Multi-type energy storage day-ahead peak regulation plan compilation method, system, equipment and medium

Abstract

The invention discloses a multi-type energy storage day-ahead peak regulation plan compilation method and system, equipment and a medium. The method comprises the steps that equipment physical data and market-oriented unit declaration data are acquired; generating a system operation data set through cleaning, checking and packaging merging; a prospective pre-clearing scheduling model is constructed, and a marketization unit clearing result is obtained by solving output arrangement under thermal power unit start-stop and segmented quotation; constructing a multi-type energy storage day-ahead peak regulation optimization model, and obtaining a non-marketization energy storage peak regulation plan by optimizing non-marketization energy storage operation; and obtaining a peak regulation plan compilation system through integrated compilation and standardized output. According to the method, the whole-process connection of the market operation boundary and the non-marketization unit scheduling is realized, and the smooth transition of power production from a plan mode to a market mode and the efficient calling of energy storage resources are supported.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method, system, equipment and medium for compiling day-ahead peak shaving plans for multiple types of energy storage. Background Technology

[0002] The current power system faces significant technical bottlenecks in the formulation of day-ahead peak-shaving plans for non-market-based energy storage. First, the planning relies heavily on manual experience, making it difficult to dynamically respond to net load fluctuations caused by the high proportion of renewable energy integration, thus failing to fully tap the regulation potential of energy storage. Secondly, existing peak-shaving algorithms are severely out of sync with the electricity market mechanism: on the one hand, traditional optimization models treat energy storage plans and spot market boundaries separately, failing to consider the cost-effectiveness of market-based units, causing energy storage plans to deviate from the optimal economic operating point of the system; on the other hand, some models that attempt to couple market factors are difficult to solve due to the introduction of complex constraints, and multi-type energy storage coordinated scheduling faces modeling dilemmas: if detailed physical operation models (such as accurate charge and discharge efficiency curves and lifetime loss constraints) are adopted, the model complexity increases exponentially, making it difficult to meet the timeliness requirements of day-ahead planning; while oversimplification fails to reflect the essential differences between energy storage technologies—for example, long-cycle regulation resources such as forced pumped storage and hydrogen energy storage complete energy cycling within a single operating day (24 hours), stifling their cross-day regulation capabilities; at the same time, setting the final state of charge (SOC) of electrochemical energy storage machinery to zero or a fixed value essentially compresses its flexible regulation capabilities to a single-day time scale. Ultimately, the aforementioned defects create a vicious cycle: the full-cycle optimization model that takes into account both market synergy and the characteristics of multiple types of energy storage cannot be used due to the curse of dimensionality (the solution time often exceeds 3 hours), while the simplified model that can be solved quickly sacrifices economic efficiency and technical rationality, forcing the dispatching end to revert to an inefficient mode dominated by human experience. Summary of the Invention

[0003] In view of the aforementioned existing problems, the present invention is proposed.

[0004] Therefore, this invention provides a method, system, equipment, and medium for compiling day-ahead peak shaving plans for various types of energy storage to address the problems of insufficient exploitation of the regulation potential, poor economic efficiency, and low solution efficiency of existing day-ahead peak shaving plans for non-market-based energy storage in power systems.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides a method for formulating day-ahead peak-shaving plans for multiple types of energy storage, including:

[0007] Obtain physical data of equipment and application data for market-oriented generating units;

[0008] Based on the acquired data, the system operation dataset is generated through cleaning, verification, and encapsulation and merging.

[0009] Based on the system operation dataset, a forward-looking pre-clearing scheduling model is constructed. By solving the power output arrangement under the start-up and shutdown of thermal power units and segmented bidding, the clearing results of market-oriented units are obtained.

[0010] Based on the results of market-based unit clearing, a multi-type energy storage day-ahead peak-shaving optimization model is constructed. By optimizing the operation of non-market-based energy storage, a non-market-based energy storage peak-shaving plan is obtained.

[0011] Based on the results of peak shaving optimization, a peak shaving plan preparation system is obtained through integrated preparation and standardized output.

[0012] As a preferred embodiment of the multi-type energy storage day-ahead peak-shaving plan preparation method described in this invention, the method for generating system operation datasets includes:

[0013] Data for units that are not connected to the grid or have been permanently decommissioned has been removed.

[0014] Verify the consistency between the predicted new energy values ​​and the historical error range;

[0015] Verify that the unit maintenance plan has been synchronized in the data;

[0016] Based on the cleaned and verified data, the data for a single operating day is packaged.

[0017] The data from two consecutive days are merged to generate a preprocessed system operation dataset.

[0018] As a preferred embodiment of the multi-type energy storage day-ahead peak-shaving plan formulation method described in this invention, wherein obtaining the non-market-based energy storage peak-shaving plan includes:

[0019] Define an objective function that minimizes the root mean square error of the net load curves for two consecutive future operating days.

[0020] Calculate the net load for each time period based on the load, interconnection lines, new energy output, and energy storage charging and discharging power;

[0021] Apply general operational constraints for energy storage;

[0022] By employing a tiered relaxation strategy, the state of charge constraints for various types of energy storage terminals are set differently.

[0023] The objective function and constraints are integrated into a mixed-integer quadratic programming problem, which is then solved using a commercial solver to obtain a non-market-based energy storage peak-shaving plan.

[0024] The beneficial effects of this preferred technical solution are as follows: by setting an objective function to minimize the net load fluctuation over consecutive two days, combining net load calculation, general constraints of energy storage and differentiated terminal state of charge constraints of hierarchical relaxation, and constructing a mixed integer quadratic programming model that can be solved efficiently, the collaborative peak-shaving optimization of multiple types of non-market energy storage is realized. Under the premise of ensuring the safe operation of various types of energy storage, the system's peak-shaving capability and the engineering feasibility of the scheduling scheme are significantly improved.

[0025] As a preferred embodiment of the multi-type energy storage day-ahead peak-shaving planning method of the present invention, wherein: the obtained peak-shaving planning system includes:

[0026] By analyzing the charge and discharge power sequence, a single power station operation plan and daily utilization report are generated;

[0027] The province's energy storage cluster power data is aggregated to construct a provincial total power curve and generate standardized market reporting documents.

[0028] Simultaneously issue refined scheduling instructions for the first day of operation to each energy storage power station and visualize monitoring information;

[0029] By using a rolling optimization and connection mechanism, the final state of energy storage operation in the current cycle is used as the initial condition for the next cycle, thus obtaining a peak-shaving plan formulation system.

[0030] As a preferred embodiment of the multi-type energy storage day-ahead peak-shaving plan formulation method described in this invention, wherein obtaining the market-based unit clearing results includes:

[0031] Define an objective function that minimizes the total operating cost of market-based generating units over the next two consecutive operating days;

[0032] Apply system-level operational constraints, physical operational constraints of thermal power units, operational constraints of new energy and hydropower, and logical constraints of unit start-up and shutdown states;

[0033] The objective function and constraints are integrated into a mixed-integer linear programming problem, which is then solved using a commercial solver to obtain the market-based unit clearing results.

[0034] The beneficial effects of this preferred technical solution are that by constructing an optimization model aimed at minimizing the total operating cost of market-based units for the next two operating days, and comprehensively considering multiple constraints such as system balance, reserve requirements, unit physical characteristics, and start-up and shutdown logic, and modeling it as a mixed integer linear programming problem that can be solved efficiently, the economic efficiency and feasibility of unit start-up and shutdown and output plans are optimized in a coordinated manner, effectively improving the rationality and engineering practicality of the day-ahead market clearing results.

[0035] As a preferred embodiment of the multi-type energy storage day-ahead peak-shaving planning method described in this invention, the rolling optimization and connection mechanism includes:

[0036] For short-term regulating energy storage, the actual state of charge at the end of the planning period is used as the initial reference value for the next cycle and the deviation range is reset.

[0037] For long-term regulating energy storage, the continuity of the state of charge across cycles is maintained;

[0038] Once the data update is complete, a new round of optimization processes will be triggered.

[0039] As a preferred embodiment of the multi-type energy storage day-ahead peak-shaving planning method of the present invention, the step of setting differentiated state-of-charge constraints for multi-type energy storage terminals through a hierarchical relaxation strategy includes:

[0040] For short-term regulating energy storage, a relaxed state-of-charge constraint at the end of the period is adopted;

[0041] For long-term regulating energy storage, a state-of-charge constraint across operating days is adopted;

[0042] The state-of-charge constraint at the end of the relaxation period is expressed as follows:

[0043]

[0044] Among them, Ω batt For electrochemical energy storage subset; Δ s The allowable deviation in state of charge;

[0045] The cross-operational day state of charge constraint is expressed as follows:

[0046]

[0047] Among them, Ω long It is a subset for long-term energy storage.

[0048] Secondly, the present invention provides a multi-type energy storage day-ahead peak-shaving planning system, comprising:

[0049] The acquisition module is used to acquire equipment physical data and market-oriented unit application data;

[0050] The preprocessing module is used to generate a system runtime dataset based on the acquired data through cleaning, verification, and encapsulation.

[0051] The forward-looking pre-clearing scheduling module is used to build a forward-looking pre-clearing scheduling model based on the system operation dataset. By solving the power output arrangement under the start-up and shutdown of thermal power units and segmented bidding, the clearing results of market-based units are obtained.

[0052] The energy storage peak shaving optimization module is used to construct day-ahead peak shaving optimization models for various types of energy storage based on the market-based unit clearing results. By optimizing the operation of non-market-based energy storage, a non-market-based energy storage peak shaving plan is obtained.

[0053] The data output and planning module is used to obtain a peak shaving plan planning system based on the peak shaving optimization results through integrated planning and standardized output.

[0054] Thirdly, the present invention provides an electronic device, comprising:

[0055] Memory, used to store programs;

[0056] A processor is configured to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the multi-type energy storage day-ahead peak shaving plan formulation method.

[0057] Fourthly, the present invention provides a computer-readable storage medium, comprising: when the program is executed by a processor, the steps of implementing the multi-type energy storage day-ahead peak shaving plan preparation method.

[0058] The beneficial effects of this invention are as follows: This invention removes the rigidity of the uniform end-period SOC equation constraint through a layered relaxation strategy. Specifically, it adopts a dynamic allowable range constraint of end-period SOC for electrochemical energy storage and retains only the lower limit constraint of SOC for long-cycle energy storage to achieve collaborative modeling for technical characteristic adaptation. It establishes a dynamic connection path between non-market resources and the spot market through a two-stage safety and economic collaborative framework: an economic boundary is generated based on the forward-looking pre-clearing of market-based unit bidding data. Under its constraint, the energy storage peak-shaving plan is optimized with the goal of minimizing the net load variance. Thus, under the premise of ensuring the physical security of the power system, the entire process of market operation boundary and non-market unit scheduling is connected, supporting the smooth transition of power production from a planned mode to a market mode and the efficient use of energy storage resources. Attached Figure Description

[0059] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0060] Figure 1 This is a basic flowchart illustrating a method for developing day-ahead peak-shaving plans for multiple types of energy storage, provided in one embodiment of the present invention.

[0061] Figure 2 An energy storage peak-shaving effect diagram is provided for a method for compiling multi-type energy storage day-ahead peak-shaving plans according to an embodiment of the present invention;

[0062] Figure 3A time-segmented total output diagram of energy storage across the province is provided as an embodiment of the present invention for a method for compiling day-ahead peak-shaving plans for multiple types of energy storage.

[0063] Figure 4 An operation plan curve of a single energy storage power station provided as an embodiment of the present invention for a method for compiling day-ahead peak shaving plans for multiple types of energy storage. Detailed Implementation

[0064] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0065] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for formulating day-ahead peak-shaving plans for multiple types of energy storage is provided, including:

[0066] S100: Obtain physical data of equipment and application data for market-oriented generating units;

[0067] S200: Based on the acquired data, the system operation dataset is generated through cleaning, verification, and encapsulation and merging.

[0068] S300: Based on the system operation dataset, a forward-looking pre-clearing scheduling model is constructed. By solving the power output arrangement under the start-up and shutdown of thermal power units and segmented bidding, the clearing results of market-oriented units are obtained.

[0069] S400: Based on the market-based unit clearing results, construct a multi-type energy storage day-ahead peak-shaving optimization model, and obtain a non-market-based energy storage peak-shaving plan by optimizing the operation of non-market-based energy storage;

[0070] S500: Based on the results of peak shaving optimization, a peak shaving plan compilation system is obtained through integrated compilation and standardized output.

[0071] It should be noted that existing methods for developing day-ahead peak-shaving plans for non-market-based energy storage in power systems face a series of complex challenges during operation. First, energy storage technologies are diverse (e.g., electrochemical storage, pumped hydro storage, hydrogen storage), with significant differences in response speed, energy density, charge / discharge efficiency, and operational lifespan, making unified modeling difficult and hindering the synergistic optimization of multiple energy storage types. Second, the charging and discharging behavior of energy storage is highly coupled with system net load fluctuations. Traditional methods often employ fixed output curves or simple peak-shaving and valley-filling strategies, lacking precise responses to the dynamic characteristics of the net load, resulting in low peak-shaving efficiency. Third, terminal state of charge (SOC) management presents a dilemma: forced zeroing or resetting may lead to energy waste or frequent equipment start-ups and shutdowns; without constraints, it may cause cross-cycle energy imbalances, affecting the feasibility of multi-day continuous dispatch. Furthermore, existing methods often neglect engineering constraints such as energy storage ramp-up rates, cycle life losses, and in-station unit output allocation, resulting in poor plan executability. Finally, with the high penetration rate of new energy sources, the forecasting error is significant, the fixed day-ahead plan lacks flexibility and is difficult to adapt to the uncertainties in actual operation, affecting the peak-shaving effect and system safety.

[0072] Therefore, addressing the issues of insufficiently explored regulation potential, poor economic efficiency, and low solution efficiency in existing day-ahead peak-shaving planning methods for non-market-based energy storage in the power system, this paper proposes a solution through steps S100-S500. By utilizing a continuous two-day optimization window, it overcomes the limitations of traditional single-day dispatch, allowing long-duration energy storage resources such as pumped hydro storage and hydrogen storage to escape the constraint of forced return to the initial state of charge each day, thus enabling cross-day energy transfer. Simultaneously, electrochemical energy storage adopts a dynamic range constraint based on the state of charge at the end of the period, significantly improving its intraday regulation freedom. This two-stage optimization framework integrates market... The system internalizes unit bidding information into a pre-clearing economic boundary, driving the peak-shaving plan of non-market-based energy storage to dynamically respond to market signals, thereby reducing the overall system operating cost while ensuring the physical security of the power grid. By decomposing it into a linear unit combination and a secondary peak-shaving model and calling a commercial solver, it achieves efficient solution to the bi-day dispatch problem under the scale of a provincial power grid, meeting the timeliness requirements of day-ahead planning. Ultimately, it forms a peak-shaving planning system that takes into account the adaptation of long-cycle energy storage characteristics, dynamic connection of market boundaries, and improvement of computational efficiency, providing key technical support for the market-oriented transformation of the power system.

[0073] Example 2, refer to Figure 1 As an embodiment of the present invention, based on the previous embodiment, a method for compiling multi-type energy storage day-ahead peak-shaving plans is provided, including:

[0074] In this embodiment, step S100 involves collecting relevant data required for subsequent forward scheduling optimization of unit combinations and the preparation of energy storage peak-shaving plans. This includes equipment physical information related to operation and market declaration information. Regarding equipment physical information, the collected data includes: 1) Unit technical parameters, including rated capacity, upper and lower limits of active power output, minimum start-up and shutdown time, and ramp rate for thermal power, hydropower, and new energy units; 2) New energy forecast data: time-of-use forecast output of wind farms / photovoltaic power plants, and daily water inflow plans for hydropower units; 3) System load data: provincial day-ahead load forecast curve, positive and negative reserve capacity demand, local non-market-based unit power generation plans, and inter-provincial AC / DC transmission arrangements; 4) Operational constraint information: unit maintenance status and corresponding outage periods. Regarding market declaration information, the main data collected includes market-based unit declaration data: electricity-cost segmented price curves (or operating cost information), start-up and shutdown costs, and available power generation capacity.

[0075] In this embodiment, step S200 involves data cleaning and validity verification. The main operations include: removing data from units that are not connected to the grid or permanently decommissioned; verifying the consistency between the predicted new energy values ​​and historical error ranges; and verifying whether the unit maintenance plan has been synchronized in the data.

[0076] In this embodiment of the application, in step S200, the data of a single operating day is encapsulated, and the data of two operating days are collected and merged to generate a preprocessed system operating dataset for use in step S300.

[0077] In this embodiment of the application, obtaining the market-based unit clearing result in step S300 includes:

[0078] Define an objective function that minimizes the total operating cost of market-based generating units over the next two consecutive operating days;

[0079] Apply system-level operational constraints, physical operational constraints of thermal power units, operational constraints of new energy and hydropower, and logical constraints of unit start-up and shutdown states;

[0080] The objective function and constraints are integrated into a mixed-integer linear programming problem, which is then solved using a commercial solver to obtain the market-based unit clearing results.

[0081] In an optional implementation, the market-based unit clearing result obtained in step S300 can also be based on multi-scenario renewable energy output and load forecast data to construct a stochastic unit combination model with the goal of minimizing the expected total operating cost. Taking into account system balance, reserve demand and unit operation constraints under each scenario, the robust unit output plan for the next two days is obtained by solving this stochastic mixed integer programming problem.

[0082] In an optional implementation, the market-based unit clearing results obtained in step S300 can also be used to train a reinforcement learning agent using historical operating data, so that the agent can generate an approximately optimal unit start-up and shutdown and output sequence online based on input information such as load, new energy forecasts and unit status, forming a unit combination plan for the next two operating days.

[0083] In this embodiment of the application, step S300 performs forward-looking unit combination optimization based on the aforementioned generated system operation dataset, taking into account the predicted information for the next two operating days to optimize the unit start-up and shutdown schedule. The corresponding mathematical model includes two parts: an objective function and constraints.

[0084] In this embodiment of the application, the objective function for minimizing the total operating cost of market-based generating units over two consecutive future operating days (192 time periods, each time period being 15 minutes) is expressed as:

[0085]

[0086] In the formula, I th I re It is a collection of thermal power and new energy units; y i,t z i,t For unit i, the start / stop state is a 0-1 variable during time period t; U i,t P represents the 0-1 variable indicating the operating state of unit i during time period t; i,t Let i be the power output level of thermal power unit i during time period t; For unit start / stop costs; For the unit's no-load cost; A function for segmented pricing of generator unit power; The cost of calling up the new energy unit j; The actual output of the new energy unit j during time period t.

[0087] In this application embodiment, system-level operational constraints include system supply and demand balance constraints and system positive and negative reserve capacity constraints; thermal power unit physical operational constraints include upper and lower output limits constraints, ramp rate constraints, and minimum start-stop time constraints; new energy and hydropower operational constraints include new energy output constraints and hydropower unit daily power generation constraints; unit start-stop state logic constraints include start-stop state and operation state relationship constraints and mutual exclusion start-stop constraints.

[0088] In this embodiment of the application, the system supply and demand balance constraint is expressed as:

[0089]

[0090] In the formula, I hyd A collection of hydroelectric generating units; D t The system load is defined as time period t. This represents the net input power of the inter-provincial connection line. This refers to the power generation of local generating units.

[0091] In this embodiment of the application, the system's positive and negative standby capacity constraints are expressed as follows:

[0092]

[0093] In the formula, P i The upper and lower limits of the output of unit i; The system's positive and negative backup requirements for time period t.

[0094] In this embodiment of the application, the upper and lower limits of output are expressed as follows:

[0095]

[0096] In this embodiment of the application, the ramp rate constraint is expressed as:

[0097]

[0098] In this embodiment of the application, the minimum start-stop time constraint is expressed as:

[0099]

[0100] In the formula, The upper and lower limits of the unit's climbing ability; This sets the start-up and shutdown time limits for unit i.

[0101] In this embodiment of the application, the power output constraint of the new energy source is expressed as follows:

[0102]

[0103] In the formula, The predicted output of the new energy unit j during time period t.

[0104] In this embodiment of the application, the daily power generation constraint of the hydropower unit is expressed as:

[0105]

[0106] In the formula, M k For the set of generating units belonging to hydropower station k; Δt represents the equivalent hydropower output of hydropower station k over two operating days; Δt is the length of a single time period, which is 15 minutes.

[0107] In this embodiment of the application, the constraint between the start / stop state and the running state is expressed as follows:

[0108]

[0109] In this embodiment of the application, the mutual exclusion start / stop constraint is expressed as:

[0110]

[0111] In the formula, U i,0 This is a 0-1 variable representing the operating status of unit i at the end of the previous operating day.

[0112] In this embodiment, the forward-looking pre-clearing scheduling model can be solved by calling commercial solvers such as Gurobi / COPT to solve mixed-integer linear programming problems, and the output result is the start-up and shutdown status U of each market-based unit in the next 192 time periods. i,t , output plan P i,t Actual output of new energy and hydropower output arrangement This can then be used to calculate the total minimum technical output of thermal power units during the peak-shaving phase, serving as the boundary condition for the model in step S400.

[0113] In this embodiment of the application, step S400 mainly involves constructing a system operation model for peak shaving optimization and multi-type energy storage. Based on common operation constraints, flexible operation state of charge constraints are constructed according to the characteristics of different types of energy storage technologies to achieve coordinated peak shaving of multiple types of energy storage.

[0114] In this embodiment of the application, step S400, which yields a non-market-based energy storage peak-shaving plan, includes:

[0115] Define an objective function that minimizes the root mean square error of the net load curves for two consecutive future operating days.

[0116] Calculate the net load for each time period based on the load, interconnection lines, new energy output, and energy storage charging and discharging power;

[0117] Apply general operational constraints for energy storage;

[0118] By employing a tiered relaxation strategy, the state of charge constraints for various types of energy storage terminals are set differently.

[0119] The objective function and constraints are integrated into a mixed-integer quadratic programming problem, which is then solved using a commercial solver to obtain a non-market-based energy storage peak-shaving plan.

[0120] In an optional implementation, the non-market-based energy storage peak-shaving plan obtained in step S400 can also be based on the forward-looking pre-clearing scheduling results, and a mixed integer quadratic programming model is constructed with the goal of minimizing the sum of thermal power fuel costs, start-up and shutdown costs, and energy storage operating costs. The model retains the general operating constraints of various types of energy storage and the differentiated terminal state of charge relaxation constraints. By solving the model, the day-ahead operation plan of non-market-based energy storage that takes into account both peak-shaving demand and economic optimization can be obtained.

[0121] In an optional implementation, the non-market-based energy storage peak-shaving plan obtained in step S400 can also be based on the forward-looking pre-clearing scheduling results, constructing a mixed integer linear programming model with the goal of minimizing wind and solar curtailment or maximizing the actual consumption of new energy. This model combines system load, tie line and minimum output constraints of thermal power, while retaining the general operating characteristics of multiple types of energy storage and differentiated terminal state of charge relaxation strategies. By solving this model, a non-market-based energy storage day-ahead peak-shaving plan that prioritizes the absorption of renewable energy can be generated.

[0122] In this embodiment of the application, the objective function of the power system peak-shaving optimization problem is to minimize the root mean square error of the net load curve, expressed as:

[0123]

[0124] In the formula, The net load is for time period t; T is 192. This represents the average net load; Ω sto For energy storage collection; The charging and discharging power of energy storage s during time period t.

[0125] In the embodiments of this application, the general operating constraints for energy storage include upper and lower limits of charge and discharge power, mutual exclusion constraints of charge and discharge, dynamic update equation of state of charge (SOC), SOC safe range constraints, limit of charge and discharge ramp rate, and limit of daily equivalent cycle count.

[0126] In this embodiment of the application, the upper and lower limits of charge and discharge power are expressed as follows:

[0127]

[0128] In this embodiment of the application, the charge-discharge mutual exclusion constraint is expressed as:

[0129]

[0130] In this embodiment, the dynamic update equation for the state of charge (SOC) is expressed as:

[0131]

[0132] In this embodiment of the application, the SOC safety interval constraint is expressed as:

[0133]

[0134] In this embodiment of the application, the charge / discharge ramp rate limit is expressed as:

[0135]

[0136] In this embodiment of the application, the daily equivalent cycle count limit is expressed as:

[0137]

[0138] In the formula, This represents the maximum charging and discharging power of the energy storage device s; SOC is a 0-1 variable representing the state of charge and discharge; s,t State of charge (SOC) is the state of charge of energy storage during a given period. s,0 The initial state of charge; SOC s , These are the upper and lower limits of the state of charge; This represents the maximum charge and discharge ramp rate for energy storage. This is the set of time periods for the operating days of day D and day D+1. This refers to the rated capacity of the energy storage. The maximum allowed number of equivalent loops per day.

[0139] In this embodiment of the application, a hierarchical relaxation strategy is used to differentiate the state of charge constraints of various types of energy storage terminals, including:

[0140] For short-term regulating energy storage (such as electrochemical energy storage), a relaxed state-of-charge constraint at the end of the time period is adopted;

[0141] For long-term regulating energy storage (such as pumped storage and hydrogen energy storage), the state of charge constraint across operating days is adopted;

[0142] In an optional implementation, the tiered relaxation strategy in step S400 can also set differentiated terminal SOC constraint strategies based on the functional roles of different energy storage types in the system (such as intraday frequency regulation, inter-day peak shaving, and new energy support).

[0143] In an optional implementation, the hierarchical relaxation strategy in step S400 can also achieve soft constraint optimization by not directly setting a hard terminal SOC constraint, but by converting the deviation from the ideal terminal SOC into an equivalent cyclic depreciation cost based on the lifetime loss characteristics of different types of energy storage, and then incorporating it into the objective function.

[0144] In this embodiment, the state of charge constraint at the end of the relaxation period is expressed as:

[0145]

[0146] Among them, Ω batt For electrochemical energy storage subset; Δ s The allowable deviation in state of charge;

[0147] In this embodiment of the application, the state of charge constraint across operating days is expressed as:

[0148]

[0149] Among them, Ω long It is a subset for long-term energy storage.

[0150] In this embodiment, the multi-type energy storage day-ahead peak-shaving optimization model can also be solved by calling commercial solvers such as Gurobi / COPT to obtain the energy storage peak-shaving output plan. Solving the optimization problem on day D-1 yields the energy storage dispatch schedule for day D and day D+1. Updating the relevant forecast data on day D and solving again updates the energy storage schedule for day D+1. Through a similar rolling optimization approach, the energy storage plan optimization schedule for multiple operating days can be achieved.

[0151] In this embodiment, after completing the calculation of the day-ahead peak-shaving optimization model for multiple types of energy storage in step S500, the integrated compilation and standardized output of the energy storage plan are performed. This includes parsing the charging and discharging power sequence obtained from the energy storage peak-shaving optimization model calculation, aggregating the time-sharing output instructions of all units within the energy storage power station according to the energy storage power station dimension, forming a 96-time-period (one operating day) operation plan for a single power station and the corresponding safe operating range of the state of charge. At the same time, a daily utilization statistical analysis report for a single energy storage unit is generated, covering the charging and discharging depth, equivalent utilization hours, and records of critical state extreme values. Furthermore, the charging and discharging power data of non-market-based energy storage power stations in the province within 192 time periods are summarized, and the total power during the discharging period, the total load during the charging period, and the net peak-shaving contribution power are calculated respectively to construct the time-sharing total power curve of the provincial energy storage cluster. Based on this, a standardized regional market boundary reporting document is generated, which includes the total energy storage power sequence (discharging is positive, charging is negative), the network constraint boundary inheriting the pre-clearing results, and the power generation plan of non-market-based units. The document format strictly follows the regional market data specification (such as CIM / E format). Simultaneously, refined dispatch instructions for the first operating day are issued to each power station, and the peak-shaving effect comparison, multi-type energy storage status coordination and safety margin monitoring information are visualized on the provincial dispatch platform, and a rolling optimization and connection mechanism is implemented; this step realizes the closed-loop management of the entire chain of "plan generation - on-site control - market reporting" for the call of its own energy storage units.

[0152] In this embodiment of the application, the rolling optimization connection mechanism includes:

[0153] For short-term regulating energy storage, the actual state of charge at the end of the planning period is used as the initial reference value for the next cycle and the deviation range is reset.

[0154] For long-term regulating energy storage, the continuity of the state of charge across cycles is maintained;

[0155] Once the data update is complete, a new round of optimization processes will be triggered.

[0156] In an optional implementation, the rolling optimization connection mechanism in step S500 may not rely entirely on the end SOC of the previous cycle as the initial value, but instead combine the latest load and new energy forecasts to dynamically adjust the target end SOC of the next cycle, guiding energy storage to prepare energy reserves for future multi-day scheduling.

[0157] In an optional implementation, the rolling optimization connection mechanism in step S500 may not force the SOC at the end of each cycle to be equal to the initial value, but requires that a net energy balance be achieved within a relatively long rolling window (such as 5 to 7 days) to avoid energy depletion or overflow caused by long-term unidirectional operation of energy storage.

[0158] Example 3, referring to Figures 2-4 This is one embodiment of the present invention, which provides a method for compiling day-ahead peak shaving plans for multiple types of energy storage. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through specific implementation methods and implementation effects.

[0159] The specific details of this embodiment are as follows:

[0160] A calculation example was conducted based on actual data from two consecutive operating days in a province in southern China. The example included 68 thermal power plants, 80 hydropower plants, 2 wind power plants, 6 photovoltaic power plants, and 14 energy storage units. The system net load curve after energy storage peak shaving is compared as follows: Figure 2 As shown. From Figure 2 As can be seen, energy storage peak shaving played a good role in peak shaving and valley filling on both operating days.

[0161] The total output of all energy storage in the province at different times is as follows: Figure 3 As shown. From Figure 3 As can be seen, the operating trend of all energy storage systems in the province is to charge during off-peak hours to avoid the system load falling below the minimum technical output of thermal power, and to discharge during peak hours to reduce system operating costs.

[0162] The operation plan curve of a single energy storage power station is as follows: Figure 4 As shown. From Figure 4 As can be seen, under the flexible operation constraint, energy storage does not need to return the state of charge to the initial value at the end of the operating day. Instead, it can carry out charging operations to further fill the load trough during the midnight period, and further leverage the flexible adjustment characteristics of energy storage.

[0163] Example 4 is an embodiment of the present invention. This embodiment differs from the first embodiment in that it provides a multi-type energy storage day-ahead peak shaving planning system.

[0164] It should be noted that the technical solution of the multi-type energy storage day-ahead peak shaving plan preparation system and the technical solution of the multi-type energy storage day-ahead peak shaving plan preparation method described above belong to the same concept. For details not described in detail in the technical solution of the multi-type energy storage day-ahead peak shaving plan preparation system in this embodiment, please refer to the description of the technical solution of the multi-type energy storage day-ahead peak shaving plan preparation method described above.

[0165] This embodiment presents a multi-type energy storage day-ahead peak-shaving planning system, comprising:

[0166] The acquisition module is used to acquire equipment physical data and market-oriented unit application data;

[0167] The preprocessing module is used to generate a system runtime dataset based on the acquired data through cleaning, verification, and encapsulation.

[0168] The forward-looking pre-clearing scheduling module is used to determine the minimum technical output of thermal power units based on the system operation dataset and through forward-looking unit combination optimization.

[0169] Multiple types of energy storage peak shaving optimization modules are used to obtain peak shaving optimization results by optimizing the charging and discharging strategies of energy storage devices based on the minimum technical output of thermal power units.

[0170] The module for compiling and reporting day-ahead peak shaving plans for energy storage is used to obtain day-ahead peak shaving plans for energy storage based on peak shaving optimization results by compiling time-segmented charging and discharging plans and standardized reporting documents.

[0171] This embodiment also provides an electronic device applicable to a multi-type energy storage day-ahead peak-shaving plan formulation method, including:

[0172] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement a method for developing a multi-type energy storage day-ahead peak-shaving plan, as proposed in the above embodiments.

[0173] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for compiling a multi-type energy storage day-ahead peak shaving plan as proposed in the above embodiments.

[0174] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for compiling a multi-type energy storage day-ahead peak shaving plan proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0175] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0176] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for compiling day-ahead peak-shaving plans for multiple types of energy storage, characterized in that, include: Obtain physical data of equipment and application data for market-oriented generating units; Based on the acquired data, the system operation dataset is generated through cleaning, verification, and encapsulation and merging. Based on the system operation dataset, a forward-looking pre-clearing scheduling model is constructed. By solving the power output arrangement under the start-up and shutdown of thermal power units and segmented bidding, the clearing results of market-oriented units are obtained. Based on the results of market-based unit clearing, a multi-type energy storage day-ahead peak-shaving optimization model is constructed. By optimizing the operation of non-market-based energy storage, a non-market-based energy storage peak-shaving plan is obtained. Based on the results of peak shaving optimization, a peak shaving plan preparation system is obtained through integrated preparation and standardized output.

2. The method for formulating day-ahead peak-shaving plans for multiple types of energy storage as described in claim 1, characterized in that: The generated system runs a dataset, including: Data for units that are not connected to the grid or have been permanently decommissioned has been removed. Verify the consistency between the predicted new energy values ​​and the historical error range; Verify that the unit maintenance plan has been synchronized in the data; Based on the cleaned and verified data, the data for a single operating day is packaged. The data from two consecutive days are merged to generate a preprocessed system operation dataset.

3. The method for formulating multi-type energy storage day-ahead peak-shaving plans as described in claim 1 or 2, characterized in that: The aforementioned non-market-based energy storage peak-shaving plan includes: Define an objective function that minimizes the root mean square error of the net load curves for two consecutive future operating days. Calculate the net load for each time period based on the load, interconnection lines, new energy output, and energy storage charging and discharging power; Apply general operational constraints for energy storage; By employing a tiered relaxation strategy, the state of charge constraints for various types of energy storage terminals are set differently. The objective function and constraints are integrated into a mixed-integer quadratic programming problem, which is then solved using a commercial solver to obtain a non-market-based energy storage peak-shaving plan.

4. The method for formulating day-ahead peak-shaving plans for multiple types of energy storage as described in claim 3, characterized in that: The obtained peak-shaving plan formulation system includes: By analyzing the charge and discharge power sequence, a single power station operation plan and daily utilization report are generated; The province's energy storage cluster power data is aggregated to construct a provincial total power curve and generate standardized market reporting documents. Simultaneously issue refined scheduling instructions for the first day of operation to each energy storage power station and visualize monitoring information; By using a rolling optimization and connection mechanism, the final state of energy storage operation in the current cycle is used as the initial condition for the next cycle, thus obtaining a peak-shaving plan formulation system.

5. The method for formulating day-ahead peak-shaving plans for multiple types of energy storage as described in claim 4, characterized in that: The results of the market-based power unit clearing process include: Define an objective function that minimizes the total operating cost of market-based generating units over the next two consecutive operating days; Apply system-level operational constraints, physical operational constraints of thermal power units, operational constraints of new energy and hydropower, and logical constraints of unit start-up and shutdown states; The objective function and constraints are integrated into a mixed-integer linear programming problem, which is then solved using a commercial solver to obtain the market-based unit clearing results.

6. The method for formulating day-ahead peak-shaving plans for multiple types of energy storage as described in claim 5, characterized in that: The rolling optimization connection mechanism includes: For short-term regulating energy storage, the actual state of charge at the end of the planning period is used as the initial reference value for the next cycle and the deviation range is reset. For long-term regulating energy storage, the continuity of the state of charge across cycles is maintained; Once the data update is complete, a new round of optimization processes will be triggered.

7. The method for formulating day-ahead peak-shaving plans for multiple types of energy storage as described in claim 6, characterized in that: The method of using a hierarchical relaxation strategy to differentiate the state-of-charge constraints for various types of energy storage terminals includes: For short-term regulating energy storage, a relaxed state-of-charge constraint at the end of the period is adopted; For long-term regulating energy storage, a state-of-charge constraint across operating days is adopted; The state-of-charge constraint at the end of the relaxation period is expressed as follows: Among them, Ω batt For electrochemical energy storage subset; Δ s The allowable deviation in state of charge; The cross-operational day state of charge constraint is expressed as follows: Among them, Ω long It is a subset for long-term energy storage.

8. A multi-type energy storage day-ahead peak-shaving planning system, using the method described in any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire equipment physical data and market-oriented unit application data; The preprocessing module is used to generate a system runtime dataset based on the acquired data through cleaning, verification, and encapsulation. The forward-looking pre-clearing scheduling module is used to build a forward-looking pre-clearing scheduling model based on the system operation dataset. By solving the power output arrangement under the start-up and shutdown of thermal power units and segmented bidding, the clearing results of market-based units are obtained. The energy storage peak shaving optimization module is used to construct day-ahead peak shaving optimization models for various types of energy storage based on the market-based unit clearing results. By optimizing the operation of non-market-based energy storage, a non-market-based energy storage peak shaving plan is obtained. The data output and planning module is used to obtain a peak shaving plan planning system based on the peak shaving optimization results through integrated planning and standardized output.

9. An electronic device, characterized in that, include: Memory, used to store programs; A processor for loading the program to perform the steps of the method as claimed in any one of claims 1-7.

10. A computer-readable storage medium storing a program, characterized in that, When the program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

Images