Electric power and electric quantity balance strategy generation method and device, equipment, medium and product

Abstract

The invention discloses an electric power and electric quantity balance strategy generation method and device, equipment, a medium and a product, and belongs to the technical field of electric power system operation and control. The method comprises the steps that basic data of a power system are acquired, a random multi-scene set is generated based on meteorological information and hydrological information in the basic data, and each scene comprises a hydropower output sequence; based on the basic data, establishing an electric power and electric quantity balance optimization model with the purpose of minimizing the total operation cost of the system; and setting unit operation constraints, cross-regional power transaction constraints and system operation constraints for the optimization model based on the random multi-scene set, solving the model by adopting an optimization algorithm to obtain a power and electric quantity balance strategy, and outputting the power and electric quantity balance strategy to guide medium and long-term scheduling operation of the power system. According to the embodiment of the invention, the accuracy of hydropower output characteristic evaluation in an extreme weather scene can be improved, and the safety of an electric power and electric quantity balance strategy formulated according to the method is further improved.

Description

Technical Field

[0001] This application relates to the field of power system operation and control technology, and in particular to a method, apparatus, equipment, medium and product for generating power balance strategy. Background Technology

[0002] Hydropower, as a crucial pillar of the power system, serves multiple functions including power generation, peak shaving, reserve, and energy storage. The stability of its output is vital for power balance. However, hydropower output, especially its available power on medium- to long-term timescales, is highly dependent on river basin inflows and is vulnerable to the direct impact of extreme weather events. For example, prolonged extreme droughts can lead to a sharp reduction in reservoir inflows, significantly decreasing the power generation capacity of hydropower units; while sudden catastrophic floods may force hydropower stations to release water, also resulting in power loss. Against the backdrop of global climate change, the frequency and intensity of such extreme hydrological events are increasing, making hydropower output exhibit significant randomness and uncertainty. To address this uncertainty, current technologies often treat hydropower as a deterministic or simply fluctuating power source, leading to insufficient reserve capacity on medium- to long-term timescales or impractical cross-regional power trading arrangements. Once an extreme hydrological event occurs, the system will face a significant risk of power shortage, jeopardizing grid security. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus, device, medium, and product for generating power balance strategies, which can effectively improve the accuracy of assessing the hydropower output characteristics under extreme weather scenarios, thereby improving the security of the power balance strategies formulated accordingly.

[0004] To achieve the above objectives, a first aspect of this application provides a method for generating a power balance strategy, comprising: Acquire basic data of the power system, and generate a random multi-scenario set based on meteorological and hydrological information in the basic data, wherein each scenario contains a hydropower output sequence; Based on the aforementioned basic data, a power balance optimization model is established with the goal of minimizing the total system operating cost. The total system operating cost includes thermal power generation cost, hydropower curtailment penalty cost, wind power curtailment penalty cost, photovoltaic curtailment penalty cost, and load loss penalty cost. Based on the aforementioned set of random multi-scenario applications, unit operation constraints, cross-regional power trading constraints, and system operation constraints are set for the optimization model. An optimization algorithm is used to solve the model to obtain a power balance strategy, which is then output to guide the medium- and long-term scheduling and operation of the power system.

[0005] Compared with existing technologies, the power balance strategy generation method provided in this application has the following advantages: By generating a random multi-scenario set of hydropower and using it as the boundary condition of the optimization model, the established power balance optimization model can fully account for the uncertainty of hydropower output under extreme weather conditions, thereby ensuring that the generated scheduling strategy has inherent robustness in dealing with multiple possible extreme scenarios, significantly improving the power supply security and resilience of the power system in the long term; at the same time, by constructing a system total operating cost objective function that comprehensively considers thermal power costs, curtailment penalties and load loss penalties, this strategy effectively guides the system to prioritize the consumption of clean energy and optimize economic operation mode under the premise of ensuring the bottom line of power supply security, realizing the multi-objective synergy of promoting the efficient consumption of new energy and reducing the overall operating cost of the system, systematically solving the technical problem that traditional power balance methods are difficult to balance safety, economy and greenness in the medium and long term.

[0006] In some embodiments, generating a random multi-scene set based on the meteorological and hydrological information in the basic data includes: Extreme weather events can be identified based on the meteorological and hydrological information in the aforementioned basic data. Using the extreme weather events as common driving factors, a random multi-scenario set is generated by employing probabilistic models or machine learning methods.

[0007] In some embodiments, the unit operating constraints include: Constraints on the output range and annual power generation of hydropower units, and total output of hydropower stations; Output range constraints and curtailment restrictions for wind and solar power units; Constraints on the output range, start-stop status, and minimum technical output of thermal power units; Constraints on the power generation and pumping power of pumped storage units.

[0008] In some embodiments, the cross-regional power trading constraints include cross-regional power trading constraints and cross-regional electricity trading constraints; The system operation constraints include power balance constraints, energy balance constraints, and reserve constraints.

[0009] In some embodiments, the power balance constraint ensures that the total power generation of the system is balanced with the total load power, inter-regional trading power, and energy storage charging and discharging power at each time period and load level; The power balance constraint ensures that the total power generation of the system is balanced with the changes in total load power, cross-regional transaction power and energy storage power within the calculation period; The backup constraints include upward backup constraints and downward backup constraints to ensure that the system has the ability to cope with unit failures and load fluctuations.

[0010] In some embodiments, the optimization algorithm is the Benders decomposition algorithm, which decomposes the optimization model into a main problem and subproblems for iterative solution.

[0011] To achieve the above objectives, a second aspect of this application provides a power balance strategy generation apparatus, the apparatus comprising: The acquisition module is used to acquire basic data of the power system and generate a random multi-scenario set based on the meteorological and hydrological information in the basic data, wherein each scenario contains a hydropower output sequence. A module is established to build a power balance optimization model based on the basic data with the goal of minimizing the total system operating cost, wherein the total system operating cost includes thermal power generation cost, hydropower curtailment penalty cost, wind power curtailment penalty cost, photovoltaic curtailment penalty cost, and load loss penalty cost; The solution module is used to set unit operation constraints, cross-regional power trading constraints and system operation constraints for the optimization model based on the random multi-scenario set, solve the model using optimization algorithms, obtain the power balance strategy, and output the power balance strategy to guide the medium and long-term scheduling and operation of the power system.

[0012] To achieve the above objectives, a third aspect of this application provides an electronic device, the electronic device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the method described in the first aspect.

[0013] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium comprising a stored computer program, wherein the computer program, when executed, controls the device containing the computer-readable storage medium to perform the method described in the first aspect.

[0014] To achieve the above objectives, a fifth aspect of the present application provides a computer program product, which includes a computer program or computer instructions, wherein the computer program or computer instructions, when executed by a processor, implement the method described in the first aspect. Attached Figure Description

[0015] Figure 1 This is a flowchart of a power balance strategy generation method provided in an embodiment of this application; Figure 2 This is a comparison chart of inter-regional weekly trade in energy under normal circumstances; Figure 3This is a bar chart comparing system performance metrics under normal scenarios; Figure 4 This is a comparison chart of inter-regional weekly trade in energy under extreme scenarios; Figure 5 It is a bar chart comparing system performance metrics under extreme scenarios; Figure 6 This is a schematic diagram of a power balance strategy generation device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0017] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0018] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0019] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0020] Hydropower, as a crucial pillar of the global power system, serves multiple functions including power generation, peak shaving, backup, and energy storage. The stability of its output is vital for maintaining power balance. However, hydropower output, especially its available capacity over medium- to long-term timescales, is highly dependent on river basin inflows and is vulnerable to the direct impact of extreme weather events. For example, prolonged extreme droughts can drastically reduce reservoir inflows, significantly decreasing the power generation capacity of hydropower units; while sudden catastrophic floods may force hydropower stations to release water, also resulting in energy losses. Against the backdrop of global climate change, the frequency and intensity of such extreme hydrological events are increasing, leading to significant randomness and uncertainty in hydropower output.

[0021] To address the uncertainty of hydropower output, existing technologies have proposed several solutions. One approach is to improve hydrological and meteorological forecasting techniques, using numerical weather prediction or statistical models to enhance the accuracy of short- to medium-term inflow forecasts, thereby optimizing reservoir operation plans. Another approach is to consider historical hydrological sequences in power system planning and scheduling, using typical year or long-term operational simulations to evaluate the long-term average performance of hydropower.

[0022] However, these existing technologies have significant limitations. First, most studies focus on routine hydrological fluctuations or simply analyze historical extremes (such as dry and wet years), lacking systematic modeling and quantitative assessment of "low-probability, high-impact" extreme hydrological scenarios (such as once-in-a-century droughts or floods). Second, in power balance models, hydropower is often treated as a deterministic or simply fluctuating power source, failing to fully reflect the substantial risk of hydropower units abruptly transforming from a "stable supporting power source" to a "power shortage source" under extreme weather conditions. This simplistic treatment may lead to insufficient reserve capacity on medium- to long-term timescales or unrealistic cross-regional power trading arrangements. Once extreme hydrological events occur, the system will face a huge risk of power shortage, endangering grid security.

[0023] Based on this, embodiments of this application provide a method, apparatus, device, medium, and product for generating power balance strategies, which can effectively improve the accuracy of assessing the hydropower output characteristics under extreme weather scenarios, thereby improving the security of the power balance strategies formulated accordingly.

[0024] Please see Figure 1 , Figure 1 This is an optional flowchart of the power balance strategy generation method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S103.

[0025] Step S101: Obtain basic data of the power system, and generate a random multi-scenario set based on meteorological and hydrological information in the basic data, wherein each scenario contains a hydropower output sequence; Step S102: Based on the basic data, establish a power balance optimization model with the goal of minimizing the total system operating cost, where the total system operating cost includes thermal power generation cost, hydropower curtailment penalty cost, wind power curtailment penalty cost, photovoltaic curtailment penalty cost, and load loss penalty cost; Step S103: Based on a random multi-scenario set, set unit operation constraints, cross-regional power trading constraints, and system operation constraints for the optimization model. Use an optimization algorithm to solve the model to obtain the power balance strategy and output the power balance strategy to guide the medium- and long-term dispatch and operation of the power system.

[0026] Steps S101 to S103 as illustrated in this application embodiment generate a random multi-scenario set of hydropower and use it as the boundary condition of the optimization model. This allows the established power balance optimization model to fully account for the uncertainty of hydropower output under extreme weather conditions, thereby ensuring that the generated scheduling strategy has inherent robustness in dealing with various possible extreme scenarios. This significantly improves the long-term power supply security and resilience of the power system. At the same time, by constructing a system total operating cost objective function that comprehensively considers thermal power costs, curtailment penalties, and load loss penalties, this strategy effectively guides the system to prioritize the consumption of clean energy and optimize economic operation modes while ensuring the bottom line of power supply security. This achieves multi-objective synergy in promoting the efficient consumption of new energy and reducing the overall operating cost of the system, systematically solving the technical problem that traditional power balance methods are unable to balance safety, economy, and greenness on medium- and long-term time scales.

[0027] In step S101 of some embodiments, the basic data can be a set of key data of the power system, which is divided into two categories according to attributes: uncertain input data and deterministic data. Uncertain input data refers to dynamic data affected by natural conditions or prediction accuracy, including historical and predicted hydrological data (i.e., hydrological information, including rainfall and runoff), historical and predicted meteorological data (i.e., meteorological information, including precipitation, temperature, wind speed, sunshine, total solar radiation, direct radiation, temperature, humidity, etc.), and prediction error distribution data of the above data. Deterministic data refers to static data that is inherent to the power system or can be accurately obtained, including physical characteristic data of wind power / photovoltaic power plants (installed capacity, power curve, power influencing factors, etc.), load data (medium and long-term prediction curves and segmented information), thermal power unit parameters (capacity, upper and lower limits of output, cost coefficient, etc.), hydropower unit parameters (minimum / maximum output, water regulation rules, etc.), power grid operation parameters (topology, transmission constraints, etc.), and equipment maintenance plans. The random multi-scenario set can be a combination of scenarios including both normal and extreme weather conditions. Each scenario corresponds to specific meteorological / hydrological conditions, and the power output sequences of hydropower, wind power, and photovoltaic power within the scenario are coupled and correlated, covering extreme power output scenarios. The power output sequence can be a continuous data sequence of power generation output in different time periods within a certain time period. The hydropower sequence is determined by rainfall / runoff and reservoir rules.

[0028] It should be noted that, on medium- to long-term timescales, the output of wind power and solar power, as well as the inflow of water for hydropower, all exhibit significant randomness. Specifically, the inflow of water into a hydropower station reservoir depends not only on rainfall but also on the combined effects of the spatial and temporal distribution of rainfall and the geographical characteristics of the watershed; while the output of wind power and solar power is directly constrained by uncontrollable natural factors such as wind speed and sunshine.

[0029] In this application embodiment, based on the relatively stable characteristics of wind and solar resources over medium to long-term timescales, the output of wind and solar power in extreme scenarios is treated deterministically, assuming that their output remains stable over a certain period. Accordingly, this application focuses its evaluation on the output characteristics of hydropower, which is more severely affected by extreme weather events and has higher uncertainty. By constructing a power balance optimization model that considers extreme hydropower output, significant economic benefits can be achieved while ensuring the safety and reliability of the power grid.

[0030] In some embodiments, a random multi-scene set is generated based on meteorological and hydrological information in the underlying data, including: Identifying extreme weather events based on meteorological and hydrological information from basic data; Using extreme weather events as common driving factors, a random multi-scenario set is generated by employing probabilistic models or machine learning methods.

[0031] Specifically, firstly, the system extracts key meteorological and hydrological indicators closely related to hydropower output from basic data, such as the number of consecutive drought days in a specific region, cumulative rainfall, typhoon path and intensity, and watershed inflow. Based on historical statistical data and climate models, thresholds for these indicators are set to identify target extreme weather events (e.g., severe droughts, severe floods) that may pose a serious threat to the operation of the power system.

[0032] Subsequently, the identified target extreme weather events are used as common driving factors. This means that an extreme weather event (such as a severe drought) will be considered a core driving scenario, around which a complete profile of new energy output is constructed. A probabilistic model (e.g., a Copula function joint distribution model based on historical data, Monte Carlo simulation, etc.) or machine learning methods (e.g., Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs, etc.) are used to generate a random multi-scenario set containing a large number of samples. In this set, each scenario represents a specific hydropower output time-series curve that may occur under the driving force of an extreme weather event. These scenarios collectively characterize the uncertainty space of hydropower output under extreme conditions, providing comprehensive and quantitative risk input for subsequent optimization models.

[0033] Through the above methods, this invention transforms qualitative extreme weather risks into quantitative, calculable extreme scenarios, laying a solid foundation for developing robust power balance strategies. Compared to traditional independent modeling methods that only target a single energy source, the scenarios generated using this approach not only provide more comprehensive coverage of extreme scenarios but also significantly reduce the correlation errors between multiple energy outputs, ultimately making the corresponding power balance strategies more reliable.

[0034] In step S102 of some embodiments, the power balance optimization model can be a medium- to long-term scheduling mathematical model, aiming to minimize the total system operating cost, integrating multiple scenario constraints and operating rules to achieve optimal resource allocation. The total system operating cost can be the sum of operating costs and penalty fees, including thermal power generation costs, hydropower curtailment penalty costs, wind power curtailment penalty costs, photovoltaic power curtailment penalty costs, and load loss penalty costs.

[0035] The objective function of the power balance optimization model can be expressed by the following formula (1):

[0036] in, Indicates a time period; Indicates the total duration of the calculation cycle, for example You can choose "one year"; This represents the segmentation of an approximate continuous load curve (dividing the continuous load curve into several segments for easier calculation). This represents the total number of segments in an approximately continuous load curve; Indicates time period Internal load level The corresponding duration, in hours; thermal power cost parameters: Indicates the designation of the thermal power generating unit; It refers to the collection of thermal power generating units, that is, the collective term for all thermal power generating units; This represents the power generation cost coefficient of thermal power generating unit g at time period t and load level b, expressed in yuan / MWh; This represents the average output of thermal power generating unit g at time period t and load level b, in MW; parameters for the water wastage penalty term: The penalty coefficient for wasted water volume is expressed in yuan / MWh; Indicates a hydroelectric power plant; Indicates the output of the hydroelectric generator unit; Indicates hydroelectric power unit Average equivalent cost coefficient of water curtailment under time period t and load level b; parameters for wind and solar power curtailment penalty term: This represents the penalty coefficient for wind power curtailment and solar power curtailment, used to incentivize the integration of wind and solar power. Indicates wind power and solar power output; This represents the cost coefficients for wind power and solar power curtailment at time period t and load level b; load loss penalty term parameters: This represents the load curtailment penalty coefficient, used to ensure power supply reliability; Indicates load output. This represents the load loss cost coefficient under load level b during time period t.

[0037] This step provides a unified decision-making framework for power balance optimization by constructing a system total cost objective function that integrates thermal power operation costs, hydropower / energy curtailment penalties, and load loss penalties, thereby quantifying economic efficiency, environmental friendliness, and power supply security simultaneously.

[0038] In step S103 of some embodiments, the random multi-scenario set can serve as the boundary conditions for model solving, requiring the strategy to meet the operational requirements of all scenarios (especially extreme scenarios). The main constraints can include three types: unit operation constraints (limitations on various power technologies, such as hydropower water balance and thermal power output range), cross-regional transaction constraints (tether line power / energy limits), and system operation constraints (power / energy balance, reserve capacity requirements, etc.). The optimization algorithm used here is the Benders decomposition algorithm, which decomposes the complex mixed-integer programming problem into "main problem (objective optimization) + sub-problems (constraint verification)," accelerating the solution efficiency.

[0039] The constraints include unit maintenance constraints, unit operation constraints, inter-regional power trading constraints, and system operation constraints. Unit maintenance should adhere to constraints such as maintenance frequency, maintenance time, maintenance continuity, and power plant maintenance capacity. Unit operation constraints include start-up status and power range limitations for thermal power, hydropower, pumped storage, wind power, and photovoltaic units. Specifically, unit operation constraints include: output range constraints and annual power generation constraints for hydropower units, and total power output constraints for hydropower stations; output range constraints and curtailment restrictions for wind power and photovoltaic units; output range constraints, start-up and shutdown status constraints, and minimum technical output constraints for thermal power units; and power generation and pumping power constraints for pumped storage units.

[0040] Considering the impact of extreme weather conditions such as drought and floods on hydropower generation, a stochastic multi-scenario method is used to model hydropower. Based on the stochastic multi-scenario set, unit operation constraints, inter-regional power trading constraints, and system operation constraints are set to optimize the model. The unit operation constraints of hydropower units are described in detail below:

[0041]

[0042]

[0043]

[0044] Among them, formula (2) is the lower limit constraint of the output of the hydropower unit under scenario hs. For scene identification, random scene It is a dataset containing future extreme operating conditions. In formula (2), This represents the actual planned power generation of hydropower unit h under time period t, scenario hs, and load level b, in MW. This represents the downward reserve provided by hydropower unit h under time period t, scenario hs, and load level b, i.e., the margin for output reduction, expressed in MW. This represents the minimum output of hydropower unit h under scenario hs, i.e., the lower limit of the unit's output during operation, in MW; This represents the operating status of hydropower unit h under load levels at time periods t and b. When =1, it is running. =0 indicates shutdown; Formula (3) is the upper limit constraint of the output of the hydropower unit under scenario hs. In Formula (3), This indicates the upward reserve provided by hydropower unit h under time period t, scenario hs, and load level b, i.e., the margin for power output that can be increased, in MW; This represents the maximum output of the hydropower unit h under scenario hs, i.e., the upper limit of the unit's output during operation, in MW. Formula (4) is the downward reserve range constraint of the hydropower unit under scenario hs, and formula (5) is the upward reserve range constraint of the hydropower unit under scenario hs. In summary, formulas (2) to (5) together constitute the output range constraint of the hydropower unit.

[0045] In addition to these constraints, hydropower operation must also meet overall hydropower constraints, mainly in the following aspects: First, the total output of the hydropower station should not be less than its forced output. Secondly, its total output should not exceed the power plant's expected output. During a period of time Inside, the hydroelectric power station generates electricity. The relationship between electricity generation and power generation is shown below:

[0046]

[0047]

[0048] Among them, formulas (6)-(7) are the total output constraints of the hydropower station under scenario hs, i.e., the upper and lower limits of the total output constraints, and formula (8) is the formula for calculating the total power generation of the hydropower station. In formula (6), Indicates a hydroelectric power station. It is a hydroelectric power unit; This represents the average power generation of hydropower unit h under time period t, scenario hs, and load level b, in MW; Indicates hydroelectric power station The forced output under time period t and scenario hs, i.e., the lower limit of the total output that the hydropower station must maintain, is expressed in MW; in formula (7), Indicates hydroelectric power station The expected output under time period t and scenario hs, i.e., the upper limit of the total planned output of the hydropower station, is expressed in MW; in formula (8), Indicates hydroelectric power station The total amount of electricity generated during the entire time period t under scenario hs, expressed in MWh; Segments representing approximate continuous load curves; This represents the total number of segments in an approximately continuous load curve; Indicates time period Internal load level The duration, in hours; Representing a scene The probability of occurrence; This refers to mathematical multiplication operations.

[0049] In one specific embodiment, when the time unit The calculation period is 1 week. When the annual regulation period is 1 year, the water allocation of the hydropower station should meet the following constraints.

[0050]

[0051]

[0052] This formula is applicable to annual regulating hydropower stations (time unit). 1 week, calculation period The water allocation constraint (for a year) is one of the core rules for the medium- and long-term operation and scheduling of hydropower, used to regulate the range of outflow power and the balance of total annual power within a given period. In formula (9), This represents the minimum outflow power of a hydropower station hp under time period t and scenario hs, expressed in MWh. This represents the total power generation of the hydropower station hp in time period t and scenario hs, expressed in MWh. hp represents the total equivalent electricity generated by the hydropower station during time period t and scenario hs, in MWh; This represents the maximum outflow of hydropower station hp under time period t and scenario hs; formula (10). This represents the total electricity generated by the hydropower station hp during the entire time period t under scenario hs, expressed in MWh. hp represents the total equivalent electricity generated by the hydropower station during time period t and scenario hs, in MWh; The total equivalent power of water discharge during the time period t of the hydropower station hp is expressed in MWh. Formula (9) is used to limit the range of outflow power within a single time period (1 week), requiring that the "total power generation + equivalent power of water discharge" be between the minimum and maximum outflow power, balancing the rationality of reservoir discharge and power generation demand; Formula (10) is used to achieve annual power balance, requiring that the sum of "total power generation + equivalent power of water discharge" in each time period of the year equals the annual total power, ensuring that the annual water allocation of the regulating hydropower station matches the power generation plan. In summary, Formulas (8)-(10) together constitute the annual power generation constraint of the hydropower unit.

[0053] In some embodiments, for thermal power units: the output range is met ( ≤ ≤ ), start / stop state constraints ( ∈{0,1}) and minimum technical output constraint; where, Indicates thermal power unit; Indicates thermal power unit Maximum output, measured in MW; Indicates thermal power unit The minimum output power, in MW; This indicates the operating status of thermal power unit g under load levels at time periods t and b. When =1, it is running. =0 indicates shutdown.

[0054] For wind and solar turbines: output range constraints ( ≤ ≤ ), and constraints on power curtailment (curtailment amount = maximum available output - planned received output); among which, This represents the total planned wind and solar power output that the system will receive during time period t and at load level b. This represents the minimum available output (power, MW) of wind power and photovoltaic power at time period t and load level b. This represents the maximum available output (power, MW) of wind power and photovoltaic power at time period t and load level b.

[0055] Energy storage units (pumped hydro storage): power generation constraints ( ≤ Pumping power constraints ≤ Furthermore, power generation and pumping operations are mutually exclusive within the same time period; among them, This represents the power generation of energy storage unit q in time period t and load segment b, in MW; This represents the discharge power of energy storage unit q during time period t and load segment b, expressed in MW. This represents the maximum power generation of energy storage unit q during time period t and load segment b; This represents the maximum discharge power of the energy storage unit q during time period t and load segment b.

[0056] In some embodiments, cross-regional power trading constraints include cross-regional power trading constraints and cross-regional electricity trading constraints. Cross-regional power trading constraints define the power and electricity boundaries of tie lines to prevent transactions from exceeding transmission capacity in extreme scenarios.

[0057] Cross-regional transaction power constraints: ≤ ≤ ,in, Let z be the power output of tie line z during time period t and load b. / These are the upper and lower limits of the tie line power. Cross-regional electricity trading constraints: ,in, For connecting lines The total annual electricity volume is evenly distributed seasonally.

[0058] In some embodiments, system operating constraints include power balance constraints, energy balance constraints, and reserve constraints.

[0059] Specifically, power balance constraints ensure that the total power generation of the system is balanced with the total load power, inter-regional trading power, and energy storage charging and discharging power at each time period and load level; The power balance constraint ensures that the total power generation of the system is balanced with the changes in total load power, cross-regional transaction power and energy storage power within the calculation period; Backup constraints include upward backup constraints and downward backup constraints to ensure that the system has the ability to cope with unit failures and load fluctuations.

[0060] Specifically, the power balance constraint is shown in the following formula (11):

[0061] Formula (11) represents the real-time power balance constraint under power system time period t and load segment b. It is one of the core constraints for stable operation of the power system, used to ensure real-time matching between total power output on the generation side and total demand on the load side (including cross-regional transmission), and to avoid frequency fluctuations or supply-demand imbalances. In Formula (11), Indicates the designation of the thermal power generating unit; Indicates the total number of thermal power units; This represents the planned output of thermal power generating unit g in time period t and load segment b, in MW; Indicates the total number of scenes; Indicates a hydroelectric power unit; Indicates the number of hydropower units; This represents the actual planned power generation of hydropower unit h under time period t, scenario hs, and load level b, in MW. This represents the total planned output of wind and solar power at time period t and load level b, expressed in MW. Indicates energy storage unit; Indicates the number of energy storage units; This represents the power generation of energy storage unit q in time period t and load segment b, in MW; This represents the discharge power of energy storage unit q during time period t and load segment b, expressed in MW. This represents the total load power of the system at load level b during time period t, in MW. An index indicating cross-regional connections or transaction contracts; Indicates the total number of cross-regional connection lines or transaction contracts; Indicates cross-regional connection line The power transmitted during time period t and load segment b is the power transmitted across regions, in MW. The left side of formula (11) is the total net output of the power generation side (thermal power output + total output of hydropower in random multi-scenario set + renewable energy output + net energy storage output, where net energy storage output = power generation - pumping power); the right side is the total demand of the load side (system load + power transmitted across regions). This constraint requires that the power on both sides be equal in real time, which is the basic condition for ensuring the frequency stability and supply-demand balance of the power system.

[0062] The system power balance constraint is shown in the following formula (12):

[0063] Formula (12) ensures that the total power generation and total power consumption are balanced within the calculation period, taking into account the effects of energy storage charging and discharging and energy curtailment. Total available power from wind and solar power (unit: MWh); The value is the amount of wind and solar power curtailment (unit: MWh), and the interpretations of the other parameters are consistent with those in formula (11).

[0064] The system standby constraint is shown in the following formula (13):

[0065]

[0066] in, / For the upward / downward standby of thermal power unit g. / For upward / downward backup of energy storage unit q / Reserved for upward / downward movement of the connecting line z; This represents the total planned wind and solar power output that the system will receive during time period t and at load level b. This represents the minimum available output (power, MW) of wind power and photovoltaic power at time period t and load level b. This represents the maximum available output (power, MW) of wind power and solar power at time period t and load level b. To cope with random factors such as unit failures and load fluctuations, the system should ensure sufficient startup capacity, as shown in the following formula (15).

[0067]

[0068] in, Indicates thermal power unit; Indicates thermal power unit Maximum output, measured in MW; This indicates the operating status of thermal power unit g under load levels at time periods t and b. When =1, it is running. =0 indicates shutdown; This represents the planned output of hydropower unit h under scenario hs at time period t and load level b. This represents the sum of the planned output of all hydropower units h under scenario hs at time t and load level b. This represents the total planned power output of wind and solar power during time period t and at load level b. Indicates energy storage unit; This represents the power generation of energy storage unit q during time period t and at load level b. This represents the discharge power of energy storage unit q during time period t and at load level b. This is the system reserve factor; This represents the total load demand of the system at time period t and load level b.

[0069] The optimization model shown in formula (1) and the constraints shown in formulas (2) to (15) constitute a mixed integer programming problem with complex constraints and numerous variables. To accelerate the solution of the model, this application uses the Benders decomposition algorithm to solve the model. The Benders decomposition algorithm decomposes the optimization model into a main problem and subproblems for iterative solution.

[0070] For example, the solution process of the Benders decomposition algorithm is as follows: Based on the mixed integer linear programming optimization model (MILP), the parameters are first initialized, the number of iterations is set, and the parameters are optimized. Then, the Benders main problem is entered: This problem needs to determine the constraint variables such as unit start-up / shutdown, maintenance plan, and inter-regional trading plan, and solve it with the goal of minimizing the total system operating cost. Then, the decision result of the main problem is passed into the Benders subproblem. Under the premise of the decision of the main problem, the subproblem checks the feasibility of the optimal economic dispatch scheme under the corresponding dispatch strategy, while satisfying the constraints such as power balance and reserve, and calculates the actual operating cost. Then, the upper and lower bounds are updated and a cutting plane is added to the main problem. Then, it is judged whether the difference between the upper and lower bounds is less than the threshold ε. If it is not satisfied, the main problem iteration is returned. If it is satisfied, the optimal power balance strategy is output, which includes various unit maintenance / start-up / shutdown plans, medium and long-term power generation plans, inter-regional trading curves, and system reserve capacity configuration.

[0071] Compared with existing technologies, this application makes the constraint modeling more complete through the quantitative constraints of formulas (2)-(15): For the first time, it constructs a full-dimensional constraint of "output-reserve-electricity" for hydropower units (formulas (2)-(10)) for extreme weather scenarios, quantifies the impact of extreme water inflow on hydropower output, and solves the problem that related technologies only qualitatively describe constraints; further, the multi-constraint collaboration is more efficient: the hydropower constraint is modeled collaboratively with the thermal power, energy storage, and cross-regional transaction constraints (formulas (10)-(15)) to ensure the resource linkage of each power source and each region under extreme scenarios; further, the constraints are more in line with the objectives: the cost of constraint violation is incorporated into the objective function through the penalty coefficient (formula (1)) to achieve the collaborative optimization of "safety constraints-economic objectives".

[0072] In one specific embodiment, to verify the effectiveness of the technical solution of this application, the improved IEEE-RTS1979 system is used as the research object, and the following experimental parameters and scenarios are set: the calculation period is 1 year, corresponding to 52 weeks; the penalty for abandoned hydropower is US$1000 / MWh; the penalty for abandoned wind / solar power is US$5 / MWh; and the penalty for load loss is US$1000 / MWh. The annual cross-regional electricity trading volume is evenly distributed according to the seasons, and the output power range of the tie line is [10MW, 500MW]. Normal scenario: no extreme weather, hydropower inflow is at the historical average, and wind and solar power output follows the typical annual curve; extreme scenario: severe drought occurs in summer and autumn, and hydropower inflow drops to 50% of the historical average.

[0073] First, a verification under normal operating conditions (without the impact of extreme weather) was conducted. The models considering extreme weather and those not considering extreme weather were solved separately. The system operating indicators and weekly values ​​of cross-regional electricity transactions under normal operating conditions were compared between the two models. The system operating indicators are shown in Table 1 below. Table 1 System performance indicators under normal operating conditions

[0074] It should be noted that the term "renewable energy" in this application refers to a collective term for wind power, photovoltaic power, and hydropower.

[0075] Figure 2 This is a comparison chart of inter-regional weekly trade in energy under normal circumstances. Figure 3 The bar chart shows the comparison of system performance indicators under normal scenarios. As can be seen from the results above, the existing technical model, which does not consider hydropower reserve constraints, has a lower short-term cost but a higher probability of renewable energy curtailment. The model in this application improves the wind and solar power absorption capacity through hydropower reserve adjustment, reducing the probability of renewable energy curtailment by 1.15 percentage points, thus verifying the role of the constraint formula in promoting the absorption of new energy.

[0076] Secondly, we conducted validation under extreme weather conditions: assuming extreme weather reduces hydropower generation to half its original level during the summer and autumn seasons. The system operation indicators and inter-regional weekly energy trade under extreme weather conditions are compared between the two models, as shown in Table 2 below.

[0077] Table 2 System performance indicators under extreme weather conditions

[0078] Figure 4 This is a comparison chart of inter-regional weekly trade in energy under extreme scenarios. Figure 5 This is a bar chart comparing system performance indicators under extreme scenarios. When extreme weather events such as drought occur, to ensure grid balance, thermal power generation increases, renewable energy consumption increases, and inter-regional energy trade decreases during spring and winter. Notably, inter-regional electricity consumption decreased by 2684 MWh in week 26. Both models result in load losses, but the load losses are smaller under extreme weather conditions. This indicates that the model considering extreme weather is better able to address climate change and ensure grid balance.

[0079] Compared with existing technologies, this application effectively overcomes the core shortcomings of traditional power balance strategies, which "only focus on normal operating conditions, lack sufficient quantification of extreme risks, and are difficult to coordinate safety and economy." Its specific advantages are significant: Firstly, by using a set of multiple scenarios under extreme weather conditions as model boundary constraints, it greatly enhances the safety resilience and risk resistance of the power system under extreme scenarios, avoiding the supply-demand imbalance problems that easily occur in extreme weather conditions due to existing technologies based on deterministic predictions or normal fluctuation range scheduling. As shown in the extreme operating condition example, the load loss of the model in this application is only 0.0242 × MWh, far lower than 0.2260× without considering extreme weather models. MWh, effectively avoiding the risk of power curtailment; on the other hand, a unified medium- and long-term power balance framework is constructed, realizing unified modeling of uncertain power sources (hydropower) and accurate quantification of the balance state under extreme scenarios. This solves the problem that existing technologies are limited to short-term scheduling or independent handling of hydropower uncertainty and are difficult to reflect the coupling effect of multiple energy sources. Case study data shows that under normal operating conditions, the wind and solar curtailment probability of the model in this application decreases from 4.1687% to 3.018%, and under extreme operating conditions, the curtailment volume decreases from 0.1686 × MWh decreased to 0.0181× MWh can accurately capture the impact of multi-energy coupling on system supply and demand, providing scientific data support for scheduling plans. At the same time, through multi-objective optimization design, it improves the overall economic benefits of system operation while ensuring safety in extreme scenarios, breaking the dilemma of existing technologies either driving up costs due to conservative backup or suffering extreme losses due to insufficient risk assessment. Although the total operating cost of the model in this application is slightly higher under normal operating conditions, the cost of thermal power generation under extreme operating conditions is reduced from US$12.743 million to US$6.238 million. By reducing power curtailment and power shortage losses throughout the entire cycle, the overall optimization of long-term system risk and cost is ultimately achieved.

[0080] Please see Figure 6 This application also provides a power balance strategy generation apparatus, which can implement the above-mentioned power balance strategy generation method. The apparatus includes: The acquisition module 601 is used to acquire basic data of the power system and generate a random multi-scenario set based on meteorological and hydrological information in the basic data, wherein each scenario contains a hydropower output sequence. Module 602 is established to build a power balance optimization model based on basic data with the goal of minimizing the total system operating cost. The total system operating cost includes thermal power generation cost, hydropower curtailment penalty cost, wind power curtailment penalty cost, photovoltaic curtailment penalty cost, and load loss penalty cost. The solver module 603 is used to set unit operation constraints, cross-regional power trading constraints and system operation constraints for the optimization model based on a random multi-scenario set, and to solve the model using an optimization algorithm to obtain the power balance strategy and output the power balance strategy to guide the medium and long-term dispatch and operation of the power system.

[0081] The specific implementation of the power balance strategy generation device is basically the same as the specific implementation of the power balance strategy generation method described above, and will not be repeated here.

[0082] Thirdly, embodiments of this application provide an electronic device, see [link to relevant documentation]. Figure 7 The diagram shown is a structural schematic of an electronic device provided in this application.

[0083] like Figure 7 As shown, the device includes: Memory 31 is used to store computer programs; Processor 32 is used to execute computer programs; When the processor 32 executes the computer program, it implements the power balance strategy generation method as described in any of the above embodiments.

[0084] For example, a computer program may be divided into one or more modules / units, one or more of which are stored in memory 31 and executed by processor 32 to complete this application. One or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in an electronic device.

[0085] The processor 32 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0086] The memory 31 can be used to store computer programs and / or modules. The processor 32 implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory 31 and calling the data stored in the memory 31. The memory 31 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 31 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0087] It should be noted that the aforementioned electronic devices include, but are not limited to, processors and memory, as will be understood by those skilled in the art. Figure 7 The structural diagram is merely an example of the electronic device described above and does not constitute a limitation on the electronic device. It may include more components than shown in the diagram, or combine certain components, or use different components.

[0088] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed, implements the power balance strategy generation method of any of the above embodiments.

[0089] It should be understood that the implementation of all or part of the above-described power balance strategy generation method can also be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above-described power balance strategy generation method. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the relevant jurisdiction. For example, in some relevant jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0090] Fifthly, embodiments of this application also provide a computer program product, which is stored in a storage medium and executed by at least one processor to implement the power balance strategy generation method of any of the above embodiments.

[0091] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0092] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A method for generating an electricity balance strategy, characterized in that, include: Acquire basic data of the power system, and generate a random multi-scenario set based on meteorological and hydrological information in the basic data, wherein each scenario contains a hydropower output sequence; Based on the aforementioned basic data, a power balance optimization model is established with the goal of minimizing the total system operating cost. The total system operating cost includes thermal power generation cost, hydropower curtailment penalty cost, wind power curtailment penalty cost, photovoltaic curtailment penalty cost, and load loss penalty cost. Based on the aforementioned set of random multi-scenario applications, unit operation constraints, cross-regional power trading constraints, and system operation constraints are set for the optimization model. An optimization algorithm is used to solve the model to obtain a power balance strategy, which is then output to guide the medium- and long-term scheduling and operation of the power system.

2. The power balance strategy generation method as described in claim 1, characterized in that, The generation of a random multi-scene set based on the meteorological and hydrological information in the basic data includes: Extreme weather events can be identified based on the meteorological and hydrological information in the aforementioned basic data. Using the extreme weather events as common driving factors, a random multi-scenario set is generated by employing probabilistic models or machine learning methods.

3. The power balance strategy generation method as described in claim 1, characterized in that, The unit operating constraints include: Constraints on the output range and annual power generation of hydropower units, and total output of hydropower stations; Output range constraints and curtailment restrictions for wind and solar power units; Constraints on the output range, start-stop status, and minimum technical output of thermal power units; Constraints on the power generation and pumping power of pumped storage units.

4. The power balance strategy generation method as described in claim 1, characterized in that, The constraints on cross-regional power trading include cross-regional power trading constraints and cross-regional electricity trading constraints. The system operation constraints include power balance constraints, energy balance constraints, and reserve constraints.

5. The power balance strategy generation method as described in claim 4, characterized in that, The power balance constraint ensures that the total power generation of the system is balanced with the total load power, the cross-regional trading power, and the energy storage charging and discharging power at each time period and load level. The power balance constraint ensures that the total power generation of the system is balanced with the changes in total load power, cross-regional transaction power and energy storage power within the calculation period; The backup constraints include upward backup constraints and downward backup constraints to ensure that the system has the ability to cope with unit failures and load fluctuations.

6. The method for generating a power balance strategy as described in claim 1, characterized in that, The optimization algorithm is the Benders decomposition algorithm, which decomposes the optimization model into a main problem and subproblems for iterative solution.

7. A power balance strategy generation device, characterized in that, include: The acquisition module is used to acquire basic data of the power system and generate a random multi-scenario set based on the meteorological and hydrological information in the basic data, wherein each scenario contains a hydropower output sequence. A module is established to build a power balance optimization model based on the basic data with the goal of minimizing the total system operating cost, wherein the total system operating cost includes thermal power generation cost, hydropower curtailment penalty cost, wind power curtailment penalty cost, photovoltaic curtailment penalty cost, and load loss penalty cost; The solution module is used to set unit operation constraints, cross-regional power trading constraints and system operation constraints for the optimization model based on the random multi-scenario set, solve the model using optimization algorithms, obtain the power balance strategy, and output the power balance strategy to guide the medium and long-term scheduling and operation of the power system.

8. An electronic device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the power balance strategy generation method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the power balance strategy generation method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program or computer instructions, which, when executed by a processor, implement the power balance strategy generation method as described in any one of claims 1 to 6.

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