Wind-solar-thermal storage integrated comprehensive energy system and multi-objective optimization method

Abstract

The invention relates to the technical field of power systems, in particular to a wind-solar-thermal storage integrated comprehensive energy system and a multi-objective optimization method. The thermal power plant is optimized by connecting the thermal power plant with the carbon capture device and the steam extraction energy storage device, carbon dioxide generated during operation of the thermal power plant is captured through the carbon capture device and stored or utilized, and therefore the carbon dioxide entering the atmosphere is reduced, and the influences of the greenhouse effect and the climate change are relieved; the amount of carbon dioxide which can be captured by the carbon capture device is increased, excess heat generated in the energy production process is stored in a steam extraction mode through the steam extraction energy storage device, then the stored heat energy is supplied to heating or driving mechanical equipment in the peak period of energy demand, the utilization efficiency and sustainability of energy are improved, and the energy utilization rate is increased. The heat storage capacity of the system is improved while carbon emission is reduced, the energy storage cost is reduced, and low-carbon economical operation of the system is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems. Background Art

[0002] A thermal power plant, abbreviated as a coal-fired power plant, is a factory that uses combustibles (such as coal) as fuel to produce electric energy. Its basic production process is as follows: the fuel heats water to generate steam during combustion, converting the chemical energy of the fuel into heat energy. The steam pressure drives the steam turbine to rotate, converting heat energy into mechanical energy. Then, the steam turbine drives the generator to rotate, converting mechanical energy into electric energy. The prime mover is usually a steam engine or a gas turbine. In some smaller power plants, an internal combustion engine may also be used. They all generate electricity by utilizing the pressure drop during the process of high-temperature, high-pressure steam or gas passing through the turbine to become low-pressure air or condensed water. However, traditional coal-fired power plants have high carbon emissions and poor economy due to the need to burn a large amount of combustibles. Summary of the Invention

[0003] To solve the technical problems of high carbon emissions and poor economy of traditional coal-fired power plants, the present invention provides a multi-objective optimization system and method for an integrated wind-solar-thermal energy storage comprehensive energy system.

[0004] The technical solution adopted by the present invention to achieve the above object is as follows:

[0005] An integrated wind-solar-thermal energy storage comprehensive energy system, where the power grid is connected to a solar energy system, a wind energy system, a coal-fired power plant, and a power supply system. The coal-fired power plant is connected to a carbon capture device and a steam extraction energy storage device. The carbon capture device is connected to a rich and poor liquid storage tank. The coal-fired power plant is connected to the power grid. The steam extraction energy storage device is connected to a gas turbine and a heating system. The gas turbine is connected to a natural gas system.

[0006] The power grid is connected to a battery and an electric boiler, and the electric boiler is connected to the heating system.

[0007] The steam extraction energy storage device is connected to a heat storage device, and the heat storage device is connected to the heating system.

[0008] A multi-objective optimization method for multi-objective optimization of any of the above integrated wind-solar-thermal energy storage comprehensive energy systems, comprising the following steps:

[0009] Establish a mathematical model for the overall operating cost of the coal-fired power plant;

[0010] Establish mathematical models for the supply-side operating cost and the energy storage-side operating cost of the integrated wind-solar-thermal energy storage comprehensive energy multi-objective optimization system;

[0011] Establish a mathematical model for the overall operating cost of the integrated wind-solar-thermal energy storage comprehensive energy multi-objective optimization system based on the mathematical model of the overall operating cost of the coal-fired power plant and the mathematical models of the supply-side operating cost and the energy storage-side operating cost;

[0012] Establish the constraint conditions for each objective in the overall operating cost mathematical model;

[0013] According to the overall mathematical model and constraint conditions, use the improved particle swarm optimization algorithm to obtain the optimal solution.

[0014] The step of establishing the overall operating cost mathematical model of a thermal power plant includes the following steps:

[0015] Establish the operating cost mathematical model of the carbon capture device:

[0016] P oi,t = P ci,t + P ri,t + P bi,t

[0017] In the formula: P oi,t is the total output power of the i-th thermal power plant in the t time period, P ci,t is the net output of the i-th thermal power plant in the t time period; P ri,t is the operating power of the carbon capture device in the i-th thermal power plant in the t time period, P bi,t is the reference power of the carbon capture device in the i-th thermal power plant in the t time period;

[0018]

[0019] In the formula: j is the energy consumed per unit of CO2 captured, V alli,t is the total amount of carbon dioxide gas captured by the carbon capture device in the i-th thermal power plant in the t time period, is the amount of carbon dioxide to be analyzed transported from the rich and lean liquid storage tanks to the analysis device by the carbon capture equipment in the i-th thermal power plant in the t time period, β i is the flue gas diversion ratio of the i-th thermal power plant, η is the carbon capture efficiency, is the total amount of CO2 generated by the i-th thermal power plant in the t time period, γ1 and γ2 are the working coefficients of the analysis device and the compressor respectively, is the carbon emission coefficient of the thermal power plant, P oi,max is the upper limit of the output power of the thermal power plant;

[0020] Establish the operating cost mathematical model of the extraction steam energy storage device:

[0021]

[0022] In the formula: C ext is the total operating cost of the extraction steam energy storage device, V e,t is the amount of steam extracted from the steam turbine of the thermal power unit in the thermal power plant by the extraction steam energy storage device from time t to T, s ext and d ext are the extraction steam and storage cost coefficients of the extraction steam energy storage device respectively.

[0023] Establish an overall operating cost mathematical model for a thermal power plant based on the operating cost mathematical model of a carbon capture device and the operating cost mathematical model of a steam extraction energy storage device:

[0024]

[0025] Where: C(P oi,t ) is the overall operating cost of the thermal power plant, N G is the number of thermal power generation units in the red dot length, T is the number of time periods in a scheduling cycle, m oi,t is the start-stop status variable of the i-th thermal power plant at time t, f(P oi,t ) is the fuel cost function of the i-th thermal power plant, B oi is the start-stop cost of the i-th thermal power plant, and are the start-up cost and shutdown cost of the carbon capture device respectively.

[0026] The above steps establish the mathematical models of the supply-side operating cost and the energy storage-side operating cost:

[0027] F g = min[C(P oi,t ) + C gt + C etb + C PV + C WP + C w + C ma

[0028] Where: F g is the total operating cost of the integrated wind-solar-thermal-storage multi-objective optimization system, C gt is the operating cost of the gas turbine unit, C etb is the operating cost of the electric heating boiler unit, C PV is the operating cost of the photovoltaic unit, C WP is the operating cost of the wind turbine unit, C w is the penalty cost for wind and light abandonment, C ma is the operation and maintenance cost of the energy storage side;

[0029] The calculation formula for the operating cost of the gas turbine is as follows:

[0030]

[0031] Where: M is the number of gas turbine units, f gt is the unit natural gas cost, Q gtj,t is the consumption of natural gas in the t time period;

[0032] The calculation formula for the operating cost of the electric heating boiler unit is as follows: ​

[0033] C etb = f etb H etb,t Δt / η etb

[0034] Where: f etb is the unit operating cost of the electric - heat boiler unit, Δt is the operating duration of the electric - heat boiler unit, H etb , t is the heat production of the electric - heat boiler unit within the t - time period, η etb is the electro - thermal conversion efficiency of the electric - heat boiler unit;

[0035] The calculation formula for the penalty cost of wind and solar curtailment is as follows:

[0036]

[0037] Where: f w is the penalty coefficient for wind and solar curtailment, c′ is the time - of - use electricity price, and are the predicted power of wind and light for the day - ahead respectively, P Wp,t and P Pv,t are the grid - connected power of wind and light during the t - time period respectively;

[0038] The calculation formula for the operation and maintenance cost on the energy storage side is as follows:

[0039]

[0040] Where: k ess,p and k ess,h are the unit aging maintenance costs of the battery and the heat storage device respectively, P am,t and H am,t are the charge - discharge power and heat power of the battery and the heat storage device at the t - moment respectively, f r is the CO2 solvent cost coefficient in the liquid storage tank of the carbon capture device, μ r is the unit CO2 solvent loss coefficient.

[0041] The above - mentioned steps establish a mathematical model for the overall operating cost of the integrated wind - solar - thermal - energy - storage multi - objective optimization system:

[0042] F = F g + F ct

[0043]

[0044] Where: F is the total system cost, F ct is the carbon trading cost, f ct is the carbon trading cost coefficient, λ ct is the unit carbon emission quota coefficient of the unit.

[0045] The above steps establish the constraint conditions for each objective in the overall operating cost mathematical model:

[0046] Electro-thermal power balance constraint:

[0047] P etb,t +P am ,t = P Ci,t +P Wp,t +P Pv,t +P gt,t

[0048] P H,t = H gt ,t + H etb ,t + H am .t + H ext,t

[0049] In the formula: P etb,t and P am,t respectively represent the electrical loads input by the electro-thermal boiler unit and the battery during the t period, P gt,t represents the electrical load output by the steam turbine during the t period, P H,t is the heat load output by the integrated wind-solar-thermal-storage multi-objective optimization system during the t period; H gt,t , H etb,t , H am.t and H ext,t respectively represent the heat loads input by the steam turbine, electro-thermal boiler, heat storage device, and extraction steam energy storage device during the t period;

[0050] Ramp constraint of the thermal power unit in the thermal power plant:

[0051]

[0052] In the formula: R oi,up and R oi,down are respectively the upper and lower limits of the ramp rate of the thermal power unit i, u oi,t and u oi,t-1 are respectively the start-stop states of the i-th thermal power unit at time t and t-1;

[0053] Constraint of the carbon capture device:

[0054]

[0055] In the formula: V Pgi,t and V Fgi,t are respectively the solvent amounts in the lean liquid storage tank and the rich liquid storage tank of the i-th thermal power unit during the t period, V Cgi,t is the solvent amount required to consume the CO2 emitted by unit i during the t period, V RY,i is the total solvent amount in the storage tank of thermal power plant i.

[0056] The above steps to obtain the optimal solution using the improved particle swarm algorithm include the following steps:

[0057] Establish a mathematical model of the grey wolf algorithm:

[0058] A = 2aR1 - a

[0059] C = 2R2

[0060] Where: R1 and R2 are vectors randomly selected from [0, 1], a is the convergence factor, A and C are two random adaptive coefficient vectors. When |A| ≥ 1, grey wolves are scattered in various regions as much as possible to search for prey. When |A| < 1, grey wolves will concentrate on searching for prey in a certain or certain regions;

[0061] Input the positions, velocities and values of a, A, C of all particles, and determine the initial α wolf, β wolf, δ wolf based on the initial positions;

[0062] Calculate the fitness values of all particles through the fitness function:

[0063] Y(F) = x ij (F g + F ct )

[0064] v ij (t + 1) = wv ij (t) + c1r1(t)[X α (t + 1) - x ij (t)] + c2r2(t)[X β (t + 1) - x ij (t)] + c3r3(t)[X δ (t + 1) - x ij (t)]

[0065] x ij (t + 1) = x ij (t) + v ij (t + 1)

[0066] w = w max - i(w max - w min ) / i max

[0067] Where: Y(F) is the particle fitness function value, x ij is the position updated by the current iteration, w is the inertia weight, c1, c2 and c3 are learning factors, v ij (t) is the velocity of the particle at the current iteration, r1(t), r2(t) and r3(t) are random numbers between [0, 1] respectively, X α (t + 1), Xβ (t + 1) and X δ (t + 1) are the updated positions of α-wolf, β-wolf and δ-wolf respectively, w max and w min are the maximum and minimum values of the inertia weight respectively, i and i max represent the current iteration number and the maximum iteration number;

[0068] Select the three particles with the smallest fitness values and define them as the new α-wolf, β-wolf, and δ-wolf;

[0069] Take the α-wolf as the updated global optimal solution, and its fitness value is the global optimal value;

[0070] Judge whether the fitness value is the global optimal value. The judgment formula is as follows:

[0071]

[0072] In the formula: d k is the individual distance in the k-th iteration formula, is the positions of all particles at the k-th iteration, is the average position of all individuals at the k-th iteration;

[0073] When d k ≤ 10 -6 then judge that the fitness value is the global optimal value and output it, and the iteration ends;

[0074] Otherwise, update the inertia weight value, and update the positions and velocities of the particles. When |v ij | > |v max |, v ij = v max ; when |v ij | < |v min |, v ij = v min , with the direction unchanged. v max is the maximum value of the particle velocity, v min is the minimum value of the particle velocity; when |x ij | > |x max |, x ij = x max ; when |x ij | > |x min |, x ij = x min , x max is the maximum value of the particle position, x min is the minimum value of the particle position;

[0075] After the update, re-execute the step to calculate the fitness values of all particles through the fitness function.

[0076] An apparatus for an integrated energy optimization scheduling method considering source-load coordination, comprising a memory and a processor, where the memory is used to store a computer program, and the computer program is used to execute the described method when loaded by the processor.

[0077] The advantages of the present invention compared with the prior art are as follows:

[0078] By connecting a thermal power plant with a carbon capture device and a steam extraction energy storage device to optimize the thermal power plant, the carbon capture device captures the carbon dioxide generated during the operation of the thermal power plant and stores or utilizes it, thereby reducing the amount of carbon dioxide entering the atmosphere and mitigating the impact of the greenhouse effect and climate change. The carbon capture device is connected to a rich and lean liquid storage tank, which increases the amount of carbon dioxide that the carbon capture device can capture. The steam extraction energy storage device stores the excess heat generated during the energy production process in the form of extracted steam, and then uses this stored thermal energy to supply heating or drive mechanical equipment during the peak energy demand period, improving the energy utilization efficiency and sustainability, increasing the heat storage capacity of the system while reducing carbon emissions, reducing the energy storage cost, and realizing the low-carbon economic operation of the system. Description of the Drawings

[0079] Figure 1 It is the overall flowchart of a multi-objective optimization method for an integrated wind-solar-thermal-storage energy system of the present invention.

[0080] Figure 2 It is the flowchart for calculating the global optimal value of a multi-objective optimization method for an integrated wind-solar-thermal-storage energy system of the present invention.

[0081] Figure 3 It is the schematic diagram of an integrated wind-solar-thermal-storage energy system of the present invention.

[0082] Figure 4 It is the bar chart of the operating cost of Mode 1.

[0083] Figure 5 It is the bar chart of the operating cost of Mode 2.

[0084] Figure 6 It is the line chart of the carbon emissions varying with time. Detailed Embodiments

[0085] An integrated wind-solar-thermal-energy-storage comprehensive energy system provided by the present invention. The power grid is connected to a solar energy system, a wind energy system, a thermal power plant, and a power supply system. The thermal power plant is connected to a carbon capture device and a steam extraction energy storage device to obtain an optimized thermal power plant. The carbon capture device is connected to a rich and lean liquid storage tank. The thermal power plant is connected to the power grid. The steam extraction energy storage device is connected to a gas turbine and a heating system. The gas turbine is connected to a natural gas system; the power grid is connected to a battery and an electric heating boiler. The electric heating boiler is connected to the heating system; the steam extraction energy storage device is connected to a heat storage device. The heat storage device is connected to the heating system. The solar energy system, the wind energy system, and the optimized thermal power plant supply power to the power grid and transmit it to the power supply system for users to use. Excess electricity is stored in the battery and the electric energy is released during the peak electricity consumption period to save energy consumption. The optimized thermal power plant and the electric heating boiler supply heat to the heating system and provide it for users to use. The heat storage device stores excess heat and releases heat during the peak energy consumption period to save energy consumption.

[0086] A multi-objective optimization method for multi-objective optimization of an integrated wind-solar-thermal-energy-storage comprehensive energy system, comprising the following steps:

[0087] Establish a mathematical model for the overall operating cost of the thermal power plant;

[0088] Establish a mathematical model for the operating cost of the carbon capture device:

[0089] P oi,t =P ci,t +P ri,t +P bi,t

[0090] In the formula: P oi,t is the total output power of the i-th thermal power plant in the t time period, P ci,t is the net output of the i-th thermal power plant in the t time period; P ri,t is the operating power of the carbon capture device in the i-th thermal power plant in the t time period, P bi,t is the reference power of the carbon capture device in the i-th thermal power plant;

[0091]

[0092] In the formula: j is the energy consumed for each unit of CO2 captured, V alli,t is the total amount of carbon dioxide gas captured by the carbon capture device in the i-th thermal power plant in the t time period, is the amount of carbon dioxide to be analyzed transported from the rich and lean liquid storage tank to the analysis device by the carbon capture equipment in the i-th thermal power plant in the t time period, β i is the flue gas diversion ratio of the i-th thermal power plant, that is, the ratio of the flue gas emission volume to the absorbed flue gas volume, η is the carbon capture efficiency, is the total amount of CO2 generated by the i-th thermal power plant during the t time period, and γ1 and γ2 are the working coefficients of the analytical device and the compressor respectively. is the carbon emission coefficient of the thermal power plant, and P oi,max is the upper limit of the output power of the thermal power plant;

[0093] "Extraction steam energy storage" can extract steam from the steam turbine and store its energy through the rich and poor liquid storage tanks, that is, the high-temperature molten salt energy storage device. Therefore, the operating cost of the extraction steam energy storage device can be divided into extraction steam cost and storage cost. A mathematical model of the operating cost of the extraction steam energy storage device is established:

[0094]

[0095] In the formula: C ext is the total operating cost of the extraction steam energy storage device, V e,t is the amount of steam extracted from the steam turbine of the thermal power unit in the thermal power plant by the extraction steam energy storage device during the time period from t to T, s ext and d ext are the cost coefficients of steam extraction and storage of the extraction steam energy storage device respectively.

[0096] Based on the mathematical model of the operating cost of the carbon capture device and the mathematical model of the operating cost of the extraction steam energy storage device, a mathematical model of the overall operating cost of the thermal power plant is established:

[0097]

[0098] In the formula: C(P oi,t ) is the overall operating cost of the thermal power plant, N G is the number of thermal power units, T is the number of time periods in a scheduling cycle, m oi,t is the start-stop status variable of the i-th thermal power plant at time t, f(P oi,t ) is the fuel cost function of the i-th thermal power plant, B oi is the start-stop cost of the i-th thermal power plant, and are the start-up cost and shutdown cost of the carbon capture device respectively.

[0099] Establish a mathematical model of the supply-side operating cost and energy storage-side operating cost of the integrated wind-solar-thermal-energy storage multi-objective optimization system;

[0100] F g = min[C(P oi,t ) + C gt + C etb + C PV + C WP + C w + C ma

[0101] In the formula: F​g is the total operating cost of the integrated wind-solar-thermal energy storage multi-objective optimization system, C gt is the operating cost of the gas turbine unit, C etb is the operating cost of the electric heating boiler unit, C PV is the operating cost of the photovoltaic unit, C WP is the operating cost of the wind turbine unit, C w is the penalty cost for wind and light curtailment, C ma is the operation and maintenance cost on the energy storage side;

[0102] The operating cost of the gas turbine is mainly the cost of natural gas, and the calculation formula is as follows:

[0103]

[0104] In the formula: M is the number of gas turbine units, f gt is the unit natural gas cost, Q gtj,t is the consumption of natural gas within the t time period;

[0105] The operating cost of the electric heating boiler unit is mainly the electricity cost of the electric heating boiler, and the calculation formula is as follows:

[0106] C etb = f etb H etb,t Δt / η etb

[0107] In the formula: f etb is the unit operating cost of the electric heating boiler unit (approximately simplified to the unit electricity price), Δt is the operating duration of the electric heating boiler unit, H etb,t is the heat output of the electric heating boiler unit within the t time period, η etb is the electro-thermal conversion efficiency of the electric heating boiler unit;

[0108] To ensure the high proportion of consumption of new energy such as wind power and photovoltaic power, in addition to the necessary operating costs of the wind and photovoltaic units, a penalty cost for wind and light curtailment needs to be set, and the calculation formula is as follows:

[0109]

[0110] In the formula: f w is the penalty coefficient for wind and light curtailment, c′ is the time-of-use electricity price, and are the predicted power of wind and light respectively for the day-ahead, P Wp,t and P Pv,t are the grid-connected power of wind and light respectively during the t time period;

[0111] The calculation formula for the operation and maintenance cost on the energy storage side is as follows:

[0112]

[0113] In the formula: k ess,p and k ess,h are respectively the unit aging maintenance costs of the battery and the heat storage device, P am,t and H am,t are respectively the charge-discharge power and heat power of the battery and the heat storage device at time t, f r is the CO2 solvent cost coefficient in the liquid storage tank of the carbon capture device, μ r is the unit CO2 solvent loss coefficient.

[0114] Based on the overall operating cost mathematical model of the thermal power plant and the mathematical models of the supply-side operating cost and the energy storage-side operating cost, establish the overall operating cost mathematical model of the integrated wind-solar-thermal-energy storage multi-objective optimization system;

[0115] F = F g + F ct

[0116]

[0117] In the formula: F is the total system cost, F ct is the carbon trading cost, f ct is the carbon trading cost coefficient, λ ct is the unit carbon emission quota coefficient of the unit. The carbon trading cost is the cost (or benefit) formed by the carbon emission party purchasing or selling carbon emission quotas through the carbon trading market on the basis of the relevant department allocating carbon emission quotas to stipulate the carbon emissions of enterprises. At the same time, considering the carbon capture behavior, the carbon emissions of the system do not consider the captured CO2.

[0118] Establish the constraint conditions for each objective in the overall operating cost mathematical model;

[0119] Electrical and thermal power balance constraint:

[0120] P etb,t + P am,t = P Ci,t + P Wp,t + P Pv,t + P gt,t

[0121] P H,t = H gt,t + H etb,t + H am.t + H ext,t

[0122] In the formula: P etb,t and P am,t respectively represent the electrical loads input by the electric boiler unit and the battery during the t period, P gt,t represents the electrical load output by the steam turbine inside the power plant during the t period, P H,tThe thermal load output by the integrated wind-solar-thermal-storage multi-objective optimization system at time t; H gt,t and H etb,t and H am.t and H ext,t respectively represent the thermal loads input by the steam turbine, electric boiler, thermal energy storage device, and extraction steam energy storage device at time t;

[0123] Ramp constraint of the thermal power unit in the thermal power plant:

[0124]

[0125] In the formula: R oi,up and R oi,down are respectively the upper and lower limits of the ramp rate of thermal power unit i, u oi,t and u oi,t-1 are respectively the start-stop states of the i-th thermal power unit at time t and t - 1.

[0126] Carbon capture device constraint:

[0127]

[0128] In the formula: V Pgi,t and V Fgi,t are respectively the solvent amounts in the lean liquid storage tank and rich liquid storage tank of the i-th thermal power unit at time t. Due to the limited capacities of the lean and rich liquid storage tanks, the amount of CO2 stored in them is limited, and the lean and rich liquid storage tanks can be reflected by the solvent volume in the storage tank, V Cgi,t is the solvent amount required to eliminate the CO2 emissions of the i-th thermal power unit at time t, V RY,i is the total solvent amount in the storage tank of the i-th thermal power plant.

[0129] According to the overall mathematical model and constraint conditions, the optimal solution is obtained by using the improved particle swarm optimization algorithm.

[0130] Establish the mathematical model of the grey wolf algorithm:

[0131] A = 2aR1 - a

[0132] C = 2R2

[0133] In the formula: R1 and R2 are vectors randomly selected from [0, 1], a is the convergence factor, A and C are two random adaptive coefficient vectors. When |A| ≥ 1, the grey wolves are as scattered as possible in each area to search for prey. When |A| < 1, the grey wolves will concentrate on searching for prey in a certain or some areas;

[0134] Input the initial values of all particles, including position, velocity, and the values of a, A, and C, and determine the initial α-wolves, β-wolves, and δ-wolves based on the initial positions; The particle swarm optimization algorithm is an optimization algorithm inspired by the foraging behavior of bird flocks. The particle swarm optimization algorithm simulates the process of individuals in a bird flock searching for the optimal solution in the solution space, and searches for the global optimal solution by continuously updating the velocity and position. This algorithm has a good ability to jump out of local optima, but may have a problem of slow convergence speed in specific problems. Combining the grey wolf algorithm with the particle swarm optimization algorithm can comprehensively utilize the advantages of the two algorithms. Through the fast convergence and global search ability of the grey wolf algorithm, it can help the algorithm converge to the region of the optimal solution faster. While the particle swarm optimization algorithm can help the algorithm jump out of local optima and further search for the global optimal solution. This fusion algorithm may have better performance and effect in solving complex optimization problems, enabling it to avoid premature convergence and falling into local extreme values to a certain extent, and can be well applied to the parameter optimization problem, improving the solution accuracy and efficiency; The grey wolf optimization algorithm was proposed by scholars such as Mirjalili from Griffith University in Australia in 2014. The grey wolf group has a strict hierarchical domination system, and the group contains three levels of wolves: α-wolves, β-wolves, and δ-wolves. The algorithm includes steps such as the social hierarchical stratification of grey wolves, tracking, surrounding, and attacking prey;

[0135] Calculate the fitness values of all particles through the fitness function:

[0136] Y(F) = x ij (F g +F ct )

[0137] v ij (t + 1) = wv ij (t) + c1r1(t)[X α (t + 1) - x ij (t)] + c2r2(t)[X β (t + 1) - x ij (t)] + c3r3(t)[X δ (t + 1) - x ij (t)]

[0138] x ij (t + 1) = x ij (t) + v ij (t + 1)

[0139] w = w max -i(w max -w min ) / i max

[0140] Where: Y(F) is the particle fitness function value, xij is the position updated for the current iteration, w is the inertia weight, c1, c2, and c3 are learning factors, and v ij (t) is the velocity of the particle at the current iteration number, r1(t), r2(t), and r3(t) are random numbers between [0, 1] respectively, and X α (t + 1), X β (t + 1), and X δ (t + 1) are the positions of the α-wolf, β-wolf, and δ-wolf after update respectively, and w max and w min are the maximum and minimum values of the inertia weight respectively, and i and i max represent the current iteration number and the maximum iteration number respectively;

[0141] Select three particles with the smallest fitness values and define them as the new α-wolf, β-wolf, and δ-wolf;

[0142] Take the α-wolf as the updated global optimal solution, and its fitness value is the global optimal value;

[0143] Judge whether the fitness value is the global optimal value, and the judgment formula is as follows:

[0144]

[0145] In the formula: d k is the individual distance at the k-th iteration formula, is the positions of all particles at the k-th iteration, is the average position of all individuals at the k-th iteration;

[0146] When d k ≤10 -6 , then judge that the fitness value is the global optimal value, output it, and end the iteration;

[0147] Otherwise, update the inertia weight value, and update the positions and velocities of the particles. When |v ij | > |v max |, v ij = v max ; when |v ij | < |v min |, v ij = v min , with the direction unchanged, v max is the maximum value of the particle velocity, and v min is the minimum value of the particle velocity; when |x ij | > |x max |, x ij = x max ; when |x ij | > |x min |, x ij = xmin , x max is the maximum value of the particle position, x min is the minimum value of the particle position;

[0148] After the update is completed, re - execute the step to calculate the fitness values of all particles through the fitness function.

[0149] An apparatus for an integrated energy optimization scheduling method considering source - load coordination, including a memory and a processor. The memory is used to store a computer program, and the computer program is used to execute the described method when loaded by the processor; A computer - readable storage medium stores a computer program, and the computer program is suitable for executing the described method when loaded by the processor.

[0150] The present invention flexibly transforms a thermal power unit through a carbon capture device and a steam extraction energy storage device, and coordinates it with high - temperature molten salt energy storage, establishing an integrated energy system of "wind - solar - thermal - storage" including carbon capture technology and steam extraction energy storage technology. A lower - layer scheduling model is established with the costs of the supply side, energy storage side, and carbon trading cost as the objective function. While ensuring the economic and stable operation of the system, carbon emissions are reduced, realizing the low - carbon economic operation of the system; In addition, an improved particle swarm optimization algorithm is proposed, which fuses the traditional particle swarm optimization algorithm with the grey wolf algorithm, can avoid the problems of premature convergence and falling into local extreme values to a certain extent, and can be well applied to the parameter optimization problem.

[0151] The present invention takes a certain integrated energy system as an example for simulation. The output equipment parameters of each unit are shown in Table 1, the energy storage equipment parameters are shown in Table 2, and the interaction costs of electricity, heat, gas, and cold in the integrated energy system are shown in Table 3.

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[0154] Table 1 Output equipment parameters of each unit

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[0156] Table 2 Energy storage equipment parameters

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[0158] Table 3 Interaction costs of electricity, heat, gas, and cold

[0159] To better verify the effectiveness of the "wind - solar - thermal - storage" integrated multi - energy complementary integrated energy system model proposed by the present invention, two scenarios are set for comparative analysis:

[0160] Scenario 1: Adopt the traditional integrated energy system scheduling modeling method that does not consider carbon capture and extraction steam energy storage technology;

[0161] Scenario 2: Adopt the optimized scheduling modeling method of the "wind-solar-thermal-storage" integrated energy system based on carbon capture and extraction steam energy storage technology proposed by the present invention.

[0162] The bar charts of the operating costs under the two scenarios are shown in Appendix Figure 4 and Appendix Figure 5 As shown. It can be seen from the simulation diagram that the supply-side operating costs of the two scenarios reach the peak at 16:00 and the trough at 2:00 in the early morning. The supply-side operating cost of Scenario 2 is reduced by about 250,000 yuan compared with Scenario 1 at the peak, and increased by about 80,000 yuan at the trough. Therefore, it can be obtained that the scheduling strategy proposed by the present invention has a slight decrease in operating cost compared with the traditional integrated energy system scheduling method.

[0163] The simulation curves of carbon emissions under the two scenarios are shown in Appendix Figure 6 As shown. It can be clearly seen from the figure that the optimized scheduling modeling method of the "wind-solar-thermal-storage" integrated energy system based on carbon capture and extraction steam energy storage technology (Scenario 2) is significantly lower in carbon emissions than Scenario 1 that does not consider carbon capture and extraction steam energy storage technology, and the carbon emissions are reduced by 13.4% at the peak moment of 18:00 in the evening. Therefore, it can be obtained that the multi-objective scheduling optimization strategy of the integrated energy system proposed by the present invention can greatly improve the environmental pollution caused by carbon emissions.

[0164] According to Appendix Figures 4 - 6 As shown, compared with Scenario 1, Scenario 2 (i.e., the scheduling strategy proposed by the present invention) can minimize carbon emissions to the greatest extent while ensuring a slight decrease in operating cost, and can reduce energy consumption and achieve peak shaving and valley filling of system operation. Therefore, it can be concluded that the scheduling strategy considering operating cost, environmental cost and energy consumption cost proposed by the present invention can reduce carbon emissions while ensuring economy and achieve low-carbon operation of the system.

[0165] The present invention is described through embodiments. Those skilled in the art know that without departing from the spirit and scope of the present invention, various changes or equivalent replacements can be made to these features and embodiments. In addition, under the teaching of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application belong to the protection scope of the present invention.

Claims

1. An integrated wind-solar-thermal-storage comprehensive energy system, characterized in that, The power grid is connected to a solar power system, a wind power system, a thermal power plant, and a power supply system. The thermal power plant is connected to a carbon capture device and a steam extraction energy storage device. The carbon capture device is connected to a rich and lean liquid storage tank. The thermal power plant is connected to the power grid. The steam extraction energy storage device is connected to a gas turbine and a heating system. The gas turbine is connected to a natural gas system.

2. The integrated wind-solar-thermal-storage comprehensive energy system according to claim 1, characterized in that The power grid is connected to a battery and an electric heating boiler, and the electric heating boiler is connected to the heating system.

3. The integrated wind-solar-thermal-energy-storage comprehensive energy system according to claim 1, characterized in that, The steam extraction energy storage device is connected to a heat storage device, and the heat storage device is connected to the heating system.

4. A multi-objective optimization method for multi-objective optimization of any one of the integrated wind-solar-thermal-storage energy systems described in claims 1-3, characterized in that, It includes the following steps: Establish a mathematical model for the overall operating cost of the thermal power plant; Establish mathematical models for the supply-side operating cost and the energy storage-side operating cost of the integrated wind-solar-thermal-energy storage multi-objective optimization system; Establish a mathematical model for the overall operating cost of the integrated wind-solar-thermal-energy storage multi-objective optimization system based on the mathematical model of the overall operating cost of the thermal power plant and the mathematical models of the supply-side operating cost and the energy storage-side operating cost; Establish the constraint conditions for each objective in the overall operating cost mathematical model; According to the overall mathematical model and the constraint conditions, use the improved particle swarm optimization algorithm to obtain the optimal solution.

5. A multi-objective optimization method for integrated wind-solar-thermal energy storage integrated energy according to claim 4, characterized in that, The step of establishing the mathematical model for the overall operating cost of the thermal power plant includes the following steps: Establish a mathematical model for the operating cost of the carbon capture device: P oi,t = P ci,t + P ri,t + P bi,t Where: P oi,t is the total output power of the i-th thermal power plant during the t time period, and P ci,t is the net output of the i-th thermal power plant during the t time period; P ri,t is the operating power of the carbon capture device in the i-th thermal power plant during the t time period, and P bi,t is the reference power of the carbon capture device in the i-th thermal power plant during the t time period; where: j is the energy consumed per unit of CO2 captured, V alli,t is the total amount of carbon dioxide gas captured by the carbon capture device in the ith thermal power plant within the time period t, is the amount of carbon dioxide to be desorbed transported from the rich and lean liquid storage tanks to the desorption device by the carbon capture equipment in the ith thermal power plant within the time period t, β i is the flue gas diversion ratio of the ith thermal power plant, η is the carbon capture efficiency, is the total amount of CO2 generated by the ith thermal power plant within the time period t, γ1 and γ2 are the working coefficients of the desorption device and the compressor respectively, is the carbon emission coefficient of the thermal power plant, P oi,max is the upper limit of the output power of the thermal power plant; Establish a mathematical model for the operating cost of the steam extraction energy storage device: Where: C ext is the total operating cost of the extraction and energy storage device, V e,t is the amount of steam extracted from the steam turbine of the thermal power unit in the thermal power plant by the extraction and energy storage device during the time period from t to T, s ext and d ext are the cost coefficients of steam extraction and storage of the extraction and energy storage device, respectively. Establish a mathematical model for the overall operating cost of the thermal power plant based on the mathematical model of the operating cost of the carbon capture device and the mathematical model of the operating cost of the steam extraction energy storage device: Where: C(P oi,t ) is the overall operating cost of the thermal power plant, N G is the number of thermal power generation units in the thermal power plant, T is the number of time periods in a scheduling cycle, m oi,t is the start-stop status variable of the i-th thermal power plant at time t, f(P oi,t ) is the fuel cost function of the i-th thermal power plant, B oi is the start-stop cost of the i-th thermal power plant, and are the start-up cost and shutdown cost of the carbon capture device, respectively.

6. A multi-objective optimization method for integrated wind-solar-thermal-energy storage integrated energy, according to claim 4, characterized in that The step of establishing the mathematical models for the supply-side operating cost and the energy storage-side operating cost: F g = min[C(P oi,t ) + C gt + C etb + C PV + C WP + C w + C ma ​ Where: F g is the total operating cost of the integrated wind-solar-thermal energy storage multi-objective optimization system, C gt is the operating cost of the gas turbine unit, C etb is the operating cost of the electric heating boiler unit, C PV is the operating cost of the photovoltaic unit, C WP is the operating cost of the wind turbine unit, C w is the penalty cost for wind and light curtailment, C ma is the operation and maintenance cost on the energy storage side; The calculation formula for the operating cost of the gas turbine is as follows: Where: M is the number of gas turbine units, f gt is the unit natural gas cost, Q gtj,t is the consumption of natural gas within the time period t; The calculation formula for the operating cost of the electric heating boiler unit is as follows: C etb = f etb H etb,t Δt / η etb In the formula: f etb is the unit operation cost of the electric heating boiler unit, Δt is the operation duration of the electric heating boiler unit, H etb,t is the heat output of the electric heating boiler unit within the t time period, η etb is the electro-thermal conversion efficiency of the electric heating boiler unit; The calculation formula for the penalty cost of wind and solar curtailment is as follows: Where: f w is the curtailment penalty coefficient for wind and solar power, c′ is the time-of-use electricity price, and are the predicted power of wind and solar for the day-ahead respectively, P Wp,t and P Pv,t are the grid-connected power of wind and solar at time t respectively; The calculation formula for the energy storage-side operation and maintenance cost is as follows: Where: k ess,p and k ess,h are the unit aging maintenance costs of the battery and the heat storage device respectively, P am,t and H am,t are the charge and discharge power and the heat power of the battery and the heat storage device at time t respectively, f r is the CO2 solvent cost coefficient in the liquid storage tank of the carbon capture device, μ r is the unit CO2 solvent loss coefficient.

7. A multi-objective optimization method for integrated wind-solar-thermal energy storage integrated energy, according to claim 4, characterized in that The step of establishing the mathematical model for the overall operating cost of the integrated wind-solar-thermal-energy storage multi-objective optimization system: F = F g + F ct Where: F is the total system cost, F ct is the carbon trading cost, f ct is the carbon trading cost coefficient, λ ct is the carbon emission quota coefficient of the unit.

8. A multi-objective optimization method for integrated wind-solar-thermal energy storage integrated energy according to claim 4, characterized in that The step of establishing the constraint conditions for each objective in the overall operating cost mathematical model: Electric heating power balance constraint: P etb,t +P am,t =P Ci,t +P Wp,t +P Pv,t +P gt,t P H,t = H gt,t + H etb,t + H am.t + H ext,t Where: P etb,t and P am,t respectively represent the electrical loads input by the electric boiler unit and the battery during the t period, P gt,t represents the electrical load output by the steam turbine during the t period, P H,t is the heat load output by the integrated wind-solar-thermal-storage comprehensive energy multi-objective optimization system during the t period; H gt,t , H etb,t , H am.t and H ext,t respectively represent the heat loads input by the steam turbine, electric boiler, heat storage device and extraction steam energy storage device during the t period; Ramp constraint of the thermal power generation unit in the thermal power plant: Where: R oi,up and R oi,down are respectively the upper and lower limits of the ramp rate of thermal power unit i, u oi,t and u oi,t-1 are respectively the start-stop states of the i-th thermal power unit at time t and t-1; Carbon capture device constraint: Where: V Pgi,t and V Fgi,t are respectively the solvent amounts in the lean liquid storage tank and the rich liquid storage tank of the i-th thermal power unit during the t period, V Cgi,t is the solvent amount required to consume the CO2 emitted by the i-th thermal power unit during the t period, V RY,i is the total solvent amount in the storage tank of the i-th thermal power plant.

9. A multi-objective optimization method for integrated wind-solar-thermal energy storage integrated energy according to claim 4, characterized in that The step of using the improved particle swarm optimization algorithm to obtain the optimal solution includes the following steps: Establish a mathematical model of the grey wolf algorithm: A = 2aR1 - a C = 2R2 In the formula: R1 and R2 are vectors randomly selected from [0, 1], a is the convergence factor, A and C are two random adaptive coefficient vectors. When |A| ≥ 1, grey wolves are as scattered as possible in each area to search for prey. When |A| < 1, grey wolves will concentrate on searching for prey in a certain or some areas; Input the positions, velocities, and values of a, A, and C of all particles, and determine the initial α-wolf, β-wolf, and δ-wolf based on the initial positions; Calculate the fitness values of all particles through the fitness function: Y(F) = x ij (F g +F ct ) v ij (t + 1) = wv ij (t) + c1r1(t)[X α (t + 1) - x ij (t)] + c2r2(t)[X β (t + 1) - x ij (t)] + c3r3(t)[X δ (t + 1) - x ij (t)] x ij (t + 1)= x ij (t)+ v ij (t + 1) w = w max -i(w max -w min ) / i max Where: Y(F) is the particle fitness function value, x ij is the position updated in the current iteration, w is the inertia weight, c1, c2, and c3 are learning factors, v ij (t) is the velocity of the particle at the current iteration, r1(t), r2(t), and r3(t) are random numbers between [0, 1] respectively, X α (t + 1), X β (t + 1), and X δ (t + 1) are the positions updated for the α-wolf, β-wolf, and δ-wolf respectively, w max and w min are the maximum and minimum values of the inertia weight respectively, i and i max represent the current iteration number and the maximum iteration number; Select three particles with the smallest fitness values and define them as the new α-wolf, β-wolf, and δ-wolf; Take the α-wolf as the updated global optimal solution, and its fitness value is the global optimal value; Judge whether the fitness value is the global optimal value, and the judgment formula is as follows: Where: d k is the individual distance of the k-th iteration formula, is the position of all particles at the k-th iteration, is the average position of all individuals at the k-th iteration; When d k ≤ 10 -6 , it is determined that the fitness value is the global optimal value, output it, and the iteration ends; Otherwise, update the inertia weight value, and update the position and velocity of the particle. When |v ij | > |v max |, v ij = v max ; when |v ij | < |v min |, v ij = v min , with the direction unchanged. v max is the maximum value of the particle velocity, and v min is the minimum value of the particle velocity. When |x ij | > |x max |, x ij = x max ; when |x ij | > |x min |, x ij = x min , x max is the maximum value of the particle position, and x min is the minimum value of the particle position; After the update, re-execute the step of calculating the fitness values of all particles through the fitness function.

10. An apparatus for an integrated energy optimization scheduling method considering source-load coordination, characterized in that, It includes a memory and a processor. The memory is used to store a computer program, and the computer program is used to execute the method described in any one of claims 4-9 when loaded by the processor.

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