c,t SU SU i,c,t C i,c,t SD =SC i,c,t SD SU i,c,t a i,c min O i,c,t ≤P i,c,t Cluster ≤O i,c,t -λ i,c Ru O i,c,t ≤P i,c,t Cluster -P i,c,t-1 Cluster ≤λ i,c Rd O i,c,t ∑ c (O i,c,t -P i,c,t Cluster )+∑ n1 R i,n1,t +∑ n2 R ’ i,n2,t ≥λ i,W ∑ n3 P i,n3,t W +λ i,V ∑ n4 P i,n4,t V +λ i,L ∑ d P i,d,t +R i,N-1 ∑ c (P i,c,t Cluster -α i,c min O i,c,t )≥λ i,W ∑ n3 P i,n3,t W +λ V ∑ n4 P i,n4,t V +λ L ∑ d P i,d,t where Cluster represents a thermal power unit cluster, j = 1 to N c,i G , c = 1 to N Cluster,i , n1 = 1 to N PH,i , n2 = 1 to N ES,i , n3 = 1 to N W,i , n4 = 1 to N V,i , d = 1 to N D,i , N c,i G is the number of thermal power units in cluster c in the ith region, N Cluster,i , N PH,i , N ES,i , N W,i , N V,i , N D,i is the number of thermal power unit clusters, pumped storage power stations, electrochemical energy storage power stations, wind farms, photovoltaic power stations, and load nodes in the ith region, respectively, P i,j,t G is the output power of the jth thermal power unit in the ith region at time t, P i,j G,max is the installed capacity of the jth thermal power unit in the ith region, P i,c,t Cluster is the total output power of the cth thermal power unit cluster in the ith region at time t, O i,c,t , SU i,c,t , SD i,c,t are the online capacity, startup capacity, and shutdown capacity of the cth thermal power unit cluster in the ith region at time t, C i,c,t SU , C i,c,t SD , SC i,c,t SU , SC i,c,t SD are the startup cost, shutdown cost, startup cost coefficient, and shutdown cost coefficient of the cluster, P i,n3,t W is the actual power generation of the nth3 wind power unit in the ith region at time t, P i,n4,t V is the actual power generation of the nth4 photovoltaic unit in the ith region at time t, P i,d,t is the load demand of the dth node in the ith region at time t, τ1 = 1 to T i,c On , τ2 = 1 to T i,c Off , T i,c On , T i,c Off , a i,c min , l i,c Ru , l i,c Rd respectively represent the minimum startup time, shutdown time, minimum load rate, maximum ramp-up rate, and maximum ramp-down rate of the cth unit cluster in the ith region, l i,W , l i,V , l i,L , R i,N-1 respectively represent the wind power output prediction error coefficient, photovoltaic output prediction error coefficient, load reserve coefficient, and maximum single machine capacity of the ith region; (6) The i-th regional multi-energy complementary source and load constraint is set according to the following formula: 0 < P i,n3,t W ≤ Cap i,n3 W δ i,n3,t W 0 < P i,n4,t V ≤ Cap i,n4 V δ i,n4,t V 0 < P i,n5,t H ≤ Cap i,n5 H δ i,n5,t H 0 < P i,d,t DR ≤ λ i,DR P i,d,t 0 < P i,d,t CT ≤ λ i,DR P i,d,t Cap i,n3 W Cap is the installed capacity of the nth3 wind turbine, δ i,n3,t W P is the normalized theoretical output of the nth3 wind turbine at time t, Cap i,n4 W Cap is the installed capacity of the nth4 photovoltaic turbine, δ i,n4,t V P is the normalized theoretical output of the nth4 photovoltaic turbine at time t, Cap i,n5,t H P is the actual power generation of the nth5 hydroelectric turbine at time t in the region, Cap i,n5 H Cap is the installed capacity of the nth5 hydroelectric turbine, δ i,n5,t H P is the normalized theoretical output of the nth5 hydroelectric turbine at time t, Cap i,d,t DR P is the normalized theoretical output of the nth5 hydroelectric turbine at time t, Cap i,d,t CT P is the demand-side response reduction power and load shedding power of the dth node at time t, respectively, λ i,DR λ is the maximum adjustment ratio of demand-side response, λ i,CT λ is the maximum load reduction rate.

5. The method of claim 1, wherein the method further comprises: The objective function in S2 is set according to the following formula: Min C Sys Ope =∑ i ∑ t [∑ n6 (V i,n6 Cg,G P i,n6,t G +C i,n6,t SU,G +C i,n6,t SD,G )+∑ d C i DR P i,d,t DR +∑ d C i CT P i,d,t CT +∑ n7 C i,n7 DC P i,n7,t DC +∑ n2 C ope,i ES (P i,n2,t ES,cha +P i,n2,t ES,dis )+∑ n1 C ope,i PH (P i,n1,t PH,cha +P i,n1,t PH,dis ) +∑ n3 C waste,i W (Cap i,n3 W δ i,n3,t W -P i,n3,t W )+∑ n4 C waste,i V (Cap i,n4 V δ i,n4,t V -P i,n4,t V )] +∑ i (∑ n2 C punish,i,n2 ES +∑ n1 C punish,i,n1 PH )+∑ n ∑ t C n Tie P n,t Tie wherein C Sys Ope is the system objective function, i = 1 to N, t = 1 to T, n = 1 to N Tie , n1=1 to N PH,i , n2=1 to N ES,i , n3=1 to N W,i , n4=1 to N V,i , n6=1 to N G,i , n7=1 to N DC,i , d=1 to N D,i , N is the number of system areas, T is the total length of the optimization scheduling, N Tie is the number of interconnections in the system, N G,i , N D,i , N ES,i , N PH,i , N W,i , N V,i , N DC,i are the number of thermal power units, the number of nodes, the number of electrochemical energy storage power stations, the number of pumped storage power stations, the number of wind farms, the number of photovoltaic power stations and the number of DC lines in the i-th area, P i,n6,t G , C i,n6,t SU,G and C i,n6,t SD,G are the power generation, start-up cost and shutdown cost of the n6-th thermal power unit in the i-th area at the t-th time, V i,n6 Cg,G is the operation cost coefficient of the n6-th thermal power unit in the i-th area, P i,d,t DR and P i,d,t CT are the demand-side response reduction power and load shedding power of the d-th node in the i-th area at the t-th time, C i DR and C i CT are the demand-side response compensation rate and load shedding penalty coefficient in the i-th area, P i,n7,t DC and C i,n7 DC are the transmission power and transmission cost coefficient of the n7-th DC in the i-th area, P i,n2,t ES,cha and P i,n2,t ES,dis are the charging and discharging power of the n2-nd electrochemical energy storage power station in the i-th area at the t-th time, P i,n1,t PH,cha and P i,n1,t PH,dis The charging and discharging power of the nth pumped storage power station in the ith region at time t, respectively ope,i ES And C ope,i PH The operation loss parameters of the unit electric quantity of the electrochemical energy storage and pumped storage in the ith region, respectively i,n3 W δ i,n3,t W And P i,n3,t W The maximum and actual power generation of the nth wind farm in the ith region at time t, respectively i,n4 V δ i,n4,t V And P i,n4,t V The maximum and actual power generation of the nth photovoltaic power station in the ith region at time t, respectively i,n3 W And Cap i,n4 V The installed capacity of the nth wind farm and the nth photovoltaic power station in the ith region, respectively i,n3,t W And δ i,n4,t V The normalized theoretical output of the nth wind farm and the nth photovoltaic power station in the ith region at time t, respectively waste,i W And C waste,i V The penalty coefficient of abandoned electricity of wind power and photovoltaic in the ith region, respectively punish,i,n1 PH And C punish,i,n2 ES The discharging compensation of the nth pumped storage power station and the nth electrochemical energy storage power station in the ith region at the peak of net load, respectively n,t Tie And C n Tie The transmission power and transmission cost coefficient of the nth tie line, respectively.

6. The method of claim 1, wherein the method further comprises: S3 is specifically: based on the network constraint relaxation cluster unit combination method, the cross-provincial cross-regional joint optimization model is solved to obtain the power optimization result.

7. The method of claim 1, wherein the method further comprises: The power optimization result in S3 includes: cross-provincial cross-regional tie-line transmission power, output power and cluster start-stop state of each regional thermal power unit, power generation power of hydropower unit and new energy unit, charge and discharge power of electrochemical energy storage and pumped storage power station, demand side response reduction power and load shedding power of each node, transmission power and direction of each line. 8.The method of claim 1, wherein the method further comprises: determining a power flow of the tie-line; and determining a power flow of the tie-line based on the power flow of the tie-line. The transmission power of the cross-provincial cross-regional tie-line in the power optimization result is obtained by day optimization.

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