Energy storage optimization operation method considering degradation cost

Abstract

The invention provides an energy storage optimization operation method considering degradation cost, and belongs to the technical field of power markets and energy storage optimization operation, and the method comprises the following steps: carrying out piecewise linear method processing for charging and discharging of an energy storage power station, and building an energy storage degradation cost model; taking the stored energy considering the degradation cost as a bidding main body of the market, and constructing a double-layer decision-making model oriented to the energy and frequency modulation market; and on the basis of a strong dual theory and a KKT condition, the double-layer decision model is reconstructed into a mixed integer programming problem to be solved, dual variables influencing price indexes are obtained, and a quantitative basis is provided for an energy storage bidding strategy which assists in considering degradation cost. The method is effective in assisting and coordinating energy storage economic benefits and life loss.

Description

Technical Field

[0001] This invention relates to an energy storage optimization operation method that takes into account degradation costs, belonging to the field of electricity market and energy storage optimization operation technology. Background Technology

[0002] On the one hand, the intermittency, volatility, and uncertainty of new energy power generation lead to a decrease in grid inertia and a decline in frequency regulation capability, posing a severe challenge to the safe and stable operation of the power system. To ensure system stability, frequency regulation has become a critical technical problem that urgently needs to be solved. On the other hand, energy storage technology can address the problems of decreased system inertia and frequency stability faced by a high proportion of new energy integration into the power system. In a power market environment, incorporating energy storage systems into the energy and frequency regulation market can solve the problems of economical system operation and frequency stability.

[0003] The design and evolution of energy storage participation mechanisms in typical international frequency regulation markets offer valuable lessons for the development of my country's frequency regulation market. Zhang Hongyu et al. introduced the entry conditions, participation models, and pricing mechanisms of frequency regulation markets. Chen Qixin et al. described the value analysis, framework, and trading mechanisms of domestic and international energy storage participation markets, providing feasible suggestions for energy storage participation in my country's market. Li Jiamei et al., combining international practical experience with my country's situation, offered suggestions on market structure, entry rules, bid clearing, and settlement allocation, providing a reference for the construction of a virtual power plant frequency regulation market mechanism. Xiao Yunpeng et al. summarized current typical domestic and international market clearing coordination mechanisms, analyzing the economic benefits of energy storage participation and its impact on clearing and settlement.

[0004] Domestic and international experience in the frequency regulation market shows that a sound market mechanism can guide energy storage power stations to participate in frequency regulation ancillary services in a standardized manner and ensure that they obtain corresponding economic benefits. However, when optimizing strategies for energy storage participation in the frequency regulation market, considering only the market benefits of the generating units while ignoring the degradation costs of energy storage will lead to an overestimation of actual economic benefits. Therefore, accurately quantifying degradation costs is one of the core issues in the bidding decision-making process for energy storage participation in the market.

[0005] While some progress has been made in energy storage system degradation modeling, shortcomings remain in model dimensionality and engineering applicability. Existing research largely focuses on the depth of discharge (DoD), but battery degradation actually involves the coupling of multiple factors, including ambient temperature, state of charge (SOC), charge / discharge rate, and health status. Given the highly nonlinear and strongly coupled nature of battery degradation, current mainstream research tends to employ simplified DoD-based modeling methods to balance model accuracy and practicality, using rainflow counting to calculate equivalent cycle counts and assess lifetime loss. Furthermore, although data-driven methods have shown potential in predicting remaining lifetime and estimating SOC, these methods are highly dependent on data and have limited generalization ability across different application scenarios. To address the engineering application needs of degradation modeling in scheduling scenarios, some studies have proposed a marginal degradation cost modeling method. This method constructs a degradation cost function to improve the scheduling accuracy and operational benefit assessment capabilities of energy storage systems in complex operating environments. The use of a piecewise linear degradation cost function improves computational efficiency while maintaining modeling accuracy. Therefore, the invention uses a piecewise linear method to construct an energy storage degradation cost model and integrates it into a fast response frequency regulation control strategy to improve the economic assessment and scheduling performance of energy storage systems in actual operation.

[0006] Currently, research has been conducted both domestically and internationally on the mechanism of energy storage participation in the spot market and the impact of energy storage lifetime on returns. Chen Zeyu et al. designed a self-dispatch strategy for energy and frequency regulation markets based on energy storage lifetime. Liu Chunyang et al. constructed an economic dispatch model for microgrids including wind, solar, energy storage, and controllable generator units, considering energy storage lifetime. Liu Qingkai et al. constructed a two-stage model for energy storage optimization configuration and operation considering cycle life depreciation. Liu Fan et al. proposed an energy storage capacity configuration method based on operation strategy for building energy storage power stations in wind power aggregation areas. Diao Rui et al. proposed a power allocation strategy to reduce the operating costs of heterogeneous energy storage systems based on cycle information to evaluate energy storage aging models. XU BL et al. evaluated the degradation caused by energy storage providing frequency control in the PJM market based on a semi-empirical lithium-ion battery degradation model. YONG P et al. established a framework for the corresponding interaction between the degradation model and the power system optimization model based on the battery degradation equation. WICKE M et al. considered the energy storage degradation cost and used hybrid energy storage systems for photovoltaic output power stabilization, market trading, and peak shaving.

[0007] In summary, the charging and discharging strategies of energy storage power stations have a significant impact on the market clearing results and returns of market participants in the spot market. However, in the bidding strategies for energy storage participation in the energy and frequency regulation markets, few methods are introduced to quantify the degradation costs of energy storage during frequency regulation in real time using piecewise linear methods. Based on this, this invention proposes a two-layer optimization model for the energy and frequency regulation market that considers energy storage degradation costs. The upper-layer model aims to maximize energy storage revenue, while the lower-layer model aims to minimize the system market bidding costs. The optimal winning bid power for each unit and power station in the energy and frequency regulation market is determined, and the impact of energy storage degradation costs on the clearing price and winning bid power in the energy and frequency regulation market is analyzed. Summary of the Invention

[0008] The technical problem solved by this invention is: This invention provides an energy storage optimization operation method that takes into account degradation costs. This invention uses a piecewise linear method to process the charging and discharging of energy storage power stations and constructs an energy storage degradation cost model. Based on strong duality theory and KKT conditions, the two-level decision model is reconstructed into a mixed integer programming problem to obtain the dual variables affecting price indicators, providing a quantitative basis for assisting in energy storage bidding strategies that take into account degradation costs.

[0009] The technical solution of this invention is: an energy storage optimization operation method that takes into account degradation costs, the method comprising:

[0010] Step 1: Apply piecewise linear method to the charging and discharging of energy storage power stations and establish an energy storage degradation cost model;

[0011] Step 2: Take energy storage, which takes into account degradation costs, as the main subject of market bidding and construct a two-level decision-making model for the energy and frequency regulation markets;

[0012] Step 3: Based on strong duality theory and KKT conditions, the two-level decision model is reconstructed into a mixed integer programming problem to obtain the dual variables that affect price indicators.

[0013] Further, Step 1 includes:

[0014] Step 1.1.1: Construct the energy storage cycle degradation function;

[0015] The energy storage cycle lifetime is negatively correlated with the cycle depth DoD. In a complete energy storage cycle, the energy storage cycle degradation function is shown in equations (1)-(2).

[0016]

[0017] Among them, D t Let DoD be the cycle depth for time period t. Let m represent the energy of the energy storage power station and the discharge power won in the frequency regulation market during time period t; Δt is the time interval; For discharge efficiency; Ccap B represents the total energy storage capacity. d (Dt) represents D t The cumulative impact function on energy storage aging;

[0018] Step 1.1.2: Construct the degradation cost function;

[0019] The piecewise linear method is used to calculate the loop depth D. t Perform segmentation processing, by using D t Discretized into N segments, the calculation of energy storage degradation cost is simplified while retaining the nonlinear characteristics of the degradation process; the piecewise linear degradation cost function is shown in equations (3)-(4);

[0020]

[0021] Wherein d(D) t ) represents the derivative of the energy storage cycle degradation function, c n C represents the marginal aging cost of the loop depth segment n; gh This represents the replacement cost of the energy storage unit; N represents the total number of cycle depth segments. For the depth of discharge D t The degradation cost approximation function falls on the (n-1) / N and n / N segments. For discharge efficiency;

[0022] Step 1.1.3: Construct the total degradation cost function;

[0023] Within each cycle depth segment n, auxiliary variables for charging and discharging power and state of charge are introduced. The total degradation cost of the energy storage power station is the sum of the degradation costs within each cycle depth segment, as shown in equation (5).

[0024]

[0025] in, N represents the total degradation cost of the energy storage power station; T represents the total number of time periods; N represents the total degradation cost of the energy storage power station. es Indicates the number of energy storage power stations; Let t be the discharge power of energy storage power station m during the cycle depth n in time period t.

[0026] Furthermore, Step 2 includes:

[0027] Step 2.1: Construct a pricing model for upper-layer energy storage participation in the market;

[0028] The objective function of the upper-layer energy storage participation market strategy pricing model is to maximize the total revenue obtained by energy storage in the energy and frequency regulation market, and to consider the degradation cost of energy storage; as shown in equation (6);

[0029]

[0030] Among them, C H For the total revenue of the energy storage power station market; v t This represents the energy market clearing price for energy storage power stations during time period t; These represent the energy m of the energy storage power station during time period t, as well as the discharge power, charging power, frequency regulation capacity, and frequency regulation mileage won in the frequency regulation market. These represent the FM capacity price and mileage price after the FM market clears out;

[0031] Step 2.2: Construct a market clearing model for lower-level energy frequency regulation;

[0032] The lower-level energy frequency regulation market clearing model optimizes the operation strategy of each unit through market clearing, rationally allocates the output of each unit in the market, meets the system supply and demand balance and frequency regulation requirements, and minimizes the application cost of each unit; as shown in equation (7):

[0033]

[0034] Among them, C L For the market declaration costs of thermal power and energy storage units; N G The number of thermal power units; These represent the winning bid output, winning bid capacity, and winning bid mileage of thermal power unit i in the energy market, frequency regulation market, and time period t, respectively. These represent the market power generation price, capacity price, and mileage price for thermal power unit i during time period t. and These represent the charging, discharging, capacity, and mileage quotes for energy storage power station m in the energy and frequency regulation market during time period t.

[0035] Furthermore, Step 2.1 includes:

[0036] Step 2.1.1: Constructing Energy Storage Charge and Discharge Constraints

[0037] During the bidding process in the energy and frequency regulation market, energy storage power stations comprehensively consider the bid price and capacity limits, as well as the constraints on energy storage charging and discharging power and state of charge within each cycle depth.

[0038] When an energy storage power station bids in the energy and frequency regulation market, the sum of the charging and discharging power within the cycle depth segment n is equal to the charging and discharging power of the energy storage power station during that period.

[0039]

[0040] in, These represent the maximum charging and discharging power of the energy storage power station m, respectively. These represent the charging and discharging states of energy storage station m during time period t; The charging and discharging power of energy storage power station m during time period t in the circulation depth segment n;

[0041] Step 2.1.2: Construct energy storage state of charge constraints;

[0042] After segmenting the DoD of the energy storage cycle, the state-of-charge relationship of the energy storage power station is shown in equation (9):

[0043]

[0044] Among them, auxiliary variable E t,m,n The state of charge of the energy storage power station during the m-cycle depth segment n in time period t; E t,m,nmax E represents the maximum state of charge of energy storage in cycle depth segment n; t,m E represents the state of charge of energy storage power station m during time period t in the application process. t,m,max E t,m,min These represent the upper and lower limits of the allowable state of charge (SOP) for the energy storage power station m during time period t. E represents the charging and discharging efficiency of an energy storage power station. 0,m E T,m The charging state at the beginning and end of the time period;

[0045] Step 2.1.3: Establish capacity constraints for energy storage applications;

[0046]

[0047] in, This represents the maximum frequency regulation capacity of the energy storage power station. Indicates the mileage-to-capacity ratio;

[0048] Step 2.1.4: Construct energy storage pricing constraints;

[0049]

[0050] in, and Price quotes for charging, discharging, capacity, and mileage of energy storage power stations in the energy and frequency regulation markets during time period t; and These represent the maximum values ​​for charging, discharging, capacity, and mileage of energy storage power station m, respectively.

[0051] Furthermore, Step 2.2 includes:

[0052] Step 2.2.1: Construct power balance constraints for power system nodes;

[0053] When generating units exit the energy and frequency regulation market, it is essential to ensure that the power generation of each node in the power system equals the load to satisfy the basic physical constraint of power balance, thereby ensuring the safe and stable operation of the power grid; the formula is shown in equation (12):

[0054]

[0055] Where, Φ G (j), Φ es (j), Φ d (j) represents the set of thermal power, energy storage, and load at node j; Φ(j) represents the set connected to node j. B represents the load of node j; ja θ is the admittance between nodes j and a; t,j and θ t,a Phase angles of node j and node a at time t, respectively; dual variable λ t,j Let $t$ be the clearing price of node $j$ during time period $t$.

[0056] Step 2.2.2: Construct line transmission capacity constraints;

[0057]

[0058] Among them, P l,max P l,min The upper and lower limits of the transmission capacity of line l are respectively defined; These are the dual variables of the corresponding constraints;

[0059] Step 2.2.3: Construct the frequency regulation capacity and frequency regulation mileage requirements for thermal power and energy storage units;

[0060]

[0061] in, These are the system frequency regulation capacity and mileage requirements, respectively. Let be the frequency regulation capacity and mileage clearing price for time period t, and be the dual variables of the unit's frequency regulation capacity and mileage demand;

[0062] Step 2.2.4: Establish market clearing rules and constraints for thermal power and energy storage units;

[0063] The constraints on the winning bid capacity, winning bid mileage, and output of thermal power units are shown in Equation (15), and the relevant constraints on energy storage power stations are shown in Equation (16).

[0064]

[0065] in, These are the maximum and minimum active power outputs of thermal power unit i, respectively; For thermal power unit i, these are the maximum frequency regulation capacity and frequency regulation mileage. The frequency regulation mileage-capacity ratio of thermal power unit i;

[0066] All of these are dual variables of the corresponding constraints of thermal power units;

[0067]

[0068] in, These are all dual variables of the corresponding constraints of the energy storage power station.

[0069] Furthermore, Step 3 includes:

[0070] Step 3.1: Linearize the two-level decision model; according to the strong duality theorem, the objective function of the two-level decision model is re-expressed as equation (17):

[0071]

[0072] Step 3.2: Transform and solve the two-level decision model;

[0073] By introducing KKT conditions, the two-level decision model is transformed into a single-level mathematical programming model. The objective function of the simplified single-level mathematical programming model is shown in equation (18):

[0074]

[0075] Where, N L Indicates the number of system lines.

[0076] The present invention also provides an energy storage optimization operation system that takes into account degradation costs, the system comprising: a module for executing the aforementioned energy storage optimization operation method that takes into account degradation costs.

[0077] The beneficial effects of this invention are:

[0078] (1) This invention uses a piecewise linear method to process the charging and discharging of energy storage power stations, and constructs an energy storage degradation cost model, which reduces the life loss cost of energy storage in the process of participating in the frequency regulation market.

[0079] (2) The present invention couples the constructed degradation cost function with the frequency modulation control strategy, which, while characterizing the nonlinear degradation characteristics of the battery, ensures the engineering applicability and computational efficiency of the model in complex operating environments.

[0080] (3) This invention constructs a two-layer decision model that optimizes the upper layer of energy storage revenue and the lower layer of system cost (including degradation cost). The model is reconstructed into a mixed integer programming problem by using strong duality theory and KKT conditions. The influence mechanism of degradation cost on market clearing price and energy storage winning bid power is quantitatively revealed.

[0081] (4) This invention has the effectiveness in assisting in coordinating the economic benefits and lifespan loss of energy storage by constructing a two-level decision-making model for energy storage participation in the energy and frequency regulation market; the dual variables of the price indicators obtained by this invention provide a quantitative basis for energy storage bidding strategies that take into account degradation costs. Attached Figure Description

[0082] Figure 1 This is a schematic diagram of the energy storage participation in the energy and frequency regulation market trading model in this invention;

[0083] Figure 2 This is a schematic diagram illustrating the bidding situation of energy units in the energy market and frequency regulation market clearing, without considering the energy storage degradation cost in this invention;

[0084] Figure 3 This is a schematic diagram illustrating the bidding situation of energy and frequency regulation market clearing units in this invention, taking into account the energy storage degradation costs.

[0085] Figure 4 This is a schematic diagram illustrating the bidding situation for the frequency regulation capacity of generating units in the energy and frequency regulation market clearing process, without considering the energy storage degradation cost in this invention.

[0086] Figure 5 This is a schematic diagram illustrating the bidding situation for the frequency regulation capacity of generating units, taking into account the energy storage degradation costs and the clearing of the frequency regulation market, in this invention.

[0087] Figure 6 This is a schematic diagram illustrating the bidding situation of frequency regulation mileage of generating units in the energy and frequency regulation market clearing process, without considering the energy storage degradation cost in this invention.

[0088] Figure 7 This is a schematic diagram illustrating the bidding results for frequency regulation mileage of generating units, taking into account the energy storage degradation costs and the clearing of the frequency regulation market, in this invention.

[0089] Figure 8 This is a schematic diagram of the energy market clearing price for energy and frequency regulation market clearing, without considering the energy storage degradation cost in this invention.

[0090] Figure 9 This is a schematic diagram of the energy market clearing price considering the energy storage degradation cost and the frequency regulation market clearing in this invention;

[0091] Figure 10 This is a schematic diagram of the energy and frequency regulation market clearing price in this invention, without considering the energy storage degradation cost;

[0092] Figure 11 This is a schematic diagram of the energy and frequency regulation market clearing price that takes into account the energy storage degradation cost in this invention. Detailed Implementation

[0093] Example 1: As Figures 1-11 As shown, an energy storage optimization operation method that takes into account degradation costs includes:

[0094] Step 1: Apply piecewise linear method to the charging and discharging of energy storage power stations and establish an energy storage degradation cost model;

[0095] Further, Step 1 includes:

[0096] Step 1.1.1: Construct the energy storage cycle degradation function;

[0097] Energy storage will be frequently charged and discharged during frequency regulation, which will lead to its lifespan decay and performance degradation. The energy storage cycle lifespan is negatively correlated with the cycle depth DoD. In a complete energy storage cycle, the energy storage cycle degradation function is shown in equations (1)-(2).

[0098]

[0099] Among them, D t Let DoD be the cycle depth for time period t. Let m represent the energy of the energy storage power station and the discharge power won in the frequency regulation market during time period t; Δt is the time interval; For discharge efficiency; C cap B represents the total energy storage capacity. d (Dt) represents D t The cumulative impact function on energy storage aging;

[0100] Step 1.1.2: Construct the degradation cost function;

[0101] Energy storage degradation costs are non-linear; a piecewise linear method is used to calculate the cycle depth D. t Perform segmentation processing, by using D t Discretized into N segments, the calculation of energy storage degradation cost is simplified while retaining the nonlinear characteristics of the degradation process; the piecewise linear degradation cost function is shown in equations (3)-(4);

[0102]

[0103] Wherein d(D) t ) represents the derivative of the energy storage cycle degradation function, c n C represents the marginal aging cost of the loop depth segment n; gh This represents the replacement cost of the energy storage unit; N represents the total number of cycle depth segments. For the depth of discharge D t The degradation cost approximation function falls on the (n-1) / N and n / N segments. For discharge efficiency;

[0104] Step 1.1.3: Construct the total degradation cost function;

[0105] To accurately describe the degradation cost of energy storage in different DoD segments, auxiliary variables of charge and discharge power and state of charge are introduced in each cycle depth segment n. The total degradation cost of the energy storage power station is the sum of the degradation costs in each cycle depth segment, as shown in equation (5).

[0106]

[0107] in, N represents the total degradation cost of the energy storage power station; T represents the total number of time periods; N represents the total degradation cost of the energy storage power station. es Indicates the number of energy storage power stations; Let t be the discharge power of energy storage power station m during the cycle depth n in time period t.

[0108] Step 2: Take energy storage that takes into account degradation costs as the main subject of market bidding, and construct a two-level decision-making model for the energy and frequency regulation market to optimize energy storage revenue and lifespan;

[0109] Furthermore, Step 2 includes:

[0110] Step 2.1: Construct a pricing model for upper-layer energy storage participation in the market;

[0111] The upper-level model is an energy storage decision model. The objective function of the upper-level energy storage participation market strategy bidding model is to maximize the total revenue obtained by energy storage in the energy and frequency regulation market, and to consider the degradation cost of energy storage; as shown in equation (6);

[0112]

[0113] Among them, C H For the total revenue of the energy storage power station market; v t This represents the energy market clearing price for energy storage power stations during time period t; These represent the energy m of the energy storage power station during time period t, as well as the discharge power, charging power, frequency regulation capacity, and frequency regulation mileage won in the frequency regulation market. These represent the FM capacity price and mileage price after the FM market clears out;

[0114] Step 2.2: Construct a market clearing model for lower-level energy frequency regulation;

[0115] The lower-level energy frequency regulation market clearing model optimizes the operation strategy of each unit through market clearing, rationally allocates the output of each unit in the market, meets the system supply and demand balance and frequency regulation requirements, and minimizes the application cost of each unit; as shown in equation (7):

[0116]

[0117] Among them, C L For the market declaration costs of thermal power and energy storage units; N G The number of thermal power units; These represent the winning bid output, winning bid capacity, and winning bid mileage of thermal power unit i in the energy market, frequency regulation market, and time period t, respectively. These represent the market power generation price, capacity price, and mileage price for thermal power unit i during time period t. and These represent the charging, discharging, capacity, and mileage quotes for energy storage power station m in the energy and frequency regulation market during time period t.

[0118] Furthermore, Step 2.1 includes:

[0119] Step 2.1.1: Constructing Energy Storage Charge and Discharge Constraints

[0120] During the bidding process in the energy and frequency regulation market, energy storage power stations comprehensively consider the bid price and capacity limits, as well as the constraints on energy storage charging and discharging power and state of charge within each cycle depth.

[0121] When an energy storage power station bids in the energy and frequency regulation market, the sum of the charging and discharging power within the cycle depth segment n is equal to the charging and discharging power of the energy storage power station during that period.

[0122]

[0123] in, These represent the maximum charging and discharging power of the energy storage power station m, respectively. These represent the charging and discharging states of energy storage station m during time period t; The charging and discharging power of energy storage power station m during time period t in the circulation depth segment n;

[0124] Step 2.1.2: Construct energy storage state of charge constraints;

[0125] After segmenting the DoD of the energy storage cycle, the state-of-charge relationship of the energy storage power station is shown in equation (9):

[0126]

[0127] Among them, auxiliary variable E t,m,n The state of charge of the energy storage power station during the m-cycle depth segment n in time period t; Et,m,nmax E represents the maximum state of charge of energy storage in cycle depth segment n; t,m E represents the state of charge of energy storage power station m during time period t in the application process. t,m,max E t,m,min These represent the upper and lower limits of the allowable state of charge (SOP) for the energy storage power station m during time period t. E represents the charging and discharging efficiency of an energy storage power station. 0,m E T,m The charging state at the beginning and end of the time period;

[0128] Step 2.1.3: Establish capacity constraints for energy storage applications;

[0129]

[0130] in, This represents the maximum frequency regulation capacity of the energy storage power station. Indicates the mileage-to-capacity ratio;

[0131] Step 2.1.4: Construct energy storage pricing constraints;

[0132]

[0133] in, and Price quotes for charging, discharging, capacity, and mileage of energy storage power stations in the energy and frequency regulation markets during time period t; and These represent the maximum values ​​for charging, discharging, capacity, and mileage of energy storage power station m, respectively.

[0134] Furthermore, Step 2.2 includes:

[0135] Step 2.2.1: Construct power balance constraints for power system nodes;

[0136] When generating units exit the energy and frequency regulation market, it is essential to ensure that the power generation of each node in the power system equals the load to satisfy the basic physical constraint of power balance, thereby ensuring the safe and stable operation of the power grid; the formula is shown in equation (12):

[0137]

[0138] Where, Φ G (j), Φ es (j), Φ d (j) represents the set of thermal power, energy storage, and load at node j; Φ(j) represents the set connected to node j. B represents the load of node j; ja θ is the admittance between nodes j and a; t,j and θ t,aPhase angles of node j and node a at time t, respectively; dual variable λ t,j Let $t$ be the clearing price of node $j$ during time period $t$.

[0139] Step 2.2.2: Construct line transmission capacity constraints;

[0140]

[0141] Among them, P l,max P l,min The upper and lower limits of the transmission capacity of line l are respectively defined; These are the dual variables of the corresponding constraints;

[0142] Step 2.2.3: Construct the frequency regulation capacity and frequency regulation mileage requirements for thermal power and energy storage units;

[0143]

[0144] in, These are the system frequency regulation capacity and mileage requirements, respectively. Let be the frequency regulation capacity and mileage clearing price for time period t, and be the dual variables of the unit's frequency regulation capacity and mileage demand;

[0145] Step 2.2.4: Establish market clearing rules and constraints for thermal power and energy storage units;

[0146] The constraints on the winning bid capacity, winning bid mileage, and output of thermal power units are shown in Equation (15), and the relevant constraints on energy storage power stations are shown in Equation (16).

[0147]

[0148] in, These are the maximum and minimum active power outputs of thermal power unit i, respectively; For thermal power unit i, these are the maximum frequency regulation capacity and frequency regulation mileage. The frequency regulation mileage-capacity ratio of thermal power unit i;

[0149] These are all dual variables of the corresponding constraints of thermal power units;

[0150]

[0151] in, These are all dual variables of the corresponding constraints of the energy storage power station.

[0152] Step 3: Based on strong duality theory and KKT conditions, the two-level decision model is reconstructed into a mixed integer programming problem to obtain the dual variables that affect price indicators.

[0153] Furthermore, Step 3 includes:

[0154] Step 3.1: Linearize the two-level decision model; according to the strong duality theorem, the objective function of the two-level decision model is re-expressed as equation (17):

[0155]

[0156] Step 3.2: Transform and solve the two-level decision model;

[0157] By introducing KKT conditions, the two-level decision model is transformed into a single-level mathematical programming model. The objective function of the simplified single-level mathematical programming model is shown in equation (18):

[0158]

[0159] Where, N L Indicates the number of system lines.

[0160] The present invention also provides an energy storage optimization operation system that takes into account degradation costs, the system comprising: a module for executing the aforementioned energy storage optimization operation method that takes into account degradation costs.

[0161] As a further aspect of the present invention, it also includes step 4: example analysis and verification. The specific steps of step 4 are as follows:

[0162] Step 4.1: Verify the correctness of the proposed energy and frequency regulation market clearing model and solution method that includes the energy storage degradation cost based on the IEEE-30 node system. At the same time, to verify the reliability of the simulation, two simulation scenarios are set in the example.

[0163] Scenario 1: Clearing out of the energy and frequency regulation market without considering the cost of energy storage degradation.

[0164] Scenario 2: Clearing out of the energy and frequency regulation market, taking into account the cost of energy storage degradation.

[0165] The topology of the IEEE-30 node system mainly includes lines, load nodes, thermal power units, and energy storage power stations. The market clearing model incentivizes units to bid at marginal cost through a scheduling mechanism and calculates node electricity based on the system node power balance constraints. Two energy storage units of 20MW / 60MW·h and 10MW / 40MW·h are added to the IEEE-30 node system, with a charge and discharge efficiency of 0.9 and maximum and minimum states of charge of 0.9 and 0.1, respectively.

[0166] Numerical fitting was used to obtain the energy storage cycle lifetime loss and DoD function. Piecewise fitting tests were performed on the energy storage cycle degradation function; a relatively smooth piecewise linear approximation of the original function was achieved using a 4-segment division. Too many segments would significantly increase the computational complexity of the model. Based on the scale and characteristics of the solution model, and after balancing model accuracy and computational cost, a total of 4 linearization segments were chosen.

[0167] Step 4.2 Analysis of Energy Market Unit Bidding Results:

[0168] Depend on Figure 2 and Figure 3 The following conclusions can be drawn:

[0169] 1) In the energy market, the cumulative output of the six thermal power units accounts for approximately 90% of the total power generation in each time period. Since the bidding prices for G1, G2, and G4 are lower than other units, they participate in the energy market first according to the principle of prioritizing lower-priced units, operating at near full capacity throughout the 24-hour period. During the peak load periods of 11-12 and 17-19, while G1, G2, and G4 operate at full capacity, G6 participates in the energy market first because its bidding price is lower than G3 and G5. Furthermore, Es1 has a capacity 1.5 times that of Es2, allowing it to obtain higher profits through off-peak charging and peak discharging in the energy market; therefore, Es1's winning bid power in the energy market is higher than that of Es2.

[0170] 2) In Scenario 1, during periods of low load, energy storage units will strategically charge to maximize revenue by taking advantage of market price fluctuations. In Scenario 2, considering the degradation costs of energy storage, energy storage power stations avoid charging during unnecessary periods. Comparing Scenario 1 and Scenario 2 shows that without considering degradation costs, energy storage power stations charge and discharge frequently, primarily optimizing the market based on electricity price differences, without considering the impact of energy storage degradation. Considering degradation costs, frequent charging and discharging accelerates energy storage degradation, thus limiting the frequency and depth of charging for energy storage units.

[0171] Step 4.3, Analysis of the bidding results for unit frequency regulation capacity:

[0172] Depend on Figure 4 and Figure 5 The following conclusions can be drawn:

[0173] 1) In the frequency regulation market, energy storage power stations (Es1 and Es2) account for approximately 65% ​​of the total winning bids for frequency regulation capacity. Because energy storage power stations have rapid response capabilities and a high mileage-to-capacity ratio, they can provide more frequency regulation mileage than thermal power units when there is sufficient frequency regulation capacity margin. Therefore, energy storage power stations with strong frequency regulation capabilities are given priority in the frequency regulation market.

[0174] 2) The frequency regulation capacity bids for thermal power units G2 and G6 were both 12 yuan / MW. Because G2 had priority dispatch in the energy market, G6 won the bid for power in the frequency regulation market. This shows that thermal power units gain revenue by providing stable frequency regulation capacity, reflecting their low marginal cost advantage. Furthermore, thermal power units are constrained by minimum output requirements and other conditions; therefore, during periods 1, 4, 11, and 19, thermal power units G1, G3, and G4 participated in the frequency regulation market on a small scale.

[0175] 3) The winning bid power for Es2 frequency regulation is greater than that for Es1. The main reasons are as follows: Firstly, the power-capacity ratios of Es1 and Es2 are 0.33 and 0.25, respectively. Since the power-capacity ratio of Es2 is lower than that of Es1, it means that Es2 can provide output power for a longer period of time under unit energy, and the change in state of charge is relatively slow, which can undertake basic frequency regulation. Secondly, the frequency regulation capacity requirement of the system is 5% of the total load. Es2 can provide enough power to meet the basic frequency regulation. It is more economical to give priority to Es2 with smaller capacity units.

[0176] 4) By comparing Scenario 1 and Scenario 2, the impact of energy storage degradation costs on the frequency regulation market can be obtained. In Scenario 1, the basic frequency regulation capacity demand is met by Es2, while the incremental demand during peak hours (10-20%) is supplemented by Es1. During off-peak hours, Es1 has no output in the frequency regulation market. In Scenario 2, the smaller rated capacity of Es2 results in a higher degradation cost per unit charge-discharge depth than Es1. During peak hours, prioritizing the use of Es1 can reduce the degradation cost per energy storage cycle. Therefore, during peak hours (11 and 19), Es1 replaces Es2 in bidding for frequency regulation market contracts.

[0177] Step 4.4 Analysis of the Bidding Results for Unit Frequency Regulation Mileage

[0178] Depend on Figure 6 and Figure 7 The following conclusions can be drawn:

[0179] 1) In the frequency regulation market of Scenario 1, Es2's frequency regulation mileage is fully utilized throughout the 24-hour period, while Es1 only participates in the frequency regulation market during the peak load period of 8-20 hours. Due to the high responsiveness, low frequency regulation capacity cost, and strong frequency regulation mileage capability of energy storage power stations, under the market mechanism that does not consider degradation costs, Es2 has the economics and incentive conditions to participate in frequency regulation services throughout the 24-hour period. Therefore, Es2 is incentivized to maximize frequency regulation revenue by operating at full capacity.

[0180] 2) Comparing Scenario 1 and Scenario 2, in Scenario 2, Es2 accounts for approximately 85% of the total power generation won in the frequency regulation market, and Es1 replaces Es2 during periods 4, 11, and 19. Considering the degradation cost of energy storage, the market participation strategy for energy storage was changed. Es2's smaller capacity leads to a higher degradation cost per unit depth of discharge, resulting in accelerated capacity decay under frequent charge-discharge conditions. During the transitional periods of 4, 11, and 19 when load changes rapidly, Es1's larger capacity, with its lower depth of discharge degradation cost and higher power response margin, becomes a more economical frequency regulation resource. Therefore, during peak periods, energy storage Es1 temporarily replaces Es2 in winning bids due to its lower overall cost, improving the overall economic efficiency of the system.

[0181] Step 4.5: Energy Market Clearing Price Analysis

[0182] Depend on Figure 8 and Figure 9 The following conclusions can be drawn:

[0183] 1) During the 24-hour period, the energy market clearing price is positively correlated with nodal load. During periods 10-14 and 16-20, sudden increases in system load drive up nodal prices. During periods 11 and 19, increased generator output during peak load periods causes some grid lines to reach their transmission capacity limits, leading to system congestion. At this time, some grid lines cannot carry more current, resulting in power transmission bottlenecks and a surge in nodal prices, showing a significant difference compared to nodal prices in other periods. During other periods, there is no system congestion, and prices at all nodes converge.

[0184] 2) A comparison of scenarios 1 and 2 shows that the introduction of energy storage degradation costs reduces the participation of energy storage power stations during peak load periods, leading to increased electricity price volatility, especially during the 10-14 and 16-20 periods. In both scenarios, while the clearing price fluctuates, it remains within the range of 190-200 yuan / MW·h.

[0185] Step 4.6: FM Market Clearing Price Analysis:

[0186] 1) From Figure 10 and Figure 11 It can be seen that the clearing price in the frequency regulation market in Scenario 1 is in the range of 12-18 yuan / MW·h; and in Scenario 2, the clearing price in the frequency regulation market is in the range of 12-14 yuan / MW·h. Due to the steep demand curve of frequency regulation during load ramp-up and peak load periods, even a small capacity shortage can cause significant price fluctuations. Therefore, the clearing price in both scenarios fluctuates significantly during the load periods of 4-5, 10-12, and 18-20.

[0187] 2) In both scenarios, during the 10-12 and 16-20 period of load fluctuation, the system's frequency regulation capacity is ample, leading to a decrease in the cost of marginally clearing resources and a decline in the frequency regulation capacity price. The system's use of frequency regulation resources for power adjustment results in an increase in regulation mileage, thus increasing the frequency regulation mileage price. During peak load periods, system frequency fluctuations intensify, increasing the demand for actual frequency regulation actions. At this time, generating units are more inclined to obtain frequency regulation mileage revenue in their market pricing strategies, and the mileage-priority strategy leads to a lower frequency regulation capacity price.

[0188] Step 4.6, Energy and Frequency Modulation Market Revenue Analysis:

[0189] Table 1 shows the revenue of each thermal power unit and energy storage power station in the energy market, frequency regulation market, and total market revenue in both scenarios.

[0190] Table 1 shows the market revenue of each generating unit.

[0191]

[0192] As shown in Table 1, in Scenario 1, thermal power units dominate the energy and frequency regulation market, accounting for a high percentage of their output. Therefore, the total revenue of thermal power units is higher than that of energy storage units. However, thermal power units have limited participation in the frequency regulation market, resulting in lower frequency regulation revenue. Energy storage power stations have relatively low output in the energy market, and without considering degradation costs, their charging and discharging in the energy market fails to generate sufficient revenue, resulting in negative returns. However, energy storage has a frequency regulation advantage in the frequency regulation market, achieving higher revenue, indicating that energy storage power stations mainly rely on the frequency regulation market to provide flexible services for profitability.

[0193] In summary, energy storage degradation costs have a significant impact on the revenue distribution of generating units in the market. By considering energy storage degradation costs, energy storage can participate in energy market transactions during optimized dispatching and rationally allocate frequency regulation service capacity, thereby improving the marginal revenue level in the energy and frequency regulation markets. Ultimately, this leads to higher overall revenue for energy storage and also drives up the total market revenue of the entire system.

[0194] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for optimizing the operation of energy storage considering degradation costs, characterized in that: The method includes: Step 1: Apply piecewise linear method to the charging and discharging of energy storage power stations and establish an energy storage degradation cost model; Step 2: Take energy storage, which takes into account degradation costs, as the main subject of market bidding and construct a two-level decision-making model for the energy and frequency regulation markets; Step 3: Based on strong duality theory and KKT conditions, the two-level decision model is reconstructed into a mixed integer programming problem to obtain the dual variables that affect price indicators.

2. The energy storage optimization operation method considering degradation costs according to claim 1, characterized in that: Step 1 includes: Step 1.1.1: Construct the energy storage cycle degradation function; The energy storage cycle lifetime is negatively correlated with the cycle depth DoD. In a complete energy storage cycle, the energy storage cycle degradation function is shown in equations (1)-(2). Among them, D t Let DoD be the cycle depth for time period t. Let m represent the energy of the energy storage power station and the discharge power won in the frequency regulation market during time period t; Δt is the time interval. For discharge efficiency; C cap B represents the total energy storage capacity. d (Dt) represents D t The cumulative impact function on energy storage aging; Step 1.1.2: Construct the degradation cost function; The piecewise linear method is used to calculate the loop depth D. t Perform segmentation processing, by using D t Discretized into N segments, the calculation of energy storage degradation cost is simplified while retaining the nonlinear characteristics of the degradation process; the piecewise linear degradation cost function is shown in equations (3)-(4); Wherein d(D) t ) represents the derivative of the energy storage cycle degradation function, c n C represents the marginal aging cost of the loop depth segment n; gh This represents the replacement cost of the energy storage unit; N represents the total number of cycle depth segments. For the depth of discharge D t Approximate degradation cost functions falling in the (n-1) / N and n / N segments. For discharge efficiency; Step 1.1.3: Construct the total degradation cost function; Within each cycle depth segment n, auxiliary variables for charging and discharging power and state of charge are introduced. The total degradation cost of the energy storage power station is the sum of the degradation costs within each cycle depth segment, as shown in equation (5). in, N represents the total degradation cost of the energy storage power station; T represents the total number of time periods; N represents the total degradation cost of the energy storage power station. es Indicates the number of energy storage power stations; Let t be the discharge power of energy storage power station m during the cycle depth n in time period t.

3. The energy storage optimization operation method considering degradation costs according to claim 1, characterized in that: Step 2 includes: Step 2.1: Construct a pricing model for upper-layer energy storage participation in the market; The objective function of the upper-layer energy storage participation market strategy pricing model is to maximize the total revenue obtained by energy storage in the energy and frequency regulation market, and to consider the degradation cost of energy storage; as shown in equation (6); Among them, C H For the total revenue of the energy storage power station market; v t This represents the energy market clearing price for energy storage power stations during time period t; These represent the energy m of the energy storage power station during time period t, as well as the discharge power, charging power, frequency regulation capacity, and frequency regulation mileage won in the frequency regulation market. These represent the FM capacity price and mileage price after the FM market clears out; Step 2.2: Construct a market clearing model for lower-level energy frequency regulation; The lower-level energy frequency regulation market clearing model optimizes the operation strategy of each unit through market clearing, rationally allocates the output of each unit in the market, meets the system supply and demand balance and frequency regulation requirements, and minimizes the application cost of each unit; as shown in equation (7): Among them, C L For the market declaration costs of thermal power and energy storage units; N G The number of thermal power units; These represent the winning bid output, winning bid capacity, and winning bid mileage of thermal power unit i in the energy market, frequency regulation market, and time period t, respectively. These represent the market power generation price, capacity price, and mileage price for thermal power unit i during time period t. and These represent the charging, discharging, capacity, and mileage quotes for energy storage power station m in the energy and frequency regulation market during time period t.

4. The energy storage optimization operation method considering degradation costs according to claim 3, characterized in that: Step 2.1 includes: Step 2.1.1: Constructing Energy Storage Charge and Discharge Constraints During the bidding process in the energy and frequency regulation market, energy storage power stations comprehensively consider the bid price and capacity limits, as well as the constraints on energy storage charging and discharging power and state of charge within each cycle depth. When an energy storage power station bids in the energy and frequency regulation market, the sum of the charging and discharging power within the cycle depth segment n is equal to the charging and discharging power of the energy storage power station during that period. in, These represent the maximum charging and discharging power of the energy storage power station m, respectively. These represent the charging and discharging states of energy storage station m during time period t; The charging and discharging power of energy storage power station m during time period t in the circulation depth segment n; Step 2.1.2: Construct energy storage state of charge constraints; After segmenting the DoD of the energy storage cycle, the state-of-charge relationship of the energy storage power station is shown in equation (9): Among them, auxiliary variable E t,m,n The state of charge of the energy storage power station during the m-cycle depth segment n in time period t; E t,m,nmax E represents the maximum state of charge of energy storage in cycle depth segment n; t,m E represents the state of charge of energy storage power station m during time period t in the application process. t,m,max E t,m,min These represent the upper and lower limits of the allowable state of charge (SOP) for the energy storage power station m during time period t. E represents the charging and discharging efficiency of an energy storage power station. 0,m E T,m The charging state at the beginning and end of the time period; Step 2.1.3: Establish capacity constraints for energy storage applications; in, This represents the maximum frequency regulation capacity of the energy storage power station. Indicates the mileage-to-capacity ratio; Step 2.1.4: Construct energy storage pricing constraints; in, and Price quotes for charging, discharging, capacity, and mileage of energy storage power stations in the energy and frequency regulation markets during time period t; and These represent the maximum values ​​for charging, discharging, capacity, and mileage of energy storage power station m, respectively.

5. The energy storage optimization operation method considering degradation costs according to claim 3, characterized in that: Step 2.2 includes: Step 2.2.1: Construct power balance constraints for power system nodes; When generating units exit the energy and frequency regulation market, it is essential to ensure that the power generation of each node in the power system equals the load to satisfy the basic physical constraint of power balance, thereby ensuring the safe and stable operation of the power grid; the formula is shown in equation (12): Where, Φ G (j), Φ es (j), Φ d (j) represents the set of thermal power, energy storage, and load at node j; Φ(j) represents the set connected to node j. B represents the load of node j; ja θ is the admittance between nodes j and a; t,j and θ t,a Phase angles of node j and node a at time t, respectively; dual variable λ t,j Let $t$ be the clearing price of node $j$ during time period $t$. Step 2.2.2: Construct line transmission capacity constraints; Among them, P l,max P l,min The upper and lower limits of the transmission capacity of line l are respectively defined; These are the dual variables of the corresponding constraints; Step 2.2.3: Construct the frequency regulation capacity and frequency regulation mileage requirements for thermal power and energy storage units; in, These are the system frequency regulation capacity and mileage requirements, respectively. Let be the frequency regulation capacity and mileage clearing price for time period t, and be the dual variables of the unit's frequency regulation capacity and mileage demand; Step 2.2.4: Establish market clearing rules and constraints for thermal power and energy storage units; The constraints on the winning bid capacity, winning bid mileage, and output of thermal power units are shown in Equation (15), and the relevant constraints on energy storage power stations are shown in Equation (16). in, These are the maximum and minimum active power outputs of thermal power unit i, respectively; For thermal power unit i, these are the maximum frequency regulation capacity and frequency regulation mileage. The frequency regulation mileage-capacity ratio of thermal power unit i; These are all dual variables of the corresponding constraints of thermal power units; in, These are all dual variables of the corresponding constraints of the energy storage power station.

6. The energy storage optimization operation method considering degradation costs according to claim 1, characterized in that: Step 3 includes: Step 3.1: Linearize the two-level decision model; according to the strong duality theorem, the objective function of the two-level decision model is re-expressed as equation (17): Step 3.2: Transform and solve the two-level decision model; By introducing KKT conditions, the two-level decision model is transformed into a single-level mathematical programming model. The objective function of the simplified single-level mathematical programming model is shown in equation (18): Where, N L Indicates the number of system lines.

7. An energy storage optimized operation system that takes into account degradation costs, characterized in that, The system includes a module for performing an energy storage optimization operation method that takes into account degradation costs as described in any one of claims 1 to 6.

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