A multi-energy storage optimization configuration method for a regional integrated energy system with a high proportion of distributed photovoltaics

Abstract

The present invention discloses a method for optimizing the configuration of multi-energy storage in a regional integrated energy system containing a high proportion of distributed photovoltaics. The method comprehensively considers the differentiated operating characteristics of multi-energy storage equipment, covers the entire planning-operation cycle of multi-energy storage equipment, and aims to minimize the investment and construction costs of the multi-energy storage system and the operating costs of the regional integrated energy system, smooth out the fluctuations in the output of high-penetration distributed photovoltaics, and ensure the stability of system operation. The multi-energy storage installation capacity and operation strategy are optimized based on multi-objective nonlinear programming technology. In view of the nonlinear constraints in the method, auxiliary variables are introduced to ensure the existence of the solution to the original problem, and the adjustable parameters introduced at the same time improve the versatility of the model and method. The method of the present invention can fully consider the impact of the volatility of high-proportion distributed photovoltaic output, scientifically and reasonably configure the multi-energy storage capacity and optimize its operation strategy, and reduce investment, construction, operation and maintenance costs.

Description

Technical Field

[0001] The invention discloses a multi-energy storage optimization configuration method for a regional comprehensive energy system containing a high proportion of distributed photovoltaics, and belongs to the field of energy system planning and optimization. Background Art

[0002] The construction of a regional integrated energy system can interconnect and coordinate the originally separated power grid and heat network, and improve the comprehensive utilization efficiency of energy. Technological progress, the improvement of the policy system and the actual needs of energy system development have led to a sharp increase in the proportion and penetration rate of distributed photovoltaic installed capacity in regional integrated energy systems. By the end of 2020, the cumulative installed capacity of distributed photovoltaics in China reached 31.1%, and the reliable consumption of renewable energy and the safe and stable operation of the system face huge challenges. Installing multi-energy storage devices with rapid response capabilities is an effective measure to deal with the impact of high-proportion distributed photovoltaics on system stability. However, the existing system energy storage configuration and operation optimization methods do not consider the impact of high-proportion distributed photovoltaic penetration on energy storage planning schemes and system operation optimization strategies; traditional energy storage optimization planning technologies are mostly aimed at the capacity optimization of single devices such as battery energy storage and supercapacitors or multiple electrical energy storage devices, and do not fully consider and utilize the differentiated operating characteristics of multi-energy storage devices. Therefore, it is necessary to study the multi-energy storage optimization configuration method for regional integrated energy systems with a high proportion of distributed photovoltaics (installed capacity accounts for more than 30%). Summary of the invention

[0003] In view of the defects of the relevant background technology, the present invention provides a multi-energy storage optimization configuration method, which fully considers the cost and performance differences of various types of electrical and thermal energy storage devices, can comprehensively consider the energy storage system planning-operation stage, minimize the energy storage system construction and regional integrated energy system operation costs, and effectively deal with the impact of high-proportion distributed photovoltaic randomness and volatility on the economic and stable operation of the system, scientifically configure the capacity of multi-energy storage facilities and optimize their operation strategies. The steps for implementing the multi-energy storage optimization configuration method for a regional integrated energy system containing a high proportion of distributed photovoltaics are as follows:

[0004] Step 1: Construct the objective function of the multi-energy storage optimization configuration model. The multi-energy storage facilities considered in this invention mainly include: battery energy storage equipment, flywheel energy storage equipment and thermal storage device. The construction cost and operation cost of the energy storage system are:

[0005]

[0006]

[0007] Where: C inv Represents the energy storage construction cost, Represent the operating costs of the battery energy storage system, flywheel energy storage system and thermal storage device respectively; cBESS , c FESS and c TSS Represent the unit capacity cost coefficients of batteries, flywheels and thermal storage devices respectively; ε BESS , ε FESS and ε TSS is the cost daily allocation coefficient; and represents the installed capacity of the battery energy storage system, flywheel energy storage system and thermal storage device respectively; r and n are the equipment depreciation rate and service life respectively; m BESS , m FESS and m TSS Respectively represent the operation and maintenance cost coefficients of each energy storage system; and are the charging power and discharging power of battery energy storage system i at time t; P j,t is the electric power of the flywheel energy storage system j at time t; and is the heat storage and heat release power of the heat storage device k at time t; N BESS , N FESS and N TSS They represent the number of installed battery energy storage systems, flywheel energy storage systems and thermal storage devices respectively.

[0008] The operation and maintenance costs of distributed generator sets, cogeneration units, photovoltaic and gas boilers and the energy purchase costs of the integrated energy system are:

[0009]

[0010] Where: and are the operation and maintenance costs and energy purchase costs of other equipment in the integrated energy system except for the multi-energy storage equipment; the operation cost of the distributed generator set is expressed by a quadratic function, a CGU , b CGU and c CGU are the quadratic term coefficient, linear term coefficient and constant term of the cost function respectively; σ CHP , σ PV and σ GB They are the operation and maintenance cost coefficients of cogeneration units, distributed photovoltaics, and gas boilers; are the electric power of small diesel units, cogeneration units and distributed photovoltaic units at time t respectively; is the thermal power of the gas boiler at time t; the subscript I represents the serial number of each energy production equipment; P t EX,e and P t EX,gas are the purchased electric power and purchased gas power of the integrated energy system at time t, respectively; and λ gas Respectively represent the electricity purchase price and gas purchase price at time t; Γ CGU , Γ CHP , Γ PV and Γ GB They represent the collection of nodes where small diesel generators, cogeneration units, distributed photovoltaics and gas boilers are located.

[0011] Then the objective function of the multi-energy storage optimization configuration model can be expressed as:

[0012]

[0013] Where: T w represents the optimization time window; Δt represents the optimization step size.

[0014] Step 2: Construct the multi-energy storage system operation constraints, multi-energy network constraints and other equipment constraints. Other equipment includes distributed photovoltaics, gas boilers, small diesel generators and cogeneration units. The multi-energy network includes the distribution network and the regional thermal network.

[0015] Both battery energy storage systems and flywheel energy storage systems have the characteristics of large specific energy and small size. Commonly used battery energy storage systems are lithium cobalt oxide batteries, lithium iron phosphate batteries and ternary lithium batteries. Battery energy storage systems have fast response speeds and almost no climbing restrictions, but their charging and discharging power must be less than their rated power:

[0016]

[0017] Where: They represent the maximum charging / discharging power of battery energy storage system i respectively.

[0018] The state of charge (SOC) of a battery energy storage system is related to its charge and discharge state and its own energy decay. At the same time, to ensure the safety of the battery energy storage system, the battery's state of charge (SOC) must also be within a reasonable range:

[0019]

[0020] Where: γ self ,γ ch ,γ dis They represent the self-discharge coefficient, charging efficiency and discharge efficiency of the battery respectively; represents the rated capacity of the battery energy storage system i; Δt represents the optimization step size. In the actual operation process, for the convenience of operation and management, it is generally required that the SOC state of the battery energy storage system at the beginning and end of the operation cycle is consistent:

[0021]

[0022] Flywheel energy storage devices store electrical energy in the form of kinetic energy. Compared with battery energy storage systems, they are safer and more stable, and are more suitable for fast and frequent charging and discharging scenarios, but they are more expensive. Similar to battery energy storage systems, flywheel energy storage devices also have charging / discharging power limitations and SOC constraints:

[0023]

[0024] Where: and oh r Respectively represent the upper and lower limits of the flywheel speed; β self and β ch / dis They represent the self-discharge rate and charge and discharge efficiency of the flywheel energy storage system respectively.

[0025] The thermal storage tank is a widely used heat storage device, which is often installed inside thermal power plants. When the heat loss of the thermal storage tank is ignored, its operating characteristics can be described by the following formula:

[0026]

[0027] Where: E k,t is the heat storage state of the heat storage tank k at time t; ch and dis Respectively represent the heat storage / heat release efficiency of the heat storage tank.

[0028] When the influence of ambient temperature on the output of distributed photovoltaic power is not considered, its output power can be described by the following formula:

[0029]

[0030] in: Indicates the rated power of photovoltaic i; I t represents the irradiance level at time t, I N is the irradiance level under standard test conditions. Assume that the photovoltaic is operated in constant power factor mode (the power factor is set to ), the reactive output of the inverter is:

[0031]

[0032] In order to improve the stability and flexibility of system operation, small diesel units with strong climbing ability are usually built close to the user side. Their operating characteristics are as follows:

[0033]

[0034] Where: is the reactive power of distributed generator set i at time t; and They represent the lower and upper limits of the active output of generator set i respectively; and Respectively represent the lower and upper limits of its reactive output; r l CGU and Respectively represent the upward / downward climbing capability of the generator set.

[0035] Gas boilers are an important type of central heating device in district heating systems, which generate heat by burning natural gas. The output thermal power is directly related to the gas power consumption, and there are also ramp constraints:

[0036]

[0037] Where: η GB Indicates the operating efficiency of the gas boiler, P t gas,GB Indicates its gas consumption power; and It is the lower and upper limits of the thermal power output of the gas boiler.

[0038] The regional heating network is generally composed of water supply pipes, return pipes and heat exchange stations. In view of the operating characteristics of the regional heating network and the transmission delay of each pipeline, the present invention adopts the node method to describe it. The heat exchange station is a place where heat is concentrated and exchanged. Through the heat exchange device, the heat at the heat source can be transferred to the primary pipe network, or the heat in the primary pipe network can be transferred to the secondary pipe network, and finally to the user end. The operating characteristics of the heat exchange station can be described as:

[0039]

[0040] in: and They represent the thermal power absorbed by the heat exchange station located at node n from the heat source or the primary pipe network at time t, and the thermal power transmitted to the primary pipe network or the secondary pipe network through the heat exchange station; and They represent the working fluid flow rates flowing into and out of node n at time t respectively; and They represent the water supply temperature of the heat exchange station at node n at time t and the hot water temperature flowing through the main heat exchange station respectively; similarly, and Represents the temperature at the outlet of the supply pipe and the inlet of the return pipe at node n at time t.

[0041] When constructing the mapping relationship between the inlet and outlet temperatures of the pipeline, the temperature change inside the pipeline is ignored, and only the transmission delay of the working fluid inside the pipeline is considered. The transmission delay of the working fluid in the pipeline can be calculated by the following formula:

[0042]

[0043] Where: m l represents the working fluid flow rate inside the pipe l; ρ is the density of water; d l and L l are the diameter and length of the pipe l respectively; M l represents the quality of hot water inside pipe l. At the same time, the temperature at the outlet of pipe l is:

[0044]

[0045] in: is the temperature at the outlet of pipe l at time t; and They are (t-τ l ) and (t-τ l +1) is the working fluid temperature injected into pipeline l at time.

[0046] When the heat medium is transported inside the pipeline, it will inevitably radiate heat energy to the surrounding environment, resulting in heat loss. Therefore, the actual temperature at the outlet of pipeline l at time t is:

[0047]

[0048] Where: K l and T ground are the thermal conductivity of the pipeline per unit length and the ground temperature around the pipeline; C is the specific heat capacity coefficient of water.

[0049] Assuming that the working fluid injected at each node is fully mixed, according to the law of conservation of energy, the temperature mixing process of each node can be described by the following formula (applicable to both the water supply network and the return network):

[0050]

[0051] Where: and are the working fluid temperatures injected into and out of node n at time t respectively; and They represent the working fluid flow rates injected into and out of node n at time t respectively; and are the sets of all pipes flowing into and out of node n, respectively.

[0052] At the same time, the temperatures at all nodes in the heat network must satisfy certain constraints:

[0053]

[0054] Where: and are the upper and lower limits of the temperature at each node in the water supply network; It is the lower limit of the temperature of each node in the return network.

[0055] The power network in the regional integrated energy system is mainly a distribution network, which is mostly a radial network and adopts the "closed-loop construction, open-loop operation" mode. The radial network linear power flow model is used to describe the energy flow relationship and node voltage changes in the distribution network:

[0056]

[0057] Where: j} and {Ι j} are the branch set for power injection and the branch set for power outflow at node j respectively; P jk , P ij , Q jk and Q ij are the active power and reactive power flowing in / out respectively; the load at node j is composed of S j =P j +Q j Indicates; U i and are the voltage amplitudes at nodes i and j respectively; U0 is the reference voltage; the parameter of line ij is the resistance R ij and reactance X ij .

[0058] The main electric-heat coupling equipment in the regional integrated energy system is a large combined heat and power (CHP) unit. Based on engineering practice, CHP units are mostly extraction-condensing cogeneration units, and the electricity and heat production processes can be decoupled from each other:

[0059]

[0060] Where: P t gas,CHP and Respectively represent the gas consumption power and thermal power of CHP at time t; η CHP and R CHP It indicates the operating efficiency and heat-to-electricity ratio of the CHP unit; r l CHP and It is the downward / upward climbing capability of the CHP unit; and It is the lower and upper limits of the CHP unit output power.

[0061] The power grid and heat network must meet the power balance constraints:

[0062]

[0063] Where: P d,t , Q d,t and H d,tRepresents the active load, reactive load and thermal load level of the system at time t.

[0064] Step 3: Introduce auxiliary variables to linearize the nonlinear constraints of the electric energy storage system and introduce adjustable parameters to improve the applicability of the method. Specifically:

[0065] In the process of multi-energy storage optimization configuration, the installed capacity and rated charging and discharging power of the electric energy storage equipment are variables to be optimized. Therefore, the multi-energy storage model constructed in step 1 contains bilinear constraints. If these constraints are directly added to the regional integrated energy system operation optimization model to construct a multi-energy storage optimization configuration model, the original problem will become non-convex and difficult to solve directly. In order to facilitate the use of existing optimization techniques to solve the multi-energy storage optimization configuration model and obtain deterministic optimization results, it is necessary to make appropriate modifications to the multi-energy storage model, specifically: 1) Introduce the residual energy of the energy storage system 2) Introducing the adjustable parameter Dur min , which indicates the minimum continuous energy release time of the energy storage system. The configured energy storage system must meet the energy release duration of not less than Dur min , Dur min The size can be selected based on actual engineering experience, generally ranging from 1-4h, and 2h is selected here.

[0066] After the above processing, the relationship between the charge / discharge power of the energy storage system state of charge becomes:

[0067]

[0068] It can be found that the original bilinear equality constraint becomes a linear equality constraint and can be solved directly. At the same time, the state of charge of the energy storage system can be solved by the remaining energy of the system at each moment:

[0069]

[0070] When introducing the minimum continuous energy release time Dur of the energy storage system min After that, the relationship between the rated charge and discharge power and the rated capacity of the battery energy storage system can be described as:

[0071]

[0072] Step 4: Construct a multi-energy storage optimization configuration model for a regional integrated energy system with a high proportion of distributed photovoltaics. With the goal of minimizing the investment and construction cost of multi-energy storage and the operating cost of the regional integrated energy system, and with the operating constraints of the multi-energy storage system, the multi-energy network constraints and other equipment constraints as constraints, a multi-energy storage optimization configuration model for a regional integrated energy system with a high proportion of distributed photovoltaics is constructed. Specifically:

[0073]

[0074] F i (x e ,x h ,x s )=0,i=1,2,...,m

[0075] G j (x e ,x h ,x s )≥0,j=1,2,...n

[0076] Where: m, n are the isomorphic constraints F in the model respectively. i and the inequality constraint G j The number of e , x h and x s are the optimization variables for the power system, heating system and energy storage system respectively.

[0077] Step 5: Setting the boundary conditions of the optimization configuration problem and solving the problem. The system electricity purchase price adopts the time-of-use electricity price, and the gas purchase price and heat purchase price are fixed. The system load level and photovoltaic output level are represented by the typical daily electrical load, thermal load and photovoltaic output. After the setting is completed, the original multi-energy storage optimization configuration problem can be solved to obtain the optimized multi-type energy storage capacity and its operation strategy.

[0078] The beneficial effects of the present invention are:

[0079] 1) Aiming at the regional integrated energy system with a high proportion of distributed photovoltaics, a multi-energy storage optimization configuration method is proposed, which can make full use of the operating characteristics of multiple types of energy storage facilities to deal with the randomness and volatility of photovoltaic output and load side fluctuations;

[0080] 2) A method for dealing with nonlinear constraints of energy storage is proposed. By introducing auxiliary variables, the difficulty of solving the model is effectively reduced and the existence of the optimal solution of the original problem is ensured. At the same time, adjustable parameters are introduced, and the values ​​of the adjustable parameters can be adjusted in time according to the actual situation, which improves the applicability of the model and method;

[0081] 3) The operating constraints and costs of the equipment are taken into account during the energy storage planning stage, achieving full-cycle optimization of multi-energy storage planning and operation, ensuring the scientific nature of the optimization results, reducing investment and construction costs, and maximizing the utilization rate of energy storage facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 It is a flow chart of a multi-energy storage optimization configuration method for a regional integrated energy system containing a high proportion of distributed photovoltaics. DETAILED DESCRIPTION

[0083] Aiming at the regional integrated energy system with a high proportion of distributed photovoltaic, in order to effectively deal with the impact of the randomness of high-penetration distributed photovoltaic output on the system operation stability and energy supply reliability, a multi-energy storage optimization configuration method for regional integrated energy system is proposed. Specifically, the differentiated operation characteristics of multi-energy storage equipment are comprehensively considered, covering the entire planning-operation cycle of multi-energy storage equipment, with the goal of minimizing the construction cost of multi-energy storage and the system operation cost, smoothing the fluctuation of high-penetration distributed photovoltaic output, and ensuring the stability of system operation, etc., to optimize the installation capacity and operation strategy of multi-energy storage.

[0084] A method for optimizing the configuration of multi-energy storage in a regional integrated energy system containing a high proportion of distributed photovoltaics, the main steps of which are as follows: 1) constructing an objective function of a multi-energy storage optimization configuration model; 2) constructing operation constraints of the multi-energy storage system, multi-energy network constraints, and other equipment constraints except multi-energy storage in the regional integrated energy system; 3) introducing auxiliary variables to linearize the nonlinear constraints of the electric energy storage system, and introducing adjustable parameters to improve the applicability of the method; 4) constructing a multi-energy storage optimization configuration model for a regional integrated energy system containing a high proportion of distributed photovoltaics; 5) setting and solving the boundary conditions of the optimization problem; 6) obtaining the optimization results of the multi-energy storage and the optimal operation strategy.

[0085] The specific implementation steps of this method are:

[0086] Step 1: Construct the objective function of the multi-energy storage optimization configuration model. The multi-energy storage facilities considered in this invention mainly include: battery energy storage equipment, flywheel energy storage equipment and thermal storage device. The construction cost and operation cost of the energy storage system are:

[0087]

[0088]

[0089] Where: C inv Represents the energy storage construction cost, Represent the operating costs of the battery energy storage system, flywheel energy storage system and thermal storage device respectively; c BESS , c FESS and c TSS Represent the unit capacity cost coefficients of batteries, flywheels and thermal storage devices respectively; ε BESS , ε FESS and ε TSS is the cost daily allocation coefficient; and represents the installed capacity of the battery energy storage system, flywheel energy storage system and thermal storage device respectively; r and n are the equipment depreciation rate and service life respectively; m BESS , m FESS and m TSSRespectively represent the operation and maintenance cost coefficients of each energy storage system; and are the charging power and discharging power of battery energy storage system i at time t; P j,t is the electric power of the flywheel energy storage system j at time t; and is the heat storage and heat release power of the heat storage device k at time t; N BESS , N FESS and N TSS They represent the number of installed battery energy storage systems, flywheel energy storage systems and thermal storage devices respectively.

[0090] The operation and maintenance costs of distributed generator sets, cogeneration units, photovoltaic and gas boilers and the energy purchase costs of the integrated energy system are:

[0091]

[0092] Where: and are the operation and maintenance costs and energy purchase costs of other equipment in the integrated energy system except for the multi-energy storage equipment; the operation cost of the distributed generator set is expressed by a quadratic function, a CGU , b CGU and c CGU are the quadratic term coefficient, linear term coefficient and constant term of the cost function respectively; σ CHP , σ PV and σ GB They are the operation and maintenance cost coefficients of cogeneration units, distributed photovoltaics, and gas boilers; are the electric power of small diesel units, cogeneration units and distributed photovoltaic units at time t respectively; is the thermal power of the gas boiler at time t; the subscript I represents the serial number of each energy production equipment; P t EX,e and P t EX,gas are the purchased electric power and purchased gas power of the integrated energy system at time t, respectively; and λ gas Respectively represent the electricity purchase price and gas purchase price at time t; Γ CGU , Γ CHP , Γ PV and Γ GB They represent the collection of nodes where small diesel generators, cogeneration units, distributed photovoltaics and gas boilers are located.

[0093] Then the objective function of the multi-energy storage optimization configuration model can be expressed as:

[0094]

[0095] Where: T w represents the optimization time window; Δt represents the optimization step size.

[0096] Step 2: Construct the multi-energy storage system operation constraints, multi-energy network constraints and other equipment constraints. Other equipment includes distributed photovoltaics, gas boilers, small diesel generators and cogeneration units. The multi-energy network includes the distribution network and the regional thermal network.

[0097] Both battery energy storage systems and flywheel energy storage systems have the characteristics of large specific energy and small size. Commonly used battery energy storage systems are lithium cobalt oxide batteries, lithium iron phosphate batteries and ternary lithium batteries. Battery energy storage systems have fast response speeds and almost no climbing restrictions, but their charging and discharging power must be less than their rated power:

[0098]

[0099] Where: They represent the maximum charging / discharging power of battery energy storage system i respectively.

[0100] The state of charge (SOC) of a battery energy storage system is related to its charge and discharge state and its own energy decay. At the same time, to ensure the safety of the battery energy storage system, the battery's state of charge (SOC) must also be within a reasonable range:

[0101]

[0102] Where: γ self ,γ ch ,γ dis They represent the self-discharge coefficient, charging efficiency and discharge efficiency of the battery respectively; represents the rated capacity of the battery energy storage system i; Δt represents the optimization step size. In the actual operation process, for the convenience of operation and management, it is generally required that the SOC state of the battery energy storage system at the beginning and end of the operation cycle is consistent:

[0103]

[0104] Flywheel energy storage devices store electrical energy in the form of kinetic energy. Compared with battery energy storage systems, they are safer and more stable, and are more suitable for fast and frequent charging and discharging scenarios, but they are more expensive. Similar to battery energy storage systems, flywheel energy storage devices also have charging / discharging power limitations and SOC constraints:

[0105]

[0106] Where: and oh r Respectively represent the upper and lower limits of the flywheel speed; β self and βch / dis They represent the self-discharge rate and charge and discharge efficiency of the flywheel energy storage system respectively.

[0107] The thermal storage tank is a widely used heat storage device, which is often installed inside thermal power plants. When the heat loss of the thermal storage tank is ignored, its operating characteristics can be described by the following formula:

[0108]

[0109] Where: E k,t is the heat storage state of the heat storage tank k at time t; ch and dis Respectively represent the heat storage / heat release efficiency of the heat storage tank.

[0110] When the influence of ambient temperature on the output of distributed photovoltaic power is not considered, its output power can be described by the following formula:

[0111]

[0112] in: Indicates the rated power of photovoltaic i; I t represents the irradiance level at time t, I N is the irradiance level under standard test conditions. Assume that the photovoltaic is operated in constant power factor mode (the power factor is set to ), the reactive output of the inverter is:

[0113]

[0114] In order to improve the stability and flexibility of system operation, distributed generator sets with strong climbing ability (such as small diesel generator sets) are generally built close to the user side. Their operating characteristics are as follows:

[0115]

[0116] Where: is the reactive power of distributed generator set i at time t; and They represent the lower and upper limits of the active output of generator set i respectively; and Respectively represent the lower and upper limits of its reactive output; r l CGU and Respectively represent the downward / upward climbing capability of the generator set.

[0117] Gas boilers are an important type of central heating device in district heating systems, which generate heat by burning natural gas. The output thermal power is directly related to the gas power consumption, and there are also ramp constraints:

[0118]

[0119] Where: η GB Indicates the operating efficiency of the gas boiler, P t gas,GB Indicates its gas consumption power; and It is the lower and upper limits of the thermal power output of the gas boiler.

[0120] The regional heating network is generally composed of water supply pipes, return pipes and heat exchange stations. In view of the operating characteristics of the regional heating network and the transmission delay of each pipeline, the present invention adopts the node method to describe it. The heat exchange station is a place where heat is concentrated and exchanged. Through the heat exchange device, the heat at the heat source can be transferred to the primary pipe network, or the heat in the primary pipe network can be transferred to the secondary pipe network, and finally to the user end. The operating characteristics of the heat exchange station can be described as:

[0121]

[0122] in: and They represent the thermal power absorbed by the heat exchange station located at node n from the heat source or the primary pipe network at time t, and the thermal power transmitted to the primary pipe network or the secondary pipe network through the heat exchange station; and They represent the working fluid flow rates flowing into and out of node n at time t respectively; and They represent the water supply temperature of the heat exchange station at node n at time t and the hot water temperature flowing through the main heat exchange station respectively; similarly, and Represents the temperature at the outlet of the supply pipe and the inlet of the return pipe at node n at time.

[0123] When constructing the mapping relationship between the inlet and outlet temperatures of the pipeline, the temperature change inside the pipeline is ignored, and only the transmission delay of the working fluid inside the pipeline is considered. The transmission delay of the working fluid in the pipeline can be calculated by the following formula:

[0124]

[0125] Where: m l represents the working fluid flow rate inside the pipe l; ρ is the density of water; d l and L l are the diameter and length of the pipe l respectively; M l represents the quality of hot water inside pipe l. At the same time, the temperature at the outlet of pipe l is:

[0126]

[0127] in: is the temperature at the outlet of pipe l at time t; and They are (t-τ l ) and (t-τ l +1) is the working fluid temperature injected into pipeline l at time.

[0128] When the heat medium is transported inside the pipeline, it will inevitably radiate heat energy to the surrounding environment, resulting in heat loss. Therefore, the actual temperature at the outlet of pipeline l at time t is:

[0129]

[0130] Where: K l and T ground are the thermal conductivity of the pipeline per unit length and the ground temperature around the pipeline; C is the specific heat capacity coefficient of water.

[0131] Assuming that the working fluid injected at each node is fully mixed, according to the law of conservation of energy, the temperature mixing process of each node can be described by the following formula (applicable to both the water supply network and the return network):

[0132]

[0133] Where: and are the working fluid temperatures injected into and out of node n at time t respectively; and They represent the working fluid flow rates injected into and out of node n at time t respectively; and are the sets of all pipes flowing into and out of node n, respectively.

[0134] At the same time, the temperatures at all nodes in the heat network must satisfy certain constraints:

[0135]

[0136] Where: and is the lower and upper temperature limits at each node in the water supply network; It is the lower limit of the temperature of each node in the return network.

[0137] The power network in the regional integrated energy system is mainly a distribution network, which is mostly a radial network and adopts the "closed-loop construction, open-loop operation" mode. The radial network linear power flow model is used to describe the energy flow relationship and node voltage changes in the distribution network:

[0138]

[0139] Where: j} and {Ι j} are the branch set for power injection and the branch set for power outflow at node j respectively; P jk , P ij , Q jk and Q ij are the active power and reactive power flowing in / out respectively; the load at node j is composed of S j =P j +Q j Indicates; U i and are the voltage amplitudes at nodes i and j respectively; U0 is the reference voltage; the parameter of line ij is the resistance R ij and reactance X ij .

[0140] The main electric-heat coupling equipment in the regional integrated energy system is a large combined heat and power (CHP) unit. Based on engineering practice, CHP units are mostly extraction-condensing cogeneration units, and the electricity and heat production processes can be decoupled from each other:

[0141]

[0142] Where: P t gas,CHP and Respectively represent the gas consumption power and thermal power of CHP at time t; η CHP and R CHP It indicates the operating efficiency and heat-to-electricity ratio of the CHP unit; r l CHP and It is the downward / upward climbing capability of the CHP unit; and It is the lower and upper limits of the CHP unit output power.

[0143] The power grid and heat network must meet the power balance constraints:

[0144]

[0145] Where: P d,t , Q d,t and H d,t Represents the active load, reactive load and thermal load level of the system at time t.

[0146] Step 3: Introduce auxiliary variables to linearize the nonlinear constraints of the electric energy storage system and introduce adjustable parameters to improve the applicability of the method. Specifically:

[0147] 1) Introducing surplus energy from the energy storage system 2) Introducing the adjustable parameter Dur min , which indicates the minimum continuous energy release time of the energy storage system. The configured energy storage system must meet the energy release duration of not less than Dur min , Durmin The size can be selected based on actual engineering experience, generally ranging from 1-4h, and 2h is selected here.

[0148] After the above processing, the relationship between the charge / discharge power of the energy storage system state of charge becomes:

[0149]

[0150] It can be found that the original bilinear equality constraint becomes a linear equality constraint and can be solved directly. At the same time, the state of charge of the energy storage system can be solved by the remaining energy of the system at each moment:

[0151]

[0152] When introducing the minimum continuous energy release time Dur of the energy storage system min After that, the relationship between the rated charge and discharge power and the rated capacity of the battery energy storage system can be described as:

[0153]

[0154] Step 4: Construct a multi-energy storage optimization configuration model for a regional integrated energy system with a high proportion of distributed photovoltaics. With the goal of minimizing the investment and construction cost of multi-energy storage and the operating cost of the regional integrated energy system, and with the operating constraints of the multi-energy storage system, the multi-energy network constraints and other equipment constraints as constraints, a multi-energy storage optimization configuration model for a regional integrated energy system with a high proportion of distributed photovoltaics is constructed. Specifically:

[0155]

[0156] F i (x e ,x h ,x s )=0,i=1,2,...,m

[0157] G j (x e ,x h ,x s )≥0,j=1,2,...n

[0158] Where: m, n are the isomorphic constraints F in the model respectively. i and the inequality constraint G j The number of e , x h and x s are the optimization variables for the power system, heating system and energy storage system respectively.

[0159] Step 5: Setting boundary conditions of the optimization configuration problem and solving the problem. The electricity purchase price adopts the time-of-use electricity price, and the gas purchase price and heat purchase price are fixed. The system load level and photovoltaic output level are represented by the typical daily electrical load, thermal load and photovoltaic output. After the setting is completed, the original multi-energy storage optimization configuration problem can be solved to obtain the optimized multi-type energy storage capacity and its operation strategy.

Claims

1. A method for optimizing the configuration of multi-energy storage in a regional integrated energy system containing a high proportion of distributed photovoltaics, characterized in that: For the regional integrated energy system with a high proportion of distributed photovoltaics, firstly, the investment and construction cost of the multi-energy storage system and the operating cost of the regional integrated energy system are comprehensively considered to construct the optimization target of the optimization configuration model; secondly, the differentiated dynamic operation characteristics, complex network characteristics and operation characteristics of other equipment in the regional integrated energy system other than multi-energy storage are taken into account to construct model constraints, which include multi-energy storage constraints, network constraints and operation constraints of other equipment other than multi-energy storage; then, auxiliary variables and adjustable parameters are introduced to linearize the bilinear constraints in the multi-energy storage constraints; finally, the boundary conditions of the optimization problem are set and solved to obtain the optimization results of the multi-energy storage capacity and the optimal operation strategy; the optimization configuration model is: s.t.F i (x e ,x h ,x s )=0,i=1,2,...,m G j (x e ,x h ,x s )≥0,j=1,2,...,n Where: are the operation and maintenance cost and energy purchase cost of the regional integrated energy system respectively; C inv is the investment and construction cost of multi-energy storage facilities; the constraints are the equality constraints of the network and the equipment F i and inequality constraints G j , where the number of equality constraints is represented by m and the number of inequality constraints is represented by n; x e , x h and x s Represent the relevant decision variables of the electrical system, thermal system and energy storage facility respectively; T w represents the optimization time window of the energy storage system; Δt is the optimization step length; The model constraints are: Multi-energy storage constraints include battery energy storage constraints BESS, flywheel energy storage constraints FESS, and thermal storage device constraints TSS: Where: Respectively represent the maximum charging and discharging power of battery energy storage system i; γ self ,γ ch ,γ dis They represent the self-discharge coefficient, charging efficiency and discharge efficiency of the battery respectively; SOC represents the state of charge of each electrical energy storage device; E represents the remaining energy inside the thermal storage device; represents the rated capacity of the battery energy storage system i; Δt represents the optimization step length; and ω r Respectively represent the upper and lower limits of the flywheel speed; β self and β ch / dis They represent the self-discharge rate and charge-discharge efficiency of the flywheel energy storage system respectively; and They represent the heat storage / heat release power of the heat storage tank k at time t; E k,t is the heat storage state of the heat storage tank k at time t; ch and dis They represent the heat storage / release efficiency of the heat storage tank respectively; The operating constraints of multi-energy equipment are: in: Indicates the rated power of photovoltaic i; Represents the reactive power output of photovoltaic device i at time t; I t Indicates the irradiance level at time t; I N Indicates the irradiance level under standard test conditions; Indicates the power factor when the inverter is running; is the reactive power output by diesel generator set i at time t; and They represent the lower and upper limits of the active output of diesel generator set i respectively; and Respectively represent the lower and upper limits of the reactive power output of diesel generator set i; r l CGU and Respectively represent the downward / upward climbing ability of the diesel generator set; η GB Indicates the operating efficiency of the gas boiler; P t gas,GB It represents the gas consumption power of the gas boiler at time t; It represents the thermal power output of the gas boiler at time t; and P is the lower and upper limits of the gas boiler output; t e,CHP , P t gas,CHP and Respectively represent the output electrical power, gas power consumption and thermal power of CHP at time t; η CHP and R CHP It indicates the operating efficiency and heat-to-electricity ratio of the CHP unit; r l CHP and It is the downward / upward climbing capability of the CHP unit; and The lower and upper limits of the output power of the CHP unit; The distribution network constraints in the network constraints are: U j =U i -(R ij P ij +X ij Q ij ) / U0 Among them: j } and {Ι j } are the branch set for power injection and the branch set for power outflow at node j respectively; P jk , P ij , Q jk and Q ij are the active power and reactive power flowing in / out respectively; the load at node j is composed of S j =P j +Q j Indicates; U i and are the voltage amplitudes at nodes i and j respectively; U0 is the reference voltage; the parameter of line ij is the resistance R ij and reactance X ij ;P d,t , Q d,t and H d,t are the system electrical load and thermal load levels at time t respectively; The heating network constraints in the network constraints are: in, and They represent the thermal power absorbed by the heat exchange station located at node n from the heat source or the primary pipe network at time t, and the thermal power transmitted to the primary pipe network or the secondary pipe network through the heat exchange station; and They represent the working fluid flow rates flowing into and out of node n at time t respectively; and They represent the water supply temperature of the heat exchange station at node n at time t and the hot water temperature flowing through the main heat exchange station respectively; similarly, and represents the temperature at the outlet of the water supply pipe and the inlet of the return pipe at the node n at the time; m l represents the working fluid flow rate inside the pipe l; ρ is the density of water; d l and L l are the diameter and length of the pipe l respectively; M l Indicates the quality of hot water inside pipe l; is the temperature at the outlet of pipe l at time t; and They are (t-τ l ) and (t-τ l +1) working fluid temperature injected into pipeline l; K l and T ground are the thermal conductivity of the pipeline per unit length and the ground temperature around the pipeline respectively; and are the working fluid temperatures injected into and out of node n at time t respectively; and They represent the working fluid flow rates injected into and out of node n at time t respectively; and are the sets of all pipes flowing into and out of node n respectively; and is the lower and upper temperature limits at each node in the water supply network; is the lower limit of the temperature of each node in the return network; Auxiliary variables and adjustable parameters are introduced to linearize the bilinear constraints in the multi-energy storage constraints. Specifically: The auxiliary variable is the residual energy The adjustable parameter is the minimum continuous energy release time Dur of the energy storage system min , where Dur min 1-4h; After introducing auxiliary variables and adjustable parameters, the battery energy storage constraint BESS and the flywheel energy storage constraint FESS are modified to linear constraints: The boundary conditions of the optimization problem are as follows: the electricity purchase price adopts the time-of-use electricity price, the gas purchase price and the heat purchase price are fixed; the system load level and the photovoltaic output level are represented by the typical daily electricity load, thermal load and photovoltaic output.

2. According to claim 1, a method for optimizing the configuration of multi-energy storage in a regional integrated energy system containing a high proportion of distributed photovoltaics, characterized in that: The optimization objective of the optimization configuration model is expressed as: Where: The operating cost of distributed generators is expressed by a quadratic function, a CGU , b CGU and c CGU are the quadratic term coefficient, linear term coefficient and constant term of the cost function respectively; σ CHP , σ PV and σ GB are the operation and maintenance cost coefficients of cogeneration units, distributed photovoltaics and gas boilers; C inv and Represents the energy storage construction cost and operation cost; c BESS , c FESS and c TSS denote the unit capacity cost coefficients of battery, flywheel and thermal storage device respectively; ε BESS , ε FESS and ε TSS is the cost daily allocation coefficient; and represents the installed capacity of the battery energy storage system, flywheel energy storage system and thermal storage device respectively; r and n are the equipment depreciation rate and service life respectively; m BESS , m FESS and m TSS Respectively represent the operation and maintenance cost coefficients of each energy storage system; N BESS , N FESS and N TSS represent the number of installed battery energy storage systems, flywheel energy storage systems and thermal storage devices respectively; Represents the active power of diesel generator set I at time t; They represent the active power output by CHP unit I and photovoltaic power generation equipment I at time t respectively; is the thermal power output of the gas boiler at time t; is the charge / discharge power of battery energy storage system i at time t; P j,t is the electric power of the flywheel energy storage system j at time t, which is greater than zero, indicating discharge, otherwise, it indicates charging; and is the charging / discharging power of the heat storage device k at time t; P t EX,e and P t EX,gas are the purchased electric power and purchased gas power of the integrated energy system at time t, respectively; and λ gas They represent the electricity price and gas price at time t respectively.

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