Optimal configuration method and system of hybrid energy storage system for green ports

Abstract

The present invention provides a hybrid energy storage system optimization configuration method and system for green ports, including: step S1: obtaining power through an offshore wind power generation system and a power grid transmission system; the power is converted into heat and cold energy through an electric conversion device to meet the heat and cold load requirements; the remaining wind power obtained by the offshore wind power generation module is stored using an energy storage device; step S2: establishing a hybrid energy storage system optimization configuration model; step S3: optimizing the hybrid energy storage system optimization configuration model through a particle swarm intelligent optimization method to achieve the optimal configuration of hybrid energy storage. The present invention combines a new energy power generation system with an energy storage system to help ports optimize their energy structure, reduce carbon emissions, and promote the development of a green and advanced low-carbon economy for ports in the new era.

Description

Technical Field

[0001] The present invention relates to the field of port integrated energy system planning and energy storage optimization configuration, and in particular, to a hybrid energy storage system optimization configuration method and system for green ports. Background Art

[0002] With the rise of distributed offshore wind power new energy technology, new energy power generation can be combined with the traditional energy system of the port. By relying on the economic and environmental characteristics of wind power, the port energy supply side structure can be optimized and energy costs can be reduced, thereby promoting the green and low-carbon circular development of the port.

[0003] However, offshore wind power output is affected by natural factors and is often uncertain and uncontrollable. In order to improve the consumption level of offshore wind power and meet the different types of energy needs of ports, such as cold, heat and electricity, energy storage systems play a vital role. This solution combines offshore wind power generation with traditional port energy systems, establishes a scientific model of a port comprehensive energy system with multiple forms of energy storage, and forms an optimal configuration plan for a hybrid energy storage system based on an intelligent optimization algorithm, in order to reduce the cost of wind abandonment while improving the economic efficiency of the port comprehensive energy system.

[0004] Patent document CN112909977A (application number: 202110322369.4) discloses a real-time economic dispatch method for a port distribution network involving electric ships. The steps include: 1) quantitatively analyzing the time and space transfer characteristics of electric ships; 2) establishing a charging and discharging model of the electric ship energy storage battery; 3) establishing a distribution network flow constraint model based on the charging and discharging model; 4) establishing a gas turbine constraint model based on the gas turbine operating characteristics; 5) determining the minimum total operating cost of the distribution network based on the flow network constraint model and the gas turbine constraint model, and obtaining a real-time economic dispatch strategy.

[0005] Pu Yue, Ji Li, Liu Haoming. Optimal operation of port energy system considering comprehensive demand response[J]. Power Demand Side Management, 2021, 23(01): 11-17. In order to meet the growing energy demand of ports and improve the environmental pollution problem in port areas, the energy supply mode of ports has gradually changed from traditional fuel oil and coal-fired power supply to comprehensive energy supply mode covering cooling, heating, electricity and natural gas. The continuous advancement of port shore power construction and facility electrification transformation projects has further increased the demand for port electricity. In view of this situation, a method for optimizing the operation of port comprehensive energy system considering comprehensive demand response is proposed. By analyzing the energy conversion coupling connection between various devices in the system, a multi-energy coupling conversion model of port comprehensive energy system based on energy hub is constructed, and a comprehensive demand response model of port is established according to the adjustable load characteristics of the port. On this basis, the influence of factors such as energy price, environmental pollution and energy loss on system operation is comprehensively considered, and the operation optimization model of port comprehensive energy system considering comprehensive demand response is established with the goal of minimizing system operation cost. The results of the example verify that the optimization method can effectively reduce the operating cost of the port integrated energy system and reduce the power load demand during the peak period of port electricity consumption. This paper proposes an optimized operation method for the port integrated energy system considering comprehensive demand response, and establishes an optimized operation model for the port integrated energy system with the goal of minimizing the system operation cost. However, this scheme only uses energy storage to alleviate load mismatch, and does not analyze the economic feasibility of its participation in system operation optimization. In contrast, this scheme not only comprehensively considers the optimized operation of energy storage in the integrated energy system, but also proposes a hybrid energy storage optimization configuration method based on the particle swarm optimization algorithm to improve the economic efficiency of system operation.

[0006] Huang Yiwen, Huang Wentao, Wei Wei, Tai Nengling, Li Ran. Logistics-energy synergistic optimization scheduling method for large-scale seaport integrated energy system [J / OL]. Proceedings of the CSEE. This paper proposes a logistics-energy synergistic optimization scheduling method for seaport integrated energy system to achieve energy conservation, emission reduction and energy efficiency improvement in ports, and fully tap the economic optimization potential of the combination of logistics system and energy system. Starting from the operation characteristics of the logistics system, the impact of quay crane scheduling on the status of ships in port is analyzed, and a port-ship logistics synergistic scheduling model is established. The energy consumption characteristics of the seaport logistics system are analyzed, and a logistics-energy system coupling load model is established in combination with the status of ships in port and load operation characteristics, so as to achieve the synergy between the logistics system and the energy system. With the lowest operating cost as the goal, combined with various energy utilization forms, the seaport logistics-energy coupling system is synergistically optimized to obtain the ship load operation strategy and quay crane scheduling plan, as well as the output of each energy equipment. Taking a port in Shanghai as an example, the simulation results show that the proposed method can effectively combine the port logistics system and the energy system on the basis of fully considering the ship's itinerary arrangement, reduce operating costs and carbon emissions, and improve the comprehensive utilization rate of energy. This document establishes a seaport integrated energy system that couples energy and logistics systems, combines multiple forms of energy utilization, and optimizes the seaport integrated energy system with the goal of minimizing operating costs. In the implementation plan, the coordinated optimization of the logistics system and the energy system is comprehensively considered, and the power grid and the natural gas grid are coupled to operate, but the comprehensive utilization of renewable energy has not yet been considered. This plan introduces a wind power generation system in the optimization of the port integrated energy system in which energy storage participates, and considers the impact of wind abandonment costs. Summary of the invention

[0007] In view of the defects in the prior art, the object of the present invention is to provide a hybrid energy storage system optimization configuration method and system for green ports.

[0008] According to the present invention, a hybrid energy storage system optimization configuration method for a green port is provided, comprising:

[0009] Step S1: obtaining power through an offshore wind power generation system and a power transmission system of a power grid; converting the power into heat energy and cold energy through an electric conversion device to meet the heat and cold load requirements; using an energy storage device to store the surplus wind power obtained by the offshore wind power generation system, thereby establishing a hybrid energy storage system;

[0010] Step S2: Establishing a hybrid energy storage system optimization configuration model;

[0011] Step S3: Optimizing the hybrid energy storage system configuration model by particle swarm intelligent optimization method to achieve optimal configuration of hybrid energy storage;

[0012] The hybrid energy storage system optimization configuration model uses a particle swarm intelligent optimization algorithm to obtain the optimal installation capacity of hybrid energy storage for electricity and heat storage.

[0013] Preferably, the wind power generation system adopts:

[0014]

[0015] Among them, P w,t represents the wind power generation at time t; ρ represents the air density; C p represents the wind energy conversion coefficient; v t represents the wind speed at time t; r represents the radius of the wind rotor blade.

[0016] Preferably, the electric conversion device comprises: an electric heating device and an electric cooling device;

[0017] The electric conversion device converts electric energy into heat energy or cold energy to meet the heat load and cold load requirements of different seasons;

[0018]

[0019] Among them, P t HC and are the output power and input power of the electric conversion device at time t respectively; η is the conversion efficiency of the electric conversion device.

[0020] Preferably, the energy storage device adopts:

[0021]

[0022] in, and are the remaining energy of the energy storage device at time t and time t-1 respectively; are the charging / heat storage power and discharging / heat release power of the energy storage device at time t respectively; is the charging / heat storage efficiency; is the discharge / heat release efficiency; τ is the energy self-loss rate.

[0023] Preferably, the step S2 adopts:

[0024]

[0025]

[0026]

[0027]

[0028]

[0029] Among them, T is the total time of the program scheduling; P represents the cost of purchasing electricity from the power grid;t GRID is the amount of electricity traded by the power grid at time t; price t is the electricity purchase price corresponding to time t; Maintenance costs for electrical conversion devices; is the output of the electric conversion device at time t; γ is the cost coefficient of its operation and maintenance fee; is the wind curtailment cost; P wy,t , P ws,t are the predicted wind power output and actual wind power output of the wind turbine at time t, respectively; λ is the wind abandonment cost coefficient; is the cost of the energy storage device; d and n are the depreciation rate and depreciation period; s EHC is the rated capacity of energy storage; is the charging and discharging power and power consumption of the energy storage device at time t; α is the unit capacity cost coefficient; μ is the operation and maintenance cost coefficient; θ is the loss energy cost coefficient; 8760 means 8760=365*24 is the total number of hours in a year;

[0030] Constraints

[0031] st

[0032]

[0033] Among them, P L,t is the electrical load at time t; P HC,t is the cooling / heating load at time t; is the maximum power output of the power grid; P HC,max Indicates the maximum conversion power of the electric conversion device; is the maximum charging and discharging power of the energy storage device; s EHC Indicates the planned rated capacity of energy storage; S min , S max are the upper and lower limit values ​​of the planned energy storage capacity, which refer to the minimum constraint capacity and maximum floor space constraint capacity to ensure system safety respectively; It represents the energy storage capacity that is satisfied when the energy storage is charged and discharged at the rated energy storage capacity at time t; are the minimum and maximum energy storage capacity that can be satisfied when charging and discharging energy storage power under rated energy storage capacity, and s EHC related; They represent the energy storage capacity at time t=1 and time t=24 respectively.

[0034] Preferably, the step S3 adopts:

[0035] Step S3.1: Using the minimum rated capacity and maximum rated capacity of the energy storage device as the upper and lower boundaries, initialize the particle swarm optimization variables, including lithium battery capacity, heat / cold storage tank capacity, lithium battery output, and heat / cold storage tank output;

[0036] Step S3.2: Randomly assign positions and velocities to each particle and set the total cost objective function of the port integrated energy system;

[0037] Step S3.3: Based on the total cost objective function of the port integrated energy system, determine the individual optimal solution pbest, and determine the global optimal solution gbest among all individual optimal solutions;

[0038] Step S3.4: Update the speed and position of the particle swarm under the constraints, compare the updated fitness value with the historical fitness value, determine the better fitness value, compare all pbest and gbest, and update gbest; determine whether the fitness function value is optimal. If not, return to continue iterative update until the optimal fitness value and hybrid energy storage device capacity are output.

[0039] According to the present invention, a hybrid energy storage system optimization configuration system for green ports includes:

[0040] Module M1: Obtain power through an offshore wind power generation system and a power transmission system of a power grid; convert the power into heat and cold energy through an electric conversion device to meet the demand for heat and cold loads; use an energy storage device to store the surplus wind power obtained by the offshore wind power generation system, thereby establishing a hybrid energy storage system;

[0041] Module M2: Establishing the optimal configuration model of hybrid energy storage system;

[0042] Module M3: Optimize the hybrid energy storage system configuration model through particle swarm intelligent optimization method to achieve the optimal configuration of hybrid energy storage;

[0043] The hybrid energy storage system optimization configuration model uses a particle swarm intelligent optimization algorithm to obtain the optimal installation capacity of hybrid energy storage for electricity and heat storage.

[0044] Preferably, the wind power generation system adopts:

[0045]

[0046] Among them, P w,t represents the wind power generation at time t; ρ represents the air density; C p represents the wind energy conversion coefficient; v t represents the wind speed at time t; r represents the radius of the wind rotor blade;

[0047] The electric conversion device includes: an electric heating device and an electric cooling device;

[0048] The electric conversion device converts electric energy into heat energy or cold energy to meet the heat load and cold load requirements of different seasons;

[0049]

[0050] Among them, P t HC and are the output power and input power of the electric conversion device at time t respectively; η is the conversion efficiency of the electric conversion device;

[0051] The energy storage device adopts:

[0052]

[0053] in, and are the remaining energy of the energy storage device at time t and time t-1 respectively; are the charging / heat storage power and discharging / heat release power of the energy storage device at time t respectively; is the charging / heat storage efficiency; is the discharge / heat release efficiency; τ is the energy self-loss rate.

[0054] Preferably, the module M2 adopts:

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] Among them, T is the total scheduling time of the scheme; P represents the cost of purchasing electricity from the power grid; t GRID is the amount of electricity traded by the power grid at time t; price t is the electricity purchase price corresponding to time t; Maintenance costs for electrical conversion devices; is the output of the electric conversion device at time t; γ is the cost coefficient of its operation and maintenance fee; is the wind curtailment cost; P wy,t , P ws,t are the predicted wind power output and actual wind power output of the wind turbine at time t, respectively; λ is the wind abandonment cost coefficient; is the cost of the energy storage device; d and n are the depreciation rate and depreciation period; s EHC is the rated capacity of energy storage; is the charging and discharging power and power consumption of the energy storage device at time t; α is the unit capacity cost coefficient; μ is the operation and maintenance cost coefficient; θ is the loss energy cost coefficient; 8760 means 8760=365*24 is the total number of hours in a year;

[0061] Constraints

[0062] st

[0063]

[0064] Among them, P L,t is the electrical load at time t; P HC,t is the cooling / heating load at time t; is the maximum power output of the power grid; P HC,max Indicates the maximum conversion power of the electric conversion device; is the maximum charging and discharging power of the energy storage device; s EHC Indicates the planned rated capacity of energy storage; S min , S max are the upper and lower limit values ​​of the planned energy storage capacity, which refer to the minimum constraint capacity and maximum floor space constraint capacity to ensure system safety respectively; It represents the energy storage capacity that is satisfied when the energy storage is charged and discharged at the rated energy storage capacity at time t; are the minimum and maximum energy storage capacity that can be satisfied when charging and discharging energy storage power under rated energy storage capacity, and s EHC related; They represent the energy storage capacity at time t=1 and time t=24 respectively.

[0065] Preferably, the module M3 adopts:

[0066] Module M3.1: Using the minimum rated capacity and maximum rated capacity of the energy storage device as the upper and lower boundaries, initialize the particle swarm optimization variables, including lithium battery capacity, heat / cold storage tank capacity, lithium battery output, and heat / cold storage tank output;

[0067] Module M3.2: Randomly assign positions and velocities to each particle and set the total cost objective function of the port integrated energy system;

[0068] Module M3.3: Based on the total cost objective function of the port integrated energy system, determine the individual optimal solution pbest, and determine the global optimal solution gbest among all individual optimal solutions;

[0069] Module M3.4: Update the speed and position of the particle swarm under the constraints, compare the updated fitness value with the historical fitness value, determine the better fitness value, compare all pbest and gbest, and update gbest; determine whether the fitness function value is optimal. If not, return to continue iterative update until the optimal fitness value and hybrid energy storage device capacity are output.

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

[0071] 1. Combined with energy storage technology, it suppresses the fluctuation of wind power output, effectively improves the level of wind power consumption, and reduces the cost of wind power abandonment;

[0072] 2. Based on the hybrid energy storage optimization configuration scheme, it effectively avoids the defect of low energy utilization rate of single energy storage, and improves the overall economy of the system while meeting the diversity needs of the energy system;

[0073] 3. Considering the relationship between energy supply and demand and combining the time-of-use electricity price of the power grid, the hybrid energy storage system participates in energy optimization, realizing peak load shifting and valley filling of the system while reducing the power purchase cost of the power grid;

[0074] 4. The combination of new energy power generation system and energy storage system can help ports optimize their energy structure, reduce carbon emissions, and promote the development of green and advanced low-carbon economy of ports in the new era. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0076] Figure 1 This is a diagram of the green port’s integrated energy system.

[0077] Figure 2 Schematic diagram of particle swarm optimization.

[0078] Figure 3 Output diagram of the port integrated energy system with hybrid energy storage. DETAILED DESCRIPTION

[0079] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0080] Combining offshore wind power generation with traditional port energy systems, an optimal configuration scheme for a hybrid energy storage system based on an intelligent optimization algorithm is proposed. The scheme has the following characteristics:

[0081] 1. In order to suppress the fluctuation of offshore wind power output, a hybrid energy storage system is configured in the port's integrated energy system.

[0082] 2. Introduce economic indicators to evaluate the effectiveness of energy storage in system optimization.

[0083] 3. Comprehensively consider the impact of factors such as differences in energy supply and demand, time-of-use electricity prices, etc., to achieve efficient use of wind power generation and hybrid energy storage.

[0084] 4. According to the typical daily cooling load and heating load requirements in different seasons, the particle swarm optimization algorithm is used to optimize the port integrated energy system to achieve the optimal configuration of the hybrid energy storage system.

[0085] The present invention is a hybrid energy storage optimization configuration based on particle swarm optimization algorithm. The structure of the green port integrated energy system is as follows: Figure 1 As shown in the figure, the electric load demand is supplied by the power grid and wind power; the heat load and cooling load demands are met by electric heating / cooling devices respectively; the energy storage system is affected by factors such as the difference between energy supply and demand and time-of-use electricity prices, and can be used as both a power generation source and an electricity load.

[0086] Example 1

[0087] According to the present invention, a hybrid energy storage system optimization configuration method for a green port is provided, comprising:

[0088] Step S1: obtaining power through an offshore wind power generation system and a power transmission system of a power grid; converting the power into heat energy and cold energy through an electric conversion device to meet the heat and cold load requirements; using an energy storage device to store the surplus wind power obtained by the offshore wind power generation system, thereby establishing a hybrid energy storage system;

[0089] Step S2: Establishing a hybrid energy storage system optimization configuration model;

[0090] Step S3: Optimizing the hybrid energy storage system configuration model by particle swarm intelligent optimization method to achieve optimal configuration of hybrid energy storage;

[0091] The hybrid energy storage system optimization configuration model of the present invention uses a particle swarm intelligent optimization algorithm to obtain the optimal installation capacity of hybrid energy storage for electricity storage and heat storage (or cold storage). The optimal configuration of hybrid energy storage avoids the irrationality of energy storage capacity configuration caused by the single energy storage optimization configuration, and reduces the investment cost of energy storage; the optimization based on the optimal hybrid energy storage can improve the operation strategy of the port integrated energy system, improve the absorption of offshore wind power, and reduce the cost of wind abandonment, thereby improving the overall economy of the port integrated energy system.

[0092] Specifically, the wind power generation system adopts: the wind turbine power generation principle: using wind power to drive the wind turbine blades to rotate, and then increasing the speed of rotation through the speed increaser to drive the generator to generate electricity. The output of offshore wind power generation is related to air density, wind speed and wind turbine equipment parameters. Wind power is the wind energy flowing through the cross-sectional area perpendicular to the wind speed per unit time;

[0093]

[0094] Among them, P w,t represents the wind power generation at time t; ρ represents the air density; C p represents the wind energy conversion coefficient; v t represents the wind speed at time t; r represents the radius of the wind rotor blade.

[0095] Specifically, the electric conversion device includes: an electric heating device and an electric cooling device; the principle is to use electricity as energy and convert it into electrical energy or cold energy.

[0096] The electric conversion device converts electric energy into heat energy or cold energy to meet the heat load and cold load requirements of different seasons;

[0097]

[0098] Among them, P t HC and are the output power and input power of the electric conversion device at time t respectively; η is the conversion efficiency of the electric conversion device.

[0099] Specifically, the energy storage device adopts:

[0100]

[0101] in, and are the remaining energy of the energy storage device at time t and time t-1 respectively; are the charging / heat storage power and discharging / heat release power of the energy storage device at time t respectively; is the charging / heat storage efficiency; is the discharge / heat release efficiency; τ is the energy self-loss rate.

[0102] Specifically, the step S2 adopts: according to the different heat and cooling load demands in the heating season and the cooling season, the present invention constructs an economic objective function that comprehensively considers the power purchase cost of the power grid, the maintenance cost of the power conversion device, the wind abandonment cost, and the energy storage cost, so as to achieve the optimal configuration of hybrid energy storage and improve the economy of the integrated energy system; the objective function is specifically expressed as follows:

[0103]

[0104]

[0105]

[0106]

[0107]

[0108] Among them, T is the total time of the program scheduling; P represents the cost of purchasing electricity from the power grid; t GRID is the amount of electricity traded by the power grid at time t; price t is the electricity purchase price corresponding to time t; Maintenance costs for electrical conversion devices; is the output of the electric conversion device at time t; γ is the cost coefficient of its operation and maintenance fee; is the wind curtailment cost; P wy,t , P ws,t are the predicted wind power output and actual wind power output of the wind turbine at time t, respectively; λ is the wind abandonment cost coefficient; is the cost of the energy storage device; d and n are the depreciation rate and depreciation period; s EHC is the rated capacity of energy storage; is the charging and discharging power and power consumption of the energy storage device at time t; α is the unit capacity cost coefficient; μ is the operation and maintenance cost coefficient; θ is the loss energy cost coefficient; 8760 means 8760=365*24 is the total number of hours in a year;

[0109] Constraints

[0110] st

[0111]

[0112] Among them, P L,t is the electrical load at time t; P HC,t is the cooling / heating load at time t; is the maximum power output of the power grid; P HC,max Indicates the maximum conversion power of the electric conversion device; is the maximum charging and discharging power of the energy storage device; s EHC Indicates the planned rated capacity of energy storage; S min , S max are the upper and lower limit values ​​of the planned energy storage capacity, which refer to the minimum constraint capacity and maximum floor space constraint capacity to ensure system safety respectively; It represents the energy storage capacity that is satisfied when the energy storage is charged and discharged at the rated energy storage capacity at time t; are the minimum and maximum energy storage capacity that can be satisfied when charging and discharging energy storage power under rated energy storage capacity, and s EHC related; They represent the energy storage capacity at time t=1 and time t=24 respectively.

[0113] The complementary constraint of energy storage limits the unification of energy storage states; It means that the energy storage capacity returns to the initial value after 24 hours of operation.

[0114] From the above scheme, we can see that the power grid adopts time-of-use electricity price for power purchase, and the energy storage system can charge when the electricity price is low and discharge when the electricity price is high according to the energy demand, that is, arbitrage in the way of "low storage and high generation". At the same time, in order to improve the economic efficiency of the system, the comprehensive energy storage cost including investment cost, operation cost and loss cost is introduced to evaluate the energy storage participating in the optimization of the operation of the port comprehensive energy system.

[0115] Specifically, the step S3 adopts: The present invention adopts a particle swarm intelligent optimization algorithm to obtain the optimal configuration scheme of the optimal hybrid energy storage, so as to obtain the best economic benefit. The optimal configuration scheme of the hybrid energy storage is related to the energy storage capacity, and the energy storage capacity is closely related to the output of the energy storage. Therefore, the lithium battery capacity, the heat storage / cold storage tank capacity, the lithium battery output, and the heat storage / cold storage tank output are taken as the initial optimization variables, and the particle swarm algorithm is used to iteratively update the optimization variables, and finally the optimal capacity is confirmed;

[0116] Specifically, the particle swarm optimization algorithm is a population-based intelligent optimization algorithm, such as Figure 2 As shown in Figure 1, it has the characteristics of high efficiency and strong robustness and has been applied in many research fields. The basic idea of ​​the particle swarm optimization algorithm is to continuously iterate and update feasible solutions according to the fitness function to find the optimal solution.

[0117] During initialization, a certain number of particles are randomly placed in the feasible domain, and the position of each particle represents a feasible solution. In each round of iteration, the position of each particle is updated according to the particle velocity, and the velocity is determined by the inertial velocity, the individual current optimal solution, and the global current optimal solution. The expressions of velocity and position are shown in formula (6) and formula (7):

[0118]

[0119]

[0120] in, represents the position of particle i in the kth iteration; represents the velocity of particle i in the kth iteration; w is the inertia weight; c1 and c2 are acceleration constants; r1 and r2 are random numbers between [0, 1]; is the individual optimal solution of particle i before the kth round; gbest k is the global optimal solution for the kth round.

[0121] Based on this iterative strategy, the movement of particles will be intelligent, simulating group activities, and each iterative update will comprehensively consider individual wisdom and group wisdom. With the help of this mechanism, the particle swarm optimization algorithm can effectively find the optimal solution.

[0122] More specifically, it includes:

[0123] Step S3.1: Using the minimum rated capacity and maximum rated capacity of the energy storage device as the upper and lower boundaries, initialize the particle swarm optimization variables, including lithium battery capacity, heat / cold storage tank capacity, lithium battery output, and heat / cold storage tank output;

[0124] Step S3.2: Randomly assign positions and velocities to each particle and set fitness, i.e., the total cost objective function of the port integrated energy system;

[0125] Step S3.3: Based on the fitness function, determine the individual optimal solution pbest, and determine the global optimal solution gbest among all individual optimal solutions;

[0126] Step S3.4: Update the speed and position of the particle swarm under the constraints, compare the updated fitness value with the historical fitness value, determine the better fitness value, compare all pbest and gbest, and update gbest; determine whether the fitness function value is optimal. If not, return to continue iterative update until the optimal fitness value and hybrid energy storage device capacity are output.

[0127] According to the present invention, a hybrid energy storage system optimization configuration system for green ports includes:

[0128] Module M1: Obtain power through an offshore wind power generation system and a power transmission system of a power grid; convert the power into heat and cold energy through an electric conversion device to meet the demand for heat and cold loads; use an energy storage device to store the surplus wind power obtained by the offshore wind power generation system, thereby establishing a hybrid energy storage system;

[0129] Module M2: Establishing the optimal configuration model of hybrid energy storage system;

[0130] Module M3: Optimize the hybrid energy storage system configuration model through particle swarm intelligent optimization method to achieve the optimal configuration of hybrid energy storage;

[0131] The hybrid energy storage system optimization configuration model of the present invention uses a particle swarm intelligent optimization algorithm to obtain the optimal installation capacity of hybrid energy storage for electricity storage and heat storage (or cold storage). The optimal configuration of hybrid energy storage avoids the irrationality of energy storage capacity configuration caused by the single energy storage optimization configuration, and reduces the investment cost of energy storage; the optimization based on the optimal hybrid energy storage can improve the operation strategy of the port integrated energy system, improve the absorption of offshore wind power, and reduce the cost of wind abandonment, thereby improving the overall economy of the port integrated energy system.

[0132] Specifically, the wind power generation system adopts: the wind turbine power generation principle: using wind power to drive the wind turbine blades to rotate, and then increasing the speed of rotation through the speed increaser to drive the generator to generate electricity. The output of offshore wind power generation is related to air density, wind speed and wind turbine equipment parameters. Wind power is the wind energy flowing through the cross-sectional area perpendicular to the wind speed per unit time;

[0133]

[0134] Among them, P w,t represents the wind power generation at time t; ρ represents the air density; C p represents the wind energy conversion coefficient; v t represents the wind speed at time t; r represents the radius of the wind rotor blade.

[0135] Specifically, the electric conversion device includes: an electric heating device and an electric cooling device; the principle is to use electricity as energy and convert it into electrical energy or cold energy.

[0136] The electric conversion device converts electric energy into heat energy or cold energy to meet the heat load and cold load requirements of different seasons;

[0137]

[0138] Among them, P t HC and are the output power and input power of the electric conversion device at time t respectively; η is the conversion efficiency of the electric conversion device.

[0139] Specifically, the energy storage device adopts:

[0140]

[0141] in, and are the remaining energy of the energy storage device at time t and time t-1 respectively; are the charging / heat storage power and discharging / heat release power of the energy storage device at time t respectively; is the charging / heat storage efficiency; is the discharge / heat release efficiency; τ is the energy self-loss rate.

[0142] Specifically, the module M2 adopts: according to the different heat and cooling load demands in the heating season and the cooling season, the present invention constructs an economic objective function that comprehensively considers the power purchase cost of the power grid, the maintenance cost of the power conversion device, the wind abandonment cost, and the energy storage cost, so as to achieve the optimal configuration of hybrid energy storage and improve the economy of the comprehensive energy system; the objective function is specifically expressed as follows:

[0143]

[0144]

[0145]

[0146]

[0147]

[0148] Among them, T is the total time of the program scheduling; P represents the cost of purchasing electricity from the power grid; t GRID is the amount of electricity traded by the power grid at time t; price t is the electricity purchase price corresponding to time t; Maintenance costs for electrical conversion devices; is the output of the electric conversion device at time t; γ is the cost coefficient of its operation and maintenance fee; is the wind curtailment cost; P wy,t , P ws,t are the predicted wind power output and actual wind power output of the wind turbine at time t, respectively; λ is the wind abandonment cost coefficient; is the cost of the energy storage device; d and n are the depreciation rate and depreciation period; s EHC is the rated capacity of energy storage; is the charging and discharging power and power consumption of the energy storage device at time t; α is the unit capacity cost coefficient; μ is the operation and maintenance cost coefficient; θ is the loss energy cost coefficient; 8760 means 8760=365*24 is the total number of hours in a year;

[0149] Constraints

[0150] st

[0151]

[0152] Among them, P L,t is the electrical load at time t; P HC,t is the cooling / heating load at time t; is the maximum power output of the power grid; P HC,max Indicates the maximum conversion power of the electric conversion device; is the maximum charging and discharging power of the energy storage device; s EHC Indicates the planned rated capacity of energy storage; S min , S max are the upper and lower limit values ​​of the planned energy storage capacity, which refer to the minimum constraint capacity and maximum floor space constraint capacity to ensure system safety respectively; It represents the energy storage capacity that is satisfied when the energy storage is charged and discharged at the rated energy storage capacity at time t; are the minimum and maximum energy storage capacity that can be satisfied when charging and discharging energy storage power under rated energy storage capacity, and s EHC related; They represent the energy storage capacity at time t=1 and time t=24 respectively.

[0153] The complementary constraint of energy storage limits the unification of energy storage states; It means that the energy storage capacity returns to the initial value after 24 hours of operation.

[0154] From the above scheme, we can see that the power grid adopts time-of-use electricity price for power purchase, and the energy storage system can charge when the electricity price is low and discharge when the electricity price is high according to the energy demand, that is, arbitrage in the way of "low storage and high generation". At the same time, in order to improve the economic efficiency of the system, the comprehensive energy storage cost including investment cost, operation cost and loss cost is introduced to evaluate the energy storage participating in the optimization of the operation of the port comprehensive energy system.

[0155] Specifically, the module M3 adopts: The present invention adopts the particle swarm intelligent optimization algorithm to obtain the optimal configuration scheme of the optimal hybrid energy storage, so as to obtain the best economic benefits. The optimal configuration scheme of hybrid energy storage is related to the energy storage capacity, and the energy storage capacity is closely related to the output of the energy storage. Therefore, the lithium battery capacity, the heat storage / cold storage tank capacity, the lithium battery output, and the heat storage / cold storage tank output are taken as the initial optimization variables, and the particle swarm algorithm is used to iteratively update the optimization variables, and finally the optimal capacity is confirmed;

[0156] Specifically, the particle swarm optimization algorithm is a population-based intelligent optimization algorithm with the characteristics of high efficiency and strong robustness, and has been applied in many research fields. The basic idea of ​​the particle swarm optimization algorithm is to continuously iteratively update feasible solutions according to the fitness function to find the optimal solution.

[0157] During initialization, a certain number of particles are randomly placed in the feasible domain, and the position of each particle represents a feasible solution. In each round of iteration, the position of each particle is updated according to the particle velocity, and the velocity is determined by the inertial velocity, the individual current optimal solution, and the global current optimal solution. The expressions of velocity and position are shown in formula (6) and formula (7):

[0158]

[0159]

[0160] in, represents the position of particle i in the kth iteration; represents the velocity of particle i in the kth iteration; w is the inertia weight; c1 and c2 are acceleration constants; r1 and r2 are random numbers between [0, 1]; is the individual optimal solution of particle i before the kth round; gbest k is the global optimal solution for the kth round.

[0161] Based on this iterative strategy, the movement of particles will be intelligent, simulating group activities, and each iterative update will comprehensively consider individual wisdom and group wisdom. With the help of this mechanism, the particle swarm optimization algorithm can effectively find the optimal solution.

[0162] More specifically, it includes:

[0163] Module M3.1: Using the minimum rated capacity and maximum rated capacity of the energy storage device as the upper and lower boundaries, initialize the particle swarm optimization variables, including lithium battery capacity, heat / cold storage tank capacity, lithium battery output, and heat / cold storage tank output;

[0164] Module M3.2: Randomly assign positions and velocities to each particle and set fitness, i.e., the total cost objective function of the port integrated energy system;

[0165] Module M3.3: Based on the fitness function, determine the individual optimal solution pbest, and determine the global optimal solution gbest among all individual optimal solutions;

[0166] Module M3.4: Update the speed and position of the particle swarm under the constraints, compare the updated fitness value with the historical fitness value, determine the better fitness value, compare all pbest and gbest, and update gbest; determine whether the fitness function value is optimal. If not, return to continue iterative update until the optimal fitness value and hybrid energy storage device capacity are output.

[0167] Example 2

[0168] Embodiment 2 is a preferred embodiment of Embodiment 1

[0169] Take the typical daily wind power output and electric heat load demand data of a port during the heating season as an example. Lithium-ion batteries and heat storage tanks are used as energy storage planning objects, and the maximum energy storage capacity is 200MWh. The unit price of lithium battery capacity is 2 million yuan / MWh, the unit price of heat storage tank capacity is 500,000 yuan / MWh, the rated power of wind turbine is 18MW, and the rated power of electric heat conversion device is 22MW. The electricity price implements the time-of-use electricity price plan, and the "peak-flat-valley" time-of-use electricity prices are 1.35 yuan / kWh, 0.9 yuan / kWh, and 0.47 yuan / kWh respectively.

[0170] This solution targets the "anti-load" characteristics of wind power output. During the wind abandonment period, the power-to-heat device will give priority to absorbing wind power, converting excess wind power into heat energy and storing it in the heat storage tank. When the maximum heat storage capacity demand per unit time is met, the lithium battery will absorb the excess wind power to increase the wind power absorption rate. During the peak load period, the heat storage tank and lithium battery will release heat and discharge to relieve the power supply pressure of the power grid. Based on the time-of-use electricity price, the energy storage device will arbitrage according to the "low storage and high generation" method to reduce the power purchase cost of the power grid.

[0171] In order to illustrate the effectiveness of this scheme, the economic feasibility of four configuration methods of energy storage in the port integrated energy system is analyzed and compared.

[0172] 1) The port integrated energy system is not equipped with energy storage devices;

[0173] 2) The port integrated energy system is only equipped with power storage devices for system optimization;

[0174] 3) The port integrated energy system is only equipped with heat storage devices for system optimization;

[0175] 4) The port’s integrated energy system is configured with a hybrid electricity / heat storage device for system optimization.

[0176] The optimization analysis results are shown in Table 1. The output of the port integrated energy system equipped with electric / thermal hybrid energy storage is as follows: Figure 3 shown.

[0177] Table 1 Economic optimization analysis of typical days in the heating season for the port integrated energy system

[0178]

[0179] From the data in the table, it can be concluded that the hybrid energy storage configuration scheme of electricity storage combined with heat storage is the most economical, and its total cost economic efficiency is improved by 26%. Compared with the single energy storage configuration scheme, the energy storage configuration capacity is also reduced, avoiding the irrationality of the energy storage device capacity configuration when the energy storage device is planned separately. The rated capacity of the electricity storage device is 29.8MWh, and the rated capacity of the heat storage device is 110.6MWh. The optimized configuration scheme of hybrid energy storage further improves the economic benefits of the port's comprehensive energy system, thereby promoting the development of a green port low-carbon economy.

[0180] Those skilled in the art know that, in addition to implementing the system, device and its various modules provided by the present invention in a purely computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers and embedded microcontrollers by logically programming the method steps. Therefore, the system, device and its various modules provided by the present invention can be considered as a hardware component, and the modules included therein for implementing various programs can also be considered as structures within the hardware component; the modules for implementing various functions can also be considered as both software programs for implementing the method and structures within the hardware component.

[0181] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A hybrid energy storage system optimization configuration method for green ports, characterized in that: include: Step S1: obtaining power through an offshore wind power generation system and a power grid transmission system; The power is converted into heat and cold energy by an electrical conversion device to meet the heat and cold load requirements; Using energy storage devices to store excess wind power from offshore wind power generation systems, thereby establishing a hybrid energy storage system; Step S2: Establishing a hybrid energy storage system optimization configuration model; Step S3: Optimizing the hybrid energy storage system configuration model by particle swarm intelligent optimization method to achieve optimal configuration of hybrid energy storage; The hybrid energy storage system optimization configuration model uses a particle swarm intelligent optimization algorithm to find the optimal installation capacity of hybrid energy storage for electricity and heat storage; The step S2 adopts: Among them, T is the total time of the program scheduling; represents the cost of purchasing electricity from the power grid; is the amount of electricity traded by the power grid at time t; price t is the electricity purchase price corresponding to time t; Maintenance costs for electrical conversion devices; is the output of the electric conversion device at time t; γ is the cost coefficient of its operation and maintenance fee; is the wind curtailment cost; P wy,t , P ws,t are the predicted wind power output and actual wind power output of the wind turbine at time t, respectively; λ is the wind abandonment cost coefficient; is the cost of the energy storage device; d and n are the depreciation rate and depreciation period; s EHC is the rated capacity of energy storage; is the charging and discharging power and power consumption of the energy storage device at time t; α is the unit capacity cost coefficient; μ is the operation and maintenance cost coefficient; θ is the loss energy cost coefficient; 8760 means 8760=365*24 is the total number of hours in a year; Constraints Among them, P L,t is the electrical load at time t; P HC,t is the cooling / heating load at time t; is the maximum power output of the power grid; P HC,max Indicates the maximum conversion power of the electric conversion device; is the maximum charging and discharging power of the energy storage device; s EHC Indicates the planned rated capacity of energy storage; S min , S max are the upper and lower limit values ​​of the planned energy storage capacity, which refer to the minimum constraint capacity and maximum floor space constraint capacity to ensure system safety respectively; It represents the energy storage capacity that is satisfied when the energy storage is charged and discharged at the rated energy storage capacity at time t; are the minimum and maximum energy storage capacity that can be satisfied when charging and discharging energy storage at rated energy storage capacity, and s EHC related; They represent the energy storage capacity at time t=1 and time t=24 respectively.

2. The method for optimizing configuration of hybrid energy storage system for green ports according to claim 1, characterized in that: The wind power generation system adopts: Among them, P w,t represents the wind power generation at time t; ρ represents the air density; C p represents the wind energy conversion coefficient; v t represents the wind speed at time t; r represents the radius of the wind rotor blade.

3. The method for optimizing configuration of hybrid energy storage system for green ports according to claim 1, characterized in that: The electric conversion device includes: an electric heating device and an electric cooling device; The electric conversion device converts electric energy into heat energy or cold energy to meet the heat load and cold load requirements of different seasons; in, and are the output power and input power of the electric conversion device at time t respectively; η is the conversion efficiency of the electric conversion device.

4. The method for optimizing configuration of hybrid energy storage system for green ports according to claim 1, characterized in that: The energy storage device adopts: in, and are the remaining energy of the energy storage device at time t and time t-1 respectively; are the charging / heat storage power and discharging / heat release power of the energy storage device at time t respectively; is the charging / heat storage efficiency; is the discharge / heat release efficiency; τ is the energy self-loss rate.

5. The method for optimizing configuration of hybrid energy storage system for green ports according to claim 1, characterized in that: The step S3 adopts: Step S3.1: Using the minimum rated capacity and maximum rated capacity of the energy storage device as the upper and lower boundaries, initialize the particle swarm optimization variables, including lithium battery capacity, heat / cold storage tank capacity, lithium battery output, and heat / cold storage tank output; Step S3.2: Randomly assign positions and velocities to each particle and set the total cost objective function of the port integrated energy system; Step S3.3: Based on the total cost objective function of the port integrated energy system, determine the individual optimal solution pbest, and determine the global optimal solution gbest among all individual optimal solutions; Step S3.4: Update the speed and position of the particle swarm under the constraints, compare the updated fitness value with the historical fitness value, determine the better fitness value, compare all pbest and gbest, and update gbest; determine whether the fitness function value is optimal. If not, return to continue iterative update until the optimal fitness value and hybrid energy storage device capacity are output.

6. A hybrid energy storage system optimization configuration system for green ports, characterized in that: include: Module M1: Obtain power through offshore wind power generation system and grid power transmission system; The power source is converted into heat and cold energy through an electric conversion device to meet the heat and cold load requirements; the energy storage device is used to store the surplus wind power obtained by the offshore wind power generation system, thereby establishing a hybrid energy storage system; Module M2: Establishing the optimal configuration model of hybrid energy storage system; Module M3: Optimize the hybrid energy storage system configuration model through particle swarm intelligent optimization method to achieve the optimal configuration of hybrid energy storage; The hybrid energy storage system optimization configuration model uses a particle swarm intelligent optimization algorithm to find the optimal installation capacity of hybrid energy storage for electricity and heat storage; The module M2 adopts: Among them, T is the total time of the program scheduling; represents the cost of purchasing electricity from the power grid; is the amount of electricity traded by the power grid at time t; price t is the electricity purchase price corresponding to time t; Maintenance costs for electrical conversion devices; is the output of the electric conversion device at time t; γ is the cost coefficient of its operation and maintenance fee; is the wind curtailment cost; P wy,t , P ws,t are the predicted wind power output and actual wind power output of the wind turbine at time t, respectively; λ is the wind abandonment cost coefficient; is the cost of the energy storage device; d and n are the depreciation rate and depreciation period; s EHC is the rated capacity of energy storage; is the charging and discharging power and power consumption of the energy storage device at time t; α is the unit capacity cost coefficient; μ is the operation and maintenance cost coefficient; θ is the loss energy cost coefficient; 8760 means 8760=365*24 is the total number of hours in a year; Constraints Among them, P L,t is the electrical load at time t; P HC,t is the cooling / heating load at time t; is the maximum power output of the power grid; P HC,max Indicates the maximum conversion power of the electric conversion device; is the maximum charging and discharging power of the energy storage device; s EHC Indicates the planned rated capacity of energy storage; S min , S max are the upper and lower limit values ​​of the planned energy storage capacity, which refer to the minimum constraint capacity and maximum floor space constraint capacity to ensure system safety respectively; It represents the energy storage capacity that is satisfied when the energy storage is charged and discharged at the rated energy storage capacity at time t; are the minimum and maximum energy storage capacity that can be satisfied when charging and discharging energy storage at rated energy storage capacity, and s EHC related; They represent the energy storage capacity at time t=1 and time t=24 respectively.

7. The hybrid energy storage system optimization configuration system for green ports according to claim 6 is characterized in that: The wind power generation system adopts: Among them, P w,t represents the wind power generation at time t; ρ represents the air density; C p represents the wind energy conversion coefficient; v t represents the wind speed at time t; r represents the radius of the wind rotor blade; The electric conversion device includes: an electric heating device and an electric cooling device; The electric conversion device converts electric energy into heat energy or cold energy to meet the heat load and cold load requirements of different seasons; in, and are the output power and input power of the electric conversion device at time t respectively; η is the conversion efficiency of the electric conversion device; The energy storage device adopts: in, and are the remaining energy of the energy storage device at time t and time t-1 respectively; are the charging / heat storage power and discharging / heat release power of the energy storage device at time t respectively; is the charging / heat storage efficiency; is the discharge / heat release efficiency; τ is the energy self-loss rate.

8. The hybrid energy storage system optimization configuration system for green ports according to claim 6, characterized in that: The module M3 adopts: Module M3.1: Using the minimum rated capacity and maximum rated capacity of the energy storage device as the upper and lower boundaries, initialize the particle swarm optimization variables, including lithium battery capacity, heat / cold storage tank capacity, lithium battery output, and heat / cold storage tank output; Module M3.2: Randomly assign positions and velocities to each particle and set the total cost objective function of the port integrated energy system; Module M3.3: Based on the total cost objective function of the port integrated energy system, determine the individual optimal solution pbest, and determine the global optimal solution gbest among all individual optimal solutions; Module M3.4: Update the speed and position of the particle swarm under the constraints, compare the updated fitness value with the historical fitness value, determine the better fitness value, compare all pbest and gbest, and update gbest; determine whether the fitness function value is optimal. If not, return to continue iterative update until the optimal fitness value and hybrid energy storage device capacity are output.

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