A shared energy storage optimal scheduling method considering default penalty

Abstract

The application discloses a kind of shared energy storage optimization scheduling method considering default penalty, and specifically relates to energy storage scheduling and energy management field, first, the multi-service transaction framework between energy storage operator and new energy station, power grid is built;Then the capacity lease default, energy transaction default and over-selling default behavior that energy storage operator can occur are quantitatively modeled, and are included in optimization target;Further, the scheduling model with the maximization of operator's income as the goal is established, the opportunity constraint of energy storage state of charge is introduced to balance economy and safety;Finally, the model is converted into mixed integer linear programming problem by Monte Carlo sampling and linearization technique to solve;The application can effectively guide energy storage operator to make optimal scheduling decision between default risk and economic benefit, improve the overall operation efficiency and economy of shared energy storage system.

Description

Technical Field

[0001] This invention relates to the field of energy storage dispatch and energy management technology, and more specifically, to a shared energy storage optimization dispatch method that takes into account penalties for breach of contract. Background Technology

[0002] Against the backdrop of energy transition, shared energy storage, as an innovative application model, aims to improve overall economic efficiency by increasing the utilization rate of energy storage resources. However, this model faces complex challenges in practice. The core issue lies in the fact that, in actual operation, the forecasting deviation of new energy output and the uncertainty of energy storage application scenarios pose significant challenges to the currently formulated energy storage trading strategies. This may lead energy storage operators to deviate from contracts or even adopt "overselling" strategies in pursuit of maximizing their own economic benefits, thereby triggering default risks. Therefore, how to formulate the optimal actual dispatch strategy for shared energy storage while balancing the potential gains from default with the possible penalties for default is a critical issue that urgently needs to be addressed. Summary of the Invention

[0003] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a shared energy storage optimization scheduling method that takes into account default penalties, in order to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] Establish a shared energy storage trading framework: Third-party energy storage operators independently construct and operate centralized shared energy storage power stations, providing capacity leasing and energy trading services to new energy power stations and frequency regulation auxiliary services to the power grid; in the day-ahead phase, capacity leasing contracts, energy trading power and frequency regulation service application capacity are determined; within the day, the demand is adjusted according to the output deviation of new energy power stations and the probability of grid frequency regulation demand, and dispatch is executed and default compensation is borne;

[0006] Quantitative modeling of default behavior: Mathematical characterization of capacity leasing default, energy trading default and overselling default are performed respectively. Among them, capacity leasing default covers energy storage capacity occupation and energy misappropriation behavior, energy trading default distinguishes between scenarios of increased or decreased demand at the site and the degree of operator satisfaction, and overselling default is based on the risk-return model constructed based on the frequency regulation service call probability.

[0007] Establish an optimized scheduling model: with the objective function of maximizing the daily operating revenue of energy storage operators, incorporating energy trading arbitrage revenue, various default costs, frequency regulation service revenue, and operation and maintenance costs; setting energy storage power constraints, power interaction constraints, frequency regulation power constraints, and state of charge opportunity constraints;

[0008] Model Solution: The Monte Carlo method is used to sample and linearly transform the charged state chance constraints. The Big M method is used to handle the max / min nonlinear terms in the objective function and constraints, transforming the model into a mixed integer linear programming problem for solution.

[0009] Preferably, in constructing a shared energy storage trading framework, third-party energy storage operators, as independent market entities, fully fund the investment, construction, and professional operation of centralized shared energy storage power stations. They undertake core responsibilities such as daily maintenance, status monitoring, and dispatch execution of physical energy storage equipment, ensuring the safe and stable operation of the energy storage system. On the service supply side, operators provide two-way services to new energy power stations (including wind farms, photovoltaic power stations, etc.): first, capacity leasing services, where new energy power stations can lease a specific amount of energy storage capacity as a buffer for output adjustment based on their own power generation fluctuation characteristics; second, energy trading services, supporting power stations to store excess electricity in energy storage power stations during peak power generation periods and purchase electricity from the power stations to supplement power during off-peak periods or peak consumption periods, realizing the spatial and temporal transfer of energy and peak shaving and valley filling. At the same time, operators provide professional frequency regulation auxiliary services to the grid side, responding to the grid frequency adjustment needs and improving the operational stability of the power system.

[0010] The transaction process follows a closed-loop mechanism of "day-ahead agreement - intraday execution - default compensation": In the day-ahead phase (the day before the dispatch date), new energy power plants and energy storage operators clarify the details of capacity leasing contracts (including leased capacity, lease period, lease unit price, etc.), the energy trading power range for each period, and the transaction price benchmark through market-based negotiation or bidding; operators then combine the frequency regulation demand forecast released by the power grid, comprehensively assess their own energy storage resource redundancy, and submit key parameters such as frequency regulation service application capacity, response rate, and quotation to the power grid to lock in the day-ahead transaction plan.

[0011] Preferably, in the quantitative modeling of the default behavior, the renewable energy power station signs a capacity leasing contract with the energy storage operator before the dispatch date, specifying that the renewable energy power station can independently decide the dispatch power of the leased energy storage units on the dispatch date, thus obtaining dispatch authority over the "virtual energy storage". However, the actual dispatch of the "physical energy storage" is the responsibility of the energy storage operator. Given this, the energy storage operator may, in pursuit of higher economic benefits, "occupy" the leased energy storage capacity and use it for transactions with other participants (such as other renewable energy power stations) to provide capacity leasing services. Furthermore, the energy storage operator may also "misappropriate" energy already stored in leased energy storage by the renewable energy power station to meet the power demands of other power stations or the power grid, thereby obtaining additional revenue. When the energy storage operator fails to execute the energy storage or release instructions issued by the renewable energy power station according to the contract, resulting in the renewable energy power station's inability to meet the energy dispatch demand of the leased energy storage units, this constitutes a capacity leasing default.

[0012] For energy storage operators, considering that the purpose of storing energy at renewable energy power plants is to compensate for energy shortages through discharge, the default caused by insufficient energy storage capacity due to "occupancy" of energy storage has a certain time delay. This means that energy storage operators can choose to declare a default when the capacity is insufficient during energy storage, or choose to "conceal" the default information until the renewable energy power plant needs to use the stored energy but cannot meet the demand, and then "confess" the default. The trading mechanism in this study considers the value differences of capacity "lease packages" of different durations and the differences in energy trading prices at different times. To fully reflect the decision-making process of energy storage operators, this paper takes both of the above situations into consideration, allowing energy storage operators to independently decide how to pay capacity lease default penalties to the power plant.

[0013] The capacity default costs that energy storage operators need to bear during the dispatch day The calculation method is as follows:

[0014]

[0015] in: and These are the charging and discharging power scheduling instructions from the new energy power station i to its leased energy storage unit u during the t-th time period; and These represent the actual charging power and discharging power of the energy storage unit u reported by the energy storage operator to the new energy power station. Let be the penalty per unit power that the energy storage operator must pay to site i for capacity leasing default during time period t. The higher the unit price per time period for capacity leasing, the higher the leasing cost incurred by the site, and therefore the higher the penalty for capacity leasing default. Therefore, we take... The specific calculation method is as follows:

[0016]

[0017] In the formula: Let $t$ be the time-period unit price for leasing energy storage unit $u$ by station $i$ during time period $t$. This represents the penalty coefficient for breach of capacity lease agreement.

[0018] During intraday dispatching, renewable energy power plants make more accurate ultra-short-time forecasts of their power generation output. Therefore, their actual energy trading demand submitted to energy storage operators will be adjusted accordingly compared to the energy trading power determined in the day-ahead contract. If renewable energy power plants fail to trade with energy storage according to the day-ahead contract, this behavior constitutes a "default". For energy storage operators, if the demand adjustment submitted by renewable energy power plants brings greater profit decision-making space, the energy storage operators may choose to violate the day-ahead energy dispatching contract and renegotiate the power decision, i.e., adopt an energy trading default strategy. By comparing the power reported by the power plants after adjusting their demand, the power value actually met by the energy storage operators, and the day-ahead trading value determined in the contract, the energy trading default strategy can be further subdivided.

[0019] Preferably, in the establishment of the optimized scheduling model, the objective function is to maximize the intraday operating revenue of energy storage operators to optimize the scheduling decisions of physical energy storage. Since the revenue from energy storage leasing capacity and energy trading has been determined through day-ahead trading contracts and has no impact on the actual decisions of energy storage operators, it is not listed in the revenue function. The objective function expression is:

[0020]

[0021] in: Revenue from energy storage dispatch includes price arbitrage profits from energy trading during actual energy storage dispatch. Capacity default cost Energy trading default costs Ancillary service revenue and energy storage operation and maintenance costs . Maintenance cost per unit power and These represent the actual charging and discharging power of the physical energy storage, respectively.

[0022] Under energy storage power constraints, the destination of energy storage charging and discharging dispatch power includes direct energy trading with renewable energy power plants, meeting the charging and discharging needs of renewable energy power plant capacity leasing, and providing frequency regulation power to the grid, which satisfies the following equation:

[0023]

[0024] (8) Wherein: the constraints are power balance constraint, dispatch power limit constraint, and energy storage charging and discharging mutual exclusion constraint, respectively, and P is the rated power of physical energy storage. and These are Boolean variables describing charging and discharging behavior, respectively.

[0025] In solving the model, the equation The uncertainty of the state of charge (SBC) of energy storage is modeled as a chance constraint, which can be approximated using risk theory. Specifically, the SBC is sampled using the Monte Carlo method, and an auxiliary variable is introduced to perform a linear equivalent transformation:

[0026]

[0027] Where: S is the number of sampling scenarios. Number the scene. This represents the probability of scenario s occurring. Let t represent the state of charge of the energy storage in scenario s during time period t. , , , These are introduced auxiliary variables used for linearizing the model.

[0028] The technical effects and advantages of this invention are as follows:

[0029] 1. It has achieved systematic modeling and quantitative management of default behavior, transforming capacity leasing default, energy trading default, and overselling default into quantifiable economic decision variables, enabling operators to scientifically weigh the benefits of default against the cost of penalties, and improving the scientific nature of dispatching decisions;

[0030] 2. Enhanced system economy and flexibility. The model allows operators to flexibly adjust strategies within a compliance framework, capture market arbitrage opportunities through proactive default decisions, while the default penalty mechanism effectively curbs malicious default behavior;

[0031] 3. By introducing opportunity constraints on the state of charge of energy storage, it is allowed to moderately exceed the safety threshold under low probability risks, which not only ensures operational safety in a statistical sense, but also significantly improves the economy and flexibility of dispatch.

[0032] 4. An efficient solution method is proposed. By using Monte Carlo scene sampling and Big M method linearization, the complex nonlinear uncertainty problem is transformed into a mixed integer linear programming problem that can be solved efficiently, and it has good engineering applicability. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0034] Figure 2 This is a schematic diagram illustrating the classification and quantification of breach of contract behavior according to the present invention.

[0035] Figure 3 This is a schematic diagram of the model solution of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Please see Figure 1 As shown, the present invention provides a shared energy storage optimization scheduling method that takes into account default penalties, comprising:

[0038] Establish a shared energy storage trading framework: Third-party energy storage operators independently construct and operate centralized shared energy storage power stations, providing capacity leasing and energy trading services to new energy power stations and frequency regulation auxiliary services to the power grid; in the day-ahead phase, capacity leasing contracts, energy trading power and frequency regulation service application capacity are determined; within the day, the demand is adjusted according to the output deviation of new energy power stations and the probability of grid frequency regulation demand, and dispatch is executed and default compensation is borne;

[0039] In constructing a shared energy storage trading framework, third-party energy storage operators, as independent market entities, fully fund the investment, construction, and professional operation of centralized shared energy storage power stations. They undertake core responsibilities such as daily maintenance, status monitoring, and dispatch execution of physical energy storage equipment, ensuring the safe and stable operation of the energy storage system. On the service supply side, operators provide two-way services to new energy power stations (including wind farms and photovoltaic power stations): first, capacity leasing services, allowing new energy power stations to lease a specific amount of energy storage capacity as a buffer for output adjustment based on their own power generation fluctuation characteristics; second, energy trading services, supporting power stations to store excess electricity in energy storage power stations during peak power generation periods and purchase electricity from the power stations to supplement power during off-peak periods or peak consumption periods, realizing the spatial and temporal transfer of energy and peak shaving and valley filling. At the same time, operators provide professional frequency regulation auxiliary services to the grid side, responding to the grid's frequency regulation needs and improving the operational stability of the power system.

[0040] The transaction process follows a closed-loop mechanism of "day-ahead agreement - intraday execution - default compensation": In the day-ahead phase (the day before the dispatch date), new energy power plants and energy storage operators clarify the details of capacity leasing contracts (including leased capacity, lease period, lease unit price, etc.), the energy trading power range for each period, and the transaction price benchmark through market-based negotiation or bidding; operators then combine the frequency regulation demand forecast released by the power grid, comprehensively assess their own energy storage resource redundancy, and submit key parameters such as frequency regulation service application capacity, response rate, and quotation to the power grid to lock in the day-ahead transaction plan;

[0041] Entering the intraday scheduling phase (real-time scheduling cycle), renewable energy power plants dynamically adjust their charging and discharging needs based on ultra-short-term output forecast data (the forecast accuracy is higher than the day-ahead forecast), and transmit the updated scheduling instructions to energy storage operators in real time; the grid-side frequency regulation demand is affected by factors such as load fluctuations and renewable energy output fluctuations, showing significant probabilistic characteristics. Operators use big data analysis and machine learning algorithms to predict the probability of grid frequency regulation service calls and demand intensity in real time for each period.

[0042] After obtaining the probability forecasts of the adjustment needs of the power plants and the frequency regulation needs of the grid, the energy storage operator will conduct global optimization scheduling based on the real-time state of charge, charging and discharging efficiency and other operating parameters of the physical energy storage power plants: giving priority to ensuring the execution of services within the scope of the contract, and flexibly allocating power to respond to sudden intraday demands, provided that energy storage resources allow; if the operator fails to meet the capacity leasing needs of the new energy power plants or the energy trading power as agreed in the previous day's contract due to excessive deviation in the forecast of new energy output, sudden surge in grid frequency regulation demand or improper scheduling decisions by the operator, or fails to deliver the declared frequency regulation services to the grid in full, the default compensation mechanism will be triggered.

[0043] Operators are required to pay corresponding penalties to the defaulting party (new energy power plant or grid) based on the type of default (capacity leasing default, energy trading default, or over-selling default), the shortfall in defaulted power, the duration of default, and the penalty standards stipulated in the contract. This is to constrain operators' default behavior, protect the legitimate rights and interests of all parties involved in the transaction, and maintain market transaction order. The entire transaction framework combines the stability of day-ahead planning with the flexibility of intraday scheduling.

[0044] Quantitative modeling of default behavior: Mathematical characterization of capacity leasing default, energy trading default and overselling default are performed respectively. Among them, capacity leasing default covers energy storage capacity occupation and energy misappropriation behavior, energy trading default distinguishes between scenarios of increased or decreased demand at the site and the degree of operator satisfaction, and overselling default is based on the risk-return model constructed based on the frequency regulation service call probability.

[0045] In the quantitative modeling of default behavior, renewable energy power plants sign capacity leasing contracts with energy storage operators, specifying that on the dispatch date, the renewable energy power plant can independently decide the dispatch power of its leased energy storage units, thus gaining dispatch authority over "virtual energy storage." However, the actual dispatch of "physical energy storage" is the responsibility of the energy storage operator. Given this, energy storage operators may, in pursuit of higher economic benefits, "occupy" the leased energy storage capacity and use it for transactions with other participants (such as other renewable energy power plants) to provide capacity leasing services. Furthermore, energy storage operators may also "misappropriate" energy already stored in leased energy storage by renewable energy power plants to meet the power demands of other power plants or the power grid, thereby obtaining additional revenue. When the energy storage operator fails to execute the energy storage or release instructions issued by the renewable energy power plant according to the contract, resulting in the renewable energy power plant's inability to meet the energy dispatch demand of the leased energy storage units, this constitutes a capacity leasing default.

[0046] For energy storage operators, considering that the purpose of storing energy at renewable energy power plants is to compensate for energy shortages through discharge, the default caused by insufficient energy storage capacity due to "occupancy" of energy storage has a certain time delay. This means that energy storage operators can choose to declare a default when the capacity is insufficient during energy storage, or choose to "conceal" the default information until the renewable energy power plant needs to use the stored energy but cannot meet the demand, and then "confess" the default. The trading mechanism in this study considers the value differences of capacity "lease packages" of different durations and the differences in energy trading prices at different times. To fully reflect the decision-making process of energy storage operators, this paper takes both of the above situations into consideration, allowing energy storage operators to independently decide how to pay capacity lease default penalties to the power plant.

[0047] The capacity default costs that energy storage operators need to bear during the dispatch day The calculation method is as follows:

[0048]

[0049] in: and These are the charging and discharging power scheduling instructions from the new energy power station i to its leased energy storage unit u during the t-th time period; and These represent the actual charging power and discharging power of the energy storage unit u reported by the energy storage operator to the new energy power station. Let be the penalty per unit power that the energy storage operator must pay to site i for capacity leasing default during time period t. The higher the unit price per time period for capacity leasing, the higher the leasing cost incurred by the site, and therefore the higher the penalty for capacity leasing default. Therefore, we take... The specific calculation method is as follows:

[0050]

[0051] In the formula: Let $t$ be the time-period unit price for leasing energy storage unit $u$ by station $i$ during time period $t$. This represents the penalty coefficient for breach of capacity lease agreement.

[0052] During intraday dispatching, renewable energy power plants make more accurate ultra-short-term forecasts of their power generation output. Therefore, their actual energy trading demand submitted to energy storage operators will be adjusted accordingly compared to the energy trading power determined in the day-ahead contract. If renewable energy power plants fail to trade with energy storage according to the day-ahead contract, this behavior constitutes a "default". For energy storage operators, if the demand adjustment submitted by renewable energy power plants brings greater profit decision-making space, energy storage operators may choose to violate the day-ahead energy dispatching contract and renegotiate the power decision, i.e., adopt an energy trading default strategy. By comparing the power reported by the power plants after adjusting their demand, the power value actually met by the energy storage operators, and the day-ahead trading value determined in the contract, the energy trading default strategy can be further subdivided.

[0053] When the actual power demand reported by renewable energy power station i is lower than the power demand in the day-ahead contract, i.e., the power demand of the power station decreases, leading to a "passive default" by the energy storage operator; this means that since part of the energy trading power provided by the energy storage operator to other power stations originates from this renewable energy power station, the decrease in power may result in the inability to fully meet the energy complementarity needs of other power stations. Therefore, the renewable energy power station must pay a "default penalty" cost to the energy storage operator; the specific calculation method for the default penalty cost is as follows:

[0054]

[0055] in, These are the fees that the energy storage facilities need to pay to the energy storage operators due to reduced demand. and This represents the day-ahead contract value for charging and discharging power in energy trading between renewable energy power station i and energy storage operators; the actual power demand reported by the power station after demand adjustment is [value missing]. and , and These represent reduced charging and discharging demands, respectively. and This represents the unit power penalty coefficient for the reduction in energy trading demand at the power station during time period t. This coefficient is determined by the charging and discharging electricity prices during time period t. and Confirmed, among which This corresponds to the energy default penalty factor;

[0056] When the actual power reported by the renewable energy power station i exceeds the power traded under the day-ahead contract, due to the scarcity of resources and the urgency of demand, the energy storage operator will meet the additional power demand at a higher trading price, i.e.:

[0057]

[0058] in, Revenue earned by energy storage operators for providing additional power trading services to power plants. and These represent the charging and discharging power that energy storage is required to meet for site i beyond the current day-ahead contract. The actual charging and discharging power that the energy storage operator meets for site i is... and . and These represent the unit price of additional trading power, This corresponds to the unit price multiplier factor;

[0059] When the actual reported power of renewable energy power station i is less than the contracted power before the day, if the transaction power that the energy storage operator can meet is still less than the actual demand of the renewable energy power station, then the energy storage operator will bear the default cost for the unmet contracted power demand, that is:

[0060]

[0061] in: The cost of default incurred by energy storage operators for failing to meet the power requirements stipulated in the trading contract. and The charging and discharging power deficit of the energy storage operator for the default of the site is calculated from the difference between the benchmark value and the actual power. and These represent the penalties for breach of contract due to insufficient charging and discharging power per unit, respectively. This corresponds to the unit price multiplier factor; and The benchmark value for calculating the penalty for breach of contract is the smaller of the contracted penalty and the actual required penalty. This means that when the actual required penalty is less than the contracted penalty, the penalty is paid based on the difference between the actual required penalty and the required penalty; if the actual required penalty exceeds the contracted penalty, the penalty is paid based on the difference between the actual required penalty and the contracted penalty.

[0062] The energy storage resources used by energy storage operators for frequency regulation services to the grid have already been used in day-ahead transactions to provide capacity leasing and energy trading services to renewable energy power plants; therefore, providing frequency regulation services constitutes an over-selling model. The revenue of energy storage operators providing frequency regulation ancillary services to the grid is as follows:

[0063]

[0064] in: This indicates that the revenue from frequency regulation ancillary services for energy storage operators is comprised of capacity revenue. Mileage Rewards And the penalty costs incurred when energy storage cannot deliver services to the grid in a timely manner due to over-selling, when the grid actually calls for ancillary services. It consists of three parts. Among them, and These represent the upward and downward frequency regulation power reported by the energy storage operator to the grid for time period t, respectively. and These represent the up-frequency regulation and down-frequency regulation power actually supplied to the power grid during time period t, respectively. and These represent the up-frequency regulation and down-frequency regulation power actually required by the power grid, respectively. , and These are the corresponding unit prices for frequency modulation capacity, frequency modulation mileage, and penalties for breach of contract. Let be the probability that the power grid will call up frequency regulation power during the t-th time period.

[0065] Establish an optimized scheduling model: with the objective function of maximizing the daily operating revenue of energy storage operators, incorporating energy trading arbitrage revenue, various default costs, frequency regulation service revenue, and operation and maintenance costs; setting energy storage power constraints, power interaction constraints, frequency regulation power constraints, and state of charge opportunity constraints;

[0066] In the established optimization scheduling model, the objective function is to maximize the intraday operating revenue of energy storage operators to optimize the scheduling decisions of physical energy storage. Since the revenue from energy storage leasing capacity and energy trading is already determined through day-ahead trading contracts and has no impact on the actual decisions of energy storage operators, it is not listed in the revenue function. The objective function expression is:

[0067]

[0068] in: Revenue from energy storage dispatch includes price arbitrage profits from energy trading during actual energy storage dispatch. Capacity default cost Energy trading default costs Ancillary service revenue and energy storage operation and maintenance costs . Maintenance cost per unit power and These represent the actual charging and discharging power of the physical energy storage, respectively.

[0069] Under energy storage power constraints, the destination of energy storage charging and discharging dispatch power includes direct energy trading with renewable energy power plants, meeting the charging and discharging needs of renewable energy power plant capacity leasing, and providing frequency regulation power to the grid, which satisfies the following equation:

[0070]

[0071]

[0072]

[0073] (8)

[0074] Wherein: the constraints are power balance constraint, dispatch power limit constraint, and energy storage charge / discharge mutual exclusion constraint, respectively; P is the rated power of the physical energy storage. and These are Boolean variables describing charging and discharging behavior, respectively.

[0075] In capacity leasing services, the actual energy storage and release dispatch power of energy storage shall not exceed the "virtual energy storage" dispatch power instruction reported by the new energy power station, that is:

[0076]

[0077] When energy storage provides frequency regulation services, its actual power regulation and power reduction do not exceed the actual demand command of the power grid, that is:

[0078]

[0079] The state of charge of energy storage can be calculated using the following formula:

[0080]

[0081] in: and These represent the charging and discharging efficiencies of the energy storage, respectively, and E is the rated capacity of the physical energy storage.

[0082] In decision-making regarding energy storage charging and discharging scheduling, to reduce battery aging and excessive lifespan degradation caused by overcharging or over-discharging, the state of charge (SOC) of energy storage is limited to a certain range. However, considering the possibility of various default behaviors and overselling strategies, allowing energy storage operators to appropriately exceed traditional constraints on the SOC may improve the economic efficiency of energy storage, but it may also cause some damage to the battery lifespan. To balance the benefits and losses of energy storage, the following opportunity constraint is introduced:

[0083]

[0084] in: This represents the probability of event A occurring. and These represent the upper and lower thresholds of the energy storage state of charge, respectively. This represents the risk probability level. The above constraint indicates that the energy storage state of charge does not exceed... and not less than The probability is no less than This ensures that, under a statistically safe level of risk, the possibility of exceeding the limit of the energy storage state of charge is allowed.

[0085] Model Solution: The Monte Carlo method is used to sample and linearly transform the charged state chance constraints. The Big M method is used to handle the max / min nonlinear terms in the objective function and constraints, transforming the model into a mixed integer linear programming problem for solution.

[0086] In solving the model, the equation The uncertainty of the state of charge (SBC) of energy storage is modeled as a chance constraint, which can be approximated using risk theory. Specifically, the SBC is sampled using the Monte Carlo method, and an auxiliary variable is introduced to perform a linear equivalent transformation:

[0087]

[0088] Where: S is the number of sampling scenarios. Number the scene. This represents the probability of scenario s occurring. Let t represent the state of charge of the energy storage in scenario s during time period t. , , , These are introduced auxiliary variables used for linearizing the model.

[0089] Secondly, the maximum and minimum value functions in the model are nonlinear terms. To facilitate the solution, the Big M method is used to transform the nonlinear terms, resulting in the equation containing the max function. It can be equivalently transformed into:

[0090]

[0091] Where M is a constant of a large order of magnitude. and It is a Boolean variable.

[0092] Similarly, expressions containing min functions It can be equivalently transformed into:

[0093]

[0094] in: and It is a Boolean variable.

[0095] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A shared energy storage optimization scheduling method considering default penalties, characterized in that, include: Establish a shared energy storage trading framework: Third-party energy storage operators independently construct and operate centralized shared energy storage power stations, providing capacity leasing and energy trading services to new energy power stations and frequency regulation auxiliary services to the power grid; in the day-ahead phase, capacity leasing contracts, energy trading power and frequency regulation service application capacity are determined; within the day, the demand is adjusted according to the output deviation of new energy power stations and the probability of grid frequency regulation demand, and dispatch is executed and default compensation is borne; Quantitative modeling of default behavior: Mathematical characterization of capacity leasing default, energy trading default and overselling default are performed respectively. Among them, capacity leasing default covers energy storage capacity occupation and energy misappropriation behavior, energy trading default distinguishes between scenarios of increased or decreased demand at the site and the degree of operator satisfaction, and overselling default is based on the risk-return model constructed based on the frequency regulation service call probability. Establish an optimized scheduling model: with the objective function of maximizing the daily operating revenue of energy storage operators, incorporating energy trading arbitrage revenue, various default costs, frequency regulation service revenue, and operation and maintenance costs; setting energy storage power constraints, power interaction constraints, frequency regulation power constraints, and state of charge opportunity constraints; Model Solution: The Monte Carlo method is used to sample and linearly transform the charged state chance constraints. The Big M method is used to handle the max / min nonlinear terms in the objective function and constraints, transforming the model into a mixed integer linear programming problem for solution.

2. The shared energy storage optimization scheduling method considering default penalties according to claim 1, characterized in that: In the aforementioned shared energy storage trading framework, the server provides capacity leasing and energy trading services to renewable energy power plants, while also providing frequency regulation auxiliary services to the power grid. The transaction follows a closed-loop mechanism of "day-ahead agreement - intraday execution - default compensation": In the day-ahead phase, renewable energy power plants and operators negotiate and lock in the details of capacity leasing and energy trading, and operators submit frequency regulation service parameters to the power grid; In the intraday phase, power plants dynamically adjust their charging and discharging demands, and operators predict the power grid's frequency regulation demands through algorithms and optimize scheduling based on the real-time status of energy storage; If the operator fails to perform its obligations as agreed due to various factors, it will pay a penalty according to the type of default, the shortfall, and the duration.

3. The shared energy storage optimization scheduling method considering default penalties according to claim 1, characterized in that: The energy storage operator can choose to declare a breach of contract if the energy storage capacity is insufficient, or choose to conceal the breach until the renewable energy power station needs to use the stored energy but cannot meet the demand, at which point the operator will "confess" to the breach. Both scenarios will be considered, and the energy storage operator will have the autonomy to decide how to pay the power station for capacity leasing breach penalties. Default on capacity lease includes the act of energy storage operators occupying leased capacity or misappropriating stored energy. The penalty for default is positively correlated with the unit price during the lease period.

4. The shared energy storage optimization scheduling method considering default penalties according to claim 3, characterized in that: The capacity default costs that energy storage operators need to bear during the dispatch day The calculation method is as follows: in: and These are the charging and discharging power scheduling instructions from the new energy power station i to its leased energy storage unit u during the t-th time period; and These respectively represent the actual charging power and discharging power of the energy storage unit u reported by the energy storage operator to the new energy power station; Let be the penalty per unit power that the energy storage operator must pay to site i due to capacity leasing default during time period t. The higher the unit price per time period for capacity leasing, the higher the leasing cost incurred by the site, and therefore the higher the penalty for capacity leasing default. Therefore, let be... The specific calculation method is as follows: In the formula: Let $t$ be the time-period unit price for leasing energy storage unit $u$ by station $i$ during time period $t$. This represents the penalty coefficient for breach of capacity lease agreement.

5. A shared energy storage optimization scheduling method considering default penalties according to claim 1, characterized in that: If a renewable energy power plant fails to trade with energy storage in accordance with the day-ahead contract, this constitutes a "default". For energy storage operators, if the demand adjustment submitted by the renewable energy power plant creates room for profit decisions, the energy storage operator may choose to violate the day-ahead energy dispatch contract and renegotiate the power decision, i.e., adopt an energy trading default strategy. The energy trading default strategy is assessed by comparing the power reported by the power plant after adjusting its demand, the power actually met by the energy storage operator, and the day-ahead trading value determined in the contract.

6. The shared energy storage optimization scheduling method considering default penalties according to claim 1, characterized in that: When the actual power demand reported by renewable energy power station i is lower than the power demand in the day-ahead contract, i.e., the power demand of the power station decreases, leading to the energy storage operator's "passive default," since part of the energy trading power provided by the energy storage operator to other power stations originates from this renewable energy power station, the renewable energy power station must pay the energy storage operator a "default penalty" cost. The specific calculation method for the default penalty cost is as follows: in, These are the fees that the energy storage facilities need to pay to the energy storage operators due to reduced demand. and This represents the day-ahead contract value for charging and discharging power in energy trading between renewable energy power station i and energy storage operators; the actual power demand reported by the power station after demand adjustment is [value missing]. and , and These represent reduced charging and discharging demands, respectively. and This represents the unit power penalty coefficient for the reduction in energy trading demand at the power station during time period t. This coefficient is determined by the charging and discharging electricity prices during time period t. and Sure, This is the corresponding energy default penalty factor.

7. A shared energy storage optimization scheduling method considering default penalties according to claim 1, characterized in that: When the actual reported power of renewable energy power station i is less than the contracted power before the day, if the transaction power that the energy storage operator can meet is still less than the actual demand of the renewable energy power station, then the energy storage operator will bear the default cost for the unmet contracted power demand, that is: (4) in: The cost of default incurred by energy storage operators for failing to meet the power requirements stipulated in the transaction contract; and The charging and discharging power deficit of the energy storage operator for the default of the site is calculated from the difference between the benchmark value and the actual power. and These represent the penalties for breach of contract due to insufficient charging and discharging power per unit, respectively. This corresponds to the unit price multiplier factor; and The benchmark value for calculating the penalty for breach of contract is the smaller of the contracted power and the actual required power. This means that when the actual demand is less than the contracted power, the penalty is paid according to the difference between the actual power met and the required power; if the actual demand exceeds the contracted power, the penalty is paid according to the difference between the actual power met and the contracted power.

8. A shared energy storage optimization scheduling method considering default penalties according to claim 1, characterized in that: In the established optimization scheduling model, the objective function is to maximize the intraday operational revenue of energy storage operators, thereby optimizing the scheduling decisions for physical energy storage. The objective function expression is as follows: (5) in: Revenue from energy storage dispatch includes price arbitrage profits from energy trading during actual energy storage dispatch. Capacity default cost Energy trading default costs Ancillary service revenue and energy storage operation and maintenance costs ; Maintenance cost per unit power and These represent the actual charging and discharging power of the physical energy storage, respectively.

9. A shared energy storage optimization scheduling method considering default penalties according to claim 1, characterized in that: In the model solution, the uncertainty of the energy storage state of charge is modeled as a chance constraint. Risk theory is used to approximate this constraint; specifically, the Monte Carlo method is used to sample the energy storage state of charge, and an auxiliary variable is introduced to perform a linear equivalent transformation. (6) Where: S is the number of sampling scenarios. Number the scene; This represents the probability of scenario s occurring; Let t represent the state of charge of the energy storage in scenario s during time period t. , , , These are introduced auxiliary variables used for linearizing the model; Secondly, the maximum and minimum value functions in the model are nonlinear terms. Using the Big M method to transform these nonlinear terms, the equation containing the max function is then... It can be equivalently transformed into: (7) Where M is a constant of a large order of magnitude. and It is a Boolean variable.

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