An industrial plant-level and microgrid load optimization scheduling method based on electricity price incentives

Abstract

An industrial plant-level and microgrid load optimization scheduling method based on electricity price incentives includes: obtaining multiple power loads in the enterprise, and calculating the daily total load according to the power load; constructing an electricity cost calculation model under a preset load time scheduling constraint condition according to the daily total load, and calculating the load demand curve under the minimum electricity cost; the objective function of the electricity cost calculation model is: minC=W j ·C j ‑x·ΔP a ·C a Among them, C j is the electricity price corresponding to time period j; ΔP a is the power of the enterprise's participation in the peak shaving or valley filling response action of the power grid; x represents the situation of the power company issuing the response demand; the load demand curve obtained is input, the economic cost and fuel emission at each moment are calculated according to the pre-built microgrid load optimization scheduling model, and the genetic algorithm is used to calculate the optimal solution; the present invention can integrate the demand response electricity price subsidy and the waste heat power generation adopted by industrial users in the production process into the optimization scheduling model, so as to realize the participation of industrial users in the peak shaving and valley filling of the large power grid and further optimize the economic efficiency of their own electricity consumption.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial load optimization scheduling, and more specifically to an industrial plant-level and microgrid load optimization scheduling method based on electricity price incentives. Background Art

[0002] As the main force of electricity consumption, industrial loads, better optimization of dispatching methods are of great significance to the stable operation of the power system. With the sustained and rapid development of my country's national economy, the power consumption on the user demand side has gradually increased, and the contradiction between power supply and demand has become increasingly prominent. In order to further deepen the reform of electricity prices, use price levers to guide power users to actively participate in power demand response, tap the load regulation capacity on the demand side, and ensure the balance of power supply and demand and the safe and stable operation of the power grid. According to the requirements of the "Notice of the National Development and Reform Commission on Further Improving the Time-of-Use Electricity Price Mechanism" (Development and Reform Price

[2021] No. 1093), many regions have implemented demand response electricity price compensation policies to encourage users to participate in demand response. In addition, many large industrial enterprises recycle waste heat for power generation. These measures, on the one hand, further ensure the stable operation of the power grid, and on the other hand, are also conducive to protecting the environment. Incorporating these factors into modeling provides a new direction for future industrial load optimization and dispatching.

[0003] Industrial users account for a large proportion of the world's electricity consumption. For the problem of optimizing the dispatch of industrial users' loads, most of the existing methods are aimed at adjusting the adjustable loads in the industrial loads. Based on the time-of-use electricity price, the loads running during peak hours are reduced or transferred to the flat or valley period. On the one hand, the electricity expenditure of enterprises is optimized, and on the other hand, the large power grid is shaving the peak and filling the valley. However, these methods are too general when establishing models and often miss some important factors, such as not considering electricity price subsidies and some industrial users using waste heat for power generation. Therefore, when optimizing the dispatch of industrial loads, it is not possible to further respond to the demand for shaving the peak and filling the valley of the large power grid and reduce the electricity expenditure of users themselves.

[0004] Therefore, how to provide an industrial plant-level and microgrid load optimization scheduling method based on electricity price incentives, which can integrate demand response electricity price subsidies and industrial users' use of waste heat power generation in the production process into the optimization scheduling model, and realize industrial users' participation in peak shaving and valley filling of the large power grid and further optimize their own electricity consumption economy is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0005] In view of this, the present invention provides an industrial plant-level and microgrid load optimization scheduling method based on electricity price incentives, which can integrate demand response electricity price subsidies and industrial users' use of waste heat power generation in the production process into the optimization scheduling model, thereby enabling industrial users to participate in peak shaving and valley filling of the large power grid and further optimize their own electricity economy.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] An industrial plant-level and microgrid load optimization scheduling method based on electricity price incentives includes the following steps:

[0008] Obtain multiple power loads within the enterprise, and calculate the daily total load based on the power loads;

[0009] Constructing an electricity cost calculation model under preset load time scheduling constraints according to the daily total load, and calculating a load demand curve under the minimum electricity cost according to the above;

[0010] The objective function of the electricity fee calculation model is:

[0011] min C=W j ·C j -x·ΔP a ·C a

[0012] Among them, C j is the electricity price corresponding to time period j; ΔP a The power of the enterprise participating in the peak shaving or valley filling response action of the power grid; x is 0 or 1, indicating the situation of the power company issuing the response demand;

[0013] The obtained load demand curve is input, and the economic cost and fuel emission at each moment are calculated according to the pre-built microgrid load optimization scheduling model. The optimal solution is calculated using a genetic algorithm to obtain the scheduling plan at each moment when the cost and fuel emission are optimal.

[0014] Furthermore, the load time scheduling constraints include:

[0015] Load operation time: 0≤t i,j <T i,j ; i = 1...m;

[0016] The load operation time in each period does not exceed the total time of the corresponding period:

[0017] The total running time of the load in a scheduling cycle remains unchanged:

[0018]

[0019]

[0020] Among them, C j is the electricity price corresponding to time period j; ΔP a The power of enterprises participating in the peak shaving or valley filling response action of the power grid; C a To compensate the electricity price for the corresponding response.

[0021] Furthermore, the objective function of the microgrid load optimization scheduling model is:

[0022] Lowest economic cost:

[0023]

[0024] Among them, P b,j and P s,j is the price of electricity purchase and sale in period j; E b,j j and E s,j The amount of electricity purchased for the time period;

[0025]

[0026] F i,t is the fuel cost of power source i in period j; M i,t is the operation and maintenance cost of power source i in period j, and is the product of the operation and maintenance coefficient of the corresponding equipment and its output power in period j;

[0027] Lowest fuel emissions:

[0028]

[0029] Among them, λ j is the treatment coefficient of pollutant j; Q ij P is the size of j pollutants generated by i micro-source; i , is the output power of power supply i.

[0030] Furthermore, the microgrid load optimization scheduling model is constructed, and the steps include:

[0031] Constructing a micro power generation model, wherein the micro power generation model includes one or more of a photovoltaic power generation model, a wind turbine power generation model, and a gas turbine power generation model;

[0032] When the micro power generation model includes a photovoltaic power generation model, a wind turbine power generation model and a gas turbine power generation model, the power balance is calculated according to the micro power generation model to form the output constraint of the micro power generation model.

[0033] Furthermore, the photovoltaic power generation model is:

[0034] P pv =A pv ·I·η pv

[0035] Among them, P pv A is the actual output power of the photovoltaic cell; pv is the area of ​​the photovoltaic panel (m 2 ); I is the solar radiation intensity (kW / m 2), η pv is the power generation efficiency of photovoltaic cells.

[0036] Furthermore, the wind turbine power generation model is:

[0037]

[0038]

[0039] Wind turbines output different electrical powers under four wind speed conditions: V is the actual wind speed of the wind turbine, V cin is the cut-in wind speed, V cout is the cut-out wind speed, V rate It indicates the rated wind speed; P rate is the rated power of the motor.

[0040] Furthermore, the output power of the gas turbine under stable combustion efficiency is:

[0041]

[0042] Among them, P MT (t) is the electrical power output of the gas turbine per unit time; V f (t) is the gas turbine output power P MT (t) The corresponding natural gas consumption, in m 3 ; η MT is the power generation efficiency of the gas turbine; R LHVng (t) is the lower calorific value of natural gas, which is 9.7kW〃h / m 3 .

[0043] Furthermore, when the micro power source generates electricity using a gas turbine, waste heat power generation is performed; the waste heat emitted by the exhaust gas of the internal gas turbine is:

[0044]

[0045] Among them, Q MT (t) is the exhaust heat of the gas turbine per unit time; P MT (t) is the power generation of the gas turbine per unit time; η MT is the power generation efficiency of the gas turbine; η Loss is the heat dissipation loss rate.

[0046] The output power of waste heat power generation is:

[0047] P C (t) = Q C (t)·η C ·λ

[0048] η Cis the conversion efficiency of the waste heat power generation system; Q C (t) is the waste heat discharged within time t; λ is the tail gas recovery rate of the waste heat collection device.

[0049] Furthermore, the microgrid load optimization scheduling model is constructed, and the steps also include:

[0050] Obtaining a battery state of charge under battery handling constraints;

[0051] Calculate the large power grid interaction power under the output constraint of the micro power source according to the battery state of charge, the micro power source generation model and the load demand curve;

[0052] The objective function value is calculated according to the large power grid interaction power and the micro power source generation model.

[0053] Furthermore, the steps of calculating the optimal solution using the genetic algorithm include:

[0054] Initialize a population, wherein the population individuals include gas turbines and batteries;

[0055] Calculating the objective function value through the microgrid load optimization scheduling model;

[0056] Sorting the individuals of the population according to the objective function value, comparing and grading the individuals by fast non-dominated sorting; selecting individuals with high sorting levels as new generation parents to select, crossover and mutate the individuals of the population to form a new population;

[0057] After reaching the maximum number of iterations, the optimal solution is output, and the output power of the micro-power generation model at each moment when the economic cost and fuel emission are optimal is obtained.

[0058] Beneficial effects of the present invention:

[0059] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses an industrial plant-level and microgrid load optimization scheduling method based on electricity price incentives. For industrial loads, when establishing the model, on the one hand, the dispatchable space of the enterprise's own load is considered, and on the other hand, the demand response electricity price subsidy and the use of waste heat power generation by industrial users in the production process are integrated into the optimization scheduling model, which is of great help to industrial users in participating in the peak shaving and valley filling of the large power grid and further optimizing their own electricity economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0061] Figure 1 The accompanying drawing is a schematic diagram of an industrial plant-level and microgrid load optimization scheduling method based on electricity price incentives provided by the present invention;

[0062] Figure 2 The accompanying drawing is a schematic diagram of the structure of the microgrid system in the present invention. DETAILED DESCRIPTION

[0063] 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 only 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 are within the scope of protection of the present invention.

[0064] The embodiment of the present invention discloses an industrial plant-level and microgrid load optimization scheduling method based on electricity price incentives, which is characterized by comprising the following steps:

[0065] S1: First, plant-level load optimization is carried out, which is actually to solve the linear programming problem with the goal of minimizing electricity costs. The variable to be solved is the working time of the dispatchable load. The GA algorithm is used to solve it, and then the most economical load power consumption curve that meets the constraints is obtained.

[0066] The specific steps include:

[0067] S11: Acquire multiple power loads in the enterprise, and calculate the daily total load based on the power loads;

[0068] S12: constructing an electricity cost calculation model under load time scheduling constraints according to the daily total load, and calculating a load demand curve under the minimum electricity cost;

[0069] Load time scheduling constraints include:

[0070] Load operation time: 0≤t i,j <T i,j ; i = 1...m;

[0071] The load operation time in each period does not exceed the total time of the corresponding period:

[0072]

[0073] The total running time of the load in a scheduling cycle remains unchanged:

[0074]

[0075]

[0076] Among them, C j is the electricity price corresponding to time period j; ΔP a The power of enterprises participating in the peak shaving or valley filling response action of the power grid; C a To compensate the electricity price for the corresponding response.

[0077] S2: Input the obtained load demand curve, obtain the equipment output curve at each moment according to the pre-built microgrid load optimization scheduling model, and calculate the optimal solution.

[0078] The microgrid load optimization dispatch model includes:

[0079] Lowest economic cost:

[0080]

[0081]

[0082] Among them, P b,j and P s,j is the price of electricity purchase and sale in period j; E b,j j and E s,j The amount of electricity purchased during the period; calculate the minimum fuel emission: F i,t is the fuel cost of power source i in period j; M i,t is the operation and maintenance cost of power source i in period j, and is the product of the operation and maintenance coefficient of the corresponding equipment and its output power in period j;

[0083] Lowest fuel emissions:

[0084]

[0085] Among them, λ j is the treatment coefficient of pollutant j; Q ij P is the size of j pollutants generated by i micro-source; i , is the output power of power supply i.

[0086] The specific steps include:

[0087] S21: constructing a micro-power generation model, wherein the micro-power generation includes one or more of photovoltaic power generation, wind turbine power generation and gas turbine power generation;

[0088] The output of photovoltaic power generation model is: P pv =A pv ·I·η pv

[0089] Among them, P pv A is the actual output power of the photovoltaic cell; pv is the area of ​​the photovoltaic panel (m 2 ); I is the solar radiation intensity (kW / m2 ), η pv is the power generation efficiency of photovoltaic cells.

[0090] The output of the wind turbine power generation model is:

[0091] Wind turbines will output different electrical power under four wind speed conditions; V cin is the cut-in wind speed, V cout is the cut-out wind speed, V cout It indicates the rated wind speed; P rate is the rated power of the motor.

[0092] Gas turbine power generation model output: When the micro power source generates electricity for the gas turbine, the output power of the gas turbine at a stable combustion efficiency is:

[0093]

[0094] Among them, P MT (t) is the electrical power output of the gas turbine per unit time; V f (t) is the gas turbine output power P MT (t) The corresponding natural gas consumption, in m 3 ; η MT is the power generation efficiency of the gas turbine; R LHVng (t) is the lower calorific value of natural gas, which is 9.7kW〃h / m 3 .

[0095] S22: predict the output curve, electricity prices at different times, and waste heat power generation generated by industry based on the photovoltaic power generation model and the wind turbine power generation model;

[0096] According to the load demand curve, the load is required to absorb the photovoltaic wind turbine and its own waste heat power generation to obtain the remaining load demand. The remaining load demand is met by gas turbines, batteries and large power grids.

[0097] S23: The remaining load demand is met by gas turbines, batteries and the large power grid.

[0098] Initialize the population. Each of the population contains a gas turbine and an energy storage unit, namely a battery. The power is output at each moment. The remaining load demand in S22 is subtracted from the corresponding gas turbine, battery output and waste heat power generation of the gas turbine in the population to obtain the power for interaction with the large power grid. Positive power indicates power purchase, and negative power indicates power sale. Infeasible values ​​are discarded according to the interaction constraints of the large power grid.

[0099] Among them, the battery output is:

[0100]

[0101] SOC min ≤SOC≤SOC max

[0102] Where SOC(t+1) and SOC(t) are the state of charge at time t+1 and time t respectively; P BAT (t) is the charge and discharge power of the battery per unit time. When the lithium battery is discharging, P BAT (t) is positive, when the lithium battery is charging, P BAT (t) is negative; η dis is the discharge efficiency of lithium battery; η ch is the charging efficiency; Q R is the rated capacity of the lithium battery; SOC min and SOC max They are the lower and upper limits of the state of charge of a lithium-ion battery pack.

[0103] The electric power balance constraint is:

[0104] P L i (t) = P PV i (t)+P WT i (t)+P BAT i (t)+P MT i (t)+P C i (t)+P GRID i (t)

[0105] P L i (t) is the electric load power at time t under scenario i, P PV i (t), P WT i (t), P BAT i (t), P MT i (t), P C i (t), P GRID i (t) are the outputs of photovoltaic, wind, gas turbine, battery, waste heat power generation and power grid respectively.

[0106] The amount of waste heat electricity generated by the gas turbine is;

[0107]

[0108] Among them, QMT (t) is the exhaust heat of the gas turbine per unit time; P MT (t) is the power generation of the gas turbine per unit time; η MT is the power generation efficiency of the gas turbine; η Loss is the heat dissipation loss rate;

[0109] In the past, waste heat power generation systems, such as organic Rankine cycle waste heat power generation systems, were operated in a grid-connected but not grid-connected mode. The present invention interacts with the energy storage unit, and its output power is:

[0110] P C (t) = Q C (t)·η C ·λ

[0111] η C is the conversion efficiency of the waste heat power generation system; Q C (t) is the waste heat discharged within time t; λ is the tail gas recovery rate of the waste heat collection device.

[0112] Interaction constraints of large power grid:

[0113]

[0114] Where: are the maximum and minimum power that the tie line can pass respectively.

[0115] S24: Sort the individuals in the population according to the objective function value, and use fast non-dominated sorting to compare and grade the individuals. The smaller the objective function value, the lower the cost and the better the corresponding individual, and then it is ranked at a higher level. Individuals with high levels are selected as the parents of the new generation. In order to keep the number of parents of the new generation unchanged, individuals of the same level are selected according to the crowding distance, and individuals with larger crowding degree are given priority to avoid falling into the local optimum.

[0116] S25: A new population is obtained through selection, crossover and mutation, and the loss of excellent individuals is prevented through constraint correction and elite strategy.

[0117] S26: After repeating steps S24-S25 and reaching the maximum number of iterations, the result is output.

[0118] The result obtained is a Pareto solution set that includes economic efficiency, i.e., operating cost value, and environmental efficiency, i.e., environmental cost value. Finally, constraints are added according to the actual situation of the microgrid to select the optimal solution. For example, the constraints on the carbon emissions and pollutant gas emissions of the microgrid cannot be simply summed up to get the lowest value as the optimal solution. Then, the output power of the micro-power generation model at each moment is obtained when the economic cost and fuel emissions are optimal.

[0119] In one embodiment, in S2, the load time scheduling constraints include:

[0120] Load operation time: 0≤t i,j <T i,j ; i = 1...m;

[0121] The load operation time in each period does not exceed the total time of the corresponding period:

[0122] The total running time of the load in a scheduling cycle remains unchanged:

[0123]

[0124]

[0125] Among them, C j is the electricity price corresponding to time period j; ΔP a The power of enterprises participating in the peak shaving or valley filling response action of the power grid; C a To compensate the electricity price for the corresponding response.

[0126] In the present invention, S1 is to smooth the peak and fill the valley of the dispatchable load in the factory, transfer the dispatchable equipment running when the electricity price is high to the time when the electricity price is low, and reduce the electricity cost. The variable sought is the dispatchable load operation time period, and then the optimized one-day electricity curve, that is, the load demand curve, is obtained; after understanding the electricity demand, S2 is to solve the output power of the power generation unit at each time from the perspective of load power supply, with economy and environmental protection as the goal, such as gas turbines, batteries, and large power grids, so as to obtain the operation plan of the power generation unit when the economy and environmental protection are optimal, and the specific operation cost and emission cost when the economy and environmental protection are optimal.

[0127] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0128] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing industrial plant-level and microgrid load scheduling based on electricity price incentives, characterized in that: The following steps are involved: Obtain multiple power loads within the enterprise, and calculate the daily total load based on the power loads; Constructing an electricity cost calculation model under a preset load time scheduling constraint condition according to the daily total load, and calculating a load demand curve under a minimum electricity cost; The objective function of the electricity fee calculation model is: min C=W j ·C j -x·ΔP a ·C a Among them, C j is the electricity price corresponding to time period j; ΔP a The power of the enterprise participating in the peak shaving or valley filling response action of the power grid; x is 0 or 1, indicating the situation of the power company issuing the response demand; The load time scheduling constraints include: Load operation time: 0≤t i,j <T i,j ; i = 1...m; The load operation time in each period does not exceed the total time of the corresponding period: The total running time of the load in a scheduling cycle remains unchanged: Among them, C j is the electricity price corresponding to time period j; ΔP a The power of enterprises participating in the peak shaving or valley filling response action of the power grid; C a To compensate the electricity price for the corresponding response; The load demand curve is input into a pre-built microgrid load optimization scheduling model to calculate the economic cost and fuel emission at each moment, and a genetic algorithm is used to calculate the optimal solution to obtain a scheduling plan at each moment when the cost and fuel emission are optimal; The objective function of the microgrid load optimization scheduling model is: Lowest economic cost: Among them, P b,j and P s,j is the price of electricity purchase and sale in period j; E b,j j and E s,j The amount of electricity purchased for the time period; F i,t is the fuel cost of power source i in period j; M i,t is the operation and maintenance cost of power source i in period j, and is the product of the operation and maintenance coefficient of the corresponding equipment and its output power in period j; Lowest fuel emissions: Among them, λ j is the treatment coefficient of pollutant j; Q ij P is the size of j pollutants generated by i micro-source; i , is the output power of power supply i; Constructing the microgrid load optimization scheduling model, the steps include: Constructing a micro power generation model, wherein the micro power generation model includes one or more of a photovoltaic power generation model, a wind turbine power generation model, and a gas turbine power generation model; When the micro power generation model includes a photovoltaic power generation model, a wind turbine power generation model and a gas turbine power generation model, the power balance is calculated according to the micro power generation model to form the output constraint of the micro power generation model.

2. The method for optimizing industrial plant-level and microgrid load scheduling based on electricity price incentives according to claim 1 is characterized in that: The photovoltaic power generation model is: P pv =A pv ·I·th pv Among them, P pv A is the actual output power of the photovoltaic cell; pv is the area of ​​the photovoltaic panel (m 2 ); I is the solar radiation intensity (kW / m 2 ), η pv is the power generation efficiency of photovoltaic cells.

3. The method for optimizing industrial plant-level and microgrid load scheduling based on electricity price incentives according to claim 1 is characterized in that: The wind turbine power generation model is: Wind turbines output different electrical powers under four wind speed conditions: V is the actual wind speed of the wind turbine, V cin is the cut-in wind speed, V cout is the cut-out wind speed, V rate It indicates the rated wind speed; P rate is the rated power of the motor; P v is the wind speed at V cin and V rate The output power of the wind turbine between .

4. The method for optimizing industrial plant-level and microgrid load scheduling based on electricity price incentives according to claim 1 is characterized in that: The output power of the gas turbine under stable combustion efficiency is: Among them, P MT (t) is the electrical power output of the gas turbine per unit time; V f (t) is the gas turbine output power P MT (t) The corresponding natural gas consumption, in m 3 ; η MT is the power generation efficiency of the gas turbine; R LHVng (t) is the lower calorific value of natural gas, which is 9.7kW·h / m 3 .

5. The method for optimizing industrial plant-level and microgrid load scheduling based on electricity price incentives according to claim 4 is characterized in that: When the micro power source is a gas turbine, waste heat power generation is performed; the waste heat emitted by the exhaust gas of the internal gas turbine is: Among them, Q MT (t) is the exhaust heat of the gas turbine per unit time; P MT (t) is the power generation of the gas turbine per unit time; η MT is the power generation efficiency of the gas turbine; η Loss is the heat dissipation loss rate; The output power of waste heat power generation is: P C (t)=Q C (t)·h C ·l η C is the conversion efficiency of the waste heat power generation system; Q C (t) is the waste heat discharged within time t; λ is the tail gas recovery rate of the waste heat collection device.

6. The method for optimizing industrial plant-level and microgrid load scheduling based on electricity price incentives according to claim 5 is characterized in that: Constructing the microgrid load optimization scheduling model, the steps also include: Obtaining a battery state of charge under battery handling constraints; Calculate the large power grid interaction power under the output constraint of the micro power source according to the battery state of charge, the micro power source generation model and the load demand curve; The objective function value is calculated according to the large power grid interaction power and the micro power source generation model.

7. The method for optimizing industrial plant-level and microgrid load scheduling based on electricity price incentives according to claim 6 is characterized in that: The steps of calculating the optimal solution using genetic algorithm include: Initialize a population, wherein the population individuals include gas turbines and batteries; Calculating the objective function value through the microgrid load optimization scheduling model; Sorting the individuals of the population according to the objective function value, comparing and grading the individuals by fast non-dominated sorting; selecting individuals with high sorting levels as new generation parents to select, crossover and mutate the individuals of the population to form a new population; After reaching the maximum number of iterations, the optimal solution is output.

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