Section power flow optimization method considering participation of load side response resources in day-ahead scheduling

Abstract

The invention relates to the technical field of section power flow optimization, and discloses a section power flow optimization method considering participation of load side response resources in day-ahead scheduling, a large-scale interruptible load response mechanism, and load reduction in an interruptible load reduction potential range. An electric vehicle cluster time transfer mechanism for guiding the electric vehicle cluster charging load to be transferred in time through a time-of-use electricity price mechanism; an electric vehicle cluster space transfer mechanism for guiding the electric vehicle cluster charging load to be transferred in space through a regional electricity price mechanism; a section power flow flexible optimization model is constructed by taking the minimum total adjustment cost of an electric vehicle cluster and a large-scale interruptible load and the minimum power generation cost of all power supplies as objective functions, and an optimization scheme is output through the section power flow flexible optimization model. The method has the advantages that an electric vehicle cluster and a large-scale interruptible load are taken as load side response resources to participate in a section power flow flexible optimization model, and the large-scale interruptible load constraint and the electric vehicle cluster constraint are clarified.

Description

Technical Field

[0001] The present invention relates to the technical field of section power flow optimization, and in particular to a section power flow optimization method considering load-side response resources participating in day-ahead scheduling. Background Art

[0002] In the power system, a group of transmission lines connecting two electrical areas with the same active power flow direction under typical operation mode is generally called a transmission section. When any line in the section is disconnected due to a fault, most of the power flow is transferred to the remaining lines in the section, which can easily cause the remaining lines to be overloaded and cut off one after another, thereby triggering a chain reaction of failures. This is one of the main reasons for major power outages at home and abroad in recent years. The optimization and control of section power flow is an important factor affecting the safe and stable operation of the power grid, and has received extensive attention and research. The research methods mainly include section identification and intelligent real-time control, source-grid-storage multi-coordination, etc. With the growth of new flexible loads and the improvement of the power market mechanism, it is gradually possible to mobilize load-side response resources to flexibly optimize section power flow.

[0003] Generally speaking, any load that can adjust its own power consumption according to the grid operation needs can be included in the load-side response resources. There are many types of load-side response resources, and the two most valuable ones are currently: large-scale interruptible loads and electric vehicle clusters. Among them, large-scale interruptible loads are large in number, large in capacity, and relatively concentrated in spatial distribution, which is convenient for dispatching, control and management; while electric vehicles will continue to develop rapidly in the future, and their strong sensitivity to electricity prices makes their dispatching flexibility very high.

[0004] Interruptible loads mainly participate in grid response through contracts. Domestic and foreign scholars have conducted research on the design, evaluation methods and dispatching methods of interruptible load contracts: the existing technology of the study of the interruptible load contract model in the power market discussed the content of the interruptible load contract and the relevant market organization form, and gave the optimal purchase model of the interruptible load and solved it; the existing technology of the construction of the evaluation system for the participation of regional flexible loads in power dispatch and its application research aimed at constructing a comprehensive evaluation index system with flexible loads as the control object and the purpose of achieving energy optimization configuration; the existing technology based on the Nx uncertain set of interruptible loads The model of the participation of interruptible loads in the power grid optimization was proposed from different angles, and the removal strategy was studied. The existing technology uses a new method of interruptible loads to solve the transmission congestion problem to establish a direct power supply relationship between the branch and the load, and adjusts the load according to this relationship to alleviate or relieve line congestion.

[0005] The main way for electric vehicles to respond to grid optimization is through electricity price incentives. Domestic and foreign scholars have conducted a lot of research on this: the existing technology proposes a time-of-use electricity price strategy based on the PSO algorithm for the two-stage optimization strategy of orderly charging and discharging of electric vehicles to reduce users' charging costs and load peak-valley differences; the existing technology considers the dynamic response of electric vehicles and fast charging stations with the interaction of multiple vehicles, giving priority to the interests of electric vehicle users, with improving the efficiency of electric vehicles in finding stations and reducing the cost of electric vehicles as the priority goals, but does not involve the impact of electric vehicles on the operating status of the grid; the existing technology considers demand response and the economic dispatch of active distribution networks involving electric vehicles, and studies the relationship between electric vehicle flexibility and grid operation indicators such as wind power absorption, network loss reduction, and load peak-valley differences, but does not consider the spatial distribution and transfer characteristics of charging loads and their impact on section currents.

[0006] In general, there are few studies at home and abroad that comprehensively consider multiple load-side response resources to flexibly optimize cross-sectional currents, and there is a lack of guidance on how to deploy and select load-side response resources to optimize cross-sectional currents in engineering. Summary of the invention

[0007] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a section power flow optimization method that takes into account the participation of load-side response resources in day-ahead scheduling.

[0008] The object of the present invention is achieved by the following technical solution: a section power flow optimization method considering the participation of load-side response resources in day-ahead scheduling, the method comprising:

[0009] Large-scale interruptible load response mechanism, which reduces load within the interruptible load reduction potential range according to the grid dispatching needs; electric vehicle cluster time transfer mechanism, which guides the time transfer of electric vehicle cluster charging load through the time-of-use electricity price mechanism, and encourages electric vehicle users to send electricity back to the grid during peak load periods and charge during off-peak periods in the V2G mode;

[0010] The spatial transfer mechanism of electric vehicle clusters guides the spatial transfer of electric vehicle cluster charging loads through a regional electricity price mechanism; a flexible optimization model for section power flow is constructed with the total regulation cost of electric vehicle clusters and large-scale interruptible loads and the lowest power generation cost of all power sources as the objective function, and the optimization plan is output through the flexible optimization model for section power flow.

[0011] Specifically, the time-of-use electricity price is formulated as follows:

[0012] C p (t1)E+C rest +C exp ≤C p (t0)E (1)

[0013] In the formula, C p(t0) and C p (t1) are the charging electricity prices for the corresponding periods before and after the electric vehicle load transfer; E is the planned charging power of the electric vehicle;

[0014] C p ≤η p η g C g (2)

[0015] In the formula, C p and C g are the charging and discharging electricity prices of electric vehicles respectively; η p and η g are the charge and discharge efficiencies, respectively.

[0016] Specifically, the regional electricity price is formulated as follows:

[0017] C p (A)E+C rest +C exp ≤C p (B) E (3)

[0018] In the formula, C p (A) and C p (B) are the charging electricity prices at nodes A and B respectively, while for electric vehicle users who transfer in space, C rest Other costs of participating in space-time transfer include extra time, power consumption, and battery life loss; C exp Profit for the user's psychological expectations, C rest Calculated by the following formula:

[0019]

[0020] Where, t road is the time consumed for space transfer; ω is the power consumption coefficient; is the time cost coefficient.

[0021] Specifically, the objective function is:

[0022] minF=F c +F g +β(K sec -1) (5)

[0023] In the formula, F c is the total adjustment cost, F g is the total power generation cost, K sec is the section flow overload multiple, and β is the section flow overload penalty coefficient.

[0024] Specifically, the total adjustment cost F c The calculation formula is as follows:

[0025] F c =C EV +C irl (6)

[0026] In the formula, C EV Adjusting the cost for electric vehicle clusters, C irl is the scaled interruptible load regulation cost; the electric vehicle cluster regulation cost C EV The calculation formula is as follows:

[0027] C EV =∑C0-∑C p +∑C g (7)

[0028] Where C0 is the charging electricity cost of the electric vehicle cluster under the conventional charging method; C p and C g They are respectively the charging electricity cost and discharging electricity cost of the electric vehicle cluster after optimized scheduling;

[0029] Large-scale interruptible load regulation cost C irl The calculation formula is as follows:

[0030]

[0031] Where C1(i) ​​and C2(i) are the unit electricity compensation cost and single response compensation cost of the i-th interruptible load user, respectively. irl (i, t) is the load value that the i-th interruptible load user actively reduces in period t, B irl (i, t) is the user’s response status at the current moment, N irl is the number of users, ΔT is the duration of a scheduling cycle, and nt is the number of scheduling periods.

[0032] Specifically, the total power generation cost F g The calculation formula is:

[0033]

[0034] Where P i，t is the output of the i-th unit in period t, N is the number of units, A i,2 , A i,1 and A i,0 is the cost coefficient of the unit.

[0035] Specifically, the constraints of the section power flow flexible optimization model include large-scale interruptible load constraints, electric vehicle cluster constraints, and power grid operation and safety constraints.

[0036] Specifically, the large-scale interruptible load constraints include:

[0037] Reduce upper and lower load constraints:

[0038]

[0039] In the formula, and P irl, i , t are the upper and lower limits of load reduction, S irl,i,t is a 0-1 variable representing the reduction status;

[0040] Maximum reduction time constraint:

[0041]

[0042] In the formula, S irl,i,max The maximum number of reduction periods agreed by the user within a scheduling plan;

[0043] Maximum reduction number constraint:

[0044]

[0045] In the formula, B irl,i,t B is a 0-1 variable representing the period of load reduction. A value of 1 indicates that load reduction starts from this period. irl,i,max The maximum number of reductions agreed by the user within a scheduling plan;

[0046] Single shortest reduction time constraint:

[0047]

[0048] In the formula, E irl,i,t is a 0-1 variable representing the end of the load shedding period, and a value of 1 indicates that the load shedding will stop from this period; T sus,i The minimum number of time periods for a single load reduction agreed upon by the user;

[0049] The shortest interval constraint between two adjacent reduction commands:

[0050]

[0051] Where, T int,i The minimum number of time periods between two load reductions agreed upon by the user.

[0052] Specifically, the electric vehicle cluster constraints include:

[0053] Electric vehicle power change constraints:

[0054]

[0055] Where E(i,t) is the battery capacity of the ith electric vehicle cluster at the end of period t; η p and η g They are charging and discharging efficiency respectively; upper and lower limits of charging and discharging power are:

[0056] 0≤P p (i,t)≤P(i,t)P p,max (i) (16)

[0057] 0≤P g (i,t)≤G(i,t)P g,max (i) (17)

[0058] Where P p,max (i) and P g,max (i) are the maximum charging power and maximum discharging power of the i-th electric vehicle cluster, respectively. P(i, t) and G(i, t) are 0-1 variables representing whether the cluster is charging or discharging in time period t;

[0059] Interlocking constraints during charging and discharging process:

[0060]

[0061] This constraint restricts the charging and discharging of electric vehicles from being carried out simultaneously and must be separated by at least two time periods;

[0062] Battery power upper and lower limit constraints:

[0063] E min (i)≤E(i,t)≤E max (i) (19)

[0064] In the formula, E max (i) E min (i) is the upper and lower limits of the battery power of the i-th electric vehicle cluster;

[0065] Electric vehicle cluster access and off-grid conditions and constraints:

[0066] E(i,t in (i)) = E in (i) (20)

[0067] E(i,t out (i)) = E out (i) (21)

[0068] P(i,t)=G(i,t)=0 t≤t in (i) |t>t out (i) (22)

[0069] Where, t in (i) and t out (i) is the grid access time and agreed grid-off time of the ith electric vehicle cluster; E in (i) and E out (i) The battery capacity when entering the network and the battery capacity when leaving the network respectively;

[0070] Electric vehicle space transfer constraints:

[0071] When an EV transfers from aggregator k to aggregator m for charging, the following constraints are satisfied:

[0072] t in (i) = t in (i′)+t road (k,m) (23)

[0073] E in (i) = E in (i′)-ω(i)t road (k,m) (24)

[0074] Where, t in (i′) and E in (i′) is the time and amount of electricity connected to the grid when the i-th electric vehicle cluster does not consider spatial transfer; the upper and lower limits of the charging and discharging power of the aggregator are:

[0075]

[0076] In the formula, Ω j represents the set of all electric vehicles charged and discharged at the jth aggregator, P p,union，max (j,t) and P g,union，max (j, t) are the maximum charging and discharging powers that the jth aggregator can exchange with the grid in period t, respectively;

[0077] Electric vehicle user response willingness constraints:

[0078]

[0079] In the formula, C c (i) is the net charging electricity cost paid by the i-th electric vehicle cluster; C0(i) is the electricity cost under conventional charging mode; C p (i, t) and C g (i,t) are the charging and discharging electricity prices of the i-th electric vehicle cluster in period t.

[0080] Specifically, the power grid operation and safety constraints include:

[0081] System power balance constraints:

[0082]

[0083] Where P source (i,t) is the output of the i-th unit in period t, P load (t) is the total network load in the corresponding period;

[0084] System network flow security constraints:

[0085]

[0086] Among them, P l (i,t) is the power flow of the ith line in period t, is the upper limit of the transmission power of the line;

[0087] For the cross section, we have:

[0088]

[0089] Among them, P sec (i,t) is the tidal current of the i-th section in period t, is the upper limit of the transmission power of the section;

[0090] Unit output upper and lower limit constraints:

[0091]

[0092] in, and P source (i) are the upper and lower limits of the output of the i-th unit, respectively, and S(i,t) is the start and stop status of the unit in the t-th scheduling period;

[0093] Unit climbing constraints:

[0094] P th (i,t)-P th (i,t-1)|≤r(i)ΔT (36)

[0095] Among them, P th (i,t) is the output of the i-th thermal power unit in period t, r(i) is the ramp rate;

[0096] Operation constraints of hydropower units:

[0097] V(i,t)-V(i,t-1)=ΔT[Q in (i,t)-Q out (i,t)] (37)

[0098] P h (i,t)=ηρgH[Q out(i,t)-Q waste (i,t)] (38)

[0099] Among them, V(i,t) is the water storage capacity of the i-th reservoir at the end of period t, Q in (i,t),Q out (i, t) are the average inflow and outflow of the period respectively; P h (i, t) is the output of the i-th hydropower station unit in the t period, η is the power generation efficiency of the hydropower unit, ρ is the density of water, g is the acceleration of gravity, H is the average head height, Q waste (i,t) is the amount of water discarded during this period;

[0100] Spinning reserve constraints:

[0101]

[0102] Where: L + % and L - % are the positive and negative spinning reserve capacity coefficients of the system respectively; s u (i,t) and s d (i, t) are respectively the positive and negative spinning reserve capacities that unit i can provide during period t, which are determined by the following formula:

[0103]

[0104] s d (i,t)=P source (i,t)-P source ( i ) S(i,t) (42)

[0105] In addition, the spinning reserve capacity of thermal power units is also limited by the ramp speed:

[0106] s u,th (i,t)≤r(i)ΔT (43)

[0107] s d,th (i,t)≤r(i)ΔT (44).

[0108] The present invention has the following advantages:

[0109] 1. The present invention proposes a flexible optimization model for power grid section power flow that simultaneously considers electric vehicle clusters and large-scale interruptible loads as load-side response resources, and clarifies the large-scale interruptible load constraints and electric vehicle cluster constraints.

[0110] 2. The flexible optimization model of section flow proposed in the present invention can significantly alleviate section overload, while promoting the consumption of clean energy, and achieving the grid operation goals of ensuring power supply, safety, and consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] Figure 1 It is a schematic diagram of the flow chart of the cross-section tidal current optimization method of the present invention;

[0112] Figure 2 A schematic diagram of the interruptible load response power grid dispatching of the present invention;

[0113] Figure 3 A schematic diagram of the time transfer of charging power for an electric vehicle according to the present invention;

[0114] Figure 4 This is a schematic diagram of the spatial transfer of charging power for an electric vehicle of the present invention;

[0115] Figure 5 This is a schematic diagram of the heavy overload flow curve of Section 1;

[0116] Figure 6 This is a schematic diagram of the heavy overload flow curve of Section 2;

[0117] Figure 7 This is a schematic diagram of the heavy overload flow curve of Section 3;

[0118] Figure 8 This is a schematic diagram of the heavy overload flow curve of Section 4;

[0119] Fig. 9 Schematic diagram of charging and discharging power curve of electric vehicle clusters in different scenarios;

[0120] Fig.10 This is a schematic diagram of the space transfer of electric vehicle clusters;

[0121] Fig.11 This is a schematic diagram of the large-scale interruptible load response in scenario three;

[0122] Fig.12 This is a schematic diagram of the large-scale interruptible load response in scenario 4; DETAILED DESCRIPTION

[0123] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the embodiments described are only part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0124] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0125] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0126] The present invention is further described below in conjunction with the accompanying drawings, but the protection scope of the present invention is not limited to the following description. Figures 1 to 12 As shown in FIG. 1 , a section power flow optimization method considering the participation of load-side response resources in day-ahead dispatching is proposed. The method includes: a mechanism for large-scale interruptible loads and electric vehicle clusters to participate in section power flow optimization:

[0127] 1. Large-scale interruptible load response mechanism, according to the grid dispatching requirements, reduce the load within the interruptible load reduction potential range; interruptible load response grid dispatching such as Figure 2 As shown, the red curve is the initial load curve predicted or declared by the user, and the green curve is the minimum guaranteed load curve that meets the basic production and life needs of the user. The range between the two curves is the reduction potential of the interruptible load, and the maximum load reduction is 60kW. According to the optimized scheduling calculation, the cross-section flow of the grid node load peak period from 18:00 to 21:00 is limited, and the user needs to reduce the load by 50kW. The grid issues a reduction instruction to the user, and the user's actual power consumption curve is executed according to the blue curve;

[0128] 2. The time transfer mechanism of electric vehicle clusters guides the time transfer of electric vehicle cluster charging load through the time-sharing electricity price mechanism, and encourages electric vehicle users to return electricity to the power grid during peak load periods and charge during off-peak periods in the V2G mode. The time transfer of electric vehicle clusters is mainly guided by the time-sharing electricity price mechanism. The charging load of electric vehicle clusters is transferred at different times to reduce the peak load of the power grid and fill the valley, thereby alleviating the problem of cross-section flow blocking during peak load periods. Taking private car clusters as an example, the time of access to the power grid is generally from 19:00 to 7:00 the next morning. Figure 3 As shown in the figure, under the conventional charging mode, electric vehicles start charging as soon as they are connected to the grid until they are fully charged. The charging load is concentrated between 19:00 and 24:00, which aggravates the evening peak operation pressure of the grid. By setting time-of-use electricity prices, increasing the electricity prices during the evening peak period and reducing the electricity prices during the early morning period, some electric vehicles can be guided to transfer to the load valley period (0:00-5:00) for charging, reducing the load peak. Further, considering the V2G model, during the peak load period, a reasonable subsidy mechanism can be used to encourage electric vehicle users to send electricity back to the grid, which can guide some electric vehicles to discharge during the evening peak period (17:00-23:00) and wait until the early morning load valley period (0:00-10:00) to charge. To achieve the above goals, the formulation of time-of-use electricity prices should follow the following principles:

[0129] C p (t1)E+C rest +C exp ≤C p (t0)E(1)

[0130] In the formula, C p (t0) and C p (t1) are the charging electricity prices in the corresponding periods before and after the load transfer of electric vehicles; E is the planned charging power of electric vehicles; Since there is power loss in the charging and discharging process of electric vehicle batteries, in order to guide electric vehicles to participate in V2G, the formulation of charging and discharging electricity prices should also follow the following principles:

[0131] C p ≤η p η g C g (2)

[0132] In the formula, C p and C g are the charging and discharging electricity prices of electric vehicles respectively; η p and η g are the charging and discharging efficiencies, respectively;

[0133] 3. Spatial transfer mechanism of electric vehicle clusters, guiding the spatial transfer of electric vehicle cluster charging load through regional electricity price mechanism; The spatial transfer of electric vehicle clusters is mainly guided by regional electricity price mechanism, and the electric vehicle charging load is transferred at different spatial nodes to improve the distribution of grid load at different nodes, thereby alleviating the section flow pressure, such as Figure 4 As shown in the figure, node A is close to the power supply side, node B is close to the load side, and the AB section power flow is seriously blocked. Therefore, the charging price of node A (non-heavy-load node) can be lowered, and the charging price of node B (heavy-load node) can be increased to guide some electric vehicles to transfer from node B to node A for charging, thereby alleviating the section power flow pressure. To achieve the above goals, the formulation of regional electricity prices should follow the following principles:

[0134] C p (A)E+C rest +C exp ≤C p (B)E(3)

[0135] In the formula, C p (A) and C p (B) are the charging electricity prices at nodes A and B respectively. For electric vehicle users who transfer in space, C rest Other costs of participating in space-time transfer include extra time, power consumption, and battery life loss; C exp Profit for the user's psychological expectations, C rest Calculated by the following formula:

[0136]

[0137] Where, t road is the time taken for space transfer; ω is the power consumption coefficient, i.e., the power consumption per unit time of driving; is the time cost coefficient, i.e. the cost corresponding to the driving unit time;

[0138] A flexible optimization model for section power flow is constructed with the total regulation cost of electric vehicle clusters and large-scale interruptible loads and the minimum power generation cost of all power sources as the objective function, and the optimization plan is output through the flexible optimization model for section power flow.

[0139] The decision variables of the cross-section flow flexible optimization model include:

[0140] The decision variables of the optimization model represent the resources that can be mobilized to some extent. In this model, the main decision variables are:

[0141] 1. Output of each unit P source: An ng*nt dimensional matrix, representing the active output of each unit in each dispatch period, where ng is the number of units and nt is the number of dispatch periods;

[0142] 2. The start and stop status of each unit S: an ng*nt dimension 0-1 variable matrix, representing the start and stop status of each unit in each scheduling period, with a value of 1 representing start and stop, and a value of 0 representing stop;

[0143] 3. The water storage capacity of each reservoir V: an nh*nt dimensional matrix, representing the water storage capacity of each reservoir at the end of each dispatching period, where nh is the number of hydropower stations;

[0144] 4. Discarded water flow Q of each hydropower station waste : An nh*nt dimensional matrix, representing the amount of water abandoned by each hydropower station in each dispatching period; since the water flow of a hydropower station is proportional to the output of the hydropower station, there is no need to make a separate decision; there is a storage capacity constraint relationship between the water flow, water flow, abandoned water flow and water storage capacity of the hydropower station;

[0145] 5. Load reduction amount P of interruptible load irl : One N irl *nt-dimensional matrix, representing the active load reduced by each interruptible load in each dispatching period, which can be equivalent to power supply, where N irl The amount of interruptible load contracted;

[0146] 6. Interruptible load reduction state S irl : One N irl *nt-dimensional 0-1 variable matrix, representing the state of load reduction of each interruptible load in each dispatching period, with a value of 1 representing that load reduction is implemented and a value of 0 representing that load reduction is not implemented;

[0147] 7. Electric vehicle battery capacity E: one N bev *nt-dimensional matrix, representing the battery power of each electric vehicle cluster at the end of each scheduling period;

[0148] 8. Electric vehicle charging and discharging power P p and P g : Two N bev *nt-dimensional matrix, representing the charging power of each electric vehicle cluster in each scheduling period and the power fed back to the grid;

[0149] 9. Electric vehicle charging and discharging states SP and SG: two N bev *nt-dimensional 0-1 variable matrix, representing the charging and discharging status of each electric vehicle cluster in each scheduling period, with a value of 1 representing charging (or discharging) and a value of 0 representing not charging (or discharging);

[0150] 10. Section tidal current overload multiple Ksec : A real variable representing the maximum value of the overload coefficients of all lines and sections in all dispatching periods;

[0151] The ultimate goal of the flexible optimization of section flow is to limit the section flow within the safety limit with the smallest possible regulation cost, so as to improve the safety and economy of power grid operation. Therefore, the objective function of the flexible optimization model of section flow is to minimize the total regulation cost of electric vehicle clusters and large-scale interruptible loads and the power generation cost of all power sources, and add the penalty term of section flow overload. The objective function is:

[0152] minF=F c +F g +β(K sec -1) (5)

[0153] In the formula, F c is the total adjustment cost, F g is the total power generation cost, K sec is the section flow overload multiple, and β is the section flow overload penalty coefficient.

[0154] The total adjustment cost F c The calculation formula is as follows:

[0155] F c =C EV +C irl (6)

[0156] In the formula, C EV Adjusting the cost for electric vehicle clusters, C irl is the scaled interruptible load regulation cost; the electric vehicle cluster regulation cost C EV The calculation formula is as follows:

[0157] C EV =∑C0-∑C p +∑C g (7)

[0158] Where C0 is the charging electricity cost of the electric vehicle cluster under the conventional charging method; C p and C g They are respectively the charging electricity cost and discharging electricity cost of the electric vehicle cluster after optimized scheduling;

[0159] For users who have executed interruptible load contracts, power supply companies should pay them a certain fee, which constitutes the regulation cost. One type of fee settlement method is based on fixed compensation fees, and its regulation cost is a fixed value, which can be excluded from the optimization scheduling calculation; the other two types of fee settlement methods are calculated based on the amount of electricity cut and the number of cuts. Therefore, the interruptible load regulation cost is calculated by the following formula: the scaled interruptible load regulation cost C irlThe calculation formula is as follows:

[0160]

[0161] Where C1(i) ​​and C2(i) are the unit electricity compensation cost and single response compensation cost of the i-th interruptible load user, respectively. irl (i, t) is the load value that the i-th interruptible load user actively reduces in period t, B irl (i, t) is the user's response status at the current moment, with a value of 1 indicating participation in the response, and N irl is the number of users, ΔT is the duration of a scheduling cycle, and nt is the number of scheduling periods.

[0162] The power generation cost mainly considers thermal power, including fuel, pollutant gas emissions and carbon emission costs, etc. It is generally believed that it has a quadratic function relationship with the unit output. The total power generation cost F g The calculation formula is:

[0163]

[0164] Where P i，t is the output of the i-th unit in period t, N is the number of units, A i,2 , A i,1 and A i,0 is the cost coefficient of the unit.

[0165] The constraints of the section power flow flexible optimization model include large-scale interruptible load constraints, electric vehicle cluster constraints, and power grid operation and safety constraints.

[0166] The scaled interruptible load constraints include:

[0167] Reduce upper and lower load constraints:

[0168]

[0169] In the formula, and P irl,i,t are the upper and lower limits of load reduction, S irl,i,t is a 0-1 variable representing the reduction status;

[0170] Maximum reduction time constraint:

[0171]

[0172] In the formula, S irl,i,max The maximum number of reduction periods agreed by the user within a scheduling plan;

[0173] Maximum reduction number constraint:

[0174]

[0175] In the formula, B irl,i,t B is a 0-1 variable representing the period of load reduction. A value of 1 indicates that load reduction starts from this period. irl,i,max The maximum number of reductions agreed by the user within a scheduling plan;

[0176] Single shortest reduction time constraint:

[0177]

[0178] In the formula, E irl,i,t is a 0-1 variable representing the end of the load shedding period, and a value of 1 indicates that the load shedding will stop from this period; T sus,i The minimum number of time periods for a single load reduction agreed upon by the user;

[0179] The shortest interval constraint between two adjacent reduction commands:

[0180]

[0181] Where, T int,i The minimum number of time periods between two load reductions agreed upon by the user.

[0182] The purpose of setting the shortest single load reduction time constraint and the shortest interval constraint between two adjacent load reduction commands is to avoid frequent issuance of load reduction commands and improve user satisfaction.

[0183] This model assumes that the time and power required to transfer electric vehicle clusters between different aggregators remain unchanged. Based on the above considerations, the constraints of electric vehicle clusters include:

[0184] Electric vehicle power change constraints:

[0185]

[0186] Where E(i,t) is the battery capacity of the ith electric vehicle cluster at the end of period t; η p and η g They are charging and discharging efficiency respectively; upper and lower limits of charging and discharging power are:

[0187] 0≤P p (i,t)≤P(i,t)P p,max (i) (16)

[0188] 0≤P g (i,t)≤G(i,t)P g,max (i) (17)

[0189] Where P p,max (i) and Pg,max (i) are the maximum charging power and maximum discharging power of the i-th electric vehicle cluster, respectively. P(i, t) and G(i, t) are 0-1 variables representing whether the cluster is charging or discharging in time period t;

[0190] Interlocking constraints during charging and discharging process:

[0191]

[0192] This constraint restricts the charging and discharging of electric vehicles to not be performed simultaneously and must be separated by at least two periods to extend the battery life;

[0193] Battery power upper and lower limit constraints:

[0194] E min (i)≤E(i,t)≤E max (i) (19)

[0195] In the formula, E max (i) E min (i) is the upper and lower limits of the battery power of the i-th electric vehicle cluster;

[0196] Electric vehicle cluster access and off-grid conditions and constraints:

[0197] E(i,t in (i)) = E in (i) (20)

[0198] E(i,t out (i)) = E out (i) (21)

[0199] P(i,t)=G(i,t)=0 t≤t in (i) |t>t out (i) (22)

[0200] Where, t in (i) and t out (i) is the grid access time and agreed grid-off time of the ith electric vehicle cluster; E in (i) and E out (i) The battery capacity when entering the network and the battery capacity when leaving the network respectively;

[0201] Electric vehicle space transfer constraints:

[0202] When an EV transfers from aggregator k to aggregator m for charging, the following constraints are satisfied:

[0203] t in (i) = t in (i′)+t road(k,m) (23)

[0204] E in (i) = E in (i′)-ω(i)t road (k,m) (24)

[0205] Where, t in (i′) and E in (i′) is the time and amount of electricity connected to the grid when the i-th electric vehicle cluster does not consider spatial transfer; the upper and lower limits of the charging and discharging power of the aggregator are:

[0206]

[0207] In the formula, Ω j represents the set of all electric vehicles charged and discharged at the jth aggregator, P p,union，max (j,t) and P g,union，max (j, t) are the maximum charging and discharging powers that the jth aggregator can exchange with the grid in period t, respectively;

[0208] In order to protect the interests of electric vehicle users and ensure that the electric vehicle cluster is willing to respond to the grid optimization dispatch, in this model, constraints are used to ensure that the total charging cost of the electric vehicle cluster after responding to the grid dispatch is not higher than the conventional charging method. The response willingness constraints of electric vehicle users are:

[0209]

[0210] In the formula, C c (i) is the net charging electricity cost paid by the i-th electric vehicle cluster; C0(i) is the electricity cost under conventional charging mode; C p (i, t) and C g (i,t) are the charging and discharging electricity prices of the i-th electric vehicle cluster in period t.

[0211] The transmission line loss ratio is generally between 1% and 2%. In this model, in order to improve the calculation efficiency, the nonlinear constraints are converted into linear constraints, and the line losses are not considered. In this case, the system power balance constraint is that the output of each unit is equal to the load power consumption. The grid operation and safety constraints include:

[0212] System power balance constraints:

[0213]

[0214] Where P source (i,t) is the output of the i-th unit in period t, P load (t) is the total network load in the corresponding period, taking into account the electric vehicle charging load and the interruptible load after reduction;

[0215] System network power flow safety constraints, including line power upper limit constraints, that is, the power transmitted by each line cannot exceed the rated output power upper limit of the line;

[0216]

[0217] Among them, P l (i,t) is the power flow of the ith line in period t, is the upper limit of the transmission power of the line; it is mainly constrained by the thermal stability limit of the line conductor; in this model, the line flow overload multiple and the section flow overload multiple are controlled by the same variable;

[0218] For the cross section, we have:

[0219]

[0220] Among them, P sec (i,t) is the tidal current of the i-th section in period t, The upper limit of the transmission power of the section is mainly determined by the operation experience and simulation calculation of the dispatcher;

[0221] The transmission power P of line km (i.e. starting point k and end point m) in a certain scheduling period k,m Calculated by the following formula:

[0222]

[0223] Where U is the system voltage, x k,m is the line reactance, θ k,m is the voltage phase angle difference between the starting point and the end point of the line, calculated by the following formula:

[0224] θ k,m =θ k -θ m (33)

[0225] The DC power flow method is used to calculate the voltage phase angle of each node:

[0226] θ=XP sp (34)

[0227] Where: θ and P sp are all n-dimensional vectors (n is the number of network nodes), X is an n-order square matrix. θ is the voltage phase angle of each node (with the balanced node as the reference). sp is the total incoming power of each node, that is, the sum of the unit output at each node minus the sum of the load of the node. X is the node reactance matrix without considering the grounding branch and branch resistance, and the row and column where the balanced node is located are set to 0;

[0228] Unit output upper and lower limit constraints:

[0229]

[0230] in, and P source (i) are the upper and lower limits of the output of the i-th unit respectively, and S(i,t) is the start and shutdown status of the unit in the t-th scheduling period. It is generally only set for units such as thermal power and nuclear power units that cannot switch the start and shutdown status or working status at any time). Usually the rated capacity, P source (i) It is the lower limit of the output of thermal power units, and it is 0 for units such as hydropower units whose output can be continuously adjusted. Obviously, the upper and lower limits of the output of the unit when it is shut down are both 0;

[0231] There are certain restrictions on the speed of increasing and decreasing the output of each unit (mainly coal-fired thermal power units), and the unit climbing constraints are:

[0232] |P th (i,t)-P th (i,t-1)|≤r(i)ΔT (36) Among them, P th (i, t) is the output of the i-th thermal power unit in the t period, and r(i) is the ramp rate. For simplification, it is assumed that the maximum ramp rate and the maximum ramp rate of the thermal power unit are equal.

[0233] For the same hydropower station, the amount of water flowing in during a certain dispatching period minus the amount of water discharged is the amount of increase in the reservoir capacity of the hydropower station. The amount of water discharged includes water used for power generation and abandoned water. In this model, considering the high linearity of the output of the hydropower unit, it is assumed that the power generation efficiency of each unit in the same hydropower station is the same. The units of the same hydropower station are merged into a virtual unit to correspond to the reservoir capacity. The operation constraints of the hydropower unit are:

[0234] V(i,t)-V(i,t-1)=ΔT[Q in (i,t)-Q out (i,t)] (37)

[0235] P h (i,t)=ηρgH[Q out (i,t)-Q waste (i,t)] (38)

[0236] Among them, V(i,t) is the water storage capacity of the i-th reservoir at the end of period t, Q in (i,t),Q out (i, t) are the average inflow and outflow of the period respectively; P h(i, t) is the output of the i-th hydropower station unit in the t period, η is the power generation efficiency of the hydropower unit, ρ is the density of water, g is the acceleration of gravity, H is the average head height, Q waste (i, t) is the amount of water abandoned during this period; since the head height of most reservoir hydropower stations does not change much within a day, in this model, for linear simplification, the head height is assumed to be a constant; in addition, the reservoir capacity and discharge flow of each hydropower station at each moment are subject to upper and lower limits.

[0237] The spinning reserve constraint means that the sum of the positive and negative spinning reserve capacities of each unit should be greater than the spinning reserve capacity required by the system:

[0238]

[0239] Where: L + % and L - % are the positive and negative spinning reserve capacity coefficients of the system, both of which are taken as 2% in this model; s u (i,t) and s d (i, t) are respectively the positive and negative spinning reserve capacities that unit i can provide during period t, which are determined by the following formula:

[0240]

[0241] s d (i,t)=P source (i,t)-P source (i) S(i,t) (42)

[0242] The spinning reserve capacity is limited by the upper and lower limits of the unit output. Obviously, the positive and negative spinning reserve capacities of the unit are both 0 when it is shut down. In addition, the spinning reserve capacity of the thermal power unit is also limited by the ramp speed:

[0243] s u,th (i,t)≤r(i)ΔT (43)

[0244] s d,th (i,t)≤r(i)ΔT (44)

[0245] A specific case simulates a receiving power grid. Taking the day-ahead dispatch of a typical working day as an example, the maximum load of the power grid is 18943MW, and the daily peak-to-valley difference is 10342MW. The total installed capacity of thermal power in the area is 2050MW, and the total installed capacity of hydropower is 3830MW. Most of the power supply comes from surrounding power sources and is transmitted to the center through a 500kV ring network. The capacity of the external power receiving channel is 14100MW, most of which is clean energy such as hydropower, wind and light, and the capacity of the power receiving channel dominated by thermal power is only 1800MW. The huge external dependence on electricity and the peak-to-valley difference in load have led to a very serious situation for the safe operation of the power grid, and the pressure of the local section flow is extremely high.

[0246] There are about 460,000 electric vehicles in the district. After preliminary investigation and research, about 58,000 of them can be included in the current dispatchable control resources and divided into 38 clusters, with a total daily charging capacity of about 2,200MWh.

[0247] In order to guide the temporal and spatial transfer of electric vehicle clusters, the charging electricity price is divided into three levels of peak, flat and valley prices in time, and two levels of electricity price in space, namely, heavy-load nodes and non-heavy-load nodes. The discharge electricity price during peak hours is 1.4 times the charging electricity price, as shown in Table 1. The peak, valley and flat periods are divided based on whether the predicted load of the corresponding period exceeds 80% of the maximum daily load, is less than 55% and is between the two respectively; the heavy-load node and non-heavy-load node are divided based on whether the maximum load of the node exceeds 85% of the upper limit of the node capacity;

[0248] Table 1 Charging and discharging electricity price Unit: Yuan / kWh

[0249]

[0250] The large-scale interruptible load resources in the area are distributed on 16 heavy-load nodes, and the maximum total load reduction can be 870MW.

[0251] In order to study the optimization effects of electric vehicle clusters and large-scale interruptible loads on section power flow, the following four different scenarios are set up:

[0252] Scenario 1: No load-side response resources;

[0253] Scenario 2: Only the electric vehicle cluster participates in the grid response;

[0254] Scenario 3: Only large-scale interruptible loads participate in grid response;

[0255] Scenario 4: Electric vehicle clusters and large-scale interruptible loads jointly participate in grid response.

[0256] The optimization calculation results under different scenarios are shown in Table 2.

[0257] Table 2 Optimization calculation results under different scenarios

[0258]

[0259]

[0260] The heavy overload section flow curves in each scenario are as follows: Figures 5 to 8 shown.

[0261] The above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any technician familiar with the art can make many possible changes and modifications to the technical solution of the present invention by using the above-mentioned technical content without departing from the scope of the technical solution of the present invention, or modify it into an equivalent embodiment of equivalent changes. Therefore, any changes, modifications, equivalent changes and modifications made to the above embodiments based on the technology of the present invention without departing from the content of the technical solution of the present invention belong to the protection scope of the present technical solution.

Claims

1. A cross-section power flow optimization method considering the participation of load-side response resources in day-ahead dispatching, characterized by: The method includes: Large-scale interruptible load response mechanism, which reduces load within the interruptible load reduction potential range according to the grid dispatching needs; electric vehicle cluster time transfer mechanism, which guides the time transfer of electric vehicle cluster charging load through the time-of-use electricity price mechanism, and encourages electric vehicle users to send electricity back to the grid during peak load periods and charge during off-peak periods in the V2G mode; The spatial transfer mechanism of electric vehicle clusters guides the spatial transfer of electric vehicle cluster charging loads through a regional electricity price mechanism; a flexible optimization model for section power flow is constructed with the total regulation cost of electric vehicle clusters and large-scale interruptible loads and the lowest power generation cost of all power sources as the objective function, and the optimization plan is output through the flexible optimization model for section power flow.

2. The section power flow optimization method considering the load-side response resources participating in the day-ahead dispatching according to claim 1 is characterized by: The time-of-use electricity price is formulated as: C p (t1)E+C rest +C exp ≤C p (t0)E (1) In the formula, C p (t0) and C p (t1) are the charging electricity prices for the corresponding periods before and after the electric vehicle load transfer; E is the planned charging power of the electric vehicle; C rest Other costs of participating in space-time transfer include extra time, power consumption, and battery life loss; C exp Profit from users' psychological expectations; C p ≤η p or g C g (2) In the formula, C p and C g are the charging and discharging electricity prices of electric vehicles respectively; η p and η g are the charge and discharge efficiencies, respectively.

3. The section power flow optimization method considering the load-side response resources participating in the day-ahead dispatching according to claim 1 is characterized by: The regional electricity prices are formulated as follows: C p (A)E+C rest +C exp ≤C p (B)E (3) In the formula, C p (A) and C p (B) are the charging electricity prices at nodes A and B respectively, while for electric vehicle users who transfer in space, C rest Calculated by the following formula: Where, t road It takes time to transfer space; ω is the power consumption coefficient; is the time cost coefficient.

4. The section power flow optimization method considering the load-side response resources participating in the day-ahead dispatching according to claim 1 is characterized by: The objective function is: min F=F c +F g +β(K sec -1) (5) In the formula, F c is the total adjustment cost, F g is the total power generation cost, K sec is the section flow overload multiple, and β is the section flow overload penalty coefficient.

5. The section power flow optimization method considering the load-side response resources participating in the day-ahead dispatching according to claim 4 is characterized by: The total adjustment cost F c The calculation formula is as follows: F c =C EV +C irl (6) In the formula, C EV Adjusting the cost for electric vehicle clusters, C irl is the scaled interruptible load regulation cost; the electric vehicle cluster regulation cost C EV The calculation formula is as follows: C EV =∑C0-∑C p +∑C g (7) Where C0 is the charging electricity cost of the electric vehicle cluster under the conventional charging method; C p and C g They are respectively the charging electricity cost and discharging electricity cost of the electric vehicle cluster after optimized scheduling; Large-scale interruptible load regulation cost C irl The calculation formula is as follows: Where C1(i) ​​and C2(i) are the unit electricity compensation cost and single response compensation cost of the i-th interruptible load user, respectively. irl (i, t) is the load value that the i-th interruptible load user actively reduces in period t, B irl (i, t) is the user’s response status at the current moment, N irl is the number of users, ΔT is the duration of a scheduling cycle, and nt is the number of scheduling periods.

6. The section power flow optimization method considering the load-side response resources participating in the day-ahead dispatching according to claim 4 is characterized by: The total electricity generation cost F g The calculation formula is: Where P i，t is the output of the i-th unit in period t, N is the number of units, A i,2 , A i,1 and A i,0 is the cost coefficient of the unit.

7. The section power flow optimization method considering the load-side response resources participating in the day-ahead dispatching according to claim 4 is characterized by: The constraints of the section power flow flexible optimization model include large-scale interruptible load constraints, electric vehicle cluster constraints, and power grid operation and safety constraints.

8. The section power flow optimization method considering the load-side response resources participating in the day-ahead dispatching according to claim 7 is characterized by: The scaled interruptible load constraints include: Reduce upper and lower load constraints: In the formula, and P irl, i , t are the upper and lower limits of load reduction, S irl,i,t is a 0-1 variable representing the reduction status; Maximum reduction time constraint: In the formula, S irl,i,max The maximum number of reduction periods agreed by the user within a scheduling plan; Maximum reduction number constraint: In the formula, B irl,i,t B is a 0-1 variable representing the period of load reduction. A value of 1 indicates that load reduction starts from this period. irl,i,max The maximum number of reductions agreed by the user within a scheduling plan; Single shortest reduction time constraint: In the formula, E irl,i,t is a 0-1 variable representing the end of the load shedding period, and a value of 1 indicates that the load shedding will stop from this period; T sus,i The minimum number of time periods for a single load reduction agreed upon by the user; The shortest interval constraint between two adjacent reduction commands: Where, T int,i The minimum number of time periods between two load reductions agreed upon by the user.

9. The section power flow optimization method considering the load-side response resources participating in the day-ahead dispatching according to claim 7 is characterized by: The electric vehicle cluster constraints include: Electric vehicle power change constraints: Where E(i,t) is the battery capacity of the ith electric vehicle cluster at the end of period t; η p and η g They are charging and discharging efficiency respectively; upper and lower limits of charging and discharging power are: 0≤P p (i,t)≤P(i,t)P p,max (i) (16) 0≤P g (i,t)≤G(i,t)P g,max (i) (17) Where P p,max (i) and P g,max (i) are the maximum charging power and maximum discharging power of the i-th electric vehicle cluster, respectively. P(i, t) and G(i, t) are 0-1 variables representing whether the cluster is charging or discharging in time period t; Interlocking constraints during charging and discharging process: This constraint restricts the charging and discharging of electric vehicles from being carried out simultaneously and must be separated by at least two time periods; Battery power upper and lower limit constraints: E min (i)≤E(i,t)≤E max (i) (19) In the formula, E max (i) E min (i) is the upper and lower limits of the battery power of the i-th electric vehicle cluster; Electric vehicle cluster access and off-grid conditions and constraints: E(i,t in (i))=E in (i) (20) E(i,t out (i))=E out (i) (21) P(i,t)=G(i,t)=0 t≤t in (i)∣t>t out (i) (22) Where, t in (i) and t out (i) is the grid access time and agreed grid-off time of the ith electric vehicle cluster; E in (i) and E out (i) The battery capacity when entering the network and the battery capacity when leaving the network respectively; Electric vehicle space transfer constraints: When an EV transfers from aggregator k to aggregator m for charging, the following constraints are satisfied: t in (i)=t in (i′)+t road (k,m) (23) E in (i)=E in (i′)-ω(i)t road (k,m) (24) Where, t in (i′) and E in (i′) is the grid access time and grid access power of the i-th electric vehicle cluster without considering spatial transfer; Aggregator charging and discharging power upper and lower limits: In the formula, Ω j represents the set of all electric vehicles charged and discharged at the jth aggregator, P p,union，max (j,t) and P g,union，max (j, t) are the maximum charging and discharging powers that the jth aggregator can exchange with the grid in period t, respectively; Electric vehicle user response willingness constraints: In the formula, C c (i) is the net charging electricity cost paid by the i-th electric vehicle cluster; C0(i) is the electricity cost under conventional charging mode; C p (i, t) and C g (i,t) are the charging and discharging electricity prices of the i-th electric vehicle cluster in period t.

10. The section power flow optimization method considering the load-side response resources participating in the day-ahead dispatching according to claim 7 is characterized by: The grid operation and safety constraints include: System power balance constraints: Where P source (i,t) is the output of the i-th unit in period t, P load (t) is the total network load in the corresponding period; System network flow security constraints: Among them, P l (i,t) is the power flow of the ith line in period t, is the upper limit of the transmission power of the line; For the cross section, we have: Among them, P sec (i,t) is the tidal current of the i-th section in period t, is the upper limit of the transmission power of the section; Unit output upper and lower limit constraints: in, and P source ( i ) are the upper and lower limits of the output of the i-th unit, respectively, and S(i,t) is the start and stop status of the unit in the t-th scheduling period; Unit climbing constraints: |P th (i,t)-P th (i,t-1)|≤r(i)ΔT (36) Among them, P th (i,t) is the output of the i-th thermal power unit in period t, r(i) is the ramp rate; Operation constraints of hydropower units: V(i,t)-V(i,t-1)=ΔT[Q in (i,t)-Q out (i,t)] (37) P h (i,t)=ηρgH[Q out (i,t)-Q waste (i,t)] (38) Among them, V(i,t) is the water storage capacity of the i-th reservoir at the end of period t, Q in (i,t),Q out (i, t) are the average inflow and outflow of the period respectively; P h (i, t) is the output of the i-th hydropower station unit in the t period, η is the power generation efficiency of the hydropower unit, ρ is the density of water, g is the acceleration of gravity, H is the average head height, Q waste (i,t) is the amount of water discarded during this period; Spinning reserve constraints: Where: L + % and L - % are the positive and negative spinning reserve capacity coefficients of the system respectively; s u (i,t) and s d (i, t) are respectively the positive and negative spinning reserve capacities that unit i can provide during period t, which are determined by the following formula: In addition, the spinning reserve capacity of thermal power units is also limited by the ramp speed: s u,th (i,t)≤r(i)ΔT (43) s d,th (i,t)≤r(i)ΔT (44)。

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