A two-stage dispatching method for power distribution network based on energy storage leasing

Abstract

The present application relates to a kind of based on energy storage leasing distribution network two-stage scheduling method, comprising the following steps: step S1: the active distribution network operation framework considering energy storage operator self-service sharing is built;Step S2: shared energy storage leasing mechanism is formulated;Step S3: the overall optimization scheduling framework is built;Step S4: the first stage energy storage leasing capacity optimization model is built;Step S5: the second stage multi-agent collaborative optimization scheduling model is built;Step S6: the first stage model is solved;Step S7: the second stage model is solved.The method can realize the benefit balance of distribution network multi-agent while effectively improving the utilization of energy storage and the utilization of renewable energy, so as to promote the collaborative development of energy storage and new energy industry.

Description

Technical Field

[0001] This invention relates to the field of shared energy storage technology, and more specifically to a two-stage dispatching method for distribution networks based on energy storage leasing. Background Technology

[0002] Against the backdrop of dual-carbon development, the renewable energy industry has experienced rapid growth. However, the increasing penetration of renewable energy in power distribution networks has also brought a series of risks and challenges, posing a significant threat to the stable, reliable, and efficient operation of the power system. Energy storage is a crucial supporting technology for achieving dual-carbon goals. As a carrier of electrical energy, it has great potential in increasing the proportion of renewable energy consumption and ensuring the safe and stable operation of the power system. However, with the gradual expansion of installed capacity across various industries, the energy storage industry has gradually revealed disadvantages such as a lack of overall planning, low utilization rates, a single business model, and limited profitability, which are detrimental to market development. Therefore, it is necessary to improve the utilization rate of energy storage while rationally improving its operation methods, thereby promoting the coordinated development of renewable energy and the energy storage industry.

[0003] Shared energy storage provides a more flexible new energy supply through capacity leasing. As an innovative form of energy storage, shared energy storage has advantages such as promoting the consumption of new energy sources, flexible and efficient operation and dispatch, controllable safety and quality, and more significant economic benefits. It has broad development prospects in the power system and can be divided into two types according to its investment and operation model: user co-construction and sharing, and operator investment and sharing.

[0004] Current research on shared energy storage focuses primarily on the user side, emphasizing its operational models and economic benefits, while neglecting the potential for grid-side shared energy storage power stations to participate in system dispatch. Existing research proposes centralizing user-side energy storage devices in the cloud and using virtual capacity in the cloud to replace physical energy storage for sharing; other studies optimize the configuration of shared energy storage to improve the utilization rate of energy storage devices; and still others apply shared energy storage to multi-microgrid grid-connected systems, validating the effectiveness of this model in improving energy storage utilization and promoting the integration of new energy sources.

[0005] Renewable energy exhibits strong seasonality, uncertainty, and uncontrollability, leading to a series of impacts on distribution network protection and power quality when it is widely integrated into the grid. Traditional methods such as wind and solar curtailment and line upgrades, aimed at reducing net load peak-valley differences and improving power quality, often result in reduced renewable energy utilization and unsatisfactory investment returns. Introducing shared energy storage allows distribution networks to address the shortcomings of traditional methods by leasing storage for peak shaving and valley filling. However, balancing leasing costs and power quality while also considering the interests of all stakeholders in the distribution network remains a critical challenge. Summary of the Invention

[0006] The purpose of this invention is to provide a two-stage dispatching method for distribution networks based on energy storage leasing. This method can effectively improve the utilization rate of energy storage and renewable energy while achieving a balance of interests among multiple stakeholders in the distribution network, thereby promoting the coordinated development of energy storage and new energy industries.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a two-stage dispatching method for distribution networks based on energy storage leasing, comprising the following steps:

[0008] Step S1: Construct an active distribution network operation framework that considers self-operation and sharing by energy storage operators;

[0009] Step S2: Establish a shared energy storage leasing mechanism;

[0010] Step S3: Construct an overall optimized scheduling framework;

[0011] Step S4: Construct the first-stage energy storage leasing capacity optimization model;

[0012] Step S5: Construct the second-stage multi-agent collaborative optimization scheduling model;

[0013] Step S6: Solve the first-stage model;

[0014] Step S7: Solve the second-stage model.

[0015] Furthermore, the implementation method of step S1 is as follows:

[0016] Construct an active distribution network operation framework that considers self-operated and shared energy storage by energy storage operators, including: active distribution network (ADN), load aggregator (LA), and shared energy storage operator (SESA);

[0017] ADN (Automatic Generation Network) includes distributed photovoltaic (PV) and load, aiming to promote local consumption of new energy and load shaving and valley filling while minimizing operating costs. First, ADN prioritizes the consumption of new energy generation to balance the load. Second, it reduces the net load peak-valley difference by leasing shared energy storage, while promoting demand response by LA (Local Area) through time-of-use pricing and incentivizing SESA (Self-Supported Energy Storage) to utilize remaining energy storage capacity for peak shaving. Finally, ADN purchases electricity from the main grid to meet the remaining unbalanced load.

[0018] LA's operational objective is to respond to demand and maximize revenue while ensuring users' electricity supply; to aggregate users' electricity demand to form a certain scale of demand response, and to execute the contract signed with ADN, namely to provide load regulation capabilities to ADN during peak load periods;

[0019] SESA's operational objective is to cooperate with ADN to reduce the peak-valley difference in net load and maximize operational efficiency. It mainly provides leasing services to ADN by building centralized shared energy storage power stations (SES) on the distribution network side. The ADN's leasing needs determine the SES charging and discharging strategy, and the remaining energy storage capacity is used for low-storage and high-discharge arbitrage based on time-of-use pricing.

[0020] Furthermore, the method for implementing step S2 is as follows:

[0021] Energy storage leasing adopts a hybrid billing method, which takes into account the energy capacity, power capacity and charging and discharging power of the energy storage leasing. The calculation methods of energy capacity and power capacity are shown in Equations (1) and (2) respectively. They reflect the demand of ADN for energy storage from different levels. Since the lifespan of energy storage will be damaged by frequent charging and discharging, thereby increasing the operating cost, the charging and discharging power is charged in the form of flow to reflect the frequency of users' use of energy storage.

[0022] P i cap =max(|P i,t |), t=1,...,24 (1)

[0023]

[0024] In the formula, and P represents the power capacity and energy capacity leased by the ADN from the i-th SES, respectively; i,t S represents the power of the i-th energy storage at time t; max,t S min,t denoted as the maximum and minimum state of charge of the i-th energy storage unit within a day; t represents the t-th hour of the day; ω represents the capacity margin coefficient.

[0025] Furthermore, the implementation method of step S3 is as follows: the shared energy storage leasing and the multi-entity collaborative optimization scheduling model of the distribution network adopt a two-stage scheduling strategy to ensure the efficient utilization of energy storage and promote the full consumption of new energy.

[0026] Furthermore, the implementation method of step S4 is as follows:

[0027] In the first stage, ADN plans the leased energy storage capacity based on load, new energy output forecasts and leasing prices, and leases shared energy storage on demand to reduce the peak-valley difference of net load. Considering the contradiction between the peak shaving and valley filling effect brought by leased energy storage and the leasing cost, a multi-objective optimization model is constructed to minimize the ADN net load variance and the energy storage leasing cost.

[0028] Objective 1: Minimize the variance of ADN net load;

[0029]

[0030]

[0031] In the formula, F1 is the net load variance after ADN energy storage leasing; N PV N SES These represent the number of PVs and SESs, respectively; P L,t P PV,i,t P dis,i,t P ch,i,t and P ave These represent the load power, PV power, SES discharge power, charging power, and equivalent load average of the ADN during time period t, where T represents the number of hours in a day.

[0032] Objective 2: Lowest cost of ADN leased energy storage;

[0033]

[0034] P i cap =max(|P i,t |), t=1,...,24 (6)

[0035]

[0036] In the formula, F2 is the rental cost of energy storage after ADN rental; α, β, and γ are the unit power capacity rental price, unit energy capacity rental price, and unit power charging and discharging cost of SESA, respectively. and These represent the power capacity and energy capacity leased by the ADN from the i-th SES, respectively, and ω represents the capacity margin factor.

[0037] This stage mainly considers system operation constraints, including power balance constraints, SES energy storage capacity constraints, SES charging and discharging power constraints, PV output constraints, power flow equation constraints and node voltage constraints, as shown in equations (8)-(14), respectively.

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] V i,min≤V i,t ≤V i,max (14)

[0045] In the formula P grid,t Electricity purchased from the mainnet; P loss,t For ADN network loss; Q grid,t Q PV,i,t Q L,t These represent the reactive power obtained from the main grid, the reactive power generated by the PV, and the reactive power required by the ADN, respectively; A t B t η is a Boolean variable. ch η dis The charging and discharging efficiencies of SES are respectively; S t S represents the energy storage capacity of SES during time period t; max Indicates the energy storage capacity; S represents the maximum active power of the PV. inv V is the inverter capacity; N is the number of distribution network nodes; θ is the power factor angle corresponding to the minimum power factor of PV; V i，t and V j，t The node voltages at nodes i and j are respectively; G ij and B ij These represent the conductance and susceptance between nodes i and j, respectively; θ ij V represents the phase angle difference between nodes i and j; i，min and V i，max These represent the minimum and maximum node voltages, respectively.

[0046] Furthermore, the implementation method of step S5 is as follows:

[0047] Based on the SES output and leasing cost obtained in the first stage, a multi-entity collaborative optimization scheduling model for the distribution network is constructed with the objectives of minimizing ADN operating costs, maximizing LA revenue, and maximizing SESA arbitrage.

[0048] Objective 1: To minimize the total operating cost of the ADN, including electricity purchase costs C. grid，t LA compensation fee C LA，t And energy storage leasing costs C rent，t ;

[0049]

[0050] In the formula, J1 represents the total operating cost of the ADN, and c grid，t The price of electricity purchased from the main grid during time period t; c LA P is the LA compensation coefficient; IL，i，t N represents the interruptible load response power during time period t; IL Number of interruptible loads;

[0051] Objective 2: Maximize the operational benefits of LA;

[0052]

[0053] In the formula, J2 represents the operational benefits of interruptible loads;

[0054] Objective 3: Maximize SESA arbitrage, including arbitrage income C in and operating costs C cost ;

[0055]

[0056] In the formula, J3 represents the SESA arbitrage profit; σ is the SES unit charge / discharge power operation and maintenance cost; P′ dis，i，t 、P′ ch，i，t These refer to the discharge and charging power during the arbitrage phase, respectively.

[0057] This stage mainly considers active power balance constraints, SES arbitrage power constraints, LA operation constraints, reactive power constraints, power flow constraints and node voltage constraints. Among them, reactive power constraints, power flow constraints and node voltage constraints are the same as in the first stage, and the rest are shown in equations (18)-(22).

[0058]

[0059] P SES,i,t =P dis,i,t -P ch,i,t +P' dis,i,t -P' ch,i,t (19)

[0060]

[0061]

[0062]

[0063] In the formula, N PV N represents the number of PVs; LA P represents the number of LAs; SES,i,t Let A be the total power of the i-th SES during time period t; t B t From the first stage; ΔS t This represents the change in SES energy storage capacity during arbitrage; ζ is the interruptibility coefficient. T represents the maximum power of user i during time period t; IL,i The duration of the interruption for user i. The maximum allowed duration of interruption for user i; t IL,i This represents the number of interruptions per day for user i. The maximum number of interruptions allowed per day for user i; n i V is the minimum time interval between two allowed load interruptions for user i. IL,i,t This is a Boolean variable representing the interruption status of user i at time t, where 1 indicates a load interruption.

[0064] Furthermore, the implementation method of step S6 is as follows: since the two objective functions in the first stage are contradictory, a fast non-dominated sorting genetic algorithm with an elite retention strategy is used to solve the first stage model.

[0065] Furthermore, the implementation method of step S7 is as follows: considering that ADN, LA and SESA have different interests and their objective functions cannot be optimized at the same time, and there are many optimization objectives, the second stage model is solved based on the SES output and rental cost obtained in the first stage, using the NSGA-III algorithm based on the reference point mechanism to maintain population diversity under high-dimensional objectives.

[0066] Compared with existing technologies, this invention has the following advantages: It provides a two-stage dispatching method for distribution networks based on energy storage leasing. This method, through multi-objective optimization, takes into account the interests of ADN, SESA, and LA, achieving a balance of interests among multiple stakeholders in the distribution network. Simultaneously, the self-operated and shared energy storage model clarifies the profit model of SESA, reducing idle energy storage capacity, effectively improving energy storage utilization, and simultaneously increasing the utilization rate of renewable energy, thereby promoting the coordinated development of energy storage and the new energy industry. Attached Figure Description

[0067] Figure 1 This is a flowchart illustrating the method implementation of an embodiment of the present invention;

[0068] Figure 2 This is an active distribution network operation framework diagram in an embodiment of the present invention;

[0069] Figure 3 This is a flowchart of solving the two-stage model according to an embodiment of the present invention;

[0070] Figure 4 These are the photovoltaic output, load curves, and time-of-use electricity price diagrams of the ADN in this embodiment of the invention;

[0071] Figure 5 This is the Pareto non-dominated solution set of Strategy 1 in the embodiments of the present invention;

[0072] Figure 6 This is the net load curve of the compromise solution in the embodiments of the present invention;

[0073] Figure 7 This refers to the energy storage output and the net load of the ADN before and after arbitrage in Scheme 1 of this invention.

[0074] Figure 8 This refers to the energy storage output and the net load of the ADN before and after arbitrage in Scheme 2 of this invention.

[0075] Figure 9 This refers to the output of each main body in Scheme 2 of this embodiment of the invention;

[0076] Figure 10 This refers to the output of each main body in Scheme 3 of this embodiment. Detailed Implementation

[0077] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0078] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0079] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0080] like Figure 1 As shown, this embodiment provides a two-stage dispatching method for distribution networks based on energy storage leasing, including the following steps:

[0081] Step S1: Construct an active distribution network operation framework that considers self-operation and sharing by energy storage operators.

[0082] Step S2: Establish a shared energy storage leasing mechanism.

[0083] Step S3: Construct an overall optimized scheduling framework.

[0084] Step S4: Construct the first-stage energy storage leasing capacity optimization model.

[0085] Step S5: Construct the second-stage multi-agent collaborative optimization scheduling model.

[0086] Step S6: Solve the first-stage model.

[0087] Step S7: Solve the second-stage model.

[0088] In this embodiment, the method for implementing step S1 is as follows:

[0089] Constructing an active distribution network operation framework that considers self-operation and sharing by energy storage operators, such as Figure 2 As shown, it includes: Active Distribution Network (ADN), Load Aggregator (LA), and Shared Energy Storage Operator (SESA).

[0090] An ADN (Automatic Generation Network) includes distributed photovoltaic (PV) power and load, aiming to promote local consumption of renewable energy and peak shaving and valley filling while minimizing operating costs. First, the ADN prioritizes the consumption of renewable energy generation to balance the load. Second, it reduces the net load peak-valley difference by leasing shared energy storage, while promoting demand response by LA (Local Energy Providers) through time-of-use pricing and incentivizing SESA (Supported Energy Storage Services) to utilize remaining energy storage capacity for peak shaving. Finally, the ADN purchases electricity from the main grid to meet the remaining unbalanced load.

[0091] LA's operational objective is to respond to demand and maximize revenue while ensuring users' electricity supply; to aggregate users' electricity demand to form a certain scale of demand response; and to execute the contract signed with ADN, namely to provide load regulation capabilities to ADN during peak load periods.

[0092] SESA's operational objective is to cooperate with ADN to reduce the peak-valley difference in net load and maximize operational efficiency. It mainly provides leasing services to ADN by building centralized shared energy storage power stations (SES) on the distribution network side. The ADN's leasing needs determine the SES charging and discharging strategy, and the remaining energy storage capacity is used for low-storage and high-discharge arbitrage based on time-of-use pricing.

[0093] In this embodiment, step S2 is implemented as follows:

[0094] Energy storage leasing adopts a hybrid billing method, which takes into account the energy capacity, power capacity and charging and discharging power of the energy storage leasing. The calculation methods of energy capacity and power capacity are shown in equations (1) and (2) respectively. They reflect the demand of ADN for energy storage from different levels. Since the lifespan of energy storage will be damaged by frequent charging and discharging, thereby increasing the operating cost, the charging and discharging power is charged in the form of flow to reflect the frequency of users' use of energy storage. Therefore, this is used as the charging standard.

[0095] P i cap =max(P i,t ), t=1,...,24 (1)

[0096]

[0097] In the formula, and P represents the power capacity and energy capacity leased by the ADN from the i-th SES, respectively; i,t S represents the power of the i-th energy storage at time t; max,t S min,trepresents the maximum and minimum state of charge of the i-th energy storage unit within a day, respectively; t represents the t-th hour of the day; ω represents the capacity margin coefficient, which is set to 1.1 here.

[0098] In this embodiment, the implementation method of step S3 is as follows: the shared energy storage leasing and the multi-entity collaborative optimization scheduling model of the distribution network adopt a two-stage scheduling strategy to ensure the efficient utilization of energy storage and promote the full consumption of new energy.

[0099] In this embodiment, step S4 is implemented as follows:

[0100] In the first stage, ADN plans the leased energy storage capacity based on load, new energy output forecasts and leasing prices, and leases shared energy storage on demand to reduce the peak-valley difference of net load. Considering the contradiction between the peak shaving and valley filling effect brought by leased energy storage and the leasing cost, a multi-objective optimization model is constructed to minimize the ADN net load variance and the energy storage leasing cost.

[0101] Objective 1: Minimize the variance of ADN net load.

[0102]

[0103]

[0104] In the formula, F1 is the net load variance after ADN energy storage leasing; N PV N SES These represent the number of PVs and SESs, respectively; P L,t P PV,i,t P dis,i,t P ch,i,t and P ave These represent the load power, PV power, SES discharge power, charging power, and equivalent load average of the ADN during time period t, where T represents the number of hours in a day.

[0105] Objective 2: Lowest cost for ADN energy storage leasing.

[0106]

[0107] P i cap =max(|P i,t |), t=1,...,24 (6)

[0108]

[0109] In the formula, F2 is the rental cost of energy storage after ADN rental; α, β, and γ are the unit power capacity rental price, unit energy capacity rental price, and unit power charging and discharging cost of SESA, respectively. and These represent the power capacity and energy capacity leased by the ADN from the i-th SES, respectively, and ω represents the capacity margin coefficient, which is taken as 1.1 here.

[0110] This stage mainly considers system operation constraints, including power balance constraints, SES energy storage capacity constraints, SES charging and discharging power constraints, PV output constraints, power flow equation constraints, and node voltage constraints, as shown in equations (8)-(14), respectively.

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] V i,min ≤V i,t ≤V i,max (14)

[0118] In the formula P grid,t Electricity purchased from the mainnet; P loss,t For ADN network loss; Q grid,t Q PV,i,t Q L,t These represent the reactive power obtained from the main grid, the reactive power generated by the PV, and the reactive power required by the ADN, respectively; A t B t η is a Boolean variable. ch η dis These represent the charge and discharge efficiencies of the SES, respectively, which are taken as 0.95 here; S t S represents the energy storage capacity of SES during time period t; max Indicates the energy storage capacity; S represents the maximum active power of the PV. inv The inverter capacity is typically [value missing]. =1 to 1.1 times; N is the number of distribution network nodes; θ is the power factor angle corresponding to the minimum power factor of PV, which is taken as Q here. PV，i，t =0.3287P PV，i，t V i，t and V j，t The node voltages at nodes i and j are respectively; G ij and B ij These represent the conductance and susceptance between nodes i and j, respectively; θ ijV represents the phase angle difference between nodes i and j; i，min and V i，max These represent the minimum and maximum node voltages, respectively.

[0119] In this embodiment, step S5 is implemented as follows:

[0120] Based on the SES output and leasing costs obtained in the first stage, a multi-entity collaborative optimization scheduling model for the distribution network is constructed with the objectives of minimizing ADN operating costs, maximizing LA revenue, and maximizing SESA arbitrage.

[0121] Objective 1: To minimize the total operating cost of the ADN, including electricity purchase costs C. grid，t LA compensation fee C LA，t And energy storage leasing costs C rent，t .

[0122]

[0123] In the formula, J1 represents the total operating cost of the ADN, and c grid，t The price of electricity purchased from the main grid during time period t; c LA P is the LA compensation coefficient; IL，i，t N represents the interruptible load response power during time period t; IL This represents the number of interruptible loads.

[0124] Objective 2: Maximize the benefits of LA operation.

[0125]

[0126] In the formula, J2 represents the operating benefit of interruptible load.

[0127] Objective 3: Maximize SESA arbitrage, including arbitrage income C in and operating costs C cost .

[0128]

[0129] In the formula, J3 represents the SESA arbitrage profit; σ is the SES unit charge / discharge power operation and maintenance cost; P′ dis，i，t 、P′ ch，i，t These refer to the discharge and charging power during the arbitrage phase, respectively.

[0130] This stage mainly considers active power balance constraints, SES arbitrage power constraints, LA operation constraints, reactive power constraints, power flow constraints and node voltage constraints. Among them, reactive power constraints, power flow constraints and node voltage constraints are the same as in the first stage, and the rest are shown in equations (18)-(22).

[0131]

[0132] P SES,i,t =P dis,i,t -P ch,i,t +P' dis,i,t -P' ch,i,t (19)

[0133]

[0134]

[0135]

[0136] In the formula, N PV N represents the number of PVs; LA P represents the number of LAs; SES,i,t Let A be the total power of the i-th SES during time period t; t B t From the first stage; ΔS t This represents the change in SES energy storage capacity during arbitrage; ζ is the interruptibility factor, which is set to 0.2 here. T represents the maximum power of user i during time period t; IL,i The duration of the interruption for user i. The maximum allowed interrupt duration for user i, here set to 2 hours; t IL,i This represents the number of interruptions per day for user i. Let n be the maximum number of interruptions allowed for user i per day, set to 2 here; i V is the minimum time interval between two allowed load interruptions for user i, which is set to 2 hours here; IL,i,t This is a Boolean variable representing the interruption status of user i at time t, where 1 indicates a load interruption.

[0137] In this embodiment, step S6 is implemented as follows: Since the two objective functions in the first stage are contradictory, the fast non-dominated sorting genetic algorithm (NAGA-II) with an elite retention strategy is used to solve the first stage model. The specific steps are as follows: Figure 3 As shown.

[0138] In this embodiment, step S7 is implemented as follows: Considering that ADN, LA, and SESA have different interests and their objective functions cannot be simultaneously optimized, and there are many optimization objectives, based on the SES output and rental cost obtained in the first stage, the NSGA-III algorithm, which maintains population diversity under high-dimensional objectives based on the reference point mechanism, is used to solve the second-stage model. The specific steps are as follows: Figure 3 As shown.

[0139] In this embodiment, an IEEE 33-node distribution network system is selected as an example, and a simulation experiment is conducted based on the MATLAB R2021b platform. The photovoltaic output, load curve, and time-of-use pricing of the ADN are shown below. Figure 4 As shown.

[0140] This example sets up two different unit power capacity leasing prices α and energy capacity leasing prices β to compare the ADN energy storage leasing capacity and peak shaving and valley filling benefits under different pricing strategies of SESA. Strategy 1: α = 0.54 yuan / kW, β = 0.20 yuan / kWh; Strategy 2: α = 0.62 yuan / kW, β = 0.31 yuan / kWh; γ = 0.27 yuan / kW, σ = 0.27 yuan / kW.

[0141] like Figure 5 The diagram shows the Pareto non-dominated solution set for strategy 1. A membership function is constructed using partial small-scale fuzzy set decision theory to evaluate the satisfaction of the Pareto solution sets for both strategies and select the optimal compromise solution. The membership function is shown in equation (23), and the satisfaction evaluation is shown in equation (24). Figure 6 The figure shows the net load curve of the compromise solution.

[0142]

[0143]

[0144] In the formula, f max,i f min,i f represents the maximum and minimum values ​​of the i-th objective, respectively; i The objective function value; The satisfaction level of the k-th solution is represented by ; N represents the number of optimal solutions; and m represents the number of objective functions.

[0145] Table 1: Compromise on Energy Storage Leasing

[0146]

[0147]

[0148] Table 1 presents the compromise solution for energy storage leasing, comparing aspects such as leasing costs, leasing profitability, remaining total capacity, and net load variance. Table 1 shows that SESA can formulate reasonable leasing prices by comprehensively considering factors such as energy storage capacity and peak-shaving benefits. ADN, on the other hand, can leverage load and renewable energy output forecast data, weighing leasing costs and power quality based on each pricing strategy, and ultimately leasing energy storage according to demand to achieve an ideal net load fluctuation mitigation effect.

[0149] Regarding multi-entity collaborative optimization, this embodiment sets up three sets of calculation examples, aiming to achieve the lowest ADN cost, the maximum SESA arbitrage, and the highest LA operating revenue, comparing the revenue of each entity and the peak shaving and valley filling effect under different strategies. Scheme 1: ADN leases energy storage according to strategy 1 of the first stage, and SESA performs "low storage, high release" arbitrage based on time-of-use electricity prices; Scheme 2: ADN leases energy storage according to strategy 2 of the first stage, and the rest is the same as Scheme 1; Scheme 3: SESA does not lease energy storage, but performs arbitrage based on time-of-use electricity prices.

[0150] Figure 7 , Figure 8 The comparison shows the energy storage output and ADN net load before and after arbitrage in Schemes 1 and 2, respectively. Figure 9 , 10 Table 2 shows the contribution of each entity in Schemes 2 and 3; Table 2 shows the operating costs and benefits of each scheme.

[0151] Table 2. Operating Costs and Benefits of Each Option

[0152]

[0153] It can be seen that the interests of ADN, SESA, and LA were taken into account in the multi-stakeholder collaborative optimization, achieving a balance of interests among the multiple stakeholders in the distribution network. At the same time, it reduced idle energy storage capacity, effectively improved energy storage utilization, and promoted the coordinated development of energy storage and the new energy industry.

[0154] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0155] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0156] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0157] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0158] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A two-stage dispatching method for distribution networks based on energy storage leasing, characterized in that, Includes the following steps: Step S1: Construct an active distribution network operation framework that considers self-operation and sharing by energy storage operators; Step S2: Establish a shared energy storage leasing mechanism; Step S3: Construct an overall optimized scheduling framework; Step S4: Construct the first-stage energy storage leasing capacity optimization model; Step S5: Construct the second-stage multi-agent collaborative optimization scheduling model; Step S6: Solve the first-stage model; Step S7: Solve the second-stage model; The implementation method of step S4 is as follows: In the first stage, ADN plans the leased energy storage capacity based on load, new energy output forecasts and leasing prices, and leases shared energy storage on demand to reduce the peak-valley difference of net load. Considering the contradiction between the peak shaving and valley filling effect brought by leased energy storage and the leasing cost, a multi-objective optimization model is constructed to minimize the ADN net load variance and the energy storage leasing cost. Objective 1: Minimize the variance of ADN net load; （3） （4） In the formula, The net load variance after ADN leases energy storage; , These represent the number of PVs and SESs, respectively. , , , and These represent the load power, PV power, SES discharge power, charging power, and equivalent load average of the ADN during time period t, where T represents the number of hours in a day. Objective 2: Lowest cost of ADN leased energy storage; （5） ， （6） （7） In the formula, The cost of leasing energy storage after leasing ADN energy storage; , , These are SESA's unit power capacity leasing price, unit energy capacity leasing price, and unit power charging / discharging cost, respectively. and These represent the power capacity and energy capacity leased by the ADN from the i-th SES, respectively. P represents the capacity margin factor. i,t S represents the power of the i-th energy storage at time t; max,t S min,t These represent the maximum and minimum states of charge of the i-th energy storage unit within one day, respectively. This stage mainly considers system operation constraints, including power balance constraints, SES energy storage capacity constraints, SES charging and discharging power constraints, PV output constraints, power flow equation constraints and node voltage constraints, as shown in equations (8)-(14); （8） （9） （10） （11） （12） （13） （14） In the formula, Electricity purchased from the mainnet; For ADN network loss; , , These are the reactive power obtained from the main grid, the reactive power generated by the PV, and the reactive power required by the ADN, respectively. , It is a Boolean variable; , These are the charge and discharge efficiencies of the SES, respectively. Let t be the energy storage capacity of SES during time period t; Indicates the energy storage capacity; This represents the maximum active power of the PV. Where N is the inverter capacity; N is the number of distribution network nodes; The power factor angle corresponding to the minimum power factor of PV; and , where i and j are the node voltages, respectively; and These are the conductance and susceptance between nodes i and j, respectively; The phase angle difference between nodes i and j; and These represent the minimum and maximum node voltages, respectively.

2. The two-stage dispatching method for distribution networks based on energy storage leasing according to claim 1, characterized in that, The implementation method of step S1 is as follows: Construct an active distribution network operation framework that considers self-operated and shared energy storage by energy storage operators, including: active distribution network (ADN), load aggregator (LA), and shared energy storage operator (SESA); ADN (Automatic Generation Network) includes distributed photovoltaic (PV) and load, aiming to promote local consumption of new energy and load shaving and valley filling while minimizing operating costs. First, ADN prioritizes the consumption of new energy generation to balance the load. Second, it reduces the net load peak-valley difference by leasing shared energy storage, while promoting demand response by LA (Local Area) through time-of-use pricing and incentivizing SESA (Self-Supported Energy Storage) to utilize remaining energy storage capacity for peak shaving. Finally, ADN purchases electricity from the main grid to meet the remaining unbalanced load. LA's operational objective is to respond to demand and maximize revenue while ensuring users' electricity supply; to aggregate users' electricity demand to form a certain scale of demand response, and to execute the contract signed with ADN, namely to provide load regulation capabilities to ADN during peak load periods; SESA's operational objective is to cooperate with ADN to reduce the peak-valley difference in net load and maximize operational efficiency. It mainly provides leasing services to ADN by building centralized shared energy storage power stations (SES) on the distribution network side. The ADN's leasing needs determine the SES charging and discharging strategy, and the remaining energy storage capacity is used for low-storage and high-discharge arbitrage based on time-of-use pricing.

3. The two-stage dispatching method for distribution networks based on energy storage leasing according to claim 1, characterized in that, The method for implementing step S2 is as follows: Energy storage leasing adopts a hybrid billing method, which takes into account the energy capacity, power capacity and charging and discharging power of the energy storage leasing. The calculation methods of energy capacity and power capacity are shown in Equations (1) and (2) respectively. They reflect the demand of ADN for energy storage from different levels. Since the lifespan of energy storage will be damaged by frequent charging and discharging, thereby increasing the operating cost, the charging and discharging power is charged in the form of flow to reflect the frequency of users' use of energy storage. ， （1） （2） In the formula, and Let represent the power capacity and energy capacity leased by ADN from the i-th SES, respectively; t represents the t-th hour of the day. This represents the capacity margin coefficient.

4. The two-stage dispatching method for distribution networks based on energy storage leasing according to claim 1, characterized in that, The implementation method of step S3 is as follows: the shared energy storage leasing and the multi-entity collaborative optimization scheduling model of the distribution network adopt a two-stage scheduling strategy to ensure the efficient utilization of energy storage and promote the full consumption of new energy.

5. The two-stage dispatching method for distribution networks based on energy storage leasing according to claim 1, characterized in that, The implementation method of step S5 is as follows: Based on the SES output and leasing cost obtained in the first stage, a multi-entity collaborative optimization scheduling model for the distribution network is constructed with the objectives of minimizing ADN operating costs, maximizing LA revenue, and maximizing SESA arbitrage. Objective 1: To minimize the total operating cost of ADN, including electricity purchase costs. LA compensation costs and energy storage leasing fees ; （15） In the formula, J1 represents the total operating cost of the ADN. The price at which electricity is purchased from the main grid during time period t; This is the LA compensation coefficient; The interruptible load response power during time period t; Number of interruptible loads; Objective 2: Maximize the operational benefits of LA; （16） In the formula, J2 represents the operational benefits of interruptible loads; Objective 3: Maximize SESA arbitrage, including arbitrage income. and operating costs ; （17） In the formula, J3 represents the SESA arbitrage profit; Maintenance cost per unit charge / discharge power for SES; , These refer to the discharge and charging power during the arbitrage phase, respectively. This stage mainly considers active power balance constraints, SES arbitrage power constraints, LA operation constraints, reactive power constraints, power flow constraints and node voltage constraints. Among them, reactive power constraints, power flow constraints and node voltage constraints are the same as in the first stage, and the rest are shown in equations (18)-(22). （18） （19） （20） （21） （22） In the formula, N PV N represents the number of PVs; LA Indicates the number of LAs; Let be the total power of the i-th SES during time period t; , From the first stage; This indicates the change in the SES energy storage capacity during arbitrage; ζ is the interruptibility coefficient; The maximum power of user i during time period t; The duration of the interruption for user i. The maximum allowed duration of interruption for user i; The number of interruptions per day for user i. The maximum number of interruptions allowed per day for user i; The minimum time interval between two allowed load interruptions for user i; This is a Boolean variable representing the interruption status of user i at time t, where 1 indicates a load interruption.

6. The two-stage dispatching method for distribution networks based on energy storage leasing according to claim 1, characterized in that, The implementation method of step S6 is as follows: Since the two objective functions in the first stage are contradictory, a fast non-dominated sorting genetic algorithm with an elite retention strategy is used to solve the first stage model.

7. A two-stage dispatching method for distribution networks based on energy storage leasing according to claim 5, characterized in that, The implementation method of step S7 is as follows: Considering that ADN, LA and SESA have different interests and their objective functions cannot be optimized at the same time, and there are many optimization objectives, the NSGA-III algorithm, which maintains population diversity under high-dimensional objectives based on the SES output and rental cost obtained in the first stage, is used to solve the second stage model.

Images