Energy storage and electricity price coordinated optimization method based on flexible resource collaboration

Abstract

The invention relates to an energy storage and electricity price coordinated optimization method based on flexible resource collaboration, which can be used for transferring the adjustment potential of flexible resources in a power distribution network by optimizing the electricity price and effectively coping with increasingly severe power balance pressure of the power distribution network. The method mainly comprises the following steps: obtaining photovoltaic and wind power load data and initial time-of-use electricity price of a certain voltage of a power distribution network, and solving energy storage operation power at each moment through a particle swarm algorithm; calculating energy storage configuration and operation cost; calculating the maximum income of a power distribution network operator, and optimizing the time-of-use electricity price through a moth flame algorithm by taking the maximum income as a target function; and outputting the energy storage optimal configuration capacity and the optimal electricity price. Compared with the prior art, the power utilization mode of the user is adjusted through the electricity price, peak clipping and valley filling are achieved, low-storage and high-generation are conducted through the new energy, on-site consumption of the new energy can be effectively promoted, the balance capacity of the power distribution network is improved, and theoretical support can be provided for safe and low-carbon operation of the power distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system planning, and specifically relates to a method for coordinated optimization of energy storage and electricity prices based on the collaboration of flexible resources. Background Art

[0002] With the continuous development of new power systems, random distributed renewable energy represented by photovoltaics has been massively connected to the distribution network, which has led to the evolution of the distribution network structure and brought severe challenges to the power balance of the distribution network. The traditional "source follows load" balancing mode cannot effectively cope with the increasingly severe power balance pressure. It is urgent to build a new flexible balancing method to effectively improve the system balancing ability.

[0003] There are many variables of distributed power sources and adjustable loads in the distribution network, which makes it difficult to globally optimize the overall balance and promote the local consumption of new energy, resulting in the phenomenon of wind and solar power abandonment. Therefore, using flexible resources in the distribution network to improve the balancing capacity of the distribution network has received widespread attention. Among them, the development of flexible resources such as energy storage and demand-side response has provided new ideas for improving the new energy consumption and balancing capacity of the distribution network. Through energy storage and demand-side response, the user's regulation potential can be fully tapped, which can promote the local consumption of new energy in the distribution network, thereby effectively improving the power balancing capacity of the system.

[0004] In summary, existing technologies make it difficult to mobilize flexible resources such as energy storage and demand-side response within the distribution network to promote local consumption of new energy and improve the system's power balancing capabilities. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a method for coordinating and optimizing energy storage and electricity prices based on the synergy of flexible resources.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] On the one hand, the present invention provides a method for optimizing energy storage and electricity price coordination based on flexible resource collaboration, comprising the following steps:

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

[0009] Step S2: Calculate the energy storage operating power at each moment through a particle swarm optimization algorithm according to the daily photovoltaic output, daily wind power output, and daily load data;

[0010] Step S3: Calculating the energy storage configuration capacity and the energy storage configuration and operating cost according to the energy storage operating power at each moment;

[0011] Step S4: Calculate the optimal time-of-use electricity price through the moth flame optimization algorithm according to the energy storage configuration and operating cost;

[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 the first preset threshold, if it is less than or equal to the first preset threshold, execute step S6, if it is greater than the first preset threshold, calculate the demand elasticity coefficient according to the optimal time-of-use electricity price and the current time-of-use electricity price, calculate the daily load change according to the demand elasticity coefficient, update the daily load data according to the daily load change, update the current time-of-use electricity price to the optimal time-of-use electricity price, and execute step S2;

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

[0014] Furthermore, the 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 new energy output using the following formula:

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

[0017] Among them, P new (t) is the daily new energy output, P PV (t) is the daily photovoltaic output, P WT (t) daily wind power output;

[0018] Step S2.2: According to the daily new energy output P new (t) and the daily load data P1(t) define the particle swarm optimization algorithm objective function, which is:

[0019]

[0020] Where S is the regional net power, P bat (t) is the ideal charging and discharging power of energy storage at each moment;

[0021] The particle swarm optimization algorithm takes the minimum regional net power S as the objective function;

[0022] Step S2.3: Setting energy storage configuration constraints, wherein the energy storage configuration constraints include: power balance constraints, energy storage system operation constraints, and energy storage charge state constraints;

[0023] Step S2.4: Use the particle swarm optimization algorithm to calculate the energy storage operating power P at each moment according to the particle swarm optimization algorithm objective function and energy storage configuration constraints. b (t).

[0024] Furthermore, the power balance constraint is:

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

[0026] Among them, P0(t) is the load at each moment of the last iteration;

[0027] The energy storage system operation constraints are:

[0028]

[0029] -P bess ≤P b (t)≤P bess

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

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

[0032]

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

[0034] Furthermore, in step S3, the energy storage configuration capacity is calculated according to the operating power of the energy storage at each moment, and the calculation formula is:

[0035]

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

[0037] Furthermore, the energy storage configuration and operating cost are calculated according to the operating power of the energy storage at each moment, and the calculation formula is:

[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 is the energy storage configuration and operation cost, C in is the daily investment cost of energy storage, C rep is the daily replacement cost corresponding to the energy storage capacity loss, C om is the daily maintenance cost of energy storage, C bess is the total investment cost of energy storage, C con is the initial construction cost of the energy storage system, P b is the energy storage operating power, E bess K is the energy storage configuration capacity, D is the equal daily value coefficient, K p is the unit power of energy storage, K E is the unit capacity cost coefficient of energy storage, K inv is the unit power cost coefficient of the energy storage auxiliary equipment inverter, K om is the annual maintenance cost coefficient of energy storage, α is the cost ratio coefficient of energy storage battery, r is the discount rate, y bess The investment period for the energy storage system.

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

[0046] Step S4.1: Based on energy storage configuration and operating cost C ope and 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] Among them, maxI is the maximum revenue of the regional operator, I sell is the electricity sales revenue of the regional operator, C DR is the incentive demand response cost, C new is the cost of purchasing electricity from new energy sources for energy storage operators, C ope is the energy storage configuration and operation cost, C PV Help users save electricity costs by using energy storage, C up.grid is the cost of electricity purchased from the upper grid by the regional operator, P1(t) is the daily load data, P s (t) is the current time-of-use electricity price, Δt is the change per unit time, and the value is 1 hour;

[0050] The moth flame optimization algorithm takes the maximum profit of regional operators as the objective function;

[0051] Step S4.2: Setting electricity price optimization constraint conditions, wherein the electricity price optimization constraint conditions include: electricity consumption constraint, time-of-use electricity price constraint and unit electricity cost constraint;

[0052] Step S4.3: Utilize the moth flame optimization algorithm to calculate the optimal time-of-use electricity price according to the moth flame optimization algorithm objective function and electricity price optimization constraints.

[0053] Furthermore, the power consumption constraint is:

[0054]

[0055] in, is the change of daily electricity consumption in the region;

[0056] The time-of-use electricity price constraint is:

[0057]

[0058] p min ≤p g <p f ≤pp ≤p max

[0059] Among them, p min 、p max is the boundary value of time-of-use electricity price; p f 、p g 、p p They are the peak-time electricity price, valley-time electricity price, and normal-time electricity price; λ1 and λ2 are the peak-valley electricity price ratio coefficients;

[0060] The unit electricity cost constraint is:

[0061]

[0062] in, are the cost before and after electricity price optimization, P s (t) is the current time-of-use electricity price, P s1 (t) is the optimal time-of-use electricity price calculated by the moth flame optimization algorithm.

[0063] Furthermore, the demand elasticity coefficient is calculated according to the optimal time-of-use electricity price, and the calculation formula is:

[0064]

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

[0066] Furthermore, the daily load change is calculated according to the demand elasticity coefficient, and the calculation formula is:

[0067]

[0068] Among them, ΔP 1i It is the daily load change at time i after the current time-of-use electricity price is updated.

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

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

[0071] (1) The present invention comprehensively considers the flexibility and rapid adjustment capability of the energy storage system and its ability to guide users to actively change their electricity consumption behavior by optimizing time-of-use electricity prices in demand-side response, and constructs a two-layer optimization model of energy storage optimization and demand-side response optimization to promote the local absorption capacity of new energy in the 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 economy of the distribution network and the economy of electricity consumption of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 is a flow chart of the present invention;

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

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

[0076] Figure 4 Optimize the electricity price map in the embodiment of the present invention;

[0077] Figure 5 This is a real-time interactive diagram of power after optimization in an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0079] Embodiment 1:

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

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

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

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

[0084] S1.2: The obtained distribution network daily photovoltaic output, daily wind power output, daily load data and time-of-use electricity sales price are used as initial data input.

[0085] Step S2: Calculate the energy storage operating power at each moment through a particle swarm optimization algorithm according to the daily photovoltaic 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 new energy output using the following formula:

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

[0089] Among them, P new (t) is the daily new energy output, P PV (t) is the daily photovoltaic output, P WT (t) daily wind power output;

[0090] Step S2.2: Optimize the regional energy storage capacity configuration with the minimum standard deviation of regional load and new energy output (regional net power) after energy storage participation as the optimization goal. Minimize net power as the intermediate goal, determine the energy storage charging and discharging power within 24 hours, and calculate the new energy consumption rate. At this time, the regional new energy consumption rate is the largest and the consumption effect is the best. According to the daily new energy output P new (t) and the daily load data P1(t) define the particle swarm optimization algorithm objective function, which is:

[0091]

[0092] Where S is the regional net power, P bat (t) is the ideal charging and discharging power of energy storage at each moment;

[0093] The particle swarm optimization algorithm takes the minimum regional net power S as the objective function;

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

[0095] (1) Power balance constraint: before and after optimization, the regional operator must maintain a balance between the real-time power it interacts with users and the real-time power it interacts with new energy operators. The power balance constraint is:

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

[0097] Among them, P0(t) is the load at each moment of the last iteration;

[0098] (2) Energy storage system operation constraints

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

[0100]

[0101] -P bess ≤P b (t)≤P bess

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

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

[0104]

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

[0106] Step S2.4: Use the particle swarm optimization algorithm to calculate the energy storage operating power P at each moment according to the particle swarm optimization algorithm objective function and energy storage configuration constraints. b (t).

[0107] Step S3: Calculating the energy storage configuration capacity and the energy storage configuration and operating cost according to the energy storage operating power at each moment;

[0108] The energy storage configuration and operating cost are calculated based on the energy storage operating power at each moment, and the calculation formula is:

[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 is the energy storage configuration and operation cost, C in is the daily investment cost of energy storage, C rep is the daily replacement cost corresponding to the energy storage capacity loss, C om is the daily maintenance cost of energy storage, C bess is the total investment cost of energy storage, C con is the initial construction cost of the energy storage system, P b is the energy storage operating power, E bess K is the energy storage configuration capacity, D is the equal daily value coefficient, K p is the unit power of energy storage, K E is the unit capacity cost coefficient of energy storage, K inv is the unit power cost coefficient of the energy storage auxiliary equipment inverter, K om is the annual maintenance cost coefficient of energy storage, α is the cost ratio coefficient of energy storage battery, r is the discount rate, y bess The investment period for the energy storage system.

[0116] Step S4: Calculate the optimal time-of-use electricity price through the moth flame optimization algorithm according to the energy storage configuration and operating cost;

[0117] Step S4 specifically includes the following steps:

[0118] Step S4.1: Based on the regional operator's time-of-use electricity price, comprehensively consider its electricity sales revenue, electricity purchase cost, energy storage daily construction cost, demand response cost and electricity purchase cost from the upper power grid, optimize the time-of-use electricity price, guide users to participate in demand-side response, maximize regional economy, and optimize the energy storage configuration and operation cost C. ope and 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] Among them, maxI is the maximum revenue of the regional operator, I sell is the electricity sales revenue of the regional operator, C DR is the incentive demand response cost, C new is the cost of purchasing electricity from new energy sources for energy storage operators, C ope is the energy storage configuration and operation cost, C PV Help users save electricity costs by using energy storage, C up.grid is the cost of electricity purchased from the upper grid by the regional operator, P1(t) is the daily load data, P s (t) is the current time-of-use electricity price, Δt is the change per unit time, and the value is 1 hour;

[0122] The moth flame optimization algorithm takes the maximum profit of regional operators as the objective function;

[0123] Step S4.2: Setting electricity price optimization constraint conditions, wherein the electricity price optimization constraint conditions include: electricity consumption constraint, time-of-use electricity price constraint and unit electricity cost constraint;

[0124] (1) Power consumption constraints

[0125] In order to ensure regional electricity demand, after implementing time-of-use electricity prices, the change in regional daily electricity consumption must be controlled within a certain range. The electricity consumption constraints are:

[0126]

[0127] in, is the change of daily electricity consumption in the region;

[0128] (2) Time-of-use electricity price constraints

[0129] The peak-valley electricity price ratio will directly affect the results of users' participation in demand-side response, so it is necessary to constrain the peak-valley electricity price ratio to prevent the inversion of peak and valley periods. The time-of-use electricity price constraint is:

[0130]

[0131] p min ≤p g <p f ≤p p ≤pmax

[0132] Among them, p min 、p max is the boundary value of time-of-use electricity price; p f 、p g 、p p They are the peak-time electricity price, valley-time electricity price, and normal-time electricity price; λ1 and λ2 are the peak-valley electricity price ratio coefficients;

[0133] (3) Unit electricity cost constraints

[0134] After the regional electricity price is optimized, the electricity cost of regional users should not be greater than the electricity cost of regional users before the electricity price optimization. The unit electricity cost constraint is:

[0135]

[0136] in, are the cost before and after electricity price optimization, P s (t) is the current time-of-use electricity price, P s1 (t) is the optimal time-of-use electricity price calculated by the moth flame optimization algorithm.

[0137] Step S4.3: Utilize the moth flame optimization algorithm to calculate the optimal time-of-use electricity price according to the moth flame optimization algorithm objective function and 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 the first preset threshold, if it is less than or equal to the first preset threshold, execute step S6, if it is greater than the first preset threshold, calculate the demand elasticity coefficient according to the optimal time-of-use electricity price and the current time-of-use electricity price, calculate the daily load change according to the demand elasticity coefficient, update the daily load data according to the daily load change, update the current time-of-use electricity price to the optimal time-of-use electricity price, and execute step S2;

[0139] Step S6: Calculate the demand elasticity coefficient according to the optimal time-of-use electricity price, and the calculation formula is:

[0140]

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

[0142] Calculate the daily load change according to the demand elasticity coefficient,

[0143] The calculation formula is:

[0144]

[0145] Among them, ΔP 1i It is the daily load change at time i after the current time-of-use electricity price is updated.

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

[0147] Embodiment 2:

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

[0149] This embodiment proposes a method for improving the balancing capability of a distribution network by coordinating flexible resources, and its flow chart is as follows: Figure 1 As shown, the method comprises the following steps:

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

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

[0152] Table 1 Initial time-of-use electricity price

[0153]

[0154] S1.2: The obtained distribution network daily photovoltaic output, daily wind power output, daily load data and time-of-use electricity sales price are used as initial data input.

[0155] In the embodiment, the regional initial power real-time interaction is as follows Figure 2 shown.

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

[0157] S2.1: Compare the initial parameters with the sum of wind power output and photovoltaic output and the load, output the rule of the smaller one, and calculate the real-time new energy consumption of the load in the region according to formula (1);

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

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

[0160] S2.2: Optimize the regional energy storage capacity configuration with the minimum standard deviation of regional load and new energy output (regional net power) after energy storage participation as the optimization goal. Minimize net power as the intermediate goal, determine the energy storage charging and discharging power within 24 hours, and calculate the new energy consumption rate. At this time, the regional new energy consumption rate is the largest and the consumption effect is the best. The calculation formula is as follows;

[0161]

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

[0163]

[0164] Where: S is the net power of the area, P bat (t) is the ideal charging and discharging power of energy storage at each moment; P new (t) is the daily new energy output, P PV (t), P WT (t) are the regional photovoltaic power output and regional wind power output respectively; R sel is the regional new energy consumption rate; P SC (t) is the output of renewable energy consumed by the regional load at each moment, and the energy storage charging time P bat (t)>0.

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

[0166] (1) Power and electricity balance constraints

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

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

[0169] Among them, P0(t) is the load at each moment of the last 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 energy storage must be equal within a demand response cycle, that is,

[0172]

[0173] -P bess ≤P b (t)≤P bess

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

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

[0176]

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

[0178] S2.4: Using the particle swarm optimization algorithm, according to the objective function and constraints of the energy storage configuration model, the energy storage operating power Pb at each moment is calculated. The existing technology and the method for improving the balancing capacity of the distribution network with flexible resource coordination according to the present invention are adopted. The energy storage operating power is as follows: Figure 3 shown.

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

[0180] Table 2 Energy storage system related parameters

[0181] parameter Numeric <![CDATA[Cost of energy storage battery (K E )]]> 1085 yuan / (kW.h) <![CDATA[Unit power cost of energy storage (K p )]]> 1085 yuan / (kW) <![CDATA[Cost of the 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 NTD <![CDATA[Charge and discharge efficiency (η c , η dc )]]> 0.95，0.95 Depth of discharge (D) 0.7 Discount rate (r) 0.04 <![CDATA[Investment period of energy storage system (y bess )]]> 15 years

[0182] Daily operation 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 is the energy storage configuration and operation cost, C in is the daily investment cost of energy storage, C rep is the daily replacement cost corresponding to the energy storage capacity loss, C om is the daily maintenance cost of energy storage, C bess is the total investment cost of energy storage, C con is the initial construction cost of the energy storage system, P b is the energy storage operating power, E bess K is the energy storage configuration capacity, D is the equal daily value coefficient, K p is the unit power of energy storage, K E is the unit capacity cost coefficient of energy storage, K inv is the unit power cost coefficient of the energy storage auxiliary equipment inverter, K om is the annual maintenance cost coefficient of energy storage, α is the cost ratio coefficient of energy storage battery, r is the discount rate, y bess The investment period for the energy storage system.

[0190] S4: Calculate the maximum profit of the distribution network operator based on the result 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 regional operator's time-of-use electricity price, comprehensively consider its electricity sales revenue, electricity purchase cost, energy storage daily construction cost, demand response cost and electricity purchase cost from the superior power grid, and optimize the time-of-use electricity price to guide users to participate in demand-side response and maximize the regional economy. The calculation formula is as follows;

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

[0193]

[0194] Among them, maxI is the maximum revenue of the regional operator, I sell is the electricity sales revenue of the regional operator, C DR is the incentive demand response cost, C new is the cost of purchasing electricity from new energy sources for energy storage operators, C ope is the energy storage configuration and operation cost, C PV Help users save electricity costs by using energy storage, C up.grid is the cost of electricity purchased from the upper grid by the regional operator, P1(t) is the daily load data, P s (t) is the current time-of-use electricity price, Δt is the change per unit time, and the value is 1 hour;

[0195] Demand-side response costs:

[0196] The demand-side response strategy can guide users to actively change their electricity consumption behavior through incentives, so that the regional load curve is closer to the new energy output curve in terms of timing, which is conducive to the local consumption of regional new energy, reducing the configuration of regional energy storage capacity, and improving the overall benefits of regional operators. Compared with the traditional single demand-side response method, this paper adopts PDR and IDR coordinated regulation to promote the local consumption of new energy. And the real-time power of the region after participating in the demand-side response is used as the basis for passing it to the lower-level energy storage optimization model.

[0197] Among them, the PDR response is closely related to the electricity price. According to the principle of economics, the demand for a certain commodity in life is closely related to the price of the commodity. The same is true for electricity as a commodity, so the change in electricity consumption caused by the change in electricity price is defined as the demand elasticity coefficient e.

[0198]

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

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

[0201]

[0202] Among them, ΔP 1i It is the daily load change at time i after the current time-of-use electricity price is updated.

[0203] The demand response costs are as follows:

[0204]

[0205] Where: C DR is the demand response cost; C IDR is the cost of incentive demand response; C PDR is the price-based demand response cost; K IDR is the unit incentive demand response cost, unit is kW / yuan; P0(t) is the load at each moment before electricity price optimization; p0(t) is the electricity price before optimization.

[0206] S4.2: In order to make the optimization result of time-of-use electricity price more scientific, the optimization constraints of electricity price are set, including: power consumption constraint, time-of-use electricity price constraint, and unit electricity cost constraint. The calculation formula is as follows;

[0207] (1) Power consumption constraints

[0208] In order to ensure regional electricity demand, after implementing time-of-use electricity prices, the fluctuation range of regional daily electricity consumption must be controlled within a certain range.

[0209]

[0210] Where: is the change of regional daily electricity consumption; P1(t) and P0(t) are the user load after the implementation of time-of-use electricity price and the original user load, respectively.

[0211] (2) Time-of-use electricity price constraints

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

[0213]

[0214] p min ≤p g <p f ≤p p ≤p max

[0215] Among them, p min 、p max is the boundary value of time-of-use electricity price; p f 、p g 、p p They are the peak-time electricity price, valley-time electricity price, and normal-time electricity price; λ1 and λ2 are the peak-valley electricity price ratio coefficients;

[0216] (3) Unit electricity cost constraints

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

[0218]

[0219] in, are the cost before and after electricity price optimization, P s (t) is the current time-of-use electricity price, P s1 (t) is the optimal time-of-use electricity price calculated by the moth flame optimization algorithm.

[0220] S4.3: Using the moth flame optimization algorithm, according to the objective function and constraints, the objective function is the maximum profit of the regional operator, and the optimal time-of-use electricity price result is calculated. The optimized electricity price and the optimized load curve are as follows: Figure 4 shown.

[0221] In order to reflect the advantages of the present invention, three strategies are set for illustration:

[0222] Strategy 1: A unified initial electricity price is adopted and only PDR is considered in the demand-side response optimization model.

[0223] Strategy 2: A unified initial electricity price is adopted, only PDR is considered in the demand-side response optimization model, and energy storage is discharged evenly during peak hours.

[0224] Strategy 3: Adopting zoning to optimize electricity prices, considering PDR and IDR in the demand-side response optimization model, and discharging energy storage evenly during peak hours, which is the method proposed in this invention.

[0225] The new energy consumption rate and energy storage capacity configuration of the three strategies are shown in Table 3.

[0226] Table 3 New energy consumption rate and energy storage capacity configuration of 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 operating costs and operator benefits of the three strategies are shown in Table 4.

[0229] Table 4 Operating costs and operator benefits of three strategies

[0230]

[0231] According to the comparison of the three methods recorded in Table 3 and Table 4, the proposed method for improving the balancing capacity of the distribution network with coordinated flexible resources has good advantages under the three strategies. Further analysis and comparison of the energy storage operation results of the three strategies show that compared with strategies 1 and 2, the energy storage rated power of strategy 3 is reduced, thereby reducing the energy storage operation loss, increasing the service life of the energy storage, saving the energy storage construction cost, and improving the economy of the distribution network. The energy storage discharge mode in strategy 3 is uniform discharge during peak hours. Its operation results show that compared with strategies 1 and 2, the energy storage capacity has increased, which promotes the full consumption of new energy. When using strategy 2, the energy storage capacity configuration is reduced compared with strategies 1 and 3, resulting in a decrease in the new energy consumption rate. In Strategy 3, the energy storage is charged during the valley period of 01:00-10:00, with a cumulative charging capacity of 7515kW·h, that is, the energy storage consumes 7515kW·h of new energy during the valley period, and supplies power to the load as a power source during the peak period of 11:00-23:00. In order to reduce the operating loss of the energy storage and avoid overcharging and over-discharging, the energy storage is evenly discharged during the peak period. At the same time, compared with the initial electricity price of Strategy 1, Strategy 3 optimizes the electricity price to reduce the electricity consumption in Zone 1. The reason is that in order to reduce the cost of energy storage construction and meet the user's unit electricity cost constraints, 1.74% of the load that can be reduced is increased, resulting in a reduction in electricity consumption.

[0232] S5: Combine the calculation results of S3 and S4 to output the optimal configuration capacity and optimal electricity price of energy storage. The embodiment adopts a method for improving the balancing capacity of a distribution network with coordinated flexibility resources involved in the present invention. The real-time interactive diagram of regional power after optimization is as follows: Figure 5 shown.

[0233] If the above functions are implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0234] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for optimizing energy storage and electricity price coordination based on flexible resource collaboration, characterized in that: The following steps are involved: Step S1: Obtain the daily photovoltaic output, daily wind power output, daily load data and current time-of-use electricity sales price of the distribution network; Step S2: Calculate the energy storage operating power at each moment through a particle swarm optimization algorithm according to the daily photovoltaic output, daily wind power output, and daily load data; Step S3: Calculating the energy storage configuration capacity and the energy storage configuration and operation cost according to the energy storage operation power at each moment; Step S4: Calculate the optimal time-of-use electricity price through the moth flame optimization algorithm according to the energy storage configuration and operating cost; 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 the first preset threshold, if it is less than or equal to the first preset threshold, execute step S6, if it is greater than the first preset threshold, calculate the demand elasticity coefficient according to the optimal time-of-use electricity price and the current time-of-use electricity price, calculate the daily load change according to the demand elasticity coefficient, update the daily load data according to the daily load change, update the current time-of-use electricity price to the optimal time-of-use electricity price, and execute step S2; Step S6: Calculate the demand elasticity coefficient according to the optimal time-of-use electricity price, calculate the daily load change according to the demand elasticity coefficient, update the daily load data according to the daily load change, calculate the energy storage configuration capacity according to the updated daily load data, and coordinate and optimize the energy storage and electricity price according to the energy storage configuration capacity and the optimal time-of-use electricity price.

2. The method for optimizing energy storage and electricity price coordination based on flexible resource collaboration according to claim 1 is characterized in that: The step S2 specifically includes the following steps: Step S2.1: Based on the daily photovoltaic output P PV (t), daily wind power output P WT (t) Calculate the daily new energy output using the following formula: P new (t)=P PV (t)+P WT (t) Among them, P new (t) is the daily new energy output, P PV (t) is the daily photovoltaic output, P WT (t) daily wind power output; Step S2.2: According to the daily new energy output P new (t) and the daily load data P1(t) define the particle swarm optimization algorithm objective function, which is: Where S is the regional net power, P bat (t) is the ideal charging and discharging power of energy storage at each moment; The particle swarm optimization algorithm takes the minimum regional net power S as the objective function; Step S2.3: Setting energy storage configuration constraints, wherein the energy storage configuration constraints include: power balance constraints, energy storage system operation constraints, and energy storage charge state constraints; Step S2.4: Use the particle swarm optimization algorithm to calculate the energy storage operating power P at each moment according to the particle swarm optimization algorithm objective function and energy storage configuration constraints. b (t).

3. The method for optimizing energy storage and electricity price coordination based on flexible resource collaboration according to claim 2 is characterized in that: The power balance constraint is: P PV (t)+P WT (t)=P0(t)+P bat (t) Among them, P0(t) is the load at each moment of the last iteration; The energy storage system operation constraints are: -P bess ≤P b (t)≤P bess Among them, P c (t) is the charging power of the energy storage at each moment, P dc (t) is the discharge power of energy storage at each moment, P b (t) is the operating power of the energy storage at each moment, P bess is the energy storage rated power boundary value; The energy storage state of charge constraint is: Among them, S min is the minimum energy storage SOC, S max is the maximum value of the energy storage SOC, S(0) and S(24) are the SOC values ​​of the energy storage at time 0 and 24 respectively.

4. The method for optimizing energy storage and electricity price coordination based on flexible resource collaboration according to claim 1 is characterized in that: In step S3, the energy storage configuration capacity is calculated according to the energy storage operating power at each moment, and the calculation formula is: Among them, E bess Configure the capacity for energy storage, P b (t) is the operating power of the energy storage at each moment.

5. The method for optimizing energy storage and electricity price coordination based on flexible resource collaboration according to claim 1 is characterized in that: The energy storage configuration and operating cost are calculated based on the energy storage operating power at each moment, and the calculation formula is: C ope =C in +C rep +C om C in =K D (C bess +C con ) C om =K D K om |] b | C bess =αK E E bess +K p P b +K inv P b Among them, C ope is the energy storage configuration and operation cost, C in is the daily investment cost of energy storage, C rep is the daily replacement cost corresponding to the energy storage capacity loss, C om is the daily maintenance cost of energy storage, C bess is the total investment cost of energy storage, C con is the initial construction cost of the energy storage system, P b is the energy storage operating power, E bess K is the energy storage configuration capacity, D is the equal daily value coefficient, K p is the unit power of energy storage, K E is the unit capacity cost coefficient of energy storage, K inv is the unit power cost coefficient of the energy storage auxiliary equipment inverter, K om is the annual maintenance cost coefficient of energy storage, α is the cost ratio coefficient of energy storage battery, r is the discount rate, y bess The investment period for the energy storage system.

6. The method for optimizing energy storage and electricity price coordination based on flexible resource collaboration according to claim 1 is characterized in that: The step S4 specifically comprises the following steps: Step S4.1: Based on energy storage configuration and operating cost C ope and 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: maxI=I sell -(C DR +C new +C ope +C PV +C up.grid ) Among them, maxI is the maximum revenue of the regional operator, I sell is the electricity sales revenue of the regional operator, C DR is the incentive demand response cost, C new is the cost of purchasing electricity from new energy sources for energy storage operators, C ope is the energy storage configuration and operation cost, C PV Help users save electricity costs by using energy storage, C up.grid is the cost of electricity purchased from the upper grid by the regional operator, P1(t) is the daily load data, P s (t) is the current time-of-use electricity price, Δt is the change per unit time, and the value is 1 hour; The moth flame optimization algorithm takes the maximum profit of regional operators as the objective function; Step S4.2: Setting electricity price optimization constraint conditions, the electricity price optimization constraint conditions include: electricity consumption constraint, time-of-use electricity price constraint and unit electricity cost constraint; Step S4.3: Utilize the moth flame optimization algorithm to calculate the optimal time-of-use electricity price according to the moth flame optimization algorithm objective function and electricity price optimization constraints.

7. The method for optimizing energy storage and electricity price coordination based on flexible resource collaboration according to claim 6 is characterized in that: The power consumption constraint is: in, is the change of daily electricity consumption in the region; The time-of-use electricity price constraint is: p min ≤p g <p f ≤p p ≤p max Among them, p min 、p max is the boundary value of time-of-use electricity price; p f 、p g 、p p They are the peak-time electricity price, valley-time electricity price, and normal-time electricity price; λ1 and λ2 are the peak-valley electricity price ratio coefficients; The unit electricity cost constraint is: in, are the cost before and after electricity price optimization, P s (t) is the current time-of-use electricity price, P s1 (t) is the optimal time-of-use electricity price calculated by the moth flame optimization algorithm.

8. The method for optimizing energy storage and electricity price coordination based on flexible resource collaboration according to claim 1, characterized in that: The demand elasticity coefficient is calculated according to the optimal time-of-use electricity price, and the calculation formula is: Among them, e i-j is the demand elasticity coefficient, P sj is the current time-of-use electricity price in period j, ΔP sj is the difference between the current time-of-use electricity price and the optimal time-of-use electricity price in period j, P i , ΔP i They are respectively the daily load data at time i and the change between the daily load data at time i and the daily load data of the previous iteration.

9. The method for optimizing energy storage and electricity price coordination based on flexible resource collaboration according to claim 1 is characterized in that: The daily load change is calculated according to the demand elasticity coefficient, and the calculation formula is: Among them, ΔP 1i It is the daily load change at time i after the current time-of-use electricity price is updated.

10. The method for optimizing energy storage and electricity price coordination based on flexible resource collaboration according to claim 1, characterized in that: The step S6 calculates the energy storage configuration capacity according to the updated daily load data, including: calculating the energy storage operating power at each moment according to the updated daily load data, and calculating the energy storage configuration capacity according to the energy storage operating power at each moment.

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