Multi-energy complementary clearing method and system for cross-provincial mutual aid of source network load storage

Abstract

The invention is suitable for the technical field of power systems, and provides a source network load storage cross-province mutual aid multi-energy complementary clearing method and system, and the method comprises the steps: obtaining the source network load storage data of each province in a cross-province interconnected power grid; constructing a clearing model taking the minimization of the total operation cost of the trans-provincial interconnected power grid as a target function based on the obtained source network load storage data; the constraint conditions comprise a trans-provincial power transmission line capacity constraint, a power balance constraint of each province, a power supply side unit output constraint, an energy storage operation constraint and a multi-energy complementary coordination constraint; solving the clearing model to obtain the clearing output of the power supply side unit of each province, the power of the trans-province power transmission line, the energy storage charging and discharging power and the clearing electricity price; and generating a day-ahead market clearing plan of the trans-provincial interconnected power grid according to a solving result, and issuing the day-ahead market clearing plan to the source network load storage main body of each province for execution. According to the method, the cost model accurately responding to the equipment state and the market environment is constructed, and the cooperation efficiency of cross-provincial mutual aid, the new energy consumption level and the system operation safety can be improved.

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 source-grid-load-storage cross-province mutual aid, 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 source-grid-load-storage cross-province mutual aid, comprising: acquiring source-grid-load-storage data of each province in a cross-province interconnected power grid, wherein the source-grid-load-storage data comprises new energy output prediction data and traditional unit output data on the power source side, cross-province 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; constructing a dispatching model with the minimum total operation cost of the cross-province interconnected power grid as an objective function based on the acquired source-grid-load-storage data; the dispatching model comprises an objective function and constraint conditions; wherein the objective function considers the power generation cost of traditional units in each province, the power transmission cost of the cross-province transmission network, the wind and light curtailment penalty cost of new energy stations, and the operation cost of energy storage systems; the constraint conditions comprise cross-province transmission line capacity constraints, each provincial power balance constraints, power source side unit output constraints, energy storage operation constraints, and multi-energy complementary coordination constraints; solving the dispatching model to obtain the dispatching output of the power source side units in each province, the power of the cross-province transmission line, the charging and discharging power of the energy storage, and the dispatching electricity price; generating a day-ahead market dispatching plan of the cross-province interconnected power grid according to the solution of the dispatching model, and issuing the dispatching plan to the source-grid-load-storage subjects in each province for execution.

[0008] Further, the power generation cost of the traditional units in each province is expressed as: 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 linear term coefficient of the incremental energy consumption cost of the traditional unit in period , is the no-load energy consumption cost of the traditional unit in period , is the no-load energy consumption cost of the traditional unit the start-stop loss cost of the time period .

[0009] Further, the expression of the transmission cost of the cross-province transmission network is: wherein: is the set of cross-province transmission lines, is the transmission line the loss cost coefficient of the time period , is the transmission line the transmission power of the time period , is the unit length resistance of the line, is the transmission line the dynamic congestion penalty coefficient of the time period , is the transmission line the dynamic security margin coefficient of the time period , is the rated maximum transmission capacity of the transmission line .

[0010] Further, the expression of the wind and light curtailment penalty cost of the new energy station is: wherein: is the set of new energy stations, is the new energy station the time-varying reference penalty coefficient of the time period , is the predicted output of the new energy station in the time period , is the actual clearing output of the new energy station in the time period , is the nonlinear penalty index, is the adjustment ability compensation coefficient of the new energy station in the time period .

[0011] Further, the expression of the operation cost of the energy storage system is: wherein: is the set of energy storage systems, , is the energy storage system in the time 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.

[0012] Furthermore, the expression for the objective function is: in: , , , These are the normalized weighting coefficients for the power generation costs of traditional generating units in each province, the transmission costs of inter-provincial power transmission networks, the cost of wind and solar curtailment penalties for new energy power plants, and the operating costs of energy storage systems.

[0013] Furthermore, the normalized weighting coefficients are determined through the following relationship: The objective function of the top-level optimization model, which aims to maximize overall operational efficiency, is obtained by solving a top-level optimization model. 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: , , , The expected cost savings are calculated based on the power generation costs of traditional generating units in each province, the transmission costs of inter-provincial power transmission networks, the costs of wind and solar power curtailment penalties at renewable energy power plants, and the operating costs of energy storage systems. The marginal contribution is obtained by calculating the Lagrange dual multipliers of the top-level optimization model.

[0014] Furthermore, the multi-energy complementary 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 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, The mutual assistance sensitivity coefficient is used to convert the fluctuation of the total power of inter-provincial power transmission into the regulating power that needs to be provided by energy storage. Source-storage spatiotemporal balance constraints: in: The permissible spatiotemporal imbalance threshold, For provinces During the period The load, A rolling time window for complementary coordination.

[0015] Furthermore, 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. extracting the decision variable values corresponding to the power of each inter-provincial transmission line in the solving result as the power of each inter-provincial transmission line; 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; obtaining the dual variable values corresponding to the power balance constraints of each province in the solving result, and taking the dual variable values as the clearing price of each province.

[0016] The second aspect of the present application provides a multi-energy complementary clearing system for source-network-load-storage inter-provincial mutual aid, comprising: a source-network-load-storage data acquisition unit configured to acquire source-network-load-storage data of each province in the inter-provincial interconnected power grid, wherein the source-network-load-storage data comprises new energy output prediction data and traditional unit output data on the power source 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; a clearing model construction unit 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-network-load-storage data; the clearing model comprises an objective function and constraint conditions; wherein the objective function considers the power generation cost of the traditional units in each province, the transmission cost of the inter-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; the constraint conditions comprise inter-provincial transmission line capacity constraints, power balance constraints of each province, unit output constraints on the power source side, energy storage operation constraints, and multi-energy complementary coordination constraints; a clearing model solving unit configured to solve the clearing model to obtain the clearing output of the power source side units in each province, the power of the inter-provincial transmission line, the charging and discharging power of the energy storage, and the clearing price; a clearing plan execution unit configured to generate a day-ahead market clearing plan for the inter-provincial interconnected power grid according to the solving result of the clearing model, and issue the day-ahead market clearing plan to the source-network-load-storage subjects of each province for execution.

[0017] As can be seen from the above technical solutions, the present application has the following advantages: The application is based on the obtained source network load storage data to construct a clearing model with the minimum total operation cost of the inter-provincial interconnected power grid as the objective function, wherein the model considers the generation cost of the traditional units in each province, the transmission cost of the inter-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, the constraint conditions include the capacity constraint of the inter-provincial transmission line, the power balance constraint of each province, the unit output constraint of the power supply side, the energy storage operation constraint and the multi-energy complementary coordination constraint; the clearing model is solved to obtain the clearing output of the power supply side units in each province, the power of the inter-provincial transmission line, the charging and discharging power of the energy storage and the clearing price; finally, the day-ahead market clearing plan of the inter-provincial interconnected power grid is generated according to the solving result of the clearing model and is issued to the source network load storage subjects in each province for execution. The application constructs a cost model that accurately responds to the device state and market environment, deeply couples the macro policy, safety demand and market mechanism through multi-dimensional constraints, so that the clearing result can not only achieve more fine economic optimization, but also actively guide the resource distribution, improve the collaborative efficiency of inter-provincial aid, the new energy consumption level and the system operation safety. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 It is an embodiment flow diagram of the multi-energy complementary clearing method of source network load storage inter-provincial aid in the application. DETAILED DESCRIPTION

[0019] The terms "first", "second", "third", "fourth" and the like in the specification of the application and the above drawings, if any, are used to distinguish similar objects, and do not necessarily have to describe a particular order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "correspond to" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0020] Embodiment one The implementation method in this embodiment can be implemented in a system, which can be implemented in a server or a terminal, and the specific implementation is not limited. From the perspective of system implementation, the method in the application will be introduced. Please refer to Figure 1 The method provided by the embodiment of the application includes the following steps: S11. Obtain the source network load storage data of each province in the inter-provincial interconnected power grid, wherein the source network load storage data includes the new energy output prediction data and the traditional unit output data of the power supply side, the inter-provincial transmission line capacity data of the power grid side, the load prediction data of the load side, and the energy storage state data of the energy storage side; This step provides complete and consistent input data basis for building a clear model by periodically and automatically collecting or receiving the following multi-dimensional, heterogeneous source network load storage real-time and predicted data through the data collection and monitoring control system deployed in each province, energy management system and market technical support system, and conducting data quality verification and format standardization processing. The specific data obtained include: 1. New energy output prediction data on the power supply side: obtain 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. This prediction is based on numerical weather prediction, historical power data and machine learning algorithm; traditional unit output data on the power supply side: obtain the real-time running state, current output and unit combination plan of all coal-fired, gas-fired and hydraulic traditional units in each province from the power plant monitoring system or dispatching plan system, as well as technical parameters determined by unit characteristic test report, including unit output upper and lower limit, climbing rate and sliding rate, micro-increment energy consumption cost coefficient, idle standard coal consumption and start-stop cost (calculated based on boiler and steam turbine life loss model).

[0021] 2. Cross-province transmission line capacity data on the power grid side: obtain the steady-state thermal stability limit of cross-province AC / DC tie lines from the power grid dispatching and operation mode department, and obtain the unit length resistance and rated voltage based on the line design parameters. At the same time, receive the line corridor environmental temperature and wind speed data provided by the meteorological system for calculating the real-time thermal stability limit margin risk index considering dynamic capacity increase.

[0022] 3. Load prediction data on the load side: obtain the total system load prediction value of each province in the future dispatching period from the load prediction system; this prediction considers historical load curve, weather forecast (temperature, humidity), date type (workday, holiday) and macroeconomic indicators.

[0023] 4. Energy storage state data on the energy storage side: obtain the current state of charge of each energy storage power station from the energy storage energy management system, as well as technical parameters provided by the battery management system or manufacturer, including rated energy capacity, maximum charge / discharge power, state of charge safe operation interval and identification of whether it has automatic generation control function.

[0024] All the data obtained are sent to the data platform for consistency check and time series alignment, and after eliminating outliers and filling in missing data, a standardized data set is formed for subsequent use in building a clear model.

[0025] S12. Construct a dispatching model with the minimum total operation cost of the cross-province interconnected power grid as an objective function based on the obtained source network load data; the dispatching model comprises the objective function and constraint conditions; wherein the objective function considers the power generation cost of the traditional units in each province, the power transmission cost of the cross-province power transmission network, the abandoned wind and light penalty cost of the new energy station and the operation cost of the energy storage system; the constraint conditions comprise the cross-province power transmission line capacity constraint, the power balance constraint of each province, the unit output constraint of the power source side, the energy storage operation constraint and the multi-energy complementary coordination constraint; Specifically, the power generation cost of the traditional units in each province is expressed as: 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 linear term coefficient of the incremental energy consumption cost of the traditional unit in period ; is the no-load energy consumption cost of the traditional unit in period , is the no-load coal consumption of the traditional unit , is the coal price of period , is the aging and health state correction factor of the traditional unit in period ; is the start-stop loss cost of the traditional unit in period , , are 0 / 1 variables of the running state of the traditional unit in period and period , is the single start-stop cost of the traditional unit .

[0026] The power transmission cost of the cross-province power transmission network is expressed as: wherein: is a set of inter-provincial transmission lines, is a transmission line in time period is the loss cost coefficient, is the system marginal price forecast value in time period is the length of transmission line is the rated voltage; is the transmission power of transmission line in time period is the unit length resistance of line; is the dynamic congestion penalty coefficient of transmission line in time period , is the weight factor, is the historical maximum price difference of sending and receiving nodes in similar time period, is the real-time thermal stability limit margin risk index of transmission line in time period is the dynamic security margin coefficient of transmission line in time period is the reference security margin, is the adjustment sensitivity coefficient; is the rated maximum transmission capacity of transmission line . The expression of the penalty cost of wind and light curtailment of new energy station is:

[0027] The expression of the penalty cost of wind and light curtailment of new energy station is: wherein: is a set of new energy stations, is a new energy station in time period is the time-varying reference penalty coefficient, is the reference penalty coefficient, is the sensitivity coefficient, which represents the influence degree of price difference on penalty coefficient, is the new energy station​​​​​​ provinces in the time period predicted marginal price of the node, average predicted price of the inter-provincial interconnected power grid in the time period ; predicted output of the new energy plant in the time period , actual clearing output of the new energy plant in the time period , nonlinear penalty index; regulation capacity compensation coefficient of the new energy plant in the time period , compensation coefficient reference value, 0 / 1 variable representing whether the plant has automatic generation control function, represents having, represents not having, growth factor of the S-shaped function, power threshold value, natural constant.

[0028] The expression of the operation cost of the energy storage system is: wherein: set of energy storage systems, , charging power and discharging power of the energy storage system in the time period , , unit charging cost coefficient and unit discharging cost coefficient of the energy storage system ; state of charge of the energy storage system in the time period , dynamic reference state of charge of the energy storage system in the time period , sensitivity coefficient representing the influence degree of the net load prediction on the reference state of charge, total system load prediction value in the time period , total new energy output prediction value in the time period , energy storage system​ the rated capacity of the energy storage system; the energy storage system in the time period a time-varying state of charge deviation penalty coefficient, a basic coefficient of state of charge deviation penalty, a weight factor representing the degree of influence of the standard deviation of the net load prediction on the penalty coefficient, the current time period to the end of the dispatch cycle the standard deviation of the system net load (load-new energy) prediction sequence, reflecting the degree of fluctuation of the net load.

[0029] According to the above various cost items, the expression of the objective function is: 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 wind and light curtailment of the new energy station, and the operation cost of the energy storage system.

[0030] The above normalized weight coefficients are determined by the following relationship: The above normalized weight coefficients are determined by the following relationship: 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, inter-provincial line congestion probability, and reserve capacity shortage risk; The relationship of the weight coefficients is: wherein: , , , are the marginal contributions of the generation cost of the traditional units in each province, the transmission cost of the inter-provincial transmission network, the penalty cost of wind and light curtailment of the new energy station, and the operation cost of the energy storage system to the expected cost saving , which are calculated by the Lagrange dual multipliers of the top-level optimization model.

[0031] Specifically, the expressions of the various constraint conditions are as follows: Inter-provincial transmission line capacity constraint: Provincial power balance constraints: where: , , denote the sets of conventional units, new energy stations and energy storage systems belonging to province , , denote the sets 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 .

[0032] Power supply side unit output constraints: where: , are the lower and upper output limits of conventional units , , are the ramp-down and ramp-up rates of conventional units .

[0033] Energy storage operation constraints: where: 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 .

[0034] 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 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, The mutual assistance sensitivity coefficient is used to convert the fluctuation of the total power of inter-provincial power transmission into the regulating power that needs to be provided by energy storage. Source-storage spatiotemporal balance constraints: in: The permissible spatiotemporal imbalance threshold, For provinces During the period The load, A rolling time window for complementary coordination.

[0035] S13. 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 price; Input the clearing model into the preset optimization solver; An optimized solver is used to solve the clearing model based on the branch and bound method, handling both continuous and integer variables in the clearing model. In the solution process, the multi-energy complementary coordination constraint is treated as a constraint condition to ensure that the solution process meets the constraints of inter-provincial new energy consumption responsibility, cross-provincial flexible resource mutual assistance, and source-storage spatiotemporal balance. 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 charge and discharge power of each energy storage system in the solving result as the charge and discharge power of each energy storage system. Obtain the dual variable values corresponding to the power balance constraints of each province in the solving result, and take the dual variable values as the clearing prices of each province.

[0036] 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 responsibility of inter-provincial consumption, flexible interconnection and time-space balance requirements. When the solver converges, the unit output decision variables of each province in the original solution are extracted 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, and the shadow prices corresponding to the power balance constraints of each province in the dual solution are obtained as the node marginal price, and finally a complete market clearing result is formed.

[0037] S14. According to the solving result of the clearing model, a day-ahead market clearing plan of the inter-provincial interconnected power grid is generated, and is issued to each source, network, load and storage subject for execution.

[0038] Based on the clearing result obtained by S13, the market operation agency automatically integrates all decision variables and dual variables through a 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 transmission power plan of each cross-provincial tie line, the 96-point charge and discharge dispatching instruction of each energy storage system, and the 96-point node marginal price sequence of each province. After being verified by a safety checking module, the unit clearing output and start-stop instruction are issued to each power generation enterprise monitoring system through the data bus of the power market operation platform, the cross-provincial transmission plan is issued to the energy management system of each level of dispatching agency, the energy storage charge and discharge instruction is issued to the energy management system of each energy storage power station, and the clearing price information is published to all market subjects. At the same time, a settlement list and a market clearing report containing all the above clearing results are generated as the only basis for the day-ahead market execution.

[0039] The above embodiment realizes the economic optimal allocation of cross-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 improves the efficiency of power grid operation and the system regulation capability.

[0040] Embodiment two An embodiment of a source, network, load and storage inter-provincial interconnection multi-energy complementary clearing system in the application includes the following: The source network load storage data acquisition unit is configured to acquire source network load storage data of each province in the inter-provincial interconnected power grid, wherein the source network load 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 energy state data on the storage energy side; 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 acquired source network load storage data. The clearing model comprises an objective function and constraint conditions. The objective function considers generation cost of traditional units in each province, transmission cost of cross-provincial transmission network, wind and light curtailment penalty cost of new energy stations, and operation cost of storage energy systems. The constraint conditions comprise cross-provincial transmission line capacity constraints, power balance constraints in each province, unit output constraints on the power source side, storage energy operation constraints, and multi-energy complementary coordination constraints. The clearing model solution unit is configured to solve the clearing model to obtain clearing output of units on the power source side in each province, power of cross-provincial transmission lines, storage energy charging and discharging power, and clearing electricity price. 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 solution result of the clearing model, and deliver the day-ahead market clearing plan to source network load storage subjects in each province for execution.

[0041] The specific limitations of the system can be referred to the limitations of the method in the above, which will not be repeated here. Each module in the above system can be realized by software, hardware and their combinations in whole or in part. 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 as to call and execute the operations corresponding to the above modules by the processor.

[0042] It can be understood that those skilled in the art can combine various embodiments in the above embodiments to obtain technical solutions of various embodiments.

[0043] The above is only the preferred embodiment of the present application, and is not used to limit the present application. Any modification, equivalent replacement and improvement 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 source, network, load and storage cross-province mutual assistance, characterized in that, The method comprises the following steps: acquiring source-grid-load-storage data of each province in the inter-provincial interconnected power grid, wherein the source-grid-load-storage data comprises new energy output prediction data and traditional unit output data on the power source side, inter-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; constructing a dispatching 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 dispatching model comprises an objective function and constraint conditions; wherein the objective function considers the power generation cost of the traditional units in each province, the power transmission cost of the inter-provincial transmission network, the wind and light curtailment penalty cost of the new energy station, and the operation cost of the storage system; the constraint conditions comprise inter-provincial transmission line capacity constraints, power balance constraints of each province, unit output constraints on the power source side, storage operation constraints, and multi-energy complementary coordination constraints; solving the dispatching model to obtain the dispatching output of the units on the power source side of each province, the power of the inter-provincial transmission line, the charging and discharging power of the storage system, and the dispatching electricity price; generating a day-ahead market dispatching plan of the inter-provincial interconnected power grid according to the solution of the dispatching model, and delivering the plan to the source-grid-load-storage subjects of each province for execution.

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 expression of the generating cost of the conventional unit in each province is: wherein: is a set of dispatch periods, is a set of conventional units, is a conventional unit is the out-of-merit output of the conventional unit in period , is the quadratic coefficient of the incremental energy cost of the conventional unit in period , is the linear coefficient of the incremental energy cost of the conventional unit in period , is the no-load energy cost of the conventional unit in period , is the start-up and shut-down loss cost of the conventional unit in period , , , , .

3. The method of claim 2, wherein the source network load storage cross-province mutual assistance multi-energy complementary clearing method is characterized in that, Transmission cost of the cross-provincial transmission network The expression is: wherein: is a set of inter-provincial transmission lines, is a transmission line in time period is a loss cost coefficient, is a transmission line in time period is a transmission power, is a unit length resistance of the line, is a transmission line in time period is a dynamic congestion penalty coefficient, is a transmission line in time period is a dynamic security margin coefficient, is a transmission line a 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 penalty cost of abandoned wind and light of the new energy station The expression is: wherein: is a set of new energy stations, is a new energy station is a time-varying reference penalty coefficient of a time period is a time-varying reference penalty coefficient of a time period is a new energy station is a predicted output of a time period is a predicted output of a time period is a new energy station is an actual clearing output of a time period is an actual clearing output of a time period is a nonlinear penalty index is a new energy station is an adjustment capacity compensation coefficient of a time period is an adjustment capacity compensation coefficient of a time period 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, Operating costs of the energy storage system The expression for the operating costs of the energy storage system is: wherein: is a set of energy storage systems, , is an energy storage system is a charging power and a discharging power of an energy storage system at a time period , is a unit charging cost coefficient and a unit discharging cost coefficient of an energy storage system , is a state of charge of an energy storage system at a time period , is a dynamic reference state of charge of an energy storage system at a time period , is a time-varying state of charge deviation penalty coefficient of an energy storage system at a time period .

6. The multi-energy complementary clearing method for cross-provincial mutual assistance between source, grid, load, and storage as described in claim 5, is characterized in that, The expression of the objective function is: wherein: , , , are the normalized weight coefficients of the generation cost of the traditional unit in each province, the transmission cost of the inter-provincial transmission network, the penalty cost of wind and light curtailment of the new energy station, and the operation cost of the energy storage system, respectively.

7. The multi-energy complementary clearing method for cross-provincial mutual assistance between source, grid, load, and storage as described in claim 6, is characterized in that, The normalized weight coefficient is determined by the following relationship: The weight coefficient is obtained by solving a top-level optimization model with the maximum comprehensive operation benefit as an objective function, and the objective function of the top-level optimization model is: wherein: The expected cost savings brought by cross-provincial mutual aid are determined based on regression analysis of historical clearing data and current supply and demand forecasts, The system operation dedication index is jointly quantified by new energy output volatility, cross-provincial line congestion probability and reserve capacity shortage risk. The relationship of the weight coefficient is: 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 wind and light curtailment penalty cost of the new energy station, 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.

8. The multi-energy complementary clearing method for cross-provincial mutual assistance between source, grid, load, and storage as described in claim 7, is characterized in that, The multi-energy complementary coordination constraints comprise inter-provincial new energy consumption responsibility constraints, inter-provincial flexible resource mutual aid constraints, and source-storage space-time balance constraints, and the expressions are as follows: Inter-provincial new energy consumption responsibility constraints: wherein: is the set of all provinces, belongs to the province is the set of new energy stations of the province, is the province is the new energy consumption responsibility weight factor of the province in the time period is pre-set by the provincial energy policy and the cross-provincial agreement, is the minimum weighted average new energy consumption rate of the whole network in the time period . Inter-provincial flexible resource mutual aid constraints: wherein: is the set of flexible energy storage systems designated to participate in the inter-provincial mutual aid, is the mutual aid sensitivity coefficient used to convert the amount of fluctuation in the total inter-provincial power transmission into the adjustment power that needs to be provided by the energy storage; Source-storage space-time balance constraints: wherein: is a threshold for the allowed spatiotemporal imbalance, is a province is a load at a time period is a load at a time period is a complementary coordinated rolling time window.

9. The multi-energy complementary clearing method for cross-provincial mutual assistance between source, grid, load, and storage as described in claim 8, is characterized in that, The solving of the dispatching model to obtain the dispatching output of the units on the power source side of each province, the power of the inter-provincial transmission line, the charging and discharging power of the storage system, and the dispatching electricity price comprises the following steps: inputting the dispatching model into a preset optimization solver; using the optimization solver to solve the dispatching model based on the branch and bound method, and processing the continuous variables and integer variables contained in the dispatching model; 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 constraints, the inter-provincial flexible resource mutual aid constraints, and the source-storage space-time balance constraints; after the optimization solver completes the solving, extracting the decision variable values corresponding to the unit output on the power source side of each province in the solution as the dispatching output of the units on the power source side of each province; extracting the decision variable values corresponding to the power of each inter-provincial transmission line in the solution as the power of each inter-provincial transmission line; extracting the decision variable values corresponding to the charging and discharging power of each storage system in the solution as the charging and discharging power of each storage system; extracting the dual variable values corresponding to the power balance constraints of each province in the solution as the dispatching electricity price of each province.

10. A multi-energy complementary clearing system for cross-provincial mutual assistance of source, network, load and storage, characterized in that, The method of any one of claims 1-9 comprises the following steps: The source network load storage data acquisition unit is configured to acquire source network load storage data of each province in the inter-provincial interconnected power grid, wherein the source network load storage data comprises new energy output prediction data and traditional unit output data on the power source side, inter-provincial transmission line capacity data on the power grid side, load prediction data on the load side, and storage energy state data on the storage energy side; 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 acquired source network 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, wind and light curtailment penalty cost of new energy stations, and operation cost of the storage energy system; and the constraint conditions comprise inter-provincial transmission line capacity constraints, power balance constraints of each province, unit output constraints on the power source side, storage energy operation constraints, and multi-energy complementary coordination constraints; The clearing model solution unit is configured to solve the clearing model to obtain clearing output of units on the power source side of each province, power of the inter-provincial transmission line, storage energy charging and discharging power, and clearing electricity price; 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 solution result of the clearing model, and deliver the day-ahead market clearing plan to source network load storage subjects of each province for execution.

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