Scheduling optimization method and system for clean energy-containing electric power system considering delivery

Abstract

The invention belongs to the related technical field of power system dispatching, and discloses a dispatching optimization method and system for a clean energy-containing power system considering delivery, and the optimization method comprises the steps: building a clean energy-containing power system model Mod1 considering the operation constraint of a unit and the network security constraint, and the unit comprises the model Mod1; the system comprises a wind turbine generator, a photovoltaic power station, a thermal power generating unit, a cascade hydropower station unit and a pumped storage unit. Establishing a direct-current delivery system model Mod2 for outputting the electric energy of the electric power system containing the clean energy to each receiving end area; integrating the model Mod1 and the model Mod2 after the upper limit and the lower limit of the safe receiving value of the active power of each receiving end area are substituted into the upper limit constraint and the lower limit constraint of the safe receiving value of the active power in the model Mod2 to obtain a clean energy-containing electric power system model Mod3 considering delivery; and solving the model Mod3 by taking the minimum operation cost as a target to obtain a scheduling scheme in the scheduling time sequence. Through the method, the scheduling strategy can be optimized, and waste of clean energy can be reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field related to power system dispatching, and more specifically, relates to a dispatching optimization method and system for a clean energy - containing power system considering external power transmission. Background Art

[0002] With the steady progress of the goals of carbon peak and carbon neutrality, the new power system is accelerating its construction, and the grid connection of non - fossil energy is increasing day by day. Promoting the green and low - carbon transformation of energy, advancing the clean and efficient utilization of energy, and enhancing the level of technology R & D are the inevitable trends of energy development. Most of the regions rich in clean energy are located in the western regions with relatively backward economic development, and the in - province consumption market is very limited. It is necessary to transmit power over long - distance transmission lines to the out - of - province load centers for consumption.

[0003] At present, the dispatching of clean energy usually adopts the isolated modeling method of a single power station. For example, for a traditional run - of - river hydropower station, if the isolated modeling method of a single power station is adopted, the relevance of the hydrological response process within the region will be ignored. Moreover, in the current situation of large - scale grid connection of wind power and photovoltaic power, the single model has certain limitations because it does not consider the collaborative dispatching potential between power stations, resulting in the underestimation of the overall system consumption efficiency. Therefore, although this modeling method is simple, the dispatching effect is not good, and it is easy to cause waste of resources.

[0004] Moreover, traditional research has not fully explored the potential for coordinated optimization of the DC external power transmission tie - lines on the transmission grid side. Currently, DC external power transmission tie - lines are usually regarded as simple transmission components within the power grid, and it is considered that they operate in a fixed or stepped constant - power mode. The fixed or stepped constant - power external power transmission mode is difficult to fully respond to the rapid fluctuations of new energy output. Especially in the case of high uncertainties of renewable energy such as wind power and photovoltaic power, problems such as untimely power adjustment, waste of transmission capacity, or insufficient safety margin are likely to occur, resulting in poor dispatching effects.

[0005] Therefore, there is an urgent need to propose a new dispatching method to improve the dispatching effect of clean energy and the utilization rate of renewable energy. Summary of the Invention

[0006] In view of the above - mentioned defects or improvement requirements of the prior art, the present invention provides a dispatching optimization method and system for a clean energy - containing power system considering external power transmission, aiming to optimize the dispatching strategy, realize the efficient utilization of large - scale clean energy, and make it play a greater economic benefit.

[0007] According to the first aspect of the present invention, there is provided a dispatching optimization method for a clean energy - containing power system considering external power transmission, which includes:

[0008] Build a clean energy integrated power system model Mod1 considering unit operation constraints and network security constraints. The units include wind turbines, photovoltaic power plants, thermal power plants, cascade hydropower units, and pumped-storage units.

[0009] Build a DC transmission system model Mod2 for outputting the electric energy of the clean energy integrated power system to each receiving area. The model Mod2 includes the upper and lower limit constraints of the active power safe receiving value for each receiving area.

[0010] After substituting the upper and lower limits of the active power safe receiving value of each receiving area into the upper and lower limit constraints of the active power safe receiving value in the model Mod2, integrate the model Mod1 and the model Mod2 to obtain a clean energy integrated power system model Mod3 considering transmission.

[0011] Solve the model Mod3 with the goal of minimizing the operating cost to obtain a scheduling plan within the scheduling time sequence.

[0012] Optionally, in the model Mod1, the network security constraints include: network power balance constraint, network power flow constraint, network spinning reserve constraint; the unit operation constraints include: operation constraints of photovoltaic and wind power, thermal power operation constraint, cascade hydropower operation constraint, pumped-storage operation constraint, electrochemical energy storage operation constraint; the model Mod2 includes converter-side power balance constraint, converter-side power flow constraint, DC line-side operation constraint.

[0013] Optionally, the method for obtaining the upper and lower limits of the active power safe receiving value of each receiving area is as follows:

[0014] Build a receiving-end model for analyzing the acceptance capacity of each receiving area. The receiving-end model includes load balance constraint, line transmission power limit constraint, spinning reserve constraint, and conventional thermal power unit constraint of the receiving area. Solve the receiving-end model to obtain the upper and lower limits of the active power safe receiving value of the receiving area.

[0015] Optionally, use a time-sequence operation order rolling algorithm to solve the model Mod3, including:

[0016] Divide the scheduling time sequence range into K equally spaced sections;

[0017] Solve the model Mod3 for each section in turn, and use the obtained optimal solution as the scheduling plan for the corresponding section.

[0018] Optionally, use a no-feasible-solution automatic rollback algorithm to solve the model Mod3, including:

[0019] S41. Initialize r = 0;

[0020] S42. Use the system operating status at the end of the (k - r - 1)-th section as the initial status of the k-th section, solve the k-th section. If there is a feasible solution for this section, use this feasible solution as the scheduling plan for this section, update k = k + 1, and then jump back to S41. If there is no feasible solution for this section, execute S43; where no feasible solution includes no solution to the model or the obtained solution being judged as unreasonable.

[0021] S43. Update r = r + 1, and jump to S42.

[0022] Optionally, when the obtained solution does not satisfy the following conditions, it is judged as unreasonable:

[0023]

[0024] In the formula, is the unit coal consumption cost, C line is the penalty coefficient for unit transmission power deviation, C v , C w are the penalty coefficient for unit light curtailment and the penalty coefficient for unit wind curtailment respectively.

[0025] According to the second aspect of the present invention, there is provided a dispatching and optimization system for a clean energy - containing power system considering external transmission, including:

[0026] The first modeling unit is used to establish a clean energy - containing power system model Mod1 considering unit operation constraints and network security constraints. The units include: wind turbine units, photovoltaic power plants, thermal power units, cascade hydropower units, and pumped - storage units;

[0027] The second modeling unit is used to establish a DC external transmission system model Mod2 for outputting the electric energy of the clean energy - containing power system to each receiving area. The upper and lower limits of the active power safety receiving value of each receiving area are included in the model Mod2;

[0028] The model integration unit is used to substitute the upper and lower limits of the active power safety receiving value of each receiving area into the upper and lower limits of the active power safety receiving value in the model Mod2, and then integrate the model Mod1 and the model Mod2 to obtain a clean energy - containing power system model Mod3 considering external transmission;

[0029] The solving unit is used to solve the model Mod3 with the goal of minimizing the operating cost to obtain the scheduling plan within the dispatching time sequence.

[0030] Optionally, in the model Mod1, the network security constraints include: network power balance constraint, network power flow constraint, network spinning reserve constraint; the unit operation constraints include: operation constraints of photovoltaic and wind power, thermal power operation constraint, cascade hydropower operation constraint, pumped storage operation constraint, electrochemical energy storage operation constraint; the model Mod2 also includes converter side power balance constraint, converter side power flow constraint, DC line side operation constraint.

[0031] Optionally, the solving unit includes:

[0032] A section dividing sub-unit, configured to divide the dispatching time sequence range into K equidistant sections;

[0033] A section solving sub-unit, configured to solve the model Mod3 for each section in sequence, and use the obtained optimal solution as the dispatching plan for the corresponding section;

[0034] The section solving sub-unit includes:

[0035] An initialization module, configured to start the operation module after initializing r = 0;

[0036] An operation module, configured to use the system operation state at the end of the (k - r - 1)-th section as the initial state of the k-th section, solve the k-th section. If there is a feasible solution for this section, use this feasible solution as the dispatching plan for this section, update k = k + 1 and then start the initialization module again. If there is no feasible solution for this section, start the rollback module; where no feasible solution includes that the model has no solution or the obtained solution is judged to be unreasonable;

[0037] A rollback module, configured to update r = r + 1 and then start the operation module again.

[0038] According to the third aspect of the present invention, there is provided a computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0039] Generally speaking, compared with the prior art through the above technical solutions conceived by the present invention, the present invention mainly has the following beneficial effects:

[0040] 1. In the present invention, models are established for both the clean energy power system and the DC power transmission system. The upper and lower limits of the safe receiving value of the active power in the receiving area are brought into the DC power transmission system model for constraint. The models of the clean energy power system and the DC power transmission system are integrated to obtain a clean energy power system model considering power transmission. Since it takes into account the optimal operation of different power sources in the clean energy system, the optimal operation of the DC power transmission system, and the safe receiving limit values of the active power in each receiving area, thus, the integrated model is more in line with the actual situation, which makes the dispatching strategy better, reduces the waste of clean energy, realizes the efficient utilization of large-scale clean energy, and enables it to exert greater economic benefits.

[0041] 2. In the present invention, the predicament of isolated modeling is broken through, and a model of a clean energy DC power transmission system with multiple sources complementing each other is constructed. The clean energy power system model takes into account wind turbine units, photovoltaic power plants, thermal power units, cascade hydropower station units, and pumped storage units. The covered energy types are very comprehensive, and the multi-energy complementarity of wind, light, and water under real scenarios is considered, which can realize the coordinated dispatching of multiple energies. Thus, a better dispatching strategy can be obtained, the waste of clean energy can be reduced, the efficient utilization of large-scale clean energy can be realized, and greater economic benefits can be exerted.

[0042] 3. In one embodiment, a sequential operation order rolling algorithm is used to optimize the dispatching plan of the long-time series system, which can reduce the operation and solution scale of the entire model while obtaining optimized operation results with sufficient category richness.

[0043] 4. In one embodiment, a non-feasible solution automatic rollback algorithm is used to solve the model, which can solve the shortage of adjustable resources in the current time period caused by meeting the optimal operation of the system in the previous time period or several previous time periods, thus resulting in the situation of no feasible solution in the current time period, and accelerating the solution speed.

[0044] 5. In one embodiment, by judging whether the solution is reasonable, the operation of the clean energy DC power transmission system can meet the requirements of giving priority to the consumption of wind and light and matching the DC power transmission power of the plan, so as to reduce the amount of wind and light abandoned and ensure the normal implementation of cross-regional electricity market transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flowchart of the steps of a dispatching optimization method for a clean energy power system considering power transmission in an embodiment of the present invention;

[0046] Figure 2 is a schematic diagram of the output of each unit in a clean energy power system considering power transmission obtained by solving in an embodiment of the present invention;

[0047] Figure 3(a) is the operating curve of the clean energy - containing power system with external power transmission on the first day obtained by solving in an embodiment of the present invention;

[0048] Figure 3(b) is the operating curve of the clean energy - containing power system with external power transmission on the 91st day obtained by solving in an embodiment of the present invention;

[0049] Figure 3(c) is the operating curve of the clean energy - containing power system with external power transmission on the 182nd day obtained by solving in an embodiment of the present invention;

[0050] Figure 3(d) is the operating curve of the clean energy - containing power system with external power transmission on the 274th day obtained by solving in an embodiment of the present invention;

[0051] Figure 4(a) is the schematic diagram of the start - stop state of the thermal power unit on the first day obtained by solving in an embodiment of the present invention;

[0052] Figure 4(b) is the schematic diagram of the start - stop state of the thermal power unit on the 91st day obtained by solving in an embodiment of the present invention;

[0053] Figure 4(c) is the schematic diagram of the start - stop state of the thermal power unit on the 182nd day obtained by solving in an embodiment of the present invention;

[0054] Figure 4(d) is the schematic diagram of the start - stop state of the thermal power unit on the 274th day obtained by solving in an embodiment of the present invention. Detailed implementation manners

[0055] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to 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. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0056] Embodiment 1

[0057] The present invention proposes a dispatching optimization method for a clean - energy - containing power system considering external power transmission. As Figure 1 shown in the flow - chart of the steps of the dispatching optimization method for a clean - energy - containing power system considering external power transmission in an embodiment of the present invention, the steps are introduced in detail below.

[0058] S1. Establish a clean - energy - containing power system model Mod1 considering unit operation constraints and network security constraints. The power station includes; the units include wind turbine units, photovoltaic power stations, thermal power units, cascade hydropower units and pumped - storage units.

[0059] Specifically, this step combines multiple clean energy sources with traditional thermal power generation, aiming to minimize the total operating cost of the system except for infrastructure construction costs and subsequent maintenance costs, which is the final solution goal. As shown in the following formula:

[0060]

[0061] In the formula, t is the number of each time period; MT is the total number of time periods; MG, MW, MV, MP, MH, and ML are the total numbers of thermal power units, wind power units, photovoltaic power stations, pumped-storage units, cascade hydropower station units, and DC tie lines respectively; g, w, v, p, h, and line are the numbers of thermal power units, wind power units, photovoltaic power stations, pumped-storage units, cascade hydropower station units, and DC tie lines respectively; q is the number of each segment after segmenting the coal consumption cost curve of the thermal power unit; is the slope of the thermal power unit g on the q-th segment of the coal consumption curve; is the power generation of the thermal power unit g on the q-th segment of the coal consumption cost curve at time period t; N g is the no-load coal consumption of the thermal power unit g; is the start-stop status of the thermal power unit g at time period t, where "1" represents starting up and "0" represents shutting down; and are the coal consumptions of the thermal power unit g starting up and shutting down at time period t respectively; C coal is the unit price of coal fuel; C w is the unit penalty coefficient for wind abandonment of the wind power unit w; and are the predicted output value and the actual output value of the wind power unit w at time period t respectively; C v is the unit penalty coefficient for power abandonment of the photovoltaic power station v; and P v t are the predicted output value and the actual output value of the photovoltaic power station v at time period t respectively; C p and C h are the start-stop costs of the hydroelectric unit p of the pumped-storage power station and the hydroelectric unit h of the cascade hydropower station respectively; and are the total start-stop times of the hydroelectric unit p of the pumped-storage power station and the hydroelectric unit h of the cascade hydropower station at time period t respectively; and are the start-up times and shutdown times of the hydroelectric unit h of the cascade hydropower station at time period t respectively; and are the start-up and shutdown times of the hydroelectric unit p of the pumped-storage power station in the pumping state at time period t respectively; and are the start-up and shutdown times of the hydroelectric unit p of the pumped-storage power station in the discharging state at time period t respectively; Cline is the penalty coefficient for the deviation of the unit transmission power of DC tie line line; is the active power on DC tie line line at time t; is the predicted active power flowing through DC tie line line in the day-ahead at time t.

[0062] In the clean energy-integrated power system model Mod1, the network security constraints include: network power balance constraint, network power flow constraint, network spinning reserve constraint; the unit operation constraints include: the operation constraints of photovoltaic and wind power, thermal power operation constraint, cascade hydropower operation constraint, pumped-storage operation constraint, electrochemical energy storage operation constraint.

[0063] The network power balance constraint is:

[0064]

[0065] In the formula, MBA is the total number of electrochemical energy storage power stations; ba is the number of the electrochemical energy storage power station; is the active power generation of cascade hydropower station unit h at time t; and are the active power generation and pumping active power of pumped-storage hydropower station unit p at time t, respectively; and are the discharging active power and charging active power of electrochemical energy storage power station ba at time t, respectively; is the active power demand of load d at time t; is the active power flowing into the outgoing converter dcts at time t.

[0066] Network power flow constraint conditions:

[0067]

[0068] In the formula, is the maximum power flow limit of branch k in the system network; and are the PTDF matrices corresponding to the thermal power unit g, cascade hydropower station unit h, wind turbine w, photovoltaic power station v, pumped-storage hydropower station unit p, electrochemical energy storage power station ba, load d and outgoing converter dcts and branch k in the system network, respectively.

[0069] Network spinning reserve constraint conditions:

[0070]

[0071]

[0072] In the formula, and are the upper and lower limits of the active power generation of the thermal power unit g, respectively; and are the upper and lower limits of the active power generation of the cascade hydropower station unit h, respectively; is the start / stop status of the cascade hydropower station unit h. "1" indicates the unit is on, and "0" indicates the unit is off; is the maximum power generation of the pumped-storage hydropower station unit p; is the maximum pumping power of the pumped-storage hydropower station unit p; is the maximum discharge power of the electrochemical energy storage power station ba; is the maximum charging power of the electrochemical energy storage power station ba; and are the lower and upper limit values of the active power flowing into the external transmission converter dcts, respectively; R d is the spinning reserve rate of the load (d), and its value is taken as 5%; and are the spinning reserve rates generated by introducing wind power (w) and photovoltaic power (v) in period t, respectively; is the upper limit of the active power generation of the wind turbine w, and its lower limit of active power generation is taken as 0; is the upper limit of the active power generation of the photovoltaic power station v, and its lower limit of active power generation is taken as 0.

[0073] Operating constraints of photovoltaic and wind power:

[0074] The active power generations of the photovoltaic power station v and the wind turbine w in period t and shall not exceed the predicted values of the active power generations in their corresponding periods and as shown in the following formula.

[0075]

[0076]

[0077] Operating constraints of thermal power:

[0078]

[0079]

[0080] In the formula, C stp is the total number of segments after segmenting the coal consumption cost curve of the thermal power unit g; su g and sd g are the unit coal consumptions of the thermal power unit g when it is on and off, respectively; UR g and UD g are the upward ramp rate and downward ramp rate of the thermal power unit g, respectively; and are the minimum start-up time and the minimum shut-down time of the thermal power unit g, respectively.

[0081] Cascade hydropower operation constraints:

[0082]

[0083]

[0084] In the formula, η h is the hydropower conversion efficiency of the hydropower unit h of the cascade hydropower station; is the generated flow rate of the hydropower unit h of the cascade hydropower station at time t; is the generated head of the hydropower unit h of the cascade hydropower station at time t; is the initial generated head of the hydropower unit h of the cascade hydropower station; α h is the head coefficient of the hydropower unit h of the cascade hydropower station; is the reservoir capacity of the hydropower unit h of the cascade hydropower station at time t; and are the minimum and maximum generated flow rates of the hydropower unit h of the cascade hydropower station; is the inflow rate of the hydropower unit h of the cascade hydropower station at time t; Δt h'h is the water flow lag time from the upstream hydropower unit h' of the cascade hydropower station to the downstream hydropower unit h of the cascade hydropower station, that is, the time required for the outflow rate of the upstream hydropower unit h' of the cascade hydropower station to reach the downstream hydropower unit h of the cascade hydropower station; is the generated flow rate released by the hydropower unit h' of the cascade hydropower station before Δt h' ; is the natural inflow rate of the hydropower unit h of the cascade hydropower station at time t; and are the maximum reservoir capacity and the minimum reservoir capacity of the hydropower unit h of the cascade hydropower station, respectively; and are the reservoir capacities of the hydropower unit h of the cascade hydropower station at the beginning and end of the specified scheduling period, respectively; is the maximum total number of start-up and shut-down operations (each start-up and shut-down is counted as 1 time, a total of 2 times) allowed for the hydropower unit h of the cascade hydropower station within the range from the beginning to the end of the unit scheduling time. The values of all hydropower units are 20; and are the start-up times and shut-down times of the hydropower unit h of the cascade hydropower station at time t, respectively.

[0085] Pumped-storage operation constraints:

[0086]

[0087] In the formula, is the minimum generating power of the pumped - storage hydropower station unit p; is the minimum pumping power of the pumped - storage hydropower station unit p; and respectively represent the switching conditions of the pumped - storage hydropower station unit p in the pumping and generating states during the t - th period. "1" indicates starting up, and "0" indicates shutting down; and respectively represent the number of start - ups and shutdowns of the pumped - storage hydropower station unit p in the pumping state during the t - th period; and respectively represent the number of start - ups and shutdowns of the pumped - storage hydropower station unit p in the discharging state during the t - th period; N p is the maximum total number of times the pumped - storage hydropower station unit p is allowed to switch units within the unit dispatching time range. The N p value for all units is 20; and respectively represent the upper reservoir capacity and lower reservoir capacity of the pumped - storage hydropower station unit p during the t - th period; η g and η p respectively represent the water conversion coefficient of the pumped - storage hydropower station unit p in the generating state and the electricity conversion coefficient in the pumping state, that is, the ratio of the conversion between electricity and water of the pumped - storage hydropower station unit p in the generating and pumping states.

[0088] Electrochemical energy storage operation constraints:

[0089]

[0090]

[0091] In the formula, is the energy stored in the electrochemical energy storage power station ba during the t - th period; μ g and μ p respectively represent the energy conversion coefficients of the electrochemical energy storage power station ba during energy release and energy charging, that is, the ratio of the conversion between power and energy of the electrochemical energy storage power station ba in the energy release and energy charging states; and respectively represent the lower limit and upper limit of the energy that the electrochemical energy storage power station can store.

[0092] S2. Establish a DC external transmission system model Mod2 for outputting the electric energy of the clean - energy - containing power system to each receiving region. The model Mod2 includes the upper and lower limit constraints of the active power safe receiving values of each receiving region.

[0093] Flexible DC transmission technology has unique advantages in the large-power and long-distance transmission of clean energy. The present invention combines the spatio-temporal complementary characteristics of various types of power sources in the clean energy base with the high flexibility of the regulation capabilities of flexible DC transmission lines, pumped-storage hydropower station units, and electrochemical energy storage power stations, to provide stable and appropriate electrical energy for the receiving-end regional power grid and improve the consumption of wind and solar resources.

[0094] The multi-terminal flexible DC grid can adopt a bipolar connection method, and both the starting end and the receiving end have the ability to operate independently. When a fault occurs in a certain pole connection line, as long as any other pole connection line has the power supply ability, the fault can be successfully transferred. Therefore, in order to ensure that there is no power cut-off phenomenon on the connection line when an N-1 fault occurs, the current flowing through the remaining connection lines at this time should not exceed the maximum current-carrying capacity of the circuit breakers of these lines. Based on this, a DC transmission system model Mod2 is established.

[0095] Among them, the model Mod2 includes the upper and lower limit constraints of the safe receiving value of the active power in each receiving-end region, which are specifically as follows:

[0096]

[0097] In the formula, is the power received by the receiving-end system B from the DC transmission system during the t period, and are respectively the lower limit and upper limit of the safe receiving value of the active power in the receiving-end region B, and their values are determined through subsequent analysis of the receiving capacity of the receiving-end region; η line is the loss (variable loss, line loss) rate of the power transmitted on the DC connection line line, and its value can be set to 4%.

[0098] Specifically, the model Mod2 also includes the power balance constraint on the converter side, the power flow constraint on the converter side, and the operation constraint on the DC line side.

[0099] Power balance constraint on the converter side:

[0100]

[0101] Power flow constraint on the converter side:

[0102]

[0103] In the formula, is the active power loss generated when flowing from the o end through the resistor R inside the outgoing converter dcts to reach the o' end during the t period. According to the principle of the DC power flow method, the pure resistance loss is 0; is the active power flowing through the o' end of the connection line side of the outgoing converter dcts during the t period; is the active power flowing out of the external transmission converter DCTS during period t; and are respectively the lower limit value and the upper limit value of the active power flowing out of the external transmission converter DCTS; is the power received by the receiving-end system B from the DC external transmission system during period t; is the maximum active power that the tie line line can withstand; MB is the total number of receiving-end systems; out is the number of the external transmission converter outlet; and are respectively the PTDF matrices corresponding to the external transmission converter outlet out and between the receiving-end system B and the external transmission tie line line.

[0104] Assume that the DC tie line is an internal connection line between interconnected power grids. Combining the power transmission plan of the DC external transmission system and the safety receiving limit value of the active power in the receiving area, the entire DC external transmission system containing clean energy is coordinated and optimized to reduce the loss of wind and solar resources.

[0105] Operating constraints on the DC line side:

[0106]

[0107] In the formula, UR line and UD line are the upper and lower limits of the adjustment of the transmission power of the DC tie line line in the adjacent period; is the upward adjustment state of the DC tie line line during period t, "1" means upward adjustment, "0" means no upward adjustment; is the downward adjustment state of the DC tie line line during period t, "1" means downward adjustment, "0" means no downward adjustment; N line is the maximum number of adjustments of the DC tie line line within the planned period (the sum of the upward and downward adjustment times); Δt is the unit time interval, and its value is 1h.

[0108] S3. After substituting the upper and lower limits of the safe receiving value of the active power in each receiving area into the upper and lower limit constraints of the safe receiving value of the active power in Model Mod2, Model Mod1 and Model Mod2 are integrated to obtain the clean energy power system model Mod3 considering external transmission.

[0109] Specifically, a receiving-end model for analyzing the acceptance capacity of each receiving-end area can be established. Considering constraints such as the load balance constraint, line transmission power limit constraint, spinning reserve constraint, and conventional thermal power unit constraint of each receiving-end area, the upper and lower limit curves of the safe receiving value of the active power of each receiving-end area can be obtained; then, the upper and lower limits of the safe receiving value of the active power obtained from this model are brought into the constraints of the DC external power transmission system model, so as to coordinately optimize the entire DC external power transmission system with clean energy.

[0110] Specifically, the receiving-end model takes the maximum and minimum values of the total transmission energy flowing into each receiving-end area during the planned period as the optimization objectives, as shown in the following formula, to analyze the acceptance capacity of each receiving-end area, and the change of the unit commitment plan of each receiving-end area is not considered during the analysis process.

[0111]

[0112] In the formula, Obj1 and Obj2 are the maximum and minimum values of the total transmission energy flowing into each receiving-end area during the planned period, respectively. The upper and lower limits of the safe receiving value of the active power of each receiving-end area are the values of the objective function.

[0113] S4. Solve the model Mod3 with the goal of minimizing the operating cost to obtain the scheduling plan within the scheduling time sequence.

[0114] Specifically, with the goal of minimizing the operating cost introduced above, solve the integrated power system model with clean energy considering external power transmission, and the scheduling plan can be obtained.

[0115] In specific operations, in order to obtain an optimized operation result with sufficient category richness while reducing the operation and solution scale of the entire model, the time-sequence operation order rolling algorithm can be used to optimize the scheduling plan of the system on a long time sequence (such as the 8760-hour scale).

[0116] The time-sequence operation order rolling algorithm includes:

[0117] Divide the scheduling time sequence range into K equally spaced sections; for example, for 8760 time periods, it can be divided into 365 sections, each section is 24 time periods, and each time period is 1 hour;

[0118] Solve the model Mod3 for each section in turn, and use the obtained optimal solution as the scheduling plan for the corresponding section.

[0119] More specifically, this algorithm can be expressed as the following specific process:

[0120] 1) Obtain the start-stop status and output power size information of all units in the system at the initial time of the optimal scheduling;

[0121] 2) Divide the time sequence range into K equally-spaced time segments, and let k = 0;

[0122] 3) Solve the sub-UC problem of UC(t k +1, t k+1 ). Meanwhile, the start-stop status and output power of all units in the system are assigned the optimal solution of this sub-UC problem of UC(t k +1, t k+1 ) during the time interval [t k +1, t k+1 ;

[0123] 4) Let k = k + 1. If k ≥ K, then exit the entire loop; otherwise, go to step 3) and continue to execute the algorithm.

[0124] Considering that when solving sequentially by segments, in order to meet the optimal operation of the system in the previous time period or several previous time periods, the lack of adjustable resources available in the system during the current time period may occur, resulting in no feasible solution in the current time period. In one embodiment, the algorithm is further optimized, and the infeasible solution automatic rollback algorithm is used for solving. The specific process is as follows:

[0125] S41. Initialize r = 0;

[0126] S42. Use the system operation state at the end of the (k - r - 1)-th segment as the initial state of the k-th segment, and solve the k-th segment. If there is a feasible solution for this segment, use this feasible solution as the scheduling plan for this segment, update k = k + 1, and then jump back to S41. If there is no feasible solution for this segment, execute S43; where, no feasible solution includes no solution to the model or the obtained solution being judged as unreasonable;

[0127] S43. Update r = r + 1, and jump to S42.

[0128] In the above loop, if the current k-th segment is the last segment of the entire full time sequence, then end the process of the annual time sequence operation simulation of the system; otherwise, continue the loop.

[0129] In one embodiment, in order to make the operation of the clean energy DC external power transmission system meet the requirements of giving priority to the consumption of wind and light and matching the DC external power transmission power of the plan, in order to reduce the amount of wind and light abandonment and ensure the normal execution of cross-regional electricity market transactions, after solving the model, calculate the unit coal consumption cost It should satisfy as follows:

[0130]

[0131] In the formula, C v and C ware the unit curtailment penalty coefficients for light and wind, respectively, and C line is the unit transmission power deviation penalty coefficient, is the new unit coal consumption cost considering start-stop cost and no-load cost.

[0132] In the above formula, C v and C w are maximized, which can ensure that the entire system preferentially absorbs wind and light resources; C line is greater than which can prevent the system from sacrificing the matching degree between the DC external transmission system transmission power and the predicted transmission power for the day-ahead in order to reduce the coal consumption cost. Since is a non-linear variable and it is difficult to linearize it, so it is not written as a constraint condition in the modeling. Instead, after the model is successfully solved, it is judged whether the entire system meets the above. If it meets, it means the model is reasonable; otherwise, it means the model does not meet the actual situation and relevant parameters need to be adjusted appropriately to meet the actual requirements.

[0133] Specifically, the solution result can be substituted into the following formula to calculate the corresponding

[0134]

[0135] In the formula, is the new unit coal consumption cost considering start-stop cost and no-load cost of thermal power unit g, with the unit of yuan / MW; is the active power generation of thermal power unit g at time t.

[0136] Generally speaking, the clean energy DC external transmission system constructed by the scheduling optimization method of the clean energy power system considering external transmission proposed by the present invention can save more total system operation costs compared with the traditional unit system and the DC external transmission system; compared with the traditional unit system and the hydropower system, it has more regulation margins and can effectively alleviate the problem of the decline in the load balancing ability of hydropower due to weather changes; compared with the traditional unit system and the pumped-storage system, it can make up for the problem that the reaction speed of the pumped-storage unit system is not fast enough due to various constraints through DC external transmission. All in all, the clean energy DC external transmission system can effectively integrate the various components of the power system, enable them to complement each other's advantages, and to a certain extent reduce the impact of their respective deficiencies on the system operation cost and stability.

[0137] Embodiment 2

[0138] The present invention also relates to a scheduling optimization system for a clean energy power system considering external transmission, including a first modeling unit, a second modeling unit, a model integration unit, and a solving unit.

[0139] The first modeling unit is used to establish a clean energy - integrated power system model Mod1 considering unit operation constraints and network security constraints. The units include: wind turbines, photovoltaic power plants, thermal power plants, cascade hydropower units, and pumped - storage units;

[0140] The second modeling unit is used to establish a DC power transmission system model Mod2 for outputting the electric energy of the clean energy - integrated power system to each receiving area. The model Mod2 includes upper and lower limit constraints on the secure receiving value of active power in each receiving area;

[0141] The model integration unit is used to substitute the upper and lower limits of the secure receiving value of active power in each receiving area into the upper and lower limit constraints on the secure receiving value of active power in the model Mod2, and then integrate the model Mod1 and the model Mod2 to obtain a clean energy - integrated power system model Mod3 considering power transmission;

[0142] The solving unit is used to solve the model Mod3 with the goal of minimizing the operating cost to obtain a scheduling plan within the scheduling time sequence.

[0143] In one embodiment, in the model Mod1, the network security constraints include: network power balance constraint, network power flow constraint, network spinning reserve constraint; the unit operation constraints include: operation constraints of photovoltaic and wind power, thermal power operation constraint, cascade hydropower operation constraint, pumped - storage operation constraint, and electrochemical energy storage operation constraint; the model Mod2 also includes converter - side power balance constraint, converter - side power flow constraint, and DC line - side operation constraint. The specific representation forms of each constraint can refer to the introduction in Embodiment 1 and will not be elaborated here.

[0144] In one embodiment, the solving unit includes:

[0145] A section division sub - unit, which is used to divide the scheduling time sequence range into K equally - spaced sections;

[0146] A section solving sub - unit, which is used to solve the model Mod3 for each section in turn, and use the obtained optimal solution as the scheduling plan for the corresponding section.

[0147] Furthermore, the section solving sub - unit is used to execute the infeasible solution automatic rollback algorithm in Embodiment 1, specifically including:

[0148] An initialization module, which is used to initialize r = 0 and then start the operation module;

[0149] An operation module is used to take the system operation state at the end of the (k - r - 1)-th section as the initial state of the k-th section, solve the k-th section. If there is a feasible solution for this section, use this feasible solution as the scheduling plan for this section, update k = k + 1, and then start the initialization module again. If there is no feasible solution for this section, start the rollback module; where no feasible solution includes that the model has no solution or the obtained solution is judged to be unreasonable;

[0150] The rollback module is used to update r = r + 1 and then start the operation module again.

[0151] Embodiment 3

[0152] The present invention also relates to a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0153] Specifically, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0154] Embodiment 4

[0155] The following uses the improved IEEE 6-bus and IEEE 14-bus systems for example analysis. The IEEE 6-bus system includes 3 thermal power generation units, 1 cascade hydropower station (consisting of an upstream hydropower station and a downstream hydropower station), 1 photovoltaic power station, 1 centralized wind turbine, 1 pumped-storage power station, 1 electrochemical energy storage power station, 3 loads, and 1 DC transmission line. The parameters of the 8760-hour scale input to this system consist of the natural water inflow of the cascade hydropower station, the active load of the clean energy system and the receiving area, the predicted active power output of the wind and light, and the predicted transmission power on the DC tie line the day before.

[0156] Substitute the basic parameters of the power system into the clean energy power system model considering power transmission, solve the model, and obtain the scheduling results of the system. As Figure 2 、 Figures 3(a) to 3(d) 、 Figures 4(a) to 4(d) are the scheduling optimization results; as Figure 2 shows the schematic diagram of the output of each unit in the clean energy power system considering power transmission; as Figures 3(a) to 3(d) shows the operation curves of the clean energy power system considering power transmission, which respectively correspond to the 1st day, the 91st day, the 182nd day, and the 274th day; as Figures 4(a) to 4(d)The figure shows a schematic diagram of the start-up and shut-down states of a thermal power unit, corresponding to the 1st day, 91st day, 182nd day, and 274th day respectively.

[0157] Two application scenarios are set for discussion: a clean energy-integrated power system without considering DC power transmission and a clean energy-integrated power system considering power transmission.

[0158] Table 1 shows the dispatching optimization results of the clean energy-integrated power system considering power transmission, and Table 2 shows the dispatching optimization results of the clean energy-integrated power system without considering DC power transmission.

[0159] Table 1 Dispatching Optimization Results of the Clean Energy-Integrated Power System Considering Power Transmission

[0160]

[0161] Table 2 Dispatching Optimization Results of the Clean Energy-Integrated Power System without Considering DC Power Transmission

[0162]

[0163] Compared with the thermal, wind, and photovoltaic systems and the hydropower system, the addition of a pumped-storage system and a DC power transmission system to this system enables the consumption of some curtailment of wind and photovoltaic power, reduces the curtailment rates of wind and photovoltaic power in the system, and due to the increase in reserve capacity, the situation of no solution to the optimization caused by resource shortage becomes less, and the number of automatic rollbacks decreases from 63 to 52 times, accelerating the solution speed.

[0164] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification. It should be noted that the "in one embodiment", "for example", "again, for example", etc. in the present invention are intended to illustrate the present invention rather than limit the present invention.

[0165] The above embodiments only represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent application. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention.

Claims

1. A method for optimizing the dispatch of a clean energy power system considering external transmission, characterized in that: include: Establish a clean energy power system model Mod1 that takes into account unit operation constraints and network security constraints. The units include: wind turbines, photovoltaic power stations, thermal power units, cascade hydropower station units and pumped storage units; Establishing a DC transmission system model Mod2 for outputting electric energy from the clean energy power system to each receiving end area, wherein the model Mod2 includes upper and lower limit constraints of the active power safety receiving value of each receiving end area; After the upper and lower limits of the safe receiving value of active power in each receiving area are brought into the upper and lower limit constraints of the safe receiving value of active power in the model Mod2, the model Mod1 and the model Mod2 are integrated to obtain the clean energy power system model Mod3 considering external transmission; The model Mod3 is solved with the goal of minimizing the operating cost to obtain a scheduling solution within the scheduling time series.

2. The method for optimizing dispatch of a power system containing clean energy and considering external transmission as claimed in claim 1, characterized in that: In the model Mod1, the network security constraints include: network power balance constraints, network flow constraints, and network rotating reserve constraints; the unit operation constraints include: photovoltaic and wind power operation constraints, thermal power operation constraints, cascade hydropower operation constraints, pumped storage operation constraints, and electrochemical energy storage operation constraints; the model Mod2 also includes converter side power balance constraints, converter side flow constraints, and DC line side operation constraints.

3. The method for optimizing dispatch of a power system containing clean energy and considering external transmission as claimed in claim 1, characterized in that: The upper and lower limits of the safe receiving value of the active power of each receiving area are obtained as follows: A receiving-end model is established to analyze the receiving capacity of each receiving-end area. The receiving-end model includes load balance constraints, line transmission power limit constraints, rotating standby constraints and conventional thermal power unit constraints in the receiving-end area. The upper and lower limits of the safe receiving value of the active power in the receiving-end area are obtained by solving the receiving-end model.

4. The method for optimizing dispatch of a power system containing clean energy and considering external transmission as claimed in claim 1, characterized in that: The model Mod3 is solved by using a sequential rolling algorithm, including: Divide the scheduling time range into K equidistant segments; The model Mod3 is solved for each section in turn, and the optimal solution obtained is used as the scheduling plan for the corresponding section.

5. The method for optimizing dispatch of a power system containing clean energy and considering external transmission as claimed in claim 4, characterized in that: The model Mod3 is solved by using an automatic rollback algorithm with no feasible solution, including: S41, initialization r=0; S42, taking the system operation state of the kr-1th section end as the initial state of the kth section, solving the kth section, if the section has a feasible solution, then using the feasible solution as the scheduling plan for the section, updating k=k+1 and jumping back to S41, if the section has no feasible solution, executing S43; wherein, no feasible solution includes that the model has no solution or the obtained solution is judged to be unreasonable; S43. Update r=r+1 and jump to S42.

6. The method for optimizing dispatch of a clean energy power system considering external transmission as claimed in claim 5, characterized in that: When the solution does not meet the following conditions, it is judged to be unreasonable: In the formula, is the unit coal consumption cost, C line is the unit transmission power deviation penalty coefficient, C v , C w They are the unit solar power abandonment penalty coefficient and the unit wind power abandonment penalty coefficient respectively.

7. A dispatch optimization system for a clean energy power system considering external transmission, characterized in that: include: The first modeling unit is used to establish a clean energy power system model Mod1 that takes into account unit operation constraints and network security constraints. The units include: wind turbines, photovoltaic power stations, thermal power units, cascade hydropower station units and pumped storage units; The second modeling unit is used to establish a DC transmission system model Mod2 for outputting electric energy from the clean energy power system to each receiving end area, wherein the model Mod2 contains upper and lower limit constraints of the active power safety receiving value of each receiving end area; A model integration unit, used for bringing the upper and lower limits of the active power safety receiving value of each receiving end area into the upper and lower limit constraints of the active power safety receiving value in the model Mod2, and integrating the model Mod1 and the model Mod2 to obtain the clean energy power system model Mod3 considering external transmission; The solving unit is used to solve the model Mod3 with the goal of minimizing the operating cost, and obtain a scheduling plan within the scheduling time sequence.

8. The dispatch optimization system for a power system containing clean energy and considering external transmission as claimed in claim 7, characterized in that: In the model Mod1, the network security constraints include: network power balance constraints, network flow constraints, and network rotating reserve constraints; the unit operation constraints include: photovoltaic and wind power operation constraints, thermal power operation constraints, cascade hydropower operation constraints, pumped storage operation constraints, and electrochemical energy storage operation constraints; the model Mod2 also includes converter side power balance constraints, converter side flow constraints, and DC line side operation constraints.

9. The dispatch optimization system for a power system containing clean energy and considering external transmission as claimed in claim 7, characterized in that: The solution unit comprises: A segment division subunit is used to divide the scheduling time range into K equidistant segments; A section solving subunit is used to solve the model Mod3 for each section in turn, and use the optimal solution obtained as the scheduling plan for the corresponding section; The segment solving subunit comprises: Initialization module, used to initialize r=0 and then start the operation module; A calculation module is used to use the system operation state of the kr-1th segment end as the initial state of the kth segment, solve the kth segment, and if the segment has a feasible solution, use the feasible solution as the scheduling plan for the segment, update k=k+1 and start the initialization module again, and if the segment has no feasible solution, start the rollback module; wherein no feasible solution includes that the model has no solution or the obtained solution is judged to be unreasonable; The rollback module is used to restart the operation module after updating r=r+1.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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