A source-grid-load generalized energy storage system optimization scheduling method and system

Abstract

The present invention relates to the technical field of peak-shaving of power systems, and discloses a method for optimizing and dispatching a generalized energy storage system of source, grid and load, including the following steps: S1, establishing a source-load double-layer optimization dispatching model, wherein the upper model aims to maximize the net operating income of source-side leased energy storage, and the lower model aims to minimize the net load variance; S2, generating energy storage configuration results according to the upper model and passing them to the lower layer, and also discloses a system for optimizing and dispatching a generalized energy storage system of source, grid and load, including: a source-load double-layer optimization dispatching model, which is used to maximize the net operating income of source-side leased energy storage and minimize the net load variance of the system. The deep peak-shaving demand is determined by the optimized net load, and the peak-shaving is cleared with the goal of minimizing the system peak-shaving cost, which ensures the fairness and effectiveness of peak-shaving transactions, enables various market players to participate in peak-shaving transactions in a fair competition environment, and improves economic efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of power system peak regulation, and in particular to a source-grid-load generalized energy storage system optimization scheduling method and system thereof. Background Art

[0002] With the rapid development of new energy sources, mainly wind power generation, the future power system will become a diversified power system dominated by new energy generation and coexisting with multiple power generation entities. Due to the volatility of wind power, large-scale grid connection will increase the peak-to-valley difference of the equivalent load of the power grid, and flexible adjustment capabilities will be an indispensable component of the new power system. However, in the context of large-scale wind power grid connection, energy storage power stations and multiple types of power sources jointly participate in peak regulation, focusing on optimizing the output of hybrid power sources and the charging and discharging behavior of energy storage based on the technical or economic goals of the overall peak regulation of the system, which well solves the problems of system peak regulation costs and increased peak-to-valley differences in net loads caused by wind power grid connection; but it ignores the willingness of energy storage to participate in the peak regulation market as an independent entity, and fails to reflect the power market value of energy storage.

[0003] At the same time, the operating mechanism of energy storage on different sides of the power system participating in the power market has, to a certain extent, increased the enthusiasm of energy storage to participate in the peak-shaving market; however, the power market is composed of various links on the supply side, the grid side and the demand side, and there is a lack of research on the coordinated optimization of energy storage at all ends of the source, grid and load participating in the power market.

[0004] Therefore, in order to solve the above problems, there is an urgent need for an optimization scheduling method and system for a source-grid-load generalized energy storage system. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a source-grid-load generalized energy storage system optimization scheduling method and system, which solves the peak-shaving problems such as net load fluctuation and increased operating costs of thermal power units caused by the grid connection of new energy such as wind power, as well as the problem that the current optimization scheduling method for multiple energy storage systems is still imperfect.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for optimizing the dispatching of a source-grid-load generalized energy storage system, comprising the following steps:

[0007] S1. Establish a source-load dual-layer optimization scheduling model. The upper layer model aims to maximize the net profit of the source-side leasing energy storage operation, and the lower layer model aims to minimize the net load variance.

[0008] S2. Generate energy storage configuration results based on the upper-layer model and pass them to the lower layer. The lower-layer model optimizes the net load and source-side leased energy storage output based on the energy storage operation simulation results.

[0009] S3. Through continuous interactive iteration of the two-layer model, the optimal configuration of the leased energy storage is achieved and the net load curve of the system under this configuration is obtained;

[0010] S4. Establish a multi-market player deep peak-shaving quotation model and trading mechanism on the power grid side to fully reflect the willingness of wind, thermal and storage to participate in peak-shaving transactions;

[0011] S5. Determine the deep peak-shaving demand based on the optimized net load, and perform peak-shaving clearing with the goal of minimizing the system peak-shaving cost.

[0012] Preferably, the step S1 specifically includes the following steps:

[0013] S1.1. Determine the objective function of the upper model, with the goal of maximizing the net profit of leasing energy storage at the source side;

[0014] S1.2. Make decisions on the configuration of leased energy storage at the wind farm based on energy storage operation data;

[0015] S1.3. The proportion of leased energy storage configuration at the wind farm side is used as a constraint condition;

[0016] S1.4. Determine the objective function of the lower model, with the goal of minimizing the net load variance;

[0017] S1.5. Calculate the expression for net load variance and set the constraints for demand response and wind curtailment rate.

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

[0019] S3.1. The upper model updates the source-side rental energy storage configuration based on the feedback results of the lower model;

[0020] S3.2, the lower model re-optimizes the net load and energy storage output according to the new configuration of the upper model;

[0021] S3.3. Repeat steps S3.1 and S3.2 until the two-layer model converges and the optimal configuration of rental energy storage and the system net load curve are obtained.

[0022] Preferably, the step S4 specifically includes the following steps:

[0023] S4.1. Establish a deep peak load quotation model for wind turbines;

[0024] S4.2. Establish a deep peak load quotation model for thermal power units;

[0025] S4.3. Establish a deep peak load quotation model for energy storage systems;

[0026] S4.4. Design a deep peak-shaving trading mechanism for multiple market players, taking into account the bidding strategies and marginal costs of each player.

[0027] Preferably, the step S5 specifically includes the following steps:

[0028] S5.1. Calculate the deep peak load demand;

[0029] S5.2. Peak load clearing is carried out according to the quotation results of market players, with the goal of minimizing the system peak load cost;

[0030] S5.3. Implement peak-shaving transactions to adjust the output and energy storage charging and discharging behavior of each market player.

[0031] Preferably, the objective function of the upper model in step S1.1 is:

[0032] Where n is the number of leased energy storage units, Δp esN is the rated power of the energy storage unit, is the net benefit of energy storage operation. The objective function of the lower model in step S1.4 is The expression of the net load variance in step S1.5 is:

[0033]

[0034] Among them, P net,ave is the average net load value, is the load data forecasted for period t, is the load transferred into period t, is the load transferred out during period t.

[0035] Preferably, the objective function in step S5.2 with the goal of minimizing the system peak-shaving cost is:

[0036] Among them, C Gi,t is the peak load-shaving cost of the i-th unit in period t, is the grid-side energy storage loss cost during period t, is the peak load storage loss cost at the source side during period t, is the penalty cost of wind farm abandonment during period t.

[0037] A source-grid-load generalized energy storage system optimization dispatching system, comprising:

[0038] Source-load dual-layer optimization dispatch model, which is used to maximize the net operating income of source-side leasing energy storage and minimize the net load variance of the system;

[0039] Energy storage configuration module, which is used to generate energy storage configuration results and pass them to the lower-level model;

[0040] Net load optimization module, which is used to optimize net load and source-side leased energy storage output based on energy storage operation simulation results;

[0041] A two-layer model interaction module, which is used to implement interactive iteration between the upper-layer model and the lower-layer model;

[0042] Deep peak-shaving quotation model, which is used to establish a deep peak-shaving quotation model for multiple market players;

[0043] The deep peak-shaving trading module is used to calculate the deep peak-shaving demand and clear the peak-shaving.

[0044] Preferably, the source-load dual-layer optimization scheduling model includes:

[0045] The upper-level model objective function calculation unit is used to calculate the net operating income of leasing energy storage on the source side.

[0046] The upper model configuration decision unit is used to make configuration decisions based on energy storage operation data.

[0047] The upper model constraint setting unit is used to set constraints such as the configuration ratio of leased energy storage on the wind farm side.

[0048] The lower model objective function calculation unit is used to calculate the net load variance.

[0049] The lower model constraint setting unit is used to set constraints such as demand response and wind abandonment rate.

[0050] Preferably, the deep peak shaving trading module includes:

[0051] A demand calculation unit, which is used to calculate the deep peak load demand;

[0052] The peak-shaving and clearing unit is used to perform peak-shaving and clearing according to the bidding results, with the goal of minimizing the system peak-shaving cost;

[0053] The transaction execution unit is used to execute peak-shaving transactions and adjust the output and energy storage charging and discharging behavior of each market player.

[0054] The present invention provides a source-grid-load generalized energy storage system optimization scheduling method and system thereof. It has the following beneficial effects:

[0055] 1. The present invention determines the deep peak-shaving demand through the optimized net load, and performs peak-shaving clearing with the goal of minimizing the system peak-shaving cost. This process ensures the fairness of peak-shaving transactions, enables various market players to participate in peak-shaving transactions in a fair competition environment, and improves the effectiveness and economy of peak-shaving.

[0056] 2. The present invention improves the stability and reliability of the power system by reducing the fluctuation of the net load of the system. The optimized scheduling method effectively reduces the wind abandonment rate, reduces the system operating cost, and enhances the reliability and continuity of power supply.

[0057] 3. The present invention designs a reasonable peak-shaving transaction mechanism by comprehensively considering the quotation strategies and marginal costs of multiple market players, thereby effectively reducing the peak-shaving cost of the system, which not only saves operating costs but also improves economic benefits.

[0058] 4. The present invention improves the system's ability to respond to load fluctuations and emergencies by optimizing energy storage configuration and peak-shaving strategies. The system can adjust energy storage and output more quickly to ensure stable operation under various working conditions.

[0059] 5. The present invention improves the stability and reliability of the power system by reducing the fluctuation of the net load of the system. The optimized scheduling method effectively reduces the wind abandonment rate, reduces the system operating cost, and enhances the reliability and continuity of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of the method of the present invention;

[0061] Figure 2 is a flow chart of the method of the present invention;

[0062] Figure 3 The load curve diagram after price-based demand response optimization of the present invention;

[0063] Figure 4 It is a market subject quotation curve chart of the present invention;

[0064] Figure 5 It is a curve diagram of the change of the source side energy storage benefit of the present invention;

[0065] Figure 6 It is a framework diagram of the system of the present invention;

[0066] Figure 7 It is a framework diagram of the source-load dual-layer optimization scheduling model of the present invention;

[0067] Figure 8 This is a framework diagram of the deep peak-shaving trading module of the present invention. DETAILED DESCRIPTION

[0068] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] Example:

[0070] Please see attached Figure 1 -Attached Figure 2The embodiment of the present invention provides a method for optimizing and dispatching a generalized energy storage system of a source, a grid and a load, comprising the following steps:

[0071] S1. Establish a source-load dual-layer optimization scheduling model. The upper layer model aims to maximize the net profit of the source-side leasing energy storage operation, and the lower layer model aims to minimize the net load variance.

[0072] S2. Generate energy storage configuration results based on the upper-layer model and pass them to the lower layer. The lower-layer model optimizes the net load and source-side leased energy storage output based on the energy storage operation simulation results.

[0073] S3. Through continuous interactive iteration of the two-layer model, the optimal configuration of the leased energy storage is achieved and the net load curve of the system under this configuration is obtained;

[0074] S4. Establish a multi-market player deep peak-shaving quotation model and trading mechanism on the power grid side to fully reflect the willingness of wind, thermal and storage to participate in peak-shaving transactions;

[0075] S5. Determine the deep peak-shaving demand based on the optimized net load, and perform peak-shaving clearing with the goal of minimizing the system peak-shaving cost.

[0076] Specifically, in step S1,

[0077] Upper model: With the goal of maximizing the net profit of leasing energy storage operation on the source side, an objective function is established, taking energy storage operation data and the configuration ratio of leased energy storage on the wind farm side as constraints.

[0078] Lower model: With the goal of minimizing the net load variance, the expression of the net load variance is calculated, and the constraints of demand response and wind curtailment rate are set.

[0079] This step is used to simultaneously maximize the economic benefits of leasing energy storage on the source side and stabilize the system load.

[0080] The problem that the traditional single-layer optimization model cannot take into account both economic efficiency and system stability at the same time is solved. Through the two-layer model, the separation and coordination of the upper and lower layer goals enable the system to achieve a balance between different goals.

[0081] In step S2, the energy storage configuration is generated according to the optimization results of the upper model, and the results are passed to the lower model for further optimization.

[0082] This step is used to ensure the rationality and operability of the energy storage configuration.

[0083] The disconnection problem between energy storage configuration and system load optimization is solved. Through the linkage of upper and lower layer models, it is ensured that the energy storage configuration results can be effectively applied in the lower layer to achieve overall system optimization.

[0084] In step S3,

[0085] Upper-level model update: Update the source-side rental energy storage configuration based on the feedback results of the lower-level model.

[0086] Lower-level model re-optimization: Re-optimize net load and energy storage output based on the new configuration of the upper-level model.

[0087] Iteration process: Repeat the above steps until the two-layer model converges and the optimal configuration of rental energy storage and the system net load curve are obtained.

[0088] This step is used to achieve the optimal solution for energy storage configuration and the optimal balance of system load.

[0089] The problem that a single optimization cannot achieve the global optimum is solved. Through the interactive iteration of the two-layer model, the energy storage configuration and system load are continuously corrected and optimized, so that the system reaches the optimal state after multiple iterations.

[0090] In step S4,

[0091] Wind turbine quotation model: Establish a deep peak-shaving quotation model for wind turbines.

[0092] Thermal power unit quotation model: Establish a deep peak load regulation quotation model for thermal power units.

[0093] Energy storage system quotation model: Establish a deep peak-shaving quotation model for energy storage systems.

[0094] Trading mechanism design: Design a deep peak-shaving trading mechanism for multiple market players, taking into account the bidding strategies and marginal costs of each player.

[0095] This step is used to mobilize the enthusiasm of multiple market players to participate in peak-shaving transactions and ensure the fairness and efficiency of peak-shaving transactions.

[0096] The problem of low willingness of market players to participate and unreasonable quotations in the traditional peak-shaving mechanism has been solved. Through the design of a reasonable quotation model and trading mechanism, the enthusiasm of various players to participate in peak-shaving and the efficiency of peak-shaving transactions have been improved.

[0097] In step S5,

[0098] Calculate the deep peak-shaving demand: Based on the optimized net load, calculate the deep peak-shaving demand of the system.

[0099] Peak load clearing: Peak load clearing is carried out based on the quotations of market players, with the goal of minimizing the system peak load cost.

[0100] Execute peak-shaving transactions: adjust the output and energy storage charging and discharging behavior of each market player, and execute the results of peak-shaving transactions.

[0101] This step is used to ensure the economy and reliability of system peak load regulation and optimize the operating costs of the power system.

[0102] The problem of high cost and poor peak-shaving effect in traditional peak-shaving methods has been solved. By optimizing the calculation of deep peak-shaving demand after net load and the design of peak-shaving clearing mechanism, the system peak-shaving cost is minimized and the peak-shaving effect is improved.

[0103] In summary:

[0104] The present invention solves the problems of unreasonable energy storage configuration, high system peak-shaving cost, low peak-shaving transaction efficiency in the prior art by establishing a source-load double-layer optimization scheduling model, optimizing energy storage configuration and net load, designing a peak-shaving quotation model and trading mechanism for multiple market entities, and optimizing peak-shaving demand and clearing process, thereby achieving efficient utilization of energy storage resources and stable operation of the power system.

[0105] The S1 step specifically includes the following steps:

[0106] S1.1. Determine the objective function of the upper model, with the goal of maximizing the net profit of leasing energy storage at the source side;

[0107] S1.2. Make decisions on the configuration of leased energy storage at the wind farm based on energy storage operation data;

[0108] S1.3. The proportion of leased energy storage configuration at the wind farm side is used as a constraint condition;

[0109] S1.4. Determine the objective function of the lower model, with the goal of minimizing the net load variance;

[0110] S1.5. Calculate the expression for net load variance and set the constraints for demand response and wind curtailment rate.

[0111] Specifically, in step S1.1, the objective function of the upper model is:

[0112] Where n is the number of leased energy storage units, Δp esN is the rated power of the energy storage unit, It is the net profit of energy storage operation.

[0113] Taking the wind farm leasing energy storage configuration as a constraint:

[0114]

[0115] in, are the upper and lower limit coefficients of energy storage configuration ratio on the wind farm side, P wN Installed capacity of wind farm.

[0116] This step is used to maximize the economic benefits of leasing energy storage at the source side.

[0117] It solves the problem of not considering the maximization of economic benefits in traditional energy storage configuration. Through a clear objective function, the economic benefits of energy storage operation can be quantified and optimized.

[0118] In step S1.2, based on the energy storage operation data, an optimization algorithm is used to make configuration decisions on the leased energy storage on the wind farm side to determine the configuration plan for each energy storage unit.

[0119] This step is used to rationally allocate energy storage resources on the wind farm side to ensure efficient use of resources.

[0120] It solves the problems of unreasonable allocation and low utilization efficiency of energy storage resources, and improves the scientificity and rationality of energy storage configuration through data-driven decision-making.

[0121] In step S1.3, the proportion of leased energy storage configuration on the wind farm side is set as a constraint of the optimization model to ensure that the energy storage configuration meets actual needs and limitations.

[0122] This step is used to ensure the rationality and feasibility of the energy storage configuration.

[0123] The problem of ignoring the actual proportion limit in energy storage configuration is solved, and the optimization results are made more realistic and operational by setting constraints.

[0124] In step S1.4, the objective function of the lower model is:

[0125]

[0126] This step is used to smooth the system load, reduce load fluctuations, and improve system stability.

[0127] The problem of large load fluctuation and poor stability in the system is solved, and load stabilization is achieved by minimizing load variance.

[0128] S1.5 In this step, the net load variance D(P net,t ) is calculated as:

[0129]

[0130] Among them, P net,ave is the average net load value, is the load data forecasted for period t, is the load transferred into period t, is the load transferred out during period t.

[0131] The constraints are as follows:

[0132] Demand Response Constraints

[0133]

[0134] Among them, ΔP load,t is the load change in period t before and after demand response, is the maximum load change rate during period t, e f 、e g They are the electricity prices during peak, flat and valley periods respectively.

[0135] Wind curtailment rate constraints

[0136]

[0137] Among them, λ max，t is the maximum wind curtailment rate allowed at time t, λ w,max It is the maximum wind curtailment rate allowed during the dispatch period.

[0138] Source-side rental energy storage operation constraints

[0139]

[0140] in, Lease the rated capacity of energy storage for the power supply side. is the rated power of the rental energy storage on the power supply side, which is determined by n and ΔP esN Joint decision, SOC min is the lower limit of energy storage charge rate, SOC min is the upper limit of energy storage charge rate, Leasing energy storage for the source side, is the energy storage power leased on the power supply side during period t.

[0141] This step is used to ensure that the optimization of system load meets the constraints of demand response and wind curtailment rate, and improve the practical applicability of the optimization results.

[0142] The problem of not considering demand response and wind curtailment restrictions in load optimization is solved. By setting relevant constraints, the optimization results are made more feasible and effective in practical applications.

[0143] The S3 step specifically includes the following steps:

[0144] S3.1. The upper model updates the source-side rental energy storage configuration based on the feedback results of the lower model;

[0145] S3.2, the lower model re-optimizes the net load and energy storage output according to the new configuration of the upper model;

[0146] S3.3. Repeat steps S3.1 and S3.2 until the two-layer model converges and the optimal configuration of rental energy storage and the system net load curve are obtained.

[0147] Specifically, in step S3.1,

[0148] Feedback mechanism: The upper-level model receives optimization results fed back by the lower-level model, mainly including net load and energy storage output.

[0149] Update configuration: The upper-level model adjusts the configuration strategy of the source-side leased energy storage based on the feedback results to continue to maximize the net operating income of the source-side leased energy storage.

[0150] Therefore, the load-side energy storage dispatch model is optimized with the goal of maximizing the benefits of low storage and high generation. The objective function is as follows:

[0151]

[0152] Among them, η c , η d are respectively the energy storage charging and discharging efficiency, s cha,t is the charging price of energy storage at the load side during period t (i.e., time-of-use electricity price and valley electricity price), S dis,t is the discharge electricity price of energy storage in period t on the load side (i.e., time-of-use electricity price and peak electricity price).

[0153] Load-side energy storage operation constraints

[0154]

[0155] in, is the rated capacity of energy storage on the load side.

[0156] This step is used to dynamically adjust the energy storage configuration in response to the lower-level optimization feedback to ensure the real-time effectiveness of the configuration strategy.

[0157] It solves the problem of lack of dynamic adjustment in static optimization methods. Through real-time feedback and adjustment, the energy storage configuration can better adapt to the actual operating conditions, thus improving the flexibility and adaptability of the system.

[0158] In step S3.2,

[0159] Receive new configuration: The lower-level model receives the energy storage configuration results adjusted by the upper-level model.

[0160] Re-optimization: Based on the new energy storage configuration, the net load and energy storage output are recalculated for optimization, and the objective function is shown above.

[0161] This step is used to optimize the system net load and energy storage output according to the new energy storage configuration plan to ensure the stable operation of the system.

[0162] The adaptability problem in the load optimization process is solved, and the net load optimization results are made more reasonable and effective by dynamically adjusting the energy storage configuration.

[0163] In step S3.3,

[0164] Iteration process: The interaction process of the upper and lower models is repeated continuously until the optimization result no longer changes significantly and reaches a convergence state.

[0165] This step is used to ensure that the system optimization reaches the global optimal state and obtains a stable energy storage configuration and system net load curve.

[0166] The problem that a single optimization cannot reach the global optimum is solved. Through repeated iterations and adjustments, global optimization is achieved, which improves the overall performance and stability of the system.

[0167] The S4 step specifically includes the following steps:

[0168] S4.1. Establish a deep peak load quotation model for wind turbines;

[0169] S4.2. Establish a deep peak load quotation model for thermal power units;

[0170] S4.3. Establish a deep peak load quotation model for energy storage systems;

[0171] S4.4. Design a deep peak-shaving trading mechanism for multiple market players, taking into account the bidding strategies and marginal costs of each player.

[0172] Specifically, in step S4.1, the main goal is to construct a bidding model for wind turbines in peak-shaving transactions, taking into account the volatility and output characteristics of wind power.

[0173] This step is used to reasonably set the quotation of wind turbines in the peak-shaving market and ensure that wind turbines can participate in peak-shaving transactions fairly.

[0174] It solves the problems of unreasonable quotations and low participation of wind turbines in the peak-shaving market. Through a scientific quotation model, wind turbines can make reasonable quotations based on the market and their own conditions, thereby improving their enthusiasm for participating in peak-shaving and increasing economic benefits.

[0175] The main goal of step S4.2 is to build a quotation model for thermal power units in peak-shaving transactions, taking into account the regulation capacity and operating costs of thermal power.

[0176] This step is used to reasonably set the quotations of thermal power units in the peak-shaving market to ensure that thermal power units can efficiently participate in peak-shaving transactions.

[0177] The problems of imperfect quotation mechanism and insufficient peak-shaving response of thermal power units in the peak-shaving market have been solved. By establishing a scientific quotation model, thermal power units can make reasonable quotations based on market demand and improve peak-shaving response efficiency.

[0178] In step S4.3, the main goal is to build a quotation model for energy storage systems in peak-shaving transactions, taking into account the charging and discharging characteristics and costs of the energy storage systems.

[0179] This step is used to reasonably set the quotation of the energy storage system in the peak-shaving market and ensure that the energy storage system can effectively participate in peak-shaving transactions.

[0180] It solves the problems of unclear bidding strategies and low economic benefits of energy storage systems in the peak-shaving market. By establishing a reasonable bidding model, the energy storage system can make reasonable bids based on market conditions, thereby improving the economic benefits and regulatory capabilities of participating in peak-shaving.

[0181] In step S4.4, the main goal is to design a deep peak-shaving trading mechanism involving multiple market players (wind power, thermal power, and energy storage), taking into account the bidding strategies, marginal costs, and market electricity prices of each player.

[0182] This step is used to coordinate the peak-shaving quotations and transactions of various market players to minimize the system peak-shaving costs.

[0183] It solves the problems of low participation and high cost of peak-shaving among market players. By designing a reasonable trading mechanism, it ensures that all players can make reasonable bids based on marginal costs and market strategies, thereby improving the efficiency and economy of peak-shaving transactions.

[0184] To sum up, this step is mainly implemented in the following ways:

[0185] On the grid side, a multi-market player deep peak load quotation model and trading mechanism is established. Usually, the quotation strategy of market trading players is based on their own marginal cost. Since wind turbines have no fuel cost and the power generation cost is zero, the unit transaction quotation of wind turbines in period t is R w,t The specific expression is as follows:

[0186]

[0187] Among them, R w,t is the unit price of deep peak regulation of wind farm in period t (yuan / MW), s max 、s min P is the upper and lower limits of the unit price quotation for wind turbine deep peak regulation. wr,t is the actual grid-connected power of the wind power plant after the peak-shaving transaction in period t, P wr,t It is the actual grid-connected power of the wind power station.

[0188] The energy storage subsidy quotation is mainly composed of marginal cost and the subsidy brought by reducing the peak-shaving cost of the system. The marginal cost of grid-side energy storage participating in the peak-shaving market is mainly the loss cost of charging and discharging. Taking grid-side energy storage as an example, the loss cost of energy storage participating in the peak-shaving market is expressed as follows:

[0189]

[0190] Among them, A and B are constants related to battery type and technology, DOD is the depth of charge and discharge of energy storage, is the unit loss cost of grid-side energy storage during period t (yuan / MW), The construction cost of the grid-side energy storage power station, SOC t is the energy storage charge rate during period t, C p is the unit energy storage power cost (10,000 yuan / MW);

[0191] Unit subsidy quotation for grid-side energy storage during period t The calculation formula is as follows:

[0192]

[0193] in, is the charging amount of the grid-side energy storage during the peak load period, K sub is the energy storage subsidy coefficient, I 01 is the wind penalty cost, I 02 To lower the peak output limit of thermal power units and reduce the operating costs of thermal power units.

[0194] Energy storage-thermal power-wind power peak-shaving transactions are cleared with the goal of minimizing the system peak-shaving cost, and its objective function is:

[0195]

[0196] Among them, C Gi,t is the peak load-shaving cost of the i-th unit in period t, is the grid-side energy storage loss cost during period t, is the peak load storage loss cost at the source side during period t, is the penalty cost of wind farm abandonment during period t.

[0197] The constraints are as follows:

[0198] (1) Energy storage operation constraints

[0199]

[0200] in, is the rated capacity of grid-side energy storage, is the rated capacity of the source-side peak-shaving energy storage, and PptdesN is the rated power of the source-side peak-shaving energy storage.

[0201] (2) Operation constraints of thermal power units

[0202] 1) Hot standby

[0203]

[0204] Among them, u i,t is the start and stop status of unit i in period t, and ρ is the hot standby coefficient.

[0205] 2) Unit output constraints

[0206] η min P GR,i ≤P Gi,t ≤u i,t P GR，i

[0207] 3) Unit climbing constraints

[0208] -R d ≤P Gi,t -P Gi,t-1 ≤R u

[0209] Among them, R d , R u It is the down / up ramp rate of the unit.

[0210] 4) Unit start and stop time constraints

[0211]

[0212] Among them, T s , T is the minimum shutdown / startup time.

[0213] 5) Constraints on paid power generation capacity transferred by units

[0214]

[0215] Step S5 specifically includes the following steps:

[0216] S5.1. Calculate the deep peak load demand;

[0217] S5.2. Peak load clearing is carried out according to the quotation results of market players, with the goal of minimizing the system peak load cost;

[0218] S5.3. Implement peak-shaving transactions to adjust the output and energy storage charging and discharging behavior of each market player.

[0219] Specifically, the main goal of step S5.1 is to calculate the deep peak load demand of the system in each time period based on the optimized net load curve.

[0220] This step is used to accurately calculate the peak-shaving demand in each time period and ensure the reasonable allocation of peak-shaving resources.

[0221] It solves the problems of inaccurate calculation of peak-shaving demand and unreasonable allocation of peak-shaving resources in traditional methods. By accurately calculating the peak-shaving demand, peak-shaving resources can be allocated on demand, thereby improving the peak-shaving efficiency and reliability of the system.

[0222] The main goal of step S5.2 is to perform clearing calculations based on the peak-shaving quotations of market entities, determine the peak-shaving output of each entity, and ensure that the system peak-shaving cost is minimized. Its objective function is:

[0223]

[0224] Among them, C Gi,t is the peak load-shaving cost of the i-th unit in period t, is the grid-side energy storage loss cost during period t, is the peak load storage loss cost at the source side during period t, is the penalty cost of wind farm abandonment during period t.

[0225] This step is used to reasonably allocate peak-shaving output to ensure that the system peak-shaving cost is minimized.

[0226] It solves the problems of high cost and low clearing efficiency in traditional peak-shaving and clearing methods. By optimizing the peak-shaving and clearing algorithm, it minimizes the peak-shaving cost and improves the efficiency and economy of peak-shaving transactions.

[0227] The main goal of step S5.3 is to execute peak-shaving transactions based on the clearing results, adjust the output of each market player and the charging and discharging behavior of the energy storage system, and ensure the smooth progress of peak-shaving transactions.

[0228] Operation process:

[0229] Publish clearing results: notify all market players of peak load clearing results.

[0230] Adjust output: Each entity adjusts its output based on the clearing results, and wind power, thermal power and energy storage systems increase or decrease their output accordingly.

[0231] Adjusting charging and discharging: The energy storage system adjusts the charging and discharging plan based on the clearing results to ensure that the stored energy is released during peak load periods and charged during low load periods.

[0232] This step is used to ensure the execution of peak-shaving transactions, adjust the output and energy storage behavior of each market player, and ensure that the system's peak-shaving needs are met.

[0233] It solves the problems of difficult coordination and slow response in the execution of traditional peak-shaving transactions. Through scientific execution mechanisms and adjustment strategies, it ensures the efficient execution of peak-shaving transactions and improves the stability and reliability of the system.

[0234] The objective function of the upper model in step S1.1 is:

[0235] Where n is the number of leased energy storage units, Δp esN is the rated power of the energy storage unit, is the net benefit of energy storage operation. The objective function of the lower model in step S1.4 is The expression for the net load variance in step S1.5 is:

[0236]

[0237] Among them, P net,ave is the average net load value, is the load data forecasted for period t, is the load transferred into period t, is the load transferred out during period t.

[0238] The objective function in step S5.2, which aims to minimize the system peak load cost, is:

[0239] Among them, C Gi,t is the peak load-shaving cost of the i-th unit in period t, is the grid-side energy storage loss cost during period t, is the peak load storage loss cost at the source side during period t, is the penalty cost of wind farm abandonment during period t.

[0240] Please see attached Figure 6 , a source-grid-load generalized energy storage system optimization dispatching system, comprising:

[0241] Source-load dual-layer optimization dispatch model, which is used to maximize the net operating income of source-side leasing energy storage and minimize the net load variance of the system;

[0242] Energy storage configuration module, which is used to generate energy storage configuration results and pass them to the lower-level model;

[0243] Net load optimization module, which is used to optimize net load and source-side leased energy storage output based on energy storage operation simulation results;

[0244] A two-layer model interaction module, which is used to implement interactive iteration between the upper-layer model and the lower-layer model;

[0245] Deep peak-shaving quotation model, which is used to establish a deep peak-shaving quotation model for multiple market players;

[0246] The deep peak-shaving trading module is used to calculate the deep peak-shaving demand and clear the peak-shaving.

[0247] Please see attached Figure 7 , the source-load two-layer optimization scheduling model includes:

[0248] The upper-level model objective function calculation unit is used to calculate the net operating income of leasing energy storage on the source side.

[0249] The upper model configuration decision unit is used to make configuration decisions based on energy storage operation data.

[0250] The upper model constraint setting unit is used to set constraints such as the configuration ratio of leased energy storage on the wind farm side.

[0251] The lower model objective function calculation unit is used to calculate the net load variance.

[0252] The lower model constraint setting unit is used to set constraints such as demand response and wind abandonment rate.

[0253] Please see attached Figure 8 , the deep peak-shaving trading module includes;

[0254] A demand calculation unit, which is used to calculate the deep peak load demand;

[0255] The peak-shaving and clearing unit is used to perform peak-shaving and clearing according to the bidding results, with the goal of minimizing the system peak-shaving cost;

[0256] The transaction execution unit is used to execute peak-shaving transactions and adjust the output and energy storage charging and discharging behavior of each market player.

[0257] Example:

[0258] Experimental background:

[0259] Please see attached Figure 3 -Attached Figure 5 In order to verify the effectiveness of the source-grid-load-generalized energy storage system optimization scheduling method of the present invention, lithium iron phosphate batteries that have been widely used are selected as the energy storage type. The specific parameters are shown in Table 1.

[0260] Table 1 Energy storage system related parameters

[0261]

[0262] Energy storage unit ΔPesN / ΔSesN is 5MW / 20MWh

[0263] The power supply side energy storage station is configured as 120MW / 480MWh

[0264] The grid-side energy storage configuration is 80MW / 320MWh

[0265] The load-side energy storage configuration is 40MW / 160MWh

[0266] Experimental methods:

[0267] 1. Source-load dual-layer optimization scheduling model

[0268] 1.1 Upper-level optimization model

[0269] Objective: To maximize the net profit from leasing energy storage operations on the source side.

[0270] Objective function: Where n is the number of leased energy storage units, Δp esN is the rated power of the energy storage unit, It is the net profit of energy storage operation.

[0271] Constraints: The wind farm leasing energy storage configuration is used as a constraint:

[0272]

[0273] in, are the upper and lower limit coefficients of energy storage configuration ratio on the wind farm side, P wN Installed capacity of wind farm.

[0274] 1.2 Lower layer optimization model

[0275] Objective: To minimize the variance of the system net load.

[0276] Objective function:

[0277] Among them, D(P net,t ) is the net load variance, and its expression is as follows:

[0278]

[0279] Among them, P net,ave is the average net load value, is the load data forecasted for period t, is the load transferred into period t, is the load transferred out during period t.

[0280] The constraints are as follows:

[0281] Demand Response Constraints

[0282]

[0283] Among them, ΔP load,t is the load change in period t before and after demand response, is the maximum load change rate during period t, e f 、e g They are the electricity prices during peak, flat and valley periods respectively.

[0284] Wind curtailment rate constraints

[0285]

[0286] Among them, λ max，t is the maximum wind curtailment rate allowed at time t, λw,max It is the maximum wind curtailment rate allowed during the dispatch period.

[0287] Source-side rental energy storage operation constraints

[0288]

[0289] in, Lease the rated capacity of energy storage for the power supply side. is the rated power of the rental energy storage on the power supply side, which is determined by n and ΔP esN Joint decision, SOC min is the lower limit of energy storage charge rate, SOC max is the upper limit of energy storage charge rate, Leasing energy storage for the source side, is the energy storage power leased on the power supply side during period t.

[0290] System optimization process:

[0291] Based on the source-load dual-layer optimization scheduling model, through iterative optimization, it is concluded that the number of wind farm leased energy storage units n is 17, and the energy storage configuration 85MW / 340MWh, configuration ratio u n The peak load storage configuration on the power supply side is 12.14%. It is 35MW / 140MWh.

[0292] like Figure 3 As shown, it is a load curve diagram after price-based demand response optimization in a source-grid-load-generalized energy storage system optimization scheduling method of the present invention. Figure 3 As shown in the figure, under the guidance of peak-valley electricity prices, the load side transfers part of the load from the peak electricity price periods of 10:00-16:00 and 18:00-22:00 to the low-valley electricity price period of 0:00-8:00; due to the anti-peaking characteristics of wind power itself, wind power is in a high-generation period during the period of 0:00-8:00, and increasing the load during the low-valley electricity price period can alleviate the system peak-shaving pressure brought by wind power grid connection. The following table shows the net load of the power grid under different scenarios.

[0293] Load-side energy storage dispatch:

[0294] The load-side energy storage obtains electricity revenue by low storage and high generation. The objective function of the dispatch model is as follows:

[0295]

[0296] Among them, η c , η d are respectively the energy storage charging and discharging efficiency, s cha,t is the charging price of energy storage at the load side during period t (i.e., time-of-use electricity price and valley electricity price), s dia,tis the discharge electricity price of energy storage in period t on the load side (i.e., time-of-use electricity price and peak electricity price).

[0297] Load-side energy storage operation constraints

[0298]

[0299] in, is the rated capacity of energy storage on the load side.

[0300] Grid-side peak load trading:

[0301] On the power grid side, a multi-market player deep peak-shaving quotation model and trading mechanism is established. Usually, the quotation strategy of market trading players is based on their own marginal cost. Since wind turbines have no fuel cost and the power generation cost is zero, the specific expression of the unit transaction quotation Rw,t of wind turbines in period t is as follows:

[0302]

[0303] Among them, R w,t is the unit price of deep peak regulation of wind farm in period t (yuan / MW), s max 、s min P is the upper and lower limits of the unit price quotation for wind turbine deep peak regulation. wr,t is the actual grid-connected power of the wind power plant after the peak-shaving transaction in period t, P wr,t It is the actual grid-connected power of the wind power station.

[0304] The energy storage subsidy quotation is mainly composed of marginal cost and the subsidy brought by reducing the peak-shaving cost of the system. The marginal cost of grid-side energy storage participating in the peak-shaving market is mainly the loss cost of charging and discharging. Taking grid-side energy storage as an example, the loss cost of energy storage participating in the peak-shaving market is expressed as follows:

[0305]

[0306] Among them, A and B are constants related to battery type and technology, DOD is the depth of charge and discharge of energy storage, is the unit loss cost of grid-side energy storage during period t (yuan / MW), The construction cost of the grid-side energy storage power station, SOC t is the energy storage charge rate during period t, C p is the unit energy storage power cost (10,000 yuan / MW);

[0307] Unit subsidy quotation for grid-side energy storage during period t The calculation formula is as follows:

[0308]

[0309] in, is the charging amount of the grid-side energy storage during the peak load period, K sub is the energy storage subsidy coefficient, I 01 is the wind penalty cost, I 02 To lower the peak output limit of thermal power units and reduce the operating costs of thermal power units.

[0310] like Figure 3 As shown, it is a market subject quotation curve diagram in a source-grid-load-generalized energy storage system optimization scheduling method of the present invention. Figure 3 As shown in the figure, during the low load period, the output of the thermal power unit is 218.9MW, the load rate is 43.78%, and it is in the second subsidy tier, with a quotation of 300 yuan / MWh. The grid-side energy storage and source-side peak-shaving energy storage are charged during the period of 0:00-10:00. As the depth of charging (DoD) increases, the charging loss cost also increases. Therefore, the grid-side energy storage quotation increases from 241.9 yuan / MWh to 400.7 yuan / MWh. Since the cost of peak-shaving energy storage on the source side participating in the peak-shaving is reduced by 12,000 yuan, compared with the peak-shaving cost reduced by 510,700 yuan by the grid-side energy storage, the difference is 498,700 yuan. Therefore, the grid-side energy storage quotation increases from 71.9 yuan / MWh to 230.8 yuan / MWh. The quotation of the wind farm fluctuates in the quotation range of 260 yuan / MWh-360 yuan / MWh according to the fluctuation of the wind power grid-connected power, which is in line with the quotation strategy.

[0311] Energy storage-thermal power-wind power peak-shaving transactions are cleared with the goal of minimizing the system peak-shaving cost, and its objective function is:

[0312]

[0313] Among them, C Gi,t is the peak load-shaving cost of the i-th unit in period t, is the grid-side energy storage loss cost during period t, is the peak load storage loss cost at the source side during period t, is the penalty cost of wind farm abandonment during period t.

[0314] The constraints are as follows:

[0315] (1) Energy storage operation constraints

[0316]

[0317] in, is the rated capacity of grid-side energy storage, is the rated capacity of the source-side peak-shaving energy storage, and PptdesN is the rated power of the source-side peak-shaving energy storage.

[0318] (2) Operation constraints of thermal power units

[0319] 1) Hot standby

[0320]

[0321] Among them, u i,t is the start and stop status of unit i in period t, and ρ is the hot standby coefficient.

[0322] 2) Unit output constraints

[0323] η min P GR,i ≤P Gi,t ≤u i,t P GR，i

[0324] 3) Unit climbing constraints

[0325] -R d ≤P Gi,t -P Gi,t-1 ≤R u

[0326] Among them, R d , R u It is the down / up ramp rate of the unit.

[0327] 4) Unit start and stop time constraints

[0328]

[0329] Among them, T s , T is the minimum shutdown / startup time.

[0330] 5) Constraints on paid power generation capacity transferred by units

[0331]

[0332] Experimental results:

[0333] Through the above optimization scheduling method, the net load of the power grid in the source-grid-load-generalized energy storage system in different scenarios is significantly smoothed, and the peak load regulation pressure of the system is effectively alleviated. The specific benefit analysis is as follows:

[0334] When the unit capacity rental price is 500 yuan / MWh and the quotation range for wind power peak regulation is different, the income from energy storage subsidies is significantly improved.

[0335] Table 2 Energy storage subsidy benefits under different wind power peak load quotation ranges

[0336]

[0337] As can be seen from Table 2, as the wind power peak-shaving quotation range continues to increase, the peak-shaving market clearing price increases accordingly: when the wind power peak-shaving quotation range is 160-260 yuan / MWh, the unit income of the source-side peak-shaving energy storage is 7200 yuan / MWh, and the unit income of the grid-side energy storage is 1000 yuan / MWh; when the wind power peak-shaving quotation range is 460-560 yuan / MWh, the unit income of the source-side peak-shaving energy storage is 1300 yuan / MWh, a year-on-year increase of 80.56%, and the unit income of the grid-side energy storage is 1600 yuan / MWh, a year-on-year increase of 60%; it can be seen that increasing the upper and lower limits of peak-shaving quotations can significantly increase the enthusiasm of energy storage to participate in deep peak-shaving market transactions.

[0338] Experimental conclusion:

[0339] The present invention achieves the coordinated peak load regulation of wind power, thermal power and energy storage systems by optimizing the configuration and dispatching of energy storage systems, significantly improving the stability and economic benefits of the power system. In particular, through a flexible bidding mechanism and an optimized dispatching method, the benefits of the energy storage system are maximized, effectively solving the peak load regulation problem caused by the grid connection of renewable energy.

[0340] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the dispatching of a source-grid-load generalized energy storage system, characterized in that: The following steps are involved: S1. Establish a source-load dual-layer optimization scheduling model. The upper layer model aims to maximize the net profit of the source-side leasing energy storage operation, and the lower layer model aims to minimize the net load variance. The S1 step specifically includes the following steps: S1.

1. Determine the objective function of the upper model, with the goal of maximizing the net profit of leasing energy storage at the source side; The objective function of the upper model in step S1.1 is: Where n is the number of leased energy storage units, Δp esN is the rated power of the energy storage unit, It is the net profit of energy storage operation; S1.

2. Make decisions on the configuration of leased energy storage at the wind farm based on energy storage operation data; S1.

3. The proportion of leased energy storage configuration at the wind farm side is used as a constraint condition; S1.

4. Determine the objective function of the lower model, with the goal of minimizing the net load variance; The objective function of the lower model in step S1.4 is: F2=min[D(P net,t )] nΔpesN ; S1.

5. Calculate the expression for net load variance and set the constraints for demand response and wind curtailment rate; The expression of the net load variance in step S1.5 is: Among them, P net,ave is the average net load value, is the load data forecasted for period t, is the load transferred into period t, is the load transferred out during period t; S2: Generate energy storage configuration results based on the upper-level model and pass them to the lower-level model. The lower-level model optimizes the net load and source-side leased energy storage output based on the energy storage operation simulation results. S3. Through the continuous interactive iteration of the two-layer model, the optimal configuration of the leased energy storage is achieved and the system net load curve under the configuration is obtained; S4. Establish a multi-market player deep peak-shaving quotation model and trading mechanism on the power grid side to fully reflect the willingness of wind, thermal and storage to participate in peak-shaving transactions; S5. Determine the deep peak-shaving demand based on the optimized net load, and perform peak-shaving clearing with the goal of minimizing the system peak-shaving cost.

2. The method for optimizing and dispatching a source-grid-load generalized energy storage system according to claim 1, characterized in that: The S3 step specifically includes the following steps: S3.

1. The upper model updates the source-side rental energy storage configuration based on the feedback results of the lower model; S3.2, the lower model re-optimizes the net load and energy storage output according to the new configuration of the upper model; S3.

3. Repeat steps S3.1 and S3.2 until the two-layer model converges and the optimal configuration of rental energy storage and the system net load curve are obtained.

3. The method for optimizing and dispatching a source-grid-load generalized energy storage system according to claim 1, characterized in that: The S4 step specifically includes the following steps: S4.

1. Establish a deep peak load quotation model for wind turbines; S4.

2. Establish a deep peak load quotation model for thermal power units; S4.

3. Establish a deep peak load quotation model for energy storage systems; S4.

4. Design a deep peak-shaving trading mechanism for multiple market players, taking into account the bidding strategies and marginal costs of each player.

4. The method for optimizing and dispatching a source-grid-load generalized energy storage system according to claim 1, characterized in that: The step S5 specifically includes the following steps: S5.

1. Calculate the deep peak load demand; S5.

2. Peak load clearing is carried out according to the quotation results of market players, with the goal of minimizing the system peak load cost; S5.

3. Implement peak-shaving transactions to adjust the output and energy storage charging and discharging behavior of each market player.

5. The method for optimizing and dispatching a source-grid-load generalized energy storage system according to claim 4, characterized in that: The objective function in step S5.2, which aims to minimize the system peak load cost, is: Among them, C Gi,t is the peak load regulation cost of the i-th unit in period t, is the grid-side energy storage loss cost during period t, is the peak load storage loss cost at the source side during period t, is the penalty cost of wind farm abandonment during period t.

6. A source-grid-load generalized energy storage system optimization dispatching system, characterized in that: Used in conjunction with a source-grid-load generalized energy storage system optimization scheduling method as described in any one of claims 1 to 5, comprising: Source-load dual-layer optimization dispatch model, which is used to maximize the net operating income of source-side leasing energy storage and minimize the net load variance of the system; Energy storage configuration module, which is used to generate energy storage configuration results and pass them to the lower-level model; Net load optimization module, which is used to optimize net load and source-side leased energy storage output based on energy storage operation simulation results; A two-layer model interaction module, which is used to implement interactive iteration between the upper-layer model and the lower-layer model; Deep peak-shaving quotation model, which is used to establish a deep peak-shaving quotation model for multiple market players; The deep peak-shaving trading module is used to calculate the deep peak-shaving demand and clear the peak-shaving.

7. The source-grid-load generalized energy storage system optimization dispatching system according to claim 6 is characterized in that: The deep peak shaving trading module includes: A demand calculation unit, which is used to calculate the deep peak load demand; The peak-shaving and clearing unit is used to perform peak-shaving and clearing according to the bidding results, with the goal of minimizing the system peak-shaving cost; The transaction execution unit is used to execute peak-shaving transactions and adjust the output and energy storage charging and discharging behavior of each market player.

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