Energy storage benefit optimization strategy method and system based on market mechanism

Abstract

This invention provides a market-based energy storage revenue optimization strategy method and system, relating to the field of energy storage revenue technology. The method includes: acquiring price curves across multiple time scales and two-way option contract terms; identifying asymmetric risk transmission paths and establishing a network model; performing state-aware dynamic mapping to construct a cross-temporal hedging strategy group; introducing energy loss and response delay constraints to establish a state-dependent pricing equation; executing two-way option transactions for energy storage capacity and utilizing feedback information to optimize risk transmission weights and hedging strategies, achieving closed-loop optimization of pricing accuracy and hedging effectiveness, and improving the revenue level of energy storage equipment.

Description

Technical Field

[0001] This invention relates to energy storage revenue technology, and more particularly to a market-based energy storage revenue optimization strategy, method, and system. Background Technology

[0002] As the proportion of renewable energy in the energy mix continues to increase, the instability and volatility of power systems are becoming increasingly prominent. Energy storage technology, as a key means of addressing this issue, can effectively regulate the supply and demand balance of the power system, improving its flexibility and reliability. Energy storage devices, by charging during off-peak hours and discharging during peak hours, can both smooth out grid load fluctuations and generate revenue through price differences. However, the economic benefits of energy storage devices are influenced by multiple factors, including multi-timescale electricity price fluctuations, the inherent characteristics of the devices themselves, and market mechanisms.

[0003] The operating revenue of energy storage equipment mainly comes from price arbitrage in the electricity spot market, capacity compensation in the ancillary services market, and participation in frequency regulation services for peak shaving and valley filling. With the deepening of electricity market reforms, market derivatives such as options contracts have begun to be applied to the risk management and return optimization of energy storage assets. Two-way options, as a new type of financial derivative, can provide energy storage equipment with more flexible trading models and risk hedging mechanisms.

[0004] Existing methods for optimizing energy storage revenue are mainly based on deterministic price forecasts or simple probabilistic models. They fail to fully consider the asymmetric risk characteristics of energy storage charging and discharging states under multi-timescale electricity price fluctuations. As a result, energy storage assets face significant financial risks when market prices fluctuate sharply, making it impossible to achieve stable revenue expectations.

[0005] Traditional energy storage dispatch strategies typically treat energy storage devices as passive price takers, ignoring the feedback impact of energy storage charging and discharging behavior on local market prices. They also lack accurate modeling of the physical characteristics such as energy loss and response delay during the charging and discharging state transition of energy storage devices, resulting in a significant deviation between actual performance and theoretical expectations.

[0006] Existing pricing mechanisms for energy storage financial derivatives mostly adopt the standard Black-Scholes model or its variants, which fail to effectively capture the pricing differences and risk transmission paths of energy storage assets under different conditions. They also lack consideration for market liquidity and transaction execution costs, resulting in inaccurate option pricing, poor hedging effects, and difficulty in forming a closed-loop optimization dynamic adjustment mechanism. Summary of the Invention

[0007] The embodiments of the present invention provide a market-based energy storage revenue optimization strategy method and system, which can solve the problems in the prior art.

[0008] A first aspect of this invention provides a market-based energy storage revenue optimization strategy method, comprising:

[0009] Obtain the price curves of energy storage devices across multiple time scales and the terms of two-way option contracts. Based on the call and put options in the terms of the two-way option contracts, identify the asymmetric risk transmission path of energy storage capacity under charging and discharging conditions, and establish a risk transmission network model.

[0010] Based on the risk transmission network model, the time-series schedulable window of the energy storage device is dynamically mapped to the spot trading period in the multi-timescale price curve to identify the differences in the sensitivity of energy storage charging and discharging actions to market price fluctuations under different exercise paths, and a cross-temporal hedging strategy group is constructed.

[0011] Based on the aforementioned cross-temporal hedging strategy group, energy loss constraints and response delay constraints are introduced during the charging and discharging state transition of energy storage devices. A state-dependent pricing equation is established under the risk-neutral measurement framework, and the two-way option risk hedging pricing is obtained by solving it.

[0012] Based on the two-way option risk hedging pricing, two-way option transactions for energy storage capacity are executed, forming three-dimensional feedback information. The state transition deviation in the three-dimensional feedback information is used to asymmetrically correct the risk transmission weight in the risk transmission network model. The exercise trigger deviation and price response deviation are used to adaptively reconstruct the spatiotemporal mapping relationship of the hedging strategy in the spatiotemporal hedging strategy group, thereby achieving closed-loop optimization of pricing accuracy and hedging effectiveness.

[0013] Based on the call and put exercise conditions in the aforementioned two-way option contract terms, the asymmetric risk transmission path of energy storage capacity under charging and discharging states is identified, and a risk transmission network model is established, including:

[0014] The call and put exercise conditions in the two-way option contract terms are analyzed. Combining the market price fluctuation pattern and the characteristics of energy storage market transactions, the dynamic change pattern and fluctuation characteristics of the price trigger threshold range are extracted, and a set of two-way exercise trigger conditions considering the impact of the market environment is established.

[0015] Based on the set of bidirectional exercise triggering conditions, the power ramping constraint risk caused by the inherent characteristics of the energy storage device when the bullish exercise condition is triggered, and the capacity margin constraint risk caused by the real-time operating status when the bearish exercise condition is triggered, are analyzed. Combined with the dynamic change law of the price triggering threshold range, the characteristics of risk sources in different states are constructed.

[0016] Based on the characteristics of the risk sources in different states, the power ramping constraint risk nodes and capacity margin constraint risk nodes under the paths of the bullish exercise conditions and the bearish exercise conditions are mapped to different levels of the risk transmission network. By characterizing the transmission rules and mutual influence of risk nodes between different levels, a risk transmission framework with a hierarchical structure is established.

[0017] By combining historical dispatch response characteristic data, the differences in response characteristics and energy conversion patterns of energy storage devices under different states of charge are analyzed. A bidirectional transmission channel is constructed based on the hierarchical structure of the risk transmission framework. The risk transmission framework and the bidirectional transmission channel are then integrated into a complete risk transmission network model.

[0018] Based on the risk transmission network model, the time-series schedulable window of the energy storage device is dynamically mapped to the spot trading period in the multi-timescale price curve with state awareness. This identifies the differences in the sensitivity of energy storage charging and discharging actions to market price fluctuations under different exercise paths, and constructs a cross-temporal hedging strategy group including:

[0019] Based on the aforementioned risk transmission network model, the dynamic operating characteristics of the discharge action node under the bullish risk transmission path and the charging action node under the bearish risk transmission path are analyzed. Combined with the physical constraint boundary of the energy storage device, a schedulable window with time-series characteristics is constructed.

[0020] By analyzing the fluctuation patterns of spot market price sequences in the multi-timescale price curves, the evolution characteristics of price increase and price decrease intervals are characterized. The price increase intervals are mapped to call option exercise-related periods, and the price decrease intervals are mapped to put option exercise-related periods, thus constructing a set of spot trading periods that reflect market volatility characteristics.

[0021] The study explores the temporal coupling mechanism between the time-series schedulable window and the set of spot trading periods. Based on the transmission weights of the risk transmission network model, a state-aware dynamic mapping relationship is established between the charging and discharging action execution node and the price fluctuation response node within the overlapping interval.

[0022] Based on the state-aware dynamic mapping relationship, the response characteristics of discharge actions to price increase fluctuations and charging actions to price decrease fluctuations are analyzed, and a sensitivity evaluation index is constructed by evaluating the differences in response characteristics under different exercise paths.

[0023] Based on the sensitivity evaluation index, optimize the discharge and charge hedging strategy layout within the bullish exercise period and the putish exercise period, establish a time-series connection relationship based on state of charge constraints, and form a complete cross-temporal hedging strategy group.

[0024] The study explores the temporal coupling mechanism between the time-series schedulable window and the set of spot trading sessions. Within the overlapping interval, based on the transmission weights of the risk transmission network model, it establishes a state-aware dynamic mapping relationship between the charging / discharging action execution node and the price fluctuation response node, including:

[0025] By establishing the dynamic operating characteristics of discharge and charging time windows in a time-schedulable window, and combining the temporal evolution of call and put option exercise periods in the spot trading session set, we explore the coupling characteristics of discharge and charging time windows in different exercise periods and construct an adaptive strength index that reflects the degree of temporal coupling.

[0026] Based on the adaptive intensity index, the dynamic evolution of the discharge time window during the bullish exercise period and the charging time window during the bearish exercise period are analyzed. By evaluating the changing characteristics of the scheduling feasibility state, a set of states containing the overlapping intervals of bullish and bearish options is established.

[0027] This study delves into the transmission relationship between the discharge node and the price increase node in the bullish path and the transmission characteristics between the charging node and the price decrease node in the bearish path within the risk transmission network model. It then systematically associates these relationships with the set of states in the overlapping intervals to form a complete mapping foundation. Based on this mapping foundation, a state-aware dynamic mapping relationship is constructed within the overlapping intervals of bullish and bearish trends.

[0028] Based on the aforementioned spatiotemporal hedging strategy group, which incorporates energy loss constraints and response delay constraints during the charging and discharging state transition of energy storage devices, a state-dependent pricing equation is established within a risk-neutral measurement framework. Solving this equation yields the following two-way option risk hedging pricing:

[0029] The dynamic characteristics of the discharge state transition process of the bullish hedging strategy and the charging state transition process of the bearish hedging strategy are analyzed from the cross-temporal hedging strategy group. The energy loss coefficient and response delay parameter corresponding to each state transition process are obtained, and a set of constraint parameters describing the state transition law is constructed.

[0030] Based on the set of constraint parameters, an energy and time delay constraint relationship is constructed. An energy loss constraint equation is established through the energy loss coefficient. A response time delay constraint equation is established by combining the response time delay parameter. The energy loss constraint equation and the response time delay constraint equation are combined to form a complete constraint equation system.

[0031] Under the risk-neutral measurement framework, the bullish hedging strategy and the put hedging strategy are modified using the constraint equation system. The modified return function is constructed as a state-dependent function that depends on the state of charge of the energy storage device. The state-dependent pricing equation is established using the constraint equation system as boundary conditions.

[0032] The state-dependent pricing equation is solved, and the boundary conditions determined by the constraint equation system are introduced to obtain the pricing functions of the bullish hedging strategy and the put hedging strategy under different charge states. Combining the dynamic evolution characteristics of the pricing function and the conversion law of charge state, a two-way option risk hedging pricing is constructed.

[0033] Solving the state-dependent pricing equation, and by introducing the boundary conditions determined by the constraint equation system, yields the pricing functions for the bullish hedging strategy and the bearish hedging strategy under different states of charge:

[0034] The charged state variables in the state-dependent pricing equation are dynamically divided into regions. Based on the energy loss constraint equation in the constraint equation system, the influence characteristics of the charged state on the charging and discharging efficiency are explored. The influence mechanism of the charged state on the conversion rate is analyzed in combination with the response delay constraint equation. Based on the critical characteristic points of charging and discharging efficiency and state conversion rate, a multi-dimensional state space decomposition structure is constructed.

[0035] For each state subspace in the multidimensional state space decomposition structure, the corresponding energy and time constraint boundaries are extracted from the constraint equation system and transformed into power and time constraints of the state-dependent pricing equation at the subspace boundary, thus establishing a dynamic mapping relationship from the constraint equation system to the pricing equation boundary.

[0036] By applying the constraints determined by the dynamic mapping relationship to the state-dependent pricing equation in each state subspace, piecewise pricing solutions for the bullish hedging strategy and the bearish hedging strategy are obtained. Continuity constraints between the piecewise pricing solutions are established through critical feature points in the multidimensional state space decomposition structure, thereby reconstructing the pricing functions of the bullish hedging strategy and the bearish hedging strategy under different charge states.

[0037] Based on the aforementioned two-way option risk hedging pricing, two-way option transactions for energy storage capacity are executed, forming three-dimensional feedback information. The state transition bias in the three-dimensional feedback information is used to asymmetrically correct the risk transmission weights in the risk transmission network model. Adaptive reconstruction of the spatiotemporal mapping relationship of the hedging strategies in the cross-spatial hedging strategy group is performed using exercise trigger bias and price response bias, including:

[0038] Based on the pricing function in the two-way option risk hedging pricing, the option trading strategy is executed in the energy storage capacity two-way option trading market. By monitoring the operating status of the energy storage equipment in real time, the state transition deviation, exercise trigger deviation and price response deviation are obtained, and three-dimensional feedback information describing the dynamic characteristics of the system is constructed.

[0039] Extract the state transition deviation from the three-dimensional feedback information, analyze the dynamic evolution of its charging and discharging components, perform asymmetric correction on the transmission weights of the charging and discharging risk transmission path in the risk transmission network model, and establish a dynamic correction mechanism that reflects the asymmetric characteristics of charging and discharging.

[0040] By combining the exercise trigger deviation and price response deviation in the three-dimensional feedback information, the degree of deviation between them and the spatiotemporal mapping parameters of each strategy in the cross-temporal hedging strategy group is evaluated. Based on the correction results of the dynamic correction mechanism, the execution spatiotemporal characteristics of the cross-temporal hedging strategy are adaptively reconstructed to form a dynamic response strategy to market fluctuations.

[0041] A second aspect of the present invention provides a market-based energy storage revenue optimization strategy system, comprising:

[0042] The first unit is used to obtain the multi-timescale price curves of energy storage devices and the terms of two-way option contracts. Based on the call and put exercise conditions in the two-way option contract terms, it identifies the asymmetric risk transmission path of energy storage capacity in the charging and discharging state and establishes a risk transmission network model.

[0043] The second unit is used to dynamically map the time-series schedulable window of the energy storage device to the spot trading period in the multi-timescale price curve based on the risk transmission network model, identify the differences in the sensitivity of energy storage charging and discharging actions to market price fluctuations under different exercise paths, and construct a cross-temporal hedging strategy group.

[0044] The third unit is used to establish a state-dependent pricing equation under the risk-neutral measurement framework based on the energy loss constraints and response delay constraints introduced in the energy storage device charging and discharging state transition process of the aforementioned cross-temporal hedging strategy group, and solve it to obtain the two-way option risk hedging pricing.

[0045] The fourth unit is used to execute two-way option transactions for energy storage capacity based on the two-way option risk hedging pricing, forming three-dimensional feedback information; using the state transition deviation in the three-dimensional feedback information to perform asymmetric correction on the risk transmission weight in the risk transmission network model; and using the exercise trigger deviation and price response deviation to adaptively reconstruct the spatiotemporal mapping relationship of the hedging strategy in the spatiotemporal hedging strategy group, thereby achieving closed-loop optimization of pricing accuracy and hedging effectiveness.

[0046] A third aspect of the present invention provides an electronic device, comprising:

[0047] processor;

[0048] Memory used to store processor-executable instructions;

[0049] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0050] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0051] The beneficial effects of this application are as follows:

[0052] This invention identifies asymmetric risk transmission paths of energy storage capacity under charging and discharging conditions, establishes a risk transmission network model, and enables accurate risk assessment of energy storage equipment under different market conditions, thereby improving the risk identification capability and the pertinence of response strategies in energy storage asset management.

[0053] By constructing a cross-temporal hedging strategy group, this invention can effectively utilize the flexible scheduling capability of energy storage devices in multi-timescale markets, identify differences in the responsiveness to price fluctuations, significantly improve the profitability and market competitiveness of energy storage assets, and reduce the overall operating cost of energy storage.

[0054] This invention achieves adaptive optimization of risk transmission models and hedging strategies based on three-dimensional feedback information, forming a closed-loop optimization mechanism for energy storage asset management. It significantly improves the pricing accuracy and hedging effectiveness of energy storage assets in complex market environments, providing more reliable and economical decision support for energy storage to participate in the electricity market. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating the energy storage revenue optimization strategy method based on market mechanisms, as described in an embodiment of the present invention.

[0056] Figure 2 This is a flowchart illustrating the construction and solution of constraints for a two-way option risk hedging strategy according to an embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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.

[0058] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0059] Figure 1 This is a flowchart illustrating the market-based energy storage revenue optimization strategy method according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0060] Obtain the price curves of energy storage devices across multiple time scales and the terms of two-way option contracts. Based on the call and put options in the terms of the two-way option contracts, identify the asymmetric risk transmission path of energy storage capacity under charging and discharging conditions, and establish a risk transmission network model.

[0061] Based on the risk transmission network model, the time-series schedulable window of the energy storage device is dynamically mapped to the spot trading period in the multi-timescale price curve to identify the differences in the sensitivity of energy storage charging and discharging actions to market price fluctuations under different exercise paths, and a cross-temporal hedging strategy group is constructed.

[0062] Based on the aforementioned cross-temporal hedging strategy group, energy loss constraints and response delay constraints are introduced during the charging and discharging state transition of energy storage devices. A state-dependent pricing equation is established under the risk-neutral measurement framework, and the two-way option risk hedging pricing is obtained by solving it.

[0063] Based on the two-way option risk hedging pricing, two-way option transactions for energy storage capacity are executed, forming three-dimensional feedback information. The state transition deviation in the three-dimensional feedback information is used to asymmetrically correct the risk transmission weight in the risk transmission network model. The exercise trigger deviation and price response deviation are used to adaptively reconstruct the spatiotemporal mapping relationship of the hedging strategy in the spatiotemporal hedging strategy group, thereby achieving closed-loop optimization of pricing accuracy and hedging effectiveness.

[0064] In one optional implementation, based on the call and put exercise conditions in the two-way option contract terms, the asymmetric risk transmission path of energy storage capacity under charging and discharging states is identified, and a risk transmission network model is established, including:

[0065] The call and put exercise conditions in the two-way option contract terms are analyzed. Combining the market price fluctuation pattern and the characteristics of energy storage market transactions, the dynamic change pattern and fluctuation characteristics of the price trigger threshold range are extracted, and a set of two-way exercise trigger conditions considering the impact of the market environment is established.

[0066] Based on the set of bidirectional exercise triggering conditions, the power ramping constraint risk caused by the inherent characteristics of the energy storage device when the bullish exercise condition is triggered, and the capacity margin constraint risk caused by the real-time operating status when the bearish exercise condition is triggered, are analyzed. Combined with the dynamic change law of the price triggering threshold range, the characteristics of risk sources in different states are constructed.

[0067] Based on the characteristics of the risk sources in different states, the power ramping constraint risk nodes and capacity margin constraint risk nodes under the paths of the bullish exercise conditions and the bearish exercise conditions are mapped to different levels of the risk transmission network. By characterizing the transmission rules and mutual influence of risk nodes between different levels, a risk transmission framework with a hierarchical structure is established.

[0068] By combining historical dispatch response characteristic data, the differences in response characteristics and energy conversion patterns of energy storage devices under different states of charge are analyzed. A bidirectional transmission channel is constructed based on the hierarchical structure of the risk transmission framework. The risk transmission framework and the bidirectional transmission channel are then integrated into a complete risk transmission network model.

[0069] This paper analyzes the exercise conditions in two-way option contracts. For call options, the condition is triggered when the market price rises to a preset threshold, requiring the energy storage device to discharge rapidly to obtain high-price gains. For put options, the condition is triggered when the market price falls to a preset threshold, requiring the energy storage device to charge to take advantage of low-price opportunities. By analyzing historical market price data, the dynamic changes in the price trigger threshold range are extracted. For example, in a regional electricity market, a call condition is triggered when the intraday spot price fluctuation reaches 120% of the previous day's average price, and a put condition is triggered when the price falls to 85% of the previous day's average price. Considering the intraday trading characteristics of the energy storage market, a set of two-way option exercise trigger conditions is established, taking into account factors such as time period, season, and load level. This set of conditions includes multi-dimensional parameters such as price trigger thresholds, duration requirements, and market environment influencing factors, forming a complete two-way option exercise trigger mechanism.

[0070] Based on the constructed set of bidirectional exercise trigger conditions, this study analyzes the risk source characteristics arising from the inherent features of energy storage devices. When bullish exercise conditions are triggered, the risk of power ramp-up constraints is identified. When an energy storage device transitions from a static or charging state to a discharging state, its power ramp-up rate is typically limited by technical parameters to 10% to 30% of the rated power per minute, making it impossible to instantly reach the maximum discharge power. For example, a 100MW / 200MWh energy storage system with a power ramp-up rate of 20% per minute requires 5 minutes to reach full power discharge from a static state, resulting in an initial response lag. When bearish exercise conditions are triggered, the risk of capacity margin constraints is identified. When the state of charge of the energy storage device is high, the remaining rechargeable capacity is limited, making it impossible to fully utilize low-price charging opportunities. Taking the same 100MW / 200MWh energy storage system as an example, when the state of charge is 85%, only 30MWh of usable capacity remains, limiting the charging duration. By analyzing the correlation between price fluctuation characteristics at different times and the energy storage operating state, a sub-state risk source characteristic matrix is ​​constructed for charging and discharging states.

[0071] Based on the analyzed risk source characteristics, a hierarchical risk transmission framework is constructed, mapping power ramping constraint risk nodes and capacity margin constraint risk nodes to different levels of the risk transmission network. At the first level, a market price fluctuation trigger node is set, including the generation and transmission mechanism of price trigger signals; the second level is the energy storage equipment status response node, describing the process of equipment transitioning from its current operating state to the target state; the third level is the constraint risk formation node, depicting how power and capacity constraints evolve into actual risks; and the fourth level is the economic loss assessment node, quantifying the revenue loss caused by insufficient response. The transmission pattern of risk between each level is described through the connections between nodes. For example, when power ramping constraint risk forms at the third level, it can lead to economic losses due to insufficient contract fulfillment at the fourth level, with an average loss rate of 30% to 50% of the unresponsive electricity.

[0072] By combining historical dispatch response characteristic data of energy storage devices, the differences in response under different states of charge (SOCs) are analyzed. Data shows that energy storage devices respond most flexibly when the SOC is between 40% and 60%, achieving over 95% of the design value in ramp-up capability. However, when the SOC is below 20% or above 80%, the response capability significantly decreases, with ramp-up capability dropping to 70%-85% of the design value. Based on these characteristics, a two-way transmission channel is constructed in the hierarchical structure of the risk transmission framework. The uplink transmission channel describes how power ramp-up constraints lead to economic losses, while the downlink transmission channel describes how capacity margin constraints affect device availability. A state coupling node is set between the two channels to describe the mutual influence of the two types of risks. For example, when energy storage devices delay responding to discharge commands due to power ramp-up constraints, it mitigates the subsequent capacity margin constraint risk; the risk mitigation coefficient calculated through the state coupling node averages 0.15 to 0.25.

[0073] By systematically integrating the risk transmission framework with the two-way transmission channel, a complete risk transmission network model is formed. This model adopts a directed weighted graph structure, where nodes represent different types of risk factors, edges represent risk transmission paths, and edge weights represent transmission strength. Model inputs include current market prices, energy storage device status parameters, and contract terms. Through the calculation of the risk transmission network, the model outputs expected risk assessment results and optimized scheduling suggestions, enabling risk warning and prevention for energy storage devices under two-way option contract conditions.

[0074] In one optional implementation, based on the risk transmission network model, the time-series schedulable window of the energy storage device is dynamically mapped with the spot trading period in the multi-timescale price curve to identify the differences in the sensitivity of energy storage charging and discharging actions to market price fluctuations under different exercise paths, and a cross-temporal hedging strategy group is constructed, including:

[0075] Based on the aforementioned risk transmission network model, the dynamic operating characteristics of the discharge action node under the bullish risk transmission path and the charging action node under the bearish risk transmission path are analyzed. Combined with the physical constraint boundary of the energy storage device, a schedulable window with time-series characteristics is constructed.

[0076] By analyzing the fluctuation patterns of spot market price sequences in the multi-timescale price curves, the evolution characteristics of price increase and price decrease intervals are characterized. The price increase intervals are mapped to call option exercise-related periods, and the price decrease intervals are mapped to put option exercise-related periods, thus constructing a set of spot trading periods that reflect market volatility characteristics.

[0077] The study explores the temporal coupling mechanism between the time-series schedulable window and the set of spot trading periods. Based on the transmission weights of the risk transmission network model, a state-aware dynamic mapping relationship is established between the charging and discharging action execution node and the price fluctuation response node within the overlapping interval.

[0078] Based on the state-aware dynamic mapping relationship, the response characteristics of discharge actions to price increase fluctuations and charging actions to price decrease fluctuations are analyzed, and a sensitivity evaluation index is constructed by evaluating the differences in response characteristics under different exercise paths.

[0079] Based on the sensitivity evaluation index, optimize the discharge and charge hedging strategy layout within the bullish exercise period and the putish exercise period, establish a time-series connection relationship based on state of charge constraints, and form a complete cross-temporal hedging strategy group.

[0080] The risk transmission network model constructs a time-series schedulable window by analyzing the dynamic operating characteristics of discharge nodes under a bullish risk transmission path and charging nodes under a bearish risk transmission path. The operating characteristics of discharge nodes involve the power output capacity decay law during the transition of the energy storage device from a fully charged state to a depleted state. Discharge capacity boundaries are determined by monitoring parameters such as the rate of change of battery state of charge, internal resistance trends, and temperature drift. The operating characteristics of charging nodes focus on the change in power acceptance capacity during the transition from a depleted state to a fully charged state, monitoring indicators such as charging current acceptance, voltage ramp-up rate, and thermal management system response delay. Physical constraints on the energy storage device include hard constraints such as maximum charge / discharge power limits, upper and lower limits of state of charge, continuous operating time windows, and cooling interval requirements. The time-series schedulable window construction process couples these dynamic characteristics with physical constraints, identifying the time intervals within which the energy storage device can perform charging or discharging operations under a specific state of charge. This forms a schedulable time series with a basic time granularity of fifteen minutes. Each time segment is labeled with key parameters such as the upper limit of available charging power, the upper limit of available discharging power, expected response delay, and energy conversion efficiency.

[0081] Multi-timescale price curve analysis characterizes price evolution by extracting fluctuation patterns from spot market price sequences. Price sequence data collection covers price information from different trading instruments, including day-ahead, real-time, and ancillary service markets, with a time resolution of up to five minutes and historical data depth of at least one year. Price fluctuation pattern identification employs a sliding window technique with a window length of two hours and a sliding step of fifteen minutes. Within each window, statistical characteristics such as price mean, standard deviation, peak-to-trough difference, and rate of change are calculated. A price increase interval is defined as a period of three or more consecutive time segments where prices show an upward trend with a cumulative increase exceeding 5%, and a price decrease interval is defined as a period of three or more consecutive time segments where prices show a downward trend with a cumulative decrease exceeding 5%. The bullish exercise period mapping extends the price increase interval forward by 30 minutes and backward by 60 minutes to form an extended time window, and the putish exercise period mapping extends the price decrease interval forward by 60 minutes and backward by 30 minutes to form an extended time window. The spot trading session set is constructed by merging overlapping extended time windows and removing fragmented sessions lasting less than one hour, ultimately forming a session division result reflecting the main fluctuation characteristics of the market.

[0082] This study explores the temporal coupling mechanism between the time-series schedulable window and the set of spot trading sessions by establishing a correlation through identifying overlapping intervals in the time dimension. The overlapping interval is calculated by finding the intersection of time intervals, comparing each schedulable time segment with each spot trading session, recording information such as the start time, end time, and duration of the overlap. The transmission weights in the risk transmission network model play a crucial role within the overlapping intervals. These weights are obtained through historical data statistical analysis, reflecting the intensity of the impact of energy storage charging and discharging actions on market price changes. The process of establishing a state-aware dynamic mapping relationship involves associating the time coordinates, power magnitude, and duration of the charging and discharging action execution nodes with the price change amplitude, rate of change, and duration of the price fluctuation response nodes. The mapping relationship uses a weighted correlation degree calculation method, with weight coefficients determined based on factors such as the overlap time length, power matching degree, and historical correlation. The correlation degree ranges from zero to one, with a larger value indicating a stronger correlation.

[0083] The state-aware dynamic mapping relationship analysis focuses on the differences in the response characteristics of discharge actions to price increases and charging actions to price decreases. Discharge action response characteristics are obtained by statistically analyzing the distribution patterns of parameters such as power regulation amplitude, response time delay, and continuous operating time when energy storage devices perform discharge operations during price increases. Charging action response characteristics are determined by analyzing the corresponding parameter change patterns when energy storage devices perform charging operations during price decreases. The evaluation of response characteristic differences under different exercise paths employs a comparative analysis method, quantitatively comparing the discharge response characteristics under call option exercise paths with the charging response characteristics under put option exercise paths. The sensitivity evaluation index is constructed including three dimensions: response speed sensitivity, power regulation sensitivity, and sustainability sensitivity. Each dimension uses a scoring standard from zero to ten. The overall sensitivity is calculated using a weighted average method, with weights allocated as follows: 40% for response speed, 40% for power regulation, and 20% for sustainability.

[0084] The sensitivity evaluation index optimization process involves a refined design of the charge / discharge hedging strategy layout during the call and put option exercise periods. The strategy layout optimization employs a dynamic programming algorithm, with the objective function being a weighted combination of maximizing option returns and minimizing operational risk. Constraints include physical constraints such as state of charge boundaries, power output limits, and continuous operating time. During the call option exercise period, the discharge strategy is prioritized during the time segment with the largest price increase, with the discharge power set between 80% and 95% of available power, reserving a 5% to 20% power margin to handle sudden adjustment needs. During the put option exercise period, the charging strategy is prioritized during the time segment with the largest price decrease, with the charging power set between 70% and 90% of available power to avoid overcharging and damage to battery life. The temporal connection of the state of charge constraints is established by tracking the state of charge changes of the energy storage device throughout the entire scheduling cycle, ensuring that the state of charge remains within the safe range of 10% to 90% during the execution of the charge / discharge strategy. The process of forming a cross-temporal hedging strategy group involves arranging the optimized charging and discharging strategies in chronological order, establishing logical relationships between strategies, and forming a complete strategy sequence covering a 24-hour scheduling cycle.

[0085] In a real-world application, the energy storage device has a rated power of 10 MW and a rated capacity of 40 MWh, with an operating state of charge range of 10% to 90%. On a certain day, the spot market price experienced a sustained increase of 15% between 10:00 AM and 12:00 PM, identified as a bullish option exercise-related period. Conversely, the price experienced a sustained decrease of 12% between 2:00 PM and 4:00 PM, identified as a put-back option exercise-related period. The time-series dispatchable window shows that the energy storage device has discharge capability between 9:30 AM and 12:30 PM and charging capability between 1:30 PM and 4:30 PM. Overlapping interval analysis indicates that 10:00 AM to 12:00 PM is the optimal discharge period, and 2:00 PM to 4:00 PM is the optimal charging period. Sensitivity evaluation index calculations show that the overall sensitivity of discharge actions to price increases is 8.2, and the overall sensitivity of charging actions to price decreases is 7.6. The final cross-temporal hedging strategy group includes a discharge operation at 8.5 MW power for two hours starting at 10:00 AM and a charging operation at 7 MW power for two hours starting at 2:00 PM. After the strategy is implemented, the state of charge of the energy storage device changes from the initial 50% to the final 45%, remaining within the safe operating range.

[0086] In one optional implementation, the temporal coupling mechanism between the time-series schedulable window and the set of spot trading periods is explored. Within the overlapping interval, based on the transmission weights of the risk transmission network model, a state-aware dynamic mapping relationship is established between the charging / discharging action execution node and the price fluctuation response node, including:

[0087] By establishing the dynamic operating characteristics of discharge and charging time windows in a time-schedulable window, and combining the temporal evolution of call and put option exercise periods in the spot trading session set, we explore the coupling characteristics of discharge and charging time windows in different exercise periods and construct an adaptive strength index that reflects the degree of temporal coupling.

[0088] Based on the adaptive intensity index, the dynamic evolution of the discharge time window during the bullish exercise period and the charging time window during the bearish exercise period are analyzed. By evaluating the changing characteristics of the scheduling feasibility state, a set of states containing the overlapping intervals of bullish and bearish options is established.

[0089] This study delves into the transmission relationship between the discharge node and the price increase node in the bullish path and the transmission characteristics between the charging node and the price decrease node in the bearish path within the risk transmission network model. It then systematically associates these relationships with the set of states in the overlapping intervals to form a complete mapping foundation. Based on this mapping foundation, a state-aware dynamic mapping relationship is constructed within the overlapping intervals of bullish and bearish trends.

[0090] When exploring the dynamic operating characteristics of discharge and charge time windows within a time-series schedulable window, the schedulable window of an energy storage system is defined in detail. The schedulable window includes two types: discharge time windows and charge time windows. Each type of window contains multiple sets of discrete time points. The discharge time window can be represented as T. dis ={t dis1 , t dis2 , ..., t disn}, where each time point t disi The corresponding time when the energy storage system can perform a discharge operation; the charging time window can be represented as T. ch ={t ch1 , t ch2 ,..., t chm}, where each time point t chj The time when the corresponding energy storage system can perform charging operations.

[0091] For the set of spot trading sessions, it is divided into a set of call option exercise-related sessions T. up ={t up1 , t up2 , ..., t upp The set of time periods associated with put option exercise T down ={t down1 , t down2 , ..., t downq By analyzing historical data, a price change characteristic spectrum for each time period is established, and the probability P of price increases is recorded. up And the probability of price decline P down For example, historical data shows that during the weekday period from 16:00 to 17:00, the probability of a price increase is 0.73 and the probability of a price decrease is 0.27; while during the period from 22:00 to 23:00, the probability of an increase is 0.31 and the probability of a price decrease is 0.69.

[0092] Construct an adaptive strength index C that reflects the degree of temporal coupling. i At that time, calculate the overlap between the discharge time window and the call option exercise period, and the overlap between the charging time window and the put option exercise period. For each discharge time point t disi With each call option exercise period t upj Calculate the time distance d(t) disi , t upj When the distance is less than the preset threshold τ up At that time, it is assumed that there is coupling between the two. For each charging time point t chi With each put option exercise period t downj Calculate the time distance d(t) chi , t downj When the distance is less than the preset threshold τ downAt that time, it is assumed that the two are coupled. An adaptive strength index C is formed by combining all pairs of coupling points. i The value range is [0,1]. In practical applications, when the system's discharge window from 15:30 to 16:30 overlaps with the bullish exercise period from 16:00 to 17:00, the overlap duration is 30 minutes, accounting for 0.5 of the total duration. At this time, the adaptive strength index is 0.78, indicating a strong coupling relationship.

[0093] When analyzing the dynamic evolution of the discharge time window during the bullish exercise period and the charging time window during the bearish exercise period based on the adaptive intensity index, a scheduling feasibility state set S={s1, s2, ..., s} is introduced. k}, where each state s i This indicates the types of operations that the energy storage system can perform at a specific time and the corresponding market price expectations. For example, state s1 indicates "discharge operations are possible in the current period and the market price is expected to rise." This is achieved through continuous monitoring of the adaptive strength index C. i The changes in C i Exceeding the preset threshold θ C When the value is 0.65, a strongly coupled state is identified and added to the state set S of the bullish and bearish overlapping intervals. overlap .

[0094] In practical applications, when the system detects the adaptive strength index C during the period from 14:00 to 16:00 on a certain day... i The value increased from 0.55 to 0.72, exceeding the preset threshold of 0.65. Therefore, the current state "high overlap between the discharge window and the call option exercise period, low overlap between the charging window and the put option exercise period" was added to the state set S. overlap Similarly, if the adaptive strength index is detected to rise from 0.60 to 0.81 during the period from 20:00 to 22:00, the system will add the state "high overlap between the charging window and the put option exercise period, low overlap between the discharging window and the call option exercise period" to the state set.

[0095] When exploring the transmission relationship between the discharge node and the price increase node in the bullish path of a risk transmission network model, a transmission weight matrix W is defined. up , where element w up (i,j) represents the degree of influence of discharge node i on price increase node j. Similarly, the transmission weight matrix W is defined under the bearish path. down , where element w down(i,j) represents the degree of influence of charging node i on price-declining node j. These weights are determined by analyzing historical data and reflect the strength of the influence relationship between different nodes. In practical applications, the transmission weight of the discharging node to the price-increasing node is 0.85 during high-load periods (such as 16:00-18:00 on weekdays), while it is only 0.32 during low-load periods (such as 2:00-4:00 AM).

[0096] When the transmission relationship is systematically associated with the set of overlapping interval states, for S overlap Each state s in i The system assesses the position and impact of the discharge node within the transmission network. For example, for the state "discharge window highly overlaps with call option exercise period," the system calculates the comprehensive impact index I of the discharge node on the price increase node under this state. up =0.79; For the state "charging window and put option exercise period highly overlap", calculate the comprehensive impact index I of the charging node on the price decline node. down =0.68.

[0097] Based on the above mapping, a state-aware dynamic mapping function M(s,t) is constructed within the overlapping bullish and bearish intervals. This function maps the state s within the overlapping interval at time t to the corresponding charging and discharging execution strategy. For example, when the state "discharging window highly overlaps with the bullish exercise period" and the current price trend is upward, the mapping function outputs the strategy of "executing maximum power discharge"; when the state "charging window highly overlaps with the bearish exercise period" and the current price trend is downward, the mapping function outputs the strategy of "executing maximum power charging". This mapping relationship is dynamically adjusted over time to ensure that the energy storage system can optimize its charging and discharging behavior in a timely manner according to market price fluctuations.

[0098] In actual operation, the system updates the state-aware mapping relationship at each decision cycle (e.g., every 15 minutes) and adjusts the charging and discharging strategy based on the latest mapping results. In this way, the energy storage system can effectively respond to market price signals while maintaining its own dispatchability, thereby improving operational economy.

[0099] In one optional implementation, based on the energy loss constraints and response delay constraints introduced during the charging and discharging state transition of the energy storage device according to the cross-temporal hedging strategy group, a state-dependent pricing equation is established under the risk-neutral measurement framework, and the two-way option risk hedging pricing is obtained by solving it, including:

[0100] The dynamic characteristics of the discharge state transition process of the bullish hedging strategy and the charging state transition process of the bearish hedging strategy are analyzed from the cross-temporal hedging strategy group. The energy loss coefficient and response delay parameter corresponding to each state transition process are obtained, and a set of constraint parameters describing the state transition law is constructed.

[0101] Based on the set of constraint parameters, an energy and time delay constraint relationship is constructed. An energy loss constraint equation is established through the energy loss coefficient. A response time delay constraint equation is established by combining the response time delay parameter. The energy loss constraint equation and the response time delay constraint equation are combined to form a complete constraint equation system.

[0102] Under the risk-neutral measurement framework, the bullish hedging strategy and the put hedging strategy are modified using the constraint equation system. The modified return function is constructed as a state-dependent function that depends on the state of charge of the energy storage device. The state-dependent pricing equation is established using the constraint equation system as boundary conditions.

[0103] The state-dependent pricing equation is solved, and the boundary conditions determined by the constraint equation system are introduced to obtain the pricing functions of the bullish hedging strategy and the put hedging strategy under different charge states. Combining the dynamic evolution characteristics of the pricing function and the conversion law of charge state, a two-way option risk hedging pricing is constructed.

[0104] like Figure 2 As shown, the method includes:

[0105] The analysis process of the cross-temporal hedging strategy group obtains dynamic characteristic parameters by extracting the discharge state transition process of the bullish hedging strategy and the charging state transition process of the bearish hedging strategy. The dynamic characteristics of the discharge state transition process involve the change in energy conversion efficiency of the energy storage device when transitioning from a high-charge state to a low-charge state. Actual energy loss is calculated by monitoring physical parameters such as voltage drop, internal resistance change, and temperature rise during the discharge process. The dynamic characteristics of the charging state transition process focus on the change in energy acceptability of the energy storage device when transitioning from a low-charge state to a high-charge state. Phenomena such as current acceptability decay, voltage ramp-up delay, and thermal management response lag are monitored during the charging process. The energy loss coefficient is determined by comparing the difference between the theoretical energy change and the actual energy change. The energy loss coefficient for the discharge process ranges from 0.05 to 0.15, and the energy loss coefficient for the charging process ranges from 0.08 to 0.20. The response delay parameter is obtained by measuring the time interval from the issuance of the control command to the actual start of the charging and discharging action of the energy storage device. The typical value for the discharge response delay parameter is three to eight seconds, and the typical value for the charging response delay parameter is five to twelve seconds. The constraint parameter set is constructed by storing the above energy loss coefficient and response delay parameter in segments according to different state of charge intervals. The state of charge intervals are divided into a low charge interval of 10% to 30%, a medium charge interval of 30% to 70%, and a high charge interval of 70% to 90%, with each interval corresponding to different loss coefficient and delay parameter values.

[0106] The energy and time delay constraint relationship based on the constraint parameter set is constructed by establishing energy loss constraint equations and response time delay constraint equations to achieve a complete constraint system. The energy loss constraint equation quantifies the relationship between the theoretical charging / discharging energy and the actual usable energy of the energy storage device. The actual usable discharging energy equals the theoretical discharging energy multiplied by one minus the energy loss coefficient, and the actual usable charging energy equals the theoretical charging energy divided by one plus the energy loss coefficient. The response time delay constraint equation synchronizes the execution time of the energy storage device with the market price change time, requiring the response time delay parameter of the energy storage device to be less than half the effective change window of the market price, ensuring that charging and discharging actions can respond promptly to price fluctuation signals. The constraint equation system combines the energy loss constraint equation as an energy balance constraint condition and the response time delay constraint equation as a timing synchronization constraint condition, together constituting the physical constraint boundary of the energy storage device's operation. The constraint equation system also includes a state of charge boundary constraint, limiting the energy storage device's state of charge variation within the range of 10% to 90%, and a power output constraint, limiting the charging and discharging power to no more than 95% of the rated power.

[0107] The strategy modification process under the risk-neutral measurement framework utilizes a system of constraint equations to adjust the feasibility of bullish and put-side hedging strategies. The bullish hedging strategy modification calculates the modified return by subtracting energy loss costs and time delay risk costs from the theoretical discharge return. Energy loss costs are calculated by multiplying the lost energy by the average electricity price, and time delay risk costs are calculated based on potential price change losses during the response delay period. The put-side hedging strategy modification uses a similar method, subtracting the corresponding energy loss costs and time delay risk costs from the theoretical charging return. The modified return function is constructed as a state-dependent function of the energy storage device's state of charge. The value of the return function exhibits a non-linear relationship with changes in the state of charge, reaching its optimal value under moderate state of charge and decaying under extremely high or low state of charge. The state-dependent pricing equation establishment process uses the modified return function as the objective function and the system of constraint equations as boundary conditions, forming a system of partial differential equations with the state of charge as the independent variable and the option value as the dependent variable. The boundary conditions of the pricing equations include multiple constraints such as upper boundary conditions for the state of charge, lower boundary conditions for the state of charge, and time boundary conditions.

[0108] The solution process for the state-dependent pricing equation uses numerical analysis to obtain the pricing functions for bullish and put-back hedging strategies under different states of charge. The algorithm employs the finite difference method, dividing the state space of charge into one hundred equally spaced grid points and the time space into a discretized grid with a time step of fifteen minutes. The boundary condition determination process transforms the constraint equation system into specific numerical boundary conditions. The pricing function value is set to zero at 10% charge, and the value at 90% charge is determined based on the maximum theoretical return. The iterative solution process uses an explicit difference scheme, with the convergence criterion set at a relative error of less than one ten-thousandth between two adjacent iterations, and a maximum iteration count limited to ten thousand. After obtaining the pricing functions, interpolation is performed using cubic spline interpolation to fill the numerical gaps between grid points, ensuring that the pricing functions are continuously differentiable throughout the entire state space of charge. The pricing function for the bullish hedging strategy peaks near 70% charge, while the pricing function for the put-back hedging strategy peaks near 30% charge. The intersection of the two pricing functions is located near 50% charge.

[0109] The dynamic evolutionary characteristic analysis of the pricing function reveals the time decay law of option value by tracking the trajectory of the pricing function over time. The evolutionary characteristic calculation uses the time derivative method to calculate the rate of change of the pricing function at each time step; a positive rate of change indicates an increase in option value, while a negative rate of change indicates a decrease in option value. The analysis of the state-of-charge transition law describes the evolution path of the energy storage device's state of charge under different charging and discharging strategies by establishing a state transition probability matrix. The state transition probabilities are obtained statistically from historical operating data, with probability values ​​maintained to four decimal places. The two-way option risk hedging pricing construction process combines the pricing functions of the call and put hedging strategies with weights. The weight coefficients are determined based on the probability distribution of market price fluctuation directions, and the sum of the call and put weights is equal to one. The combined pricing function calculation employs a dynamic weight adjustment mechanism, with the weight coefficients updated every hour based on the latest market information to ensure that the two-way option pricing can reflect market changes in a timely manner.

[0110] In a practical application case, the energy storage device has a rated capacity of 50 MWh and a current state of charge (SOC) of 40%, operating within the medium-capacity range. The constraint parameter set shows that the discharge energy loss coefficient is 0.09, the charging energy loss coefficient is 0.12, the discharge response delay is 5 seconds, and the charging response delay is 8 seconds. The energy loss constraint equation calculation results show that a theoretical discharge of 10 MWh corresponds to an actual usable discharge of 9.1 MWh, and a theoretical charging of 10 MWh requires an actual input of 11.2 MWh. The response delay constraint equation verification results show that the current market price change window is 30 seconds, and the energy storage device's response delay meets the constraint requirement of less than 15 seconds. Solving the state-dependent pricing equation yields a call hedging strategy price of 120 yuan per MWh and a put hedging strategy price of 108 yuan per MWh under the current SOC. The two-way option risk hedging price is calculated using a weighted combination, resulting in 114 yuan per MWh, with a call weight of 0.55 and a put weight of 0.45. The pricing results were verified by comparing them with actual market transaction prices, and the relative error was controlled within 3%, meeting the accuracy requirements for engineering applications. Boundary scenario tests showed that when the state of charge (SOC) approached the lower limit of 10%, the pricing of the put hedging strategy dropped to below 20 yuan per megawatt-hour, and when the SOC approached the upper limit of 90%, the pricing of the call hedging strategy dropped to below 15 yuan per megawatt-hour.

[0111] In one optional implementation, the state-dependent pricing equation is solved, and by introducing the boundary conditions determined by the constraint equation system, the pricing functions of the bullish hedging strategy and the bearish hedging strategy under different states of charge are obtained, including:

[0112] The charged state variables in the state-dependent pricing equation are dynamically divided into regions. Based on the energy loss constraint equation in the constraint equation system, the influence characteristics of the charged state on the charging and discharging efficiency are explored. The influence mechanism of the charged state on the conversion rate is analyzed in combination with the response delay constraint equation. Based on the critical characteristic points of charging and discharging efficiency and state conversion rate, a multi-dimensional state space decomposition structure is constructed.

[0113] For each state subspace in the multidimensional state space decomposition structure, the corresponding energy and time constraint boundaries are extracted from the constraint equation system and transformed into power and time constraints of the state-dependent pricing equation at the subspace boundary, thus establishing a dynamic mapping relationship from the constraint equation system to the pricing equation boundary.

[0114] By applying the constraints determined by the dynamic mapping relationship to the state-dependent pricing equation in each state subspace, piecewise pricing solutions for the bullish hedging strategy and the bearish hedging strategy are obtained. Continuity constraints between the piecewise pricing solutions are established through critical feature points in the multidimensional state space decomposition structure, thereby reconstructing the pricing functions of the bullish hedging strategy and the bearish hedging strategy under different charge states.

[0115] In the state-dependent pricing equation, the dynamic region division of the state-of-charge (POC) variable is achieved by establishing a POC impact analysis module to identify the characteristics of charging / discharging efficiency and state transition rate. This module receives real-time operating data from the energy storage device as input, including fields such as the current POC value, charging / discharging power setpoint, and response delay measurement, and outputs the efficiency and rate impact coefficients corresponding to the POC. The energy loss constraint equation explores the influence of POC on charging / discharging efficiency by establishing an efficiency feature extraction submodule. This submodule uses historical data regression analysis to identify the changing trends of charging / discharging efficiency within different POC ranges.

[0116] Efficiency feature extraction employs a sliding window analysis method, with the window length set to one-tenth of the range of state of charge variation and the sliding step size half the window length. Within each window, statistical features such as the average charging efficiency, average discharging efficiency, and efficiency change gradient are calculated. The process of analyzing the impact mechanism of state of charge on conversion rate using response delay constraint equations establishes a rate feature extraction submodule. This submodule monitors the time interval between receiving control commands and the actual execution of actions by the energy storage device. Rate feature extraction utilizes a time delay measurement database to record historical response delay data under different states of charge, and a kernel density estimation method is used to fit the time delay distribution function.

[0117] Critical feature point identification for charge / discharge efficiency is achieved by establishing a critical point detection algorithm. This algorithm traverses the charged state space to find the intersection points of the charging efficiency curve and the discharging efficiency curve. Critical feature point identification for state transition rate employs a similar detection algorithm to find the intersection points of the charging conversion rate curve and the discharging conversion rate curve. A multidimensional state space decomposition structure is constructed by establishing a region partitioning module. This module divides the charged state space into several continuous subspace intervals using the identified critical feature points as boundaries.

[0118] A constraint parameter mapping module is established to extract the constraint boundaries of each state subspace in the multidimensional state-space decomposition structure. This module reads the value ranges of the energy loss coefficient and response delay parameter from the constraint equation system. The constraint parameter mapping module receives subspace identifiers as input and queries a pre-established parameter mapping table to obtain the corresponding constraint parameter value ranges. The parameter mapping table is established through an offline calibration process. The calibration process collects actual operating data of the energy storage device under different states of charge and statistically analyzes the distribution characteristics of constraint parameters within each state interval.

[0119] The process of transforming boundary constraints into power and time constraints at the boundaries of state-dependent pricing equations involves establishing a boundary condition transformation module. This module converts physical constraint parameters into the mathematical boundary conditions required for solving the pricing equations. Power constraint transformation is achieved by establishing a power constraint calculation engine. This engine takes the theoretical maximum power value and energy loss coefficient as input and calculates the upper limit of the actual usable power. Time constraint transformation is achieved by establishing a time-series constraint calculation engine. This engine takes the target execution time and response delay parameters as input and calculates the earliest allowed command issuance time. The dynamic mapping relationship establishment process constructs a constraint mapping data structure. This data structure uses key-value pairs to store the mapping relationship between subspace identifiers and corresponding constraint conditions, supporting fast query and dynamic update operations.

[0120] A piecewise solver engine is established for the constraint application process of state-dependent pricing equations within state subspaces. This engine solves the pricing equations independently for each state subspace. The piecewise solver engine receives input parameters such as subspace boundary range, constraint parameters, and initial condition settings, and outputs an analytical expression or numerical solution of the pricing function within that subspace. A bullish strategy solver is established for the piecewise pricing solution calculation process of the bullish hedging strategy. This solver specifically handles pricing calculation tasks related to discharge. The bullish strategy solver integrates numerical solving algorithms, supporting multiple solution methods such as the finite difference method, finite element method, and Monte Carlo method. The finite difference method is used by default.

[0121] The calculation process for the segmented pricing solution of the put-side hedging strategy establishes a put-side strategy solver specifically for handling charging-related pricing calculation tasks. The segmented pricing solution storage adopts a hierarchical data structure, indexed and organized according to subspace identifiers, with each subspace corresponding to an independent data storage unit. Solution accuracy control is achieved through a convergence check module, which monitors residual changes during the iterative solution process and terminates the iteration when the residuals meet a preset convergence criterion. Anomaly handling mechanisms include functional modules for numerical divergence detection, boundary condition conflict detection, and memory overflow protection.

[0122] The process of establishing continuity constraints between piecewise pricing solutions at critical feature points constructs a continuity guarantee mechanism, which ensures the smooth connection of pricing functions at the boundaries of adjacent subspaces. This continuity guarantee mechanism comprises two core components: a function value continuity checker and a derivative continuity checker. The function value continuity checker verifies the consistency of the left and right limit values ​​at boundary points, while the derivative continuity checker verifies the consistency of the left and right derivative values ​​at boundary points. The numerical implementation of the continuity constraints is achieved by establishing a transition region processor. This processor sets up a buffer region near the critical feature points, and a smooth interpolation algorithm is used within the buffer region to ensure function continuity. The smooth interpolation algorithm supports various interpolation methods, including linear interpolation, quadratic interpolation, and cubic spline interpolation, which can be selected based on accuracy requirements and computational complexity.

[0123] The constraint equations are solved using a constraint optimization solver that integrates optimization algorithms such as the Lagrange multiplier method, sequential quadratic programming, and interior-point methods. A global function reconstructor is established for the pricing function reconstruction process of bullish and putish hedging strategies under different charge states. This reconstructor combines the piecewise pricing solutions from each subspace. The global function reconstructor maintains a global pricing function data structure, which uses a piecewise function representation to record the function expression and parameter values ​​corresponding to each charge state interval.

[0124] The pricing function reconstruction and validation process establishes a model validation framework that evaluates the accuracy of the pricing function through comparative analysis with historical market data. This framework includes components such as a data preprocessing module, an accuracy assessment module, and a sensitivity analysis module. The data preprocessing module cleans outliers and missing values ​​from historical data, using median imputation to handle missing data and quartiles to identify and remove outliers. The accuracy assessment module calculates the error statistics between the pricing function's predicted value and the actual market price, supporting parallel computation of multiple error metrics. The sensitivity analysis module evaluates the pricing function's sensitivity to changes in input parameters through parameter perturbation experiments, with the perturbation amplitude set within a certain percentage range of the parameter's standard value. The model calibration mechanism adjusts the constraint parameter values ​​through feedback control, triggering a parameter recalibration process when the validation accuracy does not meet requirements. Parameter recalibration uses the least squares method or maximum likelihood estimation to refit the constraint parameters and update the values ​​in the parameter mapping table.

[0125] In practical engineering implementation, the software architecture adopts a modular design, with data exchange between functional modules through standardized interfaces. Core data structures include a charged state array, constraint parameter matrix, and pricing function table, using double-precision floating-point numbers to ensure computational accuracy. Memory management employs a dynamic allocation strategy, dynamically adjusting memory usage based on the number of state space partitions. Concurrent processing supports multi-threaded parallel computation, allowing pricing solutions in each subspace to be executed in parallel to improve computational efficiency. Error handling mechanisms include input parameter validity checks, computational anomaly capture, and output result validity verification. A logging function records intermediate results of key computational steps, facilitating problem localization and algorithm debugging. Configuration management supports dynamic loading of parameter configuration files, allowing adjustment of algorithm parameters without restarting the program. The performance monitoring module monitors computational resource usage in real time, including metrics such as CPU utilization, memory usage, and computation time.

[0126] In one optional implementation, energy storage capacity two-way option trading is executed based on the two-way option risk hedging pricing to form three-dimensional feedback information; the state transition bias in the three-dimensional feedback information is used to asymmetrically correct the risk transmission weights in the risk transmission network model; and the exercise trigger bias and price response bias are used to adaptively reconstruct the spatiotemporal mapping relationship of the hedging strategy in the spatiotemporal hedging strategy group, including:

[0127] Based on the pricing function in the two-way option risk hedging pricing, the option trading strategy is executed in the energy storage capacity two-way option trading market. By monitoring the operating status of the energy storage equipment in real time, the state transition deviation, exercise trigger deviation and price response deviation are obtained, and three-dimensional feedback information describing the dynamic characteristics of the system is constructed.

[0128] Extract the state transition deviation from the three-dimensional feedback information, analyze the dynamic evolution of its charging and discharging components, perform asymmetric correction on the transmission weights of the charging and discharging risk transmission path in the risk transmission network model, and establish a dynamic correction mechanism that reflects the asymmetric characteristics of charging and discharging.

[0129] By combining the exercise trigger deviation and price response deviation in the three-dimensional feedback information, the degree of deviation between them and the spatiotemporal mapping parameters of each strategy in the cross-temporal hedging strategy group is evaluated. Based on the correction results of the dynamic correction mechanism, the execution spatiotemporal characteristics of the cross-temporal hedging strategy are adaptively reconstructed to form a dynamic response strategy to market fluctuations.

[0130] The pricing function for two-way option risk hedging pricing in the energy storage capacity two-way option trading strategy establishes an option trading execution engine. This engine receives the output of the pricing function as the basis for trading decisions. The option trading execution engine includes core components such as a trading signal generator, an order manager, and a risk controller. The trading signal generator compares the calculated result of the pricing function with the current market price. When the pricing function value is higher than the market price, a buy signal is generated; when the pricing function value is lower than the market price, a sell signal is generated.

[0131] The order manager is responsible for converting trading signals into specific trading orders. Order fields include key information such as option type, strike price, expiration time, trading quantity, and price limit. Real-time monitoring of the energy storage device's operating status is achieved through a status monitoring system. This system collects operational data through a sensor network deployed at key nodes of the energy storage device. The sensor network includes devices such as voltage sensors, current sensors, temperature sensors, and state-of-charge sensors. The data acquisition frequency is set to ten times per second, and data transmission uses the industrial Ethernet protocol to ensure real-time performance and reliability.

[0132] State transition deviation is calculated by comparing the actual state-of-charge trajectory with the theoretically expected trajectory; the deviation is the actual value minus the expected value. Exercise trigger deviation is calculated by comparing the time difference between the actual option exercise time and the time when the preset exercise condition triggers. Price response deviation is calculated by comparing the difference between the actual market price change and the expected price change after the energy storage device's charging and discharging actions. The three-dimensional feedback information is constructed using a triplet data structure, where each data point contains values ​​for state transition deviation, exercise trigger deviation, and price response deviation, with timestamp accuracy down to the millisecond level.

[0133] The extraction process of state transition deviations from 3D feedback information establishes a deviation analysis module, which extracts state transition deviation data from the feedback information data stream in real time. The deviation analysis module employs a sliding window data processing technique, with the window length set to include data points from the most recent hour, and the window update frequency being once per minute. The charging and discharging components of the state transition deviations are separated using a sign-based method: positive deviation values ​​are classified as charging components, and negative deviation values ​​are classified as discharging components.

[0134] The dynamic evolution analysis of charging and discharging components employs time series analysis techniques, including trend analysis, periodic analysis, and volatility analysis. Trend analysis identifies long-term trends in the deviation data using linear regression, periodic analysis identifies periodic components in the deviation data using fast Fourier transform, and volatility analysis assesses the stability of the deviation data by calculating the moving standard deviation. In the risk transmission network model, an asymmetric correction algorithm is established for the transmission weights of the charging and discharging risk transmission paths. This algorithm adjusts the transmission weight values ​​separately for the charging and discharging components based on their different characteristics.

[0135] The charging path conduction weight correction adopts a positive adjustment mechanism, increasing the conduction weight when the charging component deviation is positive and decreasing it when the deviation is negative. The discharging path conduction weight correction adopts a reverse adjustment mechanism, adjusting in the opposite direction to the charging path. The weight correction magnitude is controlled by setting a correction coefficient, which ranges from 0.01 to 0.1, with the specific value dynamically determined based on the deviation magnitude. The dynamic correction mechanism includes correction triggering conditions, correction execution logic, and correction result verification. The correction triggering condition is that the deviation exceeds a set threshold within three consecutive time windows. The correction execution logic uses an incremental update method, and the correction result verification verifies the correction effect through backtesting.

[0136] A deviation correlation analyzer was established to combine the analysis of exercise trigger deviation and price response deviation in the three-dimensional feedback information. This analyzer calculates the correlation and causal relationship between the two deviations. The deviation correlation analysis uses Pearson correlation coefficient and Granger causality test methods, with the correlation coefficient threshold set at 0.5 and the significance level for the causality test set at 5%. The deviation degree of each strategy's spatiotemporal mapping parameter in the cross-spatiotemporal hedging strategy group is evaluated by establishing a parameter deviation calculator. This calculator takes the current strategy execution result and the expected strategy effect as input and outputs a deviation quantification index.

[0137] The spatiotemporal mapping parameters include dimensions such as execution time deviation, spatial location deviation, and power magnitude deviation. Each dimension uses a normalized deviation calculation method, with deviation values ​​ranging from zero to one. The application process of the dynamic correction mechanism establishes a strategy reconfiguration engine. This engine adaptively adjusts the cross-spatiotemporal hedging strategy based on the weight correction results and deviation evaluation results. Strategy reconfiguration includes two aspects: time-dimensional reconfiguration and spatial-dimensional reconfiguration. Time-dimensional reconfiguration adjusts the time window and execution frequency of the strategy, while spatial-dimensional reconfiguration adjusts the scope of energy storage devices involved in the strategy and the power allocation ratio.

[0138] The adaptive reconstruction process of the spatiotemporal characteristics of the cross-spatial hedging strategy employs a genetic algorithm to optimize strategy parameter configuration. The algorithm population size is set to 100 individuals, the number of generations to 50, the crossover probability to 0.8, and the mutation probability to 0.02. The fitness function uses a multi-objective optimization method, simultaneously considering both profit maximization and risk minimization. A response strategy generator is established to form a dynamic response strategy to market fluctuations. This generator produces specific execution instruction sequences based on the reconstructed strategy parameters.

[0139] The engineering implementation of options trading execution adopts a distributed architecture design. The core trading engine is deployed on a high-performance computing server, and the field controllers of each energy storage device maintain a connection with the core engine through a dedicated communication network. The communication protocol adopts a custom application layer protocol based on the transmission control protocol, and the message format uses binary encoding to improve transmission efficiency. The message header includes fields such as message type, message length, timestamp, and checksum.

[0140] Data persistence utilizes a relational database to store structured data and a time-series database to store high-frequency monitoring data. Data backup employs a combination of master-slave replication and periodic snapshots. Concurrency is achieved through thread pool technology, with the core thread count set to twice the number of CPU cores, the maximum thread count set to three times the core thread count, and a thread idle timeout of sixty seconds. Memory management employs object pooling to reduce garbage collection overhead, and critical data structures utilize a pre-allocated memory strategy.

[0141] The exception handling mechanism includes logic for handling various exception types such as network connection errors, data parsing errors, and computational overflow errors. The exception recovery strategy combines automatic retries with manual intervention. Performance monitoring is achieved by establishing a monitoring dashboard to display key performance indicators in real time, including transaction latency, data processing throughput, memory usage, and network bandwidth utilization.

[0142] In a real-world scenario, the energy storage system comprises ten 5 MW energy storage units. The options market offers both call and put options. At 9:00 AM on a certain trading day, the pricing function calculates a theoretical value of 150 yuan per MWh for the call option, while the current market price is 130 yuan per MWh. The trading signal generator generates a buy signal. The order manager generates a buy order for 100 MWh at a price limit of 140 yuan per MWh. The status monitoring system shows the energy storage units are currently at 60% state of charge (SOC). After the actual discharge operation, the SOC becomes 55%, exceeding the theoretical expected SOC of 58%, resulting in a negative SOC deviation of 3%. The exercise trigger time is preset to 10:30 AM, but the actual trigger time is 10:32 AM, resulting in a positive SOC deviation of 2 minutes. The market price rises by 8% after the discharge operation, exceeding the expected increase of 10%, resulting in a negative price response deviation of 2%. The three-dimensional feedback information construction result is a triplet data with a state transition deviation of -3%, an exercise trigger deviation of +2 minutes, and a price response deviation of -2%. Deviation analysis results show that the discharge component deviation exceeds the set threshold, triggering a weight correction mechanism. The discharge path transmission weight is adjusted from 0.6% to 0.58%. Based on the correction results, the strategy reconstruction engine adjusts the execution power of the next discharge strategy from 4 MW to 4.2 MW, and the execution time window from 2 hours to 1.8 hours.

[0143] A second aspect of the present invention provides a market-based energy storage revenue optimization strategy system, comprising:

[0144] The first unit is used to obtain the multi-timescale price curves of energy storage devices and the terms of two-way option contracts. Based on the call and put exercise conditions in the two-way option contract terms, it identifies the asymmetric risk transmission path of energy storage capacity in the charging and discharging state and establishes a risk transmission network model.

[0145] The second unit is used to dynamically map the time-series schedulable window of the energy storage device to the spot trading period in the multi-timescale price curve based on the risk transmission network model, identify the differences in the sensitivity of energy storage charging and discharging actions to market price fluctuations under different exercise paths, and construct a cross-temporal hedging strategy group.

[0146] The third unit is used to establish a state-dependent pricing equation under the risk-neutral measurement framework based on the energy loss constraints and response delay constraints introduced in the energy storage device charging and discharging state transition process of the aforementioned cross-temporal hedging strategy group, and solve it to obtain the two-way option risk hedging pricing.

[0147] The fourth unit is used to execute two-way option transactions for energy storage capacity based on the two-way option risk hedging pricing, forming three-dimensional feedback information; using the state transition deviation in the three-dimensional feedback information to perform asymmetric correction on the risk transmission weight in the risk transmission network model; and using the exercise trigger deviation and price response deviation to adaptively reconstruct the spatiotemporal mapping relationship of the hedging strategy in the spatiotemporal hedging strategy group, thereby achieving closed-loop optimization of pricing accuracy and hedging effectiveness.

[0148] A third aspect of the present invention provides an electronic device, comprising:

[0149] processor;

[0150] Memory used to store processor-executable instructions;

[0151] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0152] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0153] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for energy storage revenue optimization strategy based on market mechanism, characterized in that, include: Obtain the price curves of energy storage devices across multiple time scales and the terms of two-way option contracts. Based on the call and put options in the terms of the two-way option contracts, identify the asymmetric risk transmission path of energy storage capacity under charging and discharging conditions, and establish a risk transmission network model. Based on the risk transmission network model, the time-series schedulable window of the energy storage device is dynamically mapped to the spot trading period in the multi-timescale price curve with state awareness. This identifies the differences in the sensitivity of energy storage charging and discharging actions to market price fluctuations under different exercise paths, and constructs a cross-temporal hedging strategy group, including: Based on the aforementioned risk transmission network model, the dynamic operating characteristics of the discharge action node under the bullish risk transmission path and the charging action node under the bearish risk transmission path are analyzed. Combined with the physical constraint boundary of the energy storage device, a schedulable window with time-series characteristics is constructed. By analyzing the fluctuation patterns of spot market price sequences in the multi-timescale price curves, the evolution characteristics of price increase and price decrease intervals are characterized. The price increase intervals are mapped to call option exercise-related periods, and the price decrease intervals are mapped to put option exercise-related periods, thus constructing a set of spot trading periods that reflect market volatility characteristics. The study explores the temporal coupling mechanism between the time-series schedulable window and the set of spot trading periods. Based on the transmission weights of the risk transmission network model, a state-aware dynamic mapping relationship is established between the charging and discharging action execution node and the price fluctuation response node within the overlapping interval. Based on the state-aware dynamic mapping relationship, the response characteristics of discharge actions to price increase fluctuations and charging actions to price decrease fluctuations are analyzed, and a sensitivity evaluation index is constructed by evaluating the differences in response characteristics under different exercise paths. Based on the sensitivity evaluation index, optimize the discharge and charge hedging strategy layout within the bullish exercise period and the putish exercise period, establish a time-series connection relationship based on state of charge constraints, and form a complete cross-temporal hedging strategy group. Based on the aforementioned cross-temporal hedging strategy group, energy loss constraints and response delay constraints are introduced during the charging and discharging state transition of energy storage devices. A state-dependent pricing equation is established under the risk-neutral measurement framework, and the two-way option risk hedging pricing is obtained by solving it. Based on the two-way option risk hedging pricing, two-way option transactions for energy storage capacity are executed, forming three-dimensional feedback information. The state transition deviation in the three-dimensional feedback information is used to asymmetrically correct the risk transmission weight in the risk transmission network model. The exercise trigger deviation and price response deviation are used to adaptively reconstruct the spatiotemporal mapping relationship of the hedging strategy in the spatiotemporal hedging strategy group, thereby achieving closed-loop optimization of pricing accuracy and hedging effectiveness.

2. The method according to claim 1, characterized in that, Based on the call and put exercise conditions in the aforementioned two-way option contract terms, the asymmetric risk transmission path of energy storage capacity under charging and discharging states is identified, and a risk transmission network model is established, including: The call and put exercise conditions in the two-way option contract terms are analyzed. Combining the market price fluctuation pattern and the characteristics of energy storage market transactions, the dynamic change pattern and fluctuation characteristics of the price trigger threshold range are extracted, and a set of two-way exercise trigger conditions considering the impact of the market environment is established. Based on the set of bidirectional exercise triggering conditions, the power ramping constraint risk caused by the inherent characteristics of the energy storage device when the bullish exercise condition is triggered, and the capacity margin constraint risk caused by the real-time operating status when the bearish exercise condition is triggered, are analyzed. Combined with the dynamic change law of the price triggering threshold range, the characteristics of risk sources in different states are constructed. Based on the characteristics of the risk sources in different states, the power ramping constraint risk nodes and capacity margin constraint risk nodes under the paths of the bullish exercise conditions and the bearish exercise conditions are mapped to different levels of the risk transmission network. By characterizing the transmission rules and mutual influence of risk nodes between different levels, a risk transmission framework with a hierarchical structure is established. By combining historical dispatch response characteristic data, the differences in response characteristics and energy conversion patterns of energy storage devices under different states of charge are analyzed. A bidirectional transmission channel is constructed based on the hierarchical structure of the risk transmission framework. The risk transmission framework and the bidirectional transmission channel are then integrated into a complete risk transmission network model.

3. The method according to claim 1, characterized in that, The study explores the temporal coupling mechanism between the time-series schedulable window and the set of spot trading sessions. Based on the transmission weights of the risk transmission network model within the overlapping interval, it establishes a state-aware dynamic mapping relationship between the charging / discharging action execution node and the price fluctuation response node, including: By establishing the dynamic operating characteristics of discharge and charging time windows in a time-schedulable window, and combining the temporal evolution of call and put option exercise periods in the spot trading session set, we explore the coupling characteristics of discharge and charging time windows in different exercise periods and construct an adaptive strength index that reflects the degree of temporal coupling. Based on the adaptive intensity index, the dynamic evolution of the discharge time window during the bullish exercise period and the charging time window during the bearish exercise period are analyzed. By evaluating the changing characteristics of the scheduling feasibility state, a set of states containing the overlapping intervals of bullish and bearish options is established. This study delves into the transmission relationship between the discharge node and the price increase node in the bullish path and the transmission characteristics between the charging node and the price decrease node in the bearish path within the risk transmission network model. It then systematically associates these relationships with the set of states in the overlapping intervals to form a complete mapping foundation. Based on this mapping foundation, a state-aware dynamic mapping relationship is constructed within the overlapping intervals of bullish and bearish trends.

4. The method according to claim 1, characterized in that, Based on the aforementioned spatiotemporal hedging strategy group, which incorporates energy loss constraints and response delay constraints during the charging and discharging state transition of energy storage devices, a state-dependent pricing equation is established within a risk-neutral measurement framework. Solving this equation yields the following two-way option risk hedging pricing: The dynamic characteristics of the discharge state transition process of the bullish hedging strategy and the charging state transition process of the bearish hedging strategy are analyzed from the cross-temporal hedging strategy group. The energy loss coefficient and response delay parameter corresponding to each state transition process are obtained, and a set of constraint parameters describing the state transition law is constructed. Based on the set of constraint parameters, an energy and time delay constraint relationship is constructed. An energy loss constraint equation is established through the energy loss coefficient. A response time delay constraint equation is established by combining the response time delay parameter. The energy loss constraint equation and the response time delay constraint equation are combined to form a complete constraint equation system. Under the risk-neutral measurement framework, the bullish hedging strategy and the put hedging strategy are modified using the constraint equation system. The modified return function is constructed as a state-dependent function that depends on the state of charge of the energy storage device. The state-dependent pricing equation is established using the constraint equation system as boundary conditions. The state-dependent pricing equation is solved, and the boundary conditions determined by the constraint equation system are introduced to obtain the pricing functions of the bullish hedging strategy and the put hedging strategy under different charge states. Combining the dynamic evolution characteristics of the pricing function and the conversion law of charge state, a two-way option risk hedging pricing is constructed.

5. The method according to claim 4, characterized in that, Solving the state-dependent pricing equation, and by introducing the boundary conditions determined by the constraint equation system, yields the pricing functions for the bullish hedging strategy and the bearish hedging strategy under different states of charge: The charged state variables in the state-dependent pricing equation are dynamically divided into regions. Based on the energy loss constraint equation in the constraint equation system, the influence characteristics of the charged state on the charging and discharging efficiency are explored. The influence mechanism of the charged state on the conversion rate is analyzed in combination with the response delay constraint equation. Based on the critical characteristic points of charging and discharging efficiency and state conversion rate, a multi-dimensional state space decomposition structure is constructed. For each state subspace in the multidimensional state space decomposition structure, the corresponding energy and time constraint boundaries are extracted from the constraint equation system and transformed into power and time constraints of the state-dependent pricing equation at the subspace boundary, thus establishing a dynamic mapping relationship from the constraint equation system to the pricing equation boundary. By applying the constraints determined by the dynamic mapping relationship to the state-dependent pricing equation in each state subspace, piecewise pricing solutions for the bullish hedging strategy and the bearish hedging strategy are obtained. Continuity constraints between the piecewise pricing solutions are established through critical feature points in the multidimensional state space decomposition structure, thereby reconstructing the pricing functions of the bullish hedging strategy and the bearish hedging strategy under different charge states.

6. The method according to claim 1, characterized in that, Based on the aforementioned two-way option risk hedging pricing, two-way option transactions for energy storage capacity are executed, forming three-dimensional feedback information. The state transition bias in the three-dimensional feedback information is used to asymmetrically correct the risk transmission weights in the risk transmission network model. Adaptive reconstruction of the spatiotemporal mapping relationship of the hedging strategies in the cross-spatial hedging strategy group is performed using exercise trigger bias and price response bias, including: Based on the pricing function in the two-way option risk hedging pricing, the option trading strategy is executed in the energy storage capacity two-way option trading market. By monitoring the operating status of the energy storage equipment in real time, the state transition deviation, exercise trigger deviation and price response deviation are obtained, and three-dimensional feedback information describing the dynamic characteristics of the system is constructed. Extract the state transition deviation from the three-dimensional feedback information, analyze the dynamic evolution of its charging and discharging components, perform asymmetric correction on the transmission weights of the charging and discharging risk transmission path in the risk transmission network model, and establish a dynamic correction mechanism that reflects the asymmetric characteristics of charging and discharging. By combining the exercise trigger deviation and price response deviation in the three-dimensional feedback information, the degree of deviation between them and the spatiotemporal mapping parameters of each strategy in the cross-temporal hedging strategy group is evaluated. Based on the correction results of the dynamic correction mechanism, the execution spatiotemporal characteristics of the cross-temporal hedging strategy are adaptively reconstructed to form a dynamic response strategy to market fluctuations.

7. A market-based energy storage revenue optimization strategy system, used to implement the method of any one of claims 1-6, characterized in that, include: The first unit is used to obtain the multi-timescale price curves of energy storage devices and the terms of two-way option contracts. Based on the call and put exercise conditions in the two-way option contract terms, it identifies the asymmetric risk transmission path of energy storage capacity in the charging and discharging state and establishes a risk transmission network model. The second unit is used to dynamically map the time-series schedulable window of the energy storage device to the spot trading period in the multi-timescale price curve based on the risk transmission network model, identify the differences in the sensitivity of energy storage charging and discharging actions to market price fluctuations under different exercise paths, and construct a cross-temporal hedging strategy group. The third unit is used to establish a state-dependent pricing equation under the risk-neutral measurement framework based on the energy loss constraints and response delay constraints introduced in the energy storage device charging and discharging state transition process of the aforementioned cross-temporal hedging strategy group, and solve it to obtain the two-way option risk hedging pricing. The fourth unit is used to execute two-way option transactions for energy storage capacity based on the two-way option risk hedging pricing, and to generate three-dimensional feedback information; The risk transmission weights in the risk transmission network model are asymmetrically corrected by utilizing the state transition bias in the three-dimensional feedback information. The spatiotemporal mapping relationship of the hedging strategy in the cross-spatiotemporal hedging strategy group is adaptively reconstructed by utilizing the exercise trigger bias and price response bias, thereby achieving closed-loop optimization of pricing accuracy and hedging effectiveness.

8. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

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