CN116362400A - Large-industry user electricity fee optimization method based on light storage system configuration - Google Patents

Abstract

The invention discloses a large-industry user electricity fee optimizing method based on optical storage system configuration. Firstly, for large industrial users with installation light storage optimization intention, acquiring power load data and site environment information; then, PVsyst7.2 software is used for designing a self-power-on-grid type photovoltaic system with surplus electricity, and output time sequence data of the photovoltaic module with unit capacity is generated; then, establishing a large industrial user optical storage optimization model according to the optimal monthly electricity charge of the user and the optimal average investment cost; and then, under the condition of considering the photovoltaic and energy storage installation limit values, carrying out optimization solution by using a Cplex solver, evaluating whether the system is suitable for additionally installing the optical storage system, calculating and determining the optimal photovoltaic and energy storage capacity expected to be additionally installed by a user, calculating the charge and discharge state and power of the energy storage system in the ith period of the day, and formulating a scheduling strategy. And the optimization of the demand of large industrial users and the cost control of electricity purchasing fees is completed.

Description

A Method for Optimizing Electricity Charges of Large Industrial Users Based on Optical-storage System Configuration

technical field

The invention belongs to the field of optical storage systems configured on the user side of large industries, and in particular relates to a method for optimizing electricity charges of large industrial users based on the configuration of optical storage systems.

Background technique

Photovoltaic power generation system is a power generation system that uses semiconductor materials to generate photovoltaic effect under sunlight irradiation and directly converts solar radiation energy into electrical energy. It is an inexhaustible clean energy source. There is a strong correlation between the output of the photovoltaic system and the peak power demand of the load, and the advantages of being able to consume it nearby, reducing investment in power lines and transmission losses, etc., have further promoted its rapid development. After years of technological development and innovation, the installed cost of photovoltaic modules has been greatly reduced. The application range and scale of photovoltaic systems have been further expanded, and the technology has become more mature.

Energy storage systems play a vital role in power consumption applications due to their flexible throughput performance. The power system is a stable and balanced system, and the energy storage power station is like a "reservoir" that coordinates and buffers various power sources and electricity demands. User-side battery energy storage mainly refers to an electrochemical energy storage system that can store, convert and release, and is mainly located near the user. With the vigorous promotion of distributed new energy, energy storage systems have also been more widely used. The application of the user-side distributed new energy storage system can improve the ability of distributed on-site consumption, stabilize the output of the distributed system, improve the quality of power, improve the reliability of power supply for users, and reduce the cost of electricity for users.

At present, scholars at home and abroad have done a lot of theoretical research on the configuration of large industrial user-side distributed optical storage systems. The focus of the research is on the control of the energy storage system, and the combination with the widely used new energy is not considered more, and the basic electricity fee collection method of large industries is mostly used in the research according to the maximum demand of the contract. After the policy was updated in 2018, few actual users have chosen it.

Aiming at the problems in this background, this paper will use the relevant characteristics of the optical storage system to propose an optical storage optimization model for large industrial users under the limit of the optical storage capacity, and use the solver to calculate and evaluate whether it is suitable to install the optical storage system, and select the optimal installed capacity and energy storage charging and discharging strategy control, and complete the optimization method for the demand of large industrial users and the cost control of electricity purchase fees.

Contents of the invention

Purpose of the invention: Aiming at the problems existing in the prior art, in order to promote the application of optical storage systems in large industrial users, the present invention proposes an optical storage optimization model for large industrial users under the condition of limited optical storage capacity, and uses a solver Calculate and evaluate whether it is suitable to install a solar storage system, select the optimal installed capacity and energy storage charging and discharging strategy control, and complete the optimization method for the demand of large industrial users and the cost control of electricity purchase fees. Based on the user's monthly electricity bill and amortized investment cost, determine the user's expected installed photovoltaic and energy storage capacity, and optimize the energy storage charging and discharging strategy.

Technical solution: In order to solve the above-mentioned technical problems, the present invention proposes a method of running a solver to calculate and evaluate whether it is suitable to install a solar storage system under the condition of the limit value of the solar storage capacity, and to select the optimal installed capacity and energy storage charging and discharging strategy control to complete An optimization method for controlling the demand of large industrial users and the cost of electricity purchase fees. Including the following steps:

(1) First, for large industrial users who intend to install optical storage optimization, obtain power load data and site environment information;

(2) Use the PVsyst7.2 software to import the solar radiation database Meteonorm8.0 data, carry out the design of the grid-connected photovoltaic system with surplus power for self-use, and generate time series data of photovoltaic module output per unit capacity;

(3) Based on the method of charging the basic electricity fee according to the actual demand, establish the sum of the monthly electricity fee, the basic electricity fee of the monthly demand, the monthly investment and maintenance cost converted from the optical storage system, and the reverse grid electricity sales revenue. The minimum value is the goal and meets the power balance constraints between the household system and the grid, the energy storage charge and discharge state constraints, the energy storage power constraints, the energy storage state of charge (SOC) constraints, the peak valley constraints, and the capacity limit of the optical storage system Constrained solar storage optimization model;

(4) Through the established photovoltaic storage optimization model for large industrial users, use the Cplex solver to optimize and solve, evaluate whether it is suitable for installing photovoltaic storage systems, and calculate and determine the optimal photovoltaic and energy storage capacity that users expect to install;

(5) Use the Cplex solver to calculate the charging and discharging state and power of the energy storage system in the i-th period of the day;

(6) Output dispatch instruction and end.

Further, the objective function of the optical storage system optimization model for large industrial users established in step (3) is:

The user's monthly electricity fee and amortized investment cost include electricity electricity fee, basic electricity fee charged according to actual demand, monthly investment and maintenance costs converted from optical storage system, and reverse grid electricity sales income. Considering that the actual maximum demand value is not much different from the value of the standard maximum power at an average interval of 15 minutes throughout the day, and it is convenient for energy storage charge and discharge control, the present invention uses 96 measurement time periods a day for analysis. Specifically, it can be expressed as:

minF＝C ₁ +C ₂ +C ₃ +C ₄

In the formula: F is the monthly electricity fee and the amortized investment cost; C ₁ is the electricity fee expenditure for electricity purchased by the grid in the current month; C ₂ is the basic electricity fee expenditure for the actual demand in the current month; C ₃ is the average monthly electricity cost in the whole life cycle Photovoltaic and energy storage investment and maintenance costs; C ₄ is the income from selling electricity back to the grid in the current month.

Among them, the calculation formula for the electricity fee expenditure of purchased electricity and the income of electricity sold back to the grid is as follows:

In the formula, T is the number of days in the current month; a is the time-of-use electricity price; s is the photovoltaic on-grid electricity price; P _grid (t) is the exchange power between the user and the grid during the t period, and when P _grid (t) > 0, the electricity purchased from the grid ; When P _grid (t)<0, sell electricity back to the grid.

Among them, the actual demand basic electricity expense,

C ₂ =bP _grid.max (t)

In the formula, b is the benchmark electricity price of the local unit demand electricity fee; P _grid.max (t) is the maximum value of the exchanged power between the user and the grid during the t period of the day.

Among them, photovoltaic and energy storage system investment and maintenance costs,

C ₃ =C _3-1 +C _3-2

In the formula, c is the unit price of the unit capacity of the energy storage system; E is the installed capacity of the energy storage system; u is the unit price of the unit power of the photovoltaic power generation system; W is the installed capacity of the photovoltaic system. N ₁ and N ₂ are the normal service life of energy storage and photovoltaic systems, respectively. x and y are the annual unit capacity operation and maintenance costs of energy storage and photovoltaic systems, respectively.

Further, the constraint conditions of the large-scale industrial user optical storage system optimization model established in step (3) are as follows:

(3.1): Power balance constraints between the household system and the grid

P _grid (t)+P _PV (t)+P _de (t)＝P _l (t)+P _ce (t)

In the formula, P _de (t) is the discharge power of the energy storage system during the t period, P _ce (t) is the charging power of the energy storage system during the t period, P _l (t) is the active power of the original large-scale industrial load during the t period, and P _PV (t) is the output power of photovoltaic power generation in t period.

(3.2): Energy storage charge and discharge state constraints

B _di (t)+B _ci (t)+B _si (t)＝1

In the formula: B _di (t), B _ci (t) and B _si (t) are variables that take 0 or 1, respectively representing the discharge, charge and static state of the energy storage at the tth moment of the i-th day, B _di ( t) being 1 means that the energy storage is in a discharging state, B _ci (t) being 1 means that the energy storage is in a charging state, and B _si (t) being 1 means that the energy storage is in a static state.

(3.3): energy storage power constraints

In the formula: P _d.max and P _c.max are the maximum discharge and charge power of the energy storage, respectively. P _di (t), P _ci (t) and P _si (t) represent the discharge, charge and rest power of the energy storage in the t-th period of the i-th day, respectively.

(3.4): energy storage state of charge (SOC) constraints

During the working process, the remaining power stored in the battery energy storage system is measured by the state of charge, and it will change within a certain range. Overcharge and overdischarge of energy storage will cause damage to itself and accelerate capacity decay, so it is necessary to limit the discharge depth of energy storage. Each state of charge change of energy storage is continued on the basis of the previous one. In order to make the energy storage charging and discharging strategy effective for a long time, the end of the two days before and after is set to be consistent with the initial value.

SOC _min ≤ SOC _i (t) ≤ SOC _max

SOC(1)=SOC(96)

In the formula: SOC _i (t) is the state of charge of the energy storage in the t-th period of the i-th day; SOC _min and SOC _max are the minimum and maximum state of charge of the energy storage, respectively. SOC _i (t+1) is the state of charge of the energy storage at time t+1 of day i, which is related to the value of SOC _i (t) at time t of day i and the charging and discharging capacity of the battery energy storage system in this period. Δt is the charging and discharging time, which is 15 minutes; η _c is the charging efficiency of energy storage; η _d is the discharging efficiency of energy storage.

(3.5) Peak and valley constraints:

In order to avoid the formation of a new peak load and increase the maximum demand value, the peak value of the initial industrial load is used as a constraint condition, as shown in the following formula:

P _grid (t)≤L _i.max

In the formula: L _i.max is the maximum value of the user's electricity load on the i-th day.

(3.6) Capacity limit constraints of optical storage system:

_0≤E≤Emax

_0≤W≤Wmax

In the formula: E _max and W _max are the maximum installed capacity limits of user energy storage and photovoltaic respectively.

Compared with the prior art, the present invention has the advantages of:

The invention can apply the optical storage system in the field of large industrial users' demand and electricity purchase cost control, and optimize the installed capacity of the optical storage system and the charge-storage scheduling strategy by optimizing the user's monthly electricity fee and amortized investment cost. In order to solve the problems that the maximum demand value is too large and the monthly electricity fee is too high during the electricity consumption process of large industrial users, the present invention proposes the optimal configuration of large industrial sites considering the limits of photovoltaic and energy storage installations The installed capacity of optical storage, and the method of controlling the charging and discharging strategy in different periods.

The invention installs the solar storage system, regulates the charge and discharge of the storage battery, utilizes the peak and valley electricity price, charges during the flat and valley periods, and discharges during the peak period, so that the load power after regulation is smooth, and the effect of peak shaving and valley filling is achieved, which is beneficial The regional power grid operates stably, and can carry out peak-valley arbitrage. The reduction of the maximum demand value also directly leads to the reduction of the monthly basic electricity bill, and the economic benefits brought about in the whole life cycle are also considerable. The high income brought by the user's optical storage system configuration is conducive to commercial promotion.

Description of drawings

Fig. 1 is a flowchart of the present invention;

Fig. 2 is a schematic diagram of power balance distribution of large industrial users in the embodiment;

Fig. 3 is a schematic diagram of the SOC of the storage battery state of charge of a large industrial user in the embodiment;

Fig. 4 is the comparative schematic diagram before and after the load adjustment of large industrial users in the embodiment;

Detailed ways

The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. The embodiments described in the present invention are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by persons of ordinary skill in the art without making creative efforts all fall within the protection scope of the present invention.

As shown in Figure 1, a large industrial user electricity bill optimization method based on optical storage system configuration, the specific steps are as follows:

S1: For large industrial users who intend to install optical storage optimization, obtain power load data and site environment information;

S2: Use the PVsyst7.2 software to import the solar radiation database Meteonorm8.0 data, carry out the design of the grid-connected photovoltaic system with surplus electricity for self-use, and generate time-series data of photovoltaic module output per unit capacity;

S3: Establish a solar-storage optimization model for large industrial users;

S4: Considering the installation limits of photovoltaics and energy storage, use the Cplex solver to optimize the solution, evaluate whether it is suitable to install a photovoltaic storage system, and calculate and determine the optimal photovoltaic and energy storage capacity that the user expects to install;

S5: Use the Cplex solver to calculate the charging and discharging state and power of the energy storage system in the i-th period of the day;

S6: output the scheduling instruction and end.

Preferably, the optimization model of the optical storage system for large industrial users is specifically:

1) Objective function

The user's monthly electricity fee and amortized investment cost include electricity electricity fee, basic electricity fee charged according to actual demand, monthly investment and maintenance costs converted from optical storage system, and reverse grid electricity sales income. Considering that the actual maximum demand value is not much different from the value of the standard maximum power at an average interval of 15 minutes throughout the day, and it is convenient for energy storage charge and discharge control, the present invention uses 96 measurement time periods a day for analysis. Specifically, it can be expressed as:

minF＝C ₁ +C ₂ +C ₃ +C ₄

In the formula: F is the monthly electricity fee and the amortized investment cost; C ₁ is the electricity fee expenditure for electricity purchased by the grid in the current month; C ₂ is the basic electricity fee expenditure for the actual demand in the current month; C ₃ is the average monthly electricity cost in the whole life cycle Photovoltaic and energy storage investment and maintenance costs; C ₄ is the income from selling electricity back to the grid in the current month.

(1) The calculation formula for the electricity fee expenditure of purchased electricity and the income of electricity sold back to the grid is as follows:

In the formula, T is the number of days in the current month; a is the time-of-use electricity price; s is the photovoltaic on-grid electricity price; P _grid (t) is the exchange power between the user and the grid during the t period, and when P _grid (t) > 0, the electricity purchased from the grid ; When P _grid (t)<0, sell electricity back to the grid.

(2) The actual demand basic electricity expense,

C ₂ =bP _grid.max (t)

In the formula, b is the benchmark electricity price of the local unit demand electricity fee; P _grid.max (t) is the maximum value of the exchanged power between the user and the grid during the t period of the day.

(3) Investment and maintenance costs of photovoltaic and energy storage systems,

C ₃ =C _3-1 +C _3-2

In the formula, c is the unit price of the unit capacity of the energy storage system; E is the installed capacity of the energy storage system; u is the unit price of the unit power of the photovoltaic power generation system; W is the installed capacity of the photovoltaic system. N ₁ and N ₂ are the normal service life of energy storage and photovoltaic systems, respectively. x and y are the annual unit capacity operation and maintenance costs of energy storage and photovoltaic systems, respectively.

2) Constraints

(1): Power balance constraints between the household system and the grid

P _grid (t)+P _PV (t)+P _de (t)＝P _l (t)+P _ce (t)

In the formula, P _de (t) is the discharge power of the energy storage system during the t period, P _ce (t) is the charging power of the energy storage system during the t period, P _l (t) is the active power of the original large-scale industrial load during the t period, and P _PV (t) is the output power of photovoltaic power generation in t period.

(2): Energy storage charge and discharge state constraints

B _di (t)+B _ci (t)+B _si (t)＝1

In the formula: B _di (t), B _ci (t) and B _si (t) are variables that take 0 or 1, respectively representing the discharge, charge and static state of the energy storage at the tth moment of the i-th day, B _di ( t) being 1 means that the energy storage is in a discharging state, B _ci (t) being 1 means that the energy storage is in a charging state, and B _si (t) being 1 means that the energy storage is in a static state.

(3): energy storage power constraints

In the formula: P _d.max and P _c.max are the maximum discharge and charge power of the energy storage, respectively. P _di (t), P _ci (t) and P _si (t) represent the discharge, charge and rest power of the energy storage in the t-th period of the i-th day, respectively.

(4): energy storage state of charge (SOC) constraints

During the working process, the remaining power stored in the battery energy storage system is measured by the state of charge, and it will change within a certain range. Overcharge and overdischarge of energy storage will cause damage to itself and accelerate capacity decay, so it is necessary to limit the discharge depth of energy storage. Each state of charge change of energy storage is continued on the basis of the previous one. In order to make the energy storage charging and discharging strategy effective for a long time, the end of the two days before and after is set to be consistent with the initial value.

SOC _min ≤ SOC _i (t) ≤ SOC _max

SOC(1)=SOC(96)

In the formula: SOC _i (t) is the state of charge of the energy storage in the t-th period of the i-th day; SOC _min and SOC _max are the minimum and maximum state of charge of the energy storage, respectively. SOC _i (t+1) is the state of charge of the energy storage at time t+1 of day i, which is related to the value of SOC _i (t) at time t of day i and the charging and discharging capacity of the battery energy storage system in this period. Δt is the charging and discharging time, which is 15 minutes; η _c is the charging efficiency of energy storage; η _d is the discharging efficiency of energy storage.

(5) Peak and valley constraints:

In order to avoid the formation of a new peak load and increase the maximum demand value, the peak value of the initial industrial load is used as a constraint condition, as shown in the following formula:

P _grid (t)≤L _i.max

In the formula: L _i.max is the maximum value of the user's electricity load on the i-th day.

(6) Capacity limit constraints of optical storage system:

_0≤E≤Emax

_0≤W≤Wmax

In the formula: E _max and W _max are the maximum installed capacity limits of user energy storage and photovoltaic respectively.

Specifically, the present invention selects a user of a food processing factory in Dali, Yunnan Province as an optimization calculation example. It is known that the site has the conditions for building photovoltaic power generation and energy storage, and the user chooses to charge the basic electricity fee according to the actual maximum demand. The invention uses PVsyst7.2 software, imports the data of solar radiation database Meteonorm8.0, carries out photovoltaic system design, and generates unit capacity photovoltaic module output time series data. Then use the Cplex solver to solve the optimization model. Evaluate whether it is suitable to install a photovoltaic storage system, calculate and determine the optimal photovoltaic and energy storage capacity that the user expects to install, and calculate the charging and discharging status and power of the energy storage system at the i-th period of the day.

The operating transformer capacity of this household is 2000kVA, and the daily load peak and valley difference is obvious. The actual maximum demand value of the month is 1515.6kW, and there are three occurrences of more than 1500kW. Because there is a strong regularity in the production and operation of large industrial users, in order to facilitate the load analysis of users, this paper will take the day when the maximum demand occurs in the current month as the typical daily load curve, as shown in Figure 4 before adjusting the load, with a total of 96 nodes. It can be seen that the peak load of electricity consumption is mainly concentrated in the normal working hours during the day, and the load decreases significantly at night and before going to work in the morning, which has a certain degree of similarity with the implementation time of time-of-use electricity prices.

In terms of operation mode, the user builds the solar storage system by himself, that is, the user bears the investment, construction and operation and maintenance costs of the solar storage system, and the monthly saving of electricity purchase fee and electricity sales fee is the income after installing the solar storage system. Analyze the corresponding investment and income situation. According to the pricing of the local government, for large industrial users, the time-of-use electricity price is adopted, divided into peak hours (09:00-12:00, 18:00-23:00), normal hours (07:00-09:00, 12:00 00-18:00), valley hours (23:00-07:00), see Table 1 for the time-of-use electricity price execution table.

Table 1 Time-of-use electricity price list

period of time Time-of-use electricity price On-grid electricity price Peak hours (09:00-12:00, 18:00-23:00) 0.62 0.3358 Regular hours (07:00-09:00, 12:00-18:00) 0.43 0.3358 Valley time (23:00-07:00) 0.25 0.3358

Assuming the power grid capacity of the area and the site environment of the household and other factors, the discussion limits the maximum allowable installation capacity of photovoltaics to 300kW, 600kW, and 1000kW respectively, the construction cost of photovoltaic power generation is 3500 yuan/kW, and the annual operation and maintenance cost per unit power is 0.07 yuan/kW, and the service life is 16 years.

The energy storage type is a lithium battery, which has the advantages of high energy density and long cycle life, and it is considered that the charge and discharge times of lithium batteries can generally reach tens of thousands of times. The impact of lithium battery charge and discharge times on battery loss is not considered. Suppose the maximum allowable installation capacity is 300kWh and 600kWh respectively, the installation and construction cost per unit capacity is 1000 yuan/kWh, the annual operation and maintenance cost per unit capacity is 100 yuan/kWh, the service life is 8 years, and the upper and lower limits of the state of charge are 0.9, 0.2, the initial state of charge is 0.5, the charge and discharge efficiency is 90%, the maximum charge and discharge power is limited to 0.5 times the maximum capacity, and the charge and discharge state can be adjusted every 15 minutes.

Table 2. Optimal Investment Income Table for Monthly Electricity Charges

The following conclusions can be drawn from Table 2:

After the optical storage system is configured according to different capacities, the maximum demand value is significantly reduced, which plays a role in peak shaving and valley filling. The average monthly expenses of users are also significantly reduced, and the total return on investment in the entire life cycle is high, which is suitable for configuring optical storage systems.

If conditions permit, the user should give priority to increasing the photovoltaic capacity allocation. First, because the solar radiation of the site is sufficient, in the industrial load of normal production, the daytime peak point is closer to the peak value of photovoltaic output, which can basically be absorbed by its own load. , so that under the time-of-use electricity price mechanism, the purchase of peak electricity from the grid can be reduced. The second is that the charging and discharging process of the storage battery regulates energy, while photovoltaics can directly create more value through their own production, and considering that the cost of storage batteries is higher than that of photovoltaics, under the condition of limited cost, priority can be given to increasing the installed capacity of photovoltaics. Then consider the redistribution of energy by the battery. Of course, when the photovoltaic installation conditions are limited, the adjustment function of the battery can be fully utilized, and the purpose of cost optimization can also be achieved. When the installation and operation and maintenance costs of the battery are further reduced in the future, the economic benefits brought will also become better. clear.

Because in actual production, the photovoltaic installation site is easily limited, the following will limit the photovoltaic and battery to 600kW and 600kWh respectively. In this case, the charging and discharging strategy analysis is carried out on the optimal capacity configuration of 600kW and 454kWh for photovoltaics and batteries.

The following conclusions can be drawn from the accompanying drawings 2-4, power balance distribution diagram, battery state of charge diagram and load comparison diagram:

In any time period, the power balance state will still be maintained after controlling the charging and discharging strategy. Under the optimized charging and discharging strategy of the photovoltaic storage system, energy storage is attracted by the peak-to-valley price difference, chooses to charge during the valley period and peaceful period, and discharge during the peak period. Smooth out the resulting electricity usage curve. 00:00-08:00 and 23:00-24:00 during the valley electricity price period at night, the photovoltaic power generation power is 0, and the electricity purchased by the grid is equal to the user load and battery charging consumption. 08:00-0900, this is the normal time period without charging and discharging, and the grid purchases electricity to meet the load. From 09:00 to 12:00, photovoltaics will gradually generate electricity. During the peak electricity price period, it is better to use photovoltaics and battery discharge to offset part of the load, and the peak power will be reduced during this period. From 12:00 to 18:00, photovoltaics continue to generate power. During this period of flat electricity prices, the battery is recharged by looking for a relatively low load during this period. From 18:00 to 23:00, the electricity price is again at the peak time at night, and the load demand is met through battery discharge and power purchase from the grid. There is no selling power during operation, because the on-grid electricity price is low, but the actual load consumes a lot of power, and the photovoltaic power is not enough to be absorbed by its own load, so there is no need to sell electricity online.

Claims (4)

1. The large-industry user electricity fee optimizing method based on the configuration of the optical storage system is characterized by comprising the following steps:

(1) Firstly, for large industrial users with the installation light storage optimization intention, acquiring power load data and site environment information;

(2) Designing a self-generating self-power-consumption residual electricity internet-surfing type photovoltaic system, and constructing output data of the photovoltaic system with unit capacity;

(3) Based on a basic electric charge collecting mode according to actual demand, establishing an optical storage optimization model which aims at the minimum value of the sum of monthly electric charge, monthly demand basic electric charge, investment and maintenance cost reduced to monthly by an optical storage system and reverse power grid selling income, and meets the power balance constraint, the energy storage charge-discharge state constraint, the energy storage power constraint, the energy storage state of charge (SOC) constraint, the peak-valley constraint and the capacity limit constraint of the optical storage system between a user system and a power grid;

(4) Through the established large industrial user optical storage optimization model, the Cplex solver is used for carrying out optimization solution, whether the optical storage system is suitable for being additionally arranged or not is evaluated, and the optimal photovoltaic and energy storage capacity expected to be additionally arranged by a user are calculated and determined;

(5) Calculating the charge and discharge states and the power of the energy storage system in the ith period of the day by using a Cplex solver;

(6) And outputting the scheduling instruction and ending.

2. The method for optimizing electricity charge of large industrial users based on configuration of optical storage system according to claim 1, wherein the constructing output data of photovoltaic system in the step (2) is as follows:

according to the environmental information of large industrial user sites, PVsyst7.2 software is used for importing the data of a solar irradiation database Meteonorm8.0, and the design of a self-power-on-grid type photovoltaic system with self-power-on surplus is carried out to generate output time sequence data of a photovoltaic module with unit capacity.

3. The method for optimizing electricity charge of large industrial users based on configuration of optical storage system according to claim 1, wherein the objective function of the optimization model of large industrial user optical storage system established in the step (3) is:

the monthly electricity charge of the user and the average investment cost charge comprise electricity charge, basic electricity charge charged according to actual demand, investment and maintenance cost charge converted to monthly by an optical storage system and reverse power grid selling income, and the invention analyzes 96 measurement time periods in a day by taking the fact that the actual maximum demand value is not greatly different from the value of the standard maximum power at 15 minute intervals on average throughout the day and is convenient for energy storage charge and discharge control, and can be specifically expressed as:

min F＝C ₁ +C ₂ +C ₃ +C ₄

wherein: f is month electricity charge and average investment cost charge, C ₁ C, paying for electricity consumption and electricity fee of the current month of electricity grid purchase ₂ C, paying for basic electricity charge of the actual demand in the same month ₃ To calculate the average monthly photovoltaic and energy storage investment and maintenance costs over the entire life cycle, C ₄ Selling electricity returns to the power grid for the current month;

the electricity charge expenditure of the electricity purchase quantity and the return power grid selling income are calculated according to the following formula:

wherein T is the number of days in the month, a is the time-of-use electricity price, s is the photovoltaic Internet electricity price, and P _grid (t) exchanging power between the user and the power grid for period t, when P _grid When (t) > 0, purchasing electricity to the power grid, and when P _grid When (t) is less than 0, returning to the power grid for selling electricity;

wherein, the actual demand quantity is basically paid for electricity,

C ₂ ＝bP _grid.max (t)

wherein b is the reference electricity price of the local unit electricity charge, P _grid.max (t) is the maximum value of the power exchanged between the user and the grid during time t of the day;

wherein, the investment and maintenance cost of the photovoltaic and energy storage system,

C ₃ ＝C _3-1 +C _3-2

wherein c is unit price of unit capacity of the energy storage system, E is installation capacity of the energy storage system, u is unit power unit price of the photovoltaic power generation system, W is installation capacity of the photovoltaic system, and N ₁ And N ₂ The normal service life of the energy storage system and the photovoltaic system is respectively prolonged, and x and y are annual unit capacity operation maintenance fees of the energy storage system and the photovoltaic system respectively.

4. The method for optimizing the electricity charge of the large industrial user based on the configuration of the optical storage system according to claim 1, wherein the constraint conditions of the large industrial user optical storage system optimization model established in the step (3) are specifically as follows:

(3.1): power balance constraint between household system and power grid

P _grid (t)+P _PV (t)+P _d.e (t)＝P _l (t)+P _c.e (t)

Wherein P is _d.e (t) is the discharge power of the energy storage system in the period t, P _c.e (t) is the charging power of the energy storage system in the period t, P _l (t) load active power of the original large industry in t period, P _PV (t) is the output power of the photovoltaic power generation in the period t;

(3.2): energy storage charge-discharge state constraint

B _d.i (t)+B _c.i (t)+B _s.i (t)＝1

Wherein: b (B) _d.i (t)、B _c.i (t) and B _s.i (t) is a variable of 0 or 1, and represents the discharge, charge and rest states of energy storage at the t-th moment on the i-th day respectively, B _d.i (t) 1 represents that the energy storage is in a discharge state, B _c.i (t) 1 represents that the stored energy is in a charged state, B _s.i (t) 1 represents that the stored energy is in a static state;

(3.3): energy storage power constraint

Wherein: p (P) _d.max And P _c.max And maximum discharge and charge power of stored energy, P _d.i (t)、P _c.i (t) and P _s.i (t) represents the discharge, charge and stationary power, respectively, of the stored energy on the ith day, t-th period;

(3.4): energy storage state of charge (SOC) constraints

In the working process, the residual electric quantity stored by the storage battery energy storage system is measured by the state of charge, the residual electric quantity is changed within a certain range, the overcharge and overdischarge of the stored energy can damage the storage battery, the capacity attenuation is accelerated, therefore, the discharge depth of the stored energy needs to be limited, the change of the state of charge of each stored energy is carried out continuously on the basis of the previous time, in order to enable the charging and discharging strategies of the stored energy to be carried out effectively for a long time, the end of two days before and after setting is consistent with the initial value, and the calculation formula is expressed as follows:

SOC _min ≤SOC _i (t)≤SOC _max

SOC(1)＝SOC(96)

wherein: SOC (State of Charge) _i (t) is the state of charge of the energy storage at the t-th period of the ith day, SOC _min And SOC (System on chip) _max Respectively the minimum charge state and the maximum charge state of energy storage, and SOC _i (t+1) is the energy storage charge state at the ith time t+1, which is the same as the SOC at the ith time t _i The value (t) is related to the charge and discharge electric quantity of the storage battery energy storage system in the period, delta t is 15 minutes, eta is taken as the charge and discharge time _c Charge efficiency, η, of energy storage _d Discharge efficiency for energy storage;

(3.5): peak-valley constraint

To avoid the formation of new peak loads, the maximum demand value is increased, and the peak value of the initial industrial load is taken as a constraint condition, as shown in the following formula:

P _grid (t)≤L _i.max

wherein: l (L) _i.max The maximum value of the power load of the user on the i th day is respectively;

(3.6): optical storage system capacity limit constraints

0≤E≤E _max

0≤W≤W _max

Wherein: e (E) _max 、W _max And respectively storing energy for a user and limiting the maximum installed capacity of the photovoltaic.

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