A multi-energy complementary clearing method and system for source, network, load and storage cross-province mutual assistance

Abstract

The application is suitable for the technical field of power systems, and provides a multi-energy complementary clearing method and system for source-grid-load-storage cross-province mutual aid, which comprises the following steps: obtaining source-grid-load-storage data of each province in a cross-province interconnected power grid; constructing a clearing model with the minimum total operation cost of the cross-province interconnected power grid as an objective function based on the obtained source-grid-load-storage data; the constraint conditions include cross-province transmission line capacity constraints, each province power balance constraints, power supply side unit output constraints, energy storage operation constraints and multi-energy complementary coordination constraints; solving the clearing model to obtain the clearing output of each province power supply side unit, the power of cross-province transmission lines, the charging and discharging power of energy storage and the clearing price; generating a day-ahead market clearing plan of the cross-province interconnected power grid according to the solving result, and issuing the day-ahead market clearing plan to each province source-grid-load-storage subject for execution. The application constructs a cost model that accurately responds to the state of equipment and market environment, and can improve the collaborative efficiency of cross-province mutual aid, the new energy consumption level and the system operation safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to a multi-energy complementary clearing method and system for source-grid-load-storage cross-province mutual aid. BACKGROUND

[0002] With the deepening of the energy transformation strategy, the proportion of new energy represented by wind power and photovoltaic power in the power system continues to increase. Under this background, source-grid-load-storage integrated collaborative operation and cross-province mutual aid have become a key technical path to ensure energy security and improve new energy consumption. Source-grid-load-storage emphasizes the collaborative optimization of power sources, power grids, loads, and energy storage as a whole. Multi-energy complementation improves the stability and economy of system operation by coordinating the output of different energy sources. As the core mechanism for realizing optimal allocation of resources, the scientificity and accuracy of the model directly determine the efficiency of collaborative operation.

[0003] The most similar implementation scheme to the present application mainly falls into two categories. For example, Chinese patent application CN117639044A discloses a power system source-grid-load-storage coordinated operation method, which is mainly to establish a system model considering low-carbon power generation, formulate a power plant dispatching scheme through linear programming method, and optimize power grid dispatching to find the channel combination with the lowest cost and the smallest carbon emissions. By integrating power demand forecasting, low-carbon power generation, power grid dispatching, and multi-element energy storage model, the problems of high carbon emissions and insufficient regulation capacity are solved. For another example, Chinese patent application CN120109791A discloses a regional source-grid-load-storage virtual power plant operation optimization method, which constructs an optimization function with the minimum operation cost, the minimum carbon emissions, and the minimum risk as multi-objectives, and considers equipment operation constraints and inter-regional market clearing constraints. The method solves the dispatching strategy through a multi-objective optimization algorithm, aiming to solve the problem of considering a single direction in virtual power plant operation.

[0004] As can be seen, the cost model of the prior art mostly adopts fixed coefficients or simplified linear relationships, which fails to accurately reflect the nonlinear impact of unit no-load loss, start-stop life loss, line congestion economic cost, new energy curtailment rate, and dynamic value of energy storage regulation capacity, resulting in distorted economy of clearing results and inability to achieve precise pricing and optimal allocation of resources. In addition, the constraint conditions mostly focus on traditional power balance and equipment operation limits, failing to deeply embed security and stability factors such as dynamic thermal stability limits of transmission lines and real-time demand of system regulation capacity by new energy output fluctuations into the clearing model in a forward-looking and quantitative manner, so that the market result lacks predictability and avoidance ability for overall system safety risks while pursuing economy.

[0005] In view of this, a multi-energy complementary clearing method and system for source-grid-load-storage cross-province mutual aid are proposed. SUMMARY

[0006] The application provides a multi-energy complementary dispatching method and system for cross-provincial mutual aid of source, network, load and storage, which is used for solving the problems of economic distortion of dispatching results, inability to realize accurate pricing and optimal configuration of resources, and insufficient predictability and avoidance ability of overall system security risks.

[0007] The first aspect of the application provides a multi-energy complementary dispatching method for cross-provincial mutual aid of source, network, load and storage, comprising:

[0008] obtaining source, network, load and storage data of each province in a cross-provincial interconnected power grid, wherein the source, network, load and storage data comprises new energy output prediction data and traditional unit output data on the power source side, cross-provincial transmission line capacity data on the power grid side, load prediction data on the load side, and storage state data on the storage side;

[0009] constructing a dispatching model with the obtained source, network, load and storage data, wherein the dispatching model comprises an objective function and constraint conditions, and the objective function is to minimize the total operation cost of the cross-provincial interconnected power grid;

[0010] wherein the objective function considers the generation cost of the traditional units in each province, the transmission cost of the cross-provincial transmission network, the penalty cost of abandoned wind and light of the new energy station, and the operation cost of the energy storage system; and the constraint conditions comprise cross-provincial transmission line capacity constraints, provincial power balance constraints, power source side unit output constraints, energy storage operation constraints and multi-energy complementary coordination constraints;

[0011] solving the dispatching model to obtain the dispatching output of the power source side units in each province, the power of the cross-provincial transmission line, the charging and discharging power of the energy storage, and the dispatching electricity price;

[0012] generating a day-ahead market dispatching plan of the cross-provincial interconnected power grid according to the solving result of the dispatching model, and delivering the dispatching plan to the source, network, load and storage subjects in each province for execution.

[0013] Further, the generation cost of the traditional units in each province is expressed as:

[0014]

[0015] wherein: is a set of dispatching periods, is a set of traditional units, is the dispatching output of the traditional unit in period , is the quadratic term coefficient of the incremental energy consumption cost of the traditional unit in period , is the incremental energy consumption cost of the traditional unit in period The coefficient of the first term of the incremental energy consumption cost, For traditional units During the period The no-load energy consumption cost For traditional units During the period The start-stop loss cost.

[0016] Furthermore, the transmission cost of the inter-provincial power transmission network The expression is:

[0017]

[0018] in: A collection of inter-provincial power transmission lines. For power transmission lines During the period The loss cost coefficient, For power transmission lines During the period Transmission power, The resistance per unit length of the line is denoted as . For power transmission lines During the period The dynamic blocking penalty coefficient, For power transmission lines During the period The dynamic safety margin coefficient, For power transmission lines The rated maximum transmission capacity.

[0019] Furthermore, the penalty cost for wind and solar power curtailment at the aforementioned renewable energy power stations. The expression is:

[0020]

[0021] in: A collection of new energy power stations. For new energy power stations During the period The time-varying reference penalty coefficient, For new energy power stations During the period The predicted output For new energy power stations During the period The actual clearing effort, It is a non-linear penalty exponent. For new energy power stations During the period The adjustment capacity compensation coefficient.

[0022] Further, the operation cost of the energy storage system is expressed as:

[0023]

[0024] wherein: is a set of energy storage systems, , is the charging power and the discharging power of the energy storage system in the time period , , is the unit charging cost coefficient and the unit discharging cost coefficient of the energy storage system , is the state of charge of the energy storage system in the time period , is the dynamic reference state of charge of the energy storage system in the time period , is the time-varying state of charge deviation penalty coefficient of the energy storage system in the time period .

[0025] Further, the expression of the objective function is:

[0026]

[0027] wherein: , , , are the normalized weight coefficients of the generation cost of the traditional units in each province, the transmission cost of the inter-provincial transmission network, the penalty cost of curtailment of wind and light in the new energy station, and the operation cost of the energy storage system, respectively.

[0028] Further, the normalized weight coefficients are determined by the following relationship:

[0029] The top-level optimization model is obtained by solving a top-level optimization model with the objective of maximizing the comprehensive operation benefit, and the objective function of the top-level optimization model is:

[0030]

[0031] wherein: is the expected cost saving brought by inter-provincial mutual aid, which is determined based on the regression analysis of historical clearing data and current supply and demand prediction, is the system operation contribution index, which is quantified by the new energy output fluctuation, the inter-provincial line congestion probability, and the reserve capacity shortage risk;

[0032] The relationship of the weight coefficients is:

[0033]

[0034]

[0035] wherein: , , , is the marginal contribution of the generation cost of the traditional units in each province, the transmission cost of the inter-provincial transmission network, the penalty cost of curtailment of wind and solar energy, and the operation cost of the energy storage system to the expected cost saving, which is calculated by the Lagrange dual multipliers of the top-level optimization model.

[0036] Further, the multi-energy complementary coordination constraints include inter-provincial new energy consumption responsibility constraints, inter-provincial flexible resource mutual aid constraints, and source-storage space-time balance constraints, the expressions of which are as follows:

[0037] Inter-provincial new energy consumption responsibility constraints:

[0038]

[0039] wherein: is the set of all provinces, is the set of new energy stations in the province is the new energy consumption responsibility weight factor of the province in the time period , which is pre-set by the provincial energy policy and the inter-provincial agreement, is the minimum weighted average new energy consumption rate of the whole network in the time period ;

[0040] Inter-provincial flexible resource mutual aid constraints:

[0041]

[0042] wherein: is the set of flexible energy storage systems designated to participate in inter-provincial mutual aid, is the mutual aid sensitivity coefficient, which is used to convert the fluctuation amount of the total inter-provincial transmission power into the adjustment power required to be provided by the energy storage;

[0043] Source-storage space-time balance constraints:

[0044]

[0045] wherein: is the allowed space-time imbalance threshold value, is the load of the province in the time period ,​​ complementary coordination rolling time window.

[0046] Further, the solving the clearing model obtains the clearing output of each provincial power supply side unit, the power of the cross-provincial transmission line, the charging and discharging power of the energy storage, and the clearing electricity price, and comprises the following steps:

[0047] inputting the clearing model into a preset optimization solver;

[0048] solving the clearing model based on a branch and bound method by using the optimization solver, and processing the continuous variables and integer variables contained in the clearing model;

[0049] in the solving process, the multi-energy complementary coordination constraints are processed as constraint conditions, so that the solving process meets the inter-provincial new energy consumption responsibility constraint, the cross-provincial flexible resource mutual aid constraint and the source-storage space-time balance constraint;

[0050] after the optimization solver completes the solving, extracting the decision variable values corresponding to the output of each provincial power supply side unit in the solving result as the clearing output of each provincial power supply side unit;

[0051] extracting the decision variable values corresponding to the power of each cross-provincial transmission line in the solving result as the power of each cross-provincial transmission line;

[0052] extracting the decision variable values corresponding to the charging and discharging power of each energy storage system in the solving result as the charging and discharging power of each energy storage system;

[0053] obtaining the dual variable values corresponding to each provincial power balance constraint in the solving result, and taking the dual variable values as the clearing electricity price of each province.

[0054] The second aspect of the present application provides a multi-energy complementary clearing system of source-network-load-storage cross-provincial mutual aid, comprising:

[0055] a source-network-load-storage data acquisition unit, configured to acquire source-network-load-storage data of each province in a cross-provincial interconnected power grid, wherein the source-network-load-storage data comprises new energy output prediction data and traditional unit output data of a power supply side, cross-provincial transmission line capacity data of a power grid side, load prediction data of a load side, and energy storage state data of an energy storage side;

[0056] The clearing model construction unit is configured to construct a clearing model with minimization of total operation cost of the inter-provincial interconnected power grid as an objective function based on the obtained source-grid-load-storage data; the clearing model comprises an objective function and constraint conditions; wherein the objective function considers generation cost of traditional units in each province, transmission cost of the inter-provincial transmission network, penalty cost of abandoned wind and light of new energy stations and operation cost of energy storage systems; the constraint conditions comprise inter-provincial transmission line capacity constraint, power balance constraint of each province, unit output constraint on the power supply side, energy storage operation constraint and multi-energy complementary coordination constraint;

[0057] The clearing model solving unit is configured to solve the clearing model to obtain clearing output of units on the power supply side in each province, power of the inter-provincial transmission line, charging and discharging power of the energy storage and clearing price;

[0058] The clearing plan execution unit is configured to generate a day-ahead market clearing plan of the inter-provincial interconnected power grid according to the solving result of the clearing model and issue the day-ahead market clearing plan to source-grid-load-storage subjects in each province for execution.

[0059] As can be seen from the above technical solution, the present application has the following advantages:

[0060] The present application constructs a clearing model with minimization of total operation cost of the inter-provincial interconnected power grid as an objective function based on the obtained source-grid-load-storage data, wherein the model considers generation cost of traditional units in each province, transmission cost of the inter-provincial transmission network, penalty cost of abandoned wind and light of new energy stations and operation cost of energy storage systems, and the constraint conditions comprise inter-provincial transmission line capacity constraint, power balance constraint of each province, unit output constraint on the power supply side, energy storage operation constraint and multi-energy complementary coordination constraint; the clearing model is solved to obtain clearing output of units on the power supply side in each province, power of the inter-provincial transmission line, charging and discharging power of the energy storage and clearing price; finally, a day-ahead market clearing plan of the inter-provincial interconnected power grid is generated according to the solving result of the clearing model and issued to source-grid-load-storage subjects in each province for execution. The present application constructs a cost model that accurately responds to device state and market environment, deeply couples macro policies, safety requirements and market mechanisms through multi-dimensional constraints, so that the clearing result not only realizes more fine economic optimization, but also actively guides resource distribution, improves collaborative efficiency of inter-provincial aid, new energy consumption level and system operation safety. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 It is an embodiment flowchart of a multi-energy complementary clearing method for inter-provincial aid of source-grid-load-storage in the present application. DETAILED DESCRIPTION

[0062] The terms "first", "second", "third", "fourth" and the like in the description of this application and in the claims, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of the terms so termed is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of orderly or chronological mundane operation, reverse order operation, based on circuitry availability, changes in architecture, or based on other considerations not specifically recited herein. Moreover, the terms "comprises", "comprising", "corresponds" and "corresponding", as well as any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, product or apparatus that comprises a list of steps or units not necessarily comprises only those steps or units

[0063] Embodiment one

[0064] The method implemented in this embodiment can be implemented in a system, which can be implemented in a server or in a terminal, and the specific implementation is not limited. From the perspective of system implementation, the method in this application will be introduced below. Please refer to Figure 1 The method provided by the embodiment of the application includes the following steps:

[0065] S11. Obtain source-grid-load-storage data of each province in the inter-provincial interconnected power grid, wherein the source-grid-load-storage data includes new energy output prediction data and traditional unit output data on the power supply side, cross-provincial transmission line capacity data on the power grid side, load prediction data on the load side, and storage state data on the storage side;

[0066] This step periodically and automatically collects or receives the following multi-dimensional and heterogeneous source-grid-load-storage real-time and prediction data through the data acquisition and monitoring control system, energy management system and market technical support system deployed in each province, and performs data quality verification and format standardization processing, thereby providing complete and consistent input data basis for constructing a clear model. The specific data collected includes:

[0067] 1. The new energy output prediction data on the power supply side is obtained by obtaining the short-term or ultra-short-term active power prediction value sequence of all wind farms and photovoltaic power stations in each province within a future dispatching period from the new energy power prediction system. The prediction is based on numerical weather prediction, historical power data and machine learning algorithm; the traditional unit output data on the power supply side is obtained by obtaining the real-time running state, current output and unit combination plan of all coal-fired, gas-fired and hydraulic units in each province from the power plant monitoring system or dispatching plan system, as well as the technical parameters determined by the unit characteristic test report, including the upper and lower limits of unit output, climbing rate and sliding rate, micro-increment energy consumption cost coefficient, idle standard coal consumption and start-stop cost (calculated based on the boiler and steam turbine life loss model).

[0068] 2. The capacity data of inter-provincial transmission lines on the power grid side is obtained from the power grid dispatching and operation department, which provides the steady-state thermal stability limit of inter-provincial AC / DC interconnection lines. Based on the line design parameters, the resistance per unit length and rated voltage are obtained. At the same time, the system receives environmental temperature and wind speed data of the line corridor from the meteorological system to calculate the real-time thermal stability limit margin risk index considering dynamic capacity expansion.

[0069] 3. Load forecast data on the load side is obtained from the load forecast system, which provides the total system load forecast for each province for the next scheduling cycle. This forecast takes into account historical load curves, weather forecasts (temperature, humidity), date type (weekday, holiday) and macroeconomic indicators.

[0070] 4. Energy storage status data on the energy storage side is obtained in real time from the energy storage energy management system. This includes the current state of charge of each energy storage power station, as well as technical parameters provided by the battery management system or the manufacturer, including rated energy capacity, maximum charge / discharge power, safe operating range of state of charge, and indicators that indicate whether it has automatic power generation control function.

[0071] All acquired data is sent to the data platform for consistency checks and time series alignment. Outliers are removed and missing data is filled in to form a standardized data set for use in the subsequent clearing model construction.

[0072] S12. Based on the acquired source-grid-load-storage data, a clearing model is constructed with the objective function of minimizing the total operating cost of the inter-provincial interconnected power grid. The clearing model includes the objective function and constraints. The objective function considers the generation cost of traditional generating units in each province, the transmission cost of the inter-provincial transmission network, the wind and solar curtailment penalty cost of new energy power plants, and the operating cost of the energy storage system. The constraints include inter-provincial transmission line capacity constraints, power balance constraints in each province, power generation unit output constraints, energy storage operation constraints, and multi-energy complementarity coordination constraints.

[0073] Specifically, the power generation cost of traditional generating units in various provinces The expression is:

[0074]

[0075]

[0076]

[0077] in: For the set of scheduling periods, A collection of traditional units, For traditional units During the period The clearing out of the market and the effort to clear out the market, For traditional units During the period The coefficient of the quadratic term of the incremental energy consumption cost, For traditional units During the period The coefficient of the first term of the incremental energy consumption cost; For traditional units During the period The no-load energy consumption cost For traditional units The amount of standard coal consumed under no-load conditions For time period The price of standard coal For traditional units During the period Aging and health status correction factors; For traditional units During the period The start-stop loss cost, , Traditional units During the period and time period The 0 / 1 variable of the running state, For traditional units The cost of a single start-stop operation.

[0078] Transmission costs of inter-provincial power transmission networks The expression is:

[0079]

[0080]

[0081]

[0082]

[0083] in: A collection of inter-provincial power transmission lines. For power transmission lines During the period The loss cost coefficient, For time period The system's marginal electricity price forecast, For power transmission lines Length, Rated voltage; For power transmission lines During the period Transmission power, The resistance per unit length of the line; For power transmission lines During the period dynamic congestion penalty factor of the transmission line, , weighting factor, historical maximum price difference of the sending and receiving nodes in the similar time period, real-time thermal stability limit margin risk index of the transmission line in the time period ; dynamic security margin coefficient of the transmission line in the time period , reference security margin, adjustment sensitivity coefficient; rated maximum transmission capacity of the transmission line .

[0084] penalty cost of wind and light curtailment of the new energy station is expressed as:

[0085]

[0086]

[0087]

[0088] wherein: set of new energy stations, time-varying reference penalty factor of the new energy station in the time period , reference penalty factor, sensitivity coefficient, representing the influence degree of the price difference on the penalty factor, forecasted node marginal price of the province where the new energy station is located in the time period , average forecasted price of the cross-province interconnected power grid in the time period ; forecasted output of the new energy station in the time period , actual clearing output of the new energy station in the time period , nonlinear penalty index; adjustment capacity compensation coefficient of the new energy station in the time period , compensation coefficient reference value, a 0 / 1 variable representing whether the field station has the automatic generation control function, represents having, represents not having, is a growth factor of the S-shaped function, is a power threshold, is a natural constant.

[0089] Operating cost of the energy storage system The expression is:

[0090]

[0091]

[0092]

[0093] wherein: is a set of energy storage systems, , are respectively the charging power and the discharging power of the energy storage system in the time period , , are respectively the unit charging cost coefficient and the unit discharging cost coefficient of the energy storage system ; is the state of charge of the energy storage system in the time period , is the dynamic reference state of charge of the energy storage system in the time period , is a sensitivity coefficient representing the influence degree of the net load prediction on the reference state of charge, is the total load prediction value of the system in the time period , is the total output prediction value of the new energy in the time period , is the rated capacity of the energy storage system ; is the time-varying state of charge deviation penalty coefficient of the energy storage system in the time period , is the basic coefficient of the state of charge deviation penalty, is a weight factor representing the influence degree of the standard deviation of the net load prediction on the penalty coefficient, is the standard deviation of the system net load (load-new energy) prediction sequence from the current time period to the end of the scheduling period , reflecting the fluctuation degree of the net load.

[0094] According to the above various cost items, the expression of the objective function is:

[0095]

[0096] Among them: , , , The normalized weight coefficients of the generation cost of traditional units in each province, the transmission cost of inter-provincial transmission network, the penalty cost of abandoned wind and light of new energy station and the operation cost of energy storage system are respectively.

[0097] The above normalized weight coefficients are determined by the following relationship:

[0098] By solving a top-level optimization model with the goal of maximizing comprehensive operation benefit, the objective function of the top-level optimization model is:

[0099]

[0100] Among them: The expected cost saving brought by inter-provincial mutual aid is determined based on the regression analysis of historical clearing data and current supply and demand forecast, The system operation dedication index is quantified by the new energy output fluctuation, inter-provincial line congestion probability and reserve capacity shortage risk;

[0101] The relationship of the weight coefficients is:

[0102]

[0103]

[0104] Among them: , , , The marginal contribution of the generation cost of traditional units in each province, the transmission cost of inter-provincial transmission network, the penalty cost of abandoned wind and light of new energy station and the operation cost of energy storage system to the expected cost saving Is calculated by the Lagrange dual multiplier of the top-level optimization model.

[0105] Specifically, the expressions of various constraint conditions are as follows:

[0106] Inter-provincial transmission line capacity constraint:

[0107]

[0108] The power balance constraint of each province:

[0109]

[0110] wherein: , , denote the set of conventional units, new energy stations and energy storage systems belonging to province , , denote the set of transmission lines flowing into and out of province , is the set of all provinces, is the load forecast value of province in time period .

[0111] Power source side unit output constraints:

[0112]

[0113]

[0114] wherein: , are the lower and upper output limits of conventional units , , are the ramp-down and ramp-up rates of conventional units .

[0115] Energy storage operation constraints:

[0116]

[0117]

[0118]

[0119] wherein: is the state of charge of energy storage system in time period , , are the charging and discharging efficiencies of energy storage system , is the rated energy capacity of energy storage system , is the length of each dispatching time period, , are the lower and upper limits of the state of charge of energy storage system , , are the maximum allowed charging and discharging power of energy storage system .

[0120] The multi-purpose complementary coordination constraints include inter-provincial new energy consumption responsibility constraints, cross-provincial flexible resource mutual aid constraints and source-storage space-time balance constraints, and the expressions are as follows:

[0121] The inter-provincial new energy consumption responsibility constraint is:

[0122]

[0123] Wherein: is the set of all provinces, is the set of new energy stations in the province is the set of new energy stations in the province is the new energy consumption responsibility weight factor of the province in the time period , which is pre-set by the provincial energy policy and cross-provincial agreement, is the minimum weighted average new energy consumption rate of the whole network in the time period .

[0124] The cross-provincial flexible resource mutual aid constraint is:

[0125]

[0126] Wherein: is the set of flexible energy storage systems designated to participate in cross-provincial mutual aid, is the mutual aid sensitivity coefficient, which is used to convert the fluctuation amount of cross-provincial transmission power into the adjustment power required by the energy storage,

[0127] The source-storage space-time balance constraint is:

[0128]

[0129] Wherein: is the allowed space-time imbalance threshold value, is the load of the province in the time period , is the rolling time window of complementary coordination.

[0130] S13. Solve the clearing model to obtain the clearing output of each provincial power source side unit, the power of cross-provincial transmission line, the charging and discharging power of energy storage and the clearing price;

[0131] Input the clearing model into a pre-set optimization solver;

[0132] Use the optimization solver to solve the clearing model based on the branch and bound method to handle the continuous variables and integer variables contained in the clearing model;

[0133] In the solving process, the multi-energy complementary coordination constraints are treated as constraint conditions, so that the solving process meets the inter-provincial new energy consumption responsibility constraints, cross-provincial flexible resource mutual aid constraints and source-storage space-time balance constraints.

[0134] After the optimization solver completes the solving, the decision variable values corresponding to the provincial power side unit output in the solving result are extracted as the provincial power side unit output.

[0135] The decision variable values corresponding to the power of each cross-provincial transmission line in the solving result are extracted as the power of each cross-provincial transmission line.

[0136] The decision variable values corresponding to the charge and discharge power of each energy storage system in the solving result are extracted as the charge and discharge power of each energy storage system.

[0137] The dual variable values corresponding to the provincial power balance constraints in the solving result are obtained, and the dual variable values are used as the provincial clearing price.

[0138] Specifically, first, the constructed mixed integer quadratic programming clearing model is input into a commercial optimization solver, and is solved by a branch and bound algorithm. The algorithm processes integer variables such as unit start-stop through continuous branching, determines the boundary by linear programming relaxation, and embeds multi-energy complementary coordination constraints as hard constraints in the solving process to guarantee the inter-provincial consumption responsibility, flexible mutual aid and space-time balance requirements. When the solver converges, the provincial unit output decision variables are extracted from the original solution as the clearing output, the cross-provincial line power variables are extracted as the tie line plan, the energy storage charge and discharge power variables are extracted as the energy storage dispatching instruction, the shadow prices corresponding to the provincial power balance constraints are obtained from the dual solution as the node marginal price, and finally the complete market clearing result is formed.

[0139] S14. According to the solving result of the clearing model, a day-ahead market clearing plan of the cross-provincial interconnected power grid is generated and is issued to each provincial source-grid-load-storage subject for execution.

[0140] Based on the clearing result obtained by S13, the market operation agency automatically integrates all decision variables and dual variables through the clearing plan generation module to form a day-ahead market clearing plan containing the following complete elements: the 96-point output curve and start-stop state of each traditional unit in each province, the 96-point power transmission plan of each inter-provincial tie line, the 96-point charging and discharging instruction of each energy storage system, and the 96-point node marginal price sequence of each province; After verification by the safety checking module, the unit clearing output and start-stop instruction are issued to the monitoring system of each power generation enterprise through the data bus of the power market operation platform, the inter-provincial power transmission plan is issued to the energy management system of each level of dispatching agency, the energy storage charging and discharging instruction is issued to the energy management system of each energy storage power station, and the clearing price information is published to all market participants, while generating a settlement list and a market clearing report containing all the above clearing results, which serves as the only basis for the day-ahead market execution.

[0141] The above embodiment realizes the economic optimal allocation of inter-provincial resources and efficient consumption of new energy by constructing a refined cost model and multi-dimensional complementary constraints. The method deeply couples market clearing and safe operation, significantly improving the efficiency of power grid operation and system regulation capability.

[0142] Embodiment two

[0143] An embodiment of a multi-energy complementary clearing system for source-grid-load-storage inter-provincial mutual aid in the present application includes the following:

[0144] A source-grid-load-storage data acquisition unit is configured to acquire source-grid-load-storage data of each province in an inter-provincial interconnected power grid, wherein the source-grid-load-storage data includes new energy output prediction data and traditional unit output data on the power supply side, inter-provincial transmission line capacity data on the power grid side, load prediction data on the load side, and energy storage state data on the energy storage side.

[0145] A clearing model construction unit is configured to construct a clearing model with the minimum total operation cost of the inter-provincial interconnected power grid as an objective function based on the acquired source-grid-load-storage data; the clearing model includes an objective function and constraint conditions; wherein the objective function considers the generation cost of traditional units in each province, the transmission cost of inter-provincial transmission networks, the penalty cost of abandoned wind and light of new energy stations, and the operation cost of energy storage systems; the constraint conditions include inter-provincial transmission line capacity constraints, provincial power balance constraints, power supply side unit output constraints, energy storage operation constraints, and multi-energy complementary coordination constraints.

[0146] A clearing model solving unit is configured to solve the clearing model to obtain the clearing output of power supply side units in each province, the power of inter-provincial transmission lines, the charging and discharging power of energy storage, and the clearing price.

[0147] A clearing plan execution unit is configured to generate a day-ahead market clearing plan of the inter-provincial interconnected power grid according to the solving result of the clearing model, and issue the plan to the source-grid-load-storage subjects of each province for execution.

[0148] The specific limitation of the system can refer to the above limitation of the method, which is not described here. Each module in the above system can be implemented by software, hardware and their combination. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so that the processor calls and executes the corresponding operations of the above modules.

[0149] It can be understood that those skilled in the art can combine various embodiments in the above embodiments under the guidance of the above embodiments to obtain various embodiments of the technical solutions.

[0150] The above only describes the preferred embodiments of the present application and does not limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A multi-energy complementary clearing method for cross-provincial mutual assistance between energy sources, grids, loads, and storage, characterized in that, include: Acquire source, grid, load and storage data for each province in the inter-provincial interconnected power grid. The source, grid, load and storage data includes power generation forecast data of new energy sources and power generation data of traditional units on the power source side, capacity data of inter-provincial transmission lines on the grid side, load forecast data on the load side, and energy storage status data on the energy storage side. Based on the acquired source-grid-load-storage data, a clearing model is constructed with the objective function of minimizing the total operating cost of the inter-provincial interconnected power grid. The objective function considers the power generation costs of traditional generating units in each province. Transmission costs of inter-provincial power transmission networks The penalty costs for wind and solar power curtailment at renewable energy power plants and the operating cost of energy storage systems The constraints of the clearing model include inter-provincial transmission line capacity constraints, provincial power balance constraints, power generation unit output constraints, energy storage operation constraints, and multi-energy complementarity coordination constraints. The expression for the objective function is: in: , , , They are respectively , , and The normalized weight coefficients are obtained by solving a top-level optimization model that aims to maximize overall operational efficiency. The objective function of the top-level optimization model is: in: The expected cost savings from inter-provincial mutual assistance are determined based on regression analysis using historical clearing data and current supply and demand forecasts. The contribution indicators for system operation are quantified by the volatility of new energy output, the probability of inter-provincial line congestion, and the risk of reserve capacity shortage. The formula for the weighting coefficients is: in: , , , for , , and Expected cost savings The marginal contribution is calculated using the Lagrange dual multipliers of the top-level optimization model; The multi-energy complementarity coordination constraints include inter-provincial new energy consumption responsibility constraints, cross-provincial flexible resource mutual assistance constraints, and source-storage spatiotemporal balance constraints, the expressions of which are as follows: Inter-provincial responsibility constraints for renewable energy consumption: in: For the set of all provinces, Province A collection of new energy power stations, For provinces During the period The weighting factor for the responsibility of new energy consumption is predetermined by provincial energy policies and inter-provincial agreements. , For new energy power stations During the period Actual clearing capacity and projected clearing capacity For the entire network during the time period The lowest weighted average renewable energy consumption rate; Cross-provincial flexibility and resource sharing constraints: in: As a collection of flexible energy storage systems designated to participate in inter-provincial energy sharing, A collection of inter-provincial power transmission lines. , For energy storage systems During the period The charging and discharging power, , For power transmission lines During the period and Transmission power, The mutual assistance sensitivity coefficient; Source-storage spatiotemporal balance constraints: in: A collection of new energy power stations. A collection of energy storage systems. , For new energy power stations During the period Actual clearing capacity and projected clearing capacity , For energy storage systems During the period The charging and discharging power, The permissible spatiotemporal imbalance threshold, For provinces During the period The load, A rolling time window for complementary coordination; Solving the clearing model yields the cleared output of power generation units on the power supply side of each province, the power of inter-provincial transmission lines, the charging and discharging power of energy storage, and the cleared electricity price. Based on the solution results of the clearing model, a day-ahead market clearing plan for inter-provincial interconnected power grids is generated and distributed to the source-grid-load-storage entities in each province for implementation.

2. The multi-energy complementary clearing method for cross-provincial mutual assistance between source, grid, load, and storage as described in claim 1, is characterized in that, The power generation costs of traditional generating units in each province The expression is: in: For the set of scheduling periods, A collection of traditional units, For traditional units During the period The clearing out of the market and the effort to clear out the market, For traditional units During the period The coefficient of the quadratic term of the incremental energy consumption cost, For traditional units During the period The coefficient of the first term of the incremental energy consumption cost, For traditional units During the period The no-load energy consumption cost For traditional units During the period The start-stop loss cost.

3. The multi-energy complementary clearing method for cross-provincial mutual assistance between source, grid, load, and storage as described in claim 2, is characterized in that... The transmission cost of the inter-provincial power transmission network The expression is: in: A collection of inter-provincial power transmission lines. For power transmission lines During the period The loss cost coefficient, For power transmission lines During the period Transmission power, The resistance per unit length of the line is denoted as . For power transmission lines During the period The dynamic blocking penalty coefficient, For power transmission lines During the period The dynamic safety margin coefficient, For power transmission lines The rated maximum transmission capacity.

4. The multi-energy complementary clearing method for cross-provincial mutual assistance between source, grid, load, and storage as described in claim 3, is characterized in that, The cost of wind and solar power curtailment penalties at the aforementioned new energy power stations The expression is: in: A collection of new energy power stations. For new energy power stations During the period The time-varying reference penalty coefficient, For new energy power stations During the period The predicted output For new energy power stations During the period The actual clearing effort, It is a non-linear penalty exponent. For new energy power stations During the period The adjustment capacity compensation coefficient.

5. The multi-energy complementary clearing method for cross-provincial mutual assistance between source, grid, load, and storage as described in claim 4, is characterized in that, The operating cost of the energy storage system The expression is: in: A collection of energy storage systems. , energy storage system During the period The charging power and discharging power, , energy storage system The unit charging cost coefficient and the unit discharging cost coefficient, For energy storage systems During the period The state of charge, For energy storage systems During the period The dynamic reference state of charge, For energy storage systems During the period The time-varying state of charge deviation penalty coefficient.

6. The multi-energy complementary clearing method for cross-provincial mutual assistance between source, grid, load, and storage as described in claim 1, is characterized in that, Solving the clearing model yields the cleared output of power generation units in each province, the power of inter-provincial transmission lines, the charging and discharging power of energy storage, and the cleared electricity price, including: The clearing model is input into a preset optimization solver; The optimized solver is used to solve the clearing model based on the branch and bound method, and the continuous and integer variables contained in the clearing model are processed. During the solution process, the multi-energy complementary coordination constraint is treated as a constraint condition to ensure that the solution process satisfies the inter-provincial new energy consumption responsibility constraint, the cross-provincial flexible resource mutual assistance constraint, and the source-storage spatiotemporal balance constraint. After the optimization solver completes the solution, the decision variable values ​​corresponding to the power output of the power generation units in each province are extracted from the solution results and used as the clearing output of the power generation units in each province. Extract the decision variable values ​​corresponding to the power of each inter-provincial transmission line from the solution results, and use them as the power of each inter-provincial transmission line; Extract the decision variable values ​​corresponding to the charging and discharging power of each energy storage system from the solution results, and use them as the charging and discharging power of each energy storage system. Obtain the dual variable values ​​corresponding to the power balance constraints of each province from the solution results, and use the dual variable values ​​as the clearing electricity price of each province.

7. A multi-energy complementary clearing system for cross-provincial mutual assistance between energy sources, grids, loads, and storage, characterized in that: The method described by any one of claims 1-6 comprises: The source-grid-load-storage data acquisition unit is used to acquire source-grid-load-storage data of each province in the inter-provincial interconnected power grid. The source-grid-load-storage data includes new energy output forecast data and traditional unit output data on the power supply side, inter-provincial transmission line capacity data on the grid side, load forecast data on the load side, and energy storage status data on the energy storage side. The clearing model construction unit is used to construct a clearing model based on the acquired source-grid-load-storage data, with the objective function being the minimization of the total operating cost of the inter-provincial interconnected power grid. The clearing model includes an objective function and constraints. The objective function considers the generation cost of traditional generating units in each province, the transmission cost of the inter-provincial transmission network, the wind and solar curtailment penalty cost of new energy power plants, and the operating cost of the energy storage system. The constraints include inter-provincial transmission line capacity constraints, power balance constraints in each province, power generation unit output constraints, energy storage operation constraints, and multi-energy complementarity coordination constraints. The clearing model solving unit is used to solve the clearing model to obtain the cleared output of power generation units on the power supply side of each province, the power of inter-provincial transmission lines, the charging and discharging power of energy storage, and the clearing electricity price. The clearing plan execution unit is used to generate the day-ahead market clearing plan for the inter-provincial interconnected power grid based on the solution results of the clearing model, and distribute it to the source-grid-load-storage entities in each province for execution.

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