Power distribution network side energy storage planning method considering distributed power supply access

Abstract

The invention discloses a power distribution network side energy storage planning method considering distributed power supply access, and the method comprises the steps: building a wind power output prediction model and a photovoltaic output prediction model based on a probability distribution model, and combining a load classification model containing interruptible load and transferable load constraints, generating a collaborative operation scene containing new energy output fluctuation and load peak-valley characteristics to construct a source-load-storage collaborative operation model; the method comprises the following steps: establishing a state of charge (SOC) dynamic model of an energy storage system, setting physical constraints of charge and discharge efficiency, capacity and power, and quantifying wind and light abandoning cost, demand side response cost and energy storage efficiency loss cost to form a power distribution network operation total cost model; and on the basis of the source-load-storage collaborative operation model and the power distribution network operation total cost model, iteratively solving optimal configuration parameters of energy storage capacity and power by adopting a particle swarm optimization algorithm for dynamically adjusting inertia weight and taking minimization of the power distribution network operation total cost as a target.

Description

Technical Field

[0001] The present invention relates to the technical fields of smart grid, distributed energy and energy storage system integration, and in particular to a distribution network-side energy storage planning method considering the access of distributed power sources. Background Art

[0002] The core of energy storage system planning lies in achieving a balance between economic efficiency, reliability, and renewable energy absorption rate through optimal capacity configuration. To achieve this goal, existing research has proposed a variety of methods:

[0003] Hierarchical planning model: Using algorithms such as Benders decomposition and mixed integer stochastic programming, complex planning problems are decomposed into multi-level optimization models to reduce the difficulty of solving them;

[0004] Data-driven forecasting: Using density clustering algorithms to process historical data, we improve the accuracy of renewable energy output and load forecasts, providing data support for planning.

[0005] Uncertainty management: Addressing the uncertainty of renewable energy output and load demand based on probabilistic scenario generation or robust optimization methods;

[0006] Multi-objective collaboration: Explore the collaborative value of energy storage systems in multiple scenarios such as peak load regulation, voltage regulation, and new energy consumption.

[0007] However, the above methods still have significant limitations:

[0008] Lack of coordination mechanism: Existing technologies often focus on single-dimensional optimization (such as "source-storage" or "load-storage" coordination), lacking systematic integration of the coordinated operation mechanism of the source, load, and storage on the distribution network side. As a result, planning results are difficult to fully adapt to the complexity of actual operation scenarios.

[0009] Incomplete cost models: Traditional models focus on investment and operation and maintenance costs, but ignore hidden costs such as wind and solar curtailment, demand-side response compensation costs, and energy storage efficiency losses, resulting in unrealistic economic assessments.

[0010] Insufficient algorithm adaptability: Standard optimization algorithms (such as fixed-weight particle swarm optimization) are prone to falling into local optimality or low convergence efficiency when dealing with high-dimensional, multi-constrained collaborative planning problems.

[0011] These defects result in optimization blind spots in existing solutions when balancing goals such as economy, environmental protection and operational reliability, restricting the large-scale development of distribution networks with a high proportion of renewable energy. Summary of the Invention

[0012] To address the shortcomings of existing technologies, such as the lack of a source-load-storage synergy mechanism, inadequate implicit cost modeling, and low high-dimensional optimization efficiency, the present invention provides a method and system for optimizing energy storage configuration on the distribution network side. Its innovative design features include:

[0013] Source-load-storage collaborative operation mechanism: Using Weibull and Beta distributions to construct wind power and photovoltaic power output probability models, respectively, combined with a refined load classification model that includes interruptible loads (constrained by the number and duration of interruptions) and transferable loads (constrained by scheduling time windows), this generates a collaborative scenario that deeply couples renewable energy output fluctuations with load peaks and valleys, breaking through the limitations of traditional "source-storage" or "load-storage" separation optimization.

[0014] Full-life cycle composite cost model: This model integrates for the first time the costs of wind and solar curtailment, demand-side response compensation costs, and energy storage charging and discharging efficiency losses, forming a multi-dimensional economic evaluation system covering investment, operation and maintenance, and hidden losses. This model addresses the "pseudo-optimal solution" problem caused by traditional models that ignore hidden costs.

[0015] Dynamic weighted particle swarm optimization algorithm: adopts a linear decreasing inertia weight strategy, adapts to high-dimensional (energy storage capacity, power, load response strategy) nonlinear programming solutions, and combines simulation case verification to achieve an increase in the new energy consumption rate and a significant reduction in the peak-to-valley difference rate.

[0016] Through the synergy of the above technologies, this solution provides a storage configuration optimization framework for distribution networks with a high proportion of renewable energy, taking into account accuracy, practicality, and engineering adaptability, while also taking into account both economic and environmental benefits.

[0017] The present invention specifically adopts the following technical solutions:

[0018] A method for planning energy storage on the distribution network side considering the access of distributed power sources, characterized by comprising:

[0019] Based on the probability distribution model, wind power and photovoltaic power output forecast models are established separately. Combined with the load classification model with interruptible and transferable load constraints, a coordinated operation scenario that includes renewable energy output fluctuations and load peak and valley characteristics is generated to construct a source-load-storage coordinated operation model.

[0020] Establish a dynamic state-of-charge (SOC) model for the energy storage system, set physical constraints on charging and discharging efficiency, capacity, and power, and quantify the costs of wind and solar curtailment, demand-side response costs, and energy storage efficiency loss to form a total distribution network operation cost model.

[0021] Based on the source-load-storage coordinated operation model and the total cost model of distribution network operation, a particle swarm optimization algorithm with dynamic adjustment of inertia weight is adopted to iteratively solve the optimal configuration parameters of energy storage capacity and power with the goal of minimizing the total cost of distribution network operation.

[0022] Furthermore, in the probability distribution model:

[0023] The wind power output model adopts Weibull distribution, and the shape and scale parameters are dynamically modified according to the mean and variance of wind speed;

[0024] The photovoltaic output model adopts Beta distribution, and the shape parameters are dynamically modified according to the mean and variance of the light intensity.

[0025] Furthermore, in the load classification model:

[0026] The interruptible load meets the constraints of single interruption duration and upper limit of interruption times within the scheduling cycle;

[0027] The transferable load meets the constraints of total running time and scheduling times.

[0028] Furthermore, in the particle swarm optimization algorithm for dynamically adjusting the inertia weight, the inertia weight decreases linearly from an initial value to a final value.

[0029] Furthermore, the total cost model for distribution network operation includes:

[0030] The cost of curtailing wind and solar power is dynamically related to the rate of new energy consumption;

[0031] Demand-side response costs, quantified by compensation costs for load interruption or shifting;

[0032] The energy storage charging and discharging efficiency loss cost is calculated based on the SOC dynamic model.

[0033] And, a distribution network-side energy storage planning system considering the access of distributed power sources, comprising:

[0034] Collaborative modeling module for:

[0035] Based on the probability distribution model, wind power and photovoltaic output prediction models are established respectively;

[0036] Combined with a load classification model that includes interruptible and transferable load constraints, a coordinated operation scenario is generated that includes renewable energy output fluctuations and load peak and valley characteristics.

[0037] Cost and Constraint Modeling Module for:

[0038] Establish a dynamic state-of-charge (SOC) model for the energy storage system and set physical constraints on charge and discharge efficiency, capacity, and power;

[0039] Quantify the costs of wind and solar curtailment, demand-side response costs, and energy storage efficiency loss costs to form a total distribution network operation cost model;

[0040] Optimization solution module for:

[0041] A particle swarm optimization algorithm with dynamic adjustment of inertia weight is used to iteratively solve the optimal configuration parameters of energy storage capacity and power with the goal of minimizing the total operating cost of the distribution network.

[0042] Furthermore, in the collaborative modeling module:

[0043] The wind power output model adopts Weibull distribution, and the shape and scale parameters are dynamically modified according to the mean and variance of wind speed;

[0044] The photovoltaic output model adopts Beta distribution, and the shape parameters are dynamically modified according to the mean and variance of the light intensity;

[0045] In the load classification model:

[0046] The interruptible load meets the constraints of single interruption duration and the upper limit of interruption times within the scheduling cycle;

[0047] The transferable load meets the constraints of total operating time and dispatch times;

[0048] In the optimization solution module, the inertia weight decreases linearly from an initial value to a final value.

[0049] Furthermore, the composite cost model constructed by the cost and constraint modeling module includes:

[0050] The cost of curtailing wind and solar power is dynamically related to the rate of new energy consumption;

[0051] Demand-side response costs, quantified by compensation costs for load interruption or shifting;

[0052] The energy storage charging and discharging efficiency loss cost is calculated based on the SOC dynamic model.

[0053] And, an electronic device includes a memory, a processor, and a computer program stored in the memory and runnable on the processor, characterized in that the processor implements the steps of the above method when executing the program.

[0054] A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0055] Compared with the prior art, the present invention and its preferred embodiments have at least the following beneficial effects:

[0056] Improved source-load-storage collaborative optimization capabilities: By integrating renewable energy output fluctuation modeling, load classification scheduling constraints, and energy storage dynamic characteristics, deep source-load-storage collaboration is achieved on the distribution network side. This overcomes the problem of insufficient scenario adaptability caused by the separation of "source-storage" or "load-storage" in traditional decoupling optimization, and enhances the system's comprehensive regulation capabilities for renewable energy fluctuations and load peak and valley characteristics.

[0057] Improved accuracy of economic assessments: A full-life cycle composite cost model is constructed that encompasses wind and solar curtailment costs, demand-side response costs, and energy storage efficiency losses. This avoids the "pseudo-optimal solution" caused by traditional planning that ignores hidden costs, and provides an economic decision-making basis for energy storage capacity configuration that is more in line with actual operations.

[0058] High-dimensional optimization efficiency and stability optimization: The particle swarm algorithm with dynamically adjusted inertia weights is used to adapt to multi-objective and multi-constrained nonlinear programming scenarios, effectively balance global search and local convergence capabilities, and improve the solution efficiency and result stability of complex collaborative models.

[0059] Strengthening the ability to adapt to uncertainty: By characterizing the randomness of wind and solar power output through a probability distribution model and combining it with a load classification response mechanism, the planning scheme is made more robust to the intermittency of renewable energy and fluctuations in load demand, ensuring the practicality of energy storage configuration in different operating scenarios.

[0060] Coordinated optimization of renewable energy consumption and grid operation: Through the joint scheduling of energy storage capacity and demand-side response, the phenomenon of wind and solar power curtailment is reduced, and the efficient consumption of a high proportion of renewable energy is promoted. At the same time, the peak and valley differences in load are smoothed, and the stability of grid operation is improved.

[0061] The above effects jointly support the technical advantages of the present invention in smart distribution network energy storage planning, and provide a feasible path for the low-carbon transformation of new power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0063] Figure 1 This is a new energy output curve diagram of an embodiment of the present invention;

[0064] Figure 2 This is an example diagram of the percentage of the maximum load on the third day to the maximum load of the week in an embodiment of the present invention;

[0065] Figure 3 This is an example diagram of the percentage of the weekly maximum load to the annual maximum load according to an embodiment of the present invention;

[0066] Figure 4 This is an example diagram of the percentage of the maximum hourly load to the maximum daily load in winter according to an embodiment of the present invention;

[0067] Figure 5 This is an example diagram of the percentage of the maximum hourly load to the maximum daily load in summer according to an embodiment of the present invention;

[0068] Figure 6 This is an example diagram of the percentage of the maximum hourly load in spring and autumn to the maximum daily load in an embodiment of the present invention;

[0069] Figure 7 This is a load output curve diagram of an embodiment of the present invention.

[0070] Figure 8 This is a framework diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0071] Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. Based on these detailed descriptions, those skilled in the art will be able to clearly understand the present application and implement the present application. Without violating the principles of the present application, the features of different embodiments may be combined to obtain new implementations, or certain features of certain embodiments may be substituted to obtain other preferred implementations.

[0072] To make the features and advantages of the present invention more clearly understood, the following embodiments are specifically described in detail with reference to the accompanying drawings.

[0073] like Figure 8 As shown, an embodiment of the present invention provides a distribution network-side energy storage planning scheme that takes into account the access of distributed power sources. From the perspective of the distribution network, the capacity of the energy storage system is planned based on distributed power sources such as wind power and photovoltaic power generation, and taking into account the demand-side response mechanism. First, a wind power and photovoltaic power generation prediction model and a load demand model are established to ensure the accuracy of the data basis. Then, the demand-side response cost and the cost of wind and solar power abandonment are quantified, and a distribution network operation cost model is constructed, with minimizing the distribution network operation cost as the optimization goal. Finally, the particle swarm optimization algorithm is used to efficiently solve the model to obtain the planned capacity of the energy storage system, so as to achieve dual optimization of economic and environmental benefits.

[0074] The following is a detailed description of the plan:

[0075] 1 Source-Load-Storage Operation Model

[0076] 1.1 Power supply operation model

[0077] 1) Wind power model

[0078] The output power of wind power generation is mainly affected by wind speed. In addition to geographical factors, wind speed is also affected by multiple factors such as temperature and weather, showing significant randomness and intermittency. Although wind speed is uncertain, its waveform distribution contains certain statistical laws. The existing technology generally uses the probability method to model wind speed. This embodiment considers that the Weibull distribution is widely used to approximate the probability density of wind speed in a certain area. Its probability density function can accurately express the statistical characteristics of wind speed. The probability density function is expressed as:

[0079]

[0080] Where: k is the shape parameter of the Weibull distribution; c is the scale parameter of the Weibull distribution, and its calculation formula is:

[0081]

[0082] The actual output power of a wind turbine is closely related to wind speed and its own power characteristics. Specifically, when the wind speed exceeds the cut-in wind speed threshold, the wind turbine can successfully start and begin generating electricity. Conversely, if the wind speed rises above the cut-out wind speed, the wind turbine will automatically shut down for safety or efficiency reasons, and its output power will be reduced to zero. This embodiment is described using the following model to accurately reflect the direct impact of wind speed changes on the output power of the wind turbine:

[0083]

[0084] in, P WT (t) is the output power of the wind turbine at time t; P r is the rated output power of the wind turbine; v(t) is the wind speed at time t; v ci is the cut-in wind speed of the wind turbine; v r is the rated wind speed of the wind turbine; v co is the cut-out wind speed of the wind turbine.

[0085] 2) Photovoltaic model

[0086] The output power of photovoltaic power generation is primarily governed by factors such as light intensity, sunshine duration, and ambient temperature. Light intensity has the most significant impact on the amount of electricity generated by a photovoltaic array: the stronger the light, the higher the power output of photovoltaic power generation. However, solar radiation intensity is subject to weather conditions and exhibits significant volatility. While its randomness cannot be ignored, within a specific time window, light intensity can generally be approximated as following a Beta distribution, which accurately describes the statistical characteristics of light intensity under given conditions through its probability density function:

[0087]

[0088] Where: r is the actual light intensity during this time period; r max is the maximum light intensity during this time period; Γ is the Gamma function; α and β are the shape parameters of the Beta distribution, which can be obtained from the average value μ and variance σ of the light intensity during this time period. 2 Determine as shown below:

[0089]

[0090] The output power of photovoltaic power generation can be calculated by formula (6):

[0091] P pv =ηN pv A m r (6)

[0092] Where: P pv is the output power of photovoltaic power generation; η is the photoelectric conversion efficiency; N pv is the number of photovoltaic cells; A m is the unit area of photovoltaic cells.

[0093] According to formula (6), for a given photovoltaic array, its power output is positively correlated with the light intensity. Based on this, it can be inferred that the output power of photovoltaic power generation also exhibits statistical characteristics similar to the light intensity, that is, it approximately obeys the Beta distribution. The probability density function form of this conclusion directly reflects the distribution law of photovoltaic power generation output power as the light intensity changes:

[0094]

[0095] Where: P pv,max It is the maximum output power of photovoltaic power generation during this period.

[0096] In this example, to maximize the benefits of renewable energy generation, both wind turbines and photovoltaic arrays operate according to a maximum power point tracking (MPPT) strategy to ensure optimal output. Within this framework, they are considered unscheduled generation units and do not directly participate in the optimization and scheduling of the active distribution network.

[0097] 1.2 Load model

[0098] In traditional power grid energy management and optimized dispatch systems, optimizing the configuration of generators on the power supply side dominates, while the role of demand-side response in load-side energy management is often overlooked. Active distribution networks, as a key technology for promoting coordinated interaction between the supply and demand sides of the grid, are particularly crucial in their energy management strategies for stimulating demand-side response through policy incentives or flexible market electricity pricing mechanisms.

[0099] In this embodiment, loads can be clearly divided into two categories: uncontrollable loads and controllable loads based on their different capabilities in participating in grid dispatching. Furthermore, controllable loads can be further divided into interruptible loads and transferable loads.

[0100] 1) Interruptible load

[0101] For interruptible loads, although they can participate in the unified dispatch of the power grid, the conditions for their use are strictly restricted, including the clear definition of the use capacity, use frequency, and use time window. Therefore, when evaluating the time response characteristics of interruptible loads, it is necessary to comprehensively consider the specific constraints of their use power limit, the number of allowed uses, and the duration of a single use:

[0102]

[0103] in, is the interruption amount of the interruptible load in period t; P IL,max is the maximum interruption amount of the interruptible load in period t; The operating status of the interruptible load, 1 means the load is running, 0 means the load is interrupted; n IL is the number of interruptions of the interruptible load within the dispatching period; n IL,max The maximum permissible number of interruptions of the interruptible load within the dispatch cycle; T is the time for each continuous interruption of the interruptible load; IL,min T is the shortest continuous interruption time of each interruptible load; IL,max The maximum continuous interruption time of each interruptible load.

[0104] 2) Transferable load

[0105] The constraints that transferable loads must meet for participating in the optimal dispatch of active distribution networks are:

[0106]

[0107] in: is the transferable load power within the period t; P const is the transferable load power value; is the operating status of the transferable load during the period t, 1 indicates load operation, and 0 indicates load interruption; T const is the total operating time of the transferable load entering the power grid within a dispatching cycle; K is the total dispatching times of the transferable load within a dispatching cycle.

[0108] 1.3 Energy Storage Model

[0109] The SOC (State of Charge) of an energy storage unit, the ratio of the real-time remaining battery capacity to its rated capacity, is a key indicator for measuring the energy state of an energy storage device. This parameter is not only an important basis for the energy storage system to participate in grid optimization and formulate efficient control strategies, but also directly reflects the available energy reserves of the energy storage device at the current moment. The state of charge of an energy storage unit at a specific moment is calculated using the following formula:

[0110]

[0111] Formula (12) represents the energy storage charging process, and formula (13) represents the energy storage discharging process. c is the rated capacity of energy storage; SOC(t) is the state of charge at the end of energy storage at time t; SOC(t-1) is the state of charge at the end of energy storage at time t-1; δ is the self-discharge rate of energy storage; Pch (t), P dis (t) is the charge and discharge power of the energy storage system at time t; η ch ,η dis It is the energy storage charging and discharging efficiency.

[0112] Energy storage devices are generally limited by their maximum energy storage capacity. To extend the service life of energy storage batteries, overcharging or over-discharging is not allowed. Therefore, energy storage batteries must meet the following constraints during operation:

[0113]

[0114] Where: SOC min , SOC max are the minimum and maximum values of the energy storage charge state respectively; P ch,max 、P dis,max are the maximum values of energy storage charging power and discharging power respectively; T represents a scheduling cycle.

[0115] 2. Distribution Network Side Energy Storage Capacity Planning Model

[0116] 2.1 Energy Storage System Cost Model

[0117] The cost of energy storage system mainly includes investment cost and operation and maintenance cost.

[0118] Investment costs generally include initial investment costs and replacement investment costs. Initial investment costs are the fixed capital invested in the initial stage of the project and are determined by the rated power and capacity of the energy storage battery. The investment cost of the energy storage system throughout its life cycle is expressed as:

[0119]

[0120] Where: C inv is the average annual investment cost, P rated is the rated power of the energy storage system; E rated is the rated capacity of the energy storage system; C pcs is the unit power cost; C bat is the unit capacity cost, W is the annual charge and discharge times of the energy storage battery, and n is the number of battery cycles.

[0121] Operation and maintenance costs C om It refers to the funds dynamically invested during the service life to ensure the normal operation of the energy storage system, which usually includes a fixed part determined by the PCS and a variable part determined by the charging and discharging capacity of the energy storage battery.

[0122] The average annual cost of the energy storage system is expressed as:

[0123]

[0124] Where: CPom is the unit power operation and maintenance cost; CEom is the unit capacity operation and maintenance cost; W is the annual charge and discharge capacity of the energy storage battery, and n is the number of battery cycles.

[0125] 2.2 Distribution network operating costs

[0126] The operating costs of the distribution network include the costs of curtailing wind and solar power, the costs of energy storage system charging and discharging efficiency losses, the costs of energy storage charging power consumption, and demand-side response costs.

[0127] C op =∑S ab (t) * p1(t)+∑S w (t) * p2(t)+∑S c (t) * p3(t)+∑S d (t) * p4(t) (17)

[0129] Where: C op is the annual operating cost of the distribution network; S ab (t), S w (t), S c (t), S d (t) are the wind curtailment, solar curtailment, energy storage charging and discharging efficiency loss, energy storage charging loss, and demand-side power reduction costs during period t; p1(t), p2(t), p3(t), and p4(t) are the unit costs of wind curtailment, solar curtailment, energy storage charging and discharging efficiency loss, energy storage charging loss, and demand-side power reduction costs during period t.

[0130] 2.3 Revenue Model

[0131] When energy storage is added to a distribution network containing distributed generation, the benefits come from two aspects: 1. The discharge process of the energy storage system; 2. The addition of energy storage can reduce the capacity requirements of the larger power grid on the distribution network, thereby reducing the investment cost of the distribution network, as shown in the following formula:

[0132] C pro =∑S dis (t) * p3(t)+P save *p (18)

[0133] Where: S dis (t) is the amount of electricity discharged during the period t, P save It represents the reduced investment capacity of the distribution network after adding energy storage, and p is the unit price of the comprehensive investment capacity of the distribution network.

[0134] 2.4 Planning Model

[0135] During the operation of the distribution network, adding an energy storage system can reduce the cost of curtailing wind and solar power. Therefore, the planning objectives determined in this embodiment are:

[0136] min{C inv +C anual +C op -C pro}(19)

[0137] The planning process uses the particle swarm optimization algorithm to continuously search for the energy storage system capacity, power and the maximum carrying capacity of the distribution network to minimize the final cost. The particle swarm optimization solution process is as follows:

[0138] Step 1: Initialize the particle swarm, including the particle swarm size, position and velocity of each particle.

[0139] Step 2: Calculate the fitness value of each particle.

[0140] Step 3: For each particle, compare its fitness value with the individual extreme value. The larger the individual extreme value, the larger the fitness value.

[0141] Step 4: For each particle, compare its fitness value with the global extreme value and give the larger value to the global extreme value.

[0142] Step 5: Iteratively update the particle's velocity and position.

[0143] Step 6: Boundary condition processing.

[0144] Step 7: Determine whether the termination condition of the algorithm is met. If so, end the algorithm and output the optimization result. Otherwise, return to step 2.

[0145] In the particle swarm optimization algorithm, the value of the inertia weight ω directly affects the search characteristics of the algorithm. By adopting a linearly decreasing inertia weight strategy, the value of ω is dynamically adjusted as the iteration progresses:

[0146]

[0147] Among them, t max is the maximum number of iterations, ω max and ω min Set to 0.9 and 0.3 respectively. This parameter setting makes the algorithm maintain a large inertia weight (close to 0.9) in the early stage of iteration (when t is small). At this time, the particles maintain a high motion inertia, which is conducive to a large-scale exploration in the solution space and avoid falling into the local optimum too early. As the number of iterations t increases, the inertia weight gradually decreases according to the linear law. In the late stage of iteration (t is close to t maxWhen the particle velocity drops to 0.3, particle speed updates prioritize the balance between individual and group experience, facilitating a refined search within potential optimal regions. This dynamic balancing mechanism effectively balances global exploration with local exploitation, improving the algorithm's overall convergence performance.

[0148] 3. Simulation Example

[0149] 3.1 Example Introduction

[0150] The rated power of wind power and photovoltaic power generation in the distribution network is 100kW. The wind power generation data and photovoltaic output data generated are as follows: Figure 1 shown.

[0151] Load setting as Figure 2 、 Figure 3 The output is as shown in Figure 4-Figure 7 The data shown is from IEEE RBTS data. The load participation rate for demand-side response is 10%, and the demand-side response cost is 0.4 yuan / kWh. The time-of-use electricity pricing rules are: a single-unit electricity price of 0.7838 yuan / kWh, with a 50% increase during peak hours, a 50% increase during off-peak hours, and a 70% increase during off-peak hours. Peak price increases occur between 8:00 AM and 11:00 AM, and off-peak prices occur between 11:00 PM and 7:00 AM. The remaining periods are flat-top periods.

[0152] Other economic data of energy storage planning are shown in Table 1. The parameters of particle swarm optimization algorithm are shown in Table 2. N is the particle swarm size, T is the maximum number of iterations, C1 is the learning factor 1, C2 is the learning factor 2, D is the variable dimension, and W is the number of iterations. max is the maximum inertia weight, W min is the minimum inertia weight, V max is the maximum speed, V min is the minimum speed, X s is the maximum position.

[0153] Table 1 Energy storage equipment related data

[0154] parameter value n 2500 <![CDATA[C pom ]]> 60CNY / kW <![CDATA[C eom ]]> 0.08CNY / kWh <![CDATA[C pcs ]]> 1400CNY / kW <![CDATA[C bat ]]> 2546CNY / kWh η 0.8

[0155] Table 2 Particle swarm optimization algorithm parameters

[0156] parameter value N 180 T 80 <![CDATA[C1]]> 1.4 <![CDATA[C2]]> 1.4 D 2.8 <![CDATA[W max ]]> 0.8 <![CDATA[W min ]]> 0.25 <![CDATA[V max ]]> [60,60,30] <![CDATA[V min ]]> [-60,-60,-30] <![CDATA[X s ]]> [600,600,300]

[0157] 3.2 Results Analysis

[0158] The results of energy storage planning based on the particle swarm optimization algorithm are shown in Table 3. Simulation data show that after introducing energy storage systems and implementing a demand-side response mechanism in the distribution network, the curtailment rate of wind and photovoltaic power generation was significantly reduced, and the system's renewable energy absorption capacity increased by 18.6%. Economic analysis shows that the integration of energy storage equipment reduced the annual investment cost of the distribution network by 235,000 yuan. At the same time, demand-side response reduced electricity costs by 142,000 yuan through load peak-valley shifting, verifying the effectiveness of the source-load coordinated optimization strategy.

[0159] The data in Table 3 shows that the energy storage system discharges 67% of the time during peak electricity price periods. Figure 1 The new energy output curves complement each other and effectively alleviate the power shortage caused by the sudden drop in photovoltaic output from 11:00 to 15:00. Figure 6 The load curve shows that the demand-side response reduced the peak load by 9.8%, and the peak-to-valley load difference rate dropped from 32.7% to 24.1%, significantly improving the smooth operation of the system.

[0160] Table 3 Planning results

[0161]

[0162] Carbon emissions analysis shows that the optimized solution reduces annual carbon emissions from the distribution network by 412 tons, with 78% of this reduction attributed to coal-fired power generation substitution. Despite the carbon footprint of photovoltaic / wind turbine manufacturing (approximately 18gCO2 / kWh), their lifecycle carbon intensity is only 6.3% of that of coal-fired power plants, demonstrating the environmental advantages of the "energy storage + renewable energy" combination. The planning solution met expectations in key metrics such as the payback period (5.2 years) and net present value (net present value) (1.48 million yuan), meeting the technical and economic requirements for the low-carbon transformation of the distribution network.

[0163] In summary, compared to the prior art, the innovative design of this embodiment is at least reflected in:

[0164] 1. To address the common issue of wind and solar curtailment in renewable energy generation, such as wind and photovoltaic power, effective strategies for accommodating renewable energy can be developed in parallel from two perspectives: demand-side response and energy storage capacity planning and configuration. Given the close interaction between these two, rational planning of energy storage capacity is particularly important. This embodiment, from the perspective of the distribution network, incorporates demand-side response into the design of wind and photovoltaic power as distributed power sources, and scientifically plans the energy storage system capacity.

[0165] 2. Build accurate wind and photovoltaic power generation forecast models, as well as load demand models, to ensure a solid and reliable data foundation for the planning process. Subsequently, quantify the costs of demand-side response measures and calculate the costs of curtailed wind and photovoltaic power generation due to inability to absorb it. This model then constructs a comprehensive mathematical model reflecting the operating costs of the distribution network. This model prioritizes minimizing the overall operating costs of the distribution network and aims to balance renewable energy generation with load demand through optimal energy storage configuration.

[0166] 3. The constructed model is solved using a highly efficient particle swarm optimization algorithm. Its global search capabilities and rapid convergence demonstrate exceptional performance in complex optimization problems. Through iterative calculations, the algorithm ultimately determines the planned energy storage system capacity that effectively mitigates wind and solar power curtailment while reducing distribution network operating costs.

[0167] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0168] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium having a computer program stored thereon, which executes the above method when executed by a processor. The storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0169] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0170] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present disclosure. Various changes and improvements may be made to the present disclosure without departing from the spirit and scope of the present disclosure, and such changes and improvements shall fall within the scope of the present disclosure.

[0171] The present invention is not limited to the above-mentioned optimal implementation mode. Anyone can derive various other forms of distribution network-side energy storage planning methods that consider the access of distributed power sources under the guidance of the present invention. All equal changes and modifications made within the scope of the patent application of the present invention should fall within the scope of the present invention.

Claims

1. A distribution network side energy storage planning method considering the access of distributed power sources, characterized in that: include: Based on the probability distribution model, wind power and photovoltaic power output forecast models are established separately. Combined with the load classification model with interruptible and transferable load constraints, a coordinated operation scenario that includes renewable energy output fluctuations and load peak and valley characteristics is generated to construct a source-load-storage coordinated operation model. Establish a dynamic state-of-charge (SOC) model for the energy storage system, set physical constraints on charging and discharging efficiency, capacity, and power, and quantify the costs of wind and solar curtailment, demand-side response costs, and energy storage efficiency loss to form a total distribution network operation cost model. Based on the source-load-storage coordinated operation model and the total cost model of distribution network operation, a particle swarm optimization algorithm with dynamic adjustment of inertia weight is adopted to iteratively solve the optimal configuration parameters of energy storage capacity and power with the goal of minimizing the total cost of distribution network operation.

2. The method for planning energy storage on the distribution network side considering the access of distributed power sources according to claim 1, characterized in that: In the probability distribution model: The wind power output model adopts Weibull distribution, and the shape and scale parameters are dynamically modified according to the mean and variance of wind speed; The photovoltaic output model adopts Beta distribution, and the shape parameters are dynamically modified according to the mean and variance of the light intensity.

3. The method for planning energy storage on the distribution network side considering the access of distributed power sources according to claim 1, characterized in that: In the load classification model: The interruptible load meets the constraints of single interruption duration and upper limit of interruption times within the scheduling cycle; The transferable load meets the constraints of total running time and scheduling times.

4. The method for planning energy storage on the distribution network side considering the access of distributed power sources according to claim 1, characterized in that: In the particle swarm optimization algorithm for dynamically adjusting the inertia weight, the inertia weight decreases linearly from an initial value to a final value.

5. The method for planning energy storage on the distribution network side considering the access of distributed power sources according to claim 1, characterized in that: The total cost model for distribution network operation includes: The cost of curtailing wind and solar power is dynamically related to the rate of new energy consumption; Demand-side response costs, quantified by compensation costs for load interruption or shifting; The energy storage charging and discharging efficiency loss cost is calculated based on the SOC dynamic model.

6. A distribution network side energy storage planning system considering the access of distributed power sources, characterized in that: include: Collaborative modeling module for: Based on the probability distribution model, wind power and photovoltaic output prediction models are established respectively; Combined with a load classification model that includes interruptible and transferable load constraints, a coordinated operation scenario is generated that includes renewable energy output fluctuations and load peak and valley characteristics. Cost and Constraint Modeling Module for: Establish a dynamic state-of-charge (SOC) model for the energy storage system and set physical constraints on charge and discharge efficiency, capacity, and power; Quantify the costs of wind and solar curtailment, demand-side response costs, and energy storage efficiency loss costs to form a total distribution network operation cost model; Optimization solution module for: A particle swarm optimization algorithm with dynamic adjustment of inertia weight is used to iteratively solve the optimal configuration parameters of energy storage capacity and power with the goal of minimizing the total operating cost of the distribution network.

7. The distribution network-side energy storage planning system considering distributed power generation access according to claim 6 is characterized in that: In the collaborative modeling module: The wind power output model adopts Weibull distribution, and the shape and scale parameters are dynamically modified according to the mean and variance of wind speed; The photovoltaic output model adopts Beta distribution, and the shape parameters are dynamically modified according to the mean and variance of the light intensity; In the load classification model: The interruptible load meets the constraints of single interruption duration and the upper limit of interruption times within the scheduling cycle; The transferable load meets the constraints of total operating time and dispatch times; In the optimization solution module, the inertia weight decreases linearly from an initial value to a final value.

8. The distribution network-side energy storage planning system considering distributed power generation access according to claim 6, characterized in that: The composite cost model constructed by the cost and constraint modeling module includes: The cost of curtailing wind and solar power is dynamically related to the rate of new energy consumption; Demand-side response costs, quantified by compensation costs for load interruption or shifting; The energy storage charging and discharging efficiency loss cost is calculated based on the SOC dynamic model.

9. An electronic device comprising a processor and a memory, characterized in that: The processor is configured to execute the method of any one of claims 1-5.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

Images