User-side shared energy storage multilayer optimization method considering joint pricing and demand response

Abstract

The invention provides a user side shared energy storage multilayer optimization method considering joint pricing and demand response, and relates to the technical field of integrated energy systems.The method comprises the steps that firstly, basic operation parameter data in a regional integrated energy system are collected, and the relation between the power of a shared energy storage power station and the power of the shared energy storage power station used by each user is determined; and calculating the price excitation demand response income and peak regulation cost of the user group, and further constructing a user-side shared energy storage multilayer optimization model and solving the user-side shared energy storage multilayer optimization model. Through optimizing a joint pricing and energy scheduling strategy, the model significantly reduces the overall operation cost of a user group, and effectively reduces the power purchase demand of a power grid in a peak period. Meanwhile, the system fully consumes the renewable energy generating capacity, and the problem of low utilization rate caused by resource waste is avoided. Therefore, the power supply pressure of the power grid is relieved, the operation stability is improved, the synergistic interaction of the source network load storage is realized, and the win-win situation among multiple main bodies is achieved.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system technology, specifically to a multi-layer optimization method for user-side shared energy storage that takes into account joint pricing and demand response. Background Technology

[0002] As the penetration rate of renewable energy in the power system continues to increase, its intermittency and volatility pose serious challenges to the system's safety, stability, and economic operation. Especially during peak renewable energy output periods, it often exceeds load demand and grid absorption capacity, leading to the abandonment of large amounts of clean energy and resource waste. Although distributed energy storage technology can effectively mitigate volatility and improve the utilization rate of renewable energy, its high investment cost remains a major obstacle to large-scale application. Shared energy storage models, through intensive configuration and multi-entity sharing, significantly improve equipment utilization and economic efficiency, providing an effective path to overcome the cost bottleneck of energy storage. However, the service fees for shared energy storage directly affect users' energy consumption behavior and the revenue of shared energy storage operators. Therefore, how to reasonably formulate shared energy storage service fees and balance operator revenue and user costs has become a key issue in shared energy storage research.

[0003] Meanwhile, the mismatch between renewable energy and load demand in time and space, especially during peak hours when the combined generation of renewable and conventional power sources can easily lead to short-term power surges in the grid, exceeding line transmission capacity or system regulation margins, and causing stability risks such as voltage exceeding limits and frequency deviations. Demand response strategies, through price signals or incentive mechanisms, guide users to adjust their electricity consumption behavior, implement peak shaving and valley filling, smooth the load curve, alleviate peak pressure on the grid, and improve the flexibility of system operation, becoming a key means to solve grid stability problems.

[0004] However, single strategies such as shared energy storage or demand response still have limitations in addressing the dual challenges brought about by the integration of high proportions of renewable energy. Shared energy storage primarily regulates the transfer of energy over time, while demand response focuses on adjusting load; the two are complementary in terms of response characteristics and adjustment speed. Therefore, how to synergistically optimize shared energy storage and demand response resources, fully leverage their spatiotemporal complementary advantages, and construct a comprehensive energy optimization and dispatching system adapted to the integration of high proportions of renewable energy has become an urgent technical challenge. Simultaneously, the rational formulation of time-of-use pricing and shared energy storage service fees is crucial in this process. Scientific pricing not only incentivizes users to participate in demand response but also ensures that grid companies and shared energy storage operators receive reasonable returns, thereby providing economic support for system optimization and sustainable development. Precise pricing can achieve a win-win situation for all parties, providing a more efficient solution to the challenges in the energy system. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to propose a multi-layered optimization method for user-side shared energy storage that considers joint pricing and demand response, including:

[0006] Collect basic operating parameter data of the regional integrated energy system. The basic operating parameter data includes time-of-use electricity price, energy storage service fee, demand response compensation coefficient, peak shaving cost coefficient, wind power of each user, photovoltaic data of each user, electricity load data of each user, operating parameters of each device, purchased power and dispatchable load parameters.

[0007] Determine the relationship between the power output of the shared energy storage power station and the power consumption of the shared energy storage power station by each user;

[0008] For typical user groups with independently configured wind and solar power generation equipment, the price incentive demand response revenue of the user group is calculated based on the demand response compensation coefficient, and the peak-shaving cost is calculated based on the peak-shaving cost coefficient.

[0009] Based on the relationship between the power of shared energy storage power stations and the power of shared energy storage power stations used by each user, the demand response benefits of user group price incentives and peak shaving costs, a multi-layer optimization model for user-side shared energy storage considering joint pricing and demand response is constructed. The multi-layer optimization model for user-side shared energy storage includes objective functions and constraints. The objective functions include an outer objective function, a middle objective function, and an inner objective function.

[0010] The particle swarm optimization algorithm is used to solve the outer and middle objective functions based on the constraints. The constraints are linearized, and the inner objective function is solved based on the linearized constraints to obtain the multi-party collaborative scheduling result under the optimal time-of-use electricity price and shared energy storage service fee pricing.

[0011] Optionally, the relationship between the power of the shared energy storage power station and the power used by each user in the shared energy storage power station is specifically expressed by the following formula:

[0012] ;

[0013] in, The power used to charge the shared energy storage station for the i-th user during time period t; Let N be the power discharged by the i-th user during time period t using the shared energy storage station, and let N represent the number of users. The charging power of the shared energy storage power station during time period t. The discharge power of the shared energy storage power station during time period t.

[0014] Optionally, for typical user groups with independently configured wind and solar power generation equipment, the price-incentive demand response revenue for the user group is calculated based on the demand response compensation coefficient, specifically through the following formula:

[0015] ;

[0016] in, Revenue from responding to price-incentive demand from the user base; Let be the compensation factor for the electrical load transferred during time period t. This is the compensation factor for the reduced electrical load during time period t; Let be the transferable electrical load power of the i-th user participating in the response during time period t. Let T represent the power of the electrical load that can be reduced by the i-th user participating in the response during time period t, and T represent the scheduling period. This indicates the duration of the unit scheduling period.

[0017] Optionally, the peak-shaving cost can be calculated based on the peak-shaving cost coefficient, specifically using the following formula:

[0018] ;

[0019] in, It is the peak-shaving incentive coefficient for transferring electrical load during time period t; This marks the start of the peak load period. This represents the length of the peak period interval.

[0020] Optionally, the outer objective function is expressed as:

[0021] ;

[0022] in, N represents the net revenue from electricity sales for the power grid company; N is the number of users; and T is the dispatch cycle. The duration of the scheduling period is specified in units. Let be the power purchased by the i-th user from the grid during time period t; The time-of-use electricity price for period t; The cost of maintaining electricity sold per unit;

[0023] The objective function for the middle layer is expressed as:

[0024] ;

[0025] in, To share the combined interests of energy storage operators and users; The total daily operating economic cost for the user group connected to the shared energy storage power station; λ represents the service fee paid by the user group to the shared energy storage power station; λ is the interest coordination coefficient; δ represents the price of the shared energy storage service fee paid by the user to the shared energy storage power station.

[0026] The inner objective function is expressed as:

[0027] ;

[0028] in, The cost of purchasing electricity from the grid for the user group; Service fees paid by the user group to the shared energy storage power station; The peak-shaving cost provided to the power grid by the user group; Revenue from responding to price-incentive demand from the user base;

[0029] Among them, the electricity purchase fees from the power grid by the user group It is calculated using the following formula:

[0030] ;

[0031] in, The price at which a user purchases electricity from the grid during time period t;

[0032] The service fee paid by the user group to the shared energy storage power station is calculated using the following formula:

[0033] ;

[0034] in, The power used to charge the shared energy storage station for the i-th user during time period t; Let be the power discharged by the shared energy storage station used by the i-th user during time period t. The price of shared energy storage service fees paid by the user to the shared energy storage power station during time period t.

[0035] Optionally, the constraints include time-of-use electricity price upper and lower limits, shared energy storage service fee pricing constraints, power balance constraints, curtailment constraints, shared energy storage station charging and discharging power constraints, and each user's charging and discharging power constraints for the shared energy storage station. It also includes wind power and photovoltaic power output constraints and load transfer and reduction constraints.

[0036] The upper and lower limits of the time-of-use electricity price are expressed by the following formula:

[0037] ;

[0038] in, , These are the lower limit and the upper limit of the time-of-use electricity price, respectively. , , These are the time-of-use electricity prices for off-peak hours, normal hours, and peak hours, respectively.

[0039] The pricing constraint for shared energy storage service fees is expressed by the following formula:

[0040] ;

[0041] The power balance constraint is expressed by the following formula:

[0042] ;

[0043] in, Let be the photovoltaic power generation of the i-th user during time period t; Let be the wind power output of the i-th user during time period t; Let be the power abandoned by the i-th user during time period t; Let be the electrical load power of the i-th user after demand response in time period t;

[0044] The output constraints for wind power and solar power are expressed by the following formula:

[0045] ;

[0046] in, This represents the upper limit of wind power output for the i-th user. This represents the upper limit of the photovoltaic output power for the i-th user.

[0047] The curtailment constraint is expressed by the following formula:

[0048] ;

[0049] The load transfer and reduction constraints are expressed by the following formula:

[0050] ;

[0051] ;

[0052] ;

[0053] in, , , , These represent the electrical load power, original electrical load power, transferable electrical load power, and reducible electrical load power of the i-th user after demand response in time period t, respectively. Let be the ratio of the transferable electrical load power of the i-th user to its original electrical load power. This is the ratio of the reduceable electrical load power of the i-th user to its original electrical load power;

[0054] The charging and discharging power constraint of the shared energy storage power station is expressed by the following formula:

[0055] ;

[0056] in, and For the charging and discharging status bits of the shared energy storage power station; For the maximum charging and discharging power of the shared energy storage power station, The charging power of the shared energy storage power station during time period t. These represent the discharge power of the shared energy storage power station during time period t;

[0057] The power constraints for each user's charging and discharging at the shared energy storage power station are expressed by the following formula:

[0058] ;

[0059] in, The maximum charging and discharging power for users utilizing shared energy storage power stations; and This refers to the charging and discharging status bits of the shared energy storage station used by the i-th user during time period t.

[0060] Optionally, the multi-party collaborative scheduling result under the optimal time-of-use pricing and shared energy storage service fee pricing includes the power of the i-th user charging the shared energy storage station during time period t. The power discharged by the i-th user using the shared energy storage power station during time period t. The charging power of the shared energy storage power station during time period t The discharge power of the shared energy storage power station during time period t User group pricing incentive demand response revenue The transferable electrical load power of the i-th user after demand response in time period t. Peak-shaving costs provided by the user base to the power grid Total daily operating economic cost of users connected to shared energy storage power stations The cost of electricity purchased from the power grid by the user group Service fees paid by user groups to shared energy storage power stations The power purchased by the i-th user from the grid during time period t The photovoltaic power generation of the i-th user during time period t The wind power output of the i-th user during time period t The original electrical load power of the i-th user after demand response in time period t. The power load that can be reduced after the i-th user responds to demand in time period t. Net revenue from electricity sales by power grid companies Comprehensive benefits for shared energy storage operators and user groups Time-of-use electricity pricing during off-peak hours Time-of-use electricity pricing during normal periods Time-of-use electricity pricing during peak hours The shared energy storage service fee δ paid by the user to the shared energy storage power station, and the electrical load power of the i-th user after demand response in time period t. The amount of power abandoned by the i-th user during time period t. .

[0061] The beneficial effects of adopting the above technical solution are as follows:

[0062] This invention establishes a multi-layered optimization method for user-side shared energy storage that considers joint pricing and demand response. First, it collects basic operating parameter data from a regional integrated energy system to determine the relationship between the power of shared energy storage power stations and the power consumed by each user. It then calculates the user group's price-incentive demand response revenue and peak-shaving costs. Next, it constructs and solves a multi-layered optimization model for user-side shared energy storage. The outer objective function of this model aims to maximize the net revenue of the power grid company by formulating a time-of-use pricing strategy. The middle objective function, for the shared energy storage operator, aims to maximize the comprehensive benefits for both the operator and the user group by setting shared energy storage service fees. The inner objective function, for the user cluster, aims to minimize total cost by optimizing shared energy storage scheduling, power purchase plans, and demand response schemes. By optimizing joint pricing and energy dispatch strategies, this model significantly reduces the overall operating costs of the user group and effectively reduces the grid's power purchase demand during peak hours. Simultaneously, the system fully absorbs renewable energy generation, avoiding low utilization rates due to resource waste. This not only alleviates the power grid's supply pressure and improves operational stability but also achieves synergistic efficiency among power generation, grid, load, and storage, resulting in a win-win situation for multiple stakeholders. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating the multi-layer optimization method for user-side shared energy storage that takes into account joint pricing and demand response in an embodiment of the present invention.

[0064] Figure 2 This is a time-of-use electricity price diagram in an embodiment of the present invention;

[0065] Figure 3 This is a graph showing the wind power and photovoltaic data of each user in this embodiment of the invention;

[0066] Figure 4 This is a diagram showing the electrical load data of each user in this embodiment of the invention;

[0067] Figure 5 This is a diagram of a user-side energy storage cluster sharing model in an embodiment of the present invention;

[0068] Figure 6 This is a demand response model diagram in an embodiment of the present invention;

[0069] Figure 7 This is a multi-layered theoretical framework diagram in an embodiment of the present invention;

[0070] Figure 8 This is a general diagram of the multi-layer optimization model for user-side shared energy storage that takes into account joint pricing and demand response in an embodiment of the present invention. Detailed Implementation

[0071] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0072] To address the problems existing in the prior art, this invention provides a multi-layer optimization method for user-side shared energy storage that considers joint pricing and demand response, combined with... Figure 1 This may include the following steps:

[0073] Step 1: Collect basic operating parameter data of the regional integrated energy system. This basic operating parameter data includes time-of-use electricity prices, energy storage service fees, demand response compensation coefficients, peak-shaving cost coefficients, wind power data for each user, photovoltaic data for each user, electricity load data for each user, operating parameters of each device, purchased electricity, and dispatchable load parameters. The collected time-of-use electricity prices are as follows: Figure 2 Wind power and solar power data for each user, such as Figure 3 Electricity load data for each user, such as Figure 4 .

[0074] Step 2: Determine the relationship between the power of the shared energy storage power station and the power used by each user on the shared energy storage power station. That is, the sum of the charging and discharging power of each user using the shared energy storage power station is the charging and discharging power of the shared energy storage power station, which is expressed by the following formula:

[0075] ;

[0076] in, The power used to charge the shared energy storage station for the i-th user during time period t; Let N be the power discharged by the i-th user during time period t using the shared energy storage station, and let N represent the number of users. The charging power of the shared energy storage power station during time period t. The discharge power of the shared energy storage power station during time period t;

[0077] Furthermore, based on the self-discharge efficiency, charging efficiency, and discharging efficiency of the shared energy storage power station, as well as the upper and lower limits of the state of charge (SOC) in the equipment's operating parameters, the SOC of the shared energy storage power station is determined, and the SOC of the shared energy storage power station is expressed as follows:

[0078] ;

[0079] in, The state of charge of the shared energy storage power station at time t; To share the self-discharge efficiency of energy storage power stations; This represents the state of charge of the shared energy storage power station during time period t-1. To improve the charging efficiency of shared energy storage power stations. To share the discharge efficiency of energy storage power stations The charging power of the shared energy storage power station during time period t. These represent the discharge power of the shared energy storage power station during time period t; This represents the minimum state of charge of a shared energy storage power station. This represents the maximum value of the state of charge of the shared energy storage power station. This indicates the duration of the unit scheduling period.

[0080] Furthermore, the established user-side energy storage cluster sharing model, such as Figure 5 .

[0081] Step 3: Based on the collected basic operating parameter data of the regional integrated energy system, establish a demand response model. The established demand response model is as follows: Figure 6 Specifically, for typical user groups with independently configured wind and solar power generation equipment, the price incentive demand response revenue of the user group is calculated based on the demand response compensation coefficient, and the peak-shaving cost is calculated based on the peak-shaving cost coefficient.

[0082] Step 3.1: Based on the collected demand response compensation coefficients, this step targets a typical user group with independently configured wind and solar power equipment. Therefore, compensation for transferred loads is provided by the factory's internal service provider, while compensation for actively reduced loads is provided by the upstream power grid. Considering the different willingness of users to transfer and reduce electricity load, an incentive-based response strategy with different compensation methods is adopted. Specifically, for a typical user group with independently configured wind and solar power equipment, the price incentive demand response revenue for the user group is calculated based on the demand response compensation coefficients, using the following formula:

[0083] ;

[0084] in, Revenue from responding to price-incentive demand from the user base; Let be the compensation factor for the electrical load transferred during time period t. This is the compensation factor for the reduced electrical load during time period t; Let be the transferable electrical load power of the i-th user participating in the response during time period t. Let T represent the power of the electrical load that can be reduced by the i-th user participating in the response during time period t, and T represent the scheduling period. Indicates the duration of a unit scheduling period;

[0085] Step 3.2: For typical user groups with independently configured wind and solar power generation equipment, considering only the peak-shaving cost of load transfer, calculate the peak-shaving cost based on the peak-shaving cost coefficient. Specifically, this is achieved through the following formula:

[0086] ;

[0087] in, It is the peak-shaving incentive coefficient for transferring electrical load during time period t; This marks the start of the peak load period. This refers to the length of the peak period interval;

[0088] Step 4: Based on the relationship between the power of the shared energy storage power station and the power of the shared energy storage power station used by each user, the user group's price incentive demand response revenue and peak shaving cost, construct a user-side shared energy storage multi-layer optimization model that takes into account joint pricing and demand response. The user-side shared energy storage multi-layer optimization model includes an objective function and constraints. The objective function includes an outer objective function, a middle objective function and an inner objective function.

[0089] The outer objective function aims to maximize the net revenue of the power grid company from electricity sales and formulate time-of-use pricing strategies. The middle objective function, for shared energy storage operators, aims to maximize the combined benefits of operators and user groups by setting shared energy storage service fees. The inner objective function, for user clusters, aims to minimize total cost and optimize shared energy storage scheduling, electricity purchase plans, and demand response schemes. This multi-layered theoretical framework is as follows: Figure 7 .

[0090] The outer objective function is expressed as:

[0091] ;

[0092] in, N represents the net revenue from electricity sales for the power grid company; N is the number of users; and T is the dispatch cycle. The duration of the scheduling period is specified in units. Let be the power purchased by the i-th user from the grid during time period t; The time-of-use electricity price for period t; The cost of maintaining electricity sold per unit;

[0093] The objective function for the middle layer is expressed as:

[0094] ;

[0095] in, To share the combined interests of energy storage operators and users; The total daily operating economic cost for the user group connected to the shared energy storage power station; The service fee paid by the user group to the shared energy storage power station; λ is the benefit coordination coefficient, λ∈[0,1], when λ=0 only the operator's revenue is considered, when λ=1 only the user's cost is considered; δ represents the price of the shared energy storage service fee paid by the user to the shared energy storage power station;

[0096] The inner objective function is expressed as:

[0097] ;

[0098] in, The cost of purchasing electricity from the grid for the user group; Service fees paid by the user group to the shared energy storage power station; The peak-shaving cost provided to the power grid by the user group; Revenue from responding to price-incentive demand from the user base;

[0099] Among them, the electricity purchase fees from the power grid by the user group It is calculated using the following formula:

[0100] ;

[0101] in, The price at which a user purchases electricity from the grid during time period t;

[0102] The service fee paid by the user group to the shared energy storage power station is calculated using the following formula:

[0103] ;

[0104] in, The power used to charge the shared energy storage station for the i-th user during time period t; Let be the power discharged by the shared energy storage station used by the i-th user during time period t. The price of shared energy storage service fees paid by users to the shared energy storage power station during time period t;

[0105] The constraints include upper and lower limits for time-of-use electricity prices, pricing constraints for shared energy storage service fees, power balance constraints, curtailment constraints, charging and discharging power constraints for shared energy storage power stations, charging and discharging power constraints for each user using shared energy storage power stations, as well as constraints on wind power and photovoltaic power output and load transfer and reduction constraints.

[0106] The upper and lower limits of the time-of-use electricity price are expressed by the following formula:

[0107] ;

[0108] in, , These are the lower limit and the upper limit of the time-of-use electricity price, respectively. , , These are the time-of-use electricity prices for off-peak hours, normal hours, and peak hours, respectively.

[0109] The pricing constraint for shared energy storage service fees is expressed by the following formula:

[0110] ;

[0111] The power balance constraint is expressed by the following formula:

[0112] ;

[0113] in, Let be the photovoltaic power generation of the i-th user during time period t; Let be the wind power output of the i-th user during time period t; Let be the power abandoned by the i-th user during time period t; Let be the electrical load power of the i-th user after demand response in time period t;

[0114] The output constraints for wind power and solar power are expressed by the following formula:

[0115] ;

[0116] in, This represents the upper limit of wind power output for the i-th user. This represents the upper limit of the photovoltaic output power for the i-th user.

[0117] The curtailment constraint is expressed by the following formula:

[0118] ;

[0119] The load transfer and reduction constraints are expressed by the following formula:

[0120] ;

[0121] ;

[0122] ;

[0123] in, , , , These represent the electrical load power, original electrical load power, transferable electrical load power, and reducible electrical load power of the i-th user after demand response in time period t, respectively. Let be the ratio of the transferable electrical load power of the i-th user to its original electrical load power. This is the ratio of the reduceable electrical load power of the i-th user to its original electrical load power;

[0124] The charging and discharging power constraint of the shared energy storage power station is expressed by the following formula:

[0125] ;

[0126] in, and For the charging and discharging status bits of the shared energy storage power station; For the maximum charging and discharging power of the shared energy storage power station, The charging power of the shared energy storage power station during time period t. The discharge power of the shared energy storage power station during time period t;

[0127] The power constraints for each user's charging and discharging at the shared energy storage power station are expressed by the following formula:

[0128] ;

[0129] in, The maximum charging and discharging power for users utilizing shared energy storage power stations; and For the i-th user, the charging status bit and discharging status bit of the shared energy storage station during time period t;

[0130] In summary, the complete multi-layer optimization model for user-side shared energy storage established by this invention, which considers joint pricing and demand response, is as follows: Figure 8 ;

[0131] Step 5: Using the particle swarm optimization algorithm, based on the constraints, solve the outer and middle layer objective functions, specifically solving the pricing problems of the power grid company and the shared energy storage operator. Linearize the constraints, transforming the user scheduling problem into a mixed integer linear programming (MILP) problem. Based on the linearized constraints, solve the inner layer objective function. Specifically, use the commercial solver Cplex and Yalmip toolbox in Matlab 2024b to obtain the multi-party collaborative scheduling results under the optimal time-of-use pricing and shared energy storage service fee pricing. These results include the power of the i-th user charging the shared energy storage station during time period t. The power discharged by the i-th user using the shared energy storage power station during time period t. The charging power of the shared energy storage power station during time period t The discharge power of the shared energy storage power station during time period t User group pricing incentive demand response revenue The transferable electrical load power of the i-th user after demand response in time period t. Peak-shaving costs provided by the user base to the power grid Total daily operating economic cost of users connected to shared energy storage power stations The cost of electricity purchased from the power grid by the user group Service fees paid by user groups to shared energy storage power stations The power purchased by the i-th user from the grid during time period t The photovoltaic power generation of the i-th user during time period t The wind power output of the i-th user during time period t The original electrical load power of the i-th user after demand response in time period t. The power load that can be reduced after the i-th user responds to demand in time period t. Net revenue from electricity sales by power grid companies Comprehensive benefits for shared energy storage operators and user groups Time-of-use electricity pricing during off-peak hours Time-of-use electricity pricing during normal periods Time-of-use electricity pricing during peak hours The shared energy storage service fee δ paid by the user to the shared energy storage power station, and the electrical load power of the i-th user after demand response in time period t. The amount of power abandoned by the i-th user during time period t. .

[0132] To verify the superiority of the strategy of this invention, it is compared and analyzed with four other strategies. The five schemes are set as follows:

[0133] Option 1: Under the fixed time-of-use electricity price and shared energy storage service fee, the user group will not be equipped with shared energy storage power stations and demand response will not be considered;

[0134] Option 2, under the fixed time-of-use tariff and shared energy storage service fee, does not configure shared energy storage power stations for the user group and only considers demand response;

[0135] Option 3, under the fixed time-of-use tariff and shared energy storage service fee, only shared energy storage power stations are configured for the user group, and demand response is not considered;

[0136] Option 4, with fixed time-of-use pricing and shared energy storage service fees, allows users to both configure shared energy storage power stations and consider demand response.

[0137] The present invention provides a multi-layered optimization method for user-side shared energy storage that takes into account joint pricing and demand response.

[0138] The results of solving the above five schemes are shown in Table 1;

[0139] Table 1 Summary of optimization results for each scheme

[0140]

[0141] As can be seen from the comparison of the solution results in Table 1, the advantages of the present invention compared with the existing methods are as follows:

[0142] (1) The total daily operating cost of the user group is significantly reduced. Compared with Scheme 1, the total daily operating cost of the present invention is reduced by 75.85%, compared with Scheme 2 by 51.57%, compared with Scheme 3 by 61.61%, and compared with Scheme 4 by 2.82%.

[0143] (2) Effectively reduces the total grid purchase of electricity by user groups during peak hours. Compared with Scheme 1, the total grid purchase of electricity is reduced by 86.20%; compared with Scheme 2, the total grid purchase of electricity is reduced by 79.22%; and compared with Scheme 3, the total grid purchase of electricity is reduced by 68.39%.

[0144] (3) Fully absorb renewable energy power generation and successfully avoid the problem of low resource utilization caused by renewable energy waste. Compared with Scheme 1, the total abandoned power of the user group is reduced by 1869.01 kWh, and compared with Scheme 2, the total abandoned power of the user group is reduced by 1720.91 kWh.

[0145] Compared to existing methods, the advantages of this invention lie in its multi-layered optimization method for user-side shared energy storage that considers joint pricing and demand response. By optimizing joint pricing and energy dispatch strategies, this model significantly reduces the overall operating costs for the user group and effectively reduces grid power purchase demand during peak hours. Simultaneously, the system fully absorbs renewable energy generation, avoiding low utilization rates due to resource waste. This not only alleviates grid supply pressure and improves operational stability but also achieves synergistic efficiency among power generation, grid, load, and storage, resulting in a win-win situation for multiple stakeholders.

[0146] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A multi-layered optimization method for user-side shared energy storage that considers joint pricing and demand response, characterized in that, include: Collect basic operating parameter data of the regional integrated energy system. The basic operating parameter data includes time-of-use electricity price, energy storage service fee, demand response compensation coefficient, peak shaving cost coefficient, wind power of each user, photovoltaic data of each user, electricity load data of each user, operating parameters of each device, purchased power and dispatchable load parameters. Determine the relationship between the power output of the shared energy storage power station and the power consumption of the shared energy storage power station by each user; For typical user groups with independently configured wind and solar power generation equipment, the price incentive demand response revenue of the user group is calculated based on the demand response compensation coefficient, and the peak-shaving cost is calculated based on the peak-shaving cost coefficient. Based on the relationship between the power of shared energy storage power stations and the power of shared energy storage power stations used by each user, the demand response benefits of user group price incentives and peak shaving costs, a multi-layer optimization model for user-side shared energy storage considering joint pricing and demand response is constructed. The multi-layer optimization model for user-side shared energy storage includes objective functions and constraints. The objective functions include an outer objective function, a middle objective function, and an inner objective function. The particle swarm optimization algorithm is used to solve the outer and middle objective functions based on the constraints. The constraints are linearized, and the inner objective function is solved based on the linearized constraints to obtain the multi-party collaborative scheduling result under the optimal time-of-use electricity price and shared energy storage service fee pricing.

2. The multi-layer optimization method for user-side shared energy storage considering joint pricing and demand response as described in claim 1, characterized in that, The relationship between the power of the shared energy storage power station and the power consumed by each user from the shared energy storage power station is specifically expressed by the following formula: ； in, The power used to charge the shared energy storage station for the i-th user during time period t; Let N be the power discharged by the i-th user during time period t using the shared energy storage station, and let N represent the number of users. The charging power of the shared energy storage power station during time period t. The discharge power of the shared energy storage power station during time period t.

3. The multi-layer optimization method for user-side shared energy storage considering joint pricing and demand response as described in claim 1, characterized in that, For typical user groups with independently configured wind and solar power equipment, the price-incentive demand response benefit for these user groups is calculated based on the demand response compensation coefficient, specifically through the following formula: ； in, Revenue from responding to price-incentive demand from the user base; Let be the compensation factor for the electrical load transferred during time period t. This is the compensation factor for the reduced electrical load during time period t; Let be the transferable electrical load power of the i-th user participating in the response during time period t. Let T represent the power of the electrical load that can be reduced by the i-th user participating in the response during time period t, and T represent the scheduling period. This indicates the duration of the unit scheduling period.

4. The multi-layer optimization method for user-side shared energy storage considering joint pricing and demand response as described in claim 1, characterized in that, Based on the peak-shaving cost coefficient, the peak-shaving cost is calculated using the following formula: ； in, It is the peak-shaving incentive coefficient for transferring electrical load during time period t; This marks the start of the peak load period. This represents the length of the peak period interval.

5. The multi-layer optimization method for user-side shared energy storage considering joint pricing and demand response as described in claim 1, characterized in that, The outer objective function is expressed as: ； in, N represents the net revenue from electricity sales for the power grid company; N is the number of users; and T is the dispatch cycle. The duration of the scheduling period is specified in units. Let be the power purchased by the i-th user from the grid during time period t; The time-of-use electricity price for period t; The cost of maintaining electricity sold per unit; The objective function for the middle layer is expressed as: ； in, To share the combined interests of energy storage operators and users; The total daily operating economic cost for the user group connected to the shared energy storage power station; λ represents the service fee paid by the user group to the shared energy storage power station; λ is the interest coordination coefficient; δ represents the price of the shared energy storage service fee paid by the user to the shared energy storage power station. The inner objective function is expressed as: ； in, The cost of purchasing electricity from the grid for the user group; Service fees paid by the user group to the shared energy storage power station; The peak-shaving cost provided to the power grid by the user group; Revenue from responding to price-incentive demand from the user base; Among them, the electricity purchase fees from the power grid by the user group It is calculated using the following formula: ； in, The price at which a user purchases electricity from the grid during time period t; The service fee paid by the user group to the shared energy storage power station is calculated using the following formula: ； in, The power used to charge the shared energy storage station for the i-th user during time period t; Let be the power discharged by the shared energy storage station used by the i-th user during time period t. The price of shared energy storage service fees paid by the user to the shared energy storage power station during time period t.

6. The multi-layer optimization method for user-side shared energy storage considering joint pricing and demand response as described in claim 1, characterized in that, The constraints include time-of-use pricing upper and lower limits, shared energy storage service fee pricing constraints, power balance constraints, curtailment constraints, shared energy storage station charging and discharging power constraints, and each user's charging and discharging power constraints for shared energy storage stations. It also includes wind power and photovoltaic power output constraints and load transfer and reduction constraints. The upper and lower limits of the time-of-use electricity price are expressed by the following formula: ； in, , These are the lower limit and the upper limit of the time-of-use electricity price, respectively. , , These are the time-of-use electricity prices for off-peak hours, normal hours, and peak hours, respectively. The pricing constraint for shared energy storage service fees is expressed by the following formula: ； The power balance constraint is expressed by the following formula: ； in, Let be the photovoltaic power generation of the i-th user during time period t; Let be the wind power output of the i-th user during time period t; Let be the power abandoned by the i-th user during time period t; Let be the electrical load power of the i-th user after demand response in time period t; The output constraints for wind power and solar power are expressed by the following formula: ； in, This represents the upper limit of wind power output for the i-th user. This represents the upper limit of the photovoltaic output power for the i-th user. The curtailment constraint is expressed by the following formula: ； The load transfer and reduction constraints are expressed by the following formula: ； ； ； in, , , , These represent the electrical load power, original electrical load power, transferable electrical load power, and reducible electrical load power of the i-th user after demand response in time period t, respectively. Let be the ratio of the transferable electrical load power of the i-th user to its original electrical load power. This is the ratio of the reduceable electrical load power of the i-th user to its original electrical load power; The charging and discharging power constraint of the shared energy storage power station is expressed by the following formula: ； in, and For the charging and discharging status bits of the shared energy storage power station; For the maximum charging and discharging power of the shared energy storage power station, The charging power of the shared energy storage power station during time period t. These represent the discharge power of the shared energy storage power station during time period t; The power constraints for each user's charging and discharging at the shared energy storage power station are expressed by the following formula: ； in, The maximum charging and discharging power for users utilizing shared energy storage power stations; and This refers to the charging and discharging status bits of the shared energy storage station used by the i-th user during time period t.

7. The multi-layer optimization method for user-side shared energy storage considering joint pricing and demand response as described in claim 1, characterized in that, The multi-party collaborative dispatch result under the optimal time-of-use pricing and shared energy storage service fee pricing includes the power of the i-th user charging at the shared energy storage station during time period t. The power discharged by the i-th user using the shared energy storage power station during time period t. The charging power of the shared energy storage power station during time period t The discharge power of the shared energy storage power station during time period t User group pricing incentive demand response revenue The transferable electrical load power of the i-th user after demand response in time period t. Peak-shaving costs provided by the user base to the power grid Total daily operating economic cost of users connected to shared energy storage power stations The cost of electricity purchased from the power grid by the user group Service fees paid by user groups to shared energy storage power stations The power purchased by the i-th user from the grid during time period t The photovoltaic power generation of the i-th user during time period t The wind power output of the i-th user during time period t The original electrical load power of the i-th user after demand response in time period t. The power load that can be reduced after the i-th user responds to demand in time period t. Net revenue from electricity sales by power grid companies Comprehensive benefits for shared energy storage operators and user groups Time-of-use electricity pricing during off-peak hours Time-of-use electricity pricing during normal periods Time-of-use electricity pricing during peak hours The shared energy storage service fee δ paid by the user to the shared energy storage power station, and the electrical load power of the i-th user after demand response in time period t. The amount of power abandoned by the i-th user during time period t. .

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