A method for coordinating and optimizing energy storage and electricity prices based on flexible resource synergy

Abstract

This invention relates to a method for coordinating and optimizing energy storage and electricity pricing based on flexible resource synergy. This method can effectively address the increasingly severe power balance pressure on distribution networks by optimizing electricity prices and mobilizing the adjustment potential of flexible resources within the distribution network. The main steps include: acquiring photovoltaic and wind power load data and initial time-of-use electricity prices for a given day in the distribution network; calculating the energy storage operating power at each moment using a particle swarm optimization algorithm; calculating the energy storage configuration and operating costs; calculating the maximum revenue for the distribution network operator and using this as the objective function to optimize the time-of-use electricity price using a moth-flame algorithm; and outputting the optimal energy storage configuration capacity and the optimal electricity price. Compared with existing technologies, this invention achieves peak shaving and valley filling by adjusting user electricity consumption patterns through electricity pricing, and utilizes new energy sources for low-storage, high-generation generation, effectively promoting the local consumption of new energy, improving the balance capacity of the distribution network, and providing theoretical support for the safe and low-carbon operation of the distribution network.

Description

Technical Field

[0001] This invention belongs to the field of power system planning technology, specifically relating to a method for coordinating and optimizing energy storage and electricity prices based on flexible resource synergy. Background Technology

[0002] With the continuous development of new power systems, the massive influx of stochastic distributed new energy sources, such as photovoltaics, into the distribution network has led to changes in the network structure and brought severe challenges to the power balance. The traditional "source follows load" balancing model cannot effectively cope with the increasingly severe power balance pressure, and there is an urgent need to build new flexible balancing methods to effectively improve the system's balancing capacity.

[0003] The distribution network contains numerous distributed power sources and adjustable load variables, making global optimization and overall balance difficult, and hindering the local consumption of renewable energy, resulting in wind and solar power curtailment. Therefore, leveraging flexible resources within the distribution network to enhance its balancing capacity has garnered widespread attention. Among these, the development of flexible resources such as energy storage and demand-side response (DSR) offers new approaches to improving the renewable energy consumption and balancing capabilities of the distribution network. By fully tapping into user adjustment potential through energy storage and DSR, the local consumption of renewable energy in the distribution network can be promoted, thereby effectively improving the system's power balance capacity.

[0004] In summary, existing technologies are insufficient to mobilize flexible resources such as energy storage and demand-side response within the distribution network to promote the local consumption of new energy sources and improve the system's power balance capabilities. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method for coordinating and optimizing energy storage and electricity prices based on flexible resource collaboration.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] This invention provides a method for coordinating and optimizing energy storage and electricity pricing based on flexible resource synergy, comprising the following steps:

[0008] Step S1: Obtain daily photovoltaic power output, daily wind power output, daily load data of the distribution network, and the current time-of-use electricity price;

[0009] Step S2: Calculate the operating power of the energy storage at each moment using the particle swarm optimization algorithm based on the daily photovoltaic power output, daily wind power output, and daily load data;

[0010] Step S3: Calculate the energy storage configuration capacity, energy storage configuration, and operating cost based on the energy storage operating power at each moment;

[0011] Step S4: Calculate the optimal time-of-use electricity price based on the energy storage configuration and operating costs using the moth-flame optimization algorithm;

[0012] Step S5: Calculate the absolute value of the difference between the optimal time-of-use electricity price and the current time-of-use electricity price, compare the absolute value of the difference with a first preset threshold. If it is less than or equal to the first preset threshold, proceed to step S6. If it is greater than the first preset threshold, calculate the demand elasticity coefficient based on the optimal time-of-use electricity price and the current time-of-use electricity price, calculate the daily load change based on the demand elasticity coefficient, update the daily load data based on the daily load change, update the current time-of-use electricity price to the optimal time-of-use electricity price, and proceed to step S2.

[0013] Step S6: Calculate the demand elasticity coefficient based on the optimal time-of-use electricity price, calculate the daily load change based on the demand elasticity coefficient, update the daily load data based on the daily load change, calculate the energy storage configuration capacity based on the updated daily load data, and coordinate and optimize energy storage and electricity price based on the energy storage configuration capacity and the optimal time-of-use electricity price.

[0014] Furthermore, step S2 specifically includes the following steps:

[0015] Step S2.1: Based on the daily photovoltaic output P PV (t), Daily wind power output P WT (t) Calculate the daily renewable energy output using the following formula:

[0016] P new (t)=P PV (t)+P WT (t)

[0017] Among them, P new (t) contributes to Japan's new energy source, P PV (t) represents the daily photovoltaic power output, P WT (t) represents the daily wind power output;

[0018] Step S2.2: Based on the daily new energy output P new The objective function of the particle swarm optimization algorithm is defined using the daily load data P1(t) and the data P1(t). The objective function of the particle swarm optimization algorithm is:

[0019]

[0020] Where S is the net power of the region, P bat (t) represents the ideal charge and discharge power of the energy storage at each moment;

[0021] The particle swarm optimization algorithm takes the minimum net power S of the region as its objective function.

[0022] Step S2.3: Set energy storage configuration constraints, which include: power balance constraints, energy storage system operation constraints, and energy storage state of charge constraints;

[0023] Step S2.4: Using the particle swarm optimization algorithm, calculate the energy storage operating power P at each moment based on the objective function of the particle swarm optimization algorithm and the energy storage configuration constraints. b (t).

[0024] Furthermore, the power balance constraint is as follows:

[0025] P PV (t)+P WT (t)=P0(t)+P bat (t)

[0026] Where P0(t) is the load at each moment of the previous iteration;

[0027] The operating constraints of the energy storage system are:

[0028]

[0029] -P bess ≤P b (t)≤P bess

[0030] Among them, P c (t) represents the charging power of the energy storage at each moment, P dc (t) represents the discharge power of the stored energy at each moment, P b (t) represents the operating power of the energy storage at each moment, P bess This represents the boundary value of the rated power of the energy storage.

[0031] The energy storage state of charge constraint is:

[0032]

[0033] Among them, S min S is the minimum SOC of energy storage. max S is the maximum SOC of the energy storage, and S(0) and S(24) are the SOC values ​​of the energy storage at times 0 and 24, respectively.

[0034] Furthermore, in step S3, the energy storage configuration capacity is calculated based on the energy storage's operating power at each moment, using the following formula:

[0035]

[0036] Among them, E bess To configure capacity for energy storage, P b (t) represents the operating power of the energy storage at each moment.

[0037] Furthermore, the calculation of energy storage configuration and operating cost based on the energy storage's operating power at each moment is performed using the following formula:

[0038] C ope =C in +C rep +C om

[0039] C in =K D (C bess +C con )

[0040]

[0041] C om =K D K om |P b |

[0042] C bess =αK E E bess +K p P b +K inv P b

[0043]

[0044] Among them, C ope For energy storage configuration and operating costs, C in For the daily investment cost of energy storage, C rep C represents the daily replacement cost corresponding to the loss of energy storage operating capacity. om For the daily maintenance cost of energy storage, C bess C represents the total investment cost for energy storage. con P represents the initial construction cost of the energy storage system. b For energy storage operation power, E bess To configure capacity for energy storage, K D K is the isochronous value coefficient. p K is the unit power of energy storage. E K is the unit capacity cost coefficient for energy storage. inv K is the unit power cost coefficient of the inverter for energy storage auxiliary equipment. om y is the annual maintenance cost coefficient for energy storage, α is the energy storage battery cost ratio coefficient, r is the discount rate, and y is the annual maintenance cost coefficient for energy storage. bess This refers to the investment period for the energy storage system.

[0045] Furthermore, step S4 specifically includes the following steps:

[0046] Step S4.1: Based on energy storage configuration and operating cost C ope Compared with the current time-of-use electricity price P s (t) Define the objective function of the moth-flame optimization algorithm. The objective function of the moth-flame optimization algorithm is:

[0047] maxI=I sell -(C DR +C new +C ope +C PV +C up.grid )

[0048]

[0049] Where maxI represents the maximum revenue for the regional operator, I sell For regional operators' electricity sales revenue, C DR For incentive-based demand response costs, C new For energy storage operators, the cost of purchasing new energy electricity, C ope For energy storage configuration and operating costs, C PV To help users save on electricity costs through energy storage, C up.grid P1(t) represents the cost of electricity purchased from the upper-level grid by the regional operators, and P represents the daily load data. s (t) represents the current time-of-use electricity price, and Δt represents the change per unit time, with a value of 1 hour;

[0050] The moth-flame optimization algorithm uses the maximum revenue of the regional operator as the objective function.

[0051] Step S4.2: Set electricity price optimization constraints, which include: electricity consumption constraints, time-of-use electricity price constraints, and unit electricity cost constraints;

[0052] Step S4.3: Calculate the optimal time-of-use electricity price using the moth flame optimization algorithm based on the objective function of the moth flame optimization algorithm and the electricity price optimization constraints.

[0053] Furthermore, the power consumption constraint is as follows:

[0054]

[0055] in, This represents the daily electricity consumption variation in the region.

[0056] The time-of-use electricity pricing constraint is as follows:

[0057]

[0058] p min ≤p g <p f ≤pp ≤p max

[0059] Where, p min p max p represents the boundary value for time-of-use electricity pricing. f p g p p These represent peak-hour electricity price, off-peak-hour electricity price, and normal-hour electricity price; λ1 and λ2 are peak-to-valley price ratio coefficients.

[0060] The unit electricity cost constraint is:

[0061]

[0062] in, These represent the costs before and after electricity price optimization, respectively, P. s (t) represents the current time-of-use electricity price, P s1 (t) represents the optimal time-of-use electricity price calculated by the moth-flame optimization algorithm.

[0063] Furthermore, the demand elasticity coefficient is calculated based on the optimal time-of-use electricity price, using the following formula:

[0064]

[0065] Among them, e i-j P is the demand elasticity coefficient. sj Let ΔP be the current time-of-use electricity price for period j. sj Let P be the difference between the current time-of-use electricity price and the optimal time-of-use electricity price for time period j. i ΔP i These represent the daily load data at time i and the change between the daily load data at time i and the daily load data from the previous iteration, respectively.

[0066] Furthermore, the daily load change is calculated based on the demand elasticity coefficient using the following formula:

[0067]

[0068] Where, ΔP 1i This represents the daily load change at time i after the current time-of-use electricity price has been updated.

[0069] Furthermore, in step S6, calculating the energy storage configuration capacity based on the updated daily load data includes: calculating the energy storage operating power at each moment based on the updated daily load data, and calculating the energy storage configuration capacity based on the energy storage operating power at each moment.

[0070] Compared with the prior art, the present invention has the following advantages:

[0071] (1) This invention comprehensively considers the flexibility, rapid adjustment capability of energy storage system and the ability to guide users to actively change their electricity consumption behavior by optimizing time-of-use pricing in demand-side response. It constructs a two-layer optimization model of energy storage optimization and demand-side response optimization to promote the local consumption capacity of new energy in distribution network.

[0072] (2) The present invention comprehensively considers the price-based demand response model and the incentive-based demand response model in the demand-side response model, thereby improving the overall economic efficiency of the distribution network and the economic efficiency of user electricity consumption. Attached Figure Description

[0073] Figure 1 This is a flowchart of the present invention;

[0074] Figure 2 This is a real-time interactive diagram of the initial power in an embodiment of the present invention;

[0075] Figure 3 This is a diagram showing the energy storage charging and discharging power in an embodiment of the present invention;

[0076] Figure 4 To optimize the electricity price map in this embodiment of the invention;

[0077] Figure 5 This is the optimized real-time power interaction diagram in an embodiment of the present invention. Detailed Implementation

[0078] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0079] Example 1:

[0080] This embodiment provides a method for coordinating and optimizing energy storage and electricity prices based on flexible resource synergy, including the following steps:

[0081] Step S1: Obtain daily photovoltaic power output, daily wind power output, daily load data of the distribution network, and the current time-of-use electricity price;

[0082] Furthermore, the specific steps of S1 include:

[0083] S1.1: Obtain the photovoltaic power output, wind power output, and load output data of a certain province's power distribution network on a certain day, as well as the peak, flat, and valley electricity prices of that province;

[0084] S1.2: Use the acquired daily photovoltaic power output, daily wind power output, daily load data of the distribution network, and time-of-use electricity price as initial data input.

[0085] Step S2: Calculate the operating power of the energy storage at each moment using the particle swarm optimization algorithm based on the daily photovoltaic power output, daily wind power output, and daily load data;

[0086] Step S2 specifically includes the following steps:

[0087] Step S2.1: Based on the daily photovoltaic output P PV (t), Daily wind power output P WT (t) Calculate the daily renewable energy output using the following formula:

[0088] P new (t)=P PV (t)+P WT (t)

[0089] Among them, P new (t) contributes to Japan's new energy source, P PV (t) represents the daily photovoltaic power output, P WT (t) represents the daily wind power output;

[0090] Step S2.2: Optimize the configuration of regional energy storage capacity with the goal of minimizing the standard deviation (net power) between regional load and renewable energy output after energy storage participation. Using minimum net power as an intermediate target, determine the energy storage charging and discharging power within 24 hours, then calculate the renewable energy absorption rate. At this point, the regional renewable energy absorption rate is maximized, resulting in the best absorption effect. Based on the daily renewable energy output P... new The objective function of the particle swarm optimization algorithm is defined using the daily load data P1(t) and the data P1(t). The objective function of the particle swarm optimization algorithm is:

[0091]

[0092] Where S is the net power of the region, P bat (t) represents the ideal charge and discharge power of the energy storage at each moment;

[0093] The particle swarm optimization algorithm takes the minimum net power S of the region as its objective function.

[0094] Step S2.3: To make the energy storage capacity calculation results more scientific, energy storage configuration constraints are set. The energy storage configuration constraints include: power balance constraints, energy storage system operation constraints, and energy storage state of charge constraints.

[0095] (1) Power balance constraint: Before and after optimization, regional operators must maintain a balance between the real-time power they exchange with users and the real-time power they exchange with new energy operators. The power balance constraint is as follows:

[0096] P PV (t)+P WT (t)=P0(t)+Pbat (t)

[0097] Where P0(t) is the load at each moment of the previous iteration;

[0098] (2) Energy storage system operation constraints

[0099] To ensure continuous and stable operation of the energy storage system, the total charging power and total discharging power must be equal within a demand response cycle. The operating constraints of the energy storage system are as follows:

[0100]

[0101] -P bess ≤P b (t)≤P bess

[0102] Among them, P c (t) represents the charging power of the energy storage at each moment, P dc (t) represents the discharge power of the stored energy at each moment, P b (t) represents the operating power of the energy storage at each moment, P bess This represents the boundary value of the rated power of the energy storage.

[0103] (3) The energy storage state of charge constraint is:

[0104]

[0105] Among them, S min S is the minimum SOC of energy storage. max S is the maximum SOC of the energy storage, and S(0) and S(24) are the SOC values ​​of the energy storage at times 0 and 24, respectively.

[0106] Step S2.4: Using the particle swarm optimization algorithm, calculate the energy storage operating power P at each moment based on the objective function of the particle swarm optimization algorithm and the energy storage configuration constraints. b (t).

[0107] Step S3: Calculate the energy storage configuration capacity, energy storage configuration, and operating cost based on the energy storage operating power at each moment;

[0108] The energy storage configuration and operating cost are calculated based on the energy storage's operating power at each moment. The calculation formula is as follows:

[0109] C ope =C in +C rep +C om

[0110] C in =K D (C bess +C con )

[0111]

[0112] C om =K D K om |P b |

[0113] C bess =αK E E bess +K p P b +K inv P b

[0114]

[0115] Among them, C ope For energy storage configuration and operating costs, C in For the daily investment cost of energy storage, C rep C represents the daily replacement cost corresponding to the loss of energy storage operating capacity. om For the daily maintenance cost of energy storage, C bess C represents the total investment cost for energy storage. con P represents the initial construction cost of the energy storage system. b For energy storage operation power, E bess To configure capacity for energy storage, K D K is the isochronous value coefficient. p K is the unit power of energy storage. E K is the unit capacity cost coefficient for energy storage. inv K is the unit power cost coefficient of the inverter for energy storage auxiliary equipment. om y is the annual maintenance cost coefficient for energy storage, α is the energy storage battery cost ratio coefficient, r is the discount rate, and y is the annual maintenance cost coefficient for energy storage. bess This refers to the investment period for the energy storage system.

[0116] Step S4: Calculate the optimal time-of-use electricity price based on the energy storage configuration and operating costs using the moth-flame optimization algorithm;

[0117] Step S4 specifically includes the following steps:

[0118] Step S4.1: Based on the regional operator's time-of-use pricing, and comprehensively considering its electricity sales revenue, electricity purchase cost, daily construction cost of energy storage, demand response cost, and cost of purchasing electricity from the upper-level grid, optimize the time-of-use pricing to guide users to participate in demand-side response, thereby maximizing regional economic growth. This is based on the energy storage configuration and operating cost C. ope Compared with the current time-of-use electricity price P s (t) Define the objective function of the moth-flame optimization algorithm. The objective function of the moth-flame optimization algorithm is:

[0119] maxI=I sell -(C DR +C new +C ope +C PV +C up.grid )

[0120]

[0121] Where maxI represents the maximum revenue for the regional operator, I sell For regional operators' electricity sales revenue, C DR For incentive-based demand response costs, C new For energy storage operators, the cost of purchasing new energy electricity, C ope For energy storage configuration and operating costs, C PV To help users save on electricity costs through energy storage, C up.grid P1(t) represents the cost of electricity purchased from the upper-level grid by the regional operators, and P represents the daily load data. s (t) represents the current time-of-use electricity price, and Δt represents the change per unit time, with a value of 1 hour;

[0122] The moth-flame optimization algorithm takes the maximum revenue of the regional operator as the objective function.

[0123] Step S4.2: Set electricity price optimization constraints, which include: electricity consumption constraints, time-of-use electricity price constraints, and unit electricity cost constraints;

[0124] (1) Power consumption constraints

[0125] To ensure regional electricity demand, the daily fluctuation of regional electricity consumption must be controlled within a certain range after the implementation of time-of-use pricing. The electricity consumption constraints are as follows:

[0126]

[0127] in, This represents the daily electricity consumption variation in the region.

[0128] (2) Time-of-use pricing constraints

[0129] The peak-valley electricity price ratio directly affects the outcome of user participation in demand-side response; therefore, it is necessary to constrain the peak-valley price ratio to prevent the reversal of peak and valley periods. The time-of-use pricing constraint is as follows:

[0130]

[0131] p min ≤p g <p f ≤p p ≤pmax

[0132] Where, p min p max p represents the boundary value for time-of-use electricity pricing. f p g p p These represent peak-hour electricity price, off-peak-hour electricity price, and normal-hour electricity price; λ1 and λ2 are peak-to-valley price ratio coefficients.

[0133] (3) Unit electricity cost constraint

[0134] After regional electricity pricing optimization, the electricity cost for regional users should not exceed the electricity cost for regional users before the pricing optimization. The unit electricity cost constraint is:

[0135]

[0136] in, These represent the costs before and after electricity price optimization, respectively, P. s (t) represents the current time-of-use electricity price, P s1 (t) represents the optimal time-of-use electricity price calculated by the moth-flame optimization algorithm.

[0137] Step S4.3: Calculate the optimal time-of-use electricity price using the moth flame optimization algorithm based on the objective function of the moth flame optimization algorithm and the electricity price optimization constraints.

[0138] Step S5: Calculate the absolute value of the difference between the optimal time-of-use electricity price and the current time-of-use electricity price, compare the absolute value of the difference with a first preset threshold. If it is less than or equal to the first preset threshold, proceed to step S6. If it is greater than the first preset threshold, calculate the demand elasticity coefficient based on the optimal time-of-use electricity price and the current time-of-use electricity price, calculate the daily load change based on the demand elasticity coefficient, update the daily load data based on the daily load change, update the current time-of-use electricity price to the optimal time-of-use electricity price, and proceed to step S2.

[0139] Step S6: Calculate the demand elasticity coefficient based on the optimal time-of-use electricity price. The calculation formula is as follows:

[0140]

[0141] Among them, e i-j P is the demand elasticity coefficient. sj Let ΔP be the current time-of-use electricity price for period j. sj Let P be the difference between the current time-of-use electricity price and the optimal time-of-use electricity price for time period j. i ΔP i These represent the daily load data at time i and the change between the daily load data at time i and the daily load data from the previous iteration, respectively.

[0142] Calculate the daily load change based on the aforementioned demand elasticity coefficient.

[0143] The calculation formula is:

[0144]

[0145] Wherein, ΔP 1i This represents the daily load change at time i after the current time-of-use electricity price has been updated.

[0146] The daily load data is updated based on the daily load change, the energy storage configuration capacity is calculated based on the updated daily load data, and the energy storage and electricity price are coordinated and optimized based on the energy storage configuration capacity and the optimal time-of-use electricity price.

[0147] Example 2:

[0148] The parts not mentioned in this embodiment are the same as in Embodiment 1.

[0149] This embodiment proposes a method for improving the balancing capacity of a distribution network through flexible resource coordination, the flowchart of which is shown below. Figure 1 As shown, the method includes the following steps:

[0150] S1: Obtain the photovoltaic and wind power load data and the initial time-of-use electricity price for a certain day and 24 hours in the distribution network, and set them as the initial values;

[0151] S1.1: Obtain the photovoltaic power output, wind power output, and load output data of a certain province's power distribution network for a certain day and 24 hours, as well as the peak, flat, and valley electricity prices of that province. In this embodiment, the time-of-use electricity prices shown in Table 1 are used.

[0152] Table 1 Initial Time-of-Use Electricity Price

[0153]

[0154] S1.2: Use the acquired daily photovoltaic power output, daily wind power output, daily load data of the distribution network, and time-of-use electricity price as initial data input.

[0155] Real-time interaction of initial regional power in the embodiment, such as Figure 2 As shown.

[0156] S2: Calculate the net load of the distribution network based on the data in S1, and solve the energy storage operating power at each moment using the particle swarm optimization algorithm;

[0157] S2.1: The initial parameters are compared with the load based on the sum of wind power output and photovoltaic power output, and the rule of the smaller one is output. The real-time absorption of new energy by the load in the region is calculated according to formula (1).

[0158] P SC(t)=min{P PV (t)+P WT (t), P1(t)}

[0159] In the formula: P SC (t) represents the output of new energy consumed by the regional load at each moment, P PV (t), P WT P(t) represents the regional photovoltaic power output and the regional wind power output, respectively; P1(t) represents the load at each moment after the electricity price optimization.

[0160] S2.2: The optimal configuration of regional energy storage capacity is based on minimizing the standard deviation (net power of the region) between regional load and renewable energy output after energy storage participation. Using minimum net power as an intermediate objective, after determining the energy storage charging and discharging power within 24 hours, the renewable energy absorption rate is calculated. At this point, the regional renewable energy absorption rate is maximized, resulting in the best absorption effect. The calculation formula is as follows:

[0161]

[0162] P new (t)=P PV (t)+P WT (t)

[0163]

[0164] In the formula: where S is the net power of the region, P bat (t) represents the ideal charge / discharge power of the energy storage at each moment; P new (t) contributes to Japan's new energy source, P PV (t), P WT (t) represent the regional photovoltaic power output and the regional wind power output, respectively; R sel For regional renewable energy consumption rate; P SC (t) represents the output of new energy consumed by the regional load at each moment, and P during energy storage charging. bat (t)>0.

[0165] S2.3: To make the energy storage capacity calculation results more scientific, energy storage configuration constraints are set, including: power balance constraints, energy storage system operation constraints, and energy storage state of charge constraints. The calculation formulas are as follows;

[0166] (1) Power balance constraint

[0167] Before and after optimization, regional operators must maintain a balance between the real-time power they use to interact with users and the real-time power they use to interact with new energy operators.

[0168] P PV (t)+P WT (t)=P0(t)+P bat (t)

[0169] Where P0(t) is the load at each moment of the previous iteration;

[0170] (2) Energy storage system operation constraints

[0171] To ensure continuous and stable operation of energy storage, the total charging power and total discharging power of the energy storage must be equal within a demand response cycle.

[0172]

[0173] -P bess ≤P b (t)≤P bess

[0174] Among them, P c (t) represents the charging power of the energy storage at each moment, P dc (t) represents the discharge power of the stored energy at each moment, P b (t) represents the operating power of the energy storage at each moment, P bess This represents the boundary value of the rated power of the energy storage.

[0175] (3) Energy storage state of charge constraints

[0176]

[0177] Among them, S min S is the minimum SOC of energy storage. max S is the maximum SOC of the energy storage, and S(0) and S(24) are the SOC values ​​of the energy storage at times 0 and 24, respectively.

[0178] S2.4: Using the particle swarm optimization algorithm, the operating power Pb of the energy storage at each moment is calculated based on the objective function and constraints of the energy storage configuration model. This method, employing existing technologies and the present invention, is a flexible resource-coordinated distribution network balancing capability enhancement method. The energy storage operating power is as follows: Figure 3 As shown.

[0179] S3: Based on the results of S2, the energy storage configuration and operating cost are calculated using the parameters shown in Table 2 in this embodiment. The calculation formula is as follows:

[0180] Table 2. Relevant parameters of the energy storage system

[0181] parameter numerical values <![CDATA[Energy storage battery cost (K E )]]> 1085 yuan / (kW.h) <![CDATA[Energy storage unit power cost (K p )]]> 1085 yuan / (kW) <![CDATA[Cost of energy storage auxiliary equipment inverter (K inv )]]> 1380 yuan / (kW) <![CDATA[Annual maintenance cost coefficient of energy storage (K om )]]> 90 yuan / (kW) <![CDATA[Initial construction cost of energy storage (C con )]]> 30,000 yuan <![CDATA[Charge-discharge efficiency (η c , η dc )]]> 0.95，0.95 Depth of discharge (D) 0.7 Discount rate (r) 0.04 <![CDATA[Investment years of energy storage system (y bess )]]> 15 years

[0182] Daily operating and configuration costs of energy storage:

[0183] C ope =C in +C rep +C om

[0184] C in =K D (C bess +C con )

[0185]

[0186] C om =K D K om |P b |

[0187] C bess =αK E E bess +K p P b +K inv P b

[0188]

[0189] Among them, C ope For energy storage configuration and operating costs, C in For the daily investment cost of energy storage, C rep C represents the daily replacement cost corresponding to the loss of energy storage operating capacity. om For the daily maintenance cost of energy storage, C bess C represents the total investment cost for energy storage. con P represents the initial construction cost of the energy storage system. b For energy storage operation power, E bess To configure capacity for energy storage, K D K is the isochronous value coefficient. p K is the unit power of energy storage. E K is the unit capacity cost coefficient for energy storage. inv K is the unit power cost coefficient of the inverter for energy storage auxiliary equipment. om y is the annual maintenance cost coefficient for energy storage, α is the energy storage battery cost ratio coefficient, r is the discount rate, and y is the annual maintenance cost coefficient for energy storage. bess This refers to the investment period for the energy storage system.

[0190] S4: Calculate the maximum revenue of the distribution network operator based on the results of S3, and use this as the objective function to optimize the time-of-use electricity price through the moth-flame algorithm;

[0191] S4.1: Based on the time-of-use pricing of regional operators, and taking into account their electricity sales revenue, electricity purchase cost, daily construction cost of energy storage, demand response cost, and cost of purchasing electricity from the upper-level grid, the calculation formula is as follows:

[0192] maxI=Isell -(C DR +C new +C ope +C PV +C up.grid )

[0193]

[0194] Where maxI represents the maximum revenue for the regional operator, I sell For regional operators' electricity sales revenue, C DR For incentive-based demand response costs, C new For energy storage operators, the cost of purchasing new energy electricity, C ope For energy storage configuration and operating costs, C PV To help users save on electricity costs through energy storage, C up.grid P1(t) represents the cost of electricity purchased from the upper-level grid by the regional operators, and P represents the daily load data. s (t) represents the current time-of-use electricity price, and Δt represents the change per unit time, with a value of 1 hour;

[0195] Demand-side response cost:

[0196] Demand-side response strategies can incentivize users to proactively change their electricity consumption behavior, making the regional load curve more closely match the renewable energy output curve in time series. This facilitates local renewable energy consumption, reduces the need for regional energy storage capacity allocation, and improves the overall revenue of regional operators. Compared to traditional single demand-side response methods, this paper adopts coordinated regulation of PDR and IDR to promote local renewable energy consumption. The real-time regional power after participating in demand-side response is used as the basis for transmitting to the lower-level energy storage optimization model.

[0197] The PDR response is closely related to electricity prices. According to economic principles, the quantity demanded of a commodity is closely linked to its price. Electricity, as a commodity, is no exception; therefore, the change in electricity consumption caused by a change in electricity prices is defined as the demand elasticity coefficient e.

[0198]

[0199] Among them, e i-j P is the demand elasticity coefficient. sj Let ΔP be the current time-of-use electricity price for period j. sj Let P be the difference between the current time-of-use electricity price and the optimal time-of-use electricity price for time period j. i ΔP i These represent the daily load data at time i and the change between the daily load data at time i and the daily load data from the previous iteration, respectively. When i = j, e i-j is the self-elasticity coefficient; when i≠j, it is the mutual elasticity coefficient.

[0200] If a 24-hour period is taken as a cycle, and the time-of-use electricity price for each period is known, then the change in user load during period i is as follows:

[0201]

[0202] Where, ΔP 1i This represents the daily load change at time i after the current time-of-use electricity price has been updated.

[0203] Demand response costs are shown below:

[0204]

[0205] In the formula: C DR For demand response costs; C IDR Cost of response to incentive-based demand; C PDR For price-based demand response costs; K IDR P0(t) represents the unit incentive-based demand response cost, expressed in kW / yuan; P0(t) represents the load at each moment before electricity price optimization; and p0(t) represents the electricity price before optimization.

[0206] S4.2: To make the time-of-use pricing optimization results more scientific, the following constraints are set for the pricing optimization: electricity consumption constraint, time-of-use pricing constraint, and unit electricity cost constraint. The calculation formula is as follows;

[0207] (1) Power consumption constraints

[0208] To ensure regional electricity demand, the daily fluctuation of regional electricity consumption must be controlled within a certain range after the implementation of time-of-use pricing.

[0209]

[0210] In the formula: P1(t) represents the daily electricity consumption change in the region; P1(t) and P0(t) represent the user load after the implementation of time-of-use pricing and the original user load, respectively.

[0211] (2) Time-of-use pricing constraints

[0212] The peak-valley electricity price ratio will directly affect the results of users' participation in demand-side response. Therefore, it is necessary to constrain the peak-valley electricity price ratio to prevent the reversal of peak and valley periods.

[0213]

[0214] p min ≤p g <p f ≤p p ≤p max

[0215] Where, p min p max p represents the boundary value for time-of-use electricity pricing. f p g p p These represent peak-hour electricity price, off-peak-hour electricity price, and normal-hour electricity price; λ1 and λ2 are peak-to-valley price ratio coefficients.

[0216] (3) Unit electricity cost constraint

[0217] After the regional electricity price is optimized, the electricity cost for regional users should not be greater than the electricity cost for regional users before the price optimization.

[0218]

[0219] in, These represent the costs before and after electricity price optimization, respectively, P. s (t) represents the current time-of-use electricity price, P s1 (t) represents the optimal time-of-use electricity price calculated by the moth-flame optimization algorithm.

[0220] S4.3: Using the moth-flame optimization algorithm, based on the objective function (maximizing the regional operator's revenue), the optimal time-of-use electricity price is calculated. The optimized electricity price and the optimized load curve are shown below. Figure 4 As shown.

[0221] To demonstrate the advantages of this invention, three strategies are described.

[0222] Strategy 1: Adopt a uniform initial electricity price, and only consider PDR in the demand-side response optimization model.

[0223] Strategy 2: Adopt a uniform initial electricity price, and only consider PDR in the demand-side response optimization model, so that energy storage can be discharged evenly during peak hours.

[0224] Strategy 3: Adopting zoned optimization of electricity pricing, considering PDR and IDR in the demand-side response optimization model, and discharging energy storage evenly during peak periods, which is the method proposed in this invention.

[0225] The renewable energy absorption rate and energy storage capacity configuration for the three strategies are shown in Table 3.

[0226] Table 3. New energy absorption rate and energy storage capacity configuration for three strategies

[0227] Strategy New energy consumption rate (%) <![CDATA[E bess / (kW.h)]]> <![CDATA[P N / (kW)]]> Strategy 1 100 10715 1250 Strategy 2 99.54 10345 1190 Strategy 3 100 10735 1175

[0228] The results of the operating costs and operator revenue for the three strategies are shown in Table 4.

[0229] Table 4. Operating Costs and Operator Revenues for the Three Strategies

[0230]

[0231] Based on the comparison of the three methods recorded in Tables 3 and 4, the proposed flexible resource-coordinated distribution network balancing capability improvement method has significant advantages under all three strategies. Further analysis and comparison of the energy storage operation results of the three strategies reveals that, compared to strategies 1 and 2, strategy 3 reduces the rated power of energy storage, thereby reducing energy storage operation losses, increasing energy storage lifespan, saving energy storage construction costs, and thus improving the economic efficiency of the distribution network. In strategy 3, the energy storage discharge mode is uniform discharge during peak hours, and its operation results show that the energy storage capacity is increased compared to strategies 1 and 2, promoting the full absorption of new energy. When using strategy 2, the energy storage capacity configuration is reduced compared to strategies 1 and 3, resulting in a decrease in the new energy absorption rate. In Strategy 3, energy storage is charged during the off-peak hours (01:00-10:00), accumulating a charging amount of 7515 kWh. This means the energy storage absorbs 7515 kWh of renewable energy during the off-peak hours and supplies power to the load during the peak hours (11:00-23:00). To reduce operating losses and avoid overcharging and over-discharging, the energy storage is evenly discharged during peak hours. Furthermore, compared to the initial electricity price in Strategy 1, Strategy 3 optimizes the electricity price, resulting in lower electricity consumption in Zone 1. This is because, to reduce energy storage construction costs while simultaneously meeting user unit electricity cost constraints, a 1.74% reduction in load can be achieved, leading to lower electricity consumption.

[0232] S5: Combining the calculation results of S3 and S4, output the optimal energy storage configuration capacity and the optimal electricity price. The implementation example uses the optimized regional power real-time interaction diagram of the flexible resource coordination distribution network balancing capability improvement method involved in this invention, as shown below. Figure 5 As shown.

[0233] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0234] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for coordinating and optimizing energy storage and electricity pricing based on flexible resource synergy, characterized in that, Includes the following steps: Step S1: Obtain daily photovoltaic power output, daily wind power output, daily load data of the distribution network, and the current time-of-use electricity price; Step S2: Calculate the operating power of the energy storage at each moment using the particle swarm optimization algorithm based on the daily photovoltaic power output, daily wind power output, and daily load data; Step S3: Calculate the energy storage configuration capacity, energy storage configuration, and operating cost based on the energy storage operating power at each moment; Step S4: Calculate the optimal time-of-use electricity price based on the energy storage configuration and operating costs using the moth-flame optimization algorithm; Step S5: Calculate the absolute value of the difference between the optimal time-of-use electricity price and the current time-of-use electricity price, compare the absolute value of the difference with a first preset threshold. If it is less than or equal to the first preset threshold, proceed to step S6. If it is greater than the first preset threshold, calculate the demand elasticity coefficient based on the optimal time-of-use electricity price and the current time-of-use electricity price, calculate the daily load change based on the demand elasticity coefficient, update the daily load data based on the daily load change, update the current time-of-use electricity price to the optimal time-of-use electricity price, and proceed to step S2. Step S6: Calculate the demand elasticity coefficient based on the optimal time-of-use electricity price, calculate the daily load change based on the demand elasticity coefficient, update the daily load data based on the daily load change, calculate the energy storage configuration capacity based on the updated daily load data, and coordinate and optimize energy storage and electricity price based on the energy storage configuration capacity and the optimal time-of-use electricity price. The daily load change is calculated based on the demand elasticity coefficient using the following formula: in, Updated to the current time-of-use electricity price Daily load change at any given time for Daily load data at any time This is the demand elasticity coefficient. for j Current time-of-use electricity price for the current period for j The difference between the current time-of-use electricity price and the optimal time-of-use electricity price for the specified period.

2. The method for coordinating and optimizing energy storage and electricity prices based on flexible resource synergy according to claim 1, characterized in that, Step S2 specifically includes the following steps: Step S2.1: Based on daily photovoltaic output Daily wind power output The formula for calculating daily renewable energy output is as follows: in, Contribute to Japan's new energy sources Contribute to Japan's photovoltaic industry. Powering Japanese wind power; Step S2.2: Based on the daily new energy output Daily load data Define the objective function of the particle swarm optimization algorithm as follows: Where S is the net power of the region. To achieve the ideal charging and discharging power for energy storage at every moment; The particle swarm optimization algorithm takes the minimum net power S of the region as its objective function. Step S2.3: Set energy storage configuration constraints, which include: power balance constraints, energy storage system operation constraints, and energy storage state of charge constraints; Step S2.4: Using the particle swarm optimization algorithm, calculate the operating power of the energy storage at each moment based on the objective function of the particle swarm optimization algorithm and the energy storage configuration constraints. .

3. The method for coordinating and optimizing energy storage and electricity prices based on flexible resource synergy according to claim 2, characterized in that, The power balance constraint is: in, The load at each moment of the previous iteration; The operating constraints of the energy storage system are: in, The charging power of the energy storage at every moment. The discharge power of the stored energy at each moment. For the energy storage's operating power at each moment, This represents the boundary value of the rated power of the energy storage. The energy storage state of charge constraint is: in, This represents the minimum SOC (State of Charge) for energy storage. This represents the maximum SOC (State of Charge) of the energy storage system. , These are the SOC values ​​of the stored energy at times 0 and 24, respectively.

4. The method for coordinating and optimizing energy storage and electricity prices based on flexible resource synergy according to claim 1, characterized in that, In step S3, the energy storage configuration capacity is calculated based on the energy storage's operating power at each moment. The calculation formula is as follows: in, Configure capacity for energy storage, The operating power of the energy storage at any given moment.

5. The method for coordinating and optimizing energy storage and electricity prices based on flexible resource synergy according to claim 1, characterized in that, The calculation formula for energy storage configuration and operating cost based on the energy storage's operating power at each moment is as follows: in, For energy storage configuration and operating costs, The daily investment cost for energy storage, The daily replacement cost corresponding to the loss of energy storage operating capacity. For the daily maintenance cost of energy storage, The total investment cost for energy storage, The initial construction cost of the energy storage system, For energy storage operation power, Configure capacity for energy storage, This is the isochronous value coefficient. For energy storage unit power, This is the unit capacity cost coefficient for energy storage. The unit power cost coefficient for inverters used in energy storage auxiliary equipment. This is the annual maintenance cost coefficient for energy storage. This is a cost ratio coefficient for energy storage batteries. The discount rate is... For the investment period of the energy storage system, Changes per unit of time.

6. The method for coordinating and optimizing energy storage and electricity prices based on flexible resource synergy according to claim 1, characterized in that, Step S4 specifically includes the following steps: Step S4.1: Based on energy storage configuration and operating costs Compared with the current time-of-use electricity price Define the objective function of the moth-flame optimization algorithm as follows: in, To maximize the revenue for regional operators For regional operators' electricity sales revenue, For incentive-based demand response costs, For energy storage operators, the cost of purchasing new energy electricity. For energy storage configuration and operating costs, To help users save on electricity costs by utilizing energy storage, The cost of electricity purchased from the upper-level power grid by regional operators. This is daily load data. This is the current time-of-use electricity price. The value represents a change over a unit of time, and is taken as 1 hour. The moth-flame optimization algorithm uses the maximum revenue of the regional operator as the objective function. Step S4.2: Set electricity price optimization constraints, which include: electricity consumption constraints, time-of-use electricity price constraints, and unit electricity cost constraints; Step S4.3: Calculate the optimal time-of-use electricity price using the moth flame optimization algorithm based on the objective function of the moth flame optimization algorithm and the electricity price optimization constraints.

7. The method for coordinating and optimizing energy storage and electricity prices based on flexible resource synergy according to claim 6, characterized in that, The power consumption constraint is: in, For changes in daily electricity consumption in the region, For the original user load; The time-of-use electricity pricing constraint is as follows: in, , These are the boundary values ​​for time-of-use electricity pricing; These are the peak-hour electricity price, off-peak-hour electricity price, and normal-hour electricity price; , Peak-valley electricity price ratio coefficient; The unit electricity cost constraint is: in, , These represent the costs before and after electricity price optimization, respectively. This is the current time-of-use electricity price. The optimal time-of-use electricity price is calculated using the moth-flame optimization algorithm.

8. The method for coordinating and optimizing energy storage and electricity prices based on flexible resource synergy according to claim 1, characterized in that, The demand elasticity coefficient is calculated based on the optimal time-of-use electricity price, and the calculation formula is as follows: in, This is the demand elasticity coefficient. for j Current time-of-use electricity price for the current period for j The difference between the current time-of-use electricity price and the optimal time-of-use electricity price for the specified period. , They are respectively Daily load data and The change in daily load data at a given time compared to the daily load data of the previous iteration.

9. The method for coordinating and optimizing energy storage and electricity prices based on flexible resource synergy according to claim 1, characterized in that, The step S6, which calculates the energy storage configuration capacity based on the updated daily load data, includes: calculating the energy storage operating power at each moment based on the updated daily load data, and calculating the energy storage configuration capacity based on the energy storage operating power at each moment.

Images