A multi-energy storage thermal power unit optimization scheduling method and system

Abstract

The present invention relates to the technical field of energy storage peak regulation in power systems, and discloses a method for optimizing and dispatching multiple energy storage thermal power units, including the following steps: S1, optimizing the total charging and discharging power of leased multiple energy storages, and determining the declared power of new energy stations participating in bilateral transactions, and also discloses a system for optimizing and dispatching multiple energy storage thermal power units, including: a market transaction power optimization module, used to determine the declared power of new energy stations participating in bilateral transactions, and discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. By optimizing the charging and discharging behavior of a single energy storage, the overall benefits of multiple energy storage participating in the power market are maximized; new energy stations can independently declare peak regulation transaction information, thereby improving the peak regulation enthusiasm of new energy stations; the winning bid power of new energy and the available power of thermal power units are continuously iterated, thereby achieving the optimal dispatch of new energy stations and thermal power units.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage peak regulation in power systems, and in particular to a method and system for optimizing scheduling of multiple energy storage thermal power units. Background Art

[0002] With the rapid development of new energy sources such as wind power and photovoltaics, the future power system will be dominated by new energy power generation, and will present a diversified power system with multiple power generation entities coexisting. Due to the volatility of new energy power generation, large-scale grid connection will lead to an increase in the peak-to-valley difference of the equivalent load of the power grid, and traditional thermal power units will face problems such as high operating costs when participating in deep peak regulation. Energy storage power stations have good energy time-shifting capabilities and fast charging and discharging capabilities, which can meet the future power system's demand for flexibility and effectively alleviate the peak regulation pressure brought by large-scale wind power grid connection.

[0003] Existing methods mainly focus on the technical or economic goals of the overall peak load regulation of the system, and respond to the challenges brought by wind power grid connection by optimizing the output of thermal power units and the charging and discharging behavior of energy storage. However, they ignore the market participation willingness of energy storage as an independent entity and fail to fully reflect its market value. In addition, although some methods have designed mechanisms for energy storage to participate in the electricity market and increased its enthusiasm for market participation, they mainly focus on a single energy storage market mechanism, and research on the participation of multiple energy storage systems in the electricity market is still insufficient. Therefore, in response to the above problems, there is an urgent need for an optimal scheduling method and system for multi-energy storage thermal power units. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for optimizing the scheduling of multiple energy storage thermal power units, which solves the problem of optimizing the scheduling of multiple energy storage systems and thermal power units in an environment of fluctuating renewable energy power generation.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for optimizing the scheduling of multiple energy storage thermal power units, comprising the following steps:

[0006] S1. Optimize the total charging and discharging power of the leased multi-energy storage and determine the declared power of the new energy station participating in the bilateral transaction;

[0007] S2. Designed a storage leasing declaration model and multi-storage leasing transaction mechanism based on the full life cycle cost;

[0008] S3. Based on the current operating rules of the peak load ancillary service market, a bilateral transaction between thermal power units and new energy stations was designed;

[0009] S4. Under relevant constraints, optimize the clearing with the goal of minimizing the system peak-shaving cost to obtain the output of thermal power units, the winning bid power of new energy stations and the clearing price.

[0010] Preferably, the step S1 specifically includes the following steps:

[0011] S1.1. With the goal of minimizing the net load variance, optimize the total charging and discharging power of multiple energy storages leased at new energy stations in each period;

[0012] S1.2. On this basis, new energy stations and thermal power units conduct bilateral transactions to obtain deep peak-shaving space for thermal power units.

[0013] Preferably, the step S2 specifically includes the following steps:

[0014] S2.1. The energy storage rental quotation needs to consider the charging and discharging costs of the energy storage unit;

[0015] S2.2. Calculate the unit charging and discharging cost of each energy storage power station;

[0016] S2.3. On this basis, a storage leasing quotation model is designed. The quotation should fluctuate between the maximum and minimum values ​​to reflect the operating costs and market demand of different storage power stations.

[0017] S2.4. Establish a multi-energy storage leasing transaction mechanism to ensure that each energy storage power station participates in market transactions in the order of the best price while meeting the balance between electricity supply and demand.

[0018] Preferably, the step S3 specifically includes the following steps:

[0019] S3.1. Thermal power units and new energy stations need to submit their own declarations to the trading platform, including the available power and subsidy quotes of thermal power units, and the trading power and trading quotes of new energy stations;

[0020] S3.2. The trading platform calls the thermal power units for peak load regulation in order from low to high according to the bids of the thermal power units based on the declared information;

[0021] S3.3. Adopt bilateral bidding clearing method, aiming at minimizing system peak load cost, and determine the final clearing result, i.e. the winning bid power of new energy stations, the output of thermal power units and the clearing price;

[0022] S3.4. New energy stations shall make peak-shaving quotations based on the proportion of power participating in bilateral transactions in each time period, and the peak-shaving quotations shall be adjusted within a certain range.

[0023] Preferably, the step S4 specifically includes the following steps:

[0024] S4.1. It is necessary to comprehensively consider the operating costs of thermal power units, the penalty costs for wind and solar power abandonment, and the operating costs of multiple energy storage to ensure that the peak load cost is minimized;

[0025] S4.2. Constraints must be met during the optimization process, including power balance constraints, thermal power unit operation constraints, unit start and stop time constraints, and bilateral transaction constraints.

[0026] Preferably, the formula of the objective function in step S1.1 is:

[0027] Among them, T is the scheduling period, P net,t is the net load of the power grid during period t, P net,ave is the average net load of the power grid;

[0028] The declared power of the new energy station participating in the bilateral transaction in step S1.2 is:

[0029] in, is the comprehensive charging power of multiple energy storages during period t, P ptd,t is the deep peak-shaving power of the system during period t.

[0030] Preferably, the calculation formula for the unit charging and discharging cost of each energy storage power station in step S2.2 is:

[0031] in, is the unit charging and discharging cost of the i-th energy storage power station, is the average annual cost of the energy storage power station, N y is the number of energy storage cycles per year.

[0032] A multi-energy storage thermal power unit optimization dispatching system, comprising:

[0033] Market transaction power optimization module, used to determine the declared power of new energy stations participating in bilateral transactions;

[0034] The multi-energy storage leasing transaction optimization module is used to optimize the charging and discharging behavior of a single energy storage under the constraints of power supply and demand balance, and obtain the charging and discharging power and leasing quotation of a single energy storage power station;

[0035] Bilateral transaction optimization module, used to obtain the output of thermal power units, the winning bid power of new energy stations and the clearing price under the relevant constraints

[0036] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of a method for optimizing the scheduling of multiple energy storage thermal power units when executing the computer program.

[0037] The present invention provides a method and system for optimizing the scheduling of multiple energy storage thermal power units. It has the following beneficial effects:

[0038] 1. The present invention fully reflects the market value of energy storage as an independent entity by designing an energy storage leasing declaration model and a multi-energy storage leasing transaction mechanism based on the full life cycle cost, thereby increasing the enthusiasm and benefits of energy storage power stations in participating in the electricity market.

[0039] 2. The present invention reduces the overall peak-shaving cost of the system by optimizing the coordinated scheduling of multiple energy storage and thermal power units with the goal of minimizing the system peak-shaving cost. The multi-energy storage charging and discharging optimization strategy adopted can effectively smooth out grid load fluctuations and reduce the high operating costs caused by frequent start-up and shutdown of thermal power units.

[0040] 3. The present invention improves the flexibility and stability of the power system by utilizing the rapid response capability and energy time-shifting capability of the energy storage power station. At the same time, the energy storage system can quickly adjust charging and discharging when there are large fluctuations in renewable energy power generation, thereby ensuring the stability and reliability of the power grid operation.

[0041] 4. The introduction of the multi-energy storage system of the present invention significantly improves the absorption capacity of new energy and reduces the phenomenon of wind and solar power abandonment. By rationally scheduling the energy storage system, excess electric energy can be stored during the peak period of new energy power generation and released during the peak period of demand, thereby optimizing energy utilization efficiency.

[0042] 5. The present invention designs a bilateral trading mechanism between new energy stations and thermal power units. New energy stations can independently declare peak-shaving transaction information, which improves the market competitiveness and peak-shaving enthusiasm of new energy stations. The bilateral trading mechanism ensures the fairness and efficiency of market transactions, which is conducive to the participation of new energy stations in market-oriented transactions.

[0043] 6. The present invention takes into account the operating cost of thermal power units, the penalty cost for wind and solar power abandonment, and the operating cost of multiple energy storage systems in the scheduling strategy. By optimizing the scheduling strategy, the overall operating cost is reduced and the system economy is improved. In particular, after the introduction of multiple energy storage systems, although the energy storage operating cost has increased to a certain extent, the energy consumption cost of thermal power units and the penalty cost for wind and solar power abandonment are significantly reduced through optimized scheduling.

[0044] 7. The present invention optimizes the charging and discharging behavior of a single energy storage, maximizing the benefits of multiple energy storages participating in the electricity market as a whole. During the dispatching process, the system continuously iterates and optimizes the winning bid power of new energy and the available power of thermal power units, achieving the optimal dispatch of new energy stations and thermal power units, and ensuring the maximization of the comprehensive benefits of all parties involved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a schematic flow chart of the present invention;

[0046] Figure 2 This is a diagram showing the total charge and discharge power of multiple energy storages for a new energy station with a power generation capacity of 160MWh according to the present invention;

[0047] Figure 3 It is the declaration information and clearing result diagram of the new energy station of the present invention;

[0048] Figure 4 A comparison diagram of abandoned power in different scenarios of the present invention;

[0049] Figure 5 It is a framework diagram of the system of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Please refer to the attached Figure 1 The embodiment of the present invention provides a method for optimizing the scheduling of multiple energy storage thermal power units, comprising the following steps:

[0052] S1. Optimize the total charging and discharging power of the leased multi-energy storage and determine the declared power of the new energy station participating in the bilateral transaction;

[0053] S2. Designed a storage leasing declaration model and multi-storage leasing transaction mechanism based on the full life cycle cost;

[0054] S3. Based on the current operating rules of the peak load ancillary service market, a bilateral transaction between thermal power units and new energy stations was designed;

[0055] S4. Under relevant constraints, optimize the clearing with the goal of minimizing the system peak-shaving cost to obtain the output of thermal power units, the winning bid power of new energy stations and the clearing price.

[0056] Specifically, in step S1, by optimizing the total charging and discharging power of multiple energy storages, the net load fluctuation of the power grid is minimized, the load curve of the power grid is smoothed, and the peak load regulation pressure of the power grid is reduced.

[0057] New energy stations carry out bilateral transactions with thermal power units to fully utilize the peak-shaving capacity of thermal power units, improve the new energy consumption rate, and reduce the phenomenon of wind and solar power abandonment.

[0058] The invention solves the problem in the prior art that the volatility of renewable energy power generation is large, which leads to the increase of the peak-to-valley difference of the power grid load and the high peak-shaving cost of traditional thermal power units.

[0059] This step effectively smooths grid load fluctuations, reduces the peak-shaving costs of thermal power units, and improves the stability and economy of grid operation by optimizing the charging and discharging behavior of the energy storage system and the bilateral trading mechanism.

[0060] The total charging and discharging power of multiple energy storage systems leased at new energy stations in each period is optimized mainly by adopting the objective function of minimizing the net load variance.

[0061] New energy sites and thermal power units carry out bilateral transactions to determine the declared power, utilize the deep peak-shaving space of thermal power units, and improve peak-shaving efficiency.

[0062] In step S2, by designing an energy storage leasing declaration model and considering the full life cycle cost, the quotation of the energy storage power station is made more reasonable and reflects the actual operating cost.

[0063] The multi-energy storage leasing transaction mechanism optimizes the participation of energy storage systems in the electricity market and improves the market competitiveness and revenue of energy storage power stations.

[0064] The present invention solves the problems in the prior art that the mechanism design of energy storage systems participating in market transactions is imperfect and that the energy storage costs and market value are not fully considered.

[0065] This step solves the problem of insufficient market participation willingness for energy storage systems by introducing a full life cycle cost model and optimizing energy storage leasing quotations and transaction mechanisms.

[0066] The unit charging and discharging cost of energy storage power stations is calculated, and the energy storage leasing quotation model is designed based on this. A multi-energy storage leasing transaction mechanism is established to ensure that energy storage power stations participate in market transactions within a reasonable quotation range.

[0067] In step S3, by designing a bilateral trading mechanism, we can ensure that transactions between thermal power units and new energy sites in the market are transparent and fair, thereby improving market operation efficiency.

[0068] Through the bidding and clearing method, the system peak-shaving cost is optimized, the peak-shaving capacity of thermal power units is maximized, and the overall peak-shaving cost is reduced.

[0069] It solves the problems of imperfect design of existing peak-shaving market rules, lack of effective bilateral trading mechanism, and difficulty in fully utilizing the peak-shaving capacity of thermal power units.

[0070] This step optimizes the peak-shaving transactions between thermal power units and new energy stations and improves market operation efficiency and economy by designing a bilateral trading mechanism and bidding clearing method.

[0071] The declaration information, including available power and transaction quotations, is mainly submitted to the trading platform by thermal power units and new energy stations. Based on the declared information, thermal power units are called in order from low to high quotations for peak load regulation to determine the clearing results.

[0072] In step S4, by optimizing the clearing process, the operating costs of thermal power units, the penalty costs for wind and solar power curtailment, and the operating costs of multiple energy storage units are comprehensively considered to reduce the overall system peak-shaving costs.

[0073] Ensure that thermal power units and new energy sites are optimally dispatched under the premise of meeting power balance, operation constraints and transaction constraints, so as to improve the economy and reliability of system operation.

[0074] The present invention solves the problems in the prior art, such as high peak-shaving cost, lack of comprehensive optimization means for system scheduling, and difficulty in minimizing the overall cost.

[0075] This step optimizes the clearing process by introducing multiple cost factors to ensure that the system achieves peak load regulation at the lowest cost and improve the economy and efficiency of scheduling.

[0076] The system peak-shaving cost is optimized comprehensively under the constraints of power balance, operation of thermal power units and bilateral transactions. Through optimized clearing, the output of thermal power units, the winning bid power of new energy stations and clearing prices are determined to ensure optimal system dispatch.

[0077] The S1 step specifically includes the following steps:

[0078] S1.1. With the goal of minimizing the net load variance, optimize the total charging and discharging power of multiple energy storages leased at new energy stations in each period;

[0079] S1.2. On this basis, new energy stations and thermal power units conduct bilateral transactions to obtain deep peak-shaving space for thermal power units.

[0080] Specifically, in step S1.1, by optimizing the charging and discharging behaviors of multiple energy storages, the net load curve of the power grid is made smoother, fluctuations are reduced, and the stability of power grid operation is improved.

[0081] Minimizing the net load variance can smooth out grid load fluctuations, reduce the operating costs of frequent start-up and shutdown of thermal power units, extend equipment life, and reduce maintenance costs.

[0082] It solves the problem of large volatility in renewable energy power generation, which leads to increased peak-to-valley differences in grid load, and traditional thermal power units facing high operating costs and frequent starts and stops during peak load regulation.

[0083] It mainly optimizes the charging and discharging behavior of multiple energy storage systems to keep the net load of the power grid within a smaller fluctuation range, smooth the load curve, and reduce the peak-shaving pressure on thermal power units.

[0084] Optimization goal: To minimize the net load variance, optimize the total charging and discharging power of multiple energy storages leased at new energy stations in each period. The objective function is:

[0085] Among them, T is the scheduling period, P net,t is the net load of the power grid during period t, P net,ave is the average net load of the power grid.

[0086] Through mathematical optimization models, the energy storage charging and discharging plan for each period is determined to make the grid load curve as smooth as possible and reduce fluctuations. The specific implementation steps include:

[0087] Collect grid load data and renewable energy power generation forecast data.

[0088] An energy storage charging and discharging optimization model is established with the goal of minimizing the net load variance.

[0089] An optimization algorithm (such as linear programming or dynamic programming) is used to solve the model and obtain the optimal charge and discharge power curve.

[0090] In step S1.2, through the bilateral trading mechanism, new energy sites and thermal power units can effectively coordinate and utilize the peak-shaving capacity of thermal power units to improve the new energy consumption rate and reduce the phenomenon of wind and solar power abandonment.

[0091] The bilateral trading mechanism ensures the optimal allocation of peak-shaving resources, improves the overall peak-shaving efficiency of the power system, and reduces system operating costs.

[0092] It solves the problems of imperfect market rules for existing peak-shaving ancillary services and the lack of an effective bilateral trading mechanism, which leads to low utilization of peak-shaving resources and inability to fully absorb new energy power generation.

[0093] It is mainly through the design of a reasonable bilateral trading mechanism to optimize the peak-shaving transactions between thermal power units and new energy sites to ensure the efficient use of peak-shaving resources.

[0094] Trading mechanism: New energy stations and thermal power units conduct bilateral transactions, and the declared power includes the sum of multiple energy storage charging power and the system deep peak load regulation power. The formula for declared power is:

[0095] in, is the comprehensive charging power of multiple energy storages during period t, P ptd,t is the system deep peak-shaving power during period t (i.e. the planned market trading power of the new energy station).

[0096] Formulate bilateral trading rules, including declaration, quotation, bidding and clearing. The specific implementation steps include:

[0097] New energy stations and thermal power units submit their respective declaration information to the trading platform.

[0098] The trading platform matches the declared information and calls upon thermal power units for peak load regulation in order from low to high quotations.

[0099] Determine the final clearance results, including the winning bid power of new energy sites and the output of thermal power units.

[0100] Constraints: During the transaction, the rated capacity of the energy storage power station and the planned leased multi-energy storage power generation capacity must be met to ensure the rationality and safety of the transaction. The constraint formula is:

[0101]

[0102] in, is the rated capacity of the i-th energy storage power station, Plan to lease more energy storage for new energy stations to absorb the power generation. is the rated power of the i-th energy storage power station.

[0103] Through the above specific steps, step S1 realizes the optimization of multi-energy storage charging and discharging and the bilateral transaction between new energy stations and thermal power units, effectively solves the difficulties in the existing technology and improves the stability and economy of the power system.

[0104] The S2 step specifically includes the following steps:

[0105] S2.1. The energy storage rental quotation needs to consider the charging and discharging costs of the energy storage unit;

[0106] S2.2. Calculate the unit charging and discharging cost of each energy storage power station;

[0107] S2.3. On this basis, a storage leasing quotation model is designed. The quotation should fluctuate between the maximum and minimum values ​​to reflect the operating costs and market demand of different storage power stations.

[0108] S2.4. Establish a multi-energy storage leasing transaction mechanism to ensure that each energy storage power station participates in market transactions in the order of the best price while meeting the balance between electricity supply and demand.

[0109] Specifically, in step S2.1, the charging and discharging costs of the energy storage unit are taken into consideration to make the quotation more accurate and reasonable and reflect the actual operating cost of the energy storage system.

[0110] Through a reasonable bidding mechanism, it can be ensured that the energy storage system obtains fair benefits in the market and improve its enthusiasm and stability in market participation.

[0111] It solves the problem that the existing energy storage system's quotation mechanism is imperfect and fails to fully consider the charging and discharging costs, causing market quotations to deviate from actual costs and affecting the economy and market competitiveness of the energy storage system.

[0112] By taking into account the unit charging and discharging costs of energy storage, the quotation mechanism is optimized to ensure that the quotations for energy storage systems are reasonable and reflect actual costs.

[0113] The main consideration is to add the unit charging and discharging cost into the energy storage leasing quotation model. Through the full life cycle cost analysis, the cost of the energy storage system under different operating conditions is determined and used as an important basis for quotation.

[0114] The energy storage rental quotation is related to the unit charging and discharging cost of energy storage, which is related to the annual cycle number and the average annual cost, as follows:

[0115]

[0116] in, is the unit charging and discharging cost of the i-th energy storage power station, is the average annual cost of the energy storage power station, N y is the number of energy storage cycles per year.

[0117] Energy storage rental quotation:

[0118]

[0119] Among them, β es,i,t is the quotation coefficient of the i-th energy storage power station in period t, as follows:

[0120]

[0121] in, is the upper limit of the quotation coefficient, It is the lower limit of the quotation coefficient.

[0122] In step S2.2, the unit charging and discharging costs of the energy storage power station are precisely calculated to ensure the accuracy and rationality of the quotation.

[0123] Calculating the unit charging and discharging costs can provide a scientific basis for the quotation of energy storage systems, avoiding quotations that are too high or too low, which will affect market competitiveness and profits.

[0124] This solves the problem in the prior art that there is a lack of accurate calculation of the unit charging and discharging costs of energy storage power stations, which leads to a lack of scientific basis for quotations and affects the market performance of energy storage systems.

[0125] Through scientific calculation methods, the unit charging and discharging costs of energy storage power stations can be accurately calculated to provide reliable data support for quotations.

[0126] Calculation formula: The unit charging and discharging cost calculation formula of the energy storage power station is as follows:

[0127] in, is the unit charging and discharging cost of the i-th energy storage power station, is the average annual cost of the energy storage power station, N y is the number of energy storage cycles per year.

[0128] Collect data such as the average annual cost and annual cycle number of energy storage power stations, apply the above formula to calculate, and obtain the unit charging and discharging cost of each energy storage power station.

[0129] In step S2.3, by designing a reasonable quotation model, the quotation of the energy storage system can fluctuate within a reasonable range to reflect the actual operating cost and market demand.

[0130] Through a reasonable quotation model, the market supply and demand relationship can be effectively balanced and the market competitiveness and revenue of the energy storage system can be improved.

[0131] It solves the problem that the existing quotation model is unreasonable in design, fails to reflect the actual operating costs and market demand, leads to distorted quotations of energy storage systems, and affects the fairness and efficiency of market transactions.

[0132] Based on actual operating costs and market demand, a reasonable quotation model is designed to ensure that the quotation fluctuates within a reasonable range.

[0133] Quotation model: The energy storage leasing quotation should fluctuate between the maximum and minimum values. The specific formula is:

[0134]

[0135] Among them, β es,i,t is the quotation coefficient of the i-th energy storage power station in period t, is the upper limit of the quotation coefficient, It is the lower limit of the quotation coefficient.

[0136] Based on market demand and the actual operating costs of the energy storage power station, determine the upper and lower limits of the quotation coefficient, design the quotation model, and ensure that the quotation is reasonable and scientific.

[0137] In step S2.4, a multi-energy storage leasing transaction mechanism is established to ensure that energy storage power stations can participate in market transactions in the order of optimal price while meeting the balance between electricity supply and demand, thereby improving market efficiency and fairness.

[0138] Through a reasonable trading mechanism, the participation order of energy storage power stations can be optimized to improve the overall economic benefits and operational stability of the system.

[0139] It solves the problem that the existing trading mechanism is imperfectly designed and lacks coordinated scheduling and optimization of multiple energy storage systems, which results in the inability of energy storage systems to effectively participate in market transactions, affecting market efficiency and fairness.

[0140] Establish a multi-energy storage leasing transaction mechanism, optimize the participation order of energy storage systems, and ensure efficient and fair market transactions.

[0141] Trading mechanism: The multi-energy storage leasing trading mechanism must ensure that each energy storage power station participates in market transactions in the order of the best price while meeting the balance between electricity supply and demand.

[0142] The multi-energy storage trading mechanism arranges energy storage power stations to undertake renewable energy power generation in order from low to high based on the rental prices reported by single energy storage power stations, subject to meeting relevant constraints.

[0143] The constraints are as follows:

[0144]

[0145] Among them, SOC max and SOC min is the upper and lower limits of energy storage charge rate, P bat,i,t is the power of the i-th energy storage station in period t, P dis,i,t is the discharge power of the i-th energy storage power station in period t.

[0146] With the goal of maximizing the rental income of multiple energy storages, the charging and discharging power of a single energy storage in each period is optimized and cleared to obtain a single energy storage charging and discharging power curve and a rental quotation curve, as shown below:

[0147]

[0148] Among them, E es is the total rental income in multiple energy storage dispatch cycles, and m is the number of energy storage power stations.

[0149] Formulate multi-storage energy trading rules to ensure the balance of power supply and demand during the transaction process, and conduct transactions in order of bid priority. Specific implementation steps include:

[0150] Formulate participation rules and transaction procedures for energy storage power stations.

[0151] Ensure the balance between electricity supply and demand to prevent power shortages or surpluses.

[0152] According to the quotations of energy storage power stations, the energy storage power stations are arranged to participate in market transactions in the order of the best price to ensure fair and efficient transactions.

[0153] The S3 step specifically includes the following steps:

[0154] S3.1. Thermal power units and new energy stations need to submit their own declarations to the trading platform, including the available power and subsidy quotes of thermal power units, and the trading power and trading quotes of new energy stations;

[0155] S3.2. The trading platform calls the thermal power units for peak load regulation in order from low to high according to the bids of the thermal power units based on the declared information;

[0156] S3.3. Adopt bilateral bidding clearing method, aiming at minimizing system peak load cost, and determine the final clearing result, i.e. the winning bid power of new energy stations, the output of thermal power units and the clearing price;

[0157] S3.4. New energy stations shall make peak-shaving quotations based on the proportion of power participating in bilateral transactions in each time period, and the peak-shaving quotations shall be adjusted within a certain range.

[0158] Specifically, in step S3.1, the reporting mechanism is used to ensure the transparency and openness of transaction information, so that market participants can trade on a fair basis.

[0159] The declared information of thermal power units and new energy stations provides basic data for subsequent transactions and peak load regulation, ensuring the effectiveness and rationality of transactions.

[0160] It solves the problem that the existing peak-shaving market lacks an effective information reporting mechanism, resulting in opaque transaction information and affecting market efficiency and fairness.

[0161] Establish a standardized reporting mechanism to enable the trading platform to accurately obtain the available power and quotation information of thermal power units and new energy stations, and ensure the openness and fairness of the trading process.

[0162] Application content: Thermal power units need to submit available power and subsidy quotations, and new energy stations need to submit trading power and trading quotations.

[0163] Formulate declaration rules and procedures to ensure the accuracy and timeliness of declaration information. Specific implementation steps include:

[0164] Thermal power units and new energy stations fill out the declaration form according to actual conditions and submit it to the trading platform.

[0165] The trading platform aggregates and reviews the declared information to ensure the authenticity and completeness of the data.

[0166] In step S3.2, a quotation priority mechanism is used to ensure that low-cost peak-shaving resources are called first, thereby reducing the overall system peak-shaving cost.

[0167] Optimize the configuration of peak-shaving resources, improve peak-shaving efficiency, and ensure that the system achieves peak-shaving goals at the lowest cost.

[0168] It solves the problem of lack of priority in calling peak-shaving resources in the existing peak-shaving market, failure to fully utilize low-cost peak-shaving resources, and resulting in high peak-shaving costs.

[0169] It mainly introduces a quotation priority mechanism, and calls on the quotations of thermal power units in order from low to high, to optimize the use of peak-shaving resources and improve economic benefits.

[0170] Calling order: The trading platform calls the thermal power units for peak load regulation in order from low to high according to the subsidy quotations of the thermal power units.

[0171] Formulate call rules and procedures to ensure the transparency and efficiency of the call process. Specific implementation steps include:

[0172] The trading platform sorts the subsidy quotes for thermal power units.

[0173] According to the sorting results, the thermal power units with the lowest bids are called in turn for peak load regulation until the system peak load regulation demand is met.

[0174] In step S3.3, the fairness and efficiency of market transactions are ensured through bilateral bidding and clearing, maximizing the reduction of system peak-shaving costs.

[0175] Optimize the system peak-shaving cost, make the scheduling of thermal power units and new energy stations more scientific and reasonable, and improve the overall economic benefits.

[0176] The problem that the peak-shaving market clearing method is unreasonable in the existing technology, it is difficult to minimize the system peak-shaving cost, and the economic benefits are affected is solved.

[0177] A bilateral bidding clearing method is adopted to ensure fairness and efficiency of peak-shaving transactions, and optimization is carried out with the goal of minimizing system peak-shaving costs.

[0178] Clearing method: bilateral bidding is used to clear the market, with the goal of minimizing the system peak-shaving cost to determine the final clearing result.

[0179] Formulate auction clearing rules and processes to ensure fairness and efficiency of the clearing process. Specific implementation steps include:

[0180] The trading platform aggregates the declared information of thermal power units and new energy stations and conducts bilateral bidding.

[0181] With the goal of minimizing the system peak-shaving cost, clearing optimization is carried out to determine the winning bid power of new energy sites, the output of thermal power units and the clearing price.

[0182] In step S3.4, a reasonable peak-shaving quotation mechanism is used to ensure that the peak-shaving needs of new energy sites in different time periods are met, thereby improving the flexibility and market competitiveness of peak-shaving transactions.

[0183] According to the proportion of power participating in bilateral transactions in each time period, the peak-shaving quotation shall be reasonably adjusted to ensure the scientific nature and market adaptability of the peak-shaving quotation.

[0184] It solves the problem that the existing peak-shaving quotation mechanism lacks flexibility, fails to fully consider the peak-shaving demand and market conditions in different time periods, and affects market competitiveness.

[0185] According to the proportion of power participating in bilateral transactions in each time period, the peak-shaving quotation is reasonably adjusted to ensure that the quotation fluctuates within a certain range to reflect market demand and actual cost.

[0186] Quotation adjustment: New energy stations will make peak-shaving quotations based on the proportion of power participating in bilateral transactions in each time period, and the peak-shaving quotations should be adjusted within a certain range.

[0187] Formulate peak load quotation adjustment rules to ensure that the quotation fluctuates within a reasonable range. Specific implementation steps include:

[0188] Collect peak-shaving demands and market conditions in each time period and determine a reasonable quotation range.

[0189] Adjust the peak-shaving quotation based on the proportion of power participating in bilateral transactions in each time period to ensure that the quotation is scientific and reasonable.

[0190] The specific design of the bilateral trading mechanism between renewable energy and thermal power peak load regulation is as follows:

[0191] Market participants include thermal power units (peak-shaving resource providers) and new energy stations (peak-shaving resource demanders). Bilateral transactions adopt a day-ahead transaction model with a transaction cycle of 24 hours. Thermal power units report available power ΔP to the trading platform. Gi,t , Subsidy Quote New energy stations report trading power to the trading platform Trading Quotes According to the information reported by both parties, the bids of thermal power units are called in order from low to high; the bilateral bidding clearing method is adopted to minimize the system peak load cost, and the clearing result is obtained: the winning bid power of the new energy station Thermal power unit output P Gi,t and the clearing price S t .

[0192] The new energy station will make a peak load quotation based on the proportion of the power participating in bilateral transactions in each period in the predicted power, as follows:

[0193]

[0194] in, is the peak load quotation of the new energy station in period t, S min is the lower limit of the peak load quotation, (S max The upper limit of the peak load quotation.

[0195] The S4 step specifically includes the following steps:

[0196] S4.1. It is necessary to comprehensively consider the operating costs of thermal power units, the penalty costs for wind and solar power abandonment, and the operating costs of multiple energy storage to ensure that the peak load cost is minimized;

[0197] S4.2. Constraints must be met during the optimization process, including power balance constraints, thermal power unit operation constraints, unit start and stop time constraints, and bilateral transaction constraints.

[0198] Specifically, in step S4.1, by comprehensively considering the operating costs of thermal power units, the penalty costs for wind and solar power abandonment, and the operating costs of multiple energy storage systems, the scheduling plan is optimized to ensure that the system peak-shaving costs are minimized.

[0199] This comprehensive cost consideration can fully reflect the economic efficiency of system operation, ensure that all types of costs are reasonably controlled during the peak-shaving process, and minimize the overall cost of the system.

[0200] It solves the problem that existing peak-shaving technologies often only focus on a single cost factor, fail to fully consider the comprehensive costs of thermal power units, wind and solar power curtailment, and multiple energy storage, resulting in uneconomical peak-shaving solutions.

[0201] By comprehensively considering various cost factors, a full cost optimization model is established to ensure the economy and rationality of the peak-shaving plan.

[0202] Optimization goal: Minimizing the system peak-shaving cost is the goal, comprehensively considering the operating cost of thermal power units, the penalty cost for wind and solar power abandonment, and the operating cost of multiple energy storage units to optimize the peak-shaving plan.

[0203]

[0204] in, is the penalty cost for wind and solar power abandonment during period t, is the multi-storage operation cost during period t.

[0205] Build a comprehensive optimization model that includes multiple types of costs, use optimization algorithms (such as linear programming or dynamic programming) to solve, and obtain the optimal peak-shaving solution. The specific implementation steps include:

[0206] Collect various cost data and establish a cost calculation model.

[0207] Determine the optimization objectives and constraints and build the optimization model.

[0208] Use optimization algorithms to solve and obtain the optimal peak-shaving solution to ensure that the peak-shaving cost is minimized.

[0209] In step S4.2, reasonable constraints are set to ensure that the peak-shaving scheme in the optimization process is technically feasible and safe and reliable in operation.

[0210] Ensure that the system peak-shaving plan is not only optimal in terms of economy, but also meets actual requirements in terms of technology and operation, so as to ensure the safe and stable operation of the power grid.

[0211] The problem that the various constraints of actual operation are not fully considered in the existing peak-shaving optimization process, resulting in the difficulty in implementing the optimization plan in actual operation, is solved.

[0212] During the optimization process, constraints such as power balance, thermal power unit operation, unit start and stop time, and bilateral transactions are set to ensure the feasibility and reliability of the optimization plan.

[0213] Power balance constraint: Ensure that the total power supply of the power grid is equal to the total power demand in each period. The formula is as follows:

[0214]

[0215] Where ΔP Gi,t is the available power of the i-th unit in period t, P is the bid power of the new energy station in period t, load,t is the load forecast data for period t.

[0216] Thermal power unit operation constraints: including hot standby, unit output and ramp rate constraints to ensure the safe operation of thermal power units. The constraints are as follows:

[0217] Hot Standby:

[0218]

[0219] Among them, u i,t is the start and stop status of unit i in period t, ρ is the hot standby coefficient, P Gi,t Provide power for thermal power units.

[0220] Unit output constraints:

[0221] η min P GR,i ≤P Gi,t ≤u i,t P GR,i

[0222] Unit climbing constraints:

[0223] -R d ≤P Gi,t -P Gi,t-1 ≤R u

[0224] Among them, R d , R u It is the down / up ramp rate of the unit.

[0225] Unit start-stop time constraints: Ensure the minimum shutdown and start-up time of thermal power units to prevent damage to equipment caused by frequent start-stop. The constraints are as follows:

[0226]

[0227] Among them, T s and T o Minimum shutdown / startup time.

[0228] Bilateral transaction constraints: Ensure that the transaction power and price of thermal power units and new energy stations comply with market rules. The constraints are as follows:

[0229]

[0230] Among them, S t is the clearing price, The new energy station reports the trading power to the trading platform, ΔPG it Report the available power for thermal power units to the trading platform.

[0231] The formula of the objective function in step S1.1 is:

[0232] Among them, T is the scheduling period, P net,t is the net load of the power grid during period t, P net,ave is the average net load of the power grid;

[0233] The declared power of the new energy station participating in the bilateral transaction in step S1.2 is:

[0234] P es_chat =Pp tdt , where P es_chat is the comprehensive charging power of multiple energy storages during period t, P ptdt is the deep peak-shaving power of the system during period t.

[0235] The calculation formula for the unit charging and discharging cost of each energy storage power station in step S2.2 is:

[0236] in, is the unit charging and discharging cost of the i-th energy storage power station, is the average annual cost of the energy storage power station, N y is the number of energy storage cycles per year.

[0237] Please refer to the attached Figure 5 , a multi-energy storage thermal power unit optimization dispatching system, comprising:

[0238] Market transaction power optimization module, used to determine the declared power of new energy stations participating in bilateral transactions;

[0239] The multi-energy storage leasing transaction optimization module is used to optimize the charging and discharging behavior of a single energy storage under the constraints of power supply and demand balance, and obtain the charging and discharging power and leasing quotation of a single energy storage power station;

[0240] The bilateral transaction optimization module is used to obtain the output of thermal power units, the winning bid power of new energy stations and the clearing price while satisfying relevant constraints.

[0241] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a method for optimizing the scheduling of multiple energy storage thermal power units are implemented.

[0242] Embodiment 1:

[0243] Please refer to the attached Figure 2 -Attached Figure 4 In the multi-energy storage-thermal power unit optimization scheduling method of the present invention, the energy storage power station type is selected from all-vanadium liquid flow energy storage, lithium iron phosphate energy storage and ternary lithium energy storage. When the planned leasing multi-energy storage power generation capacity of the new energy station is 160MWh, the total charging and discharging power of the multi-energy storage in each period is as follows: Figure 2 shown.

[0244] In order to verify the effectiveness of the multi-energy storage thermal power unit optimization scheduling method of the present invention in a specific environment, this embodiment will set up three operation scenarios for comparative analysis, and the designed experiment is as follows:

[0245] Experimental purpose: To verify the effectiveness of a multi-energy storage-thermal power unit optimization scheduling method, and to analyze the declaration information and clearing results of new energy stations, as well as the power abandonment and peak-shaving economy under different scenarios through experiments.

[0246] Experimental methods:

[0247] Preparation before the experiment:

[0248] Collect the declaration information of relevant new energy sites and the peak-shaving quotations of thermal power units.

[0249] Determine the charging and discharging power and cost parameters of multiple energy storage systems (all-vanadium liquid flow energy storage, lithium iron phosphate energy storage and ternary lithium energy storage).

[0250] Create an experimental scenario:

[0251] Scenario 1: All electricity generated by renewable energy connected to the grid is purchased for security purposes and consumed by thermal power units.

[0252] Scenario 2: The amount of electricity connected to the grid by new energy sources is divided into guaranteed purchase electricity and market trading electricity, and new energy stations conduct bilateral transactions with thermal power units.

[0253] Scenario 3: The amount of electricity connected to the grid by new energy sources is divided into guaranteed purchase electricity and market trading electricity. New energy stations carry out capacity leasing transactions and bilateral transactions with multiple energy storage and thermal power units respectively.

[0254] Experimental steps:

[0255] Step 1: New energy station declaration and clearance experiment

[0256] At 3:00 and 11:00, the reported power of new energy stations and their share in the predicted power were recorded as 18.32% and 8.38% respectively.

[0257] The recorded peak-shaving quotations were 360 ​​yuan / MWh and 305.71 yuan / MWh respectively.

[0258] Observe the clearance results and record the winning bid powers, which are 45MW and 38.21MW respectively.

[0259] Table 1:

[0260] Time Declared power ratio Peak load quotation (yuan / MWh) Winning bid power(MW) 3:00 18.32％ 360 45 11:00 8.38％ 305.71 38.21

[0261] Step 2: Comparison experiment of power abandonment in different scenarios

[0262] Set up three experimental scenes:

[0263] Scenario 1: All electricity generated by renewable energy connected to the grid is purchased for security purposes and consumed by thermal power units, with a maximum power abandonment rate of 10%.

[0264] Scenario 2: The electricity volume of new energy grid-connected is divided into guaranteed purchase electricity volume and market trading electricity volume, and thermal power units transfer paid peak-shaving space.

[0265] Scenario 3: The electricity generated by renewable energy connected to the grid is divided into guaranteed purchase electricity and market trading electricity, and multiple energy storage devices are used to charge and consume renewable energy power generation during the period of 4:00-8:00.

[0266] Record the power abandonment and power abandonment rate in each time period under each scenario.

[0267] Table 2:

[0268] Scenario Power curtailment period Abandoned power Abandonment rate Scenario 1 4:00-7:00 Higher than scene 2 5％ Scenario 2 1:00-3:00,8:00-11:00 Below scenario 1 5.11％ Scene 3 4:00-8:00 lowest 3.42％

[0269] Step 3: Peak load economic analysis

[0270] Calculate the peak-shaving costs and net benefits of new energy stations under each scenario.

[0271] Record the net benefits of more energy storage.

[0272] Table 3 System peak load regulation cost under different scenarios (10,000 yuan):

[0273]

[0274] Table 4 Net income of new energy stations under different scenarios (10,000 yuan)

[0275]

[0276] Table 5 Net income of multiple energy storage (ten thousand yuan)

[0277] Energy storage type Rental income Charging and discharging cost Net income All-vanadium liquid flow energy storage 3.84 3.01 0.83 Lithium iron phosphate energy storage 3.04 2.42 0.62 Ternary lithium energy storage 2.18 1.71 0.47

[0278] Experimental results analysis:

[0279] Through experimental data, it can be obtained that compared with Scenario 2 and Scenario 1, Scenario 3 adds multiple energy storage to absorb new energy power generation, and the penalty cost for wind and solar power abandonment is reduced by 107,700 yuan compared with Scenario 2. Although the charging and discharging costs of multiple energy storage are increased, the discharge of multiple energy storage causes the energy consumption cost of thermal power units to decrease by 63,800 yuan compared with Scenario 2, and the total peak-shaving cost is still reduced by 94,200 yuan; and the net income of the new energy station increases by 71,800 yuan and 168,900 yuan compared with Scenario 2 and Scenario 1 respectively.

[0280] According to Table 5, since the amount of new energy electricity absorbed by all-vanadium liquid flow energy storage is 71.11MWh, which is higher than the 53.33MWh of lithium iron phosphate energy storage and 35.56MWh of ternary lithium energy storage, the net income of all-vanadium liquid flow energy storage participating in capacity leasing transactions is the highest among the three, while the net income of ternary lithium energy storage is the lowest; it can be seen that increasing the energy storage capacity participating in leasing transactions can increase net income.

[0281] Specific analysis shows that the charging and discharging behavior of the multi-energy storage system effectively reduces the energy consumption cost of thermal power units and the penalty cost of wind and solar power abandonment, and improves the economy of overall scheduling.

[0282] Conclusion: The introduction of multi-storage systems can optimize the dispatching of thermal power units and new energy stations, improve the level of new energy consumption, reduce system peak-shaving costs, increase the net income of new energy stations, and achieve the optimal dispatching of multi-storage thermal power units.

[0283] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the scheduling of multiple energy storage thermal power units, characterized in that: The following steps are involved: S1. Optimize the total charging and discharging power of the leased multi-energy storage, with the goal of minimizing the net load variance, through the formula: Among them, T is the scheduling period, P net,t is the net load of the power grid during period t, P net,ave is the average net load of the power grid; Optimize the multi-storage charging and discharging power of new energy stations leased at different time periods to smooth the grid load curve; S2. Design a storage leasing declaration model based on the full life cycle cost, using the formula: in, is the unit charging and discharging cost of the i-th energy storage power station, is the average annual cost of the energy storage power station, N y is the number of energy storage cycles per year; Calculate the unit charging and discharging cost of energy storage, and generate dynamic quotations based on real-time charging and discharging power; S3. Based on the market rules for peak load ancillary services, design bilateral transactions between thermal power units and new energy stations, dynamically match peak load demand by iteratively optimizing the new energy bid power and the available power of thermal power units; S4. Under the conditions of satisfying power balance, thermal power unit operation constraints and bilateral transaction constraints, optimize clearing with the goal of minimizing system peak-shaving costs to obtain the thermal power unit output, new energy station winning power and clearing price.

2. The method for optimizing the dispatching of multiple energy storage thermal power units according to claim 1, characterized in that: The S1 step specifically includes the following steps: S1.

1. With the goal of minimizing the net load variance, optimize the total charging and discharging power of multiple energy storages leased at new energy stations in each period; S1.

2. On this basis, new energy stations and thermal power units conduct bilateral transactions to obtain deep peak-shaving space for thermal power units.

3. The method for optimizing the scheduling of multiple energy storage thermal power units according to claim 1, characterized in that: The S2 step specifically includes the following steps: S2.

1. The energy storage rental quotation needs to consider the charging and discharging costs of the energy storage unit; S2.

2. Calculate the unit charging and discharging cost of each energy storage power station; S2.

3. On this basis, a storage leasing quotation model is designed. The quotation should fluctuate between the maximum and minimum values ​​to reflect the operating costs and market demand of different storage power stations. S2.

4. Establish a multi-energy storage leasing transaction mechanism to ensure that each energy storage power station participates in market transactions in the order of the best price while meeting the balance between electricity supply and demand.

4. The method for optimizing the dispatching of multiple energy storage thermal power units according to claim 1, characterized in that: The S3 step specifically includes the following steps: S3.

1. Thermal power units and new energy stations need to submit their own declarations to the trading platform, including the available power and subsidy quotes of thermal power units, and the trading power and trading quotes of new energy stations; S3.

2. The trading platform calls the thermal power units for peak load regulation in order from low to high according to the bids of the thermal power units based on the declared information; S3.

3. Adopt bilateral bidding clearing method, aiming at minimizing system peak load cost, and determine the final clearing result, i.e. the winning bid power of new energy stations, the output of thermal power units and the clearing price; S3.

4. New energy stations shall make peak-shaving quotations based on the proportion of power participating in bilateral transactions in each time period, and the peak-shaving quotations shall be adjusted within a certain range.

5. The method for optimizing the dispatching of multiple energy storage thermal power units according to claim 1, characterized in that: The S4 step specifically includes the following steps: S4.

1. It is necessary to comprehensively consider the operating costs of thermal power units, the penalty costs for wind and solar power abandonment, and the operating costs of multiple energy storage to ensure that the peak load cost is minimized; S4.

2. Constraints must be met during the optimization process, including power balance constraints, thermal power unit operation constraints, unit start and stop time constraints, and bilateral transaction constraints.

6. The method for optimizing the dispatching of multiple energy storage thermal power units according to claim 1, characterized in that: The formula of the objective function in step S1.1 is: Among them, T is the scheduling period, P net,t is the net load of the power grid during period t, P net,ave is the average net load of the power grid; The declared power of the new energy station participating in the bilateral transaction in step S1.2 is: in, is the comprehensive charging power of multiple energy storages during period t, P ptd,t is the deep peak-shaving power of the system during period t.

7. An optimization dispatching system for multiple energy storage thermal power units, characterized in that: Used in conjunction with a multi-energy storage thermal power unit optimization scheduling method as described in any one of claims 1 to 6, comprising: Market transaction power optimization module, used to determine the declared power of new energy stations participating in bilateral transactions; The multi-energy storage leasing transaction optimization module is used to optimize the charging and discharging behavior of a single energy storage under the constraints of power supply and demand balance, and obtain the charging and discharging power and leasing quotation of a single energy storage power station; The bilateral transaction optimization module is used to obtain the output of thermal power units, the winning bid power of new energy stations and the clearing price while satisfying relevant constraints.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for optimizing the scheduling of multiple energy storage thermal power units described in any one of claims 1 to 6 are implemented.

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