Saggob new energy large-base scheduling method considering transmission capacity of internal and external lines
By constructing an optimized scheduling model for internal and external line transmission capacity and a mixed integer quadratic programming algorithm, the problem of the separation of internal and external constraints in the power dispatch of the Shagohuang New Energy Base was solved, achieving a balance between safety and economy, and improving the stability of the power system and the capacity for new energy absorption.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XI AN JIAOTONG UNIV
- Filing Date
- 2025-12-22
- Publication Date
- 2026-05-01
AI Technical Summary
Existing technologies in the power dispatching of the Shagohuang New Energy Base have failed to effectively consider the transmission capacity constraints of the internal power grid and the external DC transmission channel, resulting in a disconnect between the dispatching model and the actual power grid topology, which poses safety hazards and economic losses.
An optimized scheduling model considering the transmission capacity of internal and external lines is constructed. A mixed-integer quadratic programming algorithm is used to generate a scheduling plan that balances safety and economy. The model integrates multiple types of generator sets, energy storage devices and system power balance constraints, and solves the problem using the mixed-integer quadratic programming algorithm.
It significantly improves the stability, robustness, and overall operational efficiency of the large-scale power transmission system, avoids operational risks caused by the disconnect between internal and external constraints, and enhances the capacity for renewable energy absorption and the system's robustness in dealing with uncertainties.
Smart Images
Figure CN121965780A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of new energy power system dispatching technology, and in particular to a dispatching method for the Shagohuang new energy large-scale base that takes into account the transmission capacity of internal and external lines. Background Technology
[0002] In recent years, to promote the green transformation of the energy structure, my country has planned and constructed large-scale wind and solar power bases with a capacity of hundreds of millions of kilowatts in the Gobi Desert region. These bases adopt an integrated development model of wind, solar, thermal, and energy storage, aiming to transmit clean electricity over long distances to load centers in central and eastern China via ultra-high-voltage direct current (UHVDC) channels. However, renewable energy generation in the Gobi Desert region is characterized by significant intermittency and strong volatility. Coupled with the large scale of the bases and the complex power supply structure, power dispatch faces severe challenges. Existing technical solutions typically employ a hierarchical and progressive optimization framework, focusing on multi-energy complementary economic dispatch on the power supply side, but generally treating the operation of the internal power grid of large bases and external DC transmission as relatively independent links. This simplified approach has obvious limitations: on the one hand, it lacks quantitative analysis of transmission capacity constraints such as the thermal stability limit of the internal AC aggregation network of the base, leading to a disconnect between the dispatch model and the actual power grid topology; on the other hand, it does not adequately consider key constraints such as the power fluctuation tolerance range of the external DC transmission channels. As a result, under the random fluctuations in wind and solar power output, the existing scheduling strategy is difficult to coordinate the safety of internal lines with the efficiency of external transmission channels, which can easily lead to power transmission blockage, equipment overload, wind and solar curtailment or frequency and voltage instability risks, thus restricting the overall efficiency development of the base.
[0003] Specifically, existing implementation schemes often focus on the time-series coupling and economic allocation of power source resources such as wind power, photovoltaic power, thermal power, and energy storage, failing to incorporate the transmission capacity constraints of internal transmission lines and UHVDC transmission channels into a unified optimization boundary. This fragmented modeling approach, separating internal and external constraints, can lead to contradictory situations in practical engineering applications, where prioritizing DC transmission plans at the expense of internal grid security, or restricting external transmission capacity to ensure internal security. This not only poses safety risks but also results in economic losses. Therefore, there is an urgent need for a collaborative optimization scheduling method that integrates internal and external transmission capacity constraints to fundamentally address the technical challenges of safe, economical, and efficient operation of the vast desert power base. Summary of the Invention
[0004] In view of this, the present invention provides a scheduling method for the Shagohuang New Energy Base that takes into account the transmission capacity of internal and external lines, in order to solve the above problems.
[0005] This invention provides a scheduling method for the Shagohuang New Energy Base that considers the transmission capacity of internal and external lines. The method includes: constructing an optimized scheduling model that simultaneously considers the transmission capacity constraints of the internal power transmission network and the transmission capacity constraints of the UHVDC transmission channel, with the objective function of minimizing the total system operating cost; and solving the optimized scheduling model using a mixed integer quadratic programming algorithm to generate a scheduling plan that balances safety and economy.
[0006] In another implementation of the present invention, the objective function is:
[0007]
[0008]
[0009]
[0010] in, The cost for all thermal power units during the entire dispatch period; and These represent the costs of wind and solar power curtailment during the entire scheduling period; For thermal power units h The quadratic cost coefficient represents the characteristic that costs rise more rapidly as output increases; For thermal power units h One of the cost coefficients, which mainly reflects the variable costs that are linearly related to output; For thermal power units h The fixed cost coefficient reflects costs that do not change with output, such as unit depreciation, loan repayment and interest payments, and fixed wages. Indicates thermal power unit h During the period t contribution; Indicates wind turbine w During the period t contribution; Indicates photovoltaic unit s During the period t contribution; and Wind turbine w and photovoltaic units s During the period t Maximum output; H , W , S , T These represent the sets of thermal power units, wind power units, photovoltaic units, and all scheduling time periods, respectively.
[0011] In another implementation of the present invention, the optimized scheduling model integrates the operating constraints of multiple types of generator sets, the dynamic energy management constraints of energy storage devices, the system power balance constraints, and the internal and external transmission capacity safety boundary constraints.
[0012] In another implementation of the present invention, the step of using a mixed-integer quadratic programming algorithm to solve the optimized scheduling model and generate a scheduling plan that balances safety and economy includes: transforming the optimized model into a standard mixed-integer quadratic programming problem, with the objective function being a quadratic combination of thermal power operating costs and wind / solar curtailment penalty costs, and constraints including linear equality and inequality constraints as well as integer state variables of energy storage devices; constructing a continuous relaxation problem, ignoring integer constraints, using a quadratic programming solver to calculate the relaxed solution, and obtaining the lower bound of the problem; if the relaxed solution satisfies the integer requirement, then directly outputting the solution. If the optimal solution is found, the algorithm proceeds to the branch and bound phase. A non-integer variable is selected for branching, generating two subproblems. The variable is fixed to either 0 or 1, gradually narrowing the search space. During branching, the algorithm combines the cutting plane method to enhance the compactness of the relaxation problem and improves efficiency by adding effective inequalities to eliminate non-integer solution regions. Each subproblem is solved as a quadratic programming problem, and the global upper and lower bounds are updated to track the current optimal integer solution. Iterative branching, solving, and pruning operations continue until all nodes are processed or a preset tolerance is reached, outputting a scheduling plan that balances safety and economy.
[0013] Another aspect of the present invention provides a scheduling system for the Shagohuang New Energy Base that considers the transmission capacity of internal and external lines, comprising: a model building module: constructing an optimized scheduling model that simultaneously considers the transmission capacity constraints of the internal power transmission network of the large base and the transmission capacity constraints of the UHVDC transmission channel, with the objective function of minimizing the total operating cost of the system; and a model solving module: solving the optimized scheduling model using a mixed integer quadratic programming algorithm to generate a scheduling plan that balances safety and economy.
[0014] In another implementation of the present invention, the objective function is:
[0015]
[0016]
[0017]
[0018] in, The cost for all thermal power units during the entire dispatch period; and These represent the costs of wind and solar power curtailment during the entire scheduling period; For thermal power units hThe quadratic cost coefficient represents the characteristic that costs rise more rapidly as output increases; For thermal power units h One of the cost coefficients, which mainly reflects the variable costs that are linearly related to output; For thermal power units h The fixed cost coefficient reflects costs that do not change with output, such as unit depreciation, loan repayment and interest payments, and fixed wages. Indicates thermal power unit h During the period t contribution; Indicates wind turbine w During the period t contribution; Indicates photovoltaic unit s During the period t contribution; and Wind turbine w and photovoltaic units s During the period t Maximum output; H , W , S , T These represent the sets of thermal power units, wind power units, photovoltaic units, and all scheduling time periods, respectively.
[0019] In another implementation of the present invention, the optimized scheduling model integrates the operating constraints of multiple types of generator sets, the dynamic energy management constraints of energy storage devices, the system power balance constraints, and the internal and external transmission capacity safety boundary constraints.
[0020] In another implementation of the present invention, the step of using a mixed-integer quadratic programming algorithm to solve the optimized scheduling model and generate a scheduling plan that balances safety and economy includes: transforming the optimized model into a standard mixed-integer quadratic programming problem, with the objective function being a quadratic combination of thermal power operating costs and wind / solar curtailment penalty costs, and constraints including linear equality and inequality constraints as well as integer state variables of energy storage devices; constructing a continuous relaxation problem, ignoring integer constraints, using a quadratic programming solver to calculate the relaxed solution, and obtaining the lower bound of the problem; if the relaxed solution satisfies the integer requirement, then directly outputting the solution. If the optimal solution is found, the algorithm proceeds to the branch and bound phase. A non-integer variable is selected for branching, generating two subproblems. The variable is fixed to either 0 or 1, gradually narrowing the search space. During branching, the algorithm combines the cutting plane method to enhance the compactness of the relaxation problem and improves efficiency by adding effective inequalities to eliminate non-integer solution regions. Each subproblem is solved as a quadratic programming problem, and the global upper and lower bounds are updated to track the current optimal integer solution. Iterative branching, solving, and pruning operations continue until all nodes are processed or a preset tolerance is reached, outputting a scheduling plan that balances safety and economy.
[0021] In another aspect, the present invention provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a scheduling method for a large-scale new energy base in the desert considering internal and external line transmission capacity as described in any of the preceding claims. In another aspect, the present invention provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of a scheduling method for a large-scale new energy base in Shagohuang that considers the transmission capacity of internal and external lines as described in any of the preceding claims.
[0022] The present invention provides a scheduling method for the Shagohuang New Energy Large Base that considers the transmission capacity of internal and external lines. It establishes a unified quantitative framework for internal and external power transmission capacity constraints, and incorporates the internal power grid topology and DC transmission operation characteristics of the base into the same optimization dimension. This generates scheduling plans that take into account both safety boundaries and economy at multiple time scales, from day-ahead to real-time, effectively avoiding operational risks caused by the separation of internal and external constraints, and significantly improving the stability, robustness and comprehensive operational efficiency of the large base power transmission system. Attached Figure Description
[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. By reading the detailed description of the embodiments below, the advantages and benefits of the solutions will become clear to those skilled in the art. The accompanying drawings are only for illustrating preferred embodiments and are not intended to limit the present invention. In the accompanying drawings: Figure 1 This is a schematic diagram of the scheduling method for the Shagohuang New Energy Base, which takes into account the transmission capacity of internal and external lines, according to an embodiment of the present invention.
[0024] Figure 2 This is a block diagram of the internal structure of the "Shagohuang" large base according to an embodiment of the present invention.
[0025] Figure 3 This is a schematic diagram of a mixed integer quadratic programming algorithm according to an embodiment of the present invention. Detailed Implementation
[0026] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art should fall within the protection scope of the present invention.
[0027] Figure 1 This is a schematic diagram of a scheduling method for a large-scale new energy base in the desert region, taking into account the transmission capacity of internal and external lines, provided by an embodiment of the present invention. Figure 1 As shown, this embodiment mainly includes: S101. With minimizing the total system operating cost as the objective function, construct an optimization scheduling model that simultaneously considers the transmission capacity constraints of the internal power transmission network of the large base and the transmission capacity constraints of the UHV DC transmission channel.
[0028] S102. Solve the optimized scheduling model using a mixed-integer quadratic programming algorithm to generate a scheduling plan that balances safety and economy.
[0029] The present invention provides a scheduling method for the Shagohuang New Energy Large Base that considers the transmission capacity of internal and external lines. It establishes a unified quantitative framework for internal and external power transmission capacity constraints, and incorporates the internal power grid topology and DC transmission operation characteristics of the base into the same optimization dimension. This generates scheduling plans that take into account both safety boundaries and economy at multiple time scales, from day-ahead to real-time, effectively avoiding operational risks caused by the separation of internal and external constraints, and significantly improving the stability, robustness and comprehensive operational efficiency of the large base power transmission system.
[0030] In another implementation of the present invention, the objective function is:
[0031]
[0032]
[0033]
[0034] in, The cost for all thermal power units during the entire dispatch period; and These represent the costs of wind and solar power curtailment during the entire scheduling period; For thermal power units h The quadratic cost coefficient represents the characteristic that costs rise more rapidly as output increases; For thermal power units hOne of the cost coefficients, which mainly reflects the variable costs that are linearly related to output; For thermal power units h The fixed cost coefficient reflects costs that do not change with output, such as unit depreciation, loan repayment and interest payments, and fixed wages. Indicates thermal power unit h During the period t contribution; Indicates wind turbine w During the period t contribution; Indicates photovoltaic unit s During the period t contribution; and Wind turbine w and photovoltaic units s During the period t Maximum output; H , W , S , T These represent the sets of thermal power units, wind power units, photovoltaic units, and all scheduling time periods, respectively.
[0035] For example, such as Figure 2 As shown, the "Shagohuang" new energy base adopts an integrated "wind, solar, thermal, and energy storage" optimized scheduling model and transmits electricity externally through an ultra-high-voltage direct current channel. The model's objective is economic efficiency, with the objective function constructed to minimize the total operating cost, including the operating cost of thermal power units and the cost of curtailment penalties for wind and solar power. The operating cost of thermal power units is characterized by a combination of quadratic, linear, and constant terms to reflect its output-related variable and fixed costs. The cost of curtailment penalties is calculated based on the difference between the maximum available output and the actual output of wind and solar power units to promote the consumption of new energy.
[0036] In another implementation of the present invention, the optimized scheduling model integrates the operating constraints of multiple types of generator sets, the dynamic energy management constraints of energy storage devices, the system power balance constraints, and the internal and external transmission capacity safety boundary constraints.
[0037] For example, the characteristic constraints of various types of generator sets include: output constraints of thermal power units, which limit their output to between the minimum technical output and the maximum technical output; ramping constraints of thermal power units, which limit the rate of change of their output in adjacent time periods to ensure smooth output adjustment; output constraints of wind power units, which limit their output to not exceed the maximum available output in a time period; and output constraints of photovoltaic units, which limit their output to not exceed the maximum available output in a time period.
[0038] Specifically, thermal power output constraints:
[0039] In the formula, and They represent thermal power units h During the period t The minimum and maximum technical output. This constraint indicates that the output of the thermal power unit is within the technical range.
[0040] Thermal power plant ramping constraints:
[0041] In the formula, and They represent thermal power units h During the period t The minimum and maximum ramp limits are specified. This constraint prevents excessively rapid changes in thermal power output and avoids new fluctuations caused by its own adjustments. It forces thermal power to participate in regulation in a relatively smooth manner, leaving the task of rapid and drastic adjustments to energy storage.
[0042] Wind power output constraints:
[0043] In the formula, Indicates thermal power unit w During the period t The maximum available output. This constraint states that wind power output shall not exceed its maximum available output.
[0044] Photovoltaic output constraints:
[0045] In the formula, Indicates photovoltaic unit s During the period t The maximum available output. This constraint states that the photovoltaic output shall not exceed its maximum available output.
[0046] The constraints of energy storage devices include integer variables to represent the charging or discharging state of energy storage; the charging power and discharging power of energy storage devices are limited by the maximum charging power and the maximum discharging power, respectively, and integer state variables are used to avoid charging and discharging at the same time; the energy dynamic constraints of energy storage devices are described by energy balance equations, taking into account charging efficiency, discharging efficiency, and the upper and lower limits of stored energy, to ensure that energy storage participates in regulation at multiple time scales.
[0047] Specifically, the dynamic constraints on energy storage capacity:
[0048]
[0049]
[0050]
[0051] In the formula, and Energy storage devices b During the period t The charging power and discharging power. and Energy storage devices b Maximum charging power and discharge power. For energy storage devices b During the period t The state variable is an integer variable between 0 and 1. When it is 1, it indicates that the energy storage is in the charging state, and when it is 0, it indicates that the energy storage is in the discharging state. For energy storage devices b During the period t Stored energy and These represent the electrical and discharge efficiencies of energy storage (between 0 and 1). and Energy storage devices b The minimum and maximum storage energy.
[0052] The above constraints ensure that energy storage can function over the entire timescale (hours or even days) and prevent energy storage devices from charging and discharging simultaneously during a single period by setting integer state variables.
[0053] The power transmission capacity constraint of the transmission lines within the large base is calculated by the power transmission distribution factor to determine the power flow distribution of each line. The constraint limits the transmission power of each transmission line to no more than the maximum transmission capacity corresponding to its thermal stability limit, so as to prevent line overload and equipment damage and ensure the safe and stable operation of the internal power grid.
[0054] Specifically, the power transmission capacity constraints of the transmission lines within the large base:
[0055]
[0056] In the formula, I For the collection of all internal transmission lines, For nodes i For transmission lines l The power transmission distribution factor, For the node i The units during the period t Total output power For nodes i During the period t The load demand. For power transmission linesl During the period t The limitation on increasing power transmission capacity.
[0057] By calculating the power transmission distribution factor of the transmission lines within the large base for each time period and setting the maximum transmission capacity constraint, the safe and stable power transmission within the large base can be effectively guaranteed, avoiding serious problems such as line overload, equipment damage, and even system collapse caused by exceeding the transmission capacity limit, thereby improving the reliability and stability of the entire power transmission system of the large base.
[0058] The system power balance constraint ensures that the sum of the output of all generator sets and the charging and discharging power of energy storage devices within the large base, minus the load demand within the base, equals the transmission power of the UHVDC transmission channel, in order to maintain the real-time power balance of the system.
[0059] Specifically, power balance constraints:
[0060] In the formula, B It is the collection of all energy storage devices. This refers to the electricity transmitted from the large base via ultra-high voltage direct current channels.
[0061] This constraint indicates that the electricity generated by all generators within the large base, combined with the power output or absorption of energy storage devices, minus the power consumed within the large base, equals the electricity ultimately transmitted to other areas via the ultra-high voltage direct current channel. This ensures the stable operation and precise power balance of the large base's power system.
[0062] The transmission capacity constraint of the UHVDC transmission channel limits the DC transmission power to between the minimum and maximum transmission power. The minimum transmission power is used to maintain the stability of the channel operation, while the maximum transmission power is set based on the physical carrying capacity of the channel to prevent safety hazards caused by power exceeding the limit.
[0063] Specifically, the transmission capacity constraints of ultra-high voltage DC transmission channels:
[0064] In the formula, and These represent the minimum and maximum transmission power limits for the UHVDC transmission channel, respectively. The minimum transmission power limit ensures that the channel can maintain a certain power transmission level during operation, avoiding problems such as channel instability and low equipment efficiency caused by insufficient power. The maximum transmission power limit, on the other hand, is based on the channel's physical load-bearing capacity and safe operation, preventing safety hazards such as channel overheating and insulation damage caused by excessive transmission power, and even systemic failures.
[0065] This constraint, by clearly defining the upper and lower limits of the transmission power of the UHVDC channel, provides a quantitative guarantee for the safe and stable operation of the channel, and effectively maintains the reliability and security of the entire power transmission system.
[0066] In another implementation of the present invention, the optimal scheduling model is solved using a mixed-integer quadratic programming algorithm to generate a scheduling plan that balances safety and economy, such as... Figure 3 As shown, the algorithm includes: transforming the optimization model into a standard mixed-integer quadratic programming problem, with the objective function being a quadratic combination of thermal power operating costs and wind / solar curtailment penalty costs, and constraints including linear equality and inequality constraints as well as integer state variables of energy storage devices; constructing a continuous relaxation problem, ignoring integer constraints, and using a quadratic programming solver to calculate the relaxation solution and obtain the lower bound of the problem; if the relaxation solution satisfies the integer requirement, it is directly output as the optimal solution; otherwise, it enters the branch and bound stage, selecting non-integer variables (such as the charging and discharging state variables of energy storage) for branching, generating two sub-problems, fixing the variable to 0 or 1 respectively, and gradually narrowing the search space; during the branching process, the algorithm combines the cutting plane method to enhance the compactness of the relaxation problem, and improves the solution efficiency by adding effective inequalities to eliminate non-integer solution regions; each sub-problem is solved as a quadratic programming problem, and the global upper and lower bounds are updated to track the current optimal integer solution; iterating the branching, solving, and pruning operations until all nodes are processed or the preset tolerance is reached, and outputting a scheduling plan that balances safety and economy.
[0067] For example, the optimization model described above is a mixed-integer quadratic programming (MIQP) problem, which is solved using the MIQP algorithm. The MIQP algorithm ensures that the scheduling plan reaches global optimum or near-optimal while satisfying all safety and economic constraints by handling integer constraints and quadratic objective functions in stages.
[0068] The algorithm process begins with problem initialization, transforming the optimization model into the standard MIQP form. The objective function is a quadratic combination of the operating cost of thermal power and the penalty cost of wind and solar curtailment. The constraints include linear equality and inequality constraints, as well as integer state variables of the energy storage equipment.
[0069] The mixed-integer quadratic programming algorithm is used to solve quadratic programming problems with integer variables. By combining the branch and bound method, the cutting plane method, and quadratic programming techniques, it handles integer constraints and quadratic objective functions in stages, improving solution efficiency and practicality. This process ensures the model's practicality and robustness under complex constraints, and accurately handles discrete decisions for energy storage through mixed-integer programming techniques, thereby dynamically responding to wind and solar fluctuations in day-ahead to real-time scheduling. The entire algorithm focuses on computational efficiency, utilizing optimization software to achieve rapid solutions, providing reliable support for the integrated collaborative scheduling of the Shagohuang large-scale energy storage base.
[0070] The beneficial effects of this invention are reflected in: (1) Significantly improves the safety and stability of system operation. This invention is the first to uniformly quantify the thermal stability limit of the internal AC collection network of a large base and the power fluctuation tolerance range of the UHVDC transmission channel, and use them as hard boundary conditions for the optimization model. This fundamentally avoids safety problems such as internal line overload or DC channel operation exceeding limits caused by the disconnect between scheduling instructions and actual physical constraints, ensuring that the entire process from power collection to remote transmission can operate within strict safety boundaries, and greatly enhancing the overall stability of the system.
[0071] (2) Effectively enhances the economic efficiency and renewable energy absorption capacity of the system. This invention designs a multi-factor collaborative optimization model of source-grid-storage-DC, aiming to minimize the total operating cost (including thermal power cost and wind and solar curtailment penalty cost). Under the premise of meeting all safety constraints, it coordinates the cooperation between flexible resources such as thermal power and energy storage and the fluctuating output of wind and solar power through optimization algorithms. This not only reduces the regulation pressure of high-cost thermal power, but also significantly improves the utilization rate of clean energy and reduces unnecessary energy waste by setting a wind and solar curtailment penalty mechanism, thereby achieving a dual improvement in safety and economy.
[0072] (3) Comprehensively improve the robustness and scheduling accuracy of integrated operation of large-scale power bases. This invention establishes a high-fidelity hybrid integer programming model by introducing energy storage constraints with integer variables and refined network power flow constraints. The charging and discharging states of energy storage devices are accurately characterized by 0-1 variables, avoiding physically infeasible operations; at the same time, based on the internal line power flow calculation of the power transmission distribution factor, the scheduling plan can accurately reflect the actual power grid topology. The scheduling scheme generated by this method can dynamically respond to the random fluctuations in wind and solar power output, fundamentally solving the contradiction of restricting external transmission for the sake of security or sacrificing security for the sake of external transmission, and improving the system's robustness in the face of uncertainty.
[0073] Another aspect of the present invention provides a scheduling system for the Shagohuang New Energy Base that considers the transmission capacity of internal and external lines, comprising: Model building module: With minimizing the total operating cost of the system as the objective function, an optimization scheduling model is built that simultaneously considers the transmission capacity constraints of the internal power transmission network of the large base and the transmission capacity constraints of the UHV DC transmission channel.
[0074] Model Solving Module: The optimized scheduling model is solved using a mixed-integer quadratic programming algorithm to generate a scheduling plan that balances safety and economy.
[0075] The present invention provides a scheduling system for the Shagohuang New Energy Base that considers the transmission capacity of internal and external lines. It establishes a unified quantitative framework for internal and external power transmission capacity constraints, and incorporates the internal power grid topology and DC transmission operation characteristics into the same optimization dimension. This generates scheduling plans that take into account both safety boundaries and economy at multiple time scales, from day-ahead to real-time, effectively avoiding operational risks caused by the separation of internal and external constraints, and significantly improving the stability, robustness and overall operational efficiency of the large-scale power transmission system.
[0076] In another implementation of the present invention, the objective function is:
[0077]
[0078]
[0079]
[0080] in, The cost for all thermal power units during the entire dispatch period; and These represent the costs of wind and solar power curtailment during the entire scheduling period; For thermal power units h The quadratic cost coefficient represents the characteristic that costs rise more rapidly as output increases; For thermal power units h One of the cost coefficients, which mainly reflects the variable costs that are linearly related to output; For thermal power units h The fixed cost coefficient reflects costs that do not change with output, such as unit depreciation, loan repayment and interest payments, and fixed wages. Indicates thermal power unit h During the period t contribution; Indicates wind turbine w During the period t contribution; Indicates photovoltaic unit s During the period t contribution; and Wind turbine w and photovoltaic units s During the period t Maximum output; H , W , S , T These represent the sets of thermal power units, wind power units, photovoltaic units, and all scheduling time periods, respectively.
[0081] In another implementation of the present invention, the optimized scheduling model integrates the operating constraints of multiple types of generator sets, the dynamic energy management constraints of energy storage devices, the system power balance constraints, and the internal and external transmission capacity safety boundary constraints.
[0082] In another implementation of the present invention, the step of using a mixed-integer quadratic programming algorithm to solve the optimized scheduling model and generate a scheduling plan that balances safety and economy includes: transforming the optimized model into a standard mixed-integer quadratic programming problem, with the objective function being a quadratic combination of thermal power operating costs and wind / solar curtailment penalty costs, and constraints including linear equality and inequality constraints as well as integer state variables of energy storage devices; constructing a continuous relaxation problem, ignoring integer constraints, using a quadratic programming solver to calculate the relaxed solution, and obtaining the lower bound of the problem; if the relaxed solution satisfies the integer requirement, then directly outputting the solution. If the optimal solution is found, the algorithm proceeds to the branch and bound phase. A non-integer variable is selected for branching, generating two subproblems. The variable is fixed to either 0 or 1, gradually narrowing the search space. During branching, the algorithm combines the cutting plane method to enhance the compactness of the relaxation problem and improves efficiency by adding effective inequalities to eliminate non-integer solution regions. Each subproblem is solved as a quadratic programming problem, and the global upper and lower bounds are updated to track the current optimal integer solution. Iterative branching, solving, and pruning operations continue until all nodes are processed or a preset tolerance is reached, outputting a scheduling plan that balances safety and economy.
[0083] In another aspect of the present invention, the electronic device includes: a processor, a memory, and a communication bus and a communication interface.
[0084] in: The processor, memory, and communication interface communicate with each other via a communication bus.
[0085] A communication interface is used to communicate with other electronic devices or servers.
[0086] The processor is used to execute programs, specifically, it can execute any of the steps of the scheduling method for the Shagohuang New Energy Base that takes into account the transmission capacity of internal and external lines in the above embodiments.
[0087] Specifically, the program may include program code, which includes computer operation instructions.
[0088] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.
[0089] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device.
[0090] Specifically, the program can be used to cause the processor to execute the steps of any of the scheduling methods for the Shagohuang New Energy Base considering internal and external line transmission capacity described in the embodiments. The specific implementation of each step in the program can be found in the corresponding descriptions of the steps and units executed in any of the above-mentioned scheduling methods for the Shagohuang New Energy Base considering internal and external line transmission capacity, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments.
[0091] An exemplary embodiment of this application also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the methods of various embodiments of this application.
[0092] The methods described above according to embodiments of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded via a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the methods shown herein.
[0093] Specific embodiments of the present invention have now been described. Other embodiments are within the scope of the appended claims. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result.
[0094] It should be noted that all directional indications (such as up, down, left, right, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship between the components in a certain order (as shown in the figure). If the specific order changes, the directional indication will also change accordingly.
[0095] In the description of this invention, the terms "first" and "second" are used only for convenience in describing different components or names, and should not be construed as indicating or implying a sequential relationship, relative importance, or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" and "second" may explicitly or implicitly include at least one of that feature.
[0096] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
[0097] It should be noted that although specific embodiments of the present invention have been described in detail with reference to the accompanying drawings, this should not be construed as limiting the scope of protection of the present invention. Various modifications and variations that can be made by those skilled in the art without inventive effort within the scope described in the claims still fall within the scope of protection of the present invention.
[0098] The examples of the embodiments of the present invention are intended to concisely illustrate the technical features of the embodiments of the present invention, so that those skilled in the art can intuitively understand the technical features of the embodiments of the present invention, and are not intended to be an improper limitation of the embodiments of the present invention.
[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A scheduling method for a large-scale new energy base in the desert region considering the transmission capacity of internal and external lines, characterized in that, include: With the objective function of minimizing the total system operating cost, an optimization scheduling model is constructed that simultaneously considers the transmission capacity constraints of the internal power transmission network of the large base and the transmission capacity constraints of the UHV DC transmission channel. The optimal scheduling model is solved using a mixed-integer quadratic programming algorithm to generate a scheduling plan that balances safety and economy.
2. The method according to claim 1, characterized in that, The objective function is: in, The cost for all thermal power units during the entire dispatch period; and These represent the costs of wind and solar power curtailment during the entire scheduling period; For thermal power units h The quadratic cost coefficient represents the characteristic that costs rise more rapidly as output increases; For thermal power units h One of the cost coefficients, which mainly reflects the variable costs that are linearly related to output; For thermal power units h The fixed cost coefficient reflects costs that do not change with output, such as unit depreciation, loan repayment and interest payments, and fixed wages. Indicates thermal power unit h During the period t contribution; Indicates wind turbine w During the period t contribution; Indicates photovoltaic unit s During the period t contribution; and Wind turbine w and photovoltaic units s During the period t Maximum output; H , W , S , T These represent the sets of thermal power units, wind power units, photovoltaic units, and all scheduling time periods, respectively.
3. The method according to claim 1, characterized in that, The optimized scheduling model integrates the operating constraints of multiple types of generator sets, the dynamic energy management constraints of energy storage devices, the system power balance constraints, and the safety boundary constraints of internal and external power transmission capacity.
4. The method according to claim 1, characterized in that, The method of solving the optimized scheduling model using a mixed-integer quadratic programming algorithm to generate a scheduling plan that balances safety and economy includes: The optimization model is transformed into a standard mixed integer quadratic programming problem. The objective function is a quadratic combination of thermal power operating costs and wind and solar curtailment penalty costs. The constraints include linear equality and inequality constraints as well as integer state variables of energy storage devices. Construct a continuous relaxation problem, ignore integer constraints, use a quadratic programming solver to compute the relaxation solution, and obtain the lower bound of the problem. If the relaxed solution satisfies the integer property requirement, then the optimal solution is output directly. Otherwise, proceed to the branch and bound phase, select a non-integer variable to branch, generate two subproblems, fix the variable to 0 or 1 respectively, and gradually narrow the search space; During the branching process, the algorithm combines the cutting plane method to enhance the compactness of the relaxation problem and improves the solution efficiency by adding effective inequalities to eliminate non-integer solution regions. Each subproblem is solved as a quadratic programming problem, and the global upper and lower bounds are updated to track the current best integer solution; Iterate through branching, solving, and pruning operations until all nodes are processed or a preset tolerance is reached, and output a scheduling plan that balances safety and economy.
5. A scheduling system for a large-scale new energy base in the desert region, considering both internal and external line transmission capacity, characterized in that: include: Model building module: With minimizing the total system operating cost as the objective function, an optimization scheduling model is built that simultaneously considers the transmission capacity constraints of the power transmission network within the large base and the transmission capacity constraints of the UHV DC transmission channel. Model Solving Module: The optimized scheduling model is solved using a mixed-integer quadratic programming algorithm to generate a scheduling plan that balances safety and economy.
6. The system according to claim 5, characterized in that, The objective function is: in, The cost for all thermal power units during the entire dispatch period; and These represent the costs of wind and solar power curtailment during the entire scheduling period; For thermal power units h The quadratic cost coefficient represents the characteristic that costs rise more rapidly as output increases; For thermal power units h One of the cost coefficients, which mainly reflects the variable costs that are linearly related to output; For thermal power units h The fixed cost coefficient reflects costs that do not change with output, such as unit depreciation, loan repayment and interest payments, and fixed wages. Indicates thermal power unit h During the period t contribution; Indicates wind turbine w During the period t contribution; Indicates photovoltaic unit s During the period t contribution; and Wind turbine w and photovoltaic units s During the period t Maximum output; H , W , S , T These represent the sets of thermal power units, wind power units, photovoltaic units, and all scheduling time periods, respectively.
7. The system according to claim 5, characterized in that, The optimized scheduling model integrates the operating constraints of multiple types of generator sets, the dynamic energy management constraints of energy storage devices, the system power balance constraints, and the safety boundary constraints of internal and external power transmission capacity.
8. The system according to claim 5, characterized in that, The method of solving the optimized scheduling model using a mixed-integer quadratic programming algorithm to generate a scheduling plan that balances safety and economy includes: The optimization model is transformed into a standard mixed integer quadratic programming problem. The objective function is a quadratic combination of thermal power operating costs and wind and solar curtailment penalty costs. The constraints include linear equality and inequality constraints as well as integer state variables of energy storage devices. Construct a continuous relaxation problem, ignore integer constraints, use a quadratic programming solver to compute the relaxation solution, and obtain the lower bound of the problem. If the relaxed solution satisfies the integer property requirement, then the optimal solution is output directly. Otherwise, proceed to the branch and bound phase, select a non-integer variable to branch, generate two subproblems, fix the variable to 0 or 1 respectively, and gradually narrow the search space; During the branching process, the algorithm combines the cutting plane method to enhance the compactness of the relaxation problem and improves the solution efficiency by adding effective inequalities to eliminate non-integer solution regions. Each subproblem is solved as a quadratic programming problem, and the global upper and lower bounds are updated to track the current best integer solution; Iterate through branching, solving, and pruning operations until all nodes are processed or a preset tolerance is reached, and output a scheduling plan that balances safety and economy.
9. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the scheduling method for the Shagohuang New Energy Base, as described in any one of claims 1 to 4, which takes into account the transmission capacity of internal and external lines.
10. A computer storage medium, characterized in that, The computer storage medium stores a computer program, which, when executed by a processor, implements the steps in the scheduling method for the Shagohuang New Energy Base that considers the transmission capacity of internal and external lines as described in any one of claims 1 to 4.