User side long-time energy storage planning method under two-system electricity price mechanism

Abstract

The invention discloses a user side long-time energy storage planning method and system of an electric power system, a medium and equipment, and the method comprises the steps: constructing an energy storage planning cost-operation scheduling two-stage optimization model; a user side electricity utilization pricing framework is established based on a two-part electricity price mechanism, and modeling is carried out for operation scheduling actions; a time-of-use electricity price driven long-time power scheduling strategy and a demand management strategy are introduced in the operation scheduling stage, and the long-time power scheduling strategy establishes charging and discharging constraints including peak period discharging priorities; according to the demand management strategy, the flexible power adjusting capacity of energy storage is used for restraining load capacity exceeding, and capacity control punishment is converted into measurable economic benefits; establishing a comprehensive benefit evaluation mechanism of energy storage as an evaluation basis of an energy storage configuration result; an improved SA-PSO algorithm is adopted to realize collaborative optimization of capacity configuration and operation scheduling, the global search capability of simulated annealing and the local convergence characteristic of particle swarm optimization are fused, and an energy storage optimal planning result is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage in power systems, and particularly relates to a long-term energy storage planning method, system, medium and device for the user side of a power system. Background Art

[0002] As a supply method in a new energy storage system, energy storage has great advantages in aspects such as innovative allocation of production factors, optimal combination of market resources, innovation of business models, and innovation of management and systems, and has a significant role in integrating and driving the green and low-carbon industries and supply chains.

[0003] First of all, there are still research gaps in dimensions such as system economic analysis, benefit evaluation mechanism, and market application prospects. Existing research has not established a perfect economic evaluation model for energy storage projects, resulting in a lack of scientific guidance for key links such as capacity configuration, operation strategy, and benefit calculation in the user-side energy storage scheme in different application scenarios.

[0004] Secondly, starting from the characteristics of new industrial and commercial loads, it is analyzed that their peak load duration is long, seasonal fluctuations are significant, and they are prone to grid safety risks and economic penalties due to overcapacity. The traditional "valley charge peak discharge + flat charge peak discharge" strategy has insufficient economic space under the background of narrowing peak-valley price differences and complex load patterns.

[0005] Existing user-side energy storage mostly adopts a 2-hour short-term configuration and cannot adapt to the continuous peak characteristics of new industrial and commercial loads. The conventional two-stage model does not achieve in-depth coupling optimization of capacity configuration and operation scheduling.

[0006] In response to the above challenges, some energy storage system planning methods have been proposed. However, most of these methods have the following problems:

[0007] Lack of comprehensive consideration of the dynamic characteristics of the electricity spot market. Especially in the case of large electricity price fluctuations or frequent frequency regulation requirements, the flexibility and adaptability of the planning model are low.

[0008] Existing demand-side management methods only control the energy storage not to increase the load demand during the charging operation in the existing "valley charge peak discharge + flat charge peak discharge" strategy, lacking active management of demand and ensuring the continuous load smoothing ability of the energy storage. As a result, the economy in actual application fails to meet expectations.

[0009] Therefore, these methods often fail to achieve the effect of both ensuring the stability of system operation and improving the economic benefits of the energy storage system in actual application.

[0010] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0011] The present invention provides a long - term energy storage planning method, system, medium and device for the user side of a power system, achieving the effect of both ensuring the operation stability of the system and improving the economic benefits of the energy storage system.

[0012] A long - term energy storage planning method for the user side of a power system includes:

[0013] Obtain power system data to construct a two - stage optimization model for energy storage planning cost - operation scheduling. The two - stage optimization model includes a cost model and an operation scheduling model. The cost model quantifies the equivalent annual cost of the energy storage system from the perspective of the whole life cycle to provide an economic benchmark plan for capacity configuration, and the operation scheduling model takes the time - of - use electricity price arbitrage and demand control revenue as the objective function;

[0014] Based on the user - side electricity pricing framework of the two - part tariff mechanism, model the operation scheduling actions. The two - part tariff mechanism includes an electricity quantity pricing mechanism and a capacity pricing mechanism;

[0015] The operation scheduling model introduces a long - term power scheduling strategy driven by time - of - use electricity price and a demand management strategy. The long - term power scheduling strategy establishes charge - discharge constraints including the discharge priority during peak hours; the demand management strategy uses the power regulation ability of the energy storage to suppress load over - capacity and converts the capacity control penalty into measurable economic benefits;

[0016] Establish a comprehensive benefit evaluation mechanism for the energy storage as the evaluation basis for the energy storage configuration result. The comprehensive benefit evaluation mechanism includes an economic evaluation mechanism for the energy storage, an application efficiency evaluation mechanism for the energy storage, and a peak - shaving ability evaluation mechanism for the energy storage;

[0017] With the goal of optimizing the economic evaluation of the energy storage, the application efficiency of the energy storage, and the peak - shaving ability of the energy storage, adopt an improved SA - PSO algorithm to realize the collaborative optimization of capacity configuration and operation scheduling, integrating the global search ability of simulated annealing and the local convergence characteristics of particle swarm optimization, and solve to obtain the optimal energy storage planning result.

[0018] In the above - mentioned long - term energy storage planning method for the user side of a power system, constructing the two - stage optimization model for energy storage planning cost - operation scheduling includes,

[0019] (1) Planning cost model:

[0020] The expression of the planning cost model is:

[0021] minC sys =C inv +C om (1)

[0022] Where: C sys is the equivalent annual value of the total cost; C inv is the equivalent annual value of the energy storage investment cost; Com is the annual operation and maintenance cost of energy storage,

[0023]

[0024] C pur = c p P ess + c e E ess (3)

[0025]

[0026] In the formula: C pur is the procurement cost of the energy storage unit and its supporting equipment, C rep is the energy storage replacement cost within the project period; P ess is the rated power of the energy storage; E ess is the rated capacity of the energy storage; c p is the procurement cost coefficient per unit power of the energy storage; c e is the procurement cost coefficient per unit capacity of the energy storage; γ is the discount rate; Y a is the project planning period; Y ess is the energy storage life;

[0027] C om = c om P ess (5)

[0028] In the formula: c om is the annual average maintenance cost coefficient of the energy storage,

[0029] (2) Operation and scheduling model:

[0030] The objective function expression of the operation and scheduling model is:

[0031]

[0032] In the formula: f2 is the net income of energy storage scheduling under time-of-use electricity price conditions; N n is the number of typical day types; S is the number of typical days; U a is the arbitrage income of the energy storage system in S typical days; U B is the profit obtained by the energy storage through capacity reduction to avoid over-capacity penalty within a typical day.

[0033] In the described long-term energy storage planning method for the user side of the power system, the establishment of the long-term power scheduling strategy includes charge-discharge constraints with peak-hour discharge priority:

[0034] In the case of single-day scheduling, the energy storage operates according to different charge-discharge powers according to the time-of-use electricity price periods:

[0035]

[0036] Wherein: are the charging powers during the valley price period, flat price period, peak price period, and spike price period respectively, and t v , t m , t p , t t are the valley price period, flat price period, peak price period, and spike price period respectively. The discharging operation of the energy storage is the same as above;

[0037] The energy storage system obtains benefits by using the price difference between the peak and valley of the power grid. Its calculation formula is:

[0038]

[0039] Wherein: P a is the grid trading electricity price at the energy storage action time t; P dis,t , P ch,t are the discharging and charging powers of the energy storage during the period t; η C , η D are the charging and discharging efficiencies of the energy storage respectively.

[0040] In the described long-term energy storage planning method for the user side of the power system, the demand-side management strategy uses the power regulation ability of the energy storage to suppress the overload of the load, and converts the capacity control penalty into measurable economic benefits, including

[0041] The capacity electricity fee, as the basic electricity fee part in the two-part electricity price, is charged according to the operating transformer capacity, and is charged according to the sum of the capacities of the transformers in the current operating state and hot standby state. The capacity electricity fee is expressed as:

[0042]

[0043] Wherein: B t is the rated capacity of the main transformer accessed by the user; P B is the electricity price per unit based on the rated capacity of the main transformer; L max is the actual demand of the load for this month.

[0044] In the described long-term energy storage planning method for the user side of the power system, establishing a comprehensive benefit evaluation mechanism for the energy storage as the evaluation basis for the energy storage configuration results includes

[0045] According to the full life cycle cost of the energy storage power station, combined with the annual power generation of the energy storage power station, calculate the cost per unit of electricity of the energy storage power station. The calculation formula for the cost per unit of electricity of the energy storage power station is:

[0046]

[0047] Wherein: c ESSis the cost per kilowatt-hour of the energy storage power station; η is the conversion efficiency of the energy storage power station; E ESS is the installed capacity of the energy storage power station; H ESS is the annual utilization hours of electricity storage of the energy storage power station. When calculating the annual utilization hours of electricity storage of the energy storage power station, only the working hours during energy storage discharge are taken, that is and integrate according to the year to calculate the result,

[0048] IRR represents the annual average rate of return of an investment project. When IRR is greater than the cost of capital or expected rate of return of the investment, the investment is considered favorable. Its objective function f(r) is the net present value NPV:

[0049]

[0050] In the formula: CF t is the cash flow in the t-th year; r is the discount rate,

[0051] For the evaluation index of the ability of energy storage to smooth the load, according to the electricity price characteristics of the load, the discharge performance of the energy storage during peak and peak electricity price periods is used as the evaluation standard, and the formula is as follows:

[0052]

[0053] Among them: F P represents the peak shaving coefficient of the energy storage; C t,p represents the ratio of the electricity price in the peak period to the electricity price in the peak period; P load (t) is the original load when the energy storage is not in use at time t.

[0054] In the described method for long-term energy storage planning on the user side of the power system, the SA-PSO algorithm structure model clarifies the load type, screens representative typical day data covering different peak-valley-flat periods, eliminates outliers to ensure data reliability, combines the scheduling model with the goal of minimizing the energy storage capacity configuration under the condition of maximizing profit, and determines the optimal capacity of the energy storage system; and judges the constraint conditions, substitutes the capacity result in the configuration stage into the energy storage scheduling model, and determines the best charging and discharging strategies according to different electricity prices and loads through the regulations on the energy storage actions, so as to maximize the net profit or minimize the total cost. The SA-PSO algorithm includes the following stages:

[0055] (1) Initialization stage: Set the particle swarm size N = 50, the maximum number of iterations T = 100; initialize the particle positions and velocities; set the initial temperature T0 = 500, the annealing coefficient α = 0.85 - 0.95; initialize the individual optimal and global optimal solutions,

[0056] (2) Iterative calculation process: Calculate the step size t = 1:T for each iteration,

[0057] (3) Fitness calculation: Calculate the objective function value for each particle, and calculate the result of the configuration economy model of the supercapacitor.

[0058] (3) Update particle state: Update the particle velocity; update the particle position.

[0059] (4) Simulated annealing operation: Calculate ΔE for each new position, where ΔE is the difference between the new solution and the old solution; if ΔE>0, it is judged that this solution is an improved solution and directly accept the new solution; if ΔE≤0, accept the inferior solution with the probability function ; and update the temperature so that T = αT to gradually reduce the temperature.

[0060] (5) Optimal solution update: Update the individual optimal pbest and the global optimal gbest; record the current optimal energy storage planning configuration scheme (P ess 、E ess ).

[0061] Since the energy storage always needs to maintain power balance when participating in the power interaction between users and the power grid, the balance constraint is set as follows:

[0062] P L,t = P dis,t - P ch,t + P grid,t (13)

[0063] In the formula: P L,t is the actual load power; P dis,t 、P ch,t are the charging / discharging powers of the energy storage; P grid,t is the power purchased from the power grid. The power during the charging of the energy storage needs to be restricted:

[0064] P ch,t + P L,t <B t (14)

[0065]

[0066] In the formula: P ch,t 、P dismax are the charging and discharging powers of the energy storage at time t respectively, and P chmax 、P dismax are the rated charging and discharging powers of the energy storage respectively.

[0067] Since the charging process and the discharging process of the energy storage cannot exist simultaneously, the energy storage charging and discharging state constraint is set as follows:

[0068] B dis,t + B ch,t ≤ 1(17)

[0069] In the formula: Bdis,t and B ch,t are variables with values of 0 or 1, respectively representing the discharging and charging states of the energy storage at the t-th moment of the i-th day. When B dis,t is 1, it means the energy storage is in the discharging state. When B ch,t is 1, it means the energy storage is in the charging state;

[0070] Set the energy storage state of charge constraint for the actual state of charge of the energy storage:

[0071]

[0072] In the formula: S oc,t is the state of charge of the energy storage at the t-th moment; η C is the charging efficiency of the energy storage; S oc,max and S oc,min are the upper and lower limits of the state of charge of the energy storage.

[0073] In the described long-term energy storage planning method for the user side of the power system, in the improved SA-PSO algorithm, starting from a random solution, set the initial temperature T and the temperature decay function α. Randomly select a solution within the neighborhood of the current solution, which is called the candidate solution. Calculate the cost difference, that is, the energy difference, between the candidate solution and the current solution. If the candidate solution is better than the current solution, accept it; otherwise, accept the worse solution with a certain probability, and the probability decreases as the temperature drops. As the algorithm progresses, the temperature gradually decreases, and the acceptance probability of non-optimal solutions gradually decreases. When the temperature drops to a sufficiently low level or reaches the predetermined number of iterations, the algorithm terminates. The improved SA-PSO algorithm calculates the energy difference, that is, the difference in the objective function value, in each iteration and uses the following formula to decide whether to accept the worse solution:

[0074]

[0075] In the formula: ΔE is the difference in the objective function value between the new solution and the current solution, and T is the current temperature; the temperature T decreases according to the set decay function, and the decay function is:

[0076] T t+1 =αT t (3 - 18)

[0077] where α is a constant that controls the temperature decay rate, and 0 < α < 1.

[0078] Through temperature decay, the system gradually "cools" and finally converges to a local optimal or global optimal solution. The particle swarm algorithm searches for the optimal solution through the movement of particles in the solution space. Each particle updates its position and velocity in the search space and is affected by its historical best position p i and the global best position g. The particle update rule is:

[0079]

[0080] Wherein: is the velocity of particle i at the t-th moment; is the position of particle i at the t-th moment; and g t are the historical best positions of particle i and all particles respectively; c1 and c2 are learning factors that control the dependence of particles on their own experience and group experience; r1 and r2 are random numbers to ensure the diversity of search. Simulated annealing helps the algorithm maintain global search ability during the scheduling process. Through the temperature control mechanism of simulated annealing, it is avoided that the solution in the process of PSO loop calculation of the two-stage algorithm is the optimal solution of a single objective.

[0081] A system for implementing the described method includes:

[0082] A modeling unit for constructing a two-stage optimization model of energy storage planning cost - operation scheduling. The two-stage optimization model includes a cost model that quantifies the equivalent annual value cost of the energy storage system from the perspective of the whole life cycle to provide an economic benchmark plan for capacity configuration, and an operation scheduling model with the time-of-use electricity price arbitrage and demand control benefits as the objective function;

[0083] A framework unit for modeling the operation scheduling actions based on the user-side electricity pricing framework of the two-part tariff mechanism, where the two-part tariff mechanism includes an electricity quantity pricing mechanism and a capacity pricing mechanism;

[0084] An operation unit for introducing a long-term power scheduling strategy driven by time-of-use electricity price and a demand management strategy in the operation scheduling stage. The long-term power scheduling strategy establishes charge and discharge constraints including the discharge priority during peak hours; the demand management strategy uses the flexible power regulation ability of the energy storage to suppress load overcapacity and converts the capacity control penalty into measurable economic benefits;

[0085] An evaluation unit for establishing a comprehensive benefit evaluation mechanism of the energy storage as the evaluation basis for the energy storage configuration result. The comprehensive benefit evaluation mechanism includes an economic evaluation mechanism of the energy storage, an application efficiency evaluation mechanism of the energy storage, and a peak shaving capacity evaluation mechanism of the energy storage;

[0086] A calculation unit for achieving the collaborative optimization of capacity configuration and operation scheduling with the goal of optimizing the economic evaluation of the energy storage, the application efficiency of the energy storage, and the peak shaving capacity of the energy storage. It integrates the global search ability of simulated annealing and the local convergence characteristics of particle swarm optimization to solve and obtain the optimal energy storage planning result.

[0087] A computer storage medium, the storage medium includes computer instructions, when it runs on a computer, it enables the computer to execute the described method.

[0088] An electronic device, the electronic device comprising:

[0089] a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein,

[0090] when the processor executes the program, the method described above is implemented.

[0091] Compared with the prior art, the present invention has the following advantages: The present invention realizes the optimization of the configuration result, which not only ensures the stability of the system operation but also improves the economic benefit of the energy storage system, improves the economy of the energy storage configuration, and guarantees the rate of return; on the other hand, it realizes the active control of the energy storage for the demand. Except during peak and ultra-peak periods, the peak shaving ability of the energy storage is significantly enhanced; when the load exceeds the capacity, the energy storage can also actively discharge to reduce the demand for the load, avoid over-capacity penalties, and further suppress the load peak. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] By reading the following detailed description of the preferred specific embodiments, various other advantages and benefits of the present invention will become clear to those of ordinary skill in the art. The accompanying drawings in the specification are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. Moreover, throughout the drawings, the same reference numerals are used to represent the same components.

[0093] In the drawings:

[0094] Figure 1 is a working flowchart of a method for user-side long-term energy storage planning under a two-part electricity price mechanism in a preferred embodiment of the present invention;

[0095] Figure 2 is a model operation structure diagram of a method for user-side long-term energy storage planning under a two-part electricity price mechanism in a preferred embodiment of the present invention;

[0096] Figure 3 is a flowchart of the SA-PSO algorithm in a method for user-side long-term energy storage planning under a two-part electricity price mechanism in a preferred embodiment of the present invention;

[0097] Figure 4 is a system schematic diagram of the energy storage achieving economic operation in a method for user-side long-term energy storage planning under a two-part electricity price mechanism in a preferred embodiment of the present invention.

[0098] The present invention will be further explained below with reference to the drawings and embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0099] Specific embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although specific embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be fully conveyed to those skilled in the art.

[0100] It should be noted that in the description of the specification and claims, certain terms are used to refer to specific components. Those skilled in the art should understand that technicians may use different terms to refer to the same component. The specification and claims do not use the difference in terms as a way to distinguish components, but use the difference in the functions of components as the criterion for distinction. As mentioned throughout the specification and claims, "comprising" or "including" is an open-ended term and should be interpreted as "including but not limited to". The subsequent description of the specification is the preferred implementation manner for implementing the present invention, but the description is for the purpose of the general principles of the specification and is not used to limit the scope of the present invention. The protection scope of the present invention shall be subject to what is defined by the appended claims.

[0101] To facilitate the understanding of the embodiments of the present invention, the following will further explain with specific embodiments as examples in conjunction with the accompanying drawings, and each accompanying drawing does not constitute a limitation to the embodiments of the present invention.

[0102] As Figures 1 to 4 shown, the long-term energy storage planning method for the user side of the power system includes the following steps:

[0103] Obtain power system data to construct a two-stage optimization model for energy storage planning cost-operation scheduling. The two-stage optimization model includes a cost model and an operation scheduling model. The cost model quantifies the equivalent annual cost of the energy storage system from the perspective of the whole life cycle to provide an economic benchmark plan for capacity configuration. The operation scheduling model takes the time-of-use electricity price arbitrage and demand control benefits as the objective function;

[0104] Based on the electricity pricing framework for the user side under the two-part tariff mechanism, model the operation scheduling actions. The two-part tariff mechanism includes an electricity quantity pricing mechanism and a capacity pricing mechanism;

[0105] The operation scheduling model introduces a long-term power scheduling strategy driven by the time-of-use electricity price and a demand management strategy. The long-term power scheduling strategy establishes charge-discharge constraints including the discharge priority during peak hours; the demand management strategy uses the power regulation ability of the energy storage to suppress the load overcapacity and converts the capacity control penalty into measurable economic benefits;

[0106] Establish a comprehensive benefit evaluation mechanism for the energy storage as the evaluation basis for the energy storage configuration result. The comprehensive benefit evaluation mechanism includes an economic evaluation mechanism for the energy storage, an application efficiency evaluation mechanism for the energy storage, and a peak shaving ability evaluation mechanism for the energy storage;

[0107] Aiming at the economic evaluation of energy storage, the application efficiency of energy storage, and the optimal peak shaving capacity of energy storage, an improved SA-PSO algorithm is used to achieve the collaborative optimization of capacity configuration and operation scheduling, integrating the global search ability of simulated annealing and the local convergence characteristics of particle swarm optimization to solve the optimal energy storage planning results.

[0108] In the preferred implementation of the long-term energy storage planning method for the user side of the power system, constructing a two-stage optimization model of energy storage planning cost - operation scheduling includes,

[0109] (1) Planning cost model:

[0110] The expression of the planning cost model is:

[0111] minC sys = C inv + C om (1)

[0112] Where: C sys is the equivalent annual value of the total cost; C inv is the equivalent annual value of the energy storage investment cost; C om is the annual operation and maintenance cost of the energy storage,

[0113]

[0114] C pur = c p P ess + c e E ess (3)

[0115]

[0116] Where: C pur is the procurement cost of the energy storage unit and its supporting equipment, C rep is the energy storage replacement cost within the project period; P ess is the rated power of the energy storage; E ess is the rated capacity of the energy storage; c p is the purchase cost coefficient per unit power of the energy storage; c e is the purchase cost coefficient per unit capacity of the energy storage; γ is the discount rate; Y a is the project planning period; Y ess is the energy storage life;

[0117] C om = c om P ess (5)

[0118] Where: c om is the annual average maintenance cost coefficient of the energy storage,

[0119] (3) Operating scheduling model:

[0120] The objective function expression of the operating scheduling model is:

[0121]

[0122] Where: f2 is the net storage scheduling income under time-of-use electricity price conditions; N n is the number of typical day types; S is the number of typical days; U a is the arbitrage income of the energy storage system in S typical days; U B is the profit obtained by the energy storage avoiding over-capacity penalty through derating within a typical day.

[0123] In the preferred implementation of the long-term energy storage planning method for the user side of the power system, the establishment of the long-term power scheduling strategy includes charge-discharge constraints with peak-hour discharge priority:

[0124] In the case of single-day scheduling, the energy storage operates according to different charge-discharge powers according to the time-of-use electricity price periods:

[0125]

[0126] Where: are the charging powers during the valley price period, flat price period, peak price period, and spike price period respectively, t v , t m , t p , t t are the valley price period, flat price period, peak price period, and spike price period respectively. The discharge operation of the energy storage is the same as above;

[0127] The energy storage system obtains income by using the grid peak-valley electricity price difference. Its calculation formula is:

[0128]

[0129] Where: P a is the grid trading electricity price at the energy storage operation time t; P dis,t , P ch,t are the discharge and charging powers of the energy storage within the period t; η C , η D are the charge and discharge efficiencies of the energy storage respectively.

[0130] In the preferred implementation of the long-term energy storage planning method for the user side of the power system, the demand management strategy uses the power regulation ability of the energy storage to suppress load over-capacity and converts the capacity control penalty into measurable economic benefits, including

[0131] The capacity electricity charge, as the basic electricity charge in the two-part tariff, is charged according to the capacity of the operating transformer, and is collected based on the sum of the capacities of the transformers in the current operating state and the hot standby state. The capacity electricity charge is expressed as:

[0132]

[0133] In the formula: B t is the rated capacity of the main transformer connected by the user; P B is the electricity price per unit based on the rated capacity of the main transformer; L max is the actual demand of the load for this month.

[0134] In the preferred implementation of the long-term energy storage planning method for the user side of the power system, establishing a comprehensive benefit evaluation mechanism for energy storage as the evaluation basis for the energy storage configuration results includes,

[0135] According to the life cycle cost of the energy storage power station, combined with the annual power generation of the energy storage power station, calculate the cost per unit of electricity of the energy storage power station. The calculation formula for the cost per unit of electricity of the energy storage power station is:

[0136]

[0137] In the formula: c ESS is the cost per unit of electricity of the energy storage power station; η is the conversion efficiency of the energy storage power station; E ESS is the installed capacity of the energy storage power station; H ESS is the annual utilization hours of electricity storage of the energy storage power station. When calculating the annual utilization hours of electricity storage of the energy storage power station, only take the working hours during the discharge of the energy storage, that is time, and integrate according to the year to calculate the result,

[0138] IRR represents the annual average rate of return of the investment project. When IRR is greater than the cost of capital or the expected rate of return of the investment, the investment is considered favorable. Its objective function f(r) is the net present value NPV:

[0139]

[0140] In the formula: CF t is the cash flow in the t-th year; r is the discount rate,

[0141] For the evaluation index of the energy storage's ability to smooth the load, according to the electricity price characteristics of the load, taking the discharge performance of the energy storage during the peak and peak-hour electricity price periods as the evaluation standard, the formula is as follows:

[0142]

[0143] Among them: F P represents the flat peak coefficient of the energy storage; C t,p represents the ratio of the electricity price in the peak-hour period to the electricity price in the peak price period; Pload (t) is the original load when energy storage is not in use during time period t.

[0144] In the preferred implementation of the long-term energy storage planning method for the user side of the power system, the SA-PSO algorithm structure model clarifies the load type, screens representative typical day data, covering different periods of peak, valley, and flat, eliminates outliers to ensure data reliability, combines the scheduling model with the goal of minimizing the energy storage capacity configuration under the condition of maximizing profit, and determines the optimal capacity of the energy storage system; and judges the constraint conditions, substitutes the capacity result in the configuration stage into the energy storage scheduling model, and determines the best charging and discharging strategies according to different electricity prices and loads by stipulating the energy storage actions, so as to maximize the net profit or minimize the total cost. The SA-PSO algorithm includes the following stages:

[0145] (1) Initialization stage: Set the particle swarm size N = 50 and the maximum number of iterations T = 100; Initialize the particle positions and velocities; Set the initial temperature T0 = 500 and the annealing coefficient α = 0.85 - 0.95; Initialize the individual optimal and global optimal solutions.

[0146] (2) Iterative calculation process: Each iteration calculates the step size t = 1:T.

[0147] (3) Fitness calculation: Calculate the objective function value for each particle, and calculate the result of the configuration economy model of the supercapacitor.

[0148] (3) Update particle state: Update the particle velocity; Update the particle position.

[0149] (4) Simulated annealing operation: Calculate ΔE for each new position, where ΔE is the difference between the new solution and the old solution; If ΔE > 0, that is, judge that this solution is an improved solution and directly accept the new solution; If ΔE ≤ 0, accept the inferior solution with the probability function And update the temperature so that T = αT to gradually reduce the temperature.

[0150] (5) Optimal solution update: Update the individual optimal pbest and the global optimal gbest; Record the current optimal energy storage planning configuration scheme (P ess , E ess );

[0151] Since the energy storage always needs to maintain the power balance when participating in the power interaction between the user and the power grid, the balance constraint is set as:

[0152] P L,t = P dis,t - P ch,t + P grid,t (13)

[0153] In the formula: P L,tis the actual power of the load; P dis,t and P ch,t is the charge / discharge power of the energy storage; P grid,t For the power purchase from the power grid, the power of the energy storage during charging needs to be restricted:

[0154] P ch,t +P L,t <B t (14)

[0155]

[0156] In the formula: P ch,t and P dismax are the charge and discharge powers of the energy storage at time t respectively, P chmax and P dismax are the rated charge and discharge powers of the energy storage respectively,

[0157] Since the charging process and the discharging process of the energy storage cannot exist simultaneously, the energy storage charge and discharge state constraint is set:

[0158] B dis,t +B ch,t ≤1 (17)

[0159] In the formula: B dis,t and B ch,t are variables with values of 0 or 1, representing the discharging and charging states of the energy storage at the t-th moment of the i-th day respectively. B dis,t being 1 means the energy storage is in the discharging state, and B ch,t being 1 means the energy storage is in the charging state;

[0160] For the state of charge of the energy storage in fact, the energy storage state of charge constraint is set:

[0161]

[0162] In the formula: S oc,t is the state of charge of the energy storage at time t; η C is the charging efficiency of the energy storage; S oc,max and S oc,min are the upper and lower limits of the state of charge of the energy storage.

[0163] In the preferred implementation of the described long-term energy storage planning method for the user side of the power system, in the improved SA-PSO algorithm, starting from a random solution, the initial temperature T and the temperature decay function α are set. A solution randomly selected within the neighborhood of the current solution is called the candidate solution. Calculate the cost difference, that is, the energy difference, between the candidate solution and the current solution. If the candidate solution is better than the current solution, accept it; otherwise, accept the worse solution with a certain probability, and the probability decreases as the temperature drops. As the algorithm progresses, the temperature gradually decreases, and the acceptance probability of non-optimal solutions gradually decreases. When the temperature drops to a sufficiently low level or reaches the predetermined number of iterations, the algorithm terminates. The improved SA-PSO algorithm calculates the energy difference, that is, the difference in the objective function values, in each iteration and uses the following formula to determine whether to accept the worse solution:

[0164]

[0165] In the formula: ΔE is the difference in the objective function values between the new solution and the current solution, and T is the current temperature; the temperature T decreases according to the set decay function, and the decay function is:

[0166] T t+1 = αT t (3 - 18)

[0167] Among them, α is a constant that controls the temperature decay rate, and 0 < α < 1.

[0168] Through temperature decay, the system gradually "cools" and finally converges to a local optimal or global optimal solution. The particle swarm algorithm searches for the optimal solution through the movement of particles in the solution space. Each particle updates its position and velocity in the search space and is affected by its historical best position p i and the global best position g. The particle update rule is:

[0169]

[0170] In the formula: is the velocity of particle i at the t-th moment; is the position of particle i at the t-th moment; and g t are the historical best positions of particle i and all particles respectively; c1 and c2 are learning factors that control the degree of dependence of the particle on its own experience and the group experience; r1 and r2 are random numbers to ensure the diversity of the search. Simulated annealing helps the algorithm maintain the global search ability during the scheduling process. Through the temperature control mechanism of simulated annealing, it is avoided that the solution in the process of the pso loop calculation of the two-stage algorithm is the optimal solution of a single objective.

[0171] A system for implementing the described method includes:

[0172] A modeling unit, which is used to construct a two-stage optimization model for energy storage planning cost - operation scheduling. The two-stage optimization model includes a cost model that quantifies the equivalent annual cost of the energy storage system from a full life cycle perspective to provide an economic benchmark plan for capacity configuration, and an operation scheduling model with the time-of-use electricity price arbitrage and demand control benefits as the objective function;

[0173] A framework unit, which is used to model the operation scheduling actions based on the user-side electricity pricing framework of the two-part tariff mechanism. The two-part tariff mechanism includes an electricity quantity pricing mechanism and a capacity pricing mechanism;

[0174] An operation unit, which is used to introduce a long-term power scheduling strategy and a demand management strategy driven by the time-of-use electricity price in the operation scheduling stage. The long-term power scheduling strategy establishes charge and discharge constraints including the discharge priority during peak hours; the demand management strategy uses the flexible power regulation ability of the energy storage to suppress load overcapacity and converts the capacity control penalty into measurable economic benefits;

[0175] An evaluation unit, which is used to establish a comprehensive benefit evaluation mechanism for the energy storage as the evaluation basis for the energy storage configuration result. The comprehensive benefit evaluation mechanism includes an economic evaluation mechanism for the energy storage, an application efficiency evaluation mechanism for the energy storage, and a peak shaving capacity evaluation mechanism for the energy storage;

[0176] A calculation unit, which is used to take the optimization of the economic evaluation of the energy storage, the application efficiency of the energy storage, and the peak shaving capacity of the energy storage as the goal, and use an improved SA-PSO algorithm to realize the collaborative optimization of capacity configuration and operation scheduling, integrating the global search ability of simulated annealing and the local convergence characteristics of particle swarm optimization, and solving to obtain the optimal energy storage planning result.

[0177] In one embodiment, Figure 1 This is a working flowchart of a user-side long-term energy storage planning method under a two-part tariff mechanism in a preferred embodiment of the present invention. The method includes:

[0178] Obtain power system data to construct a two-stage optimization model for energy storage planning cost - operation scheduling. The two-stage optimization model includes a cost model and an operation scheduling model. The cost model quantifies the equivalent annual cost of the energy storage system from the perspective of the whole life cycle to provide an economic benchmark plan for capacity configuration. The operation scheduling model takes the time-of-use electricity price arbitrage and demand control benefits as the objective function. From the perspective of the equivalent annual value system in the whole life cycle, when modeling energy storage planning considering costs, costs such as investment and construction, equipment depreciation of energy storage are summarized as whole life cycle properties. However, in actual modeling, due to the large calculation scale, annualization processing is required to convert these periodic cost values into equivalent annual values for modeling. The mathematical expression for calculating the equivalent annual cost is the process of establishing the expression, which is the modeling process. The operation scheduling model with the time-of-use electricity price arbitrage and demand control benefits as the objective function is modeled based on the user-side electricity pricing framework of the two-part tariff mechanism, and the operation scheduling actions are modeled. The two-part tariff mechanism includes an electricity quantity pricing mechanism and a capacity pricing mechanism. It is modeled specifically based on two different pricing methods, namely electricity quantity pricing and demand pricing. Both parts of the modeling are for the operation scheduling modeling of energy storage. The two-stage optimization model for energy storage planning cost - operation scheduling means establishing two models, namely the energy storage planning cost model and the operation scheduling model, substituting them into the sa-pso optimization model for iterative solution. Its physical meaning is to find the energy storage configuration result with the lowest planning configuration cost under the condition that the energy storage scheduling proposed in the present invention can achieve its maximum economic benefit. The operation scheduling model is related to the energy storage profit, and the planning configuration model is related to the energy storage configuration cost. Moreover, the planning configuration model affects the result of the operation scheduling. Analyzed from the perspective of mathematical modeling, it is a multi-objective optimization problem.

[0179] Based on the user-side electricity pricing framework of the two-part tariff mechanism, the operation scheduling actions are modeled. The two-part tariff mechanism includes: an electricity quantity pricing mechanism and a capacity pricing mechanism;

[0180] In the operation scheduling stage, introduce a long-term power scheduling strategy driven by the time-of-use electricity price and a demand management strategy. The long-term power scheduling strategy establishes charge and discharge constraints including the discharge priority during peak hours. The demand management strategy uses the flexible power regulation ability of the energy storage to suppress load overcapacity and converts the capacity control penalty into measurable economic benefits;

[0181] Establish a comprehensive benefit evaluation mechanism for the energy storage, which includes an economic evaluation mechanism for the energy storage, an application efficiency evaluation mechanism for the energy storage, and a peak shaving ability evaluation mechanism for the energy storage, and use the above as the evaluation basis for the energy storage configuration result;

[0182] With the goal of optimizing the economic evaluation of the energy storage, the application efficiency of the energy storage, and the peak shaving ability of the energy storage, adopt an improved SA-PSO algorithm to realize the collaborative optimization of capacity configuration and operation scheduling, integrating the global search ability of simulated annealing and the local convergence characteristics of particle swarm optimization; solve to obtain the optimal energy storage planning result.

[0183] First, extract the 15-minute load data of the target user in a one-year cycle, pre-process the user-side load data, and divide the typical day types (working days and non-working days are selected in this case) according to the user's production and operation experience. After removing abnormal data points, the typical daily operation load data of each month is formed. In this typical day curve, there are 96 sampled load data, and the data covers the whole day.

[0184] Secondly, a two-stage optimization model of energy storage planning cost and operation scheduling is constructed. The planning cost model quantifies the equivalent annual cost of the energy storage system, comprehensively considers the costs of equipment purchase, replacement, maintenance, etc., and provides an economic benchmark for capacity configuration; the operation scheduling model takes time-of-use electricity price arbitrage and demand control benefits as the objective function; including:

[0185] The operating cost model takes into account the capacity and power-related investment and operation and maintenance costs of the energy storage system, and takes the minimum operating cost as the goal. Its expression is:

[0186] minC sys =C inv +C om (1) Where: C sys is the total cost equal to the annual value; C inv is the annual value of energy storage investment cost; C om is the annual operation and maintenance cost of energy storage.

[0187] The investment and construction cost of the energy storage system is the fixed capital invested in the initial stage of the energy storage system project, usually used for the purchase of major equipment, etc. Considering the time value of money, the investment cost of energy storage is corrected and converted using the equal annual value method;

[0188]

[0189] C pur =c p P ess +c e E ess (3)

[0190]

[0191] Where: C pur C is the purchase cost of the energy storage unit and its supporting equipment rep P is the energy storage replacement cost within the project life; ess E is the energy storage rated power; ess is the rated capacity of energy storage; c p c is the purchase cost coefficient of energy storage unit power; e is the purchase cost coefficient of energy storage unit capacity; γ is the discount rate; Y a Plan the project lifespan;ess is the energy storage life.

[0192] The operation and maintenance cost refers to the funds dynamically invested to ensure the normal operation of the energy storage system during its life cycle, usually including the costs of testing, installation, loss, outage, labor, overhaul and maintenance of the energy storage system, with the unit of year.

[0193] C om = c om P ess (5)

[0194] In the formula: c om is the annual average maintenance cost coefficient of the energy storage.

[0195] The objective function of the dispatching model under the time-of-use electricity price condition aims to maximize the economic benefits obtained by the energy storage through responding to peak actions or arbitrage under the time-of-use electricity price condition within the typical daily load. The expression of the objective function is:

[0196]

[0197] In the formula: f2 is the net income of energy storage dispatching under the time-of-use electricity price condition; N n is the number of typical day types; S is the number of typical days; U a is the arbitrage income of the energy storage system in S typical days; U B is the profit obtained by the energy storage through reducing capacity to avoid over-capacity penalty within a typical day.

[0198] In the operation and dispatching stage, a long-term power dispatching strategy and a demand management strategy driven by the time-of-use electricity price are introduced. The long-term power dispatching strategy establishes charge and discharge constraints including the discharge priority during peak hours; the demand management strategy uses the power regulation ability of the energy storage to suppress load over-capacity and converts the capacity control penalty into measurable economic benefits, including:

[0199] Under the time-of-use electricity price mechanism, the operating power of the energy storage is set according to the electricity price period at that time. However, in the revenue calculation formula, the charge and discharge power of the energy storage will default to the rated charge and discharge power within the allowable range. In the case of single-day dispatching, the energy storage operates according to different charge and discharge powers according to the time-of-use electricity price period:

[0200]

[0201] In the formula: are the charging powers during the valley price period, flat price period, peak price period, and spike price period respectively, t v , t m , t p , t t are the valley price period, flat price period, peak price period, and spike price period respectively. The discharge actions of the energy storage are the same as above.

[0202] The energy storage system utilizes the price difference between peak and valley electricity prices of the power grid and obtains benefits through "low storage and high discharge". Its calculation formula is as follows:

[0203]

[0204] In the formula: P a is the electricity trading price of the power grid at the energy storage operation moment t; η C and η D are the charging and discharging efficiencies of the energy storage respectively.

[0205] During the operation and scheduling stage, a long-term power scheduling strategy and a demand management strategy driven by time-of-use electricity prices are introduced. The long-term power scheduling strategy establishes charge-discharge constraints including the discharge priority during peak hours; the demand management strategy uses the flexible power regulation ability of the energy storage to suppress load overcapacity and transforms the capacity control penalty into measurable economic benefits; including:

[0206] The capacity electricity fee, as the basic electricity fee part in the two-part electricity price, is charged according to the capacity of the operating transformer. It is charged based on the sum of the capacities of the transformers in the current operating state and the hot standby state. The basic electricity fee that the user needs to pay does not change with its demand. On this basis, if the part of the actual measured maximum demand of the user that exceeds the contractually agreed value is more than 5%, a penalty will be imposed on the excess part by doubling it. If the actual load of the user exceeds the previously reported maximum load value by more than 5%, a double penalty charge will be imposed in accordance with the regulations, which can be expressed as:

[0207]

[0208] In the formula: B t is the rated capacity of the main transformer accessed by the user; P B is the electricity price per unit based on the rated capacity of the main transformer; L max is the actual demand of this load in this month.

[0209] An overall benefit evaluation mechanism for the energy storage is established. The mechanism includes an economic evaluation mechanism for the energy storage, an application efficiency evaluation mechanism for the energy storage, and a peak shaving ability evaluation mechanism for the energy storage, and uses the above as the evaluation basis for the energy storage configuration result; including:

[0210] According to the definition of the International Electrotechnical Commission (IEC) standard ICE 60300-3-3, the life cycle cost (LCC) covers the total direct and indirect costs incurred during the entire process from system planning, construction, operation to decommissioning and recycling. This model decomposes the economy of the energy storage system into dimensions such as initial investment, operation and maintenance expenses, and residual value recovery, and quantitatively evaluates the economy of energy storage through the levelized cost of storage (LCOS) - that is, the ratio of the total life cycle cost of the system to the effective discharge capacity. The research uses the LCC framework to construct a cost model, focusing on comparing the differences in the levelized cost of storage under the influence of parameters such as energy conversion efficiency and cycle life of different energy storage technologies, providing a quantitative basis for the selection of technical routes.

[0211] Based on the life cycle cost of the energy storage power station and combined with the annual power generation of the energy storage power station, the levelized cost of storage of the energy storage power station can be calculated. The calculation formula is:

[0212]

[0213] In the formula: c ESS is the levelized cost of storage of the energy storage power station; η is the conversion efficiency of the energy storage power station; E ESS is the installed capacity of the energy storage power station; H ESS is the annual utilization hours of electricity storage of the energy storage power station. When calculating the annual utilization hours of electricity storage of the energy storage power station, only the working hours during energy storage discharge are taken, that is When, and integrate according to the year to calculate the result.

[0214] The profit evaluation mechanism of energy storage uses the internal rate of return (IRR) to judge. IRR is a financial indicator that measures the return on investment and is used to evaluate the profitability of investment projects. It represents the discount rate that makes the net present value (NPV) of the investment equal to zero, that is, the annual compound growth rate of the investment project during its life cycle. IRR represents the annual average return rate of the investment project. When the IRR is greater than the cost of capital or the expected return rate of the investment, the investment is considered favorable, and its objective function f(r) is the net present value (NPV):

[0215]

[0216] In the formula: CF t is the cash flow in the t-th year; r is the discount rate.

[0217] For IRR, the goal is to find the discount rate r that makes the NPV zero. Generally, for the investment and construction of a 10-year energy storage power station, its IRR is about 8-10% under normal conditions. With the development of the energy storage industry, the profit space for energy storage construction will be higher and higher. Therefore, this paper requires that the IRR of energy storage operators be above 10%.

[0218] The evaluation index for the ability of energy storage to smooth the load is an important parameter for the effectiveness of the demand management strategy adopted. According to the electricity price characteristics of the load, taking the discharge performance of energy storage during peak and critical peak price periods as the evaluation criteria, the formula is as follows:

[0219]

[0220] Where: F P represents the peak shaving coefficient of the energy storage; C t,p represents the ratio of the electricity price during the critical peak period to that during the peak price period.

[0221] This formula represents the proportion of the total discharge amount of the energy storage during the peak price and critical peak price periods in the total demand, and is used to measure the ability of the energy storage to reduce the peak demand of the load.

[0222] Aiming at the economic evaluation of energy storage, the application efficiency of energy storage, and the optimal peak shaving ability of energy storage, an improved SA-PSO algorithm is used to realize the collaborative optimization of capacity configuration and operation scheduling, integrating the global search ability of simulated annealing and the local convergence characteristics of particle swarm optimization; the optimal planning result of the energy storage is obtained through solution; including:

[0223] The improved SA-PSO algorithm structure model first clarifies the load type, and screens representative typical day data, covering different periods of peak, valley, and flat, and eliminates outliers to ensure data reliability. Combining with the scheduling model, with the goal of minimizing cost or maximizing net present value, the optimal capacity of the energy storage system is determined; and the relevant specified constraint conditions are judged, and the capacity result in the configuration stage is substituted into the energy storage scheduling model. According to different electricity prices and loads, the best charging and discharging strategies are determined through the regulations of the energy storage actions, so as to maximize the net profit or minimize the total cost. In the algorithm, the net profit under different energy storage capacities is evaluated, and the position information of the particles is adjusted according to the update iteration of the SA-PSO algorithm, and finally the energy storage capacity that maximizes the objective function value (net profit) is found.

[0224] Since the energy storage always needs to maintain the power balance when participating in the power interaction between the user and the power grid, a balance constraint needs to be set:

[0225] P L,t = P dis,t - P ch,t + P grid,t (13)

[0226] In the formula: P L,t is the actual load power; P dis,t , P ch,t are the charging / discharging powers of the energy storage; P grid,t is the power purchased from the power grid.

[0227] Since the method of controlling the charging power of energy storage to control the demand during charging does not directly generate economic benefits, it is only necessary to limit the power of energy storage during charging:

[0228] P ch,t +P L,t <B t (14)

[0229] Since the energy storage needs to meet the configured rated power limit during operation, a constraint on the charging and discharging power of the energy storage is set:

[0230]

[0231] In the formula: P ch,t 、P dismax are the charging and discharging powers of the energy storage at time t respectively, and P chmax 、P dismax are the rated charging and discharging powers of the energy storage respectively.

[0232] Since the charging process and the discharging process of the energy storage cannot exist simultaneously, a constraint on the charging and discharging state of the energy storage is set:

[0233] B dis,t +B ch,t ≤1 (17)

[0234] In the formula: B dis,t and B ch,t are variables with values of 0 or 1, representing the discharging and charging states of the energy storage at the t-th moment of the i-th day respectively. B dis,t being 1 indicates that the energy storage is in the discharging state, and B ch,t being 1 indicates that the energy storage is in the charging state.

[0235] A constraint on the state of charge of the energy storage is set according to the actual state of charge of the energy storage:

[0236]

[0237] In the formula: S oc,t is the state of charge of the energy storage at time t; η C is the charging efficiency of the energy storage; S oc,max 、S oc,min are the upper and lower limits of the state of charge of the energy storage.

[0238] The key parameters of the energy storage equipment adopted by this system are as follows: the energy storage power cost is 350 yuan per kilowatt (yuan / kW); the energy storage capacity cost is 1080 yuan per kilowatt-hour (yuan / kW·h); the annual maintenance cost is 0.03 yuan per kilowatt-hour (yuan / kW·h); the charging efficiency is 95%; the charge and discharge efficiency are both 95%; the designed service life T = 10 years; the rated charging power is 0.5C (C is the capacity multiple); the rated discharge power is 0.5C; the discount rate is 7% (γ = 0.07); the charge and discharge power is expressed in C-rate; 0.5C corresponds to the power level that can fully charge / discharge in 2 hours under the rated capacity; the discount rate is used for the calculation of the full life cycle cost, reflecting the time value of funds. The maintenance cost is calculated according to the annual operation and maintenance cost per unit of capacity.

[0239] Starting from a random solution, set the initial temperature T and the temperature decay function α. Randomly select a solution (called the candidate solution) within the neighborhood of the current solution. Calculate the cost difference (energy difference) between the candidate solution and the current solution. If the candidate solution is better than the current solution, accept it; otherwise, accept the worse solution with a certain probability, and the probability decreases as the temperature drops. As the algorithm progresses, the temperature gradually decreases, and the acceptance probability of non-optimal solutions gradually decreases. When the temperature drops to a sufficiently low level or reaches the predetermined number of iterations, the algorithm terminates. SA calculates the energy difference (i.e., the difference in the objective function values) in each iteration and uses the following formula to determine whether to accept the worse solution:

[0240]

[0241] In the formula: ΔE is the difference in the objective function values between the new solution and the current solution, and T is the current temperature; the temperature T drops according to the set decay function, and the decay function is:

[0242] T t+1 = αT t (3 - 18)

[0243] Among them, α is a constant that controls the temperature decay rate, and 0 < α < 1.

[0244] Through temperature decay, the system gradually "cools" and finally converges to a local optimal or global optimal solution.

[0245] The particle swarm optimization (PSO) algorithm searches for the optimal solution through the movement of particles in the solution space. Each particle updates its position and velocity in the search space, influenced by its historical best position p i and the global best position g. The particle update rule is:

[0246]

[0247] In the formula: is the velocity of particle i at time t; is the position of particle i at time t; and g t are the historical best positions of particle i and all particles respectively; c1 and c2 are learning factors that control the degree of dependence of particles on their own experience and group experience; r1 and r2 are random numbers to ensure the diversity of the search. Simulated annealing helps the algorithm maintain its global search ability during the scheduling process. Through the temperature control mechanism of simulated annealing, it avoids the solution in the pso loop calculation process of the two-stage algorithm being the optimal solution for a single objective.

[0248] The core parameter settings of the hybrid optimization algorithm adopted by this system are as follows: The algorithm sets the particle swarm size to 50 particles, and the maximum number of iterations is limited to 100 times to balance the calculation efficiency and optimization accuracy. Among the particle movement parameters, the inertia weight adopts a dynamic adjustment mechanism, with a value range of 0.1 - 0.5. The individual learning factor and the group learning factor are set to 0.1 and 0.2 respectively, forming differentiated local development and global exploration capabilities. In the simulated annealing module, the initial temperature is set to 500 to ensure the wide-area search ability of the algorithm in the early stage. The temperature decay coefficient (annealing speed) is controlled in the range of 0.85 - 0.95, and the optimization process realizes a progressive transition from extensive search to fine convergence through the exponential decay mechanism. This parameter combination has been verified through multi-scenario tests and can obtain a globally optimized solution with stable convergence within 120 seconds.

[0249] As an optimization plan, it realizes an annual profit of 5.2394 million yuan with a capacity of 19.543 MWh, and the net present value reaches 17.25%, with the best comprehensive benefits.

[0250] The power configuration of each plan reflects different operation logics; centralized charging during the valley period (3.65 MWh); preferential discharging during the peak period (3.65 MWh, accounting for 63% of the total); additional configuration of 0.538 MWh of derated power; realizing the improvement of marginal revenue through dynamic adjustment of peak load; Figure 4 It is a system schematic diagram for the economic operation of the energy storage described in a long-term energy storage planning method for the user side under a two-part tariff mechanism in a preferred embodiment of this case.

[0251] Through the analysis of the trends of large industrial and commercial load changes and time-of-use tariff mechanism changes and the resulting demand for energy storage configuration, a long-term energy storage planning method for large industrial and commercial users under a two-part tariff mechanism aiming to enhance the economic efficiency of energy storage planning and dispatching and solve the load demand problem is proposed. On the one hand, compared with 2-hour energy storage, the economic efficiency of energy storage configuration is improved. The capacity configuration is 2.2 times that of it, and the annual profit is 2.43 times that of it. When the initial investment scale increases in the early stage of capacity expansion, the net present value increases by 3.03%, ensuring the rate of return. On the other hand, during the peak and peak periods, the peak-flattening ability of the energy storage under each typical day is significantly enhanced; the duration of controlling the demand changes from 4 hours to 10 hours.

[0252] Although the embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the above specific embodiments and application fields. The above specific embodiments are merely illustrative and guiding, rather than restrictive. Those of ordinary skill in the art can also make many forms under the inspiration of this specification and without departing from the scope protected by the claims of the present invention, and all of these fall within the scope of protection of the present invention.

Claims

1. A long-term energy storage planning method for the user side of a power system, characterized in that The steps are as follows: Obtain power system data to construct a two-stage optimization model for energy storage planning cost - operation scheduling. The two-stage optimization model includes a cost model and an operation scheduling model. The cost model quantifies the equivalent annual cost of the energy storage system from the perspective of the whole life cycle to provide an economic benchmark plan for capacity configuration. The operation scheduling model takes the time-of-use electricity price arbitrage and demand control revenue as the objective function; Based on the user-side electricity pricing framework of the two-part tariff mechanism, model the operation scheduling actions. The two-part tariff mechanism includes an electricity quantity pricing mechanism and a capacity pricing mechanism; The operation scheduling model introduces a long-term power scheduling strategy and a demand management strategy driven by the time-of-use electricity price. The long-term power scheduling strategy establishes charge-discharge constraints including the discharge priority during peak hours. The demand management strategy uses the power regulation ability of the energy storage to suppress load overcapacity and converts the capacity control penalty into measurable economic benefits; Establish a comprehensive benefit evaluation mechanism for the energy storage as the evaluation basis for the energy storage configuration result. The comprehensive benefit evaluation mechanism includes an economic evaluation mechanism for the energy storage, an application efficiency evaluation mechanism for the energy storage, and a peak shaving ability evaluation mechanism for the energy storage; Taking the optimal economic evaluation, application efficiency, and peak shaving ability of the energy storage as the goal, adopt an improved SA-PSO algorithm to realize the collaborative optimization of capacity configuration and operation scheduling, integrating the global search ability of simulated annealing and the local convergence characteristics of particle swarm optimization, and solve to obtain the optimal energy storage planning result.

2. The long-term energy storage planning method for the user side of a power system according to claim 1, wherein Preferably, constructing the two-stage optimization model for energy storage planning cost - operation scheduling includes (1) Planning cost model: The expression of the planning cost model is: minC sys = C inv + C om (1); Where: C sys is the equivalent annual value of the total cost; C inv is the equivalent annual value of the energy storage investment cost; C om is the annual operation and maintenance cost of the energy storage, C pur = c p P ess + c e E ess (3); Where: C pur is the procurement cost of the energy storage unit and its supporting equipment, and C rep is the energy storage replacement cost within the project period; P ess is the rated power of the energy storage; E ess is the rated capacity of the energy storage; c p is the purchase cost coefficient per unit power of the energy storage; c e is the purchase cost coefficient per unit capacity of the energy storage; γ is the discount rate; Y a is the project planning period; Y ess is the energy storage life; C om = c om P ess (5); where: c om is the annual average maintenance cost coefficient of energy storage, (2) Operation scheduling model: The expression of the objective function of the operation scheduling model is: Where: f2 is the net benefit of energy storage dispatching under time-of-use electricity price conditions; N n is the number of typical day types; S is the number of typical days; U a is the arbitrage benefit of the energy storage system over S typical days; U B is the profit obtained by the energy storage avoiding over-capacity penalty through derating within a typical day.

3. A long-term energy storage planning method for the user side of a power system according to claim 1, characterized in that, The charge-discharge constraints including the discharge priority during peak hours established by the long-term power scheduling strategy include: In the case of single-day scheduling, the energy storage acts according to different charge-discharge powers according to the time-of-use electricity price periods; Wherein: are the charging powers during the valley price period, flat price period, peak price period, and spike price period respectively, and t v , t m , t p , t t are the valley price period, flat price period, peak price period, and spike price period respectively. The discharging operation of the energy storage is the same as above; The energy storage system obtains benefits by using the peak-valley electricity price difference of the power grid. Its calculation formula is: Where: P a is the grid trading electricity price at the energy storage operation time t; P dis,t , P ch,t are the discharge and charge powers of the energy storage during the period t; η C , η D are the charge and discharge efficiencies of the energy storage respectively.

4. A long-term energy storage planning method for the user side of a power system according to claim 1, characterized in that The demand management strategy uses the power regulation ability of the energy storage to suppress load overcapacity and converts the capacity control penalty into measurable economic benefits, including The capacity electricity charge, as the basic electricity charge part in the two-part tariff, is charged according to the operating transformer capacity, and is charged according to the sum of the capacities of the transformers in the current operating state and the hot standby state. The capacity electricity charge is expressed as: Where: B t is the rated capacity of the main transformer accessed by the user; P B It is the electricity price per kilowatt-hour based on the rated capacity of the main transformer; L max It is the actual demand of this load for this month.

5. A method for long-term energy storage planning on the user side of a power system according to claim 2, characterized in that, Establishing a comprehensive benefit evaluation mechanism for the energy storage as the evaluation basis for the energy storage configuration result includes According to the total life cycle cost of the energy storage power station, combined with the annual power generation of the energy storage power station, calculate the cost per kilowatt-hour of the energy storage power station. The calculation formula for the cost per kilowatt-hour of the energy storage power station is: Where: c ESS is the cost per kilowatt-hour of the energy storage power station; η is the conversion efficiency of the energy storage power station; E ESS is the installed capacity of the energy storage power station; H ESS is the annual utilization hours of electricity storage of the energy storage power station. When calculating the annual utilization hours of electricity storage of the energy storage power station, only the working hours during energy storage discharge are taken, that is At this time, and integrate according to the year to calculate the result IRR represents the annual average rate of return of an investment project. When IRR is greater than the cost of capital or the expected rate of return of the investment, the investment is considered favorable. Its objective function f(r) is the net present value NPV: Where: CF t is the cash flow in the t-th year; r is the discount rate, For the evaluation index of the peak shaving ability of the energy storage for the load, according to the electricity price characteristics of the load, taking the discharge performance of the energy storage during peak and spike price periods as the evaluation standard, the formula is as follows: Where: F P represents the flat peak coefficient of energy storage; C t,p represents the electricity price ratio between the peak period and the peak price period; P load (t) is the original load when the energy storage is not in operation at time t.

6. A method for long-term energy storage planning on the user side of a power system according to claim 1, characterized in that, The SA-PSO algorithm structure model clarifies the load type and selects representative typical daily data, covering different periods of peak, valley and flat, and removes outliers to ensure data reliability. It combines the scheduling model with the goal of minimizing the energy storage capacity configuration under the condition of maximizing profits to determine the optimal capacity of the energy storage system; and judges the constraints, substitutes the capacity results of the configuration stage into the energy storage scheduling model, and determines the optimal charging and discharging strategy according to different electricity prices and loads through the provisions of energy storage actions, thereby maximizing net profit or minimizing total cost. The SA-PSO algorithm includes the following stages: (1) Initialization stage: set the particle swarm size N = 50, the maximum number of iterations T = 100; initialize the particle position and velocity; set the initial temperature T0 = 500, the annealing coefficient α = 0.85-0.95; initialize the individual optimal solution and the global optimal solution, (2) Iterative calculation process: Each iterative calculation step length is t = 1:T, (3) Fitness calculation: Calculate the objective function value for each particle and calculate the configuration economic model of the supercapacitor to obtain the result. (3) Update particle state: update particle velocity; Update particle positions, (4) Simulated annealing operation: Calculate ΔE for each new position, where ΔE is the difference between the new solution and the old solution; if ΔE > 0, that is, judge that the solution is an improved solution and directly accept the new solution; if ΔE ≤ 0, accept the inferior solution with the probability function Accept the inferior solution; And update the temperature so that T = αT to gradually reduce the temperature, (5) Optimal solution update: Update the individual optimal pbest and the global optimal gbest; Record the currently optimal energy storage planning configuration solution (P ess , E ess ); Since energy storage must always maintain a balance of power when users interact with the grid, a balance constraint is set: P L,t = P dis,t - P ch,t + P grid,t (13); Where: P L,t is the actual load power; P dis,t , P ch,t are the charge / discharge powers of the energy storage; P grid,t is the power purchase from the power grid, and the power of the energy storage during charging needs to be limited: P ch,t +P L,t <B t (14); Where: P ch,t , P dismax are the charging and discharging powers of the energy storage at time t, respectively, and P chmax , P dismax are the rated charging and discharging powers of the energy storage, respectively Since the charging process and discharging process of energy storage cannot exist at the same time, the energy storage charging and discharging state constraints are set as follows: B dis,t +B ch,t ≤1 (17); Where: B dis,t and B ch,t are variables with values of 0 or 1, respectively representing the discharge and charge states of the energy storage at the t-th moment on the i-th day. B dis,t being 1 indicates that the energy storage is in the discharge state, and B ch,t being 1 indicates that the energy storage is in the charge state; Set the energy storage state of charge constraint for the state of charge of the energy storage fact: Where: S oc,t is the state of charge of the energy storage at time t; η C is the charging efficiency of the energy storage; S oc,max , S oc,min are the upper and lower limits of the state of charge of the energy storage.

7. A method for long-term energy storage planning on the user side of a power system according to claim 1, characterized in that, In the improved SA-PSO algorithm, starting from a random solution, the initial temperature T and the temperature attenuation function α are set, and a solution is randomly selected in the neighborhood of the current solution, called a candidate solution. The cost difference between the candidate solution and the current solution, that is, the energy difference, is calculated. If the candidate solution is better than the current solution, it is accepted; otherwise, the inferior solution is accepted with a certain probability, and the probability decreases as the temperature decreases. As the algorithm proceeds, the temperature gradually decreases, and the probability of accepting the suboptimal solution is gradually reduced. When the temperature drops low enough or reaches the predetermined number of iterations, the algorithm terminates. The improved SA-PSO algorithm calculates the energy difference, that is, the objective function value difference, in each iteration, and uses the following formula to decide whether to accept the inferior solution: Where: ΔE is the difference between the objective function value of the new solution and the current solution, T is the current temperature; the temperature T decreases according to the set attenuation function, and the attenuation function is: Tt +1 = αT t (3 - 18); Among them, α is a constant that controls the temperature decay rate, and 0<α<1, Through temperature decay, the system gradually "cools" and finally converges to a local optimal or global optimal solution. The particle swarm algorithm searches for the optimal solution through the movement of particles in the solution space. Each particle updates its position and velocity in the search space, influenced by its historical best position p i and the global best position g. The particle update rule is as follows: where: is the velocity of particle i at the t-th moment; is the position of particle i at the t-th moment; and g t are the historical best positions of particle i and all particles respectively; c1 and c2 are learning factors that control the degree of dependence of particles on their own experience and group experience; r1 and r2 are random numbers to ensure the diversity of search. Simulated annealing helps the algorithm maintain the global search ability during the scheduling process. Through the temperature control mechanism of simulated annealing, it is avoided that the solution in the process of pso loop calculation of the two-stage algorithm is the optimal solution of a single objective.

8. A system for implementing the method according to any one of claims 1-7, characterized in that, It includes: A modeling unit is used to construct a two-stage optimization model of energy storage planning cost-operation scheduling. The two-stage optimization model includes a cost model that quantifies the equivalent annual cost of the energy storage system from a full life cycle perspective to provide an economic benchmark planning for capacity configuration, and an operation scheduling model that takes time-of-use electricity price arbitrage and demand control benefits as objective functions; A framework unit, which is used to model the operation and scheduling actions based on the user-side electricity pricing framework of the two-part electricity pricing mechanism, wherein the two-part electricity pricing mechanism includes an electricity pricing mechanism and a capacity pricing mechanism; An operating unit, which is used to run the long-term power scheduling strategy and demand management strategy driven by time-of-use electricity price introduced in the scheduling stage. The long-term power scheduling strategy establishes charge and discharge constraints including the discharge priority during peak hours; the demand management strategy uses the flexible power regulation ability of energy storage to suppress load overcapacity and converts the capacity control penalty into measurable economic benefits; An evaluation unit, which is used to establish a comprehensive benefit evaluation mechanism for energy storage as the evaluation basis for the energy storage configuration result. The comprehensive benefit evaluation mechanism includes an economic evaluation mechanism for energy storage, an application efficiency evaluation mechanism for energy storage, and a peak shaving capacity evaluation mechanism for energy storage; A calculation unit, which is used to take the optimization of the economic evaluation of energy storage, the application efficiency of energy storage, and the peak shaving capacity of energy storage as the goal, and use an improved SA-PSO algorithm to realize the collaborative optimization of capacity configuration and operation scheduling, integrating the global search ability of simulated annealing and the local convergence characteristics of particle swarm optimization, and solving to obtain the optimal energy storage planning result.

9. A computer storage medium, characterized in that, The storage medium includes computer instructions, which, when running on a computer, cause the computer to execute the method according to any one of claims 1-7.

10. An electronic device, characterized in that, The electronic device includes: A memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it realizes the method according to any one of claims 1-7.

Images